diff --git a/whisper_pipeline/HA1.wav b/whisper_pipeline/HA1.wav
deleted file mode 100644
index 4bdd53010f4a2ae1b148e4b49baababf21180954..0000000000000000000000000000000000000000
--- a/whisper_pipeline/HA1.wav
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:87fd3e947f85de5aeeae4d2f34a4774370541acf92e0f3317686e3c70572aa6a
-size 1242438
diff --git a/whisper_pipeline/api.py b/whisper_pipeline/api.py
deleted file mode 100644
index d7ef9cfc09e72fdb316c07ebc32937709e634178..0000000000000000000000000000000000000000
--- a/whisper_pipeline/api.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from pathlib import Path
-import os
-from gector import GecBERTModel
-from faster_whisper import WhisperModel, BatchedInferencePipeline
-from transformers.models.whisper.english_normalizer import BasicTextNormalizer
-from text_processing.inverse_normalize import InverseNormalizer
-import shutil
-import uvicorn
-
-# Initialize the FastAPI app
-app = FastAPI()
-
-# Initialize models and normalizer
-current_dir = Path(__file__).parent.as_posix()
-inverse_normalizer = InverseNormalizer('vi')
-whisper_model = WhisperModel("pho_distill_q8", device="auto", compute_type="auto")
-batched_model = BatchedInferencePipeline(model=whisper_model, use_vad_model=True, chunk_length=15)
-gector_model = GecBERTModel(
- vocab_path=os.path.join(current_dir, "gector/vocabulary"),
- model_paths=[os.path.join(current_dir, "gector/Model_GECTOR")],
- split_chunk=True
-)
-normalizer = BasicTextNormalizer()
-
-@app.post("/transcriptions")
-async def transcribe_audio(file: UploadFile = File(...)):
- # Save the uploaded file temporarily
- temp_file_path = Path(f"temp_{file.filename}")
- with open(temp_file_path, "wb") as buffer:
- shutil.copyfileobj(file.file, buffer)
- segments, info = batched_model.transcribe(str(temp_file_path), language="vi", batch_size=32)
- os.remove(temp_file_path)
- transcriptions = [segment.text for segment in segments]
- normalized_transcriptions = [inverse_normalizer.inverse_normalize(normalizer(text)) for text in transcriptions]
- corrected_texts = gector_model(normalized_transcriptions)
- return JSONResponse({"text": ' '.join(corrected_texts)})
-
-
-if __name__ == "__main__":
- uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=True)
\ No newline at end of file
diff --git a/whisper_pipeline/check.py b/whisper_pipeline/check.py
deleted file mode 100644
index f66823958bfcf03baa7514c1eff860ff79a6e41e..0000000000000000000000000000000000000000
--- a/whisper_pipeline/check.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from text_processing.inverse_normalize import InverseNormalizer
-import time
-normalizer = InverseNormalizer('vi')
-start = time.time()
-print(normalizer.inverse_normalize("mười hai ki lô gram"))
-print(time.time()- start)
\ No newline at end of file
diff --git a/whisper_pipeline/dockerfile b/whisper_pipeline/dockerfile
deleted file mode 100644
index 0b68a245cf57be93c1ddb573954d90ec2a55aec4..0000000000000000000000000000000000000000
--- a/whisper_pipeline/dockerfile
+++ /dev/null
@@ -1,51 +0,0 @@
-# Use Python 3.11-slim-bookworm as base
-FROM python:3.11-slim-bookworm AS base
-
-# Use args
-ARG USE_CUDA
-ARG USE_CUDA_VER
-
-## Basis ##
-ENV ENV=prod \
- PORT=5056 \
- # pass build args to the build
- USE_CUDA_DOCKER=${USE_CUDA} \
- USE_CUDA_DOCKER_VER=${USE_CUDA_VER}
-
-# Install GCC and build tools
-RUN apt-get update && \
- apt-get install -y gcc build-essential curl git pkg-config libicu-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-# Set working directory
-WORKDIR /app
-
-# Install the necessary dependencies from the requirements.txt file
-COPY ./requirements.txt .
-RUN pip3 install uv && \
- if [ "$USE_CUDA" = "true" ]; then \
- pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir; \
- else \
- pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir; \
- fi
-# Copy faster-whisper-main folder (which includes the setup file) and install
-COPY ./faster-whisper-main ./faster-whisper-main
-RUN pip3 install ./faster-whisper-main
-
-RUN uv pip install --system -r requirements.txt --no-cache-dir
-
-
-
-# Copy the remaining application code
-COPY . .
-
-# Expose the API port
-EXPOSE 5056
-
-# Set the environment variables
-ENV HOST="0.0.0.0"
-ENV PORT="5056"
-
-# Set entrypoint to run the FastAPI server
-ENTRYPOINT [ "bash", "start.sh" ]
diff --git a/whisper_pipeline/faster-whisper-main/.github/workflows/ci.yml b/whisper_pipeline/faster-whisper-main/.github/workflows/ci.yml
deleted file mode 100644
index 407661fd95398cffafac3e6f311729ddedec4766..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/.github/workflows/ci.yml
+++ /dev/null
@@ -1,90 +0,0 @@
-name: CI
-
-on:
- push:
- branches:
- - master
- tags:
- - v*
- pull_request:
- branches:
- - master
-
-jobs:
- check-code-format:
- runs-on: ubuntu-latest
-
- steps:
- - uses: actions/checkout@v3
-
- - name: Set up Python 3.8
- uses: actions/setup-python@v4
- with:
- python-version: 3.8
-
- - name: Install module
- run: |
- pip install wheel
- pip install -e .[dev]
-
- - name: Check code format with Black
- run: |
- black --check .
-
- - name: Check imports order with isort
- run: |
- isort --check-only .
-
- - name: Check code style with Flake8
- if: ${{ always() }}
- run: |
- flake8 .
-
-
- run-tests:
- runs-on: ubuntu-latest
-
- steps:
- - uses: actions/checkout@v3
-
- - name: Set up Python 3.8
- uses: actions/setup-python@v4
- with:
- python-version: 3.8
-
- - name: Install module
- run: |
- pip install wheel
- pip install -e .[dev]
-
- - name: Run pytest
- run: |
- pytest -v tests/
-
-
- build-and-push-package:
- runs-on: ubuntu-latest
- needs: [check-code-format, run-tests]
-
- steps:
- - uses: actions/checkout@v3
-
- - name: Set up Python 3.8
- uses: actions/setup-python@v4
- with:
- python-version: 3.8
-
- - name: Install dependencies
- run: |
- pip install wheel
-
- - name: Build package
- run: |
- python3 setup.py sdist bdist_wheel
-
- - name: Push package on PyPI
- if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
- uses: pypa/gh-action-pypi-publish@release/v1
- with:
- user: __token__
- password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/whisper_pipeline/faster-whisper-main/.gitignore b/whisper_pipeline/faster-whisper-main/.gitignore
deleted file mode 100644
index 8f634c289f61452594236f753e28066cf0ef2013..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/.gitignore
+++ /dev/null
@@ -1,15 +0,0 @@
-# Byte-compiled / Optimized / DLL Files
-*.pyc
-*.pyo
-*.pyd
-__pycache__/
-
-# Distribution / Packaging
-venv/
-
-# Unit Test
-.pytest_cache/
-
-# Ignore IDE, Editor Files
-.idea/
-.vscode/
diff --git a/whisper_pipeline/faster-whisper-main/CONTRIBUTING.md b/whisper_pipeline/faster-whisper-main/CONTRIBUTING.md
deleted file mode 100644
index 8d6a9c244689ded701cad9aab99a234096008afc..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/CONTRIBUTING.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Contributing to faster-whisper
-
-Contributions are welcome! Here are some pointers to help you install the library for development and validate your changes before submitting a pull request.
-
-## Install the library for development
-
-We recommend installing the module in editable mode with the `dev` extra requirements:
-
-```bash
-git clone https://github.com/SYSTRAN/faster-whisper.git
-cd faster-whisper/
-pip install -e .[dev]
-```
-
-## Validate the changes before creating a pull request
-
-1. Make sure the existing tests are still passing (and consider adding new tests as well!):
-
-```bash
-pytest tests/
-```
-
-2. Reformat and validate the code with the following tools:
-
-```bash
-black .
-isort .
-flake8 .
-```
-
-These steps are also run automatically in the CI when you open the pull request.
diff --git a/whisper_pipeline/faster-whisper-main/LICENSE b/whisper_pipeline/faster-whisper-main/LICENSE
deleted file mode 100644
index 2d92330dc294fea86caf785fa626d22917850141..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 SYSTRAN
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/whisper_pipeline/faster-whisper-main/MANIFEST.in b/whisper_pipeline/faster-whisper-main/MANIFEST.in
deleted file mode 100644
index 8a103dd64eb1866fc9fce36e0e9c7d84e482f98b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/MANIFEST.in
+++ /dev/null
@@ -1,4 +0,0 @@
-include faster_whisper/assets/silero_vad.onnx
-include requirements.txt
-include requirements.conversion.txt
-include faster_whisper/assets/pyannote_vad_model.bin
diff --git a/whisper_pipeline/faster-whisper-main/README.md b/whisper_pipeline/faster-whisper-main/README.md
deleted file mode 100644
index f7d54ee45467a8fdf06d9e5fa77ab3d7c2184472..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/README.md
+++ /dev/null
@@ -1,319 +0,0 @@
-[](https://github.com/SYSTRAN/faster-whisper/actions?query=workflow%3ACI) [](https://badge.fury.io/py/faster-whisper)
-
-# Faster Whisper transcription with CTranslate2
-
-**faster-whisper** is a reimplementation of OpenAI's Whisper model using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models.
-
-This implementation is up to 4 times faster than [openai/whisper](https://github.com/openai/whisper) for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.
-
-## Benchmark
-
-### Whisper
-
-For reference, here's the time and memory usage that are required to transcribe [**13 minutes**](https://www.youtube.com/watch?v=0u7tTptBo9I) of audio using different implementations:
-
-* [openai/whisper](https://github.com/openai/whisper)@[6dea21fd](https://github.com/openai/whisper/commit/6dea21fd7f7253bfe450f1e2512a0fe47ee2d258)
-* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[3b010f9](https://github.com/ggerganov/whisper.cpp/commit/3b010f9bed9a6068609e9faf52383aea792b0362)
-* [faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[cce6b53e](https://github.com/SYSTRAN/faster-whisper/commit/cce6b53e4554f71172dad188c45f10fb100f6e3e)
-
-### Large-v2 model on GPU
-
-| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory |
-| --- | --- | --- | --- | --- | --- |
-| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB |
-| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB |
-| faster-whisper | int8 | 5 | 59s | 3091MB | 3117MB |
-
-*Executed with CUDA 11.7.1 on a NVIDIA Tesla V100S.*
-
-### Small model on CPU
-
-| Implementation | Precision | Beam size | Time | Max. memory |
-| --- | --- | --- | --- | --- |
-| openai/whisper | fp32 | 5 | 10m31s | 3101MB |
-| whisper.cpp | fp32 | 5 | 17m42s | 1581MB |
-| whisper.cpp | fp16 | 5 | 12m39s | 873MB |
-| faster-whisper | fp32 | 5 | 2m44s | 1675MB |
-| faster-whisper | int8 | 5 | 2m04s | 995MB |
-
-*Executed with 8 threads on a Intel(R) Xeon(R) Gold 6226R.*
-
-
-### Distil-whisper
-
-| Implementation | Precision | Beam size | Time | Gigaspeech WER |
-| --- | --- | --- | --- | --- |
-| distil-whisper/distil-large-v2 | fp16 | 4 |- | 10.36 |
-| [faster-distil-large-v2](https://huggingface.co/Systran/faster-distil-whisper-large-v2) | fp16 | 5 | - | 10.28 |
-| distil-whisper/distil-medium.en | fp16 | 4 | - | 11.21 |
-| [faster-distil-medium.en](https://huggingface.co/Systran/faster-distil-whisper-medium.en) | fp16 | 5 | - | 11.21 |
-
-*Executed with CUDA 11.4 on a NVIDIA 3090.*
-
-
-testing details (click to expand)
-
-For `distil-whisper/distil-large-v2`, the WER is tested with code sample from [link](https://huggingface.co/distil-whisper/distil-large-v2#evaluation). for `faster-distil-whisper`, the WER is tested with setting:
-```python
-from faster_whisper import WhisperModel
-
-model_size = "distil-large-v2"
-# model_size = "distil-medium.en"
-# Run on GPU with FP16
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-segments, info = model.transcribe("audio.mp3", beam_size=5, language="en")
-```
-
-
-## Requirements
-
-* Python 3.8 or greater
-
-
-### GPU
-
-GPU execution requires the following NVIDIA libraries to be installed:
-
-* [cuBLAS for CUDA 12](https://developer.nvidia.com/cublas)
-* [cuDNN 8 for CUDA 12](https://developer.nvidia.com/cudnn)
-
-**Note**: Latest versions of `ctranslate2` support CUDA 12 only. For CUDA 11, the current workaround is downgrading to the `3.24.0` version of `ctranslate2` (This can be done with `pip install --force-reinstall ctranslate2==3.24.0` or specifying the version in a `requirements.txt`).
-
-There are multiple ways to install the NVIDIA libraries mentioned above. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
-
-
-Other installation methods (click to expand)
-
-
-**Note:** For all these methods below, keep in mind the above note regarding CUDA versions. Depending on your setup, you may need to install the _CUDA 11_ versions of libraries that correspond to the CUDA 12 libraries listed in the instructions below.
-
-#### Use Docker
-
-The libraries (cuBLAS, cuDNN) are installed in these official NVIDIA CUDA Docker images: `nvidia/cuda:12.0.0-runtime-ubuntu20.04` or `nvidia/cuda:12.0.0-runtime-ubuntu22.04`.
-
-#### Install with `pip` (Linux only)
-
-On Linux these libraries can be installed with `pip`. Note that `LD_LIBRARY_PATH` must be set before launching Python.
-
-```bash
-pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
-
-export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
-```
-
-**Note**: Version 9+ of `nvidia-cudnn-cu12` appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.
-
-#### Download the libraries from Purfview's repository (Windows & Linux)
-
-Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA libraries for Windows & Linux in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs). Decompress the archive and place the libraries in a directory included in the `PATH`.
-
-
-
-## Installation
-
-The module can be installed from [PyPI](https://pypi.org/project/faster-whisper/):
-
-```bash
-pip install faster-whisper
-```
-
-
-Other installation methods (click to expand)
-
-### Install the master branch
-
-```bash
-pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz"
-```
-
-### Install a specific commit
-
-```bash
-pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"
-```
-
-
-
-## Usage
-
-### Faster-whisper
-
-```python
-from faster_whisper import WhisperModel
-
-model_size = "large-v3"
-
-# Run on GPU with FP16
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-
-# or run on GPU with INT8
-# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
-# or run on CPU with INT8
-# model = WhisperModel(model_size, device="cpu", compute_type="int8")
-
-segments, info = model.transcribe("audio.mp3", beam_size=5)
-
-print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-**Warning:** `segments` is a *generator* so the transcription only starts when you iterate over it. The transcription can be run to completion by gathering the segments in a list or a `for` loop:
-
-```python
-segments, _ = model.transcribe("audio.mp3")
-segments = list(segments) # The transcription will actually run here.
-```
-
-### multi-segment language detection
-
-To directly use the model for improved language detection, the following code snippet can be used:
-
-```python
-from faster_whisper import WhisperModel
-model = WhisperModel("medium", device="cuda", compute_type="float16")
-language_info = model.detect_language_multi_segment("audio.mp3")
-```
-
-### Batched faster-whisper
-
-
-The batched version of faster-whisper is inspired by [whisper-x](https://github.com/m-bain/whisperX) licensed under the BSD-2 Clause license and integrates its VAD model to this library. We modify this implementation and also replaced the feature extraction with a faster torch-based implementation. Batched version improves the speed upto 10-12x compared to openAI implementation and 3-4x compared to the sequential faster_whisper version. It works by transcribing semantically meaningful audio chunks as batches leading to faster inference.
-
-The following code snippet illustrates how to run inference with batched version on an example audio file. Please also refer to the test scripts of batched faster whisper.
-
-```python
-from faster_whisper import WhisperModel, BatchedInferencePipeline
-
-model = WhisperModel("medium", device="cuda", compute_type="float16")
-batched_model = BatchedInferencePipeline(model=model)
-segments, info = batched_model.transcribe("audio.mp3", batch_size=16)
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-### Faster Distil-Whisper
-
-The Distil-Whisper checkpoints are compatible with the Faster-Whisper package. In particular, the latest [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
-checkpoint is intrinsically designed to work with the Faster-Whisper transcription algorithm. The following code snippet
-demonstrates how to run inference with distil-large-v3 on a specified audio file:
-
-```python
-from faster_whisper import WhisperModel
-
-model_size = "distil-large-v3"
-
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-segments, info = model.transcribe("audio.mp3", beam_size=5, language="en", condition_on_previous_text=False)
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).
-
-### Word-level timestamps
-
-```python
-segments, _ = model.transcribe("audio.mp3", word_timestamps=True)
-
-for segment in segments:
- for word in segment.words:
- print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
-```
-
-### VAD filter
-
-The library integrates the [Silero VAD](https://github.com/snakers4/silero-vad) model to filter out parts of the audio without speech:
-
-```python
-segments, _ = model.transcribe("audio.mp3", vad_filter=True)
-```
-
-The default behavior is conservative and only removes silence longer than 2 seconds. See the available VAD parameters and default values in the [source code](https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/vad.py). They can be customized with the dictionary argument `vad_parameters`:
-
-```python
-segments, _ = model.transcribe(
- "audio.mp3",
- vad_filter=True,
- vad_parameters=dict(min_silence_duration_ms=500),
-)
-```
-
-### Logging
-
-The library logging level can be configured like this:
-
-```python
-import logging
-
-logging.basicConfig()
-logging.getLogger("faster_whisper").setLevel(logging.DEBUG)
-```
-
-### Going further
-
-See more model and transcription options in the [`WhisperModel`](https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/transcribe.py) class implementation.
-
-## Community integrations
-
-Here is a non exhaustive list of open-source projects using faster-whisper. Feel free to add your project to the list!
-
-
-* [faster-whisper-server](https://github.com/fedirz/faster-whisper-server) is an OpenAI compatible server using `faster-whisper`. It's easily deployable with Docker, works with OpenAI SDKs/CLI, supports streaming, and live transcription.
-* [WhisperX](https://github.com/m-bain/whisperX) is an award-winning Python library that offers speaker diarization and accurate word-level timestamps using wav2vec2 alignment
-* [whisper-ctranslate2](https://github.com/Softcatala/whisper-ctranslate2) is a command line client based on faster-whisper and compatible with the original client from openai/whisper.
-* [whisper-diarize](https://github.com/MahmoudAshraf97/whisper-diarization) is a speaker diarization tool that is based on faster-whisper and NVIDIA NeMo.
-* [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) Standalone CLI executables of faster-whisper for Windows, Linux & macOS.
-* [asr-sd-pipeline](https://github.com/hedrergudene/asr-sd-pipeline) provides a scalable, modular, end to end multi-speaker speech to text solution implemented using AzureML pipelines.
-* [Open-Lyrics](https://github.com/zh-plus/Open-Lyrics) is a Python library that transcribes voice files using faster-whisper, and translates/polishes the resulting text into `.lrc` files in the desired language using OpenAI-GPT.
-* [wscribe](https://github.com/geekodour/wscribe) is a flexible transcript generation tool supporting faster-whisper, it can export word level transcript and the exported transcript then can be edited with [wscribe-editor](https://github.com/geekodour/wscribe-editor)
-* [aTrain](https://github.com/BANDAS-Center/aTrain) is a graphical user interface implementation of faster-whisper developed at the BANDAS-Center at the University of Graz for transcription and diarization in Windows ([Windows Store App](https://apps.microsoft.com/detail/atrain/9N15Q44SZNS2)) and Linux.
-* [Whisper-Streaming](https://github.com/ufal/whisper_streaming) implements real-time mode for offline Whisper-like speech-to-text models with faster-whisper as the most recommended back-end. It implements a streaming policy with self-adaptive latency based on the actual source complexity, and demonstrates the state of the art.
-* [WhisperLive](https://github.com/collabora/WhisperLive) is a nearly-live implementation of OpenAI's Whisper which uses faster-whisper as the backend to transcribe audio in real-time.
-* [Faster-Whisper-Transcriber](https://github.com/BBC-Esq/ctranslate2-faster-whisper-transcriber) is a simple but reliable voice transcriber that provides a user-friendly interface.
-
-## Model conversion
-
-When loading a model from its size such as `WhisperModel("large-v3")`, the corresponding CTranslate2 model is automatically downloaded from the [Hugging Face Hub](https://huggingface.co/Systran).
-
-We also provide a script to convert any Whisper models compatible with the Transformers library. They could be the original OpenAI models or user fine-tuned models.
-
-For example the command below converts the [original "large-v3" Whisper model](https://huggingface.co/openai/whisper-large-v3) and saves the weights in FP16:
-
-```bash
-pip install transformers[torch]>=4.23
-
-ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2
---copy_files tokenizer.json preprocessor_config.json --quantization float16
-```
-
-* The option `--model` accepts a model name on the Hub or a path to a model directory.
-* If the option `--copy_files tokenizer.json` is not used, the tokenizer configuration is automatically downloaded when the model is loaded later.
-
-Models can also be converted from the code. See the [conversion API](https://opennmt.net/CTranslate2/python/ctranslate2.converters.TransformersConverter.html).
-
-### Load a converted model
-
-1. Directly load the model from a local directory:
-```python
-model = faster_whisper.WhisperModel("whisper-large-v3-ct2")
-```
-
-2. [Upload your model to the Hugging Face Hub](https://huggingface.co/docs/transformers/model_sharing#upload-with-the-web-interface) and load it from its name:
-```python
-model = faster_whisper.WhisperModel("username/whisper-large-v3-ct2")
-```
-
-## Comparing performance against other implementations
-
-If you are comparing the performance against other Whisper implementations, you should make sure to run the comparison with similar settings. In particular:
-
-* Verify that the same transcription options are used, especially the same beam size. For example in openai/whisper, `model.transcribe` uses a default beam size of 1 but here we use a default beam size of 5.
-* When running on CPU, make sure to set the same number of threads. Many frameworks will read the environment variable `OMP_NUM_THREADS`, which can be set when running your script:
-
-```bash
-OMP_NUM_THREADS=4 python3 my_script.py
-```
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/benchmark.m4a b/whisper_pipeline/faster-whisper-main/benchmark/benchmark.m4a
deleted file mode 100644
index 9fc8da8c60a0dc0594b3e368b2bffdcbb02edf9c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/benchmark.m4a
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5dedec4f587a7940cfab93ff36e5014f155f80e10b7935f67d9eee8761663c34
-size 12935433
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/memory_benchmark.py b/whisper_pipeline/faster-whisper-main/benchmark/memory_benchmark.py
deleted file mode 100644
index 1fbdfbdf379b4a2b7c71fb192e471db01b37d579..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/memory_benchmark.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import argparse
-import time
-
-from typing import Callable
-
-import py3nvml.py3nvml as nvml
-
-from memory_profiler import memory_usage
-from utils import MyThread, get_logger, inference
-
-logger = get_logger("faster-whisper")
-parser = argparse.ArgumentParser(description="Memory benchmark")
-parser.add_argument(
- "--gpu_memory", action="store_true", help="Measure GPU memory usage"
-)
-parser.add_argument("--device-index", type=int, default=0, help="GPU device index")
-parser.add_argument(
- "--interval",
- type=float,
- default=0.5,
- help="Interval at which measurements are collected",
-)
-args = parser.parse_args()
-device_idx = args.device_index
-interval = args.interval
-
-
-def measure_memory(func: Callable[[], None]):
- if args.gpu_memory:
- logger.info(
- "Measuring maximum GPU memory usage on GPU device."
- " Make sure to not have additional processes running on the same GPU."
- )
- # init nvml
- nvml.nvmlInit()
- handle = nvml.nvmlDeviceGetHandleByIndex(device_idx)
- gpu_name = nvml.nvmlDeviceGetName(handle)
- gpu_memory_limit = nvml.nvmlDeviceGetMemoryInfo(handle).total >> 20
- gpu_power_limit = nvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000.0
- info = {"gpu_memory_usage": [], "gpu_power_usage": []}
-
- def _get_gpu_info():
- while True:
- info["gpu_memory_usage"].append(
- nvml.nvmlDeviceGetMemoryInfo(handle).used >> 20
- )
- info["gpu_power_usage"].append(
- nvml.nvmlDeviceGetPowerUsage(handle) / 1000
- )
- time.sleep(interval)
-
- if stop:
- break
-
- return info
-
- stop = False
- thread = MyThread(_get_gpu_info, params=())
- thread.start()
- func()
- stop = True
- thread.join()
- result = thread.get_result()
-
- # shutdown nvml
- nvml.nvmlShutdown()
- max_memory_usage = max(result["gpu_memory_usage"])
- max_power_usage = max(result["gpu_power_usage"])
- print("GPU name: %s" % gpu_name)
- print("GPU device index: %s" % device_idx)
- print(
- "Maximum GPU memory usage: %dMiB / %dMiB (%.2f%%)"
- % (
- max_memory_usage,
- gpu_memory_limit,
- (max_memory_usage / gpu_memory_limit) * 100,
- )
- )
- print(
- "Maximum GPU power usage: %dW / %dW (%.2f%%)"
- % (
- max_power_usage,
- gpu_power_limit,
- (max_power_usage / gpu_power_limit) * 100,
- )
- )
- else:
- logger.info("Measuring maximum increase of memory usage.")
- max_usage = memory_usage(func, max_usage=True, interval=interval)
- print("Maximum increase of RAM memory usage: %d MiB" % max_usage)
-
-
-if __name__ == "__main__":
- measure_memory(inference)
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/normalizer.json b/whisper_pipeline/faster-whisper-main/benchmark/normalizer.json
deleted file mode 100644
index dd6ae819ad738ac1a546e9f9282ef325c33b9ea0..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/normalizer.json
+++ /dev/null
@@ -1,1742 +0,0 @@
-{
- "accessorise": "accessorize",
- "accessorised": "accessorized",
- "accessorises": "accessorizes",
- "accessorising": "accessorizing",
- "acclimatisation": "acclimatization",
- "acclimatise": "acclimatize",
- "acclimatised": "acclimatized",
- "acclimatises": "acclimatizes",
- "acclimatising": "acclimatizing",
- "accoutrements": "accouterments",
- "aeon": "eon",
- "aeons": "eons",
- "aerogramme": "aerogram",
- "aerogrammes": "aerograms",
- "aeroplane": "airplane",
- "aeroplanes": "airplanes",
- "aesthete": "esthete",
- "aesthetes": "esthetes",
- "aesthetic": "esthetic",
- "aesthetically": "esthetically",
- "aesthetics": "esthetics",
- "aetiology": "etiology",
- "ageing": "aging",
- "aggrandisement": "aggrandizement",
- "agonise": "agonize",
- "agonised": "agonized",
- "agonises": "agonizes",
- "agonising": "agonizing",
- "agonisingly": "agonizingly",
- "almanack": "almanac",
- "almanacks": "almanacs",
- "aluminium": "aluminum",
- "amortisable": "amortizable",
- "amortisation": "amortization",
- "amortisations": "amortizations",
- "amortise": "amortize",
- "amortised": "amortized",
- "amortises": "amortizes",
- "amortising": "amortizing",
- "amphitheatre": "amphitheater",
- "amphitheatres": "amphitheaters",
- "anaemia": "anemia",
- "anaemic": "anemic",
- "anaesthesia": "anesthesia",
- "anaesthetic": "anesthetic",
- "anaesthetics": "anesthetics",
- "anaesthetise": "anesthetize",
- "anaesthetised": "anesthetized",
- "anaesthetises": "anesthetizes",
- "anaesthetising": "anesthetizing",
- "anaesthetist": "anesthetist",
- "anaesthetists": "anesthetists",
- "anaesthetize": "anesthetize",
- "anaesthetized": "anesthetized",
- "anaesthetizes": "anesthetizes",
- "anaesthetizing": "anesthetizing",
- "analogue": "analog",
- "analogues": "analogs",
- "analyse": "analyze",
- "analysed": "analyzed",
- "analyses": "analyzes",
- "analysing": "analyzing",
- "anglicise": "anglicize",
- "anglicised": "anglicized",
- "anglicises": "anglicizes",
- "anglicising": "anglicizing",
- "annualised": "annualized",
- "antagonise": "antagonize",
- "antagonised": "antagonized",
- "antagonises": "antagonizes",
- "antagonising": "antagonizing",
- "apologise": "apologize",
- "apologised": "apologized",
- "apologises": "apologizes",
- "apologising": "apologizing",
- "appal": "appall",
- "appals": "appalls",
- "appetiser": "appetizer",
- "appetisers": "appetizers",
- "appetising": "appetizing",
- "appetisingly": "appetizingly",
- "arbour": "arbor",
- "arbours": "arbors",
- "archaeologically": "archeologically",
- "archaeologist": "archeologist",
- "archaeologists": "archeologists",
- "archaeology": "archeology",
- "archeological": "archaeological",
- "ardour": "ardor",
- "armour": "armor",
- "armoured": "armored",
- "armourer": "armorer",
- "armourers": "armorers",
- "armouries": "armories",
- "armoury": "armory",
- "artefact": "artifact",
- "artefacts": "artifacts",
- "authorise": "authorize",
- "authorised": "authorized",
- "authorises": "authorizes",
- "authorising": "authorizing",
- "axe": "ax",
- "backpedalled": "backpedaled",
- "backpedalling": "backpedaling",
- "bannister": "banister",
- "bannisters": "banisters",
- "baptise": "baptize",
- "baptised": "baptized",
- "baptises": "baptizes",
- "baptising": "baptizing",
- "bastardise": "bastardize",
- "bastardised": "bastardized",
- "bastardises": "bastardizes",
- "bastardising": "bastardizing",
- "battleax": "battleaxe",
- "baulk": "balk",
- "baulked": "balked",
- "baulking": "balking",
- "baulks": "balks",
- "bedevilled": "bedeviled",
- "bedevilling": "bedeviling",
- "behaviour": "behavior",
- "behavioural": "behavioral",
- "behaviourism": "behaviorism",
- "behaviourist": "behaviorist",
- "behaviourists": "behaviorists",
- "behaviours": "behaviors",
- "behove": "behoove",
- "behoved": "behooved",
- "behoves": "behooves",
- "bejewelled": "bejeweled",
- "belabour": "belabor",
- "belaboured": "belabored",
- "belabouring": "belaboring",
- "belabours": "belabors",
- "bevelled": "beveled",
- "bevvies": "bevies",
- "bevvy": "bevy",
- "biassed": "biased",
- "biassing": "biasing",
- "bingeing": "binging",
- "bougainvillaea": "bougainvillea",
- "bougainvillaeas": "bougainvilleas",
- "bowdlerise": "bowdlerize",
- "bowdlerised": "bowdlerized",
- "bowdlerises": "bowdlerizes",
- "bowdlerising": "bowdlerizing",
- "breathalyse": "breathalyze",
- "breathalysed": "breathalyzed",
- "breathalyser": "breathalyzer",
- "breathalysers": "breathalyzers",
- "breathalyses": "breathalyzes",
- "breathalysing": "breathalyzing",
- "brutalise": "brutalize",
- "brutalised": "brutalized",
- "brutalises": "brutalizes",
- "brutalising": "brutalizing",
- "busses": "buses",
- "bussing": "busing",
- "caesarean": "cesarean",
- "caesareans": "cesareans",
- "calibre": "caliber",
- "calibres": "calibers",
- "calliper": "caliper",
- "callipers": "calipers",
- "callisthenics": "calisthenics",
- "canalise": "canalize",
- "canalised": "canalized",
- "canalises": "canalizes",
- "canalising": "canalizing",
- "cancelation": "cancellation",
- "cancelations": "cancellations",
- "cancelled": "canceled",
- "cancelling": "canceling",
- "candour": "candor",
- "cannibalise": "cannibalize",
- "cannibalised": "cannibalized",
- "cannibalises": "cannibalizes",
- "cannibalising": "cannibalizing",
- "canonise": "canonize",
- "canonised": "canonized",
- "canonises": "canonizes",
- "canonising": "canonizing",
- "capitalise": "capitalize",
- "capitalised": "capitalized",
- "capitalises": "capitalizes",
- "capitalising": "capitalizing",
- "caramelise": "caramelize",
- "caramelised": "caramelized",
- "caramelises": "caramelizes",
- "caramelising": "caramelizing",
- "carbonise": "carbonize",
- "carbonised": "carbonized",
- "carbonises": "carbonizes",
- "carbonising": "carbonizing",
- "carolled": "caroled",
- "carolling": "caroling",
- "catalogue": "catalog",
- "catalogued": "cataloged",
- "catalogues": "catalogs",
- "cataloguing": "cataloging",
- "catalyse": "catalyze",
- "catalysed": "catalyzed",
- "catalyses": "catalyzes",
- "catalysing": "catalyzing",
- "categorise": "categorize",
- "categorised": "categorized",
- "categorises": "categorizes",
- "categorising": "categorizing",
- "cauterise": "cauterize",
- "cauterised": "cauterized",
- "cauterises": "cauterizes",
- "cauterising": "cauterizing",
- "cavilled": "caviled",
- "cavilling": "caviling",
- "centigramme": "centigram",
- "centigrammes": "centigrams",
- "centilitre": "centiliter",
- "centilitres": "centiliters",
- "centimetre": "centimeter",
- "centimetres": "centimeters",
- "centralise": "centralize",
- "centralised": "centralized",
- "centralises": "centralizes",
- "centralising": "centralizing",
- "centre": "center",
- "centred": "centered",
- "centrefold": "centerfold",
- "centrefolds": "centerfolds",
- "centrepiece": "centerpiece",
- "centrepieces": "centerpieces",
- "centres": "centers",
- "channelled": "channeled",
- "channelling": "channeling",
- "characterise": "characterize",
- "characterised": "characterized",
- "characterises": "characterizes",
- "characterising": "characterizing",
- "cheque": "check",
- "chequebook": "checkbook",
- "chequebooks": "checkbooks",
- "chequered": "checkered",
- "cheques": "checks",
- "chilli": "chili",
- "chimaera": "chimera",
- "chimaeras": "chimeras",
- "chiselled": "chiseled",
- "chiselling": "chiseling",
- "circularise": "circularize",
- "circularised": "circularized",
- "circularises": "circularizes",
- "circularising": "circularizing",
- "civilise": "civilize",
- "civilised": "civilized",
- "civilises": "civilizes",
- "civilising": "civilizing",
- "clamour": "clamor",
- "clamoured": "clamored",
- "clamouring": "clamoring",
- "clamours": "clamors",
- "clangour": "clangor",
- "clarinettist": "clarinetist",
- "clarinettists": "clarinetists",
- "collectivise": "collectivize",
- "collectivised": "collectivized",
- "collectivises": "collectivizes",
- "collectivising": "collectivizing",
- "colonisation": "colonization",
- "colonise": "colonize",
- "colonised": "colonized",
- "coloniser": "colonizer",
- "colonisers": "colonizers",
- "colonises": "colonizes",
- "colonising": "colonizing",
- "colour": "color",
- "colourant": "colorant",
- "colourants": "colorants",
- "coloured": "colored",
- "coloureds": "coloreds",
- "colourful": "colorful",
- "colourfully": "colorfully",
- "colouring": "coloring",
- "colourize": "colorize",
- "colourized": "colorized",
- "colourizes": "colorizes",
- "colourizing": "colorizing",
- "colourless": "colorless",
- "colours": "colors",
- "commercialise": "commercialize",
- "commercialised": "commercialized",
- "commercialises": "commercializes",
- "commercialising": "commercializing",
- "compartmentalise": "compartmentalize",
- "compartmentalised": "compartmentalized",
- "compartmentalises": "compartmentalizes",
- "compartmentalising": "compartmentalizing",
- "computerise": "computerize",
- "computerised": "computerized",
- "computerises": "computerizes",
- "computerising": "computerizing",
- "conceptualise": "conceptualize",
- "conceptualised": "conceptualized",
- "conceptualises": "conceptualizes",
- "conceptualising": "conceptualizing",
- "connexion": "connection",
- "connexions": "connections",
- "contextualise": "contextualize",
- "contextualised": "contextualized",
- "contextualises": "contextualizes",
- "contextualising": "contextualizing",
- "cosier": "cozier",
- "cosies": "cozies",
- "cosiest": "coziest",
- "cosily": "cozily",
- "cosiness": "coziness",
- "cosy": "cozy",
- "councillor": "councilor",
- "councillors": "councilors",
- "counselled": "counseled",
- "counselling": "counseling",
- "counsellor": "counselor",
- "counsellors": "counselors",
- "crenelated": "crenellated",
- "criminalise": "criminalize",
- "criminalised": "criminalized",
- "criminalises": "criminalizes",
- "criminalising": "criminalizing",
- "criticise": "criticize",
- "criticised": "criticized",
- "criticises": "criticizes",
- "criticising": "criticizing",
- "crueller": "crueler",
- "cruellest": "cruelest",
- "crystallisation": "crystallization",
- "crystallise": "crystallize",
- "crystallised": "crystallized",
- "crystallises": "crystallizes",
- "crystallising": "crystallizing",
- "cudgelled": "cudgeled",
- "cudgelling": "cudgeling",
- "customise": "customize",
- "customised": "customized",
- "customises": "customizes",
- "customising": "customizing",
- "cypher": "cipher",
- "cyphers": "ciphers",
- "decentralisation": "decentralization",
- "decentralise": "decentralize",
- "decentralised": "decentralized",
- "decentralises": "decentralizes",
- "decentralising": "decentralizing",
- "decriminalisation": "decriminalization",
- "decriminalise": "decriminalize",
- "decriminalised": "decriminalized",
- "decriminalises": "decriminalizes",
- "decriminalising": "decriminalizing",
- "defence": "defense",
- "defenceless": "defenseless",
- "defences": "defenses",
- "dehumanisation": "dehumanization",
- "dehumanise": "dehumanize",
- "dehumanised": "dehumanized",
- "dehumanises": "dehumanizes",
- "dehumanising": "dehumanizing",
- "demeanour": "demeanor",
- "demilitarisation": "demilitarization",
- "demilitarise": "demilitarize",
- "demilitarised": "demilitarized",
- "demilitarises": "demilitarizes",
- "demilitarising": "demilitarizing",
- "demobilisation": "demobilization",
- "demobilise": "demobilize",
- "demobilised": "demobilized",
- "demobilises": "demobilizes",
- "demobilising": "demobilizing",
- "democratisation": "democratization",
- "democratise": "democratize",
- "democratised": "democratized",
- "democratises": "democratizes",
- "democratising": "democratizing",
- "demonise": "demonize",
- "demonised": "demonized",
- "demonises": "demonizes",
- "demonising": "demonizing",
- "demoralisation": "demoralization",
- "demoralise": "demoralize",
- "demoralised": "demoralized",
- "demoralises": "demoralizes",
- "demoralising": "demoralizing",
- "denationalisation": "denationalization",
- "denationalise": "denationalize",
- "denationalised": "denationalized",
- "denationalises": "denationalizes",
- "denationalising": "denationalizing",
- "deodorise": "deodorize",
- "deodorised": "deodorized",
- "deodorises": "deodorizes",
- "deodorising": "deodorizing",
- "depersonalise": "depersonalize",
- "depersonalised": "depersonalized",
- "depersonalises": "depersonalizes",
- "depersonalising": "depersonalizing",
- "deputise": "deputize",
- "deputised": "deputized",
- "deputises": "deputizes",
- "deputising": "deputizing",
- "desensitisation": "desensitization",
- "desensitise": "desensitize",
- "desensitised": "desensitized",
- "desensitises": "desensitizes",
- "desensitising": "desensitizing",
- "destabilisation": "destabilization",
- "destabilise": "destabilize",
- "destabilised": "destabilized",
- "destabilises": "destabilizes",
- "destabilising": "destabilizing",
- "dialled": "dialed",
- "dialling": "dialing",
- "dialogue": "dialog",
- "dialogues": "dialogs",
- "diarrhoea": "diarrhea",
- "digitise": "digitize",
- "digitised": "digitized",
- "digitises": "digitizes",
- "digitising": "digitizing",
- "disc": "disk",
- "discolour": "discolor",
- "discoloured": "discolored",
- "discolouring": "discoloring",
- "discolours": "discolors",
- "discs": "disks",
- "disembowelled": "disemboweled",
- "disembowelling": "disemboweling",
- "disfavour": "disfavor",
- "dishevelled": "disheveled",
- "dishonour": "dishonor",
- "dishonourable": "dishonorable",
- "dishonourably": "dishonorably",
- "dishonoured": "dishonored",
- "dishonouring": "dishonoring",
- "dishonours": "dishonors",
- "disorganisation": "disorganization",
- "disorganised": "disorganized",
- "distil": "distill",
- "distils": "distills",
- "dramatisation": "dramatization",
- "dramatisations": "dramatizations",
- "dramatise": "dramatize",
- "dramatised": "dramatized",
- "dramatises": "dramatizes",
- "dramatising": "dramatizing",
- "draught": "draft",
- "draughtboard": "draftboard",
- "draughtboards": "draftboards",
- "draughtier": "draftier",
- "draughtiest": "draftiest",
- "draughts": "drafts",
- "draughtsman": "draftsman",
- "draughtsmanship": "draftsmanship",
- "draughtsmen": "draftsmen",
- "draughtswoman": "draftswoman",
- "draughtswomen": "draftswomen",
- "draughty": "drafty",
- "drivelled": "driveled",
- "drivelling": "driveling",
- "duelled": "dueled",
- "duelling": "dueling",
- "economise": "economize",
- "economised": "economized",
- "economises": "economizes",
- "economising": "economizing",
- "editorialise": "editorialize",
- "editorialised": "editorialized",
- "editorialises": "editorializes",
- "editorialising": "editorializing",
- "edoema": "edema",
- "empathise": "empathize",
- "empathised": "empathized",
- "empathises": "empathizes",
- "empathising": "empathizing",
- "emphasise": "emphasize",
- "emphasised": "emphasized",
- "emphasises": "emphasizes",
- "emphasising": "emphasizing",
- "enamelled": "enameled",
- "enamelling": "enameling",
- "enamoured": "enamored",
- "encyclopaedia": "encyclopedia",
- "encyclopaedias": "encyclopedias",
- "encyclopaedic": "encyclopedic",
- "endeavour": "endeavor",
- "endeavoured": "endeavored",
- "endeavouring": "endeavoring",
- "endeavours": "endeavors",
- "energise": "energize",
- "energised": "energized",
- "energises": "energizes",
- "energising": "energizing",
- "enrol": "enroll",
- "enrols": "enrolls",
- "enthral": "enthrall",
- "enthrals": "enthralls",
- "epaulette": "epaulet",
- "epaulettes": "epaulets",
- "epicentre": "epicenter",
- "epicentres": "epicenters",
- "epilogue": "epilog",
- "epilogues": "epilogs",
- "epitomise": "epitomize",
- "epitomised": "epitomized",
- "epitomises": "epitomizes",
- "epitomising": "epitomizing",
- "equalisation": "equalization",
- "equalise": "equalize",
- "equalised": "equalized",
- "equaliser": "equalizer",
- "equalisers": "equalizers",
- "equalises": "equalizes",
- "equalising": "equalizing",
- "eulogise": "eulogize",
- "eulogised": "eulogized",
- "eulogises": "eulogizes",
- "eulogising": "eulogizing",
- "evangelise": "evangelize",
- "evangelised": "evangelized",
- "evangelises": "evangelizes",
- "evangelising": "evangelizing",
- "exorcise": "exorcize",
- "exorcised": "exorcized",
- "exorcises": "exorcizes",
- "exorcising": "exorcizing",
- "extemporisation": "extemporization",
- "extemporise": "extemporize",
- "extemporised": "extemporized",
- "extemporises": "extemporizes",
- "extemporising": "extemporizing",
- "externalisation": "externalization",
- "externalisations": "externalizations",
- "externalise": "externalize",
- "externalised": "externalized",
- "externalises": "externalizes",
- "externalising": "externalizing",
- "factorise": "factorize",
- "factorised": "factorized",
- "factorises": "factorizes",
- "factorising": "factorizing",
- "faecal": "fecal",
- "faeces": "feces",
- "familiarisation": "familiarization",
- "familiarise": "familiarize",
- "familiarised": "familiarized",
- "familiarises": "familiarizes",
- "familiarising": "familiarizing",
- "fantasise": "fantasize",
- "fantasised": "fantasized",
- "fantasises": "fantasizes",
- "fantasising": "fantasizing",
- "favour": "favor",
- "favourable": "favorable",
- "favourably": "favorably",
- "favoured": "favored",
- "favouring": "favoring",
- "favourite": "favorite",
- "favourites": "favorites",
- "favouritism": "favoritism",
- "favours": "favors",
- "feminise": "feminize",
- "feminised": "feminized",
- "feminises": "feminizes",
- "feminising": "feminizing",
- "fertilisation": "fertilization",
- "fertilise": "fertilize",
- "fertilised": "fertilized",
- "fertiliser": "fertilizer",
- "fertilisers": "fertilizers",
- "fertilises": "fertilizes",
- "fertilising": "fertilizing",
- "fervour": "fervor",
- "fibre": "fiber",
- "fibreglass": "fiberglass",
- "fibres": "fibers",
- "fictionalisation": "fictionalization",
- "fictionalisations": "fictionalizations",
- "fictionalise": "fictionalize",
- "fictionalised": "fictionalized",
- "fictionalises": "fictionalizes",
- "fictionalising": "fictionalizing",
- "fillet": "filet",
- "filleted": "fileted",
- "filleting": "fileting",
- "fillets": "filets",
- "finalisation": "finalization",
- "finalise": "finalize",
- "finalised": "finalized",
- "finalises": "finalizes",
- "finalising": "finalizing",
- "flautist": "flutist",
- "flautists": "flutists",
- "flavour": "flavor",
- "flavoured": "flavored",
- "flavouring": "flavoring",
- "flavourings": "flavorings",
- "flavourless": "flavorless",
- "flavours": "flavors",
- "flavoursome": "flavorsome",
- "flyer / flier": "flier / flyer",
- "foetal": "fetal",
- "foetid": "fetid",
- "foetus": "fetus",
- "foetuses": "fetuses",
- "formalisation": "formalization",
- "formalise": "formalize",
- "formalised": "formalized",
- "formalises": "formalizes",
- "formalising": "formalizing",
- "fossilisation": "fossilization",
- "fossilise": "fossilize",
- "fossilised": "fossilized",
- "fossilises": "fossilizes",
- "fossilising": "fossilizing",
- "fraternisation": "fraternization",
- "fraternise": "fraternize",
- "fraternised": "fraternized",
- "fraternises": "fraternizes",
- "fraternising": "fraternizing",
- "fulfil": "fulfill",
- "fulfilment": "fulfillment",
- "fulfils": "fulfills",
- "funnelled": "funneled",
- "funnelling": "funneling",
- "gage": "gauge",
- "gaged": "gauged",
- "gages": "gauges",
- "gaging": "gauging",
- "galvanise": "galvanize",
- "galvanised": "galvanized",
- "galvanises": "galvanizes",
- "galvanising": "galvanizing",
- "gambolled": "gamboled",
- "gambolling": "gamboling",
- "gaol": "jail",
- "gaolbird": "jailbird",
- "gaolbirds": "jailbirds",
- "gaolbreak": "jailbreak",
- "gaolbreaks": "jailbreaks",
- "gaoled": "jailed",
- "gaoler": "jailer",
- "gaolers": "jailers",
- "gaoling": "jailing",
- "gaols": "jails",
- "gasses": "gases",
- "generalisation": "generalization",
- "generalisations": "generalizations",
- "generalise": "generalize",
- "generalised": "generalized",
- "generalises": "generalizes",
- "generalising": "generalizing",
- "ghettoise": "ghettoize",
- "ghettoised": "ghettoized",
- "ghettoises": "ghettoizes",
- "ghettoising": "ghettoizing",
- "gipsies": "gypsies",
- "glamor": "glamour",
- "glamorise": "glamorize",
- "glamorised": "glamorized",
- "glamorises": "glamorizes",
- "glamorising": "glamorizing",
- "globalisation": "globalization",
- "globalise": "globalize",
- "globalised": "globalized",
- "globalises": "globalizes",
- "globalising": "globalizing",
- "glueing": "gluing",
- "goitre": "goiter",
- "goitres": "goiters",
- "gonorrhoea": "gonorrhea",
- "gramme": "gram",
- "grammes": "grams",
- "gravelled": "graveled",
- "grey": "gray",
- "greyed": "grayed",
- "greying": "graying",
- "greyish": "grayish",
- "greyness": "grayness",
- "greys": "grays",
- "grovelled": "groveled",
- "grovelling": "groveling",
- "groyne": "groin",
- "groynes": "groins",
- "gruelling": "grueling",
- "gruellingly": "gruelingly",
- "gryphon": "griffin",
- "gryphons": "griffins",
- "gynaecological": "gynecological",
- "gynaecologist": "gynecologist",
- "gynaecologists": "gynecologists",
- "gynaecology": "gynecology",
- "haematological": "hematological",
- "haematologist": "hematologist",
- "haematologists": "hematologists",
- "haematology": "hematology",
- "haemoglobin": "hemoglobin",
- "haemophilia": "hemophilia",
- "haemophiliac": "hemophiliac",
- "haemophiliacs": "hemophiliacs",
- "haemorrhage": "hemorrhage",
- "haemorrhaged": "hemorrhaged",
- "haemorrhages": "hemorrhages",
- "haemorrhaging": "hemorrhaging",
- "haemorrhoids": "hemorrhoids",
- "harbour": "harbor",
- "harboured": "harbored",
- "harbouring": "harboring",
- "harbours": "harbors",
- "harmonisation": "harmonization",
- "harmonise": "harmonize",
- "harmonised": "harmonized",
- "harmonises": "harmonizes",
- "harmonising": "harmonizing",
- "homoeopath": "homeopath",
- "homoeopathic": "homeopathic",
- "homoeopaths": "homeopaths",
- "homoeopathy": "homeopathy",
- "homogenise": "homogenize",
- "homogenised": "homogenized",
- "homogenises": "homogenizes",
- "homogenising": "homogenizing",
- "honour": "honor",
- "honourable": "honorable",
- "honourably": "honorably",
- "honoured": "honored",
- "honouring": "honoring",
- "honours": "honors",
- "hospitalisation": "hospitalization",
- "hospitalise": "hospitalize",
- "hospitalised": "hospitalized",
- "hospitalises": "hospitalizes",
- "hospitalising": "hospitalizing",
- "humanise": "humanize",
- "humanised": "humanized",
- "humanises": "humanizes",
- "humanising": "humanizing",
- "humour": "humor",
- "humoured": "humored",
- "humouring": "humoring",
- "humourless": "humorless",
- "humours": "humors",
- "hybridise": "hybridize",
- "hybridised": "hybridized",
- "hybridises": "hybridizes",
- "hybridising": "hybridizing",
- "hypnotise": "hypnotize",
- "hypnotised": "hypnotized",
- "hypnotises": "hypnotizes",
- "hypnotising": "hypnotizing",
- "hypothesise": "hypothesize",
- "hypothesised": "hypothesized",
- "hypothesises": "hypothesizes",
- "hypothesising": "hypothesizing",
- "idealisation": "idealization",
- "idealise": "idealize",
- "idealised": "idealized",
- "idealises": "idealizes",
- "idealising": "idealizing",
- "idolise": "idolize",
- "idolised": "idolized",
- "idolises": "idolizes",
- "idolising": "idolizing",
- "immobilisation": "immobilization",
- "immobilise": "immobilize",
- "immobilised": "immobilized",
- "immobiliser": "immobilizer",
- "immobilisers": "immobilizers",
- "immobilises": "immobilizes",
- "immobilising": "immobilizing",
- "immortalise": "immortalize",
- "immortalised": "immortalized",
- "immortalises": "immortalizes",
- "immortalising": "immortalizing",
- "immunisation": "immunization",
- "immunise": "immunize",
- "immunised": "immunized",
- "immunises": "immunizes",
- "immunising": "immunizing",
- "impanelled": "impaneled",
- "impanelling": "impaneling",
- "imperilled": "imperiled",
- "imperilling": "imperiling",
- "individualise": "individualize",
- "individualised": "individualized",
- "individualises": "individualizes",
- "individualising": "individualizing",
- "industrialise": "industrialize",
- "industrialised": "industrialized",
- "industrialises": "industrializes",
- "industrialising": "industrializing",
- "inflexion": "inflection",
- "inflexions": "inflections",
- "initialise": "initialize",
- "initialised": "initialized",
- "initialises": "initializes",
- "initialising": "initializing",
- "initialled": "initialed",
- "initialling": "initialing",
- "instal": "install",
- "instalment": "installment",
- "instalments": "installments",
- "instals": "installs",
- "instil": "instill",
- "instils": "instills",
- "institutionalisation": "institutionalization",
- "institutionalise": "institutionalize",
- "institutionalised": "institutionalized",
- "institutionalises": "institutionalizes",
- "institutionalising": "institutionalizing",
- "intellectualise": "intellectualize",
- "intellectualised": "intellectualized",
- "intellectualises": "intellectualizes",
- "intellectualising": "intellectualizing",
- "internalisation": "internalization",
- "internalise": "internalize",
- "internalised": "internalized",
- "internalises": "internalizes",
- "internalising": "internalizing",
- "internationalisation": "internationalization",
- "internationalise": "internationalize",
- "internationalised": "internationalized",
- "internationalises": "internationalizes",
- "internationalising": "internationalizing",
- "ionisation": "ionization",
- "ionise": "ionize",
- "ionised": "ionized",
- "ioniser": "ionizer",
- "ionisers": "ionizers",
- "ionises": "ionizes",
- "ionising": "ionizing",
- "italicise": "italicize",
- "italicised": "italicized",
- "italicises": "italicizes",
- "italicising": "italicizing",
- "itemise": "itemize",
- "itemised": "itemized",
- "itemises": "itemizes",
- "itemising": "itemizing",
- "jeopardise": "jeopardize",
- "jeopardised": "jeopardized",
- "jeopardises": "jeopardizes",
- "jeopardising": "jeopardizing",
- "jewelled": "jeweled",
- "jeweller": "jeweler",
- "jewellers": "jewelers",
- "jewellery": "jewelry",
- "judgement": "judgment",
- "kilogramme": "kilogram",
- "kilogrammes": "kilograms",
- "kilometre": "kilometer",
- "kilometres": "kilometers",
- "labelled": "labeled",
- "labelling": "labeling",
- "labour": "labor",
- "laboured": "labored",
- "labourer": "laborer",
- "labourers": "laborers",
- "labouring": "laboring",
- "labours": "labors",
- "lacklustre": "lackluster",
- "legalisation": "legalization",
- "legalise": "legalize",
- "legalised": "legalized",
- "legalises": "legalizes",
- "legalising": "legalizing",
- "legitimise": "legitimize",
- "legitimised": "legitimized",
- "legitimises": "legitimizes",
- "legitimising": "legitimizing",
- "leukaemia": "leukemia",
- "levelled": "leveled",
- "leveller": "leveler",
- "levellers": "levelers",
- "levelling": "leveling",
- "libelled": "libeled",
- "libelling": "libeling",
- "libellous": "libelous",
- "liberalisation": "liberalization",
- "liberalise": "liberalize",
- "liberalised": "liberalized",
- "liberalises": "liberalizes",
- "liberalising": "liberalizing",
- "licence": "license",
- "licenced": "licensed",
- "licences": "licenses",
- "licencing": "licensing",
- "likeable": "likable",
- "lionisation": "lionization",
- "lionise": "lionize",
- "lionised": "lionized",
- "lionises": "lionizes",
- "lionising": "lionizing",
- "liquidise": "liquidize",
- "liquidised": "liquidized",
- "liquidiser": "liquidizer",
- "liquidisers": "liquidizers",
- "liquidises": "liquidizes",
- "liquidising": "liquidizing",
- "litre": "liter",
- "litres": "liters",
- "localise": "localize",
- "localised": "localized",
- "localises": "localizes",
- "localising": "localizing",
- "louvre": "louver",
- "louvred": "louvered",
- "louvres": "louvers",
- "lustre": "luster",
- "magnetise": "magnetize",
- "magnetised": "magnetized",
- "magnetises": "magnetizes",
- "magnetising": "magnetizing",
- "manoeuvrability": "maneuverability",
- "manoeuvrable": "maneuverable",
- "manoeuvre": "maneuver",
- "manoeuvred": "maneuvered",
- "manoeuvres": "maneuvers",
- "manoeuvring": "maneuvering",
- "manoeuvrings": "maneuverings",
- "marginalisation": "marginalization",
- "marginalise": "marginalize",
- "marginalised": "marginalized",
- "marginalises": "marginalizes",
- "marginalising": "marginalizing",
- "marshalled": "marshaled",
- "marshalling": "marshaling",
- "marvelled": "marveled",
- "marvelling": "marveling",
- "marvellous": "marvelous",
- "marvellously": "marvelously",
- "materialisation": "materialization",
- "materialise": "materialize",
- "materialised": "materialized",
- "materialises": "materializes",
- "materialising": "materializing",
- "maximisation": "maximization",
- "maximise": "maximize",
- "maximised": "maximized",
- "maximises": "maximizes",
- "maximising": "maximizing",
- "meagre": "meager",
- "mechanisation": "mechanization",
- "mechanise": "mechanize",
- "mechanised": "mechanized",
- "mechanises": "mechanizes",
- "mechanising": "mechanizing",
- "mediaeval": "medieval",
- "memorialise": "memorialize",
- "memorialised": "memorialized",
- "memorialises": "memorializes",
- "memorialising": "memorializing",
- "memorise": "memorize",
- "memorised": "memorized",
- "memorises": "memorizes",
- "memorising": "memorizing",
- "mesmerise": "mesmerize",
- "mesmerised": "mesmerized",
- "mesmerises": "mesmerizes",
- "mesmerising": "mesmerizing",
- "metabolise": "metabolize",
- "metabolised": "metabolized",
- "metabolises": "metabolizes",
- "metabolising": "metabolizing",
- "metre": "meter",
- "metres": "meters",
- "mhm": "hmm",
- "micrometre": "micrometer",
- "micrometres": "micrometers",
- "militarise": "militarize",
- "militarised": "militarized",
- "militarises": "militarizes",
- "militarising": "militarizing",
- "milligramme": "milligram",
- "milligrammes": "milligrams",
- "millilitre": "milliliter",
- "millilitres": "milliliters",
- "millimetre": "millimeter",
- "millimetres": "millimeters",
- "miniaturisation": "miniaturization",
- "miniaturise": "miniaturize",
- "miniaturised": "miniaturized",
- "miniaturises": "miniaturizes",
- "miniaturising": "miniaturizing",
- "minibusses": "minibuses",
- "minimise": "minimize",
- "minimised": "minimized",
- "minimises": "minimizes",
- "minimising": "minimizing",
- "misbehaviour": "misbehavior",
- "misdemeanour": "misdemeanor",
- "misdemeanours": "misdemeanors",
- "misspelt": "misspelled",
- "mitre": "miter",
- "mitres": "miters",
- "mm": "hmm",
- "mmm": "hmm",
- "mobilisation": "mobilization",
- "mobilise": "mobilize",
- "mobilised": "mobilized",
- "mobilises": "mobilizes",
- "mobilising": "mobilizing",
- "modelled": "modeled",
- "modeller": "modeler",
- "modellers": "modelers",
- "modelling": "modeling",
- "modernise": "modernize",
- "modernised": "modernized",
- "modernises": "modernizes",
- "modernising": "modernizing",
- "moisturise": "moisturize",
- "moisturised": "moisturized",
- "moisturiser": "moisturizer",
- "moisturisers": "moisturizers",
- "moisturises": "moisturizes",
- "moisturising": "moisturizing",
- "monologue": "monolog",
- "monologues": "monologs",
- "monopolisation": "monopolization",
- "monopolise": "monopolize",
- "monopolised": "monopolized",
- "monopolises": "monopolizes",
- "monopolising": "monopolizing",
- "moralise": "moralize",
- "moralised": "moralized",
- "moralises": "moralizes",
- "moralising": "moralizing",
- "motorised": "motorized",
- "mould": "mold",
- "moulded": "molded",
- "moulder": "molder",
- "mouldered": "moldered",
- "mouldering": "moldering",
- "moulders": "molders",
- "mouldier": "moldier",
- "mouldiest": "moldiest",
- "moulding": "molding",
- "mouldings": "moldings",
- "moulds": "molds",
- "mouldy": "moldy",
- "moult": "molt",
- "moulted": "molted",
- "moulting": "molting",
- "moults": "molts",
- "moustache": "mustache",
- "moustached": "mustached",
- "moustaches": "mustaches",
- "moustachioed": "mustachioed",
- "multicoloured": "multicolored",
- "nationalisation": "nationalization",
- "nationalisations": "nationalizations",
- "nationalise": "nationalize",
- "nationalised": "nationalized",
- "nationalises": "nationalizes",
- "nationalising": "nationalizing",
- "naturalisation": "naturalization",
- "naturalise": "naturalize",
- "naturalised": "naturalized",
- "naturalises": "naturalizes",
- "naturalising": "naturalizing",
- "neighbour": "neighbor",
- "neighbourhood": "neighborhood",
- "neighbourhoods": "neighborhoods",
- "neighbouring": "neighboring",
- "neighbourliness": "neighborliness",
- "neighbourly": "neighborly",
- "neighbours": "neighbors",
- "neutralisation": "neutralization",
- "neutralise": "neutralize",
- "neutralised": "neutralized",
- "neutralises": "neutralizes",
- "neutralising": "neutralizing",
- "normalisation": "normalization",
- "normalise": "normalize",
- "normalised": "normalized",
- "normalises": "normalizes",
- "normalising": "normalizing",
- "odour": "odor",
- "odourless": "odorless",
- "odours": "odors",
- "oesophagus": "esophagus",
- "oesophaguses": "esophaguses",
- "oestrogen": "estrogen",
- "offence": "offense",
- "offences": "offenses",
- "omelette": "omelet",
- "omelettes": "omelets",
- "optimise": "optimize",
- "optimised": "optimized",
- "optimises": "optimizes",
- "optimising": "optimizing",
- "organisation": "organization",
- "organisational": "organizational",
- "organisations": "organizations",
- "organise": "organize",
- "organised": "organized",
- "organiser": "organizer",
- "organisers": "organizers",
- "organises": "organizes",
- "organising": "organizing",
- "orthopaedic": "orthopedic",
- "orthopaedics": "orthopedics",
- "ostracise": "ostracize",
- "ostracised": "ostracized",
- "ostracises": "ostracizes",
- "ostracising": "ostracizing",
- "outmanoeuvre": "outmaneuver",
- "outmanoeuvred": "outmaneuvered",
- "outmanoeuvres": "outmaneuvers",
- "outmanoeuvring": "outmaneuvering",
- "overemphasise": "overemphasize",
- "overemphasised": "overemphasized",
- "overemphasises": "overemphasizes",
- "overemphasising": "overemphasizing",
- "oxidisation": "oxidization",
- "oxidise": "oxidize",
- "oxidised": "oxidized",
- "oxidises": "oxidizes",
- "oxidising": "oxidizing",
- "paederast": "pederast",
- "paederasts": "pederasts",
- "paediatric": "pediatric",
- "paediatrician": "pediatrician",
- "paediatricians": "pediatricians",
- "paediatrics": "pediatrics",
- "paedophile": "pedophile",
- "paedophiles": "pedophiles",
- "paedophilia": "pedophilia",
- "palaeolithic": "paleolithic",
- "palaeontologist": "paleontologist",
- "palaeontologists": "paleontologists",
- "palaeontology": "paleontology",
- "panelled": "paneled",
- "panelling": "paneling",
- "panellist": "panelist",
- "panellists": "panelists",
- "paralyse": "paralyze",
- "paralysed": "paralyzed",
- "paralyses": "paralyzes",
- "paralysing": "paralyzing",
- "parcelled": "parceled",
- "parcelling": "parceling",
- "parlour": "parlor",
- "parlours": "parlors",
- "particularise": "particularize",
- "particularised": "particularized",
- "particularises": "particularizes",
- "particularising": "particularizing",
- "passivisation": "passivization",
- "passivise": "passivize",
- "passivised": "passivized",
- "passivises": "passivizes",
- "passivising": "passivizing",
- "pasteurisation": "pasteurization",
- "pasteurise": "pasteurize",
- "pasteurised": "pasteurized",
- "pasteurises": "pasteurizes",
- "pasteurising": "pasteurizing",
- "patronise": "patronize",
- "patronised": "patronized",
- "patronises": "patronizes",
- "patronising": "patronizing",
- "patronisingly": "patronizingly",
- "pedalled": "pedaled",
- "pedalling": "pedaling",
- "pedestrianisation": "pedestrianization",
- "pedestrianise": "pedestrianize",
- "pedestrianised": "pedestrianized",
- "pedestrianises": "pedestrianizes",
- "pedestrianising": "pedestrianizing",
- "penalise": "penalize",
- "penalised": "penalized",
- "penalises": "penalizes",
- "penalising": "penalizing",
- "pencilled": "penciled",
- "pencilling": "penciling",
- "personalise": "personalize",
- "personalised": "personalized",
- "personalises": "personalizes",
- "personalising": "personalizing",
- "pharmacopoeia": "pharmacopeia",
- "pharmacopoeias": "pharmacopeias",
- "philosophise": "philosophize",
- "philosophised": "philosophized",
- "philosophises": "philosophizes",
- "philosophising": "philosophizing",
- "philtre": "filter",
- "philtres": "filters",
- "phoney": "phony",
- "plagiarise": "plagiarize",
- "plagiarised": "plagiarized",
- "plagiarises": "plagiarizes",
- "plagiarising": "plagiarizing",
- "plough": "plow",
- "ploughed": "plowed",
- "ploughing": "plowing",
- "ploughman": "plowman",
- "ploughmen": "plowmen",
- "ploughs": "plows",
- "ploughshare": "plowshare",
- "ploughshares": "plowshares",
- "polarisation": "polarization",
- "polarise": "polarize",
- "polarised": "polarized",
- "polarises": "polarizes",
- "polarising": "polarizing",
- "politicisation": "politicization",
- "politicise": "politicize",
- "politicised": "politicized",
- "politicises": "politicizes",
- "politicising": "politicizing",
- "popularisation": "popularization",
- "popularise": "popularize",
- "popularised": "popularized",
- "popularises": "popularizes",
- "popularising": "popularizing",
- "pouffe": "pouf",
- "pouffes": "poufs",
- "practise": "practice",
- "practised": "practiced",
- "practises": "practices",
- "practising": "practicing",
- "praesidium": "presidium",
- "praesidiums": "presidiums",
- "pressurisation": "pressurization",
- "pressurise": "pressurize",
- "pressurised": "pressurized",
- "pressurises": "pressurizes",
- "pressurising": "pressurizing",
- "pretence": "pretense",
- "pretences": "pretenses",
- "primaeval": "primeval",
- "prioritisation": "prioritization",
- "prioritise": "prioritize",
- "prioritised": "prioritized",
- "prioritises": "prioritizes",
- "prioritising": "prioritizing",
- "privatisation": "privatization",
- "privatisations": "privatizations",
- "privatise": "privatize",
- "privatised": "privatized",
- "privatises": "privatizes",
- "privatising": "privatizing",
- "professionalisation": "professionalization",
- "professionalise": "professionalize",
- "professionalised": "professionalized",
- "professionalises": "professionalizes",
- "professionalising": "professionalizing",
- "programme": "program",
- "programmes": "programs",
- "prologue": "prolog",
- "prologues": "prologs",
- "propagandise": "propagandize",
- "propagandised": "propagandized",
- "propagandises": "propagandizes",
- "propagandising": "propagandizing",
- "proselytise": "proselytize",
- "proselytised": "proselytized",
- "proselytiser": "proselytizer",
- "proselytisers": "proselytizers",
- "proselytises": "proselytizes",
- "proselytising": "proselytizing",
- "psychoanalyse": "psychoanalyze",
- "psychoanalysed": "psychoanalyzed",
- "psychoanalyses": "psychoanalyzes",
- "psychoanalysing": "psychoanalyzing",
- "publicise": "publicize",
- "publicised": "publicized",
- "publicises": "publicizes",
- "publicising": "publicizing",
- "pulverisation": "pulverization",
- "pulverise": "pulverize",
- "pulverised": "pulverized",
- "pulverises": "pulverizes",
- "pulverising": "pulverizing",
- "pummelled": "pummel",
- "pummelling": "pummeled",
- "pyjama": "pajama",
- "pyjamas": "pajamas",
- "pzazz": "pizzazz",
- "quarrelled": "quarreled",
- "quarrelling": "quarreling",
- "radicalise": "radicalize",
- "radicalised": "radicalized",
- "radicalises": "radicalizes",
- "radicalising": "radicalizing",
- "rancour": "rancor",
- "randomise": "randomize",
- "randomised": "randomized",
- "randomises": "randomizes",
- "randomising": "randomizing",
- "rationalisation": "rationalization",
- "rationalisations": "rationalizations",
- "rationalise": "rationalize",
- "rationalised": "rationalized",
- "rationalises": "rationalizes",
- "rationalising": "rationalizing",
- "ravelled": "raveled",
- "ravelling": "raveling",
- "realisable": "realizable",
- "realisation": "realization",
- "realisations": "realizations",
- "realise": "realize",
- "realised": "realized",
- "realises": "realizes",
- "realising": "realizing",
- "recognisable": "recognizable",
- "recognisably": "recognizably",
- "recognisance": "recognizance",
- "recognise": "recognize",
- "recognised": "recognized",
- "recognises": "recognizes",
- "recognising": "recognizing",
- "reconnoitre": "reconnoiter",
- "reconnoitred": "reconnoitered",
- "reconnoitres": "reconnoiters",
- "reconnoitring": "reconnoitering",
- "refuelled": "refueled",
- "refuelling": "refueling",
- "regularisation": "regularization",
- "regularise": "regularize",
- "regularised": "regularized",
- "regularises": "regularizes",
- "regularising": "regularizing",
- "remodelled": "remodeled",
- "remodelling": "remodeling",
- "remould": "remold",
- "remoulded": "remolded",
- "remoulding": "remolding",
- "remoulds": "remolds",
- "reorganisation": "reorganization",
- "reorganisations": "reorganizations",
- "reorganise": "reorganize",
- "reorganised": "reorganized",
- "reorganises": "reorganizes",
- "reorganising": "reorganizing",
- "revelled": "reveled",
- "reveller": "reveler",
- "revellers": "revelers",
- "revelling": "reveling",
- "revitalise": "revitalize",
- "revitalised": "revitalized",
- "revitalises": "revitalizes",
- "revitalising": "revitalizing",
- "revolutionise": "revolutionize",
- "revolutionised": "revolutionized",
- "revolutionises": "revolutionizes",
- "revolutionising": "revolutionizing",
- "rhapsodise": "rhapsodize",
- "rhapsodised": "rhapsodized",
- "rhapsodises": "rhapsodizes",
- "rhapsodising": "rhapsodizing",
- "rigour": "rigor",
- "rigours": "rigors",
- "ritualised": "ritualized",
- "rivalled": "rivaled",
- "rivalling": "rivaling",
- "romanticise": "romanticize",
- "romanticised": "romanticized",
- "romanticises": "romanticizes",
- "romanticising": "romanticizing",
- "rumour": "rumor",
- "rumoured": "rumored",
- "rumours": "rumors",
- "sabre": "saber",
- "sabres": "sabers",
- "saltpetre": "saltpeter",
- "sanitise": "sanitize",
- "sanitised": "sanitized",
- "sanitises": "sanitizes",
- "sanitising": "sanitizing",
- "satirise": "satirize",
- "satirised": "satirized",
- "satirises": "satirizes",
- "satirising": "satirizing",
- "saviour": "savior",
- "saviours": "saviors",
- "savour": "savor",
- "savoured": "savored",
- "savouries": "savories",
- "savouring": "savoring",
- "savours": "savors",
- "savoury": "savory",
- "scandalise": "scandalize",
- "scandalised": "scandalized",
- "scandalises": "scandalizes",
- "scandalising": "scandalizing",
- "sceptic": "skeptic",
- "sceptical": "skeptical",
- "sceptically": "skeptically",
- "scepticism": "skepticism",
- "sceptics": "skeptics",
- "sceptre": "scepter",
- "sceptres": "scepters",
- "scrutinise": "scrutinize",
- "scrutinised": "scrutinized",
- "scrutinises": "scrutinizes",
- "scrutinising": "scrutinizing",
- "secularisation": "secularization",
- "secularise": "secularize",
- "secularised": "secularized",
- "secularises": "secularizes",
- "secularising": "secularizing",
- "sensationalise": "sensationalize",
- "sensationalised": "sensationalized",
- "sensationalises": "sensationalizes",
- "sensationalising": "sensationalizing",
- "sensitise": "sensitize",
- "sensitised": "sensitized",
- "sensitises": "sensitizes",
- "sensitising": "sensitizing",
- "sentimentalise": "sentimentalize",
- "sentimentalised": "sentimentalized",
- "sentimentalises": "sentimentalizes",
- "sentimentalising": "sentimentalizing",
- "sepulchre": "sepulcher",
- "sepulchres": "sepulchers",
- "serialisation": "serialization",
- "serialisations": "serializations",
- "serialise": "serialize",
- "serialised": "serialized",
- "serialises": "serializes",
- "serialising": "serializing",
- "sermonise": "sermonize",
- "sermonised": "sermonized",
- "sermonises": "sermonizes",
- "sermonising": "sermonizing",
- "sheikh": "sheik",
- "shovelled": "shoveled",
- "shovelling": "shoveling",
- "shrivelled": "shriveled",
- "shrivelling": "shriveling",
- "signalise": "signalize",
- "signalised": "signalized",
- "signalises": "signalizes",
- "signalising": "signalizing",
- "signalled": "signaled",
- "signalling": "signaling",
- "smoulder": "smolder",
- "smouldered": "smoldered",
- "smouldering": "smoldering",
- "smoulders": "smolders",
- "snivelled": "sniveled",
- "snivelling": "sniveling",
- "snorkelled": "snorkeled",
- "snorkelling": "snorkeling",
- "snowplough": "snowplow",
- "snowploughs": "snowplow",
- "socialisation": "socialization",
- "socialise": "socialize",
- "socialised": "socialized",
- "socialises": "socializes",
- "socialising": "socializing",
- "sodomise": "sodomize",
- "sodomised": "sodomized",
- "sodomises": "sodomizes",
- "sodomising": "sodomizing",
- "solemnise": "solemnize",
- "solemnised": "solemnized",
- "solemnises": "solemnizes",
- "solemnising": "solemnizing",
- "sombre": "somber",
- "specialisation": "specialization",
- "specialisations": "specializations",
- "specialise": "specialize",
- "specialised": "specialized",
- "specialises": "specializes",
- "specialising": "specializing",
- "spectre": "specter",
- "spectres": "specters",
- "spiralled": "spiraled",
- "spiralling": "spiraling",
- "splendour": "splendor",
- "splendours": "splendors",
- "squirrelled": "squirreled",
- "squirrelling": "squirreling",
- "stabilisation": "stabilization",
- "stabilise": "stabilize",
- "stabilised": "stabilized",
- "stabiliser": "stabilizer",
- "stabilisers": "stabilizers",
- "stabilises": "stabilizes",
- "stabilising": "stabilizing",
- "standardisation": "standardization",
- "standardise": "standardize",
- "standardised": "standardized",
- "standardises": "standardizes",
- "standardising": "standardizing",
- "stencilled": "stenciled",
- "stencilling": "stenciling",
- "sterilisation": "sterilization",
- "sterilisations": "sterilizations",
- "sterilise": "sterilize",
- "sterilised": "sterilized",
- "steriliser": "sterilizer",
- "sterilisers": "sterilizers",
- "sterilises": "sterilizes",
- "sterilising": "sterilizing",
- "stigmatisation": "stigmatization",
- "stigmatise": "stigmatize",
- "stigmatised": "stigmatized",
- "stigmatises": "stigmatizes",
- "stigmatising": "stigmatizing",
- "storey": "story",
- "storeys": "stories",
- "subsidisation": "subsidization",
- "subsidise": "subsidize",
- "subsidised": "subsidized",
- "subsidiser": "subsidizer",
- "subsidisers": "subsidizers",
- "subsidises": "subsidizes",
- "subsidising": "subsidizing",
- "succour": "succor",
- "succoured": "succored",
- "succouring": "succoring",
- "succours": "succors",
- "sulphate": "sulfate",
- "sulphates": "sulfates",
- "sulphide": "sulfide",
- "sulphides": "sulfides",
- "sulphur": "sulfur",
- "sulphurous": "sulfurous",
- "summarise": "summarize",
- "summarised": "summarized",
- "summarises": "summarizes",
- "summarising": "summarizing",
- "swivelled": "swiveled",
- "swivelling": "swiveling",
- "symbolise": "symbolize",
- "symbolised": "symbolized",
- "symbolises": "symbolizes",
- "symbolising": "symbolizing",
- "sympathise": "sympathize",
- "sympathised": "sympathized",
- "sympathiser": "sympathizer",
- "sympathisers": "sympathizers",
- "sympathises": "sympathizes",
- "sympathising": "sympathizing",
- "synchronisation": "synchronization",
- "synchronise": "synchronize",
- "synchronised": "synchronized",
- "synchronises": "synchronizes",
- "synchronising": "synchronizing",
- "synthesise": "synthesize",
- "synthesised": "synthesized",
- "synthesiser": "synthesizer",
- "synthesisers": "synthesizers",
- "synthesises": "synthesizes",
- "synthesising": "synthesizing",
- "syphon": "siphon",
- "syphoned": "siphoned",
- "syphoning": "siphoning",
- "syphons": "siphons",
- "systematisation": "systematization",
- "systematise": "systematize",
- "systematised": "systematized",
- "systematises": "systematizes",
- "systematising": "systematizing",
- "tantalise": "tantalize",
- "tantalised": "tantalized",
- "tantalises": "tantalizes",
- "tantalising": "tantalizing",
- "tantalisingly": "tantalizingly",
- "tasselled": "tasseled",
- "technicolour": "technicolor",
- "temporise": "temporize",
- "temporised": "temporized",
- "temporises": "temporizes",
- "temporising": "temporizing",
- "tenderise": "tenderize",
- "tenderised": "tenderized",
- "tenderises": "tenderizes",
- "tenderising": "tenderizing",
- "terrorise": "terrorize",
- "terrorised": "terrorized",
- "terrorises": "terrorizes",
- "terrorising": "terrorizing",
- "theatre": "theater",
- "theatregoer": "theatergoer",
- "theatregoers": "theatergoers",
- "theatres": "theaters",
- "theorise": "theorize",
- "theorised": "theorized",
- "theorises": "theorizes",
- "theorising": "theorizing",
- "tonne": "ton",
- "tonnes": "tons",
- "towelled": "toweled",
- "towelling": "toweling",
- "toxaemia": "toxemia",
- "tranquillise": "tranquilize",
- "tranquillised": "tranquilized",
- "tranquilliser": "tranquilizer",
- "tranquillisers": "tranquilizers",
- "tranquillises": "tranquilizes",
- "tranquillising": "tranquilizing",
- "tranquillity": "tranquility",
- "tranquillize": "tranquilize",
- "tranquillized": "tranquilized",
- "tranquillizer": "tranquilizer",
- "tranquillizers": "tranquilizers",
- "tranquillizes": "tranquilizes",
- "tranquillizing": "tranquilizing",
- "tranquilly": "tranquility",
- "transistorised": "transistorized",
- "traumatise": "traumatize",
- "traumatised": "traumatized",
- "traumatises": "traumatizes",
- "traumatising": "traumatizing",
- "travelled": "traveled",
- "traveller": "traveler",
- "travellers": "travelers",
- "travelling": "traveling",
- "travelog": "travelogue",
- "travelogs": "travelogues",
- "trialled": "trialed",
- "trialling": "trialing",
- "tricolour": "tricolor",
- "tricolours": "tricolors",
- "trivialise": "trivialize",
- "trivialised": "trivialized",
- "trivialises": "trivializes",
- "trivialising": "trivializing",
- "tumour": "tumor",
- "tumours": "tumors",
- "tunnelled": "tunneled",
- "tunnelling": "tunneling",
- "tyrannise": "tyrannize",
- "tyrannised": "tyrannized",
- "tyrannises": "tyrannizes",
- "tyrannising": "tyrannizing",
- "tyre": "tire",
- "tyres": "tires",
- "unauthorised": "unauthorized",
- "uncivilised": "uncivilized",
- "underutilised": "underutilized",
- "unequalled": "unequaled",
- "unfavourable": "unfavorable",
- "unfavourably": "unfavorably",
- "unionisation": "unionization",
- "unionise": "unionize",
- "unionised": "unionized",
- "unionises": "unionizes",
- "unionising": "unionizing",
- "unorganised": "unorganized",
- "unravelled": "unraveled",
- "unravelling": "unraveling",
- "unrecognisable": "unrecognizable",
- "unrecognised": "unrecognized",
- "unrivalled": "unrivaled",
- "unsavoury": "unsavory",
- "untrammelled": "untrammeled",
- "urbanisation": "urbanization",
- "urbanise": "urbanize",
- "urbanised": "urbanized",
- "urbanises": "urbanizes",
- "urbanising": "urbanizing",
- "utilisable": "utilizable",
- "utilisation": "utilization",
- "utilise": "utilize",
- "utilised": "utilized",
- "utilises": "utilizes",
- "utilising": "utilizing",
- "valour": "valor",
- "vandalise": "vandalize",
- "vandalised": "vandalized",
- "vandalises": "vandalizes",
- "vandalising": "vandalizing",
- "vaporisation": "vaporization",
- "vaporise": "vaporize",
- "vaporised": "vaporized",
- "vaporises": "vaporizes",
- "vaporising": "vaporizing",
- "vapour": "vapor",
- "vapours": "vapors",
- "verbalise": "verbalize",
- "verbalised": "verbalized",
- "verbalises": "verbalizes",
- "verbalising": "verbalizing",
- "victimisation": "victimization",
- "victimise": "victimize",
- "victimised": "victimized",
- "victimises": "victimizes",
- "victimising": "victimizing",
- "videodisc": "videodisk",
- "videodiscs": "videodisks",
- "vigour": "vigor",
- "visualisation": "visualization",
- "visualisations": "visualizations",
- "visualise": "visualize",
- "visualised": "visualized",
- "visualises": "visualizes",
- "visualising": "visualizing",
- "vocalisation": "vocalization",
- "vocalisations": "vocalizations",
- "vocalise": "vocalize",
- "vocalised": "vocalized",
- "vocalises": "vocalizes",
- "vocalising": "vocalizing",
- "vulcanised": "vulcanized",
- "vulgarisation": "vulgarization",
- "vulgarise": "vulgarize",
- "vulgarised": "vulgarized",
- "vulgarises": "vulgarizes",
- "vulgarising": "vulgarizing",
- "waggon": "wagon",
- "waggons": "wagons",
- "watercolour": "watercolor",
- "watercolours": "watercolors",
- "weaselled": "weaseled",
- "weaselling": "weaseling",
- "westernisation": "westernization",
- "westernise": "westernize",
- "westernised": "westernized",
- "westernises": "westernizes",
- "westernising": "westernizing",
- "womanise": "womanize",
- "womanised": "womanized",
- "womaniser": "womanizer",
- "womanisers": "womanizers",
- "womanises": "womanizes",
- "womanising": "womanizing",
- "woollen": "woolen",
- "woollens": "woolens",
- "woollies": "woolies",
- "woolly": "wooly",
- "worshipped": "worshiped",
- "worshipper": "worshiper",
- "worshipping": "worshiping",
- "yodelled": "yodeled",
- "yodelling": "yodeling",
- "yoghourt": "yogurt",
- "yoghourts": "yogurts",
- "yoghurt": "yogurt",
- "yoghurts": "yogurts"
-}
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/requirements.benchmark.txt b/whisper_pipeline/faster-whisper-main/benchmark/requirements.benchmark.txt
deleted file mode 100644
index 26450040b7c2ddf00b61f8092ce8d03cae258058..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/requirements.benchmark.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-transformers
-jiwer
-evaluate
-datasets
-memory_profiler
-py3nvml
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/speed_benchmark.py b/whisper_pipeline/faster-whisper-main/benchmark/speed_benchmark.py
deleted file mode 100644
index a3d6ffbc537f5a1cd950459cc0c12a1cdbbc8971..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/speed_benchmark.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import argparse
-import timeit
-
-from typing import Callable
-
-from utils import inference
-
-parser = argparse.ArgumentParser(description="Speed benchmark")
-parser.add_argument(
- "--repeat",
- type=int,
- default=3,
- help="Times an experiment will be run.",
-)
-args = parser.parse_args()
-
-
-def measure_speed(func: Callable[[], None]):
- # as written in https://docs.python.org/3/library/timeit.html#timeit.Timer.repeat,
- # min should be taken rather than the average
- runtimes = timeit.repeat(
- func,
- repeat=args.repeat,
- number=10,
- )
- print(runtimes)
- print("Min execution time: %.3fs" % (min(runtimes) / 10.0))
-
-
-if __name__ == "__main__":
- measure_speed(inference)
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/utils.py b/whisper_pipeline/faster-whisper-main/benchmark/utils.py
deleted file mode 100644
index 8e5ac466a355e62d4d1534ac4e1f1d7ae37c2d74..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import logging
-
-from threading import Thread
-from typing import Optional
-
-from faster_whisper import WhisperModel
-
-model_path = "large-v3"
-model = WhisperModel(model_path, device="cuda")
-
-
-def inference():
- segments, info = model.transcribe("benchmark.m4a", language="fr")
- for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
- formatter = logging.Formatter("%(levelname)s: %(message)s")
- logger = logging.getLogger(name)
- logger.setLevel(logging.DEBUG)
- handler = logging.StreamHandler()
- handler.setFormatter(formatter)
- logger.addHandler(handler)
- return logger
-
-
-class MyThread(Thread):
- def __init__(self, func, params):
- super(MyThread, self).__init__()
- self.func = func
- self.params = params
- self.result = None
-
- def run(self):
- self.result = self.func(*self.params)
-
- def get_result(self):
- return self.result
diff --git a/whisper_pipeline/faster-whisper-main/benchmark/wer_benchmark.py b/whisper_pipeline/faster-whisper-main/benchmark/wer_benchmark.py
deleted file mode 100644
index f7a0b792df9b137d90cf7277de9a53b9f0c11ded..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/benchmark/wer_benchmark.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import argparse
-import json
-import os
-
-from datasets import load_dataset
-from evaluate import load
-from tqdm import tqdm
-from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
-
-from faster_whisper import WhisperModel
-
-parser = argparse.ArgumentParser(description="WER benchmark")
-parser.add_argument(
- "--audio_numb",
- type=int,
- default=None,
- help="Specify the number of validation audio files in the dataset."
- " Set to None to retrieve all audio files.",
-)
-args = parser.parse_args()
-
-model_path = "large-v3"
-model = WhisperModel(model_path, device="cuda")
-
-# load the dataset with streaming mode
-dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
-
-# define the evaluation metric
-wer_metric = load("wer")
-
-with open(os.path.join(os.path.dirname(__file__), "normalizer.json"), "r") as f:
- normalizer = EnglishTextNormalizer(json.load(f))
-
-
-def inference(batch):
- batch["transcription"] = []
- for sample in batch["audio"]:
- segments, info = model.transcribe(sample["array"], language="en")
- batch["transcription"].append("".join([segment.text for segment in segments]))
- batch["reference"] = batch["text"]
- return batch
-
-
-dataset = dataset.map(function=inference, batched=True, batch_size=16)
-
-all_transcriptions = []
-all_references = []
-
-# iterate over the dataset and run inference
-for i, result in tqdm(enumerate(dataset), desc="Evaluating..."):
- all_transcriptions.append(result["transcription"])
- all_references.append(result["reference"])
- if args.audio_numb and i == (args.audio_numb - 1):
- break
-
-# normalize predictions and references
-all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions]
-all_references = [normalizer(reference) for reference in all_references]
-
-# compute the WER metric
-wer = 100 * wer_metric.compute(
- predictions=all_transcriptions, references=all_references
-)
-print("WER: %.3f" % wer)
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/__init__.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/__init__.py
deleted file mode 100644
index ad69277853742e3f9f0e0f020c0862cf4ad404fb..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from faster_whisper.audio import decode_audio
-from faster_whisper.transcribe import BatchedInferencePipeline, WhisperModel
-from faster_whisper.utils import available_models, download_model, format_timestamp
-from faster_whisper.version import __version__
-
-__all__ = [
- "available_models",
- "decode_audio",
- "WhisperModel",
- "BatchedInferencePipeline",
- "download_model",
- "format_timestamp",
- "__version__",
-]
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/__init__.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/pyannote_vad_model.bin b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/pyannote_vad_model.bin
deleted file mode 100644
index 7a0c29f0dbd55f172445e95ae1f0163a7d849c27..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/pyannote_vad_model.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea
-size 17719103
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/silero_vad.onnx b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/silero_vad.onnx
deleted file mode 100644
index 6ea96ef73cac24d33ad342094d95867c756d3f5c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/assets/silero_vad.onnx
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6b99cbfd39246b6706f98ec13c7c50c6b299181f2474fa05cbc8046acc274396
-size 2313101
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/audio.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/audio.py
deleted file mode 100644
index 7ae68d40055b0df3427c1201d9cd92f663110b12..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/audio.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from typing import BinaryIO, Union
-
-import torch
-import torchaudio
-
-
-def decode_audio(
- input_file: Union[str, BinaryIO],
- sampling_rate: int = 16000,
- split_stereo: bool = False,
-):
- """Decodes the audio.
-
- Args:
- input_file: Path to the input file or a file-like object.
- sampling_rate: Resample the audio to this sample rate.
- split_stereo: Return separate left and right channels.
-
- Returns:
- A float32 Torch Tensor.
-
- If `split_stereo` is enabled, the function returns a 2-tuple with the
- separated left and right channels.
- """
-
- waveform, audio_sf = torchaudio.load(input_file) # waveform: channels X T
-
- if audio_sf != sampling_rate:
- waveform = torchaudio.functional.resample(
- waveform, orig_freq=audio_sf, new_freq=sampling_rate
- )
- if split_stereo:
- return waveform[0], waveform[1]
-
- return waveform.mean(0)
-
-
-def pad_or_trim(array, length: int, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- axis = axis % array.ndim
- if array.shape[axis] > length:
- idx = [Ellipsis] * axis + [slice(length)] + [Ellipsis] * (array.ndim - axis - 1)
- return array[idx]
-
- if array.shape[axis] < length:
- pad_widths = (
- [
- 0,
- ]
- * array.ndim
- * 2
- )
- pad_widths[2 * axis] = length - array.shape[axis]
- array = torch.nn.functional.pad(array, tuple(pad_widths[::-1]))
-
- return array
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/feature_extractor.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/feature_extractor.py
deleted file mode 100644
index 6371d5ef6864b07ce4b7f3c1cd1075fee9c8e484..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/feature_extractor.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import torch
-
-
-# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py # noqa: E501
-class FeatureExtractor:
- def __init__(
- self,
- device: str = "auto",
- feature_size=80,
- sampling_rate=16000,
- hop_length=160,
- chunk_length=30,
- n_fft=400,
- ):
- if device == "auto":
- self.device = "cuda" if torch.cuda.is_available() else "cpu"
- else:
- self.device = device
- self.n_fft = n_fft
- self.hop_length = hop_length
- self.chunk_length = chunk_length
- self.n_samples = chunk_length * sampling_rate
- self.nb_max_frames = self.n_samples // hop_length
- self.time_per_frame = hop_length / sampling_rate
- self.sampling_rate = sampling_rate
- self.mel_filters = self.get_mel_filters(
- sampling_rate, n_fft, n_mels=feature_size
- )
-
- @staticmethod
- def get_mel_filters(sr, n_fft, n_mels=128):
- """
- Implementation of librosa.filters.mel in Pytorch
- """
- # Initialize the weights
- n_mels = int(n_mels)
-
- # Center freqs of each FFT bin
- fftfreqs = torch.fft.rfftfreq(n=n_fft, d=1.0 / sr)
-
- # 'Center freqs' of mel bands - uniformly spaced between limits
- min_mel = 0.0
- max_mel = 45.245640471924965
-
- mels = torch.linspace(min_mel, max_mel, n_mels + 2)
-
- # Fill in the linear scale
- f_min = 0.0
- f_sp = 200.0 / 3
- freqs = f_min + f_sp * mels
-
- # And now the nonlinear scale
- min_log_hz = 1000.0 # beginning of log region (Hz)
- min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
- logstep = torch.log(torch.tensor(6.4)) / 27.0 # step size for log region
-
- # If we have vector data, vectorize
- log_t = mels >= min_log_mel
- freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel))
-
- mel_f = freqs
-
- fdiff = torch.diff(mel_f)
- ramps = mel_f.view(-1, 1) - fftfreqs.view(1, -1)
-
- lower = -ramps[:-2] / fdiff[:-1].unsqueeze(1)
- upper = ramps[2:] / fdiff[1:].unsqueeze(1)
-
- # Intersect them with each other and zero, vectorized across all i
- weights = torch.maximum(torch.zeros_like(lower), torch.minimum(lower, upper))
-
- # Slaney-style mel is scaled to be approx constant energy per channel
- enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
- weights *= enorm.unsqueeze(1)
-
- return weights
-
- def __call__(self, waveform, padding=True, chunk_length=None, to_cpu=False):
- """
- Compute the log-Mel spectrogram of the provided audio.
- """
-
- if chunk_length is not None:
- self.n_samples = chunk_length * self.sampling_rate
- self.nb_max_frames = self.n_samples // self.hop_length
-
- if waveform.dtype is not torch.float32:
- waveform = waveform.to(torch.float32)
-
- waveform = (
- waveform.to(self.device)
- if self.device == "cuda" and not waveform.is_cuda
- else waveform
- )
-
- if padding:
- waveform = torch.nn.functional.pad(waveform, (0, self.n_samples))
-
- window = torch.hann_window(self.n_fft).to(waveform.device)
-
- stft = torch.stft(
- waveform, self.n_fft, self.hop_length, window=window, return_complex=True
- )
- magnitudes = stft[..., :-1].abs() ** 2
-
- mel_spec = self.mel_filters.to(waveform.device) @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
-
- # When the model is running on multiple GPUs, the output should be moved
- # to the CPU since we don't know which GPU will handle the next job.
- return log_spec.cpu() if to_cpu else log_spec
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/tokenizer.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/tokenizer.py
deleted file mode 100644
index 3bf76a5f6321d5f17cb778604b3e5e3d73275835..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/tokenizer.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import string
-
-from functools import cached_property
-from typing import List, Optional, Tuple
-
-import tokenizers
-
-
-class Tokenizer:
- """Simple wrapper around a tokenizers.Tokenizer."""
-
- def __init__(
- self,
- tokenizer: tokenizers.Tokenizer,
- multilingual: bool,
- task: Optional[str] = None,
- language: Optional[str] = None,
- ):
- self.tokenizer = tokenizer
-
- if multilingual:
- if task not in _TASKS:
- raise ValueError(
- "'%s' is not a valid task (accepted tasks: %s)"
- % (task, ", ".join(_TASKS))
- )
-
- if language not in _LANGUAGE_CODES:
- raise ValueError(
- "'%s' is not a valid language code (accepted language codes: %s)"
- % (language, ", ".join(_LANGUAGE_CODES))
- )
-
- self.task = self.tokenizer.token_to_id("<|%s|>" % task)
- self.language = self.tokenizer.token_to_id("<|%s|>" % language)
- self.language_code = language
- else:
- self.task = None
- self.language = None
- self.language_code = "en"
-
- @cached_property
- def transcribe(self) -> int:
- return self.tokenizer.token_to_id("<|transcribe|>")
-
- @cached_property
- def translate(self) -> int:
- return self.tokenizer.token_to_id("<|translate|>")
-
- @cached_property
- def sot(self) -> int:
- return self.tokenizer.token_to_id("<|startoftranscript|>")
-
- @cached_property
- def sot_lm(self) -> int:
- return self.tokenizer.token_to_id("<|startoflm|>")
-
- @cached_property
- def sot_prev(self) -> int:
- return self.tokenizer.token_to_id("<|startofprev|>")
-
- @cached_property
- def eot(self) -> int:
- return self.tokenizer.token_to_id("<|endoftext|>")
-
- @cached_property
- def no_timestamps(self) -> int:
- return self.tokenizer.token_to_id("<|notimestamps|>")
-
- @property
- def timestamp_begin(self) -> int:
- return self.no_timestamps + 1
-
- @property
- def sot_sequence(self) -> List[int]:
- sequence = [self.sot]
-
- if self.language is not None:
- sequence.append(self.language)
-
- if self.task is not None:
- sequence.append(self.task)
-
- return sequence
-
- def encode(self, text: str) -> List[int]:
- return self.tokenizer.encode(text, add_special_tokens=False).ids
-
- def decode(self, tokens: List[int]) -> str:
- text_tokens = [token for token in tokens if token < self.eot]
- return self.tokenizer.decode(text_tokens)
-
- def decode_with_timestamps(self, tokens: List[int]) -> str:
- outputs = [[]]
-
- for token in tokens:
- if token >= self.timestamp_begin:
- timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
- outputs.append(timestamp)
- outputs.append([])
- else:
- outputs[-1].append(token)
-
- return "".join(
- [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
- )
-
- @cached_property
- def non_speech_tokens(self) -> Tuple[int]:
- """
- Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
- annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
-
- - ♪♪♪
- - ( SPEAKING FOREIGN LANGUAGE )
- - [DAVID] Hey there,
-
- keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
- """
- symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
- symbols += (
- "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
- )
-
- # symbols that may be a single token or multiple tokens depending on the tokenizer.
- # In case they're multiple tokens, suppress the first token, which is safe because:
- # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
- # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
- miscellaneous = set("♩♪♫♬♭♮♯")
- assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
-
- # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
- result = {self.encode(" -")[0], self.encode(" '")[0]}
- for symbol in symbols + list(miscellaneous):
- for tokens in [
- self.encode(symbol),
- self.encode(" " + symbol),
- ]:
- if len(tokens) == 1 or symbol in miscellaneous:
- result.add(tokens[0])
-
- return tuple(sorted(result))
-
- def split_to_word_tokens(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- if self.language_code in {"zh", "ja", "th", "lo", "my", "yue"}:
- # These languages don't typically use spaces, so it is difficult to split words
- # without morpheme analysis. Here, we instead split words at any
- # position where the tokens are decoded as valid unicode points
- return self.split_tokens_on_unicode(tokens)
-
- return self.split_tokens_on_spaces(tokens)
-
- def split_tokens_on_unicode(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- decoded_full = self.decode_with_timestamps(tokens)
- replacement_char = "\ufffd"
-
- words = []
- word_tokens = []
- current_tokens = []
- unicode_offset = 0
-
- for token in tokens:
- current_tokens.append(token)
- decoded = self.decode_with_timestamps(current_tokens)
-
- try:
- replacement_char_index = decoded.index(replacement_char)
- replacement_char_index += unicode_offset
- except ValueError:
- replacement_char_index = None
-
- if replacement_char_index is None or (
- replacement_char_index < len(decoded_full)
- and decoded_full[replacement_char_index] == replacement_char
- ):
- words.append(decoded)
- word_tokens.append(current_tokens)
- current_tokens = []
- unicode_offset += len(decoded)
-
- return words, word_tokens
-
- def split_tokens_on_spaces(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
- words = []
- word_tokens = []
-
- for subword, subword_tokens in zip(subwords, subword_tokens_list):
- special = subword_tokens[0] >= self.eot
- with_space = subword.startswith(" ")
- punctuation = subword.strip() in string.punctuation
- if special or with_space or punctuation or len(words) == 0:
- words.append(subword)
- word_tokens.append(subword_tokens)
- else:
- words[-1] = words[-1] + subword
- word_tokens[-1].extend(subword_tokens)
-
- return words, word_tokens
-
-
-_TASKS = (
- "transcribe",
- "translate",
-)
-
-_LANGUAGE_CODES = (
- "af",
- "am",
- "ar",
- "as",
- "az",
- "ba",
- "be",
- "bg",
- "bn",
- "bo",
- "br",
- "bs",
- "ca",
- "cs",
- "cy",
- "da",
- "de",
- "el",
- "en",
- "es",
- "et",
- "eu",
- "fa",
- "fi",
- "fo",
- "fr",
- "gl",
- "gu",
- "ha",
- "haw",
- "he",
- "hi",
- "hr",
- "ht",
- "hu",
- "hy",
- "id",
- "is",
- "it",
- "ja",
- "jw",
- "ka",
- "kk",
- "km",
- "kn",
- "ko",
- "la",
- "lb",
- "ln",
- "lo",
- "lt",
- "lv",
- "mg",
- "mi",
- "mk",
- "ml",
- "mn",
- "mr",
- "ms",
- "mt",
- "my",
- "ne",
- "nl",
- "nn",
- "no",
- "oc",
- "pa",
- "pl",
- "ps",
- "pt",
- "ro",
- "ru",
- "sa",
- "sd",
- "si",
- "sk",
- "sl",
- "sn",
- "so",
- "sq",
- "sr",
- "su",
- "sv",
- "sw",
- "ta",
- "te",
- "tg",
- "th",
- "tk",
- "tl",
- "tr",
- "tt",
- "uk",
- "ur",
- "uz",
- "vi",
- "yi",
- "yo",
- "zh",
- "yue",
-)
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/transcribe.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/transcribe.py
deleted file mode 100644
index 8652e82b0463f1d786c50ba32209b62dd167142d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/transcribe.py
+++ /dev/null
@@ -1,2170 +0,0 @@
-import itertools
-import json
-import logging
-import os
-import random
-import zlib
-
-from collections import Counter, defaultdict
-from inspect import signature
-from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
-
-import ctranslate2
-import numpy as np
-import tokenizers
-import torch
-
-from pyannote.audio import Model
-from tqdm import tqdm
-
-from faster_whisper.audio import decode_audio, pad_or_trim
-from faster_whisper.feature_extractor import FeatureExtractor
-from faster_whisper.tokenizer import _LANGUAGE_CODES, Tokenizer
-from faster_whisper.utils import (
- download_model,
- format_timestamp,
- get_assets_path,
- get_end,
- get_logger,
-)
-from faster_whisper.vad import (
- SpeechTimestampsMap,
- VadOptions,
- VoiceActivitySegmentation,
- collect_chunks,
- get_speech_timestamps,
- merge_chunks,
-)
-
-
-class Word(NamedTuple):
- start: float
- end: float
- word: str
- probability: float
-
-
-class Segment(NamedTuple):
- id: int
- seek: int
- start: float
- end: float
- text: str
- tokens: List[int]
- avg_logprob: float
- compression_ratio: float
- no_speech_prob: float
- words: Optional[List[Word]]
- temperature: Optional[float] = 1.0
-
-
-# Added additional parameters for multilingual videos and fixes below
-class TranscriptionOptions(NamedTuple):
- beam_size: int
- best_of: int
- patience: float
- length_penalty: float
- repetition_penalty: float
- no_repeat_ngram_size: int
- log_prob_threshold: Optional[float]
- log_prob_low_threshold: Optional[float]
- no_speech_threshold: Optional[float]
- compression_ratio_threshold: Optional[float]
- condition_on_previous_text: bool
- prompt_reset_on_temperature: float
- temperatures: List[float]
- initial_prompt: Optional[Union[str, Iterable[int]]]
- prefix: Optional[str]
- suppress_blank: bool
- suppress_tokens: Optional[List[int]]
- without_timestamps: bool
- max_initial_timestamp: float
- word_timestamps: bool
- prepend_punctuations: str
- append_punctuations: str
- multilingual: bool
- output_language: Optional[str]
- max_new_tokens: Optional[int]
- clip_timestamps: Union[str, List[float]]
- hallucination_silence_threshold: Optional[float]
- hotwords: Optional[str]
-
-
-class TranscriptionInfo(NamedTuple):
- language: str
- language_probability: float
- duration: float
- duration_after_vad: float
- all_language_probs: Optional[List[Tuple[str, float]]]
- transcription_options: TranscriptionOptions
- vad_options: VadOptions
-
-
-# The code below is originally from HF pipeline and is used in whisper-x
-# (https://github.com/m-bain/whisperX) and adapted for faster_whisper
-
-
-class BatchedInferencePipeline:
- """
- Huggingface Pipeline wrapper for WhisperModel.
- Copyright (c) 2022, Max Bain
- All rights reserved.
- Modified by Mobius Labs GmbH
- """
-
- def __init__(
- self,
- model,
- use_vad_model: bool = True,
- options: Optional[NamedTuple] = None,
- tokenizer=None,
- chunk_length: int = 30,
- vad_device: Union[int, str, "torch.device"] = "auto",
- vad_onset: float = 0.500,
- vad_offset: float = 0.363,
- language: Optional[str] = None,
- ):
- self.model: WhisperModel = model
- self.tokenizer = tokenizer
- self.options = options
- self.preset_language = language
- self.use_vad_model = use_vad_model
- self.vad_onset = vad_onset
- self.vad_offset = vad_offset
- self.vad_model_path = os.path.join(get_assets_path(), "pyannote_vad_model.bin")
- if self.use_vad_model:
- self.vad_device = self.get_device(vad_device)
- self.vad_model = self.load_vad_model(
- vad_onset=self.vad_onset, vad_offset=self.vad_offset
- )
- else:
- self.vad_model = None
- self.chunk_length = chunk_length # VAD merging size
- self.last_speech_timestamp = 0.0
-
- def get_device(self, device: Union[int, str, "torch.device"]):
- """
- Converts the input device into a torch.device object.
-
- The input can be an integer, a string, or a `torch.device` object.
-
- The function handles a special case where the input device is "auto".
- When "auto" is specified, the device will default to the
- device of the model (self.model.device). If the model's device is also "auto",
- it selects "cuda" if a CUDA-capable device is available; otherwise, it selects "cpu".
- """
- if isinstance(device, torch.device):
- return device
- elif isinstance(device, str):
- if device == "auto" and self.model.device == "auto":
- device = "cuda" if torch.cuda.is_available() else "cpu"
- elif device == "auto":
- device = self.model.device
- return torch.device(device)
- elif device < 0:
- return torch.device("cpu")
- else:
- return torch.device(f"cuda:{device}")
-
- def forward(self, features, segments_metadata, **forward_params):
- encoder_output, outputs = self.model.generate_segment_batched(
- features, self.tokenizer, forward_params
- )
-
- segmented_outputs = []
- segment_sizes = []
- for segment_metadata, output in zip(segments_metadata, outputs):
- duration = segment_metadata["end_time"] - segment_metadata["start_time"]
- segment_size = int(duration * self.model.frames_per_second)
- segment_sizes.append(segment_size)
- (
- subsegments,
- seek,
- single_timestamp_ending,
- ) = self.model._split_segments_by_timestamps(
- tokenizer=self.tokenizer,
- tokens=output["tokens"],
- time_offset=segment_metadata["start_time"],
- segment_size=segment_size,
- segment_duration=duration,
- seek=0,
- )
- segmented_outputs.append(
- [
- dict(
- text=self.tokenizer.decode(subsegment["tokens"]),
- avg_logprob=output["avg_logprob"],
- no_speech_prob=output["no_speech_prob"],
- tokens=subsegment["tokens"],
- start=subsegment["start"],
- end=subsegment["end"],
- compression_ratio=get_compression_ratio(
- self.tokenizer.decode(subsegment["tokens"])
- ),
- )
- for subsegment in subsegments
- ]
- )
- if forward_params["word_timestamps"]:
- self.last_speech_timestamp = self.model.add_word_timestamps(
- segmented_outputs,
- self.tokenizer,
- encoder_output,
- segment_sizes,
- forward_params["prepend_punctuations"],
- forward_params["append_punctuations"],
- self.last_speech_timestamp,
- )
-
- return segmented_outputs
-
- def get_language_and_tokenizer(
- self, audio, task: Optional[str] = None, language: Optional[str] = None
- ):
- all_language_probs = None
- language_probability = 1.0
-
- if self.tokenizer is None:
- if not language:
- (
- language,
- language_probability,
- all_language_probs,
- ) = self.model.detect_language(audio)
- task = task or "transcribe"
- self.tokenizer = Tokenizer(
- self.model.hf_tokenizer,
- self.model.model.is_multilingual,
- task=task,
- language=language,
- )
- else:
- if task is not None:
- self.tokenizer.task = self.tokenizer.tokenizer.token_to_id(
- f"<|{task}|>"
- )
-
- if language is not None:
- self.tokenizer.language = self.tokenizer.tokenizer.token_to_id(
- f"<|{language}|>"
- )
- self.tokenizer.language_code = language
-
- return language, language_probability, task, all_language_probs
-
- @staticmethod
- def audio_split(audio, segments, sampling_rate):
- """Returns splitted audio chunks as iterator"""
- audio_segments = []
- segments_metadata = []
- for seg in segments:
- f1 = int(seg["start"] * sampling_rate)
- f2 = int(seg["end"] * sampling_rate)
- seg_metadata = {
- "start_time": seg["start"],
- "end_time": seg["end"],
- "stitched_seg": seg["segments"],
- }
- audio_segments.append(audio[f1:f2])
- segments_metadata.append(seg_metadata)
- return audio_segments, segments_metadata
-
- def load_vad_model(self, vad_onset=0.500, vad_offset=0.363):
- vad_model = Model.from_pretrained(self.vad_model_path)
- hyperparameters = {
- "onset": vad_onset,
- "offset": vad_offset,
- "min_duration_on": 0.1,
- "min_duration_off": 0.1,
- }
-
- vad_pipeline = VoiceActivitySegmentation(
- segmentation=vad_model, device=torch.device(self.vad_device)
- )
- vad_pipeline.instantiate(hyperparameters)
- return vad_pipeline
-
- def transcribe(
- self,
- audio: Union[str, torch.Tensor, np.ndarray],
- vad_segments: Optional[List[dict]] = None,
- batch_size: int = 16,
- language: Optional[str] = None,
- task: str = None,
- log_progress: bool = False,
- beam_size: int = 5,
- best_of: int = 5,
- patience: float = 1,
- length_penalty: float = 1,
- repetition_penalty: float = 1,
- no_repeat_ngram_size: int = 0,
- temperature: Union[float, List[float], Tuple[float, ...]] = [
- 0.0,
- 0.2,
- 0.4,
- 0.6,
- 0.8,
- 1.0,
- ],
- compression_ratio_threshold: Optional[float] = 2.4,
- log_prob_threshold: Optional[float] = -1.0,
- log_prob_low_threshold: Optional[float] = None,
- no_speech_threshold: Optional[float] = 0.6,
- initial_prompt: Optional[Union[str, Iterable[int]]] = None,
- prefix: Optional[str] = None,
- suppress_blank: bool = True,
- suppress_tokens: Optional[List[int]] = [-1],
- prepend_punctuations: str = "\"'“¿([{-",
- append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
- max_new_tokens: Optional[int] = None,
- hotwords: Optional[str] = None,
- word_timestamps: bool = False,
- without_timestamps: bool = True,
- ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
- """transcribe audio in chunks in batched fashion and return with language info.
-
- Arguments:
- audio: audio file as numpy array/path for batched transcription.
- vad_segments: Optionally provide list of dictionaries each containing "start", "end",
- and "segments" keys.
- "start" and "end" keys specify the start and end of the voiced region within
- 30 sec boundary. An additional key "segments" contains all the start
- and end of voiced regions within that 30sec boundary as a list of tuples.
- If no vad_segments specified, it uses internal vad model automatically segment them.
- batch_size: the maximum number of parallel requests to model for decoding.
- language: The language spoken in the audio.
- task: either "transcribe" or "translate".
- log_progress: whether to show progress bar or not.
- beam_size: Beam size to use for decoding.
- best_of: Number of candidates when sampling with non-zero temperature.
- patience: Beam search patience factor.
- length_penalty: Exponential length penalty constant.
- repetition_penalty: Penalty applied to the score of previously generated tokens
- (set > 1 to penalize).
- no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable).
- temperature: Temperature for sampling. It can be a tuple of temperatures,
- which will be successively used upon failures according to either
- `compression_ratio_threshold` or `log_prob_threshold`.
- compression_ratio_threshold: If the gzip compression ratio is above this value,
- treat as failed.
- log_prob_threshold: If the average log probability over sampled tokens is
- below this value, treat as failed.
- log_prob_low_threshold: This parameter alone is sufficient to skip an output text,
- whereas log_prob_threshold also looks for appropriate no_speech_threshold value.
- This value should be less than log_prob_threshold.
- no_speech_threshold: If the no_speech probability is higher than this value AND
- the average log probability over sampled tokens is below `log_prob_threshold`,
- consider the segment as silent.
- initial_prompt: Optional text string or iterable of token ids to provide as a
- prompt for the first window.
- prefix: Optional text to provide as a prefix for the first window.
- suppress_blank: Suppress blank outputs at the beginning of the sampling.
- suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
- of symbols as defined in `tokenizer.non_speech_tokens()`.
- prepend_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the next word
- append_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the previous word
- max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set,
- the maximum will be set by the default max_length.
- hotwords:
- Hotwords/hint phrases to the model. Has no effect if prefix is not None.
- word_timestamps: Extract word-level timestamps using the cross-attention pattern
- and dynamic time warping, and include the timestamps for each word in each segment.
- Set as False.
- without_timestamps: Only sample text tokens.
-
- Static params: (Fixed for batched version)
- max_initial_timestamp: The initial timestamp cannot be later than this, set at 0.0.
- multilingual: If True, perform transcription on multilingual videos. Set as False.
- output_language: Valid only if multilingual is set to True.
- Specifies the string representing the output language. One of
- 'en' (English) or 'hybrid' (code-switched transcription). set as None.
- condition_on_previous_text: If True, the previous output of the model is provided
- as a prompt for the next window; disabling may make the text inconsistent across
- windows, but the model becomes less prone to getting stuck in a failure loop,
- such as repetition looping or timestamps going out of sync. Set as False
- prompt_reset_on_temperature: Resets prompt if temperature is above this value.
- Arg has effect only if condition_on_previous_text is True. Set at 0.5
- #TODO: support "hallucination_silence_threshold" when "word_timestamps=True"
- hallucination_silence_threshold: Optional[float]
- When word_timestamps is True, skip silent periods longer than this threshold
- (in seconds) when a possible hallucination is detected. set as None.
- clip_timestamps:
- Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to
- process. The last end timestamp defaults to the end of the file. Set as "0".
-
- unused:
- language_detection_threshold: If the maximum probability of the language tokens is
- higher than this value, the language is detected.
- language_detection_segments: Number of segments to consider for the language detection.
- vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
- without speech. This step is using the Silero VAD model
- https://github.com/snakers4/silero-vad.
- vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
- parameters and default values in the class `VadOptions`).
- chunk_length: The length of audio segments. If it is not None, it will overwrite the
- default chunk_length of the FeatureExtractor.
-
-
- Returns:
- A tuple with:
-
- - a generator over transcribed batched segments.
- - an instance of TranscriptionInfo.
- """
-
- sampling_rate = self.model.feature_extractor.sampling_rate
-
- if isinstance(audio, np.ndarray):
- audio = torch.from_numpy(audio)
- elif not isinstance(audio, torch.Tensor):
- audio = decode_audio(audio, sampling_rate=sampling_rate)
- duration = audio.shape[0] / sampling_rate
-
- # if no segment split is provided, use vad_model and generate segments
- if not vad_segments:
- # run the audio if it is less than 30 sec even without vad_segments
- if self.use_vad_model:
- vad_segments = self.vad_model(
- {
- "waveform": audio.unsqueeze(0),
- "sample_rate": 16000,
- }
- )
- vad_segments = merge_chunks(
- vad_segments,
- self.chunk_length,
- onset=self.vad_onset,
- offset=self.vad_offset,
- )
- elif duration < self.chunk_length:
- vad_segments = [
- {"start": 0.0, "end": duration, "segments": [(0.0, duration)]}
- ]
- else:
- raise RuntimeError(
- "No vad segments found. Set 'use_vad_model' to True while loading the model"
- )
- if self.model.model.is_multilingual:
- language = language or self.preset_language
- elif language != "en":
- if language is not None:
- self.model.logger.warning(
- f"English-only model is used, but {language} language is"
- "chosen, setting language to 'en'."
- )
- language = "en"
-
- (
- language,
- language_probability,
- task,
- all_language_probs,
- ) = self.get_language_and_tokenizer(audio, task, language)
-
- duration_after_vad = sum(
- segment["end"] - segment["start"] for segment in vad_segments
- )
-
- # batched options: see the difference with default options in WhisperModel
- batched_options = TranscriptionOptions(
- beam_size=beam_size,
- best_of=best_of,
- patience=patience,
- length_penalty=length_penalty,
- repetition_penalty=repetition_penalty,
- no_repeat_ngram_size=no_repeat_ngram_size,
- log_prob_threshold=log_prob_threshold,
- log_prob_low_threshold=log_prob_low_threshold,
- no_speech_threshold=no_speech_threshold,
- compression_ratio_threshold=compression_ratio_threshold,
- temperatures=(
- temperature if isinstance(temperature, (list, tuple)) else [temperature]
- ),
- initial_prompt=initial_prompt,
- prefix=prefix,
- suppress_blank=suppress_blank,
- suppress_tokens=get_suppressed_tokens(self.tokenizer, suppress_tokens),
- prepend_punctuations=prepend_punctuations,
- append_punctuations=append_punctuations,
- max_new_tokens=max_new_tokens,
- hotwords=hotwords,
- word_timestamps=word_timestamps,
- hallucination_silence_threshold=None,
- condition_on_previous_text=False,
- clip_timestamps="0",
- prompt_reset_on_temperature=0.5,
- multilingual=False,
- output_language=None,
- without_timestamps=without_timestamps,
- max_initial_timestamp=0.0,
- )
-
- info = TranscriptionInfo(
- language=language,
- language_probability=language_probability,
- duration=duration,
- duration_after_vad=duration_after_vad,
- transcription_options=batched_options,
- vad_options=None,
- all_language_probs=all_language_probs,
- )
-
- audio_segments, segments_metadata = self.audio_split(
- audio, vad_segments, sampling_rate
- )
- to_cpu = (
- self.model.model.device == "cuda" and len(self.model.model.device_index) > 1
- )
- audio_segments = torch.nested.nested_tensor(audio_segments).to_padded_tensor(
- padding=0
- )
- features = torch.stack(
- [
- self.model.feature_extractor(audio_segment, to_cpu=to_cpu)[
- ..., : self.model.feature_extractor.nb_max_frames
- ]
- for audio_segment in audio_segments
- ]
- )
-
- segments = self._batched_segments_generator(
- features,
- segments_metadata,
- batch_size,
- batched_options,
- log_progress,
- )
-
- return segments, info
-
- def _batched_segments_generator(
- self, features, segments_metadata, batch_size, options, log_progress
- ):
- pbar = tqdm(total=len(features), disable=not log_progress, position=0)
- seg_idx = 0
- for i in range(0, len(features), batch_size):
- results = self.forward(
- features[i : i + batch_size],
- segments_metadata[i : i + batch_size],
- **options._asdict(),
- )
-
- for result in results:
- for segment in result:
- seg_idx += 1
- yield Segment(
- seek=int(result[-1]["end"] * self.model.frames_per_second),
- id=seg_idx,
- text=segment["text"],
- start=round(segment["start"], 3),
- end=round(segment["end"], 3),
- words=(
- None
- if not options.word_timestamps
- else [Word(**word) for word in segment["words"]]
- ),
- tokens=segment["tokens"],
- avg_logprob=segment["avg_logprob"],
- no_speech_prob=segment["no_speech_prob"],
- compression_ratio=segment["compression_ratio"],
- )
-
- pbar.update(1)
-
- pbar.close()
- # revert the tokenizer if multilingual inference is enabled
- if self.preset_language is None:
- self.tokenizer = None
- self.last_speech_timestamp = 0.0
-
-
-class WhisperModel:
- def __init__(
- self,
- model_size_or_path: str,
- device: str = "auto",
- device_index: Union[int, List[int]] = 0,
- compute_type: str = "default",
- cpu_threads: int = 16,
- num_workers: int = 1,
- download_root: Optional[str] = None,
- local_files_only: bool = False,
- files: dict = None,
- **model_kwargs,
- ):
- """Initializes the Whisper model.
-
- Args:
- model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en,
- small, small.en, distil-small.en, medium, medium.en, distil-medium.en, large-v1,
- large-v2, large-v3, large, distil-large-v2 or distil-large-v3), a path to a
- converted model directory, or a CTranslate2-converted Whisper model ID from the HF Hub.
- When a size or a model ID is configured, the converted model is downloaded
- from the Hugging Face Hub.
- device: Device to use for computation ("cpu", "cuda", "auto").
- device_index: Device ID to use.
- The model can also be loaded on multiple GPUs by passing a list of IDs
- (e.g. [0, 1, 2, 3]). In that case, multiple transcriptions can run in parallel
- when transcribe() is called from multiple Python threads (see also num_workers).
- compute_type: Type to use for computation.
- See https://opennmt.net/CTranslate2/quantization.html.
- cpu_threads: Number of threads to use when running on CPU (4 by default).
- A non zero value overrides the OMP_NUM_THREADS environment variable.
- num_workers: When transcribe() is called from multiple Python threads,
- having multiple workers enables true parallelism when running the model
- (concurrent calls to self.model.generate() will run in parallel).
- This can improve the global throughput at the cost of increased memory usage.
- download_root: Directory where the models should be saved. If not set, the models
- are saved in the standard Hugging Face cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the
- local cached file if it exists.
- files: Load model files from the memory. This argument is a dictionary mapping file names
- to file contents as file-like or bytes objects. If this is set, model_path acts as an
- identifier for this model.
- """
- self.logger = get_logger()
-
- tokenizer_bytes, preprocessor_bytes = None, None
- if files:
- model_path = model_size_or_path
- tokenizer_bytes = files.pop("tokenizer.json", None)
- preprocessor_bytes = files.pop("preprocessor_config.json", None)
- elif os.path.isdir(model_size_or_path):
- model_path = model_size_or_path
- else:
- model_path = download_model(
- model_size_or_path,
- local_files_only=local_files_only,
- cache_dir=download_root,
- )
- self.device = device
- # set the random seed to make sure consistency across runs
- ctranslate2.set_random_seed(42)
- self.model = ctranslate2.models.Whisper(
- model_path,
- device=self.device,
- device_index=device_index,
- compute_type=compute_type,
- intra_threads=cpu_threads,
- inter_threads=num_workers,
- files=files,
- **model_kwargs,
- )
-
- tokenizer_file = os.path.join(model_path, "tokenizer.json")
- if tokenizer_bytes:
- self.hf_tokenizer = tokenizers.Tokenizer.from_buffer(tokenizer_bytes)
- elif os.path.isfile(tokenizer_file):
- self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file)
- else:
- self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained(
- "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
- )
- self.feat_kwargs = self._get_feature_kwargs(model_path, preprocessor_bytes)
- self.feature_extractor = FeatureExtractor(
- **self.feat_kwargs, device=self.device
- )
- self.input_stride = 2
- self.num_samples_per_token = (
- self.feature_extractor.hop_length * self.input_stride
- )
- self.frames_per_second = (
- self.feature_extractor.sampling_rate // self.feature_extractor.hop_length
- )
- self.tokens_per_second = (
- self.feature_extractor.sampling_rate // self.num_samples_per_token
- )
- self.time_precision = 0.02
- self.max_length = 448
-
- @property
- def supported_languages(self) -> List[str]:
- """The languages supported by the model."""
- return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"]
-
- def _get_feature_kwargs(self, model_path, preprocessor_bytes=None) -> dict:
- config = {}
- try:
- config_path = os.path.join(model_path, "preprocessor_config.json")
- if preprocessor_bytes:
- config = json.loads(preprocessor_bytes)
- elif os.path.isfile(config_path):
- with open(config_path, "r", encoding="utf-8") as file:
- config = json.load(file)
- else:
- return config
- valid_keys = signature(FeatureExtractor.__init__).parameters.keys()
- return {k: v for k, v in config.items() if k in valid_keys}
- except json.JSONDecodeError as e:
- self.logger.warning("Could not load preprocessor config: %s", e)
-
- return config
-
- def transcribe(
- self,
- audio: Union[str, BinaryIO, torch.Tensor, np.ndarray],
- language: Optional[str] = None,
- task: str = "transcribe",
- beam_size: int = 5,
- best_of: int = 5,
- patience: float = 1,
- length_penalty: float = 1,
- repetition_penalty: float = 1,
- no_repeat_ngram_size: int = 0,
- temperature: Union[float, List[float], Tuple[float, ...]] = [
- 0.0,
- 0.2,
- 0.4,
- 0.6,
- 0.8,
- 1.0,
- ],
- compression_ratio_threshold: Optional[float] = 2.4,
- log_prob_threshold: Optional[float] = -1.0,
- log_prob_low_threshold: Optional[float] = None,
- no_speech_threshold: Optional[float] = 0.6,
- condition_on_previous_text: bool = True,
- prompt_reset_on_temperature: float = 0.5,
- initial_prompt: Optional[Union[str, Iterable[int]]] = None,
- prefix: Optional[str] = None,
- suppress_blank: bool = True,
- suppress_tokens: Optional[List[int]] = [-1],
- without_timestamps: bool = False,
- max_initial_timestamp: float = 1.0,
- word_timestamps: bool = False,
- prepend_punctuations: str = "\"'“¿([{-",
- append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
- multilingual: bool = False,
- output_language: Optional[str] = None,
- vad_filter: bool = False,
- vad_parameters: Optional[Union[dict, VadOptions]] = None,
- max_new_tokens: Optional[int] = None,
- chunk_length: Optional[int] = None,
- clip_timestamps: Union[str, List[float]] = "0",
- hallucination_silence_threshold: Optional[float] = None,
- hotwords: Optional[str] = None,
- language_detection_threshold: Optional[float] = None,
- language_detection_segments: int = 1,
- ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
- """Transcribes an input file.
-
- Arguments:
- audio: Path to the input file (or a file-like object), or the audio waveform.
- language: The language spoken in the audio. It should be a language code such
- as "en" or "fr". If not set, the language will be detected in the first 30 seconds
- of audio.
- task: Task to execute (transcribe or translate).
- beam_size: Beam size to use for decoding.
- best_of: Number of candidates when sampling with non-zero temperature.
- patience: Beam search patience factor.
- length_penalty: Exponential length penalty constant.
- repetition_penalty: Penalty applied to the score of previously generated tokens
- (set > 1 to penalize).
- no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable).
- temperature: Temperature for sampling. It can be a tuple of temperatures,
- which will be successively used upon failures according to either
- `compression_ratio_threshold` or `log_prob_threshold`.
- compression_ratio_threshold: If the gzip compression ratio is above this value,
- treat as failed.
- log_prob_threshold: If the average log probability over sampled tokens is
- below this value, treat as failed.
- log_prob_low_threshold: This parameter alone is sufficient to skip an output text,
- wheras log_prob_threshold also looks for appropriate no_speech_threshold value.
- This value should be less than log_prob_threshold.
- no_speech_threshold: If the no_speech probability is higher than this value AND
- the average log probability over sampled tokens is below `log_prob_threshold`,
- consider the segment as silent.
- condition_on_previous_text: If True, the previous output of the model is provided
- as a prompt for the next window; disabling may make the text inconsistent across
- windows, but the model becomes less prone to getting stuck in a failure loop,
- such as repetition looping or timestamps going out of sync.
- prompt_reset_on_temperature: Resets prompt if temperature is above this value.
- Arg has effect only if condition_on_previous_text is True.
- initial_prompt: Optional text string or iterable of token ids to provide as a
- prompt for the first window.
- prefix: Optional text to provide as a prefix for the first window.
- suppress_blank: Suppress blank outputs at the beginning of the sampling.
- suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
- of symbols as defined in `tokenizer.non_speech_tokens()`.
- without_timestamps: Only sample text tokens.
- max_initial_timestamp: The initial timestamp cannot be later than this.
- word_timestamps: Extract word-level timestamps using the cross-attention pattern
- and dynamic time warping, and include the timestamps for each word in each segment.
- prepend_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the next word
- append_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the previous word
- multilingual: If True, perform transcription on multilingual videos
- and return the transcript based
- on the 'output_language' flag.
- output_language: Valid only if multilingual is set to True.
- Specifies the string representing the output language. One of
- 'en' (English) or 'hybrid' (code-switched transcription).
- vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
- without speech. This step is using the Silero VAD model
- https://github.com/snakers4/silero-vad.
- vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
- parameters and default values in the class `VadOptions`).
- max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set,
- the maximum will be set by the default max_length.
- chunk_length: The length of audio segments. If it is not None, it will overwrite the
- default chunk_length of the FeatureExtractor.
- clip_timestamps:
- Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to
- process. The last end timestamp defaults to the end of the file.
- vad_filter will be ignored if clip_timestamps is used.
- hallucination_silence_threshold:
- When word_timestamps is True, skip silent periods longer than this threshold
- (in seconds) when a possible hallucination is detected
- hotwords:
- Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.
- language_detection_threshold: If the maximum probability of the language tokens is higher
- than this value, the language is detected.
- language_detection_segments: Number of segments to consider for the language detection.
- Returns:
- A tuple with:
-
- - a generator over transcribed segments
- - an instance of TranscriptionInfo
- """
-
- sampling_rate = self.feature_extractor.sampling_rate
-
- if isinstance(audio, np.ndarray):
- audio = torch.from_numpy(audio)
- elif not isinstance(audio, torch.Tensor):
- audio = decode_audio(audio, sampling_rate=sampling_rate)
-
- duration = audio.shape[0] / sampling_rate
- duration_after_vad = duration
-
- self.logger.info(
- "Processing audio with duration %s", format_timestamp(duration)
- )
-
- if vad_filter and clip_timestamps == "0":
- if vad_parameters is None:
- vad_parameters = VadOptions()
- elif isinstance(vad_parameters, dict):
- vad_parameters = VadOptions(**vad_parameters)
- speech_chunks = get_speech_timestamps(audio, vad_parameters)
- audio = collect_chunks(audio, speech_chunks)
- duration_after_vad = audio.shape[0] / sampling_rate
-
- self.logger.info(
- "VAD filter removed %s of audio",
- format_timestamp(duration - duration_after_vad),
- )
-
- if self.logger.isEnabledFor(logging.DEBUG):
- self.logger.debug(
- "VAD filter kept the following audio segments: %s",
- ", ".join(
- "[%s -> %s]"
- % (
- format_timestamp(chunk["start"] / sampling_rate),
- format_timestamp(chunk["end"] / sampling_rate),
- )
- for chunk in speech_chunks
- ),
- )
-
- else:
- speech_chunks = None
-
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- features = self.feature_extractor(
- audio, chunk_length=chunk_length, to_cpu=to_cpu
- )
-
- encoder_output = None
- all_language_probs = None
-
- # setting output_language for multilingual videos
- if multilingual:
- if output_language is None:
- output_language = "en"
- elif output_language not in ["en", "hybrid"]:
- raise ValueError("Output language needs to be one of 'en'/'hybrid'.")
-
- # detecting the language if not provided
- if language is None:
- if not self.model.is_multilingual:
- language = "en"
- language_probability = 1
- else:
- if (
- language_detection_segments is None
- or language_detection_segments < 1
- ):
- language_detection_segments = 1
- start_timestamp = (
- float(clip_timestamps.split(",")[0])
- if isinstance(clip_timestamps, str)
- else clip_timestamps[0]
- )
- content_frames = (
- features.shape[-1] - self.feature_extractor.nb_max_frames
- )
- seek = (
- int(start_timestamp * self.frames_per_second)
- if start_timestamp * self.frames_per_second < content_frames
- else 0
- )
- end_frames = min(
- seek
- + self.feature_extractor.nb_max_frames
- * language_detection_segments,
- content_frames,
- )
- detected_language_info = {}
- while seek <= end_frames:
- segment = features[
- :, seek : seek + self.feature_extractor.nb_max_frames
- ]
- encoder_output = self.encode(segment)
- # results is a list of tuple[str, float] with language names and
- # probabilities.
- results = self.model.detect_language(encoder_output)[0]
- # Parse language names to strip out markers
- all_language_probs = [
- (token[2:-2], prob) for (token, prob) in results
- ]
- # Get top language token and probability
- language, language_probability = all_language_probs[0]
- if (
- language_detection_threshold is None
- or language_probability > language_detection_threshold
- ):
- break
- detected_language_info.setdefault(language, []).append(
- language_probability
- )
- seek += segment.shape[-1]
- else:
- # If no language detected for all segments, the majority vote of the highest
- # projected languages for all segments is used to determine the language.
- language = max(
- detected_language_info,
- key=lambda lang: len(detected_language_info[lang]),
- )
- language_probability = max(detected_language_info[language])
-
- self.logger.info(
- "Detected language '%s' with probability %.2f",
- language,
- language_probability,
- )
- else:
- if not self.model.is_multilingual and language != "en":
- self.logger.warning(
- "The current model is English-only but the language parameter is set to '%s'; "
- "using 'en' instead." % language
- )
- language = "en"
-
- language_probability = 1
-
- tokenizer = Tokenizer(
- self.hf_tokenizer,
- self.model.is_multilingual,
- task=task,
- language=language,
- )
-
- options = TranscriptionOptions(
- beam_size=beam_size,
- best_of=best_of,
- patience=patience,
- length_penalty=length_penalty,
- repetition_penalty=repetition_penalty,
- no_repeat_ngram_size=no_repeat_ngram_size,
- log_prob_threshold=log_prob_threshold,
- log_prob_low_threshold=log_prob_low_threshold,
- no_speech_threshold=no_speech_threshold,
- compression_ratio_threshold=compression_ratio_threshold,
- condition_on_previous_text=condition_on_previous_text,
- prompt_reset_on_temperature=prompt_reset_on_temperature,
- temperatures=(
- temperature if isinstance(temperature, (list, tuple)) else [temperature]
- ),
- initial_prompt=initial_prompt,
- prefix=prefix,
- suppress_blank=suppress_blank,
- suppress_tokens=(
- get_suppressed_tokens(tokenizer, suppress_tokens)
- if suppress_tokens
- else suppress_tokens
- ),
- without_timestamps=without_timestamps,
- max_initial_timestamp=max_initial_timestamp,
- word_timestamps=word_timestamps,
- prepend_punctuations=prepend_punctuations,
- append_punctuations=append_punctuations,
- multilingual=multilingual,
- output_language=output_language,
- max_new_tokens=max_new_tokens,
- clip_timestamps=clip_timestamps,
- hallucination_silence_threshold=hallucination_silence_threshold,
- hotwords=hotwords,
- )
-
- segments = self.generate_segments(features, tokenizer, options, encoder_output)
-
- if speech_chunks:
- segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
-
- info = TranscriptionInfo(
- language=language,
- language_probability=language_probability,
- duration=duration,
- duration_after_vad=duration_after_vad,
- transcription_options=options,
- vad_options=vad_parameters,
- all_language_probs=all_language_probs,
- )
- return segments, info
-
- def _split_segments_by_timestamps(
- self,
- tokenizer: Tokenizer,
- tokens: List[int],
- time_offset: float,
- segment_size: int,
- segment_duration: float,
- seek: int,
- ) -> List[List[int]]:
- current_segments = []
- single_timestamp_ending = (
- len(tokens) >= 2 and tokens[-2] < tokenizer.timestamp_begin <= tokens[-1]
- )
-
- consecutive_timestamps = [
- i
- for i in range(len(tokens))
- if i > 0
- and tokens[i] >= tokenizer.timestamp_begin
- and tokens[i - 1] >= tokenizer.timestamp_begin
- ]
-
- if len(consecutive_timestamps) > 0:
- slices = list(consecutive_timestamps)
- if single_timestamp_ending:
- slices.append(len(tokens))
-
- last_slice = 0
- for current_slice in slices:
- sliced_tokens = tokens[last_slice:current_slice]
- start_timestamp_position = sliced_tokens[0] - tokenizer.timestamp_begin
- end_timestamp_position = sliced_tokens[-1] - tokenizer.timestamp_begin
- start_time = (
- time_offset + start_timestamp_position * self.time_precision
- )
- end_time = time_offset + end_timestamp_position * self.time_precision
-
- current_segments.append(
- dict(
- seek=seek,
- start=start_time,
- end=end_time,
- tokens=sliced_tokens,
- )
- )
- last_slice = current_slice
-
- if single_timestamp_ending:
- # single timestamp at the end means no speech after the last timestamp.
- seek += segment_size
- else:
- # otherwise, ignore the unfinished segment and seek to the last timestamp
- last_timestamp_position = (
- tokens[last_slice - 1] - tokenizer.timestamp_begin
- )
- seek += last_timestamp_position * self.input_stride
-
- else:
- duration = segment_duration
- timestamps = [
- token for token in tokens if token >= tokenizer.timestamp_begin
- ]
- if len(timestamps) > 0 and timestamps[-1] != tokenizer.timestamp_begin:
- last_timestamp_position = timestamps[-1] - tokenizer.timestamp_begin
- duration = last_timestamp_position * self.time_precision
-
- current_segments.append(
- dict(
- seek=seek,
- start=time_offset,
- end=time_offset + duration,
- tokens=tokens,
- )
- )
-
- seek += segment_size
-
- return current_segments, seek, single_timestamp_ending
-
- def generate_segments(
- self,
- features: torch.Tensor,
- tokenizer: Tokenizer,
- options: TranscriptionOptions,
- encoder_output: Optional[ctranslate2.StorageView] = None,
- ) -> Iterable[Segment]:
- content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
- content_duration = float(content_frames * self.feature_extractor.time_per_frame)
-
- if isinstance(options.clip_timestamps, str):
- options = options._replace(
- clip_timestamps=[
- float(ts)
- for ts in (
- options.clip_timestamps.split(",")
- if options.clip_timestamps
- else []
- )
- ]
- )
- seek_points: List[int] = [
- round(ts * self.frames_per_second) for ts in options.clip_timestamps
- ]
- if len(seek_points) == 0:
- seek_points.append(0)
- if len(seek_points) % 2 == 1:
- seek_points.append(content_frames)
- seek_clips: List[Tuple[int, int]] = list(
- zip(seek_points[::2], seek_points[1::2])
- )
-
- punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
-
- idx = 0
- clip_idx = 0
- seek = seek_clips[clip_idx][0]
- all_tokens = []
- prompt_reset_since = 0
-
- if options.initial_prompt is not None:
- if isinstance(options.initial_prompt, str):
- initial_prompt = " " + options.initial_prompt.strip()
- initial_prompt_tokens = tokenizer.encode(initial_prompt)
- all_tokens.extend(initial_prompt_tokens)
- else:
- all_tokens.extend(options.initial_prompt)
-
- last_speech_timestamp = 0.0
- # NOTE: This loop is obscurely flattened to make the diff readable.
- # A later commit should turn this into a simpler nested loop.
- # for seek_clip_start, seek_clip_end in seek_clips:
- # while seek < seek_clip_end
- while clip_idx < len(seek_clips):
- seek_clip_start, seek_clip_end = seek_clips[clip_idx]
- if seek_clip_end > content_frames:
- seek_clip_end = content_frames
- if seek < seek_clip_start:
- seek = seek_clip_start
- if seek >= seek_clip_end:
- clip_idx += 1
- if clip_idx < len(seek_clips):
- seek = seek_clips[clip_idx][0]
- continue
- time_offset = seek * self.feature_extractor.time_per_frame
- window_end_time = float(
- (seek + self.feature_extractor.nb_max_frames)
- * self.feature_extractor.time_per_frame
- )
- segment_size = min(
- self.feature_extractor.nb_max_frames,
- content_frames - seek,
- seek_clip_end - seek,
- )
- segment = features[:, seek : seek + segment_size]
- segment_duration = segment_size * self.feature_extractor.time_per_frame
- segment = pad_or_trim(segment, self.feature_extractor.nb_max_frames)
-
- if self.logger.isEnabledFor(logging.DEBUG):
- self.logger.debug(
- "Processing segment at %s", format_timestamp(time_offset)
- )
-
- previous_tokens = all_tokens[prompt_reset_since:]
-
- if encoder_output is None:
- encoder_output = self.encode(segment)
-
- # Perform language detection at every segment to update task based on output language,
- # if the language is english, task is transcribe,
- # else the task is translate to english (default)
- # or transcribe if 'output_language' is 'hybrid'.
- if options.multilingual:
- results = self.model.detect_language(encoder_output)
- language_token, language_probability = results[0][0]
- language = language_token[2:-2]
- if options.output_language == "en" and language != "en":
- task = "translate"
- else:
- task = "transcribe"
-
- # Update tokenizer based on task and language
- tokenizer.task = tokenizer.tokenizer.token_to_id(f"<|{task}|>")
- tokenizer.language = tokenizer.tokenizer.token_to_id(language_token)
- tokenizer.language_code = language
- # Update prompt based on task and language
- prompt = self.get_prompt(
- tokenizer,
- previous_tokens,
- without_timestamps=options.without_timestamps,
- prefix=options.prefix if seek == 0 else None,
- hotwords=options.hotwords,
- )
-
- if seek > 0 or encoder_output is None:
- encoder_output = self.encode(segment)
-
- (
- result,
- avg_logprob,
- temperature,
- compression_ratio,
- ) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options)
-
- if options.no_speech_threshold is not None:
- # no voice activity check
- should_skip = result.no_speech_prob > options.no_speech_threshold
-
- if (
- options.log_prob_threshold is not None
- and avg_logprob > options.log_prob_threshold
- ):
- # don't skip if the logprob is high enough, despite the no_speech_prob
- should_skip = False
-
- if should_skip:
- self.logger.debug(
- "No speech threshold is met (%f > %f)",
- result.no_speech_prob,
- options.no_speech_threshold,
- )
-
- # Skip if the logprob is very low (below the threshold value),
- # despite no_speech_prob being low (ex: Too ambiguous outputs)
- if options.log_prob_low_threshold:
- if avg_logprob < options.log_prob_low_threshold:
- should_skip = True
- self.logger.debug(
- "log prob low threshold is met (%f > %f)",
- avg_logprob,
- options.log_prob_low_threshold,
- )
-
- if should_skip:
- # fast-forward to the next segment boundary
- seek += segment_size
- continue
-
- tokens = result.sequences_ids[0]
-
- previous_seek = seek
-
- # anomalous words are very long/short/improbable
- def word_anomaly_score(word: dict) -> float:
- probability = word.get("probability", 0.0)
- duration = word["end"] - word["start"]
- score = 0.0
- if probability < 0.15:
- score += 1.0
- if duration < 0.133:
- score += (0.133 - duration) * 15
- if duration > 2.0:
- score += duration - 2.0
- return score
-
- def is_segment_anomaly(segment: Optional[dict]) -> bool:
- if segment is None or not segment["words"]:
- return False
- words = [w for w in segment["words"] if w["word"] not in punctuation]
- words = words[:8]
- score = sum(word_anomaly_score(w) for w in words)
- return score >= 3 or score + 0.01 >= len(words)
-
- def next_words_segment(segments: List[dict]) -> Optional[dict]:
- return next((s for s in segments if s["words"]), None)
-
- (
- current_segments,
- seek,
- single_timestamp_ending,
- ) = self._split_segments_by_timestamps(
- tokenizer=tokenizer,
- tokens=tokens,
- time_offset=time_offset,
- segment_size=segment_size,
- segment_duration=segment_duration,
- seek=seek,
- )
-
- if options.word_timestamps:
- self.add_word_timestamps(
- [current_segments],
- tokenizer,
- encoder_output,
- segment_size,
- options.prepend_punctuations,
- options.append_punctuations,
- last_speech_timestamp=last_speech_timestamp,
- )
- if not single_timestamp_ending:
- last_word_end = get_end(current_segments)
- if last_word_end is not None and last_word_end > time_offset:
- seek = round(last_word_end * self.frames_per_second)
-
- # skip silence before possible hallucinations
- if options.hallucination_silence_threshold is not None:
- threshold = options.hallucination_silence_threshold
-
- # if first segment might be a hallucination, skip leading silence
- first_segment = next_words_segment(current_segments)
- if first_segment is not None and is_segment_anomaly(first_segment):
- gap = first_segment["start"] - time_offset
- if gap > threshold:
- seek = previous_seek + round(gap * self.frames_per_second)
- continue
-
- # skip silence before any possible hallucination that is surrounded
- # by silence or more hallucinations
- hal_last_end = last_speech_timestamp
- for si in range(len(current_segments)):
- segment = current_segments[si]
- if not segment["words"]:
- continue
- if is_segment_anomaly(segment):
- next_segment = next_words_segment(
- current_segments[si + 1 :]
- )
- if next_segment is not None:
- hal_next_start = next_segment["words"][0]["start"]
- else:
- hal_next_start = time_offset + segment_duration
- silence_before = (
- segment["start"] - hal_last_end > threshold
- or segment["start"] < threshold
- or segment["start"] - time_offset < 2.0
- )
- silence_after = (
- hal_next_start - segment["end"] > threshold
- or is_segment_anomaly(next_segment)
- or window_end_time - segment["end"] < 2.0
- )
- if silence_before and silence_after:
- seek = round(
- max(time_offset + 1, segment["start"])
- * self.frames_per_second
- )
- if content_duration - segment["end"] < threshold:
- seek = content_frames
- current_segments[si:] = []
- break
- hal_last_end = segment["end"]
-
- last_word_end = get_end(current_segments)
- if last_word_end is not None:
- last_speech_timestamp = last_word_end
- for segment in current_segments:
- tokens = segment["tokens"]
- text = tokenizer.decode(tokens)
-
- if segment["start"] == segment["end"] or not text.strip():
- continue
-
- all_tokens.extend(tokens)
- idx += 1
-
- yield Segment(
- id=idx,
- seek=seek,
- start=segment["start"],
- end=segment["end"],
- text=text,
- tokens=tokens,
- temperature=temperature,
- avg_logprob=avg_logprob,
- compression_ratio=compression_ratio,
- no_speech_prob=result.no_speech_prob,
- words=(
- [Word(**word) for word in segment["words"]]
- if options.word_timestamps
- else None
- ),
- )
-
- if (
- not options.condition_on_previous_text
- or temperature > options.prompt_reset_on_temperature
- ):
- if options.condition_on_previous_text:
- self.logger.debug(
- "Reset prompt. prompt_reset_on_temperature threshold is met %f > %f",
- temperature,
- options.prompt_reset_on_temperature,
- )
-
- prompt_reset_since = len(all_tokens)
-
- def encode(self, features: torch.Tensor) -> ctranslate2.StorageView:
- # When the model is running on multiple GPUs, the encoder output should be moved
- # to the CPU since we don't know which GPU will handle the next job.
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
-
- if features.ndim == 2:
- features = features.unsqueeze(0)
- features = get_ctranslate2_storage(features)
-
- return self.model.encode(features, to_cpu=to_cpu)
-
- def generate_with_fallback(
- self,
- encoder_output: ctranslate2.StorageView,
- prompt: List[int],
- tokenizer: Tokenizer,
- options: TranscriptionOptions,
- ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]:
- decode_result = None
- all_results = []
- below_cr_threshold_results = []
-
- max_initial_timestamp_index = int(
- round(options.max_initial_timestamp / self.time_precision)
- )
- if options.max_new_tokens is not None:
- max_length = len(prompt) + options.max_new_tokens
- else:
- max_length = self.max_length
-
- if max_length > self.max_length:
- raise ValueError(
- f"The length of the prompt is {len(prompt)}, and the `max_new_tokens` "
- f"{max_length - len(prompt)}. Thus, the combined length of the prompt "
- f"and `max_new_tokens` is: {max_length}. This exceeds the "
- f"`max_length` of the Whisper model: {self.max_length}. "
- "You should either reduce the length of your prompt, or "
- "reduce the value of `max_new_tokens`, "
- f"so that their combined length is less that {self.max_length}."
- )
-
- for temperature in options.temperatures:
- if temperature > 0:
- kwargs = {
- "beam_size": 1,
- "num_hypotheses": options.best_of,
- "sampling_topk": 0,
- "sampling_temperature": temperature,
- }
- else:
- kwargs = {
- "beam_size": options.beam_size,
- "patience": options.patience,
- }
-
- result = self.model.generate(
- encoder_output,
- [prompt],
- length_penalty=options.length_penalty,
- repetition_penalty=options.repetition_penalty,
- no_repeat_ngram_size=options.no_repeat_ngram_size,
- max_length=max_length,
- return_scores=True,
- return_no_speech_prob=True,
- suppress_blank=options.suppress_blank,
- suppress_tokens=options.suppress_tokens,
- max_initial_timestamp_index=max_initial_timestamp_index,
- **kwargs,
- )[0]
-
- tokens = result.sequences_ids[0]
-
- # Recover the average log prob from the returned score.
- seq_len = len(tokens)
- cum_logprob = result.scores[0] * (seq_len**options.length_penalty)
- avg_logprob = cum_logprob / (seq_len + 1)
-
- text = tokenizer.decode(tokens).strip()
- compression_ratio = get_compression_ratio(text)
-
- decode_result = (
- result,
- avg_logprob,
- temperature,
- compression_ratio,
- )
- all_results.append(decode_result)
-
- needs_fallback = False
-
- if options.compression_ratio_threshold is not None:
- if compression_ratio > options.compression_ratio_threshold:
- needs_fallback = True # too repetitive
-
- self.logger.debug(
- "Compression ratio threshold is not met with temperature %.1f (%f > %f)",
- temperature,
- compression_ratio,
- options.compression_ratio_threshold,
- )
- else:
- below_cr_threshold_results.append(decode_result)
-
- if (
- options.log_prob_threshold is not None
- and avg_logprob < options.log_prob_threshold
- ):
- needs_fallback = True # average log probability is too low
-
- self.logger.debug(
- "Log probability threshold is not met with temperature %.1f (%f < %f)",
- temperature,
- avg_logprob,
- options.log_prob_threshold,
- )
-
- if (
- options.no_speech_threshold is not None
- and result.no_speech_prob > options.no_speech_threshold
- and options.log_prob_threshold is not None
- and avg_logprob < options.log_prob_threshold
- ):
- needs_fallback = False # silence
-
- if not needs_fallback:
- break
- else:
- # all failed, select the result with the highest average log probability
- decode_result = max(
- below_cr_threshold_results or all_results, key=lambda x: x[1]
- )
- # to pass final temperature for prompt_reset_on_temperature
- decode_result = (
- decode_result[0],
- decode_result[1],
- temperature,
- decode_result[3],
- )
-
- return decode_result
-
- def get_prompt(
- self,
- tokenizer: Tokenizer,
- previous_tokens: List[int],
- without_timestamps: bool = False,
- prefix: Optional[str] = None,
- hotwords: Optional[str] = None,
- ) -> List[int]:
- prompt = []
-
- if previous_tokens or (hotwords and not prefix):
- prompt.append(tokenizer.sot_prev)
- if hotwords and not prefix:
- hotwords_tokens = tokenizer.encode(" " + hotwords.strip())
- if len(hotwords_tokens) >= self.max_length // 2:
- hotwords_tokens = hotwords_tokens[: self.max_length // 2 - 1]
- prompt.extend(hotwords_tokens)
- if previous_tokens:
- prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
-
- prompt.extend(tokenizer.sot_sequence)
-
- if without_timestamps:
- prompt.append(tokenizer.no_timestamps)
-
- if prefix:
- prefix_tokens = tokenizer.encode(" " + prefix.strip())
- if len(prefix_tokens) >= self.max_length // 2:
- prefix_tokens = prefix_tokens[: self.max_length // 2 - 1]
- if not without_timestamps:
- prompt.append(tokenizer.timestamp_begin)
- prompt.extend(prefix_tokens)
-
- return prompt
-
- def add_word_timestamps(
- self,
- segments: List[dict],
- tokenizer: Tokenizer,
- encoder_output: ctranslate2.StorageView,
- num_frames: int,
- prepend_punctuations: str,
- append_punctuations: str,
- last_speech_timestamp: float,
- ) -> float:
- if len(segments) == 0:
- return
-
- text_tokens = []
- text_tokens_per_segment = []
- for segment in segments:
- segment_tokens = [
- [token for token in subsegment["tokens"] if token < tokenizer.eot]
- for subsegment in segment
- ]
- text_tokens.append(list(itertools.chain.from_iterable(segment_tokens)))
- text_tokens_per_segment.append(segment_tokens)
-
- alignments = self.find_alignment(
- tokenizer, text_tokens, encoder_output, num_frames
- )
- median_max_durations = []
- for alignment in alignments:
- word_durations = np.array(
- [word["end"] - word["start"] for word in alignment]
- )
- word_durations = word_durations[word_durations.nonzero()]
- median_duration = (
- np.median(word_durations) if len(word_durations) > 0 else 0.0
- )
- median_duration = min(0.7, float(median_duration))
- max_duration = median_duration * 2
-
- # hack: truncate long words at sentence boundaries.
- # a better segmentation algorithm based on VAD should be able to replace this.
- if len(word_durations) > 0:
- sentence_end_marks = ".。!！?？"
- # ensure words at sentence boundaries
- # are not longer than twice the median word duration.
- for i in range(1, len(alignment)):
- if alignment[i]["end"] - alignment[i]["start"] > max_duration:
- if alignment[i]["word"] in sentence_end_marks:
- alignment[i]["end"] = alignment[i]["start"] + max_duration
- elif alignment[i - 1]["word"] in sentence_end_marks:
- alignment[i]["start"] = alignment[i]["end"] - max_duration
-
- merge_punctuations(alignment, prepend_punctuations, append_punctuations)
- median_max_durations.append((median_duration, max_duration))
-
- for segment_idx, segment in enumerate(segments):
- word_index = 0
- time_offset = segment[0]["start"]
- median_duration, max_duration = median_max_durations[segment_idx]
- for subsegment_idx, subsegment in enumerate(segment):
- saved_tokens = 0
- words = []
-
- while word_index < len(alignments[segment_idx]) and saved_tokens < len(
- text_tokens_per_segment[segment_idx][subsegment_idx]
- ):
- timing = alignments[segment_idx][word_index]
-
- if timing["word"]:
- words.append(
- dict(
- word=timing["word"],
- start=round(time_offset + timing["start"], 2),
- end=round(time_offset + timing["end"], 2),
- probability=timing["probability"],
- )
- )
-
- saved_tokens += len(timing["tokens"])
- word_index += 1
-
- # hack: truncate long words at segment boundaries.
- # a better segmentation algorithm based on VAD should be able to replace this.
- if len(words) > 0:
- # ensure the first and second word after a pause is not longer than
- # twice the median word duration.
- if words[0][
- "end"
- ] - last_speech_timestamp > median_duration * 4 and (
- words[0]["end"] - words[0]["start"] > max_duration
- or (
- len(words) > 1
- and words[1]["end"] - words[0]["start"] > max_duration * 2
- )
- ):
- if (
- len(words) > 1
- and words[1]["end"] - words[1]["start"] > max_duration
- ):
- boundary = max(
- words[1]["end"] / 2, words[1]["end"] - max_duration
- )
- words[0]["end"] = words[1]["start"] = boundary
- words[0]["start"] = max(0, words[0]["end"] - max_duration)
-
- # prefer the segment-level start timestamp if the first word is too long.
- if (
- subsegment["start"] < words[0]["end"]
- and subsegment["start"] - 0.5 > words[0]["start"]
- ):
- words[0]["start"] = max(
- 0,
- min(words[0]["end"] - median_duration, subsegment["start"]),
- )
- else:
- subsegment["start"] = words[0]["start"]
-
- # prefer the segment-level end timestamp if the last word is too long.
- if (
- subsegment["end"] > words[-1]["start"]
- and subsegment["end"] + 0.5 < words[-1]["end"]
- ):
- words[-1]["end"] = max(
- words[-1]["start"] + median_duration, subsegment["end"]
- )
- else:
- subsegment["end"] = words[-1]["end"]
-
- last_speech_timestamp = subsegment["end"]
- segments[segment_idx][subsegment_idx]["words"] = words
- return last_speech_timestamp
-
- def find_alignment(
- self,
- tokenizer: Tokenizer,
- text_tokens: List[int],
- encoder_output: ctranslate2.StorageView,
- num_frames: int,
- median_filter_width: int = 7,
- ) -> List[dict]:
- if len(text_tokens) == 0:
- return []
-
- results = self.model.align(
- encoder_output,
- tokenizer.sot_sequence,
- text_tokens,
- num_frames,
- median_filter_width=median_filter_width,
- )
- return_list = []
- for result, text_token in zip(results, text_tokens):
- text_token_probs = result.text_token_probs
- alignments = result.alignments
- text_indices = np.array([pair[0] for pair in alignments])
- time_indices = np.array([pair[1] for pair in alignments])
-
- words, word_tokens = tokenizer.split_to_word_tokens(
- text_token + [tokenizer.eot]
- )
- if len(word_tokens) <= 1:
- # return on eot only
- # >>> np.pad([], (1, 0))
- # array([0.])
- # This results in crashes when we lookup jump_times with float, like
- # IndexError: arrays used as indices must be of integer (or boolean) type
- return []
- word_boundaries = np.pad(
- np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)
- )
- if len(word_boundaries) <= 1:
- return []
-
- jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(
- bool
- )
- jump_times = time_indices[jumps] / self.tokens_per_second
- start_times = jump_times[word_boundaries[:-1]]
- end_times = jump_times[word_boundaries[1:]]
- word_probabilities = [
- np.mean(text_token_probs[i:j])
- for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
- ]
-
- return_list.append(
- [
- dict(
- word=word,
- tokens=tokens,
- start=start,
- end=end,
- probability=probability,
- )
- for word, tokens, start, end, probability in zip(
- words, word_tokens, start_times, end_times, word_probabilities
- )
- ]
- )
- return return_list
-
- def generate_segment_batched(
- self,
- features: torch.Tensor,
- tokenizer: Tokenizer,
- options: dict,
- ):
- batch_size = features.shape[0]
- all_tokens = []
- prompt_reset_since = 0
-
- if options["initial_prompt"] is not None:
- initial_prompt = " " + options["initial_prompt"].strip()
- initial_prompt_tokens = tokenizer.encode(initial_prompt)
- all_tokens.extend(initial_prompt_tokens)
- previous_tokens = all_tokens[prompt_reset_since:]
- prompt = self.get_prompt(
- tokenizer,
- previous_tokens,
- without_timestamps=options["without_timestamps"],
- prefix=options["prefix"],
- )
-
- encoder_output = self.encode(features)
-
- result = self.model.generate(
- encoder_output,
- [prompt] * batch_size,
- beam_size=options["beam_size"],
- patience=options["patience"],
- length_penalty=options["length_penalty"],
- max_length=self.max_length,
- suppress_blank=options["suppress_blank"],
- suppress_tokens=options["suppress_tokens"],
- return_scores=True,
- return_no_speech_prob=True,
- )
-
- output = []
- for res in result:
- output.append({})
- # return scores
- seq_len = len(res.sequences_ids[0])
- cum_logprob = res.scores[0] * (seq_len ** options["length_penalty"])
- output[-1]["avg_logprob"] = cum_logprob / (seq_len + 1)
-
- # return no speech prob
- output[-1]["no_speech_prob"] = res.no_speech_prob
- output[-1]["tokens"] = res.sequences_ids[0]
-
- return encoder_output, output
-
- def detect_language(self, audio: torch.Tensor):
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- segment = self.feature_extractor(audio, padding=True, to_cpu=to_cpu)[
- :, : self.feature_extractor.nb_max_frames
- ]
- encoder_output = self.encode(segment)
- results = self.model.detect_language(encoder_output)
- language_token, language_probability = results[0][0]
- language = language_token[2:-2]
- self.logger.info(
- f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio..."
- )
- all_language_probs = [(token[2:-2], prob) for (token, prob) in results[0]]
- return language, language_probability, all_language_probs
-
- def detect_language_multi_segment(
- self, audio: Union[str, BinaryIO, torch.Tensor], params: Optional[dict] = None
- ):
- """
- Detect language based on N highly-confident segments of a language.
- """
- # The threshold is used to decide if the audio is silence or not.
- # The default is 0.02 (2.0%) i.e, if more than 2.0% of the audio is silent,
- # the audio is considered as silence.
- if not params:
- params = {
- "multilingual": False,
- "speech_percentage_threshold": 0.02,
- "language_detection_segments": 4,
- "vad_filter": True,
- "vad_min_silence_duration": 2500,
- "language_threshold": 0.7,
- }
-
- if params.get("multilingual", False):
- logging.warning(
- "lang_id is not supported for multilingual audios, detecting the major language."
- )
-
- speech_percentage_threshold = params.get("speech_percentage_threshold", 0.02)
- language_threshold = params.get("language_threshold", 0.7)
- num_detection_segments = params.get("language_detection_segments", 4)
- vad_filter_enabled = params.get("vad_filter", True)
- vad_params = dict(
- min_silence_duration_ms=params.get("vad_min_silence_duration", 2500)
- )
-
- if vad_filter_enabled:
- vad_params = VadOptions(**vad_params)
-
- # decode audio if it is not decoded already
- sampling_rate = self.feature_extractor.sampling_rate
- if not isinstance(audio, torch.Tensor):
- audio: torch.Tensor = decode_audio(audio, sampling_rate=sampling_rate)
-
- # calculate duration of audio as number of seconds
- # audio.shape[0] is the number of samples in the audio
- # sampling_rate is the number of samples per second
- # if we divide the number of samples by the number of samples per second,
- # we get the duration in seconds
- duration = audio.shape[0] / sampling_rate
-
- # Check if vad is enabled, and collect voiced segments
- if vad_filter_enabled:
- # get chunks of audio that contain speech
- speech_chunks = get_speech_timestamps(audio, vad_params)
- # merge chunks of audio that contain speech into a single array
- audio = collect_chunks(audio, speech_chunks)
-
- # calculate new duration of audio without silence
- duration_vad = audio.shape[0] / sampling_rate
-
- logging.debug(
- f"Lang ID: VAD filter removed {duration - duration_vad} sec of audio"
- )
-
- # if the audio after VAD is less than 2% of the original audio, consider it as silence
- if duration_vad / duration < speech_percentage_threshold:
- return {"language_code": None, "language_confidence": 1.0}
-
- # update duration to be the duration after VAD
- duration = duration_vad
-
- # if the duration of the audio is less than 1 second, consider it as silence
- if duration < 1.0:
- return {"language_code": None, "language_confidence": 1.0}
-
- # number of feature frames in 30 seconds of audio is 3000
- nb_max_frames = self.feature_extractor.nb_max_frames
-
- # extract features from audio with padding (default)
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- features = self.feature_extractor(audio, to_cpu=to_cpu)
-
- # number of segments in the audio
- num_segments = features.shape[-1] // nb_max_frames
- # more number of segments than possible with the duration of file
- if num_detection_segments > num_segments:
- logging.warning(
- f"Lang ID: Can not have more segments, setting {num_segments} segments."
- )
- num_detection_segments = num_segments
-
- # create a list of indices to randomly select segments from
- indices = list(range(num_detection_segments))
-
- # fix seed to get deterministic results
- random.seed(0)
- random.shuffle(indices)
-
- detected_languages = []
- all_language_probabilities = defaultdict(list)
- confident_language_probabilities = defaultdict(list)
- num_confident_segments_per_language = defaultdict(int)
-
- # Iterate over the randomly selected indices of the segments.
- #
- # For each segment, extract features and detect language.
- #
- # If the language is confident, add it to the list of confident segments for that language.
- #
- # If the number of confident segments for a language
- # is greater than or equal to the number of detection segments,
- # return the language and the average probability of the language.
- #
- # If we are unable to get sufficient number of confident predcitions,
- # return the most frequently detected language with maximum probability.
- #
- # We need to get sufficient number of confident predictions per language, not in total.
-
- for i in indices:
- segment_features = features[:, i * nb_max_frames : (i + 1) * nb_max_frames]
- try:
- encoder_output = self.encode(segment_features)
- results = self.model.detect_language(encoder_output)[0]
-
- except ValueError as e: # or RuntimeError
- logging.error(f"Inference error:{e}")
-
- # results is the list of classes (languages) and their probabilities (descending),
- # for eg: [('<|de|>', 0.482177734375),('<|en|>', 0.283447265625),...]
-
- # take top language token and probability
- # and parse language token to strip out markers
- # for eg: '<|de|>' -> 'de'
-
- language_token = results[0][0]
- language = language_token[2:-2]
-
- language_probability = results[0][1]
-
- detected_languages.append(language)
- all_language_probabilities[language].append(language_probability)
-
- # only consider if the language prediction is confident
- if language_probability > language_threshold:
- num_confident_segments_per_language[language] += 1
-
- # Add language and probability to the list of languages when it is confident
- confident_language_probabilities[language].append(language_probability)
-
- # return the language when sufficient number of confident segments is achieved
- if (
- num_confident_segments_per_language[language]
- >= num_detection_segments
- ):
- # Considering the average probability of only confident segments
- mean = sum(confident_language_probabilities[language]) / len(
- confident_language_probabilities[language]
- )
- return {
- "language_code": language,
- "language_confidence": mean,
- }
-
- # if we are unable to get sufficient number of confident predictions,
- # return the most frequently detected language.
- # if there is a tie, return the one with maximum average probability.
- counter = Counter(detected_languages)
-
- # Define the key function to select frequent language with attached probabilities
- def key_func(language):
- # Calculate the frequency of the language
- frequency = counter[language]
-
- # Calculate the average probability of the language
- prob_avg = sum(all_language_probabilities[language]) / len(
- all_language_probabilities[language]
- )
-
- return frequency, prob_avg
-
- if detected_languages:
- # Use the key function to find the language with maximum frequency and probability
- max_language = max(detected_languages, key=key_func)
- max_probability = sum(all_language_probabilities[max_language]) / len(
- all_language_probabilities[max_language]
- )
-
- # Do additional checks for silence for non-confident case
- # calculate RMS amplitude and DC offset
- dc_offset = audio.mean()
- audio_minus_dc_offset = audio - dc_offset
- is_silent = (
- torch.all(audio.abs() < 0.01)
- or torch.sqrt(torch.mean(audio_minus_dc_offset**2)) < 0.01
- )
-
- if is_silent:
- return {"language_code": None, "language_confidence": 1.0}
-
- return {
- "language_code": max_language,
- "language_confidence": max_probability,
- }
-
- # Language is not detected for any segment and none of prev conditions met
- return {"language_code": None, "language_confidence": 1.0}
-
-
-def restore_speech_timestamps(
- segments: Iterable[Segment],
- speech_chunks: List[dict],
- sampling_rate: int,
-) -> Iterable[Segment]:
- ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
-
- for segment in segments:
- if segment.words:
- words = []
- for word in segment.words:
- # Ensure the word start and end times are resolved to the same chunk.
- middle = (word.start + word.end) / 2
- chunk_index = ts_map.get_chunk_index(middle)
- word = word._replace(
- start=ts_map.get_original_time(word.start, chunk_index),
- end=ts_map.get_original_time(word.end, chunk_index),
- )
- words.append(word)
-
- segment = segment._replace(
- start=words[0].start,
- end=words[-1].end,
- words=words,
- )
-
- else:
- segment = segment._replace(
- start=ts_map.get_original_time(segment.start),
- end=ts_map.get_original_time(segment.end),
- )
-
- yield segment
-
-
-def get_ctranslate2_storage(segment: torch.Tensor) -> ctranslate2.StorageView:
- segment = segment.contiguous()
- segment = ctranslate2.StorageView.from_array(
- segment if segment.is_cuda else segment.numpy()
- ) # torch cpu tensors don't implement __array_interface__
- # https://github.com/pytorch/pytorch/issues/51156
- return segment
-
-
-def get_compression_ratio(text: str) -> float:
- text_bytes = text.encode("utf-8")
- return len(text_bytes) / len(zlib.compress(text_bytes))
-
-
-def get_suppressed_tokens(
- tokenizer: Tokenizer,
- suppress_tokens: Tuple[int],
-) -> Optional[List[int]]:
- if -1 in suppress_tokens:
- suppress_tokens = [t for t in suppress_tokens if t >= 0]
- suppress_tokens.extend(tokenizer.non_speech_tokens)
- elif suppress_tokens is None or len(suppress_tokens) == 0:
- suppress_tokens = [] # interpret empty string as an empty list
- else:
- assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
-
- suppress_tokens.extend(
- [
- tokenizer.transcribe,
- tokenizer.translate,
- tokenizer.sot,
- tokenizer.sot_prev,
- tokenizer.sot_lm,
- ]
- )
-
- return tuple(sorted(set(suppress_tokens)))
-
-
-def merge_punctuations(alignment: List[dict], prepended: str, appended: str) -> None:
- # merge prepended punctuations
- i = len(alignment) - 2
- j = len(alignment) - 1
- while i >= 0:
- previous = alignment[i]
- following = alignment[j]
- if previous["word"].startswith(" ") and previous["word"].strip() in prepended:
- # prepend it to the following word
- following["word"] = previous["word"] + following["word"]
- if "tokens" in alignment[0].keys():
- following["tokens"] = previous["tokens"] + following["tokens"]
- previous["tokens"] = []
- previous["word"] = ""
-
- else:
- j = i
- i -= 1
-
- # merge appended punctuations
- i = 0
- j = 1
- while j < len(alignment):
- previous = alignment[i]
- following = alignment[j]
- if not previous["word"].endswith(" ") and following["word"] in appended:
- # append it to the previous word
- previous["word"] = previous["word"] + following["word"]
- if "tokens" in alignment[0].keys():
- previous["tokens"] = previous["tokens"] + following["tokens"]
- following["tokens"] = []
- following["word"] = ""
-
- else:
- i = j
- j += 1
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/utils.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/utils.py
deleted file mode 100644
index 481bd748ec0daec58681289c38559d5968e6630d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/utils.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import logging
-import os
-import re
-
-from typing import List, Optional
-
-import huggingface_hub
-import requests
-
-from tqdm.auto import tqdm
-
-_MODELS = {
- "tiny.en": "Systran/faster-whisper-tiny.en",
- "tiny": "Systran/faster-whisper-tiny",
- "base.en": "Systran/faster-whisper-base.en",
- "base": "Systran/faster-whisper-base",
- "small.en": "Systran/faster-whisper-small.en",
- "small": "Systran/faster-whisper-small",
- "medium.en": "Systran/faster-whisper-medium.en",
- "medium": "Systran/faster-whisper-medium",
- "large-v1": "Systran/faster-whisper-large-v1",
- "large-v2": "Systran/faster-whisper-large-v2",
- "large-v3": "Systran/faster-whisper-large-v3",
- "large": "Systran/faster-whisper-large-v3",
- "distil-large-v2": "Systran/faster-distil-whisper-large-v2",
- "distil-medium.en": "Systran/faster-distil-whisper-medium.en",
- "distil-small.en": "Systran/faster-distil-whisper-small.en",
- "distil-large-v3": "Systran/faster-distil-whisper-large-v3",
-}
-
-
-def available_models() -> List[str]:
- """Returns the names of available models."""
- return list(_MODELS.keys())
-
-
-def get_assets_path():
- """Returns the path to the assets directory."""
- return os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
-
-
-def get_logger():
- """Returns the module logger."""
- return logging.getLogger("faster_whisper")
-
-
-def download_model(
- size_or_id: str,
- output_dir: Optional[str] = None,
- local_files_only: bool = False,
- cache_dir: Optional[str] = None,
-):
- """Downloads a CTranslate2 Whisper model from the Hugging Face Hub.
-
- Args:
- size_or_id: Size of the model to download from https://huggingface.co/Systran
- (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en,
- distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2,
- distil-large-v3), or a CTranslate2-converted model ID from the Hugging Face Hub
- (e.g. Systran/faster-whisper-large-v3).
- output_dir: Directory where the model should be saved. If not set, the model is saved in
- the cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the local
- cached file if it exists.
- cache_dir: Path to the folder where cached files are stored.
-
- Returns:
- The path to the downloaded model.
-
- Raises:
- ValueError: if the model size is invalid.
- """
- if re.match(r".*/.*", size_or_id):
- repo_id = size_or_id
- else:
- repo_id = _MODELS.get(size_or_id)
- if repo_id is None:
- raise ValueError(
- "Invalid model size '%s', expected one of: %s"
- % (size_or_id, ", ".join(_MODELS.keys()))
- )
-
- allow_patterns = [
- "config.json",
- "preprocessor_config.json",
- "model.bin",
- "tokenizer.json",
- "vocabulary.*",
- ]
-
- kwargs = {
- "local_files_only": local_files_only,
- "allow_patterns": allow_patterns,
- "tqdm_class": disabled_tqdm,
- }
-
- if output_dir is not None:
- kwargs["local_dir"] = output_dir
- kwargs["local_dir_use_symlinks"] = False
-
- if cache_dir is not None:
- kwargs["cache_dir"] = cache_dir
-
- try:
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
- except (
- huggingface_hub.utils.HfHubHTTPError,
- requests.exceptions.ConnectionError,
- ) as exception:
- logger = get_logger()
- logger.warning(
- "An error occured while synchronizing the model %s from the Hugging Face Hub:\n%s",
- repo_id,
- exception,
- )
- logger.warning(
- "Trying to load the model directly from the local cache, if it exists."
- )
-
- kwargs["local_files_only"] = True
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
-
-
-def format_timestamp(
- seconds: float,
- always_include_hours: bool = False,
- decimal_marker: str = ".",
-) -> str:
- assert seconds >= 0, "non-negative timestamp expected"
- milliseconds = round(seconds * 1000.0)
-
- hours = milliseconds // 3_600_000
- milliseconds -= hours * 3_600_000
-
- minutes = milliseconds // 60_000
- milliseconds -= minutes * 60_000
-
- seconds = milliseconds // 1_000
- milliseconds -= seconds * 1_000
-
- hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
- return (
- f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
- )
-
-
-class disabled_tqdm(tqdm):
- def __init__(self, *args, **kwargs):
- kwargs["disable"] = True
- super().__init__(*args, **kwargs)
-
-
-def get_end(segments: List[dict]) -> Optional[float]:
- return next(
- (w["end"] for s in reversed(segments) for w in reversed(s["words"])),
- segments[-1]["end"] if segments else None,
- )
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/vad.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/vad.py
deleted file mode 100644
index 3881fd8185b439ef714af1f28b4e7a36f4fe24d6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/vad.py
+++ /dev/null
@@ -1,596 +0,0 @@
-import bisect
-import functools
-import os
-
-from abc import ABC
-from collections.abc import Callable
-from typing import List, NamedTuple, Optional, Union
-
-import numpy as np
-import torch
-
-from pyannote.audio.core.io import AudioFile
-from pyannote.audio.pipelines import VoiceActivityDetection
-from pyannote.audio.pipelines.utils import PipelineModel
-from pyannote.core import Annotation, Segment, SlidingWindowFeature
-
-from faster_whisper.utils import get_assets_path
-
-
-# The code below is adapted from https://github.com/snakers4/silero-vad.
-class VadOptions(NamedTuple):
- """VAD options.
-
- Attributes:
- threshold: Speech threshold. Silero VAD outputs speech probabilities for each audio chunk,
- probabilities ABOVE this value are considered as SPEECH. It is better to tune this
- parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
- min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out.
- max_speech_duration_s: Maximum duration of speech chunks in seconds. Chunks longer
- than max_speech_duration_s will be split at the timestamp of the last silence that
- lasts more than 100ms (if any), to prevent aggressive cutting. Otherwise, they will be
- split aggressively just before max_speech_duration_s.
- min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
- before separating it
- speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
- """
-
- threshold: float = 0.5
- min_speech_duration_ms: int = 250
- max_speech_duration_s: float = float("inf")
- min_silence_duration_ms: int = 2000
- speech_pad_ms: int = 400
-
-
-def get_speech_timestamps(
- audio: torch.Tensor,
- vad_options: Optional[VadOptions] = None,
- **kwargs,
-) -> List[dict]:
- """This method is used for splitting long audios into speech chunks using silero VAD.
-
- Args:
- audio: One dimensional float array.
- vad_options: Options for VAD processing.
- kwargs: VAD options passed as keyword arguments for backward compatibility.
-
- Returns:
- List of dicts containing begin and end samples of each speech chunk.
- """
- if vad_options is None:
- vad_options = VadOptions(**kwargs)
-
- threshold = vad_options.threshold
- min_speech_duration_ms = vad_options.min_speech_duration_ms
- max_speech_duration_s = vad_options.max_speech_duration_s
- min_silence_duration_ms = vad_options.min_silence_duration_ms
- window_size_samples = 512
- speech_pad_ms = vad_options.speech_pad_ms
- sampling_rate = 16000
- min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
- speech_pad_samples = sampling_rate * speech_pad_ms / 1000
- max_speech_samples = (
- sampling_rate * max_speech_duration_s
- - window_size_samples
- - 2 * speech_pad_samples
- )
- min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
- min_silence_samples_at_max_speech = sampling_rate * 98 / 1000
-
- audio_length_samples = len(audio)
-
- model = get_vad_model()
- state, context = model.get_initial_states(batch_size=1)
-
- speech_probs = []
- for current_start_sample in range(0, audio_length_samples, window_size_samples):
- chunk = audio[current_start_sample : current_start_sample + window_size_samples]
- if len(chunk) < window_size_samples:
- chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
- speech_prob, state, context = model(chunk, state, context, sampling_rate)
- speech_probs.append(speech_prob)
-
- triggered = False
- speeches = []
- current_speech = {}
- neg_threshold = threshold - 0.15
-
- # to save potential segment end (and tolerate some silence)
- temp_end = 0
- # to save potential segment limits in case of maximum segment size reached
- prev_end = next_start = 0
-
- for i, speech_prob in enumerate(speech_probs):
- if (speech_prob >= threshold) and temp_end:
- temp_end = 0
- if next_start < prev_end:
- next_start = window_size_samples * i
-
- if (speech_prob >= threshold) and not triggered:
- triggered = True
- current_speech["start"] = window_size_samples * i
- continue
-
- if (
- triggered
- and (window_size_samples * i) - current_speech["start"] > max_speech_samples
- ):
- if prev_end:
- current_speech["end"] = prev_end
- speeches.append(current_speech)
- current_speech = {}
- # previously reached silence (< neg_thres) and is still not speech (< thres)
- if next_start < prev_end:
- triggered = False
- else:
- current_speech["start"] = next_start
- prev_end = next_start = temp_end = 0
- else:
- current_speech["end"] = window_size_samples * i
- speeches.append(current_speech)
- current_speech = {}
- prev_end = next_start = temp_end = 0
- triggered = False
- continue
-
- if (speech_prob < neg_threshold) and triggered:
- if not temp_end:
- temp_end = window_size_samples * i
- # condition to avoid cutting in very short silence
- if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech:
- prev_end = temp_end
- if (window_size_samples * i) - temp_end < min_silence_samples:
- continue
- else:
- current_speech["end"] = temp_end
- if (
- current_speech["end"] - current_speech["start"]
- ) > min_speech_samples:
- speeches.append(current_speech)
- current_speech = {}
- prev_end = next_start = temp_end = 0
- triggered = False
- continue
-
- if (
- current_speech
- and (audio_length_samples - current_speech["start"]) > min_speech_samples
- ):
- current_speech["end"] = audio_length_samples
- speeches.append(current_speech)
-
- for i, speech in enumerate(speeches):
- if i == 0:
- speech["start"] = int(max(0, speech["start"] - speech_pad_samples))
- if i != len(speeches) - 1:
- silence_duration = speeches[i + 1]["start"] - speech["end"]
- if silence_duration < 2 * speech_pad_samples:
- speech["end"] += int(silence_duration // 2)
- speeches[i + 1]["start"] = int(
- max(0, speeches[i + 1]["start"] - silence_duration // 2)
- )
- else:
- speech["end"] = int(
- min(audio_length_samples, speech["end"] + speech_pad_samples)
- )
- speeches[i + 1]["start"] = int(
- max(0, speeches[i + 1]["start"] - speech_pad_samples)
- )
- else:
- speech["end"] = int(
- min(audio_length_samples, speech["end"] + speech_pad_samples)
- )
-
- return speeches
-
-
-def collect_chunks(audio: torch.Tensor, chunks: List[dict]) -> torch.Tensor:
- """Collects and concatenates audio chunks."""
- if not chunks:
- return torch.tensor([], dtype=torch.float32)
-
- return torch.cat([audio[chunk["start"] : chunk["end"]] for chunk in chunks])
-
-
-class SpeechTimestampsMap:
- """Helper class to restore original speech timestamps."""
-
- def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
- self.sampling_rate = sampling_rate
- self.time_precision = time_precision
- self.chunk_end_sample = []
- self.total_silence_before = []
-
- previous_end = 0
- silent_samples = 0
-
- for chunk in chunks:
- silent_samples += chunk["start"] - previous_end
- previous_end = chunk["end"]
-
- self.chunk_end_sample.append(chunk["end"] - silent_samples)
- self.total_silence_before.append(silent_samples / sampling_rate)
-
- def get_original_time(
- self,
- time: float,
- chunk_index: Optional[int] = None,
- ) -> float:
- if chunk_index is None:
- chunk_index = self.get_chunk_index(time)
-
- total_silence_before = self.total_silence_before[chunk_index]
- return round(total_silence_before + time, self.time_precision)
-
- def get_chunk_index(self, time: float) -> int:
- sample = int(time * self.sampling_rate)
- return min(
- bisect.bisect(self.chunk_end_sample, sample),
- len(self.chunk_end_sample) - 1,
- )
-
-
-@functools.lru_cache
-def get_vad_model():
- """Returns the VAD model instance."""
- path = os.path.join(get_assets_path(), "silero_vad.onnx")
- return SileroVADModel(path)
-
-
-class SileroVADModel:
- def __init__(self, path):
- try:
- import onnxruntime
- except ImportError as e:
- raise RuntimeError(
- "Applying the VAD filter requires the onnxruntime package"
- ) from e
-
- opts = onnxruntime.SessionOptions()
- opts.inter_op_num_threads = 1
- opts.intra_op_num_threads = 1
- opts.log_severity_level = 4
-
- self.session = onnxruntime.InferenceSession(
- path,
- providers=["CPUExecutionProvider"],
- sess_options=opts,
- )
-
- def get_initial_states(self, batch_size: int):
- state = np.zeros((2, batch_size, 128), dtype=np.float32)
- context = np.zeros((batch_size, 64), dtype=np.float32)
- return state, context
-
- def __call__(self, x, state, context, sr: int):
- if len(x.shape) == 1:
- x = np.expand_dims(x, 0)
- if len(x.shape) > 2:
- raise ValueError(
- f"Too many dimensions for input audio chunk {len(x.shape)}"
- )
- if sr / x.shape[1] > 31.25:
- raise ValueError("Input audio chunk is too short")
-
- x = np.concatenate([context, x], axis=1)
-
- ort_inputs = {
- "input": x,
- "state": state,
- "sr": np.array(sr, dtype="int64"),
- }
-
- out, state = self.session.run(None, ort_inputs)
- context = x[..., -64:]
-
- return out, state, context
-
-
-# BSD 2-Clause License
-
-# Copyright (c) 2024, Max Bain
-
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-
-# 1. Redistributions of source code must retain the above copyright notice, this
-# list of conditions and the following disclaimer.
-
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-# The code below is copied from whisper-x (https://github.com/m-bain/whisperX)
-# and adapted for faster_whisper.
-class SegmentX:
- def __init__(self, start, end, speaker=None):
- self.start = start
- self.end = end
- self.speaker = speaker
-
-
-class VoiceActivitySegmentation(VoiceActivityDetection, ABC):
- """Pipeline wrapper class for Voice Activity Segmentation based on VAD scores."""
-
- def __init__(
- self,
- segmentation: PipelineModel = "pyannote/segmentation",
- device: Optional[Union[str, torch.device]] = None,
- fscore: bool = False,
- use_auth_token: Optional[str] = None,
- **inference_kwargs,
- ):
- """Initialize the pipeline with the model name and the optional device.
-
- Args:
- dict parameters of VoiceActivityDetection class from pyannote:
- segmentation (PipelineModel): Loaded model name.
- device (torch.device or None): Device to perform the segmentation.
- fscore (bool): Flag indicating whether to compute F-score during inference.
- use_auth_token (str or None): Optional authentication token for model access.
- inference_kwargs (dict): Additional arguments from VoiceActivityDetection pipeline.
- """
- super().__init__(
- segmentation=segmentation,
- device=device,
- fscore=fscore,
- use_auth_token=use_auth_token,
- **inference_kwargs,
- )
-
- def apply(
- self, file: AudioFile, hook: Optional[Callable] = None
- ) -> SlidingWindowFeature:
- """Apply voice activity detection on the audio file.
-
- Args:
- file (AudioFile): Processed file.
- hook (callable): Hook called with signature: hook("step_name", step_artefact, file=file)
-
- Returns:
- segmentations (SlidingWindowFeature): Voice activity segmentation.
- """
- # setup hook (e.g. for debugging purposes)
- hook = self.setup_hook(file, hook=hook)
-
- # apply segmentation model if needed
- # output shape is (num_chunks, num_frames, 1)
- if self.training:
- if self.CACHED_SEGMENTATION in file:
- segmentations = file[self.CACHED_SEGMENTATION]
- else:
- segmentations = self._segmentation(file)
- file[self.CACHED_SEGMENTATION] = segmentations
- else:
- segmentations: SlidingWindowFeature = self._segmentation(file)
-
- return segmentations
-
-
-class BinarizeVadScores:
- """Binarize detection scores using hysteresis thresholding.
-
- Reference:
- Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
- RNN-based Voice Activity Detection", InterSpeech 2015.
-
- Modified by Max Bain to include WhisperX's min-cut operation
- https://arxiv.org/abs/2303.00747
-
- """
-
- def __init__(
- self,
- onset: float = 0.5,
- offset: Optional[float] = None,
- min_duration_on: float = 0.0,
- min_duration_off: float = 0.0,
- pad_onset: float = 0.0,
- pad_offset: float = 0.0,
- max_duration: float = float("inf"),
- ):
- """Initializes the parameters for Binarizing the VAD scores.
-
- Args:
- onset (float, optional):
- Onset threshold. Defaults to 0.5.
- offset (float, optional):
- Offset threshold. Defaults to `onset`.
- min_duration_on (float, optional):
- Remove active regions shorter than that many seconds. Defaults to 0s.
- min_duration_off (float, optional):
- Fill inactive regions shorter than that many seconds. Defaults to 0s.
- pad_onset (float, optional):
- Extend active regions by moving their start time by that many seconds.
- Defaults to 0s.
- pad_offset (float, optional):
- Extend active regions by moving their end time by that many seconds.
- Defaults to 0s.
- max_duration (float):
- The maximum length of an active segment.
- """
- super().__init__()
-
- self.onset = onset
- self.offset = offset or onset
-
- self.pad_onset = pad_onset
- self.pad_offset = pad_offset
-
- self.min_duration_on = min_duration_on
- self.min_duration_off = min_duration_off
-
- self.max_duration = max_duration
-
- def __get_active_regions(self, scores: SlidingWindowFeature) -> Annotation:
- """Extract active regions from VAD scores.
-
- Args:
- scores (SlidingWindowFeature): Detection scores.
-
- Returns:
- active (Annotation): Active regions.
- """
- num_frames, num_classes = scores.data.shape
- frames = scores.sliding_window
- timestamps = [frames[i].middle for i in range(num_frames)]
- # annotation meant to store 'active' regions
- active = Annotation()
- for k, k_scores in enumerate(scores.data.T):
- label = k if scores.labels is None else scores.labels[k]
-
- # initial state
- start = timestamps[0]
- is_active = k_scores[0] > self.onset
- curr_scores = [k_scores[0]]
- curr_timestamps = [start]
- t = start
- # optionally add `strict=False` for python 3.10 or later
- for t, y in zip(timestamps[1:], k_scores[1:]):
- # currently active
- if is_active:
- curr_duration = t - start
- if curr_duration > self.max_duration:
- search_after = len(curr_scores) // 2
- # divide segment
- min_score_div_idx = search_after + np.argmin(
- curr_scores[search_after:]
- )
- min_score_t = curr_timestamps[min_score_div_idx]
- region = Segment(
- start - self.pad_onset, min_score_t + self.pad_offset
- )
- active[region, k] = label
- start = curr_timestamps[min_score_div_idx]
- curr_scores = curr_scores[min_score_div_idx + 1 :]
- curr_timestamps = curr_timestamps[min_score_div_idx + 1 :]
- # switching from active to inactive
- elif y < self.offset:
- region = Segment(start - self.pad_onset, t + self.pad_offset)
- active[region, k] = label
- start = t
- is_active = False
- curr_scores = []
- curr_timestamps = []
- curr_scores.append(y)
- curr_timestamps.append(t)
- # currently inactive
- else:
- # switching from inactive to active
- if y > self.onset:
- start = t
- is_active = True
-
- # if active at the end, add final region
- if is_active:
- region = Segment(start - self.pad_onset, t + self.pad_offset)
- active[region, k] = label
-
- return active
-
- def __call__(self, scores: SlidingWindowFeature) -> Annotation:
- """Binarize detection scores.
-
- Args:
- scores (SlidingWindowFeature): Detection scores.
-
- Returns:
- active (Annotation): Binarized scores.
- """
- active = self.__get_active_regions(scores)
- # because of padding, some active regions might be overlapping: merge them.
- # also: fill same speaker gaps shorter than min_duration_off
- if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
- if self.max_duration < float("inf"):
- raise NotImplementedError("This would break current max_duration param")
- active = active.support(collar=self.min_duration_off)
-
- # remove tracks shorter than min_duration_on
- if self.min_duration_on > 0:
- for segment, track in list(active.itertracks()):
- if segment.duration < self.min_duration_on:
- del active[segment, track]
-
- return active
-
-
-def merge_chunks(
- segments,
- chunk_length,
- onset: float = 0.5,
- offset: Optional[float] = None,
- edge_padding: float = 0.1,
-):
- """
- Merge operation described in whisper-x paper
- """
- curr_end = 0
- merged_segments = []
- seg_idxs = []
- speaker_idxs = []
-
- assert chunk_length > 0
- binarize = BinarizeVadScores(max_duration=chunk_length, onset=onset, offset=offset)
- segments = binarize(segments)
- segments_list = []
- for speech_turn in segments.get_timeline():
- segments_list.append(
- SegmentX(
- max(0.0, speech_turn.start - edge_padding),
- speech_turn.end + edge_padding,
- "UNKNOWN",
- )
- ) # 100ms edge padding to account for edge errors
-
- if len(segments_list) == 0:
- print("No active speech found in audio")
- return []
-
- # Make sur the starting point is the start of the segment.
- curr_start = segments_list[0].start
-
- for idx, seg in enumerate(segments_list):
- # if any segment start timing is less than previous segment end timing,
- # reset the edge padding. Similarly for end timing.
- if idx > 0:
- if seg.start < segments_list[idx - 1].end:
- seg.start += edge_padding
- if idx < len(segments_list) - 1:
- if seg.end > segments_list[idx + 1].start:
- seg.end -= edge_padding
-
- if seg.end - curr_start > chunk_length and curr_end - curr_start > 0:
- merged_segments.append(
- {
- "start": curr_start,
- "end": curr_end,
- "segments": seg_idxs,
- }
- )
- curr_start = seg.start
- seg_idxs = []
- speaker_idxs = []
- curr_end = seg.end
- seg_idxs.append((seg.start, seg.end))
- speaker_idxs.append(seg.speaker)
- # add final
- merged_segments.append(
- {
- "start": curr_start,
- "end": curr_end,
- "segments": seg_idxs,
- }
- )
- return merged_segments
diff --git a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/version.py b/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/version.py
deleted file mode 100644
index 65eaef429fb3048ee8d105fe67da4da43f5a2324..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/build/lib/faster_whisper/version.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Version information."""
-
-__version__ = "1.0.3"
diff --git a/whisper_pipeline/faster-whisper-main/docker/Dockerfile b/whisper_pipeline/faster-whisper-main/docker/Dockerfile
deleted file mode 100644
index 604c8e17b788e11c228eceec75a57d19a147a074..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/docker/Dockerfile
+++ /dev/null
@@ -1,6 +0,0 @@
-FROM nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04
-WORKDIR /root
-RUN apt-get update -y && apt-get install -y python3-pip
-COPY infer.py jfk.flac ./
-RUN pip3 install faster-whisper
-CMD ["python3", "infer.py"]
diff --git a/whisper_pipeline/faster-whisper-main/docker/infer.py b/whisper_pipeline/faster-whisper-main/docker/infer.py
deleted file mode 100644
index 5d6b12cb516f3f7942798eeb41411de835cb611b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/docker/infer.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from faster_whisper import WhisperModel
-
-jfk_path = "jfk.flac"
-model = WhisperModel("tiny", device="cuda")
-segments, info = model.transcribe(jfk_path, word_timestamps=True)
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
diff --git a/whisper_pipeline/faster-whisper-main/docker/jfk.flac b/whisper_pipeline/faster-whisper-main/docker/jfk.flac
deleted file mode 100644
index 8cf66fea45dd2f1ac2b182a37a2208657c11e8af..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/docker/jfk.flac
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63a4b1e4c1dc655ac70961ffbf518acd249df237e5a0152faae9a4a836949715
-size 1152693
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/PKG-INFO b/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/PKG-INFO
deleted file mode 100644
index 6605d3d781db119560c992155867c00cde82c11d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/PKG-INFO
+++ /dev/null
@@ -1,347 +0,0 @@
-Metadata-Version: 2.1
-Name: faster-whisper
-Version: 1.0.3
-Summary: Faster Whisper transcription with CTranslate2
-Home-page: https://github.com/SYSTRAN/faster-whisper
-Author: Guillaume Klein
-License: MIT
-Keywords: openai whisper speech ctranslate2 inference quantization transformer
-Platform: UNKNOWN
-Classifier: Development Status :: 4 - Beta
-Classifier: Intended Audience :: Developers
-Classifier: Intended Audience :: Science/Research
-Classifier: License :: OSI Approved :: MIT License
-Classifier: Programming Language :: Python :: 3
-Classifier: Programming Language :: Python :: 3 :: Only
-Classifier: Programming Language :: Python :: 3.8
-Classifier: Programming Language :: Python :: 3.9
-Classifier: Programming Language :: Python :: 3.10
-Classifier: Programming Language :: Python :: 3.11
-Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
-Requires-Python: >=3.8
-Description-Content-Type: text/markdown
-Provides-Extra: conversion
-Provides-Extra: dev
-License-File: LICENSE
-
-[](https://github.com/SYSTRAN/faster-whisper/actions?query=workflow%3ACI) [](https://badge.fury.io/py/faster-whisper)
-
-# Faster Whisper transcription with CTranslate2
-
-**faster-whisper** is a reimplementation of OpenAI's Whisper model using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models.
-
-This implementation is up to 4 times faster than [openai/whisper](https://github.com/openai/whisper) for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.
-
-## Benchmark
-
-### Whisper
-
-For reference, here's the time and memory usage that are required to transcribe [**13 minutes**](https://www.youtube.com/watch?v=0u7tTptBo9I) of audio using different implementations:
-
-* [openai/whisper](https://github.com/openai/whisper)@[6dea21fd](https://github.com/openai/whisper/commit/6dea21fd7f7253bfe450f1e2512a0fe47ee2d258)
-* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[3b010f9](https://github.com/ggerganov/whisper.cpp/commit/3b010f9bed9a6068609e9faf52383aea792b0362)
-* [faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[cce6b53e](https://github.com/SYSTRAN/faster-whisper/commit/cce6b53e4554f71172dad188c45f10fb100f6e3e)
-
-### Large-v2 model on GPU
-
-| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory |
-| --- | --- | --- | --- | --- | --- |
-| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB |
-| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB |
-| faster-whisper | int8 | 5 | 59s | 3091MB | 3117MB |
-
-*Executed with CUDA 11.7.1 on a NVIDIA Tesla V100S.*
-
-### Small model on CPU
-
-| Implementation | Precision | Beam size | Time | Max. memory |
-| --- | --- | --- | --- | --- |
-| openai/whisper | fp32 | 5 | 10m31s | 3101MB |
-| whisper.cpp | fp32 | 5 | 17m42s | 1581MB |
-| whisper.cpp | fp16 | 5 | 12m39s | 873MB |
-| faster-whisper | fp32 | 5 | 2m44s | 1675MB |
-| faster-whisper | int8 | 5 | 2m04s | 995MB |
-
-*Executed with 8 threads on a Intel(R) Xeon(R) Gold 6226R.*
-
-
-### Distil-whisper
-
-| Implementation | Precision | Beam size | Time | Gigaspeech WER |
-| --- | --- | --- | --- | --- |
-| distil-whisper/distil-large-v2 | fp16 | 4 |- | 10.36 |
-| [faster-distil-large-v2](https://huggingface.co/Systran/faster-distil-whisper-large-v2) | fp16 | 5 | - | 10.28 |
-| distil-whisper/distil-medium.en | fp16 | 4 | - | 11.21 |
-| [faster-distil-medium.en](https://huggingface.co/Systran/faster-distil-whisper-medium.en) | fp16 | 5 | - | 11.21 |
-
-*Executed with CUDA 11.4 on a NVIDIA 3090.*
-
-
-testing details (click to expand)
-
-For `distil-whisper/distil-large-v2`, the WER is tested with code sample from [link](https://huggingface.co/distil-whisper/distil-large-v2#evaluation). for `faster-distil-whisper`, the WER is tested with setting:
-```python
-from faster_whisper import WhisperModel
-
-model_size = "distil-large-v2"
-# model_size = "distil-medium.en"
-# Run on GPU with FP16
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-segments, info = model.transcribe("audio.mp3", beam_size=5, language="en")
-```
-
-
-## Requirements
-
-* Python 3.8 or greater
-
-
-### GPU
-
-GPU execution requires the following NVIDIA libraries to be installed:
-
-* [cuBLAS for CUDA 12](https://developer.nvidia.com/cublas)
-* [cuDNN 8 for CUDA 12](https://developer.nvidia.com/cudnn)
-
-**Note**: Latest versions of `ctranslate2` support CUDA 12 only. For CUDA 11, the current workaround is downgrading to the `3.24.0` version of `ctranslate2` (This can be done with `pip install --force-reinstall ctranslate2==3.24.0` or specifying the version in a `requirements.txt`).
-
-There are multiple ways to install the NVIDIA libraries mentioned above. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
-
-
-Other installation methods (click to expand)
-
-
-**Note:** For all these methods below, keep in mind the above note regarding CUDA versions. Depending on your setup, you may need to install the _CUDA 11_ versions of libraries that correspond to the CUDA 12 libraries listed in the instructions below.
-
-#### Use Docker
-
-The libraries (cuBLAS, cuDNN) are installed in these official NVIDIA CUDA Docker images: `nvidia/cuda:12.0.0-runtime-ubuntu20.04` or `nvidia/cuda:12.0.0-runtime-ubuntu22.04`.
-
-#### Install with `pip` (Linux only)
-
-On Linux these libraries can be installed with `pip`. Note that `LD_LIBRARY_PATH` must be set before launching Python.
-
-```bash
-pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
-
-export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
-```
-
-**Note**: Version 9+ of `nvidia-cudnn-cu12` appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.
-
-#### Download the libraries from Purfview's repository (Windows & Linux)
-
-Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA libraries for Windows & Linux in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs). Decompress the archive and place the libraries in a directory included in the `PATH`.
-
-
-
-## Installation
-
-The module can be installed from [PyPI](https://pypi.org/project/faster-whisper/):
-
-```bash
-pip install faster-whisper
-```
-
-
-Other installation methods (click to expand)
-
-### Install the master branch
-
-```bash
-pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz"
-```
-
-### Install a specific commit
-
-```bash
-pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/a4f1cc8f11433e454c3934442b5e1a4ed5e865c3.tar.gz"
-```
-
-
-
-## Usage
-
-### Faster-whisper
-
-```python
-from faster_whisper import WhisperModel
-
-model_size = "large-v3"
-
-# Run on GPU with FP16
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-
-# or run on GPU with INT8
-# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
-# or run on CPU with INT8
-# model = WhisperModel(model_size, device="cpu", compute_type="int8")
-
-segments, info = model.transcribe("audio.mp3", beam_size=5)
-
-print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-**Warning:** `segments` is a *generator* so the transcription only starts when you iterate over it. The transcription can be run to completion by gathering the segments in a list or a `for` loop:
-
-```python
-segments, _ = model.transcribe("audio.mp3")
-segments = list(segments) # The transcription will actually run here.
-```
-
-### multi-segment language detection
-
-To directly use the model for improved language detection, the following code snippet can be used:
-
-```python
-from faster_whisper import WhisperModel
-model = WhisperModel("medium", device="cuda", compute_type="float16")
-language_info = model.detect_language_multi_segment("audio.mp3")
-```
-
-### Batched faster-whisper
-
-
-The batched version of faster-whisper is inspired by [whisper-x](https://github.com/m-bain/whisperX) licensed under the BSD-2 Clause license and integrates its VAD model to this library. We modify this implementation and also replaced the feature extraction with a faster torch-based implementation. Batched version improves the speed upto 10-12x compared to openAI implementation and 3-4x compared to the sequential faster_whisper version. It works by transcribing semantically meaningful audio chunks as batches leading to faster inference.
-
-The following code snippet illustrates how to run inference with batched version on an example audio file. Please also refer to the test scripts of batched faster whisper.
-
-```python
-from faster_whisper import WhisperModel, BatchedInferencePipeline
-
-model = WhisperModel("medium", device="cuda", compute_type="float16")
-batched_model = BatchedInferencePipeline(model=model)
-segments, info = batched_model.transcribe("audio.mp3", batch_size=16)
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-### Faster Distil-Whisper
-
-The Distil-Whisper checkpoints are compatible with the Faster-Whisper package. In particular, the latest [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
-checkpoint is intrinsically designed to work with the Faster-Whisper transcription algorithm. The following code snippet
-demonstrates how to run inference with distil-large-v3 on a specified audio file:
-
-```python
-from faster_whisper import WhisperModel
-
-model_size = "distil-large-v3"
-
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-segments, info = model.transcribe("audio.mp3", beam_size=5, language="en", condition_on_previous_text=False)
-
-for segment in segments:
- print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
-```
-
-For more information about the distil-large-v3 model, refer to the original [model card](https://huggingface.co/distil-whisper/distil-large-v3).
-
-### Word-level timestamps
-
-```python
-segments, _ = model.transcribe("audio.mp3", word_timestamps=True)
-
-for segment in segments:
- for word in segment.words:
- print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
-```
-
-### VAD filter
-
-The library integrates the [Silero VAD](https://github.com/snakers4/silero-vad) model to filter out parts of the audio without speech:
-
-```python
-segments, _ = model.transcribe("audio.mp3", vad_filter=True)
-```
-
-The default behavior is conservative and only removes silence longer than 2 seconds. See the available VAD parameters and default values in the [source code](https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/vad.py). They can be customized with the dictionary argument `vad_parameters`:
-
-```python
-segments, _ = model.transcribe(
- "audio.mp3",
- vad_filter=True,
- vad_parameters=dict(min_silence_duration_ms=500),
-)
-```
-
-### Logging
-
-The library logging level can be configured like this:
-
-```python
-import logging
-
-logging.basicConfig()
-logging.getLogger("faster_whisper").setLevel(logging.DEBUG)
-```
-
-### Going further
-
-See more model and transcription options in the [`WhisperModel`](https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/transcribe.py) class implementation.
-
-## Community integrations
-
-Here is a non exhaustive list of open-source projects using faster-whisper. Feel free to add your project to the list!
-
-
-* [faster-whisper-server](https://github.com/fedirz/faster-whisper-server) is an OpenAI compatible server using `faster-whisper`. It's easily deployable with Docker, works with OpenAI SDKs/CLI, supports streaming, and live transcription.
-* [WhisperX](https://github.com/m-bain/whisperX) is an award-winning Python library that offers speaker diarization and accurate word-level timestamps using wav2vec2 alignment
-* [whisper-ctranslate2](https://github.com/Softcatala/whisper-ctranslate2) is a command line client based on faster-whisper and compatible with the original client from openai/whisper.
-* [whisper-diarize](https://github.com/MahmoudAshraf97/whisper-diarization) is a speaker diarization tool that is based on faster-whisper and NVIDIA NeMo.
-* [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) Standalone CLI executables of faster-whisper for Windows, Linux & macOS.
-* [asr-sd-pipeline](https://github.com/hedrergudene/asr-sd-pipeline) provides a scalable, modular, end to end multi-speaker speech to text solution implemented using AzureML pipelines.
-* [Open-Lyrics](https://github.com/zh-plus/Open-Lyrics) is a Python library that transcribes voice files using faster-whisper, and translates/polishes the resulting text into `.lrc` files in the desired language using OpenAI-GPT.
-* [wscribe](https://github.com/geekodour/wscribe) is a flexible transcript generation tool supporting faster-whisper, it can export word level transcript and the exported transcript then can be edited with [wscribe-editor](https://github.com/geekodour/wscribe-editor)
-* [aTrain](https://github.com/BANDAS-Center/aTrain) is a graphical user interface implementation of faster-whisper developed at the BANDAS-Center at the University of Graz for transcription and diarization in Windows ([Windows Store App](https://apps.microsoft.com/detail/atrain/9N15Q44SZNS2)) and Linux.
-* [Whisper-Streaming](https://github.com/ufal/whisper_streaming) implements real-time mode for offline Whisper-like speech-to-text models with faster-whisper as the most recommended back-end. It implements a streaming policy with self-adaptive latency based on the actual source complexity, and demonstrates the state of the art.
-* [WhisperLive](https://github.com/collabora/WhisperLive) is a nearly-live implementation of OpenAI's Whisper which uses faster-whisper as the backend to transcribe audio in real-time.
-* [Faster-Whisper-Transcriber](https://github.com/BBC-Esq/ctranslate2-faster-whisper-transcriber) is a simple but reliable voice transcriber that provides a user-friendly interface.
-
-## Model conversion
-
-When loading a model from its size such as `WhisperModel("large-v3")`, the corresponding CTranslate2 model is automatically downloaded from the [Hugging Face Hub](https://huggingface.co/Systran).
-
-We also provide a script to convert any Whisper models compatible with the Transformers library. They could be the original OpenAI models or user fine-tuned models.
-
-For example the command below converts the [original "large-v3" Whisper model](https://huggingface.co/openai/whisper-large-v3) and saves the weights in FP16:
-
-```bash
-pip install transformers[torch]>=4.23
-
-ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2
---copy_files tokenizer.json preprocessor_config.json --quantization float16
-```
-
-* The option `--model` accepts a model name on the Hub or a path to a model directory.
-* If the option `--copy_files tokenizer.json` is not used, the tokenizer configuration is automatically downloaded when the model is loaded later.
-
-Models can also be converted from the code. See the [conversion API](https://opennmt.net/CTranslate2/python/ctranslate2.converters.TransformersConverter.html).
-
-### Load a converted model
-
-1. Directly load the model from a local directory:
-```python
-model = faster_whisper.WhisperModel("whisper-large-v3-ct2")
-```
-
-2. [Upload your model to the Hugging Face Hub](https://huggingface.co/docs/transformers/model_sharing#upload-with-the-web-interface) and load it from its name:
-```python
-model = faster_whisper.WhisperModel("username/whisper-large-v3-ct2")
-```
-
-## Comparing performance against other implementations
-
-If you are comparing the performance against other Whisper implementations, you should make sure to run the comparison with similar settings. In particular:
-
-* Verify that the same transcription options are used, especially the same beam size. For example in openai/whisper, `model.transcribe` uses a default beam size of 1 but here we use a default beam size of 5.
-* When running on CPU, make sure to set the same number of threads. Many frameworks will read the environment variable `OMP_NUM_THREADS`, which can be set when running your script:
-
-```bash
-OMP_NUM_THREADS=4 python3 my_script.py
-```
-
-
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/SOURCES.txt b/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/SOURCES.txt
deleted file mode 100644
index 99cda55c53be74ad03fc02e764c6e689d4c30bae..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-LICENSE
-MANIFEST.in
-README.md
-requirements.conversion.txt
-requirements.txt
-setup.cfg
-setup.py
-faster_whisper/__init__.py
-faster_whisper/audio.py
-faster_whisper/feature_extractor.py
-faster_whisper/tokenizer.py
-faster_whisper/transcribe.py
-faster_whisper/utils.py
-faster_whisper/vad.py
-faster_whisper/version.py
-faster_whisper.egg-info/PKG-INFO
-faster_whisper.egg-info/SOURCES.txt
-faster_whisper.egg-info/dependency_links.txt
-faster_whisper.egg-info/requires.txt
-faster_whisper.egg-info/top_level.txt
-faster_whisper/assets/__init__.py
-faster_whisper/assets/pyannote_vad_model.bin
-faster_whisper/assets/silero_vad.onnx
-tests/test_transcribe.py
-tests/test_utils.py
\ No newline at end of file
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/dependency_links.txt b/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/dependency_links.txt
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/requires.txt b/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/requires.txt
deleted file mode 100644
index 41e7caccbf2fdcaaf21b26f84624acb386d214c2..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/requires.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-ctranslate2<5,>=4.0
-huggingface_hub>=0.13
-onnxruntime<2,>=1.14
-pyannote-audio
-tokenizers<1,>=0.13
-torch
-torchaudio
-tqdm
-
-[conversion]
-transformers[torch]>=4.23
-
-[dev]
-black==23.*
-flake8==6.*
-isort==5.*
-pytest==7.*
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/top_level.txt b/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/top_level.txt
deleted file mode 100644
index a0e0f023cbf7607475e5adaee2ab32cab009ba62..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-faster_whisper
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/__init__.py b/whisper_pipeline/faster-whisper-main/faster_whisper/__init__.py
deleted file mode 100644
index ad69277853742e3f9f0e0f020c0862cf4ad404fb..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from faster_whisper.audio import decode_audio
-from faster_whisper.transcribe import BatchedInferencePipeline, WhisperModel
-from faster_whisper.utils import available_models, download_model, format_timestamp
-from faster_whisper.version import __version__
-
-__all__ = [
- "available_models",
- "decode_audio",
- "WhisperModel",
- "BatchedInferencePipeline",
- "download_model",
- "format_timestamp",
- "__version__",
-]
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/__pycache__/__init__.cpython-310.pyc b/whisper_pipeline/faster-whisper-main/faster_whisper/__pycache__/__init__.cpython-310.pyc
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diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/assets/__init__.py b/whisper_pipeline/faster-whisper-main/faster_whisper/assets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/assets/pyannote_vad_model.bin b/whisper_pipeline/faster-whisper-main/faster_whisper/assets/pyannote_vad_model.bin
deleted file mode 100644
index 7a0c29f0dbd55f172445e95ae1f0163a7d849c27..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/assets/pyannote_vad_model.bin
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea
-size 17719103
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/assets/silero_vad.onnx b/whisper_pipeline/faster-whisper-main/faster_whisper/assets/silero_vad.onnx
deleted file mode 100644
index 6ea96ef73cac24d33ad342094d95867c756d3f5c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/assets/silero_vad.onnx
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6b99cbfd39246b6706f98ec13c7c50c6b299181f2474fa05cbc8046acc274396
-size 2313101
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/audio.py b/whisper_pipeline/faster-whisper-main/faster_whisper/audio.py
deleted file mode 100644
index 7ae68d40055b0df3427c1201d9cd92f663110b12..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/audio.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from typing import BinaryIO, Union
-
-import torch
-import torchaudio
-
-
-def decode_audio(
- input_file: Union[str, BinaryIO],
- sampling_rate: int = 16000,
- split_stereo: bool = False,
-):
- """Decodes the audio.
-
- Args:
- input_file: Path to the input file or a file-like object.
- sampling_rate: Resample the audio to this sample rate.
- split_stereo: Return separate left and right channels.
-
- Returns:
- A float32 Torch Tensor.
-
- If `split_stereo` is enabled, the function returns a 2-tuple with the
- separated left and right channels.
- """
-
- waveform, audio_sf = torchaudio.load(input_file) # waveform: channels X T
-
- if audio_sf != sampling_rate:
- waveform = torchaudio.functional.resample(
- waveform, orig_freq=audio_sf, new_freq=sampling_rate
- )
- if split_stereo:
- return waveform[0], waveform[1]
-
- return waveform.mean(0)
-
-
-def pad_or_trim(array, length: int, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- axis = axis % array.ndim
- if array.shape[axis] > length:
- idx = [Ellipsis] * axis + [slice(length)] + [Ellipsis] * (array.ndim - axis - 1)
- return array[idx]
-
- if array.shape[axis] < length:
- pad_widths = (
- [
- 0,
- ]
- * array.ndim
- * 2
- )
- pad_widths[2 * axis] = length - array.shape[axis]
- array = torch.nn.functional.pad(array, tuple(pad_widths[::-1]))
-
- return array
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/feature_extractor.py b/whisper_pipeline/faster-whisper-main/faster_whisper/feature_extractor.py
deleted file mode 100644
index 6371d5ef6864b07ce4b7f3c1cd1075fee9c8e484..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/feature_extractor.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import torch
-
-
-# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py # noqa: E501
-class FeatureExtractor:
- def __init__(
- self,
- device: str = "auto",
- feature_size=80,
- sampling_rate=16000,
- hop_length=160,
- chunk_length=30,
- n_fft=400,
- ):
- if device == "auto":
- self.device = "cuda" if torch.cuda.is_available() else "cpu"
- else:
- self.device = device
- self.n_fft = n_fft
- self.hop_length = hop_length
- self.chunk_length = chunk_length
- self.n_samples = chunk_length * sampling_rate
- self.nb_max_frames = self.n_samples // hop_length
- self.time_per_frame = hop_length / sampling_rate
- self.sampling_rate = sampling_rate
- self.mel_filters = self.get_mel_filters(
- sampling_rate, n_fft, n_mels=feature_size
- )
-
- @staticmethod
- def get_mel_filters(sr, n_fft, n_mels=128):
- """
- Implementation of librosa.filters.mel in Pytorch
- """
- # Initialize the weights
- n_mels = int(n_mels)
-
- # Center freqs of each FFT bin
- fftfreqs = torch.fft.rfftfreq(n=n_fft, d=1.0 / sr)
-
- # 'Center freqs' of mel bands - uniformly spaced between limits
- min_mel = 0.0
- max_mel = 45.245640471924965
-
- mels = torch.linspace(min_mel, max_mel, n_mels + 2)
-
- # Fill in the linear scale
- f_min = 0.0
- f_sp = 200.0 / 3
- freqs = f_min + f_sp * mels
-
- # And now the nonlinear scale
- min_log_hz = 1000.0 # beginning of log region (Hz)
- min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
- logstep = torch.log(torch.tensor(6.4)) / 27.0 # step size for log region
-
- # If we have vector data, vectorize
- log_t = mels >= min_log_mel
- freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel))
-
- mel_f = freqs
-
- fdiff = torch.diff(mel_f)
- ramps = mel_f.view(-1, 1) - fftfreqs.view(1, -1)
-
- lower = -ramps[:-2] / fdiff[:-1].unsqueeze(1)
- upper = ramps[2:] / fdiff[1:].unsqueeze(1)
-
- # Intersect them with each other and zero, vectorized across all i
- weights = torch.maximum(torch.zeros_like(lower), torch.minimum(lower, upper))
-
- # Slaney-style mel is scaled to be approx constant energy per channel
- enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
- weights *= enorm.unsqueeze(1)
-
- return weights
-
- def __call__(self, waveform, padding=True, chunk_length=None, to_cpu=False):
- """
- Compute the log-Mel spectrogram of the provided audio.
- """
-
- if chunk_length is not None:
- self.n_samples = chunk_length * self.sampling_rate
- self.nb_max_frames = self.n_samples // self.hop_length
-
- if waveform.dtype is not torch.float32:
- waveform = waveform.to(torch.float32)
-
- waveform = (
- waveform.to(self.device)
- if self.device == "cuda" and not waveform.is_cuda
- else waveform
- )
-
- if padding:
- waveform = torch.nn.functional.pad(waveform, (0, self.n_samples))
-
- window = torch.hann_window(self.n_fft).to(waveform.device)
-
- stft = torch.stft(
- waveform, self.n_fft, self.hop_length, window=window, return_complex=True
- )
- magnitudes = stft[..., :-1].abs() ** 2
-
- mel_spec = self.mel_filters.to(waveform.device) @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
-
- # When the model is running on multiple GPUs, the output should be moved
- # to the CPU since we don't know which GPU will handle the next job.
- return log_spec.cpu() if to_cpu else log_spec
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/tokenizer.py b/whisper_pipeline/faster-whisper-main/faster_whisper/tokenizer.py
deleted file mode 100644
index 3bf76a5f6321d5f17cb778604b3e5e3d73275835..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/tokenizer.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import string
-
-from functools import cached_property
-from typing import List, Optional, Tuple
-
-import tokenizers
-
-
-class Tokenizer:
- """Simple wrapper around a tokenizers.Tokenizer."""
-
- def __init__(
- self,
- tokenizer: tokenizers.Tokenizer,
- multilingual: bool,
- task: Optional[str] = None,
- language: Optional[str] = None,
- ):
- self.tokenizer = tokenizer
-
- if multilingual:
- if task not in _TASKS:
- raise ValueError(
- "'%s' is not a valid task (accepted tasks: %s)"
- % (task, ", ".join(_TASKS))
- )
-
- if language not in _LANGUAGE_CODES:
- raise ValueError(
- "'%s' is not a valid language code (accepted language codes: %s)"
- % (language, ", ".join(_LANGUAGE_CODES))
- )
-
- self.task = self.tokenizer.token_to_id("<|%s|>" % task)
- self.language = self.tokenizer.token_to_id("<|%s|>" % language)
- self.language_code = language
- else:
- self.task = None
- self.language = None
- self.language_code = "en"
-
- @cached_property
- def transcribe(self) -> int:
- return self.tokenizer.token_to_id("<|transcribe|>")
-
- @cached_property
- def translate(self) -> int:
- return self.tokenizer.token_to_id("<|translate|>")
-
- @cached_property
- def sot(self) -> int:
- return self.tokenizer.token_to_id("<|startoftranscript|>")
-
- @cached_property
- def sot_lm(self) -> int:
- return self.tokenizer.token_to_id("<|startoflm|>")
-
- @cached_property
- def sot_prev(self) -> int:
- return self.tokenizer.token_to_id("<|startofprev|>")
-
- @cached_property
- def eot(self) -> int:
- return self.tokenizer.token_to_id("<|endoftext|>")
-
- @cached_property
- def no_timestamps(self) -> int:
- return self.tokenizer.token_to_id("<|notimestamps|>")
-
- @property
- def timestamp_begin(self) -> int:
- return self.no_timestamps + 1
-
- @property
- def sot_sequence(self) -> List[int]:
- sequence = [self.sot]
-
- if self.language is not None:
- sequence.append(self.language)
-
- if self.task is not None:
- sequence.append(self.task)
-
- return sequence
-
- def encode(self, text: str) -> List[int]:
- return self.tokenizer.encode(text, add_special_tokens=False).ids
-
- def decode(self, tokens: List[int]) -> str:
- text_tokens = [token for token in tokens if token < self.eot]
- return self.tokenizer.decode(text_tokens)
-
- def decode_with_timestamps(self, tokens: List[int]) -> str:
- outputs = [[]]
-
- for token in tokens:
- if token >= self.timestamp_begin:
- timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
- outputs.append(timestamp)
- outputs.append([])
- else:
- outputs[-1].append(token)
-
- return "".join(
- [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
- )
-
- @cached_property
- def non_speech_tokens(self) -> Tuple[int]:
- """
- Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
- annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
-
- - ♪♪♪
- - ( SPEAKING FOREIGN LANGUAGE )
- - [DAVID] Hey there,
-
- keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
- """
- symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
- symbols += (
- "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
- )
-
- # symbols that may be a single token or multiple tokens depending on the tokenizer.
- # In case they're multiple tokens, suppress the first token, which is safe because:
- # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
- # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
- miscellaneous = set("♩♪♫♬♭♮♯")
- assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
-
- # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
- result = {self.encode(" -")[0], self.encode(" '")[0]}
- for symbol in symbols + list(miscellaneous):
- for tokens in [
- self.encode(symbol),
- self.encode(" " + symbol),
- ]:
- if len(tokens) == 1 or symbol in miscellaneous:
- result.add(tokens[0])
-
- return tuple(sorted(result))
-
- def split_to_word_tokens(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- if self.language_code in {"zh", "ja", "th", "lo", "my", "yue"}:
- # These languages don't typically use spaces, so it is difficult to split words
- # without morpheme analysis. Here, we instead split words at any
- # position where the tokens are decoded as valid unicode points
- return self.split_tokens_on_unicode(tokens)
-
- return self.split_tokens_on_spaces(tokens)
-
- def split_tokens_on_unicode(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- decoded_full = self.decode_with_timestamps(tokens)
- replacement_char = "\ufffd"
-
- words = []
- word_tokens = []
- current_tokens = []
- unicode_offset = 0
-
- for token in tokens:
- current_tokens.append(token)
- decoded = self.decode_with_timestamps(current_tokens)
-
- try:
- replacement_char_index = decoded.index(replacement_char)
- replacement_char_index += unicode_offset
- except ValueError:
- replacement_char_index = None
-
- if replacement_char_index is None or (
- replacement_char_index < len(decoded_full)
- and decoded_full[replacement_char_index] == replacement_char
- ):
- words.append(decoded)
- word_tokens.append(current_tokens)
- current_tokens = []
- unicode_offset += len(decoded)
-
- return words, word_tokens
-
- def split_tokens_on_spaces(
- self, tokens: List[int]
- ) -> Tuple[List[str], List[List[int]]]:
- subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
- words = []
- word_tokens = []
-
- for subword, subword_tokens in zip(subwords, subword_tokens_list):
- special = subword_tokens[0] >= self.eot
- with_space = subword.startswith(" ")
- punctuation = subword.strip() in string.punctuation
- if special or with_space or punctuation or len(words) == 0:
- words.append(subword)
- word_tokens.append(subword_tokens)
- else:
- words[-1] = words[-1] + subword
- word_tokens[-1].extend(subword_tokens)
-
- return words, word_tokens
-
-
-_TASKS = (
- "transcribe",
- "translate",
-)
-
-_LANGUAGE_CODES = (
- "af",
- "am",
- "ar",
- "as",
- "az",
- "ba",
- "be",
- "bg",
- "bn",
- "bo",
- "br",
- "bs",
- "ca",
- "cs",
- "cy",
- "da",
- "de",
- "el",
- "en",
- "es",
- "et",
- "eu",
- "fa",
- "fi",
- "fo",
- "fr",
- "gl",
- "gu",
- "ha",
- "haw",
- "he",
- "hi",
- "hr",
- "ht",
- "hu",
- "hy",
- "id",
- "is",
- "it",
- "ja",
- "jw",
- "ka",
- "kk",
- "km",
- "kn",
- "ko",
- "la",
- "lb",
- "ln",
- "lo",
- "lt",
- "lv",
- "mg",
- "mi",
- "mk",
- "ml",
- "mn",
- "mr",
- "ms",
- "mt",
- "my",
- "ne",
- "nl",
- "nn",
- "no",
- "oc",
- "pa",
- "pl",
- "ps",
- "pt",
- "ro",
- "ru",
- "sa",
- "sd",
- "si",
- "sk",
- "sl",
- "sn",
- "so",
- "sq",
- "sr",
- "su",
- "sv",
- "sw",
- "ta",
- "te",
- "tg",
- "th",
- "tk",
- "tl",
- "tr",
- "tt",
- "uk",
- "ur",
- "uz",
- "vi",
- "yi",
- "yo",
- "zh",
- "yue",
-)
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/transcribe.py b/whisper_pipeline/faster-whisper-main/faster_whisper/transcribe.py
deleted file mode 100644
index 8652e82b0463f1d786c50ba32209b62dd167142d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/transcribe.py
+++ /dev/null
@@ -1,2170 +0,0 @@
-import itertools
-import json
-import logging
-import os
-import random
-import zlib
-
-from collections import Counter, defaultdict
-from inspect import signature
-from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
-
-import ctranslate2
-import numpy as np
-import tokenizers
-import torch
-
-from pyannote.audio import Model
-from tqdm import tqdm
-
-from faster_whisper.audio import decode_audio, pad_or_trim
-from faster_whisper.feature_extractor import FeatureExtractor
-from faster_whisper.tokenizer import _LANGUAGE_CODES, Tokenizer
-from faster_whisper.utils import (
- download_model,
- format_timestamp,
- get_assets_path,
- get_end,
- get_logger,
-)
-from faster_whisper.vad import (
- SpeechTimestampsMap,
- VadOptions,
- VoiceActivitySegmentation,
- collect_chunks,
- get_speech_timestamps,
- merge_chunks,
-)
-
-
-class Word(NamedTuple):
- start: float
- end: float
- word: str
- probability: float
-
-
-class Segment(NamedTuple):
- id: int
- seek: int
- start: float
- end: float
- text: str
- tokens: List[int]
- avg_logprob: float
- compression_ratio: float
- no_speech_prob: float
- words: Optional[List[Word]]
- temperature: Optional[float] = 1.0
-
-
-# Added additional parameters for multilingual videos and fixes below
-class TranscriptionOptions(NamedTuple):
- beam_size: int
- best_of: int
- patience: float
- length_penalty: float
- repetition_penalty: float
- no_repeat_ngram_size: int
- log_prob_threshold: Optional[float]
- log_prob_low_threshold: Optional[float]
- no_speech_threshold: Optional[float]
- compression_ratio_threshold: Optional[float]
- condition_on_previous_text: bool
- prompt_reset_on_temperature: float
- temperatures: List[float]
- initial_prompt: Optional[Union[str, Iterable[int]]]
- prefix: Optional[str]
- suppress_blank: bool
- suppress_tokens: Optional[List[int]]
- without_timestamps: bool
- max_initial_timestamp: float
- word_timestamps: bool
- prepend_punctuations: str
- append_punctuations: str
- multilingual: bool
- output_language: Optional[str]
- max_new_tokens: Optional[int]
- clip_timestamps: Union[str, List[float]]
- hallucination_silence_threshold: Optional[float]
- hotwords: Optional[str]
-
-
-class TranscriptionInfo(NamedTuple):
- language: str
- language_probability: float
- duration: float
- duration_after_vad: float
- all_language_probs: Optional[List[Tuple[str, float]]]
- transcription_options: TranscriptionOptions
- vad_options: VadOptions
-
-
-# The code below is originally from HF pipeline and is used in whisper-x
-# (https://github.com/m-bain/whisperX) and adapted for faster_whisper
-
-
-class BatchedInferencePipeline:
- """
- Huggingface Pipeline wrapper for WhisperModel.
- Copyright (c) 2022, Max Bain
- All rights reserved.
- Modified by Mobius Labs GmbH
- """
-
- def __init__(
- self,
- model,
- use_vad_model: bool = True,
- options: Optional[NamedTuple] = None,
- tokenizer=None,
- chunk_length: int = 30,
- vad_device: Union[int, str, "torch.device"] = "auto",
- vad_onset: float = 0.500,
- vad_offset: float = 0.363,
- language: Optional[str] = None,
- ):
- self.model: WhisperModel = model
- self.tokenizer = tokenizer
- self.options = options
- self.preset_language = language
- self.use_vad_model = use_vad_model
- self.vad_onset = vad_onset
- self.vad_offset = vad_offset
- self.vad_model_path = os.path.join(get_assets_path(), "pyannote_vad_model.bin")
- if self.use_vad_model:
- self.vad_device = self.get_device(vad_device)
- self.vad_model = self.load_vad_model(
- vad_onset=self.vad_onset, vad_offset=self.vad_offset
- )
- else:
- self.vad_model = None
- self.chunk_length = chunk_length # VAD merging size
- self.last_speech_timestamp = 0.0
-
- def get_device(self, device: Union[int, str, "torch.device"]):
- """
- Converts the input device into a torch.device object.
-
- The input can be an integer, a string, or a `torch.device` object.
-
- The function handles a special case where the input device is "auto".
- When "auto" is specified, the device will default to the
- device of the model (self.model.device). If the model's device is also "auto",
- it selects "cuda" if a CUDA-capable device is available; otherwise, it selects "cpu".
- """
- if isinstance(device, torch.device):
- return device
- elif isinstance(device, str):
- if device == "auto" and self.model.device == "auto":
- device = "cuda" if torch.cuda.is_available() else "cpu"
- elif device == "auto":
- device = self.model.device
- return torch.device(device)
- elif device < 0:
- return torch.device("cpu")
- else:
- return torch.device(f"cuda:{device}")
-
- def forward(self, features, segments_metadata, **forward_params):
- encoder_output, outputs = self.model.generate_segment_batched(
- features, self.tokenizer, forward_params
- )
-
- segmented_outputs = []
- segment_sizes = []
- for segment_metadata, output in zip(segments_metadata, outputs):
- duration = segment_metadata["end_time"] - segment_metadata["start_time"]
- segment_size = int(duration * self.model.frames_per_second)
- segment_sizes.append(segment_size)
- (
- subsegments,
- seek,
- single_timestamp_ending,
- ) = self.model._split_segments_by_timestamps(
- tokenizer=self.tokenizer,
- tokens=output["tokens"],
- time_offset=segment_metadata["start_time"],
- segment_size=segment_size,
- segment_duration=duration,
- seek=0,
- )
- segmented_outputs.append(
- [
- dict(
- text=self.tokenizer.decode(subsegment["tokens"]),
- avg_logprob=output["avg_logprob"],
- no_speech_prob=output["no_speech_prob"],
- tokens=subsegment["tokens"],
- start=subsegment["start"],
- end=subsegment["end"],
- compression_ratio=get_compression_ratio(
- self.tokenizer.decode(subsegment["tokens"])
- ),
- )
- for subsegment in subsegments
- ]
- )
- if forward_params["word_timestamps"]:
- self.last_speech_timestamp = self.model.add_word_timestamps(
- segmented_outputs,
- self.tokenizer,
- encoder_output,
- segment_sizes,
- forward_params["prepend_punctuations"],
- forward_params["append_punctuations"],
- self.last_speech_timestamp,
- )
-
- return segmented_outputs
-
- def get_language_and_tokenizer(
- self, audio, task: Optional[str] = None, language: Optional[str] = None
- ):
- all_language_probs = None
- language_probability = 1.0
-
- if self.tokenizer is None:
- if not language:
- (
- language,
- language_probability,
- all_language_probs,
- ) = self.model.detect_language(audio)
- task = task or "transcribe"
- self.tokenizer = Tokenizer(
- self.model.hf_tokenizer,
- self.model.model.is_multilingual,
- task=task,
- language=language,
- )
- else:
- if task is not None:
- self.tokenizer.task = self.tokenizer.tokenizer.token_to_id(
- f"<|{task}|>"
- )
-
- if language is not None:
- self.tokenizer.language = self.tokenizer.tokenizer.token_to_id(
- f"<|{language}|>"
- )
- self.tokenizer.language_code = language
-
- return language, language_probability, task, all_language_probs
-
- @staticmethod
- def audio_split(audio, segments, sampling_rate):
- """Returns splitted audio chunks as iterator"""
- audio_segments = []
- segments_metadata = []
- for seg in segments:
- f1 = int(seg["start"] * sampling_rate)
- f2 = int(seg["end"] * sampling_rate)
- seg_metadata = {
- "start_time": seg["start"],
- "end_time": seg["end"],
- "stitched_seg": seg["segments"],
- }
- audio_segments.append(audio[f1:f2])
- segments_metadata.append(seg_metadata)
- return audio_segments, segments_metadata
-
- def load_vad_model(self, vad_onset=0.500, vad_offset=0.363):
- vad_model = Model.from_pretrained(self.vad_model_path)
- hyperparameters = {
- "onset": vad_onset,
- "offset": vad_offset,
- "min_duration_on": 0.1,
- "min_duration_off": 0.1,
- }
-
- vad_pipeline = VoiceActivitySegmentation(
- segmentation=vad_model, device=torch.device(self.vad_device)
- )
- vad_pipeline.instantiate(hyperparameters)
- return vad_pipeline
-
- def transcribe(
- self,
- audio: Union[str, torch.Tensor, np.ndarray],
- vad_segments: Optional[List[dict]] = None,
- batch_size: int = 16,
- language: Optional[str] = None,
- task: str = None,
- log_progress: bool = False,
- beam_size: int = 5,
- best_of: int = 5,
- patience: float = 1,
- length_penalty: float = 1,
- repetition_penalty: float = 1,
- no_repeat_ngram_size: int = 0,
- temperature: Union[float, List[float], Tuple[float, ...]] = [
- 0.0,
- 0.2,
- 0.4,
- 0.6,
- 0.8,
- 1.0,
- ],
- compression_ratio_threshold: Optional[float] = 2.4,
- log_prob_threshold: Optional[float] = -1.0,
- log_prob_low_threshold: Optional[float] = None,
- no_speech_threshold: Optional[float] = 0.6,
- initial_prompt: Optional[Union[str, Iterable[int]]] = None,
- prefix: Optional[str] = None,
- suppress_blank: bool = True,
- suppress_tokens: Optional[List[int]] = [-1],
- prepend_punctuations: str = "\"'“¿([{-",
- append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
- max_new_tokens: Optional[int] = None,
- hotwords: Optional[str] = None,
- word_timestamps: bool = False,
- without_timestamps: bool = True,
- ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
- """transcribe audio in chunks in batched fashion and return with language info.
-
- Arguments:
- audio: audio file as numpy array/path for batched transcription.
- vad_segments: Optionally provide list of dictionaries each containing "start", "end",
- and "segments" keys.
- "start" and "end" keys specify the start and end of the voiced region within
- 30 sec boundary. An additional key "segments" contains all the start
- and end of voiced regions within that 30sec boundary as a list of tuples.
- If no vad_segments specified, it uses internal vad model automatically segment them.
- batch_size: the maximum number of parallel requests to model for decoding.
- language: The language spoken in the audio.
- task: either "transcribe" or "translate".
- log_progress: whether to show progress bar or not.
- beam_size: Beam size to use for decoding.
- best_of: Number of candidates when sampling with non-zero temperature.
- patience: Beam search patience factor.
- length_penalty: Exponential length penalty constant.
- repetition_penalty: Penalty applied to the score of previously generated tokens
- (set > 1 to penalize).
- no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable).
- temperature: Temperature for sampling. It can be a tuple of temperatures,
- which will be successively used upon failures according to either
- `compression_ratio_threshold` or `log_prob_threshold`.
- compression_ratio_threshold: If the gzip compression ratio is above this value,
- treat as failed.
- log_prob_threshold: If the average log probability over sampled tokens is
- below this value, treat as failed.
- log_prob_low_threshold: This parameter alone is sufficient to skip an output text,
- whereas log_prob_threshold also looks for appropriate no_speech_threshold value.
- This value should be less than log_prob_threshold.
- no_speech_threshold: If the no_speech probability is higher than this value AND
- the average log probability over sampled tokens is below `log_prob_threshold`,
- consider the segment as silent.
- initial_prompt: Optional text string or iterable of token ids to provide as a
- prompt for the first window.
- prefix: Optional text to provide as a prefix for the first window.
- suppress_blank: Suppress blank outputs at the beginning of the sampling.
- suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
- of symbols as defined in `tokenizer.non_speech_tokens()`.
- prepend_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the next word
- append_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the previous word
- max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set,
- the maximum will be set by the default max_length.
- hotwords:
- Hotwords/hint phrases to the model. Has no effect if prefix is not None.
- word_timestamps: Extract word-level timestamps using the cross-attention pattern
- and dynamic time warping, and include the timestamps for each word in each segment.
- Set as False.
- without_timestamps: Only sample text tokens.
-
- Static params: (Fixed for batched version)
- max_initial_timestamp: The initial timestamp cannot be later than this, set at 0.0.
- multilingual: If True, perform transcription on multilingual videos. Set as False.
- output_language: Valid only if multilingual is set to True.
- Specifies the string representing the output language. One of
- 'en' (English) or 'hybrid' (code-switched transcription). set as None.
- condition_on_previous_text: If True, the previous output of the model is provided
- as a prompt for the next window; disabling may make the text inconsistent across
- windows, but the model becomes less prone to getting stuck in a failure loop,
- such as repetition looping or timestamps going out of sync. Set as False
- prompt_reset_on_temperature: Resets prompt if temperature is above this value.
- Arg has effect only if condition_on_previous_text is True. Set at 0.5
- #TODO: support "hallucination_silence_threshold" when "word_timestamps=True"
- hallucination_silence_threshold: Optional[float]
- When word_timestamps is True, skip silent periods longer than this threshold
- (in seconds) when a possible hallucination is detected. set as None.
- clip_timestamps:
- Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to
- process. The last end timestamp defaults to the end of the file. Set as "0".
-
- unused:
- language_detection_threshold: If the maximum probability of the language tokens is
- higher than this value, the language is detected.
- language_detection_segments: Number of segments to consider for the language detection.
- vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
- without speech. This step is using the Silero VAD model
- https://github.com/snakers4/silero-vad.
- vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
- parameters and default values in the class `VadOptions`).
- chunk_length: The length of audio segments. If it is not None, it will overwrite the
- default chunk_length of the FeatureExtractor.
-
-
- Returns:
- A tuple with:
-
- - a generator over transcribed batched segments.
- - an instance of TranscriptionInfo.
- """
-
- sampling_rate = self.model.feature_extractor.sampling_rate
-
- if isinstance(audio, np.ndarray):
- audio = torch.from_numpy(audio)
- elif not isinstance(audio, torch.Tensor):
- audio = decode_audio(audio, sampling_rate=sampling_rate)
- duration = audio.shape[0] / sampling_rate
-
- # if no segment split is provided, use vad_model and generate segments
- if not vad_segments:
- # run the audio if it is less than 30 sec even without vad_segments
- if self.use_vad_model:
- vad_segments = self.vad_model(
- {
- "waveform": audio.unsqueeze(0),
- "sample_rate": 16000,
- }
- )
- vad_segments = merge_chunks(
- vad_segments,
- self.chunk_length,
- onset=self.vad_onset,
- offset=self.vad_offset,
- )
- elif duration < self.chunk_length:
- vad_segments = [
- {"start": 0.0, "end": duration, "segments": [(0.0, duration)]}
- ]
- else:
- raise RuntimeError(
- "No vad segments found. Set 'use_vad_model' to True while loading the model"
- )
- if self.model.model.is_multilingual:
- language = language or self.preset_language
- elif language != "en":
- if language is not None:
- self.model.logger.warning(
- f"English-only model is used, but {language} language is"
- "chosen, setting language to 'en'."
- )
- language = "en"
-
- (
- language,
- language_probability,
- task,
- all_language_probs,
- ) = self.get_language_and_tokenizer(audio, task, language)
-
- duration_after_vad = sum(
- segment["end"] - segment["start"] for segment in vad_segments
- )
-
- # batched options: see the difference with default options in WhisperModel
- batched_options = TranscriptionOptions(
- beam_size=beam_size,
- best_of=best_of,
- patience=patience,
- length_penalty=length_penalty,
- repetition_penalty=repetition_penalty,
- no_repeat_ngram_size=no_repeat_ngram_size,
- log_prob_threshold=log_prob_threshold,
- log_prob_low_threshold=log_prob_low_threshold,
- no_speech_threshold=no_speech_threshold,
- compression_ratio_threshold=compression_ratio_threshold,
- temperatures=(
- temperature if isinstance(temperature, (list, tuple)) else [temperature]
- ),
- initial_prompt=initial_prompt,
- prefix=prefix,
- suppress_blank=suppress_blank,
- suppress_tokens=get_suppressed_tokens(self.tokenizer, suppress_tokens),
- prepend_punctuations=prepend_punctuations,
- append_punctuations=append_punctuations,
- max_new_tokens=max_new_tokens,
- hotwords=hotwords,
- word_timestamps=word_timestamps,
- hallucination_silence_threshold=None,
- condition_on_previous_text=False,
- clip_timestamps="0",
- prompt_reset_on_temperature=0.5,
- multilingual=False,
- output_language=None,
- without_timestamps=without_timestamps,
- max_initial_timestamp=0.0,
- )
-
- info = TranscriptionInfo(
- language=language,
- language_probability=language_probability,
- duration=duration,
- duration_after_vad=duration_after_vad,
- transcription_options=batched_options,
- vad_options=None,
- all_language_probs=all_language_probs,
- )
-
- audio_segments, segments_metadata = self.audio_split(
- audio, vad_segments, sampling_rate
- )
- to_cpu = (
- self.model.model.device == "cuda" and len(self.model.model.device_index) > 1
- )
- audio_segments = torch.nested.nested_tensor(audio_segments).to_padded_tensor(
- padding=0
- )
- features = torch.stack(
- [
- self.model.feature_extractor(audio_segment, to_cpu=to_cpu)[
- ..., : self.model.feature_extractor.nb_max_frames
- ]
- for audio_segment in audio_segments
- ]
- )
-
- segments = self._batched_segments_generator(
- features,
- segments_metadata,
- batch_size,
- batched_options,
- log_progress,
- )
-
- return segments, info
-
- def _batched_segments_generator(
- self, features, segments_metadata, batch_size, options, log_progress
- ):
- pbar = tqdm(total=len(features), disable=not log_progress, position=0)
- seg_idx = 0
- for i in range(0, len(features), batch_size):
- results = self.forward(
- features[i : i + batch_size],
- segments_metadata[i : i + batch_size],
- **options._asdict(),
- )
-
- for result in results:
- for segment in result:
- seg_idx += 1
- yield Segment(
- seek=int(result[-1]["end"] * self.model.frames_per_second),
- id=seg_idx,
- text=segment["text"],
- start=round(segment["start"], 3),
- end=round(segment["end"], 3),
- words=(
- None
- if not options.word_timestamps
- else [Word(**word) for word in segment["words"]]
- ),
- tokens=segment["tokens"],
- avg_logprob=segment["avg_logprob"],
- no_speech_prob=segment["no_speech_prob"],
- compression_ratio=segment["compression_ratio"],
- )
-
- pbar.update(1)
-
- pbar.close()
- # revert the tokenizer if multilingual inference is enabled
- if self.preset_language is None:
- self.tokenizer = None
- self.last_speech_timestamp = 0.0
-
-
-class WhisperModel:
- def __init__(
- self,
- model_size_or_path: str,
- device: str = "auto",
- device_index: Union[int, List[int]] = 0,
- compute_type: str = "default",
- cpu_threads: int = 16,
- num_workers: int = 1,
- download_root: Optional[str] = None,
- local_files_only: bool = False,
- files: dict = None,
- **model_kwargs,
- ):
- """Initializes the Whisper model.
-
- Args:
- model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en,
- small, small.en, distil-small.en, medium, medium.en, distil-medium.en, large-v1,
- large-v2, large-v3, large, distil-large-v2 or distil-large-v3), a path to a
- converted model directory, or a CTranslate2-converted Whisper model ID from the HF Hub.
- When a size or a model ID is configured, the converted model is downloaded
- from the Hugging Face Hub.
- device: Device to use for computation ("cpu", "cuda", "auto").
- device_index: Device ID to use.
- The model can also be loaded on multiple GPUs by passing a list of IDs
- (e.g. [0, 1, 2, 3]). In that case, multiple transcriptions can run in parallel
- when transcribe() is called from multiple Python threads (see also num_workers).
- compute_type: Type to use for computation.
- See https://opennmt.net/CTranslate2/quantization.html.
- cpu_threads: Number of threads to use when running on CPU (4 by default).
- A non zero value overrides the OMP_NUM_THREADS environment variable.
- num_workers: When transcribe() is called from multiple Python threads,
- having multiple workers enables true parallelism when running the model
- (concurrent calls to self.model.generate() will run in parallel).
- This can improve the global throughput at the cost of increased memory usage.
- download_root: Directory where the models should be saved. If not set, the models
- are saved in the standard Hugging Face cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the
- local cached file if it exists.
- files: Load model files from the memory. This argument is a dictionary mapping file names
- to file contents as file-like or bytes objects. If this is set, model_path acts as an
- identifier for this model.
- """
- self.logger = get_logger()
-
- tokenizer_bytes, preprocessor_bytes = None, None
- if files:
- model_path = model_size_or_path
- tokenizer_bytes = files.pop("tokenizer.json", None)
- preprocessor_bytes = files.pop("preprocessor_config.json", None)
- elif os.path.isdir(model_size_or_path):
- model_path = model_size_or_path
- else:
- model_path = download_model(
- model_size_or_path,
- local_files_only=local_files_only,
- cache_dir=download_root,
- )
- self.device = device
- # set the random seed to make sure consistency across runs
- ctranslate2.set_random_seed(42)
- self.model = ctranslate2.models.Whisper(
- model_path,
- device=self.device,
- device_index=device_index,
- compute_type=compute_type,
- intra_threads=cpu_threads,
- inter_threads=num_workers,
- files=files,
- **model_kwargs,
- )
-
- tokenizer_file = os.path.join(model_path, "tokenizer.json")
- if tokenizer_bytes:
- self.hf_tokenizer = tokenizers.Tokenizer.from_buffer(tokenizer_bytes)
- elif os.path.isfile(tokenizer_file):
- self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file)
- else:
- self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained(
- "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
- )
- self.feat_kwargs = self._get_feature_kwargs(model_path, preprocessor_bytes)
- self.feature_extractor = FeatureExtractor(
- **self.feat_kwargs, device=self.device
- )
- self.input_stride = 2
- self.num_samples_per_token = (
- self.feature_extractor.hop_length * self.input_stride
- )
- self.frames_per_second = (
- self.feature_extractor.sampling_rate // self.feature_extractor.hop_length
- )
- self.tokens_per_second = (
- self.feature_extractor.sampling_rate // self.num_samples_per_token
- )
- self.time_precision = 0.02
- self.max_length = 448
-
- @property
- def supported_languages(self) -> List[str]:
- """The languages supported by the model."""
- return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"]
-
- def _get_feature_kwargs(self, model_path, preprocessor_bytes=None) -> dict:
- config = {}
- try:
- config_path = os.path.join(model_path, "preprocessor_config.json")
- if preprocessor_bytes:
- config = json.loads(preprocessor_bytes)
- elif os.path.isfile(config_path):
- with open(config_path, "r", encoding="utf-8") as file:
- config = json.load(file)
- else:
- return config
- valid_keys = signature(FeatureExtractor.__init__).parameters.keys()
- return {k: v for k, v in config.items() if k in valid_keys}
- except json.JSONDecodeError as e:
- self.logger.warning("Could not load preprocessor config: %s", e)
-
- return config
-
- def transcribe(
- self,
- audio: Union[str, BinaryIO, torch.Tensor, np.ndarray],
- language: Optional[str] = None,
- task: str = "transcribe",
- beam_size: int = 5,
- best_of: int = 5,
- patience: float = 1,
- length_penalty: float = 1,
- repetition_penalty: float = 1,
- no_repeat_ngram_size: int = 0,
- temperature: Union[float, List[float], Tuple[float, ...]] = [
- 0.0,
- 0.2,
- 0.4,
- 0.6,
- 0.8,
- 1.0,
- ],
- compression_ratio_threshold: Optional[float] = 2.4,
- log_prob_threshold: Optional[float] = -1.0,
- log_prob_low_threshold: Optional[float] = None,
- no_speech_threshold: Optional[float] = 0.6,
- condition_on_previous_text: bool = True,
- prompt_reset_on_temperature: float = 0.5,
- initial_prompt: Optional[Union[str, Iterable[int]]] = None,
- prefix: Optional[str] = None,
- suppress_blank: bool = True,
- suppress_tokens: Optional[List[int]] = [-1],
- without_timestamps: bool = False,
- max_initial_timestamp: float = 1.0,
- word_timestamps: bool = False,
- prepend_punctuations: str = "\"'“¿([{-",
- append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
- multilingual: bool = False,
- output_language: Optional[str] = None,
- vad_filter: bool = False,
- vad_parameters: Optional[Union[dict, VadOptions]] = None,
- max_new_tokens: Optional[int] = None,
- chunk_length: Optional[int] = None,
- clip_timestamps: Union[str, List[float]] = "0",
- hallucination_silence_threshold: Optional[float] = None,
- hotwords: Optional[str] = None,
- language_detection_threshold: Optional[float] = None,
- language_detection_segments: int = 1,
- ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
- """Transcribes an input file.
-
- Arguments:
- audio: Path to the input file (or a file-like object), or the audio waveform.
- language: The language spoken in the audio. It should be a language code such
- as "en" or "fr". If not set, the language will be detected in the first 30 seconds
- of audio.
- task: Task to execute (transcribe or translate).
- beam_size: Beam size to use for decoding.
- best_of: Number of candidates when sampling with non-zero temperature.
- patience: Beam search patience factor.
- length_penalty: Exponential length penalty constant.
- repetition_penalty: Penalty applied to the score of previously generated tokens
- (set > 1 to penalize).
- no_repeat_ngram_size: Prevent repetitions of ngrams with this size (set 0 to disable).
- temperature: Temperature for sampling. It can be a tuple of temperatures,
- which will be successively used upon failures according to either
- `compression_ratio_threshold` or `log_prob_threshold`.
- compression_ratio_threshold: If the gzip compression ratio is above this value,
- treat as failed.
- log_prob_threshold: If the average log probability over sampled tokens is
- below this value, treat as failed.
- log_prob_low_threshold: This parameter alone is sufficient to skip an output text,
- wheras log_prob_threshold also looks for appropriate no_speech_threshold value.
- This value should be less than log_prob_threshold.
- no_speech_threshold: If the no_speech probability is higher than this value AND
- the average log probability over sampled tokens is below `log_prob_threshold`,
- consider the segment as silent.
- condition_on_previous_text: If True, the previous output of the model is provided
- as a prompt for the next window; disabling may make the text inconsistent across
- windows, but the model becomes less prone to getting stuck in a failure loop,
- such as repetition looping or timestamps going out of sync.
- prompt_reset_on_temperature: Resets prompt if temperature is above this value.
- Arg has effect only if condition_on_previous_text is True.
- initial_prompt: Optional text string or iterable of token ids to provide as a
- prompt for the first window.
- prefix: Optional text to provide as a prefix for the first window.
- suppress_blank: Suppress blank outputs at the beginning of the sampling.
- suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
- of symbols as defined in `tokenizer.non_speech_tokens()`.
- without_timestamps: Only sample text tokens.
- max_initial_timestamp: The initial timestamp cannot be later than this.
- word_timestamps: Extract word-level timestamps using the cross-attention pattern
- and dynamic time warping, and include the timestamps for each word in each segment.
- prepend_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the next word
- append_punctuations: If word_timestamps is True, merge these punctuation symbols
- with the previous word
- multilingual: If True, perform transcription on multilingual videos
- and return the transcript based
- on the 'output_language' flag.
- output_language: Valid only if multilingual is set to True.
- Specifies the string representing the output language. One of
- 'en' (English) or 'hybrid' (code-switched transcription).
- vad_filter: Enable the voice activity detection (VAD) to filter out parts of the audio
- without speech. This step is using the Silero VAD model
- https://github.com/snakers4/silero-vad.
- vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
- parameters and default values in the class `VadOptions`).
- max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set,
- the maximum will be set by the default max_length.
- chunk_length: The length of audio segments. If it is not None, it will overwrite the
- default chunk_length of the FeatureExtractor.
- clip_timestamps:
- Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to
- process. The last end timestamp defaults to the end of the file.
- vad_filter will be ignored if clip_timestamps is used.
- hallucination_silence_threshold:
- When word_timestamps is True, skip silent periods longer than this threshold
- (in seconds) when a possible hallucination is detected
- hotwords:
- Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.
- language_detection_threshold: If the maximum probability of the language tokens is higher
- than this value, the language is detected.
- language_detection_segments: Number of segments to consider for the language detection.
- Returns:
- A tuple with:
-
- - a generator over transcribed segments
- - an instance of TranscriptionInfo
- """
-
- sampling_rate = self.feature_extractor.sampling_rate
-
- if isinstance(audio, np.ndarray):
- audio = torch.from_numpy(audio)
- elif not isinstance(audio, torch.Tensor):
- audio = decode_audio(audio, sampling_rate=sampling_rate)
-
- duration = audio.shape[0] / sampling_rate
- duration_after_vad = duration
-
- self.logger.info(
- "Processing audio with duration %s", format_timestamp(duration)
- )
-
- if vad_filter and clip_timestamps == "0":
- if vad_parameters is None:
- vad_parameters = VadOptions()
- elif isinstance(vad_parameters, dict):
- vad_parameters = VadOptions(**vad_parameters)
- speech_chunks = get_speech_timestamps(audio, vad_parameters)
- audio = collect_chunks(audio, speech_chunks)
- duration_after_vad = audio.shape[0] / sampling_rate
-
- self.logger.info(
- "VAD filter removed %s of audio",
- format_timestamp(duration - duration_after_vad),
- )
-
- if self.logger.isEnabledFor(logging.DEBUG):
- self.logger.debug(
- "VAD filter kept the following audio segments: %s",
- ", ".join(
- "[%s -> %s]"
- % (
- format_timestamp(chunk["start"] / sampling_rate),
- format_timestamp(chunk["end"] / sampling_rate),
- )
- for chunk in speech_chunks
- ),
- )
-
- else:
- speech_chunks = None
-
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- features = self.feature_extractor(
- audio, chunk_length=chunk_length, to_cpu=to_cpu
- )
-
- encoder_output = None
- all_language_probs = None
-
- # setting output_language for multilingual videos
- if multilingual:
- if output_language is None:
- output_language = "en"
- elif output_language not in ["en", "hybrid"]:
- raise ValueError("Output language needs to be one of 'en'/'hybrid'.")
-
- # detecting the language if not provided
- if language is None:
- if not self.model.is_multilingual:
- language = "en"
- language_probability = 1
- else:
- if (
- language_detection_segments is None
- or language_detection_segments < 1
- ):
- language_detection_segments = 1
- start_timestamp = (
- float(clip_timestamps.split(",")[0])
- if isinstance(clip_timestamps, str)
- else clip_timestamps[0]
- )
- content_frames = (
- features.shape[-1] - self.feature_extractor.nb_max_frames
- )
- seek = (
- int(start_timestamp * self.frames_per_second)
- if start_timestamp * self.frames_per_second < content_frames
- else 0
- )
- end_frames = min(
- seek
- + self.feature_extractor.nb_max_frames
- * language_detection_segments,
- content_frames,
- )
- detected_language_info = {}
- while seek <= end_frames:
- segment = features[
- :, seek : seek + self.feature_extractor.nb_max_frames
- ]
- encoder_output = self.encode(segment)
- # results is a list of tuple[str, float] with language names and
- # probabilities.
- results = self.model.detect_language(encoder_output)[0]
- # Parse language names to strip out markers
- all_language_probs = [
- (token[2:-2], prob) for (token, prob) in results
- ]
- # Get top language token and probability
- language, language_probability = all_language_probs[0]
- if (
- language_detection_threshold is None
- or language_probability > language_detection_threshold
- ):
- break
- detected_language_info.setdefault(language, []).append(
- language_probability
- )
- seek += segment.shape[-1]
- else:
- # If no language detected for all segments, the majority vote of the highest
- # projected languages for all segments is used to determine the language.
- language = max(
- detected_language_info,
- key=lambda lang: len(detected_language_info[lang]),
- )
- language_probability = max(detected_language_info[language])
-
- self.logger.info(
- "Detected language '%s' with probability %.2f",
- language,
- language_probability,
- )
- else:
- if not self.model.is_multilingual and language != "en":
- self.logger.warning(
- "The current model is English-only but the language parameter is set to '%s'; "
- "using 'en' instead." % language
- )
- language = "en"
-
- language_probability = 1
-
- tokenizer = Tokenizer(
- self.hf_tokenizer,
- self.model.is_multilingual,
- task=task,
- language=language,
- )
-
- options = TranscriptionOptions(
- beam_size=beam_size,
- best_of=best_of,
- patience=patience,
- length_penalty=length_penalty,
- repetition_penalty=repetition_penalty,
- no_repeat_ngram_size=no_repeat_ngram_size,
- log_prob_threshold=log_prob_threshold,
- log_prob_low_threshold=log_prob_low_threshold,
- no_speech_threshold=no_speech_threshold,
- compression_ratio_threshold=compression_ratio_threshold,
- condition_on_previous_text=condition_on_previous_text,
- prompt_reset_on_temperature=prompt_reset_on_temperature,
- temperatures=(
- temperature if isinstance(temperature, (list, tuple)) else [temperature]
- ),
- initial_prompt=initial_prompt,
- prefix=prefix,
- suppress_blank=suppress_blank,
- suppress_tokens=(
- get_suppressed_tokens(tokenizer, suppress_tokens)
- if suppress_tokens
- else suppress_tokens
- ),
- without_timestamps=without_timestamps,
- max_initial_timestamp=max_initial_timestamp,
- word_timestamps=word_timestamps,
- prepend_punctuations=prepend_punctuations,
- append_punctuations=append_punctuations,
- multilingual=multilingual,
- output_language=output_language,
- max_new_tokens=max_new_tokens,
- clip_timestamps=clip_timestamps,
- hallucination_silence_threshold=hallucination_silence_threshold,
- hotwords=hotwords,
- )
-
- segments = self.generate_segments(features, tokenizer, options, encoder_output)
-
- if speech_chunks:
- segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
-
- info = TranscriptionInfo(
- language=language,
- language_probability=language_probability,
- duration=duration,
- duration_after_vad=duration_after_vad,
- transcription_options=options,
- vad_options=vad_parameters,
- all_language_probs=all_language_probs,
- )
- return segments, info
-
- def _split_segments_by_timestamps(
- self,
- tokenizer: Tokenizer,
- tokens: List[int],
- time_offset: float,
- segment_size: int,
- segment_duration: float,
- seek: int,
- ) -> List[List[int]]:
- current_segments = []
- single_timestamp_ending = (
- len(tokens) >= 2 and tokens[-2] < tokenizer.timestamp_begin <= tokens[-1]
- )
-
- consecutive_timestamps = [
- i
- for i in range(len(tokens))
- if i > 0
- and tokens[i] >= tokenizer.timestamp_begin
- and tokens[i - 1] >= tokenizer.timestamp_begin
- ]
-
- if len(consecutive_timestamps) > 0:
- slices = list(consecutive_timestamps)
- if single_timestamp_ending:
- slices.append(len(tokens))
-
- last_slice = 0
- for current_slice in slices:
- sliced_tokens = tokens[last_slice:current_slice]
- start_timestamp_position = sliced_tokens[0] - tokenizer.timestamp_begin
- end_timestamp_position = sliced_tokens[-1] - tokenizer.timestamp_begin
- start_time = (
- time_offset + start_timestamp_position * self.time_precision
- )
- end_time = time_offset + end_timestamp_position * self.time_precision
-
- current_segments.append(
- dict(
- seek=seek,
- start=start_time,
- end=end_time,
- tokens=sliced_tokens,
- )
- )
- last_slice = current_slice
-
- if single_timestamp_ending:
- # single timestamp at the end means no speech after the last timestamp.
- seek += segment_size
- else:
- # otherwise, ignore the unfinished segment and seek to the last timestamp
- last_timestamp_position = (
- tokens[last_slice - 1] - tokenizer.timestamp_begin
- )
- seek += last_timestamp_position * self.input_stride
-
- else:
- duration = segment_duration
- timestamps = [
- token for token in tokens if token >= tokenizer.timestamp_begin
- ]
- if len(timestamps) > 0 and timestamps[-1] != tokenizer.timestamp_begin:
- last_timestamp_position = timestamps[-1] - tokenizer.timestamp_begin
- duration = last_timestamp_position * self.time_precision
-
- current_segments.append(
- dict(
- seek=seek,
- start=time_offset,
- end=time_offset + duration,
- tokens=tokens,
- )
- )
-
- seek += segment_size
-
- return current_segments, seek, single_timestamp_ending
-
- def generate_segments(
- self,
- features: torch.Tensor,
- tokenizer: Tokenizer,
- options: TranscriptionOptions,
- encoder_output: Optional[ctranslate2.StorageView] = None,
- ) -> Iterable[Segment]:
- content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
- content_duration = float(content_frames * self.feature_extractor.time_per_frame)
-
- if isinstance(options.clip_timestamps, str):
- options = options._replace(
- clip_timestamps=[
- float(ts)
- for ts in (
- options.clip_timestamps.split(",")
- if options.clip_timestamps
- else []
- )
- ]
- )
- seek_points: List[int] = [
- round(ts * self.frames_per_second) for ts in options.clip_timestamps
- ]
- if len(seek_points) == 0:
- seek_points.append(0)
- if len(seek_points) % 2 == 1:
- seek_points.append(content_frames)
- seek_clips: List[Tuple[int, int]] = list(
- zip(seek_points[::2], seek_points[1::2])
- )
-
- punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
-
- idx = 0
- clip_idx = 0
- seek = seek_clips[clip_idx][0]
- all_tokens = []
- prompt_reset_since = 0
-
- if options.initial_prompt is not None:
- if isinstance(options.initial_prompt, str):
- initial_prompt = " " + options.initial_prompt.strip()
- initial_prompt_tokens = tokenizer.encode(initial_prompt)
- all_tokens.extend(initial_prompt_tokens)
- else:
- all_tokens.extend(options.initial_prompt)
-
- last_speech_timestamp = 0.0
- # NOTE: This loop is obscurely flattened to make the diff readable.
- # A later commit should turn this into a simpler nested loop.
- # for seek_clip_start, seek_clip_end in seek_clips:
- # while seek < seek_clip_end
- while clip_idx < len(seek_clips):
- seek_clip_start, seek_clip_end = seek_clips[clip_idx]
- if seek_clip_end > content_frames:
- seek_clip_end = content_frames
- if seek < seek_clip_start:
- seek = seek_clip_start
- if seek >= seek_clip_end:
- clip_idx += 1
- if clip_idx < len(seek_clips):
- seek = seek_clips[clip_idx][0]
- continue
- time_offset = seek * self.feature_extractor.time_per_frame
- window_end_time = float(
- (seek + self.feature_extractor.nb_max_frames)
- * self.feature_extractor.time_per_frame
- )
- segment_size = min(
- self.feature_extractor.nb_max_frames,
- content_frames - seek,
- seek_clip_end - seek,
- )
- segment = features[:, seek : seek + segment_size]
- segment_duration = segment_size * self.feature_extractor.time_per_frame
- segment = pad_or_trim(segment, self.feature_extractor.nb_max_frames)
-
- if self.logger.isEnabledFor(logging.DEBUG):
- self.logger.debug(
- "Processing segment at %s", format_timestamp(time_offset)
- )
-
- previous_tokens = all_tokens[prompt_reset_since:]
-
- if encoder_output is None:
- encoder_output = self.encode(segment)
-
- # Perform language detection at every segment to update task based on output language,
- # if the language is english, task is transcribe,
- # else the task is translate to english (default)
- # or transcribe if 'output_language' is 'hybrid'.
- if options.multilingual:
- results = self.model.detect_language(encoder_output)
- language_token, language_probability = results[0][0]
- language = language_token[2:-2]
- if options.output_language == "en" and language != "en":
- task = "translate"
- else:
- task = "transcribe"
-
- # Update tokenizer based on task and language
- tokenizer.task = tokenizer.tokenizer.token_to_id(f"<|{task}|>")
- tokenizer.language = tokenizer.tokenizer.token_to_id(language_token)
- tokenizer.language_code = language
- # Update prompt based on task and language
- prompt = self.get_prompt(
- tokenizer,
- previous_tokens,
- without_timestamps=options.without_timestamps,
- prefix=options.prefix if seek == 0 else None,
- hotwords=options.hotwords,
- )
-
- if seek > 0 or encoder_output is None:
- encoder_output = self.encode(segment)
-
- (
- result,
- avg_logprob,
- temperature,
- compression_ratio,
- ) = self.generate_with_fallback(encoder_output, prompt, tokenizer, options)
-
- if options.no_speech_threshold is not None:
- # no voice activity check
- should_skip = result.no_speech_prob > options.no_speech_threshold
-
- if (
- options.log_prob_threshold is not None
- and avg_logprob > options.log_prob_threshold
- ):
- # don't skip if the logprob is high enough, despite the no_speech_prob
- should_skip = False
-
- if should_skip:
- self.logger.debug(
- "No speech threshold is met (%f > %f)",
- result.no_speech_prob,
- options.no_speech_threshold,
- )
-
- # Skip if the logprob is very low (below the threshold value),
- # despite no_speech_prob being low (ex: Too ambiguous outputs)
- if options.log_prob_low_threshold:
- if avg_logprob < options.log_prob_low_threshold:
- should_skip = True
- self.logger.debug(
- "log prob low threshold is met (%f > %f)",
- avg_logprob,
- options.log_prob_low_threshold,
- )
-
- if should_skip:
- # fast-forward to the next segment boundary
- seek += segment_size
- continue
-
- tokens = result.sequences_ids[0]
-
- previous_seek = seek
-
- # anomalous words are very long/short/improbable
- def word_anomaly_score(word: dict) -> float:
- probability = word.get("probability", 0.0)
- duration = word["end"] - word["start"]
- score = 0.0
- if probability < 0.15:
- score += 1.0
- if duration < 0.133:
- score += (0.133 - duration) * 15
- if duration > 2.0:
- score += duration - 2.0
- return score
-
- def is_segment_anomaly(segment: Optional[dict]) -> bool:
- if segment is None or not segment["words"]:
- return False
- words = [w for w in segment["words"] if w["word"] not in punctuation]
- words = words[:8]
- score = sum(word_anomaly_score(w) for w in words)
- return score >= 3 or score + 0.01 >= len(words)
-
- def next_words_segment(segments: List[dict]) -> Optional[dict]:
- return next((s for s in segments if s["words"]), None)
-
- (
- current_segments,
- seek,
- single_timestamp_ending,
- ) = self._split_segments_by_timestamps(
- tokenizer=tokenizer,
- tokens=tokens,
- time_offset=time_offset,
- segment_size=segment_size,
- segment_duration=segment_duration,
- seek=seek,
- )
-
- if options.word_timestamps:
- self.add_word_timestamps(
- [current_segments],
- tokenizer,
- encoder_output,
- segment_size,
- options.prepend_punctuations,
- options.append_punctuations,
- last_speech_timestamp=last_speech_timestamp,
- )
- if not single_timestamp_ending:
- last_word_end = get_end(current_segments)
- if last_word_end is not None and last_word_end > time_offset:
- seek = round(last_word_end * self.frames_per_second)
-
- # skip silence before possible hallucinations
- if options.hallucination_silence_threshold is not None:
- threshold = options.hallucination_silence_threshold
-
- # if first segment might be a hallucination, skip leading silence
- first_segment = next_words_segment(current_segments)
- if first_segment is not None and is_segment_anomaly(first_segment):
- gap = first_segment["start"] - time_offset
- if gap > threshold:
- seek = previous_seek + round(gap * self.frames_per_second)
- continue
-
- # skip silence before any possible hallucination that is surrounded
- # by silence or more hallucinations
- hal_last_end = last_speech_timestamp
- for si in range(len(current_segments)):
- segment = current_segments[si]
- if not segment["words"]:
- continue
- if is_segment_anomaly(segment):
- next_segment = next_words_segment(
- current_segments[si + 1 :]
- )
- if next_segment is not None:
- hal_next_start = next_segment["words"][0]["start"]
- else:
- hal_next_start = time_offset + segment_duration
- silence_before = (
- segment["start"] - hal_last_end > threshold
- or segment["start"] < threshold
- or segment["start"] - time_offset < 2.0
- )
- silence_after = (
- hal_next_start - segment["end"] > threshold
- or is_segment_anomaly(next_segment)
- or window_end_time - segment["end"] < 2.0
- )
- if silence_before and silence_after:
- seek = round(
- max(time_offset + 1, segment["start"])
- * self.frames_per_second
- )
- if content_duration - segment["end"] < threshold:
- seek = content_frames
- current_segments[si:] = []
- break
- hal_last_end = segment["end"]
-
- last_word_end = get_end(current_segments)
- if last_word_end is not None:
- last_speech_timestamp = last_word_end
- for segment in current_segments:
- tokens = segment["tokens"]
- text = tokenizer.decode(tokens)
-
- if segment["start"] == segment["end"] or not text.strip():
- continue
-
- all_tokens.extend(tokens)
- idx += 1
-
- yield Segment(
- id=idx,
- seek=seek,
- start=segment["start"],
- end=segment["end"],
- text=text,
- tokens=tokens,
- temperature=temperature,
- avg_logprob=avg_logprob,
- compression_ratio=compression_ratio,
- no_speech_prob=result.no_speech_prob,
- words=(
- [Word(**word) for word in segment["words"]]
- if options.word_timestamps
- else None
- ),
- )
-
- if (
- not options.condition_on_previous_text
- or temperature > options.prompt_reset_on_temperature
- ):
- if options.condition_on_previous_text:
- self.logger.debug(
- "Reset prompt. prompt_reset_on_temperature threshold is met %f > %f",
- temperature,
- options.prompt_reset_on_temperature,
- )
-
- prompt_reset_since = len(all_tokens)
-
- def encode(self, features: torch.Tensor) -> ctranslate2.StorageView:
- # When the model is running on multiple GPUs, the encoder output should be moved
- # to the CPU since we don't know which GPU will handle the next job.
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
-
- if features.ndim == 2:
- features = features.unsqueeze(0)
- features = get_ctranslate2_storage(features)
-
- return self.model.encode(features, to_cpu=to_cpu)
-
- def generate_with_fallback(
- self,
- encoder_output: ctranslate2.StorageView,
- prompt: List[int],
- tokenizer: Tokenizer,
- options: TranscriptionOptions,
- ) -> Tuple[ctranslate2.models.WhisperGenerationResult, float, float, float]:
- decode_result = None
- all_results = []
- below_cr_threshold_results = []
-
- max_initial_timestamp_index = int(
- round(options.max_initial_timestamp / self.time_precision)
- )
- if options.max_new_tokens is not None:
- max_length = len(prompt) + options.max_new_tokens
- else:
- max_length = self.max_length
-
- if max_length > self.max_length:
- raise ValueError(
- f"The length of the prompt is {len(prompt)}, and the `max_new_tokens` "
- f"{max_length - len(prompt)}. Thus, the combined length of the prompt "
- f"and `max_new_tokens` is: {max_length}. This exceeds the "
- f"`max_length` of the Whisper model: {self.max_length}. "
- "You should either reduce the length of your prompt, or "
- "reduce the value of `max_new_tokens`, "
- f"so that their combined length is less that {self.max_length}."
- )
-
- for temperature in options.temperatures:
- if temperature > 0:
- kwargs = {
- "beam_size": 1,
- "num_hypotheses": options.best_of,
- "sampling_topk": 0,
- "sampling_temperature": temperature,
- }
- else:
- kwargs = {
- "beam_size": options.beam_size,
- "patience": options.patience,
- }
-
- result = self.model.generate(
- encoder_output,
- [prompt],
- length_penalty=options.length_penalty,
- repetition_penalty=options.repetition_penalty,
- no_repeat_ngram_size=options.no_repeat_ngram_size,
- max_length=max_length,
- return_scores=True,
- return_no_speech_prob=True,
- suppress_blank=options.suppress_blank,
- suppress_tokens=options.suppress_tokens,
- max_initial_timestamp_index=max_initial_timestamp_index,
- **kwargs,
- )[0]
-
- tokens = result.sequences_ids[0]
-
- # Recover the average log prob from the returned score.
- seq_len = len(tokens)
- cum_logprob = result.scores[0] * (seq_len**options.length_penalty)
- avg_logprob = cum_logprob / (seq_len + 1)
-
- text = tokenizer.decode(tokens).strip()
- compression_ratio = get_compression_ratio(text)
-
- decode_result = (
- result,
- avg_logprob,
- temperature,
- compression_ratio,
- )
- all_results.append(decode_result)
-
- needs_fallback = False
-
- if options.compression_ratio_threshold is not None:
- if compression_ratio > options.compression_ratio_threshold:
- needs_fallback = True # too repetitive
-
- self.logger.debug(
- "Compression ratio threshold is not met with temperature %.1f (%f > %f)",
- temperature,
- compression_ratio,
- options.compression_ratio_threshold,
- )
- else:
- below_cr_threshold_results.append(decode_result)
-
- if (
- options.log_prob_threshold is not None
- and avg_logprob < options.log_prob_threshold
- ):
- needs_fallback = True # average log probability is too low
-
- self.logger.debug(
- "Log probability threshold is not met with temperature %.1f (%f < %f)",
- temperature,
- avg_logprob,
- options.log_prob_threshold,
- )
-
- if (
- options.no_speech_threshold is not None
- and result.no_speech_prob > options.no_speech_threshold
- and options.log_prob_threshold is not None
- and avg_logprob < options.log_prob_threshold
- ):
- needs_fallback = False # silence
-
- if not needs_fallback:
- break
- else:
- # all failed, select the result with the highest average log probability
- decode_result = max(
- below_cr_threshold_results or all_results, key=lambda x: x[1]
- )
- # to pass final temperature for prompt_reset_on_temperature
- decode_result = (
- decode_result[0],
- decode_result[1],
- temperature,
- decode_result[3],
- )
-
- return decode_result
-
- def get_prompt(
- self,
- tokenizer: Tokenizer,
- previous_tokens: List[int],
- without_timestamps: bool = False,
- prefix: Optional[str] = None,
- hotwords: Optional[str] = None,
- ) -> List[int]:
- prompt = []
-
- if previous_tokens or (hotwords and not prefix):
- prompt.append(tokenizer.sot_prev)
- if hotwords and not prefix:
- hotwords_tokens = tokenizer.encode(" " + hotwords.strip())
- if len(hotwords_tokens) >= self.max_length // 2:
- hotwords_tokens = hotwords_tokens[: self.max_length // 2 - 1]
- prompt.extend(hotwords_tokens)
- if previous_tokens:
- prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
-
- prompt.extend(tokenizer.sot_sequence)
-
- if without_timestamps:
- prompt.append(tokenizer.no_timestamps)
-
- if prefix:
- prefix_tokens = tokenizer.encode(" " + prefix.strip())
- if len(prefix_tokens) >= self.max_length // 2:
- prefix_tokens = prefix_tokens[: self.max_length // 2 - 1]
- if not without_timestamps:
- prompt.append(tokenizer.timestamp_begin)
- prompt.extend(prefix_tokens)
-
- return prompt
-
- def add_word_timestamps(
- self,
- segments: List[dict],
- tokenizer: Tokenizer,
- encoder_output: ctranslate2.StorageView,
- num_frames: int,
- prepend_punctuations: str,
- append_punctuations: str,
- last_speech_timestamp: float,
- ) -> float:
- if len(segments) == 0:
- return
-
- text_tokens = []
- text_tokens_per_segment = []
- for segment in segments:
- segment_tokens = [
- [token for token in subsegment["tokens"] if token < tokenizer.eot]
- for subsegment in segment
- ]
- text_tokens.append(list(itertools.chain.from_iterable(segment_tokens)))
- text_tokens_per_segment.append(segment_tokens)
-
- alignments = self.find_alignment(
- tokenizer, text_tokens, encoder_output, num_frames
- )
- median_max_durations = []
- for alignment in alignments:
- word_durations = np.array(
- [word["end"] - word["start"] for word in alignment]
- )
- word_durations = word_durations[word_durations.nonzero()]
- median_duration = (
- np.median(word_durations) if len(word_durations) > 0 else 0.0
- )
- median_duration = min(0.7, float(median_duration))
- max_duration = median_duration * 2
-
- # hack: truncate long words at sentence boundaries.
- # a better segmentation algorithm based on VAD should be able to replace this.
- if len(word_durations) > 0:
- sentence_end_marks = ".。!！?？"
- # ensure words at sentence boundaries
- # are not longer than twice the median word duration.
- for i in range(1, len(alignment)):
- if alignment[i]["end"] - alignment[i]["start"] > max_duration:
- if alignment[i]["word"] in sentence_end_marks:
- alignment[i]["end"] = alignment[i]["start"] + max_duration
- elif alignment[i - 1]["word"] in sentence_end_marks:
- alignment[i]["start"] = alignment[i]["end"] - max_duration
-
- merge_punctuations(alignment, prepend_punctuations, append_punctuations)
- median_max_durations.append((median_duration, max_duration))
-
- for segment_idx, segment in enumerate(segments):
- word_index = 0
- time_offset = segment[0]["start"]
- median_duration, max_duration = median_max_durations[segment_idx]
- for subsegment_idx, subsegment in enumerate(segment):
- saved_tokens = 0
- words = []
-
- while word_index < len(alignments[segment_idx]) and saved_tokens < len(
- text_tokens_per_segment[segment_idx][subsegment_idx]
- ):
- timing = alignments[segment_idx][word_index]
-
- if timing["word"]:
- words.append(
- dict(
- word=timing["word"],
- start=round(time_offset + timing["start"], 2),
- end=round(time_offset + timing["end"], 2),
- probability=timing["probability"],
- )
- )
-
- saved_tokens += len(timing["tokens"])
- word_index += 1
-
- # hack: truncate long words at segment boundaries.
- # a better segmentation algorithm based on VAD should be able to replace this.
- if len(words) > 0:
- # ensure the first and second word after a pause is not longer than
- # twice the median word duration.
- if words[0][
- "end"
- ] - last_speech_timestamp > median_duration * 4 and (
- words[0]["end"] - words[0]["start"] > max_duration
- or (
- len(words) > 1
- and words[1]["end"] - words[0]["start"] > max_duration * 2
- )
- ):
- if (
- len(words) > 1
- and words[1]["end"] - words[1]["start"] > max_duration
- ):
- boundary = max(
- words[1]["end"] / 2, words[1]["end"] - max_duration
- )
- words[0]["end"] = words[1]["start"] = boundary
- words[0]["start"] = max(0, words[0]["end"] - max_duration)
-
- # prefer the segment-level start timestamp if the first word is too long.
- if (
- subsegment["start"] < words[0]["end"]
- and subsegment["start"] - 0.5 > words[0]["start"]
- ):
- words[0]["start"] = max(
- 0,
- min(words[0]["end"] - median_duration, subsegment["start"]),
- )
- else:
- subsegment["start"] = words[0]["start"]
-
- # prefer the segment-level end timestamp if the last word is too long.
- if (
- subsegment["end"] > words[-1]["start"]
- and subsegment["end"] + 0.5 < words[-1]["end"]
- ):
- words[-1]["end"] = max(
- words[-1]["start"] + median_duration, subsegment["end"]
- )
- else:
- subsegment["end"] = words[-1]["end"]
-
- last_speech_timestamp = subsegment["end"]
- segments[segment_idx][subsegment_idx]["words"] = words
- return last_speech_timestamp
-
- def find_alignment(
- self,
- tokenizer: Tokenizer,
- text_tokens: List[int],
- encoder_output: ctranslate2.StorageView,
- num_frames: int,
- median_filter_width: int = 7,
- ) -> List[dict]:
- if len(text_tokens) == 0:
- return []
-
- results = self.model.align(
- encoder_output,
- tokenizer.sot_sequence,
- text_tokens,
- num_frames,
- median_filter_width=median_filter_width,
- )
- return_list = []
- for result, text_token in zip(results, text_tokens):
- text_token_probs = result.text_token_probs
- alignments = result.alignments
- text_indices = np.array([pair[0] for pair in alignments])
- time_indices = np.array([pair[1] for pair in alignments])
-
- words, word_tokens = tokenizer.split_to_word_tokens(
- text_token + [tokenizer.eot]
- )
- if len(word_tokens) <= 1:
- # return on eot only
- # >>> np.pad([], (1, 0))
- # array([0.])
- # This results in crashes when we lookup jump_times with float, like
- # IndexError: arrays used as indices must be of integer (or boolean) type
- return []
- word_boundaries = np.pad(
- np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)
- )
- if len(word_boundaries) <= 1:
- return []
-
- jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(
- bool
- )
- jump_times = time_indices[jumps] / self.tokens_per_second
- start_times = jump_times[word_boundaries[:-1]]
- end_times = jump_times[word_boundaries[1:]]
- word_probabilities = [
- np.mean(text_token_probs[i:j])
- for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
- ]
-
- return_list.append(
- [
- dict(
- word=word,
- tokens=tokens,
- start=start,
- end=end,
- probability=probability,
- )
- for word, tokens, start, end, probability in zip(
- words, word_tokens, start_times, end_times, word_probabilities
- )
- ]
- )
- return return_list
-
- def generate_segment_batched(
- self,
- features: torch.Tensor,
- tokenizer: Tokenizer,
- options: dict,
- ):
- batch_size = features.shape[0]
- all_tokens = []
- prompt_reset_since = 0
-
- if options["initial_prompt"] is not None:
- initial_prompt = " " + options["initial_prompt"].strip()
- initial_prompt_tokens = tokenizer.encode(initial_prompt)
- all_tokens.extend(initial_prompt_tokens)
- previous_tokens = all_tokens[prompt_reset_since:]
- prompt = self.get_prompt(
- tokenizer,
- previous_tokens,
- without_timestamps=options["without_timestamps"],
- prefix=options["prefix"],
- )
-
- encoder_output = self.encode(features)
-
- result = self.model.generate(
- encoder_output,
- [prompt] * batch_size,
- beam_size=options["beam_size"],
- patience=options["patience"],
- length_penalty=options["length_penalty"],
- max_length=self.max_length,
- suppress_blank=options["suppress_blank"],
- suppress_tokens=options["suppress_tokens"],
- return_scores=True,
- return_no_speech_prob=True,
- )
-
- output = []
- for res in result:
- output.append({})
- # return scores
- seq_len = len(res.sequences_ids[0])
- cum_logprob = res.scores[0] * (seq_len ** options["length_penalty"])
- output[-1]["avg_logprob"] = cum_logprob / (seq_len + 1)
-
- # return no speech prob
- output[-1]["no_speech_prob"] = res.no_speech_prob
- output[-1]["tokens"] = res.sequences_ids[0]
-
- return encoder_output, output
-
- def detect_language(self, audio: torch.Tensor):
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- segment = self.feature_extractor(audio, padding=True, to_cpu=to_cpu)[
- :, : self.feature_extractor.nb_max_frames
- ]
- encoder_output = self.encode(segment)
- results = self.model.detect_language(encoder_output)
- language_token, language_probability = results[0][0]
- language = language_token[2:-2]
- self.logger.info(
- f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio..."
- )
- all_language_probs = [(token[2:-2], prob) for (token, prob) in results[0]]
- return language, language_probability, all_language_probs
-
- def detect_language_multi_segment(
- self, audio: Union[str, BinaryIO, torch.Tensor], params: Optional[dict] = None
- ):
- """
- Detect language based on N highly-confident segments of a language.
- """
- # The threshold is used to decide if the audio is silence or not.
- # The default is 0.02 (2.0%) i.e, if more than 2.0% of the audio is silent,
- # the audio is considered as silence.
- if not params:
- params = {
- "multilingual": False,
- "speech_percentage_threshold": 0.02,
- "language_detection_segments": 4,
- "vad_filter": True,
- "vad_min_silence_duration": 2500,
- "language_threshold": 0.7,
- }
-
- if params.get("multilingual", False):
- logging.warning(
- "lang_id is not supported for multilingual audios, detecting the major language."
- )
-
- speech_percentage_threshold = params.get("speech_percentage_threshold", 0.02)
- language_threshold = params.get("language_threshold", 0.7)
- num_detection_segments = params.get("language_detection_segments", 4)
- vad_filter_enabled = params.get("vad_filter", True)
- vad_params = dict(
- min_silence_duration_ms=params.get("vad_min_silence_duration", 2500)
- )
-
- if vad_filter_enabled:
- vad_params = VadOptions(**vad_params)
-
- # decode audio if it is not decoded already
- sampling_rate = self.feature_extractor.sampling_rate
- if not isinstance(audio, torch.Tensor):
- audio: torch.Tensor = decode_audio(audio, sampling_rate=sampling_rate)
-
- # calculate duration of audio as number of seconds
- # audio.shape[0] is the number of samples in the audio
- # sampling_rate is the number of samples per second
- # if we divide the number of samples by the number of samples per second,
- # we get the duration in seconds
- duration = audio.shape[0] / sampling_rate
-
- # Check if vad is enabled, and collect voiced segments
- if vad_filter_enabled:
- # get chunks of audio that contain speech
- speech_chunks = get_speech_timestamps(audio, vad_params)
- # merge chunks of audio that contain speech into a single array
- audio = collect_chunks(audio, speech_chunks)
-
- # calculate new duration of audio without silence
- duration_vad = audio.shape[0] / sampling_rate
-
- logging.debug(
- f"Lang ID: VAD filter removed {duration - duration_vad} sec of audio"
- )
-
- # if the audio after VAD is less than 2% of the original audio, consider it as silence
- if duration_vad / duration < speech_percentage_threshold:
- return {"language_code": None, "language_confidence": 1.0}
-
- # update duration to be the duration after VAD
- duration = duration_vad
-
- # if the duration of the audio is less than 1 second, consider it as silence
- if duration < 1.0:
- return {"language_code": None, "language_confidence": 1.0}
-
- # number of feature frames in 30 seconds of audio is 3000
- nb_max_frames = self.feature_extractor.nb_max_frames
-
- # extract features from audio with padding (default)
- to_cpu = self.model.device == "cuda" and len(self.model.device_index) > 1
- features = self.feature_extractor(audio, to_cpu=to_cpu)
-
- # number of segments in the audio
- num_segments = features.shape[-1] // nb_max_frames
- # more number of segments than possible with the duration of file
- if num_detection_segments > num_segments:
- logging.warning(
- f"Lang ID: Can not have more segments, setting {num_segments} segments."
- )
- num_detection_segments = num_segments
-
- # create a list of indices to randomly select segments from
- indices = list(range(num_detection_segments))
-
- # fix seed to get deterministic results
- random.seed(0)
- random.shuffle(indices)
-
- detected_languages = []
- all_language_probabilities = defaultdict(list)
- confident_language_probabilities = defaultdict(list)
- num_confident_segments_per_language = defaultdict(int)
-
- # Iterate over the randomly selected indices of the segments.
- #
- # For each segment, extract features and detect language.
- #
- # If the language is confident, add it to the list of confident segments for that language.
- #
- # If the number of confident segments for a language
- # is greater than or equal to the number of detection segments,
- # return the language and the average probability of the language.
- #
- # If we are unable to get sufficient number of confident predcitions,
- # return the most frequently detected language with maximum probability.
- #
- # We need to get sufficient number of confident predictions per language, not in total.
-
- for i in indices:
- segment_features = features[:, i * nb_max_frames : (i + 1) * nb_max_frames]
- try:
- encoder_output = self.encode(segment_features)
- results = self.model.detect_language(encoder_output)[0]
-
- except ValueError as e: # or RuntimeError
- logging.error(f"Inference error:{e}")
-
- # results is the list of classes (languages) and their probabilities (descending),
- # for eg: [('<|de|>', 0.482177734375),('<|en|>', 0.283447265625),...]
-
- # take top language token and probability
- # and parse language token to strip out markers
- # for eg: '<|de|>' -> 'de'
-
- language_token = results[0][0]
- language = language_token[2:-2]
-
- language_probability = results[0][1]
-
- detected_languages.append(language)
- all_language_probabilities[language].append(language_probability)
-
- # only consider if the language prediction is confident
- if language_probability > language_threshold:
- num_confident_segments_per_language[language] += 1
-
- # Add language and probability to the list of languages when it is confident
- confident_language_probabilities[language].append(language_probability)
-
- # return the language when sufficient number of confident segments is achieved
- if (
- num_confident_segments_per_language[language]
- >= num_detection_segments
- ):
- # Considering the average probability of only confident segments
- mean = sum(confident_language_probabilities[language]) / len(
- confident_language_probabilities[language]
- )
- return {
- "language_code": language,
- "language_confidence": mean,
- }
-
- # if we are unable to get sufficient number of confident predictions,
- # return the most frequently detected language.
- # if there is a tie, return the one with maximum average probability.
- counter = Counter(detected_languages)
-
- # Define the key function to select frequent language with attached probabilities
- def key_func(language):
- # Calculate the frequency of the language
- frequency = counter[language]
-
- # Calculate the average probability of the language
- prob_avg = sum(all_language_probabilities[language]) / len(
- all_language_probabilities[language]
- )
-
- return frequency, prob_avg
-
- if detected_languages:
- # Use the key function to find the language with maximum frequency and probability
- max_language = max(detected_languages, key=key_func)
- max_probability = sum(all_language_probabilities[max_language]) / len(
- all_language_probabilities[max_language]
- )
-
- # Do additional checks for silence for non-confident case
- # calculate RMS amplitude and DC offset
- dc_offset = audio.mean()
- audio_minus_dc_offset = audio - dc_offset
- is_silent = (
- torch.all(audio.abs() < 0.01)
- or torch.sqrt(torch.mean(audio_minus_dc_offset**2)) < 0.01
- )
-
- if is_silent:
- return {"language_code": None, "language_confidence": 1.0}
-
- return {
- "language_code": max_language,
- "language_confidence": max_probability,
- }
-
- # Language is not detected for any segment and none of prev conditions met
- return {"language_code": None, "language_confidence": 1.0}
-
-
-def restore_speech_timestamps(
- segments: Iterable[Segment],
- speech_chunks: List[dict],
- sampling_rate: int,
-) -> Iterable[Segment]:
- ts_map = SpeechTimestampsMap(speech_chunks, sampling_rate)
-
- for segment in segments:
- if segment.words:
- words = []
- for word in segment.words:
- # Ensure the word start and end times are resolved to the same chunk.
- middle = (word.start + word.end) / 2
- chunk_index = ts_map.get_chunk_index(middle)
- word = word._replace(
- start=ts_map.get_original_time(word.start, chunk_index),
- end=ts_map.get_original_time(word.end, chunk_index),
- )
- words.append(word)
-
- segment = segment._replace(
- start=words[0].start,
- end=words[-1].end,
- words=words,
- )
-
- else:
- segment = segment._replace(
- start=ts_map.get_original_time(segment.start),
- end=ts_map.get_original_time(segment.end),
- )
-
- yield segment
-
-
-def get_ctranslate2_storage(segment: torch.Tensor) -> ctranslate2.StorageView:
- segment = segment.contiguous()
- segment = ctranslate2.StorageView.from_array(
- segment if segment.is_cuda else segment.numpy()
- ) # torch cpu tensors don't implement __array_interface__
- # https://github.com/pytorch/pytorch/issues/51156
- return segment
-
-
-def get_compression_ratio(text: str) -> float:
- text_bytes = text.encode("utf-8")
- return len(text_bytes) / len(zlib.compress(text_bytes))
-
-
-def get_suppressed_tokens(
- tokenizer: Tokenizer,
- suppress_tokens: Tuple[int],
-) -> Optional[List[int]]:
- if -1 in suppress_tokens:
- suppress_tokens = [t for t in suppress_tokens if t >= 0]
- suppress_tokens.extend(tokenizer.non_speech_tokens)
- elif suppress_tokens is None or len(suppress_tokens) == 0:
- suppress_tokens = [] # interpret empty string as an empty list
- else:
- assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
-
- suppress_tokens.extend(
- [
- tokenizer.transcribe,
- tokenizer.translate,
- tokenizer.sot,
- tokenizer.sot_prev,
- tokenizer.sot_lm,
- ]
- )
-
- return tuple(sorted(set(suppress_tokens)))
-
-
-def merge_punctuations(alignment: List[dict], prepended: str, appended: str) -> None:
- # merge prepended punctuations
- i = len(alignment) - 2
- j = len(alignment) - 1
- while i >= 0:
- previous = alignment[i]
- following = alignment[j]
- if previous["word"].startswith(" ") and previous["word"].strip() in prepended:
- # prepend it to the following word
- following["word"] = previous["word"] + following["word"]
- if "tokens" in alignment[0].keys():
- following["tokens"] = previous["tokens"] + following["tokens"]
- previous["tokens"] = []
- previous["word"] = ""
-
- else:
- j = i
- i -= 1
-
- # merge appended punctuations
- i = 0
- j = 1
- while j < len(alignment):
- previous = alignment[i]
- following = alignment[j]
- if not previous["word"].endswith(" ") and following["word"] in appended:
- # append it to the previous word
- previous["word"] = previous["word"] + following["word"]
- if "tokens" in alignment[0].keys():
- previous["tokens"] = previous["tokens"] + following["tokens"]
- following["tokens"] = []
- following["word"] = ""
-
- else:
- i = j
- j += 1
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/utils.py b/whisper_pipeline/faster-whisper-main/faster_whisper/utils.py
deleted file mode 100644
index 481bd748ec0daec58681289c38559d5968e6630d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/utils.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import logging
-import os
-import re
-
-from typing import List, Optional
-
-import huggingface_hub
-import requests
-
-from tqdm.auto import tqdm
-
-_MODELS = {
- "tiny.en": "Systran/faster-whisper-tiny.en",
- "tiny": "Systran/faster-whisper-tiny",
- "base.en": "Systran/faster-whisper-base.en",
- "base": "Systran/faster-whisper-base",
- "small.en": "Systran/faster-whisper-small.en",
- "small": "Systran/faster-whisper-small",
- "medium.en": "Systran/faster-whisper-medium.en",
- "medium": "Systran/faster-whisper-medium",
- "large-v1": "Systran/faster-whisper-large-v1",
- "large-v2": "Systran/faster-whisper-large-v2",
- "large-v3": "Systran/faster-whisper-large-v3",
- "large": "Systran/faster-whisper-large-v3",
- "distil-large-v2": "Systran/faster-distil-whisper-large-v2",
- "distil-medium.en": "Systran/faster-distil-whisper-medium.en",
- "distil-small.en": "Systran/faster-distil-whisper-small.en",
- "distil-large-v3": "Systran/faster-distil-whisper-large-v3",
-}
-
-
-def available_models() -> List[str]:
- """Returns the names of available models."""
- return list(_MODELS.keys())
-
-
-def get_assets_path():
- """Returns the path to the assets directory."""
- return os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
-
-
-def get_logger():
- """Returns the module logger."""
- return logging.getLogger("faster_whisper")
-
-
-def download_model(
- size_or_id: str,
- output_dir: Optional[str] = None,
- local_files_only: bool = False,
- cache_dir: Optional[str] = None,
-):
- """Downloads a CTranslate2 Whisper model from the Hugging Face Hub.
-
- Args:
- size_or_id: Size of the model to download from https://huggingface.co/Systran
- (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en,
- distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2,
- distil-large-v3), or a CTranslate2-converted model ID from the Hugging Face Hub
- (e.g. Systran/faster-whisper-large-v3).
- output_dir: Directory where the model should be saved. If not set, the model is saved in
- the cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the local
- cached file if it exists.
- cache_dir: Path to the folder where cached files are stored.
-
- Returns:
- The path to the downloaded model.
-
- Raises:
- ValueError: if the model size is invalid.
- """
- if re.match(r".*/.*", size_or_id):
- repo_id = size_or_id
- else:
- repo_id = _MODELS.get(size_or_id)
- if repo_id is None:
- raise ValueError(
- "Invalid model size '%s', expected one of: %s"
- % (size_or_id, ", ".join(_MODELS.keys()))
- )
-
- allow_patterns = [
- "config.json",
- "preprocessor_config.json",
- "model.bin",
- "tokenizer.json",
- "vocabulary.*",
- ]
-
- kwargs = {
- "local_files_only": local_files_only,
- "allow_patterns": allow_patterns,
- "tqdm_class": disabled_tqdm,
- }
-
- if output_dir is not None:
- kwargs["local_dir"] = output_dir
- kwargs["local_dir_use_symlinks"] = False
-
- if cache_dir is not None:
- kwargs["cache_dir"] = cache_dir
-
- try:
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
- except (
- huggingface_hub.utils.HfHubHTTPError,
- requests.exceptions.ConnectionError,
- ) as exception:
- logger = get_logger()
- logger.warning(
- "An error occured while synchronizing the model %s from the Hugging Face Hub:\n%s",
- repo_id,
- exception,
- )
- logger.warning(
- "Trying to load the model directly from the local cache, if it exists."
- )
-
- kwargs["local_files_only"] = True
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
-
-
-def format_timestamp(
- seconds: float,
- always_include_hours: bool = False,
- decimal_marker: str = ".",
-) -> str:
- assert seconds >= 0, "non-negative timestamp expected"
- milliseconds = round(seconds * 1000.0)
-
- hours = milliseconds // 3_600_000
- milliseconds -= hours * 3_600_000
-
- minutes = milliseconds // 60_000
- milliseconds -= minutes * 60_000
-
- seconds = milliseconds // 1_000
- milliseconds -= seconds * 1_000
-
- hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
- return (
- f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
- )
-
-
-class disabled_tqdm(tqdm):
- def __init__(self, *args, **kwargs):
- kwargs["disable"] = True
- super().__init__(*args, **kwargs)
-
-
-def get_end(segments: List[dict]) -> Optional[float]:
- return next(
- (w["end"] for s in reversed(segments) for w in reversed(s["words"])),
- segments[-1]["end"] if segments else None,
- )
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/vad.py b/whisper_pipeline/faster-whisper-main/faster_whisper/vad.py
deleted file mode 100644
index 3881fd8185b439ef714af1f28b4e7a36f4fe24d6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/vad.py
+++ /dev/null
@@ -1,596 +0,0 @@
-import bisect
-import functools
-import os
-
-from abc import ABC
-from collections.abc import Callable
-from typing import List, NamedTuple, Optional, Union
-
-import numpy as np
-import torch
-
-from pyannote.audio.core.io import AudioFile
-from pyannote.audio.pipelines import VoiceActivityDetection
-from pyannote.audio.pipelines.utils import PipelineModel
-from pyannote.core import Annotation, Segment, SlidingWindowFeature
-
-from faster_whisper.utils import get_assets_path
-
-
-# The code below is adapted from https://github.com/snakers4/silero-vad.
-class VadOptions(NamedTuple):
- """VAD options.
-
- Attributes:
- threshold: Speech threshold. Silero VAD outputs speech probabilities for each audio chunk,
- probabilities ABOVE this value are considered as SPEECH. It is better to tune this
- parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
- min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out.
- max_speech_duration_s: Maximum duration of speech chunks in seconds. Chunks longer
- than max_speech_duration_s will be split at the timestamp of the last silence that
- lasts more than 100ms (if any), to prevent aggressive cutting. Otherwise, they will be
- split aggressively just before max_speech_duration_s.
- min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
- before separating it
- speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
- """
-
- threshold: float = 0.5
- min_speech_duration_ms: int = 250
- max_speech_duration_s: float = float("inf")
- min_silence_duration_ms: int = 2000
- speech_pad_ms: int = 400
-
-
-def get_speech_timestamps(
- audio: torch.Tensor,
- vad_options: Optional[VadOptions] = None,
- **kwargs,
-) -> List[dict]:
- """This method is used for splitting long audios into speech chunks using silero VAD.
-
- Args:
- audio: One dimensional float array.
- vad_options: Options for VAD processing.
- kwargs: VAD options passed as keyword arguments for backward compatibility.
-
- Returns:
- List of dicts containing begin and end samples of each speech chunk.
- """
- if vad_options is None:
- vad_options = VadOptions(**kwargs)
-
- threshold = vad_options.threshold
- min_speech_duration_ms = vad_options.min_speech_duration_ms
- max_speech_duration_s = vad_options.max_speech_duration_s
- min_silence_duration_ms = vad_options.min_silence_duration_ms
- window_size_samples = 512
- speech_pad_ms = vad_options.speech_pad_ms
- sampling_rate = 16000
- min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
- speech_pad_samples = sampling_rate * speech_pad_ms / 1000
- max_speech_samples = (
- sampling_rate * max_speech_duration_s
- - window_size_samples
- - 2 * speech_pad_samples
- )
- min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
- min_silence_samples_at_max_speech = sampling_rate * 98 / 1000
-
- audio_length_samples = len(audio)
-
- model = get_vad_model()
- state, context = model.get_initial_states(batch_size=1)
-
- speech_probs = []
- for current_start_sample in range(0, audio_length_samples, window_size_samples):
- chunk = audio[current_start_sample : current_start_sample + window_size_samples]
- if len(chunk) < window_size_samples:
- chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
- speech_prob, state, context = model(chunk, state, context, sampling_rate)
- speech_probs.append(speech_prob)
-
- triggered = False
- speeches = []
- current_speech = {}
- neg_threshold = threshold - 0.15
-
- # to save potential segment end (and tolerate some silence)
- temp_end = 0
- # to save potential segment limits in case of maximum segment size reached
- prev_end = next_start = 0
-
- for i, speech_prob in enumerate(speech_probs):
- if (speech_prob >= threshold) and temp_end:
- temp_end = 0
- if next_start < prev_end:
- next_start = window_size_samples * i
-
- if (speech_prob >= threshold) and not triggered:
- triggered = True
- current_speech["start"] = window_size_samples * i
- continue
-
- if (
- triggered
- and (window_size_samples * i) - current_speech["start"] > max_speech_samples
- ):
- if prev_end:
- current_speech["end"] = prev_end
- speeches.append(current_speech)
- current_speech = {}
- # previously reached silence (< neg_thres) and is still not speech (< thres)
- if next_start < prev_end:
- triggered = False
- else:
- current_speech["start"] = next_start
- prev_end = next_start = temp_end = 0
- else:
- current_speech["end"] = window_size_samples * i
- speeches.append(current_speech)
- current_speech = {}
- prev_end = next_start = temp_end = 0
- triggered = False
- continue
-
- if (speech_prob < neg_threshold) and triggered:
- if not temp_end:
- temp_end = window_size_samples * i
- # condition to avoid cutting in very short silence
- if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech:
- prev_end = temp_end
- if (window_size_samples * i) - temp_end < min_silence_samples:
- continue
- else:
- current_speech["end"] = temp_end
- if (
- current_speech["end"] - current_speech["start"]
- ) > min_speech_samples:
- speeches.append(current_speech)
- current_speech = {}
- prev_end = next_start = temp_end = 0
- triggered = False
- continue
-
- if (
- current_speech
- and (audio_length_samples - current_speech["start"]) > min_speech_samples
- ):
- current_speech["end"] = audio_length_samples
- speeches.append(current_speech)
-
- for i, speech in enumerate(speeches):
- if i == 0:
- speech["start"] = int(max(0, speech["start"] - speech_pad_samples))
- if i != len(speeches) - 1:
- silence_duration = speeches[i + 1]["start"] - speech["end"]
- if silence_duration < 2 * speech_pad_samples:
- speech["end"] += int(silence_duration // 2)
- speeches[i + 1]["start"] = int(
- max(0, speeches[i + 1]["start"] - silence_duration // 2)
- )
- else:
- speech["end"] = int(
- min(audio_length_samples, speech["end"] + speech_pad_samples)
- )
- speeches[i + 1]["start"] = int(
- max(0, speeches[i + 1]["start"] - speech_pad_samples)
- )
- else:
- speech["end"] = int(
- min(audio_length_samples, speech["end"] + speech_pad_samples)
- )
-
- return speeches
-
-
-def collect_chunks(audio: torch.Tensor, chunks: List[dict]) -> torch.Tensor:
- """Collects and concatenates audio chunks."""
- if not chunks:
- return torch.tensor([], dtype=torch.float32)
-
- return torch.cat([audio[chunk["start"] : chunk["end"]] for chunk in chunks])
-
-
-class SpeechTimestampsMap:
- """Helper class to restore original speech timestamps."""
-
- def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
- self.sampling_rate = sampling_rate
- self.time_precision = time_precision
- self.chunk_end_sample = []
- self.total_silence_before = []
-
- previous_end = 0
- silent_samples = 0
-
- for chunk in chunks:
- silent_samples += chunk["start"] - previous_end
- previous_end = chunk["end"]
-
- self.chunk_end_sample.append(chunk["end"] - silent_samples)
- self.total_silence_before.append(silent_samples / sampling_rate)
-
- def get_original_time(
- self,
- time: float,
- chunk_index: Optional[int] = None,
- ) -> float:
- if chunk_index is None:
- chunk_index = self.get_chunk_index(time)
-
- total_silence_before = self.total_silence_before[chunk_index]
- return round(total_silence_before + time, self.time_precision)
-
- def get_chunk_index(self, time: float) -> int:
- sample = int(time * self.sampling_rate)
- return min(
- bisect.bisect(self.chunk_end_sample, sample),
- len(self.chunk_end_sample) - 1,
- )
-
-
-@functools.lru_cache
-def get_vad_model():
- """Returns the VAD model instance."""
- path = os.path.join(get_assets_path(), "silero_vad.onnx")
- return SileroVADModel(path)
-
-
-class SileroVADModel:
- def __init__(self, path):
- try:
- import onnxruntime
- except ImportError as e:
- raise RuntimeError(
- "Applying the VAD filter requires the onnxruntime package"
- ) from e
-
- opts = onnxruntime.SessionOptions()
- opts.inter_op_num_threads = 1
- opts.intra_op_num_threads = 1
- opts.log_severity_level = 4
-
- self.session = onnxruntime.InferenceSession(
- path,
- providers=["CPUExecutionProvider"],
- sess_options=opts,
- )
-
- def get_initial_states(self, batch_size: int):
- state = np.zeros((2, batch_size, 128), dtype=np.float32)
- context = np.zeros((batch_size, 64), dtype=np.float32)
- return state, context
-
- def __call__(self, x, state, context, sr: int):
- if len(x.shape) == 1:
- x = np.expand_dims(x, 0)
- if len(x.shape) > 2:
- raise ValueError(
- f"Too many dimensions for input audio chunk {len(x.shape)}"
- )
- if sr / x.shape[1] > 31.25:
- raise ValueError("Input audio chunk is too short")
-
- x = np.concatenate([context, x], axis=1)
-
- ort_inputs = {
- "input": x,
- "state": state,
- "sr": np.array(sr, dtype="int64"),
- }
-
- out, state = self.session.run(None, ort_inputs)
- context = x[..., -64:]
-
- return out, state, context
-
-
-# BSD 2-Clause License
-
-# Copyright (c) 2024, Max Bain
-
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-
-# 1. Redistributions of source code must retain the above copyright notice, this
-# list of conditions and the following disclaimer.
-
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-# The code below is copied from whisper-x (https://github.com/m-bain/whisperX)
-# and adapted for faster_whisper.
-class SegmentX:
- def __init__(self, start, end, speaker=None):
- self.start = start
- self.end = end
- self.speaker = speaker
-
-
-class VoiceActivitySegmentation(VoiceActivityDetection, ABC):
- """Pipeline wrapper class for Voice Activity Segmentation based on VAD scores."""
-
- def __init__(
- self,
- segmentation: PipelineModel = "pyannote/segmentation",
- device: Optional[Union[str, torch.device]] = None,
- fscore: bool = False,
- use_auth_token: Optional[str] = None,
- **inference_kwargs,
- ):
- """Initialize the pipeline with the model name and the optional device.
-
- Args:
- dict parameters of VoiceActivityDetection class from pyannote:
- segmentation (PipelineModel): Loaded model name.
- device (torch.device or None): Device to perform the segmentation.
- fscore (bool): Flag indicating whether to compute F-score during inference.
- use_auth_token (str or None): Optional authentication token for model access.
- inference_kwargs (dict): Additional arguments from VoiceActivityDetection pipeline.
- """
- super().__init__(
- segmentation=segmentation,
- device=device,
- fscore=fscore,
- use_auth_token=use_auth_token,
- **inference_kwargs,
- )
-
- def apply(
- self, file: AudioFile, hook: Optional[Callable] = None
- ) -> SlidingWindowFeature:
- """Apply voice activity detection on the audio file.
-
- Args:
- file (AudioFile): Processed file.
- hook (callable): Hook called with signature: hook("step_name", step_artefact, file=file)
-
- Returns:
- segmentations (SlidingWindowFeature): Voice activity segmentation.
- """
- # setup hook (e.g. for debugging purposes)
- hook = self.setup_hook(file, hook=hook)
-
- # apply segmentation model if needed
- # output shape is (num_chunks, num_frames, 1)
- if self.training:
- if self.CACHED_SEGMENTATION in file:
- segmentations = file[self.CACHED_SEGMENTATION]
- else:
- segmentations = self._segmentation(file)
- file[self.CACHED_SEGMENTATION] = segmentations
- else:
- segmentations: SlidingWindowFeature = self._segmentation(file)
-
- return segmentations
-
-
-class BinarizeVadScores:
- """Binarize detection scores using hysteresis thresholding.
-
- Reference:
- Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
- RNN-based Voice Activity Detection", InterSpeech 2015.
-
- Modified by Max Bain to include WhisperX's min-cut operation
- https://arxiv.org/abs/2303.00747
-
- """
-
- def __init__(
- self,
- onset: float = 0.5,
- offset: Optional[float] = None,
- min_duration_on: float = 0.0,
- min_duration_off: float = 0.0,
- pad_onset: float = 0.0,
- pad_offset: float = 0.0,
- max_duration: float = float("inf"),
- ):
- """Initializes the parameters for Binarizing the VAD scores.
-
- Args:
- onset (float, optional):
- Onset threshold. Defaults to 0.5.
- offset (float, optional):
- Offset threshold. Defaults to `onset`.
- min_duration_on (float, optional):
- Remove active regions shorter than that many seconds. Defaults to 0s.
- min_duration_off (float, optional):
- Fill inactive regions shorter than that many seconds. Defaults to 0s.
- pad_onset (float, optional):
- Extend active regions by moving their start time by that many seconds.
- Defaults to 0s.
- pad_offset (float, optional):
- Extend active regions by moving their end time by that many seconds.
- Defaults to 0s.
- max_duration (float):
- The maximum length of an active segment.
- """
- super().__init__()
-
- self.onset = onset
- self.offset = offset or onset
-
- self.pad_onset = pad_onset
- self.pad_offset = pad_offset
-
- self.min_duration_on = min_duration_on
- self.min_duration_off = min_duration_off
-
- self.max_duration = max_duration
-
- def __get_active_regions(self, scores: SlidingWindowFeature) -> Annotation:
- """Extract active regions from VAD scores.
-
- Args:
- scores (SlidingWindowFeature): Detection scores.
-
- Returns:
- active (Annotation): Active regions.
- """
- num_frames, num_classes = scores.data.shape
- frames = scores.sliding_window
- timestamps = [frames[i].middle for i in range(num_frames)]
- # annotation meant to store 'active' regions
- active = Annotation()
- for k, k_scores in enumerate(scores.data.T):
- label = k if scores.labels is None else scores.labels[k]
-
- # initial state
- start = timestamps[0]
- is_active = k_scores[0] > self.onset
- curr_scores = [k_scores[0]]
- curr_timestamps = [start]
- t = start
- # optionally add `strict=False` for python 3.10 or later
- for t, y in zip(timestamps[1:], k_scores[1:]):
- # currently active
- if is_active:
- curr_duration = t - start
- if curr_duration > self.max_duration:
- search_after = len(curr_scores) // 2
- # divide segment
- min_score_div_idx = search_after + np.argmin(
- curr_scores[search_after:]
- )
- min_score_t = curr_timestamps[min_score_div_idx]
- region = Segment(
- start - self.pad_onset, min_score_t + self.pad_offset
- )
- active[region, k] = label
- start = curr_timestamps[min_score_div_idx]
- curr_scores = curr_scores[min_score_div_idx + 1 :]
- curr_timestamps = curr_timestamps[min_score_div_idx + 1 :]
- # switching from active to inactive
- elif y < self.offset:
- region = Segment(start - self.pad_onset, t + self.pad_offset)
- active[region, k] = label
- start = t
- is_active = False
- curr_scores = []
- curr_timestamps = []
- curr_scores.append(y)
- curr_timestamps.append(t)
- # currently inactive
- else:
- # switching from inactive to active
- if y > self.onset:
- start = t
- is_active = True
-
- # if active at the end, add final region
- if is_active:
- region = Segment(start - self.pad_onset, t + self.pad_offset)
- active[region, k] = label
-
- return active
-
- def __call__(self, scores: SlidingWindowFeature) -> Annotation:
- """Binarize detection scores.
-
- Args:
- scores (SlidingWindowFeature): Detection scores.
-
- Returns:
- active (Annotation): Binarized scores.
- """
- active = self.__get_active_regions(scores)
- # because of padding, some active regions might be overlapping: merge them.
- # also: fill same speaker gaps shorter than min_duration_off
- if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
- if self.max_duration < float("inf"):
- raise NotImplementedError("This would break current max_duration param")
- active = active.support(collar=self.min_duration_off)
-
- # remove tracks shorter than min_duration_on
- if self.min_duration_on > 0:
- for segment, track in list(active.itertracks()):
- if segment.duration < self.min_duration_on:
- del active[segment, track]
-
- return active
-
-
-def merge_chunks(
- segments,
- chunk_length,
- onset: float = 0.5,
- offset: Optional[float] = None,
- edge_padding: float = 0.1,
-):
- """
- Merge operation described in whisper-x paper
- """
- curr_end = 0
- merged_segments = []
- seg_idxs = []
- speaker_idxs = []
-
- assert chunk_length > 0
- binarize = BinarizeVadScores(max_duration=chunk_length, onset=onset, offset=offset)
- segments = binarize(segments)
- segments_list = []
- for speech_turn in segments.get_timeline():
- segments_list.append(
- SegmentX(
- max(0.0, speech_turn.start - edge_padding),
- speech_turn.end + edge_padding,
- "UNKNOWN",
- )
- ) # 100ms edge padding to account for edge errors
-
- if len(segments_list) == 0:
- print("No active speech found in audio")
- return []
-
- # Make sur the starting point is the start of the segment.
- curr_start = segments_list[0].start
-
- for idx, seg in enumerate(segments_list):
- # if any segment start timing is less than previous segment end timing,
- # reset the edge padding. Similarly for end timing.
- if idx > 0:
- if seg.start < segments_list[idx - 1].end:
- seg.start += edge_padding
- if idx < len(segments_list) - 1:
- if seg.end > segments_list[idx + 1].start:
- seg.end -= edge_padding
-
- if seg.end - curr_start > chunk_length and curr_end - curr_start > 0:
- merged_segments.append(
- {
- "start": curr_start,
- "end": curr_end,
- "segments": seg_idxs,
- }
- )
- curr_start = seg.start
- seg_idxs = []
- speaker_idxs = []
- curr_end = seg.end
- seg_idxs.append((seg.start, seg.end))
- speaker_idxs.append(seg.speaker)
- # add final
- merged_segments.append(
- {
- "start": curr_start,
- "end": curr_end,
- "segments": seg_idxs,
- }
- )
- return merged_segments
diff --git a/whisper_pipeline/faster-whisper-main/faster_whisper/version.py b/whisper_pipeline/faster-whisper-main/faster_whisper/version.py
deleted file mode 100644
index 65eaef429fb3048ee8d105fe67da4da43f5a2324..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/faster_whisper/version.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Version information."""
-
-__version__ = "1.0.3"
diff --git a/whisper_pipeline/faster-whisper-main/requirements.conversion.txt b/whisper_pipeline/faster-whisper-main/requirements.conversion.txt
deleted file mode 100644
index 56fdf5f004baa48a8427cd1085e73244483d1b09..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/requirements.conversion.txt
+++ /dev/null
@@ -1 +0,0 @@
-transformers[torch]>=4.23
diff --git a/whisper_pipeline/faster-whisper-main/requirements.txt b/whisper_pipeline/faster-whisper-main/requirements.txt
deleted file mode 100644
index 0c106e5e136d9c2931b5907d9aeb608b4673ce1c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-ctranslate2>=4.0,<5
-huggingface_hub>=0.13
-tokenizers>=0.13,<1
-onnxruntime>=1.14,<2
-pyannote-audio
-torch
-torchaudio
-tqdm
\ No newline at end of file
diff --git a/whisper_pipeline/faster-whisper-main/setup.cfg b/whisper_pipeline/faster-whisper-main/setup.cfg
deleted file mode 100644
index bf2da8684c8b2df8b5d2c9b732ed23018e0763a9..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/setup.cfg
+++ /dev/null
@@ -1,9 +0,0 @@
-[flake8]
-max-line-length = 100
-ignore =
- E203,
- W503,
-
-[isort]
-profile=black
-lines_between_types=1
diff --git a/whisper_pipeline/faster-whisper-main/setup.py b/whisper_pipeline/faster-whisper-main/setup.py
deleted file mode 100644
index 782f1b275264cccb501f36be8e0c3054fdd11c3a..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/setup.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import os
-
-from setuptools import find_packages, setup
-
-base_dir = os.path.dirname(os.path.abspath(__file__))
-
-
-def get_long_description():
- readme_path = os.path.join(base_dir, "README.md")
- with open(readme_path, encoding="utf-8") as readme_file:
- return readme_file.read()
-
-
-def get_project_version():
- version_path = os.path.join(base_dir, "faster_whisper", "version.py")
- version = {}
- with open(version_path, encoding="utf-8") as fp:
- exec(fp.read(), version)
- return version["__version__"]
-
-
-def get_requirements(path):
- with open(path, encoding="utf-8") as requirements:
- return [requirement.strip() for requirement in requirements]
-
-
-install_requires = get_requirements(os.path.join(base_dir, "requirements.txt"))
-conversion_requires = get_requirements(
- os.path.join(base_dir, "requirements.conversion.txt")
-)
-
-setup(
- name="faster-whisper",
- version=get_project_version(),
- license="MIT",
- description="Faster Whisper transcription with CTranslate2",
- long_description=get_long_description(),
- long_description_content_type="text/markdown",
- author="Guillaume Klein",
- url="https://github.com/SYSTRAN/faster-whisper",
- classifiers=[
- "Development Status :: 4 - Beta",
- "Intended Audience :: Developers",
- "Intended Audience :: Science/Research",
- "License :: OSI Approved :: MIT License",
- "Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3 :: Only",
- "Programming Language :: Python :: 3.8",
- "Programming Language :: Python :: 3.9",
- "Programming Language :: Python :: 3.10",
- "Programming Language :: Python :: 3.11",
- "Topic :: Scientific/Engineering :: Artificial Intelligence",
- ],
- keywords="openai whisper speech ctranslate2 inference quantization transformer",
- python_requires=">=3.8",
- install_requires=install_requires,
- extras_require={
- "conversion": conversion_requires,
- "dev": [
- "black==23.*",
- "flake8==6.*",
- "isort==5.*",
- "pytest==7.*",
- ],
- },
- packages=find_packages(),
- include_package_data=True,
-)
diff --git a/whisper_pipeline/faster-whisper-main/tests/conftest.py b/whisper_pipeline/faster-whisper-main/tests/conftest.py
deleted file mode 100644
index 0c0f42483b021a80c00bcda32e35f1107058f72b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/tests/conftest.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-
-import pytest
-
-
-@pytest.fixture
-def data_dir():
- return os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
-
-
-@pytest.fixture
-def jfk_path(data_dir):
- return os.path.join(data_dir, "jfk.flac")
-
-
-@pytest.fixture
-def physcisworks_path(data_dir):
- return os.path.join(data_dir, "physicsworks.wav")
diff --git a/whisper_pipeline/faster-whisper-main/tests/data/jfk.flac b/whisper_pipeline/faster-whisper-main/tests/data/jfk.flac
deleted file mode 100644
index 8cf66fea45dd2f1ac2b182a37a2208657c11e8af..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/tests/data/jfk.flac
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63a4b1e4c1dc655ac70961ffbf518acd249df237e5a0152faae9a4a836949715
-size 1152693
diff --git a/whisper_pipeline/faster-whisper-main/tests/data/physicsworks.wav b/whisper_pipeline/faster-whisper-main/tests/data/physicsworks.wav
deleted file mode 100644
index ee71bd791592ccaf0084099e8ba81c967f01035f..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/tests/data/physicsworks.wav
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:329d029898a53fcc1d79be9d38f23268704bd896c65928719e41593b1d9c5578
-size 6505148
diff --git a/whisper_pipeline/faster-whisper-main/tests/data/stereo_diarization.wav b/whisper_pipeline/faster-whisper-main/tests/data/stereo_diarization.wav
deleted file mode 100644
index 3f5ae75dac40be2b33c2c76ac291a4d147b0b47d..0000000000000000000000000000000000000000
Binary files a/whisper_pipeline/faster-whisper-main/tests/data/stereo_diarization.wav and /dev/null differ
diff --git a/whisper_pipeline/faster-whisper-main/tests/test_transcribe.py b/whisper_pipeline/faster-whisper-main/tests/test_transcribe.py
deleted file mode 100644
index 96eb68c3331a11dcdfbe52192ab3bd1732bda097..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/tests/test_transcribe.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import os
-
-from faster_whisper import BatchedInferencePipeline, WhisperModel, decode_audio
-from faster_whisper.tokenizer import Tokenizer
-from faster_whisper.transcribe import get_suppressed_tokens
-
-
-def test_supported_languages():
- model = WhisperModel("tiny.en")
- assert model.supported_languages == ["en"]
-
-
-def test_transcribe(jfk_path):
- model = WhisperModel("tiny")
- segments, info = model.transcribe(jfk_path, word_timestamps=True)
- assert info.all_language_probs is not None
-
- assert info.language == "en"
- assert info.language_probability > 0.9
- assert info.duration == 11
-
- # Get top language info from all results, which should match the
- # already existing metadata
- top_lang, top_lang_score = info.all_language_probs[0]
- assert info.language == top_lang
- assert abs(info.language_probability - top_lang_score) < 1e-16
-
- segments = list(segments)
-
- assert len(segments) == 1
-
- segment = segments[0]
-
- assert segment.text == (
- " And so my fellow Americans ask not what your country can do for you, "
- "ask what you can do for your country."
- )
-
- assert segment.text == "".join(word.word for word in segment.words)
- assert segment.start == segment.words[0].start
- assert segment.end == segment.words[-1].end
- batched_model = BatchedInferencePipeline(model=model, use_vad_model=False)
- result, info = batched_model.transcribe(jfk_path, word_timestamps=True)
- assert info.language == "en"
- assert info.language_probability > 0.7
- segments = []
- for segment in result:
- segments.append(
- {"start": segment.start, "end": segment.end, "text": segment.text}
- )
-
- assert len(segments) == 1
- assert segment.text == (
- " And so my fellow Americans ask not what your country can do for you, "
- "ask what you can do for your country."
- )
-
-
-def test_batched_transcribe(physcisworks_path):
- model = WhisperModel("tiny")
- batched_model = BatchedInferencePipeline(model=model)
- result, info = batched_model.transcribe(physcisworks_path, batch_size=16)
- assert info.language == "en"
- assert info.language_probability > 0.7
- segments = []
- for segment in result:
- segments.append(
- {"start": segment.start, "end": segment.end, "text": segment.text}
- )
- # number of near 30 sec segments
- assert len(segments) == 8
-
- result, info = batched_model.transcribe(
- physcisworks_path,
- batch_size=16,
- without_timestamps=False,
- word_timestamps=True,
- )
- segments = []
- for segment in result:
- assert segment.words is not None
- segments.append(
- {"start": segment.start, "end": segment.end, "text": segment.text}
- )
- assert len(segments) > 8
-
-
-def test_prefix_with_timestamps(jfk_path):
- model = WhisperModel("tiny")
- segments, _ = model.transcribe(jfk_path, prefix="And so my fellow Americans")
- segments = list(segments)
-
- assert len(segments) == 1
-
- segment = segments[0]
-
- assert segment.text == (
- " And so my fellow Americans ask not what your country can do for you, "
- "ask what you can do for your country."
- )
-
- assert segment.start == 0
- assert 10 < segment.end < 11
-
-
-def test_vad(jfk_path):
- model = WhisperModel("tiny")
- segments, info = model.transcribe(
- jfk_path,
- vad_filter=True,
- vad_parameters=dict(min_silence_duration_ms=500, speech_pad_ms=200),
- )
- segments = list(segments)
-
- assert len(segments) == 1
- segment = segments[0]
-
- assert segment.text == (
- " And so my fellow Americans ask not what your country can do for you, "
- "ask what you can do for your country."
- )
-
- assert 0 < segment.start < 1
- assert 10 < segment.end < 11
-
- assert info.vad_options.min_silence_duration_ms == 500
- assert info.vad_options.speech_pad_ms == 200
-
-
-def test_stereo_diarization(data_dir):
- model = WhisperModel("tiny")
-
- audio_path = os.path.join(data_dir, "stereo_diarization.wav")
- left, right = decode_audio(audio_path, split_stereo=True)
-
- segments, _ = model.transcribe(left)
- transcription = "".join(segment.text for segment in segments).strip()
- assert transcription == (
- "He began a confused complaint against the wizard, "
- "who had vanished behind the curtain on the left."
- )
-
- segments, _ = model.transcribe(right)
- transcription = "".join(segment.text for segment in segments).strip()
- assert transcription == "The horizon seems extremely distant."
-
-
-def test_multisegment_lang_id(physcisworks_path):
- model = WhisperModel("tiny")
- language_info = model.detect_language_multi_segment(physcisworks_path)
- assert language_info["language_code"] == "en"
- assert language_info["language_confidence"] > 0.8
-
-
-def test_suppressed_tokens_minus_1():
- model = WhisperModel("tiny.en")
-
- tokenizer = Tokenizer(model.hf_tokenizer, False)
- tokens = get_suppressed_tokens(tokenizer, [-1])
- assert tokens == (
- 1,
- 2,
- 7,
- 8,
- 9,
- 10,
- 14,
- 25,
- 26,
- 27,
- 28,
- 29,
- 31,
- 58,
- 59,
- 60,
- 61,
- 62,
- 63,
- 90,
- 91,
- 92,
- 93,
- 357,
- 366,
- 438,
- 532,
- 685,
- 705,
- 796,
- 930,
- 1058,
- 1220,
- 1267,
- 1279,
- 1303,
- 1343,
- 1377,
- 1391,
- 1635,
- 1782,
- 1875,
- 2162,
- 2361,
- 2488,
- 3467,
- 4008,
- 4211,
- 4600,
- 4808,
- 5299,
- 5855,
- 6329,
- 7203,
- 9609,
- 9959,
- 10563,
- 10786,
- 11420,
- 11709,
- 11907,
- 13163,
- 13697,
- 13700,
- 14808,
- 15306,
- 16410,
- 16791,
- 17992,
- 19203,
- 19510,
- 20724,
- 22305,
- 22935,
- 27007,
- 30109,
- 30420,
- 33409,
- 34949,
- 40283,
- 40493,
- 40549,
- 47282,
- 49146,
- 50257,
- 50357,
- 50358,
- 50359,
- 50360,
- )
-
-
-def test_suppressed_tokens_minus_value():
- model = WhisperModel("tiny.en")
-
- tokenizer = Tokenizer(model.hf_tokenizer, False)
- tokens = get_suppressed_tokens(tokenizer, [13])
- assert tokens == (13, 50257, 50357, 50358, 50359, 50360)
diff --git a/whisper_pipeline/faster-whisper-main/tests/test_utils.py b/whisper_pipeline/faster-whisper-main/tests/test_utils.py
deleted file mode 100644
index bb488feca5c95c75b6be03ce93e4c90ddd4d0587..0000000000000000000000000000000000000000
--- a/whisper_pipeline/faster-whisper-main/tests/test_utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-
-from faster_whisper import available_models, download_model
-
-
-def test_available_models():
- models = available_models()
- assert isinstance(models, list)
- assert "tiny" in models
-
-
-def test_download_model(tmpdir):
- output_dir = str(tmpdir.join("model"))
-
- model_dir = download_model("tiny", output_dir=output_dir)
-
- assert model_dir == output_dir
- assert os.path.isdir(model_dir)
- assert not os.path.islink(model_dir)
-
- for filename in os.listdir(model_dir):
- path = os.path.join(model_dir, filename)
- assert not os.path.islink(path)
-
-
-def test_download_model_in_cache(tmpdir):
- cache_dir = str(tmpdir.join("model"))
- download_model("tiny", cache_dir=cache_dir)
- assert os.path.isdir(cache_dir)
diff --git a/whisper_pipeline/gector/Model_GECTOR/config.json b/whisper_pipeline/gector/Model_GECTOR/config.json
deleted file mode 100644
index 708c207ae1bed120ad563c3a395a186ded0f120f..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/Model_GECTOR/config.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
- "_name_or_path": "dragonSwing/vibert-capu",
- "architectures": [
- "Seq2LabelsModel"
- ],
- "attention_probs_dropout_prob": 0.1,
- "classifier_dropout": null,
- "hidden_act": "gelu",
- "hidden_dropout_prob": 0.1,
- "hidden_size": 768,
- "initializer_range": 0.02,
- "intermediate_size": 3072,
- "label_smoothing": 0.0,
- "layer_norm_eps": 1e-12,
- "load_pretrained": false,
- "max_position_embeddings": 512,
- "model_type": "bert",
- "num_attention_heads": 12,
- "num_detect_classes": 4,
- "num_hidden_layers": 12,
- "pad_token_id": 0,
- "position_embedding_type": "absolute",
- "predictor_dropout": 0.0,
- "pretrained_name_or_path": "FPTAI/vibert-base-cased",
- "special_tokens_fix": true,
- "torch_dtype": "float32",
- "transformers_version": "4.28.1",
- "type_vocab_size": 2,
- "use_cache": true,
- "vocab_size": 15
-}
diff --git a/whisper_pipeline/gector/Model_GECTOR/pytorch_model.bin b/whisper_pipeline/gector/Model_GECTOR/pytorch_model.bin
deleted file mode 100644
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-{
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- }
- }
-}
\ No newline at end of file
diff --git a/whisper_pipeline/gector/Tokenize_GEC/tokenizer_config.json b/whisper_pipeline/gector/Tokenize_GEC/tokenizer_config.json
deleted file mode 100644
index 03f0f6a007aa8f152549658702b6a870e0e6b493..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/Tokenize_GEC/tokenizer_config.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
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- "cls_token": "[CLS]",
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- "do_lower_case": false,
- "mask_token": "[MASK]",
- "model_max_length": 1024,
- "never_split": null,
- "pad_token": "[PAD]",
- "sep_token": "[SEP]",
- "strip_accents": null,
- "tokenize_chinese_chars": true,
- "tokenizer_class": "BertTokenizer",
- "unk_token": "[UNK]"
-}
diff --git a/whisper_pipeline/gector/Tokenize_GEC/vocab.txt b/whisper_pipeline/gector/Tokenize_GEC/vocab.txt
deleted file mode 100644
index 191dbe0b329816a0dca59c9529607abd0fa7804b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/Tokenize_GEC/vocab.txt
+++ /dev/null
@@ -1,38168 +0,0 @@
-[PAD]
-[UNK]
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-[SEP]
-[MASK]
-,
-.
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-bạn
-##i
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-##n
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-"
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-này
-##m
-l
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-k
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-##!
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-organisms
-independence
-##brata
-besar
-Landgericht
-ingin
-largely
-##partiet
-##ziek
-friendship
-visited
-heritage
-paintings
-testament
-pilots
-agreed
-embargo
-racial
-impose
-voters
-traveling
-succeeded
-registration
-modifications
-desired
-covering
-appearance
-frequent
-consistently
-##plaints
-counted
-owners
-withdraw
-considering
-##sante
-##preis
-Polizei
-Belgique
-molte
-rossa
-##bija
-Englisch
-##lativo
-temporal
-##blia
-Technologie
-##ytet
-salud
-##ulare
-Etiopia
-espera
-carica
-violent
-##runda
-scientifica
-hidrogen
-prova
-sido
-Biografie
-##ènement
-unang
-assume
-relationships
-reaches
-##cismo
-variations
-concentration
-##fected
-retain
-holds
-relating
-ordo
-mentioned
-adel
-lowest
-ranks
-fuselage
-attitude
-prohibited
-discussion
-poter
-##karz
-presa
-massive
-##atok
-##plitude
-initiated
-compose
-##alainen
-kleine
-crashed
-##nyata
-preventing
-Partito
-partito
-Jurist
-formats
-##zlik
-coupled
-outbreak
-##kels
-Medien
-delt
-finali
-appointment
-Ministro
-##versario
-relatives
-participating
-melhor
-qualidade
-Gesundheit
-alemana
-samma
-##gène
-Conservative
-Beide
-##aag
-##kammer
-reporting
-##tener
-Kálmán
-Voogd
diff --git a/whisper_pipeline/gector/__init__.py b/whisper_pipeline/gector/__init__.py
deleted file mode 100644
index a45f8a9f5b84e9105f594bfff0082d6a1d40e7d6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .gec_model import GecBERTModel
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diff --git a/whisper_pipeline/gector/configuration_seq2labels.py b/whisper_pipeline/gector/configuration_seq2labels.py
deleted file mode 100644
index 451e7367cc36ac85bdc9023e3c1b9377da81a484..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/configuration_seq2labels.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from transformers import PretrainedConfig
-
-class Seq2LabelsConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`Seq2LabelsModel`]. It is used to
- instantiate a Seq2Labels model according to the specified arguments, defining the model architecture. Instantiating a
- configuration with the defaults will yield a similar configuration to that of the Seq2Labels architecture.
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
- documentation from [`PretrainedConfig`] for more information.
- Args:
- vocab_size (`int`, *optional*, defaults to 30522):
- Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
- `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
- pretrained_name_or_path (`str`, *optional*, defaults to `bert-base-cased`):
- Pretrained BERT-like model path
- load_pretrained (`bool`, *optional*, defaults to `False`):
- Whether to load pretrained model from `pretrained_name_or_path`
- use_cache (`bool`, *optional*, defaults to `True`):
- Whether or not the model should return the last key/values attentions (not used by all models). Only
- relevant if `config.is_decoder=True`.
- predictor_dropout (`float`, *optional*):
- The dropout ratio for the classification head.
- special_tokens_fix (`bool`, *optional*, defaults to `False`):
- Whether to add additional tokens to the BERT's embedding layer.
- Examples:
- ```python
- >>> from transformers import BertModel, BertConfig
- >>> # Initializing a Seq2Labels style configuration
- >>> configuration = Seq2LabelsConfig()
- >>> # Initializing a model from the bert-base-uncased style configuration
- >>> model = Seq2LabelsModel(configuration)
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
- model_type = "bert"
-
- def __init__(
- self,
- pretrained_name_or_path="bert-base-cased",
- vocab_size=15,
- num_detect_classes=4,
- load_pretrained=False,
- initializer_range=0.02,
- pad_token_id=0,
- use_cache=True,
- predictor_dropout=0.0,
- special_tokens_fix=False,
- label_smoothing=0.0,
- **kwargs
- ):
- super().__init__(pad_token_id=pad_token_id, **kwargs)
-
- self.vocab_size = vocab_size
- self.num_detect_classes = num_detect_classes
- self.pretrained_name_or_path = pretrained_name_or_path
- self.load_pretrained = load_pretrained
- self.initializer_range = initializer_range
- self.use_cache = use_cache
- self.predictor_dropout = predictor_dropout
- self.special_tokens_fix = special_tokens_fix
- self.label_smoothing = label_smoothing
diff --git a/whisper_pipeline/gector/gec_model.py b/whisper_pipeline/gector/gec_model.py
deleted file mode 100644
index 945b809eaf58de054f874665283c81e94cdaba4d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/gec_model.py
+++ /dev/null
@@ -1,445 +0,0 @@
-"""Wrapper of Seq2Labels model. Fixes errors based on model predictions"""
-from collections import defaultdict
-from difflib import SequenceMatcher
-import logging
-import re
-from time import time
-from typing import List, Union
-
-import torch
-from transformers import AutoTokenizer
-from gector.modeling_seq2labels import Seq2LabelsModel
-from gector.vocabulary import Vocabulary
-from gector.utils import PAD, UNK, START_TOKEN, get_target_sent_by_edits
-
-logging.getLogger("werkzeug").setLevel(logging.ERROR)
-logger = logging.getLogger(__file__)
-
-
-class GecBERTModel(torch.nn.Module):
- def __init__(
- self,
- vocab_path=None,
- model_paths=None,
- weights=None,
- device=None,
- max_len=64,
- min_len=3,
- lowercase_tokens=False,
- log=False,
- iterations=3,
- min_error_probability=0.0,
- confidence=0,
- resolve_cycles=False,
- split_chunk=False,
- chunk_size=48,
- overlap_size=12,
- min_words_cut=6,
- punc_dict={':', ".", ",", "?"},
- ):
- r"""
- Args:
- vocab_path (`str`):
- Path to vocabulary directory.
- model_paths (`List[str]`):
- List of model paths.
- weights (`int`, *Optional*, defaults to None):
- Weights of each model. Only relevant if `is_ensemble is True`.
- device (`int`, *Optional*, defaults to None):
- Device to load model. If not set, device will be automatically choose.
- max_len (`int`, defaults to 64):
- Max sentence length to be processed (all longer will be truncated).
- min_len (`int`, defaults to 3):
- Min sentence length to be processed (all shorted will be returned w/o changes).
- lowercase_tokens (`bool`, defaults to False):
- Whether to lowercase tokens.
- log (`bool`, defaults to False):
- Whether to enable logging.
- iterations (`int`, defaults to 3):
- Max iterations to run during inference.
- special_tokens_fix (`bool`, defaults to True):
- Whether to fix problem with [CLS], [SEP] tokens tokenization.
- min_error_probability (`float`, defaults to `0.0`):
- Minimum probability for each action to apply.
- confidence (`float`, defaults to `0.0`):
- How many probability to add to $KEEP token.
- split_chunk (`bool`, defaults to False):
- Whether to split long sentences to multiple segments of `chunk_size`.
- !Warning: if `chunk_size > max_len`, each segment will be truncate to `max_len`.
- chunk_size (`int`, defaults to 48):
- Length of each segment (in words). Only relevant if `split_chunk is True`.
- overlap_size (`int`, defaults to 12):
- Overlap size (in words) between two consecutive segments. Only relevant if `split_chunk is True`.
- min_words_cut (`int`, defaults to 6):
- Minimun number of words to be cut while merging two consecutive segments.
- Only relevant if `split_chunk is True`.
- punc_dict (List[str], defaults to `{':', ".", ",", "?"}`):
- List of punctuations.
- """
- super().__init__()
- if isinstance(model_paths, str):
- model_paths = [model_paths]
- self.model_weights = list(map(float, weights)) if weights else [1] * len(model_paths)
- self.device = (
- torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else torch.device(device)
- )
- self.max_len = max_len
- self.min_len = min_len
- self.lowercase_tokens = lowercase_tokens
- self.min_error_probability = min_error_probability
- self.vocab = Vocabulary.from_files(vocab_path)
- self.incorr_index = self.vocab.get_token_index("INCORRECT", "d_tags")
- self.log = log
- self.iterations = iterations
- self.confidence = confidence
- self.resolve_cycles = resolve_cycles
- assert (
- chunk_size > 0 and chunk_size // 2 >= overlap_size
- ), "Chunk merging required overlap size must be smaller than half of chunk size"
- self.split_chunk = split_chunk
- self.chunk_size = chunk_size
- self.overlap_size = overlap_size
- self.min_words_cut = min_words_cut
- self.stride = chunk_size - overlap_size
- self.punc_dict = punc_dict
- self.punc_str = '[' + ''.join([f'\{x}' for x in punc_dict]) + ']'
- # set training parameters and operations
-
- self.indexers = []
- self.models = []
- for model_path in model_paths:
- model = Seq2LabelsModel.from_pretrained(model_path)
- config = model.config
- model_name = config.pretrained_name_or_path
- special_tokens_fix = config.special_tokens_fix
- self.indexers.append(self._get_indexer(model_name, special_tokens_fix))
- model.eval().to(self.device)
- self.models.append(model)
-
- def _get_indexer(self, weights_name, special_tokens_fix):
- tokenizer = AutoTokenizer.from_pretrained(
- weights_name, do_basic_tokenize=False, do_lower_case=self.lowercase_tokens, model_max_length=1024
- )
- # to adjust all tokenizers
- if hasattr(tokenizer, 'encoder'):
- tokenizer.vocab = tokenizer.encoder
- if hasattr(tokenizer, 'sp_model'):
- tokenizer.vocab = defaultdict(lambda: 1)
- for i in range(tokenizer.sp_model.get_piece_size()):
- tokenizer.vocab[tokenizer.sp_model.id_to_piece(i)] = i
-
- if special_tokens_fix:
- tokenizer.add_tokens([START_TOKEN])
- tokenizer.vocab[START_TOKEN] = len(tokenizer) - 1
- return tokenizer
-
- def forward(self, text: Union[str, List[str], List[List[str]]], is_split_into_words=False):
- # Input type checking for clearer error
- def _is_valid_text_input(t):
- if isinstance(t, str):
- # Strings are fine
- return True
- elif isinstance(t, (list, tuple)):
- # List are fine as long as they are...
- if len(t) == 0:
- # ... empty
- return True
- elif isinstance(t[0], str):
- # ... list of strings
- return True
- elif isinstance(t[0], (list, tuple)):
- # ... list with an empty list or with a list of strings
- return len(t[0]) == 0 or isinstance(t[0][0], str)
- else:
- return False
- else:
- return False
-
- if not _is_valid_text_input(text):
- raise ValueError(
- "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
- "or `List[List[str]]` (batch of pretokenized examples)."
- )
-
- if is_split_into_words:
- is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
- else:
- is_batched = isinstance(text, (list, tuple))
- if is_batched:
- text = [x.split() for x in text]
- else:
- text = text.split()
-
- if not is_batched:
- text = [text]
-
- return self.handle_batch(text)
-
- def split_chunks(self, batch):
- # return batch pairs of indices
- result = []
- indices = []
- for tokens in batch:
- start = len(result)
- num_token = len(tokens)
- if num_token <= self.chunk_size:
- result.append(tokens)
- elif num_token > self.chunk_size and num_token < (self.chunk_size * 2 - self.overlap_size):
- split_idx = (num_token + self.overlap_size + 1) // 2
- result.append(tokens[:split_idx])
- result.append(tokens[split_idx - self.overlap_size :])
- else:
- for i in range(0, num_token - self.overlap_size, self.stride):
- result.append(tokens[i : i + self.chunk_size])
-
- indices.append((start, len(result)))
-
- return result, indices
-
- def check_alnum(self, s):
- if len(s) < 2:
- return False
- return not (s.isalpha() or s.isdigit())
-
- def apply_chunk_merging(self, tokens, next_tokens):
- # Return next tokens if current tokens list is empty
- if not tokens:
- return next_tokens
-
- source_token_idx = []
- target_token_idx = []
- source_tokens = []
- target_tokens = []
- num_keep = self.overlap_size - self.min_words_cut
- i = 0
- while len(source_token_idx) < self.overlap_size and -i < len(tokens):
- i -= 1
- if tokens[i] not in self.punc_dict:
- source_token_idx.insert(0, i)
- source_tokens.insert(0, tokens[i].lower())
-
- i = 0
- while len(target_token_idx) < self.overlap_size and i < len(next_tokens):
- if next_tokens[i] not in self.punc_dict:
- target_token_idx.append(i)
- target_tokens.append(next_tokens[i].lower())
- i += 1
-
- matcher = SequenceMatcher(None, source_tokens, target_tokens)
- diffs = list(matcher.get_opcodes())
-
- for diff in diffs:
- tag, i1, i2, j1, j2 = diff
- if tag == "equal":
- if i1 >= num_keep:
- tail_idx = source_token_idx[i1]
- head_idx = target_token_idx[j1]
- break
- elif i2 > num_keep:
- tail_idx = source_token_idx[num_keep]
- head_idx = target_token_idx[j2 - i2 + num_keep]
- break
- elif tag == "delete" and i1 == 0:
- num_keep += i2 // 2
-
- tokens = tokens[:tail_idx] + next_tokens[head_idx:]
- return tokens
-
- def merge_chunks(self, batch):
- result = []
- if len(batch) == 1 or self.overlap_size == 0:
- for sub_tokens in batch:
- result.extend(sub_tokens)
- else:
- for _, sub_tokens in enumerate(batch):
- try:
- result = self.apply_chunk_merging(result, sub_tokens)
- except Exception as e:
- print(e)
-
- result = " ".join(result)
- return result
-
- def predict(self, batches):
- t11 = time()
- predictions = []
- for batch, model in zip(batches, self.models):
- batch = batch.to(self.device)
- with torch.no_grad():
- prediction = model.forward(**batch)
- predictions.append(prediction)
-
- preds, idx, error_probs = self._convert(predictions)
- t55 = time()
- if self.log:
- print(f"Inference time {t55 - t11}")
- return preds, idx, error_probs
-
- def get_token_action(self, token, index, prob, sugg_token):
- """Get lost of suggested actions for token."""
- # cases when we don't need to do anything
- if prob < self.min_error_probability or sugg_token in [UNK, PAD, '$KEEP']:
- return None
-
- if sugg_token.startswith('$REPLACE_') or sugg_token.startswith('$TRANSFORM_') or sugg_token == '$DELETE':
- start_pos = index
- end_pos = index + 1
- elif sugg_token.startswith("$APPEND_") or sugg_token.startswith("$MERGE_"):
- start_pos = index + 1
- end_pos = index + 1
-
- if sugg_token == "$DELETE":
- sugg_token_clear = ""
- elif sugg_token.startswith('$TRANSFORM_') or sugg_token.startswith("$MERGE_"):
- sugg_token_clear = sugg_token[:]
- else:
- sugg_token_clear = sugg_token[sugg_token.index('_') + 1 :]
-
- return start_pos - 1, end_pos - 1, sugg_token_clear, prob
-
- def preprocess(self, token_batch):
- seq_lens = [len(sequence) for sequence in token_batch if sequence]
- if not seq_lens:
- return []
- max_len = min(max(seq_lens), self.max_len)
- batches = []
- for indexer in self.indexers:
- token_batch = [[START_TOKEN] + sequence[:max_len] for sequence in token_batch]
- batch = indexer(
- token_batch,
- return_tensors="pt",
- padding=True,
- is_split_into_words=True,
- truncation=True,
- add_special_tokens=False,
- )
- offset_batch = []
- for i in range(len(token_batch)):
- word_ids = batch.word_ids(batch_index=i)
- offsets = [0]
- for i in range(1, len(word_ids)):
- if word_ids[i] != word_ids[i - 1]:
- offsets.append(i)
- offset_batch.append(torch.LongTensor(offsets))
-
- batch["input_offsets"] = torch.nn.utils.rnn.pad_sequence(
- offset_batch, batch_first=True, padding_value=0
- ).to(torch.long)
-
- batches.append(batch)
-
- return batches
-
- def _convert(self, data):
- all_class_probs = torch.zeros_like(data[0]['logits'])
- error_probs = torch.zeros_like(data[0]['max_error_probability'])
- for output, weight in zip(data, self.model_weights):
- class_probabilities_labels = torch.softmax(output['logits'], dim=-1)
- all_class_probs += weight * class_probabilities_labels / sum(self.model_weights)
- class_probabilities_d = torch.softmax(output['detect_logits'], dim=-1)
- error_probs_d = class_probabilities_d[:, :, self.incorr_index]
- incorr_prob = torch.max(error_probs_d, dim=-1)[0]
- error_probs += weight * incorr_prob / sum(self.model_weights)
-
- max_vals = torch.max(all_class_probs, dim=-1)
- probs = max_vals[0].tolist()
- idx = max_vals[1].tolist()
- return probs, idx, error_probs.tolist()
-
- def update_final_batch(self, final_batch, pred_ids, pred_batch, prev_preds_dict):
- new_pred_ids = []
- total_updated = 0
- for i, orig_id in enumerate(pred_ids):
- orig = final_batch[orig_id]
- pred = pred_batch[i]
- prev_preds = prev_preds_dict[orig_id]
- if orig != pred and pred not in prev_preds:
- final_batch[orig_id] = pred
- new_pred_ids.append(orig_id)
- prev_preds_dict[orig_id].append(pred)
- total_updated += 1
- elif orig != pred and pred in prev_preds:
- # update final batch, but stop iterations
- final_batch[orig_id] = pred
- total_updated += 1
- else:
- continue
- return final_batch, new_pred_ids, total_updated
-
- def postprocess_batch(self, batch, all_probabilities, all_idxs, error_probs):
- all_results = []
- noop_index = self.vocab.get_token_index("$KEEP", "labels")
- for tokens, probabilities, idxs, error_prob in zip(batch, all_probabilities, all_idxs, error_probs):
- length = min(len(tokens), self.max_len)
- edits = []
-
- # skip whole sentences if there no errors
- if max(idxs) == 0:
- all_results.append(tokens)
- continue
-
- # skip whole sentence if probability of correctness is not high
- if error_prob < self.min_error_probability:
- all_results.append(tokens)
- continue
-
- for i in range(length + 1):
- # because of START token
- if i == 0:
- token = START_TOKEN
- else:
- token = tokens[i - 1]
- # skip if there is no error
- if idxs[i] == noop_index:
- continue
-
- sugg_token = self.vocab.get_token_from_index(idxs[i], namespace='labels')
- action = self.get_token_action(token, i, probabilities[i], sugg_token)
- if not action:
- continue
-
- edits.append(action)
- all_results.append(get_target_sent_by_edits(tokens, edits))
- return all_results
-
- def handle_batch(self, full_batch, merge_punc=True):
- """
- Handle batch of requests.
- """
- if self.split_chunk:
- full_batch, indices = self.split_chunks(full_batch)
- else:
- indices = None
- final_batch = full_batch[:]
- batch_size = len(full_batch)
- prev_preds_dict = {i: [final_batch[i]] for i in range(len(final_batch))}
- short_ids = [i for i in range(len(full_batch)) if len(full_batch[i]) < self.min_len]
- pred_ids = [i for i in range(len(full_batch)) if i not in short_ids]
- total_updates = 0
-
- for n_iter in range(self.iterations):
- orig_batch = [final_batch[i] for i in pred_ids]
-
- sequences = self.preprocess(orig_batch)
-
- if not sequences:
- break
- probabilities, idxs, error_probs = self.predict(sequences)
-
- pred_batch = self.postprocess_batch(orig_batch, probabilities, idxs, error_probs)
- if self.log:
- print(f"Iteration {n_iter + 1}. Predicted {round(100*len(pred_ids)/batch_size, 1)}% of sentences.")
-
- final_batch, pred_ids, cnt = self.update_final_batch(final_batch, pred_ids, pred_batch, prev_preds_dict)
- total_updates += cnt
-
- if not pred_ids:
- break
- if self.split_chunk:
- final_batch = [self.merge_chunks(final_batch[start:end]) for (start, end) in indices]
- else:
- final_batch = [" ".join(x) for x in final_batch]
- if merge_punc:
- final_batch = [re.sub(r'\s+(%s)' % self.punc_str, r'\1', x) for x in final_batch]
-
- return final_batch
\ No newline at end of file
diff --git a/whisper_pipeline/gector/modeling_seq2labels.py b/whisper_pipeline/gector/modeling_seq2labels.py
deleted file mode 100644
index 0ed970120d929d3e4eacf014887bb2f54203c737..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/modeling_seq2labels.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import AutoConfig, AutoModel, BertPreTrainedModel
-from transformers.modeling_outputs import ModelOutput
-
-import torch
-
-
-def get_range_vector(size: int, device: int) -> torch.Tensor:
- """
- Returns a range vector with the desired size, starting at 0. The CUDA implementation
- is meant to avoid copy data from CPU to GPU.
- """
- return torch.arange(0, size, dtype=torch.long, device=device)
-
-@dataclass
-class Seq2LabelsOutput(ModelOutput):
- loss: Optional[torch.FloatTensor] = None
- logits: torch.FloatTensor = None
- detect_logits: torch.FloatTensor = None
- hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- attentions: Optional[Tuple[torch.FloatTensor]] = None
- max_error_probability: Optional[torch.FloatTensor] = None
-
-
-class Seq2LabelsModel(BertPreTrainedModel):
-
- _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
- self.num_detect_classes = config.num_detect_classes
- self.label_smoothing = config.label_smoothing
-
- if config.load_pretrained:
- self.bert = AutoModel.from_pretrained(config.pretrained_name_or_path)
- bert_config = self.bert.config
- else:
- bert_config = AutoConfig.from_pretrained(config.pretrained_name_or_path)
- self.bert = AutoModel.from_config(bert_config)
-
- if config.special_tokens_fix:
- try:
- vocab_size = self.bert.embeddings.word_embeddings.num_embeddings
- except AttributeError:
- # reserve more space
- vocab_size = self.bert.word_embedding.num_embeddings + 5
- self.bert.resize_token_embeddings(vocab_size + 1)
-
- predictor_dropout = config.predictor_dropout if config.predictor_dropout is not None else 0.0
- self.dropout = nn.Dropout(predictor_dropout)
- self.classifier = nn.Linear(bert_config.hidden_size, config.vocab_size)
- self.detector = nn.Linear(bert_config.hidden_size, config.num_detect_classes)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def forward(
- self,
- input_ids: Optional[torch.Tensor] = None,
- input_offsets: Optional[torch.Tensor] = None,
- attention_mask: Optional[torch.Tensor] = None,
- token_type_ids: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.Tensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- inputs_embeds: Optional[torch.Tensor] = None,
- labels: Optional[torch.Tensor] = None,
- d_tags: Optional[torch.Tensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple[torch.Tensor], Seq2LabelsOutput]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
-
- if input_offsets is not None:
- # offsets is (batch_size, d1, ..., dn, orig_sequence_length)
- range_vector = get_range_vector(input_offsets.size(0), device=sequence_output.device).unsqueeze(1)
- # selected embeddings is also (batch_size * d1 * ... * dn, orig_sequence_length)
- sequence_output = sequence_output[range_vector, input_offsets]
-
- logits = self.classifier(self.dropout(sequence_output))
- logits_d = self.detector(sequence_output)
-
- loss = None
- if labels is not None and d_tags is not None:
- loss_labels_fct = CrossEntropyLoss(label_smoothing=self.label_smoothing)
- loss_d_fct = CrossEntropyLoss()
- loss_labels = loss_labels_fct(logits.view(-1, self.num_labels), labels.view(-1))
- loss_d = loss_d_fct(logits_d.view(-1, self.num_detect_classes), d_tags.view(-1))
- loss = loss_labels + loss_d
-
- if not return_dict:
- output = (logits, logits_d) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return Seq2LabelsOutput(
- loss=loss,
- logits=logits,
- detect_logits=logits_d,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- max_error_probability=torch.ones(logits.size(0), device=logits.device),
- )
diff --git a/whisper_pipeline/gector/utils.py b/whisper_pipeline/gector/utils.py
deleted file mode 100644
index af4dc55eb8eb364bf7da9dfd08170b422ce2166b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/utils.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import os
-from pathlib import Path
-import re
-
-
-VOCAB_DIR = Path(__file__).resolve().parent
-PAD = "@@PADDING@@"
-UNK = "@@UNKNOWN@@"
-START_TOKEN = "$START"
-SEQ_DELIMETERS = {"tokens": " ", "labels": "SEPL|||SEPR", "operations": "SEPL__SEPR"}
-
-
-def get_verb_form_dicts():
- path_to_dict = os.path.join(VOCAB_DIR, "verb-form-vocab.txt")
- encode, decode = {}, {}
- with open(path_to_dict, encoding="utf-8") as f:
- for line in f:
- words, tags = line.split(":")
- word1, word2 = words.split("_")
- tag1, tag2 = tags.split("_")
- decode_key = f"{word1}_{tag1}_{tag2.strip()}"
- if decode_key not in decode:
- encode[words] = tags
- decode[decode_key] = word2
- return encode, decode
-
-
-ENCODE_VERB_DICT, DECODE_VERB_DICT = get_verb_form_dicts()
-
-
-def get_target_sent_by_edits(source_tokens, edits):
- target_tokens = source_tokens[:]
- shift_idx = 0
- for edit in edits:
- start, end, label, _ = edit
- target_pos = start + shift_idx
- if start < 0:
- continue
- elif len(target_tokens) > target_pos:
- source_token = target_tokens[target_pos]
- else:
- source_token = ""
- if label == "":
- del target_tokens[target_pos]
- shift_idx -= 1
- elif start == end:
- word = label.replace("$APPEND_", "")
- # Avoid appending same token twice
- if (target_pos < len(target_tokens) and target_tokens[target_pos] == word) or (
- target_pos > 0 and target_tokens[target_pos - 1] == word
- ):
- continue
- target_tokens[target_pos:target_pos] = [word]
- shift_idx += 1
- elif label.startswith("$TRANSFORM_"):
- word = apply_reverse_transformation(source_token, label)
- if word is None:
- word = source_token
- target_tokens[target_pos] = word
- elif start == end - 1:
- word = label.replace("$REPLACE_", "")
- target_tokens[target_pos] = word
- elif label.startswith("$MERGE_"):
- target_tokens[target_pos + 1 : target_pos + 1] = [label]
- shift_idx += 1
-
- return replace_merge_transforms(target_tokens)
-
-
-def replace_merge_transforms(tokens):
- if all(not x.startswith("$MERGE_") for x in tokens):
- return tokens
- if tokens[0].startswith("$MERGE_"):
- tokens = tokens[1:]
- if tokens[-1].startswith("$MERGE_"):
- tokens = tokens[:-1]
-
- target_line = " ".join(tokens)
- target_line = target_line.replace(" $MERGE_HYPHEN ", "-")
- target_line = target_line.replace(" $MERGE_SPACE ", "")
- target_line = re.sub(r'([\.\,\?\:]\s+)+', r'\1', target_line)
- return target_line.split()
-
-
-def convert_using_case(token, smart_action):
- if not smart_action.startswith("$TRANSFORM_CASE_"):
- return token
- if smart_action.endswith("LOWER"):
- return token.lower()
- elif smart_action.endswith("UPPER"):
- return token.upper()
- elif smart_action.endswith("CAPITAL"):
- return token.capitalize()
- elif smart_action.endswith("CAPITAL_1"):
- return token[0] + token[1:].capitalize()
- elif smart_action.endswith("UPPER_-1"):
- return token[:-1].upper() + token[-1]
- else:
- return token
-
-
-def convert_using_verb(token, smart_action):
- key_word = "$TRANSFORM_VERB_"
- if not smart_action.startswith(key_word):
- raise Exception(f"Unknown action type {smart_action}")
- encoding_part = f"{token}_{smart_action[len(key_word):]}"
- decoded_target_word = decode_verb_form(encoding_part)
- return decoded_target_word
-
-
-def convert_using_split(token, smart_action):
- key_word = "$TRANSFORM_SPLIT"
- if not smart_action.startswith(key_word):
- raise Exception(f"Unknown action type {smart_action}")
- target_words = token.split("-")
- return " ".join(target_words)
-
-
-def convert_using_plural(token, smart_action):
- if smart_action.endswith("PLURAL"):
- return token + "s"
- elif smart_action.endswith("SINGULAR"):
- return token[:-1]
- else:
- raise Exception(f"Unknown action type {smart_action}")
-
-
-def apply_reverse_transformation(source_token, transform):
- if transform.startswith("$TRANSFORM"):
- # deal with equal
- if transform == "$KEEP":
- return source_token
- # deal with case
- if transform.startswith("$TRANSFORM_CASE"):
- return convert_using_case(source_token, transform)
- # deal with verb
- if transform.startswith("$TRANSFORM_VERB"):
- return convert_using_verb(source_token, transform)
- # deal with split
- if transform.startswith("$TRANSFORM_SPLIT"):
- return convert_using_split(source_token, transform)
- # deal with single/plural
- if transform.startswith("$TRANSFORM_AGREEMENT"):
- return convert_using_plural(source_token, transform)
- # raise exception if not find correct type
- raise Exception(f"Unknown action type {transform}")
- else:
- return source_token
-
-
-# def read_parallel_lines(fn1, fn2):
-# lines1 = read_lines(fn1, skip_strip=True)
-# lines2 = read_lines(fn2, skip_strip=True)
-# assert len(lines1) == len(lines2)
-# out_lines1, out_lines2 = [], []
-# for line1, line2 in zip(lines1, lines2):
-# if not line1.strip() or not line2.strip():
-# continue
-# else:
-# out_lines1.append(line1)
-# out_lines2.append(line2)
-# return out_lines1, out_lines2
-
-
-def read_parallel_lines(fn1, fn2):
- with open(fn1, encoding='utf-8') as f1, open(fn2, encoding='utf-8') as f2:
- for line1, line2 in zip(f1, f2):
- line1 = line1.strip()
- line2 = line2.strip()
-
- yield line1, line2
-
-
-def read_lines(fn, skip_strip=False):
- if not os.path.exists(fn):
- return []
- with open(fn, 'r', encoding='utf-8') as f:
- lines = f.readlines()
- return [s.strip() for s in lines if s.strip() or skip_strip]
-
-
-def write_lines(fn, lines, mode='w'):
- if mode == 'w' and os.path.exists(fn):
- os.remove(fn)
- with open(fn, encoding='utf-8', mode=mode) as f:
- f.writelines(['%s\n' % s for s in lines])
-
-
-def decode_verb_form(original):
- return DECODE_VERB_DICT.get(original)
-
-
-def encode_verb_form(original_word, corrected_word):
- decoding_request = original_word + "_" + corrected_word
- decoding_response = ENCODE_VERB_DICT.get(decoding_request, "").strip()
- if original_word and decoding_response:
- answer = decoding_response
- else:
- answer = None
- return answer
-
-
-def get_weights_name(transformer_name, lowercase):
- if transformer_name == 'bert' and lowercase:
- return 'bert-base-uncased'
- if transformer_name == 'bert' and not lowercase:
- return 'bert-base-cased'
- if transformer_name == 'bert-large' and not lowercase:
- return 'bert-large-cased'
- if transformer_name == 'distilbert':
- if not lowercase:
- print('Warning! This model was trained only on uncased sentences.')
- return 'distilbert-base-uncased'
- if transformer_name == 'albert':
- if not lowercase:
- print('Warning! This model was trained only on uncased sentences.')
- return 'albert-base-v1'
- if lowercase:
- print('Warning! This model was trained only on cased sentences.')
- if transformer_name == 'roberta':
- return 'roberta-base'
- if transformer_name == 'roberta-large':
- return 'roberta-large'
- if transformer_name == 'gpt2':
- return 'gpt2'
- if transformer_name == 'transformerxl':
- return 'transfo-xl-wt103'
- if transformer_name == 'xlnet':
- return 'xlnet-base-cased'
- if transformer_name == 'xlnet-large':
- return 'xlnet-large-cased'
-
- return transformer_name
\ No newline at end of file
diff --git a/whisper_pipeline/gector/verb-form-vocab.txt b/whisper_pipeline/gector/verb-form-vocab.txt
deleted file mode 100644
index 2ee7e41c2276d48bfed15aff2b92dff8692e16a6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/verb-form-vocab.txt
+++ /dev/null
@@ -1,36216 +0,0 @@
-không_0:VB_VBN
-một_1:VB_VBN
-hai_2:VB_VBN
-ba_3:VB_VBN
-bốn_4:VB_VBN
-năm_5:VB_VBN
-sáu_6:VB_VBN
-bảy_7:VB_VBN
-bẩy_7:VB_VBN
-tám_8:VB_VBN
-chín_9:VB_VBN
-bờ_b:VB_VBN
-bê_b:VB_VBN
-bi_b:VB_VBN
-ci_c:VB_VBN
-si_c:VB_VBN
-xi_c:VB_VBN
-cờ_c:VB_VBN
-cê_c:VB_VBN
-xê_c:VB_VBN
-xe_c:VB_VBN
-sê_c:VB_VBN
-dê_d:VB_VBN
-đê_d:VB_VBN
-đi_d:VB_VBN
-ép_f:VB_VBN
-gờ_g:VB_VBN
-hát_h:VB_VBN
-hắt_h:VB_VBN
-di_j:VB_VBN
-ri_j:VB_VBN
-gi_j:VB_VBN
-ca_k:VB_VBN
-ka_k:VB_VBN
-lờ_l:VB_VBN
-eo_l:VB_VBN
-mờ_m:VB_VBN
-em_m:VB_VBN
-nờ_n:VB_VBN
-en_n:VB_VBN
-pê_p:VB_VBN
-pờ_p:VB_VBN
-quy_q:VB_VBN
-rờ_r:VB_VBN
-er_r:VB_VBN
-ét_s:VB_VBN
-tê_t:VB_VBN
-ti_t:VB_VBN
-tờ_t:VB_VBN
-vê_v:VB_VBN
-vi_v:VB_VBN
-vờ_v:VB_VBN
-ích_x:VB_VBN
-ít_x:VB_VBN
-íc_x:VB_VBN
-dét_z:VB_VBN
-rét_z:VB_VBN
-zét_z:VB_VBN
-bờ_B:VB_VBC
-bê_B:VB_VBC
-bi_B:VB_VBC
-ci_C:VB_VBC
-si_C:VB_VBC
-xi_C:VB_VBC
-cờ_C:VB_VBC
-cê_C:VB_VBC
-xê_C:VB_VBC
-xe_C:VB_VBC
-sê_C:VB_VBC
-dê_D:VB_VBC
-đê_D:VB_VBC
-đi_D:VB_VBC
-ép_F:VB_VBC
-gờ_G:VB_VBC
-hát_H:VB_VBC
-hắt_H:VB_VBC
-di_J:VB_VBC
-ri_J:VB_VBC
-gi_J:VB_VBC
-ca_K:VB_VBC
-ka_K:VB_VBC
-lờ_L:VB_VBC
-eo_L:VB_VBC
-mờ_M:VB_VBC
-em_M:VB_VBC
-nờ_N:VB_VBC
-en_N:VB_VBC
-pê_P:VB_VBC
-pờ_P:VB_VBC
-quy_Q:VB_VBC
-rờ_R:VB_VBC
-er_R:VB_VBC
-ét_S:VB_VBC
-tê_T:VB_VBC
-ti_T:VB_VBC
-tờ_T:VB_VBC
-vê_V:VB_VBC
-vi_V:VB_VBC
-vờ_V:VB_VBC
-ích_X:VB_VBC
-ít_X:VB_VBC
-íc_X:VB_VBC
-dét_Z:VB_VBC
-rét_Z:VB_VBC
-zét_Z:VB_VBC
-aaaa_AAaa:VB_VBN
-aaabb_AAaBb:VB_VBN
-aaafloorsandingdublin_AAAfloorsandingdublin:VB_VBN
-aabbccddee_AaBbCcDdEe:VB_VBN
-aabbccde_AaBbCcDE:VB_VBN
-aabbdd_AaBbDd:VB_VBN
-aabbddeehh_AabbDdEEHh:VB_VBN
-aabbddhh_AaBbDdHh:VB_VBN
-aabbddxy_AaBbDdXY:VB_VBN
-aabbxde_AaBbXDE:VB_VBN
-aabbxdexde_AaBbXDeXdE:VB_VBN
-aabbxdxd_AaBbXdXd:VB_VBN
-aaclean_AAClean:VB_VBN
-aair_aAIR:VB_VBN
-aaj_AaJ:VB_VBN
-aalink_AALink:VB_VBN
-aalock_AALock:VB_VBN
-aamail_AAMail:VB_VBN
-aapanel_aaPanel:VB_VBN
-aapoly_AAPoly:VB_VBN
-aarduino_AArduino:VB_VBN
-aarongillion_AaronGillion:VB_VBN
-aatrox_AAtrox:VB_VBN
-aaum_AAum:VB_VBN
-aaxdeyde_AaXDEYde:VB_VBN
-aaxmxm_AaXMXm:VB_VBN
-aaxmy_aaXMY:VB_VBN
-aayys_AAyys:VB_VBN
-ababxdxd_ABabXDXd:VB_VBN
-ababxdy_ABabXDY:VB_VBN
-abank_ABank:VB_VBN
-abantecart_AbanteCart:VB_VBN
-abbank_ABBank:VB_VBN
-abbankmobile_ABBANKmobile:VB_VBN
-abbd_ABbD:VB_VBN
-abbvie_AbbVie:VB_VBN
-abcdfgh_ABcdFGH:VB_VBN
-abcer_ABCer:VB_VBN
-abcgroup_ABCGroup:VB_VBN
-abcmouse_ABCmouse:VB_VBN
-abcnews_ABCNews:VB_VBN
-abctech_ABCTech:VB_VBN
-abcuts_AbCuts:VB_VBN
-abcya_ABCya:VB_VBN
-abdeg_abDEg:VB_VBN
-abeha_AbeHa:VB_VBN
-abell_ABell:VB_VBN
-abemaprime_AbemaPrime:VB_VBN
-abfuco_AbFUCO:VB_VBN
-abgroup_ABgroup:VB_VBN
-abhouse_ABhouse:VB_VBN
-abicc_ABiCC:VB_VBN
-abicollab_AbiCollab:VB_VBN
-abiword_AbiWord:VB_VBN
-aboutphaedra_AboutPhaedra:VB_VBN
-aboutsee_AboutSee:VB_VBN
-abramsxe_AbramsXe:VB_VBN
-abroad_ABroad:VB_VBN
-abs_AbS:VB_VBN
-abshop_ABshop:VB_VBN
-absoft_ABSoft:VB_VBN
-absolutelayout_AbsoluteLayout:VB_VBN
-abstractfactory_AbstractFactory:VB_VBN
-abstracthandlerexceptionresolver_AbstractHandlerExceptionResolver:VB_VBN
-abstractjpapersistable_AbstractJpaPersistable:VB_VBN
-abstractset_AbstractSet:VB_VBN
-abstractshare_AbstractShare:VB_VBN
-abstractsocial_AbstractSocial:VB_VBN
-abudhabi_AbuDhabi:VB_VBN
-abvietfrance_ABVietFrance:VB_VBN
-abxde_abXde:VB_VBN
-abxdefaultdeeplinkactivity_AbxDefaultDeeplinkActivity:VB_VBN
-abyssrium_AbyssRium:VB_VBN
-abzesolar_AbzeSolar:VB_VBN
-acapulcoadmin_AcapulcoAdmin:VB_VBN
-acbbank_ACBbank:VB_VBN
-acbel_AcBel:VB_VBN
-acbtower_ACBTower:VB_VBN
-acceledent_AcceleDent:VB_VBN
-accentcolor_accentColor:VB_VBN
-accesskey_AccessKey:VB_VBN
-accesspoint_AccessPoint:VB_VBN
-accesspress_AccessPress:VB_VBN
-accessscience_AccessScience:VB_VBN
-accesstrade_AccessTrade:VB_VBN
-accessurl_AccessURL:VB_VBN
-acchelper_AccHelper:VB_VBN
-acchome_AccHome:VB_VBN
-accnet_AccNet:VB_VBN
-accnetba_AccNetBA:VB_VBN
-accorhotels_AccorHotels:VB_VBN
-accountaddress_AccountAddress:VB_VBN
-accountdepidor_AccountDepidor:VB_VBN
-accountgroup_accountGroup:VB_VBN
-accountholder_AccountHolder:VB_VBN
-accountingsuite_AccountingSuite:VB_VBN
-accountlist_accountList:VB_VBN
-accpro_AccPro:VB_VBN
-accradio_AccRadio:VB_VBN
-accubattery_AccuBattery:VB_VBN
-accubook_AccuBook:VB_VBN
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-babyganics_BabyGanics:VB_VBN
-babygap_BabyGap:VB_VBN
-babyguard_BabyGuard:VB_VBN
-babykid_BabyKid:VB_VBN
-babykids_BabyKids:VB_VBN
-babykiss_BabyKiss:VB_VBN
-babylonexpress_BabylonExpress:VB_VBN
-babymum_BabyMum:VB_VBN
-babypips_BabyPips:VB_VBN
-babyplus_BabyPlus:VB_VBN
-babysexy_BabySexy:VB_VBN
-babysure_babySure:VB_VBN
-babyx_BabyX:VB_VBN
-bacabank_BacABank:VB_VBN
-bacarattrong_bacaratTrong:VB_VBN
-baccarataffiliate_BaccaratAffiliate:VB_VBN
-baccaratbig_BaccaratBig:VB_VBN
-baccaratchatbot_BaccaratChatbot:VB_VBN
-baccaratdeposit_BaccaratDeposit:VB_VBN
-baccaratdigital_BaccaratDigital:VB_VBN
-baccaratdo_BaccaratDo:VB_VBN
-baccaraterp_BaccaratERP:VB_VBN
-baccaratgdp_BaccaratGDP:VB_VBN
-baccaratkhi_BaccaratKhi:VB_VBN
-baccaratngay_BaccaratNgay:VB_VBN
-baccaratsau_BaccaratSau:VB_VBN
-baccarattham_BaccaratTham:VB_VBN
-baccarattrong_BaccaratTrong:VB_VBN
-bacdau_BacDau:VB_VBN
-bacdoanh_bacDoanh:VB_VBN
-bachgia_BachGia:VB_VBN
-bachhoahongchi_BachhoaHONGCHI:VB_VBN
-bachhoaxanh_BachhoaXANH:VB_VBN
-bachhoorder_BachHoOrder:VB_VBN
-bachhop_BachHop:VB_VBN
-bachkhoasg_bachkhoaSG:VB_VBN
-bachkhoastore_BachKhoaStore:VB_VBN
-bachlong_BachLong:VB_VBN
-bachppi_bachPPI:VB_VBN
-bachthanh_BachThanh:VB_VBN
-bachvan_BachVan:VB_VBN
-baciplus_BaciPlus:VB_VBN
-backbeat_BackBeat:VB_VBN
-backberry_BackBerry:VB_VBN
-backdoanh_backDoanh:VB_VBN
-backdroptrang_backdropTrang:VB_VBN
-backend_BackEnd:VB_VBN
-background_BackGround:VB_VBN
-backgroundcolor_backgroundColor:VB_VBN
-backgroundshellexcontextmenuhandlers_BackgroundshellexContextMenuHandlers:VB_VBN
-backingsoda_BackingSoda:VB_VBN
-backitup_BackItUp:VB_VBN
-backitymac_BackityMac:VB_VBN
-backjack_BackJack:VB_VBN
-backkhoa_BackKhoa:VB_VBN
-backliners_BackLiners:VB_VBN
-backlink_BackLink:VB_VBN
-backlinkaz_BacklinkAZ:VB_VBN
-backlinkcafe_BacklinkCafe:VB_VBN
-backlinko_BacklinkO:VB_VBN
-backlinkpro_BackLinkPro:VB_VBN
-backlinks_BackLinks:VB_VBN
-backlinkvip_BacklinkVip:VB_VBN
-backpack_BackPack:VB_VBN
-backslash_BackSlash:VB_VBN
-backspace_BackSpace:VB_VBN
-backtest_BackTest:VB_VBN
-backtrack_BackTrack:VB_VBN
-backup_BackUp:VB_VBN
-backupbuddy_BackupBuddy:VB_VBN
-backupchain_BackupChain:VB_VBN
-backuplife_BackupLife:VB_VBN
-backupmanagerservice_BackupManagerService:VB_VBN
-backupnow_BackupNow:VB_VBN
-backwpup_BackWPup:VB_VBN
-bacluong_BacLuong:VB_VBN
-bacnet_BACnet:VB_VBN
-bacnguyen_BacNguyen:VB_VBN
-bacninh_BacNinh:VB_VBN
-bacninhtbd_BacNinhTBD:VB_VBN
-bacnix_BACnix:VB_VBN
-baconsoi_BaConSoi:VB_VBN
-baconx_BaconX:VB_VBN
-bacpmi_bacPMI:VB_VBN
-bacsiluongngocofficial_BacSiLuongNgocOfficial:VB_VBN
-bacsionline_BacsiOnline:VB_VBN
-bacsoi_bacSoi:VB_VBN
-bacsymaytinh_BacSyMayTinh:VB_VBN
-bactrack_BACTrack:VB_VBN
-bacung_BACung:VB_VBN
-badbit_BadBit:VB_VBN
-badblue_BadBlue:VB_VBN
-badbz_BadBz:VB_VBN
-baden_BaDen:VB_VBN
-badman_BadMan:VB_VBN
-badnews_BadNews:VB_VBN
-badoinkvr_BaDoinkVR:VB_VBN
-baekhyun_BaekHyun:VB_VBN
-baemin_BaeMin:VB_VBN
-baer_BAer:VB_VBN
-bafin_BaFin:VB_VBN
-bagaang_BaGaang:VB_VBN
-bagang_BaGang:VB_VBN
-bagcleaver_BagCleaver:VB_VBN
-bagfilter_BagFilter:VB_VBN
-baggekhi_BaggeKhi:VB_VBN
-bagmixer_BagMixer:VB_VBN
-bagofword_BagOfWord:VB_VBN
-bagsnob_BagSnob:VB_VBN
-bahnar_BahNar:VB_VBN
-bahrainlink_BahrainLink:VB_VBN
-baic_BaiC:VB_VBN
-baile_BaiLe:VB_VBN
-baitapthethao_BaitapThethao:VB_VBN
-baitme_BaitMe:VB_VBN
-baiyoke_BaiYoke:VB_VBN
-baka_BaKa:VB_VBN
-bakafood_BaKafood:VB_VBN
-bakerland_BakerLand:VB_VBN
-bakhia_BaKhia:VB_VBN
-bakien_baKien:VB_VBN
-balakplay_BalakPlay:VB_VBN
-balancenew_balanceNew:VB_VBN
-balckjack_BalckJack:VB_VBN
-bali_BaLi:VB_VBN
-ballgeogrip_ballGeoGrip:VB_VBN
-balo_BaLo:VB_VBN
-balocenter_BaloCenter:VB_VBN
-balohh_BaLoHH:VB_VBN
-balooutlet_BaloOutlet:VB_VBN
-bambam_BamBam:VB_VBN
-bambihomemade_BambiHomemade:VB_VBN
-bambo_BamBo:VB_VBN
-bamboo_BamBoo:VB_VBN
-bambooair_BambooAir:VB_VBN
-bamboocare_BambooCARE:VB_VBN
-bamboodefi_BambooDeFi:VB_VBN
-bamboofever_BambooFever:VB_VBN
-bamboohr_BambooHR:VB_VBN
-bamboovietnam_BambooVietnam:VB_VBN
-bambubuild_BambuBuild:VB_VBN
-bamfs_BaMFs:VB_VBN
-bana_BaNa:VB_VBN
-bancachich_BanCaChich:VB_VBN
-bancamera_BanCamera:VB_VBN
-bancathantai_BanCaThanTai:VB_VBN
-bancatien_BanCaTien:VB_VBN
-bancazui_BanCaZui:VB_VBN
-banchi_BanChi:VB_VBN
-bancorpsouth_BancorpSouth:VB_VBN
-bancsystem_BancSystem:VB_VBN
-bandcamp_BandCamp:VB_VBN
-bandeiracoin_BandeiraCoin:VB_VBN
-bandfest_BandFest:VB_VBN
-bandicam_BandiCam:VB_VBN
-bandlab_BandLab:VB_VBN
-bando_BanDo:VB_VBN
-bandpass_BandPass:VB_VBN
-bandsports_BandSports:VB_VBN
-bandwidth_BandWidth:VB_VBN
-bang_BanG:VB_VBN
-bangbang_BangBang:VB_VBN
-banghieudanangin_BanghieudanangIn:VB_VBN
-banghieuhopden_BangHieuHopDen:VB_VBN
-bangkenh_BangKeNH:VB_VBN
-bangkok_BangKok:VB_VBN
-bangkokdu_BangkokDu:VB_VBN
-bangnhi_BangNhi:VB_VBN
-bangtan_BangTan:VB_VBN
-bangtonghop_BangTongHop:VB_VBN
-bangtwice_BangTwice:VB_VBN
-banhat_BaNhat:VB_VBN
-banhbeovodicho_BanhBeoVoDicho:VB_VBN
-banhclub_BanhClub:VB_VBN
-banhkhuc_BanhKhuc:VB_VBN
-banhmithonhiky_BanhMiThoNhiKy:VB_VBN
-banhtrangtayninh_BanhTrangTayNinh:VB_VBN
-banhtv_BanhTV:VB_VBN
-banhtvcon_banhtvCon:VB_VBN
-banhuuduongxa_BanHuuDuongXa:VB_VBN
-banik_BaNiK:VB_VBN
-banjl_banjL:VB_VBN
-bank_BanK:VB_VBN
-bankamericard_BankAmericard:VB_VBN
-bankbank_BankBank:VB_VBN
-bankexpress_BankExpress:VB_VBN
-bankhub_BankHub:VB_VBN
-bankinh_banKinh:VB_VBN
-bankkokmirama_BankkokMirama:VB_VBN
-bankmycell_BankMyCell:VB_VBN
-bankplus_BankPlus:VB_VBN
-banksign_BankSign:VB_VBN
-banlam_banLam:VB_VBN
-banlaptop_BanLaptop:VB_VBN
-bannerid_bannerId:VB_VBN
-bannersnack_BannerSnack:VB_VBN
-bannerstandstore_BannerStandStore:VB_VBN
-bannerstore_BannerStore:VB_VBN
-bannhanhsi_BanNhanhSi:VB_VBN
-banoitro_BaNoiTro:VB_VBN
-banphucs_BanPhucs:VB_VBN
-banpnj_BanPNJ:VB_VBN
-bansimhanoi_BanSimHaNoi:VB_VBN
-banthang_BanThang:VB_VBN
-banthoconggiao_BanThoCongGiao:VB_VBN
-bantranh_BanTranh:VB_VBN
-bantt_BanTT:VB_VBN
-banvangngoc_BanVangNgoc:VB_VBN
-banvpplongbien_BanVPPLongBien:VB_VBN
-banvpptailongbien_BanVPPTaiLongBien:VB_VBN
-banxehoi_BanXeHoi:VB_VBN
-bao_BaO:VB_VBN
-baoanco_BaoAnCo:VB_VBN
-baobaohao_BaoBaoHao:VB_VBN
-baocaobang_BaoCaoBang:VB_VBN
-baocaosusagami_BaocaosuSagami:VB_VBN
-baochi_baoChi:VB_VBN
-baofeng_BaoFeng:VB_VBN
-baogam_BaoGam:VB_VBN
-baogiathietke_BaoGiaThietKe:VB_VBN
-baohaiduong_BaoHaiDuong:VB_VBN
-baohiemavia_BaohiemAvia:VB_VBN
-baohiemdulich_BaohiemDulich:VB_VBN
-baohoanthinh_baohoANTHINH:VB_VBN
-baohoxanh_BaohoXANH:VB_VBN
-baokhuyennong_BaoKhuyenNong:VB_VBN
-baokim_BaoKim:VB_VBN
-baoling_BaoLing:VB_VBN
-baominhtech_BaoMinhTech:VB_VBN
-baomoi_BaoMoi:VB_VBN
-baongoc_BaoNgoc:VB_VBN
-baophapluat_BaoPhapLuat:VB_VBN
-baophunuonline_BaoPhuNuOnline:VB_VBN
-baosteel_BaoSteel:VB_VBN
-baothanhthien_BaoThanhThien:VB_VBN
-baothyha_BaoThyHa:VB_VBN
-baotoantech_BaoToanTech:VB_VBN
-baoviet_BaoViet:VB_VBN
-baovietbank_BaoVietBank:VB_VBN
-baovietfund_BaovietFund:VB_VBN
-baovinaphone_baoVinaphone:VB_VBN
-baovn_BaoVN:VB_VBN
-baoxinviec_BaoXinViec:VB_VBN
-bap_BaP:VB_VBN
-baqthe_BAQThe:VB_VBN
-barbershop_BarberShop:VB_VBN
-barbershopvutri_BarberShopVuTri:VB_VBN
-barca_BArca:VB_VBN
-barcaarsenal_BarcaArsenal:VB_VBN
-barcamessi_BarcaMessi:VB_VBN
-barcampblock_BarCampBlock:VB_VBN
-barcatv_BarcaTV:VB_VBN
-barcelonalink_BarcelonaLink:VB_VBN
-barcelonazhou_BarcelonaZhou:VB_VBN
-barcode_BarCode:VB_VBN
-barcodeviet_BarcodeViet:VB_VBN
-bardesign_BARDesign:VB_VBN
-bardray_BardRay:VB_VBN
-baresoul_BareSoul:VB_VBN
-barevent_BarEvent:VB_VBN
-barjames_BarJames:VB_VBN
-baron_BarOn:VB_VBN
-barracuda_BarraCuda:VB_VBN
-barreamped_BarreAmped:VB_VBN
-barrelstrike_BarrelStrike:VB_VBN
-bartender_BarTender:VB_VBN
-barterdex_BarterDex:VB_VBN
-bartpe_BartPE:VB_VBN
-basaangiang_BasaAnGiang:VB_VBN
-baseactivity_BaseActivity:VB_VBN
-baseadapter_BaseAdapter:VB_VBN
-baseapiservice_BaseAPIService:VB_VBN
-baseballcap_BaseballCap:VB_VBN
-basecamp_BaseCamp:VB_VBN
-basecommand_BaseCommand:VB_VBN
-basecore_BaseCore:VB_VBN
-basedon_BasedOn:VB_VBN
-basefragment_BaseFragment:VB_VBN
-baselworld_BaselWorld:VB_VBN
-basemix_BaseMix:VB_VBN
-baseobservable_BaseObservable:VB_VBN
-baseplugin_BasePlugin:VB_VBN
-basetx_BaseTx:VB_VBN
-baseus_BaseUS:VB_VBN
-baseviewmodel_BaseViewModel:VB_VBN
-basicboard_basicBoard:VB_VBN
-basicmy_BasicMy:VB_VBN
-bason_BaSon:VB_VBN
-bassboost_BassBoost:VB_VBN
-bassebombecraft_BasseBombeCraft:VB_VBN
-bassup_BassUp:VB_VBN
-bassuptm_BassUpTM:VB_VBN
-basx_BasX:VB_VBN
-batdongsanthainguyen_BatDongSanThaiNguyen:VB_VBN
-bathao_BaThao:VB_VBN
-bathroomcheck_bathroomCheck:VB_VBN
-batman_BatMan:VB_VBN
-baton_BatON:VB_VBN
-batonriver_BatonRiver:VB_VBN
-batrider_BatRider:VB_VBN
-batrivina_BaTriVina:VB_VBN
-battcursor_BattCursor:VB_VBN
-batterybar_BatteryBar:VB_VBN
-batterycare_BatteryCare:VB_VBN
-batterycyclecount_BatteryCycleCount:VB_VBN
-batterymon_BatteryMon:VB_VBN
-batterystatusbar_BatterystatusBar:VB_VBN
-battlebots_BattleBots:VB_VBN
-battlecity_BattleCity:VB_VBN
-battleeye_BattleEye:VB_VBN
-battlefield_BattleField:VB_VBN
-battlefrontblood_BattlefrontBlood:VB_VBN
-battleground_BattleGround:VB_VBN
-battlegrounds_BattleGrounds:VB_VBN
-battlemechs_BattleMechs:VB_VBN
-battrang_BatTrang:VB_VBN
-battrangvivu_BattrangVivu:VB_VBN
-battu_BatTu:VB_VBN
-baublebar_BaubleBar:VB_VBN
-bauxitevietnam_BauxiteVietnam:VB_VBN
-bauxitvn_BauxitVN:VB_VBN
-bayang_BaYang:VB_VBN
-bayanh_bayAnh:VB_VBN
-bayarena_BayArena:VB_VBN
-bayboeing_bayBoeing:VB_VBN
-bayernlb_BayernLB:VB_VBN
-bayjetstar_bayJetstar:VB_VBN
-baymax_BayMax:VB_VBN
-baynextnext_bayNextNext:VB_VBN
-baynhé_BayNhé:VB_VBN
-baynos_bayNos:VB_VBN
-baypay_BayPay:VB_VBN
-baystreet_BayStreet:VB_VBN
-bayuber_bayUber:VB_VBN
-bayvietjet_bayVietjet:VB_VBN
-bayvip_BayVip:VB_VBN
-bazaarvoice_BazaarVoice:VB_VBN
-bazan_BaZan:VB_VBN
-bazanad_BazanAD:VB_VBN
-bazanviet_BazanViet:VB_VBN
-bazanxét_bazanXét:VB_VBN
-bazapay_BazaPay:VB_VBN
-bbawcj_BBawcj:VB_VBN
-bbbb_BBbb:VB_VBN
-bbc_bbC:VB_VBN
-bbccd_bbccD:VB_VBN
-bbcincorp_BBCIncorp:VB_VBN
-bbclone_BBClone:VB_VBN
-bbcode_BBcode:VB_VBN
-bbcodes_BBcodes:VB_VBN
-bbcooker_BBCooker:VB_VBN
-bbcosplay_BBCosplay:VB_VBN
-bbcream_BBCream:VB_VBN
-bbdd_BbDd:VB_VBN
-bber_BBer:VB_VBN
-bbers_BBers:VB_VBN
-bbgear_BBGear:VB_VBN
-bbhxh_bBHXH:VB_VBN
-bbin_BBin:VB_VBN
-bbincorp_BBIncorp:VB_VBN
-bbmtrade_BBMTrade:VB_VBN
-bbnice_BBNice:VB_VBN
-bboy_BBoy:VB_VBN
-bbplaza_BBPlaza:VB_VBN
-bbpress_bbPress:VB_VBN
-bbraun_BBraun:VB_VBN
-bbrec_BBrec:VB_VBN
-bbsilk_BBsilk:VB_VBN
-bbtalk_BBtalk:VB_VBN
-bbtc_bBTC:VB_VBN
-bbtel_BBTel:VB_VBN
-bbus_BBus:VB_VBN
-bbuzz_BBuzz:VB_VBN
-bbvietnam_BBVietnam:VB_VBN
-bbworld_BBWorld:VB_VBN
-bca_BcA:VB_VBN
-bcam_BCam:VB_VBN
-bcaquy_BCAquy:VB_VBN
-bcash_BCash:VB_VBN
-bcbgeneration_BCBGeneration:VB_VBN
-bccgroup_BCCgroup:VB_VBN
-bcdation_BCDation:VB_VBN
-bcentral_BCentral:VB_VBN
-bchrome_BChrome:VB_VBN
-bckhanh_BCkhanh:VB_VBN
-bcons_BCons:VB_VBN
-bcsthanhhoa_BCSThanhHoa:VB_VBN
-bctdo_BCTdo:VB_VBN
-bcthey_BCThey:VB_VBN
-bctquy_BCTQuy:VB_VBN
-bdatrip_BDATrip:VB_VBN
-bdomain_BDomain:VB_VBN
-bdskimoanh_BDSKimOanh:VB_VBN
-bdswiss_BDSwiss:VB_VBN
-bdtour_BDtour:VB_VBN
-beachcomber_BeachComber:VB_VBN
-beachfront_BeachFront:VB_VBN
-beachgirls_BeachGirls:VB_VBN
-beachplace_BeachPlace:VB_VBN
-beachwear_BeachWear:VB_VBN
-beagleboard_BeagleBoard:VB_VBN
-bealive_BeAlive:VB_VBN
-beamflex_BeamFlex:VB_VBN
-beamit_BeamIt:VB_VBN
-beamrecordcsvtfxio_BeamRecordCsvTFXIO:VB_VBN
-beanbag_BeanBag:VB_VBN
-beanfactory_BeanFactory:VB_VBN
-beanj_BeanJ:VB_VBN
-beanloader_BeanLoader:VB_VBN
-beanstalk_BeanStalk:VB_VBN
-beaou_BeaoU:VB_VBN
-bearbrick_BearBrick:VB_VBN
-beardbrand_BeardBrand:VB_VBN
-bearhit_BearHit:VB_VBN
-bearingpoint_BearingPoint:VB_VBN
-bearingsunits_bearingsUnits:VB_VBN
-beastfashion_BeastFashion:VB_VBN
-beastmaster_BeastMaster:VB_VBN
-beat_BeAT:VB_VBN
-beatbox_BeatBox:VB_VBN
-beatpad_BeatPad:VB_VBN
-beatsaudio_BeatsAudio:VB_VBN
-beatschinhhang_BeatsChinhHang:VB_VBN
-beatsx_BeatsX:VB_VBN
-beattik_BeatTik:VB_VBN
-beatunes_beaTunes:VB_VBN
-beatx_BeatX:VB_VBN
-beatymom_BeatyMom:VB_VBN
-beaugreen_BeauGreen:VB_VBN
-beauskin_BeauSkin:VB_VBN
-beautyblender_BeautyBlender:VB_VBN
-beautyfriend_BeautyFriend:VB_VBN
-beautyhot_BeautyHot:VB_VBN
-beautyk_BeautyK:VB_VBN
-beautymart_BeautyMart:VB_VBN
-beautyplus_BeautyPlus:VB_VBN
-beautyprincess_BeautyPrincess:VB_VBN
-beautysam_BeautySAM:VB_VBN
-beautyslim_BeautySlim:VB_VBN
-beautyspa_BeautySpa:VB_VBN
-beautystation_BeautyStation:VB_VBN
-beautyvn_BeautyVN:VB_VBN
-beautéysl_BeautéYSL:VB_VBN
-beautéyves_BeautéYves:VB_VBN
-beauxarts_BeauxArts:VB_VBN
-beavertails_BeaverTails:VB_VBN
-bebe_BeBe:VB_VBN
-bebes_bebeS:VB_VBN
-bebike_beBike:VB_VBN
-bebo_BeBo:VB_VBN
-bebook_BeBook:VB_VBN
-bebé_BeBé:VB_VBN
-becamextdc_BecamexTDC:VB_VBN
-becar_beCar:VB_VBN
-becastle_BeCastle:VB_VBN
-beconcon_BeConCon:VB_VBN
-becorp_BeCorp:VB_VBN
-bedelivery_beDelivery:VB_VBN
-bedelkorea_BedelKorea:VB_VBN
-bedensi_BeDensi:VB_VBN
-bedental_BeDental:VB_VBN
-bedit_BEdit:VB_VBN
-bedmap_BedMap:VB_VBN
-bedread_bedRead:VB_VBN
-bedsheet_BedSheet:VB_VBN
-bedtv_BedTV:VB_VBN
-bedup_BedUp:VB_VBN
-bedzed_BedZED:VB_VBN
-beeads_BeeAds:VB_VBN
-beeclean_BeeClean:VB_VBN
-beecost_BeeCost:VB_VBN
-beedoctor_BeeDoctor:VB_VBN
-beefdaily_BeefDaily:VB_VBN
-beehive_BeeHive:VB_VBN
-beehome_BeeHome:VB_VBN
-beehotel_BeeHotel:VB_VBN
-beeiq_BeeIQ:VB_VBN
-beek_BeeK:VB_VBN
-beekleave_BeekLeave:VB_VBN
-beekrowd_BeeKrowd:VB_VBN
-beemart_BeeMart:VB_VBN
-beenext_BeeNext:VB_VBN
-beeocr_BeeOCR:VB_VBN
-beeone_BeeOne:VB_VBN
-beepbeep_BeepBEEP:VB_VBN
-beeralchemy_BeerAlchemy:VB_VBN
-beerclub_BeerClub:VB_VBN
-beeschool_BeeSchool:VB_VBN
-beeseo_BeeSeo:VB_VBN
-beesister_BeeSister:VB_VBN
-beeskincare_BeeSkincare:VB_VBN
-beesmart_BeeSmart:VB_VBN
-beeso_BeeSo:VB_VBN
-beest_BeeST:VB_VBN
-beetours_BeeTours:VB_VBN
-beevsmart_BeeVsmart:VB_VBN
-beewin_BeeWin:VB_VBN
-beexpress_beExpress:VB_VBN
-beezero_BeeZero:VB_VBN
-beezmax_BeezMax:VB_VBN
-beface_BeFace:VB_VBN
-befinancial_beFinancial:VB_VBN
-befirst_beFirst:VB_VBN
-beflight_beFlight:VB_VBN
-beforedestroy_beforeDestroy:VB_VBN
-beforeeach_beforeEach:VB_VBN
-beforemethod_BeforeMethod:VB_VBN
-befunky_BeFunky:VB_VBN
-begame_beGAME:VB_VBN
-beginero_BeginerO:VB_VBN
-beginweb_BeginWeb:VB_VBN
-bego_BeGo:VB_VBN
-begreen_BeGreen:VB_VBN
-begroup_beGroup:VB_VBN
-behaviosec_BehavioSec:VB_VBN
-behoa_beHoa:VB_VBN
-behringermixer_BehringerMixer:VB_VBN
-behringertrong_BEHRINGERTrong:VB_VBN
-behrtech_BehrTech:VB_VBN
-beianmin_BeiAnMin:VB_VBN
-beicamel_BeiCamel:VB_VBN
-beid_BeID:VB_VBN
-beidak_BeiDak:VB_VBN
-beidou_BeiDou:VB_VBN
-bein_beIN:VB_VBN
-beincrypto_BeInCrypto:VB_VBN
-belarustin_BelarusTin:VB_VBN
-belfond_BelFond:VB_VBN
-belhomes_BelHomes:VB_VBN
-belladonna_BellaDonna:VB_VBN
-bellalussi_bellaLussi:VB_VBN
-bellapasta_BellaPasta:VB_VBN
-bellapple_BellApple:VB_VBN
-bellasofa_BellaSofa:VB_VBN
-bellboeing_BellBoeing:VB_VBN
-bellcleveland_BellCleveland:VB_VBN
-bellfacebook_BellFacebook:VB_VBN
-bellhertz_BellHertz:VB_VBN
-belljen_BellJen:VB_VBN
-bellmondelez_BellMondelez:VB_VBN
-bellwhite_BellWhite:VB_VBN
-bellydance_BellyDance:VB_VBN
-belmarrahealth_BelMarraHealth:VB_VBN
-belongstomany_belongsToMany:VB_VBN
-beloyalty_beLoyalty:VB_VBN
-belresort_BelResort:VB_VBN
-beltsville_BeltSville:VB_VBN
-bemeet_BeMeet:VB_VBN
-bemob_BeMob:VB_VBN
-bemusic_BeMusic:VB_VBN
-ben_BeN:VB_VBN
-benbachdang_BenBachDang:VB_VBN
-benben_BenBen:VB_VBN
-bencat_BenCat:VB_VBN
-benchmark_BenchMark:VB_VBN
-benchmarkemail_BenchmarkEmail:VB_VBN
-benchsci_BenchSci:VB_VBN
-benchtown_BenchTown:VB_VBN
-benclub_BenClub:VB_VBN
-benefitslowers_BenefitsLowers:VB_VBN
-benext_BeNext:VB_VBN
-beng_BEng:VB_VBN
-bengan_BeNgan:VB_VBN
-benglo_BenGlo:VB_VBN
-bengmea_BengMea:VB_VBN
-benhvienk_BenhvienK:VB_VBN
-benk_BeNK:VB_VBN
-benkhang_BenKhang:VB_VBN
-benkona_BenKona:VB_VBN
-benlatc_BenlatC:VB_VBN
-benow_beNow:VB_VBN
-benq_BenQ:VB_VBN
-benqm_BenQm:VB_VBN
-benriach_BenRiach:VB_VBN
-benshop_BenShop:VB_VBN
-benstyle_BenStyle:VB_VBN
-benthanh_BenThanh:VB_VBN
-bentpixels_BentPixels:VB_VBN
-bentre_BenTre:VB_VBN
-benvila_BenVila:VB_VBN
-benvip_BenVip:VB_VBN
-benzmbo_BenzMBO:VB_VBN
-beobeo_BeoBeo:VB_VBN
-beobeomarketing_BeoBeoMarketing:VB_VBN
-beokaka_BeoKaka:VB_VBN
-beolab_BeoLab:VB_VBN
-beolink_BeoLink:VB_VBN
-beone_BeOne:VB_VBN
-beopen_BeOpen:VB_VBN
-beoplay_BeoPlay:VB_VBN
-beorganic_BeOrganic:VB_VBN
-beos_BeOS:VB_VBN
-beosound_BeoSound:VB_VBN
-beoutq_beoutQ:VB_VBN
-bephoangcuong_BepHoangCuong:VB_VBN
-bepicolombo_BepiColombo:VB_VBN
-bepnk_BepNK:VB_VBN
-bepoint_bePoint:VB_VBN
-bepoppc_BepopPC:VB_VBN
-bepos_bePOS:VB_VBN
-bepro_bePro:VB_VBN
-bepxanh_BepXANH:VB_VBN
-bepxua_BepXua:VB_VBN
-bepyeu_BepYeu:VB_VBN
-bequeen_beQueen:VB_VBN
-bequyen_BeQuyen:VB_VBN
-berich_BeRich:VB_VBN
-bermaguibermagui_BermaguiBermagui:VB_VBN
-bernabeuvideo_BernabeuVideo:VB_VBN
-bernalillobernalillo_BernalilloBernalillo:VB_VBN
-berriver_BerRiver:VB_VBN
-berryalloc_BerryAlloc:VB_VBN
-berrybuzz_BerryBuzz:VB_VBN
-berrygood_BerryGood:VB_VBN
-berrylink_BerryLink:VB_VBN
-beryl_BeryL:VB_VBN
-bes_BeS:VB_VBN
-besecure_BeSECURE:VB_VBN
-beslim_BesLim:VB_VBN
-besoccer_BeSoccer:VB_VBN
-bestair_BestAir:VB_VBN
-bestb_BestB:VB_VBN
-bestbond_BestBond:VB_VBN
-bestboy_BestBoy:VB_VBN
-bestbuy_BestBuy:VB_VBN
-bestco_BestCo:VB_VBN
-bestcoat_BestCoat:VB_VBN
-bestcon_BestCon:VB_VBN
-bestcure_BestCure:VB_VBN
-bestdaxua_BestDaxua:VB_VBN
-bestdecor_BestDecor:VB_VBN
-bestfloor_BestFloor:VB_VBN
-bestflow_BestFlow:VB_VBN
-bestfood_BestFood:VB_VBN
-bestgrout_BestGrout:VB_VBN
-besthard_BestHard:VB_VBN
-besthome_BestHome:VB_VBN
-besthomecho_BestHomecho:VB_VBN
-bestlab_BestLab:VB_VBN
-bestled_BestLED:VB_VBN
-bestme_BestMe:VB_VBN
-bestmix_BestMix:VB_VBN
-bestnac_BestNac:VB_VBN
-bestnutri_BestNutri:VB_VBN
-bestprice_BestPrice:VB_VBN
-bestprimer_BestPrimer:VB_VBN
-bestprotect_BestProtect:VB_VBN
-bestrepair_BestRepair:VB_VBN
-bestscores_BestScores:VB_VBN
-bestseal_BestSeal:VB_VBN
-bestshop_BestShop:VB_VBN
-bestshot_BestShot:VB_VBN
-bestsoicau_BestSoiCau:VB_VBN
-bestsync_BestSync:VB_VBN
-bestthinner_BestThinner:VB_VBN
-besttile_BestTile:VB_VBN
-bestwyll_BestWyll:VB_VBN
-besudesu_BesuDesu:VB_VBN
-besway_BesWay:VB_VBN
-betaglucan_BetaGlucan:VB_VBN
-betahost_BetaHost:VB_VBN
-betamerica_BetAmerica:VB_VBN
-betanet_BetaNet:VB_VBN
-betathe_BetaThe:VB_VBN
-betaviet_BetaViet:VB_VBN
-betconstruct_BetConstruct:VB_VBN
-beten_BeTen:VB_VBN
-betgames_BetGames:VB_VBN
-betheme_BeTheme:VB_VBN
-betlv_BetLV:VB_VBN
-betmatch_BetMatch:VB_VBN
-betonmarkets_BetOnMarkets:VB_VBN
-betphoenix_BetPhoenix:VB_VBN
-betradar_BetRadar:VB_VBN
-betraining_BeTraining:VB_VBN
-betredkings_BetRedKings:VB_VBN
-betsfortraders_BetsForTraders:VB_VBN
-betsoft_BetSoft:VB_VBN
-betterbatterystats_BetterBatteryStats:VB_VBN
-betterlife_BetterLife:VB_VBN
-bettersnaptool_BetterSnapTool:VB_VBN
-betterwmf_BetterWMF:VB_VBN
-betterworks_BetterWorks:VB_VBN
-bettingkick_BettingKick:VB_VBN
-bettrung_betTrung:VB_VBN
-betuti_BeTuTi:VB_VBN
-betvictor_BetVictor:VB_VBN
-betvision_BetVision:VB_VBN
-beu_BeU:VB_VBN
-beucare_BeUcare:VB_VBN
-beucup_BeUcup:VB_VBN
-bevita_BEViTA:VB_VBN
-bewater_beWater:VB_VBN
-beweather_BeWeather:VB_VBN
-bewhy_BewhY:VB_VBN
-bexaclean_BexaClean:VB_VBN
-beyerdynamic_BeyerDynamic:VB_VBN
-beyoga_beYoga:VB_VBN
-beyondeyes_BeyondEyes:VB_VBN
-beyondmeat_BeyondMeat:VB_VBN
-beyondsexy_BeyondSexy:VB_VBN
-beyondunewzuii_BeyondUNewZuii:VB_VBN
-bezosexpeditions_BezosExpeditions:VB_VBN
-bfan_BFan:VB_VBN
-bfaudio_BFAudio:VB_VBN
-bfaudiopro_BFAudioPro:VB_VBN
-bfgf_bFGF:VB_VBN
-bfgoodrich_BFGoodrich:VB_VBN
-bfood_BFood:VB_VBN
-bfoodrich_BFoodrich:VB_VBN
-bfroodich_BFroodich:VB_VBN
-bfv_BfV:VB_VBN
-bgata_BGata:VB_VBN
-bgchanger_BGChanger:VB_VBN
-bgning_BGNing:VB_VBN
-bhcg_bhCG:VB_VBN
-bhflex_BHflex:VB_VBN
-bhip_bHIP:VB_VBN
-bhmedia_BHMedia:VB_VBN
-bhome_BHome:VB_VBN
-bhomes_BHomes:VB_VBN
-bhorse_BHorse:VB_VBN
-bhran_BHRan:VB_VBN
-bhutanesemonestry_BhutaneseMonestry:VB_VBN
-bhxhonline_BHXHonline:VB_VBN
-bhyt_BHyt:VB_VBN
-biabia_biaBia:VB_VBN
-biacraft_BiaCraft:VB_VBN
-bian_BiAn:VB_VBN
-bianconeri_BIanconeri:VB_VBN
-biancor_BiancoR:VB_VBN
-biang_BiAng:VB_VBN
-bianhap_BiaNhap:VB_VBN
-biankinhhoang_BiAnKinhHoang:VB_VBN
-biatuongniemnguyentohoho_BiatuongniemnguyentohoHo:VB_VBN
-bib_BiB:VB_VBN
-biball_BiBall:VB_VBN
-bibi_BiBi:VB_VBN
-bibimart_BibiMart:VB_VBN
-bibitcrypto_BiBitCrypto:VB_VBN
-bibito_BiBiTo:VB_VBN
-bibliocraft_BiblioCraft:VB_VBN
-bibo_BiBo:VB_VBN
-bibomart_BiboMart:VB_VBN
-bibon_BiBon:VB_VBN
-bibook_biBook:VB_VBN
-bibop_BiBop:VB_VBN
-bicc_BicC:VB_VBN
-biccamera_BicCamera:VB_VBN
-bici_BiCi:VB_VBN
-bicland_BicLand:VB_VBN
-bico_BiCo:VB_VBN
-bictweb_BICTweb:VB_VBN
-bicweb_BICWeb:VB_VBN
-bicyclelight_BicycleLight:VB_VBN
-bidenanh_BidenAnh:VB_VBN
-bidenlo_BidenLo:VB_VBN
-bidoup_BiDoup:VB_VBN
-bidspaceer_BidSpaceer:VB_VBN
-bidv_BiDV:VB_VBN
-bidver_BIDVer:VB_VBN
-bidvertiser_BidVertiser:VB_VBN
-biendong_BienDong:VB_VBN
-biengioitrenbien_BienGioiTrenBien:VB_VBN
-biennho_BienNho:VB_VBN
-bietko_BietKo:VB_VBN
-bietthubh_bietthuBH:VB_VBN
-bietthuvip_BietthuVIP:VB_VBN
-bifisanfo_BifiSanfo:VB_VBN
-biflex_BiFlex:VB_VBN
-big_BiG:VB_VBN
-bigassfan_BigAssFan:VB_VBN
-bigbaby_BigBaby:VB_VBN
-bigbag_BigBag:VB_VBN
-bigbang_BigBang:VB_VBN
-bigbb_BigBB:VB_VBN
-bigbear_BigBear:VB_VBN
-bigbee_BigBee:VB_VBN
-bigben_BigBen:VB_VBN
-bigbig_BigBig:VB_VBN
-bigbike_BigBike:VB_VBN
-bigbluebutton_BigBlueButton:VB_VBN
-bigboi_BigBoi:VB_VBN
-bigboss_BigBoss:VB_VBN
-bigbox_BigBox:VB_VBN
-bigc_BigC:VB_VBN
-bigcake_BigCake:VB_VBN
-bigcitygold_BigcityGold:VB_VBN
-bigcityonline_BigCityOnline:VB_VBN
-bigclub_BigClub:VB_VBN
-bigco_BigCo:VB_VBN
-bigcoi_BigCoi:VB_VBN
-bigcoin_BigCoin:VB_VBN
-bigcoinvietnam_BigcoinVietnam:VB_VBN
-bigcom_BigCom:VB_VBN
-bigcommerce_BigCommerce:VB_VBN
-bigcool_BigCool:VB_VBN
-bigdadddy_BigDadddy:VB_VBN
-bigdaddy_BigDaddy:VB_VBN
-bigdata_BigData:VB_VBN
-bigdecimal_BigDecimal:VB_VBN
-bigdog_BigDog:VB_VBN
-bigdoor_BigDoor:VB_VBN
-bigfamily_BigFamily:VB_VBN
-bigfontbutton_BigFontButton:VB_VBN
-bigfontbuttonstyle_BigFontButtonStyle:VB_VBN
-biggold_BigGold:VB_VBN
-biggreen_BigGreen:VB_VBN
-bigguy_BigGuy:VB_VBN
-bighit_BigHit:VB_VBN
-bighome_BigHome:VB_VBN
-bighouse_BigHouse:VB_VBN
-bigid_BigID:VB_VBN
-bigint_BigInt:VB_VBN
-biginteger_BigInteger:VB_VBN
-bigk_BigK:VB_VBN
-bigkey_BigKey:VB_VBN
-bigkool_BigKool:VB_VBN
-bigkooldeng_bigkoolDeng:VB_VBN
-biglai_BigLai:VB_VBN
-biglaitrong_BIGLAITrong:VB_VBN
-bigland_BigLand:VB_VBN
-bigly_BigLy:VB_VBN
-bigmouth_BigMouth:VB_VBN
-bignox_BigNox:VB_VBN
-bigoceanenm_BigOceanENM:VB_VBN
-bigoff_BigOFF:VB_VBN
-bigolive_BigoLive:VB_VBN
-bigon_BigOn:VB_VBN
-bigone_BigOne:VB_VBN
-bigoption_BigOption:VB_VBN
-bigphone_BigPhone:VB_VBN
-bigpicture_BigPicture:VB_VBN
-bigpond_BigPond:VB_VBN
-bigquery_BigQuery:VB_VBN
-bigrock_BigRock:VB_VBN
-bigrubi_BigRubi:VB_VBN
-bigschool_BigSchool:VB_VBN
-bigsea_BigSea:VB_VBN
-bigseller_BigSeller:VB_VBN
-bigseo_BigSeo:VB_VBN
-bigsize_BigSize:VB_VBN
-bigsouth_BigSouth:VB_VBN
-bigsport_BigSport:VB_VBN
-bigspy_BigSpy:VB_VBN
-bigstar_BigStar:VB_VBN
-bigsur_BigSur:VB_VBN
-bigsv_BigSV:VB_VBN
-bigtable_BigTable:VB_VBN
-bigtech_BigTech:VB_VBN
-bigtime_BigTime:VB_VBN
-bigtour_BigTour:VB_VBN
-bigzen_BigZen:VB_VBN
-bihin_BiHin:VB_VBN
-biho_BiHo:VB_VBN
-bihrp_BiHRP:VB_VBN
-bii_biI:VB_VBN
-bikae_BiKae:VB_VBN
-bikebagshop_BikeBagShop:VB_VBN
-bikekidshop_BikeKidShop:VB_VBN
-biker_BIker:VB_VBN
-bikestores_BikeStores:VB_VBN
-biketrailershop_BikeTrailerShop:VB_VBN
-bikinigenie_BikiniGenie:VB_VBN
-bilbao_BIlbao:VB_VBN
-bilibili_BiliBili:VB_VBN
-bill_BIll:VB_VBN
-billboard_BillBoard:VB_VBN
-billgate_BillGate:VB_VBN
-billgates_BillGates:VB_VBN
-billgateschool_BillgateSchool:VB_VBN
-billinfo_BillInfo:VB_VBN
-billingdetail_BillingDetail:VB_VBN
-billingdetails_BillingDetails:VB_VBN
-billygraham_BillyGraham:VB_VBN
-bilutv_BiluTV:VB_VBN
-biluxury_BiLuxury:VB_VBN
-bimayxaydungcongtrinh_BImayxaydungcongtrinh:VB_VBN
-bimayxaydungcongtrinhsites_BImayxaydungcongtrinhsites:VB_VBN
-bimbipnew_BimBipNew:VB_VBN
-bimbon_BimBon:VB_VBN
-bimemo_BiMemo:VB_VBN
-bimi_BiMi:VB_VBN
-bimore_BiMore:VB_VBN
-binancedex_BinanceDEX:VB_VBN
-binancesau_BinanceSau:VB_VBN
-binanciansvn_BinanciansVN:VB_VBN
-binarybook_BinaryBook:VB_VBN
-binaryedge_BinaryEdge:VB_VBN
-binaryonline_BinaryOnline:VB_VBN
-binaryoptionsfree_BinaryOptionsFree:VB_VBN
-binaryoptionstrading_BinaryOptionsTrading:VB_VBN
-binaryreader_BinaryReader:VB_VBN
-binarytilt_BinaryTilt:VB_VBN
-binarytrading_BinaryTrading:VB_VBN
-binarywriter_BinaryWriter:VB_VBN
-binbin_BinBin:VB_VBN
-binbon_BinBon:VB_VBN
-binclub_BinClub:VB_VBN
-bindingcontext_BindingContext:VB_VBN
-bindingsource_BindingSource:VB_VBN
-bindtype_BindType:VB_VBN
-bingamer_BinGamer:VB_VBN
-bingbang_BingBang:VB_VBN
-bingbon_BingBon:VB_VBN
-bingboong_BingBoong:VB_VBN
-bingo_BinGo:VB_VBN
-bingobox_BingoBox:VB_VBN
-bingsu_BingSu:VB_VBN
-binh_BInh:VB_VBN
-binhangps_BinhAnGPS:VB_VBN
-binhanh_BinhAnh:VB_VBN
-binhdinh_BinhDinh:VB_VBN
-binhduongweb_BinhDuongWeb:VB_VBN
-binhgolf_BinhGolf:VB_VBN
-binhli_binhLi:VB_VBN
-binhminhdigital_BinhMinhDigital:VB_VBN
-binhminhjsc_BinhMinhjsc:VB_VBN
-binhtan_BinhTan:VB_VBN
-binhtg_BinhTG:VB_VBN
-binhtrong_binhTrong:VB_VBN
-binhyen_BinhYen:VB_VBN
-binomosearching_binomoSearching:VB_VBN
-binrin_BinRin:VB_VBN
-binz_BinZ:VB_VBN
-binzim_BinZim:VB_VBN
-bio_BiO:VB_VBN
-bioaid_BioAid:VB_VBN
-bioamp_BioAMP:VB_VBN
-bioaqua_BioAqua:VB_VBN
-biobank_BioBank:VB_VBN
-biobee_BioBee:VB_VBN
-biobutton_BioButton:VB_VBN
-biocare_BioCare:VB_VBN
-biocatch_BioCatch:VB_VBN
-biocell_BioCell:VB_VBN
-biocellection_BioCellection:VB_VBN
-biocenter_BioCenter:VB_VBN
-bioceuticals_BioCeuticals:VB_VBN
-biochef_BioChef:VB_VBN
-bioci_BiOCI:VB_VBN
-bioco_BioCo:VB_VBN
-bioconsortia_BioConsortia:VB_VBN
-biocontrol_BioControl:VB_VBN
-bioentry_BioEntry:VB_VBN
-biofat_BioFat:VB_VBN
-biofine_BioFine:VB_VBN
-biofix_BioFix:VB_VBN
-biofresh_BioFresh:VB_VBN
-biogaia_BioGaia:VB_VBN
-biogasviet_BiogasViet:VB_VBN
-bioginkgo_BioGinkgo:VB_VBN
-biogold_BioGold:VB_VBN
-biogs_BioGS:VB_VBN
-biohealth_BioHealth:VB_VBN
-bioheatlh_BioHeatlh:VB_VBN
-biohorizons_BioHorizons:VB_VBN
-bioiberica_BioiBerica:VB_VBN
-bioid_BioID:VB_VBN
-bioisland_BioIsland:VB_VBN
-biokeratin_BioKeratin:VB_VBN
-biolab_BioLab:VB_VBN
-biolabs_BioLabs:VB_VBN
-biolight_BioLight:VB_VBN
-biolumin_BioLumin:VB_VBN
-biomart_BioMart:VB_VBN
-biomask_BioMask:VB_VBN
-biomat_BioMat:VB_VBN
-biomax_BioMax:VB_VBN
-biomed_BioMed:VB_VBN
-biomedicine_BioMedicine:VB_VBN
-biometrylockout_biometryLockout:VB_VBN
-biometrynotavailable_biometryNotAvailable:VB_VBN
-biometrynotenrolled_biometryNotEnrolled:VB_VBN
-biomicrobics_BioMicrobics:VB_VBN
-biomilq_BioMilq:VB_VBN
-biomind_BioMind:VB_VBN
-biomini_BioMini:VB_VBN
-biomérieux_bioMérieux:VB_VBN
-bionafem_BionaFem:VB_VBN
-bionet_BioNet:VB_VBN
-bionix_BioniX:VB_VBN
-bionixwallpaper_BioniXWallpaper:VB_VBN
-biontech_BioNTech:VB_VBN
-biontechbtm_BiontechBTM:VB_VBN
-bioperine_BioPerine:VB_VBN
-biopolymer_BioPolymer:VB_VBN
-biopria_BioPRIA:VB_VBN
-bioqueen_BioQueen:VB_VBN
-biorescue_BioRescue:VB_VBN
-biorxiv_bioRxiv:VB_VBN
-bios_BiOS:VB_VBN
-biosacotec_BioSacotec:VB_VBN
-bioscentdx_BioScentDx:VB_VBN
-bioscience_BioScience:VB_VBN
-bioscosmetic_BioSCosmetic:VB_VBN
-bioscreen_BioScreen:VB_VBN
-bioses_BIOSes:VB_VBN
-biosh_BioSH:VB_VBN
-bioshock_BioShock:VB_VBN
-bioslim_BioSlim:VB_VBN
-biospatch_BiosPatch:VB_VBN
-biosphere_BIOSphere:VB_VBN
-biospring_BioSpring:VB_VBN
-biostation_BioStation:VB_VBN
-biotech_BioTech:VB_VBN
-biotechusa_BiotechUSA:VB_VBN
-bioticstm_bioticsTM:VB_VBN
-biotime_BioTime:VB_VBN
-biotopcare_BioTopcare:VB_VBN
-biotouch_BioTouch:VB_VBN
-biotracker_BioTracker:VB_VBN
-bioultraslimplus_BioUltraslimplus:VB_VBN
-biovite_BioVite:VB_VBN
-biovyzr_BioVYZR:VB_VBN
-bioware_BioWare:VB_VBN
-biowave_BioWave:VB_VBN
-biox_BioX:VB_VBN
-bioz_BioZ:VB_VBN
-biozen_BioZen:VB_VBN
-biozone_BioZone:VB_VBN
-bipap_BiPAP:VB_VBN
-bira_BiRa:VB_VBN
-birchpress_BirchPress:VB_VBN
-birdgang_BirdGang:VB_VBN
-birdlb_BirdLB:VB_VBN
-birdlife_BirdLife:VB_VBN
-birshreshto_BirShreshto:VB_VBN
-birthdaybluecaptain_birthdayblueCaptain:VB_VBN
-biscell_BisCell:VB_VBN
-bish_BiSH:VB_VBN
-bishub_BisHub:VB_VBN
-bissellther_BissellTHER:VB_VBN
-bitagged_biTagged:VB_VBN
-bitangels_BitAngels:VB_VBN
-bitarg_BitARG:VB_VBN
-bitasset_BitAsset:VB_VBN
-bitbank_BitBank:VB_VBN
-bitbay_BitBay:VB_VBN
-bitbtc_BitBTC:VB_VBN
-bitbucket_BitBucket:VB_VBN
-bitcafe_BITCafe:VB_VBN
-bitcasino_BitCasino:VB_VBN
-bitcherry_BitCherry:VB_VBN
-bitclave_BitClave:VB_VBN
-bitcluster_BitCluster:VB_VBN
-bitcoin_BitCoin:VB_VBN
-bitcoinbd_BitcoinBD:VB_VBN
-bitcoincore_BitcoinCore:VB_VBN
-bitcoinnext_BitcoinNext:VB_VBN
-bitcointalk_BitcoinTalk:VB_VBN
-bitcoinvisuals_BitcoinVisuals:VB_VBN
-bitcoinvn_BitcoinVN:VB_VBN
-bitcoloan_BitcoLoan:VB_VBN
-bitcomet_BitComet:VB_VBN
-bitcomposer_bitComposer:VB_VBN
-bitconnect_BitConnect:VB_VBN
-bitcup_BITcup:VB_VBN
-bitdefender_BitDefender:VB_VBN
-bitdegree_BitDegree:VB_VBN
-bitdi_BiTDI:VB_VBN
-bitdrive_BitDrive:VB_VBN
-bitechpharm_BitechPharm:VB_VBN
-biten_BiTen:VB_VBN
-bitesms_BiteSMS:VB_VBN
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-boxbox_BoxBox:VB_VBN
-boxcnbc_BoxCNBC:VB_VBN
-boxdr_BoxDr:VB_VBN
-boxitvn_BoxitVN:VB_VBN
-boxme_BoxMe:VB_VBN
-boxmod_BoxMod:VB_VBN
-boxmohamed_BoxMohamed:VB_VBN
-boxphanmem_BoxPhanMem:VB_VBN
-boxrec_BoxRec:VB_VBN
-boxsigns_BoxSigns:VB_VBN
-boxstores_BoxStores:VB_VBN
-boxvnpt_BoxVNPT:VB_VBN
-boxvr_BoxVR:VB_VBN
-boxyzvn_BoxyzVN:VB_VBN
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-boylover_BoyLover:VB_VBN
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-bqer_BQer:VB_VBN
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-brainchip_BrainChip:VB_VBN
-braincoach_BrainCoach:VB_VBN
-brainex_BrainEx:VB_VBN
-braingames_BrainGames:VB_VBN
-braingroup_BrainGroup:VB_VBN
-brainhearing_BrainHearing:VB_VBN
-brainhq_BrainHQ:VB_VBN
-brainmark_BrainMark:VB_VBN
-brainpickings_BrainPickings:VB_VBN
-brainpill_BrainPill:VB_VBN
-brainsbreaker_BrainsBreaker:VB_VBN
-braintalent_BrainTalent:VB_VBN
-brainwork_BrainWork:VB_VBN
-brainytab_BrainyTab:VB_VBN
-braithwaitereal_BraithwaiteReal:VB_VBN
-branchcache_BranchCache:VB_VBN
-brandbeats_BrandBeats:VB_VBN
-brandextract_BrandExtract:VB_VBN
-brandgift_BrandGift:VB_VBN
-brandindex_BrandIndex:VB_VBN
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-brandmentions_BrandMentions:VB_VBN
-brandname_BrandName:VB_VBN
-brandscxt_brandsCXT:VB_VBN
-brandsvietnam_BrandsVietnam:VB_VBN
-brandtop_brandTop:VB_VBN
-brandx_BrandX:VB_VBN
-brandyourself_BrandYourself:VB_VBN
-brandz_BrandZ:VB_VBN
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-bravebits_BraveBits:VB_VBN
-braveheart_BraveHeart:VB_VBN
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-bravomang_BRAVOmang:VB_VBN
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-brazilrap_BrazilRAP:VB_VBN
-braziltrung_BrazilTrung:VB_VBN
-brazin_BraZin:VB_VBN
-breadboard_BreadBoard:VB_VBN
-breadcrumblist_BreadcrumbList:VB_VBN
-breadnsalt_BreadnSalt:VB_VBN
-breadtalk_BreadTalk:VB_VBN
-breadtalkvietnam_BreadTalkvietnam:VB_VBN
-breakdown_BreakDown:VB_VBN
-breakfree_BreakFree:VB_VBN
-breakout_BreakOut:VB_VBN
-breakupafter_breakupAfter:VB_VBN
-breezelux_BreezeLux:VB_VBN
-breviman_BreviMan:VB_VBN
-brewmaster_BrewMaster:VB_VBN
-brexitkdb_BrexitKDB:VB_VBN
-brgland_BRGLand:VB_VBN
-brgmart_BRGMart:VB_VBN
-briansolis_BrianSolis:VB_VBN
-brickhouse_BrickHouse:VB_VBN
-brickone_BrickOne:VB_VBN
-bricmag_BricMag:VB_VBN
-bricscad_BricsCAD:VB_VBN
-bridgechelsea_BridgeChelsea:VB_VBN
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-brightburn_BrightBurn:VB_VBN
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-brighton_BrightOn:VB_VBN
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-brightridge_BrightRidge:VB_VBN
-brightside_BrightSide:VB_VBN
-brightview_BrightView:VB_VBN
-brightwin_BrightWin:VB_VBN
-brilliantcolor_BrilliantColor:VB_VBN
-brilliantcolortm_BrilliantColorTM:VB_VBN
-brisasia_BrisAsia:VB_VBN
-briskheat_BriskHeat:VB_VBN
-brislaw_BrisLaw:VB_VBN
-britaindavostheresa_BritainDavosTheresa:VB_VBN
-britesmile_BriteSmile:VB_VBN
-britishcouncil_BritishCouncil:VB_VBN
-broadband_BroadBand:VB_VBN
-broadcast_BroadCast:VB_VBN
-broadcastasia_BroadcastAsia:VB_VBN
-broadcaster_BroadCaster:VB_VBN
-broadcastlivevideo_BroadcastLiveVideo:VB_VBN
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-broadlink_BroadLink:VB_VBN
-broadsoft_BroadSoft:VB_VBN
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-broccoraphanin_BroccoRaphanin:VB_VBN
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-brohotgod_BroHotgod:VB_VBN
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-brombxh_BromBXH:VB_VBN
-bronzev_BronzeV:VB_VBN
-broodwar_BroodWar:VB_VBN
-brother_BROther:VB_VBN
-brotherhood_BrotherHood:VB_VBN
-brotherhoodvn_BrotherhoodVN:VB_VBN
-browseranimationsmodule_BrowserAnimationsModule:VB_VBN
-browserleaks_BrowserLeaks:VB_VBN
-browsermark_BrowserMark:VB_VBN
-browsermob_BrowserMob:VB_VBN
-browserrouter_BrowserRouter:VB_VBN
-browsershots_BrowserShots:VB_VBN
-browsertexting_BrowserTexting:VB_VBN
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-brtgiay_BRTgiay:VB_VBN
-brushsync_BrushSync:VB_VBN
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-bscki_BsCKI:VB_VBN
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-btctrade_BTCTrade:VB_VBN
-btcusdt_BtcUsdt:VB_VBN
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-buben_BuBen:VB_VBN
-bucketlistjourney_BucketlistJourney:VB_VBN
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-buddchiari_BuddChiari:VB_VBN
-buddhanet_BuddhaNet:VB_VBN
-buddpress_BuddPress:VB_VBN
-buddybackup_BuddyBackup:VB_VBN
-buddycheck_BuddyCheck:VB_VBN
-buddymedia_BuddyMedia:VB_VBN
-buddyphones_BuddyPhones:VB_VBN
-buddypress_BuddyPress:VB_VBN
-buddyroids_BuddyRoids:VB_VBN
-buddytag_BuddyTag:VB_VBN
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-bufferapp_BufferApp:VB_VBN
-bufferedinputstream_BufferedInputStream:VB_VBN
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-buflu_BufLu:VB_VBN
-bugsplat_BugSplat:VB_VBN
-bugtracker_BugTracker:VB_VBN
-bugun_BuGun:VB_VBN
-buian_BuiAn:VB_VBN
-buildconfig_BuildConfig:VB_VBN
-buildcraft_BuildCraft:VB_VBN
-builddatastream_buildDataStream:VB_VBN
-builderall_BuilderAll:VB_VBN
-buildfire_BuildFire:VB_VBN
-building_BUilding:VB_VBN
-buildit_BuildIt:VB_VBN
-buildmacosinstallapp_BuildmacOSInstallApp:VB_VBN
-builtwith_BuiltWith:VB_VBN
-buinland_BuinLand:VB_VBN
-bulkeditor_BulkEditor:VB_VBN
-bullerriver_BullerRiver:VB_VBN
-bulletproof_BulletProof:VB_VBN
-bullguard_BullGuard:VB_VBN
-bullionvault_BullionVault:VB_VBN
-bullpro_BullPro:VB_VBN
-bumblebee_BumbleBee:VB_VBN
-bumclub_BumClub:VB_VBN
-bumpvisiochangeid_BumpVisioChangeId:VB_VBN
-bumvip_BumVip:VB_VBN
-bunchofaccount_bunchOfAccount:VB_VBN
-bunda_BunDa:VB_VBN
-bundesliga_BundesLiga:VB_VBN
-bundlespwnagetool_bundlesPwnageTool:VB_VBN
-bungarinextnext_BungariNextNext:VB_VBN
-bungkarno_BungKarno:VB_VBN
-bunniesfairy_BunniesFairy:VB_VBN
-bunnycdn_BunnyCDN:VB_VBN
-bunnynhoc_BunnyNhoc:VB_VBN
-bunpimay_BunPiMay:VB_VBN
-buocchanviet_BuocChanViet:VB_VBN
-buocdieuky_BuocDieuKy:VB_VBN
-buoidoanhung_BuoiDoanHung:VB_VBN
-buonho_BuonHo:VB_VBN
-buoonma_BuoonMa:VB_VBN
-burberry_BurBerry:VB_VBN
-burgerswap_BurgerSwap:VB_VBN
-burgertron_BurgerTron:VB_VBN
-burgr_BurGR:VB_VBN
-burjuma_BurJuma:VB_VBN
-burnaware_BurnAware:VB_VBN
-burnintest_BurnInTest:VB_VBN
-buros_BurOS:VB_VBN
-burpsuite_BurpSuite:VB_VBN
-burstcoin_BurstCoin:VB_VBN
-busamask_BusaMask:VB_VBN
-busbar_BusBar:VB_VBN
-businessescalifornia_BusinessesCalifornia:VB_VBN
-businessforyou_BusinessForYou:VB_VBN
-businessinsider_BusinessInsider:VB_VBN
-businesskorea_BusinessKorea:VB_VBN
-businessline_BusinessLine:VB_VBN
-businessweek_BusinessWeek:VB_VBN
-businesswoman_BusinessWoman:VB_VBN
-busmap_BusMap:VB_VBN
-busno_busNo:VB_VBN
-busobj_BusObj:VB_VBN
-buspar_BuSpar:VB_VBN
-busstrade_BussTrade:VB_VBN
-busterbottom_BusterBottom:VB_VBN
-bustime_BusTime:VB_VBN
-busybox_BusyBox:VB_VBN
-busycal_BusyCal:VB_VBN
-busycontacts_BusyContacts:VB_VBN
-butiqlab_ButiqLab:VB_VBN
-butterfly_ButterFly:VB_VBN
-butterflyeffect_ButterflyEffect:VB_VBN
-butternutrition_ButterNutrition:VB_VBN
-buttonbaritem_ButtonBarItem:VB_VBN
-butylene_ButyLene:VB_VBN
-buumart_buumArt:VB_VBN
-buvchallenge_BUVChallenge:VB_VBN
-buyback_BuyBack:VB_VBN
-buybitcoin_BuyBitcoin:VB_VBN
-buybowie_BuyBowie:VB_VBN
-buydomains_BuyDomains:VB_VBN
-buye_BuyE:VB_VBN
-buyingusemedicinesafely_buyingUSEmedicinesafely:VB_VBN
-buymed_BuyMed:VB_VBN
-buynow_BuyNow:VB_VBN
-buysellads_BuySellAds:VB_VBN
-buyselleth_BuysellETH:VB_VBN
-buyshares_BuyShares:VB_VBN
-buytra_BuyTra:VB_VBN
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-buzzfeed_BuzzFeed:VB_VBN
-buzzmetrics_BuzzMetrics:VB_VBN
-buzzstream_BuzzStream:VB_VBN
-buzzsumo_BuzzSumo:VB_VBN
-buzzvideo_BuzzVideo:VB_VBN
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-bylayer_ByLayer:VB_VBN
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-bytearraytodecimal_ByteArrayToDecimal:VB_VBN
-bytecoin_ByteCoin:VB_VBN
-bytedance_ByteDance:VB_VBN
-bytefence_ByteFence:VB_VBN
-bytenext_ByteNext:VB_VBN
-bytestream_ByteStream:VB_VBN
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-cableanalyzer_CableAnalyzer:VB_VBN
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-cachecade_CacheCade:VB_VBN
-cachedu_cacheDu:VB_VBN
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-canvasdemo_CanvasDemo:VB_VBN
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-capitagreen_CapitaGreen:VB_VBN
-capitaland_CapitaLand:VB_VBN
-capitalhouse_CapitalHouse:VB_VBN
-capitaliq_CapitalIQ:VB_VBN
-capitalland_CapitalLand:VB_VBN
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-capitavalue_CapitaValue:VB_VBN
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-capolicy_CAPolicy:VB_VBN
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-capsulecrm_CapsuleCRM:VB_VBN
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-careerbuilde_CareerBuilde:VB_VBN
-careerbuilder_CareerBuilder:VB_VBN
-careerday_CareerDay:VB_VBN
-careerlink_CareerLink:VB_VBN
-careerone_CareerOne:VB_VBN
-careerprep_CareerPrep:VB_VBN
-careersfanpage_CareersFanpage:VB_VBN
-careertoday_CareerToday:VB_VBN
-carefor_CareFor:VB_VBN
-carehome_CareHome:VB_VBN
-careliefgrant_CAreliefgrant:VB_VBN
-careline_CareLine:VB_VBN
-caremart_CareMart:VB_VBN
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-caremobile_CareMobile:VB_VBN
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-carenextnext_CareNextNext:VB_VBN
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-careplus_CarePlus:VB_VBN
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-caresens_CareSens:VB_VBN
-caretaker_CareTaker:VB_VBN
-careu_CareU:VB_VBN
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-caristrap_CariStrap:VB_VBN
-carkey_CarKey:VB_VBN
-carlossan_CarlosSan:VB_VBN
-carlyrose_CarlyRose:VB_VBN
-carlzeiss_CarlZeiss:VB_VBN
-carmart_CarMart:VB_VBN
-carmax_CarMax:VB_VBN
-carmouse_CarMouse:VB_VBN
-carnitinelycopene_CarnitineLycopene:VB_VBN
-carolinarustica_CarolinaRustica:VB_VBN
-caroriemate_CarorieMate:VB_VBN
-carotdav_CarotDAV:VB_VBN
-carouselpage_CarouselPage:VB_VBN
-carpassion_CarPassion:VB_VBN
-carpenterboggs_CarpenterBoggs:VB_VBN
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-carplays_CarPlays:VB_VBN
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-cascade_CasCade:VB_VBN
-cascadetype_CascadeType:VB_VBN
-caseesports_CaseEsports:VB_VBN
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-casetify_CASETiFY:VB_VBN
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-cashplay_CashPlay:VB_VBN
-cashshop_CashShop:VB_VBN
-cashsplash_CashSplash:VB_VBN
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-cashvay_CashVay:VB_VBN
-cashvn_CashVN:VB_VBN
-casinobit_casinoBit:VB_VBN
-casinocasino_CasinoCasino:VB_VBN
-casinodaddy_CasinoDaddy:VB_VBN
-casinofirday_CasinoFirday:VB_VBN
-casinofriday_CasinoFriday:VB_VBN
-casinonextnext_casinoNextNext:VB_VBN
-casinorpg_CasinoRPG:VB_VBN
-casinosecret_CasinoSecret:VB_VBN
-casinosuning_casinoSuning:VB_VBN
-casinotagged_CASINOTagged:VB_VBN
-casiohe_casioHe:VB_VBN
-casiotri_casioTri:VB_VBN
-casiovietnam_CasioVietnam:VB_VBN
-casitabi_CasiTabi:VB_VBN
-caslatency_CasLatency:VB_VBN
-casperlabs_CasperLabs:VB_VBN
-cassandratm_CassandraTM:VB_VBN
-cassavaviet_CassavaViet:VB_VBN
-castelgandolfo_CastelGandolfo:VB_VBN
-castingasia_CastingAsia:VB_VBN
-catalog_CAtalog:VB_VBN
-catalysthost_CatalystHost:VB_VBN
-catancatan_CATANCatan:VB_VBN
-catarrhaus_catarrhaUs:VB_VBN
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-catdrive_CATDrive:VB_VBN
-categoriesadmin_CategoriesAdmin:VB_VBN
-categorieskinh_CategoriesKinh:VB_VBN
-categorybylength_CategoryByLength:VB_VBN
-categorytableseeder_CategoryTableSeeder:VB_VBN
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-catking_CatKing:VB_VBN
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-catscat_CatScat:VB_VBN
-catstop_CatStop:VB_VBN
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-cauqua_CauQua:VB_VBN
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-caylua_CayLua:VB_VBN
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-cbottom_CBottom:VB_VBN
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-chaeyoung_ChaeYoung:VB_VBN
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-chainsaw_ChainSAW:VB_VBN
-chainxchange_ChainXchange:VB_VBN
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-champa_ChamPa:VB_VBN
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-chamveda_ChamVeDa:VB_VBN
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-changeschanges_ChangesChanges:VB_VBN
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-changxin_ChangXin:VB_VBN
-changyou_ChangYou:VB_VBN
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-chanh_CHanh:VB_VBN
-chanhtuoi_ChanhTuoi:VB_VBN
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-channelsurf_ChannelSurf:VB_VBN
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-chantang_ChanTang:VB_VBN
-chanyeol_ChanYeol:VB_VBN
-chao_ChaO:VB_VBN
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-chaoex_ChaoEX:VB_VBN
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-chaotua_ChaoTua:VB_VBN
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-chapologia_CHapologia:VB_VBN
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-chartboost_ChartBoost:VB_VBN
-chasen_ChaSen:VB_VBN
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-chatadding_ChatAdding:VB_VBN
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-chathead_ChatHead:VB_VBN
-chatlineguide_ChatlineGuide:VB_VBN
-chatmiup_ChatMiUp:VB_VBN
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-chemistwarehouse_ChemistWarehouse:VB_VBN
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-chemorbis_ChemOrbis:VB_VBN
-chemrar_ChemRar:VB_VBN
-chemxinen_CHEMXINen:VB_VBN
-chenglin_ChengLin:VB_VBN
-chenglong_ChengLong:VB_VBN
-chengshin_ChengShin:VB_VBN
-chengta_ChengTa:VB_VBN
-chengzhong_ChengZhong:VB_VBN
-chenshing_ChenShing:VB_VBN
-cheondong_CheonDong:VB_VBN
-cheongkwanjang_CheongKwanJang:VB_VBN
-cheongsongwon_CheongSongWon:VB_VBN
-cheonnok_CheonNok:VB_VBN
-chephim_ChePhim:VB_VBN
-cherrycredtits_CherryCredtits:VB_VBN
-cherrynguyen_CherryNguyen:VB_VBN
-cherrystars_CherryStars:VB_VBN
-chetri_cheTri:VB_VBN
-cheyenne_CHeyenne:VB_VBN
-chi_chI:VB_VBN
-chiacontinue_chiaContinue:VB_VBN
-chiangmai_ChiangMai:VB_VBN
-chiasegame_ChiaSeGame:VB_VBN
-chiasenhac_ChiaSeNhac:VB_VBN
-chiasetutam_ChiaSeTuTam:VB_VBN
-chiasewp_ChiaseWP:VB_VBN
-chiba_ChiBa:VB_VBN
-chibaomacd_ChibaoMACD:VB_VBN
-chibi_ChiBi:VB_VBN
-chibiemon_ChibiEmon:VB_VBN
-chiboy_ChiBoy:VB_VBN
-chicagovps_ChicagoVPS:VB_VBN
-chicbanana_ChicBanana:VB_VBN
-chicharitohernandez_ChicharitoHernandez:VB_VBN
-chickendeli_ChickenDeli:VB_VBN
-chickensfdtu_ChickensFDTU:VB_VBN
-chicomotsuthat_ChiCoMotSuThat:VB_VBN
-chidoanh_chiDoanh:VB_VBN
-chidt_ChiDT:VB_VBN
-chiem_CHiem:VB_VBN
-chienthan_ChienThan:VB_VBN
-chienv_ChienV:VB_VBN
-chieru_CHIeru:VB_VBN
-chieudai_chieuDai:VB_VBN
-chieuninh_ChieuNinh:VB_VBN
-chieurong_chieuRong:VB_VBN
-chieuta_ChieuTa:VB_VBN
-chieuthanhnghia_ChieuThanhNghia:VB_VBN
-childcomponent_ChildComponent:VB_VBN
-childfun_ChildFun:VB_VBN
-childfund_ChildFund:VB_VBN
-childhope_ChildHope:VB_VBN
-childitem_ChildItem:VB_VBN
-childlife_ChildLife:VB_VBN
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-childrenmeet_childrenMeet:VB_VBN
-chile_ChiLe:VB_VBN
-chileave_ChiLeave:VB_VBN
-chileye_chileYe:VB_VBN
-chilimall_ChiliMall:VB_VBN
-chilinh_ChiLinh:VB_VBN
-chiliz_ChiliZ:VB_VBN
-chillhouse_ChillHouse:VB_VBN
-chimayochimayo_ChimayoChimayo:VB_VBN
-chimorpho_chiMorpho:VB_VBN
-chimpmate_ChimpMate:VB_VBN
-chimthung_chimThung:VB_VBN
-chinaafricaadvisory_ChinaAfricaAdvisory:VB_VBN
-chinabond_ChinaBond:VB_VBN
-chinadaily_ChinaDaily:VB_VBN
-chinafile_ChinaFile:VB_VBN
-chinajoy_ChinaJoy:VB_VBN
-chinamart_ChinaMart:VB_VBN
-chinamil_ChinaMil:VB_VBN
-chinapages_ChinaPages:VB_VBN
-chinatown_ChinaTown:VB_VBN
-chinatv_ChinaTV:VB_VBN
-chinawatch_ChinaWatch:VB_VBN
-chindangshun_ChinDangShun:VB_VBN
-chinemaster_ChineMaster:VB_VBN
-chineseskill_ChineseSkill:VB_VBN
-chinext_ChiNext:VB_VBN
-chingchuankang_ChingChuanKang:VB_VBN
-chinghai_ChingHai:VB_VBN
-chingluh_ChingLuh:VB_VBN
-chinh_CHinh:VB_VBN
-chinhanh_ChinhAnh:VB_VBN
-chinhdanh_ChinhDanh:VB_VBN
-chinhuei_ChinHuei:VB_VBN
-chinsu_CHinsu:VB_VBN
-chint_ChiNT:VB_VBN
-chip_CHip:VB_VBN
-chipbanana_ChipBanana:VB_VBN
-chipbi_ChipBi:VB_VBN
-chipboy_ChipBoy:VB_VBN
-chipchip_ChipChip:VB_VBN
-chipchipshop_ChipChipShop:VB_VBN
-chipdepxinh_ChipDepXinh:VB_VBN
-chipkool_ChipKool:VB_VBN
-chipsnapdragon_chipSnapdragon:VB_VBN
-chipu_ChiPu:VB_VBN
-chisoncung_CHISONcung:VB_VBN
-chitchat_ChitChat:VB_VBN
-chitieu_ChiTieu:VB_VBN
-chiun_ChiUn:VB_VBN
-chkawai_CHKawai:VB_VBN
-chkdsk_ChkDsk:VB_VBN
-chlbcontinue_CHLBContinue:VB_VBN
-chlorophyii_ChlorophyII:VB_VBN
-chlorun_ChloRun:VB_VBN
-chnii_CHnII:VB_VBN
-chnn_chNn:VB_VBN
-choa_ChoA:VB_VBN
-choangclub_ChoangClub:VB_VBN
-chochen_ChoChen:VB_VBN
-chochi_choChi:VB_VBN
-chocho_choCho:VB_VBN
-chocochips_ChocoChips:VB_VBN
-chocofit_ChocoFit:VB_VBN
-chocongnghiep_ChoCongNghiep:VB_VBN
-chocontinue_choContinue:VB_VBN
-chocoslim_ChocoSlim:VB_VBN
-chodotot_chodoTOT:VB_VBN
-chogodaddy_choGoDaddy:VB_VBN
-chogoogle_choGoogle:VB_VBN
-choicemail_ChoiceMail:VB_VBN
-chokershop_chokerShop:VB_VBN
-chokh_choKH:VB_VBN
-choki_ChoKi:VB_VBN
-chokwang_ChoKwang:VB_VBN
-cholab_ChoLab:VB_VBN
-cholestoff_CholestOff:VB_VBN
-cholin_CHolin:VB_VBN
-cholon_ChoLon:VB_VBN
-cholonsjc_CholonSJC:VB_VBN
-choluoi_ChoLuoi:VB_VBN
-chomalaysia_choMalaysia:VB_VBN
-choman_choMan:VB_VBN
-chome_CHome:VB_VBN
-chomicrosoft_choMicrosoft:VB_VBN
-chompsms_ChompSMS:VB_VBN
-chonam_choNam:VB_VBN
-chonamkhing_choNamkhing:VB_VBN
-choncafe_chonCafe:VB_VBN
-chonchon_ChonChon:VB_VBN
-chonghua_ChongHua:VB_VBN
-chongluadao_ChongLuaDao:VB_VBN
-chonmuachuan_ChonMuaChuan:VB_VBN
-chopardxie_chopardXie:VB_VBN
-chopdrop_ChopDrop:VB_VBN
-chophien_ChoPhien:VB_VBN
-chophilippin_choPhilippin:VB_VBN
-chophongthuy_ChoPhongThuy:VB_VBN
-chophun_choPhun:VB_VBN
-chopperorange_ChopperOrange:VB_VBN
-choppers_Chopper:VB_VBN
-choprobuy_choProbuy:VB_VBN
-chordpulse_ChordPulse:VB_VBN
-choread_choRead:VB_VBN
-chormecast_ChormeCast:VB_VBN
-chorokbaem_ChorokBaem:VB_VBN
-choseo_choSeo:VB_VBN
-chosg_ChoSG:VB_VBN
-chosuntv_ChosunTV:VB_VBN
-chotiki_choTiki:VB_VBN
-chotq_choTQ:VB_VBN
-chotrung_choTrung:VB_VBN
-chotrungquoc_ChoTrungQuoc:VB_VBN
-chouchou_ChouChou:VB_VBN
-choummalysayasone_ChoummalySayasone:VB_VBN
-chovaytiennhanh_ChoVayTienNhanh:VB_VBN
-chovinahure_choVinahure:VB_VBN
-chovinamilk_choVinamilk:VB_VBN
-chovn_choVN:VB_VBN
-chovncb_choVNCB:VB_VBN
-chowkit_ChowKit:VB_VBN
-choxesaigon_ChoXeSaiGon:VB_VBN
-chplay_CHPlay:VB_VBN
-chplaya_ChPlaya:VB_VBN
-chplays_ChPlays:VB_VBN
-chqtagged_chqTagged:VB_VBN
-chrisj_ChrisJ:VB_VBN
-chrismacneil_ChrisMacNeil:VB_VBN
-chrispc_ChrisPC:VB_VBN
-chrisstratton_ChrisStratton:VB_VBN
-christianlenart_ChristianLenart:VB_VBN
-christina_ChristinA:VB_VBN
-christinelagarde_ChristineLagarde:VB_VBN
-chromabrite_ChromaBrite:VB_VBN
-chromagar_CHROMagar:VB_VBN
-chromagartm_CHROMagarTM:VB_VBN
-chromapure_chromaPure:VB_VBN
-chromaway_ChromaWay:VB_VBN
-chromebook_ChromeBook:VB_VBN
-chromecast_ChromeCast:VB_VBN
-chromecasting_ChromeCasting:VB_VBN
-chromecleanupreportingenables_ChromeCleanupReportingEnables:VB_VBN
-chromeos_ChromeOS:VB_VBN
-chromeplus_ChromePlus:VB_VBN
-chromesau_ChromeSau:VB_VBN
-chronicle_CHRoNiCLE:VB_VBN
-chronoluxcb_ChronoluxCB:VB_VBN
-chuaheinz_chuaHeinz:VB_VBN
-chuan_ChuaN:VB_VBN
-chuanhoatienganh_ChuanHoaTiengAnh:VB_VBN
-chuankai_ChuanKai:VB_VBN
-chuankhoedep_ChuanKhoeDep:VB_VBN
-chuanxiao_chuanXiao:VB_VBN
-chuanxing_ChuanXing:VB_VBN
-chuar_chuaR:VB_VBN
-chubarwang_ChuBarWang:VB_VBN
-chubblife_ChubbLife:VB_VBN
-chuc_CHuc:VB_VBN
-chuchu_ChuChu:VB_VBN
-chuchubaby_ChuChuBaby:VB_VBN
-chuduinfo_ChuduInfo:VB_VBN
-chugye_ChuGye:VB_VBN
-chulairiverside_ChuLaiRiverside:VB_VBN
-chungang_ChungAng:VB_VBN
-chungauto_ChungAuto:VB_VBN
-chungba_chungBa:VB_VBN
-chungban_chungBan:VB_VBN
-chungbrucella_chungBrucella:VB_VBN
-chungby_chungBy:VB_VBN
-chungcard_chungCard:VB_VBN
-chungcheong_ChungCheong:VB_VBN
-chungcommon_chungCommon:VB_VBN
-chungcorydalis_chungCorydalis:VB_VBN
-chunghieu_ChungHieu:VB_VBN
-chungho_ChungHo:VB_VBN
-chunghydroxymethylbutyrate_chungHydroxymethylbutyrate:VB_VBN
-chungimmortelle_chungImmortelle:VB_VBN
-chungkem_chungKem:VB_VBN
-chungquan_chungQuan:VB_VBN
-chungsn_ChungSn:VB_VBN
-chungsoi_chungSoi:VB_VBN
-chungsucmicrofinance_chungsucMicrofinance:VB_VBN
-chungsuy_chungSuy:VB_VBN
-chungtrung_chungTrung:VB_VBN
-chungvai_chungVai:VB_VBN
-chungvideo_chungVideo:VB_VBN
-chungxét_ChungXét:VB_VBN
-churchholy_ChurchHoly:VB_VBN
-churchill_ChurChill:VB_VBN
-chus_chuS:VB_VBN
-chusudung_ChuSuDung:VB_VBN
-chuthese_chuThese:VB_VBN
-chutuocls_ChuTuocLS:VB_VBN
-chuyenbatam_ChuyenBaTam:VB_VBN
-chuyendoihoadondientuvoinganhang_ChuyenDoiHoaDonDienTuVoiNganHang:VB_VBN
-chuyengiadaquy_ChuyenGiaDaquy:VB_VBN
-chuyenhangchinhhang_ChuyenHangChinhHang:VB_VBN
-chuyenlubu_ChuyenLuBu:VB_VBN
-chuyennhatrongoi_ChuyenNhaTronGoi:VB_VBN
-chuyentactical_ChuyenTactical:VB_VBN
-chuyi_ChuYi:VB_VBN
-chémhai_chémHai:VB_VBN
-chémthan_ChémThan:VB_VBN
-chériskin_ChériSkin:VB_VBN
-ciarb_CIArb:VB_VBN
-cic_CiC:VB_VBN
-cica_CiCa:VB_VBN
-cican_CICan:VB_VBN
-cicassosidetm_CicassosideTM:VB_VBN
-cicc_CiCC:VB_VBN
-cici_CiCi:VB_VBN
-cieljj_CielJJ:VB_VBN
-cielodigital_cieloDigital:VB_VBN
-cig_CiG:VB_VBN
-cigapantocrin_CigaPantocrin:VB_VBN
-cimaher_CIMAher:VB_VBN
-cimatrone_CimatronE:VB_VBN
-cimdata_CIMdata:VB_VBN
-cinealta_CineAlta:VB_VBN
-cinebeam_CineBeam:VB_VBN
-cinebench_CineBench:VB_VBN
-cinecrystal_CineCrystal:VB_VBN
-cineeye_CineEye:VB_VBN
-cinemablend_CinemaBlend:VB_VBN
-cinemacolor_CinemaColor:VB_VBN
-cinemacon_CinemaCon:VB_VBN
-cinemadng_CinemaDNG:VB_VBN
-cinemaitaliano_CinemaItaliano:VB_VBN
-cinemamaster_CinemaMaster:VB_VBN
-cinemascore_CinemaScore:VB_VBN
-cinemasound_CinemaSound:VB_VBN
-cinemaster_CineMaster:VB_VBN
-cinemastream_CinemaStream:VB_VBN
-cinematiccolor_CinematicColor:VB_VBN
-cinemawide_CinemaWide:VB_VBN
-cinemax_CinemaX:VB_VBN
-cineshots_CineShots:VB_VBN
-cinestar_CineStar:VB_VBN
-cinesync_cineSync:VB_VBN
-cinevibe_CineVibe:VB_VBN
-cinsulin_CinSulin:VB_VBN
-cintanotes_CintaNotes:VB_VBN
-cipherbox_CipherBox:VB_VBN
-cipherlab_CipherLab:VB_VBN
-ciphouston_CIPHouston:VB_VBN
-cipmedia_CIPmedia:VB_VBN
-ciputra_CiPuTra:VB_VBN
-ciputraclubhanoi_CiputraClubHanoi:VB_VBN
-circleavatar_CircleAvatar:VB_VBN
-circleci_CircleCI:VB_VBN
-circledna_CircleDNA:VB_VBN
-circlek_CircleK:VB_VBN
-circleme_CircleMe:VB_VBN
-circlepay_CirclePay:VB_VBN
-circleup_CircleUp:VB_VBN
-circo_CirCO:VB_VBN
-circularprogressindicator_CircularProgressIndicator:VB_VBN
-ciscoworks_CiscoWorks:VB_VBN
-cisflatbed_CISFlatbed:VB_VBN
-cisspers_CISSPers:VB_VBN
-cita_CiTa:VB_VBN
-citescore_CiteScore:VB_VBN
-citeseerx_CiteSeerX:VB_VBN
-citi_CiTi:VB_VBN
-citialto_CitiAlto:VB_VBN
-citibank_CitiBank:VB_VBN
-citiesto_CitiEsto:VB_VBN
-citigrand_CitiGrand:VB_VBN
-citigym_CitiGym:VB_VBN
-citihome_CitiHome:VB_VBN
-citiland_CitiLand:VB_VBN
-citilight_CitiLight:VB_VBN
-citiliter_CitiLiteR:VB_VBN
-citinewyork_CitiNewYork:VB_VBN
-citisoho_CitiSoho:VB_VBN
-citizen_CitiZen:VB_VBN
-citizenm_CitizenM:VB_VBN
-citya_CityA:VB_VBN
-citybiden_CityBiden:VB_VBN
-citybus_CityBus:VB_VBN
-citycat_CityCat:VB_VBN
-citycentre_CityCentre:VB_VBN
-citydisc_CityDisc:VB_VBN
-cityford_CityFord:VB_VBN
-citygat_CityGat:VB_VBN
-citygate_CityGate:VB_VBN
-citygml_CityGML:VB_VBN
-cityhighlight_CityHighlight:VB_VBN
-citykhi_CITYKhi:VB_VBN
-citykhoa_CityKhoa:VB_VBN
-citylab_CityLab:VB_VBN
-cityland_CityLand:VB_VBN
-citylight_CityLight:VB_VBN
-cityline_CityLine:VB_VBN
-cityliner_CityLiner:VB_VBN
-cityliverpool_CityLiverpool:VB_VBN
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-citymart_CityMart:VB_VBN
-citynext_CityNext:VB_VBN
-citynextnext_cityNextNext:VB_VBN
-cityoffer_CityOffer:VB_VBN
-cityoffers_CityOffers:VB_VBN
-cityofzion_CityofZion:VB_VBN
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-cityphong_CityPhong:VB_VBN
-citypilot_CityPilot:VB_VBN
-cityplace_CityPlace:VB_VBN
-cityscape_CityScape:VB_VBN
-citysmart_CitySmart:VB_VBN
-citytheo_CityTheo:VB_VBN
-citythi_CityThi:VB_VBN
-citytree_CityTree:VB_VBN
-citytrump_CityTrump:VB_VBN
-citytrung_CityTrung:VB_VBN
-cityu_CityU:VB_VBN
-cityurdaneta_CityUrdaneta:VB_VBN
-cityvideo_CityVideo:VB_VBN
-cityview_CityView:VB_VBN
-citywalk_CityWalk:VB_VBN
-citywikipedia_CityWikipedia:VB_VBN
-citywolf_CityWolf:VB_VBN
-cityzoo_CityZoo:VB_VBN
-ciub_CIub:VB_VBN
-civic_CIvic:VB_VBN
-civicx_CivicX:VB_VBN
-civicxi_CivicXI:VB_VBN
-civilview_CivilView:VB_VBN
-cjoption_CJoption:VB_VBN
-cjtrade_CJtrade:VB_VBN
-cjvina_CJVina:VB_VBN
-ckbyte_CKByte:VB_VBN
-ckdgemtan_CKDGemtan:VB_VBN
-ckeditor_CKeditor:VB_VBN
-ckeyin_CkeyiN:VB_VBN
-ckhoang_CKhoang:VB_VBN
-cki_CkI:VB_VBN
-ckii_CkII:VB_VBN
-ckjerry_ckJerry:VB_VBN
-claimsunemployment_ClaimsUnemployment:VB_VBN
-claimweek_claimWeek:VB_VBN
-clamav_ClamAV:VB_VBN
-clampfc_ClampFC:VB_VBN
-clamxav_ClamXav:VB_VBN
-clan_CLan:VB_VBN
-clarenceriver_ClarenceRiver:VB_VBN
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-clariseb_ClariSEB:VB_VBN
-clarityhd_ClarityHD:VB_VBN
-clarivu_ClariVu:VB_VBN
-clas_CLas:VB_VBN
-class_CLass:VB_VBN
-classa_ClassA:VB_VBN
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-classdiagram_ClassDiagram:VB_VBN
-classflow_ClassFlow:VB_VBN
-classic_CLassic:VB_VBN
-classicboy_ClassicBoy:VB_VBN
-classicman_ClassicMan:VB_VBN
-classid_ClassID:VB_VBN
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-classin_ClassIn:VB_VBN
-classlist_classList:VB_VBN
-classmethods_ClassMethods:VB_VBN
-classname_className:VB_VBN
-classpass_ClassPass:VB_VBN
-classpath_ClassPath:VB_VBN
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-classx_ClassX:VB_VBN
-classétagged_classéTagged:VB_VBN
-clauncher_CLauncher:VB_VBN
-clay_CLay:VB_VBN
-clb_cLB:VB_VBN
-clbcaravan_CLBCaravan:VB_VBN
-clcr_CLcr:VB_VBN
-clean_CLean:VB_VBN
-cleanair_CleanAir:VB_VBN
-cleancel_CleanCel:VB_VBN
-cleancoat_CleanCoat:VB_VBN
-cleaneffectstm_CleanEffectsTM:VB_VBN
-cleanhouse_CleanHouse:VB_VBN
-cleanmaid_CleanMaid:VB_VBN
-cleanmaster_CleanMaster:VB_VBN
-cleanmax_CleanMax:VB_VBN
-cleanmymac_CleanMyMac:VB_VBN
-cleanpro_CleanPro:VB_VBN
-cleanserv_CleanServ:VB_VBN
-cleansource_CleanSource:VB_VBN
-cleansteel_CleanSteel:VB_VBN
-cleansui_CleanSui:VB_VBN
-cleansuivn_CleansuiVN:VB_VBN
-cleantec_CleanTec:VB_VBN
-cleantech_CleanTech:VB_VBN
-cleantop_CleanTop:VB_VBN
-cleanup_CleanUp:VB_VBN
-cleanview_CleanView:VB_VBN
-cleanvision_CleanVision:VB_VBN
-cleanzone_CleanZone:VB_VBN
-clearaudio_ClearAudio:VB_VBN
-clearblack_ClearBlack:VB_VBN
-clearcadvanceeffector_ClearCAdvanceEffector:VB_VBN
-clearcast_ClearCast:VB_VBN
-clearcmos_ClearCMOS:VB_VBN
-clearlock_ClearLock:VB_VBN
-clearlove_ClearLove:VB_VBN
-clearmymac_ClearMyMac:VB_VBN
-clearpass_ClearPass:VB_VBN
-clearphase_ClearPhase:VB_VBN
-clearpro_ClearPro:VB_VBN
-clearskin_ClearSkin:VB_VBN
-clearsky_ClearSky:VB_VBN
-clearspace_ClearSpace:VB_VBN
-clearstart_ClearStart:VB_VBN
-clearstory_ClearStory:VB_VBN
-cleartantien_ClearTanTien:VB_VBN
-cleartype_ClearType:VB_VBN
-clearview_ClearView:VB_VBN
-clearvison_ClearVison:VB_VBN
-clearvoice_ClearVoice:VB_VBN
-clearwayx_ClearwayX:VB_VBN
-clenziderm_CLENZIderm:VB_VBN
-clepro_ClePro:VB_VBN
-cleprox_CleproX:VB_VBN
-cleverads_CleverAds:VB_VBN
-clevercfo_CleverCFO:VB_VBN
-cleverfood_CleverFood:VB_VBN
-clevergroup_CleverGroup:VB_VBN
-cleverlearn_CleverLearn:VB_VBN
-clevertech_CleverTech:VB_VBN
-clevertube_CleverTube:VB_VBN
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-clickandbuy_ClickandBuy:VB_VBN
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-clickbuy_ClickBuy:VB_VBN
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-clickinsights_ClickInsights:VB_VBN
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-clickon_ClickOn:VB_VBN
-clickpack_ClickPack:VB_VBN
-clickpad_ClickPad:VB_VBN
-clickpay_ClickPay:VB_VBN
-clicktoflash_ClickToFlash:VB_VBN
-clicktotweet_ClickToTweet:VB_VBN
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-clientaddress_ClientAddress:VB_VBN
-clientearth_ClientEarth:VB_VBN
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-clientportal_ClientPortal:VB_VBN
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-clientsocket_ClientSocket:VB_VBN
-clientstub_ClientStub:VB_VBN
-cliffsnotes_CliffsNotes:VB_VBN
-clima_CLima:VB_VBN
-climacool_ClimaCool:VB_VBN
-climalite_ClimaLite:VB_VBN
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-climber_CLIMBeR:VB_VBN
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-clinic_CLinic:VB_VBN
-clinics_CLinics:VB_VBN
-clinimacs_CliniMACS:VB_VBN
-clio_CliO:VB_VBN
-clion_CLion:VB_VBN
-cliona_CLiona:VB_VBN
-clipart_ClipArt:VB_VBN
-clipbucket_ClipBucket:VB_VBN
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-cookbook_CookBook:VB_VBN
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-cookierobot_CookieRobot:VB_VBN
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-cookierobotbuilder_CookieRobotBuilder:VB_VBN
-cookiescookie_CookiesCookie:VB_VBN
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-coolui_CoolUI:VB_VBN
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-coolzilla_CoolZilla:VB_VBN
-coolzone_CoolZone:VB_VBN
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-degroote_DeGroote:VB_VBN
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-dehart_DeHart:VB_VBN
-dehoctot_DeHocTot:VB_VBN
-dehp_DeHP:VB_VBN
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-dejesus_DeJesus:VB_VBN
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-delaval_DeLaval:VB_VBN
-delay_DeLay:VB_VBN
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-deliverycung_DeliveryCung:VB_VBN
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-deltae_DeltaE:VB_VBN
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-deluxe_DeLuxe:VB_VBN
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-demetech_DemeTech:VB_VBN
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-demhong_DemHong:VB_VBN
-demille_DeMille:VB_VBN
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-demo_DEmo:VB_VBN
-demoattribute_DemoAttribute:VB_VBN
-demohelper_DemoHelper:VB_VBN
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-denegri_DeNegri:VB_VBN
-denhatdoc_DeNhatDoc:VB_VBN
-deniro_DeNiro:VB_VBN
-denlednhat_DenLEDNhat:VB_VBN
-denmark_DenMark:VB_VBN
-densenet_DenseNet:VB_VBN
-denseshield_DenseShield:VB_VBN
-denso_DenSo:VB_VBN
-dentalvibe_DentalVibe:VB_VBN
-dentplus_DentPlus:VB_VBN
-dentsply_DentSply:VB_VBN
-deoball_DeoBall:VB_VBN
-deoxit_DeoxIT:VB_VBN
-deoxyribonucleic_DeoxyriboNucleic:VB_VBN
-depalma_DePalma:VB_VBN
-depaul_DePaul:VB_VBN
-depaulo_DePaulo:VB_VBN
-deped_DepEd:VB_VBN
-depetris_DePetris:VB_VBN
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-depositfiles_DepositFiles:VB_VBN
-deprimo_DePrimo:VB_VBN
-depspa_DepSpa:VB_VBN
-depteditor_DeptEditor:VB_VBN
-depthimua_DepthiMua:VB_VBN
-depthtagged_DepthTagged:VB_VBN
-depthvision_DepthVision:VB_VBN
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-deptraivn_DepTraiVN:VB_VBN
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-derfussi_DerFussi:VB_VBN
-derivativesderivatives_derivativesDerivatives:VB_VBN
-dermaangel_dermaAngel:VB_VBN
-dermabio_DermaBio:VB_VBN
-dermaclear_DermaClear:VB_VBN
-dermacontrol_DermaControl:VB_VBN
-dermanutrix_DermaNutrix:VB_VBN
-dermascar_DermaScar:VB_VBN
-dermaveen_DermaVeen:VB_VBN
-dermeden_DermEden:VB_VBN
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-derozan_DeRozan:VB_VBN
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-desaria_DeSaria:VB_VBN
-descevidiom_descEVIdiom:VB_VBN
-description_DescriptioN:VB_VBN
-descriptionthay_descriptionThay:VB_VBN
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-deshawn_DeShawn:VB_VBN
-designbetter_DesignBetter:VB_VBN
-designbold_DesignBold:VB_VBN
-designbuddy_DesignBuddy:VB_VBN
-designcad_DesignCAD:VB_VBN
-designcap_DesignCap:VB_VBN
-designerin_DesignerIn:VB_VBN
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-designevo_DesignEvo:VB_VBN
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-designmaker_DesignMaker:VB_VBN
-designmax_DesignMax:VB_VBN
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-detoxgan_DetoxGan:VB_VBN
-detoxgreen_DetoxGreen:VB_VBN
-detoxslim_DetoxSlim:VB_VBN
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-devapha_DevApha:VB_VBN
-devc_DevC:VB_VBN
-devcon_DevCon:VB_VBN
-devdependencies_devDependencies:VB_VBN
-developher_DevelopHer:VB_VBN
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-devi_DeVi:VB_VBN
-devianart_DevianArt:VB_VBN
-deviantart_DeviantArt:VB_VBN
-devicediagnostic_DeviceDiagnostic:VB_VBN
-deviceid_deviceId:VB_VBN
-devicelink_DeviceLink:VB_VBN
-devicenet_DeviceNet:VB_VBN
-deviceownerauthentication_deviceOwnerAuthentication:VB_VBN
-deviceownerauthenticationwithbiometrics_deviceOwnerAuthenticationWithBiometrics:VB_VBN
-devicepath_DevicePath:VB_VBN
-devicepixelratio_devicePixelRatio:VB_VBN
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-devonagent_DEVONagent:VB_VBN
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-dewine_DeWine:VB_VBN
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-dex_DeX:VB_VBN
-dexbinance_DEXBinance:VB_VBN
-deyoli_DeYoLi:VB_VBN
-dezeen_DeZeen:VB_VBN
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-dfirtriage_DFIRtriage:VB_VBN
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-dfoob_DfOOB:VB_VBN
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-dftexpress_DFTexpress:VB_VBN
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-dgh_dGH:VB_VBN
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-dhama_DhaMa:VB_VBN
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-dictionaryofobscuresorrows_DictionaryOfObscureSorrows:VB_VBN
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-didan_DiDan:VB_VBN
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-didfinishlaunchingwithoptions_didFinishLaunchingWithOptions:VB_VBN
-didi_DiDi:VB_VBN
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-differencefirst_DifferenceFirst:VB_VBN
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-difranco_DiFranco:VB_VBN
-digdeep_DigDeep:VB_VBN
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-digestservlet_DigestServlet:VB_VBN
-digi_DiGi:VB_VBN
-digibank_DigiBank:VB_VBN
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-digibyte_DigiByte:VB_VBN
-digicert_DigiCert:VB_VBN
-digicity_DigiCity:VB_VBN
-digiclean_DigiClean:VB_VBN
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-digifoto_DigiFoto:VB_VBN
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-digilab_DigiLab:VB_VBN
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-digioia_DiGioia:VB_VBN
-digiorno_DiGiorno:VB_VBN
-digipat_DigiPat:VB_VBN
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-digipower_DigiPower:VB_VBN
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-digit_digIT:VB_VBN
-digitalarts_DigitalArts:VB_VBN
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-digitaltrend_DigitalTrend:VB_VBN
-digitaltrends_DigitalTrends:VB_VBN
-digitel_DigiTel:VB_VBN
-digitimes_DigiTimes:VB_VBN
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-digixdao_DigixDAO:VB_VBN
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-diii_DiII:VB_VBN
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-doctorhome_DoctorHome:VB_VBN
-doctorhouses_DoctorHouses:VB_VBN
-doctorlaptop_DoctorLaptop:VB_VBN
-doctorloan_DoctorLoan:VB_VBN
-doctornuoc_DoctorNuoc:VB_VBN
-doctorviet_DoctorViet:VB_VBN
-doctorwho_DoctorWHO:VB_VBN
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-documentroot_DocumentRoot:VB_VBN
-documentsmy_DocumentsMy:VB_VBN
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-doe_DoE:VB_VBN
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-dof_DoF:VB_VBN
-dofollow_DoFollow:VB_VBN
-dog_DoG:VB_VBN
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-doglost_DogLost:VB_VBN
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-dolokplayday_DolokPlayday:VB_VBN
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-domainevenconsumer_DomainEvenConsumer:VB_VBN
-domaineventsconsumer_DomainEventsConsumer:VB_VBN
-domainkeys_DomainKeys:VB_VBN
-domainscope_DomainScope:VB_VBN
-domaintools_DomainTools:VB_VBN
-domcop_DomCop:VB_VBN
-domia_doMIA:VB_VBN
-dominickbr_DominickBr:VB_VBN
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-donaldjtrump_DonaldJTrump:VB_VBN
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-download_DownLoad:VB_VBN
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-downloaddownload_downloadDownload:VB_VBN
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-downloadsmanager_DownloadsManager:VB_VBN
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-gamehub_GameHub:VB_VBN
-gameindustry_GameIndustry:VB_VBN
-gameinformer_GameInformer:VB_VBN
-gamejolt_GameJolt:VB_VBN
-gamek_GameK:VB_VBN
-gameland_GameLand:VB_VBN
-gamelandmobile_GameLandMobile:VB_VBN
-gamelandvn_GameLandVN:VB_VBN
-gameleave_GameLeave:VB_VBN
-gamelink_GameLink:VB_VBN
-gamelittle_gameLittle:VB_VBN
-gamelmht_GameLMHT:VB_VBN
-gameloft_GameLoft:VB_VBN
-gamelogin_GameLogin:VB_VBN
-gameloop_GameLoop:VB_VBN
-gamemaker_GameMaker:VB_VBN
-gamemarketing_GameMarketing:VB_VBN
-gamemax_GameMax:VB_VBN
-gamemode_GameMode:VB_VBN
-gamemoi_GameMoi:VB_VBN
-gamen_GameN:VB_VBN
-gamenet_GameNet:VB_VBN
-gamenhtsa_gameNHTSA:VB_VBN
-gamenoob_GameNoob:VB_VBN
-gameota_GameOta:VB_VBN
-gameoverwatch_gameOverwatch:VB_VBN
-gamepad_GamePad:VB_VBN
-gamepass_GamePass:VB_VBN
-gameplay_GamePlay:VB_VBN
-gameplus_GamePlus:VB_VBN
-gameportable_GamePortable:VB_VBN
-gameprivate_GamePrivate:VB_VBN
-gamequitterscoms_GameQuitterscoms:VB_VBN
-gameradar_GameRadar:VB_VBN
-gamerikvip_GameRikVip:VB_VBN
-gamersnexus_GamersNexus:VB_VBN
-gamerstorm_GamerStorm:VB_VBN
-games_GAmes:VB_VBN
-gamesamba_GameSamba:VB_VBN
-gamesao_GameSao:VB_VBN
-gamescenter_GamesCenter:VB_VBN
-gamesessions_GameSessions:VB_VBN
-gameshow_GameShow:VB_VBN
-gamesinasia_GamesInAsia:VB_VBN
-gamesir_GameSir:VB_VBN
-gamesniper_GameSniper:VB_VBN
-gamesos_GamesOS:VB_VBN
-gamespot_GameSpot:VB_VBN
-gamespy_GameSpy:VB_VBN
-gamesradar_GamesRadar:VB_VBN
-gamessau_GamesSau:VB_VBN
-gamestar_GameStar:VB_VBN
-gamestop_GameStop:VB_VBN
-gamestorchlight_gamesTorchlight:VB_VBN
-gamestudio_GameStudio:VB_VBN
-gametay_GameTay:VB_VBN
-gametham_gameTham:VB_VBN
-gametheo_gameTheo:VB_VBN
-gametrong_gameTrong:VB_VBN
-gametrung_gameTrung:VB_VBN
-gametv_GameTV:VB_VBN
-gametvplus_GameTvPlus:VB_VBN
-gameungdungmienphi_GameUngDungMienPhi:VB_VBN
-gamevac_GamEvac:VB_VBN
-gamevh_GameVH:VB_VBN
-gameview_GameView:VB_VBN
-gamevip_GameVip:VB_VBN
-gamevisual_GameVisual:VB_VBN
-gamevl_GameVL:VB_VBN
-gamevn_GameVN:VB_VBN
-gamevui_GameVui:VB_VBN
-gamewatcher_GameWatcher:VB_VBN
-gamewin_GameWin:VB_VBN
-gameyiwu_gameYiwu:VB_VBN
-gamily_GAMily:VB_VBN
-gamingx_GamingX:VB_VBN
-gamspro_gAMSPro:VB_VBN
-gamvip_GamVip:VB_VBN
-gan_GaN:VB_VBN
-gandcrab_GandCrab:VB_VBN
-ganfast_GaNFast:VB_VBN
-gangchondu_GangchonDu:VB_VBN
-ganghang_GangHang:VB_VBN
-gangkiz_GangKiz:VB_VBN
-gangnam_GangNam:VB_VBN
-gangrim_GangRim:VB_VBN
-ganheposal_ganHeposal:VB_VBN
-ganleave_ganLeave:VB_VBN
-gantzvn_GantzVN:VB_VBN
-gaongononline_GaoNgonOnline:VB_VBN
-gaosachonline_GaoSachOnline:VB_VBN
-gap_GaP:VB_VBN
-gapgap_GapGap:VB_VBN
-gaprovietnam_GaproVietnam:VB_VBN
-gapw_gApW:VB_VBN
-garageband_GarageBand:VB_VBN
-garai_GaRai:VB_VBN
-garan_GaRan:VB_VBN
-garantipay_GarantiPay:VB_VBN
-garatham_garaTham:VB_VBN
-garden_GArden:VB_VBN
-gardenbay_GardenBay:VB_VBN
-gardenson_GardenSon:VB_VBN
-garenalive_GarenaLive:VB_VBN
-garenatacchien_GarenaTacChien:VB_VBN
-garenatv_GarenaTV:VB_VBN
-garenatw_GarenaTW:VB_VBN
-garenavn_GarenaVN:VB_VBN
-garlandjungle_GarlandJungle:VB_VBN
-garper_GARPer:VB_VBN
-garpers_GARPers:VB_VBN
-gart_GArt:VB_VBN
-gasalert_GasAlert:VB_VBN
-gasalertquattro_GasAlertQuattro:VB_VBN
-gasbanmai_GasBanMai:VB_VBN
-gasbao_gasBao:VB_VBN
-gasbuddy_GasBuddy:VB_VBN
-gasmix_GasMix:VB_VBN
-gasover_GasOver:VB_VBN
-gaspetrolimex_gasPetrolimex:VB_VBN
-gastiminhp_GastiminHP:VB_VBN
-gastimunhp_GastimunHP:VB_VBN
-gastrozcurmin_GastrozCurmin:VB_VBN
-gataway_GataWay:VB_VBN
-gatehub_GateHub:VB_VBN
-gateway_GateWay:VB_VBN
-gatewaycon_GatewayCon:VB_VBN
-gathanh_GaThanh:VB_VBN
-gathrawn_GAThrawn:VB_VBN
-gatimunhp_GatimunHP:VB_VBN
-gatop_gaTOP:VB_VBN
-gattontrong_GattonTrong:VB_VBN
-gaugau_GauGau:VB_VBN
-gaugaushop_GauGauShop:VB_VBN
-gauto_GAuto:VB_VBN
-gawo_gAWo:VB_VBN
-gaystarnews_GaystarNews:VB_VBN
-gazmetan_GazMetan:VB_VBN
-gazprom_GazProm:VB_VBN
-gbe_GbE:VB_VBN
-gbgamez_GBGameZ:VB_VBN
-gbhex_GBHex:VB_VBN
-gbiphone_GBiPhone:VB_VBN
-gbmobile_GBmobile:VB_VBN
-gbmoblie_GBmoblie:VB_VBN
-gboil_GBoil:VB_VBN
-gbonline_GBonline:VB_VBN
-gbplus_GBplus:VB_VBN
-gbpluser_GBpluser:VB_VBN
-gbram_GBRam:VB_VBN
-gbrand_GBrand:VB_VBN
-gbtlnet_GbTLnet:VB_VBN
-gbvui_GbVui:VB_VBN
-gbwhatsapp_GBWhatsApp:VB_VBN
-gbxdcom_GbXDcom:VB_VBN
-gbxml_XML:VB_VBN
-gcafe_GCafe:VB_VBN
-gcafé_GCafé:VB_VBN
-gcare_GCare:VB_VBN
-gcatholic_GCatholic:VB_VBN
-gcdasyncsocket_GCDAsyncSocket:VB_VBN
-gcenter_GCenter:VB_VBN
-gcldsever_GCLDSever:VB_VBN
-gcleather_GCLeather:VB_VBN
-gcleathet_GCleathet:VB_VBN
-gclether_GCLether:VB_VBN
-gcmachinary_GCMachinary:VB_VBN
-gcode_GCode:VB_VBN
-gday_GDay:VB_VBN
-gdd_gDD:VB_VBN
-gdelectric_GDelectric:VB_VBN
-gdmss_gDMSS:VB_VBN
-gdpkinh_GDPkinh:VB_VBN
-gdragon_GDragon:VB_VBN
-gdrive_GDrive:VB_VBN
-gdtrh_GDTrH:VB_VBN
-gdtrhgdtx_GDTrHGDTX:VB_VBN
-gducky_GDucky:VB_VBN
-gdwbet_GDWBet:VB_VBN
-gearbest_GearBest:VB_VBN
-geartrax_GearTrax:VB_VBN
-gearvn_GearVN:VB_VBN
-gearvr_GearVR:VB_VBN
-gebiomized_GebioMized:VB_VBN
-geckodriver_GeckoDriver:VB_VBN
-gedworks_GEDWorks:VB_VBN
-geekbench_GeekBench:VB_VBN
-geekbuddy_GeekBuddy:VB_VBN
-geeksquad_GeekSquad:VB_VBN
-geekvape_GeekVape:VB_VBN
-geekwire_GeekWire:VB_VBN
-geetone_GeeTone:VB_VBN
-geforce_GeForce:VB_VBN
-geforcegtx_GeforceGTX:VB_VBN
-gegenpressing_GegenPressing:VB_VBN
-geifu_GeiFu:VB_VBN
-gekkoscience_GekkoScience:VB_VBN
-gelcreme_GelCreme:VB_VBN
-gellyimages_GellyImages:VB_VBN
-gelpatch_GelPatch:VB_VBN
-gemaxxie_gemaxXie:VB_VBN
-gemininestexpert_GeminiNestExpert:VB_VBN
-gemix_GEMiX:VB_VBN
-gemsilver_GemSilver:VB_VBN
-gemtvt_GEMtvt:VB_VBN
-genab_genAB:VB_VBN
-genc_genC:VB_VBN
-gencasa_GenCasa:VB_VBN
-genclaims_GenClaims:VB_VBN
-genecode_GeneCode:VB_VBN
-genemapper_GeneMapper:VB_VBN
-generalinternetdirectory_GeneralInternetDirectory:VB_VBN
-generatepress_GeneratePress:VB_VBN
-genesisgenesis_genesisGenesis:VB_VBN
-geneva_GenEva:VB_VBN
-genevadays_GenevaDays:VB_VBN
-genexpert_GeneXpert:VB_VBN
-geng_GenG:VB_VBN
-genhome_GenHome:VB_VBN
-genhpv_GenHPV:VB_VBN
-geniescout_GenieScout:VB_VBN
-geniusc_GeniusC:VB_VBN
-genk_GenK:VB_VBN
-genoptics_GenOptics:VB_VBN
-genrad_GenRad:VB_VBN
-gensokyovn_GensokyoVN:VB_VBN
-gentacolenro_GentaColenro:VB_VBN
-genteal_GenTeal:VB_VBN
-genting_GenTing:VB_VBN
-gentm_GenTM:VB_VBN
-genuisdj_GenuisDJ:VB_VBN
-genvec_GenVec:VB_VBN
-genviet_GenViet:VB_VBN
-genvita_GenVita:VB_VBN
-geny_GenY:VB_VBN
-genymotion_GenyMotion:VB_VBN
-genz_GenZ:VB_VBN
-geochemistry_GEochemistry:VB_VBN
-geocities_GeoCities:VB_VBN
-geodatabase_GeoDatabase:VB_VBN
-geoenvironmental_GeoEnvironmental:VB_VBN
-geofantex_GeofanTex:VB_VBN
-geogebra_GeoGebra:VB_VBN
-geognetics_GeoGnetics:VB_VBN
-geographic_GeoGraphic:VB_VBN
-geohive_GeoHive:VB_VBN
-geoip_GeoIP:VB_VBN
-geojson_GeoJSON:VB_VBN
-geonet_GeoNet:VB_VBN
-geopackages_GeoPackages:VB_VBN
-georadar_GeoRadar:VB_VBN
-georeference_GeoReference:VB_VBN
-georesonance_GeoResonance:VB_VBN
-georgetown_GeorgeTown:VB_VBN
-georgiabold_GeorgiaBold:VB_VBN
-geoslope_GeoSlope:VB_VBN
-geotiff_GeoTIFF:VB_VBN
-geotrust_GeoTrust:VB_VBN
-geoviet_GeoViet:VB_VBN
-geovision_GeoVision:VB_VBN
-geoxh_GeoXH:VB_VBN
-gerbertrao_GerberTrao:VB_VBN
-gerlittlecosmetics_GerlittleCosmetics:VB_VBN
-germfalcon_GermFalcon:VB_VBN
-gerustar_GeruStar:VB_VBN
-gestibio_GestiBio:VB_VBN
-get_GeT:VB_VBN
-getamped_GetAmped:VB_VBN
-getarray_getArray:VB_VBN
-getassociatedobject_getAssociatedObject:VB_VBN
-getback_GetBack:VB_VBN
-getbtc_GetBTC:VB_VBN
-getby_getBy:VB_VBN
-getbyid_getById:VB_VBN
-getcake_GetCake:VB_VBN
-getcapabilities_GetCapabilities:VB_VBN
-getcheckeditemposition_getCheckedItemPosition:VB_VBN
-getcited_getCITED:VB_VBN
-getconnection_getConnection:VB_VBN
-getcurrentposition_getCurrentPosition:VB_VBN
-getcurrentuser_getCurrentUser:VB_VBN
-getdropbox_GetDropbox:VB_VBN
-getelementbyid_getElementById:VB_VBN
-getfly_GetFly:VB_VBN
-getgo_GetGo:VB_VBN
-getincompleteitemsync_GetIncompleteItemSync:VB_VBN
-getinsta_GetInsta:VB_VBN
-getitem_getItem:VB_VBN
-getjar_GetJar:VB_VBN
-getkahoot_GetKahoot:VB_VBN
-getlink_GetLink:VB_VBN
-getlinks_GetLinks:VB_VBN
-getmetadata_getMetadata:VB_VBN
-getname_getName:VB_VBN
-getnextrequest_GetNextRequest:VB_VBN
-getoemdro_GetOEMDRO:VB_VBN
-getoemled_GetOEMLED:VB_VBN
-getparts_getParts:VB_VBN
-getphoto_getPhoto:VB_VBN
-getprocaddress_GetProcAddress:VB_VBN
-getprofile_getProfile:VB_VBN
-getreponse_GetReponse:VB_VBN
-getresponse_GetResponse:VB_VBN
-getrobot_getRobot:VB_VBN
-getsalarygrade_GetSalaryGrade:VB_VBN
-getsession_getSession:VB_VBN
-getsingleresult_getSingleResult:VB_VBN
-getsize_getSize:VB_VBN
-gettuple_getTuple:VB_VBN
-getty_GEtty:VB_VBN
-getuser_getUser:VB_VBN
-getuserprofile_getUserProfile:VB_VBN
-getuserspn_GetUserSPN:VB_VBN
-getvideo_GetVideo:VB_VBN
-getview_getView:VB_VBN
-getyourguide_GetYourGuide:VB_VBN
-geumju_GeumJu:VB_VBN
-gev_GeV:VB_VBN
-geylanggeylang_GeylangGeylang:VB_VBN
-gfg_GfG:VB_VBN
-gfirst_GFirst:VB_VBN
-gfk_GfK:VB_VBN
-gflife_GFLife:VB_VBN
-gformula_GFormula:VB_VBN
-gfriend_GFriend:VB_VBN
-gfxbench_GFXBench:VB_VBN
-ggaranti_GGaranti:VB_VBN
-ggbinary_GGBinary:VB_VBN
-ggdr_GGdr:VB_VBN
-ggem_GGem:VB_VBN
-ggil_GGil:VB_VBN
-ggmap_GGMap:VB_VBN
-ggmedia_GGmedia:VB_VBN
-ggtrade_GGtrade:VB_VBN
-ggtranslate_GGTranslate:VB_VBN
-gguard_GGuard:VB_VBN
-ghana_GhaNa:VB_VBN
-ghanasoccernet_GHANAsoccernet:VB_VBN
-ghdwoodhead_GHDWoodhead:VB_VBN
-ghgrc_ghGRC:VB_VBN
-ghiencongnge_GhienCongNge:VB_VBN
-ghiencongnghe_GhienCongNghe:VB_VBN
-ghigiao_ghiGiao:VB_VBN
-ghine_GHine:VB_VBN
-ghinghin_GhinGhin:VB_VBN
-ghmask_GHMask:VB_VBN
-ghoodew_gHoodEW:VB_VBN
-ghostark_GhostArk:VB_VBN
-ghostteam_GhostTeam:VB_VBN
-ghostvn_GhostVN:VB_VBN
-ghostvolt_GhostVolt:VB_VBN
-ghostwire_GhostWire:VB_VBN
-ghouse_GHouse:VB_VBN
-ghtop_GHTop:VB_VBN
-ghzi_GHzi:VB_VBN
-giaapple_giaApple:VB_VBN
-giabq_GiaBQ:VB_VBN
-giacongbikini_GiacongBikini:VB_VBN
-giacontinue_giaContinue:VB_VBN
-giacovid_giaCovid:VB_VBN
-giadinhnet_GiadinhNet:VB_VBN
-giadinhviet_GiaDinhViet:VB_VBN
-giadunghn_GiadungHN:VB_VBN
-giadunguytin_GiaDungUyTin:VB_VBN
-giafifa_giaFIFA:VB_VBN
-giahot_giaHot:VB_VBN
-giahy_GiaHy:VB_VBN
-giai_GiaI:VB_VBN
-giaidinhnet_GiaidinhNet:VB_VBN
-giaidnhnet_GiaidnhNet:VB_VBN
-giaimagiacmo_GiaiMaGiacMo:VB_VBN
-giainhnet_GiainhNet:VB_VBN
-giainvestment_GiaInvestment:VB_VBN
-giaiphapseo_GiaiPhapSEO:VB_VBN
-giaiphapthoathiem_GiaiPhapThoatHiem:VB_VBN
-giajohn_giaJohn:VB_VBN
-giakephuhung_GiaKePhuHung:VB_VBN
-giakhanhland_GiaKhanhLand:VB_VBN
-gialang_GialanG:VB_VBN
-gialeave_giaLeave:VB_VBN
-giamc_giaMC:VB_VBN
-giamentv_giaMENTV:VB_VBN
-giamgia_GiamGia:VB_VBN
-giaminhthinh_GiaMinhThinh:VB_VBN
-giammobung_GiamMoBung:VB_VBN
-giamthu_giamThu:VB_VBN
-gianchia_GIANchia:VB_VBN
-giancollagen_gianCollagen:VB_VBN
-giancontinue_gianContinue:VB_VBN
-gianextnext_giaNextNext:VB_VBN
-gianfranco_GianFranco:VB_VBN
-giang_GIang:VB_VBN
-gianganh_GiangAnh:VB_VBN
-giangblog_GiangBLOG:VB_VBN
-giangii_GiangII:VB_VBN
-giangposted_GiangPosted:VB_VBN
-giangtour_GiangTour:VB_VBN
-giangtp_GiangTp:VB_VBN
-giangvovtv_GiangVOVTV:VB_VBN
-gianread_gianRead:VB_VBN
-gianseries_gianSeries:VB_VBN
-giantproblems_GiantProblems:VB_VBN
-gianvqs_giaNVQS:VB_VBN
-giao_GIao:VB_VBN
-giaoanmamnon_GiaoAnMamNon:VB_VBN
-giaohangtietkiem_GiaoHangTietKiem:VB_VBN
-giaonhanh_GiaoNhanh:VB_VBN
-giaothong_GiaoThong:VB_VBN
-giaothongvn_GiaoThongVN:VB_VBN
-giaotochanh_GiaoTocHanh:VB_VBN
-giaouyen_GiaoUyen:VB_VBN
-giapcoach_GiapCoach:VB_VBN
-giapgs_giaPGS:VB_VBN
-giaphat_GiaPhat:VB_VBN
-giaphatdoor_GiaPhatDoor:VB_VBN
-giaphucgate_GIAPHUCgate:VB_VBN
-giapschool_GiapSchool:VB_VBN
-giaq_giaQ:VB_VBN
-giaqcvn_giaQCVN:VB_VBN
-giaread_giaRead:VB_VBN
-giasquawk_giaSquawk:VB_VBN
-giasy_GiaSy:VB_VBN
-giathinh_GiaThinh:VB_VBN
-giatp_giaTP:VB_VBN
-giatricuocsong_GiaTriCuocSong:VB_VBN
-giatuan_GiaTuan:VB_VBN
-giaunhanh_GiauNhanh:VB_VBN
-giavaidantuong_GiaVaiDanTuong:VB_VBN
-giavang_GiaVang:VB_VBN
-giavangvietnam_GiaVangVietNam:VB_VBN
-giavemaybayonline_GiavemaybayOnline:VB_VBN
-giaviettelviettel_giaViettelViettel:VB_VBN
-giaybom_giayBOM:VB_VBN
-giaypatin_GiayPatin:VB_VBN
-giaytagged_giayTagged:VB_VBN
-giaythethao_GiayTheThao:VB_VBN
-gib_GiB:VB_VBN
-gibmacos_gibMacOS:VB_VBN
-gibson_GIbson:VB_VBN
-gidivi_GiDiVi:VB_VBN
-giesy_GieSy:VB_VBN
-giffgaff_GiffGaff:VB_VBN
-gifrun_GIFRun:VB_VBN
-gift_GiFT:VB_VBN
-giftcard_GiftCard:VB_VBN
-giftcode_GiftCode:VB_VBN
-giftme_GiftMe:VB_VBN
-giftnow_GiftNow:VB_VBN
-giftone_GiftOne:VB_VBN
-giftpanda_GiftPanda:VB_VBN
-giftshigh_GiftsHigh:VB_VBN
-gifu_GiFU:VB_VBN
-gifyoutube_GIFyoutube:VB_VBN
-giga_GiGa:VB_VBN
-gigaace_gigaACE:VB_VBN
-gigabyte_GigaByte:VB_VBN
-gigaexpress_GigaExpress:VB_VBN
-gigamall_GigaMall:VB_VBN
-giganet_GigaNet:VB_VBN
-gigaom_GigaOm:VB_VBN
-gigashot_GigaShot:VB_VBN
-gige_GigE:VB_VBN
-gigi_GiGi:VB_VBN
-gigsky_GigSky:VB_VBN
-gigundocopr_GigundoCopr:VB_VBN
-gigundocorp_GigundoCorp:VB_VBN
-gihotech_GiHoTech:VB_VBN
-gik_GiK:VB_VBN
-gikad_GiKaD:VB_VBN
-gilenchi_GilenChi:VB_VBN
-gilisoft_GiliSoft:VB_VBN
-gillestooling_GillesTooling:VB_VBN
-gillielam_GillieLam:VB_VBN
-gillsans_GillSans:VB_VBN
-gimgoon_GimGoon:VB_VBN
-gimhae_GimHae:VB_VBN
-ginet_GiNET:VB_VBN
-gingerbread_GingerBread:VB_VBN
-ginkakujimap_GinkakujiMap:VB_VBN
-ginkgobiloba_GinkgoBiloba:VB_VBN
-ginmatsuba_GinMatsuba:VB_VBN
-gioakim_GioaKim:VB_VBN
-gioan_GIoan:VB_VBN
-gioankim_GioanKim:VB_VBN
-giorgio_GIorgio:VB_VBN
-giotsautrongmua_GiotSauTrongMua:VB_VBN
-giperoforte_GiperoForte:VB_VBN
-girl_GirL:VB_VBN
-girlchanh_GirlChanh:VB_VBN
-girlneya_GirlneYa:VB_VBN
-girlphan_girlPhan:VB_VBN
-girlplayers_GirlPlayers:VB_VBN
-girlsentertainment_girlsEntertainment:VB_VBN
-girlspace_GirlSpace:VB_VBN
-git_GiT:VB_VBN
-github_GitHub:VB_VBN
-gitkraken_GitKraken:VB_VBN
-gitlab_GitLab:VB_VBN
-gitoi_giToi:VB_VBN
-giupbankinhdoanh_GiupBanKinhDoanh:VB_VBN
-giupme_GiupMe:VB_VBN
-giupvayvon_GiupVayVon:VB_VBN
-giuse_GiuSe:VB_VBN
-giuseart_GiuseArt:VB_VBN
-givasolar_GivaSolar:VB_VBN
-giveaway_GiveAway:VB_VBN
-giveback_GiveBack:VB_VBN
-givemesport_GiveMeSport:VB_VBN
-gix_GiX:VB_VBN
-gizchina_GizChina:VB_VBN
-gizmochina_GizmoChina:VB_VBN
-gjuatropqgijwlm_gJuAtrOpqGijwLM:VB_VBN
-gkconcept_GKconcept:VB_VBN
-gkconcpet_GKConcpet:VB_VBN
-gkfxprime_GKFXPrime:VB_VBN
-gkhair_GKhair:VB_VBN
-gla_GlA:VB_VBN
-gladafrica_GladAfrica:VB_VBN
-glados_GLaDOS:VB_VBN
-glamourworld_GlamourWorld:VB_VBN
-glaricare_GlariCare:VB_VBN
-glassegg_GlassEgg:VB_VBN
-glassfish_GlassFish:VB_VBN
-glassgaming_GlassGaming:VB_VBN
-glassgo_GlassGo:VB_VBN
-glassionnomer_GlassIonnomer:VB_VBN
-glasskote_GlassKote:VB_VBN
-glasslock_GlassLock:VB_VBN
-glasspockets_GlassPockets:VB_VBN
-glassprotect_GlassProtect:VB_VBN
-glassvac_GlassVac:VB_VBN
-glasswire_GlassWire:VB_VBN
-glasswool_GlassWool:VB_VBN
-glassyzone_GlassyZone:VB_VBN
-glasweld_GlasWeld:VB_VBN
-glaw_GLaw:VB_VBN
-glaxosmithkline_GlaxoSmithKline:VB_VBN
-glbench_GLBench:VB_VBN
-gled_GLed:VB_VBN
-gleehome_GleeHome:VB_VBN
-glei_GLei:VB_VBN
-glenndoman_GlennDoman:VB_VBN
-glitizan_GliTizan:VB_VBN
-globagap_GlobaGAP:VB_VBN
-globalcheck_GlobalCheck:VB_VBN
-globalchef_GlobalChef:VB_VBN
-globalcoin_GlobalCoin:VB_VBN
-globalconnect_GlobalConnect:VB_VBN
-globaldata_GlobalData:VB_VBN
-globalenglish_GlobalEnglish:VB_VBN
-globalexam_GlobalExam:VB_VBN
-globalfoundries_GlobalFoundries:VB_VBN
-globalg_GlobalG:VB_VBN
-globalgap_GlobalGAP:VB_VBN
-globalgiving_GlobalGiving:VB_VBN
-globalhue_GlobalHue:VB_VBN
-globalmissionsvision_GlobalMissionsVision:VB_VBN
-globalsecurity_GlobalSecurity:VB_VBN
-globalsign_GlobalSign:VB_VBN
-globaltestmarket_GlobalTestMarket:VB_VBN
-globaltrans_GlobalTrans:VB_VBN
-globalwebindex_GlobalWebIndex:VB_VBN
-globalx_GlobalX:VB_VBN
-globeco_GLOBEco:VB_VBN
-globedr_GlobeDr:VB_VBN
-globepass_GlobePass:VB_VBN
-globobux_GloboBux:VB_VBN
-globtek_GlobTek:VB_VBN
-glocall_GLoCALL:VB_VBN
-gloftmvhm_GloftMVHM:VB_VBN
-glogistics_GLogistics:VB_VBN
-glong_GLong:VB_VBN
-glorybites_GloryBites:VB_VBN
-glorystar_GloryStar:VB_VBN
-glove_GloVe:VB_VBN
-glovesringhorns_GlovesRinghorns:VB_VBN
-glowlash_GlowLash:VB_VBN
-gltf_glTF:VB_VBN
-glucoactive_GlucoActive:VB_VBN
-glucodr_GlucoDr:VB_VBN
-glucoresistance_GlucoResistance:VB_VBN
-glucoruler_GlucoRuler:VB_VBN
-glucosamin_GLucosamin:VB_VBN
-glucosaminechondroitin_GlucosamineChondroitin:VB_VBN
-gluneo_GluNEO:VB_VBN
-glusterfs_GlusterFS:VB_VBN
-glutazorb_GlutaZorb:VB_VBN
-gluwhite_GluWhite:VB_VBN
-glxxe_GLXxe:VB_VBN
-glycofast_GlycoFast:VB_VBN
-glzip_GLZip:VB_VBN
-gmail_GMail:VB_VBN
-gmaildelaysend_GmailDelaySend:VB_VBN
-gmailprofiler_GmailProfiler:VB_VBN
-gmapcatcher_GmapCatcher:VB_VBN
-gmapextractor_GmapExtractor:VB_VBN
-gmark_GMark:VB_VBN
-gmarks_GMarks:VB_VBN
-gmax_GMax:VB_VBN
-gmbh_GmbH:VB_VBN
-gmedia_GMedia:VB_VBN
-gmfood_GMFood:VB_VBN
-gmo_gMO:VB_VBN
-gmobile_GMobile:VB_VBN
-gmocoin_GMOcoin:VB_VBN
-gmrack_GMRack:VB_VBN
-gmsarena_GMSArena:VB_VBN
-gmshop_GMShop:VB_VBN
-gmusic_GMusic:VB_VBN
-gmwatch_GMWatch:VB_VBN
-gnet_GNet:VB_VBN
-gnews_GNews:VB_VBN
-gngshop_GNGshop:VB_VBN
-gnhan_GNhan:VB_VBN
-gnodeb_gNodeB:VB_VBN
-gnomemeeting_GnomeMeeting:VB_VBN
-gnrh_GnRH:VB_VBN
-gnrha_GnRHa:VB_VBN
-gnsp_GNsP:VB_VBN
-gnters_GNTers:VB_VBN
-gnucash_GnuCash:VB_VBN
-goadr_GoADR:VB_VBN
-goahead_GoAhead:VB_VBN
-goalref_GoalRef:VB_VBN
-goandfly_GoandFly:VB_VBN
-goanimate_GoAnimate:VB_VBN
-goapp_GoAPP:VB_VBN
-goatz_GoatZ:VB_VBN
-gobanhkingrates_GOBanhkingRates:VB_VBN
-gobankingrates_GOBankingRates:VB_VBN
-gobattle_GoBattle:VB_VBN
-gobd_GoBD:VB_VBN
-gobear_GoBear:VB_VBN
-gobearmong_GoBearmong:VB_VBN
-gobeauty_GoBeauty:VB_VBN
-gobi_GoBi:VB_VBN
-gobike_GoBike:VB_VBN
-gobiz_GoBiz:VB_VBN
-gobreath_GoBreath:VB_VBN
-gocall_GoCALL:VB_VBN
-gocar_GoCar:VB_VBN
-gocare_GoCare:VB_VBN
-gocash_GoCash:VB_VBN
-goccuaru_goccuaRu:VB_VBN
-gocentral_GoCentral:VB_VBN
-gocharger_GoCharger:VB_VBN
-gocharm_GoCharm:VB_VBN
-gocheap_GoCheap:VB_VBN
-gochi_GoChi:VB_VBN
-goclean_GoClean:VB_VBN
-gocmod_GocMod:VB_VBN
-goco_GoCo:VB_VBN
-gocompare_GoCompare:VB_VBN
-gocontent_goContent:VB_VBN
-gocphongthuy_GocPhongThuy:VB_VBN
-gocphosau_GocPhoSau:VB_VBN
-gocrypt_GoCrypt:VB_VBN
-gocxanh_GocXanh:VB_VBN
-god_GoD:VB_VBN
-godaddy_GoDaddy:VB_VBN
-godcoast_GodCoast:VB_VBN
-godex_GoDEX:VB_VBN
-godfather_GodFather:VB_VBN
-godigoden_GodiGoden:VB_VBN
-godlike_GodLike:VB_VBN
-godmode_GodMode:VB_VBN
-godnue_GodNue:VB_VBN
-godofwar_GodOfWar:VB_VBN
-godoxphoto_GodoxPhoto:VB_VBN
-goedu_GoEdu:VB_VBN
-goehkdftewc_GoEHKDfteWc:VB_VBN
-goertek_GoerTek:VB_VBN
-goesdanh_GoesDanh:VB_VBN
-goeuro_GoEuro:VB_VBN
-goexport_GoEXPORT:VB_VBN
-gofarm_GoFarm:VB_VBN
-gofarms_GOFarms:VB_VBN
-gofazon_GofaZon:VB_VBN
-gofazone_GofaZone:VB_VBN
-goflex_GoFlex:VB_VBN
-gofood_GoFood:VB_VBN
-goforward_goForward:VB_VBN
-gofpt_GoFPT:VB_VBN
-gofundme_GoFundMe:VB_VBN
-gogame_goGame:VB_VBN
-gogear_GoGear:VB_VBN
-goget_GoGet:VB_VBN
-gogglesanti_GogglesAnti:VB_VBN
-gogi_GoGi:VB_VBN
-gogo_GoGo:VB_VBN
-gogokids_GoGoKids:VB_VBN
-gogreen_GoGreen:VB_VBN
-gohome_GoHome:VB_VBN
-gohoo_GoHoo:VB_VBN
-gohub_GoHub:VB_VBN
-goi_GoI:VB_VBN
-goidonet_GoidoNET:VB_VBN
-goioe_goIOE:VB_VBN
-goitour_GoiTour:VB_VBN
-gojapan_GoJapan:VB_VBN
-gojek_GoJek:VB_VBN
-gokids_GoKids:VB_VBN
-golang_GoLang:VB_VBN
-goldacoustic_GoldAcoustic:VB_VBN
-goldage_GoldAge:VB_VBN
-goldbee_GoldBee:VB_VBN
-goldcat_GoldCat:VB_VBN
-goldcoast_GoldCoast:VB_VBN
-goldcup_GoldCup:VB_VBN
-golddragon_GoldDragon:VB_VBN
-golddriver_GoldDriver:VB_VBN
-golden_GolDen:VB_VBN
-goldenagemt_GoldenAgeMT:VB_VBN
-goldenbay_GoldenBay:VB_VBN
-goldenchai_GoldenChai:VB_VBN
-goldenchoice_GoldenChoice:VB_VBN
-goldencoast_GoldenCoast:VB_VBN
-goldendict_GoldenDict:VB_VBN
-goldeneye_GoldenEye:VB_VBN
-goldenfoil_GoldenFoil:VB_VBN
-goldenkids_GoldenKids:VB_VBN
-goldenlab_GoldenLAB:VB_VBN
-goldenland_GoldenLand:VB_VBN
-goldenlink_GoldenLink:VB_VBN
-goldenlion_GoldenLion:VB_VBN
-goldenpeak_GoldenPeak:VB_VBN
-goldenservices_GoldenServices:VB_VBN
-goldensilk_GoldenSilk:VB_VBN
-goldentour_GoldenTour:VB_VBN
-goldenugget_GoldeNugget:VB_VBN
-goldfin_GoldFin:VB_VBN
-goldfinx_GoldFinX:VB_VBN
-goldfire_GoldFire:VB_VBN
-goldguard_GoldGuard:VB_VBN
-goldhealth_GoldHealth:VB_VBN
-goldkaraoke_GoldKaraoke:VB_VBN
-goldland_GoldLand:VB_VBN
-goldlife_GoldLife:VB_VBN
-goldmark_GoldMark:VB_VBN
-goldmart_GoldMart:VB_VBN
-goldmedal_GoldMedal:VB_VBN
-goldmine_GoldMine:VB_VBN
-goldmining_GoldMining:VB_VBN
-goldpen_GoldPen:VB_VBN
-goldra_GoldRa:VB_VBN
-goldreward_GoldReward:VB_VBN
-goldrush_GoldRush:VB_VBN
-goldseason_GoldSeason:VB_VBN
-goldsilk_GoldSilk:VB_VBN
-goldsound_GoldSound:VB_VBN
-goldsport_GoldSport:VB_VBN
-goldstar_GoldStar:VB_VBN
-goldstardance_GoldstarDance:VB_VBN
-goldstarkids_GoldstarKids:VB_VBN
-goldstocktrades_GoldStockTrades:VB_VBN
-goldsun_GoldSun:VB_VBN
-goldtech_GoldTech:VB_VBN
-goldterm_GoldTerm:VB_VBN
-goldtime_GoldTime:VB_VBN
-goldtimes_GoldTimes:VB_VBN
-goldview_GoldView:VB_VBN
-goldvina_GoldVina:VB_VBN
-goldvish_GoldVish:VB_VBN
-goldwave_GoldWave:VB_VBN
-goldwell_GoldWell:VB_VBN
-goldwing_GoldWing:VB_VBN
-goldxpert_GoldXpert:VB_VBN
-goldyip_GOLDYiP:VB_VBN
-golead_GoLEAD:VB_VBN
-golfbeer_GolfBeer:VB_VBN
-golfcity_GolfCity:VB_VBN
-golfgroup_GolfGroup:VB_VBN
-golflove_GolfLove:VB_VBN
-golfmaster_GolfMaster:VB_VBN
-golfmedia_GolfMedia:VB_VBN
-golfnews_GolfNews:VB_VBN
-golfplan_GolfPlan:VB_VBN
-golftech_GolfTech:VB_VBN
-golftimes_GolfTimes:VB_VBN
-golftourviet_GolfTourViet:VB_VBN
-golfviet_GolfViet:VB_VBN
-golfweek_GolfWeek:VB_VBN
-golfwith_GolfWith:VB_VBN
-golfzon_GolfZon:VB_VBN
-golinharris_GolinHarris:VB_VBN
-golou_GoLou:VB_VBN
-gomap_GOMap:VB_VBN
-gomedia_GoMedia:VB_VBN
-gomesandre_GomesAndre:VB_VBN
-gomistore_GomiStore:VB_VBN
-gomusic_GoMusic:VB_VBN
-gonafood_GoNaFood:VB_VBN
-gonden_GOnden:VB_VBN
-gonepal_GoNepal:VB_VBN
-gongcha_GongCha:VB_VBN
-gongjinhyang_GongJinHyang:VB_VBN
-goninhthuan_GoNinhthuan:VB_VBN
-gonjoy_GOnJOY:VB_VBN
-gonline_GOnline:VB_VBN
-gonner_GoNNER:VB_VBN
-gonoodle_GoNoodle:VB_VBN
-gonow_GoNow:VB_VBN
-gonspace_GonSpace:VB_VBN
-gonstack_GonStack:VB_VBN
-gonutrition_GoNutrition:VB_VBN
-goodball_GoodBall:VB_VBN
-goodcv_GoodCV:VB_VBN
-gooddr_GoodDr:VB_VBN
-goodfinancialcents_GoodFinancialCents:VB_VBN
-goodfit_GoodFit:VB_VBN
-goodfood_GoodFood:VB_VBN
-goodhealth_GoodHealth:VB_VBN
-goodhouse_GoodHouse:VB_VBN
-goodhousekeeping_GoodHouseKeeping:VB_VBN
-goodlife_GoodLife:VB_VBN
-goodm_GoodM:VB_VBN
-goodmaid_GoodMaid:VB_VBN
-goodmarket_GoodMarket:VB_VBN
-goodreads_GoodReads:VB_VBN
-goodrx_GoodRx:VB_VBN
-goodsmart_GoodSmart:VB_VBN
-goodsmartvn_GoodSmartVn:VB_VBN
-goodsync_GoodSync:VB_VBN
-goodtask_GoodTask:VB_VBN
-goodtherapy_GoodTherapy:VB_VBN
-goodui_GoodUI:VB_VBN
-goodwe_GoodWe:VB_VBN
-goodwillkét_GoodwillKét:VB_VBN
-goodwood_GoodWood:VB_VBN
-goodwork_GoodWork:VB_VBN
-goodyear_GoodYear:VB_VBN
-googleaccount_GoogleAccount:VB_VBN
-googleads_GoogleAds:VB_VBN
-googleapps_GoogleApps:VB_VBN
-googlecalendar_GoogleCalendar:VB_VBN
-googlechrome_googleChrome:VB_VBN
-googlefan_googleFan:VB_VBN
-googleguy_GoogleGuy:VB_VBN
-googlemap_GoogleMap:VB_VBN
-googlemapreview_GoogleMapReview:VB_VBN
-googlemapreviewer_GoogleMapReviewer:VB_VBN
-googlemaps_GoogleMaps:VB_VBN
-googlemusic_GoogleMusic:VB_VBN
-googlepanicimages_GooglePanicImages:VB_VBN
-googlepay_GooglePay:VB_VBN
-googleplay_GooglePlay:VB_VBN
-googlequery_GoogleQuery:VB_VBN
-googlesheet_GoogleSheet:VB_VBN
-googlesuggest_GoogleSuggest:VB_VBN
-googleupdate_GoogleUpdate:VB_VBN
-googleupdatebroker_GoogleUpdateBroker:VB_VBN
-googleupdatecore_GoogleUpdateCore:VB_VBN
-googleupdatehelper_GoogleUpdateHelper:VB_VBN
-googleupdateondemand_GoogleUpdateOnDemand:VB_VBN
-goomin_GooMin:VB_VBN
-goonline_GoOnline:VB_VBN
-goov_GoOV:VB_VBN
-gopay_GoPay:VB_VBN
-gopcpro_GoPcPro:VB_VBN
-gopet_GoPet:VB_VBN
-goplay_goPlay:VB_VBN
-gopos_GoPOS:VB_VBN
-gopro_GoPro:VB_VBN
-gopros_GoPros:VB_VBN
-goptions_GOptions:VB_VBN
-goracing_GoRacing:VB_VBN
-goradio_GoRadio:VB_VBN
-goride_GoRide:VB_VBN
-gorilla_GorillA:VB_VBN
-gos_GoS:VB_VBN
-gosafe_GoSafe:VB_VBN
-gosau_GoSau:VB_VBN
-goscreen_goScreen:VB_VBN
-gosell_GoSELL:VB_VBN
-goseller_GoSELLER:VB_VBN
-gosend_GoSend:VB_VBN
-goshopback_GoShopBack:VB_VBN
-goslim_GoSlim:VB_VBN
-gossipcop_GossipCop:VB_VBN
-gostation_GoStation:VB_VBN
-gostop_GoStop:VB_VBN
-gostream_GoStream:VB_VBN
-gostudio_GoStudio:VB_VBN
-gosutv_GosuTV:VB_VBN
-got_GoT:VB_VBN
-gotech_GoTech:VB_VBN
-gotg_GotG:VB_VBN
-gotiengviet_GoTiengViet:VB_VBN
-gotit_GotIt:VB_VBN
-goto_GoTo:VB_VBN
-gotoend_goToEnd:VB_VBN
-gotojapan_GoToJapan:VB_VBN
-gotomeeting_GoToMeeting:VB_VBN
-gotowebinar_GoToWebinar:VB_VBN
-gotravel_GoTravel:VB_VBN
-gotrump_GoTrump:VB_VBN
-gotrusty_GoTrusty:VB_VBN
-goucoland_GoucoLand:VB_VBN
-goutcherry_goutCherry:VB_VBN
-goutclear_GoutClear:VB_VBN
-goutpro_GoutPro:VB_VBN
-goutxét_goutXét:VB_VBN
-govalue_GoValue:VB_VBN
-governmentsecurity_GovernmentSecurity:VB_VBN
-goviet_GoViet:VB_VBN
-goviett_GoViett:VB_VBN
-gowatch_GoWatch:VB_VBN
-goweb_GoWEB:VB_VBN
-gowest_GoWest:VB_VBN
-gowin_GoWin:VB_VBN
-gowise_GoWise:VB_VBN
-gowithmi_GoWithMi:VB_VBN
-goyourmarket_GoYourMarket:VB_VBN
-goz_GoZ:VB_VBN
-gparted_GParted:VB_VBN
-gpbank_GPBank:VB_VBN
-gpcoderdirectexchange_GPCoderDirectExchange:VB_VBN
-gpeople_GPeople:VB_VBN
-gphitech_GPHitech:VB_VBN
-gphone_GPhone:VB_VBN
-gplus_GPlus:VB_VBN
-gplxonline_GPLXonline:VB_VBN
-gpmedia_GPmedia:VB_VBN
-gpone_GPOne:VB_VBN
-gportal_GPortal:VB_VBN
-gprinter_GPrinter:VB_VBN
-gpsmap_GPSmap:VB_VBN
-gpsmapedit_GPSMapEdit:VB_VBN
-gpsolar_GPsolar:VB_VBN
-gpucheck_GPUCheck:VB_VBN
-gpxviewer_GPXviewer:VB_VBN
-gqlb_GQlb:VB_VBN
-grab_GRab:VB_VBN
-grabads_GrabAds:VB_VBN
-grabassistant_GrabAssistant:VB_VBN
-grabbenefits_GrabBenefits:VB_VBN
-grabbike_GrabBike:VB_VBN
-grabbus_GrabBus:VB_VBN
-grabcar_GrabCar:VB_VBN
-grabcare_GrabCare:VB_VBN
-grabchat_GrabChat:VB_VBN
-grabdriver_GrabDriver:VB_VBN
-grabexpress_GrabExpress:VB_VBN
-grabfood_GrabFood:VB_VBN
-grabfoods_GrabFoods:VB_VBN
-grabkitchen_GrabKitchen:VB_VBN
-grabling_GrabLing:VB_VBN
-grabmart_GrabMart:VB_VBN
-grabmerchant_GrabMerchant:VB_VBN
-grabnow_GrabNow:VB_VBN
-grabpay_GrabPay:VB_VBN
-grabprotect_GrabProtect:VB_VBN
-grabrent_GrabRent:VB_VBN
-grabrewards_GrabRewards:VB_VBN
-grabshare_GrabShare:VB_VBN
-grabtaxi_GrabTaxi:VB_VBN
-gradcg_GradCG:VB_VBN
-graffstar_GraffStar:VB_VBN
-grammarbook_GrammarBook:VB_VBN
-grammersoft_GRAMMERsoft:VB_VBN
-grancabrio_GranCabrio:VB_VBN
-granclass_GranClass:VB_VBN
-grand_GranD:VB_VBN
-grandbuilding_GrandBuilding:VB_VBN
-grandchase_GrandChase:VB_VBN
-grandglory_GrandGlory:VB_VBN
-grandma_grandMA:VB_VBN
-grandmun_GrandMUN:VB_VBN
-grandpa_GrandPa:VB_VBN
-grandplaza_GrandPlaza:VB_VBN
-grandprix_GrandPrix:VB_VBN
-grandpro_GrandPro:VB_VBN
-grandstand_GrandStand:VB_VBN
-grandtouch_GrandTouch:VB_VBN
-grandtour_GrandTour:VB_VBN
-grandviet_GrandViet:VB_VBN
-grandworld_GrandWorld:VB_VBN
-grandx_GrandX:VB_VBN
-graniteshares_GraniteShares:VB_VBN
-granmonte_GranMonte:VB_VBN
-gransport_GranSport:VB_VBN
-grantthomton_GrantThomton:VB_VBN
-grantthornton_GrantThornton:VB_VBN
-granturismo_GranTurismo:VB_VBN
-grantursimo_GranTursimo:VB_VBN
-grapenet_GrapeNet:VB_VBN
-grapeseed_GrapeSEED:VB_VBN
-graphapi_graphAPI:VB_VBN
-graphicoverlay_GraphicOverlay:VB_VBN
-graphicriver_GraphicRiver:VB_VBN
-graphicsgale_GraphicsGale:VB_VBN
-graphicsprings_GraphicSprings:VB_VBN
-graphicview_GraphicView:VB_VBN
-graphql_GraphQL:VB_VBN
-graphrel_GraphREL:VB_VBN
-graphx_GraphX:VB_VBN
-grappleapp_GrappleApp:VB_VBN
-grassjelly_GrassJelly:VB_VBN
-gratispest_GratisPest:VB_VBN
-gravastar_GravaStar:VB_VBN
-gravityform_GravityForm:VB_VBN
-gravityforms_GravityForms:VB_VBN
-graykey_GrayKey:VB_VBN
-grayscale_GrayScale:VB_VBN
-grayshift_GrayShift:VB_VBN
-grb_GrB:VB_VBN
-grdp_GrDP:VB_VBN
-grealishm_GrealishM:VB_VBN
-greatcad_GreatCAD:VB_VBN
-greatfire_GreatFire:VB_VBN
-greatresumesfast_GreatResumesFast:VB_VBN
-greatwaves_GreatWaves:VB_VBN
-grecon_GreCon:VB_VBN
-greedfall_GreedFall:VB_VBN
-greeeen_GreeeeN:VB_VBN
-greehouse_GreeHouse:VB_VBN
-greenair_GreenAir:VB_VBN
-greenams_GreenAms:VB_VBN
-greenbalo_GreenBalo:VB_VBN
-greenbay_GreenBay:VB_VBN
-greenbee_GreenBee:VB_VBN
-greenbiz_GreenBiz:VB_VBN
-greenbm_GreenBM:VB_VBN
-greenbox_GreenBox:VB_VBN
-greenbrowser_GreenBrowser:VB_VBN
-greencanaltour_GreenCanalTour:VB_VBN
-greencloudvps_GreenCloudVPS:VB_VBN
-greencone_GreenCone:VB_VBN
-greencons_GreenCons:VB_VBN
-greencook_GreenCook:VB_VBN
-greendao_GreenDao:VB_VBN
-greendeck_GreenDeck:VB_VBN
-greendecor_GreenDecor:VB_VBN
-greendetech_GreenDetech:VB_VBN
-greendoglearnsphd_GreendoglearnsPHD:VB_VBN
-greendreams_GreenDreams:VB_VBN
-greenecolife_GreenEcoLife:VB_VBN
-greeneyedharpy_GreenEyedHarpy:VB_VBN
-greenfeed_GreenFeed:VB_VBN
-greenfeet_GreenFeet:VB_VBN
-greenfield_GreenField:VB_VBN
-greenfoodhanoi_GreenFoodHanoi:VB_VBN
-greengar_GreenGar:VB_VBN
-greengrown_GreenGrown:VB_VBN
-greenguard_GreenGuard:VB_VBN
-greenhome_GreenHome:VB_VBN
-greenhouse_GreenHouse:VB_VBN
-greenhouses_GreenHouses:VB_VBN
-greenhub_GreenHub:VB_VBN
-greenid_GreenID:VB_VBN
-greenlabel_GreenLabel:VB_VBN
-greenland_GreenLand:VB_VBN
-greenlandscape_GreenLandscape:VB_VBN
-greenlaw_GreenLaw:VB_VBN
-greenlife_GreenLife:VB_VBN
-greenlight_GreenLight:VB_VBN
-greenlinesdp_GreenlinesDP:VB_VBN
-greenmap_GreenMAP:VB_VBN
-greenmark_GreenMark:VB_VBN
-greenmax_GreenMax:VB_VBN
-greenmedinfo_GreenMedInfo:VB_VBN
-greennet_GREENnet:VB_VBN
-greennetworks_GreenNetworks:VB_VBN
-greenoffice_GreenOffice:VB_VBN
-greenpark_GreenPark:VB_VBN
-greenpearl_GreenPearl:VB_VBN
-greenphar_GreenPhar:VB_VBN
-greenpix_GreenPix:VB_VBN
-greenpoints_GreenPoints:VB_VBN
-greenprint_GreenPrint:VB_VBN
-greenriverside_GreenRiverside:VB_VBN
-greensea_GreenSea:VB_VBN
-greensock_GreenSock:VB_VBN
-greenspot_GreenSpot:VB_VBN
-greenstar_GreenStar:VB_VBN
-greenstars_GreenStars:VB_VBN
-greenstone_GreenStone:VB_VBN
-greentea_GreenTea:VB_VBN
-greenteabanana_GreenteaBanana:VB_VBN
-greenteaseed_GreenTeaSeed:VB_VBN
-greentech_GreenTech:VB_VBN
-greentest_GreenTest:VB_VBN
-greentesteco_GreentestEco:VB_VBN
-greentree_GreenTree:VB_VBN
-greenup_GreenUp:VB_VBN
-greenviet_GreenViet:VB_VBN
-greenview_GreenView:VB_VBN
-greenvinci_GreenVinci:VB_VBN
-greenvision_GreenVision:VB_VBN
-greenvpn_GreenVPN:VB_VBN
-greenwall_GreenWall:VB_VBN
-greenway_GreenWay:VB_VBN
-greenwindow_GreenWindow:VB_VBN
-greenworld_GreenWorld:VB_VBN
-greenyellow_GreenYellow:VB_VBN
-greenzone_GreenZone:VB_VBN
-greetingprovider_greetingProvider:VB_VBN
-greetingserver_GreetingServer:VB_VBN
-grencty_GrenCty:VB_VBN
-grepacobags_GrepacoBags:VB_VBN
-grexpress_GrExpress:VB_VBN
-gridcontrol_GridControl:VB_VBN
-gridfs_GridFS:VB_VBN
-gridinsoft_GridinSoft:VB_VBN
-gridoptions_gridOptions:VB_VBN
-gridpane_GridPane:VB_VBN
-gridsplitter_GridSplitter:VB_VBN
-gridstack_GridStack:VB_VBN
-gridview_GridView:VB_VBN
-grimmheroes_GrimmHeroes:VB_VBN
-gripbao_GripBao:VB_VBN
-gripcontrol_GripControl:VB_VBN
-grna_gRNA:VB_VBN
-grohe_GROhe:VB_VBN
-groknet_GrokNet:VB_VBN
-groko_GroKo:VB_VBN
-grokstyle_GrokStyle:VB_VBN
-groovefunnels_GrooveFunnels:VB_VBN
-groovepages_GroovePages:VB_VBN
-groovylock_GroovyLock:VB_VBN
-group_GRoup:VB_VBN
-groupbox_groupBox:VB_VBN
-groupbutton_GroupButton:VB_VBN
-groupg_GroupG:VB_VBN
-groupid_groupId:VB_VBN
-groupingseparator_groupingSeparator:VB_VBN
-grouplayout_GroupLayout:VB_VBN
-grouplens_GroupLens:VB_VBN
-groupm_GroupM:VB_VBN
-groupno_GroupNo:VB_VBN
-groupoh_groupOh:VB_VBN
-groupshied_GroupShied:VB_VBN
-groupspaces_GroupSpaces:VB_VBN
-groupthe_GroupThe:VB_VBN
-grouptrung_GroupTrung:VB_VBN
-groupx_GroupX:VB_VBN
-growbig_GrowBig:VB_VBN
-growfx_GrowFX:VB_VBN
-growgreen_GrowGreen:VB_VBN
-growmorequi_GrowmoreQui:VB_VBN
-growplus_GrowPLUS:VB_VBN
-growsteak_GrowSteak:VB_VBN
-growtech_GrowTech:VB_VBN
-growth_GRowTH:VB_VBN
-growthclub_GrowthClub:VB_VBN
-growthhackers_GrowthHackers:VB_VBN
-growviews_GrowViews:VB_VBN
-growwise_GrowWise:VB_VBN
-growwithmoth_GrowwithMoth:VB_VBN
-grpc_gRPC:VB_VBN
-grubhub_GrubHub:VB_VBN
-grumpymonkey_GrumpyMonkey:VB_VBN
-grusz_GrusZ:VB_VBN
-gsanalytic_GsAnalytic:VB_VBN
-gsanlytic_GsAnlytic:VB_VBN
-gsconnect_GSConnect:VB_VBN
-gshutdown_GShutdown:VB_VBN
-gskill_GSKill:VB_VBN
-gsmarena_GSMArena:VB_VBN
-gsmart_GSmart:VB_VBN
-gsmdome_GSMDome:VB_VBN
-gsmoon_GSMoon:VB_VBN
-gstacad_GstaCAD:VB_VBN
-gstarcad_GstarCAD:VB_VBN
-gstnetwork_GSTnetwork:VB_VBN
-gstreamer_GStreamer:VB_VBN
-gstvietnam_GSTvietnam:VB_VBN
-gsuite_GSuite:VB_VBN
-gsvlabs_GSVlabs:VB_VBN
-gtacarde_GTAcarde:VB_VBN
-gtaforums_GTAForums:VB_VBN
-gtalk_GTalk:VB_VBN
-gtall_GTall:VB_VBN
-gtan_gTAN:VB_VBN
-gtarcade_GTArcade:VB_VBN
-gtastrazeneca_GTAstraZeneca:VB_VBN
-gtav_gtaV:VB_VBN
-gtavlauncher_GTAVLauncher:VB_VBN
-gtbayer_GTBayer:VB_VBN
-gtctelecom_GTCtelecom:VB_VBN
-gtedu_GTEdu:VB_VBN
-gtel_GTel:VB_VBN
-gtelmobile_GtelMobile:VB_VBN
-gtg_GtG:VB_VBN
-gtgtquy_GTGTquy:VB_VBN
-gtgttheo_GTGTtheo:VB_VBN
-gthana_GTHana:VB_VBN
-gtir_GTiR:VB_VBN
-gtld_gTLD:VB_VBN
-gtlds_gTLDs:VB_VBN
-gtlekar_GTLekar:VB_VBN
-gtline_GTLine:VB_VBN
-gtmetrix_GTMetrix:VB_VBN
-gtnfoods_GTNFoods:VB_VBN
-gtoken_GToken:VB_VBN
-gtranslate_GTranslate:VB_VBN
-gtrenata_GTRenata:VB_VBN
-gtsolution_GTSolution:VB_VBN
-gtspirit_GTSpirit:VB_VBN
-gtthe_GTThe:VB_VBN
-gtunique_GTUnique:VB_VBN
-gturbo_GTurbo:VB_VBN
-gtvtread_GTVTRead:VB_VBN
-gtworks_GTWorks:VB_VBN
-guangdong_GuangDong:VB_VBN
-guangyang_GuangYang:VB_VBN
-guangzhou_GuangZhou:VB_VBN
-guanlin_GuanLin:VB_VBN
-guardduty_GuardDuty:VB_VBN
-guardian_GuardiaN:VB_VBN
-guardianapp_GuardianApp:VB_VBN
-guatemalagerardo_GuatemalaGerardo:VB_VBN
-guaweb_GuaWeb:VB_VBN
-gucci_GucCi:VB_VBN
-guessppi_guessPPI:VB_VBN
-guesthouse_GuestHouse:VB_VBN
-gufoods_GUfoods:VB_VBN
-guhakata_GuHakata:VB_VBN
-guibanve_guiBanve:VB_VBN
-guidelineswhat_GuidelinesWhat:VB_VBN
-guillainbarré_GuillainBarré:VB_VBN
-guitarbadon_GuitarBaDon:VB_VBN
-guitartrung_GuitarTrung:VB_VBN
-guitartuna_GuitarTuna:VB_VBN
-gumac_GuMac:VB_VBN
-gumdobot_GumdoBot:VB_VBN
-gumgum_GumGum:VB_VBN
-gumsan_GumSan:VB_VBN
-gunboundm_GunboundM:VB_VBN
-guncrasher_GunCrasher:VB_VBN
-guncrusher_GunCrusher:VB_VBN
-gundamstorevn_GundamstoreVN:VB_VBN
-gungho_GungHo:VB_VBN
-gungun_GunGun:VB_VBN
-gunmetal_GunMetal:VB_VBN
-gunnychip_GunnyChip:VB_VBN
-gunnygun_GunnyGun:VB_VBN
-gunnytrong_gunnyTrong:VB_VBN
-gunpow_GunPow:VB_VBN
-gunsan_GunSan:VB_VBN
-gunshipiii_gunshipIII:VB_VBN
-gunshop_GunShop:VB_VBN
-gunvip_GunVip:VB_VBN
-gunx_GunX:VB_VBN
-gunz_GunZ:VB_VBN
-guocoland_GuocoLand:VB_VBN
-guoqiang_GuoQiang:VB_VBN
-guqintai_GuQinTai:VB_VBN
-gurneydrive_GurneyDrive:VB_VBN
-gurunavi_GuruNavi:VB_VBN
-gutcheck_GutCheck:VB_VBN
-gutenberg_GutenBerg:VB_VBN
-gvcho_GVcho:VB_VBN
-gvim_GVim:VB_VBN
-gvtech_GVTech:VB_VBN
-gwangju_GwangJu:VB_VBN
-gwth_GWth:VB_VBN
-gxg_GxG:VB_VBN
-gxh_GxH:VB_VBN
-gxp_GxP:VB_VBN
-gxworks_GXWorks:VB_VBN
-gyawang_GyaWang:VB_VBN
-gymdesign_GYMdesign:VB_VBN
-gymer_GYMer:VB_VBN
-gymermarket_GymerMarket:VB_VBN
-gymhaus_GymHaus:VB_VBN
-gymhome_GymHome:VB_VBN
-gymlord_GymLord:VB_VBN
-gympact_GymPact:VB_VBN
-gympartner_GymPartner:VB_VBN
-gymrun_GymRun:VB_VBN
-gyptee_GypTEE:VB_VBN
-gypwall_GypWall:VB_VBN
-gyroglove_GyroGlove:VB_VBN
-gyronet_GyroNet:VB_VBN
-gyumai_GyuMai:VB_VBN
-gzdoom_GZDoom:VB_VBN
-gzip_GZip:VB_VBN
-gzmax_GZmax:VB_VBN
-haaahaaaa_HaaaHAaaa:VB_VBN
-haarmenszoon_HAarmenszoon:VB_VBN
-hab_HaB:VB_VBN
-habitminder_HabitMinder:VB_VBN
-habourfront_HabourFront:VB_VBN
-habubanktp_HabubankTP:VB_VBN
-hacademy_HAcademy:VB_VBN
-hacekombix_HacekoMbix:VB_VBN
-hacha_HaCha:VB_VBN
-hachiha_HaChiHa:VB_VBN
-hachun_HaChun:VB_VBN
-hackbkav_hackBkav:VB_VBN
-hackchong_HackChong:VB_VBN
-hackerearth_HackerEarth:VB_VBN
-hackerone_HackerOne:VB_VBN
-hackerptg_HackerPTG:VB_VBN
-hackerrank_HackerRank:VB_VBN
-hackillinois_HackIllinois:VB_VBN
-hacking_HaCkiNG:VB_VBN
-hackpower_hackPower:VB_VBN
-hacktp_hackTP:VB_VBN
-hacoled_HacoLED:VB_VBN
-hada_HaDa:VB_VBN
-hadalabo_HadaLabo:VB_VBN
-hadee_HaDee:VB_VBN
-hado_HaDo:VB_VBN
-hadoantv_HaDoanTV:VB_VBN
-hadong_HaDong:VB_VBN
-hadoopr_HadoopR:VB_VBN
-hadra_HaDra:VB_VBN
-haehyuk_HaeHyuk:VB_VBN
-haeva_HaEva:VB_VBN
-haho_HaHo:VB_VBN
-hahoi_HaHoi:VB_VBN
-hahungvuong_HaHungVuong:VB_VBN
-haian_HaiAn:VB_VBN
-haianhland_HaiAnhLand:VB_VBN
-haiau_HaiAu:VB_VBN
-haiaudio_HaiAudio:VB_VBN
-haibao_HaiBao:VB_VBN
-haichi_HAichi:VB_VBN
-haichienthang_HaiChienThang:VB_VBN
-haicung_haiCung:VB_VBN
-haidang_HaiDang:VB_VBN
-haidf_HaiDF:VB_VBN
-haidilao_HaiDiLao:VB_VBN
-haidoco_HaiDoCo:VB_VBN
-haiduong_HaiDuong:VB_VBN
-haiha_HaiHa:VB_VBN
-haihacorp_HaiHacorp:VB_VBN
-haihaishop_HaiHaiShop:VB_VBN
-haihoailinh_HaiHoaiLinh:VB_VBN
-haileave_HaiLeave:VB_VBN
-hailecao_HaiLeCao:VB_VBN
-haily_HaiLy:VB_VBN
-hailyboutique_HaiLyBoutique:VB_VBN
-hainam_HaiNam:VB_VBN
-hainguyen_HaiNguyen:VB_VBN
-hainguyendoanh_hainguyenDoanh:VB_VBN
-haiphanhuyen_HaiPhanHuyen:VB_VBN
-haiphantinh_HaiPhanTinh:VB_VBN
-haiphanxa_HaiPhanXa:VB_VBN
-hair_HAir:VB_VBN
-hairandfur_HairAndFur:VB_VBN
-hairando_HaiRanDo:VB_VBN
-hairburst_HairBurst:VB_VBN
-hairgain_HairGain:VB_VBN
-hairomega_HairOmega:VB_VBN
-hairpin_HairPin:VB_VBN
-hairsalon_HairSalon:VB_VBN
-hairshow_HairShow:VB_VBN
-hairstroke_HairStroke:VB_VBN
-hairx_HairX:VB_VBN
-haitrung_HaiTrung:VB_VBN
-haiyan_HaiYan:VB_VBN
-hakachi_HaKaChi:VB_VBN
-hakkoryu_HakkoRyu:VB_VBN
-halcyonl_HalcyonL:VB_VBN
-halenb_HalenB:VB_VBN
-halfload_HalfLoad:VB_VBN
-hali_HaLi:VB_VBN
-haligroup_HaliGroup:VB_VBN
-halili_HaLiLi:VB_VBN
-halinh_HaLinh:VB_VBN
-halinhtravel_HaLinhtravel:VB_VBN
-halion_HALion:VB_VBN
-hallostar_HalloStar:VB_VBN
-hallove_HalLove:VB_VBN
-halo_HaLo:VB_VBN
-halohalo_HaloHalo:VB_VBN
-halolighting_HALOLighting:VB_VBN
-halomua_HALOMua:VB_VBN
-halong_HaLong:VB_VBN
-haloshop_HaloShop:VB_VBN
-halotech_HaloTech:VB_VBN
-halotravel_HaloTravel:VB_VBN
-haloxyltm_HaloxylTM:VB_VBN
-hamdzui_HamDzui:VB_VBN
-hamet_HaMet:VB_VBN
-hamiruby_HamiRuby:VB_VBN
-hamkam_HamKam:VB_VBN
-hammu_HamMU:VB_VBN
-hamonypark_HamonyPark:VB_VBN
-hamoptv_hamopTV:VB_VBN
-hamsphire_HamSphire:VB_VBN
-hamtrong_hamTrong:VB_VBN
-hana_HaNa:VB_VBN
-hanaame_HanaAme:VB_VBN
-hanabeach_HanaBeach:VB_VBN
-hanacaca_HanaCaca:VB_VBN
-hanacans_HaNaCans:VB_VBN
-hanagaming_HanaGaming:VB_VBN
-hanagold_HanaGold:VB_VBN
-hanakbn_HanaKBN:VB_VBN
-hanakidz_HanaKidz:VB_VBN
-hanakimi_HanaKimi:VB_VBN
-hanalady_HanaLady:VB_VBN
-hananhviet_HanAnhViet:VB_VBN
-hanb_HanB:VB_VBN
-hanbat_HanBat:VB_VBN
-hancapquang_HanCapQuang:VB_VBN
-hancatemc_hancatEMC:VB_VBN
-hanchung_HanChung:VB_VBN
-hancook_HanCook:VB_VBN
-hancorp_HANCorp:VB_VBN
-handbrake_HandBrake:VB_VBN
-handfund_HandFund:VB_VBN
-handgrips_HandGRIPS:VB_VBN
-handheld_HandHeld:VB_VBN
-handlechange_handleChange:VB_VBN
-handlefetchusers_handleFetchUsers:VB_VBN
-handlerequest_handleRequest:VB_VBN
-handlescroll_handleScroll:VB_VBN
-handleviewclick_handleViewClick:VB_VBN
-handmade_HandMade:VB_VBN
-handmadevtshare_HandmadeVTshare:VB_VBN
-handoff_HandOff:VB_VBN
-handong_HanDong:VB_VBN
-handylab_HandyLab:VB_VBN
-hanelsoft_HanelSoft:VB_VBN
-hanespress_HANEspress:VB_VBN
-hanexingxay_HanexingXay:VB_VBN
-hangaroo_hangARoo:VB_VBN
-hangcha_HangCha:VB_VBN
-hangdachat_HangDaChat:VB_VBN
-hanghang_HangHang:VB_VBN
-hanghieusales_HangHieuSales:VB_VBN
-hangkongguan_HangKongGuan:VB_VBN
-hangout_HangOut:VB_VBN
-hangouts_HangOuts:VB_VBN
-hanguangyuan_HanGuangYuan:VB_VBN
-hangxom_HangXom:VB_VBN
-hangyao_hangYao:VB_VBN
-hanhdung_HanhDung:VB_VBN
-hanhdungedu_HanhDungEDU:VB_VBN
-hanhngamvet_HanhngamVet:VB_VBN
-hanhphucthanghoa_HanhPhucThangHoa:VB_VBN
-hanhtrinhdelta_HanhTrinhDelta:VB_VBN
-hanir_HaniR:VB_VBN
-hanixhani_HANIxHANI:VB_VBN
-hankhost_HankHost:VB_VBN
-hanko_HanKo:VB_VBN
-hankyung_HanKyung:VB_VBN
-hannahed_HannahEd:VB_VBN
-hannaholala_HannahOlala:VB_VBN
-hannesfostie_HannesFostie:VB_VBN
-hanngoc_HanNgoc:VB_VBN
-hannsjoachimfriedrichs_HannsJoachimFriedrichs:VB_VBN
-hanoibags_HanoiBags:VB_VBN
-hanoibus_HanoiBus:VB_VBN
-hanoicab_HanoiCab:VB_VBN
-hanoictt_HanoiCTT:VB_VBN
-hanoiedu_HanoiEdu:VB_VBN
-hanoietoco_HanoiEtoco:VB_VBN
-hanoievents_HanoiEvents:VB_VBN
-hanoiflycam_HanoiFlycam:VB_VBN
-hanoigame_HaNoiGame:VB_VBN
-hanoihitech_HanoiHitech:VB_VBN
-hanoihub_HanoiHub:VB_VBN
-hanoilanguage_HanoiLanguage:VB_VBN
-hanoilink_HanoiLink:VB_VBN
-hanoiredtour_HanoiRedtour:VB_VBN
-hanoiscrum_HanoiScrum:VB_VBN
-hanoisoft_HanoiSoft:VB_VBN
-hanoitoplist_HaNoitoplist:VB_VBN
-hanoitv_HanoiTV:VB_VBN
-hanoiwatch_HanoiWatch:VB_VBN
-hanotours_HanoTours:VB_VBN
-hanparis_HanParis:VB_VBN
-hanpdpick_HanpdPick:VB_VBN
-hansdevriesnl_HansDeVriesNL:VB_VBN
-hansemerkur_HanseMerkur:VB_VBN
-hansfoody_HansFoody:VB_VBN
-hansgrohe_HansGrohe:VB_VBN
-hanshop_HanShop:VB_VBN
-hansma_HansMa:VB_VBN
-hansnam_HansNam:VB_VBN
-hansol_HanSol:VB_VBN
-hanstudio_HanStudio:VB_VBN
-hansynguyen_HanSyNguyen:VB_VBN
-hanvico_HanVico:VB_VBN
-hanyang_HanYang:VB_VBN
-hanyoung_HanYoung:VB_VBN
-haobajiang_haobaJiang:VB_VBN
-haobtc_HaoBTC:VB_VBN
-haocha_HaoCha:VB_VBN
-haochi_HaoChi:VB_VBN
-haolocbuiphongsach_HaoLocbuiPhongsach:VB_VBN
-haominh_HaoMinh:VB_VBN
-haothien_HaoThien:VB_VBN
-haphamxx_HaPhamXX:VB_VBN
-hapmusictransfer_HAPMusicTransfer:VB_VBN
-hapnature_HapNature:VB_VBN
-hapodigital_HapoDigital:VB_VBN
-happhome_HappHome:VB_VBN
-happiercitizens_HappierCitizens:VB_VBN
-happistar_HappiStar:VB_VBN
-happycard_HappyCard:VB_VBN
-happyclean_HappyClean:VB_VBN
-happycook_HappyCook:VB_VBN
-happycow_HappyCow:VB_VBN
-happyfamily_HappyFamily:VB_VBN
-happyfood_HappyFood:VB_VBN
-happygold_HappyGold:VB_VBN
-happyhome_HappyHome:VB_VBN
-happyhouse_HappyHouse:VB_VBN
-happyland_HappyLand:VB_VBN
-happylife_HappyLife:VB_VBN
-happylive_HappyLive:VB_VBN
-happyluk_HappyLuk:VB_VBN
-happyluke_HappyLuke:VB_VBN
-happyluky_HappyLuky:VB_VBN
-happyme_HappyMe:VB_VBN
-happymusic_HappyMusic:VB_VBN
-happyoffice_HappyOffice:VB_VBN
-happystore_HappyStore:VB_VBN
-happytime_HappyTime:VB_VBN
-happytom_HappyTom:VB_VBN
-happytours_HappyTours:VB_VBN
-happytravel_HappyTravel:VB_VBN
-happyvet_HappyVet:VB_VBN
-happywater_HappyWater:VB_VBN
-happywedding_HappyWedding:VB_VBN
-hapre_HAPre:VB_VBN
-haprofood_HaproFood:VB_VBN
-haproinfo_HaproInfo:VB_VBN
-haproxy_HAProxy:VB_VBN
-haptickeys_HapticKeys:VB_VBN
-hapycom_HapYcom:VB_VBN
-haqa_HaQa:VB_VBN
-haraads_HaraAds:VB_VBN
-harafunnel_HaraFunnel:VB_VBN
-haraloyalty_HaraLoyalty:VB_VBN
-hararetail_HaraRetail:VB_VBN
-harashop_HARAShop:VB_VBN
-harasocial_HaraSocial:VB_VBN
-harborbay_HarborBay:VB_VBN
-harborwalk_HarborWalk:VB_VBN
-harbourfront_HarbourFront:VB_VBN
-harbourfrontharbourfront_HarbourfrontHarbourfront:VB_VBN
-hardboilder_HardBoilder:VB_VBN
-hardfork_HardFork:VB_VBN
-hardmammother_HardMammother:VB_VBN
-hardrock_HardRock:VB_VBN
-hardrywood_HardryWood:VB_VBN
-hardywood_HardyWood:VB_VBN
-hariwon_HariWon:VB_VBN
-harman_HarMan:VB_VBN
-harmankardon_HarmanKardon:VB_VBN
-harmonyhuawei_HarmonyHuawei:VB_VBN
-harmonyos_HarmonyOS:VB_VBN
-harpercollins_HarperCollins:VB_VBN
-harperone_HarperOne:VB_VBN
-harrisx_HarrisX:VB_VBN
-harryson_HarrySon:VB_VBN
-harvarduniversity_HarvardUniversity:VB_VBN
-harvestfresh_HarvestFresh:VB_VBN
-hasharon_HaSharon:VB_VBN
-hashayyara_HaShayyara:VB_VBN
-hashcalc_HashCalc:VB_VBN
-hashcode_hashCode:VB_VBN
-hashflare_HashFlare:VB_VBN
-hashfunction_HashFunction:VB_VBN
-hashid_hashId:VB_VBN
-hashimada_HashiMada:VB_VBN
-hashmap_HashMap:VB_VBN
-hashmaps_HashMaps:VB_VBN
-hashpower_HashPower:VB_VBN
-hashset_HashSet:VB_VBN
-hashtagsforlikes_HashtagsForLikes:VB_VBN
-hashtop_HashTop:VB_VBN
-hasitectest_HasitecTEST:VB_VBN
-hasmoney_hasMoney:VB_VBN
-hason_HaSon:VB_VBN
-hasownproperty_hasOwnProperty:VB_VBN
-hasredirect_hasRedirect:VB_VBN
-hasselblad_HasselBlad:VB_VBN
-hatapluz_HataPluz:VB_VBN
-hatduaphuocthanh_HatduaPhuocThanh:VB_VBN
-hatgiongnhapngoai_HatGiongNhapNgoai:VB_VBN
-hathanhauto_HaThanhAuto:VB_VBN
-hathinh_HaThinh:VB_VBN
-hatinh_HaTinh:VB_VBN
-hatomentry_HatomEntry:VB_VBN
-hatra_HaTra:VB_VBN
-hatsuinu_HatsuInu:VB_VBN
-hattrick_HatTrick:VB_VBN
-hatyai_HatYai:VB_VBN
-haugiangtv_HauGiangTV:VB_VBN
-haui_HaUI:VB_VBN
-haupk_HauPK:VB_VBN
-hausbelo_HausBelo:VB_VBN
-hausemaster_HauseMaster:VB_VBN
-hausland_HausLand:VB_VBN
-hausneo_HausNeo:VB_VBN
-hausnima_HausNima:VB_VBN
-hausviva_HausViva:VB_VBN
-havang_HaVang:VB_VBN
-havico_HaViCo:VB_VBN
-hawaii_HawaiI:VB_VBN
-hawkhost_HawkHost:VB_VBN
-hawksem_HawkSEM:VB_VBN
-haxdor_HaxDor:VB_VBN
-hay_HaY:VB_VBN
-hayaoniyazaki_HayaoNiyazaki:VB_VBN
-haybeckham_hayBeckham:VB_VBN
-haycpi_hayCPI:VB_VBN
-haydanh_hayDanh:VB_VBN
-hayfreelancer_hayFreelancer:VB_VBN
-haygame_hayGame:VB_VBN
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-hoangpr_HoangPR:VB_VBN
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-hoangviet_HoangViet:VB_VBN
-hoangviettourist_HoangViettourist:VB_VBN
-hoangvnxk_HoangVNXK:VB_VBN
-hoanleave_HoanLeave:VB_VBN
-hoantn_HoanTN:VB_VBN
-hoanui_HoaNui:VB_VBN
-hoaphat_HoaPhat:VB_VBN
-hoaphatpro_HoaphatPro:VB_VBN
-hoaquaonline_HoaquaOnline:VB_VBN
-hoaquynh_HoaQuynh:VB_VBN
-hoasala_HoaSala:VB_VBN
-hoasao_HoaSao:VB_VBN
-hoasheng_hoaSheng:VB_VBN
-hoastex_HoAstex:VB_VBN
-hoatdonganh_HoatDongAnh:VB_VBN
-hoatech_HoaTech:VB_VBN
-hoatho_HoaTho:VB_VBN
-hoatieu_HoaTieu:VB_VBN
-hoatita_HoaTiTa:VB_VBN
-hoatrung_HoaTrung:VB_VBN
-hoatuoidep_HoaTuoiDep:VB_VBN
-hoavt_HoaVT:VB_VBN
-hoavy_HoaVy:VB_VBN
-hoaxanh_HoaXanh:VB_VBN
-hoayeuthuong_HoaYeuThuong:VB_VBN
-hobbytech_HobbyTech:VB_VBN
-hobbywing_HobbyWing:VB_VBN
-hocautocadonline_HocAutoCADonline:VB_VBN
-hocdot_HocDot:VB_VBN
-hocexcelonline_HocExcelOnline:VB_VBN
-hocggsheet_HocGgSheet:VB_VBN
-hochay_HocHay:VB_VBN
-hochiminh_HoChiMinh:VB_VBN
-hochoa_HocHoa:VB_VBN
-hoclaixehaan_HoclaixeHaan:VB_VBN
-hoclammonngon_HocLamMonNgon:VB_VBN
-hocled_HocLed:VB_VBN
-hocmmo_HocMMO:VB_VBN
-hocmo_HocMo:VB_VBN
-hocmon_HocMon:VB_VBN
-hocnhanh_HocNhanh:VB_VBN
-hoco_HoCo:VB_VBN
-hoctaphay_HocTapHay:VB_VBN
-hocthatnhanh_HocThatNhanh:VB_VBN
-hocthuc_HocThuc:VB_VBN
-hoctiengtrungonline_HocTiengTrungOnline:VB_VBN
-hoctiengtrungtudau_HocTiengTrungTuDau:VB_VBN
-hoctudau_HocTuDau:VB_VBN
-hocvienawe_HocvienAWE:VB_VBN
-hocvientit_HOCVIENTit:VB_VBN
-hocvps_HocVPS:VB_VBN
-hod_HoD:VB_VBN
-hodangvietnam_HoDangVietNam:VB_VBN
-hodiled_HodiLed:VB_VBN
-hodlbot_HodlBot:VB_VBN
-hodler_HODLer:VB_VBN
-hodlers_HODLers:VB_VBN
-hodling_HODLing:VB_VBN
-hodo_HoDo:VB_VBN
-hodrickprescott_HodrickPrescott:VB_VBN
-hoecloderm_HoeCloderm:VB_VBN
-hoeneb_HoeneB:VB_VBN
-hoetramsone_HoeTramsone:VB_VBN
-hofaco_HoFaco:VB_VBN
-hofh_HoFH:VB_VBN
-hogaming_HoGaming:VB_VBN
-hogi_HoGi:VB_VBN
-hohan_HoHan:VB_VBN
-hohl_HoHL:VB_VBN
-hoi_hoI:VB_VBN
-hoian_HoiAn:VB_VBN
-hoidap_HoiDap:VB_VBN
-hoidongtruongquay_HoiDongTruongQuay:VB_VBN
-hoithanh_HoiThanh:VB_VBN
-hoiyeumeo_HoiYeuMeo:VB_VBN
-hojashop_HOJAShop:VB_VBN
-hoka_HoKa:VB_VBN
-hola_HoLa:VB_VBN
-holadong_HolaDong:VB_VBN
-holahola_HolaHola:VB_VBN
-holatravel_HoLaTravel:VB_VBN
-holdcoin_HoldCoin:VB_VBN
-holdenholdenlang_HoldenHoldenLang:VB_VBN
-holders_HOLDers:VB_VBN
-holdingshung_HoldingsHung:VB_VBN
-hole_HoLe:VB_VBN
-holinut_HoliNut:VB_VBN
-hollygout_HollyGout:VB_VBN
-hollyngo_HollyNgo:VB_VBN
-hollyspirit_HollySpirit:VB_VBN
-hollywood_HollyWood:VB_VBN
-hololens_HoloLens:VB_VBN
-holonotes_HoloNotes:VB_VBN
-holovista_HoloVista:VB_VBN
-holynight_HolyNighT:VB_VBN
-homazone_HomazOne:VB_VBN
-homeair_HomeAir:VB_VBN
-homeaway_HomeAway:VB_VBN
-homebank_HomeBank:VB_VBN
-homebanking_HomeBanking:VB_VBN
-homebar_HomeBar:VB_VBN
-homeblockcoin_HomeBlockCoin:VB_VBN
-homeboy_HomeBoy:VB_VBN
-homebrew_HomeBrew:VB_VBN
-homebudget_HomeBudget:VB_VBN
-homebus_HomeBus:VB_VBN
-homebyme_HomeByMe:VB_VBN
-homecare_HomeCare:VB_VBN
-homecinema_HomeCinema:VB_VBN
-homeclean_HomeClean:VB_VBN
-homecoming_HomeComing:VB_VBN
-homeconnect_HomeConnect:VB_VBN
-homecre_HomeCre:VB_VBN
-homecredit_HomeCredit:VB_VBN
-homedeco_HomeDeco:VB_VBN
-homedepot_HomeDepot:VB_VBN
-homedics_HoMedics:VB_VBN
-homedirect_HomeDirect:VB_VBN
-homedoctors_HomeDoctors:VB_VBN
-homedomain_HomeDomain:VB_VBN
-homedu_HomeDu:VB_VBN
-homefarm_HomeFarm:VB_VBN
-homefed_HomeFed:VB_VBN
-homefloor_HomeFloor:VB_VBN
-homefoods_HomeFoods:VB_VBN
-homefree_HomeFree:VB_VBN
-homegarden_HomeGarden:VB_VBN
-homegredit_HomeGredit:VB_VBN
-homegroup_HomeGroup:VB_VBN
-homeguard_HomeGuard:VB_VBN
-homehero_HomeHero:VB_VBN
-homehub_HomeHub:VB_VBN
-homeiq_HomeIQ:VB_VBN
-homekhu_HomeKhu:VB_VBN
-homekinh_HomeKinh:VB_VBN
-homekit_HomeKit:VB_VBN
-homelab_HomeLab:VB_VBN
-homeland_HomeLand:VB_VBN
-homelink_HomeLink:VB_VBN
-homelock_HomeLock:VB_VBN
-homematic_HomeMatic:VB_VBN
-homemua_HomeMua:VB_VBN
-homemy_HomeMy:VB_VBN
-homen_HomeN:VB_VBN
-homenext_HomeNext:VB_VBN
-homentv_HoMENTV:VB_VBN
-homeoffice_HomeOffice:VB_VBN
-homephim_HomePhim:VB_VBN
-homephone_HomePhone:VB_VBN
-homephun_HomePhun:VB_VBN
-homeplaza_HomePlaza:VB_VBN
-homepob_HomePob:VB_VBN
-homepod_HomePod:VB_VBN
-homepods_HomePods:VB_VBN
-homepoe_HomePOE:VB_VBN
-homepro_HomePro:VB_VBN
-homerun_HomeRun:VB_VBN
-homescapes_HomeScapes:VB_VBN
-homesecurity_HomeSecurity:VB_VBN
-homeset_HomeSet:VB_VBN
-homesetting_HomeSetting:VB_VBN
-homesheel_HomeSheel:VB_VBN
-homeshop_HomeShop:VB_VBN
-homesolution_HomeSolution:VB_VBN
-homesong_HomeSong:VB_VBN
-homestar_HomeStar:VB_VBN
-homestay_HomeStay:VB_VBN
-homestaydalatorg_HomestayDalatORG:VB_VBN
-homestayleave_homestayLeave:VB_VBN
-homesun_HomeSun:VB_VBN
-homesupply_HomeSupply:VB_VBN
-hometin_HomeTin:VB_VBN
-hometintuckarik_HometintucKarik:VB_VBN
-hometrainer_HomeTrainer:VB_VBN
-hometrendy_HomeTrendy:VB_VBN
-hometv_HomeTV:VB_VBN
-homeup_HomeUp:VB_VBN
-homevietland_HomeVietLand:VB_VBN
-homeview_HomeView:VB_VBN
-homevison_HomeVison:VB_VBN
-homework_HomeWork:VB_VBN
-homexinh_HomeXinh:VB_VBN
-homextra_HomeXtra:VB_VBN
-homieshop_HomieShop:VB_VBN
-homm_HoMM:VB_VBN
-homopolypropylene_HomoPolyPropylene:VB_VBN
-homyland_HomyLand:VB_VBN
-homysun_HomySun:VB_VBN
-hon_HoN:VB_VBN
-honda_HonDa:VB_VBN
-hondaelemax_HondaELEMAX:VB_VBN
-hondagx_HondaGX:VB_VBN
-hondajet_HondaJet:VB_VBN
-hondapack_HondaPack:VB_VBN
-hondaren_hondaRen:VB_VBN
-hondavac_HondaVac:VB_VBN
-hondazhu_hondaZhu:VB_VBN
-hondhonda_HondHonda:VB_VBN
-honeydewd_HoneydewD:VB_VBN
-honeyland_HoneyLand:VB_VBN
-honeywell_HoneyWell:VB_VBN
-hongce_HongCe:VB_VBN
-hongdae_HongDae:VB_VBN
-hongfeng_HongFeng:VB_VBN
-honghaxinh_HongHaXinh:VB_VBN
-hongji_HongJi:VB_VBN
-hongkhong_HongKhong:VB_VBN
-hongkhue_HongKhue:VB_VBN
-hongkong_HongKong:VB_VBN
-hongkongnextnext_HongKongNextNext:VB_VBN
-hongky_HongKy:VB_VBN
-hongleong_HongLeong:VB_VBN
-hongmen_HongMen:VB_VBN
-hongmeng_HongMeng:VB_VBN
-hongnghi_HONGNghi:VB_VBN
-hongoanh_HongOanh:VB_VBN
-hongphuong_HongPhuong:VB_VBN
-hongsamsidae_HongSamSiDae:VB_VBN
-hongyan_HongYan:VB_VBN
-honkong_HonKong:VB_VBN
-hoocmon_HoocMon:VB_VBN
-hoodtube_HoodTube:VB_VBN
-hoogendijkhope_HoogendijkHope:VB_VBN
-hoohoohaha_HooHooHaHa:VB_VBN
-hookerriver_HookerRiver:VB_VBN
-hoopcc_HoopCC:VB_VBN
-hoosierlottery_HoosierLottery:VB_VBN
-hootsuite_HootSuite:VB_VBN
-hopamchuan_HopAmChuan:VB_VBN
-hopecom_HopeCom:VB_VBN
-hopgiaysi_HopGiaySi:VB_VBN
-hophap_HoPhap:VB_VBN
-hopkinssource_HopkinsSource:VB_VBN
-hopnhat_HopNhat:VB_VBN
-hore_HoRE:VB_VBN
-horea_HoREA:VB_VBN
-horeca_HoReCa:VB_VBN
-horecavn_HorecaVN:VB_VBN
-horikashi_HoriKashi:VB_VBN
-horizonetech_HoriZonetech:VB_VBN
-horizontalalignment_HorizontalAlignment:VB_VBN
-hormonetalk_HormoneTalk:VB_VBN
-hormuznga_HormuzNga:VB_VBN
-horsepower_HorsePower:VB_VBN
-hosan_HoSan:VB_VBN
-hose_HoSE:VB_VBN
-hoseok_HoSeok:VB_VBN
-hoso_HoSo:VB_VBN
-hospitalnih_hospitalNIH:VB_VBN
-hostadsense_HostAdsense:VB_VBN
-hostadvice_HostAdvice:VB_VBN
-hostarmada_HostArmada:VB_VBN
-hostelbookers_HostelBookers:VB_VBN
-hosteltraveler_HostelTraveler:VB_VBN
-hostelworld_HostelWorld:VB_VBN
-hostfilix_HostFilix:VB_VBN
-hostgator_HostGator:VB_VBN
-hostingviet_HostingViet:VB_VBN
-hostingvn_HostingVN:VB_VBN
-hostjava_HostJava:VB_VBN
-hostmantis_HostMantis:VB_VBN
-hostmonster_HostMonster:VB_VBN
-hostname_HostName:VB_VBN
-hostpapa_HostPapa:VB_VBN
-hostscore_HostScore:VB_VBN
-hostupon_HostUpon:VB_VBN
-hostus_HostUS:VB_VBN
-hostvn_HostVN:VB_VBN
-hostwinds_HostWinds:VB_VBN
-hostxnow_HostXNow:VB_VBN
-hotaireco_HotairEco:VB_VBN
-hotavn_HotaVN:VB_VBN
-hotbets_HotBets:VB_VBN
-hotbit_HotBit:VB_VBN
-hotcat_HotCat:VB_VBN
-hotclip_HotClip:VB_VBN
-hotcon_HotCon:VB_VBN
-hotdeal_HotDeal:VB_VBN
-hotdo_HotDo:VB_VBN
-hotdog_HotDog:VB_VBN
-hotdogswap_HotdogSwap:VB_VBN
-hotelengine_HotelEngine:VB_VBN
-hotelgrand_HotelGrand:VB_VBN
-hotelkdm_HotelKDM:VB_VBN
-hotelquickly_HotelQuickly:VB_VBN
-hotelscombined_HotelsCombined:VB_VBN
-hotelsheraton_HotelSHERATON:VB_VBN
-hoten_HoTen:VB_VBN
-hotfile_HotFile:VB_VBN
-hotfix_HotFix:VB_VBN
-hotforex_HotForex:VB_VBN
-hotfucoidan_hotFucoidan:VB_VBN
-hothai_HotHai:VB_VBN
-hothanhpon_HoThanhPon:VB_VBN
-hothardware_HotHardware:VB_VBN
-hotheo_HOTheo:VB_VBN
-hotjar_HotJar:VB_VBN
-hotkey_HotKey:VB_VBN
-hotkhi_HotKhi:VB_VBN
-hotli_HotLi:VB_VBN
-hotlinel_HotlineL:VB_VBN
-hotlob_HotLob:VB_VBN
-hotmeowth_HotMEOWTH:VB_VBN
-hotmobiz_hotMobiz:VB_VBN
-hoto_HoTo:VB_VBN
-hotoc_HoToc:VB_VBN
-hotornot_HotOrNot:VB_VBN
-hotpocket_HotPocket:VB_VBN
-hotpot_HotPot:VB_VBN
-hotread_HotRead:VB_VBN
-hotroeviews_hotroEviews:VB_VBN
-hots_HotS:VB_VBN
-hotsau_HotSau:VB_VBN
-hotshot_HotShot:VB_VBN
-hotshotgg_HotshotGG:VB_VBN
-hotson_hotSon:VB_VBN
-hotspot_HotSpot:VB_VBN
-hotspur_HotSpur:VB_VBN
-hottin_HotTin:VB_VBN
-hottop_hotTop:VB_VBN
-hottrackingcolor_HotTrackingColor:VB_VBN
-hottri_hotTRI:VB_VBN
-hotwheels_HotWheels:VB_VBN
-hourofcode_HourOfCode:VB_VBN
-hourswe_hoursWe:VB_VBN
-housecall_HouseCall:VB_VBN
-housecare_HouseCare:VB_VBN
-housefit_HouseFit:VB_VBN
-housenumber_houseNumber:VB_VBN
-houseqh_HouseQH:VB_VBN
-housetech_HouseTech:VB_VBN
-housex_HouSeX:VB_VBN
-housingmap_HousingMap:VB_VBN
-houstonchronicle_HoustonChronicle:VB_VBN
-houyen_HoUyen:VB_VBN
-hoviettrungpops_HoVietTrungPOPS:VB_VBN
-howstuffworks_HowStuffWorks:VB_VBN
-howtogeek_HowToGeek:VB_VBN
-hoyeah_HoYeah:VB_VBN
-hoyeonposted_HoyeonPosted:VB_VBN
-hoypoloi_HoyPoloi:VB_VBN
-hpconnect_HPConnect:VB_VBN
-hpcviet_HPCViet:VB_VBN
-hpdoor_HPdoor:VB_VBN
-hpelitebook_HPElitebook:VB_VBN
-hpg_HpG:VB_VBN
-hpham_HPham:VB_VBN
-hphosts_hpHosts:VB_VBN
-hpkhuong_hpKhuong:VB_VBN
-hplaserjet_HPLaserJet:VB_VBN
-hplight_HPLight:VB_VBN
-hplsport_HPLSport:VB_VBN
-hpoe_HPoE:VB_VBN
-hpos_HPoS:VB_VBN
-hpphukienpc_HPPhukienpc:VB_VBN
-hpscan_HPScan:VB_VBN
-hptech_HPtech:VB_VBN
-hptrade_HpTrade:VB_VBN
-hptwindow_HPTwindow:VB_VBN
-hqcook_HQCook:VB_VBN
-hqkhanh_HQKhanh:VB_VBN
-hqmusic_HQMusic:VB_VBN
-hqreishi_HQReishi:VB_VBN
-hqsoft_HQsoft:VB_VBN
-hqtrung_HQTrung:VB_VBN
-hrchannels_HRchannels:VB_VBN
-hrdacademy_HRDAcademy:VB_VBN
-hrhub_HRHub:VB_VBN
-hrmcloud_HrmCloud:VB_VBN
-hronline_HrOnline:VB_VBN
-hrutovtrong_HrutovTrong:VB_VBN
-hrvietnam_HRVietnam:VB_VBN
-hrydroconquest_HrydroConquest:VB_VBN
-hsacademy_HSAcademy:VB_VBN
-hsaha_HSaHa:VB_VBN
-hsb_HsB:VB_VBN
-hsbcnet_HSBCnet:VB_VBN
-hscode_HScode:VB_VBN
-hsedu_HSEdu:VB_VBN
-hsh_HsH:VB_VBN
-hsmquy_hsmQuy:VB_VBN
-hsslink_HssLink:VB_VBN
-hstex_HStex:VB_VBN
-htcgame_HTCGame:VB_VBN
-htcom_HTCom:VB_VBN
-htcop_HTcop:VB_VBN
-htcsense_HTCSense:VB_VBN
-htcshop_HTCshop:VB_VBN
-htdkids_HTDkids:VB_VBN
-htemotion_HTemotion:VB_VBN
-htfashion_HTFashion:VB_VBN
-htfood_HTFood:VB_VBN
-htgoods_HTGoods:VB_VBN
-hthao_HThao:VB_VBN
-htiki_HTiki:VB_VBN
-htin_HTin:VB_VBN
-htkids_HTkids:VB_VBN
-htkonline_HTKOnline:VB_VBN
-htland_HTLand:VB_VBN
-htlight_HTlight:VB_VBN
-htlinh_HTLinh:VB_VBN
-htmart_HTmart:VB_VBN
-htmlhelper_HtmlHelper:VB_VBN
-htmlminifier_HTMLMinifier:VB_VBN
-htmlsupplied_htmlSupplied:VB_VBN
-htmlvi_htmlVI:VB_VBN
-htshop_HTShop:VB_VBN
-htsoft_HTsoft:VB_VBN
-htsolution_HTSolution:VB_VBN
-htthuyen_HTTHuyen:VB_VBN
-http_hTTP:VB_VBN
-httparchive_HTTPArchive:VB_VBN
-httpclient_HttpClient:VB_VBN
-httpcontext_HttpContext:VB_VBN
-httpcookie_HttpCookie:VB_VBN
-httpfox_HTTPFox:VB_VBN
-httpget_HttpGet:VB_VBN
-httphandlers_HttpHandlers:VB_VBN
-httpmethod_HTTPMethod:VB_VBN
-httponly_HttpOnly:VB_VBN
-httpproviders_HTTPProviders:VB_VBN
-httpservletresponse_HttpServletResponse:VB_VBN
-httpsurlconnection_HttpsUrlConnection:VB_VBN
-htvaward_HTVAward:VB_VBN
-htviet_HTViet:VB_VBN
-htweb_HTWeb:VB_VBN
-htx_hTX:VB_VBN
-hua_HuA:VB_VBN
-huaan_HuaAn:VB_VBN
-huahin_HuaHin:VB_VBN
-huangshi_HuangShi:VB_VBN
-huangzhl_HuangZhl:VB_VBN
-huashuang_HuaShuang:VB_VBN
-huasipan_HuaSiPan:VB_VBN
-huawei_HuaWei:VB_VBN
-huaweiai_HuaweiAi:VB_VBN
-huaweisamsung_HuaweiSamsung:VB_VBN
-huaxin_HuaXin:VB_VBN
-huayang_HuaYang:VB_VBN
-hubhoa_HubHoa:VB_VBN
-hubic_hubiC:VB_VBN
-hublot_HUblot:VB_VBN
-hubspot_HubSpot:VB_VBN
-hucafood_HucaFood:VB_VBN
-hucatu_HuCaTu:VB_VBN
-hueandsun_HueandSun:VB_VBN
-huebet_HueBet:VB_VBN
-huecit_HueCIT:VB_VBN
-hues_HueS:VB_VBN
-huewaco_HueWACO:VB_VBN
-huffingtonpost_HuffingtonPost:VB_VBN
-huffpo_HuffPo:VB_VBN
-huffpost_HuffPost:VB_VBN
-hugadore_HugAdore:VB_VBN
-hugegroup_HugeGroup:VB_VBN
-hugo_HuGo:VB_VBN
-hugofarm_HugoFarm:VB_VBN
-huhanhvietnam_huhanhVietNam:VB_VBN
-huijun_HuiJun:VB_VBN
-huile_HuiLe:VB_VBN
-huiqi_HuiQi:VB_VBN
-huitong_HuiTong:VB_VBN
-huizhou_HuiZHou:VB_VBN
-hulktastic_HULKtastic:VB_VBN
-huloc_HuLoc:VB_VBN
-humanbeatbox_HumanBeatbox:VB_VBN
-humanos_humanOS:VB_VBN
-humblenet_HumbleNet:VB_VBN
-humhub_HumHub:VB_VBN
-humindex_HumIndex:VB_VBN
-hummingbird_HummingBird:VB_VBN
-huna_HuNa:VB_VBN
-hunatalk_HunaTalk:VB_VBN
-hung_HUng:VB_VBN
-hungapptimeout_HungAppTimeOut:VB_VBN
-hungaryghi_HungaryGhi:VB_VBN
-hungcoi_HungCoi:VB_VBN
-hunghau_HungHau:VB_VBN
-hungmbobile_HungMbobile:VB_VBN
-hungmobie_HungMobie:VB_VBN
-hungmobile_HungMobile:VB_VBN
-hungmobilevn_HungMobilevn:VB_VBN
-hungmoblie_HungMoblie:VB_VBN
-hungmoblile_HungMoblile:VB_VBN
-hungmoibile_HungMoibile:VB_VBN
-hungmoile_HungMoile:VB_VBN
-hungrypanda_HungryPanda:VB_VBN
-hungthinhreals_HungThinhreals:VB_VBN
-hungtrinh_HungTrinh:VB_VBN
-hungvap_hungVAP:VB_VBN
-hungvinh_HungVinh:VB_VBN
-hunhf_hUNHF:VB_VBN
-huniepop_HuniePop:VB_VBN
-hunifood_HuniFood:VB_VBN
-hunoclub_HuNoClub:VB_VBN
-hunupitiyagangarama_HunupitiyaGangarama:VB_VBN
-hunxen_HunXen:VB_VBN
-huobiglobal_HuobiGlobal:VB_VBN
-huongdanbitcoin_HuongDanBitcoin:VB_VBN
-huongleave_HuongLeave:VB_VBN
-huonglee_HuongLee:VB_VBN
-huonglotus_HuongLotus:VB_VBN
-huongthuy_HuongThuy:VB_VBN
-huongxua_HuongXua:VB_VBN
-huoyi_HuoYi:VB_VBN
-hurrytimer_HurryTimer:VB_VBN
-hutbephotdh_HutbephotDH:VB_VBN
-hutech_HuTech:VB_VBN
-hutechuniversity_HutechUniversity:VB_VBN
-huto_HuTo:VB_VBN
-hutop_HuTop:VB_VBN
-huucuong_HuuCuong:VB_VBN
-huuduyen_HuuDuyen:VB_VBN
-huyair_HUYAir:VB_VBN
-huyanh_huyAnh:VB_VBN
-huybq_HuyBQ:VB_VBN
-huyct_HuyCT:VB_VBN
-huyenlam_HuyenLam:VB_VBN
-huyentt_HuyenTT:VB_VBN
-huyhoa_HuyHoa:VB_VBN
-huyin_HuyIn:VB_VBN
-huykinh_HuyKinh:VB_VBN
-huylab_HUYlab:VB_VBN
-huylananh_HuyLanAnh:VB_VBN
-huymc_HuyMC:VB_VBN
-huyme_HuyMe:VB_VBN
-huyna_HuynA:VB_VBN
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-jordan_JOrdan:VB_VBN
-josemourinho_JoseMourinho:VB_VBN
-joseyhan_JoseyHan:VB_VBN
-joshlowenthal_JoshLowenthal:VB_VBN
-jotashield_JotaShield:VB_VBN
-joton_JoTon:VB_VBN
-jotonpaint_JotonPaint:VB_VBN
-jotun_JoTun:VB_VBN
-journalleave_JournalLeave:VB_VBN
-joustmax_JoustMax:VB_VBN
-joycity_JoyCity:VB_VBN
-joyfm_JoyFM:VB_VBN
-joykids_JoyKids:VB_VBN
-joyluckclub_JoyLuckClub:VB_VBN
-joyluckspa_JoyLuckSpa:VB_VBN
-joymore_JoyMore:VB_VBN
-joynews_JoyNews:VB_VBN
-joysrovn_JoySroVN:VB_VBN
-joystick_JoyStick:VB_VBN
-jpanel_JPanel:VB_VBN
-jpanwall_JpanWall:VB_VBN
-jpanwell_JpanWell:VB_VBN
-jpegview_JPEGView:VB_VBN
-jpelli_JPelli:VB_VBN
-jpflow_JPflow:VB_VBN
-jpkoi_JPKoi:VB_VBN
-jpland_JPLand:VB_VBN
-jplay_JPlay:VB_VBN
-jpmorgan_JPMorgan:VB_VBN
-jproskowglobal_JProskowGlobal:VB_VBN
-jpshop_JPshop:VB_VBN
-jpsoftbank_JPSoftBank:VB_VBN
-jpwach_JPWach:VB_VBN
-jpwatch_JPWatch:VB_VBN
-jpweb_JPweb:VB_VBN
-jqnet_JQnet:VB_VBN
-jquery_JQuery:VB_VBN
-jqueryui_jQueryUI:VB_VBN
-jreviews_JReviews:VB_VBN
-jrii_JrII:VB_VBN
-jriv_JrIV:VB_VBN
-jriver_JRiver:VB_VBN
-jrockit_JRockit:VB_VBN
-jsb_jsB:VB_VBN
-jsbin_JSBin:VB_VBN
-jschritte_JSchritte:VB_VBN
-jscript_JScript:VB_VBN
-jsdelivr_jsDelivr:VB_VBN
-jshint_JSHint:VB_VBN
-jsjquerynpmajaxmore_jsjQuerynpmAjaxMore:VB_VBN
-jslint_JSLint:VB_VBN
-jsobject_JSObject:VB_VBN
-jsol_JSol:VB_VBN
-json_JSon:VB_VBN
-jsondecoder_JSONDecoder:VB_VBN
-jsonencoder_JSONEncoder:VB_VBN
-jsonobject_JsonObject:VB_VBN
-jsonresult_JsonResult:VB_VBN
-jsonwebtoken_JsonWebToken:VB_VBN
-jsp_JsP:VB_VBN
-jspinner_JSpinner:VB_VBN
-jstlview_JstlView:VB_VBN
-jsxpro_JSXPro:VB_VBN
-jtbc_jTBC:VB_VBN
-jtech_JTech:VB_VBN
-jtextcomponent_JtextComponent:VB_VBN
-juanguaido_JuanGuaido:VB_VBN
-jubgkook_JubgKook:VB_VBN
-jubjubeiei_JubJubeiei:VB_VBN
-juby_JuBy:VB_VBN
-jud_JuD:VB_VBN
-jugglingpandas_JugglingPandas:VB_VBN
-juicebar_JuiceBar:VB_VBN
-juiceplotter_JuicePlotter:VB_VBN
-juji_JuJi:VB_VBN
-jukebox_JukeBox:VB_VBN
-juki_JuKi:VB_VBN
-juliaquinn_JuliaQuinn:VB_VBN
-julswap_JulSwap:VB_VBN
-julyhouse_JulyHouse:VB_VBN
-julyhousetinh_JULYHOUSETinh:VB_VBN
-jumbo_JumBo:VB_VBN
-jumboy_JumBoy:VB_VBN
-jumpdrive_JumpDrive:VB_VBN
-jumpstar_JumpStar:VB_VBN
-jumpstart_JumpStart:VB_VBN
-jumpsuit_JumpSuit:VB_VBN
-jundental_JunDental:VB_VBN
-jung_jUNG:VB_VBN
-junge_JunGe:VB_VBN
-junghansdavos_junghansDavos:VB_VBN
-junghee_JungHee:VB_VBN
-jungkook_JungKook:VB_VBN
-jungletimers_JungleTimers:VB_VBN
-jungmissing_JungMissing:VB_VBN
-jungsik_JungSik:VB_VBN
-jungsoo_JungSoo:VB_VBN
-junpyo_JunPyo:VB_VBN
-junyuan_JunYuan:VB_VBN
-jupicell_JupiCell:VB_VBN
-jupiter_JuPiTer:VB_VBN
-jupiterimages_JupiterImages:VB_VBN
-jupsofa_JupSofa:VB_VBN
-jupspa_JupSpa:VB_VBN
-jupviec_JupViec:VB_VBN
-jura_JUra:VB_VBN
-jurassicraft_JurassiCraft:VB_VBN
-juredolzan_JureDolzan:VB_VBN
-justanswer_JustAnswer:VB_VBN
-justatee_JustaTee:VB_VBN
-justco_JustCo:VB_VBN
-justcosigning_JustCosigning:VB_VBN
-justenoughitems_JustEnoughItems:VB_VBN
-justfly_JustFly:VB_VBN
-justforex_JustForex:VB_VBN
-justgivemeareason_JustGiveMeaReason:VB_VBN
-justgrab_JustGrab:VB_VBN
-justhost_JustHost:VB_VBN
-justi_justI:VB_VBN
-justifycontent_justifyContent:VB_VBN
-justintv_JustinTv:VB_VBN
-justlink_JustLink:VB_VBN
-justlove_JustLove:VB_VBN
-justmarry_JustMarry:VB_VBN
-justpark_JustPark:VB_VBN
-justswap_JustSwap:VB_VBN
-justtype_JustType:VB_VBN
-justwink_justWink:VB_VBN
-juta_JuTa:VB_VBN
-jutun_JuTun:VB_VBN
-jutvhz_JUTVhZ:VB_VBN
-juvee_JuVee:VB_VBN
-juventusmu_JuventusMU:VB_VBN
-juventuszlatan_JuventusZlatan:VB_VBN
-juvidermal_JuviDermal:VB_VBN
-juvifill_JuviFill:VB_VBN
-juviskincare_JuviSkincare:VB_VBN
-juztalent_JuzTalent:VB_VBN
-jvb_JvB:VB_VBN
-jvceco_JVCeco:VB_VBN
-jververmind_JVervermind:VB_VBN
-jvevermind_JVevermind:VB_VBN
-jvmroute_jvmRoute:VB_VBN
-jvroup_JVroup:VB_VBN
-jvspin_JVSpin:VB_VBN
-jweb_JWeb:VB_VBN
-jxwin_JxWin:VB_VBN
-kaachii_KaaChii:VB_VBN
-kabum_KaBuM:VB_VBN
-kabyflowers_KabyFlowers:VB_VBN
-kabylake_KabyLake:VB_VBN
-kacherclothing_KacherClothing:VB_VBN
-kachikachi_KachiKachi:VB_VBN
-kachikochi_KachiKochi:VB_VBN
-kadanhtrang_kadanhTrang:VB_VBN
-kadick_KaDick:VB_VBN
-kadlubowskithe_KadlubowskiThe:VB_VBN
-kaekai_KaeKai:VB_VBN
-kafe_KAfe:VB_VBN
-kafeville_KafeVille:VB_VBN
-kagfloor_KagFloor:VB_VBN
-kagg_kaGG:VB_VBN
-kaikai_KaiKai:VB_VBN
-kaios_KaiOS:VB_VBN
-kaipartale_KaiPartale:VB_VBN
-kaisa_KaiSa:VB_VBN
-kaiserslots_KaiserSlots:VB_VBN
-kaixgucci_KAIxGUCCI:VB_VBN
-kaiyang_KaiYang:VB_VBN
-kaiyen_KaiYen:VB_VBN
-kaiyokukan_KaiyoKukan:VB_VBN
-kaizenyoshidaschool_KaizenYoshidaSchool:VB_VBN
-kak_kAK:VB_VBN
-kaka_KaKa:VB_VBN
-kakafast_KaKaFast:VB_VBN
-kakaometro_KakaoMetro:VB_VBN
-kakaopay_KakaoPay:VB_VBN
-kakaostory_KakaoStory:VB_VBN
-kakaotalk_KakaoTalk:VB_VBN
-kakaotaxi_KakaoTaxi:VB_VBN
-kaki_KaKi:VB_VBN
-kakinkinh_kakinKinh:VB_VBN
-kakinphong_kakinPhong:VB_VBN
-kalamsat_KalamSat:VB_VBN
-kalanchoe_KalanChoe:VB_VBN
-kali_KaLi:VB_VBN
-kalisa_KaLisa:VB_VBN
-kam_KaM:VB_VBN
-kami_KaMi:VB_VBN
-kamkaew_KamKaew:VB_VBN
-kan_kAN:VB_VBN
-kanaflooring_KanaFlooring:VB_VBN
-kanai_KaNai:VB_VBN
-kanak_KaNak:VB_VBN
-kanayomi_KanaYomi:VB_VBN
-kanbanflow_KanbanFlow:VB_VBN
-kancolle_KanColle:VB_VBN
-kandental_KanDental:VB_VBN
-kanegaeyuki_KanegaeYuki:VB_VBN
-kangaroohanoi_KangarooHanoi:VB_VBN
-kangaroohome_KangarooHome:VB_VBN
-kangaroostore_KangarooStore:VB_VBN
-kangarootrong_kangarooTrong:VB_VBN
-kangensui_KangenSUI:VB_VBN
-kangfa_KangFa:VB_VBN
-kanghwa_KangHwa:VB_VBN
-kangin_KangIn:VB_VBN
-kangjin_KangJin:VB_VBN
-kanglim_KangLim:VB_VBN
-kangnam_KangNam:VB_VBN
-kangyi_KangYi:VB_VBN
-kanji_KAnji:VB_VBN
-kankan_KanKan:VB_VBN
-kankyo_KanKyo:VB_VBN
-kankyowood_KankyoWood:VB_VBN
-kansai_KansaI:VB_VBN
-kantae_KanTae:VB_VBN
-kantarmedia_KantarMedia:VB_VBN
-kaobb_KaoBB:VB_VBN
-kaos_KaOs:VB_VBN
-kapan_KaPan:VB_VBN
-kapo_KaPO:VB_VBN
-kappab_kappaB:VB_VBN
-karabox_KaraBox:VB_VBN
-karafun_KaraFun:VB_VBN
-karakeyoke_KaraKEYoke:VB_VBN
-karaoke_KaraOke:VB_VBN
-karaokeconnect_KaraokeConnect:VB_VBN
-karaokemedia_KaraokeMedia:VB_VBN
-karatedo_KarateDo:VB_VBN
-karatitlemaker_KaraTitleMaker:VB_VBN
-karativi_KaraTivi:VB_VBN
-kardiachain_KardiaChain:VB_VBN
-kardonwireless_KardonWireless:VB_VBN
-karinahotanal_KarinaHotAnal:VB_VBN
-karlmarx_KarlMarx:VB_VBN
-karofivietnam_KarofiVietNam:VB_VBN
-kartrider_KartRider:VB_VBN
-kasa_KaSa:VB_VBN
-kasing_kaSing:VB_VBN
-kasperskyos_KasperskyOS:VB_VBN
-kasrduy_KASRduy:VB_VBN
-kasslerkl_KasslerKL:VB_VBN
-kat_KaT:VB_VBN
-kata_KaTa:VB_VBN
-katahome_KataHome:VB_VBN
-katana_KaTaNa:VB_VBN
-kataralover_KataraLover:VB_VBN
-katchup_KatchUp:VB_VBN
-katflow_KATflow:VB_VBN
-katg_katG:VB_VBN
-kathleenlights_KathleenLights:VB_VBN
-katn_KatN:VB_VBN
-katoon_KaToon:VB_VBN
-kavo_KaVo:VB_VBN
-kawaeco_KawaEco:VB_VBN
-kawasan_KawaSan:VB_VBN
-kayan_KaYan:VB_VBN
-kayn_KayN:VB_VBN
-kayz_KayZ:VB_VBN
-kazaa_KaZaA:VB_VBN
-kazat_KaZaT:VB_VBN
-kazik_KaZik:VB_VBN
-kbang_KBang:VB_VBN
-kbcband_KBCband:VB_VBN
-kbcision_KBcision:VB_VBN
-kbeabox_KbeaBox:VB_VBN
-kbeatbox_KBeatBox:VB_VBN
-kbhxh_kBHXH:VB_VBN
-kbivms_KBiVMS:VB_VBN
-kblbest_KBLBest:VB_VBN
-kbntrippyfamily_KBNtrippyfamily:VB_VBN
-kboley_KBoley:VB_VBN
-kbone_KBone:VB_VBN
-kbps_KBps:VB_VBN
-kbusolar_KBUSolar:VB_VBN
-kbview_KBView:VB_VBN
-kbvision_KBvision:VB_VBN
-kbvison_KBvision:VB_VBN
-kcleaner_KCleaner:VB_VBN
-kclkcl_KClKCl:VB_VBN
-kcn_kCN:VB_VBN
-kcoffee_KCoffee:VB_VBN
-kconcept_KConcept:VB_VBN
-kcutpro_KcutPro:VB_VBN
-kdagjxlqvi_kdaGjxlqVI:VB_VBN
-kdbg_KDbg:VB_VBN
-kdc_KdC:VB_VBN
-kddo_KdDo:VB_VBN
-kdesign_KDesign:VB_VBN
-kdfund_KDFund:VB_VBN
-kdgiaitri_KDgiaitri:VB_VBN
-kdict_KDict:VB_VBN
-kdpoker_KDPoker:VB_VBN
-kdpokers_KDPokers:VB_VBN
-kdslot_KDslot:VB_VBN
-kdslots_KDSlots:VB_VBN
-keangnam_KeangNam:VB_VBN
-kebabtorki_KebabTorki:VB_VBN
-keendry_KeenDry:VB_VBN
-keengmovies_KeengMovies:VB_VBN
-keenlab_KeenLab:VB_VBN
-keenland_KeenLand:VB_VBN
-keepalive_KeepAlive:VB_VBN
-keepass_KeePass:VB_VBN
-keepc_KeepC:VB_VBN
-keepcup_KeepCup:VB_VBN
-keepdri_KeepDri:VB_VBN
-keepfly_KeepFly:VB_VBN
-keepkey_KeepKey:VB_VBN
-keepout_KeepOut:VB_VBN
-keepsafe_KeepSafe:VB_VBN
-keepsolid_KeepSolid:VB_VBN
-keepvid_KeepVid:VB_VBN
-keeweb_KeeWeb:VB_VBN
-keffiyeh_KeffIyeh:VB_VBN
-keiky_KeiKy:VB_VBN
-keilc_KeilC:VB_VBN
-keingsangbook_KeingSangbook:VB_VBN
-keisupimi_KEIsupimi:VB_VBN
-kejazz_KeJazz:VB_VBN
-kelloggther_KelloggTHER:VB_VBN
-kellypang_KellyPang:VB_VBN
-kelvinphan_KelvinPhan:VB_VBN
-kemangkhi_KemangKhi:VB_VBN
-kemgelato_kemGelato:VB_VBN
-kemken_KemKen:VB_VBN
-kemsamguoyao_KemSamGuoyao:VB_VBN
-kemseiseda_KemSeiseda:VB_VBN
-kena_KeNa:VB_VBN
-kenauto_KenAuto:VB_VBN
-kenbo_KenBo:VB_VBN
-kenck_KenCk:VB_VBN
-kenctvbl_kenctvBL:VB_VBN
-kenda_KenDA:VB_VBN
-kendecor_KenDecor:VB_VBN
-kendesgin_KenDesgin:VB_VBN
-kendesign_KenDesign:VB_VBN
-kengnam_KengNam:VB_VBN
-kenhapple_KenhApple:VB_VBN
-kenhchothuexe_KenhChoThueXe:VB_VBN
-kenhkhoedep_KenhKhoeDep:VB_VBN
-kenhota_kenhOTA:VB_VBN
-kenhshop_KenhShop:VB_VBN
-kenhthieunhi_KenhThieunhi:VB_VBN
-kenhthuexe_KenhThueXe:VB_VBN
-kenhtulieumamnon_KenhTuLieuMamNon:VB_VBN
-kenivinh_KeniVinh:VB_VBN
-kenjiojia_kenjiOjia:VB_VBN
-kenken_KenKen:VB_VBN
-kenko_KenKo:VB_VBN
-kenlox_KenLox:VB_VBN
-kenly_KenLy:VB_VBN
-kenmark_KenMark:VB_VBN
-kennys_KennyS:VB_VBN
-kenolb_KenoLB:VB_VBN
-kenrich_KenRich:VB_VBN
-kensofa_KenSOFA:VB_VBN
-kentamax_KentaMax:VB_VBN
-kentom_KenTom:VB_VBN
-kenve_KenVe:VB_VBN
-kenvip_KenVIP:VB_VBN
-kenwood_KenWood:VB_VBN
-kenyawikipedia_KenyaWikipedia:VB_VBN
-kenz_KenZ:VB_VBN
-kenzitc_kenzitC:VB_VBN
-kenzoflower_KenzoFlower:VB_VBN
-keobongda_KeoBongDa:VB_VBN
-keocali_KeoCali:VB_VBN
-keockeo_KeoCkeo:VB_VBN
-keocopa_KeoCopa:VB_VBN
-keonhacai_KeoNhaCai:VB_VBN
-keonhacaisoi_keonhacaiSoi:VB_VBN
-keonhacaitin_keonhacaiTin:VB_VBN
-keonhanh_KeoNhanh:VB_VBN
-keonhietlevo_KeoNhietLeVo:VB_VBN
-keosoi_keoSoi:VB_VBN
-keosung_KeoSung:VB_VBN
-keothom_KeoThom:VB_VBN
-keoty_keoTy:VB_VBN
-keplerland_KeplerLand:VB_VBN
-keppelfels_KeppelFels:VB_VBN
-keppelland_KeppelLand:VB_VBN
-keracrete_KeraCRETE:VB_VBN
-keraseal_KeraSEAL:VB_VBN
-kerasys_KeraSys:VB_VBN
-kerbcrack_KerbCrack:VB_VBN
-kernelmode_KernelMode:VB_VBN
-keservicedescriptortable_KeServiceDescriptorTable:VB_VBN
-keservicedescriptortableshadow_KeServiceDescriptorTableShadow:VB_VBN
-kespa_KeSPA:VB_VBN
-ketcausoft_KetcauSoft:VB_VBN
-ketnoisingapore_KetnoiSingapore:VB_VBN
-ketoan_KeToan:VB_VBN
-ketoanmvb_KetoanMVB:VB_VBN
-ketoguru_KetoGuru:VB_VBN
-ketohedixine_KetoHedixine:VB_VBN
-ketoslim_KetoSlim:VB_VBN
-ketquadientoan_KetQuaDienToan:VB_VBN
-ketquasoxoli_ketquasoxoLi:VB_VBN
-kev_keV:VB_VBN
-keyapply_KeyApply:VB_VBN
-keyarena_KeyArena:VB_VBN
-keybanc_KeyBanc:VB_VBN
-keybanquyen_KeyBanQuyen:VB_VBN
-keybinding_KeyBinding:VB_VBN
-keyblaze_KeyBlaze:VB_VBN
-keyboard_KeyBoard:VB_VBN
-keyboardevent_KeyboardEvent:VB_VBN
-keyboardlocker_KeyboardLocker:VB_VBN
-keycdn_KeyCDN:VB_VBN
-keyeast_KeyEast:VB_VBN
-keyframe_KeyFrame:VB_VBN
-keyfreeze_KeyFreeze:VB_VBN
-keyfunctions_KeyFunctions:VB_VBN
-keygen_KeyGen:VB_VBN
-keyghost_KeyGhost:VB_VBN
-keyhero_KeyHero:VB_VBN
-keyless_KeyLess:VB_VBN
-keyloggers_KeyLoggers:VB_VBN
-keynote_KeyNote:VB_VBN
-keynotfoundexception_KeyNotFoundException:VB_VBN
-keyone_KEYone:VB_VBN
-keypay_KeyPay:VB_VBN
-keyphanmem_KeyPhanMem:VB_VBN
-keyresults_KeyResults:VB_VBN
-keyshot_KeyShot:VB_VBN
-keystone_KeysTone:VB_VBN
-keystones_KeyStones:VB_VBN
-keystore_KeyStore:VB_VBN
-keystorepass_keystorePass:VB_VBN
-keyutilities_KeyUtilities:VB_VBN
-keywe_KeyWe:VB_VBN
-keyword_KeyWord:VB_VBN
-keywordgrouperpro_KeywordGrouperPro:VB_VBN
-keywordio_KeywordIO:VB_VBN
-keywordkeg_KeywordKeg:VB_VBN
-keywordmap_KeywordMap:VB_VBN
-keywordplanner_KeywordPlanner:VB_VBN
-keywords_KeyWords:VB_VBN
-keywordtool_KeywordTool:VB_VBN
-keywordxp_KeywordXP:VB_VBN
-kfmount_KFmount:VB_VBN
-kfresh_KFresh:VB_VBN
-kfw_KfW:VB_VBN
-kgbeast_KGBeast:VB_VBN
-kgcao_kgCao:VB_VBN
-kgccao_KGCCao:VB_VBN
-kgcrane_KGcrane:VB_VBN
-kgcvina_KGCVina:VB_VBN
-kget_KGet:VB_VBN
-kgnot_KgNot:VB_VBN
-kgoe_kgOE:VB_VBN
-kgp_kgP:VB_VBN
-kgrand_KGrand:VB_VBN
-kgtrung_kgTrung:VB_VBN
-khacduy_KhacDuy:VB_VBN
-khachquaduong_KhachQuaDuong:VB_VBN
-khai_KHai:VB_VBN
-khaigiang_KhaiGiang:VB_VBN
-khaiktv_khaiKTV:VB_VBN
-khaisilk_KhaiSilk:VB_VBN
-khaitri_KhaiTri:VB_VBN
-khandaia_KhanDaiA:VB_VBN
-khang_KHang:VB_VBN
-khangia_KhanGia:VB_VBN
-khangnamwindow_KhangNamWindow:VB_VBN
-khangvuong_KhangVuong:VB_VBN
-khanhcasa_KhanhCasa:VB_VBN
-khanhi_KhaNhi:VB_VBN
-khanhtldta_KhanhTLDTA:VB_VBN
-khani_KhaNi:VB_VBN
-khaodawk_KhaoDawk:VB_VBN
-khaosdb_KhaosDB:VB_VBN
-khaphaco_KhaPhaCo:VB_VBN
-khasa_KhaSa:VB_VBN
-khausoy_KHausoy:VB_VBN
-khauvai_KhauVai:VB_VBN
-khbvptr_kHBVPTR:VB_VBN
-kheranh_kheRanh:VB_VBN
-khetheo_kheTheo:VB_VBN
-khezu_KheZu:VB_VBN
-khicontinue_khiContinue:VB_VBN
-khieng_KhienG:VB_VBN
-khiu_khIU:VB_VBN
-khkd_kHKD:VB_VBN
-khlong_KhLong:VB_VBN
-khmua_khMua:VB_VBN
-khoa_KHoa:VB_VBN
-khoahoc_KhoaHoc:VB_VBN
-khoahoctuvi_KhoaHocTuvi:VB_VBN
-khoaimage_khoaImage:VB_VBN
-khoairead_khoaiRead:VB_VBN
-khoaitv_KhoaiTV:VB_VBN
-khoapham_KhoaPham:VB_VBN
-khoasachngoaingu_KhoaSachNgoaiNgu:VB_VBN
-khoasteam_KhoaSteam:VB_VBN
-khoatagged_KhoaTagged:VB_VBN
-khoavantayvn_KhoavantayVn:VB_VBN
-khoavip_KhoaVip:VB_VBN
-khoay_khoaY:VB_VBN
-khobacnam_KhoBacNam:VB_VBN
-khoban_KhoBan:VB_VBN
-khoca_KhoCa:VB_VBN
-khochi_khoChi:VB_VBN
-khodanhapkhau_KhoDaNhapKhau:VB_VBN
-khoe_KHoe:VB_VBN
-khoedep_KhoeDep:VB_VBN
-khogabotoi_KhoGaBoToi:VB_VBN
-khogangonposted_khogangonPosted:VB_VBN
-khogasi_KhoGaSi:VB_VBN
-khohangadamis_KhohangAdamis:VB_VBN
-khonextnext_khoNextNext:VB_VBN
-khong_KHong:VB_VBN
-khongyeuanhdau_KhOnGyEuAnHdAu:VB_VBN
-khonkaen_KhonKaen:VB_VBN
-khosachngoaingu_KhoSachNgoaiNgu:VB_VBN
-khothevn_KhotheVN:VB_VBN
-khounboulom_KhounBoulom:VB_VBN
-khouse_KHouse:VB_VBN
-khread_khRead:VB_VBN
-khuchampagnetown_khuChampagneTown:VB_VBN
-khudabukhsh_KhudaBukhsh:VB_VBN
-khuinternational_khuInternational:VB_VBN
-khung_KHung:VB_VBN
-khungautomatically_khungAutomatically:VB_VBN
-khungthanh_KhungThanh:VB_VBN
-khungtheo_khungTheo:VB_VBN
-khuvuichoitreem_KhuVuiChoiTreEm:VB_VBN
-khuyenmaijanus_khuyenmaiJANUS:VB_VBN
-khuyennong_KhuyenNong:VB_VBN
-khvatec_KHVatec:VB_VBN
-khyentse_KhyenTse:VB_VBN
-kia_KiA:VB_VBN
-kiamorning_KiaMorning:VB_VBN
-kiaora_KiaOra:VB_VBN
-kiaquy_kiaQuy:VB_VBN
-kiba_KiBa:VB_VBN
-kibbtunexpectedrange_KiBBTUnexpectedRange:VB_VBN
-kibiil_KiBiil:VB_VBN
-kibu_KiBu:VB_VBN
-kibum_KiBum:VB_VBN
-kichducnu_KichDucNu:VB_VBN
-kichenaid_KichenAid:VB_VBN
-kichfit_KichFit:VB_VBN
-kichhoat_KichHoat:VB_VBN
-kichhoatbanquyenvinhvien_KichHoatBanQuyenVinhVien:VB_VBN
-kichmen_KichMen:VB_VBN
-kickback_KickBack:VB_VBN
-kickcity_KickCity:VB_VBN
-kickex_KickEx:VB_VBN
-kickfit_KickFit:VB_VBN
-kickfitness_KickFitness:VB_VBN
-kickscooter_KickScooter:VB_VBN
-kickscooters_KickScooters:VB_VBN
-kickstarter_KickStarter:VB_VBN
-kico_KiCo:VB_VBN
-kicotrans_KicoTrans:VB_VBN
-kiddihub_KiddiHub:VB_VBN
-kidica_KidiCa:VB_VBN
-kidmodel_KidModel:VB_VBN
-kidnania_KidNania:VB_VBN
-kido_KiDo:VB_VBN
-kidplus_KidPlus:VB_VBN
-kidpro_KidPrO:VB_VBN
-kidproof_KidProof:VB_VBN
-kidprooftm_KidProofTM:VB_VBN
-kidsclip_KidsClip:VB_VBN
-kidsfashion_KidsFashion:VB_VBN
-kidshealth_KidsHealth:VB_VBN
-kidsmoov_KIDSmoov:VB_VBN
-kidsonline_KidsOnline:VB_VBN
-kidsphone_KidsPhone:VB_VBN
-kidsplaza_KidsPlaza:VB_VBN
-kidsplorer_KidSplorer:VB_VBN
-kidspolrer_KidSpolrer:VB_VBN
-kidsreads_KidsReads:VB_VBN
-kidstars_KidStars:VB_VBN
-kidstyle_KidStyle:VB_VBN
-kidszonevn_KidszoneVN:VB_VBN
-kidtalent_KidTalent:VB_VBN
-kidworld_KidWorld:VB_VBN
-kidzania_KidZania:VB_VBN
-kidzmondo_KidzMondo:VB_VBN
-kidzworld_KidzWorld:VB_VBN
-kiemtiennhacai_KiemTienNhaCai:VB_VBN
-kienbank_KienBank:VB_VBN
-kienfo_KienFo:VB_VBN
-kienlong_KienLong:VB_VBN
-kienlongbank_KienLongBank:VB_VBN
-kiennguyen_KienNguyen:VB_VBN
-kienthucmoingay_KienThucMoiNgay:VB_VBN
-kienthucseo_KienthucSEO:VB_VBN
-kienthucvui_KienThucVui:VB_VBN
-kientructruehouse_KientrucTruehouse:VB_VBN
-kientructv_KientrucTV:VB_VBN
-kientrucyb_KienTrucYB:VB_VBN
-kienviet_KienViet:VB_VBN
-kienvuongtech_KienVuongTech:VB_VBN
-kieuchinh_KieuChinh:VB_VBN
-kieuchuc_KieuChuc:VB_VBN
-kieutrang_KieuTrang:VB_VBN
-kihik_KiHik:VB_VBN
-kikai_KiKai:VB_VBN
-kikfc_kikFC:VB_VBN
-kiki_KiKi:VB_VBN
-kiko_KiKo:VB_VBN
-kiku_KiKu:VB_VBN
-kikwang_KiKwang:VB_VBN
-kikyoufc_KikyouFC:VB_VBN
-kila_KiLa:VB_VBN
-kilburnparking_KilburnParking:VB_VBN
-killdisk_KillDisk:VB_VBN
-killedvip_killedVIP:VB_VBN
-killemall_KillEmAll:VB_VBN
-killex_KiLLEX:VB_VBN
-killx_KillX:VB_VBN
-kilometersperhour_KilometersPerHour:VB_VBN
-kiloohm_kiloOhm:VB_VBN
-kilowatt_KiloWatt:VB_VBN
-kimcare_KIMcare:VB_VBN
-kimeng_KimEng:VB_VBN
-kimetsu_KImetsu:VB_VBN
-kimfacebook_KimFaceBook:VB_VBN
-kimfashion_KimFashion:VB_VBN
-kimfullhouse_KimFullHouse:VB_VBN
-kimjojo_KimJOJO:VB_VBN
-kimjong_KimJong:VB_VBN
-kimkardashian_KimKardashian:VB_VBN
-kimkat_KimKat:VB_VBN
-kimkha_KimKha:VB_VBN
-kimmygroup_KimmyGroup:VB_VBN
-kimnhung_KimNhung:VB_VBN
-kimostar_KimoStar:VB_VBN
-kimpap_KimPap:VB_VBN
-kimsacanada_kimsaCanada:VB_VBN
-kimsachi_kimsaChi:VB_VBN
-kimsachina_kimsaChina:VB_VBN
-kimsachinanews_kimsaChinanews:VB_VBN
-kimsading_kimsaDing:VB_VBN
-kimsaghi_kimsaGhi:VB_VBN
-kimsakhi_kimsaKhi:VB_VBN
-kimsakim_kimsaKim:VB_VBN
-kimsaquan_kimsaQuan:VB_VBN
-kimsasau_kimsaSau:VB_VBN
-kimsashenhua_kimsaShenhua:VB_VBN
-kimsatheo_kimsaTheo:VB_VBN
-kimsathu_kimsaThu:VB_VBN
-kimsatin_kimsaTin:VB_VBN
-kimsatrang_kimsaTrang:VB_VBN
-kimsatrong_kimsaTrong:VB_VBN
-kimsawho_kimsaWHO:VB_VBN
-kimsoonsik_KimSoonSik:VB_VBN
-kimthu_KimThu:VB_VBN
-kimtwitter_KimTwitter:VB_VBN
-kimvip_KimVip:VB_VBN
-kimyong_KimYong:VB_VBN
-kimyoutube_KimYouTube:VB_VBN
-kinbar_KINBar:VB_VBN
-kinbu_kinBu:VB_VBN
-kindcoffee_KindCoffee:VB_VBN
-kinderstar_KinderStar:VB_VBN
-kinderworld_KinderWorld:VB_VBN
-kinemaster_KineMaster:VB_VBN
-kinet_KiNET:VB_VBN
-king_KIng:VB_VBN
-kingbank_KingBank:VB_VBN
-kingbass_KingBass:VB_VBN
-kingbay_KingBay:VB_VBN
-kingbbq_KingBBQ:VB_VBN
-kingbell_KingBell:VB_VBN
-kingbird_KingBird:VB_VBN
-kingbridal_KingBridal:VB_VBN
-kingcaedo_KingCaedo:VB_VBN
-kingcamp_KingCamp:VB_VBN
-kingchoice_KingChoice:VB_VBN
-kingcom_KingCom:VB_VBN
-kingcrab_KingCrab:VB_VBN
-kingcut_KingCut:VB_VBN
-kingdian_KingDian:VB_VBN
-kingdom_KingDom:VB_VBN
-kingdong_kingDong:VB_VBN
-kingdoor_KingDoor:VB_VBN
-kingeco_KingECO:VB_VBN
-kingfast_KingFast:VB_VBN
-kingfloor_KingFloor:VB_VBN
-kingfun_KingFun:VB_VBN
-kinggame_KingGame:VB_VBN
-kinggems_KingGems:VB_VBN
-kinggift_KingGift:VB_VBN
-kinghome_KingHome:VB_VBN
-kingj_KingJ:VB_VBN
-kingkicker_KingKicker:VB_VBN
-kingkoil_KingKoil:VB_VBN
-kingkong_KingKong:VB_VBN
-kingland_KingLand:VB_VBN
-kingled_KingLED:VB_VBN
-kinglight_KingLight:VB_VBN
-kinglive_KingLive:VB_VBN
-kingma_KingMa:VB_VBN
-kingmac_KingMac:VB_VBN
-kingmarketing_KingMarketing:VB_VBN
-kingmax_KingMax:VB_VBN
-kingnuts_KingNuts:VB_VBN
-kingoapp_KingoApp:VB_VBN
-kingoffice_KingOffice:VB_VBN
-kingpaint_KingPaint:VB_VBN
-kingparts_KingParts:VB_VBN
-kingpetss_KingPetss:VB_VBN
-kingphar_KingPhar:VB_VBN
-kingpos_KingPos:VB_VBN
-kingqueen_KingQueen:VB_VBN
-kingroon_KingRoon:VB_VBN
-kingroot_KingRoot:VB_VBN
-kings_KIngs:VB_VBN
-kingseam_KingSeam:VB_VBN
-kingsgroup_KingsGroup:VB_VBN
-kingshark_KingShark:VB_VBN
-kingshoes_KingShoes:VB_VBN
-kingshop_KingShop:VB_VBN
-kingsmith_KingSmith:VB_VBN
-kingsoft_KingSoft:VB_VBN
-kingsonic_KINGsonic:VB_VBN
-kingspec_KingSpec:VB_VBN
-kingspeed_KingSpeed:VB_VBN
-kingsport_KingSport:VB_VBN
-kingsteam_KingSteam:VB_VBN
-kingston_KingSton:VB_VBN
-kingstoncognate_KingstonCognate:VB_VBN
-kingstondt_KingstonDT:VB_VBN
-kingsun_KingSun:VB_VBN
-kingsup_KingsUp:VB_VBN
-kingsurf_KingSurf:VB_VBN
-kingtigerprawn_KingTigerPrawn:VB_VBN
-kingtong_KingTong:VB_VBN
-kingtony_KingTony:VB_VBN
-kingtools_KingTools:VB_VBN
-kingtop_KingTop:VB_VBN
-kingup_KingUp:VB_VBN
-kingwear_KingWear:VB_VBN
-kingwin_KingWin:VB_VBN
-kingzone_KingZone:VB_VBN
-kinh_KInh:VB_VBN
-kinhantoanvietnhat_KinhAnToanVietNhat:VB_VBN
-kinhbacweb_KinhbacWeb:VB_VBN
-kinhcuonglucquangnamphat_KinhcuonglucQuangnamphat:VB_VBN
-kinhdoanhairrbnb_KinhdoanhAirrBnB:VB_VBN
-kinhdoanhmlm_KinhDoanhMlm:VB_VBN
-kinhome_KinHome:VB_VBN
-kinhsabril_kinhSabril:VB_VBN
-kinhtedothi_KInhtedothi:VB_VBN
-kinhuni_kinhUni:VB_VBN
-kinj_kinJ:VB_VBN
-kinkakujimap_KinkakujiMap:VB_VBN
-kinlong_KinLong:VB_VBN
-kinnporsche_KinnPorsche:VB_VBN
-kinophis_KinOphis:VB_VBN
-kinscreen_KinScreen:VB_VBN
-kinsec_KinSEC:VB_VBN
-kio_KiO:VB_VBN
-kioora_KioOra:VB_VBN
-kiotpro_KiotPro:VB_VBN
-kiotviet_KiotViet:VB_VBN
-kirkpatrick_KirkPatrick:VB_VBN
-kiss_KiSS:VB_VBN
-kissa_KissA:VB_VBN
-kissanime_KissAnime:VB_VBN
-kisstartup_KisStartup:VB_VBN
-kisstoy_KissToy:VB_VBN
-kissty_KissTy:VB_VBN
-kisugame_KisuGame:VB_VBN
-kitbasic_KitBasic:VB_VBN
-kitbctc_KiTBCTC:VB_VBN
-kitchenaid_KitchenAid:VB_VBN
-kitchenclean_kitchenClean:VB_VBN
-kitchenflower_KitchenFlower:VB_VBN
-kitkat_KitKat:VB_VBN
-kito_KiTo:VB_VBN
-kitv_KiTV:VB_VBN
-kity_KiTy:VB_VBN
-kiwi_KiWi:VB_VBN
-kiwibird_KiWiBiRD:VB_VBN
-kiwibox_KiwiBox:VB_VBN
-kiwifood_KiwiFood:VB_VBN
-kiwuki_KiWuKi:VB_VBN
-kizciti_KizCiti:VB_VBN
-kizcity_KizCity:VB_VBN
-kizfarm_KizFarm:VB_VBN
-kjelroc_KjelROC:VB_VBN
-kjhi_KJhi:VB_VBN
-kkday_KKday:VB_VBN
-kkem_KKem:VB_VBN
-kkhi_KKhi:VB_VBN
-kkhung_KKhung:VB_VBN
-kknews_KKNews:VB_VBN
-kkoma_kkOma:VB_VBN
-kktechcho_KKtechCHo:VB_VBN
-klai_KLai:VB_VBN
-klang_KLang:VB_VBN
-klaras_KlaraS:VB_VBN
-klarjet_KlarJet:VB_VBN
-klasercutter_KLaserCutter:VB_VBN
-klasercuttercontroller_kLaserCutterController:VB_VBN
-kleague_KLeague:VB_VBN
-kleptocats_KleptoCats:VB_VBN
-kleze_KlezE:VB_VBN
-klickex_KlickEx:VB_VBN
-klikaanklikuit_KlikaanKlikuit:VB_VBN
-klipmix_KlipMix:VB_VBN
-klipsch_KlipSch:VB_VBN
-klook_KLook:VB_VBN
-kloon_KLoon:VB_VBN
-klovers_KLovers:VB_VBN
-klx_KlX:VB_VBN
-kmai_KMai:VB_VBN
-kmktourist_KmkTourist:VB_VBN
-kmpisco_KMpisco:VB_VBN
-kmplayer_KMPlayer:VB_VBN
-kmresort_kmResort:VB_VBN
-kmsauto_KMSAuto:VB_VBN
-kmspico_KMSpico:VB_VBN
-kmspiso_KMSpiso:VB_VBN
-knb_KnB:VB_VBN
-kncminer_KnCMiner:VB_VBN
-knic_KNiC:VB_VBN
-knightsffa_KnightsFFA:VB_VBN
-knightsgotham_KnightsGotham:VB_VBN
-knngh_KNNgh:VB_VBN
-knockon_KnockOn:VB_VBN
-knowem_KnowEm:VB_VBN
-knowoncology_KNOWoncology:VB_VBN
-knowyourself_KnowYourself:VB_VBN
-kntd_kNTD:VB_VBN
-kny_KnY:VB_VBN
-knyktrong_KnykTrong:VB_VBN
-kobayashichai_KobayashiChai:VB_VBN
-kobe_KoBe:VB_VBN
-kobewood_KobeWood:VB_VBN
-kochamongkol_KoChaMongKol:VB_VBN
-kogafansub_KogaFansub:VB_VBN
-koh_KoH:VB_VBN
-kohaku_KohaKu:VB_VBN
-kohchangtrip_KohChangTrip:VB_VBN
-kohllock_KohLLock:VB_VBN
-kohm_KOhm:VB_VBN
-koibus_KoiBus:VB_VBN
-koimucho_KoiMUCHO:VB_VBN
-koja_KoJa:VB_VBN
-kojikan_KoJikan:VB_VBN
-koken_KoKen:VB_VBN
-koko_KoKo:VB_VBN
-kokofit_KOKOFiT:VB_VBN
-kokoko_KoKoKo:VB_VBN
-kokokrunch_KoKoKrunch:VB_VBN
-kokonogia_KokonoGia:VB_VBN
-kola_KoLa:VB_VBN
-kolstuff_KOLstuff:VB_VBN
-komatsu_KOMAtSU:VB_VBN
-kompongthom_KompongThom:VB_VBN
-kompozer_KompoZer:VB_VBN
-komtum_KomTum:VB_VBN
-kongimage_KongImage:VB_VBN
-kongmoka_KongMoka:VB_VBN
-konigkid_KonigKid:VB_VBN
-konkakinh_KonKaKinh:VB_VBN
-konklor_KonKlor:VB_VBN
-konkuk_KonKuk:VB_VBN
-konmari_KonMari:VB_VBN
-konosuke_KonoSuke:VB_VBN
-konplong_KonPlong:VB_VBN
-konpne_KonPne:VB_VBN
-konpring_KonPring:VB_VBN
-kontum_KonTum:VB_VBN
-kontuminan_kontumINAN:VB_VBN
-koobits_KooBits:VB_VBN
-kooldic_KOOLdic:VB_VBN
-koolsoft_KoolSoft:VB_VBN
-koolstye_KoolStye:VB_VBN
-koplayer_KoPlayer:VB_VBN
-kor_KoR:VB_VBN
-korea_KoReA:VB_VBN
-koreabeaty_KoreaBeaty:VB_VBN
-koreader_KoReader:VB_VBN
-koreaking_KoreaKing:VB_VBN
-koreanair_KoreanAir:VB_VBN
-koreangamers_KoreanGamers:VB_VBN
-koreatimes_KoreaTimes:VB_VBN
-koreatv_KoreaTV:VB_VBN
-korihome_KoriHome:VB_VBN
-kornshell_KornShell:VB_VBN
-korokke_KoroKKe:VB_VBN
-korsthanh_korsThanh:VB_VBN
-kosher_KoSher:VB_VBN
-kostersfilms_KostersFilms:VB_VBN
-kotam_KoTam:VB_VBN
-kotamo_KoTaMo:VB_VBN
-kotl_KotL:VB_VBN
-kova_KoVa:VB_VBN
-kovadulux_KovaDulux:VB_VBN
-kowgear_KOWGear:VB_VBN
-koyobond_KoyoBond:VB_VBN
-kparts_KParts:VB_VBN
-kpdsu_KpdSU:VB_VBN
-kphucsinh_KPhucsinh:VB_VBN
-kpop_KPop:VB_VBN
-kqxsdn_kqXSDN:VB_VBN
-kqxsgl_kqXSGL:VB_VBN
-kqxsma_kqxsMa:VB_VBN
-kqxsmn_kqxsMN:VB_VBN
-kqxsst_kqXSST:VB_VBN
-kraftheinzcompany_KraftHeinzCompany:VB_VBN
-krasia_KrASIA:VB_VBN
-kraz_KrAZ:VB_VBN
-krazevina_KrazeVina:VB_VBN
-krazinoyze_KraziNoyze:VB_VBN
-krebsonsecurity_KrebsOnSecurity:VB_VBN
-kriorus_KrioRus:VB_VBN
-krisenergy_KrisEnergy:VB_VBN
-krishna_KRIShNA:VB_VBN
-kriswiktor_KrisWiktor:VB_VBN
-kritenbrink_KritenBrink:VB_VBN
-kriyoga_KriYoga:VB_VBN
-krongpa_KrongPa:VB_VBN
-kronikare_KroniKare:VB_VBN
-kronoswiss_KronoSwiss:VB_VBN
-kronshtadt_KroNShtadt:VB_VBN
-ksclauncher_KSCLauncher:VB_VBN
-ksecclass_kSecClass:VB_VBN
-ksecclassgenericpassword_kSecClassGenericPassword:VB_VBN
-ksiolajidebt_KSIOlajidebt:VB_VBN
-ksix_KSix:VB_VBN
-ksnd_kSND:VB_VBN
-ksor_KSor:VB_VBN
-ksvpro_KsvPro:VB_VBN
-ksxxx_KSxxx:VB_VBN
-ktcity_KTcity:VB_VBN
-kteam_KTeam:VB_VBN
-ktfoody_KTfoody:VB_VBN
-ktgroup_KTgroup:VB_VBN
-kthu_KThu:VB_VBN
-ktlab_KTLab:VB_VBN
-ktmart_KTMart:VB_VBN
-ktoe_kTOE:VB_VBN
-ktrt_KTrT:VB_VBN
-ktruc_KTruc:VB_VBN
-kts_kTS:VB_VBN
-ktttsleave_KTTTSLeave:VB_VBN
-ktuy_KTuy:VB_VBN
-ktv_kTV:VB_VBN
-ktvdecor_KTVDecor:VB_VBN
-ktweb_KTWeb:VB_VBN
-kuala_KuaLa:VB_VBN
-kualalumpur_KualaLumpur:VB_VBN
-kubecon_KubeCon:VB_VBN
-kubet_KuBet:VB_VBN
-kubetatletico_kubetAtletico:VB_VBN
-kubetlink_KubetLink:VB_VBN
-kubets_KuBets:VB_VBN
-kubetthomas_kubetThomas:VB_VBN
-kubetvn_KuBetVn:VB_VBN
-kubin_KuBin:VB_VBN
-kucasino_KuCasino:VB_VBN
-kuching_KuChing:VB_VBN
-kucoin_KuCoin:VB_VBN
-kudragon_KuDragon:VB_VBN
-kuenling_KuenLing:VB_VBN
-kuesports_KuEsports:VB_VBN
-kuhntucker_KuhnTucker:VB_VBN
-kuken_KuKen:VB_VBN
-kuking_KuKing:VB_VBN
-kuku_KuKu:VB_VBN
-kulav_kulaV:VB_VBN
-kulgame_KulGame:VB_VBN
-kulpi_KulPi:VB_VBN
-kumbu_KumBu:VB_VBN
-kumoi_KuMoi:VB_VBN
-kumovina_KumoVina:VB_VBN
-kungfu_KungFu:VB_VBN
-kuninstall_KUninstall:VB_VBN
-kunist_KUnist:VB_VBN
-kunlisa_KunLisa:VB_VBN
-kunshan_KunShan:VB_VBN
-kunwoo_KunWoo:VB_VBN
-kuongngan_KuongNgan:VB_VBN
-kups_KuPS:VB_VBN
-kuro_KurO:VB_VBN
-kuroemon_KuroEmon:VB_VBN
-kuroky_KuroKy:VB_VBN
-kurosakijin_KurosakiJin:VB_VBN
-kusuriya_kusuriYa:VB_VBN
-kutephone_KutePhone:VB_VBN
-kuteshop_KuteShop:VB_VBN
-kuturl_KutURL:VB_VBN
-kuvideos_KUvideos:VB_VBN
-kuxxx_KUxxx:VB_VBN
-kva_kVA:VB_VBN
-kvay_KVay:VB_VBN
-kvbro_KVBro:VB_VBN
-kvegetarian_KVegetarian:VB_VBN
-kvg_KvG:VB_VBN
-kvmart_KVmart:VB_VBN
-kwangdong_KwangDong:VB_VBN
-kwangju_KWangju:VB_VBN
-kwazulu_KwaZulu:VB_VBN
-kwfinder_KWFinder:VB_VBN
-kwk_KwK:VB_VBN
-kword_KWord:VB_VBN
-kwtrio_KWtrio:VB_VBN
-kwwh_kWWh:VB_VBN
-kyabje_KyabJe:VB_VBN
-kyaikhteeyoo_KyaikHteeYoo:VB_VBN
-kybernetwork_KyberNetwork:VB_VBN
-kyberswap_KyberSwap:VB_VBN
-kybook_KyBook:VB_VBN
-kybsin_KyBSin:VB_VBN
-kyc_kYC:VB_VBN
-kyduyenhouse_KyDuyenHouse:VB_VBN
-kyki_KyKi:VB_VBN
-kyky_KyKy:VB_VBN
-kylieminogueishouldbesolucky_KylieMinogueIShouldBeSoLucky:VB_VBN
-kylieminoguespinningaround_KylieMinogueSpinningAround:VB_VBN
-kylieminoguethelocomotion_KylieMinogueTheLocomotion:VB_VBN
-kyliemoulin_KylieMoulin:VB_VBN
-kymdan_KymDan:VB_VBN
-kynaforkids_KynaForKids:VB_VBN
-kynangmmo_KyNangMMO:VB_VBN
-kynangquanlytaichinh_KyNangQuanLyTaiChinh:VB_VBN
-kyoan_KyoAn:VB_VBN
-kyoani_KyoAni:VB_VBN
-kyongsang_KyongSang:VB_VBN
-kyonguyen_KyoNguyen:VB_VBN
-kyoryowon_KyoRyoWon:VB_VBN
-kysudulich_KySuDuLich:VB_VBN
-kyuhyun_KyuHyun:VB_VBN
-kyungdong_KyungDong:VB_VBN
-kyunghee_KyungHee:VB_VBN
-kyvy_KyVy:VB_VBN
-kèmroot_kèmRoot:VB_VBN
-kèoty_kèoTy:VB_VBN
-kéoleave_KéoLeave:VB_VBN
-kéosee_kéoSee:VB_VBN
-kéotai_kéoTai:VB_VBN
-kéotsc_kéoTSC:VB_VBN
-képhonor_képHonor:VB_VBN
-képqualcomm_képQualcomm:VB_VBN
-képsim_képSim:VB_VBN
-képyu_képYu:VB_VBN
-kétphong_KétPhong:VB_VBN
-laba_LaBa:VB_VBN
-labambi_LaBambi:VB_VBN
-labankey_LabanKey:VB_VBN
-labarge_LaBarge:VB_VBN
-labcamera_LabCamera:VB_VBN
-labcorp_LabCorp:VB_VBN
-labelfranceeducation_LabelFranceEducation:VB_VBN
-labelwriter_LabelWriter:VB_VBN
-labeouf_LaBeouf:VB_VBN
-labgenomics_LabGenomics:VB_VBN
-labhok_LabHok:VB_VBN
-labianca_LaBianca:VB_VBN
-labios_LaBios:VB_VBN
-lablift_LabLift:VB_VBN
-labobo_LaBobo:VB_VBN
-laboratorytuy_LaboratoryTuy:VB_VBN
-labrie_LaBrie:VB_VBN
-labtab_LabTab:VB_VBN
-labtop_LabTop:VB_VBN
-labu_LaBu:VB_VBN
-labulabu_LabuLabu:VB_VBN
-labute_LaBute:VB_VBN
-labuzetta_LaBuzetta:VB_VBN
-labvietchem_LabVIETCHEM:VB_VBN
-labview_LabVIEW:VB_VBN
-lacasa_LaCasa:VB_VBN
-lacasta_LaCasta:VB_VBN
-lachydrin_LacHydrin:VB_VBN
-laci_lacI:VB_VBN
-lacie_LaCie:VB_VBN
-lacimbali_LaCimbali:VB_VBN
-lacirkem_LacirKem:VB_VBN
-lacmin_LACmin:VB_VBN
-laco_LaCo:VB_VBN
-lacosme_LAcosme:VB_VBN
-lacosteromberggravimeters_LaCosteRombergGravimeters:VB_VBN
-lacphap_LacPhap:VB_VBN
-lacquan_LacQuan:VB_VBN
-lacquereglips_LacquerEglips:VB_VBN
-lacrosse_LaCrosse:VB_VBN
-lacticare_LactiCare:VB_VBN
-lactiumr_LactiumR:VB_VBN
-lacviet_LacViet:VB_VBN
-lacviettravel_LacVietTravel:VB_VBN
-lacz_lacZ:VB_VBN
-ladi_LaDi:VB_VBN
-ladibet_LaDiBET:VB_VBN
-ladipage_LadiPage:VB_VBN
-lado_LaDo:VB_VBN
-ladodetox_LadoDetox:VB_VBN
-ladora_LaDora:VB_VBN
-ladou_LaDou:VB_VBN
-ladyboss_LadyBoss:VB_VBN
-ladygaga_LadyGaga:VB_VBN
-ladykillah_LadyKillah:VB_VBN
-ladymarmaladecap_LaDymarmaladecap:VB_VBN
-ladyr_LadyR:VB_VBN
-ladytv_ladyTV:VB_VBN
-ladyxtina_LadyXtina:VB_VBN
-laerror_LAError:VB_VBN
-lafarge_LaFarge:VB_VBN
-lafargeholcim_LafargeHolcim:VB_VBN
-lafayette_LaFayette:VB_VBN
-laferrari_LaFerrari:VB_VBN
-laforce_LaForce:VB_VBN
-lafubrand_LafuBrand:VB_VBN
-lagarden_LAgarden:VB_VBN
-lagfix_LagFix:VB_VBN
-lagg_LaGG:VB_VBN
-lagi_LaGI:VB_VBN
-lagihitech_LagiHitech:VB_VBN
-lagu_LaGu:VB_VBN
-laguardia_LaGuardia:VB_VBN
-lagyp_LaGyp:VB_VBN
-laha_LaHa:VB_VBN
-lahabana_LaHabana:VB_VBN
-lahara_LaHara:VB_VBN
-lahay_LaHay:VB_VBN
-lahood_LaHood:VB_VBN
-lahwethanh_lahWethanh:VB_VBN
-lai_laI:VB_VBN
-laisasac_laiSASAC:VB_VBN
-laixie_laiXie:VB_VBN
-lakchi_LakChi:VB_VBN
-lakebtc_LakeBTC:VB_VBN
-lakeram_LakeRAM:VB_VBN
-lakeside_LakeSide:VB_VBN
-lakeview_LakeView:VB_VBN
-lala_LaLa:VB_VBN
-lalachai_LalaChai:VB_VBN
-lalafly_LalaFLY:VB_VBN
-lalafood_LalaFood:VB_VBN
-lalaha_LaLaHa:VB_VBN
-lalamove_LalaMove:VB_VBN
-lalaurie_LaLaurie:VB_VBN
-lalb_lAlB:VB_VBN
-lalifa_LaLiFa:VB_VBN
-laliga_LaLiga:VB_VBN
-laluna_LaLuna:VB_VBN
-lalung_LaLung:VB_VBN
-lama_LaMa:VB_VBN
-lamaisondusexe_LaMaisonDuSexe:VB_VBN
-lamanh_LamAnh:VB_VBN
-lamaquahouse_LamaquaHouse:VB_VBN
-lamarcus_LaMarcus:VB_VBN
-lamarine_LaMarine:VB_VBN
-lamarque_LaMarque:VB_VBN
-lambangcapgiarehcm_lambangcapgiareHCM:VB_VBN
-lambox_LamboX:VB_VBN
-lambui_LamBui:VB_VBN
-lamdepnhe_LamDepNhe:VB_VBN
-lame_LaMe:VB_VBN
-lameila_LaMeiLa:VB_VBN
-lamelo_LaMelo:VB_VBN
-lamem_LaMem:VB_VBN
-lamhien_LamHien:VB_VBN
-lamikid_LamiKid:VB_VBN
-laminatepark_LaminatePark:VB_VBN
-laminkid_LaminKid:VB_VBN
-lamnguyen_LamNguyen:VB_VBN
-lamnguyenz_LamNguyenZ:VB_VBN
-lamode_LaMode:VB_VBN
-lamora_LaMora:VB_VBN
-lamour_LaMour:VB_VBN
-lampang_LamPang:VB_VBN
-lampardcahill_LampardCahill:VB_VBN
-lampardklopp_LampardKlopp:VB_VBN
-lampardliverpool_LampardLiverpool:VB_VBN
-lampassport_LamPassPort:VB_VBN
-lamphongchina_LamPhongChina:VB_VBN
-lampsave_LampSave:VB_VBN
-lamrim_LamRim:VB_VBN
-lamsaodevao_LamSaoDeVao:VB_VBN
-lamspa_LamSpa:VB_VBN
-lamsw_LamSW:VB_VBN
-lamtamnhu_LamTamNhu:VB_VBN
-lamthao_LamThao:VB_VBN
-lamthenao_LamTheNao:VB_VBN
-lamtuan_LamTuan:VB_VBN
-lamvt_LamVT:VB_VBN
-lanbercu_LanBercu:VB_VBN
-lanchimart_LanChiMart:VB_VBN
-landcadviet_LandCadViet:VB_VBN
-landcruiser_LandCruiser:VB_VBN
-landecor_LanDecor:VB_VBN
-landerbrau_LanderBrau:VB_VBN
-landmark_LandMark:VB_VBN
-landmarktower_LandmarkTower:VB_VBN
-landmart_LAndmart:VB_VBN
-landmax_LandMax:VB_VBN
-landprocess_LandProcess:VB_VBN
-landrover_LandRover:VB_VBN
-landscapefragment_LandscapeFragment:VB_VBN
-landsoft_LandSoft:VB_VBN
-landtoday_LandToday:VB_VBN
-landtube_LandTube:VB_VBN
-landviewer_LandViewer:VB_VBN
-landxml_LandXML:VB_VBN
-laneve_LaNeve:VB_VBN
-lanewatch_LaneWatch:VB_VBN
-langbian_LangBian:VB_VBN
-langbiang_LangBiang:VB_VBN
-langchia_LangChia:VB_VBN
-langdon_LangDon:VB_VBN
-langgo_LangGo:VB_VBN
-langmaster_LANGMaster:VB_VBN
-langsonshop_LangSonShop:VB_VBN
-langtrung_LangTrung:VB_VBN
-langtu_LangTu:VB_VBN
-languagecode_LanguageCode:VB_VBN
-languagetool_LanguageTool:VB_VBN
-languard_LanGuard:VB_VBN
-langvinfast_LangVinFast:VB_VBN
-langwaki_LangwaKi:VB_VBN
-langzhou_langZhou:VB_VBN
-lanhoang_LanHoang:VB_VBN
-lanhodiep_LanHoDiep:VB_VBN
-lanholland_lanHolland:VB_VBN
-lanhollandnetherlandsdutch_LanHollandNetherlandsDutch:VB_VBN
-lanhtheo_lanhTheo:VB_VBN
-lanhung_LanHung:VB_VBN
-lanhuong_LanHuong:VB_VBN
-lankaminevik_lankaMinevik:VB_VBN
-lanlan_LanLan:VB_VBN
-lanmark_LANmark:VB_VBN
-lanmc_LanMC:VB_VBN
-lannam_LAnnam:VB_VBN
-lannavitasthe_lanNavitasThe:VB_VBN
-lannextnext_LanNextNext:VB_VBN
-lannhl_lanNHL:VB_VBN
-lanoire_LANoire:VB_VBN
-lanq_LanQ:VB_VBN
-lansyzm_LansyZM:VB_VBN
-lantai_lanTai:VB_VBN
-lantam_lanTam:VB_VBN
-lantop_LanTop:VB_VBN
-lantrankal_LANTrankal:VB_VBN
-lanzajet_LanzaJet:VB_VBN
-lanzatech_LanzaTech:VB_VBN
-laobaitd_laobaiTD:VB_VBN
-laocai_LaoCai:VB_VBN
-laocl_LaOCl:VB_VBN
-laongoandong_LaoNgoanDong:VB_VBN
-laoread_LaoRead:VB_VBN
-laovietbank_LaoVietBank:VB_VBN
-lapaglia_LaPaglia:VB_VBN
-lapassione_LaPassione:VB_VBN
-lapata_LaPata:VB_VBN
-lapaz_LaPaz:VB_VBN
-lapdoanh_lapDOANH:VB_VBN
-laperm_LaPerm:VB_VBN
-lapiere_LaPiere:VB_VBN
-laporta_LaPorta:VB_VBN
-laptop_LapTop:VB_VBN
-laptopdell_laptopDELL:VB_VBN
-laptopdt_LaptopDT:VB_VBN
-laptoph_laptoPh:VB_VBN
-laptopkhanhtran_LaptopKhanhTran:VB_VBN
-laptopmag_LaptopMag:VB_VBN
-laptopplaza_LaptopPlaza:VB_VBN
-laptopre_LaptopRe:VB_VBN
-laptopreabc_laptopreABC:VB_VBN
-laptopsn_LaptopSN:VB_VBN
-laquan_LaQuan:VB_VBN
-larelle_LaRelle:VB_VBN
-laroche_LaRoche:VB_VBN
-larose_LaRose:VB_VBN
-lasalle_LaSalle:VB_VBN
-lasan_LaSan:VB_VBN
-lasandra_LaSandra:VB_VBN
-lasen_LaSen:VB_VBN
-lasenza_LaSenza:VB_VBN
-laserit_LaserIt:VB_VBN
-laserjet_LaserJet:VB_VBN
-laserjetmobileprinting_LaserJetMobilePrinting:VB_VBN
-laserlight_LaserLight:VB_VBN
-laserliner_LaserLiner:VB_VBN
-laserlipo_LaserLipo:VB_VBN
-lasermech_LaserMech:VB_VBN
-lasermonks_LaserMonks:VB_VBN
-laserpro_LaserPro:VB_VBN
-lasertec_LaserTec:VB_VBN
-laserwhiterning_LaserWhiterning:VB_VBN
-laserwriter_LaserWriter:VB_VBN
-lasik_LaSik:VB_VBN
-lasota_LaSota:VB_VBN
-lasspass_LassPass:VB_VBN
-lastactivityview_LastActivityView:VB_VBN
-lastchildfill_LastChildFill:VB_VBN
-lasthit_LastHit:VB_VBN
-lastitem_LastItem:VB_VBN
-lastpass_LastPass:VB_VBN
-lasun_laSun:VB_VBN
-lasvegas_LasVegas:VB_VBN
-laten_LaTen:VB_VBN
-latentstyles_LatentStyles:VB_VBN
-laterrazza_LaTerrazza:VB_VBN
-latex_LaTeX:VB_VBN
-latina_LAtina:VB_VBN
-latinh_LaTinh:VB_VBN
-latoken_LAToken:VB_VBN
-latourette_LaTourette:VB_VBN
-latrobe_LaTrobe:VB_VBN
-latulip_LaTulip:VB_VBN
-laubongda_LauBongDa:VB_VBN
-lauchi_lauChi:VB_VBN
-lauderdaletravelers_LauderdaleTravelers:VB_VBN
-launchbox_LaunchBox:VB_VBN
-launcherone_LauncherOne:VB_VBN
-launchoptions_launchOptions:VB_VBN
-launchpad_LaunchPad:VB_VBN
-launchsettings_launchSettings:VB_VBN
-launchsinstall_launchSinstall:VB_VBN
-laurdiy_LaurDIY:VB_VBN
-laurentyves_LaurentYves:VB_VBN
-laustar_LauStar:VB_VBN
-lavabototo_LavaboTOTO:VB_VBN
-lavang_LaVang:VB_VBN
-lavar_LaVar:VB_VBN
-lavaughn_LaVaughn:VB_VBN
-lavcup_LavCup:VB_VBN
-lavendercare_LavenderCare:VB_VBN
-lavenderquy_LavenderQuy:VB_VBN
-laviagri_LaviAgri:VB_VBN
-lavie_LaVie:VB_VBN
-lavilla_LaVilla:VB_VBN
-lavine_LaVine:VB_VBN
-laviska_LaViska:VB_VBN
-lavita_LaVita:VB_VBN
-lavor_LaVor:VB_VBN
-lavorgna_LaVorgna:VB_VBN
-lawbreakers_LawBreakers:VB_VBN
-lawfirm_LawFirm:VB_VBN
-lawkey_LawKey:VB_VBN
-lawkeyco_LawKeyCo:VB_VBN
-lawkeycong_LawKeyCong:VB_VBN
-lawkeydich_LawKeydich:VB_VBN
-lawkeydoanh_LawKeyDoanh:VB_VBN
-lawkeyho_LawKeyHo:VB_VBN
-lawkeykham_LawKeyKham:VB_VBN
-lawsoft_LawSoft:VB_VBN
-layerslider_LayerSlider:VB_VBN
-layout_LayOut:VB_VBN
-layoutcontrolsadvance_LayoutControlsAdvance:VB_VBN
-layoutinflater_LayoutInflater:VB_VBN
-layoutlm_LayoutLM:VB_VBN
-layoutparams_LayoutParams:VB_VBN
-lazada_LaZADA:VB_VBN
-lazadacombo_LAZADACombo:VB_VBN
-lazadamall_LazadaMall:VB_VBN
-lazadavietnam_LazadaVietnam:VB_VBN
-lazebnik_LaZebnik:VB_VBN
-lazgame_LazGame:VB_VBN
-laziophong_lazioPhong:VB_VBN
-lazlive_LazLive:VB_VBN
-lazmall_LazMall:VB_VBN
-lazytown_LazyTown:VB_VBN
-lbank_LBank:VB_VBN
-lblb_lBlB:VB_VBN
-lblfirstname_lblFirstName:VB_VBN
-lblname_lblName:VB_VBN
-lbnga_LBNga:VB_VBN
-lbrgroup_LBRGroup:VB_VBN
-lbtechvina_LBTechvina:VB_VBN
-lcb_lCB:VB_VBN
-lcdcho_LCDcho:VB_VBN
-lcma_lcMa:VB_VBN
-lcos_LCoS:VB_VBN
-ldnam_LDNam:VB_VBN
-ldplayer_LDPlayer:VB_VBN
-ldso_LDso:VB_VBN
-leadcard_LeadCard:VB_VBN
-leader_LEADer:VB_VBN
-leaderreal_LeaderReal:VB_VBN
-leadershipdanh_LeadershipDanh:VB_VBN
-leadertalk_LeaderTalk:VB_VBN
-leadertask_LeaderTask:VB_VBN
-leadingkitchen_leadingKitchen:VB_VBN
-leadingstar_LeadingStar:VB_VBN
-leadpages_LeadPages:VB_VBN
-leads_LeAds:VB_VBN
-leadsites_LeadSites:VB_VBN
-leadviet_LeadViet:VB_VBN
-leafcare_LeafCare:VB_VBN
-leafmotion_LeafMotion:VB_VBN
-leafsnap_LeafSnap:VB_VBN
-leagleseo_LeagleSEO:VB_VBN
-league_LEague:VB_VBN
-leaguecska_LeagueCSKA:VB_VBN
-leaguekante_LeagueKante:VB_VBN
-leaguekèo_LeagueKèo:VB_VBN
-leaguemessi_LeagueMessi:VB_VBN
-leaguemu_LeagueMU:VB_VBN
-leagueneymar_LeagueNeymar:VB_VBN
-leaguetrong_LeagueTrong:VB_VBN
-leaguevalenciavalencia_leagueValenciaValencia:VB_VBN
-leakedsource_LeakedSource:VB_VBN
-leakid_LeakID:VB_VBN
-leaksapplepro_LeaksApplePro:VB_VBN
-leamchambang_LeamChambang:VB_VBN
-leanbody_LeanBody:VB_VBN
-leandomainsearch_LeanDomainSearch:VB_VBN
-leanerp_LeanERP:VB_VBN
-leanin_LeanIn:VB_VBN
-leann_LeAnn:VB_VBN
-leanpro_LeanPro:VB_VBN
-leantalks_LeanTalks:VB_VBN
-leap_LEaP:VB_VBN
-leapfrog_LeapFrog:VB_VBN
-leappad_LeapPad:VB_VBN
-learn_LEaRN:VB_VBN
-learndash_LearnDash:VB_VBN
-learndesk_LearnDesk:VB_VBN
-learnenglish_LearnEnglish:VB_VBN
-learningsql_LearningSQL:VB_VBN
-learningware_LearningWare:VB_VBN
-learnpress_LearnPress:VB_VBN
-learnvest_LearnVest:VB_VBN
-lebenlang_LEbenlang:VB_VBN
-lebinh_LeBinh:VB_VBN
-leblanc_LeBlanc:VB_VBN
-leblond_LeBlond:VB_VBN
-leboff_LeBoff:VB_VBN
-lebon_LeBon:VB_VBN
-leboutillier_LeBoutillier:VB_VBN
-lebrock_LeBrock:VB_VBN
-lebron_LeBron:VB_VBN
-lebros_LeBros:VB_VBN
-lebuef_LeBuef:VB_VBN
-lecanfruits_LeCanFruits:VB_VBN
-lecilaire_LeCilaire:VB_VBN
-leclair_LeClair:VB_VBN
-lecoultre_LeCoultre:VB_VBN
-lecturebank_LectureBank:VB_VBN
-lecturemaker_LectureMaker:VB_VBN
-lecturenotes_LectureNotes:VB_VBN
-lecun_LeCun:VB_VBN
-lecuong_LeCuong:VB_VBN
-ledcf_LedCF:VB_VBN
-ledcho_ledCho:VB_VBN
-ledgerjoker_LedgerJoker:VB_VBN
-ledgerx_LedgerX:VB_VBN
-ledin_LEDiN:VB_VBN
-ledinhno_ledinhNo:VB_VBN
-ledlenser_LedLenser:VB_VBN
-ledmofan_LEDMofan:VB_VBN
-ledqc_LedQC:VB_VBN
-ledshowtw_LedshowTW:VB_VBN
-ledspot_LEDspot:VB_VBN
-ledstate_ledState:VB_VBN
-ledtech_LEDtech:VB_VBN
-ledtube_LEDtube:VB_VBN
-ledviet_LedViet:VB_VBN
-leeann_LeeAnn:VB_VBN
-leeauto_LeeAuto:VB_VBN
-leebk_LeeBK:VB_VBN
-leecam_LeeCam:VB_VBN
-leeco_LeEco:VB_VBN
-leeds_LEeds:VB_VBN
-leedung_LeeDung:VB_VBN
-leegroup_LeeGroup:VB_VBN
-leelong_LeeLong:VB_VBN
-leenin_LeeNin:VB_VBN
-leeorder_LeeOrder:VB_VBN
-leeroy_LeeRoy:VB_VBN
-leesandy_LeeSandy:VB_VBN
-leesin_LeeSin:VB_VBN
-leeteuk_LeeTeuk:VB_VBN
-leewoo_LeeWoo:VB_VBN
-lefou_LeFou:VB_VBN
-legacybios_LegacyBIOS:VB_VBN
-legaltech_LegalTech:VB_VBN
-legalzone_LegalZone:VB_VBN
-legco_LegCo:VB_VBN
-legendsea_LegendSea:VB_VBN
-lego_LeGo:VB_VBN
-legoland_LegoLand:VB_VBN
-legostore_LEGOStore:VB_VBN
-legraindesable_LeGrainDeSable:VB_VBN
-legrand_LeGrand:VB_VBN
-legras_LeGras:VB_VBN
-legross_LeGross:VB_VBN
-lehair_LeHair:VB_VBN
-lehe_LeHe:VB_VBN
-lehienducblog_LeHienDucBlog:VB_VBN
-lehonghiepposted_lehonghiepPosted:VB_VBN
-leicester_LEicester:VB_VBN
-leicestercity_LeicesterCity:VB_VBN
-lejapan_LeJapan:VB_VBN
-lekhamart_LeKhaMart:VB_VBN
-lelts_lELTS:VB_VBN
-lelydesign_LeLyDesign:VB_VBN
-lelét_LeLét:VB_VBN
-lemahieu_LeMahieu:VB_VBN
-leman_LeMan:VB_VBN
-lemans_LeMans:VB_VBN
-lemart_LeMart:VB_VBN
-lemavietnam_LemaVietnam:VB_VBN
-lemax_LeMax:VB_VBN
-leminhstore_leminhSTORE:VB_VBN
-lemonchili_LemonChili:VB_VBN
-lemongrass_LemonGrass:VB_VBN
-lemonnation_LemonNation:VB_VBN
-lemotifs_leMOTIFs:VB_VBN
-lenart_LenArt:VB_VBN
-lendingmemo_LendingMemo:VB_VBN
-lendup_LendUp:VB_VBN
-lenet_LeNet:VB_VBN
-lenforu_LenforU:VB_VBN
-lengthefabulous_LengtheFabulous:VB_VBN
-lenguyentran_LenguyenTran:VB_VBN
-lenhan_LeNhan:VB_VBN
-lenid_LeNid:VB_VBN
-lenin_LeNin:VB_VBN
-lennon_LenNon:VB_VBN
-lenovothinkpad_LenovoThinkpad:VB_VBN
-lenspen_LensPen:VB_VBN
-lenstag_LensTag:VB_VBN
-leoart_LeoArt:VB_VBN
-leocoin_LeoCoin:VB_VBN
-leocustomruu_LeoCustomRUU:VB_VBN
-leogrande_LeoGrande:VB_VBN
-leoj_LeoJ:VB_VBN
-leolabs_LeoLabs:VB_VBN
-leonbets_LeonBets:VB_VBN
-leongaming_LeonGaming:VB_VBN
-leopardnguyen_leopardNguyen:VB_VBN
-leosat_LeoSat:VB_VBN
-leovegas_LeoVegas:VB_VBN
-leparc_LePARC:VB_VBN
-lepavillon_LePavillon:VB_VBN
-lephone_LePhone:VB_VBN
-lepro_LePro:VB_VBN
-lepure_LePure:VB_VBN
-leroux_LeRoux:VB_VBN
-lesean_LeSean:VB_VBN
-lesemnoz_LeSemnoz:VB_VBN
-lesimole_LeSimoLe:VB_VBN
-lespaul_LesPaul:VB_VBN
-lesportsac_LeSportsac:VB_VBN
-let_LeT:VB_VBN
-letamanh_LeTamAnh:VB_VBN
-letao_LeTao:VB_VBN
-letappsruninbackground_LetAppsRunInBackground:VB_VBN
-letex_LeTeX:VB_VBN
-letitbit_LetItBit:VB_VBN
-letmejerk_LetMeJerk:VB_VBN
-letmetrade_LetMeTrade:VB_VBN
-letou_LeTou:VB_VBN
-letourneau_LeTourneau:VB_VBN
-letsenhance_LetsEnhance:VB_VBN
-letsgodigital_LetsGoDigital:VB_VBN
-letspro_LetsPro:VB_VBN
-letuan_LeTuan:VB_VBN
-letv_LeTV:VB_VBN
-letviet_LetViet:VB_VBN
-levanton_LeVanTon:VB_VBN
-leveldb_LevelDB:VB_VBN
-leveluk_LeveLuk:VB_VBN
-leverkusenbayern_LeverkusenBayern:VB_VBN
-leverplus_LeverPlus:VB_VBN
-levert_LeVert:VB_VBN
-levo_LeVo:VB_VBN
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-meistersinger_MeisterSinger:VB_VBN
-meistertask_MeisterTask:VB_VBN
-meizu_MeiZu:VB_VBN
-mekong_MeKong:VB_VBN
-mekongcert_MekongCert:VB_VBN
-mekongorganics_MekongOrganics:VB_VBN
-mekongrestop_MekongRestop:VB_VBN
-mekongsoft_MekongSoft:VB_VBN
-mekostem_MekoStem:VB_VBN
-mela_MeLa:VB_VBN
-melacrusher_MelaCrusher:VB_VBN
-melantv_melanTV:VB_VBN
-melast_MeLast:VB_VBN
-melbet_MELbet:VB_VBN
-melbournez_melbourneZ:VB_VBN
-meliiuma_MeliiuMa:VB_VBN
-melinh_MeLinh:VB_VBN
-melissabray_MelissaBray:VB_VBN
-melloncorp_MellonCorp:VB_VBN
-melmel_MelMel:VB_VBN
-melmelbrows_MelMelBrows:VB_VBN
-melody_MeLody:VB_VBN
-melon_MelOn:VB_VBN
-melonds_MelonDS:VB_VBN
-melservo_MELServo:VB_VBN
-meltv_MelTV:VB_VBN
-membean_MemBean:VB_VBN
-memberassistant_MemberAssistant:VB_VBN
-memberexpression_MemberExpression:VB_VBN
-memberpress_MemberPress:VB_VBN
-membership_MemberShip:VB_VBN
-membranequant_MembraneQuant:VB_VBN
-membrapure_MembraPure:VB_VBN
-memecrunch_MemeCrunch:VB_VBN
-memegenerator_MemeGenerator:VB_VBN
-memes_MEMes:VB_VBN
-memo_MeMO:VB_VBN
-memomi_MemoMi:VB_VBN
-memopad_MemoPad:VB_VBN
-memoq_MemoQ:VB_VBN
-memorydiagnostic_MemoryDiagnostic:VB_VBN
-memoryerror_MemoryError:VB_VBN
-memorykeeper_MemoryKeeper:VB_VBN
-memorystick_MemoryStick:VB_VBN
-memorystream_MemoryStream:VB_VBN
-memoryuser_MemoryUser:VB_VBN
-memosuite_MemoSuite:VB_VBN
-memsql_MemSQL:VB_VBN
-memu_MEmu:VB_VBN
-memuplay_MemuPlay:VB_VBN
-memusic_meMUSIC:VB_VBN
-mencoder_MEncoder:VB_VBN
-mendaily_MenDaily:VB_VBN
-mendardatrong_MendardaTrong:VB_VBN
-mendybenjamin_MendyBenjamin:VB_VBN
-menevit_MenEvit:VB_VBN
-menf_MenF:VB_VBN
-meng_MEng:VB_VBN
-menhgia_MenhGia:VB_VBN
-menlust_MenLust:VB_VBN
-menpower_MenPower:VB_VBN
-menpro_MenPro:VB_VBN
-menshealth_MensHealth:VB_VBN
-mensmax_MensMax:VB_VBN
-menu_MeNu:VB_VBN
-menubar_MenuBar:VB_VBN
-menustart_MenuStart:VB_VBN
-menviglacera_menViglacera:VB_VBN
-menxeed_MenXeed:VB_VBN
-menz_MenZ:VB_VBN
-meobrows_MeoBrows:VB_VBN
-meoconnt_meoconNT:VB_VBN
-meoh_MeOH:VB_VBN
-meomad_meoMAD:VB_VBN
-meomeo_MeoMeo:VB_VBN
-meoquy_MeoQuy:VB_VBN
-meou_MeoU:VB_VBN
-meow_MeOw:VB_VBN
-meowmeow_MEOwmeow:VB_VBN
-meowwoofvn_MeowwoofVN:VB_VBN
-meozodiac_MeoZodiac:VB_VBN
-mephi_MEPhI:VB_VBN
-mephl_MEPhl:VB_VBN
-meplusplus_MePlusPlus:VB_VBN
-mepun_MePun:VB_VBN
-mepuzz_MePuzz:VB_VBN
-mequib_MeQuib:VB_VBN
-meramg_MerAMG:VB_VBN
-mercadolibre_MercadoLibre:VB_VBN
-mercedes_MerCedes:VB_VBN
-mercedeslian_mercedesLian:VB_VBN
-mercedestrophy_MercedesTrophy:VB_VBN
-mercerdescard_MercerdesCard:VB_VBN
-merchantcircle_MerchantCircle:VB_VBN
-merchantwords_MerchantWords:VB_VBN
-merchforest_MerchForest:VB_VBN
-mercurialx_MercurialX:VB_VBN
-mercury_MErcury:VB_VBN
-mergeall_mergeAll:VB_VBN
-meriadb_MeriaDB:VB_VBN
-merimobiles_MeriMobiles:VB_VBN
-merlion_MerLion:VB_VBN
-meroxyltm_MeroxylTM:VB_VBN
-merpay_MerPay:VB_VBN
-merperle_MerPerle:VB_VBN
-merryfair_MerryFair:VB_VBN
-mesalpha_MESalpha:VB_VBN
-mesh_MeSH:VB_VBN
-meshmixer_MeshMixer:VB_VBN
-meshsmooth_MeshSmooth:VB_VBN
-meshtalk_MeshTalk:VB_VBN
-mesobeauty_MesoBeauty:VB_VBN
-mesowhite_MesoWhite:VB_VBN
-messageboxa_MessageBoxA:VB_VBN
-messageboxw_MessageBoxW:VB_VBN
-messagecontext_MessageContext:VB_VBN
-messageofguest_MessageofGuest:VB_VBN
-messagepack_MessagePack:VB_VBN
-messagepresent_messagePresent:VB_VBN
-messagesservice_MessagesService:VB_VBN
-messagex_MessageX:VB_VBN
-messengertrong_messengerTrong:VB_VBN
-messengerzhou_messengerZhou:VB_VBN
-messerschmittstrabe_MesserschmittstraBe:VB_VBN
-messilionel_MessiLionel:VB_VBN
-messimessimessi_MessiMessiMessi:VB_VBN
-messimu_MessiMU:VB_VBN
-messinapoli_MessiNapoli:VB_VBN
-mesubjav_meSubJav:VB_VBN
-metabisunfit_MetabiSunfit:VB_VBN
-metabox_MetaBox:VB_VBN
-metacafe_MetaCafe:VB_VBN
-metacare_MetaCare:VB_VBN
-metadata_MetaData:VB_VBN
-metadefender_MetaDefender:VB_VBN
-metafacts_MetaFacts:VB_VBN
-metager_MetaGer:VB_VBN
-metahash_MetaHash:VB_VBN
-metalgreymon_MetalGreymon:VB_VBN
-metalwork_MetalWork:VB_VBN
-metalzone_MetalZone:VB_VBN
-metamask_MetaMask:VB_VBN
-metapoint_MetaPoint:VB_VBN
-metaproducts_MetaProducts:VB_VBN
-metaquotes_MetaQuotes:VB_VBN
-metastock_MetaStock:VB_VBN
-metatderinal_MetaTderinal:VB_VBN
-metatrader_MetaTrader:VB_VBN
-metatraders_MetaTraders:VB_VBN
-metatrading_MetaTrading:VB_VBN
-metatreader_MetaTreader:VB_VBN
-metavn_METAvn:VB_VBN
-meteoearth_MeteoEarth:VB_VBN
-meteotrend_MeteoTrend:VB_VBN
-metforminjanuvia_MetforminJANUVIA:VB_VBN
-meth_mETH:VB_VBN
-methacrylate_MethAcrylate:VB_VBN
-methug_metHuG:VB_VBN
-metic_METiC:VB_VBN
-metisdao_MetisDAO:VB_VBN
-metkstar_MetKstar:VB_VBN
-metlife_MetLife:VB_VBN
-metone_MetOne:VB_VBN
-metpak_MetPak:VB_VBN
-metrascan_MetraSCAN:VB_VBN
-metro_MeTro:VB_VBN
-metrocity_MetroCity:VB_VBN
-metrolinq_MetroLinq:VB_VBN
-metronet_MetroNET:VB_VBN
-metropcs_MetroPCS:VB_VBN
-metub_MeTub:VB_VBN
-meu_MeU:VB_VBN
-mev_MeV:VB_VBN
-mewconnect_MEWconnect:VB_VBN
-mewconnnect_MEWconnnect:VB_VBN
-mewe_MeWe:VB_VBN
-mexgroup_MexGroup:VB_VBN
-mexicocheng_MexicoCheng:VB_VBN
-mexicocon_MexicoCon:VB_VBN
-mextgen_MextGen:VB_VBN
-meygen_MeyGen:VB_VBN
-meyhomes_MeyHomes:VB_VBN
-meyland_MeyLand:VB_VBN
-meyresort_MeyResort:VB_VBN
-mezoom_meZOOM:VB_VBN
-mezzo_MEzzo:VB_VBN
-mfast_MFast:VB_VBN
-mfctuy_mfcTuy:VB_VBN
-mfgmatch_MFGmatch:VB_VBN
-mfgpartners_MFGpartners:VB_VBN
-mfi_mFI:VB_VBN
-mfilm_MFilm:VB_VBN
-mfly_MFly:VB_VBN
-mforce_MForce:VB_VBN
-mfstar_MFStar:VB_VBN
-mgallery_MGallery:VB_VBN
-mgame_MGame:VB_VBN
-mgate_MGate:VB_VBN
-mgcl_MgCl:VB_VBN
-mggtiki_mggTiki:VB_VBN
-mgland_MGLand:VB_VBN
-mgmcar_MGMcar:VB_VBN
-mgnuggets_MgNuggets:VB_VBN
-mgo_MgO:VB_VBN
-mgqe_mgQE:VB_VBN
-mgre_mGRE:VB_VBN
-mgreen_MGreen:VB_VBN
-mhd_mHD:VB_VBN
-mhdviet_mHDViet:VB_VBN
-mhome_MHome:VB_VBN
-mia_MiA:VB_VBN
-miaccount_MiAccount:VB_VBN
-miai_MiAI:VB_VBN
-mialala_MiaLala:VB_VBN
-mianlan_MianLan:VB_VBN
-miaovintage_MiaoVintage:VB_VBN
-mib_MiB:VB_VBN
-mibact_MiBACT:VB_VBN
-miband_MiBand:VB_VBN
-mibet_MiBet:VB_VBN
-miboxer_MiBOXER:VB_VBN
-mica_MiCa:VB_VBN
-micaebenin_MicaeBeNin:VB_VBN
-micellair_MicellAir:VB_VBN
-micgadget_MICGadget:VB_VBN
-micgeek_MicGeek:VB_VBN
-michaelkors_MichaelKors:VB_VBN
-michikotabe_MichiKotabe:VB_VBN
-michio_MiChiO:VB_VBN
-micloud_MiCloud:VB_VBN
-micom_MiCOM:VB_VBN
-micomho_MiCOMho:VB_VBN
-micrisiftwindows_MicrisiftWINDOWS:VB_VBN
-microa_microA:VB_VBN
-microad_MicroAd:VB_VBN
-microatx_microATX:VB_VBN
-microb_microB:VB_VBN
-microbandx_MicroBandX:VB_VBN
-microbilt_MicroBilt:VB_VBN
-microbit_MicroBit:VB_VBN
-microbt_MicroBT:VB_VBN
-microchips_MicroCHIPS:VB_VBN
-microclear_MicroClear:VB_VBN
-microdock_MicroDock:VB_VBN
-microdragon_MicroDragon:VB_VBN
-microfiber_MicroFiber:VB_VBN
-microfit_microFIT:VB_VBN
-microflash_microFlash:VB_VBN
-microflow_MicroFlow:VB_VBN
-microfocus_MicroFocus:VB_VBN
-microfund_MicroFund:VB_VBN
-microg_microG:VB_VBN
-microgaming_MicroGaming:VB_VBN
-microhdmi_microHDMI:VB_VBN
-microhenry_microHenry:VB_VBN
-microlessons_MicroLessons:VB_VBN
-micromanagement_MicroManagement:VB_VBN
-micromasters_MicroMasters:VB_VBN
-micromodal_MicroModal:VB_VBN
-micromotion_MicroMotion:VB_VBN
-micronc_MicronC:VB_VBN
-microphone_MicroPhone:VB_VBN
-micropulse_MicroPulse:VB_VBN
-microrna_microRNA:VB_VBN
-microsation_MicroSation:VB_VBN
-microscada_MicroSCADA:VB_VBN
-microsd_microSD:VB_VBN
-microsdhc_microSDHC:VB_VBN
-microsdhd_MicroSDHD:VB_VBN
-microsdxc_microSDXC:VB_VBN
-microsensor_MicroSensor:VB_VBN
-microserver_MicroServer:VB_VBN
-microsex_MicroSex:VB_VBN
-microshift_MicroShift:VB_VBN
-microshots_MicroShots:VB_VBN
-microsim_microSIM:VB_VBN
-micrositemaster_MicrositeMaster:VB_VBN
-micrositemasters_MicroSiteMasters:VB_VBN
-microsizer_MicroSizer:VB_VBN
-microsofti_MicrosoftI:VB_VBN
-microsoftmicrosoft_MicrosoftMicrosoft:VB_VBN
-microspot_MicroSpot:VB_VBN
-microstation_MicroStation:VB_VBN
-microstrategy_MicroStrategy:VB_VBN
-microtag_MicroTag:VB_VBN
-microtarget_MicroTarget:VB_VBN
-microtargeting_MicroTargeting:VB_VBN
-microtese_microTESE:VB_VBN
-microtex_MicroTex:VB_VBN
-microtouch_MicroTouch:VB_VBN
-microtrend_MicroTrend:VB_VBN
-microusb_microUSB:VB_VBN
-microwarehouse_MicroWarehouse:VB_VBN
-microwin_MicroWIN:VB_VBN
-micxm_MiCXM:VB_VBN
-midaata_MIDaATA:VB_VBN
-midamerica_MidAmerica:VB_VBN
-midesk_MiDesk:VB_VBN
-midiyoke_MidiYoke:VB_VBN
-midnight_MidNight:VB_VBN
-midoctor_miDoctor:VB_VBN
-midone_MidOne:VB_VBN
-midsmob_MidsMob:VB_VBN
-midtown_MiDTown:VB_VBN
-midtran_MIDTran:VB_VBN
-midu_MiDu:VB_VBN
-miduspa_MiduSpa:VB_VBN
-mienbaclianping_mienbacLianping:VB_VBN
-mienliu_mienLiu:VB_VBN
-mientayxanh_MienTayXanh:VB_VBN
-mientrung_MienTrung:VB_VBN
-mientrungtourist_MienTrungTourist:VB_VBN
-mieuung_MieuUng:VB_VBN
-miev_MiEV:VB_VBN
-mifa_MiFa:VB_VBN
-mifare_MiFare:VB_VBN
-mifi_MiFi:VB_VBN
-mifid_MiFID:VB_VBN
-mig_MiG:VB_VBN
-migame_MIGame:VB_VBN
-migcap_MiGCAP:VB_VBN
-mighty_MIghty:VB_VBN
-mightytext_MightyText:VB_VBN
-miguelbes_MiguelBeS:VB_VBN
-mihome_MiHome:VB_VBN
-mihoyo_miHoYo:VB_VBN
-mihub_MiHub:VB_VBN
-miine_MiiNe:VB_VBN
-miitao_MiiTao:VB_VBN
-miix_MiiX:VB_VBN
-mijafit_MijaFit:VB_VBN
-mijia_MiJia:VB_VBN
-mikandi_MiKandi:VB_VBN
-mike_MIke:VB_VBN
-mikenco_MikenCo:VB_VBN
-mikenguyen_MikeNguyen:VB_VBN
-mikeyeung_MikeYeung:VB_VBN
-mikgroup_MIKGroup:VB_VBN
-mikibloodymoto_MikiBloodyMoto:VB_VBN
-mikiehara_MikieHara:VB_VBN
-mikrotik_MikroTik:VB_VBN
-mikyx_MIkyx:VB_VBN
-mil_MiL:VB_VBN
-mila_MiLa:VB_VBN
-milano_MiLano:VB_VBN
-milanplaza_MilanPlaza:VB_VBN
-mileagelands_MileageLands:VB_VBN
-mileageplus_MileagePlus:VB_VBN
-mileiq_MileIQ:VB_VBN
-milestone_MileStone:VB_VBN
-miley_MIley:VB_VBN
-mileypham_MileyPham:VB_VBN
-milfie_MILFie:VB_VBN
-mili_MiLi:VB_VBN
-milidecor_MiliDecor:VB_VBN
-milistore_MiliStore:VB_VBN
-militarit_MIlitarit:VB_VBN
-militaryrussia_MilitaryRussia:VB_VBN
-militarywatch_MilitaryWatch:VB_VBN
-milkfoam_MilkFoam:VB_VBN
-milkyway_MilkyWay:VB_VBN
-milkywayads_MilkywayAds:VB_VBN
-mill_MiLL:VB_VBN
-millennialmillennial_MillennialMillennial:VB_VBN
-millennialz_MillennialZ:VB_VBN
-millercoors_MillerCoors:VB_VBN
-milliaspired_millIASpireD:VB_VBN
-milor_MIlor:VB_VBN
-milq_milQ:VB_VBN
-mima_MiMa:VB_VBN
-mimail_MiMail:VB_VBN
-mimax_MiMax:VB_VBN
-mimaxsv_MimaxSV:VB_VBN
-mimaxsvvas_MimaxSVVAS:VB_VBN
-mimblewimble_MimbleWimble:VB_VBN
-mimi_MiMi:VB_VBN
-mimimama_MimiMama:VB_VBN
-mimingmm_MimingMM:VB_VBN
-mimodong_MimoDong:VB_VBN
-mimosa_MiMoSa:VB_VBN
-mimosatek_MimosaTEK:VB_VBN
-minahq_MinaHQ:VB_VBN
-minan_minAn:VB_VBN
-minaotp_MinaOTP:VB_VBN
-minausb_MinaUSB:VB_VBN
-minbffs_minBFFs:VB_VBN
-minc_MinC:VB_VBN
-mincapacity_minCapacity:VB_VBN
-minci_MinCi:VB_VBN
-mind_MinD:VB_VBN
-mindalife_MindaLife:VB_VBN
-mindenergy_MindEnergy:VB_VBN
-mindesign_MinDesign:VB_VBN
-mindgenius_MindGenius:VB_VBN
-mindjet_MindJet:VB_VBN
-mindmanager_MindManager:VB_VBN
-mindmanuber_MindManuber:VB_VBN
-mindmap_MindMap:VB_VBN
-mindmapstina_MindmapsTina:VB_VBN
-mindmaster_MindMaster:VB_VBN
-mindmeister_MindMeister:VB_VBN
-mindmup_MindMup:VB_VBN
-mindscience_MindScience:VB_VBN
-mindshift_MindSHIFT:VB_VBN
-mindtriibe_MindTriibe:VB_VBN
-mindweb_MindWeb:VB_VBN
-mindx_MindX:VB_VBN
-minecraft_MineCraft:VB_VBN
-minefs_MineFS:VB_VBN
-minelord_MineLord:VB_VBN
-minestore_MineStore:VB_VBN
-minestrike_MineStrike:VB_VBN
-minfontsize_minFontsize:VB_VBN
-mingid_MingID:VB_VBN
-mingw_MinGW:VB_VBN
-mingzhen_MingZhen:VB_VBN
-minh_MInh:VB_VBN
-minhanfoods_MinhAnFoods:VB_VBN
-minhash_MinHash:VB_VBN
-minhchay_MinhChay:VB_VBN
-minhchinhlottery_MinhChinhLottery:VB_VBN
-minhdecor_MinhDecor:VB_VBN
-minhdepzai_MinhDepZai:VB_VBN
-minhdriver_MinhDRIVER:VB_VBN
-minhfixme_MinhFixme:VB_VBN
-minhhagroup_MinhHaGroup:VB_VBN
-minhhotline_MinhHotline:VB_VBN
-minhhumanhuman_minhHumanHuman:VB_VBN
-minhhy_MinhHy:VB_VBN
-minhkhang_MinhKhang:VB_VBN
-minhkuala_MinhKuala:VB_VBN
-minhlee_MinhLee:VB_VBN
-minhlush_MinhLush:VB_VBN
-minhminh_MinhMinh:VB_VBN
-minhmotor_MinhMotor:VB_VBN
-minhnextnext_minhNextNext:VB_VBN
-minhngoc_MinhNgoc:VB_VBN
-minho_MinHo:VB_VBN
-minhphim_minhPhim:VB_VBN
-minhphnom_MinhPhnom:VB_VBN
-minhpig_MinhPig:VB_VBN
-minhpv_MinhPV:VB_VBN
-minhquangphutho_MinhQuangPhuTho:VB_VBN
-minhread_MINHRead:VB_VBN
-minhshare_minhShare:VB_VBN
-minhtamblog_MinhTamBlog:VB_VBN
-minhtamdo_MinhtamDo:VB_VBN
-minhtel_MinhTel:VB_VBN
-minhthailand_MinhthaiLand:VB_VBN
-minhtham_minhTham:VB_VBN
-minhthuvcvip_minhthuvcVIP:VB_VBN
-minhthycos_MinhThyCos:VB_VBN
-minhtran_MinhTran:VB_VBN
-minhtri_MinhTri:VB_VBN
-minhtrieu_MinhTrieu:VB_VBN
-minhverified_MinhVerified:VB_VBN
-minhvietcorp_MinhvietCorp:VB_VBN
-minhview_MinhView:VB_VBN
-minhvinhomes_MinhVinhomes:VB_VBN
-minhvpgd_MinhVPGD:VB_VBN
-minhwatch_MinhWatch:VB_VBN
-minhyun_MinHyun:VB_VBN
-minhzootopiazootopia_minhZootopiaZootopia:VB_VBN
-mini_MIni:VB_VBN
-minia_miniA:VB_VBN
-miniapps_MiniApps:VB_VBN
-minib_miniB:VB_VBN
-minibb_MiniBB:VB_VBN
-minibusvolkswagen_minibusVolkswagen:VB_VBN
-minicapri_MiniCapri:VB_VBN
-miniclat_MiniClat:VB_VBN
-minicutter_MiniCutter:VB_VBN
-minidisplay_MiniDisplay:VB_VBN
-minidisplayport_MiniDisplayport:VB_VBN
-minidp_MiniDP:VB_VBN
-minifreeshipkét_MiniFREESHIPKét:VB_VBN
-minifyenabled_minifyEnabled:VB_VBN
-minigame_MiniGame:VB_VBN
-minigbic_MiniGBIC:VB_VBN
-minigt_MiniGT:VB_VBN
-minij_MiniJ:VB_VBN
-minikhi_MINIKhi:VB_VBN
-minikit_MiniKit:VB_VBN
-miniknot_MiniKnot:VB_VBN
-minilab_MiniLab:VB_VBN
-minilar_MinilaR:VB_VBN
-miniled_miniLED:VB_VBN
-miniluxe_MiniLuxe:VB_VBN
-minilyrics_MiniLyrics:VB_VBN
-minimart_MiniMart:VB_VBN
-minimax_MINImax:VB_VBN
-minimba_MiniMBA:VB_VBN
-minimini_MiniMini:VB_VBN
-miningcity_MiningCity:VB_VBN
-miningforgood_MiningForGood:VB_VBN
-miningstore_MiningStore:VB_VBN
-minipcie_miniPCIe:VB_VBN
-minipoker_MiniPoker:VB_VBN
-minipresso_MiniPresso:VB_VBN
-miniq_miniQ:VB_VBN
-minirite_MiniRite:VB_VBN
-miniscada_miniSCADA:VB_VBN
-miniscape_miniSCAPE:VB_VBN
-minisd_miniSD:VB_VBN
-minisim_miniSIM:VB_VBN
-minismart_MiniSmart:VB_VBN
-minispace_MiniSpace:VB_VBN
-ministop_MiniStop:VB_VBN
-minitool_MiniTool:VB_VBN
-minixp_miniXP:VB_VBN
-minkextension_MinkExtension:VB_VBN
-minled_MinLED:VB_VBN
-minlee_minLee:VB_VBN
-minmobile_MinMobile:VB_VBN
-minostore_MinoStore:VB_VBN
-minoximed_MinoxiMed:VB_VBN
-minp_MinP:VB_VBN
-minprosniper_MinProSniper:VB_VBN
-minsdkversion_minSdkVersion:VB_VBN
-minttech_MintTech:VB_VBN
-mintuoide_minTuoide:VB_VBN
-mintvine_MintVine:VB_VBN
-minup_MinUp:VB_VBN
-minuteclinic_MinuteClinic:VB_VBN
-minutephysics_MinutePhysics:VB_VBN
-minx_MinX:VB_VBN
-minyoung_MinYoung:VB_VBN
-mio_MiO:VB_VBN
-mioskin_MioSkin:VB_VBN
-mipad_MiPad:VB_VBN
-mipax_MiPAX:VB_VBN
-mipbx_MiPBX:VB_VBN
-mipex_MiPex:VB_VBN
-mipow_MiPow:VB_VBN
-mipro_MiPro:VB_VBN
-miptv_MipTV:VB_VBN
-mir_miR:VB_VBN
-mira_MiRa:VB_VBN
-mirabeauwine_MirabeauWine:VB_VBN
-miracast_MIracast:VB_VBN
-miracosta_MiraCosta:VB_VBN
-miraculous_MiraCulous:VB_VBN
-miradry_MiraDry:VB_VBN
-miragloss_MiraGloss:VB_VBN
-mirai_MiRai:VB_VBN
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-mirc_mIRC:VB_VBN
-mircosd_MircoSD:VB_VBN
-mircousb_MircoUSB:VB_VBN
-mire_MiRe:VB_VBN
-mirosd_miroSD:VB_VBN
-mirrorlink_MirrorLink:VB_VBN
-mirrortrader_MirrorTrader:VB_VBN
-misaer_MISAer:VB_VBN
-misis_MISiS:VB_VBN
-misms_MiSMS:VB_VBN
-miso_MiSo:VB_VBN
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-missdigitalworld_MissDigitalWorld:VB_VBN
-missearth_MissEarth:VB_VBN
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-missiobot_MissioBot:VB_VBN
-missionkit_MissionKit:VB_VBN
-missourivietcatholic_MissouriVietCatholic:VB_VBN
-missteen_MissTeen:VB_VBN
-misswhite_MissWhite:VB_VBN
-mistake_MiSTakE:VB_VBN
-mistar_MiStar:VB_VBN
-mistep_MiSTEP:VB_VBN
-mistgunz_MistGunz:VB_VBN
-misthy_MisThy:VB_VBN
-misu_MiSu:VB_VBN
-mit_MiT:VB_VBN
-mita_MiTA:VB_VBN
-mitadoor_MitaDoor:VB_VBN
-mith_MiTH:VB_VBN
-mithi_MiThi:VB_VBN
-mitm_MitM:VB_VBN
-mitrans_MITrans:VB_VBN
-mitsubishielectric_MitsubishiElectric:VB_VBN
-mitsubishigrandis_MitsubishiGrandis:VB_VBN
-mitv_MiTV:VB_VBN
-miu_mIU:VB_VBN
-miui_MiUI:VB_VBN
-miumiu_MiuMiu:VB_VBN
-miunglab_MiungLab:VB_VBN
-miura_MIura:VB_VBN
-mivaly_MiVaLy:VB_VBN
-mivue_MiVue:VB_VBN
-mivv_MiVV:VB_VBN
-miway_MiWay:VB_VBN
-miwi_MiWi:VB_VBN
-miwifi_MiWifi:VB_VBN
-mixbit_MixBit:VB_VBN
-mixbus_MixBus:VB_VBN
-mixcloud_MixCloud:VB_VBN
-mixcolumns_MixColumns:VB_VBN
-mixconsole_MixConsole:VB_VBN
-mixerled_MixerLed:VB_VBN
-mixgo_MixGo:VB_VBN
-mixi_MiXi:VB_VBN
-mixifood_MixiFood:VB_VBN
-mixigaming_MixiGaming:VB_VBN
-miximiser_MixiMiser:VB_VBN
-mixmeister_MixMeister:VB_VBN
-mixplorer_MiXplorer:VB_VBN
-mixtourist_MixTourist:VB_VBN
-mixxmix_MixxMix:VB_VBN
-mixzing_MixZing:VB_VBN
-miyin_MiYin:VB_VBN
-mizuland_MizuLand:VB_VBN
-mizzzee_MizzZee:VB_VBN
-mkchem_MKChem:VB_VBN
-mkdir_MkDir:VB_VBN
-mkdirections_MKDirections:VB_VBN
-mkii_MkII:VB_VBN
-mkiii_MkIII:VB_VBN
-mkmocggudui_mKmocgGUDUI:VB_VBN
-mknow_MKnow:VB_VBN
-mkrdezign_MKRdezign:VB_VBN
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-mktoolnix_MKToolnix:VB_VBN
-mkvtoolnix_MKVToolNix:VB_VBN
-mlaas_MlaaS:VB_VBN
-mland_MLand:VB_VBN
-mlcasestudy_MLCaseStudy:VB_VBN
-mlee_MLee:VB_VBN
-mlemmmmlin_mlemmmmLin:VB_VBN
-mlg_MlG:VB_VBN
-mlili_mLiLi:VB_VBN
-mlive_MLive:VB_VBN
-mliving_MLiving:VB_VBN
-mlkit_MLKit:VB_VBN
-mllib_MLlib:VB_VBN
-mlteisseire_MLTeisseire:VB_VBN
-mlu_mlU:VB_VBN
-mmarchive_MMArchive:VB_VBN
-mmbthe_MMBThe:VB_VBN
-mmbtu_mmBTU:VB_VBN
-mmcplus_MMCplus:VB_VBN
-mmdidau_MMDiDau:VB_VBN
-mmguardian_MMGuardian:VB_VBN
-mmhg_mmHg:VB_VBN
-mmhga_mmHgA:VB_VBN
-mminh_mMInh:VB_VBN
-mmista_MMista:VB_VBN
-mmmmm_MmMmm:VB_VBN
-mmnn_MmNN:VB_VBN
-mmnnxp_MmNnXp:VB_VBN
-mmoer_MMOer:VB_VBN
-mmojourney_MMOJourney:VB_VBN
-mmong_MMong:VB_VBN
-mmonlineleave_MMonlineLeave:VB_VBN
-mmorpggame_MMORPGgame:VB_VBN
-mmorpggmo_MMORPGgMO:VB_VBN
-mmware_mmWare:VB_VBN
-mmwave_mmWave:VB_VBN
-mmwavmi_mmWavmi:VB_VBN
-mnam_MNam:VB_VBN
-mnb_MnB:VB_VBN
-mncatholic_MnCatholic:VB_VBN
-mnhg_mmHg:VB_VBN
-mnhi_MNhi:VB_VBN
-mnhnhland_mnhnhLand:VB_VBN
-mnig_MniG:VB_VBN
-mnm_MnM:VB_VBN
-mnmy_MnMy:VB_VBN
-mno_MnO:VB_VBN
-mnoy_MnOy:VB_VBN
-mns_MnS:VB_VBN
-moan_MOan:VB_VBN
-moba_MoBa:VB_VBN
-mobaxterm_MobaXterm:VB_VBN
-mobdata_MobData:VB_VBN
-mobez_MobEZ:VB_VBN
-mobi_MObi:VB_VBN
-mobicard_MobiCard:VB_VBN
-mobicast_MobiCast:VB_VBN
-mobiclip_MobiClip:VB_VBN
-mobicms_MobiCMS:VB_VBN
-mobiecity_MobieCity:VB_VBN
-mobiedu_MobiEdu:VB_VBN
-mobietrans_MobieTrans:VB_VBN
-mobieyes_MobiEyes:VB_VBN
-mobiez_MobiEZ:VB_VBN
-mobif_mobiF:VB_VBN
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-mobifo_MobiFo:VB_VBN
-mobifone_MobiFone:VB_VBN
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-mobifonele_MobiFonele:VB_VBN
-mobigo_MobiGo:VB_VBN
-mobigold_MobiGold:VB_VBN
-mobihome_MobiHome:VB_VBN
-mobiistar_MobiiStar:VB_VBN
-mobikin_MobiKin:VB_VBN
-mobile_MoBile:VB_VBN
-mobileadsinitprovider_MobileAdsInitProvider:VB_VBN
-mobilebank_MobileBank:VB_VBN
-mobilecare_MobileCare:VB_VBN
-mobilechien_mobileChien:VB_VBN
-mobilecity_MobileCity:VB_VBN
-mobiledit_MobilEdit:VB_VBN
-mobilefun_MobileFun:VB_VBN
-mobilegeeks_MobileGeeks:VB_VBN
-mobilego_MobileGo:VB_VBN
-mobilekids_MobileKids:VB_VBN
-mobilelite_MobileLite:VB_VBN
-mobilelmht_mobileLMHT:VB_VBN
-mobilemark_MobileMark:VB_VBN
-mobileme_MobileMe:VB_VBN
-mobilemonkey_MobileMonkey:VB_VBN
-mobilenet_MobileNet:VB_VBN
-mobilenha_MobileNha:VB_VBN
-mobilepay_MobilePay:VB_VBN
-mobilestudio_MobileStudio:VB_VBN
-mobilestudion_MobileStudion:VB_VBN
-mobilesubstrate_MobileSubstrate:VB_VBN
-mobiletech_MobileTech:VB_VBN
-mobiletrans_MobileTrans:VB_VBN
-mobiletv_MobileTV:VB_VBN
-mobileworld_MobileWorld:VB_VBN
-mobilexxon_MobilExxon:VB_VBN
-mobileye_MobilEye:VB_VBN
-mobilimb_MobiLimb:VB_VBN
-mobilityware_MobilityWare:VB_VBN
-mobilr_mobilR:VB_VBN
-mobimoney_MobiMoney:VB_VBN
-mobimover_MobiMover:VB_VBN
-mobinam_mobiNam:VB_VBN
-mobion_MobiON:VB_VBN
-mobione_MobiOne:VB_VBN
-mobipay_MobiPay:VB_VBN
-mobiphone_MobiPhone:VB_VBN
-mobipro_MobiPro:VB_VBN
-mobiprotrung_MobiproTrung:VB_VBN
-mobiq_MobiQ:VB_VBN
-mobisaver_MobiSaver:VB_VBN
-mobishow_MobiShow:VB_VBN
-mobistar_MobiStar:VB_VBN
-mobitv_MobiTV:VB_VBN
-mobius_MobiUS:VB_VBN
-mobiwifi_MobiWifi:VB_VBN
-mobiwork_MobiWork:VB_VBN
-mobiworkdms_MobiworkDMS:VB_VBN
-mobizone_MobiZone:VB_VBN
-moboplay_MoboPlay:VB_VBN
-mobymart_MobyMart:VB_VBN
-moc_MoC:VB_VBN
-moca_MoCA:VB_VBN
-mocache_MOCache:VB_VBN
-mochi_MoChi:VB_VBN
-mochimochi_MochiMochi:VB_VBN
-moctoc_MocToc:VB_VBN
-moctv_MocTV:VB_VBN
-moda_ModA:VB_VBN
-modaviet_ModaViet:VB_VBN
-modbus_ModBus:VB_VBN
-modcloth_ModCloth:VB_VBN
-moddao_ModDao:VB_VBN
-moddb_ModDB:VB_VBN
-modelandview_ModelAndView:VB_VBN
-modelbuilder_ModelBuilder:VB_VBN
-modelfit_ModelFit:VB_VBN
-modelingcafe_ModelingCafe:VB_VBN
-modelspace_ModelSpace:VB_VBN
-modelstate_ModelState:VB_VBN
-modelswardrobe_ModelsWardrobe:VB_VBN
-modelx_ModelX:VB_VBN
-modemvietel_modemVietel:VB_VBN
-modenz_ModenZ:VB_VBN
-modernchair_ModernChair:VB_VBN
-moderncoffeetable_ModernCoffeeTable:VB_VBN
-moderndecor_ModernDecor:VB_VBN
-moderndoor_ModernDoor:VB_VBN
-modernfurniturefactory_ModernFurnitureFactory:VB_VBN
-modernhome_ModernHome:VB_VBN
-modernlife_ModernLife:VB_VBN
-modernmix_ModernMix:VB_VBN
-modernsofa_ModernSofa:VB_VBN
-modeto_MoDeTo:VB_VBN
-modiface_ModiFace:VB_VBN
-modifiedfiles_ModifiedFiles:VB_VBN
-modingpc_ModingPC:VB_VBN
-modlauncher_ModLauncher:VB_VBN
-modloader_ModLoader:VB_VBN
-modmyi_ModMyi:VB_VBN
-modulemanager_ModuleManager:VB_VBN
-modulobox_ModuloBox:VB_VBN
-modultrade_ModulTrade:VB_VBN
-modunsoft_ModunSoft:VB_VBN
-moe_MoE:VB_VBN
-mof_MoF:VB_VBN
-mofuse_MoFuse:VB_VBN
-mogway_MogWay:VB_VBN
-mohamed_MoHaMed:VB_VBN
-mohammach_MohamMach:VB_VBN
-mohms_MOhms:VB_VBN
-moigioiforex_MoigioiForex:VB_VBN
-moinhat_MoiNhat:VB_VBN
-moistfresh_MoistFresh:VB_VBN
-mojo_MoJo:VB_VBN
-molcho_molCho:VB_VBN
-molecularshell_molecularShell:VB_VBN
-moltenbasketball_MoltenBasketball:VB_VBN
-moltensvolleyball_MoltensVolleyball:VB_VBN
-moltenvolleyball_MoltenVolleyball:VB_VBN
-molympiad_MOlympiad:VB_VBN
-mom_MoM:VB_VBN
-moma_MoMA:VB_VBN
-momcare_MomCare:VB_VBN
-momentcam_MomentCam:VB_VBN
-momi_MoMi:VB_VBN
-momjunction_MomJunction:VB_VBN
-momkitty_MomKitty:VB_VBN
-momma_MOmma:VB_VBN
-mommomcare_MomMomCare:VB_VBN
-momo_MoMo:VB_VBN
-momopay_MomoPay:VB_VBN
-momsbangteens_MomsBangTeens:VB_VBN
-mon_MoN:VB_VBN
-monacosau_MonacoSau:VB_VBN
-monadesign_MonaDesign:VB_VBN
-monager_MoNager:VB_VBN
-monami_MonAmi:VB_VBN
-monamie_MonAmie:VB_VBN
-monavie_MonaVie:VB_VBN
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-moncara_MonCaRa:VB_VBN
-monchengladbachyi_monchengladbachYi:VB_VBN
-moncity_MonCity:VB_VBN
-monclub_MonClub:VB_VBN
-moncover_MonCover:VB_VBN
-mondial_MondiaL:VB_VBN
-monetcat_MonetCat:VB_VBN
-moneybeach_MoneyBeach:VB_VBN
-moneybinary_MONEYBinary:VB_VBN
-moneybookers_MoneyBookers:VB_VBN
-moneycare_MoneyCare:VB_VBN
-moneycat_MoneyCat:VB_VBN
-moneyconf_MoneyConf:VB_VBN
-moneygram_MoneyGram:VB_VBN
-moneyit_MoneyIt:VB_VBN
-moneykeeper_MoneyKeeper:VB_VBN
-moneyline_MoneyLine:VB_VBN
-moneylovertinhte_MoneyLoverTinhte:VB_VBN
-moneyminded_MoneyMinded:VB_VBN
-moneypak_MoneyPak:VB_VBN
-moneysafe_MoneySafe:VB_VBN
-moneysavingexpert_MoneySavingExpert:VB_VBN
-moneysense_MoneySense:VB_VBN
-moneysmart_MoneySmart:VB_VBN
-moneytap_MoneyTap:VB_VBN
-moneytoken_MoneyToken:VB_VBN
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-moneyveo_MoneyVeo:VB_VBN
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-mong_MOng:VB_VBN
-mongoclient_MongoClient:VB_VBN
-mongodb_MongoDB:VB_VBN
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-mongthu_MongThu:VB_VBN
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-monivisor_MoniVisor:VB_VBN
-monkeyjunior_MonkeyJunior:VB_VBN
-monngonhanoi_MonngonHanoi:VB_VBN
-monngonsaigon_MonngonSaigon:VB_VBN
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-monobase_MonoBase:VB_VBN
-monobind_MonoBind:VB_VBN
-monobloc_MonoBloc:VB_VBN
-monobook_MonoBook:VB_VBN
-monocell_MonoCell:VB_VBN
-monogame_MonoGame:VB_VBN
-monoji_MoNoJi:VB_VBN
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-monospace_MonoSpace:VB_VBN
-monosteam_MonoSteam:VB_VBN
-monotaro_MonotaRO:VB_VBN
-monpay_MonPay:VB_VBN
-monst_MonSt:VB_VBN
-monsterinsight_MonsterInsight:VB_VBN
-monsterinsights_MonsterInsights:VB_VBN
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-moonphase_MoonPhase:VB_VBN
-moonphin_MoonPhin:VB_VBN
-moonwalk_MoonWalk:VB_VBN
-moosocial_mooSocial:VB_VBN
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-mopos_moPOS:VB_VBN
-morabanc_MoraBanc:VB_VBN
-moredu_moreDu:VB_VBN
-morehe_moreHe:VB_VBN
-morehome_MoreHome:VB_VBN
-morelocale_MoreLocale:VB_VBN
-morelocare_MoreLocare:VB_VBN
-morephone_MorePhone:VB_VBN
-morepin_morePin:VB_VBN
-moresee_MoreSee:VB_VBN
-moreso_moreSo:VB_VBN
-moresofa_MoreSofa:VB_VBN
-moretop_moreTop:VB_VBN
-morfai_MorFai:VB_VBN
-morgananouk_MorganAnouk:VB_VBN
-mori_MoRi:VB_VBN
-moris_MoriS:VB_VBN
-morningcoffee_MorningCoffee:VB_VBN
-morningconsult_MorningConsult:VB_VBN
-morphos_MorphOS:VB_VBN
-morphvox_MorphVOX:VB_VBN
-morriconeennio_MorriconeEnnio:VB_VBN
-mos_MoS:VB_VBN
-mosa_MoSa:VB_VBN
-mosaic_MosaiC:VB_VBN
-mosalingua_MosaLingua:VB_VBN
-mosami_MoSaMi:VB_VBN
-moscowcska_MoscowCSKA:VB_VBN
-moshimoshi_MoshiMoshi:VB_VBN
-mosscelltec_MossCellTec:VB_VBN
-mostchall_MostChall:VB_VBN
-mot_MoT:VB_VBN
-motherboard_MotherBoard:VB_VBN
-motherbox_MotherBox:VB_VBN
-mothercare_MotherCare:VB_VBN
-motherwearvn_MotherwearVN:VB_VBN
-motidram_MoTidram:VB_VBN
-motilityboost_MotilityBoost:VB_VBN
-motioncontrol_MotionControl:VB_VBN
-motioncore_MotionCore:VB_VBN
-motiondd_MotionDD:VB_VBN
-motionflow_MotionFlow:VB_VBN
-motionlayout_MotionLayout:VB_VBN
-motionmemory_MotionMemory:VB_VBN
-motionplus_MotionPlus:VB_VBN
-motionsensor_MotionSensor:VB_VBN
-motionx_MotionX:VB_VBN
-motoblur_MotoBlur:VB_VBN
-motobot_MotoBot:VB_VBN
-motocorp_MotoCorp:VB_VBN
-motocrazy_MotoCrazy:VB_VBN
-motocyclesdata_MotocyclesData:VB_VBN
-motogp_MotoGP:VB_VBN
-motoheroz_MotoHeroz:VB_VBN
-motom_MotoM:VB_VBN
-motopress_MotoPress:VB_VBN
-motor_MoTor:VB_VBN
-motorcity_MotorCity:VB_VBN
-motorhome_MotorHome:VB_VBN
-motorjapanese_MotorJapanese:VB_VBN
-motoroid_MOTOROiD:VB_VBN
-motorolatuy_motorolaTuy:VB_VBN
-motorsafe_MotorSafe:VB_VBN
-motorshow_MotorShow:VB_VBN
-motorsport_MotorSport:VB_VBN
-motorstore_MotorStore:VB_VBN
-motortrend_MotorTrend:VB_VBN
-motorvina_MotorVina:VB_VBN
-motosaigon_MotoSaigon:VB_VBN
-motosoft_MotoSoft:VB_VBN
-motospeak_MotoSpeak:VB_VBN
-motospeed_MotoSpeed:VB_VBN
-mototech_MotoTech:VB_VBN
-mototxt_MotoTxt:VB_VBN
-motovlog_MotoVlog:VB_VBN
-moturiver_MotuRiver:VB_VBN
-mou_MoU:VB_VBN
-mountcarl_MountCarl:VB_VBN
-mountcung_MountCung:VB_VBN
-mountlocker_MountLocker:VB_VBN
-mourinhohaaland_MourinhoHaaland:VB_VBN
-mouseenter_MouseEnter:VB_VBN
-mousehovertime_MouseHoverTime:VB_VBN
-mouseleave_MouseLeave:VB_VBN
-mousepad_MousePad:VB_VBN
-mousescroll_MouseScroll:VB_VBN
-movay_MoVay:VB_VBN
-movehome_MoveHome:VB_VBN
-movementthe_MovementThe:VB_VBN
-movenpick_MovenPick:VB_VBN
-moveon_MoveOn:VB_VBN
-movepro_MovePro:VB_VBN
-moviebar_MovieBar:VB_VBN
-moviebloc_MovieBloc:VB_VBN
-moviebox_MovieBox:VB_VBN
-moviefactory_MovieFactory:VB_VBN
-movielens_MovieLens:VB_VBN
-moviemaker_MovieMaker:VB_VBN
-moviemob_MovieMob:VB_VBN
-moviepass_MoviePass:VB_VBN
-moviepro_MoviePro:VB_VBN
-moviesaver_MovieSaver:VB_VBN
-moviestreamer_MovieStreamer:VB_VBN
-movieweek_MovieWeek:VB_VBN
-movilift_MoviLift:VB_VBN
-moving_MoVing:VB_VBN
-movinghouse_MovingHouse:VB_VBN
-movingtaxi_movingTaxi:VB_VBN
-movitracker_MoviTracker:VB_VBN
-mowo_MoWo:VB_VBN
-moyu_MoYu:VB_VBN
-mozabook_MozaBook:VB_VBN
-mozadong_MozaDong:VB_VBN
-mozalog_MozaLog:VB_VBN
-mozardx_MozardX:VB_VBN
-mozaweb_mozaWeb:VB_VBN
-mozbackup_MozBackup:VB_VBN
-mozbar_MozBar:VB_VBN
-mozcast_MozCast:VB_VBN
-mozkeyword_MozKeyword:VB_VBN
-mozplugger_MozPlugger:VB_VBN
-mozrank_MozRank:VB_VBN
-mozun_MoZun:VB_VBN
-mozyhome_MozyHome:VB_VBN
-mpag_MPaG:VB_VBN
-mpan_mpaN:VB_VBN
-mpavilion_MPavilion:VB_VBN
-mpbx_mPBX:VB_VBN
-mpcie_mPCIe:VB_VBN
-mpdf_mPDF:VB_VBN
-mpenableplus_MpEnablePlus:VB_VBN
-mphat_MPhat:VB_VBN
-mphikvision_mpHIKVISION:VB_VBN
-mphil_MPhil:VB_VBN
-mpin_mPIN:VB_VBN
-mplayer_MPlayer:VB_VBN
-mplaza_MPlaza:VB_VBN
-mpobipro_MpobiPro:VB_VBN
-mpos_mPOS:VB_VBN
-mpros_MPros:VB_VBN
-mpseno_MPseno:VB_VBN
-mptelecom_MPTelecom:VB_VBN
-mptool_MPTool:VB_VBN
-mqrobotics_MQRobotics:VB_VBN
-mqskin_MQSkin:VB_VBN
-mqwidnows_MQwidnows:VB_VBN
-mqwindows_MQwindows:VB_VBN
-mra_MrA:VB_VBN
-mractor_MrActor:VB_VBN
-mrarch_mrArch:VB_VBN
-mrbeast_MrBeast:VB_VBN
-mrbossftw_MrBossFTW:VB_VBN
-mrcall_MrCall:VB_VBN
-mrd_MrD:VB_VBN
-mrdj_MrDJ:VB_VBN
-mremoteng_mRemoteNG:VB_VBN
-mrey_MRey:VB_VBN
-mrfarm_MrFarm:VB_VBN
-mrgangrim_MrGangRim:VB_VBN
-mrgrath_MrGrath:VB_VBN
-mrhatuyen_MrHaTuyen:VB_VBN
-mrhe_MrHe:VB_VBN
-mrhien_MrHien:VB_VBN
-mrhieu_mrHieu:VB_VBN
-mrhoa_MrHoa:VB_VBN
-mrjohnson_MrJohnson:VB_VBN
-mrkcool_MrKcool:VB_VBN
-mrkelvin_MrKelvin:VB_VBN
-mrlaptop_MrLaptop:VB_VBN
-mrlecongnhan_MrLecongnhan:VB_VBN
-mrmobile_MrMobile:VB_VBN
-mrna_mRNA:VB_VBN
-mrnai_MrNai:VB_VBN
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-niea_NieA:VB_VBN
-nienhsing_NienHsing:VB_VBN
-nier_NieR:VB_VBN
-nietzsche_NietZsche:VB_VBN
-nietzscheanai_NietzscheanAI:VB_VBN
-nightfromday_NightFromDay:VB_VBN
-nightlapse_NightLapse:VB_VBN
-nightlight_NightLight:VB_VBN
-nightlink_NightLink:VB_VBN
-nightmareindustries_NightmareIndustries:VB_VBN
-nightmode_NightMode:VB_VBN
-nightscout_NightScout:VB_VBN
-nightsight_NightSight:VB_VBN
-nightsword_NightSword:VB_VBN
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-niha_NiHa:VB_VBN
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-nikatei_NiKatei:VB_VBN
-nike_NiKe:VB_VBN
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-nikelab_NikeLab:VB_VBN
-niken_NiKen:VB_VBN
-niki_NiKi:VB_VBN
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-nikolatesla_NikolaTesla:VB_VBN
-nikotinoff_NikotinOff:VB_VBN
-nilainilai_NilaiNilai:VB_VBN
-nilclass_NilClass:VB_VBN
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-nnfacebook_nnFacebook:VB_VBN
-nnlink_nnLink:VB_VBN
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-nnxin_nnXin:VB_VBN
-noautoupdate_NoAutoUpdate:VB_VBN
-nobelclinician_NobelClinician:VB_VBN
-nobi_NoBi:VB_VBN
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-noblecoin_NobleCoin:VB_VBN
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-nono_NoNo:VB_VBN
-nonolive_NonoLive:VB_VBN
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-parkmen_ParkMen:VB_VBN
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-sameweb_SameWeb:VB_VBN
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-shbdoanh_SHBDoanh:VB_VBN
-shbfinance_SHBfinance:VB_VBN
-shcgroup_SHCgroup:VB_VBN
-shden_SHden:VB_VBN
-sheaghana_SheaGhana:VB_VBN
-sheaterra_SheaTerra:VB_VBN
-shebelieves_SheBelieves:VB_VBN
-sheetworks_SheetWorks:VB_VBN
-sheffieldsoi_SheffieldSoi:VB_VBN
-sheffieldunited_SheffieldUnited:VB_VBN
-shellcheck_ShellCheck:VB_VBN
-shena_SheNa:VB_VBN
-shengbo_ShengBo:VB_VBN
-shengli_ShengLi:VB_VBN
-shengshing_ShengShing:VB_VBN
-shengshou_ShengShou:VB_VBN
-shenlong_ShenLong:VB_VBN
-shenyi_ShenYi:VB_VBN
-shenyu_ShenYu:VB_VBN
-shenzhen_ShenZhen:VB_VBN
-shepherdbarron_ShepherdBarron:VB_VBN
-sherngyuan_SherngYuan:VB_VBN
-sherrygin_SherryGin:VB_VBN
-shesabigstar_ShesaBigstar:VB_VBN
-shescribes_SheScribes:VB_VBN
-shgarden_SHgarden:VB_VBN
-shichi_ShiChi:VB_VBN
-shiftcam_ShiftCam:VB_VBN
-shiftrows_ShiftRows:VB_VBN
-shigella_SHigella:VB_VBN
-shika_SHika:VB_VBN
-shillerlearning_ShillerLearning:VB_VBN
-shimang_ShiMang:VB_VBN
-shine_SHine:VB_VBN
-shinee_SHINee:VB_VBN
-shinemate_ShineMate:VB_VBN
-shinemoist_ShineMoist:VB_VBN
-shineraysn_ShineraySN:VB_VBN
-shinesky_ShineSky:VB_VBN
-shinevn_ShineVN:VB_VBN
-shineye_ShineYe:VB_VBN
-shinfansub_ShinFansub:VB_VBN
-shinghanbank_ShinghanBank:VB_VBN
-shingmark_ShingMark:VB_VBN
-shinhan_ShinHan:VB_VBN
-shinhanbank_ShinhanBank:VB_VBN
-shinichikudo_ShinichiKudo:VB_VBN
-shinkaiweathering_ShinkaiWeathering:VB_VBN
-shinlong_ShinLong:VB_VBN
-shinmaywa_ShinMaywa:VB_VBN
-shinnee_ShinNee:VB_VBN
-shinosaka_ShinOsaka:VB_VBN
-shinshun_ShinShun:VB_VBN
-shinstar_ShinStar:VB_VBN
-shinxran_ShinxRan:VB_VBN
-shinxxran_ShinXXRan:VB_VBN
-shinyak_ShinYak:VB_VBN
-shinyang_ShinYang:VB_VBN
-shinyhunters_ShinyHunters:VB_VBN
-shinyoung_ShinYoung:VB_VBN
-shinzoabe_ShinzoAbe:VB_VBN
-shipchung_ShipChung:VB_VBN
-shipcod_shipCOD:VB_VBN
-shipdoandemff_ShipdoanDemFF:VB_VBN
-shipmua_shipMua:VB_VBN
-shipmyvina_ShipMyVina:VB_VBN
-shipstation_ShipStation:VB_VBN
-shipvn_ShipVN:VB_VBN
-shiroemn_ShiroEmn:VB_VBN
-shiroemon_ShiroEmon:VB_VBN
-shizukudocp_ShizukuDOCP:VB_VBN
-shoex_ShoeX:VB_VBN
-shootingstar_ShootingStar:VB_VBN
-shopaha_shopAha:VB_VBN
-shopback_ShopBack:VB_VBN
-shopbao_shopBao:VB_VBN
-shopbaocaosuvungtau_ShopBaoCaoSuVungTau:VB_VBN
-shopbase_ShopBase:VB_VBN
-shopbevame_ShopBevaMe:VB_VBN
-shopblackberry_ShopBlackBerry:VB_VBN
-shopbot_ShopBot:VB_VBN
-shopbuayeu_ShopBuaYeu:VB_VBN
-shopby_ShopBy:VB_VBN
-shopchothuetrangphuc_ShopChoThueTrangPhuc:VB_VBN
-shopclick_ShopClick:VB_VBN
-shopdochobeyeu_ShopDoChoBeYeu:VB_VBN
-shopdogoleave_shopdogoLeave:VB_VBN
-shopdunk_ShopDunk:VB_VBN
-shopeemall_ShopeeMall:VB_VBN
-shopeeplus_ShopeePlus:VB_VBN
-shopgao_ShopGao:VB_VBN
-shopgovn_ShopGoVN:VB_VBN
-shophangbay_ShopHangBay:VB_VBN
-shophangnhat_ShopHangNhat:VB_VBN
-shophanhphuc_ShopHanhPhuc:VB_VBN
-shophat_ShopHAT:VB_VBN
-shophoa_ShopHoa:VB_VBN
-shophoavip_ShopHoaVip:VB_VBN
-shophouse_ShopHouse:VB_VBN
-shopifyshopify_ShopifyShopify:VB_VBN
-shopisle_ShopIsle:VB_VBN
-shopit_ShopIT:VB_VBN
-shopkhala_ShopKhaLa:VB_VBN
-shopkit_ShopKit:VB_VBN
-shoplazti_ShopLazTi:VB_VBN
-shopleu_ShopLeu:VB_VBN
-shoplinhmakeup_ShopLinhMakeup:VB_VBN
-shopmexoai_ShopMeXoai:VB_VBN
-shopmeyeucon_ShopMeYeuCon:VB_VBN
-shopnangoi_ShopNangOi:VB_VBN
-shopngo_shopnGo:VB_VBN
-shopnhatviet_ShopNhatViet:VB_VBN
-shopnow_ShopNow:VB_VBN
-shopon_shopON:VB_VBN
-shoponlinept_ShoponlinePT:VB_VBN
-shoppertrak_ShopperTrak:VB_VBN
-shopphutung_ShopPhuTung:VB_VBN
-shoppinggives_ShoppingGives:VB_VBN
-shoppro_ShopPRO:VB_VBN
-shoprunner_ShopRunner:VB_VBN
-shopsocquay_ShopSocQuay:VB_VBN
-shoptalk_ShopTalk:VB_VBN
-shoptech_ShopTech:VB_VBN
-shoptraitim_ShopTraiTim:VB_VBN
-shoptranhtreotuong_ShopTranhTreoTuong:VB_VBN
-shoptretho_ShopTreTho:VB_VBN
-shoptuixachdulich_ShopTuiXachDuLich:VB_VBN
-shopviettel_ShopViettel:VB_VBN
-shopvinu_ShopViNu:VB_VBN
-shopvnb_ShopVNB:VB_VBN
-shopvnexpress_ShopVnExpress:VB_VBN
-shopwatch_ShopWatch:VB_VBN
-shopweb_ShopWeb:VB_VBN
-shopxanh_ShopXanh:VB_VBN
-shopyourway_ShopYourWay:VB_VBN
-shortbook_shortBook:VB_VBN
-shortcode_ShortCode:VB_VBN
-shortcut_ShortCut:VB_VBN
-shortlink_ShortLink:VB_VBN
-shortpixel_ShortPixel:VB_VBN
-shortstack_ShortStack:VB_VBN
-shoshugen_ShoshuGen:VB_VBN
-shotbymi_ShotByMi:VB_VBN
-shouldcomponentupdate_shouldComponentUpdate:VB_VBN
-shouldreturndefault_shouldReturnDefault:VB_VBN
-shoutcast_ShoutCast:VB_VBN
-shoutmeloud_ShoutMeLoud:VB_VBN
-shoutout_ShoutOut:VB_VBN
-showatch_ShoWatch:VB_VBN
-showbiz_ShowBiz:VB_VBN
-showbizdanh_showbizDanh:VB_VBN
-showdown_ShowDown:VB_VBN
-showermate_ShowerMate:VB_VBN
-showgender_showGender:VB_VBN
-showmaker_ShowMaker:VB_VBN
-showmatch_ShowMatch:VB_VBN
-showmb_ShowMB:VB_VBN
-showname_showName:VB_VBN
-showsee_ShowSee:VB_VBN
-showspray_ShowSpray:VB_VBN
-showstudio_SHOWstudio:VB_VBN
-showsuccess_showSuccess:VB_VBN
-showtext_showText:VB_VBN
-showtime_ShoWTimE:VB_VBN
-showtsubasa_showTsubasa:VB_VBN
-shplastic_SHPlastic:VB_VBN
-shplatic_SHplatic:VB_VBN
-shr_ShR:VB_VBN
-shredit_ShredIt:VB_VBN
-shrinkcolumns_shrinkColumns:VB_VBN
-shuangqi_ShuangQi:VB_VBN
-shuangqing_ShuangQing:VB_VBN
-shuangquing_ShuangQuing:VB_VBN
-shuankou_ShuanKou:VB_VBN
-shub_SHub:VB_VBN
-shubin_ShuBin:VB_VBN
-shugoshin_ShugoShin:VB_VBN
-shunteh_ShunTeh:VB_VBN
-shuntu_ShunTu:VB_VBN
-shure_SHure:VB_VBN
-shureplus_ShurePlus:VB_VBN
-shutdown_ShutDown:VB_VBN
-shutdownguard_ShutdownGuard:VB_VBN
-shutterstock_ShutterStock:VB_VBN
-shuttleflow_ShuttleFlow:VB_VBN
-shvak_ShVAK:VB_VBN
-shwemaw_ShweMaw:VB_VBN
-shyu_shYu:VB_VBN
-siaa_SiaA:VB_VBN
-siacoin_SiaCoin:VB_VBN
-siacoins_SiaCoins:VB_VBN
-siam_SiAm:VB_VBN
-siambrothers_SiamBrothers:VB_VBN
-siamsport_SiamSport:VB_VBN
-sibername_SiberName:VB_VBN
-sibodiet_SIBODiet:VB_VBN
-sibomealplan_SIBOMealPlan:VB_VBN
-siborecipe_SIBORecipe:VB_VBN
-sic_SiC:VB_VBN
-sicam_sICAM:VB_VBN
-sicbo_SicBo:VB_VBN
-sichuan_SiChuan:VB_VBN
-sick_SicK:VB_VBN
-sickbay_SickBay:VB_VBN
-sickkids_SickKids:VB_VBN
-sickleflow_SickleFlow:VB_VBN
-sickranger_SickRanger:VB_VBN
-sicoma_SiComa:VB_VBN
-sida_SiDA:VB_VBN
-sidebar_SideBar:VB_VBN
-sidebardiagnostics_SidebarDiagnostics:VB_VBN
-sidefx_SideFX:VB_VBN
-sideopening_SideOpening:VB_VBN
-sideral_SiderAl:VB_VBN
-sideslide_SideSlide:VB_VBN
-sidesync_SideSync:VB_VBN
-sideview_SideView:VB_VBN
-sidkohlitv_SidKohliTv:VB_VBN
-sidushq_SidusHQ:VB_VBN
-sieeunoob_SieeuNoob:VB_VBN
-siematic_SieMatic:VB_VBN
-siemreap_SiemReap:VB_VBN
-siemriep_SiemRiep:VB_VBN
-sieubet_SieuBet:VB_VBN
-sieucategories_SieuCategories:VB_VBN
-sieudong_SieuDong:VB_VBN
-sieuhost_SieuHost:VB_VBN
-sieuhu_SieuHu:VB_VBN
-sieumua_SieuMua:VB_VBN
-sieuno_SieuNo:VB_VBN
-sieupet_SieuPet:VB_VBN
-sieusale_SieuSale:VB_VBN
-sieusex_SieuSex:VB_VBN
-sieuthibepquangvinh_SieuThiBepQuangVinh:VB_VBN
-sieuthidemonline_SieuthidemOnline:VB_VBN
-sieuthidienmayxanh_SieuThiDienMayXanh:VB_VBN
-sieuthidiy_SieuthiDIY:VB_VBN
-sieuthihangviet_SieuthihangViet:VB_VBN
-sieuthijean_SieuThiJean:VB_VBN
-sieuthikorea_SieuthiKorea:VB_VBN
-sieuthikythuatso_SieuThiKyThuatSo:VB_VBN
-sieuthilamdep_SieuThiLamDep:VB_VBN
-sieuthitaigia_SieuThiTaiGia:VB_VBN
-sieuthitet_SieuThiTet:VB_VBN
-sieuthithungrac_SieuThiThungRac:VB_VBN
-sieuthithuocdongy_SieuThiThuocDongY:VB_VBN
-sigma_SIgma:VB_VBN
-sigmanest_SigmaNEST:VB_VBN
-signagecms_SignageCMS:VB_VBN
-signalchem_SignalChem:VB_VBN
-signalpush_SignalPush:VB_VBN
-signalr_SignalR:VB_VBN
-signatureofguest_SignatureOfGuest:VB_VBN
-signaturetm_SignatureTM:VB_VBN
-signblob_signBlob:VB_VBN
-signeasy_SignEasy:VB_VBN
-signnow_SignNow:VB_VBN
-signorev_signoreV:VB_VBN
-siguientelmht_siguienteLMHT:VB_VBN
-sigup_SigUp:VB_VBN
-sihanoukvillecoronacovid_SihanoukvilleCoronacovid:VB_VBN
-sikabit_SikaBit:VB_VBN
-sikagrout_SikaGrout:VB_VBN
-sikaplast_SikaPlast:VB_VBN
-sikapump_SikaPump:VB_VBN
-sikaswell_SikaSwell:VB_VBN
-sikatop_SikaTop:VB_VBN
-sil_SiL:VB_VBN
-sila_SiLa:VB_VBN
-silderm_SilDerm:VB_VBN
-silentfade_SilentFade:VB_VBN
-silhouetteac_silhouetteAC:VB_VBN
-silicomangan_SilicoMangan:VB_VBN
-silicon_SIlicon:VB_VBN
-siliconz_SiliconZ:VB_VBN
-silkair_SilkAir:VB_VBN
-silkamino_SilkAmino:VB_VBN
-silkid_SilkID:VB_VBN
-silkmove_SilkMove:VB_VBN
-silkroad_SilkRoad:VB_VBN
-silkstar_SilkStar:VB_VBN
-silkygirl_SilkyGirl:VB_VBN
-silkylive_SilkyLive:VB_VBN
-sillion_SilLion:VB_VBN
-silvercrest_SilverCrest:VB_VBN
-silverlance_SilverLance:VB_VBN
-silverlife_SilverLife:VB_VBN
-silversofttm_SilversoftTM:VB_VBN
-silverstar_SilverStar:VB_VBN
-silverstone_SilverStone:VB_VBN
-simacai_SiMaCai:VB_VBN
-simba_SimBa:VB_VBN
-simbooster_SimBooster:VB_VBN
-simcard_SIMcard:VB_VBN
-simcash_SimCash:VB_VBN
-simcity_SimCity:VB_VBN
-simdoanhnhan_SimDoanhNhan:VB_VBN
-simhome_SimHome:VB_VBN
-similarweb_SimilarWeb:VB_VBN
-simjacker_SimJacker:VB_VBN
-simlab_SimLab:VB_VBN
-simmayman_SimMayMan:VB_VBN
-simno_simNo:VB_VBN
-simonneetam_SimonneEtam:VB_VBN
-simplcommerce_SimplCommerce:VB_VBN
-simplecarry_SimpleCarry:VB_VBN
-simplehome_SimpleHome:VB_VBN
-simplehttpserver_SimpleHTTPServer:VB_VBN
-simplekhi_SimpleKhi:VB_VBN
-simplemic_SimpleMic:VB_VBN
-simplemind_SimpleMind:VB_VBN
-simplemoneylife_SimpleMoneyLife:VB_VBN
-simplemovement_SimpleMovement:VB_VBN
-simpleos_SimplEOS:VB_VBN
-simplepage_SimplePage:VB_VBN
-simplescripts_SimpleScripts:VB_VBN
-simplesite_SimpleSite:VB_VBN
-simpleswap_SimpleSwap:VB_VBN
-simplesync_SimpleSync:VB_VBN
-simplivity_SimpliVity:VB_VBN
-simplyclean_SimplyClean:VB_VBN
-simplygo_SimplyGo:VB_VBN
-simplyhired_SimplyHired:VB_VBN
-simplyjoy_SimplyJoy:VB_VBN
-simplypdf_SimplyPDF:VB_VBN
-simplyprotein_SimplyProtein:VB_VBN
-simplyvital_SimplyVital:VB_VBN
-simscale_SimScale:VB_VBN
-simscity_SimsCity:VB_VBN
-simsieure_SimSieuRe:VB_VBN
-simsn_SimSn:VB_VBN
-simso_SimSo:VB_VBN
-simsodep_SimSoDep:VB_VBN
-simsodepgiarevn_SimsodepgiareVN:VB_VBN
-simsovietnam_SimSoVietNam:VB_VBN
-simthanglong_SimThangLong:VB_VBN
-simulscan_SimulScan:VB_VBN
-simvep_SimvEP:VB_VBN
-simviphanoi_SimVipHaNoi:VB_VBN
-simvr_SimVR:VB_VBN
-sinaiai_SinaiAi:VB_VBN
-sinat_SiNat:VB_VBN
-sinb_SinB:VB_VBN
-sinbibi_SinBiBi:VB_VBN
-sing_SIng:VB_VBN
-singapore_SIngapore:VB_VBN
-singaporegrab_SingaporeGrab:VB_VBN
-singaporeimagine_SingapoReimagine:VB_VBN
-singaporemua_SingaporeMua:VB_VBN
-singaporesingapore_SingaporeSingapore:VB_VBN
-singcare_SingCare:VB_VBN
-singflo_SingFlo:VB_VBN
-singlemode_SingleMode:VB_VBN
-singlepage_SinglePage:VB_VBN
-singleroad_SingleRoad:VB_VBN
-singpass_SingPass:VB_VBN
-singpc_SingPC:VB_VBN
-singplay_SingPlay:VB_VBN
-singpost_SingPost:VB_VBN
-singsmart_SingSmart:VB_VBN
-singtel_SingTel:VB_VBN
-singtnhh_SingTNHH:VB_VBN
-singularitynet_SingularityNET:VB_VBN
-singuyen_SiNguyen:VB_VBN
-sinh_SInh:VB_VBN
-sinhcafe_SinhCafe:VB_VBN
-sinhclip_sinhClip:VB_VBN
-sinhconthongminh_SinhConThongMinh:VB_VBN
-sinhdc_sinhDC:VB_VBN
-sinhhot_sinhHot:VB_VBN
-sinhkhung_sinhKHUNG:VB_VBN
-sinhkinh_sinhKinh:VB_VBN
-sinhplaza_SinhPlaza:VB_VBN
-sinhstress_sinhStress:VB_VBN
-sinhsuntory_sinhSuntory:VB_VBN
-sinhtin_sinhTin:VB_VBN
-sinhvien_SinhVien:VB_VBN
-sinhvienhalan_sinhvienHaLan:VB_VBN
-sinhvienit_SinhvienIT:VB_VBN
-sinhvienshare_SinhVienShare:VB_VBN
-sinocolor_SinoColor:VB_VBN
-sinopharm_SinoPharm:VB_VBN
-sinoship_SinoShip:VB_VBN
-sinotruk_SinoTruk:VB_VBN
-sinovac_SinoVac:VB_VBN
-sinviet_SinViet:VB_VBN
-sio_SiO:VB_VBN
-sioffice_SIOffice:VB_VBN
-siogreen_SioGreen:VB_VBN
-siol_SiOL:VB_VBN
-sip_SiP:VB_VBN
-sipearl_SiPearl:VB_VBN
-siphonmax_SiphonMax:VB_VBN
-sipm_SiPM:VB_VBN
-sipower_SIPower:VB_VBN
-siptrunk_SIPtrunk:VB_VBN
-sirikit_SiriKit:VB_VBN
-sirimote_SiriMote:VB_VBN
-sirinmed_SirinMed:VB_VBN
-siriusdecutions_SiriusDecutions:VB_VBN
-siriusxm_SiriusXM:VB_VBN
-sis_SiS:VB_VBN
-sisa_SiSa:VB_VBN
-sisi_SiSi:VB_VBN
-sisindex_SiSindex:VB_VBN
-sisoft_SiSoft:VB_VBN
-sisoftware_SiSoftware:VB_VBN
-sistani_SIstani:VB_VBN
-sistivetouch_sistiveTouch:VB_VBN
-sistr_SiStr:VB_VBN
-sisu_SiSu:VB_VBN
-sita_SiTa:VB_VBN
-sitabecamex_SitaBecamex:VB_VBN
-siteadvisor_SiteAdvisor:VB_VBN
-siteaudit_SiteAudit:VB_VBN
-sitebuilderreport_SiteBuilderReport:VB_VBN
-sitebulb_SiteBulb:VB_VBN
-sitecampaign_siteCampaign:VB_VBN
-sitecheck_SiteCheck:VB_VBN
-sitecontroller_SiteController:VB_VBN
-sitedata_siteData:VB_VBN
-siteground_SiteGround:VB_VBN
-siteguarding_SiteGuarding:VB_VBN
-siteguending_SiteGuending:VB_VBN
-siteinspector_SiteInspector:VB_VBN
-siteminder_SiteMinder:VB_VBN
-siteorigin_SiteOrigin:VB_VBN
-sitepages_SitePages:VB_VBN
-sitepoint_SitePoint:VB_VBN
-siterecognition_SiteRecognition:VB_VBN
-sitesecure_SiteSecure:VB_VBN
-sitespinner_SiteSpinner:VB_VBN
-siteswe_sitesWe:VB_VBN
-sitevision_SiteVision:VB_VBN
-sitewide_SiteWide:VB_VBN
-siusop_SiuSop:VB_VBN
-sivhd_SivHD:VB_VBN
-sividuc_SiviDuc:VB_VBN
-siwon_SiWon:VB_VBN
-sixsenses_SixSenses:VB_VBN
-sixteenbar_SixteenBar:VB_VBN
-sixtyfour_SixtyFour:VB_VBN
-sixtyone_SixtyOne:VB_VBN
-siyang_SiYang:VB_VBN
-sizedbox_SizedBox:VB_VBN
-sizeeventargs_SizeEventArgs:VB_VBN
-sizeon_SizeOn:VB_VBN
-sizeup_SizeUp:VB_VBN
-sjcam_SJcam:VB_VBN
-sjia_sJIA:VB_VBN
-sjsock_SJSock:VB_VBN
-skadnetwork_SKAdNetwork:VB_VBN
-skbio_SKBio:VB_VBN
-sketchbook_SketchBook:VB_VBN
-sketchmee_SketchMee:VB_VBN
-sketchup_SketchUp:VB_VBN
-sketup_SketUp:VB_VBN
-skginseng_SKGinseng:VB_VBN
-skgold_SKGold:VB_VBN
-skhddt_skHDDT:VB_VBN
-skii_SKii:VB_VBN
-skill_SKill:VB_VBN
-skillfuture_SkillFuture:VB_VBN
-skillselect_SkillSelect:VB_VBN
-skillshop_SkillShop:VB_VBN
-skilltwins_SkillTwins:VB_VBN
-skin_SKin:VB_VBN
-skinactive_SkinActive:VB_VBN
-skinbibi_SkinBiBi:VB_VBN
-skincare_SkinCare:VB_VBN
-skincarei_SkincareI:VB_VBN
-skinceuticals_SkinCeuticals:VB_VBN
-skinclinic_SkinClinic:VB_VBN
-skinfood_SkinFood:VB_VBN
-skinfresh_SkinFresh:VB_VBN
-skinfull_SkinFull:VB_VBN
-skingenecellenmei_SkingenecellEnmei:VB_VBN
-skinguard_SkinGuard:VB_VBN
-skingym_SkinGym:VB_VBN
-skinmd_SkinMD:VB_VBN
-skinme_SkinMe:VB_VBN
-skinnyme_SkinnyMe:VB_VBN
-skinperfect_SkinPerfect:VB_VBN
-skinpro_SkinPro:VB_VBN
-skinribbon_SkinRibbon:VB_VBN
-skinshot_SkinShot:VB_VBN
-skinsiogel_SkinSiogel:VB_VBN
-skintm_SkinTM:VB_VBN
-skintouch_SkinTouch:VB_VBN
-skinwhite_SkinWhite:VB_VBN
-skipio_SKipio:VB_VBN
-skipscreen_SkipScreen:VB_VBN
-sklearn_SKlearn:VB_VBN
-skmobile_SKMobile:VB_VBN
-skrillskrill_SkrillSkrill:VB_VBN
-sktags_SKTags:VB_VBN
-sktitan_SKTitan:VB_VBN
-skullcandy_SkullCandy:VB_VBN
-skullgreymon_SkullGreymon:VB_VBN
-skumbu_sKumBu:VB_VBN
-skyactiv_SkyActiv:VB_VBN
-skyactive_SkyActive:VB_VBN
-skyair_SkyAir:VB_VBN
-skyauto_SkyAuto:VB_VBN
-skybar_SkyBar:VB_VBN
-skyblinds_SkyBlinds:VB_VBN
-skyblock_SkyBlock:VB_VBN
-skybonus_SkyBonus:VB_VBN
-skyboss_SkyBoss:VB_VBN
-skybox_SKybox:VB_VBN
-skybridge_SkyBridge:VB_VBN
-skybus_SkyBus:VB_VBN
-skycenter_SkyCenter:VB_VBN
-skycity_SkyCity:VB_VBN
-skycolor_SkyColor:VB_VBN
-skycouch_SkyCouch:VB_VBN
-skycount_SkyCount:VB_VBN
-skycourts_SkyCourts:VB_VBN
-skydeck_SkyDeck:VB_VBN
-skydragon_SkyDragon:VB_VBN
-skydrive_SkyDrive:VB_VBN
-skydriver_SkyDriver:VB_VBN
-skyerp_SkyERP:VB_VBN
-skyfall_SkyFall:VB_VBN
-skyfarm_SkyFarm:VB_VBN
-skygarden_SkyGarden:VB_VBN
-skyhawk_SkyHawk:VB_VBN
-skyhome_SKyhome:VB_VBN
-skyhouse_SkyHouse:VB_VBN
-skyitalia_SkyItalia:VB_VBN
-skyjet_SkyJet:VB_VBN
-skylake_SkyLake:VB_VBN
-skylark_SkyLark:VB_VBN
-skymall_SkyMall:VB_VBN
-skymen_SKymen:VB_VBN
-skymiles_SkyMiles:VB_VBN
-skynest_SkyNest:VB_VBN
-skynext_SkyNext:VB_VBN
-skynrg_skyNRG:VB_VBN
-skyoffice_SkyOffice:VB_VBN
-skyos_skyOS:VB_VBN
-skypark_SkyPark:VB_VBN
-skype_SKype:VB_VBN
-skypeenglish_SkypeEnglish:VB_VBN
-skypein_SkypeIn:VB_VBN
-skypeout_SkypeOut:VB_VBN
-skypixel_SkyPixel:VB_VBN
-skypoint_SkyPoint:VB_VBN
-skyport_SkyPort:VB_VBN
-skyresidences_SkyResidences:VB_VBN
-skyresidenses_SkyResidenses:VB_VBN
-skyrun_SkyRun:VB_VBN
-skyscanner_SkyScanner:VB_VBN
-skyscrapercity_SkyscraperCity:VB_VBN
-skysocial_SkySocial:VB_VBN
-skysport_SkySport:VB_VBN
-skyteam_SkyTeam:VB_VBN
-skytech_SkyTech:VB_VBN
-skyterrace_SkyTerrace:VB_VBN
-skytg_SkyTG:VB_VBN
-skytouch_SkyTouch:VB_VBN
-skytown_SkyTown:VB_VBN
-skytrax_SkyTrax:VB_VBN
-skytree_SkyTree:VB_VBN
-skytruth_SkyTruth:VB_VBN
-skyviet_SkyViet:VB_VBN
-skyview_SkyView:VB_VBN
-skywalk_SkyWalk:VB_VBN
-skywatcher_SkyWatcher:VB_VBN
-skyway_SkyWay:VB_VBN
-skywayexperts_SkyWayexperts:VB_VBN
-skywest_SkyWest:VB_VBN
-skywind_SKywind:VB_VBN
-skywirex_SkywireX:VB_VBN
-skywish_SkyWish:VB_VBN
-skyworld_SkyWorld:VB_VBN
-skyworth_SkyWorth:VB_VBN
-skyx_SkyX:VB_VBN
-skyxsport_SkyXsport:VB_VBN
-slackapp_SlackApp:VB_VBN
-slady_SLady:VB_VBN
-slalomslalom_slalomSlalom:VB_VBN
-sland_SLand:VB_VBN
-slashdata_SlashData:VB_VBN
-slashgear_SlashGear:VB_VBN
-slashgen_SlashGen:VB_VBN
-slashleaks_SlashLeaks:VB_VBN
-slaudio_SLaudio:VB_VBN
-slc_sLC:VB_VBN
-sleague_SLeague:VB_VBN
-sleek_SLeek:VB_VBN
-sleepbox_SleepBox:VB_VBN
-sleepmode_SleepMode:VB_VBN
-sleeppod_SleepPod:VB_VBN
-sleepwatch_SleepWatch:VB_VBN
-sleepwell_SleepWell:VB_VBN
-sleevecase_SleeveCase:VB_VBN
-slicedupbeef_SlicedUpBeef:VB_VBN
-slickrun_SlickRun:VB_VBN
-slide_SLide:VB_VBN
-slidedynamic_SlideDynamic:VB_VBN
-slidegameloft_SlideGameloft:VB_VBN
-slidegeeksntrang_SlideGeeksnTrang:VB_VBN
-slidehunter_SlideHunter:VB_VBN
-slidelab_SlideLab:VB_VBN
-slidelock_SlideLock:VB_VBN
-slideplus_SlidePlus:VB_VBN
-sliderocket_SlideRocket:VB_VBN
-slidertags_SliderTags:VB_VBN
-slidertouch_SliderTouch:VB_VBN
-slideshare_SlideShare:VB_VBN
-slideshowzilla_SlideshowZilla:VB_VBN
-slidetoshutdown_SlideToShutdown:VB_VBN
-slife_SLife:VB_VBN
-slim_SLim:VB_VBN
-slimads_SlimAds:VB_VBN
-slimbooster_SlimBooster:VB_VBN
-slimbrowser_SlimBrowser:VB_VBN
-slimcleaner_SlimCleaner:VB_VBN
-slimcomputer_SlimComputer:VB_VBN
-slimcrm_SlimCRM:VB_VBN
-slimeking_SlimeKing:VB_VBN
-slimemail_SlimEmail:VB_VBN
-slimeviscous_SlimeViscous:VB_VBN
-slimfit_SlimFit:VB_VBN
-slimherbal_SlimHerbal:VB_VBN
-slimjet_SlimJet:VB_VBN
-slimlipo_SlimLipo:VB_VBN
-slimpak_SlimPak:VB_VBN
-slimpaks_SlimPaks:VB_VBN
-slimright_SlimRight:VB_VBN
-slimsoft_SlimSoft:VB_VBN
-slimstat_SLIMStat:VB_VBN
-slimv_SlimV:VB_VBN
-slimvita_SlimVita:VB_VBN
-sline_SLine:VB_VBN
-slnalich_SLNAlich:VB_VBN
-slnatuy_SLNATuy:VB_VBN
-slnqhwethanh_slnqhWethanh:VB_VBN
-slot_SLot:VB_VBN
-slotcatalog_SlotCatalog:VB_VBN
-slotocash_SlotoCash:VB_VBN
-slotrank_SlotRank:VB_VBN
-slotsadviser_SlotsAdviser:VB_VBN
-slotsmillion_SlotsMillion:VB_VBN
-slotsup_SlotsUp:VB_VBN
-slovoed_SlovoEd:VB_VBN
-slugfest_SlugFest:VB_VBN
-slushpool_SlushPool:VB_VBN
-smallfire_SmallFire:VB_VBN
-smallg_SmallG:VB_VBN
-smallhd_SmallHD:VB_VBN
-smallhome_SmallHome:VB_VBN
-smallnet_SmallNET:VB_VBN
-smallpdf_SmallPDF:VB_VBN
-smallrig_SmallRig:VB_VBN
-smalltalk_SmallTalk:VB_VBN
-smarbuilding_SmarBuilding:VB_VBN
-smartactive_SmartActive:VB_VBN
-smartads_SmartAds:VB_VBN
-smartahc_SmartAHC:VB_VBN
-smartalo_SmartALO:VB_VBN
-smartamo_SmartAmo:VB_VBN
-smartand_SmartAnd:VB_VBN
-smartapp_SmartApp:VB_VBN
-smartart_SmartArt:VB_VBN
-smartasset_SmartAsset:VB_VBN
-smartband_SmartBand:VB_VBN
-smartbank_SmartBank:VB_VBN
-smartbanking_SmartBanking:VB_VBN
-smartboard_SmartBoard:VB_VBN
-smartbooks_SmartBooks:VB_VBN
-smartbox_SmartBox:VB_VBN
-smartbright_SmartBright:VB_VBN
-smartbuddy_SmartBuddy:VB_VBN
-smartbuild_SmartBuild:VB_VBN
-smartbusiness_SmartBusiness:VB_VBN
-smartbuy_SmartBuy:VB_VBN
-smartbuyvn_SmartBuyvn:VB_VBN
-smartbyte_SmartByte:VB_VBN
-smartcache_SmartCache:VB_VBN
-smartcalculator_SmartCalculator:VB_VBN
-smartcam_SmartCam:VB_VBN
-smartcamera_SmartCamera:VB_VBN
-smartcapture_SmartCapture:VB_VBN
-smartcar_SmartCar:VB_VBN
-smartcard_SmartCard:VB_VBN
-smartcash_SmartCash:VB_VBN
-smartcenter_SmartCENTER:VB_VBN
-smartcharts_SmartCharts:VB_VBN
-smartcity_SmartCity:VB_VBN
-smartclass_SmartClass:VB_VBN
-smartclean_SmartClean:VB_VBN
-smartcloud_SmartCloud:VB_VBN
-smartcluster_SmartCluster:VB_VBN
-smartcondotel_SmartCondotel:VB_VBN
-smartconnect_SmartConnect:VB_VBN
-smartcontract_SmartContract:VB_VBN
-smartcontrast_SmartContrast:VB_VBN
-smartcool_SmartCool:VB_VBN
-smartcredit_SmartCredit:VB_VBN
-smartd_SmartD:VB_VBN
-smartdesk_SmartDesk:VB_VBN
-smartdev_SmartDev:VB_VBN
-smartdever_SmartDever:VB_VBN
-smartdevicebox_SmartDeviceBox:VB_VBN
-smartdoc_SmartDoc:VB_VBN
-smartdock_SmartDock:VB_VBN
-smartdraw_SmartDraw:VB_VBN
-smartdry_SmartDry:VB_VBN
-smartebook_SmartEbook:VB_VBN
-smartecotechnology_SmartEcoTechnology:VB_VBN
-smartecotm_SmartEcoTM:VB_VBN
-smarterhq_SmarterHQ:VB_VBN
-smartexit_SmartExit:VB_VBN
-smartexttm_SmarTextTM:VB_VBN
-smartface_SmartFace:VB_VBN
-smartfactoryvn_SmartFactoryVN:VB_VBN
-smartfilte_SmartFilte:VB_VBN
-smartfinance_SmartFinance:VB_VBN
-smartfit_SmartFIT:VB_VBN
-smartflow_SmartFlow:VB_VBN
-smartfollow_SmartFollow:VB_VBN
-smartforce_SmartForce:VB_VBN
-smartforecast_SmartForecast:VB_VBN
-smartformat_SmartFormat:VB_VBN
-smartftp_SmartFTP:VB_VBN
-smartgen_SmartGen:VB_VBN
-smartgesture_SmartGesture:VB_VBN
-smartglass_SmartGlass:VB_VBN
-smartgps_SmartGPS:VB_VBN
-smartgrid_SmartGrid:VB_VBN
-smartguide_SmartGuide:VB_VBN
-smartguides_SmartGuides:VB_VBN
-smartgym_SmartGym:VB_VBN
-smarthdr_SmartHDR:VB_VBN
-smartheart_SmartHeart:VB_VBN
-smartheat_SmartHeat:VB_VBN
-smarthinq_SmarthinQ:VB_VBN
-smarthome_SmartHome:VB_VBN
-smarthomehn_SmartHomeHN:VB_VBN
-smarthomeplus_SmartHomePlus:VB_VBN
-smarthomes_SmartHomes:VB_VBN
-smarthomeshare_smarthomeShare:VB_VBN
-smarthub_SmartHub:VB_VBN
-smarthug_SmartHug:VB_VBN
-smarthustle_SmartHustle:VB_VBN
-smartid_SmartID:VB_VBN
-smartimer_SmarTimer:VB_VBN
-smartk_SmartK:VB_VBN
-smartkey_SmartKey:VB_VBN
-smartlab_SmartLab:VB_VBN
-smartland_SmartLand:VB_VBN
-smartled_SmartLED:VB_VBN
-smartlife_SmartLife:VB_VBN
-smartlight_SmartLight:VB_VBN
-smartlighting_SmartLighting:VB_VBN
-smartline_SmartLINE:VB_VBN
-smartlink_SmartLink:VB_VBN
-smartlipo_SmartLipo:VB_VBN
-smartlog_SmartLog:VB_VBN
-smartlogcan_SmartLogcan:VB_VBN
-smartlogsystem_SmartLogsystem:VB_VBN
-smartlpm_SmartLPM:VB_VBN
-smartmat_SmartMat:VB_VBN
-smartmatic_SMARTmatic:VB_VBN
-smartmedia_SmartMedia:VB_VBN
-smartmemory_SmartMemory:VB_VBN
-smartmic_SmartMic:VB_VBN
-smartmotion_SmartMotion:VB_VBN
-smartmovie_SmartMovie:VB_VBN
-smartnavi_SmartNavi:VB_VBN
-smartnet_SmartNet:VB_VBN
-smartnew_SmartNew:VB_VBN
-smartnic_SmartNIC:VB_VBN
-smartnode_SmartNode:VB_VBN
-smartobject_SmartObject:VB_VBN
-smartocr_SmartOCR:VB_VBN
-smartoffice_SmartOffice:VB_VBN
-smartone_SmartOne:VB_VBN
-smartos_SmartOS:VB_VBN
-smartosc_SmartOSC:VB_VBN
-smartotp_SmartOTP:VB_VBN
-smartpairing_SmartPairing:VB_VBN
-smartpassiveincome_SmartPassiveIncome:VB_VBN
-smartpattern_SmartPattern:VB_VBN
-smartpay_SmartPay:VB_VBN
-smartphone_SmartPhone:VB_VBN
-smartphonemaxmobile_SmartphoneMaxmobile:VB_VBN
-smartphonespixel_smartphonesPixel:VB_VBN
-smartphonestore_SmartPhoneStore:VB_VBN
-smartphonexiaomi_SmartphoneXiaomi:VB_VBN
-smartphoneyotaphone_smartphoneYotaPhone:VB_VBN
-smartphoto_SmartPhoto:VB_VBN
-smartpixel_smartPixel:VB_VBN
-smartplay_SmartPlay:VB_VBN
-smartpls_SmartPLS:VB_VBN
-smartplug_SmartPlug:VB_VBN
-smartplus_SmartPlus:VB_VBN
-smartports_SmartPorts:VB_VBN
-smartpos_SmartPOS:VB_VBN
-smartpower_SmartPower:VB_VBN
-smartprice_SmartPrice:VB_VBN
-smartpro_SmartPro:VB_VBN
-smartpss_SmartPSS:VB_VBN
-smartpumps_SmartPumps:VB_VBN
-smartpumpus_SmartPumpus:VB_VBN
-smartqos_SmartQoS:VB_VBN
-smartr_SmartR:VB_VBN
-smartrain_SmartRain:VB_VBN
-smartramp_SmartRamp:VB_VBN
-smartrealtors_SmartRealtors:VB_VBN
-smartreply_SmartReply:VB_VBN
-smartresponse_SmartResponse:VB_VBN
-smartretail_SmartRetail:VB_VBN
-smartrike_SmarTrike:VB_VBN
-smartripper_SmartRipper:VB_VBN
-smartscent_SmartScent:VB_VBN
-smartscreen_SmartScreen:VB_VBN
-smartsearh_SmartSearh:VB_VBN
-smartsenors_SmartSenors:VB_VBN
-smartsense_SmartSense:VB_VBN
-smartsensor_SmartSensor:VB_VBN
-smartseries_SmartSeries:VB_VBN
-smartshare_SmartShare:VB_VBN
-smartsheet_SmartSheet:VB_VBN
-smartsign_SmartSign:VB_VBN
-smartsigncung_SmartSigncung:VB_VBN
-smartskill_SmartSkill:VB_VBN
-smartskills_SmartSkills:VB_VBN
-smartslide_SmartSlide:VB_VBN
-smartslot_SmartSlot:VB_VBN
-smartstage_SmartStage:VB_VBN
-smartstart_SmartStart:VB_VBN
-smartstep_SmartStep:VB_VBN
-smartstream_SmartStream:VB_VBN
-smartsurface_SmartSurfACE:VB_VBN
-smartsystem_SmartSystem:VB_VBN
-smartsystems_SmartSystems:VB_VBN
-smarttag_SmartTag:VB_VBN
-smarttags_SmartTags:VB_VBN
-smartthings_SmartThings:VB_VBN
-smartthinq_SmartThinQ:VB_VBN
-smarttivi_SmartTivi:VB_VBN
-smarttone_SmartTone:VB_VBN
-smarttools_SmartTools:VB_VBN
-smarttrack_SmartTrack:VB_VBN
-smarttrader_SmartTrader:VB_VBN
-smarttv_SmartTV:VB_VBN
-smartviewer_SmartViewer:VB_VBN
-smartvision_SmartVision:VB_VBN
-smartvpn_SmartVPN:VB_VBN
-smartware_SmartWare:VB_VBN
-smartwasher_SmartWasher:VB_VBN
-smartwatch_SmartWatch:VB_VBN
-smartwebsite_SmartWebsite:VB_VBN
-smartwifi_SmartWifi:VB_VBN
-smartwood_SmartWood:VB_VBN
-smartx_SmartX:VB_VBN
-smartypanst_SmartyPanst:VB_VBN
-smartypants_SmartyPants:VB_VBN
-smartz_SmartZ:VB_VBN
-smartzhome_SmartZHome:VB_VBN
-smartzone_SmartZone:VB_VBN
-smarz_SmarZ:VB_VBN
-smashmouth_SmashMouth:VB_VBN
-smatphone_SmatPhone:VB_VBN
-smax_SMax:VB_VBN
-smcfancontrol_smcFanControl:VB_VBN
-smco_SmCo:VB_VBN
-smecare_SMECare:VB_VBN
-smedx_SMEdx:VB_VBN
-smfixer_SMFixer:VB_VBN
-smhome_SMHome:VB_VBN
-smic_SMic:VB_VBN
-smileboard_SmileBoard:VB_VBN
-smilegate_SmileGate:VB_VBN
-smilelite_SmileLite:VB_VBN
-smilenuts_SmileNuts:VB_VBN
-smiletravel_SmileTravel:VB_VBN
-smileyworld_SmileyWorld:VB_VBN
-smiser_SMISer:VB_VBN
-smisers_SMISers:VB_VBN
-smitemarker_SmiteMarker:VB_VBN
-smithkline_SmithKline:VB_VBN
-smithmaran_SmithMaran:VB_VBN
-smithsonian_SmithSonian:VB_VBN
-smlogis_SMLogis:VB_VBN
-smobile_SMobile:VB_VBN
-smod_SMod:VB_VBN
-smofkabiven_SmofKabiven:VB_VBN
-smoflipid_SMOFlipid:VB_VBN
-smoktech_SMOKTech:VB_VBN
-smoothe_SmoothE:VB_VBN
-smoothgrip_SmoothGrip:VB_VBN
-smoothiev_SmoothieV:VB_VBN
-smoothstream_SmoothStream:VB_VBN
-smoothtrack_SmoothTrack:VB_VBN
-smplayer_SMPlayer:VB_VBN
-sms_SmS:VB_VBN
-smsauthconfig_SmsAuthConfig:VB_VBN
-smsbanking_SMSBanking:VB_VBN
-smsbongda_SMSBongDa:VB_VBN
-smsdirect_SMSDirect:VB_VBN
-smseagle_SMSEagle:VB_VBN
-smsgo_SmsGo:VB_VBN
-smsgroup_SMSGroup:VB_VBN
-smspro_SMSPro:VB_VBN
-smstin_SMSTin:VB_VBN
-smstrieuniemvui_SmsTrieuNiemVui:VB_VBN
-smtfly_SMTfly:VB_VBN
-smucox_SMUCox:VB_VBN
-smugmug_SmugMug:VB_VBN
-smushit_SmushIt:VB_VBN
-snackinu_SnackInu:VB_VBN
-snagit_SnagIt:VB_VBN
-snailgame_SnailGame:VB_VBN
-snaodragon_SnaoDragon:VB_VBN
-snapback_SnapBack:VB_VBN
-snapbox_SnapBox:VB_VBN
-snapbridge_SnapBridge:VB_VBN
-snapchat_SnapChat:VB_VBN
-snapdragon_SnapDragon:VB_VBN
-snapdrangon_SnapDrangon:VB_VBN
-snapex_SnapEx:VB_VBN
-snaphappen_SnapHappen:VB_VBN
-snapnews_SnapNews:VB_VBN
-snapseed_SnapSeed:VB_VBN
-snapshot_SnapShot:VB_VBN
-snaptik_SnapTik:VB_VBN
-snaptube_SnapTube:VB_VBN
-snb_SnB:VB_VBN
-snbcan_SNBcan:VB_VBN
-sndway_SNDWay:VB_VBN
-snes_SNeS:VB_VBN
-sngv_SNgV:VB_VBN
-snippetsau_SnippetSau:VB_VBN
-snippetsvisual_SnippetsVisual:VB_VBN
-sno_SnO:VB_VBN
-snowball_SnowBall:VB_VBN
-snowfox_SnowFox:VB_VBN
-snowhu_SnowHu:VB_VBN
-snowtown_SnowTown:VB_VBN
-snowwolf_SnowWolf:VB_VBN
-snuggleup_SnuggleUp:VB_VBN
-snusnu_SnuSnu:VB_VBN
-sny_SnY:VB_VBN
-soapquy_SoapQuy:VB_VBN
-soapui_SoapUI:VB_VBN
-soaz_sOAZ:VB_VBN
-soc_SoC:VB_VBN
-socailoomph_SocailOomph:VB_VBN
-socbay_SocBay:VB_VBN
-soccerking_SoccerKing:VB_VBN
-soccerscore_SoccerScore:VB_VBN
-socialbaker_SocialBaker:VB_VBN
-socialbakers_SocialBakers:VB_VBN
-socialblade_SocialBlade:VB_VBN
-socialclerks_SocialClerks:VB_VBN
-socialfeed_SocialFeed:VB_VBN
-socialflow_SocialFlow:VB_VBN
-socialheat_SocialHeat:VB_VBN
-socialmediablogster_SocialMediaBlogster:VB_VBN
-socialone_SocialOne:VB_VBN
-socialpet_SocialPet:VB_VBN
-socialpeta_SocialPeta:VB_VBN
-socialreviver_SocialReviver:VB_VBN
-socialscope_SocialScope:VB_VBN
-socialtraining_SocialTraining:VB_VBN
-societyleave_SocietyLeave:VB_VBN
-socketalter_socketALTER:VB_VBN
-socketcan_SocketCAN:VB_VBN
-socketchannel_SocketChannel:VB_VBN
-socketserver_SocketServer:VB_VBN
-socola_socolA:VB_VBN
-socross_soCross:VB_VBN
-socthousashop_SocthoUSAshop:VB_VBN
-socvip_SocVip:VB_VBN
-socvps_SocVPS:VB_VBN
-sodabanking_sodaBanking:VB_VBN
-sodepviettel_SoDepViettel:VB_VBN
-sodo_SoDo:VB_VBN
-soeul_SoEul:VB_VBN
-soeurcon_SoeurCon:VB_VBN
-sofa_SoFa:VB_VBN
-sofamix_SofaMix:VB_VBN
-sofathienminh_SofaThienMinh:VB_VBN
-sofavn_SofaVN:VB_VBN
-sofaz_SofaZ:VB_VBN
-sofi_SoFi:VB_VBN
-sofia_SoFIA:VB_VBN
-sofiaviet_SofiaViet:VB_VBN
-soflitq_SoflitQ:VB_VBN
-sofm_SofM:VB_VBN
-softac_SoftAC:VB_VBN
-softaview_SoftAView:VB_VBN
-softbank_SoftBank:VB_VBN
-softbut_softBut:VB_VBN
-softclippingtm_SoftClippingTM:VB_VBN
-softclix_SoftClix:VB_VBN
-softclose_SoftClose:VB_VBN
-softdeletes_SoftDeletes:VB_VBN
-softdream_SoftDream:VB_VBN
-softdreams_SoftDreams:VB_VBN
-softether_SoftEther:VB_VBN
-softex_SofTex:VB_VBN
-softgamings_SoftGamings:VB_VBN
-softlayer_SoftLayer:VB_VBN
-softlite_SoftLite:VB_VBN
-softopening_SoftOpening:VB_VBN
-softouch_SofTouch:VB_VBN
-softpad_SoftPad:VB_VBN
-softperfect_SoftPerfect:VB_VBN
-softpos_SoftPos:VB_VBN
-softstuds_SoftStuds:VB_VBN
-softswiss_SoftSwiss:VB_VBN
-softwaredistribution_SoftwareDistribution:VB_VBN
-softwater_SoftWater:VB_VBN
-softwear_SoftWear:VB_VBN
-sogagame_SogaGame:VB_VBN
-sogou_SoGou:VB_VBN
-sogrape_SoGrape:VB_VBN
-sogreen_SoGREEN:VB_VBN
-soha_SoHa:VB_VBN
-sohacare_SohaCare:VB_VBN
-sohacoin_SohaCoin:VB_VBN
-sohagame_SohaGame:VB_VBN
-sohapay_SohaPay:VB_VBN
-soherbs_soHERBs:VB_VBN
-soho_SoHo:VB_VBN
-sohotech_SohoTech:VB_VBN
-sohotel_SoHotel:VB_VBN
-sohyang_SoHyang:VB_VBN
-soicauviet_SoiCauViet:VB_VBN
-soicauvn_SoiCauVN:VB_VBN
-soict_SoICT:VB_VBN
-soidu_SoiDu:VB_VBN
-soigiareviews_SoigiaReviewS:VB_VBN
-soikeoaz_SoikeoAZ:VB_VBN
-soikeoio_SoiKeoIO:VB_VBN
-soikeoplus_SoiKeoPlus:VB_VBN
-soikeotot_SoiKeoTot:VB_VBN
-soikeotv_SoikeoTV:VB_VBN
-soiphukienpc_soiPhukienpc:VB_VBN
-sojin_SoJin:VB_VBN
-sokany_SoKaNy:VB_VBN
-sol_SoL:VB_VBN
-solantech_SolanTech:VB_VBN
-solarbk_SolarBK:VB_VBN
-solarcity_SolarCity:VB_VBN
-solarcon_SOLARcon:VB_VBN
-solare_SolarE:VB_VBN
-solaredge_SolarEdge:VB_VBN
-solarflat_SolarFlat:VB_VBN
-solarfree_SolarFree:VB_VBN
-solargates_SolarGATES:VB_VBN
-solargis_SolarGis:VB_VBN
-solarglow_SolarGlow:VB_VBN
-solarhu_SolarHu:VB_VBN
-solarligh_SolarLigh:VB_VBN
-solarnation_SolarNation:VB_VBN
-solarv_SolarV:VB_VBN
-solarwinds_SolarWinds:VB_VBN
-solarworld_SolarWorld:VB_VBN
-solarzone_SolarZone:VB_VBN
-solax_SolaX:VB_VBN
-solbridge_SolBridge:VB_VBN
-solbrigde_SolBrigde:VB_VBN
-soldout_SoldOut:VB_VBN
-solematch_SoleMatch:VB_VBN
-soleseat_SoleSeat:VB_VBN
-solesociety_SoleSociety:VB_VBN
-solfflex_SolfFlex:VB_VBN
-solid_SOLiD:VB_VBN
-solidcam_SolidCAM:VB_VBN
-solidchrome_SolidChrome:VB_VBN
-solidnetwork_SolidNetWork:VB_VBN
-solidplant_SolidPlant:VB_VBN
-solids_SolidS:VB_VBN
-solidsquad_SolidSQUAD:VB_VBN
-solidsquadloaderenabler_SolidSQUADLoaderEnabler:VB_VBN
-solidsteel_SolidSteel:VB_VBN
-solidtrustpay_SolidTrustPay:VB_VBN
-solidwork_SolidWork:VB_VBN
-solidworks_SolidWorks:VB_VBN
-solidx_SolidX:VB_VBN
-solitairesolitaire_SolitaireSolitaire:VB_VBN
-solitarybee_SolitaryBee:VB_VBN
-solji_SolJi:VB_VBN
-solo_SoLo:VB_VBN
-solocast_SoloCast:VB_VBN
-sololaure_SoloLaure:VB_VBN
-sololearn_SoloLearn:VB_VBN
-sololeveling_SoloLeveling:VB_VBN
-solomid_SoloMid:VB_VBN
-solostar_SoloStar:VB_VBN
-solovevdizayner_SolovevDizayner:VB_VBN
-solusvm_SolusVM:VB_VBN
-solutionmarine_SolutionMarine:VB_VBN
-solutionsmore_solutionsMore:VB_VBN
-solutionwifi_SolutionWifi:VB_VBN
-somay_SoMay:VB_VBN
-somaymanabxl_SoMayManABXL:VB_VBN
-someadminuser_someAdminUser:VB_VBN
-somebymi_SomeByMi:VB_VBN
-somemethod_someMethod:VB_VBN
-somipress_SOMIPress:VB_VBN
-sommeill_SommeilL:VB_VBN
-somove_SoMove:VB_VBN
-sonaly_SonaLy:VB_VBN
-sonanh_SonAnh:VB_VBN
-sonbus_SonBus:VB_VBN
-soncamedia_SoncaMedia:VB_VBN
-sonclip_SonClip:VB_VBN
-sonderjyske_SonderjyskE:VB_VBN
-sonespoir_SonEspoir:VB_VBN
-song_SOng:VB_VBN
-songanhlogs_SongAnhLogs:VB_VBN
-songbook_SongBook:VB_VBN
-songfreaks_SongFreaks:VB_VBN
-songgenie_SongGenie:VB_VBN
-songhong_SongHong:VB_VBN
-songkong_SongKong:VB_VBN
-songkran_SongKran:VB_VBN
-songla_SongLa:VB_VBN
-songli_SongLi:VB_VBN
-songma_SongMa:VB_VBN
-songnguyen_SongNguyen:VB_VBN
-songnguyentravel_SongNguyenTravel:VB_VBN
-songoaivu_SoNgoaivu:VB_VBN
-songohan_SonGoHan:VB_VBN
-songoku_SonGoKu:VB_VBN
-songpal_SongPal:VB_VBN
-songpha_SongPha:VB_VBN
-songshift_SongShift:VB_VBN
-songspace_SongSpace:VB_VBN
-songthanhcong_SongThanhCong:VB_VBN
-songthao_SongThao:VB_VBN
-songtredep_SongTreDep:VB_VBN
-songvietlaptop_SongvietLaptop:VB_VBN
-songvuikhoe_SongVuiKhoe:VB_VBN
-songwol_SongWol:VB_VBN
-songyi_SongYi:VB_VBN
-sonhuong_SonHuong:VB_VBN
-sonicare_SoniCare:VB_VBN
-sonice_SoNice:VB_VBN
-sonicexpert_SonicExpert:VB_VBN
-sonicfox_SonicFox:VB_VBN
-sonicguard_SonicGuard:VB_VBN
-sonicmaster_SonicMaster:VB_VBN
-sonicmode_SonicMode:VB_VBN
-sonicwall_SonicWALL:VB_VBN
-sonkim_SonKim:VB_VBN
-sonmac_sonMac:VB_VBN
-sonorender_SonoRender:VB_VBN
-sonoscape_SonoScape:VB_VBN
-sontruong_SonTruong:VB_VBN
-sonvip_SonVip:VB_VBN
-sonweb_SonWeb:VB_VBN
-sonymusic_SonyMusic:VB_VBN
-sonyviet_SonyViet:VB_VBN
-sonyxperia_SonyXperia:VB_VBN
-sonzim_SonZim:VB_VBN
-soobin_SooBin:VB_VBN
-soojung_SooJung:VB_VBN
-sooksiam_SookSiam:VB_VBN
-soonhari_SoonHari:VB_VBN
-soonhyung_SoonHyung:VB_VBN
-soonr_SoonR:VB_VBN
-soorin_SooRin:VB_VBN
-soorng_SOOrng:VB_VBN
-sooyeon_SooYeon:VB_VBN
-sop_SoP:VB_VBN
-sopakvina_SopakVina:VB_VBN
-sopcast_SopCast:VB_VBN
-sopha_SOpha:VB_VBN
-soptech_SopTech:VB_VBN
-sorentocamera_SorentoCamera:VB_VBN
-sortby_sortBy:VB_VBN
-sortedmap_SortedMap:VB_VBN
-sorttcvn_SortTcvn:VB_VBN
-sortwith_sortWith:VB_VBN
-sos_SoS:VB_VBN
-sosanhgia_SoSanhGia:VB_VBN
-sosdol_SOSDol:VB_VBN
-soshareit_SoShareIT:VB_VBN
-soshop_SOshop:VB_VBN
-sosmart_SoSmart:VB_VBN
-sosvmax_sosvMax:VB_VBN
-sot_SoT:VB_VBN
-sotaikhoan_SoTaikhoan:VB_VBN
-sotaykhoedep_SoTayKhoeDep:VB_VBN
-sotc_SoTC:VB_VBN
-sotret_SotRet:VB_VBN
-sotttt_SoTTTT:VB_VBN
-souche_SouChe:VB_VBN
-soudcloud_SoudCloud:VB_VBN
-soulcalibur_SoulCalibur:VB_VBN
-soulcycle_SoulCycle:VB_VBN
-soulmate_SoulMate:VB_VBN
-soulofsagittarius_SoulOfSagittarius:VB_VBN
-soulsilver_SoulSilver:VB_VBN
-soulworker_SoulWorker:VB_VBN
-sounblaster_SounBlaster:VB_VBN
-souncraft_SounCraft:VB_VBN
-soundalive_SoundAlive:VB_VBN
-soundbar_SoundBar:VB_VBN
-soundblade_SoundBlade:VB_VBN
-soundboost_SoundBoost:VB_VBN
-soundbox_SoundBox:VB_VBN
-soundbuds_SoundBuds:VB_VBN
-soundcard_SoundCard:VB_VBN
-soundcards_SoundCards:VB_VBN
-soundcasting_SoundCasting:VB_VBN
-soundclear_SoundClear:VB_VBN
-soundcloud_SoundCloud:VB_VBN
-soundcore_SoundCore:VB_VBN
-soundcraft_SoundCraft:VB_VBN
-sounddevice_SoundDevice:VB_VBN
-sounddna_SoundDNA:VB_VBN
-soundflow_SoundFlow:VB_VBN
-soundgear_SoundGear:VB_VBN
-soundguys_SoundGuys:VB_VBN
-soundhound_SoundHound:VB_VBN
-soundlink_SoundLink:VB_VBN
-soundmagc_SoundMAGC:VB_VBN
-soundmagic_SoundMagic:VB_VBN
-soundmax_SoundMax:VB_VBN
-soundoff_SoundOff:VB_VBN
-soundpeats_SoundPEATS:VB_VBN
-soundplus_SoundPlus:VB_VBN
-soundpump_SoundPump:VB_VBN
-soundshare_SoundShare:VB_VBN
-soundsport_SoundSport:VB_VBN
-soundstandard_SoundStandard:VB_VBN
-soundstation_SoundStation:VB_VBN
-soundsteer_SoundSteer:VB_VBN
-soundsticks_SoundSticks:VB_VBN
-soundstructure_SoundStructure:VB_VBN
-soundtm_SoundTM:VB_VBN
-soundtop_SoundTop:VB_VBN
-soundtouch_SoundTouch:VB_VBN
-soundtrack_SoundTrack:VB_VBN
-soundtrax_SoundTrax:VB_VBN
-soundwear_SoundWear:VB_VBN
-soungwoong_SoungWoong:VB_VBN
-sour_SOur:VB_VBN
-sourcearray_SourceArray:VB_VBN
-sourceclockperiod_SourceClockPeriod:VB_VBN
-sourceforge_SourceForge:VB_VBN
-sourcerect_sourceRect:VB_VBN
-sourcetm_SourceTM:VB_VBN
-sourcetree_SourceTree:VB_VBN
-sourcis_SOURCiS:VB_VBN
-sousousuki_SouSouSuki:VB_VBN
-southamptonleeds_SouthamptonLeeds:VB_VBN
-southbeach_SouthBeach:VB_VBN
-southcity_SouthCity:VB_VBN
-southernbank_SouthernBank:VB_VBN
-southernhomes_SouthernHomes:VB_VBN
-southfront_SouthFront:VB_VBN
-southgate_SouthGate:VB_VBN
-southstar_SouthStar:VB_VBN
-southxchange_SouthXchange:VB_VBN
-sovecon_SovEcon:VB_VBN
-sovereignwallet_SovereignWallet:VB_VBN
-soyacincau_SoyaCincau:VB_VBN
-soyaoil_SoyaOil:VB_VBN
-soyeon_SoYeon:VB_VBN
-soyou_SoYou:VB_VBN
-soyoung_SoYoung:VB_VBN
-spa_SpA:VB_VBN
-spacafe_SpaCafe:VB_VBN
-space_SPace:VB_VBN
-spacedeck_SPACEdeck:VB_VBN
-spacededicated_SpaceDedicated:VB_VBN
-spacefit_SpaceFit:VB_VBN
-spacemax_SpaceMax:VB_VBN
-spacemouse_SpaceMouse:VB_VBN
-spacenavigator_SpaceNavigator:VB_VBN
-spacenews_SpaceNews:VB_VBN
-spacerunner_SpaceRunner:VB_VBN
-spacescribble_SpaceScribble:VB_VBN
-spaceshare_SpaceShare:VB_VBN
-spaceshipone_SpaceShipOne:VB_VBN
-spaceshiptwo_SpaceShipTwo:VB_VBN
-spacespeaker_SpaceSpeaker:VB_VBN
-spacespeakers_SpaceSpeakers:VB_VBN
-spacev_SpaceV:VB_VBN
-spacex_SpaceX:VB_VBN
-spadegaming_SpadeGaming:VB_VBN
-spaghettibox_SpaghettiBox:VB_VBN
-spalaluong_SpaLaluong:VB_VBN
-spam_SPam:VB_VBN
-spamassassin_SpamAssassin:VB_VBN
-spamblocker_SpamBlocker:VB_VBN
-spambox_SpamBox:VB_VBN
-spamcop_SpamCop:VB_VBN
-spamer_SPAMer:VB_VBN
-spamkiller_SpamKiller:VB_VBN
-spammer_SPAMmer:VB_VBN
-spamnick_SpamNick:VB_VBN
-spamscore_SpamScore:VB_VBN
-spamshield_SpamShield:VB_VBN
-spanbert_SpanBERT:VB_VBN
-spandexsize_SPANDEXSize:VB_VBN
-spanghe_spaNghe:VB_VBN
-spankchain_SpankChain:VB_VBN
-spankpay_SpankPay:VB_VBN
-spantech_SpanTech:VB_VBN
-spaquy_spaQuy:VB_VBN
-sparekey_SpareKey:VB_VBN
-sparelast_spareLast:VB_VBN
-sparitual_SpaRitual:VB_VBN
-spark_SPark:VB_VBN
-sparkchain_SparkChain:VB_VBN
-sparkdefi_SparkDeFi:VB_VBN
-sparklearn_SparkLearn:VB_VBN
-sparklestock_SparkleStock:VB_VBN
-sparkpoint_SparkPoint:VB_VBN
-sparkpost_SparkPost:VB_VBN
-sparktoro_SparkToro:VB_VBN
-sparoom_SpaRoom:VB_VBN
-sparpoint_SparPoint:VB_VBN
-sparrowskid_SparrowsKid:VB_VBN
-sparseadd_SparseAdd:VB_VBN
-sparsebooleanarray_SparseBooleanArray:VB_VBN
-sparsetensor_SparseTensor:VB_VBN
-sparsetensorsmap_SparseTensorsMap:VB_VBN
-spartabeer_SpartaBeer:VB_VBN
-spartace_SpartAce:VB_VBN
-spartanrace_SpartanRace:VB_VBN
-spasteam_SpaSteam:VB_VBN
-spatag_SpaTag:VB_VBN
-spathanhmai_SpaThanhMai:VB_VBN
-spatialnote_SpatialNote:VB_VBN
-spatin_spaTin:VB_VBN
-spatropic_SpaTropic:VB_VBN
-spaxex_SpaxeX:VB_VBN
-spazone_SpaZone:VB_VBN
-spbook_SPBook:VB_VBN
-speakerwireless_speakerWireless:VB_VBN
-speakingleave_SpeakingLeave:VB_VBN
-speakingpal_SpeakingPal:VB_VBN
-speakon_speakON:VB_VBN
-speakonly_SpeakOnly:VB_VBN
-speakpipe_SpeakPipe:VB_VBN
-speakup_SpeakUp:VB_VBN
-specfp_SPECfp:VB_VBN
-speciesi_speciesI:VB_VBN
-specopselite_SpecOpsElite:VB_VBN
-spectrakia_SpectraKia:VB_VBN
-spectralight_SpectraLight:VB_VBN
-specviewperf_SPECviewperf:VB_VBN
-speechtek_SpeechTEK:VB_VBN
-speed_SpeeD:VB_VBN
-speedcharlotte_SpeedCharlotte:VB_VBN
-speedclean_SpeedClean:VB_VBN
-speedcrunch_SpeedCrunch:VB_VBN
-speeddevil_SpeedDevil:VB_VBN
-speedex_SpeedEx:VB_VBN
-speedexpert_speedExpert:VB_VBN
-speedface_SpeedFace:VB_VBN
-speedfan_SpeedFan:VB_VBN
-speedfusion_SpeedFusion:VB_VBN
-speedl_SpeedL:VB_VBN
-speedlink_SpeedLink:VB_VBN
-speedmaint_SpeedMaint:VB_VBN
-speedmaxpc_SpeedMaxPc:VB_VBN
-speedmesh_SpeedMesh:VB_VBN
-speedmoto_SpeedMoto:VB_VBN
-speedome_SpeeDome:VB_VBN
-speedperfect_SpeedPerfect:VB_VBN
-speedperinf_speedPerinf:VB_VBN
-speedpro_SpeedPro:VB_VBN
-speedtest_SpeedTest:VB_VBN
-speedwash_SpeedWash:VB_VBN
-speedyads_SpeedyAds:VB_VBN
-speedykvm_SpeedyKVM:VB_VBN
-speeryamaha_SpeerYamaha:VB_VBN
-spen_SPen:VB_VBN
-spendingpulse_SpendingPulse:VB_VBN
-spermq_SpermQ:VB_VBN
-spgetnumber_spGetNumber:VB_VBN
-sphere_SPhere:VB_VBN
-spherestm_SpheresTM:VB_VBN
-sphereview_SphereView:VB_VBN
-spicejet_SpiceJet:VB_VBN
-spicyplumpers_SpicyPlumpers:VB_VBN
-spiderfx_SpiderFx:VB_VBN
-spiderman_SpiderMan:VB_VBN
-spinachis_SpinachIs:VB_VBN
-spinatorium_SpinAtorium:VB_VBN
-spinbooster_SpinBooster:VB_VBN
-spincontrol_SpinControl:VB_VBN
-spineditor_SpinEditor:VB_VBN
-spinflow_SpinFlow:VB_VBN
-spinmop_SpinMop:VB_VBN
-spinnermodel_SpinnerModel:VB_VBN
-spiralfibers_SpiralFibers:VB_VBN
-spiraxsarco_SpiraxSarco:VB_VBN
-spiremt_SpireMT:VB_VBN
-spiritbank_SpiritBank:VB_VBN
-splashcontrol_SplashControl:VB_VBN
-splashscreen_SplashScreen:VB_VBN
-splitcam_SplitCam:VB_VBN
-splus_SPlus:VB_VBN
-spma_spMa:VB_VBN
-spobio_SpoBio:VB_VBN
-spobiosos_SpobioSOS:VB_VBN
-spoiler_SPoiler:VB_VBN
-spokelit_SpokeLit:VB_VBN
-spongebob_SpongeBob:VB_VBN
-spongeobobppppp_SpongeobobPPPPP:VB_VBN
-sporopay_SporoPay:VB_VBN
-sport_SPort:VB_VBN
-sportbike_SportBike:VB_VBN
-sportbild_SportBild:VB_VBN
-sportcity_SportCity:VB_VBN
-sportclassic_SportClassic:VB_VBN
-sportdanh_SPORTDanh:VB_VBN
-sportdeck_SPORTdeck:VB_VBN
-sportmail_SportMail:VB_VBN
-sportmediaset_SportMediaset:VB_VBN
-sportmitsubishi_SportMitsubishi:VB_VBN
-sportonline_SportOnline:VB_VBN
-sportplus_SportPlus:VB_VBN
-sports_SPorts:VB_VBN
-sportsaccess_SportsAccess:VB_VBN
-sportsbar_SportsBar:VB_VBN
-sportsbookreview_SportsbookReview:VB_VBN
-sportscamp_SportsCamp:VB_VBN
-sportscenter_SportsCenter:VB_VBN
-sportsdirect_SportsDirect:VB_VBN
-sportsexxonmobil_SportsExxonMobil:VB_VBN
-sportslink_SportsLink:VB_VBN
-sportsmania_SportsMania:VB_VBN
-sportspress_SportsPress:VB_VBN
-sportspro_SportsPro:VB_VBN
-sportsquy_SportsQuy:VB_VBN
-sportstheo_SportsTheo:VB_VBN
-sportstrong_SportsTrong:VB_VBN
-sportstyle_SportStyle:VB_VBN
-sporttags_sportTags:VB_VBN
-sporttouring_SportTouring:VB_VBN
-sporttrong_SportTrong:VB_VBN
-sporttv_SportTV:VB_VBN
-sportx_SportX:VB_VBN
-spotbright_SpotBright:VB_VBN
-spotjobs_SpotJobs:VB_VBN
-spotlight_SpotLight:VB_VBN
-spotmini_SpotMini:VB_VBN
-spotoption_SpotOption:VB_VBN
-spotpass_SpotPass:VB_VBN
-sppextcomobjhook_SppExtComObjHook:VB_VBN
-spreadsheet_SpreadSheet:VB_VBN
-spreadsheetml_SpreadsheetML:VB_VBN
-sprin_SprIn:VB_VBN
-spring_SPring:VB_VBN
-springboard_SpringBoard:VB_VBN
-springboardseo_SpringBoardSEO:VB_VBN
-springboot_SpringBoot:VB_VBN
-springd_SpringD:VB_VBN
-springhill_SpringHill:VB_VBN
-springleaf_SpringLeaf:VB_VBN
-springo_SprinGO:VB_VBN
-sprint_SprInt:VB_VBN
-sprintray_SprintRay:VB_VBN
-spro_SPro:VB_VBN
-spsmart_SPSmart:VB_VBN
-sptest_SPTest:VB_VBN
-spvgg_SpVgg:VB_VBN
-spyagent_SpyAgent:VB_VBN
-spybot_SpyBot:VB_VBN
-spycam_SpyCam:VB_VBN
-spyderx_SpyderX:VB_VBN
-spyeye_SpyEye:VB_VBN
-spyfu_SpyFu:VB_VBN
-spyglass_SpyGlass:VB_VBN
-spyhunter_SpyHunter:VB_VBN
-spytoapp_SpyToApp:VB_VBN
-sqladvice_SQLAdvice:VB_VBN
-sqlcipher_SQLCipher:VB_VBN
-sqlcow_SQLcow:VB_VBN
-sqlcpher_SQLCpher:VB_VBN
-sqldataadapter_SqlDataAdapter:VB_VBN
-sqlexception_SQLException:VB_VBN
-sqlite_SQLite:VB_VBN
-sqlitedb_SQlitedb:VB_VBN
-sqlmap_SQLmap:VB_VBN
-sqlnettiersprovider_SqlNetTiersProvider:VB_VBN
-sqlserver_SQLServer:VB_VBN
-square_SQuare:VB_VBN
-squareenix_SquareEnix:VB_VBN
-squarepants_SquarePants:VB_VBN
-squarespace_SquareSpace:VB_VBN
-squaretrade_SquareTrade:VB_VBN
-squashfs_SquashFS:VB_VBN
-sque_SQue:VB_VBN
-squeezecenter_SqueezeCenter:VB_VBN
-squidanalyzer_SquidAnalyzer:VB_VBN
-squidhup_SquidHup:VB_VBN
-srccompat_srcCompat:VB_VBN
-sreg_SReg:VB_VBN
-srgb_sRGB:VB_VBN
-srihome_SriHome:VB_VBN
-srilanca_SriLanca:VB_VBN
-srilanka_SriLanka:VB_VBN
-srilankan_SriLankan:VB_VBN
-sro_sRO:VB_VBN
-sroptionset_SROptionSet:VB_VBN
-srpb_SrPB:VB_VBN
-srware_SRWare:VB_VBN
-srwatch_SRwatch:VB_VBN
-ssal_SSal:VB_VBN
-ssam_SSam:VB_VBN
-ssangyong_SsangYong:VB_VBN
-ssao_SSao:VB_VBN
-ssdlife_SSDLife:VB_VBN
-ssevent_SSevent:VB_VBN
-ssgalaxy_SSGalaxy:VB_VBN
-ssgarden_SSGarden:VB_VBN
-ssggroup_SSGgroup:VB_VBN
-ssgroup_SSGroup:VB_VBN
-ssharing_SSharing:VB_VBN
-ssiw_ssiW:VB_VBN
-sslexception_SSLException:VB_VBN
-ssltrust_SSLTrust:VB_VBN
-ssop_SSop:VB_VBN
-ssris_SSRis:VB_VBN
-ssrna_ssRNA:VB_VBN
-ssshadow_SSShadow:VB_VBN
-sssniperwolf_SSSniperWolf:VB_VBN
-ssstutter_SSStutter:VB_VBN
-sstruyen_SStruyen:VB_VBN
-ssuite_SSuite:VB_VBN
-stablecoinlbx_StablecoinLBX:VB_VBN
-stablecoinstablecoin_stablecoinStablecoin:VB_VBN
-stablehost_StableHost:VB_VBN
-stableprofit_StableProfit:VB_VBN
-stablesmart_StableSmart:VB_VBN
-stabnet_StabNet:VB_VBN
-stackblitz_StackBlitz:VB_VBN
-stackexchange_StackExchange:VB_VBN
-stackiq_StackIQ:VB_VBN
-stackiqtmcluster_StackIQTMCluster:VB_VBN
-stackpanel_StackPanel:VB_VBN
-stackpower_StackPower:VB_VBN
-stackstorm_StackStorm:VB_VBN
-stacktrace_StackTrace:VB_VBN
-stackwise_StackWise:VB_VBN
-staffcop_StaffCop:VB_VBN
-stagefright_StageFright:VB_VBN
-stageline_StageLine:VB_VBN
-stahlwille_STAHLWille:VB_VBN
-stainexpert_StainExpert:VB_VBN
-stainmaster_StainMaster:VB_VBN
-staircase_StairCase:VB_VBN
-stairway_StairWay:VB_VBN
-stakelogic_StakeLogic:VB_VBN
-stakeproof_stakeProof:VB_VBN
-stanhomevn_StanhomeVN:VB_VBN
-stanleypark_StanleyPark:VB_VBN
-star_STar:VB_VBN
-starban_starBan:VB_VBN
-starboyvn_StarboyVN:VB_VBN
-starbuck_StarBuck:VB_VBN
-starbucks_StarBucks:VB_VBN
-starcare_StarCARE:VB_VBN
-starcasino_StarCasino:VB_VBN
-starcemt_StarCemt:VB_VBN
-starchesfatsfruits_starchesFatsFruits:VB_VBN
-starcity_StarCity:VB_VBN
-starclub_StarClub:VB_VBN
-starcodec_StarCodec:VB_VBN
-starcraft_StarCraft:VB_VBN
-stardict_StarDict:VB_VBN
-stardust_StarDust:VB_VBN
-starfire_StarFire:VB_VBN
-starfood_StarFOOD:VB_VBN
-starfoods_StarFoods:VB_VBN
-stargalaxy_StarGalaxy:VB_VBN
-starglobal_StarGlobal:VB_VBN
-starhd_StarHD:VB_VBN
-starhill_StarHill:VB_VBN
-starhub_StarHub:VB_VBN
-starkids_StarKids:VB_VBN
-starkleave_StarkLeave:VB_VBN
-starladder_StarLadder:VB_VBN
-starlake_StarLake:VB_VBN
-starlight_StarLight:VB_VBN
-starlink_StarLink:VB_VBN
-starmart_StarMart:VB_VBN
-starmeter_STARMeter:VB_VBN
-starmovies_StarMovies:VB_VBN
-starnet_StarNet:VB_VBN
-starnews_StarNews:VB_VBN
-staroffice_StarOffice:VB_VBN
-starpoint_StarPoint:VB_VBN
-starpower_StarPower:VB_VBN
-starprog_StarProg:VB_VBN
-starrun_starRun:VB_VBN
-stars_STaRS:VB_VBN
-starsboba_StarsBoba:VB_VBN
-starseries_StarSeries:VB_VBN
-starsport_StarSport:VB_VBN
-starstruck_StarStruck:VB_VBN
-startalk_StarTalk:VB_VBN
-startbooking_StartBooking:VB_VBN
-startbooks_StartBooks:VB_VBN
-startcounter_StartCounter:VB_VBN
-startdate_startDate:VB_VBN
-startdeceleration_startDeceleration:VB_VBN
-starther_StartHer:VB_VBN
-startisback_StartIsBack:VB_VBN
-startjobs_StartJobs:VB_VBN
-startmenu_StartMenu:VB_VBN
-startpage_StartPage:VB_VBN
-startprogramswindows_StartProgramsWindows:VB_VBN
-startprojectingasync_StartProjectingAsync:VB_VBN
-startracker_StarTracker:VB_VBN
-startscreen_StartScreen:VB_VBN
-startsearchus_StartSearchUs:VB_VBN
-startsmart_StartSmart:VB_VBN
-startssl_StartSSL:VB_VBN
-startupcamp_StartupCamp:VB_VBN
-startupdelayinmsec_StartupDelayInMSec:VB_VBN
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-taeyeon_TaeYeon:VB_VBN
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-tagcung_TagCung:VB_VBN
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-tagitvn_TagITVn:VB_VBN
-tagkinh_tagKinh:VB_VBN
-tagloi_tagLoi:VB_VBN
-tagmanager_TagManager:VB_VBN
-tagmaster_TagMaster:VB_VBN
-tagpesa_TagPesa:VB_VBN
-tagphim_tagPhim:VB_VBN
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-tags_TAgs:VB_VBN
-tagsphong_TagsPhong:VB_VBN
-tagstruy_TagsTruy:VB_VBN
-tagtagtai_tagtagTai:VB_VBN
-tagtai_tagTai:VB_VBN
-tagtm_TagTM:VB_VBN
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-taiappmod_TaiAppMod:VB_VBN
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-taig_TaiG:VB_VBN
-taigame_TaiGame:VB_VBN
-taigamemienphi_TaiGameMienPhi:VB_VBN
-taigamepikachu_TaiGamePikachu:VB_VBN
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-taiktha_TaikTha:VB_VBN
-taikwang_TaiKwang:VB_VBN
-tailieu_TaiLieu:VB_VBN
-tailieummo_TaiLieuMMO:VB_VBN
-taima_taiMa:VB_VBN
-taimahal_TaiMaHal:VB_VBN
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-tainhacvn_TaiNhacVn:VB_VBN
-taiphanmemhay_TaiPhanMemHay:VB_VBN
-taiphanmemnhanh_TaiPhanMemNhanh:VB_VBN
-taisao_TaiSao:VB_VBN
-taitran_TaiTran:VB_VBN
-taitructiep_TaiTrucTiep:VB_VBN
-taiwan_TaiWan:VB_VBN
-taiwandu_taiwanDu:VB_VBN
-taiwei_taiWei:VB_VBN
-taixin_TaiXin:VB_VBN
-tajermy_TajerMy:VB_VBN
-tajmasago_TajmaSago:VB_VBN
-takahashi_TaKahashi:VB_VBN
-takanashimemori_TakanashiMemori:VB_VBN
-takara_TaKara:VB_VBN
-takemotoyouta_TakemotoYouta:VB_VBN
-takeoff_TakeOff:VB_VBN
-takeownershipex_TakeOwnershipEx:VB_VBN
-takeprofit_TakeProfit:VB_VBN
-takeuni_TakeUni:VB_VBN
-tala_TaLa:VB_VBN
-talasilvertm_TalasilverTM:VB_VBN
-talentbankers_TalentBankers:VB_VBN
-talentbin_TalentBin:VB_VBN
-talentbold_TalentBold:VB_VBN
-talenthub_TalentHub:VB_VBN
-talentmind_TalentMind:VB_VBN
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-tech_TEch:VB_VBN
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-tructiepbongdatv_TrucTiepBongDaTV:VB_VBN
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-tructiepkqxs_TrucTiepKQXS:VB_VBN
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-tructuyenli_tructuyenLi:VB_VBN
-tructuyenzhou_tructuyenZhou:VB_VBN
-trucuyenguan_trucuyenGuan:VB_VBN
-trudiestyler_TrudieStyler:VB_VBN
-trueaudio_TrueAudio:VB_VBN
-truebalance_TrueBalance:VB_VBN
-truebeam_TrueBeam:VB_VBN
-trueblock_TrueBlock:VB_VBN
-trueblue_TrueBlue:VB_VBN
-truecaller_TrueCaller:VB_VBN
-truecapsule_TrueCapsule:VB_VBN
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-trumphai_TrumpHai:VB_VBN
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-trutv_TruTV:VB_VBN
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-tuloshop_TuloShop:VB_VBN
-tumblebooks_TumbleBooks:VB_VBN
-tumeropine_TumeroPine:VB_VBN
-tumifoods_TumiFoods:VB_VBN
-tummicare_TummiCare:VB_VBN
-tumnextnext_TumNextNext:VB_VBN
-tunaline_TunaLine:VB_VBN
-tunein_TuneIn:VB_VBN
-tuneitup_TuneItUp:VB_VBN
-tunescare_TunesCare:VB_VBN
-tuneskit_TunesKit:VB_VBN
-tunesmate_TunesMate:VB_VBN
-tuneup_TuneUp:VB_VBN
-tungkang_TungKang:VB_VBN
-tunglam_TungLam:VB_VBN
-tungphatcomputer_TungPhatComputer:VB_VBN
-tungshin_TungShin:VB_VBN
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-tupleubactom_tupleubacTom:VB_VBN
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-turboboost_TurboBoost:VB_VBN
-turboc_TurboC:VB_VBN
-turbocab_TurboCAB:VB_VBN
-turbocad_TurboCAD:VB_VBN
-turbodrum_TurboDrum:VB_VBN
-turboforex_TurboForex:VB_VBN
-turbografx_TurboGrafx:VB_VBN
-turbojet_TurboJet:VB_VBN
-turbonuke_TurboNuke:VB_VBN
-turboo_TurBoo:VB_VBN
-turbopower_TurboPower:VB_VBN
-turbosd_TurboSD:VB_VBN
-turbosplit_TurboSplit:VB_VBN
-turbosquid_TurboSquid:VB_VBN
-turbostar_TurboStar:VB_VBN
-turbostat_TurboStat:VB_VBN
-turbotax_TurboTax:VB_VBN
-turbowash_TurboWash:VB_VBN
-turbowire_TurboWire:VB_VBN
-turbowrite_TurboWrite:VB_VBN
-turkstream_TurkStream:VB_VBN
-turocksteihardt_TurockSteihardt:VB_VBN
-tusin_TusiN:VB_VBN
-tutaylam_TuTayLam:VB_VBN
-tuticare_TutiCare:VB_VBN
-tuticarekhi_TutiCareKhi:VB_VBN
-tutitu_TuTiTu:VB_VBN
-tutorialmongodb_tutorialMongoDB:VB_VBN
-tutorpen_TutorPen:VB_VBN
-tutru_TuTru:VB_VBN
-tutsplus_TutsPlus:VB_VBN
-tuttojuve_TuttoJuve:VB_VBN
-tuttomercatoweb_TuttoMercatoWeb:VB_VBN
-tuttosport_TuttoSport:VB_VBN
-tutu_TuTu:VB_VBN
-tutuapp_TutuApp:VB_VBN
-tuvangiamsatxaydung_TuVanGiamSatXayDung:VB_VBN
-tuvangiayphep_TuVanGiayPhep:VB_VBN
-tuvanungthu_TuVanUngThu:VB_VBN
-tuviblog_TuViBlog:VB_VBN
-tuviglobal_TuviGLOBAL:VB_VBN
-tuvilyso_TuviLyso:VB_VBN
-tuvo_TuVo:VB_VBN
-tuxnvape_TuxnVape:VB_VBN
-tuy_tuY:VB_VBN
-tuycan_TuyCan:VB_VBN
-tuycontinue_TuyContinue:VB_VBN
-tuyendung_TuyenDung:VB_VBN
-tuyenhe_tuyenHe:VB_VBN
-tuyenhsbc_tuyenHSBC:VB_VBN
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-tuyenma_tuyenMa:VB_VBN
-tuyenmiao_tuyenMiao:VB_VBN
-tuyenninhbinh_TuyenNinhBinh:VB_VBN
-tuyensoi_tuyenSoi:VB_VBN
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-tuyentrong_tuyenTrong:VB_VBN
-tuyentruyenatgt_tuyentruyenATGT:VB_VBN
-tuyenwang_tuyenWang:VB_VBN
-tuyenxie_tuyenXie:VB_VBN
-tuyenyu_tuyenYu:VB_VBN
-tuyetlinhdesign_TUYETLINHDesign:VB_VBN
-tuyetnganguyenthuy_TuyetNgaNguyenThuy:VB_VBN
-tuyetvy_TuyetVy:VB_VBN
-tuyhoa_TuyHoa:VB_VBN
-tvabp_tvABP:VB_VBN
-tvai_tvAi:VB_VBN
-tvan_TVan:VB_VBN
-tvbox_TVBox:VB_VBN
-tvchelsea_tvChelsea:VB_VBN
-tvcrank_TVCrank:VB_VBN
-tvdamvler_tvDamvler:VB_VBN
-tvdb_TvDB:VB_VBN
-tvface_TVFace:VB_VBN
-tvfox_TVFox:VB_VBN
-tvgolf_TVGolf:VB_VBN
-tviet_TViet:VB_VBN
-tvis_TViS:VB_VBN
-tvissan_TVissan:VB_VBN
-tvix_TViX:VB_VBN
-tvjia_tvJia:VB_VBN
-tvlas_TVLas:VB_VBN
-tvlines_TVLines:VB_VBN
-tvmienphi_TvMienPhi:VB_VBN
-tvmobili_TVMobili:VB_VBN
-tvn_tvN:VB_VBN
-tvnews_TVnews:VB_VBN
-tvnguyen_tVNguyen:VB_VBN
-tvod_TVoD:VB_VBN
-tvone_tvONE:VB_VBN
-tvonenews_tvOneNews:VB_VBN
-tvontario_TVOntario:VB_VBN
-tvos_tvOS:VB_VBN
-tvphimsexsub_tvPhimsexsub:VB_VBN
-tvreport_TvReport:VB_VBN
-tvsau_TVSau:VB_VBN
-tvsextop_TvSextop:VB_VBN
-tvshopping_TVShopping:VB_VBN
-tvshow_TVshow:VB_VBN
-tvsouthamptonmanchester_TVsouthamptonmanchester:VB_VBN
-tvt_TvT:VB_VBN
-tvtap_TvTap:VB_VBN
-tvtaz_TvTaz:VB_VBN
-tvtich_TVTich:VB_VBN
-tvtrans_TVTrans:VB_VBN
-tweakbit_TweakBit:VB_VBN
-tweakbox_TweakBox:VB_VBN
-tweakram_TweakRAM:VB_VBN
-tweakslogon_TweaksLogon:VB_VBN
-tweaktown_TweakTown:VB_VBN
-tweakui_TweakUI:VB_VBN
-tweenlite_TweenLite:VB_VBN
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-tweesteden_TweeSteden:VB_VBN
-tweetcraft_TweetCraft:VB_VBN
-tweetcreator_TweetCreator:VB_VBN
-tweetdeck_TweetDeck:VB_VBN
-tweetreader_TweetReader:VB_VBN
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-twelvesky_TwelveSky:VB_VBN
-twentyseventeen_TwentySeventeen:VB_VBN
-twentyten_TwentyTen:VB_VBN
-twinair_TwinAir:VB_VBN
-twincat_TwinCat:VB_VBN
-twinlite_TwinLite:VB_VBN
-twinmaster_TwinMaster:VB_VBN
-twinmax_TwinMax:VB_VBN
-twinpower_TwinPower:VB_VBN
-twinview_TwinView:VB_VBN
-twinwash_TWINWash:VB_VBN
-twistfree_TwistFree:VB_VBN
-twistloc_TwistLoc:VB_VBN
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-twit_TWiT:VB_VBN
-twitchcon_TwitchCon:VB_VBN
-twitchtv_TwitchTV:VB_VBN
-twitlonger_TwitLonger:VB_VBN
-twitterfall_TwitterFall:VB_VBN
-twitterstreaming_TwitterStreaming:VB_VBN
-twocal_TwoCal:VB_VBN
-twodots_TwoDots:VB_VBN
-twog_TwoG:VB_VBN
-twopointzero_TwoPointZero:VB_VBN
-twt_TwT:VB_VBN
-txe_TxE:VB_VBN
-txid_TxID:VB_VBN
-txreceipt_TxReceipt:VB_VBN
-txt_TxT:VB_VBN
-txtcollector_TXTCollector:VB_VBN
-txtdisplay_txtDisplay:VB_VBN
-txtghichu_txtGhiChu:VB_VBN
-txtm_TxTM:VB_VBN
-txtninja_TXTNinja:VB_VBN
-txtquan_txtQuan:VB_VBN
-tyalex_TyAlex:VB_VBN
-tyc_TyC:VB_VBN
-tycorona_tyCORONA:VB_VBN
-tycsc_tyCSC:VB_VBN
-tyhd_TyhD:VB_VBN
-tyhuu_TyHuu:VB_VBN
-tyi_tyI:VB_VBN
-tyin_tyIN:VB_VBN
-tymer_TYMer:VB_VBN
-tymicheal_tyMicheal:VB_VBN
-tympview_TympView:VB_VBN
-tymy_TyMy:VB_VBN
-tyna_TyNa:VB_VBN
-tynanofilm_tyNANOFILM:VB_VBN
-typea_TypeA:VB_VBN
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-typeerror_TypeError:VB_VBN
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-typetester_TypeTester:VB_VBN
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-typingmaster_TypingMaster:VB_VBN
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-tzero_tZERO:VB_VBN
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-uberone_UberOne:VB_VBN
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-ucbrowser_UCBrowser:VB_VBN
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-ucmax_UCmax:VB_VBN
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-uconcept_UConcept:VB_VBN
-uconn_UConn:VB_VBN
-ucpm_uCPM:VB_VBN
-udac_uDAC:VB_VBN
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-ueber_UEBer:VB_VBN
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-uefi_uefI:VB_VBN
-ueher_UEHer:VB_VBN
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-uff_uFF:VB_VBN
-ugreen_UGreen:VB_VBN
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-uhf_UhF:VB_VBN
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-uhunter_UHunter:VB_VBN
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-uisin_UIsin:VB_VBN
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-uitm_UiTM:VB_VBN
-uitoolbar_UIToolbar:VB_VBN
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-uiview_UIView:VB_VBN
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-uiviewcontrolleranimatedtransitioning_UIViewControllerAnimatedTransitioning:VB_VBN
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-ulamp_ULamp:VB_VBN
-uledger_ULedger:VB_VBN
-ultimate_UltiMate:VB_VBN
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-ultimatewaff_UltimateWaff:VB_VBN
-ultraabsorbent_UltraAbsorbent:VB_VBN
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-ultrabrilliant_UltraBrilliant:VB_VBN
-ultrachrome_UltraChrome:VB_VBN
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-vimo_ViMo:VB_VBN
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-vinaguard_VinaGuard:VB_VBN
-vinahealth_VinaHealth:VB_VBN
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-vinahous_VinaHous:VB_VBN
-vinahouse_VinaHouse:VB_VBN
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-vinaktv_VinaKTV:VB_VBN
-vinakyoei_VinaKyoei:VB_VBN
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-vinaland_VinaLand:VB_VBN
-vinalatex_VinaLatex:VB_VBN
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-vinamrt_VInamrt:VB_VBN
-vinanuts_VinaNuts:VB_VBN
-vinaofc_VinaOFC:VB_VBN
-vinaonline_VinaOnline:VB_VBN
-vinaorgaic_VinaOrgaic:VB_VBN
-vinaorgani_VinaOrgani:VB_VBN
-vinaorganic_VinaOrganic:VB_VBN
-vinaphone_VinaPhone:VB_VBN
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-vinaphonevn_VinaPhonevn:VB_VBN
-vinapol_VinaPol:VB_VBN
-vinapot_VinaPot:VB_VBN
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-vinaqua_VinAqua:VB_VBN
-vinareal_VinaReal:VB_VBN
-vinared_VinaRed:VB_VBN
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-vinaresearch_VinaResearch:VB_VBN
-vinari_VinaRI:VB_VBN
-vinarin_VinaRin:VB_VBN
-vinaroom_VinaRoom:VB_VBN
-vinart_VinArt:VB_VBN
-vinas_VinaS:VB_VBN
-vinasave_VinaSave:VB_VBN
-vinaschools_VinaSchools:VB_VBN
-vinaseo_VinaSeo:VB_VBN
-vinaship_VinaShip:VB_VBN
-vinasite_VinaSite:VB_VBN
-vinasolar_VinaSolar:VB_VBN
-vinasoy_VinaSoy:VB_VBN
-vinaspa_VinaSpa:VB_VBN
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-vinastudy_VinaStudy:VB_VBN
-vinasun_VinaSun:VB_VBN
-vinasupport_VinaSupport:VB_VBN
-vinasve_VinaSve:VB_VBN
-vinatab_VinaTAB:VB_VBN
-vinatap_VinaTap:VB_VBN
-vinatech_VInatech:VB_VBN
-vinatechjsc_VinatechJSC:VB_VBN
-vinatechnic_VinaTechnic:VB_VBN
-vinatexland_VinatexLand:VB_VBN
-vinatext_VinaText:VB_VBN
-vinatown_VinaTown:VB_VBN
-vinatrain_VinaTrain:VB_VBN
-vinatravel_VinaTravel:VB_VBN
-vinatrends_VinaTrends:VB_VBN
-vinatrucking_VinaTrucking:VB_VBN
-vinaucare_VinaUCare:VB_VBN
-vinaurl_VinaURL:VB_VBN
-vinavisa_VinaVisa:VB_VBN
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-vinawind_VinaWind:VB_VBN
-vinaxtra_VinaXtra:VB_VBN
-vinaxtral_VinaXtraL:VB_VBN
-vinbet_VinBet:VB_VBN
-vinbrain_VinBrain:VB_VBN
-vinbus_VinBus:VB_VBN
-vinca_VinCA:VB_VBN
-vincar_VinCar:VB_VBN
-vincent_VinCent:VB_VBN
-vincenzovincenzo_VincenzoVincenzo:VB_VBN
-vincharm_VinCharm:VB_VBN
-vincity_VinCity:VB_VBN
-vincom_VinCom:VB_VBN
-vincomb_VincomB:VB_VBN
-vincommerce_VinCommerce:VB_VBN
-vincommere_VinCommere:VB_VBN
-vincomplus_VincomPlus:VB_VBN
-vincomtower_VincomTower:VB_VBN
-vincool_VinCool:VB_VBN
-vincoplast_VincoPlast:VB_VBN
-vincss_VinCSS:VB_VBN
-vindesign_VinDesign:VB_VBN
-vinds_VinDS:VB_VBN
-vinec_VinEc:VB_VBN
-vineco_VinEco:VB_VBN
-vinecom_VinEcom:VB_VBN
-vinedu_VinEdu:VB_VBN
-vinegrab_VineGrab:VB_VBN
-vinenid_VinenID:VB_VBN
-vinews_VInews:VB_VBN
-vinfa_VinFa:VB_VBN
-vinfadil_VinFadil:VB_VBN
-vinfast_VinFast:VB_VBN
-vinfastchevroletvinh_VinFastChevroletVinh:VB_VBN
-vinfaster_VinFaster:VB_VBN
-vinfastvinfast_VinFastVinFast:VB_VBN
-vinfats_VinFats:VB_VBN
-vinfruits_VinFruits:VB_VBN
-vinfuture_VinFuture:VB_VBN
-ving_VinG:VB_VBN
-vinggroup_VinGgroup:VB_VBN
-vingo_VIngo:VB_VBN
-vingrand_VinGrand:VB_VBN
-vingroup_VinGroup:VB_VBN
-vingroupvingroupvinsmartvinsmart_VingroupVingroupVinSmartVinSmart:VB_VBN
-vingruop_VinGruop:VB_VBN
-vinh_VInh:VB_VBN
-vinhbaoclub_VinhBaoClub:VB_VBN
-vinhbinhvn_VinhBinhVn:VB_VBN
-vinhclub_VinhClub:VB_VBN
-vinhho_VinhHo:VB_VBN
-vinhkhang_VinhKhang:VB_VBN
-vinhlong_VinhLong:VB_VBN
-vinhlongland_VinhLongLand:VB_VBN
-vinhlongtv_VinhLongTV:VB_VBN
-vinhlx_VinhLX:VB_VBN
-vinhoanhglass_VinhOanhGlass:VB_VBN
-vinholidays_VinHolidays:VB_VBN
-vinhome_VinHome:VB_VBN
-vinhomes_VinHomes:VB_VBN
-vinhomescentral_VinhomesCentral:VB_VBN
-vinhomescentralparktc_VinhomesCentralParktc:VB_VBN
-vinhomescorp_VinhomesCorp:VB_VBN
-vinhomesgrandpark_VinhomesGrandPark:VB_VBN
-vinhouse_VinHouse:VB_VBN
-vinhphatmachinery_VinhPhatMachinery:VB_VBN
-vinhquanglaw_VinhQuangLaw:VB_VBN
-vinhr_VinHR:VB_VBN
-vinhtp_VinhTP:VB_VBN
-vinid_VinID:VB_VBN
-vinif_VinIF:VB_VBN
-vinit_VinIT:VB_VBN
-vinix_ViniX:VB_VBN
-vinkc_VinKC:VB_VBN
-vinke_VinKE:VB_VBN
-vinland_VinLand:VB_VBN
-vinlawyer_VinLawyer:VB_VBN
-vinlift_VinLift:VB_VBN
-vinman_VinMan:VB_VBN
-vinmart_VinMart:VB_VBN
-vinmartli_vinmartLi:VB_VBN
-vinmarts_VinMarts:VB_VBN
-vinmec_VinMec:VB_VBN
-vinoasis_VinOasis:VB_VBN
-vinp_VinP:VB_VBN
-vinpearl_VinPearl:VB_VBN
-vinpearland_VinpearLand:VB_VBN
-vinpearllan_VinpearlLan:VB_VBN
-vinpearlland_VinpearlLand:VB_VBN
-vinphone_VinPhone:VB_VBN
-vinplay_VinPlay:VB_VBN
-vinpot_VinPot:VB_VBN
-vinpower_VinPower:VB_VBN
-vinpro_VinPro:VB_VBN
-vinreal_VinReal:VB_VBN
-vinretail_VinRetail:VB_VBN
-vinretain_VinRetain:VB_VBN
-vinsafari_VinSafari:VB_VBN
-vinschool_VinSchool:VB_VBN
-vinsep_VinSEP:VB_VBN
-vinser_VinSer:VB_VBN
-vinshop_VinShop:VB_VBN
-vinsmart_VinSmart:VB_VBN
-vinsmartvsmartvsmart_vinsmartVsmartvsmart:VB_VBN
-vinsteel_VinSTEEL:VB_VBN
-vinsuite_VinSuite:VB_VBN
-vinsun_VinSun:VB_VBN
-vintagephong_vintagePhong:VB_VBN
-vintagevali_VINTAGEVali:VB_VBN
-vintata_VinTaTa:VB_VBN
-vintech_VinTech:VB_VBN
-vintesgroup_VintesGroup:VB_VBN
-vinthai_VinThai:VB_VBN
-vintrain_VinTrain:VB_VBN
-vinuni_VinUni:VB_VBN
-vinuniversity_VinUniversity:VB_VBN
-vinus_ViNus:VB_VBN
-vinwin_VinWin:VB_VBN
-vinwonder_VinWonder:VB_VBN
-vinwonders_VinWonders:VB_VBN
-vinyard_VinyArd:VB_VBN
-vinylclorua_vinylClorua:VB_VBN
-vio_ViO:VB_VBN
-vioedu_VioEdu:VB_VBN
-vioeu_VioEu:VB_VBN
-violaswitch_VIOLASwitch:VB_VBN
-violet_ViOLET:VB_VBN
-violetpham_VioletPham:VB_VBN
-violetwedding_VioletWedding:VB_VBN
-violympic_ViOlympic:VB_VBN
-vione_ViOne:VB_VBN
-viones_VIones:VB_VBN
-viot_VIoT:VB_VBN
-viotrans_VioTrans:VB_VBN
-viparc_ViParc:VB_VBN
-vipbank_VIPbank:VB_VBN
-vipclub_VipClub:VB_VBN
-vipdeposits_VipDeposits:VB_VBN
-vipemoney_VipEmoney:VB_VBN
-viper_VIPer:VB_VBN
-viperchill_ViperChill:VB_VBN
-vipet_ViPET:VB_VBN
-vipfood_VipFood:VB_VBN
-vipgame_VipGame:VB_VBN
-viphaitong_vipHaitong:VB_VBN
-viphome_VipHome:VB_VBN
-vipinsiders_VIPinsiders:VB_VBN
-viplab_VipLab:VB_VBN
-viplauxanh_VipLauXanh:VB_VBN
-vipluss_VIpluss:VB_VBN
-vipmax_VipMax:VB_VBN
-vipmember_VIPmember:VB_VBN
-vipmssam_VIpMssam:VB_VBN
-vipmua_VIPmua:VB_VBN
-vipost_VIpost:VB_VBN
-vippage_VIPPage:VB_VBN
-vippoint_VipPoint:VB_VBN
-vipriser_VipRiser:VB_VBN
-vips_vIPS:VB_VBN
-vipsearch_VIPsearch:VB_VBN
-viptagged_vipTagged:VB_VBN
-viradairy_ViraDairy:VB_VBN
-viralhog_ViralHog:VB_VBN
-viralworks_ViralWorks:VB_VBN
-virbac_VirBac:VB_VBN
-vircleaner_VirCleaner:VB_VBN
-virgin_VIrgin:VB_VBN
-virginpulse_VirginPulse:VB_VBN
-virmax_VirMax:VB_VBN
-virnetx_VirnetX:VB_VBN
-virosmartlock_VIROSMARTlock:VB_VBN
-virscan_VirScan:VB_VBN
-virtualbee_VirtualBee:VB_VBN
-virtualbox_VirtualBox:VB_VBN
-virtualdj_VirtualDJ:VB_VBN
-virtualdrive_VirtualDrive:VB_VBN
-virtualdub_VirtualDub:VB_VBN
-virtualmachineerror_VirtualMachineError:VB_VBN
-virtualnes_VirtualNES:VB_VBN
-virtualrealporn_VirtualRealPorn:VB_VBN
-virtualspeech_VirtualSpeech:VB_VBN
-virtuemart_VirtueMart:VB_VBN
-virusbarrier_VirusBarrier:VB_VBN
-virusblokada_VirusBlokAda:VB_VBN
-viruscc_VirusCC:VB_VBN
-viruscorona_VirusCorona:VB_VBN
-viruscybergame_VirusCyberGame:VB_VBN
-virusfeline_virusFeline:VB_VBN
-viruskeeper_VirusKeeper:VB_VBN
-viruss_ViruSs:VB_VBN
-virussars_virusSARS:VB_VBN
-virusscan_VirusScan:VB_VBN
-virustotal_VirusTotal:VB_VBN
-virusvirus_virusVirus:VB_VBN
-viruswall_VirusWall:VB_VBN
-visacard_VisaCard:VB_VBN
-visacbaosean_ViSacBaoSean:VB_VBN
-visadebit_VisaDebit:VB_VBN
-visaf_VisaF:VB_VBN
-visaguo_visaGuo:VB_VBN
-visahq_VisaHQ:VB_VBN
-visamastercard_VisaMasterCard:VB_VBN
-visanet_VisaNet:VB_VBN
-visanhanh_VisaNhanh:VB_VBN
-visapm_VisaPM:VB_VBN
-visasaigon_VisaSaigon:VB_VBN
-viscocrete_ViscoCrete:VB_VBN
-viscotec_ViscoTec:VB_VBN
-visef_ViSEF:VB_VBN
-viser_VISer:VB_VBN
-viship_ViShip:VB_VBN
-vishows_VIshows:VB_VBN
-visicalc_VisiCalc:VB_VBN
-visioncop_VisionCop:VB_VBN
-visioncritical_VisionCritical:VB_VBN
-visiondrive_VisionDrive:VB_VBN
-visionimageprocessor_VisionImageProcessor:VB_VBN
-visionlink_VisionLINK:VB_VBN
-visip_ViSip:VB_VBN
-visipics_VisiPics:VB_VBN
-visitkhaosamroiyotnational_visitKhaoSamRoiYotNational:VB_VBN
-visitorcontact_VisitorContact:VB_VBN
-visitsingapore_VisitSingapore:VB_VBN
-viskeo_visKeo:VB_VBN
-viskin_ViSkin:VB_VBN
-visnagartrong_VisnagarTrong:VB_VBN
-vispotm_VispoTM:VB_VBN
-vissolpublish_VisSolPublish:VB_VBN
-vista_VisTa:VB_VBN
-vistumbler_ViStumbler:VB_VBN
-visualbasic_VisualBasic:VB_VBN
-visualboyadvance_VisualBoyAdvance:VB_VBN
-visualcamc_VisualCAMc:VB_VBN
-visualcpp_VisualCpp:VB_VBN
-visualmind_VisualMind:VB_VBN
-visualstudioonline_VisualStudioOnline:VB_VBN
-visualstuioonline_VisualStuioOnline:VB_VBN
-visualviet_VisualViet:VB_VBN
-visualwebtechnology_VisualWebTechnology:VB_VBN
-visuastation_VisuaStation:VB_VBN
-vita_ViTa:VB_VBN
-vitaa_VitaA:VB_VBN
-vitac_VitaC:VB_VBN
-vitacare_VitaCare:VB_VBN
-vitad_VitaD:VB_VBN
-vitadairy_VitaDairy:VB_VBN
-vitadiary_VitaDiary:VB_VBN
-vitafresh_VitaFresh:VB_VBN
-vitagrow_VitaGrow:VB_VBN
-vitagummies_VitaGummies:VB_VBN
-vitalcare_VitalCare:VB_VBN
-vitalitysm_VitalitySM:VB_VBN
-vitalsource_VitalSource:VB_VBN
-vitalstrike_vitalStrike:VB_VBN
-vitamart_VitaMart:VB_VBN
-vitaminart_VitaminArt:VB_VBN
-vitaminb_vitaminB:VB_VBN
-vitamingo_vitaminGo:VB_VBN
-vitamink_vitaminK:VB_VBN
-vitaminsaustralia_VitaminsAustralia:VB_VBN
-vitaminsea_vitaminSea:VB_VBN
-vitaminviet_VitaminViet:VB_VBN
-vitaminwater_VitaminWater:VB_VBN
-vitaniacin_VitaNiacin:VB_VBN
-vitasackville_VitaSackville:VB_VBN
-vitashield_VitaShield:VB_VBN
-vitax_ViTax:VB_VBN
-vitd_VitD:VB_VBN
-vite_VIte:VB_VBN
-vitest_ViTest:VB_VBN
-vitex_ViteX:VB_VBN
-vitexnutrition_VitexNutrition:VB_VBN
-vithanh_ViThanh:VB_VBN
-viti_ViTi:VB_VBN
-vitinhanphat_ViTinhAnPhat:VB_VBN
-vitokenize_ViTokenize:VB_VBN
-vitravel_ViTravel:VB_VBN
-vitroceramic_VitroCeramic:VB_VBN
-vitrua_VitRua:VB_VBN
-vittrung_VitTrung:VB_VBN
-vitualxposed_VitualXposed:VB_VBN
-viutv_ViuTV:VB_VBN
-viva_ViVa:VB_VBN
-vivabeauty_VivaBeauty:VB_VBN
-vivachek_VivaChek:VB_VBN
-vivaconsulting_VivaConsulting:VB_VBN
-vivacut_VivaCut:VB_VBN
-vivant_ViVant:VB_VBN
-vivastudio_VIVAStudio:VB_VBN
-vivatv_VivaTV:VB_VBN
-vivavideo_VivaVideo:VB_VBN
-vivaviet_ViVaViet:VB_VBN
-vivekkevin_ViveKKevin:VB_VBN
-vivi_ViVi:VB_VBN
-vivianchia_VIVIANchia:VB_VBN
-vividir_VividIR:VB_VBN
-vividmotion_VividMotion:VB_VBN
-vivify_ViviFy:VB_VBN
-vivitek_ViviTek:VB_VBN
-vivk_ViVK:VB_VBN
-vivo_ViVo:VB_VBN
-vivobook_VivoBook:VB_VBN
-vivobookt_VivoBookT:VB_VBN
-vivocity_VivoCity:VB_VBN
-vivocloud_vivoCloud:VB_VBN
-vivosport_VivoSport:VB_VBN
-vivostick_VivoStick:VB_VBN
-vivotab_VivoTab:VB_VBN
-vivowatch_VivoWatch:VB_VBN
-vivutv_VivuTV:VB_VBN
-viwoon_ViWoon:VB_VBN
-vixs_ViXS:VB_VBN
-vizova_ViZova:VB_VBN
-vjair_VJair:VB_VBN
-vjbooking_VJBooking:VB_VBN
-vjcamerastore_VJcamerastore:VB_VBN
-vjcare_VJcare:VB_VBN
-vjet_VJet:VB_VBN
-vjing_VJing:VB_VBN
-vjwatch_VJwatch:VB_VBN
-vkauto_VKAuto:VB_VBN
-vkdbq_VKDbq:VB_VBN
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-vkook_VKook:VB_VBN
-vlan_VLan:VB_VBN
-vland_VLand:VB_VBN
-vleague_VLeague:VB_VBN
-vlegal_VLegal:VB_VBN
-vline_VLine:VB_VBN
-vlink_VLink:VB_VBN
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-vlive_VLive:VB_VBN
-vlocker_VLocker:VB_VBN
-vlogplus_VlogPlus:VB_VBN
-vlstock_VLStock:VB_VBN
-vltea_VLTea:VB_VBN
-vlxd_VlXD:VB_VBN
-vlxx_VLxx:VB_VBN
-vlxxvietsub_VLXXVietsub:VB_VBN
-vmaforum_VMAForum:VB_VBN
-vmakerhost_VMakerHOST:VB_VBN
-vmanly_VManly:VB_VBN
-vmate_VMate:VB_VBN
-vmax_VMax:VB_VBN
-vmeet_VMeet:VB_VBN
-vmessage_VMessage:VB_VBN
-vmgers_VMGers:VB_VBN
-vmgroup_VMGroup:VB_VBN
-vmintech_VMinTech:VB_VBN
-vmonkey_VMonkey:VB_VBN
-vmpack_VMPack:VB_VBN
-vmstat_VmStat:VB_VBN
-vmua_VMua:VB_VBN
-vmusic_VMusic:VB_VBN
-vmware_VMware:VB_VBN
-vmworld_VMworld:VB_VBN
-vmzinc_VMZinc:VB_VBN
-vnaholidays_VNAHolidays:VB_VBN
-vnahomes_VnaHomes:VB_VBN
-vnailad_VNailAd:VB_VBN
-vnailmarket_VNailMarket:VB_VBN
-vnailnews_VNailNews:VB_VBN
-vnailpro_VNailPro:VB_VBN
-vnair_VNair:VB_VBN
-vnalert_VnAlert:VB_VBN
-vnallshare_VNAllshare:VB_VBN
-vnallshares_VNAllshares:VB_VBN
-vnam_VNam:VB_VBN
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-zika_ZiKa:VB_VBN
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-zil_ZiL:VB_VBN
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-zilis_ZIlis:VB_VBN
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-zimken_ZimKen:VB_VBN
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-zinc_ZinC:VB_VBN
-zindenon_zinDENON:VB_VBN
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-zktco_ZKtco:VB_VBN
-zkteco_ZKTeco:VB_VBN
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-zlatanibrahimovic_ZlatanIbrahimovic:VB_VBN
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-zno_ZnO:VB_VBN
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-zoeetree_ZoeeTree:VB_VBN
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-zongkai_ZongKai:VB_VBN
-zonvip_ZonVip:VB_VBN
-zoodoo_ZooDoo:VB_VBN
-zookeeper_ZooKeeper:VB_VBN
-zookeys_ZooKeys:VB_VBN
-zoomplus_ZoomPlus:VB_VBN
-zoomsounds_ZoomSounds:VB_VBN
-zoomsource_ZoomSource:VB_VBN
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-zoomtravel_ZoomTravel:VB_VBN
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-zoomx_ZoomX:VB_VBN
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-zzuuiii_ZZuuiii:VB_VBN
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-zzzsovipzzz_zZzSoVipzZz:VB_VBN
-zzztraveling_zZzTraveling:VB_VBN
diff --git a/whisper_pipeline/gector/vocabulary.py b/whisper_pipeline/gector/vocabulary.py
deleted file mode 100644
index c508c7d5e33f7254dd25c7ffe08ec9c76c8011d6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/vocabulary.py
+++ /dev/null
@@ -1,277 +0,0 @@
-import codecs
-from collections import defaultdict
-import logging
-import os
-import re
-from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Union
-from filelock import FileLock
-
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_NON_PADDED_NAMESPACES = ("*tags", "*labels")
-DEFAULT_PADDING_TOKEN = "@@PADDING@@"
-DEFAULT_OOV_TOKEN = "@@UNKNOWN@@"
-NAMESPACE_PADDING_FILE = "non_padded_namespaces.txt"
-_NEW_LINE_REGEX = re.compile(r"\n|\r\n")
-
-
-def namespace_match(pattern: str, namespace: str):
- """
- Matches a namespace pattern against a namespace string. For example, `*tags` matches
- `passage_tags` and `question_tags` and `tokens` matches `tokens` but not
- `stemmed_tokens`.
- """
- if pattern[0] == "*" and namespace.endswith(pattern[1:]):
- return True
- elif pattern == namespace:
- return True
- return False
-
-
-class _NamespaceDependentDefaultDict(defaultdict):
- """
- This is a [defaultdict]
- (https://docs.python.org/2/library/collections.html#collections.defaultdict) where the
- default value is dependent on the key that is passed.
- We use "namespaces" in the :class:`Vocabulary` object to keep track of several different
- mappings from strings to integers, so that we have a consistent API for mapping words, tags,
- labels, characters, or whatever else you want, into integers. The issue is that some of those
- namespaces (words and characters) should have integers reserved for padding and
- out-of-vocabulary tokens, while others (labels and tags) shouldn't. This class allows you to
- specify filters on the namespace (the key used in the `defaultdict`), and use different
- default values depending on whether the namespace passes the filter.
- To do filtering, we take a set of `non_padded_namespaces`. This is a set of strings
- that are either matched exactly against the keys, or treated as suffixes, if the
- string starts with `*`. In other words, if `*tags` is in `non_padded_namespaces` then
- `passage_tags`, `question_tags`, etc. (anything that ends with `tags`) will have the
- `non_padded` default value.
- # Parameters
- non_padded_namespaces : `Iterable[str]`
- A set / list / tuple of strings describing which namespaces are not padded. If a namespace
- (key) is missing from this dictionary, we will use :func:`namespace_match` to see whether
- the namespace should be padded. If the given namespace matches any of the strings in this
- list, we will use `non_padded_function` to initialize the value for that namespace, and
- we will use `padded_function` otherwise.
- padded_function : `Callable[[], Any]`
- A zero-argument function to call to initialize a value for a namespace that `should` be
- padded.
- non_padded_function : `Callable[[], Any]`
- A zero-argument function to call to initialize a value for a namespace that should `not` be
- padded.
- """
-
- def __init__(
- self,
- non_padded_namespaces: Iterable[str],
- padded_function: Callable[[], Any],
- non_padded_function: Callable[[], Any],
- ) -> None:
- self._non_padded_namespaces = set(non_padded_namespaces)
- self._padded_function = padded_function
- self._non_padded_function = non_padded_function
- super().__init__()
-
- def add_non_padded_namespaces(self, non_padded_namespaces: Set[str]):
- # add non_padded_namespaces which weren't already present
- self._non_padded_namespaces.update(non_padded_namespaces)
-
-
-class _TokenToIndexDefaultDict(_NamespaceDependentDefaultDict):
- def __init__(self, non_padded_namespaces: Set[str], padding_token: str, oov_token: str) -> None:
- super().__init__(non_padded_namespaces, lambda: {padding_token: 0, oov_token: 1}, lambda: {})
-
-
-class _IndexToTokenDefaultDict(_NamespaceDependentDefaultDict):
- def __init__(self, non_padded_namespaces: Set[str], padding_token: str, oov_token: str) -> None:
- super().__init__(non_padded_namespaces, lambda: {0: padding_token, 1: oov_token}, lambda: {})
-
-
-class Vocabulary:
- def __init__(
- self,
- counter: Dict[str, Dict[str, int]] = None,
- min_count: Dict[str, int] = None,
- max_vocab_size: Union[int, Dict[str, int]] = None,
- non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
- pretrained_files: Optional[Dict[str, str]] = None,
- only_include_pretrained_words: bool = False,
- tokens_to_add: Dict[str, List[str]] = None,
- min_pretrained_embeddings: Dict[str, int] = None,
- padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
- oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
- ) -> None:
- self._padding_token = padding_token if padding_token is not None else DEFAULT_PADDING_TOKEN
- self._oov_token = oov_token if oov_token is not None else DEFAULT_OOV_TOKEN
-
- self._non_padded_namespaces = set(non_padded_namespaces)
-
- self._token_to_index = _TokenToIndexDefaultDict(
- self._non_padded_namespaces, self._padding_token, self._oov_token
- )
- self._index_to_token = _IndexToTokenDefaultDict(
- self._non_padded_namespaces, self._padding_token, self._oov_token
- )
-
- @classmethod
- def from_files(
- cls,
- directory: Union[str, os.PathLike],
- padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
- oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
- ) -> "Vocabulary":
- """
- Loads a `Vocabulary` that was serialized either using `save_to_files` or inside
- a model archive file.
- # Parameters
- directory : `str`
- The directory or archive file containing the serialized vocabulary.
- """
- logger.info("Loading token dictionary from %s.", directory)
- padding_token = padding_token if padding_token is not None else DEFAULT_PADDING_TOKEN
- oov_token = oov_token if oov_token is not None else DEFAULT_OOV_TOKEN
-
- if not os.path.isdir(directory):
- raise ValueError(f"{directory} not exist")
-
- # We use a lock file to avoid race conditions where multiple processes
- # might be reading/writing from/to the same vocab files at once.
- with FileLock(os.path.join(directory, ".lock")):
- with codecs.open(os.path.join(directory, NAMESPACE_PADDING_FILE), "r", "utf-8") as namespace_file:
- non_padded_namespaces = [namespace_str.strip() for namespace_str in namespace_file]
-
- vocab = cls(
- non_padded_namespaces=non_padded_namespaces,
- padding_token=padding_token,
- oov_token=oov_token,
- )
-
- # Check every file in the directory.
- for namespace_filename in os.listdir(directory):
- if namespace_filename == NAMESPACE_PADDING_FILE:
- continue
- if namespace_filename.startswith("."):
- continue
- namespace = namespace_filename.replace(".txt", "")
- if any(namespace_match(pattern, namespace) for pattern in non_padded_namespaces):
- is_padded = False
- else:
- is_padded = True
- filename = os.path.join(directory, namespace_filename)
- vocab.set_from_file(filename, is_padded, namespace=namespace, oov_token=oov_token)
-
- return vocab
-
- @classmethod
- def empty(cls) -> "Vocabulary":
- """
- This method returns a bare vocabulary instantiated with `cls()` (so, `Vocabulary()` if you
- haven't made a subclass of this object). The only reason to call `Vocabulary.empty()`
- instead of `Vocabulary()` is if you are instantiating this object from a config file. We
- register this constructor with the key "empty", so if you know that you don't need to
- compute a vocabulary (either because you're loading a pre-trained model from an archive
- file, you're using a pre-trained transformer that has its own vocabulary, or something
- else), you can use this to avoid having the default vocabulary construction code iterate
- through the data.
- """
- return cls()
-
- def set_from_file(
- self,
- filename: str,
- is_padded: bool = True,
- oov_token: str = DEFAULT_OOV_TOKEN,
- namespace: str = "tokens",
- ):
- """
- If you already have a vocabulary file for a trained model somewhere, and you really want to
- use that vocabulary file instead of just setting the vocabulary from a dataset, for
- whatever reason, you can do that with this method. You must specify the namespace to use,
- and we assume that you want to use padding and OOV tokens for this.
- # Parameters
- filename : `str`
- The file containing the vocabulary to load. It should be formatted as one token per
- line, with nothing else in the line. The index we assign to the token is the line
- number in the file (1-indexed if `is_padded`, 0-indexed otherwise). Note that this
- file should contain the OOV token string!
- is_padded : `bool`, optional (default=`True`)
- Is this vocabulary padded? For token / word / character vocabularies, this should be
- `True`; while for tag or label vocabularies, this should typically be `False`. If
- `True`, we add a padding token with index 0, and we enforce that the `oov_token` is
- present in the file.
- oov_token : `str`, optional (default=`DEFAULT_OOV_TOKEN`)
- What token does this vocabulary use to represent out-of-vocabulary characters? This
- must show up as a line in the vocabulary file. When we find it, we replace
- `oov_token` with `self._oov_token`, because we only use one OOV token across
- namespaces.
- namespace : `str`, optional (default=`"tokens"`)
- What namespace should we overwrite with this vocab file?
- """
- if is_padded:
- self._token_to_index[namespace] = {self._padding_token: 0}
- self._index_to_token[namespace] = {0: self._padding_token}
- else:
- self._token_to_index[namespace] = {}
- self._index_to_token[namespace] = {}
- with codecs.open(filename, "r", "utf-8") as input_file:
- lines = _NEW_LINE_REGEX.split(input_file.read())
- # Be flexible about having final newline or not
- if lines and lines[-1] == "":
- lines = lines[:-1]
- for i, line in enumerate(lines):
- index = i + 1 if is_padded else i
- token = line.replace("@@NEWLINE@@", "\n")
- if token == oov_token:
- token = self._oov_token
- self._token_to_index[namespace][token] = index
- self._index_to_token[namespace][index] = token
- if is_padded:
- assert self._oov_token in self._token_to_index[namespace], "OOV token not found!"
-
- def add_token_to_namespace(self, token: str, namespace: str = "tokens") -> int:
- """
- Adds `token` to the index, if it is not already present. Either way, we return the index of
- the token.
- """
- if not isinstance(token, str):
- raise ValueError(
- "Vocabulary tokens must be strings, or saving and loading will break."
- " Got %s (with type %s)" % (repr(token), type(token))
- )
- if token not in self._token_to_index[namespace]:
- index = len(self._token_to_index[namespace])
- self._token_to_index[namespace][token] = index
- self._index_to_token[namespace][index] = token
- return index
- else:
- return self._token_to_index[namespace][token]
-
- def add_tokens_to_namespace(self, tokens: List[str], namespace: str = "tokens") -> List[int]:
- """
- Adds `tokens` to the index, if they are not already present. Either way, we return the
- indices of the tokens in the order that they were given.
- """
- return [self.add_token_to_namespace(token, namespace) for token in tokens]
-
- def get_token_index(self, token: str, namespace: str = "tokens") -> int:
- try:
- return self._token_to_index[namespace][token]
- except KeyError:
- try:
- return self._token_to_index[namespace][self._oov_token]
- except KeyError:
- logger.error("Namespace: %s", namespace)
- logger.error("Token: %s", token)
- raise KeyError(
- f"'{token}' not found in vocab namespace '{namespace}', and namespace "
- f"does not contain the default OOV token ('{self._oov_token}')"
- )
-
- def get_token_from_index(self, index: int, namespace: str = "tokens") -> str:
- return self._index_to_token[namespace][index]
-
- def get_vocab_size(self, namespace: str = "tokens") -> int:
- return len(self._token_to_index[namespace])
-
- def get_namespaces(self) -> Set[str]:
- return set(self._index_to_token.keys())
diff --git a/whisper_pipeline/gector/vocabulary/.lock b/whisper_pipeline/gector/vocabulary/.lock
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/whisper_pipeline/gector/vocabulary/d_tags.txt b/whisper_pipeline/gector/vocabulary/d_tags.txt
deleted file mode 100644
index 56e05041ae1bcdee6b1b4c78fda114064a90de7d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/vocabulary/d_tags.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-CORRECT
-INCORRECT
-@@UNKNOWN@@
-@@PADDING@@
diff --git a/whisper_pipeline/gector/vocabulary/labels.txt b/whisper_pipeline/gector/vocabulary/labels.txt
deleted file mode 100644
index 0de3942a59ce8cc0bf9c403afc3330da042f7dc1..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/vocabulary/labels.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-$KEEP
-$TRANSFORM_CASE_CAPITAL
-$APPEND_,
-$APPEND_.
-$TRANSFORM_VERB_VB_VBN
-$TRANSFORM_CASE_UPPER
-$APPEND_:
-$APPEND_?
-$TRANSFORM_VERB_VB_VBC
-$TRANSFORM_CASE_LOWER
-$TRANSFORM_CASE_CAPITAL_1
-$TRANSFORM_CASE_UPPER_-1
-$MERGE_SPACE
-@@UNKNOWN@@
-@@PADDING@@
\ No newline at end of file
diff --git a/whisper_pipeline/gector/vocabulary/non_padded_namespaces.txt b/whisper_pipeline/gector/vocabulary/non_padded_namespaces.txt
deleted file mode 100644
index d5dee50c4400824e195a609940a9f9c9abad69b5..0000000000000000000000000000000000000000
--- a/whisper_pipeline/gector/vocabulary/non_padded_namespaces.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-*tags
-*labels
diff --git a/whisper_pipeline/infer.py b/whisper_pipeline/infer.py
deleted file mode 100644
index ead780a16e5cfedb000e2ee1786fde0b639ce94c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/infer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-from pathlib import Path
-from gector import GecBERTModel
-from faster_whisper import WhisperModel, BatchedInferencePipeline
-from transformers.models.whisper.english_normalizer import BasicTextNormalizer
-from text_processing.inverse_normalize import InverseNormalizer
-import time
-inverse_normalizer = InverseNormalizer('vi')
-current_dir = Path(__file__).parent.as_posix()
-whisper_model = WhisperModel("pho_distill_q8", device="auto", compute_type="auto")
-batched_model = BatchedInferencePipeline(model=whisper_model, use_vad_model=True, chunk_length=15)
-gector_model = GecBERTModel(
- vocab_path=os.path.join(current_dir, "gector/vocabulary"),
- model_paths=[os.path.join(current_dir, "gector/Model_GECTOR")],
- split_chunk=True
-)
-normalizer = BasicTextNormalizer()
-
-####start transcriptions#####
-start = time.time()
-segments, info = batched_model.transcribe("HA1.wav", language="vi", batch_size=32)
-transcriptions = [segment.text for segment in segments]
-normalized_transcriptions = [inverse_normalizer.inverse_normalize(normalizer(text)) for text in transcriptions]
-corrected_texts = gector_model(normalized_transcriptions)
-print(''.join(text for text in corrected_texts))
-print(time.time() - start)
diff --git a/whisper_pipeline/pho_distill_q8/config.json b/whisper_pipeline/pho_distill_q8/config.json
deleted file mode 100644
index e5047537059bd8f182d9ca64c470201585015187..0000000000000000000000000000000000000000
--- a/whisper_pipeline/pho_distill_q8/config.json
+++ /dev/null
@@ -1,239 +0,0 @@
-{
- "alignment_heads": [
- [
- 5,
- 3
- ],
- [
- 5,
- 9
- ],
- [
- 8,
- 0
- ],
- [
- 8,
- 4
- ],
- [
- 8,
- 7
- ],
- [
- 8,
- 8
- ],
- [
- 9,
- 0
- ],
- [
- 9,
- 7
- ],
- [
- 9,
- 9
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-{
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- "Ġf un",
- "j ect",
- "Ġimport ant",
- "Ġs ur",
- "e w",
- "at es",
- "Ġ 5",
- "Ġd i",
- "Ġm ade",
- "Ġin s",
- "Ġas k",
- "Ġ et",
- "Ġn um",
- "Ġc ar",
- "ĠO kay",
- "Ġs im",
- "i k",
- "Ġl ast",
- "ĠG o",
- "Ġm us",
- "Ġre l",
- "ul ar",
- "´ ì",
- "ĠWe ll",
- "pe ct",
- "ĠTh ank",
- "Ġth ree",
- "Ã £",
- "ã ĥ",
- "Ġin v",
- "Ġg en",
- "l ic",
- "Ġhapp en",
- "ë Ĭ",
- "i en",
- "e ver",
- "оР²",
- "Ġst r",
- "ĠA ll",
- "Ġin st",
- "Ġâ Ģ",
- "Ġde f",
- "Ġs l",
- "Ġm ight",
- "un g",
- "Ġye ar",
- "Ġo wn",
- "Ġke ep",
- "b ody",
- "d er",
- "Ġ ÑĤ",
- "ĠÐ ´",
- "Ġan other",
- "Ġm od",
- "Ġe v",
- "Ġgu ys",
- "Ġab le",
- "ã o",
- "qu e",
- "id ent",
- "ĠY es",
- "Ġit s",
- "Ġpl ace",
- "Ġpro du",
- "ar n",
- "ĠÐ ¼",
- "Ġre p",
- "Ġex per",
- "Ġf am",
- "it ies",
- "if ic",
- "Ġh igh",
- "i ed",
- "o ol",
- "ie w",
- "е ÑĤ",
- "re n",
- "Ġdon e",
- "Ġ ...",
- "ëĬ Ķ",
- "st em",
- "ĠS e",
- "Ġbet ter",
- "c ome",
- "Ġd el",
- "Ġt y",
- "Ġu m",
- "Ġh o",
- "ĠA n",
- "Ġm on",
- "ing s",
- "Ġs k",
- "Ġo b",
- "c om",
- "ble m",
- "op e",
- "st and",
- "' d",
- "ment s",
- "Ġe le",
- "ĠI s",
- "Ġd a",
- "Ġre g",
- "le ase",
- "i ke",
- "al s",
- "iz e",
- "ê °",
- "Ġc are",
- "Ġne ver",
- "ìĿ ´",
- "es e",
- "Ġm et",
- "ol og",
- "ĠWh en",
- "u ck",
- "е ÑĢ",
- "Ġ é",
- "Ġd at",
- "Ã §",
- "Ġex am",
- "il ity",
- "Ġd et",
- "c ri",
- "Ġus ed",
- "ĠD o",
- "Ġtr ans",
- "e g",
- "t en",
- "Ñ İ",
- "c us",
- "Ġsec ond",
- "Ġb est",
- "Ġh ard",
- "Ġ ide",
- "Ġpro blem",
- "ê ³",
- "ĠU n",
- "Ñ ħ",
- "Ġ Î",
- "Ġw atch",
- "ĠS h",
- "at ter",
- "Ġpre t",
- "Ġd er",
- "Ġcour se",
- "Å Ł",
- "at ive",
- "ic s",
- "Ġquest ion",
- "ut e",
- "ì Ĺ",
- "ĠF or",
- "at her",
- "Ġc ol",
- "i end",
- "Ġ í",
- "Ġ Z",
- "Ġdoes n",
- "ar ch",
- "Ġinter est",
- "Ġp ol",
- "Ġc or",
- "i ence",
- "Ġp res",
- "Ġe ach",
- "Ġsy stem",
- "Ġf act",
- "i el",
- "ab ly",
- "Ġ er",
- "Ġr un",
- "Ġì Ŀ",
- "Ġto p",
- "n er",
- "Ġth ought",
- "Ġe as",
- "i ent",
- "Ġc re",
- "Ñ Ī",
- "Ġcomm un",
- "y e",
- "re ady",
- "ll ow",
- "Ġevery thing",
- "om m",
- "Ġm ed",
- "ļ Ķ",
- "Ġc ount",
- "it s",
- "Ġcom pl",
- "h ip",
- "Ù Ħ",
- "o ok",
- "Ġto get",
- "Ġtoget her",
- "am p",
- "Ġg ame",
- "Ġal ready",
- "аР»",
- "Ġcall ed",
- "al e",
- "Å Ĥ",
- "ĠM y",
- "Ġunder stand",
- "Ġd r",
- "Ġm om",
- "it ed",
- "оР»",
- "Ġus ing",
- "z y",
- "Ġnum ber",
- "ãĢ ģ",
- "c ed",
- "Ġc le",
- "н о",
- "ëĭ ¤",
- "in ce",
- "Ġlook ing",
- "Ġpret ty",
- "Ġpro b",
- "ĠS he",
- "Ġ ve",
- "Ġget ting",
- "Ġwe ek",
- "Ġe ff",
- "u ff",
- "a ir",
- "u es",
- "er n",
- "Ġ Q",
- "ou p",
- "ent ion",
- "Ġs ide",
- "оР¼",
- "Ġfor m",
- "Ġb us",
- "Ġas s",
- "Ġ ed",
- "as on",
- "we en",
- "âĢ ¦",
- "Ġt urn",
- "Ġc ur",
- "Ġco ll",
- "Ġd ire",
- "ĠG od",
- "Ġ1 0",
- "Ġe qu",
- "ĠÐ ±",
- "Ġop en",
- "Ġsu ch",
- "ir d",
- "аРº",
- "Ġe ar",
- "Ä Ļ",
- "g an",
- "Ġpart ic",
- "Ġfr iend",
- "Ġex p",
- "Ġex t",
- "Ġh ome",
- "Ġw ater",
- "ĠO n",
- "ÑĤ ÑĮ",
- "or k",
- "Ġп ÑĢ",
- "Ġmo ve",
- "n ess",
- "en se",
- "h o",
- "Ġch ar",
- "c o",
- "in s",
- "Ġb oth",
- "Ġ1 9",
- "Ġg ra",
- "Ġbet ween",
- "á »",
- "Ġì ķ",
- "as h",
- "ĠR e",
- "a i",
- "al th",
- "u res",
- "em ber",
- "Ġa v",
- "Ġ ver",
- "Ã ª",
- "one y",
- "Ġth ank",
- "Ġmay be",
- "u c",
- "im e",
- "ê³ ł",
- "Ġa way",
- "Ġn ame",
- "ou se",
- "Ġac c",
- "Ġmus ic",
- "Ġch ange",
- "Ġp ass",
- "g er",
- "Ġbu ild",
- "Ġv al",
- "in ess",
- "an y",
- "Ġfe w",
- "´ ë",
- "t a",
- "Ġl ist",
- "Ã ¥",
- "Ġo ld",
- "Ġì ŀ",
- "Ġs ort",
- "Ġme m",
- "Ġc a",
- "ce pt",
- "Ġgen er",
- "Ġye ah",
- "Ġwh ile",
- "Ġany thing",
- "r ic",
- "gr am",
- "Ġe in",
- "c y",
- "ur ing",
- "ĠD e",
- "Ġp ower",
- "Ġcom ing",
- "Ġwor d",
- "Ġ- -",
- "Ġbel ie",
- "Ġf ound",
- "t o",
- "Ð ¿",
- "Ġme ans",
- "Ġin form",
- "Ġ Ø",
- "Ġ Ñĩ",
- "Ġsm all",
- "00 0",
- "Ġc ame",
- "Ġ íķ",
- "w h",
- "Ġwork ing",
- "Ġexam ple",
- "Ġp os",
- "Ġde p",
- "ê ²",
- "ä º",
- "ot e",
- "Ġde m",
- "ì §",
- "t s",
- "Ġv ar",
- "a ut",
- "Ġt ri",
- "ch n",
- "Ġhe ad",
- "Ġwho le",
- "× Ļ",
- "z e",
- "Ġtry ing",
- "Ġt em",
- "Ġc ou",
- "et s",
- "Ġ 6",
- "Ġf il",
- "vel op",
- "Ġc ase",
- "à ¯",
- "Ġprob ably",
- "Ġo kay",
- "Ġpl an",
- "Ġs it",
- "Ġsch ool",
- "ĠTh en",
- "¸ ë",
- "m e",
- "Ġpro cess",
- "Ġf ar",
- "Ġre ad",
- "Ġp oss",
- "Ġb re",
- "Ġso l",
- "ic ht",
- "Ġsupp ort",
- "ĠT o",
- "ert ain",
- "Ġstart ed",
- "Ġc ap",
- "Ġle ft",
- "Ġdat a",
- "Ġtim es",
- "еР»",
- "Ġwant ed",
- "а н",
- "Ġtalk ing",
- "Ġis t",
- "Ġha ving",
- "um p",
- "Ġcont in",
- "Ġsu b",
- "ĠÐ ·",
- "p r",
- "ëĭ Ī",
- "in a",
- "Å ¼",
- "Ġc reat",
- "od e",
- "× ķ",
- "æ ĺ",
- "! !",
- "Ġt erm",
- "is m",
- "оР´",
- "ĠBe cause",
- "Ġw ent",
- "id er",
- "Ġpro v",
- "Ġch ild",
- "Ġd en",
- "Ġl ight",
- "b r",
- "³ о",
- "o h",
- "Ġbo ok",
- "Ġ Ù",
- "ut ion",
- "ĠJ ust",
- "en e",
- "Ġf our",
- "Ġv is",
- "ê° Ģ",
- "Ġh ope",
- "Ġmak ing",
- "ĠL e",
- "ì ķ",
- "Ġo pp",
- "a u",
- "Ġm oney",
- "Ġpro gram",
- "Ã ¨",
- "Ġst and",
- "I N",
- "Ġs ign",
- "Ġle arn",
- "Ã ł",
- "ĠD on",
- "Ġte am",
- "Ġн а",
- "l ud",
- "Ġre st",
- "ic es",
- "æ ľ",
- "Ġ ÑĢ",
- "Ġa ut",
- "Ġle ad",
- "ation al",
- "d e",
- "g y",
- "Ġn ice",
- "Ġd as",
- "Ġd ist",
- "Ġh um",
- "ĠO ne",
- "æ Ī",
- "Ġcom es",
- "Ġj o",
- "Ġc ent",
- "Ġex pl",
- "Ġm ark",
- "re en",
- "l ed",
- "g in",
- "ì ļĶ",
- "Ġle vel",
- "Ġcon f",
- "us h",
- "Ġde velop",
- "Ġt est",
- "en g",
- "v ious",
- "at ure",
- "еР¼",
- "re t",
- "Ġj e",
- "Ġst uff",
- "Ġcl ass",
- "ow s",
- "Ġê ·",
- "Ġs i",
- "Ġl es",
- "ro p",
- "ç ļ",
- "Ġp or",
- "Ġw ar",
- "ìĹ IJ",
- "Ġevery one",
- "Ġg e",
- "Ġche ck",
- "ot t",
- "Ġs ing",
- "Ġar t",
- "Ġfo llow",
- "Ġ20 1",
- "ĠF r",
- "a is",
- "ì ĸ",
- "Î ±",
- "å °",
- "ĠÃ ł",
- "im es",
- "Ġre t",
- "Ġch ang",
- "Ġp ub",
- "Ġin f",
- "Ġte chn",
- "ad a",
- "iv es",
- "Ġbe h",
- "æĺ ¯",
- "Ġlook s",
- "ãĢ Ĥ",
- "Ð ·",
- "ĠWh y",
- "çļ Ħ",
- "Ġen ough",
- "Ġb ra",
- "it ch",
- "ä »",
- "Ġad v",
- "Ð ±",
- "Ġwith out",
- "w er",
- "mer ic",
- "d en",
- "Ġcompl et",
- "Ġide a",
- "ter s",
- "o ck",
- "Ġdef in",
- "Ġe ver",
- "Ġg l",
- "Ġon ce",
- "Ġbr ing",
- "Ġsay ing",
- "Ġan s",
- "Ġhe ar",
- "n ect",
- "Ġl ess",
- "g o",
- "re am",
- "ad o",
- "ì ŀ",
- "Ġm ind",
- "ent e",
- "Ġf ull",
- "Ġb ad",
- "Ġw om",
- "Ġsome one",
- "Ġd u",
- "Ġw on",
- "Ġcont ro",
- "ort un",
- "Ġhe alth",
- "Ġch o",
- "ĠA r",
- "Ġcon c",
- "Ġinform ation",
- "Ġst op",
- "at t",
- "at ely",
- "ä ½",
- "Ġgr oup",
- "Ġ Ñĥ",
- "Ġqu ite",
- "Ġres p",
- "E R",
- "ug ht",
- "ê ¸",
- "m an",
- "iz ed",
- "ĠB r",
- "Ġrem ember",
- "Ġfam ily",
- "Ġbus iness",
- "a w",
- "Ġspe c",
- "Ġa u",
- "ĠO r",
- "Ä ħ",
- "Ġse en",
- "Ġl ar",
- "Ġ 7",
- "g g",
- "b ers",
- "Ġd ra",
- "Ġmon th",
- "Ġsay s",
- "Ġis s",
- "Ġli ve",
- "Ġl ine",
- "Ġmom ent",
- "Ġex c",
- "el s",
- "Ġs ound",
- "Ġco ol",
- "Ġlo c",
- "Ġc ertain",
- "Ġd ri",
- "о ÑĤ",
- "am es",
- "Ġm ust",
- "n y",
- "и ÑĤ",
- "Ġk id",
- "Ġinc lud",
- "ìĿ Ħ",
- "at or",
- "Ä Ł",
- "h a",
- "are d",
- "Ġse em",
- "Ð ¹",
- "ì Ħ",
- "Ġel se",
- "Ġì ł",
- "ir l",
- "Ġ 8",
- "Ġv o",
- "Ġquest ions",
- "in es",
- "e e",
- "æĪ ij",
- "ü r",
- "ĠA meric",
- "Ġst ory",
- "Ġser v",
- "ver n",
- "ag es",
- "l and",
- "ĠâĢ ĵ",
- "er a",
- "ĠC an",
- "Ġp op",
- "et her",
- "Ġn a",
- "Ġor der",
- "Ġmak es",
- "Ġs ince",
- "c on",
- "ct or",
- "Ġth ough",
- "Ġprodu ct",
- "л и",
- "Ġle g",
- "Ġme et",
- "al f",
- "Ñģ Ñı",
- "un ch",
- "it er",
- "o ve",
- "×ķ ×",
- "i et",
- "аР¼",
- "it al",
- "Ġsu per",
- "l ing",
- "Ġp ay",
- "Ġpar a",
- "Ġj ob",
- "ĠH ere",
- "Ġs w",
- "k s",
- "pt ion",
- "m a",
- "Ġbelie ve",
- "¬ ë",
- "Ġw ait",
- "оР¹",
- "Ġun t",
- "Ġqu ick",
- "h r",
- "ĠÑ į",
- "ĠP ro",
- "Ġm en",
- "à ¹",
- "Ġday s",
- "Ġgo es",
- "Ġspe ak",
- "ĠA t",
- "em ent",
- "Ġm iss",
- "Ġa w",
- "Ġdes ign",
- "Ġpro ject",
- "о ÑĢ",
- "i j",
- "ant s",
- "at s",
- "ĠCh r",
- "Ġ 9",
- "Ġc ut",
- "Ġre qu",
- "Ġн е",
- "ĠN ot",
- "as ter",
- "Ġm ill",
- "Ġpartic ular",
- "Ġp ie",
- "Ġstud ents",
- "Ġf ive",
- "ou n",
- "ĠN e",
- "Ġg i",
- "Ġp as",
- "Ġf ree",
- "ĠS p",
- "l ich",
- "Ġpro f",
- "Ġen g",
- "Ġpr ot",
- "ĠL ike",
- "os ed",
- "Ġcon nect",
- "a pp",
- "Ġë §",
- "it ing",
- "Ġb lo",
- "Ġl os",
- "ist s",
- "Ġexper ience",
- "re nt",
- "Ġst ay",
- "Ġfo od",
- "t on",
- "ru ct",
- "Ġh ist",
- "v iew",
- "in ing",
- "m ost",
- "i vers",
- "b o",
- "ãģ Ħ",
- "ĠT r",
- "g en",
- "Ġp lease",
- "Ġcommun ity",
- "Ġc e",
- "A N",
- "n o",
- "Ġb ody",
- "Ġh our",
- "Ġ vers",
- "á º",
- "c er",
- "Ġê °",
- "Ġre ason",
- "ĠR ight",
- "Ġl ater",
- "Ï Ħ",
- "Ġh ouse",
- "Ġ X",
- "оР½",
- "Ġst ate",
- "f ic",
- "å ¤",
- "Å Ľ",
- "iel d",
- "Ġp ri",
- "Ġp ast",
- "Ġw alk",
- "olog y",
- "er ing",
- "an na",
- "Ġt er",
- "Ġho ld",
- "Ġor gan",
- "b en",
- "Î ¿",
- "ó n",
- "Ġeff ect",
- "Ġyour self",
- "Ġpl us",
- "a j",
- "and o",
- "ur al",
- "Ġro om",
- "le ct",
- "ê² Į",
- "? \"",
- "s ide",
- "Ġbe come",
- "Ñ Ĩ",
- "Ġ Â",
- "o od",
- "Ġcon st",
- "Ġn ight",
- "ut es",
- "Ð ¶",
- "Ġbre ak",
- "Ġp ain",
- "Ġst ep",
- "ire d",
- "Ġnot hing",
- "Ġunt il",
- "Ñ ĸ",
- "аР²",
- "Ù Ĭ",
- "Ġd uring",
- "ì§ Ģ",
- "l ess",
- "o ll",
- "н Ñĭ",
- "Î ¹",
- "f ect",
- "i ver",
- "ı Ħ",
- "ith er",
- "y ing",
- "Ġbe gin",
- "×Ļ ×",
- "iv id",
- "ĠÃ §",
- "Ġs al",
- "Ġt a",
- "Ġp ot",
- "Ġ $",
- "Ġm ar",
- "Ġcle ar",
- "Ġf ace",
- "Ġgr ow",
- "Ġ *",
- "Ġins ide",
- "Ġfriend s",
- "Ġle ave",
- "en n",
- "Ġeas y",
- "Ġare a",
- "al ity",
- "ou d",
- "Ġe at",
- "Ù Ĩ",
- "Ġp ur",
- "or n",
- "Ġsa w",
- "Ġans wer",
- "Ġfr ont",
- "Ġbe aut",
- "¼ ë",
- "Ġm atter",
- "Ġs on",
- "ĠN ew",
- "Ġres ult",
- "id es",
- "ch e",
- "Ġf ut",
- "p s",
- "Ġfo cus",
- "Ġinterest ing",
- "å ¥",
- "Ġa p",
- "\" .",
- "Ġcre ate",
- "о Ñģ",
- "Ġp ress",
- "r oss",
- "Ġp ick",
- "l ine",
- "Ġto ok",
- "ĠM ay",
- "r ow",
- "Ġ ich",
- "ĺ ë",
- "Ġre f",
- "Ġm or",
- "r act",
- "are nt",
- "A R",
- "Ġex act",
- "Ġsp ace",
- "w ork",
- "н и",
- "Ġb ir",
- "Ġde v",
- "Ð ³",
- "Ġto ld",
- "Ġpub lic",
- "ci ally",
- "Ġv iew",
- "ĠHe y",
- "m ed",
- "ll o",
- "c c",
- "Ġf ac",
- "Ġcou ple",
- "Ġhe art",
- "l er",
- "Ġre ady",
- "Ġal most",
- "ar ing",
- "Ġh alf",
- "ĠM e",
- "av or",
- "i que",
- "Ġchar ac",
- "Ġpr act",
- "O N",
- "an e",
- "Ġ il",
- "н а",
- "Ġv i",
- "l ish",
- "he ad",
- "Ġle ast",
- "Ġbas ically",
- "as ed",
- "r ight",
- "Ġy et",
- "Ġtak ing",
- "Ġcount ry",
- "Ġw in",
- "Ġis n",
- "Ġposs ible",
- "Ġc am",
- "Ġinc re",
- "Ġp at",
- "Ġw anna",
- "Ġcons ider",
- "Ġab s",
- "Ġwith in",
- "Ġhum an",
- "Ġthink ing",
- "Ġo h",
- "¡ ľ",
- "Ġqu i",
- "as es",
- "Ġ 0",
- "it ely",
- "ä¸ į",
- "Ġk ill",
- "Ġm il",
- "Ġinv est",
- "is ter",
- "Ġsu c",
- "ion al",
- "el f",
- "Ġwh ether",
- "Ġcontro l",
- "Ġagain st",
- "ot s",
- "ëĭĪ ëĭ¤",
- "i or",
- "Ġpres ent",
- "Ġ ا",
- "Ġwatch ing",
- "u be",
- "er v",
- "Ġn icht",
- "Ġgo vern",
- "ĠTh ese",
- "Ġ :",
- "u it",
- "ug h",
- "Ġwork s",
- "o o",
- "Ġw ir",
- "Ġa ir",
- "ĠT e",
- "аР·",
- "is ion",
- "wh ere",
- "Ġto t",
- "j oy",
- "ì ĭ",
- "Ġv ol",
- "ĠÐ µ",
- "Ġcl ose",
- "ĠA d",
- "Ñ ī",
- "in ed",
- "Ġun a",
- "Ġê· ¸ë",
- "° ë",
- "or ry",
- "Ġb ro",
- "Ġfil m",
- "if t",
- "2 0",
- "Ġty pe",
- "Ġhappen ed",
- "ĠA m",
- "Ġg irl",
- "ĠA re",
- "ward s",
- "Ġp our",
- "Ġcol or",
- "el t",
- "а Ñģ",
- "Ġs ense",
- "le x",
- "ĠW ith",
- "us s",
- "ri b",
- "Ġre se",
- "Ġn orm",
- "Ġfut ure",
- "Ġde al",
- "end ing",
- "e y",
- "Ġ x",
- "er o",
- "ĠC l",
- "u k",
- "Ġwhat ever",
- "sel ves",
- "Ġyou ng",
- "ì Ĭ",
- "ĠM ar",
- "ĠChr ist",
- "Ġgu ess",
- "Ġper form",
- "Ġen er",
- "r on",
- "Ġh it",
- "Ġw ond",
- "Ġdire ct",
- "ĠE very",
- "Ġof ten",
- "Ġf a",
- "Ġal ong",
- "Ġcl ick",
- "ĠL ook",
- "Ġsit u",
- "Ġhapp y",
- "e ad",
- "Ġag o",
- "Ġen c",
- "Ġmy self",
- "Ġco ver",
- "оР±",
- "Ġm id",
- "Ġc ost",
- "Ġt en",
- "ĠS ch",
- "Ġex pect",
- "Ġwas n",
- "Ġstr ong",
- "if ul",
- "Ġopp ortun",
- "in al",
- "y le",
- "Ġsh are",
- "Ġtr ue",
- "Ġapp ro",
- "Ġch all",
- "Ġmin utes",
- "Ġch ann",
- "Ġë Ĥ",
- "Î µ",
- "l i",
- "Ġm ess",
- "or ies",
- "pe cially",
- "Ġwr ong",
- "Ġy es",
- "Ġì Ĺ",
- "ir on",
- "Ġall ow",
- "Ġsu bs",
- "Ġf ore",
- "Ġf ight",
- "Ġso cial",
- "Ġc ra",
- "an a",
- "Ġa ff",
- "Ġ ess",
- "Ġway s",
- "Ġsh ort",
- "Ġf all",
- "Ġla w",
- "ĠWh o",
- "Ġen joy",
- "Ġc al",
- "Ġac cess",
- "f e",
- "Ġn on",
- "Ġac ross",
- "er y",
- "vious ly",
- "ĠE x",
- "id ed",
- "Ġl ink",
- "ĠP r",
- "Ġterm s",
- "ac es",
- "Ġl and",
- "az ing",
- "Ġ1 5",
- "Ġm ult",
- "Ġspe cial",
- "å Ģ",
- "iv ing",
- "ìĿ Ģ",
- "Ġty p",
- "Ġst e",
- "Ġ Ä",
- "Ġfor ward",
- "å ı",
- "Ġf re",
- "å¥ ½",
- "Ġrese arch",
- "௠į",
- "а ÑĤ",
- "Ġma in",
- "Ġrec ord",
- "Ġh u",
- "Ġdefin itely",
- "Ġe ither",
- "Ġlist en",
- "Ġke y",
- "Ġmark et",
- "ĠÑĩ ÑĤо",
- "iz ation",
- "Ġvide os",
- "Ġgu y",
- "Ġf ig",
- "Ġst ra",
- "ĠP l",
- "ull y",
- "am os",
- "Ġm ention",
- "Ġs ong",
- "Ġinter n",
- "r al",
- "ur s",
- "Ġh on",
- "Ġval ue",
- "Ġb ar",
- "c le",
- "оР¶",
- "Ä ĩ",
- "ľ ë",
- "Ġz u",
- "и м",
- "ä½ ł",
- "Ġsing le",
- "Ġa uch",
- "cus s",
- "Ġget s",
- "Ġsomet imes",
- "å ¾",
- "am b",
- "m m",
- "c ing",
- "Ġper fect",
- "ĠB l",
- "out h",
- "ì ł",
- "Ġs ci",
- "p ar",
- "Ġre d",
- "Ġp ost",
- "Ġm ot",
- "Ġele ct",
- "ĠE u",
- "it ive",
- "ĠS ome",
- "Ġdes cri",
- "Ġcur rent",
- "é s",
- "Ġt re",
- "ĠE n",
- "Ġm it",
- "E N",
- "Ī ë",
- "i um",
- "Ġhe ard",
- "Ġsim ple",
- "l ar",
- "Ġevery body",
- "il ar",
- "Ġneed s",
- "Ġdif fic",
- "ĠGo od",
- "um ent",
- "c ent",
- "Ġo per",
- "а ÑĤÑĮ",
- "et y",
- "Ġbl ack",
- "Ġgi ven",
- "on es",
- "Ġwe l",
- "é Ģ",
- "Ġìķ Ħ",
- "Ġ3 0",
- "A T",
- "Ġst at",
- "ou ch",
- "ĠM r",
- "а ÑĢ",
- "Ġsh o",
- "Ġcon d",
- "× Ķ",
- "m y",
- "Ġchild ren",
- "Ġe u",
- "еР´",
- "ìķ Ħ",
- "ter n",
- "Ġu h",
- "Ġh ar",
- "Ġpr om",
- "Ġp ull",
- "re w",
- "Ġcomp any",
- "Ġbeaut iful",
- "ust om",
- "íķ ĺ",
- "к и",
- "Ġst re",
- "Ġam azing",
- "ri es",
- "Ġsuc cess",
- "Ġm ach",
- "n ot",
- "Ġdis cuss",
- "Ġn at",
- "¦ ¬",
- "Ġun e",
- "Ġdiffic ult",
- "Ġr is",
- "Î ½",
- "Ġc amp",
- "Ġbu y",
- "ä¸ Ģ",
- "Ġma g",
- "p o",
- "ĠY our",
- "Ġbeh ind",
- "ic a",
- "ı n",
- "ĠO K",
- "Ġl ang",
- "Ġwom en",
- "Ġen v",
- "Ġre ce",
- "Ġchann el",
- "i ally",
- "u le",
- "Ġ1 2",
- "th ers",
- "Ġb ott",
- "Ġrep ort",
- "ent ly",
- "f ully",
- "T he",
- "Ġs ent",
- "Ġev ent",
- "Ġener gy",
- "l t",
- "Ġword s",
- "ar r",
- "d le",
- "Ġa head",
- "ard s",
- "Ø ±",
- "äº Ĩ",
- "Ġto ol",
- "con om",
- "е Ñģ",
- "Ġexact ly",
- "Ġf avor",
- "Ġl ow",
- "Ġpro per",
- "Ġìŀ Ī",
- "Ġ !",
- "Ġrel ations",
- "Ġm as",
- "Ġkid s",
- "Ġent ire",
- "ud e",
- "Ù ħ",
- "ĠWh ere",
- "Ġon es",
- "Ġc ity",
- "ol ut",
- "Ġs ix",
- "ab ility",
- "ö r",
- "il i",
- "ĠE s",
- "Ġhapp ens",
- "ain s",
- "Ġmod el",
- "Ġp ict",
- "Ġes pecially",
- "Ġ1 00",
- "k t",
- "Ġso on",
- "b y",
- "ro du",
- "Ġan n",
- "Ġsubs cri",
- "ĠQ u",
- "Ġav ail",
- "im ent",
- "Ġv oc",
- "k a",
- "Ġ2 00",
- "ap er",
- "ĠI nd",
- "Ġì §",
- "h or",
- "į °",
- "j or",
- "и л",
- "Ġs qu",
- "A U",
- "ar ning",
- "ĠÐ ³",
- "I S",
- "ĠÐ »",
- "еР¹",
- "y es",
- "å ħ",
- "ĠÐ Ĵ",
- "Ġor ig",
- "оР³Ð¾",
- "Ġask ed",
- "il t",
- "оР³",
- "Ġcontin ue",
- "Ġì ĺ",
- "r am",
- "Ġo thers",
- "E S",
- "oh n",
- "Ġl ay",
- "Ġbas ed",
- "Ġp u",
- "Ġapp e",
- "Ġl im",
- "Ġpro p",
- "Ģ ë",
- "m in",
- "Ġh ot",
- "ĠL a",
- "Ġf ast",
- "Ġprot ect",
- "Ġam ount",
- "Ġa qu",
- "Ġf und",
- "Ġc ustom",
- "Ġc ult",
- "Ġhand s",
- "Ġha ven",
- "Ġa ud",
- "Ġout side",
- "ĠA fter",
- "ap s",
- "Ġan im",
- "pl oy",
- "Ġh at",
- "ĠF irst",
- "Ġt reat",
- "Ġe p",
- "Ġm ater",
- "Ġbuild ing",
- "Ġë °",
- "å IJ",
- "ìĦ ľ",
- "z a",
- "ught er",
- "ĠP e",
- "ne y",
- "et er",
- "at ic",
- "Ġed uc",
- "ê¸ °",
- "Ġmo v",
- "ĵ ¤",
- "am a",
- "r ation",
- "Ġs n",
- "Ù Ī",
- "Ġs um",
- "Ġph ot",
- "ĠÐ Ŀ",
- "Ġ .",
- "æľ ī",
- "Ġfin ish",
- "itt ing",
- "å ®",
- "Ġlar ge",
- "Ġì ĸ",
- "Ġwh ite",
- "ar a",
- "Ġma is",
- "ĠH i",
- "Ġd am",
- "Ġا ÙĦ",
- "Ġbo x",
- "ĠHe llo",
- "Ġs le",
- "Ġo pt",
- "ri ed",
- "¥ ¼",
- "Ġact iv",
- "Ġn ão",
- "ĠC om",
- "Ġplay ing",
- "T h",
- "Ġavail able",
- "Ġp ort",
- "å Ī",
- "ĠA h",
- "Ġl as",
- "Ġear ly",
- "Ġwond er",
- "± °",
- "Ġ1 8",
- "c ul",
- "Ġfun ction",
- "Ġmor ning",
- "ll e",
- "i ents",
- "u x",
- "Ġc ir",
- "it ions",
- "Ġde ep",
- "Ġpol it",
- "y or",
- "m p",
- "ak ing",
- "Į ë",
- "ĠM an",
- "Ġmill ion",
- "Ġ /",
- "Ġind ivid",
- "Ġp an",
- "Ġgovern ment",
- "Ġwr ite",
- "ĠT od",
- "am ent",
- "Ġ Ï",
- "Ġw ind",
- "ĠE ng",
- "ch en",
- "W h",
- "ì ľ",
- "Ġ ident",
- "ãģ §",
- "v ent",
- "ur ch",
- "Ġh y",
- "Ġy a",
- "Ġtr ad",
- "Ġrelations hip",
- "Ã º",
- "Ġd ou",
- "O R",
- "Ġs we",
- "Ġne g",
- "in ation",
- "Ġte xt",
- "i pp",
- "Ġf ine",
- "á s",
- "ĠD r",
- "ĠC ome",
- "Ġmonth s",
- ", \"",
- "ен и",
- "Ġhour s",
- "Ġp od",
- "ir t",
- "Ġinv ol",
- "Ġcoll ect",
- "Ġau f",
- "Ġp a",
- "Ġhist ory",
- "m b",
- "if y",
- "Ġ ?",
- "Ġbel ow",
- "as ure",
- "ab y",
- "Ġlang u",
- "Ġan t",
- "Ġcom b",
- "at o",
- "Ġex ist",
- "Ġë ĭ",
- "Ġtak es",
- "Ġcharac ter",
- "a ff",
- "Ġf ield",
- "Ġe conom",
- "ie f",
- "Ġpie ce",
- "å ľ",
- "Ġre ach",
- "Ġê ²",
- "on y",
- "Ġmater ial",
- "Ġd ig",
- "Ġph ys",
- "Ġimp ro",
- "Ġsim ilar",
- "I C",
- "Ġn et",
- "y n",
- "Ġpos ition",
- "Ã Ł",
- "Ġb ene",
- "re ad",
- "Ġle arning",
- "um e",
- "Ġcle an",
- "ÑĤо ÑĢ",
- "Ġco ok",
- "Ġseem s",
- "Ġo l",
- "ĠU S",
- "ĠJ es",
- "Ġ à®",
- "ent ial",
- "ivers ity",
- "ac y",
- "Ġ Ñı",
- "olut ely",
- "re ct",
- "ĠP lease",
- "Ġrep res",
- "Ġt ouch",
- "m en",
- "ĠÐ °",
- "i ón",
- "ĠThank s",
- "Ġan g",
- "Ġma jor",
- "Ġit self",
- "ill s",
- "\" ,",
- "i ans",
- "Ġsc reen",
- "Ġh or",
- "Ġknow n",
- "Ġenv iron",
- "Ġfin al",
- "Ġfig ure",
- "ĠT w",
- "Ġe yes",
- "Ġim ag",
- "Ġsee ing",
- "Ġha ir",
- "re m",
- "Ġapp lic",
- "end s",
- "p ut",
- "Ġnew s",
- "Ġcomplet ely",
- "ugh s",
- "Ġkn ew",
- "if ied",
- "ĠJ e",
- "ĠD id",
- "Ġsitu ation",
- "Ġf lo",
- "m s",
- "Ġph one",
- "Ġb all",
- "d o",
- "Ġp arent",
- "Ġs orry",
- "ur y",
- "и н",
- "ip s",
- "аР´",
- "Ġinst ead",
- "Ġhu ge",
- "Ġt u",
- "Ġ ãģ",
- "ĠG r",
- "Ġdet ail",
- "ĠÐ Ł",
- "Ġindivid ual",
- "Ġf ire",
- "Ġcl os",
- "Ġw er",
- "un e",
- "Ġrun ning",
- "Ġcon vers",
- "Ġrec omm",
- "Ġcom o",
- "Ġsome body",
- "ĠJ ohn",
- "ĠìĿ ´",
- "ĠO ur",
- "pl es",
- "ĠP h",
- "Ġan al",
- "Ġ5 0",
- "Ġof fer",
- "Ġ <",
- "ition al",
- "g est",
- "Ġv ous",
- "l et",
- "ic y",
- "Ġfeel ing",
- "L E",
- "r os",
- "Ġth ird",
- "оРº",
- "Ġser ies",
- "ĠAn y",
- "is ed",
- "o ld",
- "Ġdra w",
- "Ġserv ice",
- "Ġcan not",
- "b al",
- "ãģ Ĩ",
- "Ġli ving",
- "ı m",
- "Ġdiffer ence",
- "Ġopportun ity",
- "Ġne ar",
- "or th",
- "k en",
- "Ġloc al",
- "Ø ª",
- "ĠC on",
- "Ġob ject",
- "Ġd ass",
- "ãģ Ļ",
- "IJ ×",
- "Ġquick ly",
- "ra ph",
- "Ġiss ues",
- "éĢ Ļ",
- "ĠAmeric an",
- "Ġpre p",
- "en ces",
- "Ġprof ess",
- "ll ing",
- "o f",
- "Ġfo ot",
- "b re",
- "Ġus ually",
- "Ġgener al",
- "d a",
- "an ces",
- "Ġd est",
- "Ġo cc",
- "Ġmem bers",
- "Ġd ans",
- "Ġequ al",
- "z t",
- "Ġbe com",
- "Ġmo ving",
- "Ġspec ific",
- "ÃŃ a",
- "Ġf ur",
- "Ġne cess",
- "Ġcomm on",
- "Ġatt ack",
- "ĠÑį ÑĤо",
- "ĠTod ay",
- "Ġun s",
- "ĠG u",
- "i od",
- "Ġacc ount",
- "Ġgra nd",
- "Ġs elf",
- "ĠE l",
- "Ġt ast",
- "Ġcont ent",
- "Ġc u",
- "Ħ ë",
- "ĠMay be",
- "ĠJes us",
- "ore s",
- "p ort",
- "© ´",
- "Ġg ives",
- "Ġnorm al",
- "ÑĢ Ñĥ",
- "Ġimp act",
- "ä r",
- "Ġd ies",
- "Ġl ab",
- "s h",
- "i os",
- "ĠP res",
- "ĠU nd",
- "ĠO f",
- "Ġfin ally",
- "Ġdo ll",
- "Ġvoc ê",
- "p ly",
- "ĠA g",
- "Ġtak en",
- "Ġgr ound",
- "f ort",
- "Ġg ave",
- "ĠIn st",
- "Ġl ost",
- "Ġwork ed",
- "Ġl iter",
- "Ġiss ue",
- "Ġind ust",
- "Ġret urn",
- "Ġhappen ing",
- "Ġwant s",
- "и в",
- "Ġproblem s",
- "ĠC ar",
- "Ŀ ¼",
- "ĠAl so",
- "Ġs ize",
- "Ġob viously",
- "ĠS u",
- "ĠS c",
- "Ġrecomm end",
- "our ces",
- "ast ic",
- ".. ..",
- "Ġm i",
- "l ier",
- "ĠE ven",
- "ci a",
- "Ġh ur",
- "v a",
- "Ġm ass",
- "Ġwould n",
- "un t",
- "ck s",
- "Ġf elt",
- "os p",
- "l ight",
- "ол ÑĮ",
- "n ie",
- "Ġbott om",
- "Ġб Ñĭ",
- "ore d",
- "is on",
- "Ġgr ad",
- "Ġum a",
- "Ġv a",
- "Ġì Ĥ",
- "ress ion",
- "ul ation",
- "I D",
- "id ence",
- "Ġb ur",
- "Ġg one",
- "l u",
- "ìĸ ´ì",
- "Ġre du",
- "Ġj a",
- "ìĿ ĺ",
- "it a",
- "Ġso ft",
- "Ġç a",
- "ic o",
- "er al",
- "Ã ±",
- "a f",
- "Ġpoint s",
- "g u",
- "Ġd é",
- "ap t",
- "a x",
- "ĠAl right",
- "Ġcam era",
- "Ġa ch",
- "Ġп о",
- "Ġse ver",
- "5 0",
- "Ġs ie",
- "Ï ģ",
- "Ġm al",
- "Ġcomp ut",
- "Ġmid dle",
- "Ġcould n",
- "m ing",
- "Ġì ĭ",
- "ĠH is",
- "Ġg ames",
- "Ġint rodu",
- "Ġc ell",
- "p or",
- "Ġsle ep",
- "Ġë ³",
- "id ing",
- "Ġ ou",
- "Ġde g",
- "Ġdr ink",
- "Ġenviron ment",
- "ĠUn ited",
- "Ġtalk ed",
- "Ġcho ose",
- "Ġj our",
- "e ge",
- "ĠM in",
- "Ġint e",
- "Ġr ather",
- "Ġoff ic",
- "к а",
- "ac hing",
- "Ġmention ed",
- "Ġf ill",
- "Ġtr ack",
- "Ġn ie",
- "Ġ ut",
- "Ġв Ñĭ",
- "ib ility",
- "Ġv ac",
- "Ġr ad",
- "Ġp ack",
- "Ġs end",
- "ĠD as",
- "ĠA b",
- "Ġeng ine",
- "ãģ Ĺ",
- "Ġcomp et",
- "Ã ´",
- "Ġв Ñģ",
- "Ġdo or",
- "Ġlong er",
- "å° į",
- "Ġlangu age",
- "Ġext ra",
- "pl ay",
- "Ġwe bs",
- "um b",
- "ro om",
- "ç ľ",
- "Ġbegin ning",
- "Ġre fer",
- "A M",
- "n en",
- "ig her",
- "f ace",
- "er c",
- "Ġfor get",
- "Ġcom ment",
- "еРº",
- "л Ñı",
- "r or",
- "ż e",
- "ĠG e",
- "Ġd ark",
- "Ġany one",
- "ant e",
- "g es",
- "ìĬ µ",
- "Ñ ij",
- "b ed",
- "j e",
- "ruct ure",
- "Ġpr im",
- "id a",
- "è ¦",
- "ãģ ¾",
- "Ġm ix",
- "Ġstart ing",
- "ĠìĿ ´ë",
- "Ġprov ide",
- "act ion",
- "Ġm other",
- "Ġper iod",
- "Ġst ick",
- "ĠYou T",
- "Ġtechn ology",
- "ê ¹",
- "Ġb ed",
- "Ġg iving",
- "Ġexpl ain",
- "z en",
- "im ate",
- "Ġrepres ent",
- "lo ad",
- "ĠHow ever",
- "Ġli ves",
- "ut h",
- "ir it",
- "og n",
- "Ġli k",
- "Ġresp ons",
- "Ġpri v",
- "Ġto m",
- "ç ão",
- "i am",
- "Ġexc ited",
- "Ġc ard",
- "gr ound",
- "Ġ× Ķ",
- "Ġs ens",
- "Ġte ach",
- "id o",
- "h od",
- "Ġep is",
- "Ġwel come",
- "Ġw all",
- "ä ¹",
- "Ġch ance",
- "h en",
- "ĠÐ ¡",
- "ĠÄ ij",
- "Ġsim ply",
- "ĠÑĤ ак",
- "r ing",
- "j a",
- "b ook",
- "Ġsever al",
- "st e",
- "Ġcreat ed",
- "Ġо ÑĤ",
- "Ġp ush",
- "= =",
- "Ġh igher",
- "u f",
- "our ce",
- "o ke",
- "Ġon line",
- "Ġre le",
- "Ġt on",
- "ens ive",
- "Ġfavor ite",
- "Ñĥ д",
- "Ġlook ed",
- "Ġv on",
- "âĢ Ķ",
- "Ġf ür",
- "Ġbut ton",
- "Ġb ill",
- "Ġchang es",
- "! \"",
- "Ġsl ow",
- "ab les",
- "Ġde ath",
- "and s",
- "ate g",
- "Ġthem selves",
- "ãģ £",
- "Ġc op",
- "ãģ ®",
- "Ġperson al",
- "ug hing",
- "Ġ1 1",
- "g ar",
- "ad es",
- "Ġneed ed",
- "Ġstud y",
- "ag ed",
- "ÑģÑĤ в",
- "in o",
- "Ġdis c",
- "k i",
- "Ġadd ress",
- "× ¨",
- "itt en",
- "es ome",
- "ĠÐ ¶",
- "¤ ë",
- "ur a",
- "Ġm u",
- "Ġcontin u",
- "f or",
- "Ġm atch",
- "ãģ ¦",
- "Ġstra ight",
- "IJ ë",
- "n ers",
- "Ġdo g",
- "Ġde b",
- "ĠC O",
- "Ġo s",
- "g ed",
- "c ame",
- "Ġcor rect",
- "et te",
- "ĠSe e",
- "Ġinclud ing",
- "ĠEu ro",
- "est er",
- "Ġj ump",
- "ĠWh ich",
- "Ġк ак",
- "s on",
- "y a",
- "IN G",
- "Ġe ine",
- "os h",
- "en cy",
- "Ġmed ia",
- "Ġsubscri be",
- "é Ĥ",
- "Ġpr in",
- "Ġha b",
- "ĠP er",
- "ĠW as",
- "Ġp age",
- "it or",
- "Ġto wards",
- "Ġtri ed",
- "en ge",
- "art ment",
- "Ġvar i",
- "Ġp aper",
- "Ġpict ure",
- "Ġvers ion",
- "Ġbr ought",
- "w are",
- "ĠSt ates",
- "Ġs ich",
- "led ge",
- "Ġper cent",
- "Ġgo d",
- "e c",
- "ĠC omm",
- "Ġdec ided",
- "Ġse lect",
- "íķ ľ",
- ") .",
- "ur ity",
- "Ġfur ther",
- "Ġcom ments",
- "le ment",
- "Ġd ream",
- "Ġcent er",
- "m i",
- "Ġc as",
- "Ġwom an",
- "Ġro ad",
- "Ġf ail",
- "Ġbe came",
- "l us",
- "il ities",
- "ãģ ¯",
- "ĠC o",
- "Ġman age",
- "Ġrec ogn",
- "Ġact ion",
- "Ġbene f",
- "Ġear lier",
- "× ľ",
- "Ġspe ed",
- "Ġm ent",
- "Ġso ci",
- "Ġsho ot",
- "u i",
- "ĠÃ ¤",
- "Ġapp ly",
- "v o",
- "x im",
- "Ġca use",
- "Ġsur pr",
- "Ġha ben",
- "D I",
- "Ġf ather",
- "ĠNe xt",
- "ĠYouT ube",
- "Ġc ode",
- "Ġro le",
- "g ress",
- "Ġg reen",
- "et t",
- "Ġbu ilt",
- "Ġfl ow",
- "Ġb ase",
- "Ġtra ining",
- "Ġr ound",
- "ĠW ill",
- "Ġp ath",
- "ĠR o",
- "Ġinterest ed",
- "ìĸ ´",
- "Ġres pect",
- "Ġchang ed",
- "iss ion",
- "Ġstud ent",
- "og raph",
- "Ġappro ach",
- "Ġshow s",
- "å° ±",
- "Ġt ar",
- "Ġcr it",
- "Ġg lo",
- "ìĬµ ëĭĪëĭ¤",
- "Ġde ad",
- "ĠPres ident",
- "Ġth ous",
- "Ġb al",
- "st er",
- "e x",
- "Ġabs olutely",
- "Ġm ic",
- "Ġpract ice",
- "Ġqu ality",
- "Ġl ower",
- "og le",
- "Ġse par",
- "b all",
- "med i",
- "Ġre view",
- "ĠA pp",
- "Ġo k",
- "âĢ ĭ",
- "Ġexper ien",
- "Ġconc ern",
- "ent ially",
- "m ore",
- "ĠJ o",
- "ap an",
- "ĠI ch",
- "ist ic",
- "Ġf air",
- "Ġwebs ite",
- "i res",
- "ĠB y",
- "Ġtra vel",
- "Ġris k",
- "Ġm ir",
- "Ġbo ard",
- "Ġs en",
- "Ġparent s",
- "ĠW ow",
- "Ġfe ed",
- "Ġsa ve",
- "Ġser ious",
- "Ġin it",
- "E L",
- "und red",
- "A S",
- "Ġv an",
- "or row",
- "Ġwor th",
- "Ġse arch",
- "Ġ1 6",
- "Ġpart s",
- "ÑģÑĤ ÑĮ",
- "Ġcomp an",
- "Ġmov ie",
- "Ġmet hod",
- "Ġ ill",
- "Ġw ish",
- "d y",
- "Ġit em",
- "Ġmin us",
- "ang er",
- "Ġvo ice",
- "Ġsk in",
- "Ġare as",
- "Ġe ight",
- "Ġo bs",
- "Ġ ,",
- "аР¹",
- "Ġo il",
- "Ġc y",
- "Ġb aby",
- "s y",
- "Ġem ploy",
- "ĠK e",
- "Ġpl aces",
- "Ġf ix",
- "Ġest á",
- "ãģ ¨",
- "iv ed",
- "Ġlot s",
- "Ġse ason",
- "un k",
- "al t",
- "Ġt able",
- "ĠÐ ¢",
- "Ã ¢",
- "Ġatt ention",
- "ãģ ª",
- "ĠH er",
- "Ġa ge",
- "Ġp ra",
- "b ack",
- "c il",
- "Ġnet work",
- "r it",
- "Ġdo c",
- "Ġare n",
- "ig en",
- "Ġë Ħ",
- "Ø ¯",
- "end er",
- "Ġtot al",
- "Ġpr ice",
- "Ġcra zy",
- "ì ļ",
- "i qu",
- "th ough",
- "Y ou",
- "Ù ĩ",
- "ãĤ ĵ",
- "Ï ħ",
- "Ġs at",
- "Ġb i",
- "ĠD ie",
- "Ġsh a",
- "Ġthank s",
- "u h",
- "Ġst age",
- "аР¶",
- "ĠF l",
- "Ġle av",
- "Ġbo y",
- "Ġa f",
- "ö n",
- "ĠG et",
- "Ġac cept",
- "Ġent er",
- "Ġt ur",
- "Ġsi ÄĻ",
- "Ġhon est",
- "ãĢ Į",
- "Ġs am",
- "Ġre pl",
- "g ing",
- "Ġdevelop ment",
- "ĠA ct",
- "or a",
- "ãĢ į",
- "ä ¾",
- "Ġknow s",
- "Ġim age",
- "ĠL ord",
- "и ÑĤÑĮ",
- "Ġweek s",
- "Ġse x",
- "Ķ ë",
- "Ġh undred",
- "Ġsound s",
- "Ġlearn ed",
- "Ġb ud",
- "ĠÑģ ÑĤ",
- "Ġinc red",
- "â Ļ",
- "Ġn os",
- "Ġd rop",
- "Ġb en",
- "ĠÐ ĺ",
- "Ġsa fe",
- "at a",
- "Ġf uck",
- "so ci",
- "Ġd an",
- "Ġcr oss",
- "1 0",
- "m o",
- "ver t",
- "Ġ1 7",
- "z ie",
- "å ķ",
- "Ġd om",
- "ĠB o",
- "Ġset ting",
- "Ġinvol ved",
- "ar ily",
- "Ġs ind",
- "Ġs us",
- "Ġwor ry",
- "et h",
- "ê¹ Į",
- "Ġs un",
- "Ġh ier",
- "Ġcertain ly",
- "ou l",
- "ort s",
- "ĠE r",
- "ĠU m",
- "Ġca us",
- "Ġnat ural",
- "ĠÃ ¼",
- "Ġc ry",
- "ĠSe c",
- "Ġs om",
- "æ ²",
- "Ġeduc ation",
- "а еÑĤ",
- "Ġmult ip",
- "Ġal one",
- "Ġe ye",
- "Ġr ate",
- "ĠEuro pe",
- "è ¿",
- "m on",
- "Ġf it",
- "iz ing",
- "pp ed",
- "Ġpress ure",
- "th e",
- "и Ñģ",
- "it es",
- "ĠA f",
- "re ci",
- "att le",
- "Ġserv ices",
- "ĠGo ogle",
- "é ģ",
- "Ġc ases",
- "Ġdri ve",
- "Ġchall eng",
- "u z",
- "ĠM o",
- "ìľ ¼ë",
- "v al",
- "åĢ ĭ",
- "Ġf ol",
- "Ġì ¢",
- "ff ic",
- "Ġr a",
- "Ġs in",
- "Ġbl ue",
- "Ġaff ect",
- "Ġm is",
- "Ġsh ot",
- "Ġо б",
- "as ing",
- "Ġsign ific",
- "ĠC he",
- "Ġê ³",
- "Ġpos itive",
- "ì £",
- "Ġw ie",
- "Ġ4 0",
- "ord ing",
- "ĠFr om",
- "ê µ",
- "Ġbra nd",
- "Ġtr ust",
- "Ġp le",
- "Ġcommun ic",
- "Ġwe ight",
- "Ġask ing",
- "Ġta x",
- "ĠJ apan",
- "ãģ Ł",
- "Ġíķ ĺ",
- "op s",
- "Ï Ĥ",
- "Ġput ting",
- "Ġro ll",
- "ĠAmeric a",
- "re g",
- "ŀ ×",
- "at ures",
- "ens ion",
- "ĠS omet",
- "Ġorig inal",
- "p ing",
- "Ġ ÅŁ",
- "Ġproduct s",
- "ãĥ ¼",
- "Ġcont act",
- "ol ution",
- "Ġgo al",
- "Ġp ow",
- "Ġperform ance",
- "Ġblo od",
- "at ors",
- "ĠM ich",
- "Ġtem per",
- "ĠD an",
- "Ġsu gg",
- "ÑĤ и",
- "Ġim m",
- "Ġoff ice",
- "Ġar ri",
- "Ġcom fort",
- "ĠÐ Ķ",
- "Ġsugg est",
- "Ġpl at",
- "Ĥ ĺ",
- "1 9",
- "Ġo m",
- "Ġse ven",
- "ĠC ent",
- "ill e",
- "Ġcon cept",
- "Ġb ag",
- "ü n",
- "ive ly",
- "Ġd iv",
- "m os",
- "æ ī",
- "Ġfeel s",
- "Ġ ir",
- "ak es",
- "le y",
- "Ġpartic ip",
- "ĠÐ ļ",
- "f l",
- "j ust",
- "Ġs il",
- "ĠP a",
- "A L",
- "Ġgot ta",
- "Ġf an",
- "Ġchall enge",
- "Ġcompan ies",
- "ĠPe ople",
- "< /",
- "оР·",
- "Ġp en",
- "is ing",
- "Ġa us",
- "em ic",
- "am ente",
- "Ġmeet ing",
- "Ġvis it",
- "Ġsupp osed",
- "ĠOn ce",
- "д а",
- "or ld",
- "3 0",
- "U S",
- "Ġvi ol",
- "Ġnot ice",
- "ĠÐ IJ",
- "h an",
- "p ed",
- "ì ĺ",
- "h h",
- "Ġtr ou",
- "Ġmin ute",
- "ĠP ar",
- "r ay",
- "Ġt it",
- "Ġup d",
- "Ġblo ck",
- "Ġd ue",
- "a ur",
- "Ġfor ce",
- "Ġcou n",
- "ĠâĢ Ķ",
- "Ġtyp es",
- "ë §",
- "Ġl ate",
- "Ġimpro ve",
- "Ġì Ī",
- "Ġa ve",
- "ul es",
- "c l",
- "am ed",
- "Ġaw esome",
- "ĠO k",
- "Ġv ot",
- "Ġmach ine",
- "Ġfollow ing",
- "Ġme asure",
- "ac ión",
- "u el",
- "ch an",
- "Ġab ility",
- "Ġt out",
- "Ġide as",
- "Ġincre ase",
- "Ġen s",
- "ĠÑ ħ",
- "Ġë ª",
- "Ġj est",
- "ĠÐ ľ",
- "Ġtr uth",
- "h y",
- "Ġsp end",
- "Ġsci ence",
- "et e",
- "Ġ1 4",
- "Ġepis ode",
- "Ġal g",
- "end ed",
- "ãģ ĵ",
- "ar i",
- "ll a",
- "Ġf ish",
- "Ġthr ow",
- "m it",
- "å ¹",
- "Ġcir c",
- "ĠC al",
- "Ġt our",
- "Ġdire ction",
- "Ġno ch",
- "еР²",
- "é n",
- "Ġcount ries",
- "Ġindust ry",
- "in y",
- "ic le",
- "Ġfe et",
- "I t",
- "Ġlead ers",
- "et zt",
- "Ġst aff",
- "ç Ķ",
- "Ġpur p",
- "it o",
- "? !",
- "ĠJ a",
- "Ġst ore",
- "et ic",
- "ĠCh ina",
- "Ġë IJ",
- "ĠUn iversity",
- "Ġ #",
- "Ġdec ision",
- "Ġach ie",
- "Ġact ual",
- "u ly",
- "Ġse ction",
- "Ġresult s",
- "Ġst ar",
- "Ġm ist",
- "ib ly",
- "Ġd ad",
- "Ġnum bers",
- "om b",
- "è ª",
- "ĠS pe",
- "Ġm er",
- "Ġ2 5",
- "Ġaut om",
- "Ġco ld",
- "Ø ¨",
- "Ħ ľ",
- "ag er",
- "ĠT V",
- "ĠS ie",
- "ĠH ave",
- "Ġ że",
- "ug g",
- "ain ed",
- "Ġup on",
- "Ġlo g",
- "Ġcomplet e",
- "Ġbra in",
- "ag ing",
- "ĠM us",
- "o ver",
- "Ġeas ier",
- "Ġinte gr",
- "Ġm ás",
- "Ġturn ed",
- "Ġst ri",
- "iv al",
- "Ġhe av",
- "ĠT H",
- "Ġwr iting",
- "ÑĢ а",
- "åľ ¨",
- "å¤ §",
- "Ġcl a",
- "d ing",
- "Ġtell ing",
- "и д",
- "ic ated",
- "ä» ¥",
- "ac ht",
- "ãģ Ĥ",
- "h aps",
- "ĠSt e",
- "Ġres ources",
- "Ġd ann",
- "Ġpart y",
- "Ġ ÏĦ",
- "Ġsa f",
- "is es",
- "t re",
- "o int",
- "Ġknow ledge",
- "Ġany more",
- "Ġf ly",
- "Ġma int",
- "и к",
- "å ij",
- "Ġse ll",
- "la ughs",
- "ĠY ork",
- "Ġb ien",
- "Ġo d",
- "Ġeas ily",
- "Ġr ange",
- "Ġo ption",
- "Ø ¹",
- "Ġapp reci",
- "oc r",
- "Ġdet erm",
- "Ñ Ħ",
- "Ġmean ing",
- "Ġs ite",
- "Ġdis co",
- "ver age",
- "Ġl ose",
- "Ġinst all",
- "Ġem ot",
- "ant ly",
- "ä t",
- "Ġt amb",
- "ĠW ar",
- "ĠH o",
- "ĠG en",
- "em y",
- "еР·",
- "ĠP ol",
- "Ġmess age",
- "Ġnot e",
- "Į Ģ",
- "Ġh et",
- "Ġim medi",
- "Ġav o",
- "Ġbook s",
- "Ġbecom es",
- "res h",
- "è s",
- "as ons",
- "Ġhim self",
- "ut s",
- "Ġj u",
- "Ġaw are",
- "Ġrequ ire",
- "Ġsystem s",
- "ĠH ar",
- "Ġam ong",
- "Ġh om",
- "Ġb reat",
- "Ġwe ird",
- "Ġë ¶",
- "Î »",
- "Ø ©",
- "if f",
- "or ing",
- "Ġplat form",
- "ĠT ake",
- "Ġhelp s",
- "ut ions",
- "Ġfor g",
- "Ġl uck",
- "ĠEng lish",
- "Ġwe b",
- "Ġneg ative",
- "Ġt ut",
- "Ġab ove",
- "ng th",
- "Ġê ±°",
- "Ġst ories",
- "Ġlo ad",
- "Ġback ground",
- "Ġsw itch",
- "g a",
- "Ġprin ci",
- "Ġfin an",
- "Ġvar ious",
- "Ġl Ãł",
- "Ġkind s",
- "ain ing",
- "Ġn ature",
- "ĠÐ ŀ",
- "c z",
- "Ġpr ay",
- "Ġg ar",
- "ir m",
- "Ġ &",
- "Ġì ĥ",
- "n s",
- "ĠR ep",
- "ĠF e",
- "Ġre v",
- "ra nd",
- "Ġlike ly",
- "Ġunderstand ing",
- "ı r",
- "ãģ ĭ",
- "Ġf al",
- "Ġ1 3",
- "ÑĨ и",
- "Ġsu d",
- "Ġbr other",
- "Ġpl ant",
- "Ġthrough out",
- "w ise",
- "p re",
- "Ġcult ure",
- "ĠÙ ħ",
- "Ġwonder ful",
- "Ġa h",
- "pp er",
- "Ġso ld",
- "Ġstart s",
- "Ġwr itten",
- "Î ¯",
- "n i",
- "Ġ×Ķ ×",
- "ĠD av",
- "Ġu lt",
- "Ġar m",
- "Ġro ck",
- "Ġwe ar",
- "ë į°",
- "an o",
- "ra g",
- "Ġsqu are",
- "ан и",
- "c ast",
- "le br",
- "Ġliter ally",
- "Ġplay ed",
- "Ġhe at",
- "on se",
- "r ict",
- "Ġins p",
- "id s",
- "Ġpop ular",
- "ë ıĦ",
- "Ġc atch",
- "Ġm ount",
- "Ġj ud",
- "Wh at",
- "еР±",
- "R A",
- "a ud",
- "к о",
- "Ġsur face",
- "Ġcon v",
- "Ġpie ces",
- "O h",
- "æ Ģ",
- "Ġst yle",
- "pp ing",
- "Ġread ing",
- "Ġconvers ation",
- "оР¿",
- "ä¾ Ĩ",
- "ĠAg ain",
- "Ġb ank",
- "t ime",
- "Ñĥ ÑĤ",
- "er ve",
- "ĠG reat",
- "Ġcap t",
- "аР±",
- "ay s",
- "ĠF in",
- "ific ation",
- "Ġä r",
- "а Ñİ",
- "Ġe gg",
- "ĠW el",
- "Ġtar get",
- "ul a",
- "ch es",
- "an i",
- "O O",
- "ic ious",
- "n ow",
- "Ï ĥ",
- "bo ard",
- "Ġg ente",
- "Ġd ro",
- "ĠE t",
- "Ġd in",
- "Ġc os",
- "Ġaut hor",
- "Ø ³",
- "Ġo ch",
- "Ġem ail",
- "Ġsp irit",
- "Ġs itting",
- "m as",
- "Ġstre ngth",
- "Ġbig ger",
- "ĠW ait",
- "Ġm at",
- "Ġpol ice",
- "ress ed",
- "Ġwait ing",
- "is hing",
- "Ġdoll ars",
- "ho od",
- "s s",
- "Ġimag ine",
- "in i",
- "Ġm es",
- "Ġdis e",
- "id ge",
- "ab or",
- "Ġp et",
- "Ġh op",
- "ĠK ing",
- "Ġcomput er",
- "Ġgo ld",
- "Ġn u",
- "Ġf ing",
- ") ,",
- "Ġsec urity",
- "ru ction",
- "Ġsol ution",
- "e xt",
- "Ġp atter",
- "ick en",
- "ure d",
- "Ġstand ard",
- "ìĭ ľ",
- "Ġdou ble",
- "Î ·",
- "Ġw ife",
- "is a",
- "Ġdirect ly",
- "ac ed",
- "Ġb unch",
- "ĠÂ ¿",
- "ал ÑĮ",
- "Ġreg ard",
- "Ġswe et",
- "Ġun ique",
- "ĠâĻ «",
- "Ġtra in",
- "ĠG erm",
- "Î ¬",
- "R E",
- "Ġbeh av",
- "Ġpre d",
- "ì ĥ",
- "s et",
- "Ġdescri ption",
- "é e",
- "Ġc at",
- "å ĵ",
- "Ġcoll ege",
- "ì Ľ",
- "Ġapplic ation",
- "ĠS en",
- "as k",
- "Ġc red",
- "ub lic",
- "Ġmultip le",
- "Ġn i",
- "Ġpres ident",
- "Ġadd ed",
- "Ġro b",
- "Ġaqu i",
- "Ġh osp",
- "Ġtool s",
- "Ġg un",
- "Ġbas ic",
- "Ġl ines",
- "Ġst ructure",
- "ĠR uss",
- "Ġtot ally",
- "Ġbig gest",
- "Ġe en",
- "Ġar g",
- "Ġ× ľ",
- "Ġp ark",
- "ĠD es",
- "Ġce lebr",
- "Ġf ait",
- "ен ÑĮ",
- "Ġsu ff",
- "Ġreg ular",
- "¨ ë",
- "Ġm ine",
- "ĠK ore",
- "Ġpre vious",
- "Ġp i",
- "Ġse g",
- "Ġpol icy",
- "Ġк о",
- "ĠTr ump",
- "Ġvac c",
- "ó w",
- "ĠS y",
- "и Ñĩ",
- "it ter",
- "Ġpolit ical",
- "r as",
- "Ġal s",
- "ел ÑĮ",
- "Ġsha pe",
- "an z",
- "Ġon to",
- "Ġar ch",
- "Ġam b",
- "ag ram",
- "ĠS m",
- "ct ions",
- "Ġjo in",
- "b or",
- "å Ľ",
- "Ġfr ame",
- "ł ĩ",
- "Ġcho ice",
- "௠ģ",
- "Ñĥ Ñİ",
- "ĠC or",
- "ĠS w",
- "I T",
- "Ġt end",
- "ĠE ar",
- "Ġto r",
- "Ġev ents",
- "Ġcla im",
- "ĠD a",
- "ĠM ark",
- "Ġgroup s",
- "Ġe ating",
- "ĠW orld",
- "Ġrec ently",
- "Ġtast e",
- "Ġsur v",
- "à ¤",
- "Ġsk ills",
- "Ġи з",
- "itt ed",
- "Ġsh op",
- "ìĿ ´ì",
- "Ġest ab",
- "ĠëĤ ĺ",
- "Ġsecond s",
- "ĠTh ose",
- "ĠE nt",
- "Ġì Ħ",
- "ers on",
- "Ġto wn",
- "Ġc and",
- "Ġopt ions",
- "Ġ ing",
- "V ID",
- "Ġenc our",
- "Ġr é",
- "âĻ ª",
- "Ġent re",
- "Ġmove ment",
- "ĠB en",
- "Ġbir th",
- "Ġwh e",
- "Ġh ang",
- "ĠE m",
- "ig e",
- "ro ll",
- "Ġun f",
- "ì Ĥ",
- "Ġr id",
- "Ġsp read",
- "Ġh ost",
- "al d",
- "ĠE d",
- "Ġcons um",
- "U N",
- "Ġop in",
- "it ar",
- "ĠM ed",
- "Ġsub ject",
- "Ġp al",
- "Ġcar ry",
- "Ġag ree",
- "ĠWh ile",
- "Ġcare er",
- "Ġsci ent",
- "Ġsud den",
- "Ġf ile",
- "z i",
- "Ġex cept",
- "é º",
- "Ġpot ential",
- "ĠAn other",
- "Ġcomp lex",
- "ĠS im",
- "end o",
- "Ġr ais",
- "Ġphys ical",
- "Ġd ate",
- "ak er",
- "ĠC ol",
- "Ġpower ful",
- "Ġmem ber",
- "ra p",
- "Ġsp ot",
- "Ġs ource",
- "Ġf em",
- "é m",
- "Ġem p",
- "j i",
- "iet y",
- "Ġinf lu",
- "Ġd ry",
- "Ġlo ck",
- "Ġz ero",
- "ĠU h",
- "Ġr out",
- "Ġpor que",
- "Ġ2 4",
- "Ġt al",
- "Ġfol ks",
- "Ġla unch",
- "Ġcomp on",
- "ĠWel come",
- "Ġk ann",
- "ä n",
- "ĠÑį ÑĤ",
- "e es",
- "ĠÙ Ī",
- "Ġany way",
- "Ġaud ience",
- "äº º",
- "Ġsl ight",
- "on a",
- "Ġu r",
- "Ġrel ig",
- "Ġext rem",
- "ı z",
- "ĠM a",
- "Î ¼",
- "ĠÃ ¶",
- "Ġall ows",
- "Ġf at",
- "ĠF ace",
- "Ġn ational",
- "Ġinter view",
- "ĠM c",
- "é t",
- "Ġc ute",
- "el a",
- "Ġsec ret",
- "ĠW est",
- "ĠD ep",
- "Ġex erc",
- "Ġhist or",
- "Ġpri or",
- "Ġ6 0",
- "av a",
- "ac her",
- "y ond",
- "ĠH a",
- "Ġest e",
- "in ary",
- "ĠN orth",
- "on st",
- "Ġsm art",
- "am s",
- "ал и",
- "Ġd ar",
- "er ed",
- "Ġfun ny",
- "ĠO b",
- "ĠBl ack",
- "Ġrel ated",
- "ĠB u",
- "Ġsome where",
- "ĠR em",
- "n es",
- "ment e",
- "ĠRe ally",
- "Ġcreat ing",
- "Ġfam il",
- "Ġsoci ety",
- "Ġg el",
- "Ġtrans form",
- "Ä ĥ",
- "Ġinclud e",
- "Ġh ol",
- "l ike",
- "k o",
- "air s",
- "Ġп од",
- "Ġpers pect",
- "Ġb es",
- "Ġparticular ly",
- "Ġshow ing",
- "ĠP art",
- "Ġqu al",
- "lo ck",
- "Ġreal ity",
- "ho ld",
- "ict ion",
- "o on",
- "Ġv ir",
- "ãģ «",
- "it ary",
- "Ġdr ug",
- "Ġfe ature",
- "Ġre asons",
- "Ġ× ©",
- "Ġwr ote",
- "Ġf ant",
- "Ġb and",
- "Ù ĥ",
- "en a",
- "ke y",
- "Ġear th",
- "d om",
- "Ġfe atures",
- "Ġflo or",
- "Ġspeak ing",
- "Ġt ip",
- "ĠA ust",
- "Ġst ock",
- "Ġch urch",
- "Ġr ac",
- "ìľ¼ë ¡ľ",
- "ภĻ",
- "ãĤ Į",
- "k y",
- "Ġresp onse",
- "Û Į",
- "ul ations",
- "Ġsl ide",
- "Ġgrad u",
- "ci ous",
- "Ġme ant",
- "Ġ ==",
- "Ġ× IJ×",
- "ã ħ",
- "Ġkind a",
- "Ġsc ene",
- "Ġm uit",
- "Ġê° Ģ",
- "r ast",
- "re st",
- "Ġplay ers",
- "w a",
- "Ġbro ad",
- "Ġtom orrow",
- "oc ol",
- "ĠÑģ в",
- "ĠB ar",
- "ı k",
- "Ġse a",
- "Ġrem ove",
- "Ġrem ind",
- "ом Ñĥ",
- "ĠS ince",
- "Ġave c",
- "ce ll",
- "и Ñħ",
- "Ġdoc ument",
- "Ġê·¸ë Ł",
- "Ġne igh",
- "be at",
- "Ġp Ã¥",
- "Ġas pect",
- "Ġd ed",
- "lish ed",
- "il s",
- "Ġour selves",
- "u ce",
- "Ġhe y",
- "ĠпÑĢ о",
- "ent y",
- "Ġas soci",
- "ad os",
- "um ber",
- "Ġ ]",
- "éĤ £",
- "no v",
- "Ġì Ļ",
- "Ñĥ Ñĩ",
- "Ġcond ition",
- "ëĬĶ ëį°",
- "Ġval ues",
- "Ġsc en",
- "min ist",
- "Ġc ast",
- "Ġgrow ing",
- "Ġus er",
- "Ġresp ond",
- "l im",
- "é r",
- "y m",
- "çľ ĭ",
- "os es",
- "sy ch",
- "ĠÑĢ аз",
- "Ġappe ar",
- "Ġpro gress",
- "eng th",
- "Ġj ak",
- "ĠD is",
- "Ġpat ients",
- "ĠS er",
- "Ġg as",
- "è re",
- "ìĸ´ì ļĶ",
- "Ġre ci",
- "ìĿ ¸",
- "Ġs ca",
- "ep end",
- "Ñģ к",
- "аР¿",
- "Ġb atter",
- "Ġve h",
- "ð Ł",
- "Ġac com",
- "Ġbe at",
- "Ġpain t",
- "Ġcont rib",
- "Ġs ad",
- "Æ °",
- "al es",
- "Ġt ree",
- "b a",
- "Ġb orn",
- "ic ed",
- "à® ķ",
- "b and",
- "Ġme chan",
- "ĠD et",
- "Ġcap ital",
- "Ġdel iver",
- "Ġfe ar",
- "ŀ ĺ",
- "ĠS outh",
- "Ġb ought",
- "Ġst ress",
- "Ġv or",
- "? ?",
- "i h",
- "ìķ ¼",
- "Ġer a",
- "ìĿ´ ë",
- "а Ñı",
- "is ions",
- "iv ity",
- "Ġhelp ed",
- "Ġass ist",
- "Ġplay er",
- "r an",
- "Ġimmedi ately",
- "Ġmo ved",
- "c ie",
- "ê ±",
- "Ġann oun",
- "å ¿",
- "ìŀ IJ",
- "Ġprodu ction",
- "Ġsum mer",
- "Ġt un",
- "Ġprogram s",
- "G H",
- "al ing",
- "ir a",
- "el ess",
- ". )",
- "Ġa verage",
- "è¦ ģ",
- "Ġgl ass",
- "om an",
- "if ically",
- "Ġëĭ ¤",
- "ĠC ong",
- "ĠV er",
- "Ġtr ick",
- "Ġbe gan",
- "Ġv ill",
- "ê ±°",
- "h ow",
- "æ Ń",
- "Ġt ill",
- "Ġ9 0",
- "ber t",
- "Ġê ¸",
- "Ġtemper ature",
- "Ã ²",
- "๠Ī",
- "Ġgra ph",
- "Ġê· ¸",
- "Ġr ot",
- "Ġmo b",
- "A Y",
- "a el",
- "Ġre pe",
- "Ġdev ice",
- "Ġ19 9",
- "Ġte le",
- "Ġke pt",
- "p a",
- "æ ĸ",
- "ver se",
- "Ġst ream",
- "е Ñĩ",
- "ess ion",
- "Ġstr ugg",
- "z z",
- "Ġdeg ree",
- "Ġhelp ing",
- "Ġsm ell",
- "Ġper haps",
- "p ro",
- "Ġcont ext",
- "Ġi k",
- "Ġп еÑĢ",
- "Ġcal cul",
- "éº ¼",
- "b ing",
- "Ġreal ize",
- "l am",
- "ĠCh ar",
- "y t",
- "ĠìĿ ´ì",
- "Ġd anger",
- "ĠI m",
- "a a",
- "Ġlo ved",
- "Ġpurp ose",
- "Ġfinish ed",
- "Ġpe ace",
- "Ġo t",
- "Ġglo bal",
- "Ï Ģ",
- "Ġab er",
- "ĸ Ī",
- "Ġcharac ters",
- "Ġn ur",
- "Ġdam age",
- "Ġem er",
- "Ġpre c",
- "ĠW ir",
- "Ġinst it",
- "ij ×",
- "Ġallow ed",
- "b on",
- "Ġto d",
- "еР³Ð¾",
- "Ġj etzt",
- "Ġmed ic",
- "Ġsmall er",
- "ce ed",
- "Ġlevel s",
- "Ġint ell",
- "W e",
- "Ġse m",
- "Ġcurrent ly",
- "Ġmod ern",
- "Ġcont ract",
- "Ġdetail s",
- "ortun ately",
- "O S",
- "Ġst ates",
- "Ġad just",
- "ant age",
- "e z",
- "ĠV ery",
- "Ġsc ale",
- "Ġre lease",
- "Ġf az",
- "Ġ ic",
- "it ude",
- "A C",
- "ĠP at",
- "id en",
- "Ń IJ",
- "Ġpre fer",
- "olog ical",
- "ĠFace book",
- "Ġê° Ļ",
- "Ġ ..",
- "ĠM ake",
- "Ġко ÑĤоÑĢ",
- "ĠDav id",
- "ĠAf ric",
- "Ġmod e",
- "ĠC ity",
- "Ġsh all",
- "ĠÑ Ħ",
- "im in",
- "Ġз а",
- "r om",
- "u a",
- "Ġbe yond",
- "Ġdist rib",
- "к Ñĥ",
- "ĠDo es",
- "Ġv ict",
- "r ate",
- "Ġv ai",
- "Ġsuccess ful",
- "Ġh ous",
- "ah a",
- "est s",
- "ĠE st",
- "Ġdisco ver",
- "Ġthere fore",
- "ch a",
- "Ġc up",
- "Ġpop ulation",
- "ĠI l",
- "s c",
- "Ġsp ent",
- "re l",
- "Ġuse ful",
- "Ġt ab",
- "æ Ŀ",
- "Ġ Å",
- "Ġìł ľ",
- "Ġcon se",
- "Ġqu ant",
- "ay a",
- "Ġb on",
- "åı ¯",
- "ĠCh in",
- "Ġê² ĥ",
- "ound s",
- "е ÑĪ",
- "ell e",
- "Ġ ice",
- "2 1",
- "Ġk ick",
- "ä¸ ĭ",
- "Ġstep s",
- "Ġton ight",
- "нÑĭ й",
- "ren ch",
- ". '",
- "Ġgra b",
- "Ġimp lement",
- "ĠìĪ ĺ",
- "Ġmiss ion",
- "Ġclear ly",
- "Ġappreci ate",
- "è Ģ",
- "Ġf resh",
- "ar m",
- "ĠTw o",
- "Ġex ec",
- "Ġproject s",
- "Ġcommun ities",
- "ri ble",
- "Ġreg ion",
- "Ġfre qu",
- "ro y",
- "Ġhow ever",
- "Ġpart ners",
- "an c",
- "Ġmin im",
- "Ġl at",
- "Ġfamil ies",
- "Ġev idence",
- "Ġp un",
- "ra ft",
- "Ġl oss",
- "Ġma p",
- "Ġany body",
- "Ġchang ing",
- "Ġr ules",
- "Ġorgan ization",
- "Ġess entially",
- "ĠR ed",
- "Ġele ment",
- "æ Ĺ",
- "Ġv irt",
- "r at",
- "Ġpr int",
- "and er",
- "are n",
- "em os",
- "ο Ïħ",
- "Ġcond itions",
- "ab e",
- "Ġd ance",
- "и ÑĢ",
- "Ġd os",
- "о Ñĩ",
- "ĠQ ue",
- "Ġwalk ing",
- "Ġt ro",
- "Ġ id",
- "Ġadd itional",
- "Ġfull y",
- "Ġf ans",
- "Ġadd ition",
- "Ġlik ed",
- "Ġü ber",
- "Ġb ow",
- "d i",
- "Ġm aster",
- "o ff",
- ") :",
- "m ber",
- "Ġë ¬",
- "å ¯",
- "åĪ °",
- "la use",
- "Ġo der",
- "Ġsaf ety",
- "Ġre act",
- "à® ¿",
- "b t",
- "Ġdis app",
- "Ġgirl s",
- "S t",
- "ĠA ng",
- "Ġfa ith",
- "Ġturn s",
- "Ġt ight",
- "Ġm outh",
- "am i",
- "z er",
- "Ġwe ap",
- "Ġб Ñĥд",
- "Ġhosp ital",
- "ra id",
- "Ġmic ro",
- "ĠSt ate",
- "ĠM ost",
- "ag n",
- "Ġdec ide",
- "Ġpat ient",
- "Ġcor ner",
- "Ġdi ed",
- "N o",
- "ĠSt ud",
- "re nd",
- "em pt",
- "Ġli e",
- "Ġl if",
- "ĠBe fore",
- "t ó",
- "ĠSu per",
- "Ġbe ll",
- "6 0",
- "Ġpriv ate",
- "ĠPa ul",
- "Ġg ib",
- "Ġag re",
- "´ì Ħľ",
- "Ġs ig",
- "Ġinvest ig",
- "Ñı ÑĤ",
- "en ing",
- "Ġdist ance",
- "Ġwar m",
- "Ġdig ital",
- "å¾ Ī",
- "in er",
- "Ġp and",
- "ĠCO VID",
- "Ð ³Ð¾",
- "g n",
- "Ġr ace",
- "Ġpr oud",
- "Ġte aching",
- "Ġ ÑĤо",
- "ìŀ ¥",
- "ĠAll ah",
- "I n",
- "Ġw ood",
- "Ġcol ors",
- "Ġw ird",
- "u j",
- "id ad",
- "Ġcustom ers",
- "Ġconnect ed",
- "Ġlay er",
- "Ġachie ve",
- "Ġperspect ive",
- "ĠC oll",
- "Ù Ĥ",
- "Ġcl oud",
- "!! !",
- "Ġend ed",
- "łĩ ê²Į",
- "Ġmanage ment",
- "Ġr ich",
- "Ġsub st",
- "Ġrem o",
- "Ġser ve",
- "Ġres ist",
- "Ġthought s",
- "Ġgrow th",
- "ili ar",
- "Ġright s",
- "Ġchar ge",
- "Ġcons ist",
- "Ġwer den",
- "Ġem b",
- "and om",
- "Ġhur t",
- "Ġk an",
- "i as",
- "л о",
- "Ġsh it",
- "Ġbe g",
- "Ġrece ived",
- "it ation",
- "Ġme at",
- "Ġis so",
- "ff ee",
- "Ġfam ous",
- "Ġcomfort able",
- "I L",
- "ĠB ye",
- "èª ª",
- "åĢ ij",
- "oth es",
- "Ġmed ical",
- "Ġenjoy ed",
- "Ġhealth y",
- "Ġw y",
- "c ies",
- "Ġeff ort",
- "Ġdo ctor",
- "Ġmil itary",
- "L AU",
- "Ġg ro",
- "Ġb attle",
- "Ġf ed",
- "Ġcap ac",
- "Ġaf raid",
- "iv il",
- "ĠвÑģ е",
- "Ġl ength",
- "ys is",
- "Ġbe i",
- "¤ í",
- "Ġorgan iz",
- "or g",
- "in c",
- "Ġinter act",
- "ĠChin ese",
- "Ġacc ording",
- "Ġincred ible",
- "Ġkill ed",
- "Ġda ughter",
- "ĠÏ Ģ",
- "Ñĭ в",
- "Ġschool s",
- "ĠÂ «",
- "ll er",
- "Ġshould n",
- "n al",
- "Ġcr is",
- "Ġch icken",
- "Ġf aster",
- "Ġextrem ely",
- "Ġopp os",
- "Ġn ous",
- "Ġ +",
- "ri a",
- "Ġfinan cial",
- "Ġexc iting",
- "Ġjour ney",
- "×Ļ× Ŀ",
- "ł ë",
- "Ġdis play",
- "Ġmem ory",
- "Ġheav y",
- "н е",
- "Ġpass ed",
- "ÑĢ и",
- "il es",
- "Ġp sych",
- "Ġspec ifically",
- "Ġeng age",
- "Ġl ed",
- "or ge",
- "ĠD em",
- "ord er",
- "Ġ8 0",
- "Ġcre am",
- "ester day",
- "Ġed ge",
- "Ġп ол",
- "Ġbu ll",
- "Ġind ic",
- "Ġk tó",
- "Ġhope fully",
- "um ents",
- "ag en",
- "н ого",
- "Ġh ate",
- "ch t",
- "8 0",
- "Ġeff ic",
- "Ġì§ Ģ",
- "Ġintern et",
- "Ġbud get",
- "Ġproper ty",
- "id ay",
- "Ġì ļ",
- "Ġм ож",
- "ol a",
- "Ġshow ed",
- "ĠM on",
- "Ġthous and",
- "A P",
- "Ġpo or",
- "us ed",
- "ĠJ ack",
- "Ġs Ã¥",
- "ĥ ½",
- "Ġes c",
- "Ġsoft ware",
- "Ġqu ar",
- "ĠØ ¨",
- "Ġnecess arily",
- "om en",
- "i y",
- "Ġevent ually",
- "ish ed",
- "Ġbr ight",
- "E D",
- "Ġs pl",
- "Ġdem and",
- "Ġth reat",
- "Ġs ir",
- "Ġrele ased",
- "ck et",
- "ĠâĢ «",
- "Ġrequ ired",
- "Ġv ote",
- "ì ¹",
- "à® ¤",
- "Ġdevelop ed",
- "ĠìĤ ¬",
- "at ory",
- "Ġd ir",
- "ca pe",
- "Ġslight ly",
- "Ã ¬",
- "๠ī",
- "re et",
- "Ġdise ase",
- "Ġcour t",
- "Ġitem s",
- "ĠEar th",
- "ÑģÑĤ и",
- "ж е",
- "ì ²",
- "Ġchalleng es",
- "ĠBr it",
- "Ġdesign ed",
- "1 2",
- "Ġhear ing",
- "Ġlisten ing",
- "z o",
- "ĠÑģ л",
- "ãģ§ ãģĻ",
- "Ġper o",
- "Ġwe aring",
- "pl ic",
- "Ġch em",
- "Ġbal ance",
- "Ġb a",
- "Ġrece ive",
- "im a",
- "Ġsignific ant",
- "Ġм Ñĭ",
- "an ch",
- "ĠC r",
- "ĠC oun",
- "ê¸ Ī",
- "Ġjo bs",
- "Ġoffic ial",
- "Ġper m",
- "om s",
- "Ġopportun ities",
- "Ġover all",
- "Ġh us",
- "od es",
- "Ġn ation",
- "ĠR eg",
- "Ġor d",
- "Ġrest aur",
- "Ġì Ĩ",
- "Ġm el",
- "v in",
- "Ġw enn",
- "Ġk ön",
- "æ ĥ",
- "Ġopin ion",
- "ãĤ Ĥ",
- "è ¬",
- "ĠSomet imes",
- "ç Ĥ",
- "Ñī е",
- "as c",
- "O U",
- "Ġ20 20",
- "Ġdel icious",
- "ig er",
- "Ġìķ Ī",
- "o le",
- "Ġhand le",
- "Ġc it",
- "Ġíķ ľ",
- "Ġf ör",
- "o oth",
- "Ġnecess ary",
- "Ġind epend",
- "æ Ħ",
- "ist en",
- "h am",
- "Ġé t",
- "ãĥ ³",
- "Ġmult i",
- "Ï Į",
- "? )",
- "Ġcamp us",
- "Ġtop ic",
- "Ġr ain",
- "Ġpan el",
- "ĠS am",
- "Ġlar ger",
- "aud ience",
- "Ġpa id",
- "Ġeconom ic",
- "ol t",
- "Ġstre et",
- "ĠC ont",
- "Ġdri ving",
- "Ġìł Ģ",
- "Ġh ay",
- "Ġprofess ional",
- "ĠIn tern",
- "å ¸",
- "Ġin put",
- "Ġc ateg",
- "Ġc ro",
- "Ġ ll",
- "E T",
- "Ñĭ й",
- "* *",
- "ĠZ e",
- "B LE",
- "Ġì ¤",
- "re es",
- "ĠÐ ¯",
- "ed e",
- "ier t",
- "Ġfo ld",
- "Ġd ur",
- "ĠN ational",
- "Ġìĸ ´ë",
- "an ced",
- "Ġfa ire",
- "ut ed",
- "Ġk ing",
- "Ġw ild",
- "o i",
- "up beat",
- "Ġpre vent",
- "i us",
- "ĠÃ ¨",
- "Ġw ide",
- "Ġr ing",
- "Ġtit le",
- "Ġstand ing",
- "Ġal though",
- "Ġh i",
- "Ġsa uce",
- "Ġs ides",
- "Ġanim als",
- "il ing",
- "at ives",
- "ìĹIJ ìĦľ",
- "ĠO ver",
- "Ġdes p",
- "Ġconsider ed",
- "ar ies",
- "i ers",
- "Ġein en",
- "Ġs ister",
- "Ġë ķ",
- "ĠS ure",
- "ãĤ ĭ",
- "ri end",
- "a ign",
- "Ġsh own",
- "Ġs ac",
- "Ġs ont",
- "Ġcent ury",
- "Ġt ien",
- "ĠÎ º",
- "ĠS T",
- "åķ Ĭ",
- "Ġold er",
- "ie m",
- "Ġtr uly",
- "ĠS i",
- "Ġwind ow",
- "iqu es",
- "ar io",
- "æ² Ĵ",
- "Ġloc ation",
- "Î º",
- "Ġì ľ",
- "v i",
- "ag ue",
- "ĠS orry",
- "Ġdis p",
- "Ġhe ll",
- "ĠÃ ī",
- "Ġtr ade",
- "Ġcrit ical",
- "Ġê ±",
- "Ġn amed",
- "Ġprep ared",
- "ĠH ouse",
- "al u",
- "Ġt ough",
- "Ġtri p",
- "Ġs and",
- "c el",
- "ü z",
- "ĠP ut",
- "Ġap art",
- "is f",
- "v is",
- "Ġli br",
- "a ven",
- "Ġv ie",
- "Ġeffect ive",
- "ภ²",
- "Ġmag n",
- "Ġmuit o",
- "Ġê µ",
- "h al",
- "Ġlim it",
- "Ġn ine",
- "Ġwill ing",
- "ı ÅŁ",
- "s p",
- "еР³",
- "h i",
- "Ġal t",
- "ĠJ an",
- "Ġorig in",
- "ĠU s",
- "Ġele ments",
- "Ġus es",
- "Ġhelp ful",
- "Ġfl at",
- "Ġfam iliar",
- "ĠP ark",
- "Ġc ore",
- "Ġclos er",
- "Ġact ive",
- "Ġad minist",
- "C E",
- "нÑĭ е",
- "ç Ħ",
- "Ġrel ative",
- "Ġment al",
- "Ġr andom",
- "Ġpart ner",
- "Ġut il",
- "ph one",
- "Ġr ule",
- "w w",
- "Ġìł ķ",
- "Ġsch on",
- "Ġco ffee",
- "H A",
- "Ġconnect ion",
- "Ġun it",
- "la ughing",
- "l og",
- "Ġapp l",
- "л а",
- "us ic",
- "ĠB ra",
- "Ġany where",
- "AU DI",
- "Ġsepar ate",
- "bo x",
- "Ġd ivid",
- "Ġtest ing",
- "Ġs ick",
- "Ġwer en",
- "ä» ĸ",
- "Ġ׾ ×",
- "Ġadv antage",
- "Ġtrans fer",
- "' .",
- "Ġë ¹",
- "Ġfind ing",
- "н ой",
- "Ġì¢ ĭ",
- "Ġfor t",
- "Ġeconom y",
- "Ġl ack",
- "Ġleav ing",
- "Ġd im",
- "å İ",
- "ĠR es",
- "Ø Ń",
- "Ġdiscuss ion",
- "еР¿",
- "Ġg es",
- "du ct",
- "Ġch ain",
- "Ġus ers",
- "e ch",
- "ÅĤ a",
- "Ġdis h",
- "Ġcare ful",
- "Ġte acher",
- "Ġopt im",
- "Ġfl u",
- "at ically",
- "Ġref lect",
- "Ġtreat ment",
- "e ed",
- "i ÄĻ",
- "Ã ¹",
- "à® ¾",
- "Ġequ ip",
- "Ġplan ning",
- "Ġsol ve",
- "ãģ Ŀ",
- "ĠT om",
- "Ġavo id",
- "Ġp ou",
- "Ġgreat er",
- "l in",
- "O L",
- "ĠL u",
- "ĠM ore",
- "Ġatt ract",
- "ê n",
- "un a",
- "Ġphot o",
- "er ation",
- "Ġplan et",
- "Ġcop y",
- "Ġvis ual",
- "ir ing",
- "Ġintern ational",
- "Ġla ughing",
- "Ġth ick",
- "Ġhold ing",
- "Ġbring ing",
- "Ġlet ter",
- "Ġb urn",
- "Ġeffect s",
- "it é",
- "our s",
- "O T",
- "ê me",
- "ĠSch ool",
- "×ķ× ª",
- "rop ri",
- "l ig",
- "α ι",
- "Ġad ult",
- "Ġsu gar",
- "Ġr ide",
- "Ġhigh light",
- "Ġno body",
- "Ġ2 1",
- "Ġch at",
- "ĠпÑĢ и",
- "Ġin nov",
- "ung en",
- "Ġatt ach",
- "ed om",
- "å Ĭ",
- "y l",
- "Ġleg al",
- "Ġr ice",
- "Ġcoll abor",
- "k ing",
- "d own",
- "æ Ļ",
- "ãĤ Ĭ",
- "Ġi h",
- "ĠA c",
- "ous ly",
- "Ġr ap",
- "Ġsol id",
- "Ġgener ally",
- "Ġpatter n",
- "al i",
- "ภŃ",
- "Ġtrans l",
- "in ter",
- "a ult",
- "Ġë ¨",
- "Ġexp ress",
- "Ġexam ples",
- "Ġch ose",
- "Ġtell s",
- "ÃŃ s",
- "ain t",
- "ĠT ell",
- "ĠMich ael",
- "æ ¨",
- "ĠN umber",
- "Ġt ap",
- "Ġexper iment",
- "Ġbenef it",
- "Ġì °",
- "Ġse qu",
- "Ġexp ensive",
- "Ġgener ation",
- "ĠM any",
- "Ġadd ing",
- "Ġk il",
- "Ġcamp aign",
- "ĠA nt",
- "ra w",
- "omm en",
- "Ġs oul",
- "j o",
- "ĠAct ually",
- "am m",
- "ê² ł",
- "Ġma xim",
- "Ġsal t",
- "Ġc ru",
- "Ġcall ing",
- "ãģ Į",
- "Ġbas is",
- "b an",
- "Ġkeep ing",
- "ĠM or",
- "ed s",
- "ì Ĩ",
- "Ġto do",
- "ам и",
- "н Ñı",
- "Ġli ved",
- "ĠD u",
- "ãĤ ī",
- "å® ¶",
- "for ce",
- "å¹ ´",
- "fer ence",
- "al a",
- "Ġocc ur",
- "s k",
- "Ġrec ent",
- "Ġc ars",
- "Ġtrad itional",
- "ent le",
- "² Ī",
- "Ġhel d",
- "Ġn ach",
- "ĠCent er",
- "er en",
- "Ġb in",
- "Ù ģ",
- "Ġcomm e",
- "Ġre ve",
- "Ġìĺ ¤",
- "Ġexpect ed",
- "ab il",
- "Ġfocus ed",
- "o v",
- "Ġi P",
- "or ial",
- "i ro",
- "Ġet c",
- "am ing",
- "ĠS on",
- "Ġy esterday",
- "Ġstr ate",
- "ĠÑ Ĩ",
- "Ġë ı",
- "p es",
- "Ġactiv ity",
- "Ġadv ice",
- "Ġopen ing",
- "f in",
- "Ġre la",
- "é ĸ",
- "Ġinst ance",
- "ĠEvery one",
- "b l",
- "p en",
- "Ġvis ion",
- "ĠA lex",
- "if orn",
- "Ġt ick",
- "H e",
- "Ġstrate gy",
- "Ġk om",
- "P E",
- "ĠG l",
- "Ġelect ric",
- "1 5",
- "Ġda ily",
- "Ġhus band",
- "Ġst ation",
- "Ġanal ysis",
- "yn am",
- "Ġatt empt",
- "Ġbill ion",
- "v ant",
- "Ġfor th",
- "Ġm ath",
- "al y",
- "Ġbehav ior",
- "ĠM as",
- "k an",
- "ĠD ay",
- "Ġbl ess",
- "Ġg ut",
- "ĠH igh",
- "o x",
- "Ġd ress",
- "Ġj ed",
- "è ¯",
- "å ĸ",
- "Ġexperien ces",
- "ist a",
- "Ġfight ing",
- "å ·",
- "ĠÑģ к",
- "Ġmost ly",
- "a use",
- "Ġpict ures",
- "ен ÑĤ",
- "Ġm ad",
- "Ġmod els",
- "ÑĪ е",
- "ĠC ount",
- "Å Ħ",
- "ÅĤ o",
- "ep t",
- "O M",
- "ĠA N",
- "Ġtrou ble",
- "4 0",
- "Ġb ird",
- "ul ate",
- "Ġm ur",
- "Ġprodu ce",
- "Ġmar ried",
- "b it",
- "Ġthe ory",
- "í ĺ",
- "Ġlead er",
- "ĠL ast",
- "A A",
- "è µ",
- "Ġim ages",
- "Ġexp and",
- "ĠP or",
- "Ġpur ch",
- "ĠS an",
- "ĠChrist mas",
- "ĠAust ral",
- "Ġw id",
- "ĠM iss",
- "Ġknow ing",
- "Ġz e",
- "s hip",
- "k u",
- "Ñħ од",
- "ĠInst agram",
- "ĠInd ia",
- "Ġest a",
- "ĠCal iforn",
- "Ġ7 0",
- "Ġdra g",
- "Ġbr ush",
- "Ġn ames",
- "A nd",
- "Ġy o",
- "ill a",
- "Ġsch ed",
- "Ġdest roy",
- "ye ar",
- "Ġv amos",
- "Ġ ÙĦ",
- "ç a",
- "Ġforg ot",
- "и е",
- "Ġra ise",
- "re me",
- "íķ ´",
- "ĠG ive",
- "Ġcont ain",
- "ra b",
- "Ġg ift",
- "ĠÑģ п",
- "Ġrequ est",
- "Ġsh ut",
- "Ġdeg rees",
- "Ġbenef its",
- "Ñĭ е",
- "Ġstud ies",
- "Ġend s",
- "Ġevery where",
- "Ġher o",
- "op h",
- "er ry",
- "Ġmaterial s",
- "en ed",
- "N A",
- "å į",
- "Ġmu y",
- "Ġwor se",
- "ä» Ģ",
- "ĠM ad",
- "Ġdec isions",
- "ion e",
- "Ġfore ign",
- "la ughter",
- "i ber",
- "ени Ñı",
- "ãħ ĭ",
- "Ġreal ized",
- "Ġ ign",
- "Ġwe ak",
- "ĠÎ ¼",
- "Ġsca red",
- "Ġass um",
- "A K",
- "ï ¿",
- "ï¿ ½",
- "Ġcover ed",
- "ĠS at",
- "Ġо н",
- "Ġindividual s",
- "Ġcomp ared",
- "1 1",
- "ĠAd d",
- "ic les",
- "Ġc ert",
- "r ar",
- "Ġbr ief",
- "Ġactiv ities",
- "Ġf ab",
- "b ar",
- "Ġa st",
- "ĠO ther",
- "Ġclass es",
- "Ġo g",
- "Ġmiss ing",
- "ãģ ł",
- "é Ŀ",
- "w ers",
- "× ©",
- "Ġintrodu ce",
- "Ġequ ation",
- "ãģ¾ ãģĻ",
- "Ġn om",
- "Ġpain ting",
- "us hing",
- "ĠA P",
- "Ġencour age",
- "Ġsh ip",
- "itt ee",
- "iver se",
- "ot a",
- "n am",
- "ãĥ »",
- "Ġexerc ise",
- "ĠÐ Ń",
- "Ġn as",
- "Ġthous ands",
- "ĠCaliforn ia",
- "Ġs es",
- "Ġr ow",
- "ŀ Ī",
- "Ġpand emic",
- "Ġsk ill",
- "b el",
- "Ġdire ctor",
- "Ġmil k",
- "Ġn ut",
- "Ġmot ion",
- "Ġcl osed",
- "è ¨",
- "Ġcred it",
- "ah r",
- "Ġche ese",
- "Ġal tern",
- "im ately",
- "Ġs ust",
- "ĠT ra",
- "Ġgl ad",
- "Ġhigh ly",
- "Ġw a",
- "Ġredu ce",
- "Ġb le",
- "ad or",
- "in ated",
- "ion es",
- "ci ent",
- "Ġdep ending",
- "Ġsh aring",
- "Ġca ught",
- "ra el",
- "Ġme hr",
- "Ġpass ion",
- "ç Ľ",
- "Ġr u",
- "Ġfar m",
- "T I",
- "av es",
- "ĠR ob",
- "ĠB ro",
- "Ġmot iv",
- "ret ch",
- "ru pt",
- "ĠB ig",
- "Ġall e",
- "Ġet t",
- "ub s",
- "ĠJapan ese",
- "ĠH all",
- "и ли",
- "AUDI BLE",
- "ç ¬",
- "Ġcell s",
- "ik a",
- "el ine",
- "il er",
- "Ġì £",
- "Ġsk y",
- "IN AUDIBLE",
- "end e",
- "ap ter",
- "Ġp in",
- "Ġg ather",
- "h ol",
- "le ction",
- "Ġsy n",
- "Ġpl ug",
- "r ound",
- "Ġun iversity",
- "h ib",
- "Ġfant astic",
- "k n",
- "Ġho le",
- "ĠRem ember",
- "in ct",
- "ak s",
- "C H",
- "Ġbro ken",
- "Ġstr ateg",
- "Ġal ive",
- "Ġt ank",
- "Ġc art",
- "r ated",
- "r ie",
- "ĠSt ep",
- "ĠEvery thing",
- "Ġb ound",
- "Ġso bre",
- "Ġcustom er",
- "¡ Į",
- "ur g",
- "ĠB ill",
- "L a",
- "wh at",
- "Ġre action",
- "Ġs ession",
- "Ġpl ans",
- "ĠìĿ´ë łĩê²Į",
- "Ġdown load",
- "ì Ļ",
- "u er",
- "Ġc ab",
- "Ġinst r",
- "if ying",
- "ĠN ice",
- "Ġteam s",
- "ı l",
- "Ġgo als",
- "is ch",
- "Ġtrans port",
- "Ġanim al",
- "Ġcost s",
- "Ġcall s",
- "Ġse hr",
- "ì Ī",
- "ri an",
- "Ġd ial",
- "Ġwe ather",
- "๠Ģ",
- "Ġв оÑĤ",
- "ĠPl ay",
- "Ġsh ared",
- "Ġsm ooth",
- "ab a",
- "Ġleav es",
- "à® ©",
- "Ġconc ent",
- "Ġsh ift",
- "ĠëIJ ĺ",
- "ĠGo vern",
- "Ġdem onst",
- "Ġbut ter",
- "ĠìĹ ¬",
- "Ġsat isf",
- "Īë ¬",
- "Ġrecogn ize",
- "ĠF rench",
- "Ġvol ume",
- "ä nd",
- "Ñĥ м",
- "Ġì§ Ħ",
- "ĠKe ep",
- "ow a",
- "ipp ed",
- "ÑģÑĤ ÑĢ",
- "Ġdet ect",
- "ĠÏ ĥ",
- "Ġl ift",
- "Ġcl othes",
- "ĠSt op",
- "Ã µ",
- "m et",
- "Ġcl in",
- "Ġar r",
- "f riend",
- "Ġst uck",
- "Y e",
- "h and",
- "um a",
- "Ġsc ri",
- "Ġfuck ing",
- "ct ors",
- "× ª",
- "Ġjo ining",
- "Ġc ette",
- "ĠØ £",
- "ĠWh ite",
- "Ġi hr",
- "Î Ń",
- "ãģ Ń",
- "Ġinclud ed",
- "ess o",
- "Ġac ad",
- "b um",
- "Ġs ab",
- "Ġд лÑı",
- "è¿ Ļ",
- "uf act",
- "ĠRep ublic",
- "r im",
- "Ġye llow",
- "Ġlim ited",
- "T ER",
- "ĠT y",
- "Ġnot es",
- "v est",
- "и з",
- "al ed",
- "Ġph ase",
- "and a",
- "ĠM om",
- "R I",
- "Ġim mer",
- "m al",
- "Ġin j",
- "Ġy ang",
- "ud ible",
- "аР³",
- "Ġset t",
- "Ġmag ic",
- "Ġens ure",
- "Ġsp ring",
- "Ġsh ock",
- "Ġwhe el",
- "ог да",
- "ãĤ Ī",
- "Ġcan cer",
- "Ġro ot",
- "Ð IJ",
- "gen cy",
- "Ġë į",
- "i i",
- "Ġout put",
- "Ġcomm it",
- "Ġwork ers",
- "ìķĦ ìļĶ",
- "ĠÑģ ам",
- "ve y",
- "Ġpe u",
- "Ġc ivil",
- "is c",
- "Ġbr ings",
- "ÑĢ ав",
- "an ia",
- "Ä ģ",
- "c raft",
- "mb ol",
- "Ġintell ig",
- "b i",
- "ac ing",
- "y ou",
- "Ġbecom ing",
- "ĠD er",
- "em a",
- "å°± æĺ¯",
- "Ġing red",
- "Ġcomm and",
- "Ġupd ate",
- "Ġpre m",
- "Ġopen ed",
- "Ħ ¤",
- "ени е",
- "Ġg ard",
- "Ġstat ement",
- "Ġsc rew",
- "Ġpr ote",
- "Ġc ards",
- "Ġt ask",
- "Ġeven ing",
- "Ġst itch",
- "in en",
- "ĠB er",
- "m ark",
- "ĠD ad",
- "Ġе ÑģÑĤÑĮ",
- "Ġ× ŀ×",
- "ìĹ Ī",
- "Ġb an",
- "Ġcl im",
- "Ġfre edom",
- "Ġnorm ally",
- "еÑģ ÑĮ",
- "å ¦",
- "Ġprov ided",
- "Ġìŀ IJ",
- "ĠìķĦ ëĭĪ",
- "ĠK im",
- "ied er",
- "ìĿ Į",
- "Ġcit iz",
- "Ġb ike",
- "Ġb ak",
- "Ġno ise",
- "Ġcl imate",
- "iz es",
- "å¾ Į",
- "Ġincre asing",
- "ĠTH E",
- "Ġli qu",
- "Ġperson ally",
- "e f",
- "res p",
- "Ġleg s",
- "ind er",
- "Ġp ed",
- "Ġë§ İ",
- "Ġdep end",
- "Ġvar iety",
- "ĠIs rael",
- "Ġwas h",
- "å Ĩ",
- "Ġqu iet",
- "ĠJ ames",
- "ĠJ ew",
- "Ġfore ver",
- "ĠI nt",
- "Ġcoun ter",
- "ur ance",
- "ĠAny way",
- "ca re",
- "ĠOn ly",
- "ci ón",
- "ad i",
- "ĠE v",
- "ëĭĪ ê¹Į",
- "ĠÎ ±",
- "Ġslow ly",
- "Ġо д",
- "Ġnot iced",
- "ier en",
- "Ġfe ll",
- "ĠÐ ij",
- "Ġm ême",
- "Ġwhen ever",
- "! )",
- "ĠH y",
- "å ¼",
- "ord s",
- "us ion",
- "ĠSt ar",
- "Ġí ĺ",
- "ĠM ac",
- "ä¸ Ĭ",
- "i ven",
- "Ġìĭ ľ",
- "ĠìĹ Ĩ",
- "ĠT ur",
- "Ġg er",
- "r is",
- "Ġve z",
- "Ġл Ñİ",
- "Ġvers us",
- "ا Ø",
- "ocol ate",
- "Ġplan e",
- "Ġz o",
- "Ġsu it",
- "Th is",
- "Ġn erv",
- "ĠA cc",
- "Ñĥ ж",
- "ìĤ ¬",
- "n h",
- "em e",
- "Ġa uss",
- "Ġme as",
- "Ġtr ès",
- "Ï ī",
- "Ñģ ли",
- "ĠAr t",
- "ĠSec ond",
- "олÑĮ ко",
- "ch o",
- "it ect",
- "е ÑģÑĤ",
- "Ġb oss",
- "Ġinc ome",
- "ł ¤",
- "Ġsh ad",
- "Ġapp ropri",
- "ĠM al",
- "op t",
- "Ġart ist",
- "Ġplay s",
- "oth ers",
- "ĠIn ter",
- "Ġvir us",
- "Ġh ung",
- "Ġconst ant",
- "Ġscri pt",
- "Ġsn ow",
- "ul f",
- "k et",
- "Ġdev ices",
- "Ġmet al",
- "ight s",
- "ìĦ ¸",
- "Ġsal es",
- "Ġve get",
- "Ġcollect ion",
- "Ġv ia",
- "k er",
- "Ġgot ten",
- "O W",
- "i én",
- "Ġacc ur",
- "Ġw ave",
- "ult y",
- "ĠA ir",
- "Ġlead ing",
- "ic ing",
- "Ġcent ral",
- "ĠChrist ian",
- "f r",
- "ĠAl though",
- "Ġsong s",
- "Ġf if",
- "нÑĭ Ñħ",
- "Ġbel ong",
- "oss ible",
- "ì °",
- "Ġphot os",
- "is l",
- "Ġrela x",
- "s a",
- "US IC",
- "ê ·",
- "Ġman ufact",
- "ĠTw itter",
- "Ġdanger ous",
- "Ġhy d",
- "le ar",
- "i ant",
- "ĠâĢ ¦",
- "Ġsudden ly",
- "Ġla ugh",
- "Ġang le",
- "ĠG ot",
- "Ġwor ried",
- "о е",
- "Ġp ap",
- "ĠM art",
- "en o",
- "Ġbatter y",
- "Ġп оÑģ",
- "Ġlight s",
- "Ġar ms",
- "ĠA bs",
- "m es",
- "âĢ ĵ",
- "use um",
- "Ġte a",
- "ĠM ic",
- "Ġfor mer",
- "ograph y",
- "Ġapplic ations",
- "ĠD ire",
- "çĦ ¶",
- "Ġfeed back",
- "itch en",
- "yor um",
- "u ed",
- "ig t",
- "Æ° á»",
- "os ition",
- "ĠD el",
- "Ġíķ ĺë",
- "ĠB ack",
- "ad s",
- "Ġpr ime",
- "ì£ ¼",
- "ì£ ł",
- "× ij",
- "Ġm ut",
- "] .",
- "ĠÐ Ĺ",
- "lo c",
- "k in",
- "Ġexper t",
- "Ġal right",
- "ung s",
- "Ġsupp ly",
- "Ġleaders hip",
- "ĠF ra",
- "Ġtyp ically",
- "Ġs el",
- "Ġtre es",
- "Ġ2 2",
- "h ar",
- "Ġwor st",
- "Ġbus y",
- "ant o",
- "ĠU p",
- "ĠB as",
- "Ġpresent ation",
- "Ġstr ange",
- "Ġth in",
- "ÑĤ е",
- "Ġveh icle",
- "Ġд о",
- "cell ent",
- "7 0",
- "Ġt ired",
- "Ġcris is",
- "Ġt iny",
- "as y",
- "Ġr an",
- "é ĩ",
- "Ġfor ces",
- "Ġо Ñĩ",
- "Ġident ify",
- "Ġass ess",
- "иÑĤ е",
- "S E",
- "Ġcreat ive",
- "ç Ł",
- "Ġdep artment",
- "Ġinit ial",
- "æĪij åĢij",
- "ĠD am",
- "ak t",
- "v ere",
- "Ġinf ect",
- "Ġp ump",
- "Ạ¡",
- "Ġv iel",
- "Ġr are",
- "Ġd ot",
- "ash ion",
- "em pl",
- "Ġf lex",
- "Ġk on",
- "Ġtr uck",
- "Ġle ct",
- "Ġpl astic",
- "la w",
- "Ġlik es",
- "Ġr ough",
- "ĠM AT",
- "í ŀĪ",
- "Ġcomm er",
- "Ġas se",
- "Ġc ake",
- "Ġact ions",
- "Ġad m",
- "Ġother wise",
- "ĠHe alth",
- "Ġcoll e",
- "à¹Ģ à¸",
- "Ġr ub",
- "å¾ Ĺ",
- "æ Ķ",
- "Ġsc r",
- "Ġz um",
- "ĠH im",
- "Ġch amp",
- "Ġconcern ed",
- "Ġ5 00",
- "Ġpl ate",
- "ĠO ut",
- "Ġdon c",
- "Ġequip ment",
- "Ġta ught",
- "ll ed",
- "Ġí Ļ",
- "iv a",
- "Ġmot or",
- "Â »",
- "Ġgu ide",
- "å ī",
- "Ġstop ped",
- "Ġr at",
- "Ġlab or",
- "Ġa im",
- "Ġprep are",
- "ĠÑ Ī",
- "Ġshoot ing",
- "ann ed",
- "cri pt",
- "Ġen emy",
- "Ġdep ends",
- "Ġn av",
- "Ġb er",
- "Ġland s",
- "Ġun ivers",
- "i u",
- "Ġfact or",
- "ok ing",
- "Ġcar bon",
- "b ut",
- "ĠL ove",
- "el d",
- "ĠÎ µ",
- "Ġg a",
- "Ġé s",
- "Ġbre ad",
- "Ġvol t",
- "í Ĭ",
- "Ġwas te",
- "Ġkeep s",
- "æī Ģ",
- "Ġst or",
- "Ġhon or",
- "Ġun less",
- "Ġcol um",
- "Ġë ĮĢ",
- "Ġpl ants",
- "Ye ah",
- "Ġinclud es",
- "ä¸ Ń",
- "Ġo x",
- "Ġpe ut",
- "ë§ Į",
- "ìĥ ģ",
- "ist ry",
- "ภ±",
- "ĠDep artment",
- "ant a",
- "Ġfing er",
- "Ġst retch",
- "Ġsy mbol",
- "Ġneigh bor",
- "æ ¬",
- "ê° Ħ",
- "~ ~",
- "ĠÑĤ Ñĭ",
- "ĠA ber",
- "k es",
- "Ġmass ive",
- "ĠC H",
- "ĠS al",
- "× ł",
- "ãĤ Ĵ",
- "Ġd ynam",
- "ach e",
- "ĠP re",
- "Ġmon itor",
- "ent ed",
- "E O",
- "Ġrais ed",
- "ist ics",
- "Ú ©",
- "Ġv ou",
- "it en",
- "¡ °",
- "Ġbusiness es",
- "Ġe arn",
- "Ġmob ile",
- "id ade",
- "Ġha be",
- "y r",
- "l ict",
- "Ġcon duct",
- "Ġfed eral",
- "Ġw o",
- "b u",
- "Ġn one",
- "Ġteach ers",
- "ĠاÙĦ Ø",
- "éģ ĵ",
- "id ents",
- "ا ÙĦ",
- "Ġtre nd",
- "еР¶",
- "Ġal bum",
- "Ġm ich",
- "b ased",
- "ภµ",
- "Ġtrans ition",
- "Ġн о",
- "õ es",
- "h ost",
- "ed y",
- "ĠPro f",
- "p an",
- "ij n",
- "Ġcapac ity",
- "und o",
- "Ġ× ij×",
- "Ġbreat h",
- "Ġм ен",
- "Ġm ü",
- "í Ļ",
- "ĠA ut",
- "hing ton",
- "Ġn or",
- "Ġg ain",
- "po int",
- "Y es",
- "ĠØ ª",
- "ĠN a",
- "Ã¥ r",
- "Ġi ç",
- "ĠM ary",
- "Ġsp in",
- "Ġant i",
- "åIJ §",
- "Ġsome how",
- "Ġlaw s",
- "Ġmom ents",
- "Ġg re",
- "Ġmo ves",
- "ĠW ould",
- "Ġpred ict",
- "Ġv ra",
- "Ġ201 9",
- "¶ Ħ",
- "Ġfund ament",
- "2 5",
- "Ġp ure",
- "Ġw ow",
- "Ġis land",
- "Ġinvest ment",
- "Ġb ath",
- "ĠY a",
- "Ġhard er",
- "Ġt ips",
- "å Ĺ",
- "Ġelect ron",
- "ĠB ob",
- "Ġb ond",
- "od ies",
- "ĠA ug",
- "Ġgib t",
- "Ġch air",
- "Ġtw ice",
- "w ood",
- "Ġcl ar",
- "Ġmas k",
- "Ġhonest ly",
- "Ġ201 8",
- "t ies",
- "' ,",
- "Ġp ens",
- "Ġsurpr ised",
- "Ġcommunic ation",
- "ãģ£ ãģ¦",
- "Ġsp r",
- "Ġwh ose",
- "Ġst ars",
- "× IJ×",
- "ĠâĢ ĭ",
- "Ġproper ly",
- "Ġg rew",
- "os ing",
- "Ġdi vers",
- "A D",
- "Ġem pt",
- "Ġexp ression",
- "Ạ¿",
- "ĠP al",
- "ãģ Ĭ",
- "Ġjust ice",
- "Ġp air",
- "w o",
- "Ġse at",
- "or ter",
- "Ġlink s",
- "ĠM er",
- "Ġre nd",
- "но е",
- "up id",
- "ĠH el",
- "ĠM arch",
- "ĠL o",
- "Ñģ ÑĮ",
- "Ġhas n",
- "Ġev alu",
- "ãģ ı",
- "å¤ ©",
- "il os",
- "Ġfund ing",
- "Ġv en",
- "u an",
- "ĠM aster",
- "ĠO l",
- "ĠF re",
- "Ġy ap",
- "ĠS ir",
- "s ch",
- "Ġmist ake",
- "am an",
- "Ġdin ner",
- "ĠWas hington",
- "Ġorganiz ations",
- "Ġж е",
- "av ing",
- "Ġv ÃŃ",
- "Ġbirth day",
- "Ġbe ar",
- "ĠÙ ģ",
- "Ġaff ord",
- "Ġre ven",
- "Ġrelationship s",
- "r ough",
- "ĠT ime",
- "Ġt ag",
- "ĠS un",
- "u ary",
- "ĠP o",
- "c ar",
- "ab ilities",
- "Ġpr ison",
- "Ġl ic",
- "ìł ķ",
- "id den",
- "Ġspec ies",
- "é »",
- "Ġf irm",
- "Ġsc ore",
- "Ġd it",
- "Ġspe ct",
- "Ġp el",
- "Ġcompl icated",
- "æ¨ £",
- "Ġr ank",
- "Ġoppos ite",
- "Ġpick ed",
- "Ġк он",
- "el er",
- "Ġm ig",
- "ĠS l",
- "ĠN et",
- "Ġne ck",
- "ĠFr ance",
- "Ġtechn ical",
- "ภ¡",
- "Ġmil es",
- "Ġprim ary",
- "Ġse in",
- "s es",
- "Ġla ughs",
- "b ra",
- "ÅĽ ci",
- "ri age",
- "Ġn ic",
- "et ers",
- "ĠÃ ª",
- "olog ies",
- "ĠI S",
- "r ad",
- "ud o",
- "ı nd",
- "m ar",
- "Ġex ch",
- "Ġcompet ition",
- "Ġauss i",
- "ĠS erv",
- "Ġre nt",
- "Ġch ocolate",
- "Ġw ieder",
- "Ġnear ly",
- "Ġspe ech",
- "Ġun c",
- "Ġpar am",
- "ĠBrit ish",
- "Ġrem ain",
- "ภģ",
- "ur t",
- "ĠØ ¹",
- "Ġcr ack",
- "ail s",
- "Ġprom ise",
- "Ġpay ing",
- "i ÃŁ",
- "Ġad apt",
- "ал а",
- "Ġmov ies",
- "Ġw ire",
- "Ł ¬",
- "æľ ĥ",
- "Ġter rible",
- "Ġs ó",
- "Ġperfect ly",
- "åij ¢",
- "ord in",
- "Ġj á",
- "Ġimp ossible",
- "ĠTh ree",
- "Ġn h",
- "Ġtur ning",
- "r um",
- "ĠB el",
- "ig g",
- "Ġrespons ible",
- "и й",
- "Ġincred ibly",
- "w i",
- "ian o",
- "Ġhum ans",
- "ĠÃ ĩ",
- "Ġsetting s",
- "Ġj oy",
- "o ot",
- "Ġdeal ing",
- "ill ed",
- "Ġsur round",
- "Ġfollow ed",
- "Ġposs ibly",
- "Ġinit i",
- "st en",
- "Ġpr os",
- "Ġcand id",
- "Ġass ign",
- "Ġviol ence",
- "W ell",
- "Ġr ise",
- "P S",
- "Ġtamb ém",
- "Ġë ĵ¤",
- "i ance",
- "y an",
- "Ġaud io",
- "ĠB et",
- "ĠAmeric ans",
- "ĠAs s",
- "is chen",
- "ìŀ ħ",
- "Ġult imately",
- "Ġpol ic",
- "Ġmajor ity",
- "éĢĻ åĢĭ",
- "ĠFin ally",
- "er ap",
- "Ġgu ard",
- "ĠMAT T",
- "Ġbr own",
- "м и",
- "Ġch a",
- "ĠHo ly",
- "Ġnerv ous",
- "ipp ing",
- "ÄĻ d",
- "ĠS a",
- "ĵ ľë",
- "¶ Ģ",
- "l ie",
- "çľ Ł",
- "Ġn uc",
- "ĠA pr",
- "é Ľ",
- "ĠKore a",
- "eg o",
- "ĠCan ada",
- "Ġkön nen",
- "Ġcomp ar",
- "Ġg anz",
- "ĠM ais",
- "Ġthem e",
- "Ġk i",
- "Ġdraw ing",
- "az on",
- "ĠO ff",
- "t t",
- "ĠW ind",
- "Ġtod os",
- "Ġob vious",
- "на Ñı",
- "I M",
- "ĠÐ ł",
- "we ll",
- "Ġbl ow",
- "Ġho ok",
- "Ġcir cle",
- "Ġë³ ´",
- "Ġarch itect",
- "ĠK r",
- "Ġc ó",
- "Ġprotect ion",
- "eg a",
- "å ĩ",
- "Ġwatch ed",
- "Ġans wers",
- "Ġdi et",
- "iv o",
- "Ġpow der",
- "Ġyour s",
- "Ġhigh est",
- "çĤ º",
- "F F",
- "å º",
- "Ġbo ys",
- "ö yle",
- "Ġl unch",
- "è¬ Ŀ",
- "ĠI I",
- "Ġset s",
- "Ġmo le",
- "Û ģ",
- "Ġwin ter",
- "Ġluck y",
- "Ġrespons ibility",
- "Ġsign al",
- "Ġwond ering",
- "Ġa x",
- "Ġcook ing",
- "ов оÑĢ",
- "le g",
- "Ġп оÑĤ",
- "Ġsurpr ise",
- "Ġdem ocr",
- "Ġlo op",
- "Ġj ag",
- "Ġcur ious",
- "Ġmarket ing",
- "Ð Ŀ",
- "ar on",
- "ĠApp le",
- "Ġvirt ual",
- "Ġ19 8",
- "no on",
- "ĠM et",
- "оÑģ ÑĤо",
- "об Ñĭ",
- "it u",
- "ĠA w",
- "Ġbu ying",
- "Ġrestaur ant",
- "ĠB ud",
- "Ġdou bt",
- "Ġgr ant",
- "Ġver d",
- "Ġc ash",
- "Ġfac ulty",
- "Th at",
- "ĠE in",
- "å¤ ļ",
- "Ġw ed",
- "it ness",
- "ĠM ag",
- "n el",
- "Ġn arr",
- "Ġacc ident",
- "Ġmed ium",
- "em ents",
- "Ġcr ow",
- "n ight",
- "ìĿ ¼",
- "ä¹ Ł",
- "Ġlibr ary",
- "аÑİ ÑĤ",
- "Ġtamb ién",
- "Ġrefer ence",
- "Ġfour th",
- "h ouse",
- "v ention",
- "Ġfill ed",
- "ĠC our",
- "ib r",
- "Ġn g",
- "Ġdevelop ing",
- "Ġprov ides",
- "Ġpo ll",
- "Ġtra ffic",
- "arent ly",
- "à® Ł",
- "Ġform s",
- "Ġcl ient",
- "Ġg entle",
- "Ġmus s",
- "ĠCong ress",
- "ĠInd ian",
- "ce an",
- "Ġp il",
- "Ġc zy",
- "st ood",
- "ut y",
- "Ġn ä",
- "Ġsp ending",
- "Ġconst ruction",
- "ina udible",
- "Ġë§ Ī",
- "Īë¬ ´",
- "Ġìĥ Ŀ",
- "om a",
- "os en",
- "ag o",
- "Ġlar gest",
- "ãħĭ ãħĭ",
- "Ġun iverse",
- "b es",
- "os a",
- "Ġе го",
- "Ġd ude",
- "ĠM AR",
- "Ġind eed",
- "ε ι",
- "Ġman aged",
- "ĠSh ould",
- "S o",
- "Ġappl ied",
- "Ġfair ly",
- "ĠD en",
- "Ġanal y",
- "Ġconst antly",
- "Ñģ п",
- "H ow",
- "ĠS ay",
- "en cies",
- "ĠP C",
- "Ġegg s",
- "à® °",
- "Ġet h",
- "ĠEnt ão",
- "in ar",
- "i ot",
- "Ġc z",
- "ĠEurope an",
- "ãģ Ī",
- "ĠA M",
- "Ġc á",
- "Ġrad io",
- "§ Į",
- "Ġh ide",
- "ä» Ĭ",
- "ĠSt art",
- "Ġcl ub",
- "ĠH ope",
- "Ġeff orts",
- "lus ion",
- "Ġc ities",
- "h one",
- "Ġreach ed",
- "Ġgu id",
- "ro id",
- "Ġhar m",
- "Ġcut ting",
- "Ġb ul",
- "1 8",
- "i est",
- "ĠMe x",
- "Ġ iron",
- "çŁ ¥",
- "Ġafter noon",
- "Ġha ll",
- "Ġpr zy",
- "Ġg osh",
- "Ġinflu ence",
- "Ġв ид",
- "Ġincre ased",
- "ĠMin ister",
- "Ġdis ci",
- "ĠP eter",
- "Ġver t",
- "Ġmen u",
- "Ġse lling",
- "ur ally",
- "Ġqu ote",
- "ĠÂ ¡",
- "Ġcontin ues",
- "mp re",
- "ĠÅŁ ey",
- "it ution",
- "Ġна Ñģ",
- "c les",
- "ĠGerm an",
- "c zy",
- "ĠÐ £",
- "B e",
- "Ġk itchen",
- "ĠT ry",
- "i pe",
- "Ġic on",
- "ar p",
- "Ġprov iding",
- "ĠTr ans",
- "Ġtechn ique",
- "Ġh är",
- "Ġinf rast",
- "Ġsus p",
- "ü ck",
- "ic ip",
- "ĠÐ ķ",
- "Ġc in",
- "ìĸ ´ë",
- "Ġpr z",
- "Ġcompon ent",
- "Ġby e",
- "ĠB ible",
- "iz er",
- "C h",
- "Ġsol utions",
- "Ġaccom pl",
- "Ġ201 6",
- "I E",
- "ĠT a",
- "Ġass ume",
- "Ġliqu id",
- "Ġë¨ ¹",
- "Ġquar ter",
- "Ġfem ale",
- "ĠTh ink",
- "Ġstat us",
- "it ute",
- "Ġco ach",
- "Ġre in",
- "Ġcomb ination",
- "è ·",
- "ĠT er",
- "Ġobject s",
- "Ġdist rict",
- "Ġmake up",
- "Ġmur der",
- "w as",
- "f en",
- "Ġbow l",
- "Ġpub lished",
- "Ġsp orts",
- "ãģ ¡",
- "Ġident ity",
- "Ġseem ed",
- "Ġact ing",
- "л Ñİ",
- "ri x",
- "Ġup load",
- "Ġh ast",
- "Ġbo at",
- "ĠM od",
- "ri o",
- "Ġ =",
- "Ġcy cle",
- "¯ ¸",
- "Ġl oud",
- "ust ed",
- "com ing",
- "Ġ201 7",
- "Ġon t",
- "Ġleg isl",
- "Ġst ruct",
- "ĠSomet hing",
- "Ġconf lict",
- "Ġu pper",
- "Ġman ager",
- "Ġm ort",
- "Ġf ra",
- "ĠÄ °",
- "ĠM ike",
- "ĠW ork",
- "Ġn ó",
- "ph ere",
- "ĠìĤ ¬ë",
- "ĠL and",
- "Ġfil ter",
- "Ġprom ot",
- "æ °",
- "æĻ Ĥ",
- "ķ ¼",
- "Ġrecord ing",
- "× Ŀ",
- "Ġassoci ated",
- "Ġf uel",
- "und er",
- "Ġele ction",
- "Ġemploy ees",
- "ĠCom p",
- "ÑĢÑĥ г",
- "ĠW o",
- "ro l",
- "Ġsa ved",
- "ĠH on",
- "ĠV i",
- "åĪ Ĩ",
- "ac a",
- "p ret",
- "Ġw et",
- "Ġst upid",
- "Ġl ad",
- "Ġf est",
- "Ġw ake",
- "Ġи н",
- "Ġgreat est",
- "ĠJ im",
- "Ġserious ly",
- "Ġì ¹",
- "Ġfeel ings",
- "Ġ3 00",
- "i ation",
- "Ġbeaut y",
- "Ġìŀ ĺ",
- "Ġs an",
- "ĵ ł",
- "Ġ- (",
- "Ġcons cious",
- "Ġд ел",
- "b ye",
- "ç Ļ",
- "M an",
- "Ġlet s",
- "Ġsho es",
- "y d",
- "ä¹ Ī",
- "Ġdisapp e",
- "ĠCount y",
- "ĠSc ott",
- "Ġbut t",
- "Ġaqu ÃŃ",
- "Ġconf ig",
- "resp ond",
- "LAU GH",
- "© ëĭĪëĭ¤",
- "Ġdivid ed",
- "Ġac qu",
- "Ġz one",
- "Ġk omm",
- "a ção",
- "ì§ ľ",
- "c ut",
- "Ġ2 3",
- "Ġmaxim um",
- "ro g",
- "Ġrun s",
- "Ġcompon ents",
- "Ġarri ved",
- "Ġconf ident",
- "ÑĢ ов",
- "Ġhe ight",
- "Ġpro ced",
- "E M",
- "ĠÐŃ ÑĤо",
- "ĠM en",
- "Ġtalk s",
- "Ġconf idence",
- "ĠChr is",
- "Ġlead s",
- "Ġn ose",
- "f all",
- "b b",
- "ĠNot hing",
- "is er",
- "Ġindepend ent",
- "Ġmin or",
- "Ġsy m",
- "l en",
- "ci ence",
- "Ġf ashion",
- "Ġsex ual",
- "Ġb un",
- "h ere",
- "Ġso il",
- "Ġdies e",
- "Ġsh ap",
- "Ġempt y",
- "Ġjour nal",
- "ag on",
- "ĠThe ir",
- "Ġweek end",
- "ÃŃ t",
- "Ġer ror",
- "Ġn ar",
- "Ã ¸",
- "è ©",
- "an cy",
- "Ġìķ Ĭ",
- "Ġfore st",
- "Ġha cer",
- "Ġmiss ed",
- "ãģ ķ",
- "åı¯ 以",
- "Ġev il",
- "Ġstor age",
- "Ġsing ing",
- "in ha",
- "Ġkn ock",
- "Ġimp ress",
- "ĠоÑĩ енÑĮ",
- "ĠGo ld",
- "ĠS ur",
- "ĠP ort",
- "åİ »",
- "ĠL ond",
- "Ġfaz er",
- "ot y",
- "ot o",
- "Ġan x",
- "ĠWill iam",
- "Ġexist ing",
- "pl ace",
- "ĠC D",
- "Î ³",
- "ĠColl ege",
- "l or",
- "ĠE ast",
- "s en",
- "f ach",
- "o ft",
- "Ġexperien ced",
- "Ġlo ves",
- "im m",
- "Ġpo ly",
- "Ġes se",
- "ì ¤",
- "ĠG rand",
- "è §",
- "ch er",
- "Ġvict im",
- "ĠG es",
- "л ÑĮ",
- "v ision",
- "Ġt all",
- "Ġl ens",
- "Ġз на",
- "ĠB oth",
- "Ġì ²",
- "Ġsust ain",
- "Ġarg ument",
- "Ġfact ors",
- "Ġautom atically",
- "Ġfr uit",
- "Ġli ber",
- "Ġa le",
- "ĠP ress",
- "ĠB a",
- "ĠÐ ³Ð¾",
- "Ġhundred s",
- "th at",
- "ĠR ich",
- "Ġreci pe",
- "ĠI T",
- "è ĩ",
- "Ạ¥",
- "Ġdescri be",
- "Ġdri ver",
- "ĠO ct",
- "ĠM at",
- "д е",
- "Ġme al",
- "Ġlat est",
- "Ġth erap",
- "Ġcomp are",
- "ĠAm azon",
- "Ġì¢ Ģ",
- "ĠRuss ia",
- "Ġstr ing",
- "Ġk a",
- "ĠComm un",
- "Ġd ia",
- "I s",
- "Ġmill ions",
- "Ġcor por",
- "Ġcor respond",
- "Ġfix ed",
- "ĠJo e",
- "Ù İ",
- "Ġview s",
- "Ġr iver",
- "Ġstud io",
- "ig ger",
- "Ġfl avor",
- "Ġpres ence",
- "Ġun its",
- "Ġsa ving",
- "av our",
- "Ġp esso",
- "or ith",
- "Ġh ers",
- "ĠN at",
- "as ion",
- "ĠFr ank",
- "о ÑĪ",
- "ÅĤ y",
- "í Ħ",
- "Ġein em",
- "Ġfun ctions",
- "um an",
- "Ġn orth",
- "Ġìł Ħ",
- "Ġhor se",
- "v id",
- "Ġple asure",
- "а ÑĪ",
- "é es",
- "ind a",
- "Ġt ail",
- "Ġexpl ore",
- "S T",
- "Ġcommer cial",
- "ĠD uring",
- "ar l",
- "] :",
- "f it",
- "Ġr ates",
- "æ ³",
- "M USIC",
- "Ġhous ing",
- "Ġein er",
- "Ġsitu ations",
- "æ ĭ",
- "Ġdec re",
- "Ġappropri ate",
- "ен но",
- "% .",
- "Ġb ac",
- "Ġw at",
- "ens ity",
- "ä h",
- "kn own",
- "it z",
- "Ġemot ional",
- "erv ation",
- "Ġbl ind",
- "1 6",
- "í ĥ",
- "大 家",
- "Ġjo ined",
- "Ġloc ated",
- "ĠÑģ м",
- "ad as",
- "ber g",
- "Ġd ess",
- "Ġde ar",
- "ed en",
- "c os",
- "Ġad opt",
- "1 00",
- "ow e",
- "ĠChe ck",
- "ism o",
- "Ġsim pl",
- "Ġang ry",
- "Ġмен Ñı",
- "ĠC am",
- "Ġp ad",
- "Ġatt end",
- "Ġsam ple",
- "æĹ ¥",
- "Ġì Ľ",
- "ĠI N",
- "ul ous",
- "ĠS ar",
- "ĠSh ow",
- "Ġinfrast ructure",
- "ĠAug ust",
- "Ġless on",
- "Ġn iet",
- "æ İ",
- "Ġfo i",
- "Ġbro ke",
- "t r",
- "ç ķ",
- "Ġ4 5",
- "Ġg ew",
- "Ñĥ п",
- "at i",
- "Ġmaint ain",
- "Ġart ists",
- "ing er",
- "æĿ ¥",
- "er ved",
- "I A",
- "Ġequ als",
- "Ġoper ation",
- "ill y",
- "ĠëĤ ´",
- "Ġcrow d",
- "Ġintern al",
- "Ġtest s",
- "ĠR ock",
- "ĠC ons",
- "ĠëĦ Ī무",
- "w ar",
- "Ġs ou",
- "Ġch art",
- "ĠJ une",
- "ĠApr il",
- "g ent",
- "Ġv ent",
- "Ġqu and",
- "ĠKore an",
- "im o",
- "ç ī",
- "id ers",
- "Ġmount ain",
- "ÑģÑĤ ав",
- "æľ Ī",
- "ij k",
- "Ġdiscover ed",
- "ĠS und",
- "ĠS il",
- "Ġso lo",
- "Â ´",
- "Ġsch ol",
- "ĠE ach",
- "ç µ",
- "Ġb are",
- "Ġí Į",
- "ĠvÃŃ de",
- "Ġingred ients",
- "ĠIt s",
- "Ŀ¼ ê³ł",
- "Ġì Ĭ",
- "Ï į",
- "ĠLe e",
- "Ġsc ary",
- "Ġprinci p",
- "Ġspirit ual",
- "ì ħ",
- "ĠH old",
- "æ²Ĵ æľī",
- "Ġdef ine",
- "ĠL es",
- "ĠN or",
- "ĠE nd",
- "Ġbl og",
- "ĠG reen",
- "аеÑĤ ÑģÑı",
- "p art",
- "el es",
- "äº ĭ",
- "ĠUnd er",
- "Ġpart e",
- "Ġ3 5",
- "Ġse ctor",
- "ĠS ept",
- "Ġaut h",
- "à® ®",
- "om in",
- "Ġcl ients",
- "Ġc i",
- "ĠFr iday",
- "er as",
- "Ġtw e",
- "ul ated",
- "Ġcult ural",
- "ĠÑģв о",
- "Ġëį Ķ",
- "ĠÃ º",
- "Ġpar ce",
- "à® ²",
- "Ġtrad ition",
- "Ġjud ge",
- "ĠGen eral",
- "Ġdeterm ine",
- "ĠIs n",
- "ĠP L",
- "ne ath",
- "Ġmatter s",
- "íķ ´ì",
- "! ]",
- "а Ñħ",
- "Ġpo ol",
- "Ġvari able",
- "Ġvacc ine",
- "Ġcaus ed",
- "Ġw est",
- "ĠY ep",
- "f ast",
- "Ġph ilos",
- "hor a",
- "Ġcontinu ed",
- "Ġunf ortunately",
- "ãģ į",
- "æ ķ",
- "Ġfl ight",
- "Ġw rap",
- "Ġhu h",
- "ĠAbs olutely",
- "Ġp ink",
- "Ġrem ains",
- "Ġn é",
- "Ġf le",
- "ĠS ol",
- "Ġlos ing",
- "Ġalg orith",
- "Ġrequ ires",
- "Ġfound ation",
- "ĠB ur",
- "Ġprofess ion",
- "ĠM id",
- "Ġë ŃIJ",
- "c an",
- "ĠM il",
- "Ġyoung er",
- "Ġappe ars",
- "ter m",
- "íķĺ ê³ł",
- "ac le",
- "ĠLond on",
- "Ġengine ering",
- "ภ¢",
- "Ġadv ent",
- "ìĦ¸ ìļĶ",
- "Ġê¸ °",
- "ĠM aj",
- "ÑĢ ем",
- "ing u",
- "ĠU K",
- "u ro",
- "s pe",
- "Ġt ent",
- "Ġreport ed",
- "ĠA L",
- "H ey",
- "Ġë§ IJ",
- "Ġd ent",
- "ĠAustral ia",
- "ĠJan uary",
- "³ ´",
- "ag ues",
- "ars h",
- "r ig",
- "Ġtien e",
- "ภ£",
- "Î ®",
- "Ġmach en",
- "un te",
- "Ñĥ Ñģ",
- "Ġelect r",
- "Ġtut orial",
- "Ġpl aced",
- "ĠìĿ´ ê±°",
- "ĠCoun cil",
- "í ĸĪ",
- "°ë ¦¬",
- "ah ren",
- "Ġê·¸ë ŀĺ",
- "Ġpro ve",
- "f ol",
- "Ġqu er",
- "Ġche ap",
- "ĠF ather",
- "ĠP ower",
- "ĵ ľ",
- "Ġpur s",
- "Ġes p",
- "ĠB re",
- "ê¸ °ë",
- "om as",
- "æĥ ³",
- "ил ÑĮ",
- "Ġge ht",
- "os ter",
- "ê³ ¼",
- "Ġfil es",
- "ĠÐ §",
- "be ll",
- "Ġwh om",
- "Ġë ĺ",
- "Ġex cellent",
- "Ġdat ab",
- "Ġg ö",
- "Ġì§Ħ ì§ľ",
- "Ġbelie f",
- "j et",
- "Ġj ack",
- "Ġsw im",
- "ri al",
- "um in",
- "a uc",
- "Ġso ll",
- "Ġess ential",
- "íķĺ ëĬĶ",
- "Ġev ol",
- "cha ft",
- "ain e",
- "th let",
- "Ġinc or",
- "Ġreport s",
- "Ġdefin ition",
- "ke l",
- "Ġcirc um",
- "Ġprodu ced",
- "Ġ× Ľ",
- "ant ic",
- "n et",
- "Ġa ward",
- "Ġd urch",
- "Ġtrans p",
- "Ġm ale",
- "¦ ¬ë",
- "Ġmo on",
- "ĠGe orge",
- "Ġfly ing",
- "i ó",
- "Ġs ources",
- "Ġpl enty",
- "ĠDem ocr",
- "R O",
- "Ġ 00",
- "Ġsec ure",
- "ĠB ir",
- "ra in",
- "Ġz ur",
- "Ġeffic ient",
- "Ġrepe at",
- "Ġmethod s",
- "Ġcal m",
- "Ġdiscuss ed",
- "ĠìŀĪ ëĬĶ",
- "Ġser ver",
- "an ie",
- "ĠInst ead",
- "Ġide al",
- "Ġcon ven",
- "Ġhop ing",
- "ĠT or",
- "Ġdep th",
- "Ġhe aven",
- "EN CE",
- "Ġhab it",
- "gr ad",
- "Ġfl ag",
- "Ġin e",
- "Ġk h",
- "ĠL I",
- "Ġfac ing",
- "ĠA U",
- "ĠT im",
- "Ġg em",
- "ĠJ ul",
- "Ġel a",
- "iz za",
- "Ġfe llow",
- "Ġqu el",
- "Ġsp oke",
- "Ġcitiz ens",
- "u ge",
- "é ĥ½",
- "Ġp ages",
- "Ġf asc",
- "Ġrelig ious",
- "at en",
- "Ġch apter",
- "ĠV al",
- "Ġcons ult",
- "ĠM ill",
- "g l",
- "op er",
- "Ġinf in",
- "Ġmar riage",
- "Ġmedic ine",
- "Ġд в",
- "Ġdog s",
- "Ġinstr ument",
- "ĠEx act",
- "á n",
- "Ġ20 21",
- "Ġf er",
- "Ġwe alth",
- "Ġgr ade",
- "Ñĭ Ñħ",
- "Ġcr ime",
- "Ġth read",
- "Ġess a",
- "Ġw ine",
- "co hol",
- "ph a",
- "ภĩ",
- "og ue",
- "Ġins urance",
- "arr ator",
- "ĠSept ember",
- "Ġv id",
- "ĠSp irit",
- "Ġg est",
- "ĠRuss ian",
- "Ġproper ties",
- "Ġart icle",
- "Ġunder neath",
- "y er",
- "Ġjo int",
- "Ġrelative ly",
- "Ġin ch",
- "Ġdesp ite",
- "ĠG ree",
- "Ġclass ic",
- "Ġsupport ing",
- "Ġinst ruct",
- "lus ive",
- "Ġdi agn",
- "æ Ĭ",
- "Ġadminist ration",
- "аб оÑĤ",
- "ĠO pen",
- "æīĢ 以",
- "Ġп ок",
- "Ġdoll ar",
- "Ġconse qu",
- "o ber",
- "ĠGerm any",
- "Ġter r",
- "ĠQ U",
- "ĠÐ ĵ",
- "ç ¾",
- "Ġstrong er",
- "É Ļ",
- "ĠÙ Ĭ",
- "ĠiP hone",
- "Ġfab ric",
- "ü h",
- "Ġen em",
- "æ ¯",
- "Ġsub t",
- "E E",
- "ond e",
- "Ġcre w",
- "Ġremo ved",
- "Ġl ady",
- "Ġpot entially",
- "ĠÐĿ о",
- "y al",
- "Ġsym pt",
- "Ġar my",
- "Ġintrodu ced",
- "t es",
- "Ġaspect s",
- "1 4",
- "ĠL ou",
- "Ġ )",
- "Ġde ploy",
- "p et",
- "Ġh an",
- "ĠW atch",
- "Ġweap ons",
- "Ġph en",
- "Ġreg ister",
- "Ġein fach",
- "Ġsp ort",
- "Ġbr idge",
- "Ġin ner",
- "Ġminim um",
- "Ġw itness",
- "Ġes o",
- "Ġvill age",
- "Ġown er",
- "¦¬ ê³ł",
- "Ġsc ream",
- "il ed",
- "Ġp itch",
- "b ru",
- "Ġadv ance",
- "ä¸į æĺ¯",
- "Ġsupp ose",
- "ĠAt t",
- "еÑĤ ÑģÑı",
- "Ġdiffer ences",
- "ak ed",
- "Ġinter pret",
- "Ã ¦",
- "iend o",
- "Ġabs ol",
- "ĠбÑĥд еÑĤ",
- "Ġë ²",
- "Ġtri al",
- "Ġthink s",
- "ly ing",
- "cept ion",
- "ĠAfric an",
- "Ġchem ical",
- "Ġta pe",
- "Ġconvers ations",
- "Ġdistrib ution",
- "t i",
- "ĠA I",
- "Ġfl ash",
- "Ġunder stood",
- "ĠGovern ment",
- "å° ı",
- "! ?",
- "ĠS k",
- "ê± °ë",
- "ri er",
- "T S",
- "ĠAcc ording",
- "Ñİ ÑĤ",
- "Ġsp ons",
- "ÑĤ обÑĭ",
- "Ġval u",
- "ere m",
- "icht ig",
- "Ġresist ance",
- "ĠG al",
- "ger y",
- "Ġbeg ins",
- "Ġadv anced",
- "Ġrele vant",
- "Ġpolit ics",
- "ĠF am",
- "Ġç ok",
- "ĠN ever",
- "ill ing",
- "Ġfoot ball",
- "и и",
- "ĠI D",
- "ĠAfric a",
- "Ġfing ers",
- "Ġб олÑĮ",
- "ĠÃ ¡",
- "Ġcl ip",
- "ĠL at",
- "ãĤ Ħ",
- "Ġì§Ģ ê¸Ī",
- "es se",
- "Ġvo or",
- "Ġas ide",
- "æ ŀ",
- "Ġto ward",
- "Ġb at",
- "Ġval id",
- "ĠM ens",
- "Ġcomplet ed",
- "ı ģ",
- "Ġpod cast",
- "ĠB on",
- "Û Ĵ",
- "ĠJ uly",
- "il a",
- "Ġpack age",
- "Ġpull ed",
- "ch ar",
- "ĠM el",
- "o is",
- "Ġs outh",
- "Ġë Ķ",
- "Ġimport ance",
- "Ġp ushing",
- "Ġis ol",
- "Ġstand s",
- "c ill",
- "ä ¼",
- "Ġ ðŁ",
- "or i",
- "ê° ģ",
- "Ġhom es",
- "Ġconcern s",
- "Ġb iz",
- "å ½",
- "b ie",
- "Ġb is",
- "Ġge ar",
- "ĠM S",
- "Ġh un",
- "ĠM att",
- "Ạ£",
- "se y",
- "ĠSec ret",
- "Ġod d",
- "ĠM ax",
- "oll y",
- "f ord",
- "ĠS H",
- "Ġrepl ace",
- "Ġnav ig",
- "Ġin i",
- "и Ñı",
- "Ġgi ant",
- "Ġma nd",
- "ĠH app",
- "TI ON",
- "g un",
- "iam o",
- "ìŀħ ëĭĪëĭ¤",
- "Ġg ap",
- "Ġê tre",
- "Ġclass room",
- "Ġhy p",
- "ak i",
- "è ®",
- "is ters",
- "ack s",
- "ĠÑģ о",
- "Ġb ug",
- "Ġgra v",
- "am in",
- "Ġevery day",
- "Ġì ¡°",
- "Ġgard en",
- "ce mber",
- "Ġest o",
- "åĹ İ",
- "Ø ¬",
- "Ł °",
- "å ģ",
- "Ġr om",
- "Ġìłľ ê°Ģ",
- "Ġfall ing",
- "Ġfa ult",
- "ell y",
- "Ġch est",
- "Ġл и",
- "Ġpot ato",
- "Ġbuild ings",
- "Ġoper ating",
- "Ġp are",
- "w r",
- "D on",
- "ĠF our",
- "Ġv ul",
- "Ġl á",
- "Ġfr ust",
- "ĠD ann",
- "ol es",
- "ny a",
- "Ġì ¶",
- "ĠÑĢ аÑģ",
- "× Ľ",
- "Ġa ÃŃ",
- "w ord",
- "Ġweap on",
- "Ġob t",
- "ĠF all",
- "ĠSte ve",
- "Ġmix ed",
- "Ġp ode",
- "ĠA S",
- "ĠL eg",
- "Ġdes c",
- "Ġspl it",
- "Ġemer gency",
- "ĠS ing",
- "Ġprof it",
- "Ġtyp ical",
- "ĠDon c",
- "Ġannoun ce",
- "ĠTe x",
- "Ġsac r",
- "tern al",
- "Ġcomm ittee",
- "ig o",
- "Ġdi am",
- "ph as",
- "Ġdef e",
- "ĠProf ess",
- "Ġdec l",
- "Ñĥ ÑĢ",
- "2 2",
- "ol f",
- "ĠM ond",
- "u y",
- "Ġa y",
- "Ġl em",
- "Ġlove ly",
- "ĠC ould",
- "Ġgu ar",
- "H H",
- "Ġcare fully",
- "ĠL isten",
- "Ġк ÑĢ",
- "Ġyou th",
- "ĠThere fore",
- "Ġdream s",
- "ĠJe ff",
- "? ]",
- "Ġë Ī",
- "D A",
- "Ġb odies",
- "au x",
- "Ġtechn iques",
- "Ġmechan ism",
- "× ĵ",
- "Ġо ни",
- "Ġdes ire",
- "Ã ®",
- "ĠV o",
- "qu es",
- "ĠÑĥ же",
- "ĠWho a",
- "ĠG ame",
- "Ġh al",
- "an ish",
- "Ġpract ices",
- "5 00",
- "Ġsort s",
- "up s",
- "ate ful",
- "Ġhers elf",
- "Ġgu itar",
- "Ġprop os",
- "Ġsit es",
- "Ġbe ach",
- "Ġ× ¢",
- "ç¬ ¬",
- "н Ñĥ",
- "Ġdr am",
- "ĠNo ve",
- "V E",
- "r ant",
- "Ġpl ot",
- "ĠìŬ 기",
- "ĠC a",
- "Ġestab lished",
- "Ġ201 5",
- "Ġinsp ired",
- "Ġannoun ced",
- "ä¸ ª",
- "ĠÑĤ ÑĢ",
- "Ġ2 6",
- "Ġv oy",
- "Ġte ch",
- "ìł ģ",
- "Ġprocess es",
- "ont o",
- "ĠP an",
- "Ġrap id",
- "ist an",
- "Ġ19 7",
- "Ġrelig ion",
- "Ġ2 8",
- "Ġsm ile",
- "Ġb ab",
- "Ġ Ú©",
- "ĠV ir",
- "Ġsched ule",
- "Ġexec ut",
- "Ġpr on",
- "Ñ į",
- "ĠÐĿ Ñĥ",
- "m usic",
- "ìĽ IJ",
- "Ġg an",
- "ìĭ ł",
- "Ġdef ault",
- "Ġbe m",
- "Ù ī",
- "Ġfor ced",
- "ĠOb viously",
- "Ġst one",
- "Ġt ie",
- "Ġdrink ing",
- "Ġser ved",
- "C ause",
- "Ġcon ference",
- "ĠExact ly",
- "ãĥ Ī",
- "ł ľ",
- "ìĻ Ģ",
- "ĠR a",
- "Ġf ake",
- "Ġdif f",
- "ãģ ©",
- "Ġchalleng ing",
- "Ġì¤ ij",
- "Ï ĩ",
- "ä»Ģ 麼",
- "Ġintellig ence",
- "re te",
- "Ġstud ying",
- "Ġapp oint",
- "Ġt an",
- "Ġи м",
- "Ġcur ve",
- "ĠTe am",
- "ĠA z",
- "Ġз д",
- "ĠMus ic",
- "f ield",
- "ir ation",
- "Ġfail ed",
- "Ġno vel",
- "Ġdifferent ly",
- "Ġes cape",
- "ĠY o",
- "ĠOct ober",
- "ı yor",
- "Ġdescri bed",
- "Ġcon vert",
- "ac ement",
- "Ġhot el",
- "is ation",
- "Ġsu is",
- "ãģ ij",
- "å ŃIJ",
- "æĢ İ",
- "Ġwalk ed",
- "2 00",
- "Ġneighbor hood",
- "is p",
- "ĠL os",
- "Ġh idden",
- "Ġ2 7",
- "л е",
- "Ġph r",
- "ĠIs land",
- "ĠSt reet",
- "end a",
- "hip s",
- "os ure",
- "Ġdefin ed",
- "ภ§",
- "Ġv ida",
- "Ġlab el",
- "ĠEvery body",
- "Ġjo ke",
- "ia o",
- "ا ÙĨ",
- "Ġa thlet",
- "... \"",
- "ĠF ire",
- "D o",
- "Ġdef ense",
- "Ġent ertain",
- "á t",
- "Ġpolic ies",
- "Ġal cohol",
- "ĠEng ine",
- "Ġg al",
- "ĠJ ud",
- "Ġvol unte",
- "ick s",
- "et a",
- "ag t",
- "Ġ× ķ",
- "Ġm ö",
- "1 3",
- "Ġenc oun",
- "Ġe h",
- "Ġor ange",
- "Ġabs or",
- "Ġsp aces",
- "ĠNove mber",
- "êµ ¬",
- "i at",
- "Ġt am",
- "ck now",
- "Ġst orm",
- "ĠDire ctor",
- "Ġpre gn",
- "ĠìĿ ¼",
- "Ġо п",
- "Ġres ource",
- "Ġb ard",
- "ne w",
- "ĠDe cember",
- "u its",
- "Ġwe il",
- "Ġconst ruct",
- "s i",
- "n ic",
- "Ġfl our",
- "Ġrest rict",
- "ü t",
- "Ġentire ly",
- "Ġbreak ing",
- "ent lich",
- "Ġtw enty",
- "Ġcaus es",
- "Ġele v",
- "ĠS pr",
- "ĠIntern et",
- "Ġk iss",
- "Ġoper ations",
- "s zy",
- "Ġë Ĭ",
- "Ġscient ists",
- "Ġgr own",
- "Ġown ers",
- "out s",
- "Ġcour ses",
- "Ġus ual",
- "Ġin n",
- "Ġtrans m",
- "ñ o",
- "Ġnu est",
- "к ов",
- "Ġcateg ory",
- "ĠL ife",
- "ĠPl us",
- "Ġat mos",
- "wh ile",
- "Ġrecord s",
- "Ġde ÄŁ",
- "ëĭ¤ ê³ł",
- "ĠìĤ¬ë ŀ",
- "Ġrequire ments",
- "in n",
- "Ġimm ig",
- "Ġdeep er",
- "ç ´",
- "Ġapp s",
- "Ġcolle agues",
- "ż y",
- "Ġoff ers",
- "Ġt á",
- "Ġcolum n",
- "la ud",
- "I R",
- "ĠM s",
- "Ġexch ange",
- "l as",
- "ĠL aw",
- "ĠJ on",
- "is se",
- "ro gen",
- "Ġmo i",
- "× Ĺ",
- "Ġs ending",
- "Ġhe llo",
- "е е",
- "ÅĽ Äĩ",
- "Ġsuc ceed",
- "Ġsuff ering",
- "Ġad vert",
- "Ġì£ ¼",
- "çŁ¥ éģĵ",
- "Ġrec o",
- "ın ı",
- "Ġк ом",
- "all ey",
- "Ġfail ure",
- "ie j",
- "Ġëķ Į",
- "Ġdrug s",
- "Ġcu ando",
- "Ġìĸ´ë ĸ",
- "ĠAb out",
- "Ġqu ando",
- "9 0",
- "ĠF ed",
- "1 7",
- "S h",
- "in ho",
- "ĠSund ay",
- "ĠPh il",
- "Ġacad emic",
- "ĠIn c",
- "Ġmaint en",
- "åĩ º",
- "Ġre ward",
- "er d",
- "Ġcomm itted",
- "ìĬ ¤",
- "г ÑĢ",
- "Ġstand ards",
- "Ġk al",
- "Ġint ention",
- "ĠZ h",
- "Ġa cknow",
- "ä ¿",
- "Ġ== =",
- "og y",
- "å §",
- "Ġfilm s",
- "is k",
- "Ġte eth",
- "Ġstrugg le",
- "r d",
- "u en",
- "Ġdis s",
- "ĠD ar",
- "am y",
- "Ġenem ies",
- "Ġve loc",
- "ĠC all",
- "um bs",
- "иÑĤ елÑĮ",
- "Ġo cean",
- "é d",
- "ìļ °",
- "Ġtre m",
- "ient o",
- "еÑĪ ÑĮ",
- "ffic ient",
- "Ġbott le",
- "Ġinstit ution",
- "est y",
- "ĠH an",
- "h ab",
- "ëĬ ĺ",
- "Ġar rest",
- "éĤ Ħ",
- "Ġlet ters",
- "oun ce",
- "í Į",
- "A n",
- "Ġcreat es",
- "Ġcl ock",
- "Ġdeb t",
- "Ġan cient",
- "ific ations",
- "g i",
- "B ut",
- "ĠT u",
- "k l",
- "Ġb order",
- "Ġo ok",
- "ĠB ay",
- "est a",
- "Ġë³ ´ì",
- "Ġw ra",
- "pre ne",
- "Ġê² Į",
- "ang le",
- "Ġbelie ved",
- "ien cy",
- "ak a",
- "Ġcrit ic",
- "Ġb omb",
- "Ġha m",
- "ĠÐ Ľ",
- "êµ Ń",
- "ĠGu ys",
- "ros oft",
- "Ġcr im",
- "et ch",
- "AR R",
- "Ġs ight",
- "и на",
- "Ġa in",
- "á» ij",
- "is che",
- "Ġau x",
- "Ġnum er",
- "Ġsurv ive",
- "A ll",
- "B C",
- "Ġs z",
- "Ł ¬ë",
- "Ġj am",
- "ĠCour t",
- "Ġall es",
- "Ġtr igger",
- "Ð ŀ",
- "Ġform at",
- "Ġdec ades",
- "Ġc es",
- "Ġsign s",
- "Ġrob ot",
- "ĠCh urch",
- "Ġa z",
- "Ġs oup",
- "ĠTex as",
- "ut en",
- "ĠÑĩ ÑĤобÑĭ",
- "Ġneigh b",
- "ĸ ×Ķ",
- "Ġcommunic ate",
- "Å ¡",
- "Ġel imin",
- "Ġfrequ ency",
- "her n",
- "id os",
- "Ġem phas",
- "Ġmess ages",
- "Ġg ender",
- "ĠW enn",
- "Ġв о",
- "Ġpr ices",
- "ol o",
- "Ġп он",
- "w ing",
- "ĠF il",
- "а ем",
- "ĠC ur",
- "Ġfal se",
- "Ġfield s",
- "Ġs é",
- "2 4",
- "Ġm ac",
- "u ÅŁ",
- "Ġlay ers",
- "Ġadv oc",
- "w an",
- "Ġk ar",
- "ĠÅ ŀ",
- "Ġdec or",
- "Ġwall s",
- "o e",
- "iss ions",
- "Ġres ol",
- "× ¢",
- "ĠCar ol",
- "ĠV ide",
- "le ep",
- "ĠY OU",
- "Ġfl ip",
- "Ġsur gery",
- "Ġch op",
- "U R",
- ". ,",
- "Ġag ency",
- "Ġwant ing",
- "Ġsol ar",
- "Ġhor iz",
- "ĠAd am",
- "Ġstay ing",
- "ol ic",
- "Ġgr ateful",
- "Ġrem ark",
- "Ġtechn ologies",
- "Ġprote in",
- "å¿ ĥ",
- "д ел",
- "ĠM ont",
- "Ġshould er",
- "Ġz a",
- "re y",
- "ĠO oh",
- "Ġst y",
- "ic ar",
- "оÑĤ ÑĢ",
- "Ġrout e",
- "ĠT urn",
- "Ġb om",
- "Ġdeb ate",
- "Ġposs ibility",
- "Ġíķ ´ì",
- "ap a",
- "Ġinv ent",
- "ür lich",
- "Ġprof ile",
- "Ġsen ior",
- "pp y",
- "v as",
- "Ġm undo",
- "ate ver",
- "Ġapp arently",
- "en er",
- "× IJ",
- "ç Ń",
- "Ġprec is",
- "Ġal ign",
- "Ġkn ife",
- "ĠRo bert",
- "å ĭ",
- "Ġfo ol",
- "Ġinv ite",
- "us ing",
- "Ġcircum st",
- "Ġcapt ure",
- "Ġd ough",
- "ĠS and",
- "Ġse u",
- "ĠNew s",
- "Ġb ite",
- "Ġne ut",
- "w ide",
- "Ġlect ure",
- "Ġëĺ IJ",
- "Ġorigin ally",
- "Ġcho ices",
- "ĠG ar",
- "Ġver se",
- "Ġl it",
- "Ġ19 6",
- "íķ ł",
- "Ġmeas ures",
- "ç ões",
- "w ater",
- "ri ve",
- "Ġz ijn",
- "í ģ",
- "ĠB us",
- "Ġhe b",
- "е Ñħ",
- "ĠK ar",
- "ĠN ão",
- "Ġkill ing",
- "à® ª",
- "Ġmir ror",
- "m od",
- "Ġm ol",
- "Ġcre ation",
- "Ġest im",
- "Ġatmos phere",
- "Ġg am",
- "Ġt ables",
- "is i",
- "ĠL ittle",
- "Ġt as",
- "ĠE le",
- "é l",
- "Ġscen es",
- "Ġt one",
- "Ġaffect ed",
- "ĠAU DI",
- "ĠBr own",
- "I f",
- "ĠÙ ĩ",
- "ĠDan iel",
- "羣 çļĦ",
- "qu er",
- "ch i",
- "íķ ĺë",
- "Ġmist akes",
- "Ġs la",
- "ãĤ ¤",
- "Ġent r",
- "Ġе Ñģли",
- "Ġsh out",
- "Ġport ion",
- "Ñ Ĺ",
- "Ġpre viously",
- "á» Ļ",
- "ĠпÑĢ ед",
- "оÑģ ÑĮ",
- "Ġhead s",
- "ç İ",
- "å Ń",
- "åľ ĭ",
- "Ġgr ass",
- "ภ°",
- "cri be",
- "Ġqu é",
- "ĠSp anish",
- "Ġoffer ed",
- "ĠбÑĭ ло",
- "ĠCl oud",
- "Ġve ctor",
- "ĠH uh",
- "Ġk ad",
- "if ts",
- "ĠÎ ½",
- "Ġhung ry",
- "Ð ¡",
- "Ġpar all",
- "AN D",
- "ĠvÃŃde o",
- "iz z",
- "Ġocc up",
- "Ġí Ķ",
- "Ġsee k",
- "h es",
- "Ġdo ors",
- "Ġhous es",
- "Ġconsider ing",
- "Ġgradu ate",
- "Ġf ulf",
- "è ¡Į",
- "è £",
- "Ġext reme",
- "Ġflow ers",
- "it ate",
- "ĠP ri",
- "Ġfundament al",
- "Ñĩ аÑģ",
- "è¯ ´",
- "Ġtext ure",
- "į ĺ",
- "ĠAN D",
- "à® ±",
- "ĠT em",
- "Ġn ada",
- "ì§ Ħ",
- "Ġcelebr ate",
- "um s",
- "Ġp ill",
- "Ġи ли",
- "go ing",
- "Ġh ip",
- "Ġsupport ed",
- "Ġper man",
- "Ġagre ement",
- "Ġty m",
- "Ġë ij",
- "ĵ¤ ìĿ´",
- "Ġpurch ase",
- "í Ķ",
- "ĠPl an",
- "eg en",
- "Ġrec over",
- "P U",
- "ĠMic rosoft",
- "du c",
- "Ġhol es",
- "Ġdro pped",
- "Ġp ig",
- "Ġend ing",
- "Ġattack s",
- "be c",
- "Ġre n",
- "Ġr app",
- "Ġìļ °ë¦¬",
- "Ġter ror",
- "Ġ× Ļ",
- "Ġed it",
- "Ġa o",
- ". ",
- "Ġhero es",
- "ĠB oston",
- "Ġdepend ent",
- "Ġmotiv ation",
- "fl ix",
- "Ġse am",
- "ки е",
- "Ġdra in",
- "od ed",
- "Ġgu ilty",
- "ĠJ enn",
- "ing en",
- "Ġgrant ed",
- "ĠK elly",
- "ĠS av",
- "ĠUn cle",
- "ĠHon estly",
- "EL I",
- "Ġnavig ate",
- "Ġbless ed",
- "c ore",
- "Ġear ning",
- "Ġsign als",
- "Ġdis k",
- "ial s",
- "Ġag es",
- "æ ħ",
- "Ġpartic le",
- "ĠÑĩ еÑĢ",
- "Ġcan n",
- "Ġt ier",
- "Ġstat ements",
- "ê³ł ìļĶ",
- "ĠëķĮ문 ìĹIJ",
- "ĠCh o",
- "Ġpol ar",
- "an ç",
- "ĠK enn",
- "ĠN i",
- "ĠF ight",
- "or gan",
- "é ķ",
- "ĠCh a",
- "ĠS ÃŃ",
- "ãĥ ª",
- "Ġs lic",
- "Ġcert ific",
- "Ġtempl ate",
- "ĠFed eral",
- "Ġconsider ation",
- "Ġexpl o",
- "ĠM ain",
- "ĠN E",
- "Ġalong side",
- "Ġd ressed",
- "ĠP oint",
- "Ġenviron ments",
- "Ġpró xim",
- "Ġda ar",
- "Ġprom pt",
- "Ġpurs ue",
- "Ġentertain ment",
- "Ġth roat",
- "Ġproblem a",
- "Ġm art",
- "ì ¼",
- "Ġprov ider",
- "Ø Į",
- "Ġ× Ĺ",
- "int e",
- "m aking",
- "Ġstro ke",
- "Ġtiss ue",
- "U n",
- "Ġpre cious",
- "ĠAr ts",
- "ink ing",
- "ĠÐŀ н",
- "Ġи Ñģ",
- "n ah",
- "ĠÐķ Ñģли",
- "Ġcor ners",
- "Ġtrick y",
- "in ch",
- "l ijk",
- "Ġpress ing",
- "le vel",
- "AN G",
- "Ġrad iation",
- "ìĦ ł",
- "Ġconf ront",
- "Ġv et",
- "Ġrepresent ative",
- "Ġprop ag",
- "Ġcra p",
- "ĠDe c",
- "Ġr amp",
- "еп еÑĢÑĮ",
- "u és",
- "ess en",
- "cri ption",
- "Ġb ills",
- "ĠMatth ew",
- "Ġan ime",
- "ấ t",
- "Ġlow est",
- "h as",
- "sc reen",
- "og rap",
- "ал о",
- "int on",
- "ĠJ ah",
- "èĢ ħ",
- "it Ãł",
- "Ġk ay",
- "Ġrot ation",
- "ĠW ere",
- "abe i",
- "Ġtri als",
- "Ġle ver",
- "ight y",
- "Ġsp oon",
- "Ġh unt",
- "c ling",
- "Ġdis m",
- "ĠболÑĮ ÑĪ",
- "Ġass ault",
- "Ġíĺ ķ",
- "Ġweek ly",
- "Ġm ismo",
- "Ġgen etic",
- "ul pt",
- "ĠStud ent",
- "Ġreal istic",
- "Ġauthent ic",
- "æī ĵ",
- "ast a",
- "Ġarrest ed",
- "Ġguid elines",
- "Ġ×ľ× IJ",
- "Ġд ав",
- "ĠCom ing",
- "f ür",
- "Ġrequ ests",
- "ĥ IJ",
- "Ġanaly ze",
- "Ġinter ess",
- "Ġh alt",
- "ĠO per",
- "on om",
- "Ġd uck",
- "Ġwith d",
- "s er",
- "ĠÏ Į",
- "ĠHist ory",
- "Ġyout ube",
- "ãĤ į",
- "Ġsab er",
- "w alk",
- "f ont",
- "Ġover view",
- "3 9",
- "ü y",
- "ett i",
- "Ġfro zen",
- "Ġf lesh",
- "ÄŁ i",
- "ĠP M",
- "ĠìĻ Ģ",
- "é ¢",
- "ÑĨи и",
- "Ġê¸ °ë",
- "íģ ¬",
- "Ġpr ose",
- "oo oo",
- "r ates",
- "W S",
- "Ġautom atic",
- "Ġcollect ing",
- "Å ij",
- "Ġneighb ors",
- "» .",
- "ĠEx pl",
- "Ġcir cul",
- "co ver",
- "we g",
- "Ġstick s",
- "Ġe ller",
- "Ġw ww",
- "Ġd orm",
- "ĠEx per",
- "Ġstat istics",
- "Ġemail s",
- "Ġgra ve",
- "im iz",
- "H S",
- "Ġu it",
- ", '",
- "Ġlas er",
- "è ī",
- "ĠÑĤ ем",
- "Ñĭ ÑĪ",
- "Ñī Ñij",
- "Ġgen au",
- "Ġtien en",
- "Ġmed itation",
- "ĠOr gan",
- "Ġest imate",
- "Ġë¬ ´ì",
- "l ets",
- "Ġn Ãły",
- "Ġmind set",
- "Ġres on",
- "Ġm és",
- "Ġnumer ous",
- "Ġvie lleicht",
- "ĠTh ird",
- "u ous",
- "ĠDe ad",
- "ан д",
- "H N",
- "Ġrac ing",
- "Ġag ents",
- "ĠU t",
- "Ġte ar",
- "ĠH P",
- "Ġchem istry",
- "Ġsurv ival",
- "æĸ °",
- "Ġconvin ced",
- "Ġ ;",
- "Ġreg ulations",
- "ĠE S",
- "åĴ Į",
- "3 00",
- "Ġen se",
- "Ġì µ",
- "Ġd ict",
- "G A",
- "Ġah ÃŃ",
- "åĭ ķ",
- "Ġte j",
- "Ġо ÑģÑĤ",
- "ĠE lect",
- "Ġintellect ual",
- "Ġbi as",
- "Ġbur den",
- "çĤ ¹",
- "Ġìĸ´ëĸ »",
- "Ġche er",
- "Ġso ph",
- "Ġportfol io",
- "ub a",
- "Ġest os",
- "T V",
- "F or",
- "Ġas h",
- "Ġkom mer",
- "Ġcollect ive",
- "Ġw rest",
- "ĠJ etzt",
- "ĠW at",
- "re ich",
- "Ġprim er",
- "act ive",
- "Ġm ie",
- "ick ed",
- "Ġhun ting",
- "Ġtest im",
- "Ġcompass ion",
- "ĠØ ±",
- "Ġbr ut",
- "Ġsal ad",
- "об Ñīе",
- "Ġsol ving",
- "Ġflo ating",
- "ç ·",
- "Ġattract ive",
- "ÙĪ ÙĦ",
- "Ġper d",
- "if fer",
- "Ġsc ulpt",
- "hh h",
- "ĠWe ek",
- "Ġent hus",
- "Ġn ad",
- "Ġmer ch",
- "ĠíĻ ķ",
- "Ġm ile",
- "好 äºĨ",
- "ĠÎ ¸",
- "ĠëĤ ĺë",
- "éĩ į",
- "3 8",
- "Ġch ains",
- "ĠAl most",
- "Ġtick ets",
- "r in",
- "ĠC C",
- "Ġdistrib uted",
- "abet es",
- "Ġtemper atures",
- "Ġg ained",
- "Ġflex ibility",
- "Ġscream ing",
- "Ġab road",
- "un o",
- "Ġentreprene urs",
- "ĠNet work",
- "ĠCanad ian",
- "Ġpre v",
- "Ġs ö",
- "ĠÑĤеб Ñı",
- "ĠP oke",
- "ĠP od",
- "ĠTur key",
- "çı¾ åľ¨",
- "Ġabst ract",
- "Ġsn ake",
- "ĠAm y",
- "ĠëĬIJëĤ Į",
- "Ġbra ve",
- "ĠìŀĪ ìĸ´ìļĶ",
- "ĠK al",
- "Ġ200 7",
- "á rio",
- "Ġmark ed",
- "gin es",
- "Ġall oc",
- "ON G",
- "Ġscient ist",
- "Ġes ca",
- "Ġrac ism",
- "× ij×",
- "ĠS ams",
- "ĠP enn",
- "Ġload s",
- "Ġà® ¨",
- "ü ber",
- "M e",
- "ix ò",
- "Ġper ò",
- "an ne",
- "Ġexp ressed",
- "м еÑĢ",
- "Ġmo et",
- "Ġret urning",
- "n ia",
- "Ġexp on",
- "P ro",
- "Ġlo yal",
- "M L",
- "Ġl amp",
- "Ġsh y",
- "Ġcomp osition",
- "ĠL y",
- "Ġmagn etic",
- "Ġprem ier",
- "Ġmeasure d",
- "Ġsumm ary",
- "Ġattack ed",
- "Ġfin ishing",
- "Ð Ĺ",
- "ç ¥",
- "Ġs its",
- "Ġhyd rogen",
- "Ġma i",
- "ĠDeuts ch",
- "as ı",
- "Ġobt ain",
- "v ie",
- "Ġso it",
- "Ġë° Ķ",
- "Ġl ane",
- "Ġconse gu",
- "в о",
- "Ġe ase",
- "ak in",
- "ĠF a",
- "Ġunt uk",
- "Ġbur st",
- "Ġc um",
- "al ım",
- "ú blic",
- "id i",
- "ĠRoy al",
- "ĠK on",
- "Ġcommon ly",
- "Ġremo ving",
- "Ġj ur",
- "il ib",
- "Ġan ch",
- "íĸ ī",
- "Æ°á» £",
- "ĠÐľ Ñĭ",
- "ĠAn th",
- "ĠS Ã¥",
- "Ġinter rupt",
- "Ġst ere",
- "ĠO S",
- "ony m",
- "ter y",
- "ĠMar ia",
- "ê² ĥ",
- "Ġexpl oring",
- "Ġtransp arent",
- "Ġf ate",
- "ĠJ ung",
- "Ġgr up",
- "Ġdark er",
- "ĠD oug",
- "Ġman e",
- "æĶ ¾",
- "ạ i",
- "d ri",
- "lo ok",
- "ĠDes ign",
- "Ġtut aj",
- "Ġhorizont al",
- "re on",
- "ort e",
- "ĠCor rect",
- "ĠSte ven",
- "Ġv ine",
- "0 2",
- "i Äĩ",
- "Ġsie mpre",
- "ĠK ey",
- "åĥ ı",
- "ĠG ames",
- "Ġna ar",
- "Ġshock ed",
- "el ve",
- "ĠR ose",
- "ìĭ ¬",
- "Ġstop ping",
- "oh l",
- "ĠM ix",
- "Ġsuff ered",
- "Ġsig ma",
- "Ġweak ness",
- "ĠO w",
- "ี à¹Ī",
- "I F",
- "Ġà® ħ",
- "ad ed",
- "ĠNet flix",
- "an es",
- "Ġrem ained",
- "ir y",
- "Ġr ip",
- "ell t",
- "Ġsil ent",
- "Ġpro ven",
- "Ġtox ic",
- "Ġal umin",
- "Ġmulti pl",
- "al and",
- "Ġ3 4",
- "0 6",
- "ĠB ru",
- "Ġìłķ ë§IJ",
- "J ust",
- "b oy",
- "Ġsho e",
- "Ġcreat ure",
- "Ġhead ed",
- "ĠоÑĤ к",
- "æ ±",
- "Ġess ence",
- "Ġremark able",
- "Ġnú mer",
- "Ġd rew",
- "Ġpu zzle",
- "ĠLibr ary",
- "ĠF u",
- "ash es",
- "k k",
- "ĠI st",
- "¦ °",
- "ĠB ry",
- "Ġc eremony",
- "Ġà® İ",
- "Ġc ri",
- "e qu",
- "ãĤ ¢",
- "Ġpri ze",
- "Ġdim ensions",
- "og ram",
- "Ġle ather",
- "Ġpop ulations",
- "u um",
- "Ġve gan",
- "Ñı д",
- "Ġcó mo",
- "å Ħ",
- "Ġstri p",
- "å £",
- "Ġvac ation",
- "ħ ķ",
- "Ġme als",
- "ili pp",
- "Ġ ents",
- "ar am",
- "ric ht",
- "Ġgra in",
- "ĠSp ain",
- "Ġche ek",
- "ĠA ff",
- "I ON",
- "ĠBr ing",
- "Ġ3 8",
- "iel en",
- "ul u",
- "ĠболÑĮ ÑĪе",
- "Ġannounce ment",
- "ĠÑĤ ÑĥÑĤ",
- "ĠPro phet",
- "ard o",
- "3 7",
- "Ġw oke",
- "Ġtransl ation",
- "ĠN OT",
- "ĠC L",
- "Ġd Ã¼ÅŁ",
- "ÑĨ Ñĸ",
- "ac er",
- "ĠL oc",
- "Ġper ception",
- "N O",
- "Ġdies en",
- "L ook",
- "he art",
- "av ed",
- "Ġbound ary",
- "Ġfl ows",
- "Ñij м",
- "Ġarg uments",
- "Ġelect ions",
- "ı s",
- "Ġhe ck",
- "Ġsuit able",
- "Ġf iber",
- "ĠSt ra",
- "x y",
- "ĠH um",
- "Ġmonth ly",
- "u per",
- "Ġgol f",
- "Ġl ately",
- "ĠG ard",
- "ĠR en",
- "ĠA st",
- "ĠF ant",
- "аÑģ Ñģ",
- "Ġobs er",
- "ë ¡ľ",
- "Ġeas iest",
- "į Ķë",
- "Ġwebs ites",
- "p ol",
- "Ġco con",
- "Ġà® ĩ",
- "ĠV eg",
- "Ġwalk s",
- "Ġint ro",
- "Ġdirect ed",
- "ĠAn na",
- "Ġëĵ¤ ìĸ´",
- "ĠEaster n",
- "ĠS aint",
- "ĠB ow",
- "Ġro ast",
- "ĠU RL",
- "Ġjed en",
- "ur as",
- "aj a",
- "Ġse mi",
- "Ġrapid ly",
- "Ġtarget s",
- "ĠCont rol",
- "Ġb ah",
- "Ġref lection",
- "Ġcreat ivity",
- "hold ers",
- "Ġìĺ ¬ë",
- "Ġamong st",
- "Ġfeed ing",
- "ÑįÑĤ омÑĥ",
- "Ġвид е",
- "Ġë§Įë ĵ¤",
- "ĠSm art",
- "Ġrel iable",
- "Ġvez es",
- "Ġ× ¨",
- "ch uckles",
- "az ione",
- "ĠWilliam s",
- "Ġa ç",
- "Ġsle e",
- "е Ñī",
- "Ġtim eline",
- "Ġthor ough",
- "á» į",
- "ĠO t",
- "ạ n",
- "Ġimag ination",
- "Ġmechan ics",
- "r ist",
- "Ġclaim ed",
- "ÏĦ η",
- "ê te",
- "ĠHur ry",
- "ĠiP ad",
- "Ġconst ru",
- "ĠC la",
- "ĠAl s",
- "ä¼ ļ",
- "ut z",
- "Ġcult ures",
- "Ġìĸ´ëĸ» ê²Į",
- "Ġbelong s",
- "Ġy er",
- "ĠDoes n",
- "Ġge omet",
- "Ġb id",
- "Ġfo am",
- "Ġh ob",
- "ĠBrit ain",
- "Ġsubst ance",
- "Ġann iversary",
- "ĠëĦ Ī",
- "Ġnot ed",
- "Ġgovern or",
- "Ġstock s",
- "3 1",
- "Ġdi ye",
- "ìĬ ¤ë",
- "Ġre b",
- "z el",
- "Ġmultip ly",
- "Ġoper ator",
- "Ħ¤ ìļĶ",
- "Ġwat ers",
- "Ġd är",
- "Ġuns er",
- "ĠEliz abeth",
- "é« ĺ",
- "Ġincreasing ly",
- "ĠG ro",
- "Ġen gines",
- "ir s",
- "Ø «",
- "Ġtre asure",
- "P C",
- "in ction",
- "ir i",
- "Ġacc um",
- "Ġvari ation",
- "Ġp om",
- "Ġtit les",
- "ĠF est",
- "ó s",
- "Ġeld er",
- "ny m",
- "r un",
- "Ñı в",
- "Ġinnov ative",
- "Ġnom bre",
- "Ġco inc",
- "Ġfr anch",
- "Ġent onces",
- "Ġnicht s",
- "Ġexc lusive",
- "ĠChe ers",
- "ĠB i",
- "u je",
- "æŃ ¡",
- "Ġp ok",
- "ĠP rem",
- "Ġrock et",
- "ELI PE",
- "Ġhosp itals",
- "ri um",
- "Ġjust e",
- "Ġham mer",
- "Ġquant um",
- "Ġrespons es",
- "ll y",
- "end i",
- "Ġact ively",
- "Ġfr idge",
- "i ate",
- "l ong",
- "Ġqu em",
- "Ġdeath s",
- "Ġsuper ior",
- "ck en",
- "ìĿ´ì ĹIJ",
- "kt op",
- "Ġgather ed",
- "£ ¨",
- "Ġd azu",
- "Ġreci pes",
- "Ġbu zz",
- "c en",
- "Ġany time",
- "ons ense",
- "Ġcirc les",
- "Ġsol ved",
- "Ġìĭ ł",
- "Ġcoron avirus",
- "ĠLu ke",
- "Ġbu bb",
- "Ġcont empor",
- "r zy",
- "ĠJ ane",
- "Ġд ом",
- "Ġscrew s",
- "Ġhy brid",
- "Ġcas ual",
- "Ġsel bst",
- "be ing",
- "ĠÄ IJ",
- "ĠCol umb",
- "ĠÑħ оÑĩ",
- "Ġbu cket",
- "Ġevalu ate",
- "Ġid ol",
- "Ġrep utation",
- "ĠìĨ Įë",
- "ÙĪ ر",
- "Ġhe cho",
- "Ġpo em",
- "Ġsubject s",
- "pl ant",
- "ĠBe h",
- "ĠSpe aking",
- "Ġbatter ies",
- "Ġfollow ers",
- "ö l",
- "Ġg ently",
- "Ġsi xt",
- "Ġparam eter",
- "Ġik ke",
- "ĠT our",
- "ĠD J",
- "ot te",
- "ĠJ ahren",
- "Ġprepar ation",
- "Ġд Ñĥм",
- "Ġ8 00",
- "c op",
- "ik ing",
- "Ġë¬ ¸",
- "Ġн Ñĥ",
- "Ġл еÑĤ",
- "åIJ Į",
- "ĠI de",
- "Ġì¡° ê¸Ī",
- "Ġla ughter",
- "Ġmole cules",
- "ĠR est",
- "Ġobs erved",
- "d zie",
- "Ġadvert ising",
- "ert o",
- "Ġmo ins",
- "ĠM IT",
- "Ġexc it",
- "Ġt um",
- "Ġty l",
- "Ġinvest ed",
- "Ġph arm",
- "Ġunex pected",
- "Ġph i",
- "oty pe",
- "we ise",
- "Ġge ç",
- "jour d",
- "Ġhors es",
- "n Äħ",
- "= \"",
- "ĠS M",
- "Ġf ib",
- "Ġcl ips",
- "çķ ¶",
- "å¦Ĥ æŀľ",
- "Ġreg ime",
- "Ġrot ate",
- "r ou",
- "n ik",
- "Ġarm or",
- "ðŁ ĺ",
- "еÑĢ а",
- "åº ¦",
- "ĠO ch",
- "Ġr ichtig",
- "üz el",
- "ane ously",
- "m ek",
- "éĮ ¯",
- "ĠX iao",
- "Ġexist ed",
- "w orth",
- "ãģ£ ãģ¨",
- "Ġna ught",
- "Ġhe iÃŁt",
- "ĠB al",
- "Ġres id",
- "iv ot",
- "om atic",
- "Ġh ired",
- "Ġgrad ually",
- "Ġon ions",
- "Ġcomp at",
- "Ġint im",
- "Ġj ew",
- "Ġcontrib ution",
- "ĠI re",
- "ac ji",
- "Ġsl ice",
- "Ġimm un",
- "ĠR us",
- "Ġgr ows",
- "ĠSimilar ly",
- "Ġhard est",
- "Ġst ruck",
- "Ġmeasure ment",
- "... ]",
- "th ey",
- "Ġìł Ģë",
- "Ġsne ak",
- "Ġappl ies",
- "Ġн ем",
- "æ ĵ",
- "×ij ר",
- "ĠЧ ÑĤо",
- "Ġout ro",
- "Ġinnoc ent",
- "Ġm og",
- "ĠSams ung",
- "Ġmer cy",
- "Ġhand ling",
- "Ġinter vention",
- "id ays",
- "g ot",
- "Ġcur ric",
- "Ġbound aries",
- "Ġconf using",
- "Ŀ¼ ëĬĶ",
- "æ ĩ",
- "Ġstitch es",
- "ÃŃ vel",
- "Ġtun nel",
- "it ä",
- "Ġg ost",
- "im y",
- "Ġcz as",
- "Ġm é",
- "Ġcat al",
- "ĠSim on",
- "ĠLI AM",
- "m ic",
- "ĠÐ ¤",
- "Ġey el",
- "is as",
- "ĠC PU",
- "ĠD ou",
- "Ġnä ch",
- "Ġinfin ity",
- "Ġr if",
- "ĠPe ace",
- "ĠC u",
- "Ġminim al",
- "Ġlisten ed",
- "Ġpo le",
- "hal b",
- "Ġload ed",
- "Ġste ady",
- "ĠBes ides",
- "ê m",
- "Ġl ap",
- "Ġco op",
- "Ġfriends hip",
- "w orld",
- "Ġge h",
- "Ġtyl ko",
- "ĠLa ura",
- "Ġsurround ed",
- "ĠE vent",
- "Ġch ap",
- "ĠW onder",
- "bre ak",
- "Ġdro ve",
- "Ġbroad er",
- "Ġch i",
- "F i",
- "Ġge hen",
- "Ġwest ern",
- "Ġintellig ent",
- "Ġpers ist",
- "Ġfound ed",
- "ãģĵ ãģ¨",
- "Ġhistor ic",
- "Ġfr Ã¥",
- "cks å",
- "Ġhand y",
- "Ġsy mp",
- "Ġr ows",
- "Ġnut ri",
- "b ur",
- "ĠLe on",
- "Ġsist ema",
- "Ġext ensive",
- "ĠÑĥ в",
- "í ı",
- "Ġnight s",
- "Ġcá c",
- "Ġcount ing",
- "ĠM ust",
- "all ow",
- "еÑģ Ñģ",
- "M om",
- "Ġнад о",
- "Ġbar rel",
- "ãĥ ŀ",
- "AR D",
- "Ġinstall ation",
- "Ġin sect",
- "Ġëħ ¸ë",
- "uj Äħ",
- "ĠÄij i",
- "Ġpack ed",
- "Ġf iction",
- "N ow",
- "ĠY ay",
- "Ġper t",
- "r ons",
- "und e",
- "ach es",
- "Ġsty les",
- "Ġapr ès",
- "ok u",
- "ĠV ice",
- "ın ız",
- "com m",
- "Ġassign ed",
- "Ġinteract ions",
- "Ġac ab",
- "F ELIPE",
- "Ġresc ue",
- "Ġindust ries",
- "ĠAnd y",
- "Ġpra ise",
- "Ġfl ame",
- "Ġsn ack",
- "í Ĥ",
- "ç ģ",
- "Ġsw o",
- "rend er",
- "Ġbo ards",
- "ĠÑĤ ом",
- "en ne",
- "Ġpast a",
- "Ġdev il",
- "ĠF el",
- "Ġhat te",
- "Ġcoll eg",
- "e h",
- "ì »",
- "ãģĵ ãģ®",
- "Ġproduct ive",
- "for ward",
- "и п",
- "Ġsmart phone",
- "Ġinv is",
- "Ġb um",
- "Ġwho a",
- "ìŀ Ħ",
- "Ġo cksÃ¥",
- "ĠL ang",
- "ĠSy ria",
- "Ġses i",
- "ί α",
- "Ġappro val",
- "4 8",
- "Ġод ин",
- "Ġë ĸ",
- "ĠH arr",
- "ĠAd minist",
- "Ġ× ¤",
- "ĠDe an",
- "f i",
- "Ġcitiz en",
- "Ġsh ark",
- "0 5",
- "Ġbo il",
- "Ġindic ate",
- "å ¡",
- "A re",
- "Ġlay out",
- "Ġref r",
- "ĠPac ific",
- "AA AA",
- "ĠAustral ian",
- "g ression",
- "V oice",
- "ал ÑģÑı",
- "Ġshel ter",
- "T o",
- "au pt",
- "Ġevalu ation",
- "ap or",
- "Ġcur rency",
- "Ġм ного",
- "ig os",
- "ãģ °",
- "Ġo ct",
- "Ġro yal",
- "è ³",
- "as il",
- "ĠChild ren",
- "Ġr ien",
- "Ġë ĵľë",
- "Ġbar rier",
- "Ġej emplo",
- "Ġe k",
- "N D",
- "es p",
- "ен а",
- "Ġp ic",
- "Ġkill er",
- "Ġintegr ate",
- "Ġfew er",
- "Ġdis abilities",
- "Ġ ....",
- "Ġtri angle",
- "Ġfe es",
- "Ġwid ely",
- "em i",
- "Ġoverwhel ming",
- "Ġz omb",
- "Ġb ere",
- "Ġho od",
- "ĠA ye",
- "ĠHar vard",
- "e v",
- "ĠÏĦ οÏħ",
- "Ġcup s",
- "ĠA uch",
- "z ona",
- "Ġ199 0",
- "Ġwe iÃŁ",
- "Ġcr unch",
- "æ ¥",
- "Ġз ав",
- "Ġmeas uring",
- "Ġst ations",
- "ĠStep hen",
- "Ġshort ly",
- "Ġsig ning",
- "Ġcom edy",
- "om o",
- "Ġsuggest ions",
- "Ġsign ature",
- "ĠпÑĢ ив",
- "Ġdis order",
- "as ka",
- "Ġworld s",
- "Ġprecis ely",
- "n orm",
- "ra v",
- "ĠC ivil",
- "In ter",
- "ĠC ertain",
- "Ġinj ured",
- "Ġsuggest s",
- "ĠGold en",
- "Ġcy ber",
- "ĠØ ´",
- "Ġtempor ary",
- "Ġco oper",
- "Ġvot ed",
- "Ġ ought",
- "ấ y",
- "x ual",
- "Ġpan els",
- "Ġ9 5",
- "Ġhands ome",
- "ĠпÑĢ ов",
- "Ġper mit",
- "Ġke in",
- "Ġbad ly",
- "Ġnot ifications",
- "iz a",
- "ĠNot ice",
- "Ġinc lusive",
- "Ġanswer ing",
- "Ġí Ĺ",
- "u ld",
- "íħ Į",
- "Ġnow adays",
- "Ġ3 7",
- "Ġb olt",
- "Ġstat ic",
- "ĠH op",
- "Ġav ant",
- "aj o",
- "Ġ맼 ìŀĪ",
- "Ġfif ty",
- "ĠF inal",
- "Ġsc ores",
- "ĠT ap",
- "Ġcy l",
- "Ġconv ince",
- "Ġany ways",
- "od a",
- "Ġìķ ¼",
- "Ġser ves",
- "ĠÑĤак ой",
- "ĠZo om",
- "Ġsaving s",
- "ul o",
- "Ġs outhern",
- "view er",
- "Ġho je",
- "Ġse ja",
- "Ġrepresent ing",
- "Īë įĺ",
- "l ik",
- "ĠSome body",
- "Ġbe ast",
- "Ġstick ing",
- "Ġins ist",
- "Ġtal ented",
- "Ġexplain ing",
- "Ġatt orney",
- "éĥ ¨",
- "Ġst airs",
- "ĠD og",
- "í ĭ",
- "Ġc ig",
- "Ġshap ed",
- "Ġs ons",
- "Ïģ ι",
- "ut t",
- "Ġì Ķ",
- "Ġpar ad",
- "ìĿ¸ë į°",
- "Ġh orn",
- "ĠJ our",
- "ann o",
- "Ġworld wide",
- "åĬ Ľ",
- "Ġparticip ation",
- "¦ Ħ",
- "Ġm ów",
- "Ġburn ed",
- "Ġwrit ers",
- "all ah",
- "ĠF und",
- "Ġcle ver",
- "ĠLe ute",
- "b in",
- "Ġbe ating",
- "f oot",
- "ĠìĽ IJ",
- "ĠStud io",
- "Ġv ag",
- "be y",
- "r ze",
- "Ġoppos ition",
- "Ġж из",
- "w ho",
- "Ġê± ´",
- "Ġtr ace",
- "Ġд енÑĮ",
- "Ġep id",
- "Ġges ch",
- "ĠN ar",
- "ĠB E",
- "Ñĥ й",
- "ĠS ign",
- "ed ly",
- "Ġcl ay",
- "Ġinst antly",
- "Ġgather ing",
- "ĠGal axy",
- "Ġb ored",
- "ĠBudd h",
- "c é",
- "Ġm am",
- "Ġsl ope",
- "Ġëĭ¤ ìĿĮ",
- "Ġsch ön",
- "Ġp ir",
- "ge f",
- "am er",
- "Ġh ö",
- "Ġcolle ague",
- "Ġpres ents",
- "ad ium",
- "Ġà® µ",
- "Ġfal ar",
- "be ep",
- "Ġdri ed",
- "ism s",
- "Ġro pe",
- "Ġworks hop",
- "Ġest ud",
- "Ġb ands",
- "Ġthem es",
- "åħ ¬",
- "ÙĬ ر",
- "åIJ İ",
- "Ġremind er",
- "ÑĤ Ñĥ",
- "ĠB h",
- "Ġcocon ut",
- "ĠÑģ ÑĤо",
- "ĠCh annel",
- "Ġimmig ration",
- "ä s",
- ".. ...",
- "ä¸ »",
- "çĻ ½",
- "st op",
- "Ġк аÑĢ",
- "Ġco ins",
- "ĠÑĩ аÑģ",
- "Ġdest ruction",
- "l ined",
- "Ġbar riers",
- "ant ine",
- "Ġprint ed",
- "Ġcongrat ulations",
- "ĠHe art",
- "Ġin qu",
- "th a",
- "Ġhard ly",
- "ĠA ven",
- "Ġt inha",
- "ĠS ony",
- "ĠN F",
- "Ġgradu ates",
- "Ġsque eze",
- "ere my",
- "ÏĦ ι",
- "Ġep ic",
- "ĠJ u",
- "Ġol m",
- "ĠLa ughter",
- "Ġbelief s",
- "ĠC ru",
- "ĠTr ue",
- "ĠS oul",
- "owe en",
- "Ġrom antic",
- "Ġз в",
- "Ġan os",
- "ĠY up",
- "éĺ ¿",
- "d im",
- "Ġin fer",
- "Ġз ам",
- "Ġso c",
- "uk a",
- "Ġprec ise",
- "Ġdro pping",
- "Ġcl ue",
- "Ġer rors",
- "char ge",
- "ĠP u",
- "omet er",
- "Ġlamb da",
- "ac ional",
- "ĠD ong",
- "Ġcham ber",
- "Ġthank ful",
- "ĠN u",
- "ĠHaw ai",
- "Ġinf o",
- "Ġactiv ate",
- "ĠQ ual",
- "Ġqu ed",
- "Ñĥ лÑĮ",
- "Ġcl oth",
- "åĸ ľ",
- "Ġw ichtig",
- "5 5",
- "Ġot ra",
- "ograp her",
- "Ġcur ios",
- "Ġ19 80",
- "Ġemp res",
- "d ess",
- "e ur",
- "Ġcl uster",
- "ar ter",
- "ob ile",
- "ĠY an",
- "ĠAd v",
- "Ġdiscipl ine",
- "Ġìłķ ëıĦ",
- "ĠPl ace",
- "ĠSe lect",
- "T E",
- "ĠбÑĭ ла",
- "Ġwh is",
- "Ġb ay",
- "ĠD or",
- "en cing",
- "Ġrep et",
- "Ġf icar",
- "p ad",
- "Ġf og",
- "u yor",
- "Ġsn ap",
- "ib t",
- "Ġso bie",
- "Ġappoint ment",
- "ĠR y",
- "Ġce iling",
- "our se",
- "Ġwr ites",
- "ĠAfghan istan",
- "Ġm os",
- "az e",
- "Ġpen al",
- "Ġcry stal",
- "IC E",
- "ê° IJ",
- "é Ł",
- "ĠTes la",
- "Ġthe ories",
- "Ġappe al",
- "Ġnewsp aper",
- "Ġcook ies",
- "æ ©",
- "ĠاÙĦ ÙĦ",
- "Ġma j",
- "ĠGet ting",
- "k ommen",
- "ĠHe aven",
- "ell s",
- "Ġdiv ine",
- "Ä «",
- "Ġa kt",
- "Ġhop es",
- "ĠCh en",
- "we gen",
- "** *",
- "ĠFra ge",
- "Ġн и",
- "ภ¹",
- "min ister",
- "nes ota",
- "wh ich",
- "Ġexpl icit",
- "Ġverd ad",
- "Ġgradu ated",
- "ĠPh ilipp",
- "Q L",
- "ĠM I",
- "Ġdev ot",
- "Ġc ure",
- "Ġclos est",
- "ĠÃ Ħ",
- "Ġsex y",
- "ãģ Ľ",
- "ĠDe ath",
- "ok o",
- "ug u",
- "ĠAn ne",
- "itar ian",
- "es a",
- "ег од",
- "ĠD ur",
- "Ġ 000",
- "ze it",
- "Ġtour nament",
- "Ġmel hor",
- "ภª",
- "Ġin du",
- "Ġf law",
- "Ġw ars",
- "ĠM ind",
- "ĠI ron",
- "ÑĤ ак",
- "ĠV R",
- "Ġs iz",
- "ĠS outhern",
- "Ġê·¸ëŁ ¬ë",
- "Ġaw ak",
- "Ġìķ ŀ",
- "Ġc ube",
- "believ able",
- "if all",
- "d is",
- "Ġabandon ed",
- "m ind",
- "Ġpar l",
- "Ġclass ical",
- "è ĭ",
- "á»Ļ t",
- "ĠAut o",
- "ĠB or",
- "ç ©",
- "4 00",
- "ĠSoci ety",
- "Ġsubt le",
- "Ġmiss ions",
- "Ġremember ed",
- "ĠE ither",
- "Ġda für",
- "OR D",
- "Ġint ensity",
- "ES IN",
- "ĠC up",
- "Ġrare ly",
- "Ġto ys",
- "ĠChar lie",
- "á» Ł",
- "Ġgla ube",
- "Ġround s",
- "T IN",
- "Ġcap ability",
- "Ġderiv ative",
- "Ġrefer ring",
- "Ġd Ã¥",
- "ĠT ALI",
- "Ġcott on",
- "Ġcon fer",
- "Ġcolum ns",
- "Ġliber al",
- "Ġnun ca",
- "Ġμ ε",
- "Ġind o",
- "ib en",
- "ĠBe ispiel",
- "Ġê·¸ë łĩ",
- "ĠÑĥ Ñĩ",
- "Ġh oy",
- "Ġfr y",
- "ĠScott ish",
- "è Ĭ",
- "Ġc iv",
- "Ġconserv ative",
- "Ġair pl",
- "Ġs ar",
- "r us",
- "Ġinvest ments",
- "Ġinfin ite",
- "Ġà® ķ",
- "ĠTALI ESIN",
- "ĠG ary",
- "ue ll",
- "Ġа к",
- "ĠC ir",
- "Ġrit ual",
- "Ġ>> >",
- "Ġtem pt",
- "ĠTe ch",
- "ĠPoke mon",
- "Ġimprove ments",
- "Ġsp are",
- "Ġtransl ate",
- "Ġson ra",
- "ĠFil m",
- "w ort",
- "Ġм и",
- "Ġperiod s",
- "Ġje alous",
- "ãģĦ ãģĦ",
- "Ġt ir",
- "M I",
- "Ġconduct ed",
- "ĠìķĪë ħķ",
- "0 9",
- "ĠPol it",
- "ĠWhere as",
- "Ġmoist ure",
- "Ġs ins",
- "Ġk ap",
- "ĠÑį к",
- "Ġben im",
- "Ġelimin ate",
- "Ġathlet es",
- "ĠMan ager",
- "Ġfeature d",
- "ap ore",
- "äº Ľ",
- "Ġë° ľ",
- "Ġper f",
- "ĠTh us",
- "Ġdeb ut",
- "об ÑĢ",
- "Ġse ñ",
- "Ġmyster ious",
- "w ords",
- "Ķ ê°Ģ",
- "Ġcheck s",
- "Ġvolunte er",
- "Ġwas hing",
- "ĠMar vel",
- "ĠA B",
- "iss ors",
- "! '",
- "ĠF ull",
- "ye on",
- "Ġwe igh",
- "ĠJO HN",
- "Ġv os",
- "Ġproced ures",
- "Ġaddress ed",
- "ĠBer lin",
- "put er",
- "ĠB an",
- "Ġmedic ation",
- "Ġdr one",
- "ĠÑĥ б",
- "ĠJe an",
- "Ġcap s",
- "Ġdisappoint ed",
- "Ġw ore",
- "Ġêµ Ń",
- "Ġorgan ize",
- "ĠHall oween",
- "Ġfant asy",
- "y ard",
- "Ġnos otros",
- "Ġjump ed",
- "Ġphot ography",
- "ĠN ame",
- "re c",
- "A B",
- "Ġbless ing",
- "ĠSh ut",
- "Ġbit ter",
- "p op",
- "ãģĿ ãĤĮ",
- "Ġde i",
- "Ġfulf ill",
- "çIJ Ĩ",
- "Ġden gan",
- "Ġbe lo",
- "ĠMean while",
- "Ġdep ois",
- "Ġdi abetes",
- "Ġbu nd",
- "ĠZe aland",
- "Ġdig est",
- "Ġt ires",
- "Ġdo d",
- "ag ne",
- "ế t",
- "Ġpe el",
- "Ġз аб",
- "Ġn odes",
- "Ġtrend s",
- "ĠSw itch",
- "ĠA ward",
- "ĠOr ig",
- "ĠH al",
- "Ġest as",
- "Ġ3 60",
- "Ġsim ult",
- "Ġcom ic",
- "Ġm Ãł",
- "Ġbal anced",
- "ĠPrin cess",
- "Ġkilomet ers",
- "á» ©",
- "Ġpart ir",
- "ì¤ ij",
- "so ft",
- "ĠV iew",
- "Ġbi ological",
- "in st",
- "4 4",
- "Ġman era",
- "Ġcompreh ensive",
- "ĠS ab",
- "Ġcr imes",
- "y ers",
- "ĠComp any",
- "ĠPh ot",
- "Ġpou co",
- "i ac",
- "Ġbe im",
- "in ate",
- "Ġsub sequ",
- "ĠMay or",
- "Ġcent uries",
- "è res",
- "ìŀĸ ìķĦìļĶ",
- "Ġê·¸ëŁ ¼",
- "ĠFra u",
- "ĠO H",
- "Ġëģ Ŀ",
- "ĠN ah",
- "ĠSer ies",
- "Ġover night",
- "íĴ Ī",
- "ĠâĢ ¢",
- "Ġtra ve",
- "atter ed",
- "Ġwar ri",
- "ĠGru nd",
- "ĠInd ones",
- "Ġsc ra",
- "ob y",
- "ĠBro ok",
- "Ġcur s",
- "Ġë ¸",
- "Ġexpl ains",
- "ram atic",
- "Ġparticip ating",
- "Ġmin ut",
- "Ġcontract s",
- "Ġg egen",
- "Ġdisappe ared",
- "ĠS N",
- "Ġrob ust",
- "ap h",
- "Ġsh rim",
- "Ġdev ast",
- "c ope",
- "Ġme ets",
- "Ġpeace ful",
- "m ate",
- "Ġwe ld",
- "Ġ× ª",
- "d on",
- "Ñĥ ÑĤÑĮ",
- "Ġregister ed",
- "ĠN ik",
- "j in",
- "Ġc av",
- "Ġe cht",
- "io x",
- "Ġflow ing",
- "но ÑģÑĤи",
- "Ġto e",
- "Ġent ity",
- "ов а",
- "f its",
- "ĠPat rick",
- "ÑĤ ÑĢ",
- "Ġle verage",
- "Ġcor rel",
- "i ah",
- "Ġstr ings",
- "ist inct",
- "Ġg ue",
- "arch y",
- "Ġteng o",
- "ım ız",
- "Ġor bit",
- "ä¸ º",
- "Ġе ÑīÑij",
- "ca ke",
- "Ġ׾ ×Ķ",
- "ĠMin nesota",
- "Ġbra ke",
- "ow ie",
- "Ġcra w",
- "ê¸°ë ¥¼",
- "Ġprogram me",
- "ĠÑģл ÑĥÑĩ",
- "åı ª",
- "ien ces",
- "ĠO ui",
- "ĠP ers",
- "im iento",
- "ĠIn vest",
- "Ġsl ower",
- "æĻĤ åĢĻ",
- "ĠB eth",
- "Ġnur se",
- "ĠSpr ing",
- "S p",
- "Ġun employ",
- "д и",
- "Ġgen ius",
- "ĠA aron",
- "Ġê·¸ëŁ ¬",
- "Ġe i",
- "ãģĹ ãĤĩ",
- "Ġtank s",
- "Ġau jourd",
- "Ġcomplex ity",
- "ĠÑĢ еÑĪ",
- "Ġold est",
- "Ġlet z",
- "åħ ¥",
- "Ġphenomen on",
- "pr int",
- "ĠBund es",
- "it at",
- "ê» ĺ",
- "Ġ4 2",
- "ĠW i",
- "Ġinc om",
- "Ġg ek",
- "Ġembr ace",
- "Ġt ies",
- "out e",
- "Ġd ose",
- "ĠF riends",
- "Ñĭ ÑĤ",
- "егод нÑı",
- "Ġor g",
- "Ħë ¡ľ",
- "ó g",
- "Ġex ceed",
- "Ġgod s",
- "Ġê±° ìĺĪìļĶ",
- "Ġsoci et",
- "ĠUn ivers",
- "it ät",
- "Ġword en",
- "Ġsm oking",
- "Ġint ens",
- "ab ul",
- "em ia",
- "è ij",
- "4 7",
- "f ly",
- "Ġ200 6",
- "ĠSer iously",
- "Ġprze z",
- "æ ¼",
- "c re",
- "Ġn an",
- "Ġmod es",
- "ов аÑĤÑĮ",
- "ĠH ang",
- "em en",
- "Ġbenefic ial",
- "Ġvot ers",
- "ĠBro ad",
- "Ġb ent",
- "W ow",
- "Ġm ul",
- "åĵ ¥",
- "ĠU C",
- "Ġdam aged",
- "ĠUk raine",
- "Ġw ipe",
- "Ġst ones",
- "Ġman agers",
- "Ġr ab",
- "ÑģÑĤÑĢ о",
- "l at",
- "Ġde ce",
- "Ġgraph ic",
- "Ġf oss",
- "Ġdisag ree",
- "ĠAm en",
- "Ġsec rets",
- "ho le",
- "ink le",
- "Ġfortun ate",
- "Ġì ±",
- "ìľ Ħ",
- "èIJ ¬",
- "Ġhab its",
- "Ġbur ied",
- "Ġh in",
- "Ġvirt ually",
- "ol as",
- "ĠR P",
- "ĠT ab",
- "l ow",
- "Ġsacr ific",
- "Ġestim ated",
- "ol n",
- "Ù ĭ",
- "c ur",
- "ĠFe el",
- "Ġcast le",
- "Ġus eless",
- "Ġdis g",
- "ĠJac ob",
- "Ġga an",
- "Ġup side",
- "Ġpare ce",
- "ãĥ³ ãĥ",
- "Ġsh ipping",
- "ĠC R",
- "Ġdis rupt",
- "ac ter",
- "UN D",
- "f u",
- "å® Į",
- "ĠP ick",
- "ĠChar l",
- "ĠB ull",
- "Ġenter prise",
- "Ġpunish ment",
- "ack ing",
- "Ġfr action",
- "Ġtab let",
- "Ġch ord",
- "Ġsimilar ly",
- "åħ¶ 實",
- "ĠTor onto",
- "Ġcour ts",
- "ÄŁ l",
- "esz cze",
- "Ġpron oun",
- "ĠS ister",
- "ĠM P",
- "Ġgreat ly",
- "ĠD ank",
- "ic op",
- "Ġgar bage",
- "Ġresol ve",
- "ĠS af",
- "ĠG un",
- "Ġcomp ound",
- "Ġë° °",
- "ĠMus ik",
- "âĻ «",
- "Ġcha os",
- "ĠWhen ever",
- "Ġe uros",
- "Ġor chest",
- "Ġrefr iger",
- "al an",
- "ภ·",
- "ĠAm azing",
- "Ġp ud",
- "ag an",
- "Ġj eszcze",
- "is y",
- "Ġaccur acy",
- "ĠA ma",
- "is ode",
- "ë ĮĢ",
- "Ġinterpret ation",
- "ĠL iber",
- "æ ·",
- "c am",
- "Ġevol ved",
- "ĠK ay",
- "ÑĨ Ñĭ",
- "Ġcreat or",
- "it as",
- "Ġal arm",
- "Ġcelebr ation",
- "z ent",
- "Ġfun cion",
- "Ġo v",
- "umb ling",
- "Ġ %",
- "ภĪ",
- "Ġrestrict ions",
- "Ġн ав",
- "ĠK inder",
- "Ġban ana",
- "ÑĮ Ñı",
- "Ġdiam eter",
- "Ġnor thern",
- "ur ers",
- "ĠP as",
- "æĪij çļĦ",
- "Ġwork force",
- "Ġj ung",
- "Ġguar ante",
- "Ġequ ilib",
- "Ġsu ite",
- "Ġeu ro",
- "Ġdel iber",
- "S te",
- "Ġdownt own",
- "Ġch in",
- "Ġc odes",
- "ed ia",
- "Ġshe ep",
- "res hold",
- "wn ie",
- "ó b",
- "Ġunder lying",
- "l ia",
- "j er",
- "ÏĢ ÏĮ",
- "ç Ŀ",
- "th rop",
- "Ġz ap",
- "Ġvac uum",
- "ĠH ab",
- "Ġwra pped",
- "ì ¢",
- "Ġinvent ory",
- "м а",
- "Ġco ord",
- "Ġpl ates",
- "Ġsy mm",
- "T e",
- "ĠwÅĤa ÅĽnie",
- "Ġreach es",
- "Ġlon ely",
- "S cript",
- "le e",
- "ess er",
- "Ġê± ¸",
- "ĠGes ch",
- "ĠMo ving",
- "Ġré p",
- "ĠV ill",
- "åIJ Ī",
- "ĠR achel",
- "Ġtem os",
- "ON E",
- "Ġstra in",
- "Ġang el",
- "Ġf Ã¥",
- "T r",
- "Ġach o",
- "Ġhighlight s",
- "ĠW er",
- "ĠCar l",
- "Ġbl ur",
- "Ġreg ards",
- "Â ·",
- "ил ÑģÑı",
- "Ġrec re",
- "ĠY ani",
- "U CK",
- "ł ¸",
- "Ġelectr ons",
- "ĠSp iel",
- "Ġv ed",
- "Ú ¾",
- "Ġbe am",
- "Ġid iot",
- "ë ĵ¤",
- "на Ñĩ",
- "id d",
- "Ġsk i",
- "it ative",
- "Ġhyp othes",
- "ãģ§ãģĻ ãģŃ",
- "ent er",
- "ĠìķĦëĭĪ ë",
- "Ġih re",
- "Ġpre view",
- "ang el",
- "Ġdem on",
- "Ġd us",
- "Ġd ic",
- "ĠK om",
- "LE Y",
- "... !",
- "Ġsie ht",
- "ĠSon ic",
- "Ġten ho",
- "an as",
- "Ġdig it",
- "ĠMa ar",
- "Ġunder grad",
- "oun cer",
- "uff y",
- "Ġconvers ion",
- "Ġdis connect",
- "Ġe cho",
- "om er",
- "Ġcurric ulum",
- "Ġper ché",
- "Ġw and",
- ".. ?",
- "Ġroll ed",
- "Ġentreprene ur",
- "Ġtheore t",
- "ĠÑī о",
- "Ġins ights",
- "Ġzus ammen",
- "o in",
- "ret t",
- "p rodu",
- "Ġvisit ors",
- "e ous",
- "Ġgrand mother",
- "Ġhum or",
- "Ġн иÑħ",
- "zen ia",
- "ins on",
- "Ġres et",
- "Ġbase ball",
- "Ġmatch ing",
- "ëĭ¤ ê°Ģ",
- "Ġpun to",
- "ì ¡",
- "Ġre de",
- "Ġaddress ing",
- "Ġfore cast",
- "ĠB ol",
- "Ġcol ored",
- "Ġdocument ation",
- "Ġexpect ation",
- "ĠNor thern",
- "Ġcre o",
- "Ġà® ļ",
- "f on",
- "Ġuns ere",
- "U M",
- "Ġcop ies",
- "Ġexpand ed",
- "Ġveter ans",
- "ĠAl m",
- "Ġво обÑīе",
- "Ġpsych ological",
- "Ġnos so",
- "Ġpay ments",
- "im eters",
- "Ġ-- >",
- "ĠJenn ifer",
- "Ġvolunte ers",
- "os se",
- "or ious",
- "ĠбÑĭ ли",
- "è Ĥ",
- "ĠEs s",
- "w s",
- "ĠB C",
- "ĠI C",
- "W oman",
- "Ġv ont",
- "Ġeth nic",
- "EN N",
- "им о",
- "Ġlo b",
- "Ġou i",
- "c s",
- "Ġre he",
- "Ġìł ģ",
- "Ġch ick",
- "ús ica",
- "Ġk ont",
- "ĠDist rict",
- "Ġp ile",
- "Ġа в",
- "ей ÑģÑĤв",
- "ĠÂ £",
- "Ġiss ued",
- "Ġком п",
- "Ġpros per",
- "Ġprof ound",
- "ĠDe ar",
- "Ġãģ ĵ",
- "Ġfund ed",
- "Ġb isa",
- "ŀ ĺë",
- "× Ł",
- "ĠìĿ ĺ",
- "Ġtw elve",
- "ĠChamp ions",
- "éĿŀ 常",
- "Ñģ л",
- "Ġ200 5",
- "p m",
- "Ġon de",
- "Ġdiff é",
- "ĠCh all",
- "Ġdifficult ies",
- "Ġgar age",
- "Ġd á",
- "ün k",
- "Ġë¬ ¼",
- "Ġtr an",
- "Ġsubm itted",
- "z w",
- "ÙĪ ا",
- "Ġar k",
- "ĠìĦ ±",
- "Ġgrocer y",
- "он а",
- "i ere",
- "Ġa est",
- "Ġexhib ition",
- "Ġr és",
- "Ġconsist ency",
- "Ġcook ie",
- "н ей",
- "Ġrepl acement",
- "æ² ¹",
- "ĠS em",
- "ĠìĤ¬ ìļ©",
- "8 00",
- "Ġgen es",
- "Ġtrans action",
- "ĠE L",
- "Ġdur ante",
- "ib les",
- "ĠE at",
- "t ail",
- "iss ance",
- "Ġto ss",
- "Ġsurv ived",
- "Ġoff ices",
- "Ġsupport ive",
- "Wh ere",
- "Ġtout es",
- "Ġë§ ī",
- "Ġj okes",
- "ier on",
- "ap ers",
- "Ġm ature",
- "ĠM arsh",
- "Ġs ido",
- "k ind",
- "Ġreal mente",
- "ĠChe f",
- "Ġquel que",
- "Ġjud ges",
- "e ft",
- "ER S",
- "Ġj et",
- "Ġpers ons",
- "è »",
- "iz ations",
- "ri k",
- "Ġsh ops",
- "ĠW y",
- "Ġele g",
- "qu è",
- "qu oi",
- "Ġjug a",
- "Ġíķľë ²Ī",
- "ĠQuest ion",
- "ĠGlo bal",
- "Ġìķ½ ê°Ħ",
- "ĠSt ation",
- "æİ ¥",
- "ĠOh io",
- "Ġstick y",
- "Ġst ressed",
- "Ġg ün",
- "Ġí Ŀ",
- "ÑģÑĤ Ñĥп",
- "é ¡Į",
- "ĠPh D",
- "im mer",
- "Ġment or",
- "Ġinv ented",
- "Ġre un",
- "Ġine vit",
- "Ġpol ÃŃt",
- "Ġexec ute",
- "ĠSt ory",
- "Ġout standing",
- "Ġgu er",
- "ĠR ain",
- "Ġch oses",
- "ĠT it",
- "ĠÑģ еÑĢ",
- "ĠSing apore",
- "ĠN one",
- "Ġch ronic",
- "°ë į°",
- "Ġe go",
- "æł ·",
- "ES T",
- "ãģĤ ãĤĬ",
- "ĠW ang",
- "ĠN AT",
- "Ġa ug",
- "Ġdes ktop",
- "Ġetern al",
- "ĠìĤ¬ ìĭ¤",
- "ĠConst itution",
- "ìĤ ¬ë",
- "×Ļ× ľ",
- "p res",
- "ĠТ Ñĭ",
- "Ġinter f",
- "Ġlist s",
- "Ġfight s",
- "ft en",
- "ĠI owa",
- "Ġmotiv ated",
- "ĠH osp",
- "Ġelse where",
- "Ġpath s",
- "Ġinst ances",
- "B l",
- "r ange",
- "á» ±",
- "ĠS it",
- "man a",
- "Ġìĭľ ìŀij",
- "Ġm ình",
- "ans as",
- "Ġs na",
- "Ġphilos oph",
- "Ġpas se",
- "Æ°á» Ŀi",
- "ak h",
- "ent al",
- "Ġih n",
- "ru ctor",
- "Ġв аÑĪ",
- "Ġgener ous",
- "Ġp ivot",
- "п ол",
- "Ġjam ais",
- "Ġcom ent",
- "ĠL ew",
- "od zi",
- "ĠX box",
- "Ġв од",
- "Ġcons ent",
- "ī ìŀ¥",
- "Ġdis par",
- "l ass",
- "ĠGovern or",
- "Be ifall",
- "Ġê° ľ",
- "Ġbelo ved",
- "׳ ×ķ",
- "se ll",
- "Ġhon ored",
- "le h",
- "Ġw äre",
- "un ting",
- "Ġfra ud",
- "ĠR AM",
- "ê± ¸",
- "Ġkill s",
- "Ġeconom ics",
- "0 4",
- "п еÑĢ",
- "Ġco isas",
- "Ġи гÑĢ",
- "ÃŃ m",
- "Ġmö chte",
- "Ġìµ ľ",
- "Ġstim ul",
- "Ġfast est",
- "l v",
- "Ġg én",
- "ĠS ounds",
- "Ġ19 70",
- "Ġhome work",
- "spe aking",
- "Ġencour aging",
- "Ġqu ery",
- "Ġre vers",
- "pro fit",
- "Ġd y",
- "Ġìŀ ij",
- "ëĬĶëį° ìļĶ",
- "Ġso ap",
- "ĠG all",
- "ĠC N",
- "ĠAn s",
- "Ġf ic",
- "ank s",
- "Ġdess ert",
- "ĠìłĢ íĿ¬",
- "ĠM aking",
- "Ġcome ç",
- "ê³ Ħ",
- "Ġassoci ation",
- "D ad",
- "he e",
- "Ġh ogy",
- "Ġap ro",
- "Ġinvis ible",
- "Americ an",
- "í İ",
- "Ġvi be",
- "Ġem issions",
- "Ġadvoc ate",
- "Ġkick ed",
- "Ġ vel",
- "Ġsum mar",
- "Ġfre aking",
- "ch ron",
- "Ġpin ch",
- "Ġwszyst k",
- "isc al",
- "Ġpro ved",
- "Ġmind ful",
- "Ġt ä",
- "Ġno ises",
- "Ġisol ated",
- "Ġcross ed",
- "Ġê° ķ",
- "Ġvo ilÃł",
- "Ġch ore",
- "ĠR A",
- "C om",
- "Ġrelax ed",
- "at ro",
- "Ġpre vention",
- "Voice over",
- "O D",
- "ĠCo vid",
- "Ġsepar ation",
- "Ġ- [",
- "иÑĩ его",
- "çĻ ¼",
- "ĠS D",
- "ble ep",
- "Ġindepend ence",
- "Ġpart ial",
- "Ġalgorith ms",
- "ĠAny one",
- "Ġassoci ate",
- "h um",
- "ic ular",
- "Ġb ạn",
- "Ġbatt les",
- "G ood",
- "App lause",
- "Ġbast ante",
- "Ġadv ant",
- "ĠS weet",
- "Ġref used",
- "ãĤ ¸",
- "ĠÑĤеб е",
- "pl et",
- "Ġencour aged",
- "åĵ ¦",
- "Ġmir acle",
- "ĠB un",
- "ĠV ar",
- "rim ination",
- "e lect",
- "ĠM ult",
- "Ġdeliver ing",
- "e ing",
- "Ġc m",
- "ne hmen",
- "ĠL ine",
- "Ġë§ Į",
- "en ced",
- "ĠS ound",
- "ĠCont in",
- "ij d",
- "UN G",
- "k le",
- "Ġth reshold",
- "Ġcomp act",
- "ad t",
- "Ġto es",
- "ĠP ur",
- "own ed",
- "ment ed",
- "Ġdes igning",
- "Ġvacc inated",
- "Ġexha ust",
- "Ġbas ics",
- "Ġcons ists",
- "ĠGu y",
- "ac zy",
- "Ġm ÃŃ",
- "w on",
- "å® ³",
- "Ġ8 5",
- "æ Ĥ",
- "Ġm um",
- "Ġign or",
- "Ġprint ing",
- "ac ular",
- "p ow",
- "Ġexpand ing",
- "Ġg ir",
- "ĠC ab",
- "íĺ ¸",
- "ÑĤÑĮ ÑģÑı",
- "ĠìĹ¬ëŁ¬ë ¶Ħ",
- "Ġang les",
- "Ġterm inal",
- "ĠW on",
- "ĠInter esting",
- "Ġcross ing",
- "Ġbond s",
- "Ġpu eden",
- "Ġor b",
- "lar ın",
- "Ġcreep y",
- "Ġnutr ition",
- "Ġall ies",
- "Ġwire less",
- "Ġdes ired",
- "Ġcomp ute",
- "ĠAri zona",
- "ĠBeaut iful",
- "Ġprodu ces",
- "Ġnuest ro",
- "t ed",
- "Ġel igible",
- "ĠÑģ оз",
- "ic ial",
- "ĠH ero",
- "Ġcons ume",
- "Ġrob ots",
- "Ġpurch ased",
- "c ción",
- "Ġ iz",
- "ượ c",
- "ίν αι",
- "ĠØ£ ÙĨ",
- "Ġshad ows",
- "ĠMed ia",
- "Ġprin cess",
- "Ġk lar",
- "Ġwood en",
- "Ġus ar",
- "Ġg üzel",
- "Ġsl ot",
- "r ade",
- "Ġë Ĵ",
- "Ġhar mon",
- "Ġingred ient",
- "ors hip",
- "ek i",
- "Ġgrand father",
- "Ġexcit ement",
- "Ġpolit icians",
- ".. !",
- "Ġout s",
- "Ġsepar ately",
- "ĠÑı к",
- "ĠW elt",
- "ĠP ow",
- "j an",
- "Ġorient ation",
- "åı ĭ",
- "L C",
- "age m",
- "ÛĮ Úº",
- "åIJ Ĺ",
- "Ġbran ches",
- "ad en",
- "rent e",
- "ĠI hr",
- "as m",
- "Ġest ão",
- "ĠN ic",
- "Ġsla ve",
- "Ġcomp ress",
- "c rowd",
- "Ġclim bing",
- "ĠMan agement",
- "ĠB ah",
- "Ġpan ic",
- "Ġk or",
- "Ġcool ing",
- "Ġb ind",
- "Ġз ад",
- "Ġr ack",
- "Ġent it",
- "Ġs ends",
- "Ġyour selves",
- "d es",
- "ĠMuslim s",
- "Ġí ļ",
- "ism a",
- "cy cle",
- "un kt",
- "ĠC ore",
- "Ġinj uries",
- "Ġident ical",
- "ка Ñı",
- "ĠDeutsch land",
- "Ġе е",
- "is an",
- "Ġtr uc",
- "let on",
- "Ġback up",
- "Ġult ra",
- "Ġab und",
- "ille urs",
- "Ġby ÅĤo",
- "åħ ĥ",
- "ort ed",
- "Ġearth qu",
- "Ġк л",
- "Ġobs ervation",
- "Ġmainten ant",
- "el en",
- "Ġsett led",
- "Ġp ela",
- "ĠE conom",
- "Ġ Õ",
- "Ġste ering",
- "ĠAL L",
- "ĠC her",
- "Ġpat ience",
- "ĠS now",
- "Ġb or",
- "Ġworth y",
- "Ġcá i",
- "Ġ× §",
- "Ġκ α",
- "d og",
- "ĠK aren",
- "ill es",
- "Î ²",
- "Ġagric ulture",
- "×ķ× Ł",
- "ĠSe an",
- "Ġsens ors",
- "íķ ´ë",
- "ag h",
- "Ġpublic ly",
- "Ġpe ux",
- "ĠAlex ander",
- "Ġprior it",
- "Ġla zy",
- "ard on",
- "atter ing",
- "Ġcost ume",
- "س ت",
- "è¿ ĺ",
- "Ġun w",
- "Ð Ľ",
- "Ġthick ness",
- "qu ito",
- "g unt",
- "ist as",
- "ne ys",
- "ĠëIJĺ ê²Į",
- "ĠBr asil",
- "Ġto ken",
- "Ġaff ili",
- "l on",
- "Ġf Ã¥r",
- "ĠBe ach",
- "Ġw itch",
- "ĠSe ven",
- "Ġp ant",
- "λ λ",
- "Ġcapt ain",
- "å Ŀ",
- "Ġve ut",
- "Ġpou voir",
- "ac z",
- "ĠBar b",
- "Ġut ility",
- "Ġcontempor ary",
- "Ġobt ained",
- "Ġpainting s",
- "e ar",
- "Ġpe an",
- "ĠO g",
- "Ġc ust",
- "л ем",
- "Ĥ ĺë",
- "ĠIs so",
- "Ġac onte",
- "ĠTe le",
- "ĠAss istant",
- "Ã ī",
- "íĸĪ ìĬµëĭĪëĭ¤",
- "Ġcount s",
- "Ġbu ck",
- "ĠDe ep",
- "Ġtack le",
- "Ġh arsh",
- "Ġdec ides",
- "éĹ ľ",
- ". âĢĭ",
- "éĤ Ĭ",
- "ĠAng el",
- "Ġlay ing",
- "Ġcal ories",
- "Ġcontro lling",
- "Ġadvant ages",
- "ĠÑįÑĤ ой",
- "Ġappro aching",
- "Ġthreat s",
- "ak an",
- "em atic",
- "m ann",
- "ê³ µ",
- "m umbles",
- "ac ió",
- "Ġmaint aining",
- "Ġfound er",
- "l ah",
- "f ight",
- "Ġadm itted",
- "âĢ¦ .",
- "ķ Į",
- "ab ol",
- "Ġus age",
- "Ġn onsense",
- "ĠPal est",
- "Ġcont re",
- "ĠDemocr atic",
- "ĠE R",
- "j ekt",
- "Ġar bit",
- "Ġг ол",
- "ĠMich elle",
- "ich er",
- "es h",
- "ĠP ho",
- "к ом",
- "4 9",
- "ĠEner gy",
- "ο Ïį",
- "Ġc ents",
- "Ġref ers",
- "Ġg ospel",
- "ĠSh a",
- "ĠSh are",
- "×Ļ× ł",
- "Ġclin ic",
- "ĠëĦ £",
- "Ġequ ality",
- "ug s",
- "Ġsh ed",
- "Ġplan es",
- "Ġtout e",
- "re ck",
- "Ġstra nd",
- "Ġbi ology",
- "Ġle ague",
- "ĠP ok",
- "Ġnúmer o",
- "ĠCo ast",
- "Ġconsist ently",
- "Ġnuc le",
- "OO OO",
- "Ġob jet",
- "Ġch or",
- "Ġg inger",
- "Ġd abei",
- "Ġcoop eration",
- "à¯į .",
- "nt en",
- "ç ¤",
- "l Ãł",
- "ìĸ ij",
- "r ado",
- "Ġpass ive",
- "Ġglo ves",
- "Ġunder ground",
- "Ġlog ical",
- "Ġk et",
- "Ġfunction ality",
- "¸ë ¦¬",
- "Ġport al",
- "ell er",
- "×Ļ× ¨",
- "ĠT ed",
- "ĠG re",
- "IJ ľ",
- "Ġperson nel",
- "Ġemer ging",
- "ĠF ür",
- "Ġmeant ime",
- "usal em",
- "ĠC lear",
- "Ġtra pped",
- "Ġìļ °",
- "Ġdis pl",
- "Ġmet tre",
- "Ġmun icip",
- "Ġwithd raw",
- "Ġsp at",
- "un es",
- "Ġaccess ibility",
- "æĪij 们",
- "Ġap are",
- "Ġpros pect",
- "Ġн аз",
- "Ġcop per",
- "ĠP RO",
- "Ïħ ÏĦ",
- "Ġattack ing",
- "ĠV in",
- "ĠSt one",
- "Ġinvestig ate",
- "st yle",
- "ĠÎ »",
- "ë ¡Ŀ",
- "ë§ Ī",
- "Ġins pect",
- "Ġli ver",
- "ал иÑģÑĮ",
- "Ġser a",
- "hal ten",
- "em an",
- "Ġmin istry",
- "' '",
- "Ġd ots",
- "ãħĭãħĭ ãħĭãħĭ",
- "Ñĥ ÑģÑĤ",
- "ĠJ ak",
- "AK E",
- "Ġg aps",
- "uck er",
- "ĠинÑĤеÑĢ еÑģ",
- "ĠEm ily",
- "Ġinter val",
- "Ġt ender",
- "ĠTechn ology",
- "g ame",
- "Ġtri b",
- "ÙĦ ا",
- "ĠDevelop ment",
- "Ùħ ا",
- "Ġwr ist",
- "Ġf ires",
- "Ġtarget ed",
- "ìł IJ",
- "Ġso d",
- "íļ Į",
- "Ġoldu ÄŁ",
- "Ġse asons",
- "vent ions",
- "Ġн его",
- "Ġsomet ime",
- "ли в",
- "n é",
- "Ġt ú",
- "ĠDe us",
- "Ġexec ution",
- "á p",
- "ĠCh ange",
- "ĠInd eed",
- "Ġreg ulation",
- "ĠH ung",
- "é is",
- "Ġwish es",
- "Ġj azz",
- "Ġstruct ural",
- "Ġblow ing",
- "Ġby Äĩ",
- "Ġtherm al",
- "ph ant",
- "ÑĢÑĥ з",
- "ан ÑĤ",
- "ĠP ull",
- "Ġconf usion",
- "нÑĭ ми",
- "Ġscen arios",
- "ìłģ ìľ¼ë¡ľ",
- "Ġд еÑĤ",
- "Ġtatto o",
- "Ġaut re",
- "Ġhe ating",
- "Ġtreat ing",
- "Ġпон им",
- "Ġexc lus",
- "ĠL OL",
- "we ar",
- "ag le",
- "Ġzur ück",
- "Ġr ational",
- "s u",
- "Ġdet er",
- "ĠN ative",
- "à®ķ ள",
- "ach ed",
- "Ġ ãĥ",
- "ĠEnt onces",
- "Ġhor a",
- "ìĿ´ìĹIJ ìļĶ",
- "Ġl ite",
- "Ã «",
- "Ġsix th",
- "Ġбол ее",
- "act or",
- "Ġpsych ology",
- "çĽ ¸",
- "Ġdem ands",
- "Ġpe er",
- "Ġnew ly",
- "ĠWW E",
- "Don ald",
- "ĠBo x",
- "Ġp ine",
- "Ġload ing",
- "ĠN ico",
- "Ġs ÅĤ",
- "omm e",
- "AR T",
- "Ġrecru it",
- "Ġbug s",
- "arent s",
- "ĠпÑĢ об",
- "ĠIn side",
- "ipp er",
- "d ramatic",
- "Ġplan ets",
- "ord e",
- "Ġy oga",
- "ch ild",
- "ĠMar ie",
- "Ġãģ Ĥ",
- "ĠB L",
- "Ġfil med",
- "Ġref resh",
- "Ġtomato es",
- "Ġf et",
- "Qu é",
- "Ġ !!",
- "ĠëĤ ´ë",
- "r ine",
- "Ġinteract ive",
- "s al",
- "ann ah",
- "pe z",
- "ç¶ ĵ",
- "Ġunderstand s",
- "ĠTok yo",
- "Ġlibr aries",
- "Ġread er",
- "ij IJ",
- "o z",
- "ĠEnd e",
- "ĠF lo",
- "Ġm ild",
- "Ġpo etry",
- "Ġж ив",
- "æĦ Ľ",
- "Ġbeh ave",
- "Ġdo en",
- "ĠSus an",
- "p age",
- "ra ham",
- "Ġcommunic ations",
- "Ġtun ing",
- "Ġp ac",
- "Ġanx ious",
- "I O",
- "M ark",
- "Ġhi ç",
- "book s",
- "Ġp iss",
- "Ġen abled",
- "achel or",
- "ĠF OR",
- "Ġé c",
- "ĠT R",
- "il st",
- "h at",
- "ĠìĿ Į",
- "Ġty ch",
- "Ġj ar",
- "Ġbuild s",
- "ĠAr gent",
- "Ġinter medi",
- "Ġl ou",
- "Ġa ra",
- "Ġassign ment",
- "Ġcabin et",
- "Ġretire ment",
- "ãģ »",
- "Ġdis abled",
- "ric a",
- "Ġa wards",
- "Ġbo ots",
- "Ġacknow led",
- "Ġth y",
- "Ġêµ ¬",
- "Ġsy nd",
- "ни й",
- "il ton",
- "Ġprob l",
- "ĠF al",
- "Ġverd ade",
- "Ġ7 00",
- "ĠLe arning",
- "oc us",
- "Ġpal ace",
- "N ot",
- "t ain",
- "c m",
- "Ġmagn et",
- "inc oln",
- "Ġfig uring",
- "ĠL yn",
- "ĠB oss",
- "ĠV O",
- "Ġdiagn osis",
- "Ġequ ipped",
- "w atch",
- "in os",
- "ad ers",
- "Ġsh elf",
- "Ġorgan is",
- "Ġn od",
- "Ġk ız",
- "pp ers",
- "Ġrest ore",
- "Ġart ic",
- "ĠVo ice",
- "ı yorum",
- "ê² ©",
- "Ġspread ing",
- "Ġh ips",
- "Ġw ard",
- "ure au",
- "Ġinter section",
- "6 6",
- "Ġ3 9",
- "ç ³",
- "Ġwait ed",
- "ì ´",
- "hh hh",
- "Ġd ys",
- "ĠE N",
- "Ġb atch",
- "Ġca f",
- "Ġmark er",
- "大家 好",
- "or able",
- "ó ria",
- "Ġste pped",
- "Ġcelebr ating",
- "ан а",
- "Ġwor n",
- "ĠF ol",
- "Ġpl a",
- "Ġattempt s",
- "Ġtwe et",
- "Ġr ust",
- "g ence",
- "í Ĩµ",
- "Ġre vel",
- "Ġre cept",
- "en ess",
- "Ġ( (",
- "ãĥ¼ ãĥ",
- "! âĢĭ",
- "ĠìĨ IJ",
- "Ġinfluen ced",
- "и ж",
- "Ġкон еÑĩно",
- "Ġcolleg es",
- "ion i",
- "Ġs ag",
- "An n",
- "ol ar",
- "Ġexpress ions",
- "Ġsu its",
- "Ġowners hip",
- "el and",
- "pie ce",
- "æĢİ ä¹Ī",
- "Ġdesp ués",
- "Ġt el",
- "Ġins ult",
- "Ġêµ īìŀ¥",
- "ĠSm all",
- "ĠF R",
- "ok a",
- "ber ries",
- "ĠAnt on",
- "ел Ñı",
- "Ñı Ñģ",
- "Ġval ve",
- "act s",
- "Ġwood s",
- "à® £",
- "Ġcult iv",
- "Ġf á",
- "ãģ¨ ãģĦãģĨ",
- "Ġche ers",
- "Ġassum ption",
- "Ġfit ness",
- "ÃŃ cul",
- "Ġpod r",
- "Ġwe it",
- "ĠH ind",
- "Ġd ign",
- "Ġз н",
- "Ġsqu ad",
- "Ġdest ro",
- "c ere",
- "sh irt",
- "imm t",
- "eng ers",
- "Ġs ä",
- "k ÅĤad",
- "Ġ ÈĻ",
- "Ġocc as",
- "Ġì¤ Ħ",
- "Ġprocess or",
- "ĠD M",
- "ĠDad dy",
- "Ġsoon er",
- "Ġstraight forward",
- "Ġdepart ments",
- "ĠChr ome",
- "Ġwork place",
- "ĠPy thon",
- "Ġm eng",
- "ĠD AN",
- "ĠI ce",
- "ĠëĪ Ī",
- "ĠG i",
- "Ġh iring",
- "Ġland ed",
- "Ġdemocr atic",
- "ied z",
- "ãģĺ ãĤĥ",
- "Ġse v",
- "ic ia",
- "Ġespe cial",
- "ĠN ous",
- "Ġh ät",
- "Ġb ou",
- "per t",
- "ies z",
- "åij Ģ",
- "Ġv il",
- "ÅĽ li",
- "Ġî n",
- "Ġloss es",
- "éķ ·",
- "Ġto ast",
- "Ġreal m",
- "ĠAust in",
- "ĠIn formation",
- "Ġres ume",
- "Ġch ase",
- "Ġsal ary",
- "Ġë¶ Ħ",
- "ли Ñĩ",
- "ĠÑģл ед",
- "ĠFur ther",
- "Ġcar ing",
- "Ġv ig",
- "Ġval or",
- "è¿Ļ 个",
- "ĠÑĩ а",
- "Ġanalyt ics",
- "Ġglo be",
- "ĠM AN",
- "Ġn el",
- "ìĿ´ì ķ¼",
- "Ł ¼",
- "Ġo y",
- "íķĺ ìĦ¸ìļĶ",
- "j en",
- "Ġtrou bles",
- "ah aha",
- "Ġchurch es",
- "u et",
- "Ġmeasure ments",
- "b il",
- "ì ½",
- "if ully",
- "ин Ñĥ",
- "ĠWil son",
- "¦ ´",
- "ĠíĮ Į",
- "Ġì° ¨",
- "Ġp úblic",
- "ĠJer usalem",
- "Ġn ails",
- "Ġsp ine",
- "Ġhe mos",
- "Ġz n",
- "qu is",
- "ĠLe ben",
- "Ġrefer ences",
- "IT H",
- "i per",
- "ĠÑģеб Ñı",
- "ì ģ",
- "ĠW a",
- "st ate",
- "§ Ŀ",
- "åħ ±",
- "ĠGen er",
- "Ġact ress",
- "ĠEn joy",
- "๠ĥ",
- "Ġ× Ĵ",
- "Ġinfect ed",
- "Ġsh aking",
- "Ġn ick",
- "ภ¸",
- "Ġf ot",
- "Ġaccompl ished",
- "u ke",
- "Ġshe ets",
- "Ġf ence",
- "Ġnurs ing",
- "Ġintrodu cing",
- "Ġfe at",
- "O ne",
- "T O",
- "Ġcl ubs",
- "ĠBru ce",
- "on ge",
- "ch ange",
- "ĠBat man",
- "åı °",
- "ĠOffic er",
- "Ġhyd ro",
- "Ġsupp lement",
- "Ġc ela",
- "Ġlong est",
- "Ġcompet ing",
- "Ġcon he",
- "g iving",
- "Ġbra ins",
- "Ġlo ans",
- "Ġw age",
- "ĠCl inton",
- "Ġs Äĥ",
- "ane ous",
- "Ġl ord",
- "ÑĢÑĥ ж",
- "Ġqu iz",
- "Ġst iff",
- "ĠL GB",
- "s z",
- "M E",
- "m are",
- "th ere",
- "Ġn är",
- "ĠM and",
- "l ast",
- "Ġd ag",
- "Ġhalf way",
- "ĠB and",
- "Ġëĭ¤ ìĭľ",
- "ĠA ren",
- "Ġi le",
- "P N",
- "ent o",
- "Ġalg um",
- "Ġsoc cer",
- "Ġblock ed",
- "ĠJon athan",
- "Ġse w",
- "ĠTest ament",
- "Ġv ale",
- "Ġbehav i",
- "å§ ĭ",
- "Ġcon na",
- "IC H",
- "Ġaud iences",
- "m l",
- "amm ad",
- "ĠìĤ ´ì",
- "I GH",
- "Ġr aces",
- "em ed",
- "Ġm á»Ļt",
- "Ã ¯",
- "Ġover s",
- "Ġdecl ared",
- "Ġs ana",
- "ĠU na",
- "ĠÑĢ е",
- "uck s",
- "Ġp airs",
- "Ġan ge",
- "N e",
- "Ġup s",
- "av y",
- "ø r",
- "ree k",
- "Ġbehav iors",
- "Ġreflect ed",
- "Ġprior ities",
- "Ġcon du",
- "Ġret reat",
- "Ġexp enses",
- "Ġë´ IJ",
- "Ġtri ple",
- "Ġêµīìŀ¥ íŀĪ",
- "ä lt",
- "Ġind igenous",
- "Ġmin ing",
- "Ġaccept able",
- "Ġru in",
- "C A",
- "u ine",
- "Ġpip eline",
- "ct ic",
- "ê t",
- "ĠвÑģ его",
- "Ġb oun",
- "ĠDig ital",
- "ĠBo om",
- "ÑĨ е",
- "Ġл ÑĥÑĩ",
- "Ġas c",
- "ĮĢë ¡ľ",
- "ĠGood bye",
- "Ġrend er",
- "ene z",
- "ar re",
- "ĠTH AT",
- "b our",
- "ic ión",
- "ãĤ Ń",
- "E very",
- "Ġw ires",
- "ĠPar liament",
- "n ung",
- "ate ur",
- "ĠS ave",
- "ĠPh ys",
- "Ġam or",
- "ĠE ve",
- "Ġfr ight",
- "Ġgam ma",
- "Ġmic ros",
- "m itt",
- "ĠC ode",
- "ĠBe y",
- "pl ed",
- "ĠиÑģп олÑĮз",
- "ç Ĺ",
- "ìĥ ī",
- "å¥ ¹",
- "Ġmon et",
- "ĠJah re",
- "Ġlux ury",
- "Ġde af",
- "Ġbet ray",
- "Ġê² °",
- "и ки",
- "Ġdefe ated",
- "Ġunder t",
- "Ġwe g",
- "Ġcool er",
- "ãģķ ãĤĵ",
- "iam i",
- "éĤĦ æľī",
- "ĠJess ica",
- "ĠJ oy",
- "Ġsoph istic",
- "ени и",
- "ðĿ ĺ",
- "Ġch ili",
- "ĠTy pe",
- "Ġprote ins",
- "Ġpresent ing",
- "al ia",
- "ìļ ¸",
- "ĠMaj or",
- "Ġmolec ule",
- "um er",
- "Ġcoll apse",
- "ĠAny ways",
- "ĠMount ain",
- "ant ed",
- "ãĢ IJ",
- "Ġвиде о",
- "æ° ´",
- "A ud",
- "Ġcon qu",
- "Ġvo ll",
- "Ġkn it",
- "Ġmem br",
- "ĠMark et",
- "Ġd ari",
- "Ġcalcul ated",
- "г и",
- "Ġshrim p",
- "ĠM u",
- "ĠпÑĢ оÑĤ",
- "Ġìĺģ ìĥģ",
- "Ġproduct ivity",
- "Ġcogn itive",
- "ĠHe b",
- "ict ions",
- "ê² ½",
- "Ġcr é",
- "f ör",
- "Ġpray ing",
- "ash i",
- "ĠT ik",
- "ó r",
- "w en",
- "ÑĮ Ñİ",
- "ix o",
- "Ġ( \"",
- "ĠÑĤ ел",
- "Ġìĸ´ëĸ ¤",
- "ĠпеÑĢ ед",
- "ĠD rive",
- "ãĢ ij",
- "ĠE qu",
- "Ġequilib rium",
- "Ġdescri bes",
- "не е",
- "4 2",
- "ĠCur rent",
- "y y",
- "Ġabsor b",
- "Ġsold ier",
- "d ers",
- "Ġtestim ony",
- "Ġdec line",
- "ľë ¡ľ",
- "g age",
- "Ġinsp ire",
- "la pping",
- "Ġspin ning",
- "Ġsla very",
- "Ġfac ial",
- "Ġtrad itions",
- "ári os",
- "ĠHosp ital",
- "Ġn est",
- "ĠëĪ Ħ",
- "Ġto i",
- "Ġfe ars",
- "ìħ ¨",
- "ĠM uh",
- "Ġgradu ation",
- "Ġimpact ed",
- "Ġa unt",
- "ĠLet s",
- "Ġalumin um",
- "Ġdomin ant",
- "ĠDav is",
- "ĠNav y",
- "Ġcom pt",
- "op les",
- "Ġest ava",
- "è ¥",
- "Ġsc al",
- "Ġpres erve",
- "ĠO pp",
- "Ġpract ically",
- "Ġmagn itude",
- "Ġf itting",
- "Ġcoordin ate",
- "Ġfurn iture",
- "ĠFam il",
- "Ġexplos ion",
- "Ġdocument ary",
- "ĠS cript",
- "Ġport ray",
- "m at",
- "Ġschedul ed",
- "Ġdynam ics",
- "ph y",
- "ak y",
- "ĠU I",
- "C he",
- "Ġcontinu ously",
- "ĠPro v",
- "å° ij",
- "Ñĥ з",
- "ra h",
- "Ġger ne",
- "pro of",
- "Ġsecret ary",
- "ĠPat reon",
- "sc ream",
- "ĠK ids",
- "á»ĵ i",
- "Ġk g",
- "Ġuncertain ty",
- "Ġк ажд",
- "Ġmit ig",
- "Ġread s",
- "å· ²",
- "ĠR u",
- "Ġpri est",
- "Ġн ед",
- "Ġlimit ations",
- "Ġflo at",
- "6 00",
- "ĠT oy",
- "ĠJim my",
- "Ġoff ensive",
- "en i",
- "ĠX i",
- "Ġeye br",
- "ĠTur k",
- "Ġaccident ally",
- "Ġoh ne",
- "ĠS aud",
- "9 5",
- "ĠD utch",
- "ан Ñģ",
- "ĠSe attle",
- "Ġëĵ ±",
- "che ck",
- "k ÄĻ",
- "Ġcontrib utions",
- "Ġbes ide",
- "Ġqu indi",
- "Ġfle w",
- "æĹ ¶",
- "ذ ا",
- "ĠL O",
- "Ġwa ist",
- "ĠE V",
- "Ġhol idays",
- "j on",
- "Ġmis under",
- "Ñı н",
- "Ġb out",
- "Ġd imin",
- "Ạ½",
- "ó l",
- "ĠGr ace",
- "Ġinput s",
- "Ġden y",
- "Ġform ing",
- "ĠB ild",
- "Ġad equ",
- "Ġfol k",
- "Ġreject ed",
- "se mb",
- "Ġfrust rated",
- "op en",
- "ĠBet ter",
- "il on",
- "Ġtow el",
- "Ġdifferent ial",
- "Ġsac red",
- "Ġsa il",
- "éĩ Į",
- "ent imes",
- "Ġgentle man",
- "Ġicon ic",
- "Ġcomp aring",
- "Ġs agt",
- "Ġtext s",
- "Ġgrand ma",
- "Ġroll s",
- "Ġcont ents",
- "ä¸į 好",
- "оÑģ Ñģ",
- "Ġsusp ension",
- "ro it",
- "¦ ¼",
- "Ġasse z",
- "Ġd ort",
- "ĠM ath",
- "ĠVict or",
- "ĠJava Script",
- "ä¸į å°į",
- "Ġen han",
- "Å Ļ",
- "ĠB ush",
- "Ġpromot ion",
- "Ġk in",
- "Ġmon sters",
- "ĠColor ado",
- "ĠÎ ²",
- "íķ´ì ļĶ",
- "æŃ £",
- "iffer ent",
- "Ġn aked",
- "Ġpro d",
- "et ics",
- "ĠW oman",
- "Ġtreat ments",
- "Ġest oy",
- "v é",
- "Ġlif ting",
- "Ġy apt",
- "ĠRo ber",
- "Ġì¹ ľ",
- "Ġsubst itute",
- "ak u",
- "r idge",
- "Ġê± °ë",
- "Ġrespond ed",
- "Ġb é",
- "ĠEngine er",
- "Ġtransfer red",
- "ë ²",
- "Ġha ber",
- "o op",
- "ĠW E",
- "Ġv est",
- "Ġfor ty",
- "ĠD S",
- "Ġ200 4",
- "Ġco aching",
- "n om",
- "ĠB ab",
- "Ġn ossa",
- "ĠJ ake",
- "Ġg y",
- "Ġde leg",
- "Ġìŀ ł",
- "ĠкÑĢ аÑģ",
- "Ġstand point",
- "Ġdis ad",
- "Ġart work",
- "A d",
- "ill o",
- "ĠÄij ược",
- "ĠPr om",
- "ĠL ib",
- "Ġcritic ism",
- "Ġcontact s",
- "ÑĢ ам",
- "Ġachieve ment",
- "ÐĶ а",
- "Ġdiss ol",
- "ĠVeg as",
- "Ġstream s",
- "ĠK ent",
- "ĠعÙĦ Ùī",
- "Ġrad ius",
- "Ġsu cks",
- "ĠA ch",
- "Ġf i",
- "ou st",
- "ĠлÑİд и",
- "Ġpal ette",
- "ĠH az",
- "ĠAnth ony",
- "Ġtem a",
- "ĠC os",
- "Ġsa fer",
- "α ÏĤ",
- "Ġcont rad",
- "Ġma ior",
- "Ġinfl ation",
- "ĠSil ver",
- "Ġatt ending",
- "íķľ íħĮ",
- "art o",
- "Ġapplaud ing",
- "Ġcomput ing",
- "ĠH at",
- "æ »",
- "k now",
- "mak ers",
- "Ġcon oc",
- "Ġeduc ated",
- "Ġmod ified",
- "Ġinc lusion",
- "ment al",
- "ŀ IJ",
- "is ia",
- "ĠÏĢ οÏħ",
- "Ġa un",
- "ĠIre land",
- "Ġk ö",
- "Ġcompl iance",
- "Ġinsp iring",
- "иÑĤелÑĮ но",
- "Ġdisp os",
- "ì° ¨",
- "Ġw ip",
- "r ical",
- "raw d",
- "Ġt res",
- "Ġmob il",
- "olut ions",
- "B O",
- "Ġb ounce",
- "Ġassum ed",
- "ĠMed ical",
- "Ġf iscal",
- "Ġng Æ°á»Ŀi",
- "ition ally",
- "Ġst olen",
- "ĠB M",
- "Ġmechanism s",
- "ε ί",
- "Ġqual ified",
- "Ġìŀ IJë",
- "ught ers",
- "ĠH IV",
- "ĠL ots",
- "Ġser vers",
- "Ġcar r",
- "ĠT ogether",
- "Ġattract ed",
- "Ġk r",
- "æĪij æĺ¯",
- "th ur",
- "in in",
- "ĠH alf",
- "È Ľ",
- "ĠP ap",
- "Ġremind ed",
- "AL L",
- "Ġhel met",
- "Ġbott les",
- "Ġprofess ors",
- "Ġse ine",
- "ÅĤ Äħ",
- "ãĥ ı",
- "Ġê±° ìķ¼",
- "Ġ×¢ ׾",
- "f un",
- "ĠB ird",
- "Ġfight er",
- "ĠëĶ °ë",
- "ĠT ool",
- "Ġt in",
- "ino is",
- "ë ¶Ħ",
- "×Ļ× Ł",
- "ĠC AR",
- "åIJ į",
- "irst y",
- "Ġout door",
- "ĠN S",
- "ãħ İ",
- "ff en",
- "Ġl ud",
- "H ello",
- "Ġroll er",
- "ie le",
- "ĠPol and",
- "Ġap a",
- "ex p",
- "Ġcertific ate",
- "ĠT own",
- "аÑİÑĤ ÑģÑı",
- "ild e",
- "Ġdeterm in",
- "P R",
- "Ġfree ze",
- "Ġmain stream",
- "Ġobject ives",
- "b lo",
- "Ġtak ie",
- "åĵĪ åĵĪ",
- "Ġë°Ķë ¡ľ",
- "el et",
- "ĠI V",
- "ĠF ast",
- "Ġd ere",
- "em p",
- "ĠD ra",
- "ĠìŀĪ ìĹĪ",
- "Ġdisc rimination",
- "Ġε ίναι",
- "ne cess",
- "æ ®",
- "ıģ ı",
- "Ġpost ing",
- "wi ÅĽcie",
- "Ġl ub",
- "Ġol ive",
- "Ġr im",
- "Ġmodel ing",
- "Ġa ño",
- "ĠPak istan",
- "Ġover l",
- "Ġinf lam",
- "N E",
- "ìĹIJ ê²Į",
- "Ġatt ended",
- "Ġdeal t",
- "ĠAl t",
- "ĠL incoln",
- "Ġaw ake",
- "Ġfil ters",
- "ĠWith in",
- "czy wiÅĽcie",
- "Ġs û",
- "ĠJohn ny",
- "Ġintegr ity",
- "Ġisol ation",
- "ĠE asy",
- "ĠпÑĢ ин",
- "ĠAl ice",
- "Ġsm iling",
- "en ix",
- ", ...",
- "Î ¶",
- "Ġbeg un",
- "Ġjew el",
- "Ġconvention al",
- "Ġstat ist",
- "Ġhand ed",
- "Ġir re",
- "Ġpro hib",
- "Ġsatell ite",
- "é¦ Ļ",
- "ĠInd ust",
- "Ġtra ged",
- "Ġtra va",
- "Ġih m",
- "Ġcru el",
- "ĠAg ora",
- "ĠD oc",
- "Ġz ones",
- "Ġm all",
- "Ġtr ay",
- "×ķ× ł",
- "Ġir rit",
- "Ġk ans",
- "ĠBe at",
- "ud ge",
- "ie lle",
- "Ġtrust ed",
- "Ġb ikes",
- "ĠÑĥ п",
- "ĠM ember",
- "w ick",
- "Ġcreat ors",
- "Ġher itage",
- "ind istinct",
- "Ġres ur",
- "enn en",
- "C ome",
- "Ġf iring",
- "ĠBu eno",
- "ĠТ о",
- "ik an",
- "ett es",
- "Ġk es",
- "Ġtri ps",
- "Ġdivor ce",
- "ĠK l",
- "Ġcons ol",
- "ke ep",
- "기 ê°Ģ",
- "ĠRep ort",
- "Ġhost ing",
- "Ġdiam ond",
- "Ġcompl ic",
- "Ġhel icop",
- "Ġdep uis",
- "d s",
- "ĠCh an",
- "Ñı л",
- "Ġsc issors",
- "il ation",
- "Ġprop ortion",
- "ER E",
- "ĠÙĪ اÙĦ",
- "int a",
- "Ġmuch as",
- "u ation",
- "it is",
- "æĬ Ĭ",
- "Ñı Ñī",
- "Ġni in",
- "Ġemphas ize",
- "uel a",
- "Ġprodu cers",
- "Ġr ze",
- "änd er",
- "ET H",
- "æ º",
- "Ġconst itu",
- "åĽ ½",
- "Ġperform ances",
- "ist le",
- "go v",
- "ĠL iter",
- "Ġincorpor ate",
- "Ġeduc ate",
- "ĠN in",
- "ì ª½",
- "Ùĩ Ùħ",
- "el eration",
- "×ķ× ij",
- "Ġya ÅŁ",
- "or ous",
- "ĠC as",
- "Ġgr ants",
- "ëĬ ¥",
- "am el",
- "Ġê·¸ë łĩê²Į",
- "ĠE ste",
- "Ñħод иÑĤ",
- "ĠпоÑģ ле",
- "Ġg ent",
- "Ġfocus es",
- "al ities",
- "ĠR h",
- "ë ³´",
- "æ° ij",
- "ĠD ance",
- "r r",
- "Ġam er",
- "Ġutil ize",
- "Ġl ÃŃ",
- "ĠAm ong",
- "Ġpregn ancy",
- "Ġlo ops",
- "ал оÑģÑĮ",
- "ĠM oh",
- "Ġcatch ing",
- "Ġglo b",
- "Ġa jud",
- "Ġ[ ?",
- "ĠAn al",
- "lo oking",
- "Ġsurf aces",
- "Ġprogress ive",
- "Ġvir al",
- "0 8",
- "Î ¾",
- "K A",
- "Ġ ży",
- "Ġpick s",
- "ann on",
- "Ġbul k",
- "ĠR oss",
- "Ġdescri bing",
- "ĠG el",
- "Ġloc ally",
- "Ġend less",
- "Ġmass age",
- "Ġclean ed",
- "Ġtravel ed",
- "ен Ñĭ",
- "Ġsent iment",
- "ig ma",
- "ĠN as",
- "Ġchemical s",
- "Ġright eous",
- "ĠMag ic",
- "Ġrel ates",
- "Ġtruck s",
- "Ġ19 60",
- "åĪ ¥",
- "Ġapp et",
- "Ġsn acks",
- "ĠSum mer",
- "Ġy üz",
- "Ġpr is",
- "ĠMex ican",
- "Ġtransp aren",
- "Ġminor ity",
- "Ġver te",
- "Ġl assen",
- "4 6",
- "л ек",
- "é p",
- "ĠÑĦ илÑĮ",
- "Ġi yi",
- "Ġsp an",
- "íķĺ ì§Ģ",
- "Ġind icated",
- "qu ar",
- "Ġscholars hip",
- "ĠLGB T",
- "Ġhistor ically",
- "ó ÅĤ",
- "Ġmin ist",
- "Ġpen et",
- "ĠR ap",
- "Ġcons ervation",
- "çĽ ´",
- "ĠH oney",
- "ĠBe i",
- "id el",
- "Ġrespons ibilities",
- "Ġmess y",
- "ĠEx cept",
- "OR E",
- "Ġiniti atives",
- "Ġjun ior",
- "Ġdesign ers",
- "Ġexpl oration",
- "Ġspons or",
- "Ġmob ility",
- "Ġint eg",
- "land o",
- "Ġb ark",
- "Ġindic ates",
- "à ¶",
- "Ġemploy er",
- "å® ī",
- "Ġcous in",
- "Ġbo iling",
- "Ġch rom",
- "Ġç al",
- "Ġper pet",
- "Ġcont ained",
- "Ġpark s",
- "Ð «",
- "ĠEngine ering",
- "P lease",
- "ĠStart ing",
- "her o",
- "Ġlaw yers",
- "è¥ ¿",
- "Ġz d",
- "Ġfranch ise",
- "ra ge",
- "Ġint uit",
- "ĠG L",
- "re ach",
- "ĠE lle",
- "Ġnh Æ°",
- "ĠN ord",
- "Ġbe an",
- "0 7",
- "Ġple asant",
- "å½ ĵ",
- "v iron",
- "Ġgrad ient",
- "z us",
- "ĠE M",
- "Ġess ay",
- "ìĹIJ ìļĶ",
- "ế n",
- "n u",
- "á» «",
- "ĠÃī s",
- "Ġden omin",
- "ĠGirl s",
- "Ġperson nes",
- "ĠاÙĦØ £",
- "b ild",
- "ĠSt at",
- "Ġcompl iment",
- "ĠK ate",
- "Ġoptim al",
- "Ġh id",
- "د ÙĬ",
- "Ġquick er",
- "w all",
- "E n",
- "IN E",
- "?? ?",
- "ì² ´",
- "ĠA ction",
- "å Ł",
- "Ġpenal ty",
- "ĠK az",
- "' ?",
- "Ġc ried",
- "Ġcan vas",
- "ft e",
- "Ġexc lud",
- "¸ë ¡ľ",
- "Ġemphas is",
- "Ġen zy",
- "ĠH ou",
- "Ġoverse as",
- "ÃŃ amos",
- "å¸ «",
- "ö glich",
- "Ġhead phones",
- "c n",
- "ĠA ge",
- "Ġa kan",
- "Ġcharacter istic",
- "íķĺë ©´",
- "get s",
- "Ġë¶ Ī",
- "Ġr ival",
- "Ġb orders",
- "em ente",
- "em ás",
- "Ġy ol",
- "Ġcom pe",
- "end ers",
- "ınd an",
- "Ġmö glich",
- "Ġbubb les",
- "nat ural",
- "Ġar med",
- "Ġel abor",
- "ĠìĿ´ë ²Ī",
- "Ġwash ed",
- "οÏħ με",
- "è« ĭ",
- "Ġfl avors",
- "Ġexist e",
- "Ġpre st",
- "ĠThe ma",
- "оп ÑĢоÑģ",
- "er on",
- "U E",
- "er i",
- "Ġconc er",
- "Ġa ixò",
- "åħ ©",
- "Ġprotect ive",
- "Ġзна Ñİ",
- "ĠëĤ ł",
- "ĠII I",
- "Ġme er",
- "ĠSh op",
- "ll i",
- "ĠOr der",
- "ĠM Y",
- "ĠG host",
- "ãĤĤ ãģĨ",
- "ad el",
- "Ġst ole",
- "Ġrele asing",
- "ĠCom ment",
- "Ġtra ins",
- "ë ªħ",
- "Ġw issen",
- "ens ed",
- "Ġdesc end",
- "Ġf ier",
- "Ġrad i",
- "Ġpers u",
- "ç ¢",
- "Ġм н",
- "ĠD est",
- "Ġwor ries",
- "it et",
- "b as",
- "Ġst ab",
- "n ame",
- "or ic",
- "ĠCl ose",
- "Ġalum ni",
- "ĠS elf",
- "ff e",
- "it ating",
- "ather ine",
- "ĠRight s",
- "Ġell os",
- "Ġwar rant",
- "Ġn erve",
- "Ġveget able",
- "ĠTe il",
- "Ġê°Ļ ìĿ´",
- "R Y",
- "Ġsustain ability",
- "Ġste ht",
- "Ġbr id",
- "ada ÅŁ",
- "Ġt v",
- "Ġdur ation",
- "Ġpesso a",
- "Ġmet rics",
- "Ġad am",
- "c as",
- "аÑĢ и",
- "Ġev ident",
- "Ġdisplay ed",
- "Ø§Ø ¦",
- "Ġre ck",
- "ĠBudd ha",
- "Ġde le",
- "ĠDie go",
- "os ph",
- "Ġb la",
- "ĠM ik",
- "ul ator",
- "Ġ200 1",
- "Ġpromot ing",
- "y ch",
- "ĠE X",
- "Ġlast ly",
- "Ġout line",
- "Ġspir its",
- "Ġve ux",
- "Ġsubt ract",
- "ĠÅŁ imdi",
- "Ġp ins",
- "Ġbur ger",
- "Ġmol to",
- "Ġhab ÃŃa",
- "Ġë° ĺ",
- "ig u",
- "er st",
- "Ġn en",
- "Ġbac on",
- "it ious",
- "Ġcar ries",
- "Ġprom ises",
- "nd e",
- "ĠLe ft",
- "ĠL im",
- "æ £",
- "Ġ4 4",
- "Ġcare ers",
- "Ġì£ ¼ë",
- "Ġspeed s",
- "qu é",
- "m ad",
- "mark et",
- "is me",
- "Ġ200 3",
- "Ġre cess",
- "ĠJ UD",
- "Ġrac ist",
- "ĠSch l",
- "Ġpar ler",
- "Ġot ros",
- "ish es",
- "Ġconvert ed",
- "aa aa",
- "ани и",
- "ĠAr k",
- "ĠCh ance",
- "Ġelement ary",
- "ε ν",
- "ink s",
- "Inter viewer",
- "Ġfre ely",
- "al ah",
- "Ġëĭ¤ë ¥¸",
- "Ġrequest ed",
- "Ġtor que",
- "no ÅĽci",
- "ou red",
- "ĠSt aff",
- "Ġst ain",
- "ĠAl an",
- "Ġv ere",
- "ĠW inter",
- "Ġdef ect",
- "ied y",
- "Ġbe ats",
- "Ġh á",
- "um n",
- "o ons",
- "it udes",
- "Ġse it",
- "o ly",
- "Ġres erv",
- "Ġext r",
- "Ġphys ician",
- "vis or",
- "Ġhand ful",
- "ĠN ations",
- "Ġì¢ĭ ìĿĢ",
- "uc cess",
- "Ġup stairs",
- "ĠSqu are",
- "Ġhe in",
- "ĠSe ason",
- "ol is",
- "Ġpr ince",
- "Ġdef ensive",
- "ç ½",
- "Ġм еÑģÑĤ",
- "Ñĸ й",
- "Ġا ÙĨ",
- "um ble",
- "ê¹Į ìļĶ",
- "Ġass ass",
- "Ġcirc ular",
- "Ġqual ities",
- "Ġh mm",
- "Ġbl own",
- "ĠL iz",
- "ĠK ur",
- "ĠS A",
- "Ġfind ings",
- "Ġcol ours",
- "Ġde lle",
- "ĠI R",
- "ĠA th",
- "ĠD ub",
- "ĠO x",
- "ĠØ ®",
- "Ġpo ckets",
- "Ġgr ill",
- "Ġswitch ing",
- "Ġprefer red",
- "ĠW ales",
- "Ġex emplo",
- "Ġchop ped",
- "Ġvacc ination",
- "Ġne uro",
- "Ġspec ify",
- "iv os",
- "Ġser á",
- "Ġz ie",
- "Ġà® ®",
- "Ġresult ing",
- "ĠU gh",
- "Ġmess ed",
- "C D",
- "Ġpa ar",
- "Ġcom er",
- "Ġcou ch",
- "ĠFest ival",
- "Ġ4 9",
- "v ous",
- "z ens",
- "ç¨ ®",
- "ĠKenn edy",
- "ĠT s",
- "Ġë³´ì Ĺ",
- "Ġdemonst ration",
- "Ġun to",
- "Ġfrust rating",
- "Ġlabor atory",
- "Ġe gy",
- "Ġbeaut ifully",
- "Ġìŀ ¬ë",
- "Ġal gu",
- "Ġö yle",
- "ä½ł çľĭ",
- "ĠP H",
- "Ġfort une",
- "Ġclean er",
- "ĠRob in",
- "Ġsa us",
- "ĠG eld",
- "Ġk at",
- "o bs",
- "Ġol ur",
- "Ġm att",
- "Ġquest a",
- "Ġsuggest ion",
- "en cer",
- "о ÑģÑĤ",
- "Ġrad ar",
- "Ġìŀ ¡",
- "ish a",
- "à® ¨",
- "ãĤĵ ãģª",
- "j es",
- "Ġve el",
- "ìĤ °",
- "Ġauth ors",
- "ãĢ İ",
- "pl an",
- "Ġcollabor ative",
- "Ġinst inct",
- "Ġfar ming",
- "au ge",
- "E du",
- "Ġmembers hip",
- "Ġsimult aneously",
- "Ġb ake",
- "Ġk ä",
- "Ġlect ures",
- "Ñĩ еÑģ",
- "Ġprend re",
- "Ġcoll aps",
- "ĠS aya",
- "ĠF ut",
- "Ġy og",
- "ĠR ather",
- "ر ÙĬ",
- "Ġcamp s",
- "ол од",
- "Ġsim ulation",
- "ĠM ak",
- "La ughs",
- "Ġgre y",
- "Ġsent ences",
- "y en",
- "ĠUn less",
- "J e",
- "ĠSat an",
- "ĠÑĤак же",
- "ĠN A",
- "Ġbr on",
- "Ġ? ]",
- "Ġsoul s",
- "Ġlight ning",
- "Ġimag ined",
- "Ġczy li",
- "ps ilon",
- "et ta",
- "Ġbelie ving",
- "Ġstrong est",
- "ĠC ON",
- "Ġquel ques",
- "Ġimmig rants",
- "Ġwall et",
- "éĢĻ æĺ¯",
- "ĠJer sey",
- "Ġimplic ations",
- "Ġfor b",
- "ãĢ ı",
- "Ġun believable",
- "Ø§Ø ¡",
- "Ġoper ational",
- "ü s",
- "ĠG M",
- "Ġê·¸ëŁ °ëį°",
- "Ġgrac ias",
- "Ġent end",
- "ĠReg ard",
- "ro b",
- "ĠÑĤ еÑħ",
- "è ı",
- "ĠRev olution",
- "Ġwa ar",
- "ĠB iz",
- "th eless",
- "Ġspons ored",
- "qu ier",
- "ĠìĿ ¼ë",
- "Ġte k",
- "ĠëIJ ł",
- "ig keit",
- "ĠL uck",
- "ĠCertain ly",
- "Ġto ll",
- "Ġн иÑĩего",
- "ĠM oney",
- "ĠÑģ ÑĤоÑĢ",
- "ĠDou ble",
- "ĠW olf",
- "Ġch unk",
- "ά ν",
- "it és",
- "on ing",
- "M ar",
- "Ġgrand es",
- "Ġcollect ions",
- "ĠEurop a",
- "Ġа ÑĢ",
- "ĠâĢĭâĢĭ âĢĭ",
- "Ġê·¸ëŁ¬ë ©´",
- "Ġоб ÑĬ",
- "Ġãģ ª",
- "Ġìĭľ ê°Ħ",
- "ĠC ustom",
- "Ġì² ĺ",
- "Ñĸ лÑĮ",
- "Ġindivid ually",
- "í Ĺ",
- "Ġdo zen",
- "Ġo we",
- "ĠVict oria",
- "åı¯ èĥ½",
- "Ġbe et",
- "ur b",
- "Ġanal og",
- "i ção",
- "Ĥ ľ",
- "so ever",
- "Ġmod o",
- "Ġsubscri bed",
- "ìŀ ¬",
- "Ġent ities",
- "çī ĩ",
- "Ġclos et",
- "Ġrespond ing",
- "Ġprin ter",
- "ĠStep han",
- "Ġby ÅĤ",
- "ĠD om",
- "ĠF ern",
- "ĠP ier",
- "ĠwiÄĻ c",
- "Ġh ence",
- "Ġmod ules",
- "ãĥ ¬",
- "ĠëĶ ±",
- "ĠDann y",
- "ĠÑģеб е",
- "Ġv ad",
- "ĠìĹ Ħ",
- "Ġs ous",
- "Ġsp here",
- "B Y",
- "ĠP ed",
- "ign ed",
- "Ġwhe at",
- "Ġund ers",
- "Ġevol ve",
- "Ġdec lar",
- "Ġlight ly",
- "Ġident ifying",
- "æĦı æĢĿ",
- "Ġlegend ary",
- "Ġgen uine",
- "Ġgr ind",
- "ĠU ne",
- "ge ben",
- "Ġb icy",
- "Ġjump s",
- "Ġprov ince",
- "zi ÄĻ",
- "Ġ×IJ× ł×Ļ",
- "Ġh oc",
- "Ġб л",
- "ĠGr ad",
- "Ġreven ge",
- "ĠاÙĦ ت",
- "o oh",
- "æĭ ľ",
- "аÑĨи и",
- "å¹ ³",
- "Ġelect ro",
- "ĠëIJ IJ",
- "ãģ§ ãģ¯",
- "Ġf als",
- "ri el",
- "ok er",
- "ĠEx cellent",
- "ĠMor gan",
- "Ġbr ick",
- "Ġsubstant ial",
- "Ġpoll ution",
- "ĠT ür",
- "ĠEv et",
- "Ġl ung",
- "ãģ ĸ",
- "×Ļ× ©",
- "omm es",
- "Ġreal izing",
- "Ġhum ble",
- "ĠL ock",
- "Ġb od",
- "Ġìĸ ¸",
- "Ġpe ers",
- "uz z",
- "Ġembed ded",
- "Ġclar o",
- "Ġag greg",
- "Ġemploy ers",
- "ĠR aj",
- "Ġãģ ¨",
- "ĠY i",
- "Ġje u",
- "at ers",
- "Ġstri kes",
- "n os",
- "aut res",
- "d r",
- "op her",
- "ĠApp arently",
- "íĺ Ħ",
- "Ġinf ant",
- "ا ب",
- "ÑĤ Ñĭ",
- "í Ľ",
- "Ú ¯",
- "Ġred es",
- "acaģ ım",
- "ĠDA VID",
- "ĠCh icken",
- "Ġperspect ives",
- "Ġview er",
- "Ġsh ar",
- "ĠпÑĢо из",
- "lig t",
- "er os",
- "it able",
- "ил оÑģÑĮ",
- "Ġdif ÃŃ",
- "´ë į°",
- "Ġret ired",
- "Ġthat s",
- "zen ie",
- "be iten",
- "Ġmy cket",
- "ĠR ab",
- "Ġinflam m",
- "ì° ®",
- "Ġd um",
- "Ġdad dy",
- "æľ Ł",
- "Ġimm ers",
- "Ġplay list",
- "௠Ĩ",
- "Ġtra um",
- "Ġref use",
- "st ep",
- "à® ļ",
- "c up",
- "Ġpop s",
- "r imin",
- "ay ım",
- "Ġa ld",
- "Ġun necess",
- "Ġd ah",
- "ĠIr ish",
- "Ġcomp r",
- "la ÅŁ",
- "T P",
- "Ġtransl ated",
- "S c",
- "ce ÄŁim",
- "´ IJ",
- "Ġd rei",
- "ĠлÑİд ей",
- "Ġqu iero",
- "Ġhe le",
- "z lich",
- "Ġapp les",
- "Ġdistrict s",
- "Ġcred its",
- "Ġas p",
- "Ġëĭ ¨",
- "or al",
- "å½ ±",
- "Ġste pping",
- "ĠV a",
- "Ġg ains",
- "6 5",
- "Ġnuest ra",
- "ed ay",
- "ass ador",
- "ĠL ind",
- "Ġcrop s",
- "ci endo",
- "ig ue",
- "Ġb ana",
- "A m",
- "Ġp ent",
- "Ġadd iction",
- "Ġpack aging",
- "ä d",
- "ª ¨",
- "Ġper què",
- "Ġcampaign s",
- "Ġste ep",
- "Ġne ue",
- "Ġembarrass ed",
- "Ġdist inction",
- "it zer",
- "åij Ĭ",
- "Ġregist ration",
- "Ġll am",
- "ĠAlm ighty",
- "li est",
- "Ġu z",
- "n ak",
- "ç º",
- "Ġter az",
- "iam ente",
- "Ġtrans actions",
- "Ġc ôt",
- "Ġswitch ed",
- "Ġcom bo",
- "Ġpray ers",
- "Ġintern ship",
- "Ġaddress es",
- "Ġchar ity",
- "ĠW OO",
- "Ġb ait",
- "è¿ ĩ",
- "Ġ �",
- "Ġf ica",
- "ĠTy ler",
- "ar u",
- "Ġat oms",
- "ĠLe vel",
- "ĠпоÑĤ ом",
- "Ġf ame",
- "ul k",
- "Ġteach es",
- "Ġre build",
- "ед ÑĮ",
- "ĠIndones ia",
- "ush i",
- "ĠSh ort",
- "Ġens uring",
- "f s",
- "e le",
- "Ġmargin al",
- "Ġconclud e",
- "am t",
- "Ġver ify",
- "ĠMc Donald",
- "Ġsk al",
- "Ġrec onst",
- "ĠM ann",
- "Ġbas ement",
- "Ġtransform ed",
- "Ġoccasion ally",
- "z one",
- "ĠD ans",
- "Ġкак ой",
- "Ġdiagn osed",
- "ĠÏĦ α",
- "Ġcomm ands",
- "Ġpresident ial",
- "Ġab b",
- "Ġbrack et",
- "ĠL em",
- "Ã¥ ng",
- "Ġfavor ites",
- "Ġrev ol",
- "ĠíĬ ¹",
- "Ġhar ass",
- "é ħ",
- "Ġcle ans",
- "st änd",
- "Ġknock ed",
- "Ġpe oples",
- "Ġmusic ians",
- "Ġmut ual",
- "ĠC old",
- "8 8",
- "ze j",
- "at ie",
- "ĠHon or",
- "Ġobs essed",
- "ĠM USIC",
- "ĠBre ak",
- "ú ng",
- "Ġmod ify",
- "Ġs öyle",
- "Ġ×ŀ ×Ķ",
- "ĠOn line",
- "f o",
- "ĠMill er",
- "Ġlik ing",
- "Ġin hab",
- "Ġgrat itude",
- "ĠJour nal",
- "arn ess",
- "J ohn",
- "ĠG it",
- "åī Ľ",
- "Ġsin cere",
- "ĠS ci",
- "ĠE li",
- "Ġsymbol s",
- "Ġman ually",
- "ε ÏĤ",
- "Ġв Ñĸд",
- "ĠF at",
- "Ġlab els",
- "Ġsophistic ated",
- "ump s",
- "Ġrele ases",
- "Ġ4 7",
- "ĠO M",
- "ê°Ģ ë",
- "ĠB ien",
- "ĠRe f",
- "è¨ ĺ",
- "ĠSt a",
- "ĠE gg",
- "Ġindic ator",
- "ps on",
- "Ġnas ıl",
- "R ight",
- "Ġcon vey",
- "Ġkn ot",
- "Ġconnect s",
- "ul as",
- "Ġpre ced",
- "Ġine quality",
- "am iento",
- "Ġrep ly",
- "O Y",
- "Ġdism iss",
- "ĠëIJ ľ",
- "çĦ ¡",
- "ĠÑħоÑĢоÑĪ о",
- "Ġm éd",
- "Ġrandom ly",
- "ĠO nt",
- "u ard",
- "Ġpull s",
- "ĠÑĤ епеÑĢÑĮ",
- "ĠNe ed",
- "ĠSo ft",
- "Ġstrength s",
- "Ġgo ed",
- "um en",
- "æŃ »",
- "Ġíİ ¸",
- "Ġд об",
- "Ġclar ity",
- "ĠA i",
- "Ġball oon",
- "ĠP and",
- "ĠìķĦ ëĭ",
- "Ġsh iny",
- "Ġsmall est",
- "on ia",
- "h ill",
- "ot ing",
- "Ġe ing",
- "Ġmere ly",
- "Ġse us",
- "Ġн еп",
- "Ġí Ĩµ",
- "Ġgu ides",
- "Ġspecial ist",
- "Ġste ak",
- "ãĤĪ ãģĨ",
- "Ġmig ration",
- "que le",
- "Ġru ined",
- "Ġpu pp",
- "å¥ ³",
- "Ġk end",
- "ang an",
- "Ġpal m",
- "Ġunf air",
- "Ġz m",
- "ĠD V",
- "ch ester",
- "и Ñİ",
- "Ġo oh",
- "er g",
- "AT H",
- "° ©",
- "åĵ ª",
- "r ison",
- "Ġinvol ving",
- "Ġpart ly",
- "anç ais",
- "Ġv ow",
- "Ġprom inent",
- "Ġcry st",
- "ib a",
- "Ġdes erves",
- "Ġover t",
- "Ġsens it",
- "ĠWh e",
- "Ġtight en",
- "Ġintim id",
- "Ġal iment",
- "w ill",
- "Ġstrength en",
- "ĠT an",
- "åı Ī",
- "ãģĹ ãģ¾ãģĻ",
- "on i",
- "ĠM un",
- "Ġpro ph",
- "Ġrehe ars",
- "ĠK le",
- "Ġve ces",
- "Ġwonder ed",
- "ok i",
- "Ġsens es",
- "´ì ĭ",
- "Æ°á» Ľ",
- "ĠÈĻ i",
- "Ġmuch os",
- "Ġwatch es",
- "ortun ate",
- "ĠJ uan",
- "ìŀĸ ìķĦ",
- "ÑĢ е",
- "e i",
- "ion en",
- "Ġexperiment al",
- "Ġda ughters",
- "ภĽ",
- "Ġment ally",
- "bec ca",
- "aw are",
- "ìĦ Ŀ",
- "Ġwhat soever",
- "Ġen ables",
- "ĠL ow",
- "o id",
- "ภĬ",
- "ó d",
- "Ø º",
- "Ġconstruct ed",
- "ĠLad ies",
- "Ġaccus ed",
- "Ġа н",
- "D an",
- "Ġsp awn",
- "Ġcontain ers",
- "Ġart istic",
- "ı p",
- "Ġdisc l",
- "Ġaut res",
- "in as",
- "ĠN ation",
- "Ġn ag",
- "be an",
- "w he",
- "ľë ıĦ",
- "ĠSe oul",
- "Ġíı ¬",
- "ĠN ich",
- "Ġcomp lement",
- "Ġinter ven",
- "ĠMod el",
- "ĠOr ange",
- "nam on",
- "Ġcalcul ation",
- "se e",
- "Ġusted es",
- "Ġle b",
- "Ġdo ct",
- "Ñĸ н",
- "Ġf oster",
- "Ġel astic",
- "ĠAh h",
- "Ġa ce",
- "ĠP ink",
- "ĠJ eg",
- "Ġde er",
- "ãģĹ ãģĦ",
- "s is",
- "Ġjak o",
- "ĠEm ma",
- "ÑģÑĤв енно",
- "Ġport rait",
- "Ġmak er",
- "Ġa ument",
- "ÑĢ об",
- "Ġairpl ane",
- "Ġtransparen cy",
- "Ġadjust ment",
- "ĠCD C",
- "ç on",
- "Ġupload ed",
- "Ġд ейÑģÑĤв",
- "Ġго ÑĤов",
- "Ġit er",
- "Ġcur se",
- "ô n",
- "mer ce",
- "ar an",
- "Ġle ak",
- "çµ IJ",
- "Ġabs ence",
- "Ñģ кий",
- "Ġread ers",
- "al er",
- "Ġbene ath",
- "ang o",
- "h etic",
- "Ġfin ns",
- "Ġpo op",
- "Ġdu plic",
- "H i",
- "ig s",
- "olog ically",
- "op p",
- "Ġd izer",
- "ĠAll en",
- "Ġgl i",
- "Ġacc eleration",
- "Ġvit amin",
- "ãĥ Ń",
- "v ä",
- "ĠAc cess",
- "à® Ļ",
- "r ás",
- "Ġappreci ated",
- "Ġn ah",
- "Ġpos ter",
- "Ġt ale",
- "Ġhighlight ed",
- "æĸ ĩ",
- "ż eli",
- "Ġblock chain",
- "Ġmic row",
- "Ġcin ema",
- "ĠCh ang",
- "ĠSe arch",
- "ust ers",
- "ĠZ ero",
- "ĠDiv ision",
- "ÑĢ аÑģ",
- "Ġsca re",
- "Ġj elly",
- "ĠAdminist ration",
- "S O",
- "Ġl ined",
- "Ġê° Ħ",
- "Ġge ben",
- "Ġso da",
- "Ġwin ners",
- "³ ¼",
- "Ù Ĵ",
- "ĠAm b",
- "åķı é¡Į",
- "å Ķ",
- "Ġpe g",
- "å· ±",
- "4 3",
- "Ġra us",
- "Ġre wards",
- "Ġinc lus",
- "Ġhigh way",
- "Ġha h",
- "Ġmultipl ied",
- "Ġs ẽ",
- "Ġdisci ples",
- "Ġn ing",
- "Ġdress ing",
- "Ġattrib utes",
- "ĠM osc",
- "ĠGree ce",
- "Ġse k",
- "ĠLe arn",
- "Ġj us",
- "rend re",
- "Ġperson ne",
- "pl ete",
- "Ġpl acing",
- "Ġl uego",
- "ill ance",
- "Ġоб Ñī",
- "Ġprov ision",
- "Ġl ion",
- "t ra",
- "bo ards",
- "Ġbehavi our",
- "he y",
- "Ġsubscri ption",
- "Ġprot agon",
- "ãĥ £",
- "Ġvar a",
- "ĠÅŁ u",
- "Ġha ha",
- "Ġteas poon",
- "æ Ł",
- "av oir",
- "Ġcrypt o",
- "ĠÑģÑĤ аÑĢ",
- "ĠSt ore",
- "ab s",
- "ĠStud ents",
- "Ġla und",
- "int o",
- "Ġapproach ed",
- "° ľ",
- "ÑĥÑİ Ñī",
- "ĠL abor",
- "ot es",
- "iat ric",
- "Ġgro ÃŁ",
- "ut ive",
- "Ġи д",
- "ĠG ib",
- "Ġpl acement",
- "ĠdifÃŃ cil",
- "Ġf rog",
- "ĠвÑģе Ñħ",
- "ĠJ r",
- "az ed",
- "Ñĥ Ñī",
- "Ġê ¼",
- "fr ame",
- "а еÑĪÑĮ",
- "Ġlock down",
- "åij ³",
- "Ġmed i",
- "Ġ×Ķ× ŀ×",
- "ени й",
- "em ale",
- "ì¢ ħ",
- "ater al",
- "Ġdist ant",
- "Ġbe ars",
- "Ġjournal ist",
- "è§ £",
- "ĠMarsh all",
- "ĠIh nen",
- "uet ooth",
- "b ag",
- "ĠÄij ã",
- "ĠHigh ness",
- "Ġì° į",
- "и ка",
- "ĠW u",
- "ĠFr an",
- "Ġp eng",
- "Ġf on",
- "Ġhypothes is",
- "ĠÑĢ Ñĥ",
- "Ġl y",
- "× ļ",
- "ìĽ Ķ",
- "ĠRad io",
- "ภŀ",
- "D av",
- "Ġembarrass ing",
- "ĠìŀĪ ìĸ´",
- "Ġcast ing",
- "Ġc age",
- "ĠP sych",
- "ĠìĿ¼ ëĭ¨",
- "ĠÅ ¾",
- "im b",
- "Ġdirect ors",
- "S H",
- "ĠÏĦη ν",
- "á»ģ u",
- "Ġkon uÅŁ",
- "Ġoption al",
- "quar ters",
- "ik er",
- "ĠS ant",
- "Ġvers es",
- "ë ¶Ģ",
- "Ġo lar",
- "ĠÏ ĩ",
- "ãĥ ķ",
- "Ġγ ια",
- "ĠI mm",
- "Ġcontrovers ial",
- "Ġer sten",
- "Ġreci p",
- "ĠChristian ity",
- "Ġê´ ľ",
- "ord on",
- "×ķ× ©",
- "Ġsl ash",
- "ĠP f",
- "Ñĥд ÑĮ",
- "×ķ× Ŀ",
- "ĠPer ry",
- "Ġm amy",
- "Ġbackground s",
- "Ġà®İ ன",
- "Ġpend ant",
- "ĠColumb ia",
- "Ġin verse",
- "ĠÑĩеÑĢ ез",
- "Ġs v",
- "Ġdig ging",
- "4 1",
- "ch em",
- "Ġnavig ation",
- "ĠSh in",
- "ĠFr ont",
- "P D",
- "Ġbe aring",
- "ĠW asser",
- "Ġw ax",
- "ĠCH RIS",
- "ch ing",
- "Ġpress ed",
- "E l",
- "ĠD al",
- "ons in",
- "Ġb inding",
- "Ñģк ой",
- "po ons",
- "Ġmo ck",
- "are st",
- "к ÑĢа",
- "M M",
- "Ġcor rupt",
- "st orm",
- "Ġref res",
- "ĠCo ach",
- "ll ä",
- "ĠTH IS",
- "Ġpar ag",
- "Ġìĵ °",
- "p ool",
- "Ġbill ions",
- "Ġê¹ Ģ",
- "gr oup",
- "Ġwel coming",
- "cell ence",
- "ĠDu ke",
- "ê¸ ´",
- "Ġprim era",
- "ìł ¸",
- "Ġp ond",
- "Ġstat ue",
- "Ġêµ ¬ë",
- "Ġh atch",
- "Ġinstrument al",
- "Ġresident ial",
- "ì» ¤",
- "Ġaccept ing",
- "osh i",
- "d ate",
- "ĠìĶ ¨",
- "Ġplant ed",
- "Ġj oking",
- "Ġì Ħľ",
- "Ġh ated",
- "ĠÑĢаÑģ Ñģк",
- "Ġsle pt",
- "Ġpack ages",
- "Ġisland s",
- "es en",
- "ģ ı",
- "Ġdi agon",
- "ĠO sc",
- "Ġmes h",
- "Ġsc ales",
- "ar ity",
- "ĠDef ense",
- "ãģ¡ ãĤĩ",
- "ĠLew is",
- "ĠÑģ егоднÑı",
- "Ġfl ies",
- "uin ely",
- "ĠCons ider",
- "Ġst ark",
- "he w",
- "ĠAs ÃŃ",
- "³ ´ë",
- "Ġprop ose",
- "Ġíķĺë ©´",
- "od o",
- "ĠNorm ally",
- "Ġhe eft",
- "ĠHarr is",
- "g ro",
- "ĠBlo od",
- "b ase",
- "Ġi OS",
- "Ġtouch es",
- "Ġinsp ir",
- "Ġ× ĵ",
- "Ġb inary",
- "Ġì¶ Ķ",
- "Ġser ial",
- "Ġ ion",
- "Ġunemploy ment",
- "Ġodd s",
- "ĠF ab",
- "ĠF BI",
- "BR UN",
- "Ġweight s",
- "ν ο",
- "at ile",
- "Ġnurs es",
- "Ġinvolve ment",
- "ĠíĶ ¼",
- "Ġgovern ance",
- "Ġâ Ĥ¬",
- "ÑĢÑĥ п",
- "ier ra",
- "íĺ ķ",
- "ĠJ erry",
- "Ġbe ard",
- "Ġsal vation",
- "ĠAl ong",
- "g entle",
- "ĠK i",
- "b ol",
- "ĠPl at",
- "Ġhas ht",
- "è¿ ij",
- "Ġw are",
- "Ġpart ie",
- "y cz",
- "Ġint r",
- "F ih",
- "n ent",
- "Ġche at",
- "il en",
- "Ġë ¯",
- "or ie",
- "Ġfá cil",
- "et ric",
- "Ġaffect ing",
- "unci ation",
- "Ġaff airs",
- "Ġbe e",
- "Ġview ing",
- "Ġor ang",
- "ĠL an",
- "ĠС ÑĤ",
- "ä¸ ĸ",
- "ĠM es",
- "ĥ ģ",
- "er ie",
- "Ġes pa",
- "Ġinter pre",
- "Ġposs ess",
- "Ġpure ly",
- "rit o",
- "f ound",
- "as ma",
- "ìłģ ìĿ¸",
- "Ġexam ine",
- "ĠÑĥ м",
- "Ġbes ch",
- "ĠTom orrow",
- "ĠB lock",
- "Ġvari ant",
- "Ġprefer ence",
- "Ġcoach es",
- "Ġmedic ations",
- "Ġíĺ Ħ",
- "Ġemp ire",
- "ë Ħ¤",
- "ĠIll inois",
- "Ġcris py",
- "Ġth ì",
- "Ġbe es",
- "7 7",
- "Ġgl ow",
- "è º",
- "ĠStud ies",
- "åIJ Ħ",
- "ĠChall enge",
- "Ġunlike ly",
- "Ð §",
- "ıy orsun",
- "DI E",
- "Ġminim ize",
- "iz ard",
- "Ġú n",
- "Ġencont rar",
- "ĠK ill",
- "å »",
- "Ġvan illa",
- "ĠGr ant",
- "ĠG T",
- "se a",
- "Ġs ought",
- "в од",
- "Ġnä m",
- "ĠA unt",
- "OW N",
- "Ġpump kin",
- "st ellen",
- "Ġr ag",
- "ег да",
- "Ġstory t",
- "Ġfor um",
- "æ© Ł",
- "Ġestab a",
- "uch e",
- "Ġcon gress",
- "ĠRe y",
- "Ġdram atically",
- "ĠSp ort",
- "ĠYe llow",
- "Ġê³Ħ ìĨį",
- "Ġdisg usting",
- "ĠRe cent",
- "Ġacqu ired",
- "Ġc ables",
- "çĶ ļ",
- "d in",
- "Ġv isto",
- "Ġcommunic ating",
- "ÑģÑĤав лÑı",
- "еÑģ ÑĤо",
- "ãĥ»ãĥ» ãĥ»",
- "Ġré g",
- "Ġso cks",
- "Ġpro ces",
- "be cause",
- "Ġut ter",
- "Ġcoloc ar",
- "Ġnew est",
- "Ġgr amm",
- "è¡ ¨",
- "ä¸į çŁ¥éģĵ",
- "Ġsh ifting",
- "Ġcar rier",
- "ĠÑģк оÑĢ",
- "ĠSch w",
- "Ġexec uted",
- "Ġmaint ained",
- "ĠÏ Ĩ",
- "ĠM oses",
- "Ġdis se",
- "Ġhor r",
- "ãĢ ľ",
- "Ġr ally",
- "Ġall em",
- "ĠEvent ually",
- "Ġdi yor",
- "lv ania",
- "Ġsch nell",
- "Ġê³ ¼",
- "Ġë§ ¤",
- "Ġstrugg les",
- "l ate",
- "Ġclar ify",
- "é ment",
- "Ġmulti plic",
- "иб о",
- "Ġjour n",
- "Ġfra gr",
- "Ġsurprising ly",
- "Ġdesper ate",
- "5 2",
- "Ġs ul",
- "ĠRe ad",
- "ĠF ried",
- "Ġm ond",
- "w oo",
- "Ġorgan izing",
- "ãģĹãĤĩ ãģĨ",
- "ĠSo on",
- "Ġв опÑĢоÑģ",
- "ĠN ur",
- "ĠÐĹ Ð´",
- "Ġsp ider",
- "е ÑģÑı",
- "Ġtutorial s",
- "Ġnutri ents",
- "or er",
- "Ġcoe fficient",
- "Ġarrange ment",
- "Ġpr icing",
- "n an",
- "y u",
- "B L",
- "Ġtri be",
- "ĠHow ard",
- "un ks",
- "Ġnew er",
- "Ġprov in",
- "Ġpred iction",
- "h os",
- "Ġol sun",
- "ĠAr ound",
- "Ġv ier",
- "ĠÑģÑĤоÑĢ он",
- "Ġv alley",
- "ĠE la",
- "if i",
- "Ġgal axy",
- "Ġtran qu",
- "Ġad vers",
- "ĠTem ple",
- "iff s",
- "ig ence",
- "èĩª å·±",
- "Ġkön nte",
- "ĠÄij ó",
- "D id",
- "Ġphotograph s",
- "ĠA WS",
- "ÑĨи Ñı",
- "Ġgu ards",
- "Ġappoint ed",
- "ĠG il",
- "Ġм ом",
- "Ġc od",
- "ĠUn like",
- "Ġeven ly",
- "isc onsin",
- "Ġest ou",
- "Ġm nie",
- "ĠEx ec",
- "ĠM V",
- "ĠE ine",
- "ä¿ ¡",
- "ĠRog er",
- "ĠF ac",
- "ĠL ist",
- "Ġf uer",
- "аеÑĤ е",
- "om ed",
- "Ġattract ion",
- "èī ²",
- "Ġter rain",
- "ĠD rop",
- "Ġcorpor ations",
- "Ġsci ences",
- "Ġthr one",
- "ãģĦ ãģŁ",
- "Ġa j",
- "ĠR ot",
- "çī ¹",
- "Ġsupp orters",
- "ĠB ere",
- "H ere",
- "Ġdifer entes",
- "Ġsignific ance",
- "Ïĥ η",
- "æĪij 覺å¾Ĺ",
- "Ġcl amp",
- "Ġë ĮĢë",
- "Ġfab ulous",
- "re z",
- "æĮ ģ",
- "Ġassum ptions",
- "ut her",
- "w id",
- "p ot",
- "è¿ İ",
- "Ġy an",
- "ul in",
- "ÑĢ Ñĭв",
- "ĠSl ow",
- "ĠPenn sy",
- "Ġíķ ´ìĦľ",
- "Ġme io",
- "Ġwealth y",
- "ĠE ight",
- "Ġpul se",
- "Ġfr iction",
- "id ity",
- "ĠH oll",
- "i yorum",
- "Ġsound ed",
- "ĠC arr",
- "Ġfor k",
- "â ĺ",
- "ĠP A",
- "Ġcons pir",
- "Ġc oding",
- "r t",
- "ĠTy p",
- "Ġìĸ ij",
- "Ġп ог",
- "Ġmis er",
- "ĠÑģм оÑĤÑĢ",
- "ĠSw eden",
- "Ġolar ak",
- "ĠZh ang",
- "ĠCh i",
- "ĠT itan",
- "Ġscreen ing",
- "ĠSp ider",
- "ĠÅŀ imdi",
- "Ġobst acles",
- "lar a",
- "Ġchalleng ed",
- "p se",
- "T ON",
- "á» ¥",
- "ĠP i",
- "Ġlag i",
- "ie urs",
- "Ġhur ting",
- "Ġneg lect",
- "Ġgener ating",
- "Ġyoung est",
- "Ġaud it",
- "ĠÑĢ ез",
- "Ïģ ά",
- "Ġdon ate",
- "ĠPD F",
- "Ġvis its",
- "Ġcru ise",
- "P P",
- "as er",
- "Ġw sp",
- "back s",
- "iv als",
- "ãģĨ ãĤĵ",
- "Ġde ve",
- "Ġprop ort",
- "Ġc ath",
- "ĠE ffect",
- "Ġwind s",
- "ĠìĻ Ķ",
- "Ġchart s",
- "Ġs ama",
- "Ġautom ation",
- "Ġпок а",
- "Ġol an",
- "Ġbo ats",
- "Ġca fe",
- "Ġden ied",
- "ĠM ama",
- "Ġblock ing",
- "ĠTh or",
- "Ġphenomen al",
- "Ġstake holders",
- "Ġun os",
- "Ñĥ еÑĤ",
- "ĠAb raham",
- "ãģ§ ãĤĤ",
- "Ġdetect ion",
- "Ġjur is",
- "Ġpower ed",
- "z ial",
- "Ġwel fare",
- "Ġup grad",
- "Ġmoż na",
- "ĠC ase",
- "c ular",
- "Ķ ìĿ´",
- "ãĥ ģ",
- "ĠGu ess",
- "Ġcy cles",
- "ä¾ ĭ",
- "çµ ¦",
- "ro ck",
- "um i",
- "Ġel ite",
- "Ġqu è",
- "åł ±",
- "ÑĤ ом",
- "Ġsh ore",
- "gun ta",
- "Ġk u",
- "Ġfaith ful",
- "ĠJ eremy",
- "a id",
- "à ·",
- "ug al",
- "å°į åķĬ",
- "ĠV el",
- "Ġvra i",
- "st ell",
- "¨ ¸",
- "Ġk ol",
- "è ½",
- "Ġquant o",
- "Ġз аÑĢ",
- "Ġ200 2",
- "es y",
- "Ġres erve",
- "Ġмом енÑĤ",
- "Ġdeploy ed",
- "Ġdefin ing",
- "Ġsa u",
- "Ġga at",
- "\" )",
- "Ġtrans mit",
- "Ġpubl ishing",
- "Ġrank ing",
- "Ġoff ense",
- "Ġ4 6",
- "p in",
- "ĠT aking",
- "Ġentit led",
- "Ġgen uinely",
- "Ġvari ations",
- "Ġfind e",
- "Ġt au",
- "Ġunf ortunate",
- "ĠR ah",
- "port s",
- "Ġc Å",
- "Ġmon key",
- "Ġbr ac",
- "we i",
- "l ung",
- "Ġart if",
- "Ġsy rup",
- "ĠÐĶ ав",
- "Ġlift ed",
- "Ġche z",
- "ĠAd vent",
- "ĠSt ock",
- "Ġdo l",
- "м ен",
- "иÑĪ ÑĮ",
- "Ġy n",
- "g io",
- "d et",
- "Ġdes se",
- "Ġg ri",
- "ĠChair man",
- "ç ħ",
- "Ġcu enta",
- "an im",
- "Ġcra b",
- "Ġesc al",
- "Ġpremi ère",
- "ĠGe f",
- "Ġd ining",
- "Ġsevent h",
- "Ġch asing",
- "ĠT ower",
- "Ġbrut al",
- "Ġfundament ally",
- "ãģ¨ ãģĨ",
- "л ениÑı",
- "st age",
- "Ġacqu is",
- "Ġcyl inder",
- "Ġcomm ander",
- "m em",
- "ĠU V",
- "ha ppy",
- "Ġe psilon",
- "Ġinv itation",
- "Ġfar mer",
- "ch air",
- "Ġdest iny",
- "Ġso vere",
- "ĠHeb rew",
- "Ġserv ant",
- "Ġbe w",
- "Ġg ast",
- "ut ies",
- "Ġadministr ative",
- "ĠComm and",
- "é ta",
- "Ġnit rogen",
- "ê· ¼",
- "Ġab i",
- "Ġvill ain",
- "Ġblank et",
- "ĠS end",
- "Ġbeat en",
- "² Ħ",
- "Ġvol unt",
- "Ġschol ar",
- "ĠEm peror",
- "Ġ4 3",
- "v able",
- "ĠD us",
- "ĠG U",
- "Ġtarget ing",
- "ww w",
- "Ġamend ment",
- "ìĨ Įë",
- "Ġt ing",
- "Ġn asty",
- "Ġg auge",
- "ĠÑĢ од",
- "ĠH ans",
- "Y our",
- "α ν",
- "Ġpro jet",
- "ĠHawai i",
- "Ġsusp icious",
- "Ġsch w",
- "Ġremo val",
- "Ġint rig",
- "ĠM U",
- "Ġp onto",
- "ठ¾",
- "Ġоб ÑĢаз",
- "Ġguess ing",
- "p ace",
- "Ġm others",
- "Ġmill imeter",
- "л ение",
- "没 æľī",
- "Ġavail ability",
- "ic z",
- "æŃ ¤",
- "Ġfr act",
- "Ġbas es",
- "k m",
- "ĠB TS",
- "ĠF ield",
- "Ġd zie",
- "Ġseg undo",
- "ĠëĤĺ ëĬĶ",
- "Ġlegit imate",
- "im as",
- "Ġв н",
- "Ġcor ruption",
- "Ġsm ash",
- "ĠVal ent",
- "Ġalign ed",
- "ĠPennsy lvania",
- "Ġg ab",
- "ĠE un",
- "ent h",
- "ĠMor ning",
- "Ġcand le",
- "Ġback pack",
- "ĠIslam ic",
- "a ções",
- "Ġenc ry",
- "Ġmushroom s",
- "íĮ Į",
- "d it",
- "Ġtrans it",
- "ĠW isconsin",
- "Ġparticip ated",
- "ĠIl s",
- "Ġunf old",
- "¶ Ģë",
- "Ġprof its",
- "Ġwar ming",
- "ĠG ang",
- "Ġnetwork ing",
- "Ġme ga",
- "Ġthorough ly",
- "le ments",
- "ĠH m",
- "Ġdec iding",
- "Ġemotion ally",
- "Ġexha usted",
- "ĠÐŁ оÑĤ",
- "c ido",
- "ĠHT ML",
- "Ġcopy right",
- "Ġmel ody",
- "y im",
- "Ġand ers",
- "osh op",
- "Ġë³ ¼",
- "Ġathlet e",
- "ĠG E",
- "Ġfrequ ent",
- "Ġdes ires",
- "Ġneed ing",
- "ĠY un",
- "Ġrif le",
- "Ġlo ver",
- "' T",
- "Ġd ense",
- "Ġt ão",
- "Ġnot ified",
- "Ġid i",
- "ìĹ Ń",
- "í Ĩ",
- "Ġinteract ing",
- "Ġrapp ort",
- "еÑĢ и",
- "s ki",
- "Ġb esser",
- "Ġmanufact urer",
- "ĠK yle",
- "Ġaccount able",
- "ĠS ak",
- "ĠP il",
- "ĠD omin",
- "Ġpres um",
- "ĠÐĴÑģ е",
- "Ġvine gar",
- "Ġguarante ed",
- "çľĭ åĪ°",
- "Ġhand led",
- "éŁ ³",
- "c at",
- "Ġcivil ization",
- "Ġaccom p",
- "ĠV M",
- "é mon",
- "Ġde ze",
- "Ġgrad es",
- "Ġsoll te",
- "Ġst aring",
- "×IJ× ª",
- "ar nt",
- "Ġhoriz on",
- "Ġtrav ail",
- "h our",
- "第 ä¸Ģ",
- "ĠE D",
- "ĠD ak",
- "Ġn y",
- "Ġcon ve",
- "ĠCh am",
- "Ġfir ms",
- "ĠL iu",
- "ĠÑģÑĤ ÑĢан",
- "Ġli bert",
- "Ġlens es",
- "Ġint ake",
- "ĠвÑĭ б",
- "Ġmens en",
- "h el",
- "Ġpract ition",
- "Ġ3 50",
- "ãĤ ³",
- "F O",
- "Ġbed s",
- "Ġancest ors",
- "ĠìĹĦ ì²Ń",
- "Ġdistur b",
- "ĠLast ly",
- "ĠSupp ort",
- "ี à¹ī",
- "ĠCor ona",
- "Ġenthus i",
- "Ġвоз м",
- "ĠìĤ¬ëŀ Įë",
- "Ġ5 2",
- "b ird",
- "Ġredu ces",
- "ĠìŀĪ ìĿĦ",
- "ĠG ene",
- "êµ IJ",
- "ÄĻ p",
- "ĠÃľ ber",
- "Ġconcer ning",
- "us er",
- "Ġconcent rate",
- "ĠWH AT",
- "ish op",
- "onym ous",
- "no ld",
- "Ġsuggest ing",
- "© °",
- "ĠF ish",
- ".... ....",
- "Ġvess el",
- "Ġtrabaj o",
- "ãģ µ",
- "ĠO cean",
- "å§ IJ",
- "y g",
- "Ġtown s",
- "d el",
- "Ġterr ifying",
- "Ġçal Ä±ÅŁ",
- "Ġs ino",
- "Ġe ats",
- "Ġge z",
- "Ġg eme",
- "ĠìĻ Ħ",
- "Ġcomp art",
- "Ġimplement ing",
- "ĠPot ter",
- "ĠGerm ans",
- "Ġg ÅĤ",
- "Ġt ennis",
- "Ġcar pet",
- "au er",
- "ĠSaud i",
- "ye ong",
- "Ġcur ry",
- "ĠFore st",
- "Ñĭ л",
- "Ġfif teen",
- "Ġbol ts",
- "Ġ{ \\",
- "¬ ´",
- "Ġsett lement",
- "Ġl ange",
- "Ġb am",
- "G et",
- "íķ Ļ",
- "Ġsw ap",
- "ĠK han",
- "Ġcomm ence",
- "Ġquar antine",
- "Ġsc ored",
- "ç ĸ",
- "Ġ19 50",
- "Ġthick er",
- "Ġsû r",
- "åı £",
- "ĠLar ry",
- "Ġall ez",
- "ìĭľ ëĬĶ",
- "Ġg ü",
- "Ġspect acular",
- "/ /",
- "b oth",
- "Ġst ats",
- "å¦ ³",
- "ĠN ancy",
- "Ġbun u",
- "Ġcr ust",
- "Ġactiv ated",
- "Ġê·¸ë ŀ",
- "out he",
- "Ġport s",
- "Ġne ural",
- "Ġj aw",
- "Ġobserv ations",
- "Ġvo it",
- "ab an",
- "ả i",
- "¦¬ë ¥¼",
- "om es",
- "௠ĭ",
- "qu i",
- "Ġkind ness",
- "Ð ij",
- "Ġ4 1",
- "Ġmoder ate",
- "Ġang els",
- "ĠT amb",
- "è t",
- "Ġch lor",
- "ĠBill y",
- "ì² ĺë",
- "ac on",
- "Ġselect ing",
- "ĠDel ta",
- "Ġn ull",
- "den ly",
- "Ġci ud",
- "Ġtend ency",
- "Ġbreak down",
- "Ġm int",
- "ÑĦ оÑĢм",
- "or ph",
- "Ġda wn",
- "s pr",
- "ĠW ILL",
- "äch lich",
- "Ġpu ppy",
- "7 00",
- "Ġà® ¤",
- "Ġfail s",
- "ĠCon c",
- "Ġrel atives",
- "Ġinv iting",
- "Ġaut onom",
- "Ġcomp osed",
- "Ġun ity",
- "Ġdec is",
- "Ġaccess ories",
- "ĠC ass",
- "Ġb ist",
- "ĠT ip",
- "ì§ ¸",
- "Ġp unt",
- "Ġr áp",
- "éĢ ²",
- "AN K",
- "ãģ ļ",
- "ex ist",
- "Ġcompat ible",
- "Ġn er",
- "Ġе мÑĥ",
- "Ġa plic",
- "Ġb apt",
- "Ġfail ing",
- "ĠTam am",
- "Ġos cill",
- "Ġletz ten",
- "Ġrepeated ly",
- "Ġjung le",
- "ĠP ush",
- "h ai",
- "ĠÎ ·",
- "Ġdead ly",
- "Ñı ж",
- "wi Äħ",
- "ĠComm on",
- "ĠÎ ķ",
- "Ġsk ate",
- "T C",
- "ĠMin i",
- "Ġhob by",
- "ầ n",
- "Ġrout es",
- "Ġam igos",
- "Ġcon jun",
- "Ġpartners hips",
- "Ġno vo",
- "Ġa ver",
- "Ġpou vez",
- "br idge",
- "Ġpre oc",
- "h im",
- "Ġtur b",
- "Ġso b",
- "ĠSn ap",
- "Ġì° ¸",
- "min ute",
- "Ġtra ject",
- "uj ÄĻ",
- "Ġe ager",
- "Ġregul atory",
- "Ġbank ing",
- "b ling",
- "ÑĪ ÑĮ",
- "a ż",
- "Ġbiz arre",
- "it ated",
- "d ire",
- "Ġthreat ened",
- "Ġsh ining",
- "Ġn esse",
- "Ġcor ps",
- "ĠÑģ Ñĥ",
- "Ġt eles",
- "Ġtem p",
- "t em",
- "Ġк ан",
- "Ġfe ver",
- "N ew",
- "Ġheav ier",
- "ĠS ah",
- "b ud",
- "Ġout ros",
- "Ġì° ¾",
- "Ġëª ħ",
- "arr ing",
- "Ġê´ľ ì°®",
- "ĠN ap",
- "Ġse min",
- "ĠTh an",
- "if s",
- "Ġdes en",
- "ĠÑĤак ое",
- "Ġlos es",
- "ĠB alt",
- "k on",
- "Ġнап ÑĢ",
- "Ġvo is",
- "ĠMosc ow",
- "Ġch airs",
- "h is",
- "Ġrefuge es",
- "k g",
- "Ġk ole",
- "į ¨",
- "аÑģ ибо",
- "¦ ½",
- "ĠUn iverse",
- "ĠDire ct",
- "Ġche ating",
- "ĠC in",
- "Ġpat ri",
- "Ġadv ise",
- "ĠN ether",
- "Ġprime iro",
- "Ġmention ing",
- "n ut",
- "5 6",
- "ar ı",
- "Ġpet ite",
- "b led",
- "Ġpens ar",
- "ic io",
- "IN D",
- "Ġveter an",
- "Ġlad der",
- "Ġconsequ ence",
- "ож ал",
- "ĠB urn",
- "Ġr ug",
- "ĠM ade",
- "Ġg it",
- "\" ...",
- "Ġcompet itors",
- "Ġprz ed",
- "Ġapp arent",
- "ĠArgent ina",
- "ĠWork ing",
- "Ġcollabor ate",
- "w oman",
- "Ġret ain",
- "Ġle urs",
- "Ġdash board",
- "×Ļ× ĵ",
- "ĠEar ly",
- "B M",
- "Ġе Ñij",
- "ол ог",
- "Ġsatisf ying",
- "Ġoft entimes",
- "Ġma pping",
- "ünk ü",
- "ar th",
- "f old",
- "Ġlaunch ing",
- "Ġa ura",
- "Ġprec ision",
- "work s",
- "G od",
- "Ġstra p",
- "ĠIm per",
- "Ġr ivers",
- "Ġ |",
- "Ġcu er",
- "reg on",
- "Ġarri val",
- "ка Ñħ",
- "ĠM iami",
- "ан Ñĭ",
- "Ġsurviv ors",
- "ĠSen ior",
- "Dav id",
- "Ġest ado",
- "Ġse ctors",
- "Ġpop ping",
- "Ġch im",
- "ay ı",
- "Ġkun nen",
- "Ġgall ery",
- "Ġsun light",
- "ese hen",
- "Ġye lling",
- "ĠMe in",
- "ĠPho enix",
- "Ġman o",
- "Ġhistor ia",
- "Ġoccur ring",
- "æ¬ ¸",
- "ì ¸",
- "ад и",
- "å¾ ħ",
- "Ġinstitution al",
- "ĠT ut",
- "ç ²",
- "Ġsl aves",
- "ãģ© ãģĨ",
- "Ġforg iveness",
- "Ġtw in",
- "ĠHy un",
- "н ÑĮ",
- "ĠK omm",
- "and ra",
- "sh ot",
- "ss ä",
- "ĠÑĨ е",
- "at ta",
- "Ġexp ense",
- "ĠG PU",
- "ĠP ast",
- "rib ly",
- "ĠëŃIJ ìķ¼",
- "Ġгод а",
- "Ġresp ir",
- "æĿ ±",
- "ĠQue ens",
- "h ops",
- "Ġs érie",
- "Ġpre f",
- "Ġcom ed",
- "Ġpl ut",
- "ĠOver all",
- "Ġãģ Ŀ",
- "Ġc ush",
- "Ġring ing",
- "Ġincor rect",
- "ĠÑģÑĤ ÑĢ",
- "Ġgeomet ry",
- "Ġadvert is",
- "ĠÐ ¨",
- "Ġreview ed",
- "ãģĤ ãģĤ",
- "Ġdo zens",
- "Ġdeterm ination",
- "ĠPh ill",
- "Ġcontrib uted",
- "ĠC it",
- "Ġpass engers",
- "Ġcôt é",
- "Ġre ver",
- "Ġtechn ological",
- "Ġall en",
- "Ġr aining",
- "av i",
- "Ġsal ty",
- "Ġtyp ing",
- "ĠÑĤ е",
- "Ġt ilt",
- "Ġì¹ ĺ",
- "Ġо ÑĢ",
- "ĠпÑĢ Ñıм",
- "Ġr ou",
- "Ġare na",
- "ar at",
- "åĪ «",
- "HH HH",
- "Ġmanufact urers",
- "ĠEd ward",
- "Ġt uck",
- "Ġbl ows",
- "ing o",
- "ĠMar c",
- "ìķĦ ìĦľ",
- "M ich",
- "ĠCle an",
- "è ´",
- "est o",
- "ĠP ack",
- "Ġsha ft",
- "BRUN O",
- "Ġa ven",
- "u ur",
- "Ñģк олÑĮко",
- "ê´ Ģ",
- "Ġautom ated",
- "Ġvent ure",
- "Ġsurve illance",
- "ĠG row",
- "ĠE mer",
- "Ġд оÑĢ",
- "Ġinvest or",
- "ĠY ok",
- "Ġl atter",
- "ĠN I",
- "Ġfunction ing",
- "ĠHam ilton",
- "Ġ5 1",
- "Ġmurder ed",
- "Ġanch or",
- "Ġc uc",
- "ĠSC P",
- "ĠMad am",
- "Ġconstra ints",
- "Ġb arn",
- "ank en",
- "Ġë§İ ìĿĢ",
- "ĠMot or",
- "ĠDo ing",
- "Ġam en",
- "et ts",
- "Ġinst ructor",
- "eg t",
- "ak o",
- "Ġpost ure",
- "iv ia",
- "ĠPol ish",
- "Ġдв а",
- "Ġcolor ful",
- "Ġel bow",
- "Ġpar le",
- "Ġpass er",
- "Ġcond em",
- "ort al",
- "Ġfert il",
- "ا د",
- "ĠCol omb",
- "Ġalign ment",
- "Ġastron aut",
- "ĠM ut",
- "Ġsal mon",
- "Ġstructure d",
- "ŀ ר",
- "Ġclick s",
- "Ġm iej",
- "æĶ ¿",
- "ãģĦ ãĤĦ",
- "ĠR ound",
- "Ġrain bow",
- "ĠV A",
- "ãģĶ ãģĸ",
- "ì§ Ī",
- "ot z",
- ", ",
- "Ġch ords",
- "ĠSand ers",
- "Ġë¶ Ħë",
- "B en",
- "Ġdar über",
- "ili ans",
- "Ġorder ing",
- "ĠMan h",
- "Ġkil ogram",
- "Ġkar ÅŁ",
- "Ġgr asp",
- "Ġghost s",
- "al en",
- "ĠJ edi",
- "Ġб ли",
- "Ġdownload ed",
- "Ġconduct ing",
- "ĠH ak",
- "Ġresearch er",
- "il an",
- "go od",
- "ĠH annah",
- "ĠdÃ¼ÅŁ ün",
- "ĠMess iah",
- "u ity",
- "ion a",
- "Ġprob able",
- "ĠY E",
- "Ġindepend ently",
- "Ġbuff er",
- "b urn",
- "our d",
- "ĠMc K",
- "Ġl ingu",
- "uj emy",
- "еÑĢ ÑĤ",
- "Ġintuit ive",
- "Ġcrack s",
- "app ropri",
- "nt y",
- "Ġge en",
- "Ġl end",
- "Ġcert ification",
- "ID S",
- "un ter",
- "pe es",
- "Ġtr ump",
- "Ġbank rupt",
- "Ġfe as",
- "è Ĺ",
- "Ġdu ż",
- "æ¸ ħ",
- "Ġvirus es",
- "Ġ5 8",
- "g od",
- "Ġж ел",
- "Ġst alk",
- "I nd",
- "ach i",
- "ĠC F",
- "ĠC ond",
- "Ġsan ct",
- "Ġcont en",
- "Ġfre ed",
- "ĠR T",
- "Ġment ors",
- "ì¡ ±",
- "Ġport able",
- "ĠPaul o",
- "r ane",
- "HA HA",
- "ĠS ection",
- "ç Ĩ",
- "hy un",
- "ĠÎŃ Ïĩ",
- "ĠP ub",
- "ĠInd epend",
- "Ġcomp ounds",
- "ĠÑģ Ñĭ",
- "Ġmess aging",
- "Ġded ication",
- "Ġnot icing",
- "Ġdevot ed",
- "ÑİÑĤ ÑģÑı",
- "Ġsn akes",
- "Ġbattle field",
- "p ers",
- "Ġdel a",
- "9 2",
- "Ġha i",
- "ill ä",
- "ér er",
- "e very",
- "Ġrespons ive",
- "×Ļ ×ķ",
- "op f",
- "é ī",
- "Ĭ ¸",
- "Be cause",
- "Ġtour ism",
- "Ġê·¸ ê²Į",
- "×ķ× ¦",
- "Ġcan s",
- "st üt",
- "Ġdon ne",
- "ĠD ios",
- "ĠU ber",
- "act ory",
- "Ġorient ed",
- "ĠH erm",
- "Ġpat ron",
- "ur f",
- "be i",
- "Ġprogram a",
- "ĠOh h",
- "gen er",
- "Ġf ist",
- "ĠW endy",
- "Ġand a",
- "Ġguess ed",
- "Ġfre ak",
- "ä¸Ń åľĭ",
- "ĠK ings",
- "ch ool",
- "Ġoff line",
- "ĠIndian a",
- "ĠAll iance",
- "Ġ5 3",
- "Ġpartic ul",
- "ĠF ocus",
- "Ġinhab it",
- "Ġê°ĻìĿĢ ëį°",
- "ĠMc G",
- "ows ki",
- "ĠìĿ´ ê±´",
- "Ġpa ÅĦst",
- "он и",
- "itt a",
- "Ġconfirm ation",
- "ĠBrook lyn",
- "Ġnood le",
- "f und",
- "it ud",
- "Ġgrand parents",
- "Ġbar becue",
- "ει ÏĤ",
- "Ġ á",
- "Ġball ot",
- "ĠV eter",
- "Ġpip es",
- "ig ious",
- "ĠG raph",
- "est ed",
- "Ġë¸ Įë",
- "ĠK E",
- "ãģ¡ãĤĩ ãģ£ãģ¨",
- "Ġe ins",
- "Ġhat red",
- "ãģij ãģ©",
- "Ġd ang",
- "ee ee",
- "Ġarch ae",
- "ĠJes se",
- "Ġdetect ed",
- "Ġsen i",
- "burg h",
- "Ġdispl acement",
- "Ġdo p",
- "Ġcondition ing",
- "Ġне ÑģколÑĮко",
- "Ġdistur bing",
- "P H",
- "Ġthin ner",
- "Ġwound ed",
- "ĠCu ando",
- "Ġcush ion",
- "Ġwh ites",
- "Ġprefer ences",
- "Ġì¤Ģë ¹Ħ",
- "Ġka ż",
- "ĠG ate",
- "ĠP ath",
- "d les",
- "à¸Ħ ร",
- "im ore",
- "Ġë³´ìĹ ¬",
- "Ġdiscipl ines",
- "á» ı",
- "Ġmes ma",
- "Ġìĥ Īë",
- "Ġìĭ ¬",
- "Ġg ing",
- "Ġumbre lla",
- "IGH T",
- "Ġp ension",
- "Ġcomb ining",
- "S S",
- "Ġrect angle",
- "á»ĩ t",
- "Ġpro xim",
- "ĠC ow",
- "¸ Į",
- "Ġintention al",
- "æķ Ļ",
- "Ġdec id",
- "ĠÑģк аж",
- "ĠU ma",
- "ias m",
- "b uz",
- "Ġdebr is",
- "Ġc ass",
- "ĠP rop",
- "is ka",
- "ë ł¥",
- "ester ol",
- "uss ian",
- "ìĿ´ë ŀij",
- "Ġun limited",
- "Ġadm ire",
- "Ġtight ly",
- "Ġgen ome",
- "ĠJun ior",
- "ven ir",
- "g us",
- "Ġc Äĥ",
- "ĠV lad",
- "Ġí Ĥ",
- "Ġrel ativ",
- "in ci",
- "Ġaun que",
- "ĠBo ys",
- "ÑĨи он",
- "ĠSw iss",
- "Ġphys icians",
- "Ġíı ī",
- "ĠP ET",
- "Ġw ounds",
- "ab out",
- "Ãł i",
- "on z",
- "ur ities",
- "ĠÑĥв ид",
- "å· ¦",
- "Ġment ality",
- "Ġvari ance",
- "Ġseg unda",
- "Ġvol cano",
- "al ie",
- "ॠĩ",
- "Ġt iles",
- "ĠT erry",
- "ĠاÙĦÙĦ Ùĩ",
- "Ġcan on",
- "Ġsc attered",
- "pt on",
- "Ġdefin itions",
- "Ġal gebra",
- "ot en",
- "ab lo",
- "ij uana",
- "Ġwra pping",
- "Ġses ame",
- "ĠнаÑĩ ина",
- "ĠAl f",
- "ĠÐł оÑģÑģ",
- "or no",
- "Ġan kle",
- "Ġspecial ty",
- "Ġattempt ing",
- "ili ation",
- "Ġ19 20",
- "Ġphen omena",
- "ĠPro duct",
- "ĠB uck",
- "ĠA ww",
- "se en",
- "Ġvo id",
- "ĠFrank lin",
- "Ġadvoc acy",
- "ĠS ep",
- "Ġcool est",
- "ĠÑģ ÑĢазÑĥ",
- "ĠQu and",
- "Ġ9 00",
- "ĠTr ad",
- "d ies",
- "Ġhas h",
- "æĪij å°±",
- "ä¹Ł æĺ¯",
- "Ġpot s",
- "Ġsad ly",
- "Ġvi able",
- "ĠT iger",
- "ĠON E",
- "Ġneur ons",
- "ow anie",
- "Ä Ĺ",
- "ĠSh ar",
- "ĠLand es",
- "Ġconfer ences",
- "è© ²",
- "Ġcred ential",
- "Ġl ime",
- "ine e",
- "x it",
- "p ay",
- "Ġinc ons",
- "Ġ>> :",
- "èª į",
- "Ġí ŀĺë",
- "Ġless er",
- "Ġsp ill",
- "Ġprem ise",
- "Ġ36 5",
- "ĠH ost",
- "Ġtom ar",
- "×IJ× ľ",
- "ë ²Ī",
- "ĠWhat s",
- "Ġlight weight",
- "ĠM ap",
- "f ia",
- "ells chaft",
- "Ġvend ors",
- "uest o",
- "ĠM ister",
- "ĠÐŁ ÑĢи",
- "åı ³",
- "h ma",
- "Ġintention ally",
- "ĠT ang",
- "éĹ ®",
- "Ġident ification",
- "Ġetc etera",
- "ĠN ee",
- "ĠÑĤ ÑĢи",
- "ê· ¸",
- "Ġcrypt ocur",
- "Ġin hale",
- "Ġadd ict",
- "åIJĦ ä½į",
- "Ġma u",
- "ĠÑĤак аÑı",
- "Ġë² Ħ",
- "Ġcomp rar",
- "ied zieÄĩ",
- "ĠоÑĤ но",
- "Ġbegin ner",
- "Ġм Ñĥж",
- "Ġobs c",
- "Ġlim iting",
- "asc ular",
- "Ġins pection",
- "ac i",
- "Ġre jo",
- "M us",
- "Ġz aten",
- "Ġsz cz",
- "ĠMad rid",
- "Ġvar ieties",
- "Ġest Ãł",
- "ĠSh akes",
- "Ġk its",
- "Ġad minister",
- "Ġla va",
- "Ġg Ã¥",
- "è© ¦",
- "ת ×Ļ",
- "ĠWay ne",
- "Ġinst agram",
- "Ġr ated",
- "p aper",
- "Ġb ild",
- "Ġpret ending",
- "Ġobser ving",
- "ĠÑģам ом",
- "Ġtr or",
- "Ġorgan isms",
- "Ġfal ta",
- "Ġh ometown",
- "ç ±",
- "Ġí ĭ",
- "Ġche g",
- "Ġì ¡",
- "Ġcomm a",
- "is é",
- "Ġlike lihood",
- "av ored",
- "Ġgel di",
- "ни ков",
- "Ġmed io",
- "Ġjak ie",
- "ĠJ up",
- "Ġgreen house",
- "Ġsp it",
- "ко е",
- "Ġк аж",
- "ĠG ram",
- "ĠCon ference",
- "Ġdef icit",
- "s ın",
- "in se",
- "u ÄŁ",
- "Ġr icht",
- "Ġcoinc idence",
- "åı į",
- "Ġeu rop",
- "Ġbutter fly",
- "p read",
- "Ġìĸ ¼",
- "èĢ ¶",
- "Ġwa vel",
- "ĠIn fin",
- "ĠPlan et",
- "Ġself ie",
- "ient ras",
- "Ġar rog",
- "os er",
- "id al",
- "ł×Š׳×ķ",
- "üt ün",
- "Ġfresh man",
- "ĠMach ine",
- "Ïĥ ÏĦ",
- "ĠD ia",
- "ìĿ´ ëĭ¤",
- "ãģĵ ãģĨ",
- "ne a",
- "Ġlist ing",
- "Ġconfig ure",
- "ut or",
- "U p",
- "ts chaft",
- "ri ère",
- "Ġup wards",
- "ĠÑħоÑĩ Ñĥ",
- "Ġswe ep",
- "B r",
- "Ġexpress ing",
- "Ġun happy",
- "Ġmand atory",
- "g ender",
- "ĠA ÃŃ",
- "Ġindic ators",
- "Ġoil s",
- "n ote",
- "Ġseg ur",
- "ож еÑĤ",
- "yn asty",
- "Ġdist ances",
- "Ġmer ge",
- "BER T",
- "Ġsur render",
- "Ġbu at",
- "ĠA wards",
- "Ġseñ or",
- "od ox",
- "Ġfl avour",
- "Ġab dom",
- "Ġconfig ur",
- "8 6",
- "ĠDI Y",
- "Ġrig id",
- "° ĺ",
- "Ġcorpor ation",
- "Ġg room",
- "j aw",
- "ĠNe ar",
- "ил о",
- "Ġoper a",
- "ĠIn nov",
- "и ÑĢа",
- "ĵ ±",
- "Ġspec ified",
- "Ġcos m",
- "ĠFre edom",
- "Ġcl own",
- "ĠN em",
- "Ġв ол",
- "Ñij н",
- "Ġchar ger",
- "à¹ģ ล",
- "Ġinflu ential",
- "äs ident",
- "é ¤",
- "ĠìĦ łë",
- "Ġvol umes",
- "æ IJ",
- "Ġout ras",
- "ĠTw itch",
- "Ġfound ing",
- "Ġa while",
- "Ġco il",
- "ê° Ļ",
- "Ġc ả",
- "ĠTh row",
- "ĠH ence",
- "omm t",
- "ĠBen jamin",
- "глÑı д",
- "T ime",
- "ob ic",
- "Ġm our",
- "Ġd read",
- "ĠL Ãł",
- "ĠCh ile",
- "Ġpre val",
- "Ġv ain",
- "Ġart ık",
- "Ġpres erved",
- "ĠоÑĤ д",
- "Ġware house",
- "Ġbest e",
- "ĠSever al",
- "ĠS ituation",
- "Ġcard board",
- "T od",
- "er na",
- "Ġgar ant",
- "Ġgest ure",
- "Ġh en",
- "Ġspe lling",
- "ose xual",
- "Ġan ne",
- "Ġm ice",
- "ĠMe ine",
- "c ard",
- "Ġre bell",
- "Ġcert o",
- "Ġìľ łë",
- "Ġvers chied",
- "ĠB os",
- "Ġinv ention",
- "Ġtr ze",
- "Ġman ière",
- "ĠCh ad",
- "Ġsp re",
- "Ġorganis ations",
- "Ġpoor ly",
- "Ġan terior",
- "Ġst air",
- "к ÑĢ",
- "Ġatom ic",
- "Ġsymp ath",
- "Ġcontin ually",
- "Ġkle ine",
- "è te",
- "и Ñī",
- "ο ÏĤ",
- "pe ut",
- "Ġrep osit",
- "Ġent ra",
- "E m",
- "Ġfinan cing",
- "Ġмн ог",
- "Ġthe sis",
- "ĠCom puter",
- "e au",
- "ĠT ree",
- "Ġbr ide",
- "ons ieur",
- "sh ire",
- "w ic",
- "D E",
- "ĠìĪ ĺë",
- "Ġac om",
- "ĠP O",
- "ers ch",
- "Ġпом оÑī",
- "ĠAr men",
- "Ġì£ ½",
- "Ġz or",
- "Ġprint s",
- "ĠD ass",
- "æ¸ ¯",
- "Ġdur able",
- "ĠTrans port",
- "ìŀIJ ê°Ģ",
- "Ġл ег",
- "Ġdé t",
- "ô le",
- "am ous",
- "Y N",
- "Ġcl iff",
- "Ġgramm ar",
- "ĠÐŁÐ¾ ÑįÑĤомÑĥ",
- "ĠlÃł m",
- "es ch",
- "Ġmiser able",
- "Ġvol ts",
- "ĠC ad",
- "uk an",
- "ÑĤ ив",
- "r ust",
- "Ġìĺ¬ë Ŀ¼",
- "Ġver k",
- "Ġchick ens",
- "ĠY oo",
- "Ġout fits",
- "c ode",
- "Ġhier archy",
- "net es",
- "Ġcounter part",
- "Ġt ôi",
- "Ġt ed",
- "ĠB art",
- "Ġë Ŀ¼",
- "ĠGen au",
- "Ġinc oming",
- "ĠA BC",
- "ri que",
- "ĠоÑĤ п",
- "qu al",
- "Ġincent ive",
- "Ġih ren",
- "׳ ×Ļ",
- "lo e",
- "Ġ19 30",
- "Ġbar g",
- "Ġd iction",
- "Ġön ce",
- "IN S",
- "Ġre h",
- "isia j",
- "m outh",
- "Ġsc oring",
- "l ık",
- "ĠìķĦ 주",
- "OR IA",
- "ĠEst ados",
- "Ġcompan ion",
- "Ġasse mble",
- "Ġpun ished",
- "Ġit al",
- "Ġprev ents",
- "ist es",
- "ĠKent ucky",
- "Ġloc ate",
- "Ġfast ing",
- "ãģ¨ æĢĿ",
- "ĥ Ģ",
- "ĠSe b",
- "ĠCr own",
- "op ia",
- "Ġwh ip",
- "us z",
- "к ами",
- "Ġdatab ases",
- "åŃ Ĺ",
- "Ġprose c",
- "Ġ199 7",
- "ĠìĤ´ì §Ŀ",
- "ĠSol ar",
- "ĠP ues",
- "ĠZ en",
- "oll o",
- "ĠG uru",
- "Ġsque ez",
- "ĠÐĹ Ð°",
- "ĠÄ į",
- "cept ions",
- "c ca",
- "iz able",
- "m and",
- "Ġbreak through",
- "Ġtables poon",
- "ĠS EC",
- "ik h",
- "ĠS ão",
- "Ġп ло",
- "am en",
- "Ġpr ac",
- "Ġdar ling",
- "Ġtall er",
- "Ġrend ering",
- "Ġìļ°ë¦¬ ê°Ģ",
- "ĠÏĦη ÏĤ",
- "Ġm ã",
- "Ġes os",
- "uer do",
- "ĠÑģ ÑĩиÑĤ",
- "all er",
- "ìĹĪ ìĸ´ìļĶ",
- "Ġmill ones",
- "ler in",
- "Ġpe gar",
- "on ne",
- "Ġenroll ment",
- "Ġli egt",
- "Ġbo a",
- "w iÄĻ",
- "bs p",
- "Ġcy cling",
- "ĠBern ie",
- "Ġ198 9",
- "Ġд алÑĮ",
- "ĠDak ota",
- "ĠÑģв Ñıз",
- "ĠC P",
- "Ġst are",
- "íĤ ¤",
- "Ġprosper ity",
- "Ġarrange ments",
- "Ġarri ving",
- "m ä",
- "Ġkay ak",
- "ip t",
- "Ġp ardon",
- "Ġrel at",
- "Ġver ste",
- "ĠF ig",
- "Ġfo il",
- "ĠTalk ing",
- "pe are",
- "Ġno i",
- "ĠпÑĢи ÑĪ",
- "Ġhoc key",
- "Ġad o",
- "ĠO UT",
- "6 7",
- "Ġhorm ones",
- "ĠAven ue",
- "ĠSuper man",
- "Ġpres cription",
- "uber netes",
- "C L",
- "ot ive",
- "N IS",
- "ien en",
- "Ġsad ness",
- "ĠV it",
- "T y",
- "Ġstar ter",
- "Ġbed e",
- "Ġfound ations",
- "Ġso re",
- "åº Ĺ",
- "Ñīе ÑģÑĤв",
- "ìļ °ë",
- "ĠÑĩ Ñĥв",
- "l ink",
- "Ġmane u",
- "work ing",
- "Ãł n",
- "ĠAtt ack",
- "ĠC art",
- "ve is",
- "ĠRes p",
- "ens ing",
- "Ġì¢ĭ ìķĦìļĶ",
- "Ġesc uch",
- "ĠR NA",
- "Ĥ ´",
- "Ġad op",
- "Ġb ending",
- "ع د",
- "Ġman ages",
- "us p",
- "Ġt art",
- "Ġrout er",
- "B o",
- "Ġestab lishing",
- "Ġbal ancing",
- "Ġathlet ic",
- "ĠS lo",
- "Ġf ills",
- "Ġн аб",
- "Ġд ал",
- "Ġpos so",
- "ĠV ielen",
- "Ġcrit ics",
- "Ġlaws uit",
- "ĠIsa ac",
- "ĠÑĦилÑĮ м",
- "Ġtr as",
- "Ġpra w",
- "ĠCra zy",
- "Ġne u",
- "Ġk ull",
- "Ġtum or",
- "ĠAP P",
- "g ate",
- "ĠA RE",
- "9 8",
- "ĠSte am",
- "Ġfuck ed",
- "l age",
- "ĠâĻ ¬",
- "ĠM D",
- "f y",
- "Ġshell s",
- "ĠSe ems",
- "iz ers",
- "Ġr anges",
- "ĠAnton io",
- "AT ION",
- "ĠB aba",
- "Ġìĥ ī",
- "k un",
- "Ġpray ed",
- "ÑĢ Ñı",
- "ĠпÑĢоÑĤ ив",
- "Ġse as",
- "b ury",
- "Ġ×Ķ× ©",
- "Ġtra it",
- "ĠDep ending",
- "Ġd re",
- "Ġkön nt",
- "ÑĨ Ñĥ",
- "Ġlip stick",
- "ee z",
- "ĠпÑĢ имеÑĢ",
- "Ġassign ments",
- "B ob",
- "Ġmet als",
- "Ġspe cially",
- "å°į ä¸įå°į",
- "Ġìĺ Īë",
- "ĠÅ ¡",
- "Ġv ista",
- "ĠÎ ¬",
- "Ġtw ins",
- "Ġnot able",
- "ĠS au",
- "Ġdé velop",
- "Ġç ek",
- "Ġpoly nom",
- "av am",
- "Ġtamb é",
- "он ом",
- "Ġpl asma",
- "Ġe fect",
- "Ġlä ng",
- "Ġcas i",
- "Ñģ а",
- "ım ı",
- "ãģĻ ãĤĭ",
- "ĵ¤ ìĿĢ",
- "Ġlab our",
- "oss en",
- "ĠP un",
- "r if",
- "Ġd oses",
- "Ġoper ates",
- "ил ли",
- "Ġja ar",
- "st aw",
- "ĠìĤ¬ëŀ ij",
- "Ġat m",
- "Ġprotect s",
- "Ġimp ed",
- "H O",
- "Ġc ima",
- "Ġto ch",
- "ab is",
- "Ġsend o",
- "la us",
- "Ġcur l",
- "ĠN um",
- "Ġspons ors",
- "Ġdé but",
- "ĠAlex a",
- "ĠB ür",
- "ĠA mer",
- "Ġc ope",
- "Ġиз в",
- "j al",
- "Ġ199 5",
- "ap at",
- "res se",
- "ĠPri ze",
- "ĠCla ire",
- "ĠBrand on",
- "Ġwszyst ko",
- "Ġval ued",
- "à¸Ļ ะ",
- "Ġse ct",
- "Ġsecret ly",
- "Ġdiam onds",
- "ĠEv an",
- "ĠRP G",
- "ãģ« ãģª",
- "Īë ıĦ",
- "ĠUnivers al",
- "Ġdoub ts",
- "ĠP in",
- "wiÄħ z",
- "ļ ©",
- "Ġal bo",
- "Ġbra ucht",
- "AU L",
- "ĠM obile",
- "gr ades",
- "Ġsch em",
- "wh y",
- "ĠN icht",
- "p i",
- "g le",
- "Ġchor us",
- "Ġg ly",
- "Ġrein force",
- "Ġm uff",
- "ĠSh en",
- "ĠH ola",
- "Ñĥ г",
- "vid emment",
- "v ial",
- "ac ious",
- "laim ed",
- "ĠR ico",
- "Ġve gg",
- "Ġillust ration",
- "ĠBut ter",
- "ow ad",
- "Ġeu x",
- "Ġenf ants",
- "ĠLe ader",
- "ĠVill age",
- "et ically",
- "ÙĨ ÙĬ",
- "Ġst ew",
- "Ġsurpr ises",
- "Ġc ue",
- "ĠGrand ma",
- "ĠC elsius",
- "ĠR icht",
- "en c",
- "Ġpet ition",
- "Ġher b",
- "Ġw icked",
- "Ġsch le",
- "oc aly",
- "Ġtrans f",
- "Ġtok ens",
- "ĠGr ay",
- "ĠB BC",
- "I K",
- "Ġ15 00",
- "z n",
- "ĠNe v",
- "Ġk oy",
- "Ġz ar",
- "Ġbull shit",
- "ĠColomb ia",
- "ul ative",
- "Ġwides pread",
- "y ect",
- "k it",
- "Ġempres a",
- "Ġn our",
- "Ġburn s",
- "at in",
- "a ired",
- "Ġrevolution ary",
- "Ġгод Ñĥ",
- "ĠLog an",
- "Ġ199 6",
- "ĠGra ham",
- "re b",
- "ĠN HS",
- "æľ Ľ",
- "Ġcost umes",
- "Ġnaw et",
- "Ġlo vers",
- "ĠLuc y",
- "ĠInd igenous",
- "íķĺ 기",
- "Ġimmun ity",
- "¥ ´ë",
- "uit o",
- "Ġexcess ive",
- "Ġdon ations",
- "Ġ×Ķ ר",
- "Ġì² «",
- "éī Ħ",
- "Ġdry ing",
- "mel on",
- "Ġsurve ys",
- "Ġ무ì Ĭ¨",
- "é¢ ¨",
- "aa a",
- "Ġpro be",
- "an cial",
- "Ġlou der",
- "Ġhot els",
- "ü ÄŁ",
- "ag ner",
- "Ġorig ins",
- "Ġë§Ī ì§Ģë§ī",
- "Ġ* *",
- "Ġstr angers",
- "ĠHa us",
- "com ed",
- "Ġan throp",
- "Ġus o",
- "ĠìķĦ ì§ģ",
- "ĠY uan",
- "ĠíķĦ ìļĶ",
- "pl er",
- "ress ive",
- "Ġsp raw",
- "ĠSt ew",
- "Ġ199 4",
- "Ġeld ers",
- "Ġme inen",
- "Ġj unt",
- "Ġac oust",
- "ĠW ohn",
- "Ġban anas",
- "Ġproject ion",
- "ĠSt ick",
- "leg t",
- "spe ed",
- "ĠcÅ ©ng",
- "ĠW ort",
- "ĠBalt imore",
- "ĠÑĨ ел",
- "Ġdun no",
- "å¼ ·",
- "? ,",
- "ãĥī ãĥ³",
- "ĠLoc al",
- "ost o",
- "Ð Ń",
- "од а",
- "ĠPort uguese",
- "Ġtheir s",
- "Ġdé m",
- "åı ¦",
- "Ġdra uf",
- "ĠBuddh ist",
- "ert a",
- "G e",
- "Ġcar rot",
- "ĠWonder ful",
- "Ġso ak",
- "Ġchair man",
- "gg i",
- "IC A",
- "f ried",
- "Ġfl ick",
- "ĠThrough out",
- "Ġìļ °ë",
- "Ġc ough",
- "Ġfl uffy",
- "sch ool",
- "Ġr ipped",
- "---- ----",
- "ĠZuk unft",
- "Ġн еб",
- "Ġst o",
- "ĠB O",
- "p ent",
- "ĠLaw rence",
- "Ïī ÏĤ",
- "st icks",
- "ĠE ins",
- "ĠÑĢ Ñĭ",
- "ĠStr ong",
- "Ġcar amel",
- "Ġsp ite",
- "az ar",
- "éĥ½ æĺ¯",
- "Ġcrit ically",
- "Ġob ra",
- "ow itz",
- "ĠZ one",
- "ĠÑĢ ек",
- "Ġsu g",
- "ard ed",
- "Ġg ì",
- "ff entlich",
- "an che",
- "Ø Ł",
- "ast ically",
- "ìĿ ¼ë",
- "л ав",
- "Ġsimpl est",
- "ĠF riend",
- "Ġque llo",
- "Ġamb ition",
- "Ġabb iamo",
- "åº ķ",
- "ĠÑĦ оÑĢм",
- "ĠEs sa",
- "Ġeduc ators",
- "Ġstatist ical",
- "éĢĻ éĤĬ",
- "Ġchang er",
- "Ġat au",
- "éta is",
- "ĠShakes peare",
- "ë IJĺ",
- "Ġtr iggers",
- "Ġreal iz",
- "Ġcel ui",
- "whe el",
- "Ġloyal ty",
- "Ġscream s",
- "ke hr",
- "ĠM ega",
- "e ast",
- "Ġtop s",
- "ĠTot ally",
- "ount ain",
- "l ord",
- "Ġviol ation",
- "ĠG A",
- "Ġnic er",
- "ĠF resh",
- "ĠMel issa",
- "fun ction",
- "Ġra pe",
- "Ġexcept ions",
- "Ġsil icon",
- "Ġliber ty",
- "Ġhousehold s",
- "ãģį ãģ¾ãģĻ",
- "ĠC A",
- "ĠÐŀ б",
- "Ġli b",
- "ŀ Į",
- "c ific",
- "Ġtrop ical",
- "Ġinvestig ating",
- "H D",
- "Ġad apter",
- "ĠP itt",
- "an cia",
- "ĠShe ll",
- "friend ly",
- "Ġconclus ions",
- "Ġtur tle",
- "Ġdec omp",
- "Ġanim ations",
- "ĠÑģ ек",
- "ins i",
- "Ġret ention",
- "k ie",
- "Ġinject ion",
- "ĠMad ison",
- "ì° °",
- "Ġv ient",
- "Ġvar ied",
- "Ġviol in",
- "ĠB il",
- "Ġluck ily",
- "Ġh tt",
- "l ä",
- "Ġr anch",
- "çľĭ çľĭ",
- "Ġsó lo",
- "ìķ ħ",
- "ĠD erek",
- "ĠScript ure",
- "оÑĢ а",
- "Ġclassroom s",
- "av il",
- "form ed",
- "Ġbefore hand",
- "ĠG em",
- "pre ch",
- "Ġl in",
- "Ġgre ens",
- "ÑĨ ев",
- "ĠMer cedes",
- "Ġdr ought",
- "gas ps",
- "Ġab ortion",
- "Ġter ribly",
- "Ġspos ób",
- "Ġsec ured",
- "Ġat rás",
- "Ġwavel ength",
- "Ġgra ins",
- "ect ive",
- "Ġspace craft",
- "Ġtour s",
- "Ġprof es",
- "Ġsur geon",
- "ĠP ie",
- "Ġide ally",
- "arn er",
- "U P",
- "op ard",
- "s ce",
- "Ġimm ense",
- "ĠOr t",
- "roll er",
- "ĠD allas",
- "ĠNich olas",
- "Ġs ulf",
- "ĠToy ota",
- "Ġquant ities",
- "ce ans",
- "Ġcu i",
- "an ça",
- "ĠC AN",
- "itzer land",
- "åĦ ¿",
- "Ġz ou",
- "ĠCy ber",
- "le gen",
- "ĠIn it",
- "ed u",
- "Ġa pert",
- "Ġad jac",
- "ou v",
- "èĢĮ ä¸Ķ",
- "r s",
- "Ġcab bage",
- "Ġwheel chair",
- "iny l",
- "ĠD ynam",
- "ĠìķĦëĭĪë Ŀ¼",
- "Ġl ing",
- "h l",
- "Ġмог Ñĥ",
- "Ġcris p",
- "Ġm ij",
- "Ġd ug",
- "n in",
- "Ġbl oss",
- "Ġbelong ing",
- "Ġloud ly",
- "Ġminer als",
- "Ġconclud ed",
- "Ġsearch ed",
- "9 6",
- "ĠMe et",
- "ĠS EO",
- "ĠС к",
- "ĠH ob",
- "ot ta",
- "Ġpropag anda",
- "Ġcin namon",
- "Ġhun ter",
- "Ġgeme ins",
- "Ġsculpt ure",
- "uls ion",
- "Ġv äl",
- "Ġmagaz ines",
- "Ġcontrovers y",
- "ä¸Ģ 樣",
- "Ġsequ ences",
- "ãģĦ ãĤĭ",
- "Ġíļ Į",
- "Ġdel eted",
- "ä½ ¿",
- "IJë ıĦ",
- "Ġvary ing",
- "ãĥ Ĩ",
- "Ġmount ing",
- "Ġaff air",
- "Ġpath ways",
- "æ ¦",
- "Ġdig o",
- "äº ®",
- "Ġд ок",
- "A lex",
- "Ġtob acco",
- "ĠC V",
- "Ġbother ed",
- "Ġamb ient",
- "ink y",
- "ĠS L",
- "Ġh ates",
- "Ġje żeli",
- "Ġcon greg",
- "Ġel as",
- "Ġde uts",
- "ĠStud ios",
- "ch ÄĻ",
- "Ġdocument ed",
- "ĠCru z",
- "ĠL en",
- "ĠDoug las",
- "ĠPort ugal",
- "ent i",
- "Ġsp ouse",
- "Ġanal ys",
- "av ia",
- "Ġed ited",
- "Ġl ại",
- "bu ilt",
- "Ġv ille",
- "ad ora",
- "Ġbrac elet",
- "Ġs ushi",
- "Ġp m",
- "Ġtra ils",
- "Ġl ug",
- "Ġö ver",
- "Ġs orrow",
- "Ġcol ony",
- "ado x",
- "Ġser ie",
- "any ak",
- "ĠØ ·",
- "ĠG ulf",
- "æĺ¯ ä¸įæĺ¯",
- "ĠP V",
- "ĠSam uel",
- "ĠK it",
- "ĠR al",
- "ont in",
- "ex pl",
- "Ġent ries",
- "Ġactiv ists",
- "P s",
- "Ġs ant",
- "ĠÑĤо Ñĩ",
- "ĠBr uno",
- "ke ley",
- "Ġtut to",
- "é Ķ",
- "Ġv intage",
- "Ġterr ified",
- "Ġпо Ñħ",
- "us ive",
- "ow ers",
- "ай ÑĤ",
- "ë ıĻ",
- "Ġtwist ed",
- "ĠTh ought",
- "Ġt ah",
- "Ġshr ink",
- "Ġshe er",
- "l it",
- "Ġdal am",
- "Ġd ib",
- "Ġv ard",
- "ow ane",
- "Ġdo br",
- "ĠR ena",
- "ĠÑģво Ñİ",
- "ĠpaÃŃs es",
- "ĠE ra",
- "ãģ® ãģ§",
- "ĠB UT",
- "s ighs",
- "Ġê·¸ ê±°",
- "Ġgro ÃŁen",
- "Ġë¹ ¨ë¦¬",
- "Ġn erves",
- "Ġconst it",
- "Ġpreoc up",
- "ĠG ay",
- "ĠX u",
- "keep er",
- "he ure",
- ".. )",
- "ĠCal m",
- "ĠUn idos",
- "ĠìĿ´ ê²ĥ",
- "ĠAqu i",
- "Ġìłľ ìĿ¼",
- "d ır",
- "ì¦ ĺ",
- "y our",
- "ĠÑįÑĤ им",
- "20 20",
- "Ġr und",
- "ĠH O",
- "ĠC atherine",
- "iel i",
- "Ġf usion",
- "Ġide ology",
- "Ġfor am",
- "sh aped",
- "ĠíĽ Ħë",
- "Ġw t",
- "Ġret r",
- "Ġpr éc",
- "Ġê° ij",
- "Ġopen ly",
- "v ity",
- "구 ìļĶ",
- "Ġobst acle",
- "Ġbo o",
- "Ġse iner",
- "ic orn",
- "Ġeigen lijk",
- "Ġhead er",
- "are mos",
- "Ġso fter",
- "ĠÐŁ од",
- "Ġpre jud",
- "Ġdefin es",
- "ier te",
- "Ġbl ending",
- "Ġbelie vers",
- "ĠWo chen",
- "Ġник ак",
- "ĠÐļ огда",
- "ĠTyp ically",
- "Ġíģ ¬",
- "ç® ¡",
- "ci os",
- "Ġmiss iles",
- "Ġsp onge",
- "ĠK itchen",
- "Ġt ren",
- "ning en",
- "Ġsc rap",
- "Ġser ait",
- "´ì ł",
- "ç ¹",
- "Ġë° ĺë",
- "Ġrest ored",
- "Ġprzy kÅĤad",
- "ĠK ubernetes",
- "Ġsa it",
- "Ġu w",
- "Ġen abling",
- "Ġtra vers",
- "amp s",
- "åı Ĺ",
- "ĠOM G",
- "ens or",
- "Ġz osta",
- "Ġpronoun ced",
- "A ng",
- "norm al",
- "Ġeconom ies",
- "t in",
- "ĠChamp ion",
- "iz en",
- "Ġar beiten",
- "ĠG ospel",
- "ĠZ u",
- "ng a",
- "Ġliter acy",
- "ĠM ans",
- "Ġcircul ation",
- "Ġad ap",
- "ĠTot al",
- "Ġmere ka",
- "Ġol acak",
- "ÑģÑĤ аÑĤи",
- "J ack",
- "Ġm und",
- "Ġth ief",
- "b ies",
- "Ġê² ģ",
- "a que",
- "ĠÚ© ÛĮ",
- "ĠSc ar",
- "å ²",
- "Ġab ol",
- "Ġdev ote",
- "Ġ0 1",
- "Ġs itten",
- "ĠVis ual",
- "we ek",
- "s ome",
- "ing t",
- "Ġjournal ism",
- "ĠH ir",
- "ĠB achelor",
- "in ery",
- "Ãľ ND",
- "ãĥ Ł",
- "ç» Ļ",
- "Ġcolor ing",
- "ĠCr ist",
- "Ġcelebr ities",
- "ĠÑĩ иÑģ",
- "ĠC rit",
- "Ġdifferent iate",
- "ĠÐľ не",
- "el im",
- "Ġse afood",
- "Ġalgum as",
- "otherap y",
- "æĪ °",
- "Ġgla ub",
- "Ġarbitr ary",
- "g ens",
- "ĠбÑĥд ем",
- "Ġt av",
- "Ġcream y",
- "ĠCount ry",
- "a ñ",
- "м еÑĤ",
- "Ġh inter",
- "Ġm ism",
- "Ġillust rate",
- "ÃľND NIS",
- "Ġdecre asing",
- "Ġwen iger",
- "AK I",
- "ix on",
- "Ġн ей",
- "Ġfat to",
- "Ġn erd",
- "ç ł",
- "Ġb itte",
- "P er",
- "Ġt ane",
- "Ġgö z",
- "Ġfor te",
- "ĠE y",
- "Ġнав еÑĢ",
- "è¢ «",
- "ĠWord Press",
- "ĠM is",
- "Å ¯",
- "z äh",
- "Ġinté ress",
- "osa urs",
- "ĠFall s",
- "Ġn essa",
- "9 7",
- "Ġmuseum s",
- "Ġcorrespond s",
- "Ġs ings",
- "f our",
- "Ġed er",
- "ĠCommun ist",
- "o a",
- "ne k",
- "ĠWH O",
- "Ġcor po",
- "Ġmess ing",
- "ÏĦ αι",
- "Ġbrush es",
- "Ġb isc",
- "ĠAr beits",
- "ĠT ax",
- "Ġse le",
- "Ġflag s",
- "ou pe",
- "Ġanticip ated",
- "ãĥ ij",
- "ĠN ad",
- "Ġpou red",
- "Ġm l",
- "Ġll ama",
- "Ġvisual ize",
- "Ġlisten ers",
- "ÙĦ Ùĥ",
- "al ten",
- "Mich ael",
- "Ġcos ì",
- "Õ¡ Õ",
- "op us",
- "Ġíķ´ì £¼",
- "Ġh ike",
- "ĠAtt orney",
- "ĠHill ary",
- "ud ed",
- "Ġíķĺ ì§Ģë§Į",
- "Ġdo ve",
- "Ġstorm s",
- "ак Ñģ",
- "Ġdoct rine",
- "Ġhe x",
- "ik s",
- "no ÅĽÄĩ",
- "Ġscript s",
- "Ġδ εν",
- "ĠÑįÑĤи Ñħ",
- "ĠÐ Ĩ",
- "ab er",
- "ĠV as",
- "Ġcent imeters",
- "×ŀ ×Ķ",
- "ни б",
- "Ġrid ers",
- "ĠT rib",
- "åĮ ħ",
- "Ġtak że",
- "Ġn oun",
- "Ġic ons",
- "Ġsole ly",
- "mind ed",
- "Ġdisp on",
- "ĠSw itzerland",
- "Ġcl usters",
- "Ġqu eda",
- "ail ing",
- "Ġman ga",
- "Ġ6 8",
- "Ħ Ī",
- "Ġt et",
- "g ins",
- "ha us",
- "ç© º",
- "å· ¥",
- "ĠO P",
- "ot ed",
- "Ġnouve au",
- "AL LY",
- "ÙĪ د",
- "ò n",
- "Ġmort ality",
- "ĠGit Hub",
- "d rop",
- "Ġdis gu",
- "Ġrec om",
- "Ġloc als",
- "Ġhome made",
- "amb a",
- "Ġpron unciation",
- "Ġal phabet",
- "ан ÑĮ",
- "ow any",
- "ir as",
- "id ency",
- "OM E",
- "ĠÑĢаÑģ Ñģ",
- "ar ak",
- "v iamente",
- "Ġnon profit",
- "ĠYouT uber",
- "Ġp arenth",
- "ĠB oo",
- "v at",
- "ĠSt ir",
- "Ġpre cip",
- "Ġan ts",
- "Ġall y",
- "ĠMa ori",
- "ĠëĮĢ íķľ",
- "åı¯ æĺ¯",
- "og ene",
- "ĠLab our",
- "aret te",
- "Ġrecy cling",
- "ens a",
- "Ġpurs uit",
- "Ġs ak",
- "ĠÐĹд еÑģÑĮ",
- "Ġtoler ance",
- "Ġsa at",
- "Ġclick ed",
- "âĻ ¥",
- "Ġface book",
- "ĠInt o",
- "Ġincent ives",
- "기 ëĬĶ",
- "ĠD ennis",
- "ĠW ik",
- "ges ch",
- "à¹ĢภĽ",
- "ĠÏĢ α",
- "ĠWh oo",
- "Ġround ed",
- "Ġdo pe",
- "Ġcapt uring",
- "ĠWar ri",
- "Ġcivil ian",
- "Ġchar ming",
- "Ġes as",
- "Ġsust ained",
- "Ġle aning",
- "Ġabund ance",
- "ÃŃ lia",
- "алÑĮ нÑĭй",
- "Ġph ải",
- "ac ja",
- "Ġê°Ļ ìķĦ",
- "act iv",
- "า ย",
- "Ġ9 7",
- "Ġм ой",
- "c ro",
- "ĠJack ie",
- "itt ees",
- "br acht",
- "ul ent",
- "Ġìł ľë",
- "Ġplug in",
- "v antage",
- "part y",
- "Ġsu as",
- "Ġan te",
- "Ñĥ л",
- "ÐĿ ÐIJ",
- "æĤ ¨",
- "ĠÏĥ Ïħ",
- "Ġmet h",
- "Ġenthus iasm",
- "ÑıÑĤ ÑģÑı",
- "íĻ Ķë",
- "Ġsynth etic",
- "Ġseason ing",
- "ĠL ost",
- "on omy",
- "ĠSp ark",
- "Ġb ure",
- "Ġass ured",
- "Ġimag in",
- "Ġcar ro",
- "S ha",
- "Äħ t",
- "нÑĥ ÑĤÑĮ",
- "át ica",
- "T Y",
- "Ġk ern",
- "ĠBrazil ian",
- "Ã °",
- "Ġsusp ended",
- "ĠCar ib",
- "Ġbiz im",
- "ĠOl iver",
- "ãģ ¶",
- "T om",
- "Ġпл ан",
- "Ġn ope",
- "omet hing",
- "Ġbe iden",
- "ÑĨ ен",
- "Ġflu ct",
- "Ġμ οÏħ",
- "Ġf athers",
- "ĠBl ake",
- "Ġup ward",
- "ĠD ash",
- "ĠL il",
- "ĠìĪ ĺëıĦ",
- "Ġrevel ation",
- "Ġelev ated",
- "ĠJi ang",
- "LE D",
- "ĠThom pson",
- "Ġмог ÑĥÑĤ",
- "ÑģÑĤ ÑĢÑĥ",
- "if iers",
- "Ġcome back",
- "Ġbuy ers",
- "ê² °",
- "ĠS ales",
- "иÑĩ е",
- "c iones",
- "Ġwh istle",
- "Ġd ull",
- "LE X",
- "Ġíķĺ ê²łìĬµëĭĪëĭ¤",
- "Ġcrimin als",
- "Ġdes cent",
- "ipp le",
- "mas ı",
- "Ġfool ish",
- "ĠдÑĥм аÑİ",
- "t ar",
- "Ġman go",
- "Ġchore ography",
- "M att",
- "Ġterr itor",
- "Ġac aba",
- "ĠEin stein",
- "ĠI BM",
- "ĠMet al",
- "ĠCry stal",
- "Ġr ah",
- "Ġf oul",
- "ĠIsland s",
- "Ġint act",
- "ĠR ail",
- ". :",
- "Ġac á",
- "ĠпÑĢ оп",
- "еÑĢ е",
- "ĠWr ite",
- "he he",
- "ĠF O",
- "ĠÏĥ ÏĦη",
- "Ġdo in",
- "h eld",
- "Ġappropri ately",
- "Ġdeliber ately",
- "Ġarch ive",
- "Ġgive away",
- "ãģĵ ãģĵ",
- "Ġfin ale",
- "л аÑģ",
- "ен о",
- "Æ¡ n",
- "æ£ Ĵ",
- "og o",
- "çī ©",
- "ĠAud ience",
- "ãħ ł",
- "Ġsub ur",
- "Ġhead ache",
- "ан нÑı",
- "ĠW itch",
- "ĠSwed ish",
- "ĠB I",
- "Ġer ase",
- "Ġk hi",
- "Ġcomment ary",
- "ĠS ultan",
- "íĥ Ŀ",
- "ĠLe ban",
- "Ġë³´ì ĭ",
- "ĠP am",
- "pe kt",
- "mon th",
- "Ġground ed",
- "ê ¾",
- "ĠÅŁek ilde",
- "2 50",
- "ĠS CH",
- "ios o",
- "Ġin aug",
- "he imer",
- "Ġreflect ing",
- "ĠR uth",
- "ĠO il",
- "Ġtrou ver",
- "u ep",
- ".. ]",
- "Ġìŀ Īë",
- "Ġol ha",
- "Ġreason ably",
- "Ġgl itch",
- "U B",
- "ĠGr an",
- "Ġad alah",
- "Ġl ent",
- "ر ا",
- "Ġtr action",
- "Ġadjust ing",
- "´ ¤",
- "ниб ÑĥдÑĮ",
- "Ġд оп",
- "Ġstretch ed",
- "Ġor t",
- "Ġcos ine",
- "vi ol",
- "Ġì ħ",
- "c ir",
- "Ġbast ard",
- "ä¸ ĩ",
- "ĠÑħ од",
- "Ġqu ier",
- "Ġpress ures",
- "ĠAn h",
- "å¹ ¾",
- "Ġell es",
- "Ġд ÑĢÑĥз",
- "ĠможеÑĤ е",
- "Ġch á»",
- "ĠM é",
- "ö k",
- "ầ u",
- "ìł Ī",
- "z in",
- "Ġca ution",
- "ib an",
- "Ġjud ging",
- "ÑĥÑİ ÑĤ",
- "Ġb aj",
- "ĠС ейÑĩаÑģ",
- "ĠPo or",
- "ĠNaz i",
- "Ġup beat",
- "y ang",
- "Ġweek ends",
- "ĠEss entially",
- "Ġol uyor",
- "Ġspat ial",
- "ack er",
- "Ġsell er",
- "Ġ×IJ ×ķת",
- "ij ׾",
- "Ġv ivid",
- "ĠB ond",
- "ê ¶Į",
- "is kt",
- "ãĤ µ",
- "Ġgo at",
- "dri ver",
- "Ġm ug",
- "ict ional",
- "Ġall t",
- "ĠIn iti",
- "ĠR and",
- "Ġfinish es",
- "Ġê° Ī",
- "Ġvit am",
- "Ġteen agers",
- "ĠMor ris",
- "ì¤ Ħ",
- "ĠO ri",
- "i ya",
- "Ġmy ös",
- "St ep",
- "ĠK re",
- "è¾ ¦",
- "Ġdin osaur",
- "Ġëª ĩ",
- "aff e",
- "ĠëIJ ©ëĭĪëĭ¤",
- "Ġz eg",
- "åĪ ĩ",
- "ĠManh attan",
- "Ġsu jet",
- "ue lle",
- "st off",
- "Ġd ür",
- "Ġsub mar",
- "es es",
- "Ġa quele",
- "Ġn ou",
- "ĠFa ith",
- "t z",
- "ĠÑĤ омÑĥ",
- "ace ut",
- "li ers",
- "Ġband width",
- "Æ°á» Ŀ",
- "Ġrespect ive",
- "ĠA ve",
- "Ġspread she",
- "ĠS ent",
- "ic amente",
- "Ġinf ra",
- "Ġlearn ers",
- "Ġà® ī",
- "ai ah",
- "ren al",
- "Ġmust ard",
- "Ġhab t",
- "ç ĥ",
- "ĠQu é",
- "Ġanaly zing",
- "æ¯ ı",
- "Ġso lic",
- "Ġ×Ķ ×ķ×IJ",
- "Ġcaus a",
- "Ġwel comed",
- "ĠS uccess",
- "Ġfac ile",
- "ĠÐŁÐ¾ÑĤ омÑĥ",
- "sche in",
- "Ġf etch",
- "Ġstr at",
- "ĠÑģÑĤо иÑĤ",
- "ìĹIJìĦľ ëĬĶ",
- "ĠÑģп оÑģоб",
- "m am",
- "Ġser ÃŃa",
- "nam ents",
- "wr iter",
- "Ġconsult ing",
- "íĺ Ģ",
- "ĠBer keley",
- "e u",
- "as ive",
- "U U",
- "ĠAnal yt",
- "Ġsubm ission",
- "Ġmagnific ent",
- "en za",
- "Ġe con",
- "Ġprof iles",
- "Ġinc ar",
- "A b",
- "ĠN un",
- "Ġh ic",
- "scream ing",
- "Ġresil ient",
- "åĪ ©",
- "gr und",
- "Ġconc ur",
- "Ġbere its",
- "L D",
- "Ġnur t",
- "ì ī",
- "Ġfe ast",
- "Ġenc uent",
- "ĠMich el",
- "Ġsup rem",
- "\" ]",
- "Ġfeed s",
- "ĠKoll egen",
- "iss er",
- "ĠF eng",
- "ĠW en",
- "m un",
- "Ġten ÃŃa",
- "ĠW rest",
- "Ġìĺ¤ëĬĺ ìĿĢ",
- "Ġst ead",
- "Ġrest oration",
- "Ġdon ated",
- "Ġdel s",
- "Ġc ensus",
- "Ġdesper ately",
- "worth y",
- "H E",
- "ĠSp a",
- "ĠBry an",
- "Ġh j",
- "ĠR aw",
- "ìķĦ ë",
- "ĠCam era",
- "Ġz ien",
- "Ġst yl",
- "ĠT W",
- "ĠChe ese",
- "bor ne",
- "Ġob l",
- "ĠAl ready",
- "Ġunst able",
- "Ġfl ames",
- "p ost",
- "H a",
- "rom agn",
- "ĠìĹ Ħë§Ī",
- "d est",
- "Ġkole j",
- "Ġtempor arily",
- "Ġdeterm ining",
- "ĠGl ass",
- "ÑĢ он",
- "ol an",
- "Ġdom inated",
- "åĮ ĸ",
- "__ __",
- "ĠÙĩ ذا",
- "ĠD ana",
- "Ġdin heiro",
- "a qu",
- "ë ¯¼",
- "ĠÃł s",
- "ĠJo ey",
- "ĠGr iff",
- "Ġatt ain",
- "Ġtrans itions",
- "ĠLiter ally",
- "ен д",
- "ĠHa ven",
- "Ġgrab bing",
- "Ġcryst als",
- "ĠFour th",
- "Ġcand les",
- "ĠÑģлÑĥÑĩ а",
- "ric o",
- "Ġ5 000",
- "et to",
- "Ġund o",
- "Ġk to",
- "Ġdi vert",
- "Ġch ir",
- "Ġper sec",
- "Ġh iking",
- "Ġannounce ments",
- "çĶ ±",
- "з Ñĭ",
- "Ġa uc",
- "Ġsystem ic",
- "ĠR M",
- "Ïĥ α",
- "ĠÐĶ ж",
- "Ġy ar",
- "ĠW ard",
- "Ġpiss ed",
- "Ġcar n",
- "Ġautonom ous",
- "ãħİ ãħİ",
- "so ver",
- "æ²Ĵ éĮ¯",
- "å¾Ī 好",
- "Ġref lex",
- "Ġgard ens",
- "Ġd ated",
- "ì ±",
- "ami ÄĻ",
- "Ġcontinu ity",
- "Ġcitizens hip",
- "Ġsch wer",
- "Ġz ak",
- "t able",
- "ĠÑģ Ñĩ",
- "è§ ģ",
- "ĠÏĥ ε",
- "Ġgener ates",
- "구ë Ĥĺ",
- "ö h",
- "ó m",
- "al am",
- "ĠJUD Y",
- "ĠB ug",
- "Ġãģ ¦",
- "Ġdr ones",
- "Ġá gua",
- "ac aks",
- "æ ļ",
- "ĠÐļ он",
- "× ĸ×Ķ",
- "Ġstri ve",
- "ĠAl tern",
- "Ġne arest",
- "Ġpro yect",
- "ter a",
- "ĠASH LEY",
- "Ġwor m",
- "Ġre play",
- "Ġt ara",
- "ĠInd ians",
- "ãĤ °",
- "ica id",
- "ĠìĪ ľ",
- "Ġappe aling",
- "ĠW es",
- "Ġment ions",
- "Ġдел е",
- "Ġk w",
- "Ġfrag ile",
- "is z",
- "k ów",
- "h ang",
- "col or",
- "Ġpresident e",
- "8 7",
- "е ÑĦ",
- "çĪ ¸",
- "Ġдоб ав",
- "ĠN elson",
- "á fic",
- "ĠMIC HAEL",
- "Ġmechan ic",
- "Ġmet res",
- "Ġo czywiÅĽcie",
- "ĠC ind",
- "Ġog sÃ¥",
- "Ġlands ca",
- "AC E",
- "Ġhead lines",
- "Ġcat alyst",
- "ĠC atch",
- "ink les",
- "Ġp ills",
- "ord o",
- "Ġimmig rant",
- "Ġexam ination",
- "Ġacc idents",
- "zÄħ d",
- "Ġqui ere",
- "Ġne lla",
- "Ġ6 7",
- "Ġpass a",
- "Ġsuper fic",
- "ist or",
- "Ġno v",
- "ëĭ µ",
- "Ġmand ate",
- "is ons",
- "ĠVirt ual",
- "Ġsel ber",
- "Ġcounsel ing",
- "ĠN BA",
- "Ġse pt",
- "Ġbelie ver",
- "Ġmar vel",
- "ĠInte gr",
- "Ġм Ñĸ",
- "Ġor ph",
- "Ġback ward",
- "ĠGen eration",
- "ĠP ict",
- "ĠÑĤо ÑĤ",
- "Ġtap i",
- "pro chen",
- "Ġhall way",
- "ht e",
- "ĠÛģ ÛĴ",
- "ĠZ um",
- "èĢģ 師",
- "ach ment",
- "iqu er",
- "fol g",
- "ĠEd die",
- "ĠK il",
- "Ġwell ness",
- "st ock",
- "è¼ ĥ",
- "Ġka ç",
- "Ġterror ism",
- "Ġpo inter",
- "O f",
- "her ic",
- "ĠUlt imately",
- "Ġmes es",
- "ĠTr ade",
- "Ġp int",
- "Ġtu ition",
- "Ġdisag re",
- "Ġê²Į ìŀĦ",
- "Ġmanus cript",
- "Ġro omm",
- "Ġoutput s",
- "е ÑĨи",
- "Ġr ies",
- "Ġsal ud",
- "otz dem",
- "Ġmass es",
- "Ġby ÅĤa",
- "Ġclear ing",
- "Ġdisc ourse",
- "ats on",
- "Ġfold ed",
- "ĠJ ar",
- "ÙĦ Ùī",
- "9 00",
- "ĠÑĥ Ñģп",
- "Ġprophe cy",
- "Ġinterf ere",
- "иÑħ од",
- "๠Į",
- "Ġth ri",
- "Ġ×ŀ× ©",
- "Ġlaz ım",
- "Ġ199 2",
- "Ġfut uro",
- "Ġlock ing",
- "Ġembar go",
- "ĠNe ither",
- "iv amente",
- "ĠmÃ¥ ste",
- "Ġm ik",
- "Ġcollect or",
- "еко ÑĤоÑĢ",
- "ĠG and",
- "Ġsent ir",
- "ĠM ight",
- "å¡ Ķ",
- "Ġgan zen",
- "U C",
- "Ġrel ating",
- "S D",
- "Ġmos quito",
- "G R",
- "Ġho llow",
- "âĺ ħ",
- "ĠWalk er",
- "Ġaffili ate",
- "Ġduplic ate",
- "н ем",
- "Ġgra pe",
- "ĠOrgan ization",
- "Ġsy nt",
- "J oe",
- "Ġg eg",
- "Ġreve aling",
- "ĠEth an",
- "out er",
- "Ġy ay",
- "é« Ķ",
- "л аÑĢ",
- "Ġreported ly",
- "Ġihr er",
- "Ġrecogn ise",
- "Ġbum per",
- "ĠR andy",
- "ĠVen us",
- "t les",
- "Ġappet ite",
- "Ġgluc ose",
- "Ġch odzi",
- "ĠFurther more",
- "t ir",
- "Ġcont a",
- "Ġint uition",
- "Ġalt itude",
- "Ġch unks",
- "ĠJosh ua",
- "ıģ ım",
- "ry lic",
- "le ans",
- "ĠíĶ ¼ë",
- "L L",
- "Q ue",
- "Ġg or",
- "Ġзна ÑĩиÑĤ",
- "Ġpo ems",
- "Ġexc el",
- "Ġexpl ored",
- "Ġpop ul",
- "Ġinclus o",
- "st ä",
- "ĠG avin",
- "all ing",
- "ĠÏĦο ν",
- "é ©",
- "ar beit",
- "ĠG as",
- "Ġgl orious",
- "rie ben",
- "Ġsp am",
- "Ġindo or",
- "Ġthr ust",
- "ĠA ld",
- "ĠPri or",
- "Ġon board",
- "ãģł ãģķãģĦ",
- "o ca",
- "AS H",
- "£ ł",
- "ĠChrist ine",
- "Ġdra wer",
- "Ġno on",
- "Ġìŀ ĺë",
- "Ġperman ently",
- "æ· ±",
- "ĠнапÑĢ имеÑĢ",
- "Ġpodcast s",
- "era peut",
- "pr it",
- "Ġstain less",
- "ĠÚ© ÛĴ",
- "Ġfamil ia",
- "ĠÑĢаз ÑĢ",
- "un to",
- "ĠÑģÑĤ ол",
- "Ġh ä",
- "ĠH ai",
- "ĠP B",
- "iz on",
- "Ġkon nte",
- "Ġbüy ük",
- "Ġutil izar",
- "Ú Ĩ",
- "Ġaqu esta",
- "Ġmix er",
- "ud ent",
- "лек Ñģ",
- "ÅĤ u",
- "ĠÑģиÑģÑĤ ем",
- "Ġн оÑĢм",
- "Ġfat al",
- "Ġconsider ations",
- "Ġvalid ation",
- "Ġo li",
- "Ġk ardeÅŁ",
- "ĠGL ORIA",
- "Ġp all",
- "еÑģÑĤ е",
- "Ġrect ang",
- "Ġmed ieval",
- "allah i",
- "ast i",
- "ĠSy rian",
- "Ġshe ar",
- "Ġdeb ug",
- "ĠM ai",
- "Ġknock ing",
- "ĠLe x",
- "ard an",
- "ro v",
- "Ġmem orial",
- "æ° £",
- "ook y",
- "Ġstuff ed",
- "Ġpass é",
- "Ġw ig",
- "Ĥ ł",
- "Ġpróxim a",
- "Ġ199 1",
- "Ġм еждÑĥ",
- "Ġnuest ros",
- "ĠBe ast",
- "Ġsm o",
- "atch ed",
- "olog ia",
- "Ġм од",
- "Ġge e",
- "Ġconcept ual",
- "ĠÃ ´",
- "Ġdecre ases",
- "Ġquer ies",
- "олÑĮ ÑĪ",
- "ĠA part",
- "Ġex empl",
- "å± ±",
- "Ġfl ed",
- "ĠO FF",
- "gg ak",
- "Ġbe ad",
- "h ir",
- "l ies",
- "ĠClear ly",
- "ı lar",
- "Ġch ess",
- "Ġwhich ever",
- "Ġ9 6",
- "Ạ±",
- "Ġrespect s",
- "Ġм оÑĢ",
- "Ġorgan ism",
- "Ġgrand pa",
- "ĠV ie",
- "è·Ł ä½ł",
- "Ġflo oding",
- "Ġupgrad ed",
- "Ñij ÑĢ",
- "Ġcheek s",
- "Ġcon quer",
- "Ġstub born",
- "Ġpuzz les",
- "Ġau ction",
- "Ġre lying",
- "ĠPRO F",
- "ĠEs per",
- "ĠÐľ У",
- "Ġhy pe",
- "Ġposs ibil",
- "Ġimp rison",
- "ĠEr n",
- "ìĹĪ ìĬµëĭĪëĭ¤",
- "Ġenv ie",
- "Ġresur rection",
- "ä¸į è¡Į",
- "Ġs per",
- "ĠVenez uela",
- "s om",
- "Ġìŀł ê¹",
- "Ġnouve lle",
- "Ġclos es",
- "Ġ19 40",
- "Ġqu a",
- "ĠJ ared",
- "ĠP ir",
- "Ġind e",
- "Ġscr ub",
- "uk u",
- "Ġrequ iring",
- "Ġв ами",
- "Ġconsider able",
- "åIJ Ľ",
- "il ia",
- "Ġin ne",
- "Ġmein em",
- "Ġhard ship",
- "Ġtra ps",
- "ro c",
- "ĠìĦ ¤ë",
- "Ġresearch ing",
- "ĠMarg aret",
- "Ġpen ny",
- "Ġbı rak",
- "Ñij л",
- "Ġw ool",
- "Ġr het",
- "Ġflat ten",
- "ç ĩ",
- "à¹Ģภ£",
- "Ġp ied",
- "ĠCh ap",
- "Ġunder m",
- "Ġf ret",
- "Ġcrash ed",
- "ĠFra uen",
- "Ø° Ùĩ",
- "iv an",
- "Ġliter ary",
- "late go",
- "Ġsp äter",
- "Ġsimilar ities",
- "â Ĩ",
- "ĠCor on",
- "ĠC reek",
- "Ġboss es",
- "Ġaccompan ied",
- "Ġdeb ates",
- "Ġassemb led",
- "ĠÃ ģ",
- "ĠV ai",
- "Ġtr act",
- "Ġsimple ment",
- "ĠAr in",
- "Ġvulner ability",
- "Ġhorm one",
- "I EL",
- "OO K",
- "Ġrel ay",
- "ĠAnd rea",
- "r il",
- "Ġnecess ity",
- "aceut ical",
- "Ñİ Ñī",
- "ous ing",
- "nah men",
- "Ġfoot print",
- "m ap",
- "ĠT ier",
- "ann ya",
- "int end",
- "åĸ ®",
- "å ¢",
- "Ġdecor ate",
- "Ġzomb ies",
- "ĠHy d",
- "ĠSu z",
- "Ġcampus es",
- "ĠE mb",
- "Ġthr ottle",
- "Ġad min",
- "Ġop ortun",
- "Ġmir rors",
- "Ġident ities",
- "ĠCl in",
- "Ġë¹ Ħë",
- "á¹ £",
- "ĠO tt",
- "Ġbl ues",
- "Ġimpress ions",
- "- ,",
- "Ġv ague",
- "a fe",
- "Ġinfer ior",
- "eral d",
- "Ġmedic ines",
- "Ġpre gunta",
- "os ely",
- "Ġt élé",
- "ĠMon th",
- "ĠLe aders",
- "ĠEgypt ian",
- "Ġr ation",
- "k ers",
- "he its",
- "Ġre cht",
- "P lay",
- "Ġe g",
- "Ġpoll s",
- "ĠWOO DR",
- "Ġsl ots",
- "j am",
- "B oth",
- "ĠR at",
- "ÑĢ аж",
- "ĠBr ight",
- "ä¸Ģ å®ļ",
- "á»ij i",
- "ur ious",
- "Ġsing ers",
- "Ġlo gin",
- "Ġt êm",
- "l ation",
- "ĠM um",
- "Æ°á»Ŀ ng",
- "ĠEd itor",
- "åIJ ij",
- "Ġinnov ations",
- "h ave",
- "ĠS ek",
- "Ġwe aker",
- "ĠG ob",
- "A fter",
- "´ì §Ģ",
- "Ġ문 ìłľ",
- "ãĥ¼ ãĥ¼",
- "Ġdisad vantage",
- "ç¢ º",
- "Ġg aze",
- "ĠM ack",
- "Ïģ ί",
- "ĠK iss",
- "ĠH olo",
- "ĠBir th",
- "iz i",
- "b ab",
- "ä¿ Ŀ",
- "ìĭľ ê³ł",
- "д еÑĢж",
- "Ġsqu at",
- "кÑĥ Ñģ",
- "un i",
- "ĠComm e",
- "ĠWOODR UFF",
- "ĠChampions hip",
- "Ġwel che",
- "ĠY outh",
- "z em",
- "Ġod pow",
- "Ġpersist ent",
- "r ut",
- "ìĶ ©",
- "íĸ ¥",
- "la ir",
- "ik u",
- "Ġvend or",
- "Ġch úng",
- "Ġfinan ci",
- "Ġover ly",
- "â u",
- "Ġgl uten",
- "Ġ18 00",
- "Ġdiv isions",
- "Ġciud ad",
- "Ġob ed",
- "Ġwar um",
- "Ġe her",
- "Ġel im",
- "ĠÐĴ о",
- "Ġpeu vent",
- "ĠW anna",
- "Ġattend ance",
- "Ġassess ments",
- "ĠB og",
- "Ġimag ery",
- "Ġcollect ively",
- "Ġinform al",
- "ĠSch we",
- "Ġde utlich",
- "ĠCh el",
- "ĠP E",
- "ow ed",
- "Ġb anner",
- "Ġshel ves",
- "ĠRet urn",
- "æĭ ¿",
- "LAUGH S",
- "Ġcongrat ulate",
- "ĠNor way",
- "Ġd well",
- "ĠCarib bean",
- "Ġnorm s",
- "ĠAn imal",
- "ĠValent ine",
- "Ġext ending",
- "ĠV ou",
- "or r",
- "ĠCh eng",
- "Â ¡",
- "ĠдоÑĢ ог",
- "Ġve g",
- "Ġh Ã¥",
- "ĠX in",
- "Ġì¹ ´ë",
- "em et",
- "Ġhyp oth",
- "Ġinteress ante",
- "ric es",
- "I Z",
- "ĠUS D",
- "Ġrun ner",
- "ĠB ag",
- "Ġê ½",
- "Ġcomeç ar",
- "Ġpig s",
- "Ġweakness es",
- "P h",
- "ĠVi ol",
- "ä¸į çĶ¨",
- "Ġdra gging",
- "ĠAqu ÃŃ",
- "ĠCS S",
- "Ġmill imeters",
- "Ġest ás",
- "Ġac ute",
- "Ġde jar",
- "i ÄŁ",
- "ob ra",
- "L ove",
- "Ġsil k",
- "** **",
- "Ġjo ins",
- "Ġpro l",
- "Ġê°IJìĤ¬ íķ©ëĭĪëĭ¤",
- "æĶ ¯",
- "ØŃ Ø¯",
- "agh etti",
- "än ner",
- "Ġstr ang",
- "Ġdoub led",
- "Ġdescri ptions",
- "Ġst ellen",
- "Ġpart i",
- "ç« ĭ",
- "² Ħë",
- "Ġö ÄŁ",
- "ig hing",
- "Ġang ular",
- "Ġnat uur",
- "ĠSh el",
- "Æ° Æ¡",
- "Ġr ays",
- "Ġse per",
- "st art",
- "v ised",
- "Ġrush ed",
- "Ġinternation ally",
- "Ġnive l",
- "Ġbox ing",
- "fall en",
- "á»ij c",
- "Ġse inen",
- "plic ity",
- "Ġcarb oh",
- "ĠTra vis",
- "us o",
- "ĠPh ase",
- "Ġactiv ation",
- "Ġop io",
- "· ¨",
- "Ġdecre ased",
- "C ar",
- "Ġbund le",
- "Ġexp end",
- "orm al",
- "Ġadjac ent",
- "Ġme e",
- "ĠоÑĢ г",
- "Ġtrans cript",
- "ĠLang uage",
- "G S",
- "è§ ī",
- "Ġse ul",
- "Ãł nh",
- "Ġn ya",
- "ning s",
- "Ġìĭ ľë",
- "ĠëĶ°ë Ŀ¼",
- "ĠA gr",
- "ÃŃ d",
- "çķ Ļ",
- "Ġab y",
- "ĠNe o",
- "ıyor uz",
- "ĠThink ing",
- "a ime",
- "Ġv ite",
- "Ġtrav és",
- "Ġ×ij× ¢",
- "Ġм ед",
- "O ur",
- "ho ot",
- "Ġl iner",
- "ĠP izza",
- "Ġhy g",
- "fl ies",
- "ĠContin ue",
- "Ġdent al",
- "ĠT ib",
- "Ġreg ulate",
- "lie ÃŁ",
- "AL K",
- "ĠTa e",
- "ê¸ ¸",
- "ĠBre xit",
- "ĠG ut",
- "Ġoccup ation",
- "Ġz robi",
- "â m",
- "Ġwh isk",
- "ä¸ĸ çķĮ",
- "Ġkans ke",
- "om on",
- "ro be",
- "Ġwar fare",
- "Ġth á»ĥ",
- "Ġjak i",
- "Ġstro kes",
- "Ġpe as",
- "ĠDam it",
- "H AN",
- "Ġinter ference",
- "Ġмин ÑĥÑĤ",
- "N ER",
- "out ing",
- "Ġtext ures",
- "Ł ī",
- "ow i",
- "Ġíķ Ļ",
- "Ġd ens",
- "Ġprotagon ist",
- "än n",
- "Ġgod dess",
- "Ġwoll te",
- "ij o",
- "ĠWo che",
- "ĠV PN",
- "st ory",
- "Ġkind erg",
- "Ġfun nel",
- "Ġdist ress",
- "ноÑģÑĤÑĮ Ñİ",
- "Ġno isy",
- "ĠпÑĢод олж",
- "Ġdar an",
- "Ġenzy me",
- "л ож",
- "Ġm ute",
- "Ġd war",
- "Ġا س",
- "Ġkom pl",
- "Ġmer it",
- "Ġf osse",
- "ĠDr ink",
- "Ġfor a",
- "Ġw ohl",
- "Ġbree ze",
- "Ġsan it",
- "Ġdr in",
- "ĠìĿ´ê±° ëĬĶ",
- "Ġ6 2",
- "Ġì° ¨ë",
- "aby tes",
- "Ġde eds",
- "ĠÐ ¹",
- "i ème",
- "igg ling",
- "Ġ\" '",
- "ĠÑĩа ÑģÑĤÑĮ",
- "ĠAns wer",
- "Ġev angel",
- "Ġ10 80",
- "ĠVis it",
- "ic ient",
- "Ġreli ability",
- "Ñİ ÑģÑĮ",
- "ĠEar lier",
- "Ġf id",
- "çŃī ä¸Ģä¸ĭ",
- "Ġslee ves",
- "iy orsun",
- "Ġb ib",
- "ĠAcc ount",
- "Ñı ли",
- "cipl inary",
- "z as",
- "Ġб еÑĢ",
- "Ġneck lace",
- "Ġbl ender",
- "ĠPhill ips",
- "et i",
- "ĠJup iter",
- "Ġprov oc",
- "ĠYe ars",
- "ent re",
- "ac io",
- "Ġk ü",
- "Ġanten na",
- "Ġnovel s",
- "Ġf art",
- "ĠS ugar",
- "ĠJud y",
- "Ġcollaps ed",
- "ç °",
- "rit is",
- "Ġìĥģ íĻ©",
- "ÐĹ Ð«",
- "ĠVer f",
- "rane an",
- "ere um",
- "ĠTar get",
- "Ġ8 8",
- "ĠÐĺ з",
- "ide o",
- "Ġreg ression",
- "ì¶ ľ",
- "Ġmów i",
- "Ġstud ios",
- "i ens",
- "ip h",
- "Ġfr ying",
- "Ġfasc inated",
- "ĠW ah",
- "b ucks",
- "m aya",
- "ĠSat urn",
- "ĠM ommy",
- "Ġrating s",
- "Ġaut umn",
- "Æ°Æ¡ ng",
- "Ġlos er",
- "Ġcent ro",
- "érie ur",
- "ĠF old",
- "Ġsuper visor",
- "ĠNo bel",
- "Ġunder est",
- "ob ia",
- "Ġв ÑģÑı",
- "Ġver w",
- "Ġfu els",
- "Ġartif acts",
- "Ġë¶ Ļ",
- "ĠAut om",
- "çļĦ æĺ¯",
- "Û Ķ",
- "×ķ× ¡",
- "Ġih nen",
- "Ġ5 9",
- "ound ing",
- "еÑĢ Ñĭ",
- "in ars",
- "ch ant",
- "Ġadd icted",
- "Ġexplos ive",
- "Ġdisp ers",
- "â ĸĪ",
- "ax is",
- "AR Y",
- "Ġl um",
- "ĠÑĥ Ñģл",
- "ĠØ Į",
- "Ġru pees",
- "ĠPe arl",
- "c amp",
- "t v",
- "oy a",
- "Ġconclud es",
- "Ġcoll ision",
- "Ġbuy er",
- "Ġplay ground",
- "Ġspr ings",
- "Ġfemin ine",
- "ĠR as",
- "Ġincar cer",
- "íĹ ĺ",
- "Ġdial ect",
- "Ġclos ure",
- "Ġchat ting",
- "Ġb abe",
- "Ġspot light",
- "Ġnot ation",
- "è· ¯",
- "St ar",
- "i ão",
- "Ġt ête",
- "Ġt ide",
- "Ġjun to",
- "Ġsen ator",
- "Ð ¥",
- "Ġexcus es",
- "Ġbl ink",
- "Ġadm ission",
- "ĠL ily",
- "Ñĭ ми",
- "Ġam igo",
- "Ġl ust",
- "ëĭ ¬",
- "Ġam ino",
- "äºĭ æĥħ",
- "Ġconsult ant",
- "ĠElect ric",
- "Ġëħ¸ë ŀĺ",
- "uj ah",
- "Ġshoot er",
- "icht en",
- "ĠUkrain ian",
- "Ġaim s",
- "ĠEnter tain",
- "Ġmir acles",
- "èŃ °",
- "Ġze igen",
- "Ġl am",
- "Ġres s",
- "ĠJ ill",
- "yl an",
- "Ġro ok",
- "Ġh aya",
- "Ġpass port",
- "ad ata",
- "Ġju icy",
- "con f",
- "л ей",
- "ĠS z",
- "Ġinter cept",
- "ãģĤãĤĬãģĮãģ¨ãģĨ ãģĶãģĸ",
- "ĠTe ams",
- "Ġmak en",
- "ir rel",
- "ĠLI KE",
- "áºŃ y",
- "êµ °",
- "Ġshort age",
- "Ġparad igm",
- "Ġpap el",
- "Ġast ero",
- "ãģ¾ ãģŁ",
- "Ġsoll en",
- "ĠMic key",
- "ĠOr leans",
- "Ġchol esterol",
- "Ġgo ose",
- "ÑĨи Ñİ",
- "ãģĤ ãĤĭ",
- "ĠF L",
- "Ġгол ов",
- "Ġtrib ute",
- "ĠG am",
- "Ġé videmment",
- "Ñı Ñħ",
- "å® ŀ",
- "çĶ °",
- "Ġin appropri",
- "uh an",
- "Ġorganiz ational",
- "ail ed",
- "Ġend ure",
- "Ġ7 6",
- "Ġshot gun",
- "Ġliv re",
- "Ġsu ited",
- "Ġwarm th",
- "ĠS IM",
- "Ġenv ision",
- "Ġde grad",
- "î ne",
- "La ughing",
- "ĠWho ever",
- "ĠBuddh ism",
- "Ġspr inkle",
- "ceÄŁ iz",
- "Ġru ins",
- "Ġst arch",
- "ĠHer z",
- "Ġinjust ice",
- "Ġhum idity",
- "ожал Ñĥй",
- "ĠOb ject",
- "ĠI gn",
- "ĠEx am",
- "ig ers",
- "Ġth ou",
- "ĠSo y",
- "iv as",
- "Ġpol es",
- "m ath",
- "Ġв ним",
- "ING ING",
- "ed ral",
- "Ġexpl or",
- "Ġroast ed",
- "Ġcraw l",
- "Ġco ff",
- "Ġan om",
- "Ġw ij",
- "Ġimpro ves",
- "Ġtreat y",
- "Ġdiscover ing",
- "Ġstat ute",
- "Ġmerc ado",
- "ĠÑģ ил",
- "Ġint el",
- "ĠChance llor",
- "ĠMed icaid",
- "ug i",
- "Ġver bal",
- "Ġd ön",
- "Ġscript ure",
- "Ġit eration",
- "ek s",
- "ĠOx ford",
- "Ġw äh",
- "ĠV ad",
- "ĠA K",
- "ĠìķĦ ìĿ´ë",
- "Ġi ets",
- "Ġneed les",
- "Ùĥ Ùħ",
- "Ġpas ado",
- "Ġalbum s",
- "Ġye a",
- "et zen",
- "Ħë ıĦ",
- "Ġdeterm ines",
- "Ġthe e",
- "ĠPlay ing",
- "är t",
- "Ġ× ¦",
- "c led",
- "Ġdown ward",
- "al one",
- "Ġsol u",
- "Ġpart ition",
- "Ġw z",
- "d d",
- "Ġpesso al",
- "å ª½",
- "Ġfact ories",
- "Ġble ibt",
- "ม า",
- "als a",
- "ĠNF L",
- "Ġfu era",
- "Ġres erved",
- "ĠE arn",
- "Ġhel t",
- "Ġshort cut",
- "Ġconvin cing",
- "sp ace",
- "Ġen force",
- "Ġc ores",
- "Ġe fter",
- "Ġrecess ion",
- "x ico",
- "Ġprop osition",
- "ar ians",
- "rop ol",
- "Ġëª °ë",
- "ĠÎ ľ",
- "ĠìļĶ ì¦ĺ",
- "Ġactiv ist",
- "Ġconv iction",
- "Ġz ab",
- "Ġcancel ed",
- "ÑĤо Ñĩно",
- "ĠÎ ®",
- "éĢĻ樣 åŃIJ",
- "n ite",
- "Ġfund ra",
- "buz zer",
- "ел о",
- "ic ations",
- "Ġz ona",
- "Ġte ens",
- "Ġmethod ology",
- "Ġì¤ij ìļĶ",
- "th an",
- "ĠU l",
- "ĠG rey",
- "Ġh og",
- "IN K",
- "ĠS ung",
- "ĠC laud",
- "ĠCN N",
- "Ġdel ivers",
- "al in",
- "ĠAd obe",
- "ot he",
- "ĠDes wegen",
- "ภ³",
- "Ġwer de",
- "Ġgre ase",
- "Ġup grades",
- "ĠFin land",
- "ac cept",
- "Ġinter rog",
- "be e",
- "Ġãģ «",
- "Ġpre de",
- "ĠN ep",
- "ĠCam bridge",
- "Ġgraph s",
- "Ġha unted",
- "Ñģ ем",
- "æ §",
- "åħ ĭ",
- "S ome",
- "ĠM all",
- "Ġrehears al",
- "ĠUr ban",
- "ĠL ag",
- "Ġn im",
- "ê° ķ",
- "Ġposition ed",
- "Ġavo ided",
- "EM A",
- "Ġlleg ar",
- "Ġráp ido",
- "Ġgou vern",
- "Ġh ing",
- "Ġdeal er",
- "Ġreform s",
- "Ġfat ty",
- "к ол",
- "ĠA ce",
- "Ġne p",
- "Ġì² Ń",
- "Ġcomput ation",
- "ĠSt ream",
- "bour ne",
- "t ur",
- "P or",
- "Ġsleep y",
- "Ġbang et",
- "ãģĤ ãģ®",
- "Ġwe ighs",
- "Ġble iben",
- "ĠG ren",
- "Ġun ions",
- "Ġêµ IJ",
- "Ġap render",
- "uit ar",
- "ĠJ est",
- "um ing",
- "ĠPlay er",
- "ĠExt rem",
- "Ġinteg er",
- "аÑĩ е",
- "Ġconcert s",
- "×ķ× Ľ",
- "Ġtro chÄĻ",
- "ĠRe pe",
- "éĩį è¦ģ",
- "๠Ĥ",
- "ż en",
- "Ġsound ing",
- "Ġan onymous",
- "Ġex ca",
- "ĠIran ian",
- "Ġener getic",
- "Ġw ives",
- "ĠÑĨ веÑĤ",
- "Ġa is",
- "ãģĭ ãģª",
- "Ġsud ah",
- "Ġunder wear",
- "Ġcrunch y",
- "ĠP ain",
- "Ġger çek",
- "red ict",
- "Ġm isma",
- "Ñĸ ÑĤ",
- "Ġsurv iving",
- "ÎŃ ÏĤ",
- "Ġparticip ant",
- "ĠH essen",
- "ári as",
- "Ġsub way",
- "ist ä",
- "Ġcor al",
- "Ġmar ijuana",
- "ĠMem orial",
- "ÑĪ ий",
- "ri z",
- "Ġsatell ites",
- "Ġle ase",
- "ĠCam eron",
- "um ph",
- "Ġclass mates",
- "äh än",
- "ÑģÑĤв е",
- "Ġh ue",
- "ĵ¤ ìĿĦ",
- "Ġproport ional",
- "Ġn oss",
- "Ġl aps",
- "r å",
- "Ġbit coin",
- "ÐĹЫ ÐļÐIJ",
- "Ġì¶ ©",
- "ĠÙĦ ÙĦ",
- "ĠM ort",
- "ĠEs p",
- "arn os",
- "ĠÑģказ ал",
- "Ġä nd",
- "åħ Ħ",
- "×Ļ ×Ļ×Ŀ",
- "ĠGe b",
- "ge hen",
- "I naudible",
- "bor ough",
- "ÑĦ ÑĦ",
- "Ġfellow ship",
- "ĠP aper",
- "Ġcur ved",
- "ĠGE OR",
- "Ġcalcul ator",
- "ĠCat al",
- "ĠvÃł o",
- "Ġby pass",
- "л еÑĤ",
- "à ³",
- "tr ans",
- "ren cies",
- "ì ¡Į",
- "ig ent",
- "Ġtast ed",
- "Ġo ceans",
- "u ft",
- "erv ice",
- "ĠÐľÐ£ ÐĹЫÐļÐIJ",
- "ĠClass ic",
- "Ġrespect ively",
- "~ )",
- "î tre",
- "ĠN ash",
- "Ġz it",
- "ĠìĽ ĥ",
- "ĠëĨ Ĵ",
- "qu ote",
- "ĠUn s",
- "Ġt ac",
- "Ġpro ves",
- "ĠPort land",
- "b ly",
- "Ġ ere",
- "ì¶ Ķ",
- "Ġépo ca",
- "ĠÑĤÑĭ ÑģÑıÑĩ",
- "7 6",
- "Ġhad e",
- "ĠF ro",
- "ĠpolÃŃt ica",
- "t ag",
- "Ġíķ Ń",
- "Ġsch ö",
- "are tt",
- "Ġprov isions",
- "Ġmot ors",
- "Ġimag ing",
- "Ġdo k",
- "ul ously",
- "Ġme ille",
- "çİ° åľ¨",
- "ë IJ",
- "ĠIS O",
- "ĠST EM",
- "ĠBow l",
- "Ġto wers",
- "ĠE e",
- "ĠPerform ance",
- "Ġlo in",
- "cuss ion",
- "Ġcoast al",
- "ial e",
- "com pass",
- "Ġspell s",
- "Ġdisappoint ing",
- "Ġë²Ī 째",
- "E ER",
- "Ġvers atile",
- "as ury",
- "Ġen fin",
- "Ġdown side",
- "Ġgu iding",
- "ĠاÙĦ ÙĤ",
- "Ġnin ety",
- "char ged",
- "ĠF ans",
- "Ġphilosoph ical",
- "Ġg arn",
- "ĠmÃ¥ nga",
- "Ġwilling ness",
- "Ġport ions",
- "ab en",
- "Ġ ï",
- "Â ¿",
- "ra ul",
- "Ġspr int",
- "if en",
- "ıy la",
- "Ġк Ñĥп",
- "ãģı ãģłãģķãģĦ",
- "Ġens uite",
- "ĠCap itol",
- "Ġ6 3",
- "ĠговоÑĢ иÑĤ",
- "Ġappoint ments",
- "æī ¾",
- "omi ast",
- "Ġcare g",
- "Ġpubl isher",
- "Ġher aus",
- "Ġε ί",
- "ĠV S",
- "ãģĿ ãģĹãģ¦",
- "ä¸Ń åħ±",
- "Ġsacrific es",
- "th ird",
- "Ġhuman itarian",
- "ĠëĤ ´ì",
- "im on",
- "Ġine qu",
- "Ġz ob",
- "Ġcomfort ably",
- "ĠD inge",
- "Ġcancell ed",
- "ĠPS AKI",
- "ĠRob inson",
- "Ġfin s",
- ") ?",
- "ĠHist or",
- "ĠÑĩеловек а",
- "Ġt bsp",
- "te xt",
- "k im",
- "Ġupd ating",
- "Ġgel d",
- "f eld",
- "ı ¼",
- "Ġm ä",
- "Ġcaf é",
- "Ö Ģ",
- "ĠS ri",
- "ĠReg ion",
- "ĠH ahaha",
- "Ġfin ances",
- "ĠاÙĦØ ´",
- "Ġb unk",
- "ru k",
- "ha ft",
- "Ġlater al",
- "Ġext ensions",
- "ĠìķĦ ìĿ´",
- "Ġdefin ite",
- "ĠZ hao",
- "ĠLu is",
- "st y",
- "Ġcas os",
- "ĠK lim",
- "Ġ199 3",
- "Ġreal ization",
- "Ġhistor ian",
- "Ġcrack ed",
- "ëĤ ´",
- "Ġsyst ème",
- "ĠC IA",
- "ĠÑĤ во",
- "osp heric",
- "Ġfle e",
- "Ġr ất",
- "ĠRegard less",
- "Ġrel uct",
- "Ġtim ely",
- "ĠJul ian",
- "G M",
- "é Ĵ",
- "ad ura",
- "é£ Ł",
- "Ġdress es",
- "çģ £",
- "ĠëĶ Ķ",
- "Ġnom inated",
- "Ġadvoc ates",
- "ym ph",
- "Ġrecord ings",
- "Ġdev iation",
- "Ġpriorit ize",
- "Ġspir al",
- "ĠYOU R",
- "Ġtransp ose",
- "amp oo",
- "ĠìĽIJë ŀĺ",
- "ĠV ision",
- "Ġpol ite",
- "Ġha mb",
- "ĠPat ient",
- "æ¯Ķ è¼ĥ",
- "íģ ¬ë",
- "Ġs ia",
- "Ġê³ ³",
- "Ġž e",
- "è§ Ģ",
- "Ġsuper market",
- "ë ¹",
- "ĠS ierra",
- "Ġgr illed",
- "ĠUp on",
- "Ġabs ent",
- "Ġme c",
- "ĠAp ollo",
- "Ġp unk",
- "ĠPa ÅĦst",
- "ĠÑģв ой",
- "Ġê±° 기",
- "G irl",
- "Ġskin ny",
- "ĠPrem ier",
- "Ġterrit ories",
- "Ġli ability",
- "Ġj erk",
- "r atic",
- "Ġdan cers",
- "ĠÑĥ ÑĢов",
- "Ġê´ Ģë",
- "on ly",
- "ĠSt u",
- "Ġske leton",
- "ĠëŃ IJë",
- "Ġзак он",
- "ı kt",
- "ĠMI KE",
- "Ġl ö",
- "m ie",
- "Ġre iter",
- "ãģĵãĤĮ ãģ¯",
- "ĠKoll eg",
- "ĠAd ams",
- "lich er",
- "Ġçoc uk",
- "Ñı г",
- "Ġbl ush",
- "Ġsun shine",
- "Ġe z",
- "ĠDev il",
- "Ġê¸ ¸",
- "Ġãģ Ĭ",
- "ad d",
- "Ġlic ensed",
- "Ġv inyl",
- "ĠC zech",
- "im ag",
- "Ġcrack ing",
- "Ġì º",
- "Ġud ah",
- "Ġs ommes",
- "Ġìĸ¼ êµ",
- "wa Äĩ",
- "Ġf res",
- "åij ½",
- "ĠWal mart",
- "ĠТ епеÑĢÑĮ",
- "at isf",
- "C I",
- "l ang",
- "Ġdiff usion",
- "çĶ ·",
- "Ġsom os",
- "ĠM akes",
- "æĪij æĥ³",
- "ĠRick y",
- "Ġmuch a",
- "íķ ¨",
- "Ġhorse power",
- "as ia",
- "Ġfib ers",
- "Ġ erm",
- "Ñģ кие",
- "Ġjest e",
- "Ġfire fight",
- "Ġcu isine",
- "Ġbesond ers",
- "d ig",
- "Ġì¢ ħ",
- "ĠÑĥ ж",
- "Ġtr acing",
- "Ġcertain s",
- "ĠApp ly",
- "Ñĭв аÑĤÑĮ",
- "ç Į",
- "Ġbr u",
- "ĠY ES",
- "ĠB ai",
- "ĠD it",
- "ĠB is",
- "Ġun le",
- "ÑģÑĤа ÑĤоÑĩно",
- "ĠAw ak",
- ".. \"",
- "Ġ12 5",
- "Ġroot ed",
- "Ġcaut ious",
- "con st",
- "Ġorchest ra",
- "çľ ¼",
- "Ġвн ÑĥÑĤ",
- "Ġquel qu",
- "ĠоÑĤ веÑĤ",
- "ĠMet hod",
- "ì¹ ľ",
- "Ġμ αÏĤ",
- "l ü",
- "ĠìķĦ ê¹Į",
- "Ġn aming",
- "C har",
- "ĠS icher",
- "Ġprivile ged",
- "ĠF ly",
- "Ġãģ ĭ",
- "áºŃ t",
- "Ġadv ances",
- "ĠZel da",
- "Ġand ra",
- "Ġgr inding",
- "ĠEd ition",
- "p f",
- "Ġwarri ors",
- "Ġh edge",
- "Ġuns eren",
- "ĠÑģÑİ Ð´Ð°",
- "el iness",
- "Ġpersonal ities",
- "Ġf ö",
- "' M",
- "ĠÑĤо Ñĩно",
- "Ġsh ipped",
- "Ġmete or",
- "Ġsurround ings",
- "ĠF ill",
- "u esta",
- "ĠPerson al",
- "ĠAll e",
- "OR T",
- "ä¹ ħ",
- "ĠS che",
- "V I",
- "Ġcompar able",
- "dam n",
- "Ġd itch",
- "Y AN",
- "ism us",
- "Ġpick up",
- "Ġd ak",
- "ĠE P",
- "b est",
- "ĠS ue",
- "äll t",
- "Ġpop corn",
- "Ġfold ing",
- "h ome",
- "ив аеÑĤ",
- "å·² ç¶ĵ",
- "Ġan not",
- "ch uck",
- "Ġfier ce",
- "Ġdam aging",
- "Ġfl op",
- "Ġpas ar",
- "Ġre ef",
- "ĠÑģво ей",
- "Ġz oo",
- "o vers",
- "j ets",
- "Ġpr ès",
- "ĠSil icon",
- "te ok",
- "ĠS eth",
- "at amente",
- "Ġtransm itted",
- "Ġrepl icate",
- "Ġsl im",
- "ĠC ream",
- "æĦŁ ãģĺ",
- "Ġside walk",
- "ìĪ ĺë",
- "Ġжиз нÑĮ",
- "ĠMon ica",
- "ä¾Ĩ äºĨ",
- "Ġcop ied",
- "ĠTer ra",
- "ist ent",
- "ç³ »",
- "Ġо но",
- "Ġwh ale",
- "ĠW ITH",
- "л ÑĥÑĪ",
- "å½± çīĩ",
- "ĠE en",
- "ĠÑģво и",
- "Ġord in",
- "Ġpl ural",
- "Ġsp okes",
- "Ġdisp ute",
- "Ġsens ible",
- "Ġpre aching",
- "Ġktó rzy",
- "pt ed",
- "av ier",
- "Ġpist ol",
- "ĠTap i",
- "Ġ ÅĤ",
- "ff ff",
- "Ġac rylic",
- "Ġignor ance",
- "ĠZ iel",
- "r ans",
- "Ġweld ing",
- "m id",
- "æĪij ä¸į",
- "Ġзан им",
- "Ġlan es",
- "Ġmin es",
- "Ġmom s",
- "×ķ× Ĺ",
- "ĠCham ber",
- "t ier",
- "Ġmod est",
- "ĠìĹ¬ê¸° ìĦľ",
- "Ġun as",
- "Ġw rench",
- "hand ed",
- "Ġsatur ated",
- "ĠF ang",
- "ĠCommission er",
- "ठ°",
- "Ġ× ĸ",
- "ĠLouis iana",
- "ĠM ask",
- "Ġcub es",
- "ìĶ ¨",
- "Ġvidé os",
- "ĠnÃ¥ gon",
- "Ġr ider",
- "Ġì¶ ľ",
- "Ġs ón",
- "ĠLat ino",
- "b ank",
- "íķ´ì £¼",
- "ĠB rend",
- "Ġsexual ity",
- "... ,",
- "Ġforget ting",
- "Ġ ÛĮ",
- "ĠAven gers",
- "ĠBon jour",
- "cess or",
- "кÑĢа ÑĹ",
- "c ence",
- "Ġge ograph",
- "cul o",
- "о ÑģÑĤÑĮ",
- "Ġswe ating",
- "íĥ Ģ",
- "Ġsymm etry",
- "ts å",
- "Ġj an",
- "ĠFer r",
- "é¦ ĸ",
- "Ġamb assador",
- "ziÄĻ k",
- "Ġmus un",
- "ĠÑĥ ÑĤ",
- "ĠL G",
- "iss ent",
- "comm un",
- "Ġcour s",
- "Ġdevelop s",
- "Ġbron ze",
- "Ġsubst ances",
- "dri ven",
- "주 ìĦ¸ìļĶ",
- "Ġa os",
- "åĦ Ħ",
- "ĠPROF ESS",
- "h alf",
- "Ġsort ed",
- "ĠB omb",
- "л аг",
- "ĠMalays ia",
- "ĠChrist ina",
- "Ġteam mate",
- "èģ ŀ",
- "F T",
- "Ġk ı",
- "heart ed",
- "+ +",
- "ogen ic",
- "Ġbell s",
- "ĠOu ais",
- "Ġspecial ists",
- "б Ñĭ",
- "dep th",
- "lass es",
- "g ies",
- "ĠCo ffee",
- "Ġmark ing",
- "Ġfo ll",
- "ul i",
- "Ġad hesive",
- "ĠB ot",
- "ĠP unkt",
- "e ye",
- "ĠB ub",
- "el ong",
- "åĪ ¶",
- "ĠпÑĢ ик",
- "Ġdon or",
- "8 4",
- "Ġen for",
- "Ġcatch es",
- "Ġbr icks",
- "Ġkn itting",
- "ĠKnow ing",
- "ok s",
- "H Y",
- "r ide",
- "ĠFant asy",
- "im an",
- "Ġp se",
- "Ġìĺ ¨",
- "Ġв д",
- "Ġrest ra",
- "Ġevalu ated",
- "ÑĢ ев",
- "Ġfortun ately",
- "Ġche gar",
- "ر ب",
- "Ġdom ains",
- "ib i",
- "ar ry",
- "Ġshut ter",
- "Ġfic ou",
- "M ike",
- "Ġinc lu",
- "Ġdon ors",
- "Ġa pl",
- "ĠL ower",
- "Ġimport ed",
- "Ġacad emy",
- "Ġfin als",
- "Ġdisappe ars",
- "ÙĬ ا",
- "Ġadministr ator",
- "j s",
- "Ġcut ter",
- "Ġr anging",
- "ör per",
- "Ġconstra int",
- "ĠT able",
- "ĠSh an",
- "v ic",
- "ĠF ix",
- "ĠSw ift",
- "oun ces",
- "ĠWar um",
- "Ġlett uce",
- "app elle",
- "Ġsh ave",
- "Ġb ás",
- "Ġ7 7",
- "ĠO oo",
- "a o",
- "ĠMc M",
- "ĠD rew",
- "Ġl ump",
- "Ġl ashes",
- "schein lich",
- "R ep",
- "in is",
- "ĠC ette",
- "Ġcompos ite",
- "emet ery",
- "Ġsort e",
- "ĠFin ancial",
- "он е",
- "ron es",
- "ĠV oy",
- "Ġt éc",
- "ł ¹",
- "ĠNin ja",
- "ĠCor in",
- "ен нÑı",
- "ìĿ´ìĹ Ī",
- "Ġn ich",
- "Ġdetect ive",
- "âĢ¦ \"",
- "Ïĥ ε",
- "Ŀ¼ë ıĦ",
- "Ġë³ Ģ",
- "Ġë¸ Ķë",
- "Ġpro pe",
- "ĠW right",
- "Ġ×Ķ× ª",
- "ĠSh i",
- "Ġãģ Ł",
- "Ġinvestig ations",
- "éĤĦ æĺ¯",
- "ĠPower Point",
- "ĠCh u",
- "Ġìĺ ¤í",
- "ĠìĻĦ ìłĦ",
- "ĠFra gen",
- "un ning",
- "Ġpour rait",
- "Ġtext book",
- "м Ñĭ",
- "Ġf ahren",
- "Ġ ÑĤоÑĢ",
- "Ġl akes",
- "ünd e",
- "I nt",
- "ĠMet ro",
- "Ġmans ion",
- "Ġа б",
- "ĠZh ou",
- "Ġcorrid or",
- "Ġesc ol",
- "Ġindic ating",
- "ia ÅĤa",
- "Ġm ommy",
- "Ġarch ives",
- "Ġfound ers",
- "eng ine",
- "ĠDie u",
- "Ġsick ness",
- "Ġë³´ ëĭĪê¹Į",
- "Ġar b",
- "Ġn ed",
- "ĠCh op",
- "Ġco vid",
- "Ġsl am",
- "Ġpublic ations",
- "D C",
- "Ġsp ends",
- "æ ¾",
- "Ġrefuge e",
- "Ġd ile",
- "Ġ×IJ× ĸ",
- "ific ar",
- "ĠS ach",
- "G u",
- "Ġre load",
- "?? ??",
- "Ġje ÅĽli",
- "ĠÑģ оÑģÑĤо",
- "Ġsim plicity",
- "Ġbull ying",
- "Ġм ол",
- "Ġreal idad",
- "Ġuncle ar",
- "app a",
- "le vant",
- "ĠIS IS",
- "ĠW atson",
- "Ġde in",
- "ĠMic ro",
- "íķ ľë",
- "ü g",
- "Ġdev am",
- "Ġtwe eted",
- "å° İ",
- "Ġunderstand able",
- "at an",
- "Ġvers a",
- "Ġpre ca",
- "Ġv á»ģ",
- "ĠCop y",
- "ĠOr acle",
- "Ġmindful ness",
- "Ġdisc ret",
- "ern en",
- "ĠP le",
- "H ave",
- "Ġisol ate",
- "Ġde u",
- "Ġsevent y",
- "ĠH ills",
- "Ġarc ade",
- "ĠÑģп еÑĨи",
- "Ġsigu iente",
- "ĠB ÃľNDNIS",
- "lig a",
- "ĠвÑģÑĤÑĢ еÑĩ",
- "ô m",
- "Ġtwe ets",
- "Ġsch auen",
- "Ġcrit ique",
- "ĠðŁİ µ",
- "Ġst att",
- "ĠÑģам ое",
- "ân cia",
- "Ġsuper natural",
- "Ġplug ged",
- "F l",
- "yn ı",
- "ĠTamb ién",
- "Ġencourage ment",
- "ĠSer ver",
- "ëĤ ľ",
- "up a",
- "Ġast on",
- "Ġhe ars",
- "ÑĢа Ñħ",
- "Ġsch e",
- "Ġr ats",
- "Ġrec uper",
- "Ġun ten",
- "ĠFight ing",
- "Ġacadem ics",
- "ç¤ º",
- "ĠS ü",
- "Ñģ киÑħ",
- "Ġpa ired",
- "Ģ ìĿĦ",
- "Ġá rea",
- "Ġsweet ness",
- "åı Ĭ",
- "Ġde fer",
- "Ġmuit as",
- "ĠAud io",
- "Ġlock er",
- "ÙĬ د",
- "ĠÑģÑĤ ав",
- "Ġbu ena",
- "AN S",
- "Ġdetect or",
- "av o",
- "be k",
- "Ġα ν",
- "íİ ¸",
- "Ġdra gged",
- "Ġдолж ен",
- "Ã ĸ",
- "ر ة",
- "ìĿ´ì §Ģ",
- "Ġcell e",
- "ck ing",
- "ĠاÙĦØ ¬",
- "ĠCan vas",
- "Ġespa ñ",
- "Ġgl imp",
- "Ġspread s",
- "ong o",
- "ĠM ason",
- "ĠIn g",
- "Ġê°Ģ ëĬ¥",
- "ÏĦ ικ",
- "Ġsec ular",
- "Ġb ater",
- "Ġinqu iry",
- "Ġenerg ies",
- "Ġmanufact ured",
- "Ġveget arian",
- "Ġpine apple",
- "ÑıÑĤ а",
- "Ġpractition ers",
- "2 000",
- "Ġíķ´ì ļĶ",
- "ĠìĹ¬ëŁ¬ë ¶Ħëĵ¤",
- "Ġë¶ Īë",
- "ĠJeff erson",
- "ĠJo an",
- "Ġtr am",
- "å® ¹",
- "ch mal",
- "ĠH ait",
- "á¹ ĩ",
- "Ġun real",
- "Ġsymbol ic",
- "Ġste alth",
- "Ġspl ash",
- "ĠEntertain ment",
- "Ġmetall ic",
- "?\" .",
- "è¶ Ĭ",
- "ar ound",
- "Ġdesp air",
- "ĠNev ada",
- "ĠFin ance",
- "Ġk rie",
- "ĠL ux",
- "ĠSm ash",
- "ke eping",
- "Ġз аг",
- "Ġnarc iss",
- "Ġdz isiaj",
- "Ġtoler ate",
- "o ard",
- "Ġlink ing",
- "ĠEconom ic",
- "Ġì ¼",
- "Ġmor ph",
- "ĠN ak",
- "ĠB aker",
- "at on",
- "r ings",
- "ĠP eng",
- "ĠAir port",
- "ãģĭ ãģ£ãģŁ",
- "íķĺ ëĭ¤",
- "§ ģ",
- "pr ints",
- "Ġhad i",
- "Ġemp ir",
- "ĠL ives",
- "ann ers",
- "Ġн им",
- "ĠPROFESS OR",
- "Ġpositive ly",
- "ant om",
- "Ġbad ge",
- "ke lt",
- "Ġinter fer",
- "Ġfulf illing",
- "Ġvisual ization",
- "éĹľ ä¿Ĥ",
- "ĠPr ice",
- "� �",
- "Ġscen ery",
- "Ġpr one",
- "Ġw izard",
- "Ġb anyak",
- "ver b",
- "s ky",
- "Ġwish ed",
- "Ġrail way",
- "Ġü zer",
- "Ġalgu ien",
- "ĠA W",
- "Ġкол иÑĩе",
- "Ġreact ing",
- "ĠB uch",
- "ภ¶",
- "Ġan th",
- "Ġsi h",
- "Ġh ust",
- "ĠSc reen",
- "il ant",
- "ah o",
- "Ġfragr ance",
- "Ġelev ation",
- "ĠMed iter",
- "Ġë ¿",
- "Ġé qu",
- "Ġwra ps",
- "Ġin ert",
- "Ġrecre ate",
- "л аÑĤ",
- "Ġbo leh",
- "Ġharass ment",
- "unk y",
- "Ġglimp se",
- "reg ierung",
- "Ġfut ur",
- "Ġreposit ory",
- "Ġeng ra",
- "Ġtraff icking",
- "ass is",
- "ĠTre k",
- "Ġë² Į",
- "Ġë§ Īë",
- "ĠK ab",
- "ani u",
- "g ive",
- "Ġdin osaurs",
- "Ġfe ather",
- "Ġatt itudes",
- "Ġpl um",
- "ĠR S",
- "ĠAn fang",
- "ill ery",
- "ĠìĬ ¤",
- "M Y",
- "Ġtrze ba",
- "Ġsk ies",
- "ĠA j",
- "ur able",
- "C U",
- "ĠSh ane",
- "Ġdepart ure",
- "ĠT ON",
- "iet en",
- "r ats",
- "æ° Ĺ",
- "is u",
- "Ġb ord",
- "Ġinteresting ly",
- "çĻ »",
- "oug hing",
- "Ġr ushing",
- "Ġvol atility",
- "Ġp yt",
- "Ġform ats",
- "Ġз аÑĤ",
- "Ġê¼ Ń",
- "Ġwhat not",
- "Ġcomp ort",
- "s w",
- "ore an",
- "ĠRel ax",
- "Ġcl an",
- "ĠA H",
- "Ġpe w",
- "Ġdiction ary",
- "T ake",
- "sh irts",
- "ĠH ugh",
- "ĠعÙĦ ÙĬ",
- "ĠP ic",
- "Ġenroll ed",
- "Ġjed nak",
- "Ġoffer ings",
- "Ġcor az",
- "L ife",
- "Ġ !!!",
- "Ġcl er",
- "ĠVide os",
- "ĠRod rig",
- "ĠId ent",
- "ĠP os",
- "ĠSt age",
- "ĠR ace",
- "Ġen act",
- "ãģĦ ãģ¾ãģĹãģŁ",
- "ĠG y",
- "ĠHis pan",
- "Ġdef ence",
- "ĠCamp bell",
- "m atic",
- "Ġrele v",
- "Ġpe ach",
- "Ħ¸ ìļĶ",
- "Ġparad ise",
- "Ġcere mon",
- "Ġannoy ed",
- "æĮ ĩ",
- "la x",
- "Ġexplo it",
- "Ġcla use",
- "ek er",
- "ĠBlo om",
- "n ant",
- "ate urs",
- "Ġhe ights",
- "E ven",
- "Ñģ он",
- "Ġoutra ge",
- "ĠVietnam ese",
- "ãģ¯ ãģ¯",
- "T R",
- "Ġe er",
- "Ġcann on",
- "ĠCom b",
- "IJë §Į",
- "è» Ĭ",
- "Ġê²ĥ ëıĦ",
- "Ġaccomplish ments",
- "ĠAnalyt ics",
- "Ġshap ing",
- "re iben",
- "Ġb achelor",
- "Ġfing ert",
- "ack ed",
- "Ġpyram id",
- "ĠStew art",
- "á st",
- "Ġsurviv or",
- "Ġdu ct",
- "Ġdeal ers",
- "æ´ »",
- "ع Ùħ",
- "ли н",
- "Ġed e",
- "×ķ× ¢",
- "ĠÙĥ اÙĨ",
- "ĠÏĦ ι",
- "Ġcho oses",
- "ĠO wn",
- "го ÑĤов",
- "h ire",
- "алÑĮ нÑĭе",
- "ĠÐĽ Ñİ",
- "Ġо ÑģÑĤав",
- "te ch",
- "Ġdro it",
- "Ġsubject ive",
- "en es",
- "Ġdiv is",
- "ave z",
- "Ġmaneu ver",
- "à¹Ħ à¸Ķ",
- "ade ce",
- "ĠEn s",
- "ac ial",
- "ĠProt ection",
- "ĸ ´",
- "Ġform ally",
- "Ġwy d",
- "ingu ém",
- "Ġz iem",
- "Ġrecru iting",
- "×Ļ× ļ",
- "n em",
- "Ġforb idden",
- "ĠB apt",
- "×IJ× ł×Ļ",
- "Ġsubs et",
- "ĠMag az",
- "n ement",
- "Ġaqu ela",
- "rag on",
- "Ġcomm ittees",
- "Ġéta ient",
- "ud i",
- "ĠDa wn",
- "Ġb ore",
- "Ġcompos er",
- "ĠwiÄĻ cej",
- "ang a",
- "Ġdis like",
- "ĠD ays",
- "åŁ º",
- "Ġpar al",
- "Ġm ientras",
- "Ġheaven s",
- "ãģ Ĵ",
- "he id",
- "Ġtrad ers",
- "on ce",
- "Ġmasc ara",
- "ĠÏĢ Ïģο",
- "Ġwhis per",
- "ĠMus k",
- "éĽ Ĩ",
- "ĠFamil ie",
- "All ah",
- "ĠOl ivia",
- "ĠPr os",
- "Ġol ika",
- "il im",
- "Ġrép ond",
- "ĠP eters",
- "Ġ å¾Ī",
- "Ġbit es",
- "Ġv ic",
- "ĠN Y",
- "em ption",
- "Ġ4 50",
- "Ġvisual s",
- "Ġlie u",
- "ück en",
- "ĠSte el",
- "ĠG P",
- "w ait",
- "Ġnotice able",
- "uch a",
- "Ġreh abil",
- "Ġreject ion",
- "ĠÑģлед ÑĥÑİÑī",
- "Ġsl ider",
- "Ġregard ed",
- "Ġgrav it",
- "ĠRes erve",
- "c ount",
- "Ġbre eding",
- "Ġlon ge",
- "ale b",
- "Ġkn ight",
- "Ġв ой",
- "Ġprés ent",
- "Ĥĺ ìļĶ",
- "ĠSpec ifically",
- "Ġpos es",
- "Ġve ure",
- "ok ay",
- "em as",
- "Ġ ãģ§ãģĻ",
- "Ġma jÄħ",
- "Ġweb inars",
- "Ġcann abis",
- "Ġdam als",
- "ĠNorth west",
- "Ġp ada",
- "Ġcrowd s",
- "Ġfut ures",
- "Ġä n",
- "Ġciv ilians",
- "ĠS achen",
- "æ į",
- "Ġtr aces",
- "Ġ먹 ê³ł",
- "Q U",
- "é¡ĺ ãģĦ",
- "ĠI F",
- "an ın",
- "ìĤ ´",
- "Ġb iblical",
- "ĠV ed",
- "Ġst oring",
- "ÑĢав лÑı",
- "æĩī 該",
- "Ġn ast",
- "Ġd ö",
- "ÑĢ оп",
- "el ia",
- "Ġside ways",
- "ĠUnder stand",
- "ĠQ ur",
- "Ġper pend",
- "ĠMill ionen",
- "Ġwater melon",
- "ĠDiv ine",
- "ult ur",
- "ab ord",
- "Ġsuccess es",
- "Ġhom bre",
- "Ġcar p",
- "Ġsus cept",
- "ung kin",
- "Ġk ij",
- "ul us",
- "Ø§Ø ¬",
- "Ġnot ch",
- "Ġpolynom ial",
- "å¹ ²",
- "å ©",
- "Ġún ico",
- "Ġteles cope",
- "Ġpolit ique",
- "k iem",
- "ĠÎŃ Î½Î±",
- "Ġaggreg ate",
- "ĠGe off",
- "Ġtr il",
- "ĠG RA",
- "Ġsubscri ber",
- "im et",
- "Ġдол лаÑĢ",
- "op ing",
- "Ġth erapeut",
- "ĠCan cer",
- "Ġpar ade",
- "Ġir rig",
- "âĻª âĻª",
- "Ġclear er",
- "Ġb og",
- "ĠM aur",
- "า à¸ĩ",
- "ĠShang hai",
- "acht e",
- "ĠK ol",
- "el ujah",
- "Ġha v",
- "ĠCr ime",
- "se k",
- "Ġë ¡ľ",
- "ien na",
- "ĠG or",
- "è Ľ",
- "ĠпоÑĤ ÑĢ",
- "Ġкаж еÑĤÑģÑı",
- "ĠL ift",
- "ĠS ort",
- "ĠP sal",
- "Ġp ing",
- "ĵ Ŀ",
- "ph is",
- "ĠF UCK",
- "ĠS yn",
- "Ġbam boo",
- "¬ ìĺģ",
- "c uts",
- "Ġm mm",
- "Ġfunktion iert",
- "Ġ _",
- "ÃŃ cio",
- "St op",
- "Ġimag inary",
- "Ġnot amment",
- "ĠIniti ative",
- "ãĥ ¥",
- "ĠK urt",
- "Ġlo osen",
- "Ġbus car",
- "çģ «",
- "Ġz elf",
- "Ġpro ps",
- "åĽ ī",
- "Ġmoet en",
- "Ġmill i",
- "Ġhall s",
- "ĠM atch",
- "Ġbrack ets",
- "ĠC ou",
- "æ¦ Ĥ",
- "ĠÐľ аÑĢ",
- "IS A",
- "Ġcig arette",
- "Ġcompet itions",
- "ĠM IN",
- "Ġbeh ö",
- "vo or",
- "Ġ ust",
- "ĠZ i",
- "ĠO cc",
- "ul ates",
- "Ġball oons",
- "Ġpr onto",
- "ĠM iy",
- "ĠF ile",
- "Ġкл аÑģÑģ",
- "нÑĥ л",
- "Ġcere al",
- "Ġincre ment",
- "Ġref ined",
- "åı¦ å¤ĸ",
- "pr ising",
- "ĠR F",
- "Ġrespect ful",
- "Ġlo ot",
- "ask et",
- "Ġdeix a",
- "ing le",
- "Ġfuncion a",
- "ĠRe vel",
- "Ġso ber",
- "Ġperform s",
- "ĠG entle",
- "ãĤ ¨",
- "Ġrecip ient",
- "ĠHa use",
- "Ġë ĥ",
- "F rom",
- "Ġmin isters",
- "Ġpar adox",
- "å°±æĺ¯ èªª",
- "Ġtast ing",
- "Ġ×Ķ× Ĺ",
- "Ġre use",
- "ĠL ane",
- "ĠÑģов еÑĢÑĪ",
- "Ġremem bers",
- "Ġfemin ist",
- "Ġcommit ments",
- "Ġproject ed",
- "Ġg az",
- "iyor uz",
- "Ġoblig ations",
- "R o",
- "z ar",
- "Ġch w",
- "ĠJ AM",
- "ĠbÄĻd Äħ",
- "asp berry",
- "Ġм еÑģÑĤо",
- "ë² ķ",
- "Ġreg ulated",
- "Ġw icht",
- "ĠTre vor",
- "Ġsecond ly",
- "ĠIh re",
- "els h",
- "Ġrep orters",
- "ÑĤоÑĢ а",
- "oy o",
- "G I",
- "Ġinter connect",
- "é IJĺ",
- "OS H",
- "æŃ ²",
- "Ġbr ass",
- "Ġign oring",
- "ä»Ĭ æĹ¥",
- "in fect",
- "Ġpro jekt",
- "ore t",
- "ÏĦα ν",
- "ĠÑĤ ип",
- "Ġmut ta",
- "Ġunbox ing",
- "Ħ °",
- "å¡ Ĭ",
- "Ġadv ised",
- "ĠDen ver",
- "Ġsevere ly",
- "ĠM hm",
- "Ġfl ipped",
- "Ġp ien",
- "Ġkomm un",
- "ĠF RE",
- "Ġà®ĩ à®°",
- "aint ed",
- "Ġkn ives",
- "Ġhab l",
- "Ġgew orden",
- "arett es",
- "C S",
- "Ġмал енÑĮ",
- "Ġgal ax",
- "Ġnin ete",
- "ê±°ë Ĥĺ",
- "Ġs is",
- "Ġadvis ory",
- "Ġdr illing",
- "ĠWould n",
- "ün f",
- "gest ellt",
- "ĠHel en",
- "Ġ×ŀ× IJ",
- "ap olis",
- "Ġrze czy",
- "Ġter ra",
- "Ġhe p",
- "Ġalg ún",
- "ik k",
- "Ġastron om",
- "ĠStar bucks",
- "k Äħ",
- "Ġpat rol",
- "Ġì½ Ķ",
- "Ġg on",
- "Ġ ãĢIJ",
- "Ġson st",
- "Ġencoun ters",
- "Ġret rou",
- "Ġshark s",
- "Ġd or",
- "ĠR ever",
- "Ġev apor",
- "Ġreserv oir",
- "Ġalleg ed",
- "ul er",
- "Ġver m",
- "Ġcommer ce",
- "Ġf itted",
- "ge m",
- "Ġtact ical",
- "Ġl ith",
- "éīĦ å¡Ķ",
- "h ad",
- "è® Ĭ",
- "Ġcarboh yd",
- "Ġlength s",
- "ι ο",
- "Ġdem ographic",
- "R ob",
- "ĠS kin",
- "cc oli",
- "Ġsimpl ified",
- "Ġread ily",
- "ĠC um",
- "ades h",
- "ĠD Ã¥",
- "us st",
- "ig ne",
- "et on",
- "Ġmen or",
- "q i",
- "OO M",
- "à¸Ń à¸Ļ",
- "Ġpsych iat",
- "Ġeight y",
- "Ġм илли",
- "ĠT ob",
- "ed o",
- "ç¶ ²",
- "ĠÄij ến",
- "Ġcirc uits",
- "ĠLAU GH",
- "ic ism",
- "em or",
- "Ġreg ener",
- "eg ree",
- "Ġbure auc",
- "ĠAl ber",
- "ä¹ĭ å¾Į",
- "ĠW or",
- "å¤ «",
- "Ġres in",
- "Ġby ÅĤy",
- "ĠI G",
- "à¯į ,",
- "Ġ7 8",
- "Ġwe eds",
- "ĠMy th",
- "9 3",
- "æ ¿",
- "ĠëĤĺ ìĻĶ",
- "é v",
- "á ½",
- "ö ren",
- "ç ar",
- "ĠP AUL",
- "Ġdisad vant",
- "Ġposition ing",
- "Ġcock tail",
- "Ġagre es",
- "n n",
- "ĠS ally",
- "M s",
- "Ġinher ent",
- "Ġmonet ary",
- "Ġnat ur",
- "ĠN h",
- "ĠImp ort",
- "Ġle ben",
- "Ġw i",
- "uss y",
- "Ġob es",
- "Ġwand ering",
- "Ġìĭ łë",
- "Äħ da",
- "etch up",
- "Ġdispos al",
- "ĠJ A",
- "ĠC er",
- "z illa",
- "Ġvir gin",
- "ĠSl ide",
- "and el",
- "Ġrighteous ness",
- "ĠÎ £",
- "Ġide ia",
- "ä½ł 好",
- "иÑĢов аÑĤÑĮ",
- "ר ×IJ",
- "Com ment",
- "Ġpre lim",
- "ĠV ale",
- "Ġì§Ģë Ĥľ",
- "ĠV anc",
- "OM AN",
- "Ġп Ñĸд",
- "Ġy um",
- "st re",
- "ce m",
- "Ġpo cz",
- "Ġfrag ment",
- "ĠÑģлÑĥÑĩа е",
- "Ġunder go",
- "ĠH ank",
- "ce ks",
- "ĠF PS",
- "Ġoc ur",
- "Ġdeter ior",
- "æ³ ¨",
- "Ġempres as",
- "Pa ul",
- "Ġ) ))",
- "ĠвÑĢем ени",
- "Ġsc old",
- "×Ļ× ¢",
- "Ġsuspect ed",
- "Ġaccess ing",
- "Ġsubst it",
- "Ġhistor ians",
- "ä» »",
- "Ġдел о",
- "Ġsoci ed",
- "r one",
- "Ġre den",
- "Ġext ends",
- "epher d",
- "Ġbal con",
- "ä¸į èµ·",
- "ĠSol o",
- "Ġpolit ician",
- "олÑĮ но",
- "Ġirgend w",
- "Ġtraum atic",
- "Ġrapp er",
- "ĠRO BERT",
- "Re ally",
- "æģ ¯",
- "Ġline up",
- "AS E",
- "Ġcontract or",
- "ĠCorpor ation",
- "g or",
- "ĠTod o",
- "ÑģÑĤÑĢ ой",
- "F BE",
- "Ġnews letter",
- "Ġko ÅĦ",
- "alt ies",
- "ĠпÑĢ иÑĩ",
- "ĠHe avy",
- "Ġsw ords",
- "Ġmanip ulation",
- "Ġfun k",
- "Ġv Ã¥r",
- "ĠTal iban",
- "Ġë° ¥",
- "Ġac ne",
- "ür ü",
- "Ġdes wegen",
- "ĠD ust",
- "Ġsil ic",
- "Ġhook s",
- "Ġbl ij",
- "Ġpet its",
- "Ġfil me",
- "ĠBere ich",
- "ĠSa id",
- "Ġimp osed",
- "Ġdi ary",
- "Ġго ÑĢ",
- "ĠG ates",
- "Ġal ta",
- "å¸ Į",
- "Ġch cia",
- "ple asant",
- "Ġë° Ŀ",
- "Ġmoż emy",
- "ĠAust ria",
- "Ġbro ker",
- "Ġsuck ed",
- "èĢ ĥ",
- "Ġcomp artment",
- "Ġcl one",
- "Ġ×Ķ× ¢",
- "ĠDan ke",
- "Ġnoch mal",
- "ез д",
- "Ġad renal",
- "Ġkle inen",
- "ãģ¾ ãģĹãĤĩãģĨ",
- "Ġsubsequ ently",
- "Ġdecent ral",
- "Ġgen etics",
- "Ġê´ ij",
- "Ġmon itors",
- "ĠApp lic",
- "ĠRep orter",
- "w ert",
- "Ġwie m",
- "ĠMove ment",
- "Ġinterview ing",
- "Ġhair s",
- "Ġpu ò",
- "ĠChel sea",
- "Ġco her",
- "Ġc ot",
- "Ġz as",
- "Ġpatch es",
- "Ġl ah",
- "Ñĥн к",
- "ĠRe agan",
- "ĠMar co",
- "c ity",
- "Ġdef ender",
- "Ġdecor ation",
- "ij i",
- "Ġl itter",
- "Ð ¨",
- "Ġj ego",
- "RE W",
- "ĠP ik",
- "ĠHe e",
- "ĠI v",
- "Ġи де",
- "ĠThe ater",
- "ĠÑĩаÑģ ÑĤо",
- "Ġswe ater",
- "Ġhighlight ing",
- "Ġa insi",
- "Ġdipl omatic",
- "ĠNever theless",
- "å ³",
- "AS ON",
- "Ġpúblic o",
- "Ġf erm",
- "reat ed",
- "c od",
- "Ġë¬ ¼ë",
- "Ġm ister",
- "ĠVanc ouver",
- "Ġrecogn izes",
- "ec d",
- "Ġcomplic ations",
- "en cial",
- "ãģĹ ãģı",
- "Ġê°Ģ ì§Ģ",
- "ĠUlt imate",
- "Ġva ig",
- "ĠM erry",
- "×ķ× Ĵ",
- "ĠMar cus",
- "ç¸ ½",
- "ow ego",
- "Ġm ente",
- "S m",
- "Ġa ja",
- "ĠTa o",
- "Ġjud icial",
- "Ġentrepreneurs hip",
- "Ġнем ного",
- "Ġp is",
- "Ġer g",
- "Ġch rist",
- "ĠC urt",
- "ĠÑĢаÑģ п",
- "λ ε",
- "ens ch",
- "ÃŃ re",
- "Ġfo cal",
- "ĠDiam ond",
- "av ÃŃa",
- "Ġh anno",
- "ĠSqu ad",
- "Ġassoci ations",
- "ĠCreat ive",
- "Ġmess enger",
- "Ġbe gging",
- "Ġdec imal",
- "Ġd Ä±ÅŁ",
- "Ġmet adata",
- "sel s",
- "ĠÄ° ÅŁ",
- "ữ a",
- "Ġdiffic ile",
- "d ı",
- "Ġs laughter",
- "ĠVer g",
- "Ġ×Ĵ ×Ŀ",
- "ç° ¡",
- "æĮ ī",
- "ĠTe a",
- "ass es",
- "O k",
- "Ġsynth es",
- "ot iation",
- "Ġpain ter",
- "Ġel bows",
- "Ġarchitect ural",
- "ĠÑĢ ад",
- "Ġgl or",
- "im age",
- "amp a",
- "cul iar",
- "ł ¨",
- "Ġte ve",
- "ĠSt elle",
- "ĠB am",
- "Ġì´ Ī",
- "as is",
- "ip edia",
- "ĠG I",
- "ĠAct ive",
- "çĦ¶ åIJİ",
- "az i",
- "ãĤĮ ãģ¦",
- "ĠL ucky",
- "íķ ©",
- "ĠпÑĢ иÑħод",
- "Ġrun way",
- "Ġauthent ication",
- "Ġpos ible",
- "Ġsupp lements",
- "Ġsurg ical",
- "G en",
- "Ġfeas ible",
- "D O",
- "Ġout look",
- "Ġinter vals",
- "Ġan ecd",
- "Ãł ng",
- "Ġstra ps",
- "ĠSh u",
- "ud d",
- "iss enschaft",
- "Ġport e",
- "Ġcomm itting",
- "Ġall ey",
- "Ġco venant",
- "ĠPed ro",
- "less ness",
- "ĠSol id",
- "ĠM olly",
- "Ġн екоÑĤоÑĢ",
- "Ġcooper ate",
- "åĮ Ĺ",
- "oll en",
- "Ġtun a",
- "Ġkinderg arten",
- "ĠS iz",
- "Ġduż o",
- "ĠM BA",
- "ĠGEOR GE",
- "ĠF isher",
- "å¿ ĺ",
- "ĠCa esar",
- "ĠкÑĢаÑģ ив",
- "ĠDel hi",
- "zy m",
- "Ġexpl icar",
- "ê°Ģ ì§Ģ",
- "un s",
- "gr ow",
- "ĠпÑĢ иÑģ",
- "Ġ8 6",
- "Ġst ating",
- "Ġmass a",
- "ch ter",
- "Ġì»¬ë Ł¬",
- "Ġdep uty",
- "S M",
- "n oc",
- "Ġge ography",
- "ĠEnter prise",
- "ĠC ant",
- "ö z",
- "Ġun pack",
- "ĠíĻ Ķë",
- "Ġsearch es",
- "Ġpres idency",
- "Ġtri vial",
- "Ġp ige",
- "ou bt",
- "ãĤ ļ",
- "ì¼ ĢìĿ´",
- "Ġbudget s",
- "Ġu b",
- "Ġp ne",
- "ĠY ale",
- "ĠÅŁ öyle",
- "reg ular",
- "Ġimper fect",
- "AR A",
- "Ġfam ÃŃlia",
- "ur m",
- "ĠAdvent ure",
- "ãĥ Ĭ",
- "c is",
- "em ark",
- "Ġne go",
- "Ġinappropri ate",
- "ĠпÑĢи з",
- "ĠÑĢ ол",
- "Ġdream ed",
- "B ry",
- "Ġshut tle",
- "Ġpill ars",
- "Ġb ik",
- "in um",
- "ĠÑĥ Ñģ",
- "ĠNe br",
- "Ġperpend icular",
- "Ġbook ed",
- "ber y",
- "Ġv ikt",
- "be ar",
- "es us",
- "Ġвозм ожно",
- "¨ ¹",
- "Ġpresum ably",
- "ĠMem phis",
- "Ġambul ance",
- "×ķ× ŀר",
- "Ġthumbna il",
- "Ġmod ification",
- "éĩ ı",
- "Ġinterpret ed",
- "Ġprom o",
- "Ġκ ά",
- "Ġε ÏĢ",
- "Ġacoust ic",
- "ĠD B",
- "åĵ İ",
- "Ġnon etheless",
- "ou le",
- "Ġpe qu",
- "Ġkn ob",
- "ãĤ £",
- "ĠëıĮ ìķĦ",
- "Ġpurch ases",
- "ĠÃĩ ünkü",
- "Ġdivid ing",
- "per form",
- "ract ion",
- "health y",
- "ĠTit le",
- "Ġu k",
- "Ġcer ca",
- "Ġargu ably",
- "Ġf ale",
- "ë³ µ",
- "Ġgam ers",
- "Ġutil izing",
- "Ġoff ended",
- "Ġt ava",
- "al ı",
- "Ġmed ian",
- "Ġinfect ious",
- "ĠAn nie",
- "Ġsmart phones",
- "Ġpar ole",
- "åĸ Ŀ",
- "ĠEp ic",
- "z za",
- "Ġun ified",
- "Ġê·¸ë ķĮ",
- "Ġcur tain",
- "ĠÄ ĥ",
- "Ġsex ually",
- "Ġuns erem",
- "ĠCon vention",
- "Ġalleg edly",
- "Y a",
- "ĠH oo",
- "en ment",
- "æĢ ª",
- "íĽ Ħ",
- "Ġgig antic",
- "Ġnot ing",
- "Ġre bo",
- "ĠJ ama",
- "ĠAl z",
- "Ġborrow ed",
- "ì¹ ¨",
- "Ġper ipher",
- "оÑĤ а",
- "ĠG B",
- "ĠGe ar",
- "Ġeconom ically",
- "Ġtele fon",
- "Ġqu eremos",
- "ĠдалÑĮ ÑĪе",
- "Ġr as",
- "ĠTe ach",
- "ic ios",
- "at os",
- "Ġpl edge",
- "b au",
- "ĠHim self",
- "L ink",
- "Ġesper o",
- "Ġchrom os",
- "ĠP ER",
- "Ġer le",
- "Ġpod ium",
- "ç os",
- "Ġnie u",
- "Ġf en",
- "ĠGO D",
- "ĠCh ocolate",
- "wer k",
- "Ġt ừ",
- "Ġsupp ress",
- "λ η",
- "Ġ24 0",
- "Ġsit ä",
- "Ġhonest y",
- "ĠB io",
- "ĠB ard",
- "ĠобÑī ем",
- "Ġм Ñĥз",
- "Ġmar ble",
- "ĠÑĨ енÑĤ",
- "Ġproc ure",
- "Ġrot or",
- "ber n",
- "Ġtu h",
- "Ġhead set",
- "at em",
- "Ġwarrant y",
- "à® ´",
- "Ġfil ing",
- "ι ά",
- "Ġcomp rendre",
- "Ġimp ulse",
- "Ġsal v",
- "wr itten",
- "Ġinstit ute",
- "K im",
- "ĠLGBT Q",
- "fic iente",
- "H is",
- "ĠαÏħÏĦ ÏĮ",
- "Ġteen age",
- "or us",
- "ĠÑĢаз б",
- "S ee",
- "ĠCons erv",
- "á»ģ n",
- "ful ness",
- "Ġstraw berries",
- "ĠAb u",
- "и он",
- "Ġo lla",
- "NO ISE",
- "ĠEm ploy",
- "Ġwip ed",
- "ur ger",
- "Ġmod ifications",
- "Ġíķĺ ì§Ģ",
- "Ġfoot steps",
- "Ġhon ors",
- "Ġad ul",
- "Ġfl ipping",
- "ĠH U",
- "Z Y",
- "Ġintegr ating",
- "ب ر",
- "ull a",
- "Ġnatuur lijk",
- "ĠíĹ Ī",
- "ĠEth ereum",
- "ÙĬ ÙĦ",
- "w ed",
- "Ġpe aks",
- "ĠK es",
- "Ġblo om",
- "Ġcr ashing",
- "Ġ9 11",
- "ĠоÑĤ лиÑĩ",
- "Ġcontro llers",
- "ĠD od",
- "Ġвм еÑģÑĤе",
- "Ġsort ir",
- "å¥ ĩ",
- "ĠStra ight",
- "ĠGrac ias",
- "Ġgro ove",
- "Ġto gg",
- "Ġìĭ¶ ìĿĢ",
- "é ro",
- "Ġout ward",
- "ĠW A",
- "ĠRock y",
- "Ġsc am",
- "Ġhay at",
- "ig nty",
- "â Ħ",
- "pl ings",
- "Ġantibiot ics",
- "Ġ ä¸Ģ",
- "Ġnever theless",
- "j ang",
- "com merce",
- "Ġspo iler",
- "Ġglo ve",
- "Ġch atter",
- "ĠB Y",
- "~ ?",
- "Ġíĺ ¸",
- "Ġdem ol",
- "we chsel",
- "im ir",
- "Ġra id",
- "еÑĢ Ñħ",
- "ìŀIJ 기",
- "en f",
- "Ġcomment ed",
- "Ġoptim ized",
- "Ġconv icted",
- "Ġb ats",
- "ĠS B",
- "ĠA ur",
- "ĠT ong",
- "Ġimplic it",
- "ĠJan et",
- "Ġre ag",
- "ãģ ²",
- "ĠAdv anced",
- "Ġimp ose",
- "ש ×Ķ",
- "Ġschem es",
- "oug her",
- "ab olic",
- "Ġê±° ì£ł",
- "Ġslow ing",
- "Ġwt edy",
- "Ġdest ructive",
- "Ġоп ÑĢед",
- "Ġland mark",
- "Ġëı Ī",
- "ĠWalk ing",
- "Ạ¹",
- "Ġt ijd",
- "ĠK N",
- "ĠQu ant",
- "ìĺ ¤ë",
- "Ġк ÑĢÑĥ",
- "Ġper der",
- "Ġno ve",
- "änd e",
- "Ġãģ Ĺ",
- "b ia",
- "Ġcust ody",
- "Ġb iod",
- "æĿ± 西",
- "Ġdirect ing",
- "... âĢĭ",
- "Ġre loc",
- "Ġdemand e",
- "ãĤĵ ãģł",
- "Ġo ÄŁlum",
- "Ġод на",
- "ĠMil k",
- "åı ·",
- "ĠK ra",
- "ĠH onda",
- "Ġp ue",
- "Ġele kt",
- "Ġbegin ners",
- "Ġspe ar",
- "ÃŃ nh",
- "ĠLu ft",
- "Ġn ig",
- "ĠSchool s",
- "Ġfor ums",
- "ĠQ in",
- "pp o",
- "Ġz ag",
- "ĠÐ ®",
- "Ġtooth p",
- "ĠSt yle",
- "ì´ Ī",
- "Ġpun ct",
- "Ġrep s",
- "ĠA ly",
- "Ġamend ments",
- "Ġö z",
- "Ġdig its",
- "ur ai",
- "Ġcha otic",
- "ĠMas ters",
- "e on",
- "ĠC ash",
- "ĠC uz",
- "Ġbede utet",
- "Ġscan ning",
- "Ġж д",
- "н еÑĤ",
- "Ġcertain ty",
- "j ek",
- "Ġdi jo",
- "ĠCl imate",
- "Ġr inse",
- "Ġk rij",
- "vel and",
- "Ġsound track",
- "ĠSa fe",
- "ĠNo va",
- "9 4",
- "Ġa the",
- "ĠVer b",
- "ol er",
- "ìĿ´ì £ł",
- "Ġv in",
- "Ġrespir atory",
- "ĠStud y",
- "ĠC AM",
- "Ġav ocado",
- "ĠZ hen",
- "Ġlat ency",
- "Ġfe athers",
- "Ġcont ar",
- "Ġв еÑī",
- "Ġf ark",
- "Ġbl ended",
- "Ġexpl oded",
- "ĠX X",
- "ĠBen im",
- "Ġalgu ém",
- "isto ire",
- "Ġconfident ial",
- "Ġm ast",
- "Ġì ¿",
- "ge h",
- "Ġdis respect",
- "ĠSystem s",
- "Æ° a",
- "E d",
- "Ġw ys",
- "Ġex otic",
- "Ġgl owing",
- "ù ng",
- "oun ge",
- "è Ħ",
- "ани з",
- "Ġpal av",
- "ĠSw ord",
- "Ġg im",
- "ĠC row",
- "Ġpot ent",
- "b ish",
- "Ġab used",
- "ĠJ ed",
- "Ġg ambling",
- "ĠS pect",
- "Ġinvestig ators",
- "æĻ ļ",
- "Ġr att",
- "Ġdo b",
- "ĠD ES",
- "h og",
- "ĠоÑĤк ÑĢÑĭ",
- "íĮ ħ",
- "ĠденÑĮ ги",
- "Ġíĺ ¹",
- "Ġë¨ ¸ë¦¬",
- "Ġsat uration",
- "Ġinher ited",
- "ĠInnov ation",
- "ìĹ Īëįĺ",
- "Ġtang ible",
- "Ġdep ri",
- "h ed",
- "Ġпом ог",
- "Ġslic ed",
- "ॠį",
- "Ġth ế",
- "Å ¥",
- "6 8",
- "Ġcor ona",
- "Ġgift ed",
- "Ġso ir",
- "Ġhum ility",
- "ĠìĿ´ 걸",
- "Ġflaw s",
- "ĠпÑĢ акÑĤи",
- "Ġk ald",
- "wa ż",
- "y w",
- "ãĤĵ ãģ§ãģĻ",
- "ir teen",
- "Ġcroch ets",
- "¦¬ ê°Ģ",
- "ĠìłĦ ìĹIJ",
- "Ġdes e",
- "æ¥ Ń",
- "Ġм аг",
- "Ġdz iaÅĤ",
- "Ġl ég",
- "ch anging",
- "Ġlle v",
- "ÅĦ sk",
- "çĶ »",
- "Ġ198 4",
- "orn s",
- "ĠW elsh",
- "Ġpharm aceutical",
- "Ġpump ing",
- "ĠSh aw",
- "p unk",
- "Ġva ult",
- "Ġkin etic",
- "Ġhur ricane",
- "ĠInc luding",
- "ứ c",
- "ĠGrand pa",
- "ans hip",
- "é¦Ļ 港",
- "ĠвÑĭ Ñħод",
- "н ож",
- "ľ ł",
- "ut ta",
- "Ġê²ģ ëĭĪëĭ¤",
- "Ġb az",
- "Ġпо ÑĪ",
- "Ġpe culiar",
- "zy Äĩ",
- "ĠEll ie",
- "Ġlearn s",
- "ĠKr ishna",
- "Ġconse cut",
- "Ġemp ath",
- "ĠD in",
- "Ġtrad ed",
- "ĠBor is",
- "ugg age",
- "oll a",
- "Ġназ в",
- "Ġetern ity",
- "Ġв п",
- "è mes",
- "Ġgra pp",
- "b é",
- "ĠпÑĢед ÑģÑĤав",
- "ĠF C",
- "į ëĭĪëĭ¤",
- "e ven",
- "ĠNebr aska",
- "ortun e",
- "Ġk arena",
- "ĠAg ent",
- "Ġst ing",
- "ĠP I",
- "Ġmunicip al",
- "power ed",
- "Ġconse gue",
- "ĠMan chester",
- "Ġrain y",
- "Ġbl i",
- "Ġk ost",
- "Ġhal ten",
- "ĠAh hh",
- "ins ula",
- "er ting",
- "ĠاÙĦ Ùģ",
- "Ġrel acion",
- "Ġk omen",
- "Ġd ome",
- "Ġpri ests",
- "ĠInt rodu",
- "rop he",
- "sh ore",
- "vel t",
- "clip se",
- "ĠÑĢ ÑĥÑģ",
- "×Ļ× ¡",
- "Ġsab emos",
- "ĠHoll and",
- "og i",
- "ank i",
- "ĠM ats",
- "Ġsm oked",
- "ull ie",
- "Ġeuro pe",
- "ĠдейÑģÑĤв иÑĤелÑĮно",
- "Ġbard ziej",
- "Ġtransform ing",
- "ĠE z",
- "op ath",
- "Ġìĸ¸ ëĭĪ",
- "ĠÑģÑĤ ан",
- "ằ ng",
- "ั à¹ī",
- "ĠO uch",
- "Ġclear ance",
- "ust ain",
- "Ġsolid arity",
- "Ġpro ving",
- "ĠÐĺ н",
- "ĠÑģ ÑĬ",
- "Ġpro long",
- "ад но",
- "Ġs os",
- "ĠDe al",
- "Ġ17 0",
- "m ons",
- "Ġз ем",
- "Ġlo gged",
- "Ġlif elong",
- "Ġsens ory",
- "Ġbe hold",
- "ĠF AR",
- "èt ement",
- "ĠFed eration",
- "Ġdod ge",
- "ĠSh ir",
- "Ġdrag ons",
- "ĠAr ctic",
- "Äħ ż",
- "Å į",
- "Â º",
- "Ġden ke",
- "Ġpodr ÃŃa",
- "co le",
- "ÑĥлÑĮÑĤ аÑĤ",
- "Ġsystem atic",
- "ам а",
- "ch os",
- "Ġclin ics",
- "ĠB S",
- "Ġtal es",
- "us ions",
- "Ġí Ī¬",
- "Ġpres ervation",
- "Ġl ore",
- "ĠProt est",
- "á» Ľ",
- "å¸ Ĥ",
- "Ġacknowled ged",
- "ĠIs aiah",
- "ĠëķĮ ëĬĶ",
- "Ġ× ĺ",
- "Ġcompet itor",
- "Ġadv ancing",
- "z ip",
- "Ġtent h",
- "ĠLa ure",
- "Ġh ints",
- "Ġexerc ising",
- "ŀ ľë",
- "ĠIntell igence",
- "u ated",
- "OU T",
- "op ed",
- "Ġaut onomy",
- "Ġbrand ing",
- "ĠMediter ranean",
- "Ñĸ к",
- "Ġscrew driver",
- "Ġsu pre",
- "Ġst ap",
- "Ġjurisd iction",
- "ĠSetting s",
- "Ġfore front",
- "ĠF emale",
- "com fort",
- "Ġmultiplic ation",
- "ĠMur ray",
- "Ġbo b",
- "ĠT as",
- "Ġt ahu",
- "Ġon un",
- "et ter",
- "Ġproph ets",
- "l ag",
- "Ġreven ues",
- "Ġpr á",
- "Ġupload ing",
- "Ġmach inery",
- "asc al",
- "ĠEst á",
- "ĠG oth",
- "ĠB ald",
- "ĠS aw",
- "Ġstri pes",
- "ìł ij",
- "Ġpow in",
- "æĹ¥ æľ¬",
- "Ġhost ile",
- "Ġdar um",
- "Ġprevent ed",
- "ожалÑĥй ÑģÑĤа",
- "Ġalgun as",
- "Ġhop eless",
- "Ġz naj",
- "Ġread ings",
- "Ġcra ving",
- "t at",
- "ĠP ig",
- "Ġli ar",
- "çĪ ±",
- "Ġmulti player",
- "Ġd ale",
- "ĠCour se",
- "íģ ¼",
- "ĠK ita",
- "Ġcustom s",
- "Ġrespond s",
- "end ra",
- "è¦ ĸ",
- "Ġmet ro",
- "Ñģ ол",
- "Ġmitig ate",
- "Ġopp ression",
- "Ġ æĪijåĢij",
- "qu inho",
- "Ġam mo",
- "Ġen fer",
- "Ġp ony",
- "Ġ ounces",
- "° Ķ",
- "ĠìĪĺ ê°Ģ",
- "Ġdich o",
- "ĠDe b",
- "Ġwond ers",
- "ĠRo ose",
- "Ġpri zes",
- "ĠA LEX",
- "Ġthank fully",
- "Ġtiss ues",
- "ĠÑĢав но",
- "ĠL una",
- "intell igible",
- "ĠìĻ ¸",
- "ê° ij",
- "ĠHe at",
- "ĠÑģ ид",
- "ĠQu i",
- "Ġ ions",
- "Ġaccommod ation",
- "ä¾ ¿",
- "ĠK art",
- "ien st",
- "Ġt arde",
- "Ġso aked",
- "ĠCase y",
- "Ġì´ Ŀ",
- "ĠÑĢ Ñĥб",
- "Ġdifferent i",
- "Ġleft over",
- "Ġexch anges",
- "sec ond",
- "Ġfirst ly",
- "Ġbuild er",
- "ri en",
- "Ġd w",
- "Ġboun cing",
- "? <",
- "olog ÃŃa",
- "we alth",
- "Ġmed itate",
- "ĵ¤ ìĿĺ",
- "ĠC raft",
- "è§ī å¾Ĺ",
- "æĻ ®",
- "ri v",
- "ĠAgain st",
- "Ġcer amic",
- "esp ère",
- "Ġcompet ent",
- "ĠHop kins",
- "Ġkil os",
- "Ġgra vel",
- "Ġpist on",
- "Ġfriends hips",
- "Ġesc re",
- "Ġvo z",
- "ĠGes ellschaft",
- "Ġunter stüt",
- "Ġmu j",
- "Ġwarning s",
- "p os",
- "ĠProfess ional",
- "w szy",
- "od le",
- "b ands",
- "Ġteam work",
- "stell ung",
- "Ġd x",
- "åį Ĭ",
- "Ġatt orneys",
- "Ġweit ere",
- "ãħĭãħĭ ãħĭ",
- "ĠOrig inal",
- "×Ļ× Ĺ",
- "Ġbroadcast ing",
- "ĠпеÑĢв Ñĭй",
- "uch i",
- "Ġhe ure",
- "Ġgra bs",
- "ĠW OR",
- "ĠPla id",
- "M in",
- "Ġp az",
- "ĠP uis",
- "um u",
- "it ates",
- "Ġco ats",
- "Ġbu en",
- "Ġhe ir",
- "Ġpne um",
- "ש ר",
- "ens er",
- "ĠJUD GE",
- "Ġbl onde",
- "á¹ Ľ",
- "Ġg ak",
- "Ġs ık",
- "Ġquot ed",
- "Ġequip o",
- "Ġw ishing",
- "ÃŃ cia",
- "Ġver bs",
- "çµ Ħ",
- "ĠCanad ians",
- "Ġgover ning",
- "ĠEv ans",
- "E uro",
- "Ġgen res",
- "Ġunters chied",
- "ĠBeck y",
- "³¼ ê²ĮìļĶ",
- "Ġe inge",
- "ĠRa ise",
- "ol and",
- "ĠStr ateg",
- "Ġer es",
- "ĠVeter ans",
- "Ġbreak out",
- "Ġsant é",
- "Ġad el",
- "Ġinvestig ated",
- "Ġpe ur",
- "Ġag ile",
- "Ġrail road",
- "ans ka",
- "Ġе й",
- "Ġexp os",
- "ator ies",
- "ĠCont ent",
- "Ġtruth s",
- "ĠTra il",
- "Ġgu a",
- "Ġp ores",
- "Ġwrit ings",
- "ĠU hr",
- "ĠThat s",
- "Ġic ing",
- "O C",
- "ĠProdu ction",
- "Ġcar ne",
- "IS S",
- "Ġn inguém",
- "n on",
- "Ġv icious",
- "×ķ× Ķ",
- "Ġrecon nect",
- "Ġcent res",
- "ĠK em",
- "Ġcre ase",
- "ĠìĿ´ë ¯¸",
- "айÑĤ еÑģÑĮ",
- "Ġб оÑĢ",
- "ĠHay ır",
- "ĠÑģ Ñĥд",
- "Ġún ica",
- "owa ÅĤ",
- "Ġad her",
- "h ua",
- "Z Z",
- "Ġprecis o",
- "Ġcurrent s",
- "Ġseason ed",
- "ĠIo T",
- "ĠB ishop",
- "è¨ Ī",
- "st ed",
- "ĠBern ard",
- "ì¤ ĺ",
- "æ² »",
- "ĠGl enn",
- "Ġktóry m",
- "ื à¹Ī",
- "Ġast rolog",
- "ĠK ot",
- "å¤ ľ",
- "Ġparf ois",
- "Ġfor wards",
- "ĠW iÄĻ",
- "ĠÎ ĺ",
- "Ġn ano",
- "è» į",
- "s ub",
- "ĠBr ill",
- "Ġgr it",
- "Ġc ited",
- "g ado",
- "Ġmel ts",
- "Ġfor cé",
- "âĸĪ âĸĪ",
- "Ġb ajo",
- "Ġdiscret ion",
- "° °",
- "at ivity",
- "Ġsitu ated",
- "ãĥ« ãĤ¯",
- "Ñīе е",
- "åľ° æĸ¹",
- "ĠпÑĢин ÑĨип",
- "am az",
- "Ġaqu arium",
- "Ġdissol ve",
- "ĠGod s",
- "S uper",
- "Ġam id",
- "z k",
- "Ġ ãģĦ",
- "éł IJ",
- "amp f",
- "Ġhel a",
- "' !",
- "Ġdevelopment al",
- "ĠD ise",
- "ĠÑĢабоÑĤ аеÑĤ",
- "Ġsnaps hot",
- "好 好",
- "Õ ¸",
- "ĠY ue",
- "ĠH ulk",
- "ĠDo om",
- "ĠFel ix",
- "Ġré f",
- "M ale",
- "ç· Ĭ",
- "ph ants",
- "EN S",
- "ĠMe chan",
- "ĠG olf",
- "åĨį è¦ĭ",
- "Ġgener osity",
- "ät ze",
- "Ġunlock ed",
- "Ġ ãĤĴ",
- "íĥ ģ",
- "ocaly pse",
- "Al right",
- "Ġê° ľë",
- "Ġ×IJ× ij׾",
- "ĠKeep ing",
- "Ġcollabor ating",
- "ch ief",
- "ĠFern ando",
- "Ġchef s",
- "ĠíĶ¼ë ¶Ģ",
- "Ġsk ipped",
- "Ġperson n",
- "Ġax e",
- "che z",
- "Ġextract ion",
- "ĠA V",
- "ĠGib bs",
- "Ġí ľ",
- "Ġs ı",
- "I AM",
- "V iew",
- "ĠGR ANT",
- "Ġëª ¸",
- "Ġver ification",
- "Ġdep icted",
- "ĠMo z",
- "ou x",
- "Ġt ul",
- "Ġsc anner",
- "Ġcomed ian",
- "ĠVol ks",
- "ĠJE FF",
- "è¨Ĥ éĸ±",
- "§ Ħ",
- "Ġdistract ion",
- "r á",
- "ĠIN TER",
- "Ġsin cer",
- "Ġ×ŀ× ª",
- "Ġש ׳",
- "Ġconstruct ive",
- "ar f",
- "ĠëĪ Ħë",
- "Ġe co",
- "r amos",
- "Ġrenew ed",
- "in ement",
- "ĠU b",
- "ĠPe pper",
- "ì§Ģ ê°Ģ",
- "ĠDar win",
- "Ġmerch and",
- "Ġv árias",
- "è ce",
- "N G",
- "ĠìľĦ íķ´ìĦľ",
- "Ġак ÑĤив",
- "ĠUn ters",
- "ع ÙĦ",
- "Ġint ric",
- "omm a",
- "ie ving",
- "ĠCarol ine",
- "åĵ ģ",
- "ĠPR ES",
- "Ġperform er",
- "Ġaut our",
- "ãģ¾ãģĽ ãĤĵ",
- "Ġutter ly",
- "Ġsynth esis",
- "Ġles bian",
- "Ġretrie ve",
- "Ġmane ira",
- "Ġimp air",
- "Ġment oring",
- "ĠSoul s",
- "ĠGo Pro",
- "ÑĢ аÑĤÑĮ",
- "Ġc ose",
- "ĠSS D",
- "I RE",
- "Ġup front",
- "ĠA un",
- "Ġgam er",
- "Ġl itt",
- "Ġag gression",
- "ĠLike wise",
- "ĠBet ty",
- "ĠD art",
- "ĠD LC",
- "ish ment",
- "ìŀ¥ ìĿĦ",
- "Ġ 对",
- "ç» ı",
- "c ream",
- "ĠBaby lon",
- "Ġn ug",
- "br ar",
- "Ġa ynı",
- "am ily",
- "b ike",
- "ahah aha",
- "lo yd",
- "Ġmir a",
- "Ġper me",
- "ĠG aming",
- "Ġfirm ware",
- "M a",
- "Ġassist ed",
- "at ics",
- "Ġìķŀ ìľ¼ë¡ľ",
- "ĠM ental",
- "niej s",
- "ĠI z",
- "ow Äħ",
- "Ġt ougher",
- "Ġde ed",
- "èĭ ¦",
- "Ġsty lish",
- "ĠTool s",
- "ĠH amp",
- "Ġsun screen",
- "Ġartic ulate",
- "i ye",
- "и ÑĦ",
- "ĠSp read",
- "ĠHA VE",
- "Ġsw irl",
- "Ġspons oring",
- "ä» ĭ",
- "iov ascular",
- "mes i",
- "Ġrelax ation",
- "ĠÑģво иÑħ",
- "Ġmar gins",
- "Ġsa ÄŁ",
- "ĠPr ide",
- "ĠÏĦοÏħ ÏĤ",
- "и ÑĨи",
- "en ci",
- "Do es",
- "Ġcor pse",
- "Ġend urance",
- "Ġí ŀĺ",
- "ì¹ ´",
- "Ġhair cut",
- "Ġinterrupt ed",
- "Ġwind y",
- "ĠC aleb",
- "Ïģ Ïĩ",
- "ĠPour quoi",
- "Ġhol istic",
- "uc lear",
- "ĠWho le",
- "å£ «",
- "A ct",
- "Ġgall on",
- "c ade",
- "ĠReg ional",
- "ro ads",
- "ĠSch ne",
- "á ng",
- "Ġиз мен",
- "ãĤĪ ãģŃ",
- "Ġmen us",
- "Ġspl itting",
- "Ġpr iced",
- "ĠÎ ĵ",
- "Ġus ername",
- "ĠÐŀ Ñĩ",
- "Ġcomp ressed",
- "y in",
- "Ġguard ian",
- "Ġgo of",
- "Ġcheck list",
- "Ġinter change",
- "Ġexped ition",
- "Ġex tern",
- "Ġinfra red",
- "eng o",
- "Ġden ying",
- "Ġpack ets",
- "on ent",
- "B B",
- "ĠInc re",
- "Ġsin i",
- "ÃŁ er",
- "è g",
- "ma al",
- "gen eration",
- "Ġminor ities",
- "Ġlle var",
- "Ġnom ination",
- "Ġcons id",
- "Ġ×ľ× ¢",
- "m uÅŁ",
- "ĠEs c",
- "Ġnumer ator",
- "Ġka ik",
- "Ġktóry ch",
- "ies en",
- "Ġv ê",
- "ĠUS S",
- "ĠPri vate",
- "Ġод но",
- "Ġal ém",
- "ÃŃt ulo",
- "Ġlim b",
- "Ġforg iven",
- "Ġdiscl osure",
- "ÏĦ ί",
- "Ġning ún",
- "Ġtherapeut ic",
- "Ġnegoti ating",
- "ĠN ike",
- "ense ful",
- "Ġin cap",
- "Ġflag ship",
- "t own",
- "â Ī",
- "ĠÏĢ ολ",
- "Ġwol ves",
- "Ġviol ations",
- "ĠAr nold",
- "Ġinterven e",
- "Ġhe ater",
- "Ġrecurs os",
- "Ġma id",
- "ê² ¼",
- "Ġдав айÑĤе",
- "ĠCe lebr",
- "Ġca pe",
- "ĠSt y",
- "ain en",
- "s ite",
- "b ij",
- "Ġп олÑĮз",
- "Ġfr amed",
- "Ġpublish ers",
- "ĠÑĩ ÑĥÑĤÑĮ",
- "Ġtempt ation",
- "Ġcert eza",
- "Ġex empt",
- "ìĬ ¹",
- "se lling",
- "ĠT ask",
- "ho on",
- "ĠC oc",
- "ĠPark s",
- "Ġrepet ition",
- "ĠÑĤ Ñĥда",
- "Ġens l",
- "ĠdeÄŁ iÅŁ",
- "ĠOr lando",
- "ĠMain ten",
- "æŃ ¢",
- "oc ument",
- "ĠH C",
- "Ġscoot er",
- "Ġнап иÑģ",
- "Ġtight er",
- "Ġte ase",
- "Ġremo ves",
- "Ġkij ken",
- "ĠÑģÑĥ ÑīеÑģÑĤв",
- "Ġth é",
- "ĠвÑĭ глÑıд",
- "Ġrel ieve",
- "Ġmit ä",
- "Ġstation ary",
- "ö ff",
- "p able",
- "Ġar ter",
- "Ġdé f",
- "r ative",
- "Ġcon ect",
- "Ġsad dle",
- "ĠD iane",
- "Ġcomm emor",
- "fend im",
- "S ÃŃ",
- "Ġíģ ´ë",
- "Ġman ge",
- "at te",
- "Ġarrog ant",
- "Ġrobot ic",
- "Ġgi Ãł",
- "æĺ¯ çļĦ",
- "Ġneighbour hood",
- "iss on",
- "Ġдв иж",
- "ĠR I",
- "ĠNorm an",
- "b rand",
- "am ation",
- "Ġraz or",
- "Ġmur ders",
- "ĠÑĤ Ñĥ",
- "Ġwszystk im",
- "Ġut ilities",
- "Ġmicros cop",
- "ê ¿",
- "Ġda qui",
- "oll ar",
- "ĠÐĶав айÑĤе",
- "Ġann ée",
- "Ġkilomet res",
- "Ġhom osexual",
- "Ġarchitect s",
- "ãģ¡ ãģ¯",
- "Ġni ye",
- "L ER",
- "Ġmicro phones",
- "ĠSt unden",
- "Ġconsecut ive",
- "iend a",
- "v änd",
- "D ER",
- "Ġlif ts",
- "ĠMe at",
- "Ġsave z",
- "íĸ Īëįĺ",
- "M en",
- "Ġdism ant",
- "ê±°ë ¥¼",
- "Ġins ulation",
- "Ġsc all",
- "Ġsp ooky",
- "Ġpar c",
- "Ġball et",
- "ĠWhats App",
- "Ġfr anc",
- "Ġdeliber ate",
- "Ġíħ Į",
- "Ġm ars",
- "ĠZ ur",
- "P r",
- "dis ciplinary",
- "Ġobs ession",
- "м е",
- "Ġmarch ing",
- "ĠEmer gency",
- "ig uous",
- "Ġs zy",
- "ĠL ands",
- "Ġboard ing",
- "ĠпоÑĩ ÑĤи",
- "Ġenv y",
- "Ġcompassion ate",
- "Ġmer ci",
- "Ġdes irable",
- "d ale",
- "Ġcan ım",
- "ĠAnt ar",
- "tem ps",
- "Ġconfig ured",
- "ĠComp ared",
- "ne h",
- "ic ating",
- "Ġnic kel",
- "ÙĪ ÙĤ",
- "Ùĥ ÙĪÙĨ",
- "op es",
- "Ġform ulas",
- "ĠÐķ ÑģÑĤÑĮ",
- "Ġpo bl",
- "ĠP J",
- "ĠL ud",
- "ä»Ĭ åĽŀ",
- "ĠBr id",
- "ĠH og",
- "ĠBr is",
- "J en",
- "Ġshad ing",
- "ĠY as",
- "Ġdistur bed",
- "Ġrecomm ending",
- "Ġc é",
- "ĠH OW",
- "ìĹĪ ìĸ´",
- "Ġrevers ed",
- "ĠInteresting ly",
- "iox id",
- "åħ Ń",
- "Ġìĺ¤ ì¼ĢìĿ´",
- "ế u",
- "x x",
- "Ġou ais",
- "ĠYouT ubers",
- "ĠR osa",
- "ĠH aupt",
- "j adi",
- "Ġvlog s",
- "Ġcult ura",
- "ĠLeaders hip",
- "ĠH ep",
- "Ġill um",
- "´ë ıĻ",
- "Ġcustom ized",
- "Ġmar ca",
- "Ġqu atro",
- "Ġн аг",
- "ĠSpace X",
- "ĠE igen",
- "ast ing",
- "ĠolduÄŁ u",
- "Ġfor ts",
- "ãģ ī",
- "r iment",
- "ien cia",
- "Ġten ir",
- "ro ffen",
- "Ġ197 9",
- "Ġc ie",
- "ĠëIJĺ ê³ł",
- "Ġes cri",
- "ÏĮ ÏĤ",
- "íı ¬",
- "uz zy",
- "C ong",
- "ìĿ¸ ìĿ´",
- "G reat",
- "s il",
- "é ch",
- "ãģ¨ ãģĭ",
- "Ġmult ic",
- "ĠDis k",
- "² ķ",
- "Ġfaz la",
- "Ġle vant",
- "Ġab ajo",
- "ur ry",
- "st ru",
- "Ġ먹 ëĬĶ",
- "Ġaccess ory",
- "Ġдв иг",
- "ĠR id",
- "20 19",
- "Ġdown stream",
- "æķ ¸",
- "Ġk az",
- "ut an",
- "Ġchar coal",
- "Ġa fect",
- "w u",
- "Ġcontext s",
- "Ġfe ared",
- "ĠìĦ ¤",
- "Ġhist ories",
- "Ġf as",
- "ens ible",
- "Ġcoco a",
- "ill ar",
- "ge ons",
- "Ġspiritual ity",
- "ĠP ew",
- "Ġpharm acy",
- "Ġpass ions",
- "Ġb os",
- "Ġall á",
- "Ġthri ving",
- "ĠRe act",
- "Ġoccup y",
- "Ġwithdraw al",
- "Ġallow ance",
- "ĠFra ktion",
- "Ġbud dies",
- "Ġid le",
- "Ġdissol ved",
- "Ġpreval ent",
- "Ġmil itar",
- "Ġsens ing",
- "Ġpo jaw",
- "Ġanc ora",
- "Ġabund ant",
- "Ġha irst",
- "ãģĤ ãĤĮ",
- "Ġtw ee",
- "Ġnäch ste",
- "ĠMöglich keit",
- "Ġho o",
- "uff icient",
- "Ġfant ast",
- "Ġed ible",
- "Ġëĸ¨ ìĸ´ì",
- "ìĽ ĥ",
- "Ġve in",
- "uc ci",
- "Ġdevot ion",
- "Ġconce aler",
- "in come",
- "Ġrecy cled",
- "ĠìĬ¤í ĥĢ",
- "Ġpont os",
- "Ġdess us",
- "Ġvé rit",
- "Ġreflect ions",
- "ĠA A",
- "Ġtake away",
- "b are",
- "ĠCont act",
- "e il",
- "ĠHe ar",
- "Ġmir ac",
- "ĠGer ilim",
- "ĠÑģам Ñĭй",
- "Ġv ivo",
- "Ġkilogram s",
- "ĠCr im",
- "û t",
- "7 8",
- "Ġsincere ly",
- "ra z",
- "Ġë³ µ",
- "Ġarri v",
- "Ġconcept ion",
- "ĠPers ian",
- "Ġsj äl",
- "Ġst arring",
- "ĠìķĦë ¬´",
- "ĠFore ver",
- "е ÑģÑĤÑĮ",
- "Ġve il",
- "Ġsubt it",
- "od ka",
- "ĠоÑĤно ÑĪ",
- "Ġcook s",
- "ен Ñı",
- "K ay",
- "Ġni ños",
- "ĠPh one",
- "Ġstitch ing",
- "Ġfinger print",
- "é¢ ĺ",
- "λ ά",
- "Ġded icate",
- "ĠL ob",
- "Ġblack s",
- "ĠB le",
- "b out",
- "ĠÄij ang",
- "Ġe ks",
- "Ġsqu ash",
- "ĠK ü",
- "od i",
- "Ġn Æ°á»Ľc",
- "Ġvoy age",
- "Ġplay ful",
- "ĠØ¥ ÙĦÙī",
- "an ic",
- "Ġcondem n",
- "ĠB öyle",
- "ĠPol ize",
- "ãĤ¿ ãĥ¼",
- "Ġay uda",
- "Ġp am",
- "à¹Ħ à¸Ľ",
- "ĠK athy",
- "ед ин",
- "нов а",
- "Ġbr ig",
- "eg er",
- "Ġe agle",
- "Ġvis ions",
- "ĠíķŃ ìĥģ",
- "Ġsh itty",
- "Ġh ott",
- "ĠBr itt",
- "ut ors",
- "ENT E",
- "æĽ ²",
- "Ġph on",
- "ĠB ing",
- "Ġпод деÑĢж",
- "spr ing",
- "æĸ ¯",
- "et ten",
- "Ġpil gr",
- "Ġed iyor",
- "енÑĤ Ñĭ",
- "ag gio",
- "Ġj ul",
- "Ġcomp rend",
- "te il",
- "ĠØ ²",
- "Ġperform ers",
- "Ġinf amous",
- "ĠM K",
- "ç ª",
- "æ³ ģ",
- "ot le",
- "e ff",
- "ĠH ash",
- "Ġcow ard",
- "ĠB RA",
- "ĠD D",
- "Ġcom ida",
- "Ġpl ata",
- "Ġfl ap",
- "ĠMe hr",
- "rib ution",
- "ĠY emen",
- "Ġmyster ies",
- "ĠÄ° yi",
- "Ġst ell",
- "Ġeyel iner",
- "Ġdel es",
- "Ġnail ed",
- "Ġillness es",
- "Ġst acks",
- "Ġtrabaj ar",
- "fl ower",
- "ci u",
- "Ġcr ude",
- "Ġsubstant ially",
- "Ġhome m",
- "Ġnep hew",
- "Ġstamp s",
- "Ġcar bs",
- "ÑĮ ÑĤе",
- "mo oth",
- "Ġtun nels",
- "ac ie",
- "æ³ ¢",
- "ĠSe ñ",
- "ĠH era",
- "ĠìķĦëĭĪ ìĹIJìļĶ",
- "ĠWy oming",
- "ĠHD MI",
- "ĠL is",
- "u ción",
- "Ġste er",
- "о Ñİ",
- "иÑĤ а",
- "N T",
- "Ġìĸ¼êµ ´",
- "Ġpal ms",
- "Ġne on",
- "ов аниÑı",
- "Ġfilter ing",
- "Ġjou er",
- "ĠH ö",
- "Ġне Ñģ",
- "ê²ł ìĸ´ìļĶ",
- "Ġ8 1",
- "Ġstory line",
- "Ġprz ep",
- "Ġthank ing",
- "ĠBo eing",
- "Ġsoft ly",
- "j em",
- "алÑĮ нÑĭÑħ",
- "Ġflash light",
- "Ġп Ñĥ",
- "ĠW OMAN",
- "ắ c",
- "ÃŃ ch",
- "Ġlux urious",
- "Ġw ün",
- "Ġimpact ful",
- "Ġcons on",
- "re u",
- "ir ring",
- "if ter",
- "Ġconstitu ents",
- "èIJ ½",
- "Ġ9 4",
- "ĠT ou",
- "g om",
- "ĠìĥĿê°ģ ìĿĦ",
- "Ġstere otypes",
- "Ġmoż li",
- "åĪĨ 享",
- "Ĥ ¨",
- "Ġpencil s",
- "ĠÑģл ож",
- "Ġih rem",
- "ĠBes ch",
- "ĠK oh",
- "ĠEnt scheid",
- "Ġle k",
- "Ġför s",
- "Ġtotal mente",
- "Ġlive ly",
- "Ġent ropy",
- "Ġdisc ern",
- "ĠÐĹ Ð½Ð°",
- "Ġdo v",
- "Ġmyth ology",
- "è¨ĺ å¾Ĺ",
- "apan ese",
- "Ġapprox imate",
- "аÑĤ ив",
- "if iable",
- "ĠSe o",
- "åĢ Ĵ",
- "´ìĭ¬ íŀĪ",
- "Ġìĺ ·",
- "Ġtempor al",
- "Ġi T",
- "Ġest at",
- "к им",
- "Ġspr ink",
- "Ġgr und",
- "Ġinfant ry",
- "Ġsch affen",
- "ç´ Ħ",
- "Ġan k",
- "ri ages",
- "ĠYe on",
- "ĠMor oc",
- "Ġinv asive",
- "ģ Ķ",
- "Ġparent ing",
- "ĠR is",
- "ib ile",
- "Ġmod s",
- "å½ ¢",
- "ĠпÑĢов еÑĢ",
- "ĠTh ing",
- "ĠWhere ver",
- "Ġacknowled ging",
- "Ġpa wn",
- "um mer",
- "or b",
- "6 9",
- "Ġretr ouve",
- "Ġrel ies",
- "ĠHigh way",
- "Ġa we",
- "ãģ§ãģĻ ãģĭ",
- "ita ire",
- "Ġapplic ant",
- "Ġais le",
- "w orm",
- "Ġpay load",
- "Ġcar re",
- "ĠB ach",
- "æł ¼",
- "Ġì¹ľ 구ë",
- "ни е",
- "Ġit ÃŃs",
- "onna ise",
- "s ol",
- "èı ¯",
- "alg ia",
- "Ġrock ing",
- "Ġbest en",
- "rit es",
- "^ ^",
- "ин ой",
- "Ġba ixo",
- "Ġ기 ìĸµ",
- "оÑĤ ÑĢи",
- "s im",
- "Ġinc arn",
- "ëĭ¤ ìĿĮ",
- "Ġl ick",
- "s ided",
- "Ġ7 1",
- "f order",
- "Ġreson ance",
- "Ġte gen",
- "Ġmet aph",
- "ows er",
- "Ġ×IJ× ł×Ĺ׳×ķ",
- "? ãĢį",
- "Ġsp ielen",
- "Ġvoll ey",
- "ĶìĿ´íģ¬ ìĹħ",
- "lo oked",
- "Ġsent enced",
- "Ġmultip lying",
- "Ġide als",
- "Ġwahr scheinlich",
- "Ġdepos its",
- "bil ir",
- "Ġeff et",
- "ill on",
- "Īë §Į",
- "Ġtestim on",
- "Ġz awsze",
- "ĠпÑĢоÑĨ еÑģÑģ",
- "ĠL av",
- "ä¸į éĮ¯",
- "Ġtrava iller",
- "Ġla isse",
- "ĠMount ains",
- "ĠÑĢ об",
- "Ġexam ined",
- "it us",
- "W as",
- "л Ñĭ",
- "Ġattrib uted",
- "ĠìĬ ¹",
- "ĠBar on",
- "Ġg ep",
- "Ġatt ent",
- "ĠColl ection",
- "Ġthe at",
- "ĠC ai",
- "Ġwell s",
- "Ġhuman o",
- "çĹ ħ",
- "ĠH ast",
- "ĠÑħоÑĤ Ñı",
- "cz as",
- "Ġperm its",
- "Ġle gg",
- "Ġe po",
- "ĠF en",
- "Ġth i",
- "ĠF oi",
- "Ġé lect",
- "Ġ8 3",
- "Ġover th",
- "Ġ è¬Ŀè¬Ŀ",
- "Ġten ant",
- "è² ·",
- "N ext",
- "Ġpra ised",
- "sec urity",
- "ĠImp act",
- "为 ä»Ģä¹Ī",
- "Ġv ouch",
- "Ġneg ó",
- "Ġun ve",
- "Ġcritic ize",
- "ĠKen ya",
- "Ġtact ic",
- "Ġlo gr",
- "Ġpo is",
- "Ġpap a",
- "spe aks",
- "ðŁ ij",
- "isp ers",
- "Ġsur plus",
- "Ġcold er",
- "åį Ĺ",
- "åIJ ¬",
- "pl ets",
- "ĠV ienna",
- "ĠLe ad",
- "Ġaer ial",
- "ĠT ah",
- "енÑĤ ов",
- "ĠGree ks",
- "C am",
- "Ġmá xim",
- "Ġk uin",
- "ch io",
- "Ġdemonst rates",
- "an os",
- "ĠC ert",
- "ĠÑį н",
- "Ġblog s",
- "ĠìĦľ ìļ¸",
- "Ġbe ams",
- "ик ов",
- "Ġprompt ed",
- "Ġfright ening",
- "ĠPors che",
- "ãģĪ ãģ¦",
- "lar ını",
- "Ġch illing",
- "is phere",
- "Ġfl ashing",
- "ĠK ard",
- "b read",
- "Ġex h",
- "Ġty cker",
- "Ġec ological",
- "ĠMa e",
- "Ġ×ŀ×IJ ×ķ×ĵ",
- "ĠëĤ ĺëıĦ",
- "л он",
- "ys s",
- "Ġper gunt",
- "Ġpri x",
- "izz ard",
- "Ġcan cers",
- "Ġ9 1",
- "s usp",
- "ĠIt em",
- "ÅŁ a",
- "Ġp est",
- "Ġtak Äħ",
- "Ġl ymph",
- "ĠPat ri",
- "f ill",
- "Ġrec onna",
- "Ġoptim ism",
- "Ġmim ic",
- "Ġì² ľ",
- "ĠMad ame",
- "oc y",
- "l ining",
- "åijĬ 訴",
- "erm e",
- "Ġfold ers",
- "Ġcz ÅĤ",
- "uch ar",
- "Ġcur so",
- "Ġbre ach",
- "ни ÑĤÑĮ",
- "Ġp amiÄĻ",
- "Ġel ig",
- "Ġaut op",
- "F low",
- "Ġprogram med",
- "ĠPro cess",
- "Ġfig ur",
- "ĠS F",
- "ĠE les",
- "Ġprogram mes",
- "Ġdiz zy",
- "ìĭľ ê°Ħ",
- "Ġли бо",
- "Ġsn iff",
- "ĠSeb astian",
- "ĠH ye",
- "Ġ4 000",
- "Ġperm ite",
- "æ¢ Ŀ",
- "Ġза Ñī",
- "Ġgu it",
- "ĠD ais",
- "Ġaccord ance",
- "Ġmod ular",
- "ogene ous",
- "æĭ į",
- "Ġpou quinho",
- "Ġart illery",
- "Ġlub ric",
- "Ġvol can",
- "ĠN H",
- "ðŁ ¤",
- "Ġde an",
- "R h",
- "Ġminist re",
- "åĿ IJ",
- "ĠIn v",
- "ĠBul gar",
- "ĠD aten",
- "è İ",
- "I m",
- "Ġorigin ated",
- "ĠN ixon",
- "inte gr",
- "Ġlack s",
- "ĠN acht",
- "ìĸ´ë Ĥĺ",
- "cam era",
- "Ġrad ish",
- "ki ye",
- "Ġang es",
- "Ġpré f",
- "j uk",
- "ĠBe e",
- "ĠB U",
- "ĠвоÑģ п",
- "ĠB T",
- "ê mes",
- "ĠSt ück",
- "ĠIn k",
- "æĪĸ èĢħ",
- "ĠSerge ant",
- "ĠMult ip",
- "Ġhiç bir",
- "ĠС ам",
- "ĠD é",
- "ol ph",
- "ìĸ ¸",
- "Ġimp at",
- "ĠìķĬ ê³ł",
- "ĠÑĤак ого",
- "ĠнавеÑĢ ное",
- "Ġunpredict able",
- "Ġm end",
- "ĠìĹĨ ìĸ´ìļĶ",
- "Ġjakie ÅĽ",
- "Ġann i",
- "Ġdon né",
- "ĠK irsty",
- "Ġrectang ular",
- "Ġempez ar",
- "ĠEx change",
- "ê° Ķ",
- "Ġé conom",
- "ãģĵ ãĤĵ",
- "el in",
- "re ibt",
- "Ġ×Ķ× ¤",
- "Ġc emetery",
- "Ġespañ ol",
- "ol in",
- "лÑİ Ð´",
- "Ġgr âce",
- "all en",
- "ĠPh ilos",
- "ĠEr st",
- "Ġìĥ Ī",
- "ĠV id",
- "G ive",
- "O H",
- "μ ο",
- "ĠP are",
- "Ġmetabol ism",
- "Ġma ple",
- "Ġax le",
- "ĠD y",
- "Ġkomm e",
- "Ïİ Î½",
- "Ġgreat ness",
- "Ġver ified",
- "Ġsp é",
- "ĠFahren heit",
- "ĠB ren",
- "ĠConf eder",
- "Ġhist oire",
- "Ġelimin ating",
- "ĠAd ding",
- "ĠAb i",
- "æĿ İ",
- "Ġhospital ity",
- "t im",
- "Ġbon ito",
- "Ġpart es",
- "ĠдÑĢÑĥг иÑħ",
- "ĠSh ay",
- "ĠS ed",
- "Ġreg rets",
- "Ñı ми",
- "Ġten ants",
- "éĢ Ł",
- "ĠP TS",
- "Ġdev i",
- "ĠL ate",
- "ue z",
- "Ġsö yl",
- "ãĤ »",
- "Ġìŀ¬ë °Į",
- "Ġtogg le",
- "Ġmas king",
- "алÑĮ ного",
- "Ġpers ön",
- "Ġamer ican",
- "f ik",
- "ĠR GB",
- "ens on",
- "ĠK A",
- "ww ww",
- "ĠÑĢ ег",
- "met ics",
- "Ġeduc ator",
- "ãĤ· ãĥ«ãĤ¯",
- "p ark",
- "елÑĮ зÑı",
- "ar us",
- "ÑĢ еÑĤ",
- "Ġfe ito",
- "Ġcho ir",
- "Ġlar go",
- "Ġe ens",
- "Ġwat ts",
- "ĠSing le",
- "Ġsuscept ible",
- "ic er",
- "Ġв клÑİÑĩ",
- "Ġp us",
- "íĻ ĺ",
- "E ng",
- "Ġfant as",
- "Ġspecific ation",
- "Ġconfront ed",
- "ĠColumb us",
- "ив еÑĤ",
- "ar ım",
- "Ġcaffe ine",
- "mun ition",
- "Ġmig rants",
- "l ide",
- "it ations",
- "ĠG eme",
- "Ạ«",
- "Ġpl anner",
- "Ġstim ulate",
- "Ġapro xim",
- "ce u",
- "ĠN om",
- "Ġv og",
- "ĠÑĢ аÑģÑĤ",
- "Ġense ñ",
- "Ġsell ers",
- "Ġgut en",
- "z d",
- "C al",
- "Ġdescri pt",
- "Ġrecon ciliation",
- "z inho",
- "á¹ĩ a",
- "ãģĺãĤĥ ãģĤ",
- "acy j",
- "ĠCO L",
- "s aw",
- "ĠíĻķ ìĿ¸",
- "Ġvar it",
- "Ġpartner ing",
- "Ġdet ention",
- "Ġbomb ing",
- "c lapping",
- "ien cies",
- "ond u",
- "AM E",
- "Ġê°Ļ ìĬµëĭĪëĭ¤",
- "c ÃŃa",
- "ĠпоÑģ ÑĤо",
- "ĠAS MR",
- "Ġhome page",
- "Ġsi è",
- "an tha",
- "ĠP oll",
- "Ġ igen",
- "cy ch",
- "Ġê°ij ìŀIJ기",
- "Ġconsider ably",
- "ä»ĸ çļĦ",
- "ĠAr ist",
- "Ġwith stand",
- "Ġqual itative",
- "ĠK raft",
- "ĠÑį лекÑĤ",
- "ĠBe ad",
- "екÑĤ ив",
- "Ġcr ushing",
- "ì³ IJ",
- "Ġnav y",
- "ÙĪ Úº",
- "s ho",
- "Ġo ak",
- "ipp ers",
- "Ġso ils",
- "Ġpig ment",
- "Ġev itar",
- "ãĥ ĩ",
- "Ġf use",
- "ĠD ale",
- ": \"",
- "Ġcompl ètement",
- "Ġke l",
- "๠Ĩ",
- "Ġqu atre",
- "ĠU M",
- "Ġë§ IJë",
- "æł ¹",
- "ÃŃ r",
- "Ġle isure",
- "ĠH ousing",
- "Ġfold s",
- "est ion",
- "AR S",
- "Ġm ash",
- "urp ose",
- "Ġaccum ulated",
- "ĠSt uff",
- "èª ŀ",
- "Ġtap es",
- "ĠÑģ илÑĮно",
- "ĠLO VE",
- "Ġ198 2",
- "Ġsc ars",
- "Ġcapital ist",
- "ĠN ed",
- "Ġsoft en",
- "Ġnot ably",
- "Ġforcé ment",
- "ĠRa um",
- "Ġнеоб Ñħод",
- "Ġtrad emark",
- "Ġfert ig",
- "Ġ? !",
- "æĹ ł",
- "Ġreinfor ced",
- "Ġre charge",
- "ĠPut ting",
- "Ġvill ains",
- "Ġhand ic",
- "Ġadvertis ement",
- "ت ÙĬ",
- "ĠÑģ Ñĥм",
- "ĠR iley",
- "×ķ× ij×",
- "äº ¬",
- "O s",
- "Ø§Ø ²",
- "B oy",
- "Ġsqu ish",
- "ock et",
- "Ġtest ify",
- "æ¼ Ķ",
- "Ġ×ľ× ŀ×",
- "Ġм аÑģÑģ",
- "man uel",
- "ĠArk ansas",
- "if fe",
- "Ġanalyst s",
- "ĠDe af",
- "Ġj ó",
- "Ġgrocer ies",
- "ĠWhe el",
- "ĠÑĢ иÑģ",
- "Ġc òn",
- "ĠC ob",
- "Ġpris ons",
- "è ve",
- "ĠCab inet",
- "Ġpos ed",
- "Ġguer re",
- "ĠL loyd",
- "Ġcl erk",
- "Ġcr ises",
- "ĠSh o",
- "ĠO re",
- "ĠFoot ball",
- "ĠAd vis",
- "ĠZh eng",
- "è į",
- "ĠAM Y",
- "Ġun for",
- "Ġmon aster",
- "Ġcomp ile",
- "Ġimm ortal",
- "at able",
- "Ġpar ano",
- "Ġt iver",
- "ĠStep h",
- "ĠFu ÃŁ",
- "Ġdisc ontin",
- "Ġr ipe",
- "Ġhack ing",
- "Ġs iendo",
- "Ġsegu ro",
- "alt res",
- "Ġand eres",
- "Ġë ¦¬ë",
- "Ġexp orts",
- "æŃ ¥",
- "Ġtab ii",
- "Ġ기 ëĭ¤ë",
- "Ġbother ing",
- "Ġpick le",
- "ĠBRI AN",
- "Ġalt ar",
- "ĠпÑĢи б",
- "Ġtransfer ring",
- "ĠV ors",
- "ĠÙĩ ÙĪ",
- "ĠZ a",
- "ĠFr ances",
- "Ġbrow se",
- "em it",
- "Ġche wing",
- "ĠFred dy",
- "Ġedit ors",
- "ä lle",
- "Ġí ĮĢ",
- "ĠS que",
- "ĠC ultural",
- "aw k",
- "ĠS ache",
- "ĠCar bon",
- "ắ t",
- "F L",
- "ĠN GO",
- "pe ÅĤ",
- "ĠS ou",
- "Ġh vor",
- "un intelligible",
- "Ġë² ķ",
- "ĠÂ °",
- "i in",
- "Ġ×¢ ×Ŀ",
- "Ġder rière",
- "Ġczy m",
- "ĠAp ost",
- "Ġregard er",
- "Ġag rade",
- "ĠC andy",
- "Ġma re",
- "Ġintrodu ces",
- "bird s",
- "Ġuniqu ely",
- "Ġm uk",
- "Ġcook er",
- "Ġcrew s",
- "Ġje ito",
- "ER T",
- "¶ Ħë",
- "n isse",
- "Ġe f",
- "Ġcart e",
- "ĠY ak",
- "ĠP AT",
- "и но",
- "bok ki",
- "Ġm ates",
- "Ġdist int",
- "Ġì½Ķë¡ľ ëĤĺ",
- "Ġy ıl",
- "Ġκ άν",
- "Ġconfigur ations",
- "eng a",
- "re cht",
- "H appy",
- "ãĤĦ ãģ£ãģ¦",
- "in vest",
- "Ġreconst ruct",
- "ĠÑįÑĤ омÑĥ",
- "Ġmos que",
- "ra um",
- "Ġvoy ez",
- "ĠN BC",
- "ĠìŀIJ ìĭł",
- "Ġstur dy",
- "Ġк ап",
- "Ġans ch",
- "al id",
- "Ġmas ih",
- "ĠR EP",
- "Ġì½ Ķë",
- "Ġded uct",
- "Ġsal ir",
- "w urf",
- "il ot",
- "ĠM utter",
- "old s",
- "ĠF EMA",
- "ĠB ib",
- "Ġneighb oring",
- "Ġbl iss",
- "Ġíĺ ¼",
- "ли ÑģÑĮ",
- "ĠÑĤÑĢ еб",
- "Ġ å°±æĺ¯",
- "Ġgren ade",
- "Ġe gal",
- "Ġfin ely",
- "Ġpet als",
- "Ġke er",
- "Ġch yba",
- "Ġsk ipping",
- "Ġth irteen",
- "Ġgrav y",
- "ĠS AT",
- "6 1",
- "Ġн ог",
- "Ġmin s",
- "IT E",
- "Ġso zial",
- "íķĺë ©´ìĦľ",
- "rukt ur",
- "Ġвозм ож",
- "Ġоп ÑıÑĤÑĮ",
- "Ġar th",
- "ĠCub an",
- "Ġtre asures",
- "Ġfertil izer",
- "Ġawak ening",
- "Ġë°± ìĭł",
- "Ġr all",
- "Ġdep ict",
- "ĠP ablo",
- "Ġninete en",
- "Ġw att",
- "Ġentire ty",
- "K S",
- "ĠWood s",
- "S ch",
- "ĠÚ© ÙĪ",
- "ĠD ry",
- "ãģ ŀ",
- "u ve",
- "Ġreconst ruction",
- "Ġanat omy",
- "Īë ¥¼",
- "Ġb aba",
- "Ġlisten er",
- "Ġshar pen",
- "ĠPer u",
- "ĠвÑĭ з",
- "Ġrecre ation",
- "Ġiniti ate",
- "Ġcal or",
- "ĠN aj",
- "ge e",
- "ĠFe els",
- "ĠSnap chat",
- "ĠT et",
- "ĠN est",
- "ĠD af",
- "ĠFin ish",
- "ĠÑĤак им",
- "ú c",
- "iz ens",
- "Ġsp ins",
- "Ġemb ry",
- "Ġpass ages",
- "Ġc ient",
- "Ġjust ification",
- "ä»ĸ 說",
- "Ġolm az",
- "Ġflood ed",
- "Ġemo ji",
- "Ġembr acing",
- "Ġdisc ard",
- "ĠBas ic",
- "ag og",
- "ĠìľĦ íķ´",
- "Ġas ylum",
- "er in",
- "Ġf im",
- "Ġnin ja",
- "Ġautom ate",
- "Ġaller gic",
- "ÿÿ ÿÿ",
- "am am",
- "Ġм аÑĢ",
- "ĠO i",
- "ä us",
- "Ġin duct",
- "ĠB EN",
- "Ġz ÅĤ",
- "Ġkaż dy",
- "ĠAM P",
- "n ÄĽ",
- "S ure",
- "Ġqu il",
- "Ġespe c",
- "ro k",
- "BS CRI",
- "Ġlie be",
- "p us",
- "ach sen",
- "Ġcr icket",
- "ëĬ IJ",
- "ĠFr ame",
- "ekk ür",
- "ar b",
- "Ġp ÅĻ",
- "иÑģ Ñģ",
- "Ġzeg gen",
- "Ġdou bles",
- "ĠD re",
- "t est",
- "ins p",
- "bo ys",
- "Ġm ão",
- "ĠVer se",
- "Ġmus cular",
- "ĠMA LE",
- "Ġd ulu",
- "Ġoccas ional",
- "L o",
- "conom ic",
- "Ġv ak",
- "Ġrem edy",
- "å¤ ł",
- "ĠâĻªâĻª âĻª",
- "ve m",
- "Ġön em",
- "ĠkarÅŁ ı",
- "ĠSh arp",
- "h ur",
- "Ġë°© ë²ķ",
- "Ġgrand son",
- "Ġakt iv",
- "ĠTh rones",
- "ĠìķĪ ìĹIJ",
- "Ġto ts",
- "Ġsub d",
- "ĠPa ula",
- "Ġgra ves",
- "ĠB rent",
- "Ġник ÑĤо",
- "Ġsö z",
- "Ġcre c",
- "ĠVlad imir",
- "çĸ «",
- "Ġп ой",
- "Ġ\" -",
- "Ġp sy",
- "at ri",
- "id an",
- "Ġa ún",
- "Ġstandard ized",
- "ì¹ ĺë",
- "Ġк ÑĢов",
- "ĠZh u",
- "s omething",
- "Ġ7 50",
- "Ġmuj eres",
- "Ġa it",
- "éĹ ´",
- "ag u",
- "Ġcorrect ed",
- "ik ka",
- "el ed",
- "ĠCare er",
- "ow ym",
- "Ġroomm ate",
- "Ġdescend ants",
- "ĠNapole on",
- "ĠÐĶ о",
- "íĸĪ ìĸ´ìļĶ",
- "Ġbun un",
- "ĠMich a",
- "ç· ļ",
- "Ġdesc ob",
- "P I",
- "Ġpalab ra",
- "Ġtrack ed",
- "Ġdepend ence",
- "ĠBar ack",
- "åģ ĩ",
- "Ġfert ility",
- "ĠSouth west",
- "Ġincom plete",
- "Ġcomun ic",
- "Ġcomp ris",
- "ĠRest aur",
- "Ġac ron",
- "κ α",
- "Ġapprent ices",
- "Ġmus st",
- "ĠA br",
- "Ġpent ru",
- "ĠCons ort",
- "ĠAve c",
- "Ġdum plings",
- "L R",
- "Ġwszystk ie",
- "Ġsw amp",
- "н ев",
- "ugg le",
- "Ġwater color",
- "Ġprot on",
- "ĠEspa ña",
- "ock ing",
- "ов ал",
- "Ġtak im",
- "V ery",
- "Ġdement ia",
- "ĠÅŁey i",
- "J ac",
- "ĠMac Book",
- "ĠL iv",
- "ffic ients",
- "ĠH unt",
- "Ġover lay",
- "æĦŁ 覺",
- "ĠSky pe",
- "p unkt",
- "Ġconf ined",
- "ĠAd rian",
- "ر Ùĥ",
- "ĠJe ep",
- "Ġenqu anto",
- "Ġan est",
- "оÑĤ веÑĤ",
- "Ġм енÑĮ",
- "Ġirrig ation",
- "á»ij n",
- "Ġeight een",
- "ĠP on",
- "Ġresc ued",
- "Ġ198 3",
- "r ü",
- "ja e",
- "ĠJe ong",
- "Ġamazing ly",
- "ĠF DP",
- "Ġback stage",
- "c ue",
- "ĠÏĥÏĦη ν",
- "ĠاÙĦØ µ",
- "Ġlivest ock",
- "ĠW arner",
- "Ġmaj ors",
- "ãĥģ ãĥ£",
- "Ġcooper ative",
- "ĠBr ady",
- "ra ined",
- "rie b",
- "Ġ×ij× ŀ×",
- "Ġдов олÑĮно",
- "ĠF E",
- "Ġle aked",
- "ĠMerc ury",
- "Ġpersu ade",
- "Ġtransform er",
- "ĠNor weg",
- "ĠìĹ¬ë Ł¬",
- "Ġzrobi Äĩ",
- "Ġcard iovascular",
- "ĠCr ash",
- "Ġg ossip",
- "а ÑģÑĤÑĮ",
- "Ġì ª½",
- "Ġsw ept",
- "ĠH orn",
- "ĠAt é",
- "Ġbu kan",
- "ĠK aw",
- "K Y",
- "ĠSt ories",
- "G ary",
- "Ġgard ening",
- "ĠQuick ly",
- "ĠFal con",
- "Ġov at",
- "c ı",
- "ĠCom plet",
- "ĠD ate",
- "ĠпÑĢ им",
- "Ġlä uft",
- "ĠAud rey",
- "ĠW ent",
- "Ġpel ÃŃcul",
- "Ġcar riage",
- "Ġun acceptable",
- "ny mi",
- "ĠÑģл ÑĭÑĪ",
- "Ġter re",
- "uell ement",
- "EE EE",
- "Ġpharm ac",
- "h ões",
- "Ġz ich",
- "Ġmig rate",
- "ĠF ry",
- "ñ ana",
- "ĠM uito",
- "EO VER",
- "Ġfort ress",
- "ĠCom pan",
- "ĠJ SON",
- "ord nung",
- "Ġw arto",
- "Ġun gef",
- "ìħĶ ìĦľ",
- "ĠÑĢ ок",
- "Ġpad dle",
- "J ared",
- "Ġsubm itting",
- "Ġl atch",
- "Ġf ug",
- "Ġк оÑģ",
- "ĠE f",
- "Ġlaunch es",
- "Ġf t",
- "ote chn",
- "Ġtrave lled",
- "ا Ùģ",
- "éģ ķ",
- "Ġpro ch",
- "Ġded im",
- "8 3",
- "Ġreb ound",
- "ĠL U",
- "p ath",
- "ĠÑģп ÑĢав",
- "Ġö l",
- "ĠíĤ ¤",
- "Ġpriv at",
- "Ġtr actor",
- "ĠAtt ention",
- "S er",
- "Ġcos es",
- "á ria",
- "p al",
- "ĠìĿ Ģ",
- "Ġsuccess or",
- "Ġconnect ors",
- "ĠÑĥÑģÑĤ анов",
- "Ġgen ocide",
- "Ġsufficient ly",
- "ĠA ixò",
- "Ġstabil ize",
- "Ġcon gest",
- "Ġcar ving",
- "Ġz ost",
- "ĠбÑĭ ÑģÑĤÑĢо",
- "Ġshort est",
- "Ġli vel",
- "Ġ8 9",
- "éģ Ĭ",
- "Ġer k",
- "Ġport raits",
- "ॠĢ",
- "è ĺ",
- "bo at",
- "ll ah",
- "AN C",
- "Ġempir ical",
- "ĠE cho",
- "ĠNeder land",
- "è¿Ļ ä¹Ī",
- "N et",
- "Ġcuid ado",
- "ĠR oma",
- "Ġc alf",
- "Ġgi ants",
- "ĠExpl orer",
- "ĠColl ect",
- "al ition",
- "ĠDest iny",
- "Ġaus ge",
- "ĠE du",
- "ĠC lo",
- "Ġear rings",
- "ĠTr ack",
- "ĠR OS",
- "ĠBe lle",
- "çĻ ¾",
- "Ġpu eda",
- "Ġday time",
- "Ġsupp lier",
- "ĠS V",
- "ĠEx hale",
- "Ġgal era",
- "c ourse",
- "Ġcent imeter",
- "ĠB ast",
- "m ud",
- "Ġsang at",
- "ĠPhys ical",
- "Ġpriv ately",
- "Ġtr ata",
- "lyn n",
- "ill i",
- "Ġë© ĶìĿ´íģ¬ìĹħ",
- "Ġcryst all",
- "Ġpod s",
- "ả n",
- "in ator",
- "ĠRec ords",
- "å® ĺ",
- "ÄŁim iz",
- "isse ment",
- "h are",
- "h adow",
- "ĠD K",
- "ĠìķĮ ê³ł",
- "Ġw yn",
- "Ġrequest ing",
- "ĠD onna",
- "ĠìĹ ´ìĭ¬íŀĪ",
- "ine a",
- "Ġex ert",
- "ĠDun can",
- "Ġв еÑĩ",
- "ĠH ah",
- "ठĤ",
- "ĠL if",
- "ĠF inding",
- "ĠNo v",
- "Ġзн ак",
- "Ġо ÑĦ",
- "ĠQu è",
- "Ġquarter back",
- "ĠÑĦ ак",
- "Ġbipart isan",
- "ÄŁ in",
- "Ġné cess",
- "Ġrefer endum",
- "Ġcomp iler",
- "Ġprob abil",
- "ед и",
- "Ġtrad er",
- "æĺ ĵ",
- "ĠR um",
- "ge me",
- "Ġd io",
- "ĠbÄĻdzie my",
- "ĠÏĢ ά",
- "ê¾ ¸",
- "×ķ× ĺ",
- "Ġठķ",
- "Ġбл аг",
- "Ġscal p",
- "ĠPa use",
- "Ġcapt ion",
- "Ġend anger",
- "Ġen lar",
- "Ġrot ten",
- "ãĥĥ ãĥĪ",
- "Ġw ah",
- "èĤ ī",
- "Ġd zi",
- "ĠInst all",
- "A y",
- "Ġcre ar",
- "енÑĤ а",
- "Ġwe ighing",
- "Ġbutter flies",
- "ĠG ast",
- "äº ķ",
- "h orn",
- "war z",
- "IC EOVER",
- "Ġнай ÑĤи",
- "Ġcoe fficients",
- "ç°¡ åĸ®",
- "ĠSp encer",
- "ĠH igher",
- "Ġcow ork",
- "å¨ ĺ",
- "ĠкоÑĤоÑĢ ое",
- "Ġmon it",
- "Ġdys function",
- "ĠÑģÑĤ анов",
- "Ġtour naments",
- "Ġoy ster",
- "B N",
- "Ġtr ud",
- "sl ow",
- "ĠPen ny",
- "ĠOd ys",
- "æ r",
- "Ġf ou",
- "Ġenjoy ment",
- "аÑĤ Ñĭ",
- "Ġwygl Äħda",
- "алÑĮ наÑı",
- "ĠProt ect",
- "Ġmo y",
- "Ġcl aw",
- "Ġsusp icion",
- "Ġsacrific ed",
- "Ġgost o",
- "B ig",
- "Ġaggress ively",
- "Ġvor ne",
- "ãĥ ł",
- "Ġbl amed",
- "ĠSe hr",
- "פ ר",
- "c ito",
- "Ġse als",
- "Ġmu jer",
- "ĠWe ird",
- "Ġfore ns",
- "Ġcontrib utes",
- "est ra",
- "Ġp og",
- "L OL",
- "Ġhacer lo",
- "о ÑĤÑĮ",
- "f iction",
- "7 9",
- "λ ο",
- "大 æ¦Ĥ",
- "å£ °",
- "ĠÑĤ об",
- "ĠG S",
- "ĠCl ara",
- "ite z",
- "Ġadvoc ating",
- "ĠíĶ Ħë",
- "s ung",
- "Ġvert ices",
- "Ġnavig ating",
- "Ġeurop é",
- "çļ Ĩ",
- "Ġslow ed",
- "Ġfore ground",
- "ĠIndust rial",
- "Ġad ore",
- "ìĭ Ń",
- "Ġcré er",
- "æŀ Ĺ",
- "chn itt",
- "Ġun aware",
- "Ġcur ly",
- "ent ar",
- "Ġl er",
- "Ġprohib ited",
- "ĠHero es",
- "ĠRe ed",
- "u ca",
- "Ġsm ok",
- "Ġkun na",
- "zeit ig",
- "im men",
- "ĠL un",
- "Ġаб ÑģолÑİÑĤ",
- "Ġdeg li",
- "Ġvill agers",
- "Ġpres et",
- "z ept",
- "ud s",
- "Ġem it",
- "ä½ł è¦ģ",
- "Ġë ī",
- "ëĬĶ ì§Ģ",
- "нак о",
- "Ġos ób",
- "Ġ196 9",
- "ĠÐIJ ÑĢ",
- "Ġman chmal",
- "ĠBro ck",
- "Ġmant ra",
- "ĠW IL",
- "b ach",
- "in ä",
- "el as",
- "kel n",
- "Ġdisci ple",
- "Ġqual c",
- "Ġde hyd",
- "ìĿ´ë Ŀ¼ëĬĶ",
- "A f",
- "ìĦ± ìĿ´",
- "R yan",
- "Ġpupp et",
- "ĠдÑĢÑĥг ие",
- "Ġr ud",
- "Ġp ending",
- "P lus",
- "ĠìķĬ ìĿĦ",
- "Ġb á»ĭ",
- "ĠSe ga",
- "ç e",
- "Ġprogram mer",
- "b li",
- "Ġun l",
- "Ġensl aved",
- "Ġsoci été",
- "Äģ h",
- "Ġinherit ance",
- "ĠBang l",
- "erm aid",
- "Ġpractition er",
- "ĠSt alin",
- "ĠUs er",
- "ci ble",
- "Ġcard iac",
- "ĠKore ans",
- "Ġdump ed",
- "Ġ×Ķ ×Ļ×Ķ",
- "á is",
- "Ġhydraul ic",
- "oubt edly",
- "ĠP it",
- "Ġpic nic",
- "Ġbehö ver",
- "ĠÑģм ог",
- "Ġbra king",
- "é» ij",
- "ut ar",
- "ĠìĦ ¸ë",
- "ub l",
- "Ġü z",
- "Ġmaj esty",
- "Ġb ers",
- "ut able",
- "Ġhot ter",
- "çħ §",
- "ÛĮ ÙĨ",
- "Ġbi ases",
- "Ġsubject ed",
- "Ġnaught y",
- "Ġcir cus",
- "ãģĹ ãģĭ",
- "ĠIm medi",
- "ĠSte fan",
- "ĠTri ple",
- "en k",
- "Ġw it",
- "Ġrecy cle",
- "em ie",
- "d ated",
- "Ġun load",
- "Ġpop ula",
- "ch in",
- "Ġyield s",
- "Ġeng lish",
- "ĠBon nie",
- "Ġsp iders",
- "Ã ģ",
- "Ġer osion",
- "éĥ¨ åĪĨ",
- "ĠN ICK",
- "иÑı Ñħ",
- "Ġimp art",
- "Ġк ни",
- "Ġres olutions",
- "Ġlith ium",
- "Ġconver gence",
- "ĠT ara",
- "Ġдв е",
- "th s",
- "ĠCind y",
- "æĪij è¦ģ",
- "å¹ «",
- "ĠD IE",
- "Ġass urance",
- "Ġоп иÑģ",
- "Ġbu ckets",
- "Ġc ues",
- "ĠQu iet",
- "Ġsimilar ity",
- "Ġfound ational",
- "ĠMin ist",
- "æ» ¿",
- "Ġp ian",
- "Ġcent r",
- "Ġnum b",
- "Ġmon ks",
- "uj ourd",
- "en zie",
- "Ġskate board",
- "Ġd latego",
- "ĠÑģ оÑĤ",
- "ĠA E",
- "Ġmaster piece",
- "ĠSol omon",
- "ĠRed dit",
- "Ġr iot",
- "ab l",
- "ĠJ azz",
- "Ġelectromagn etic",
- "Ġinsec ure",
- "ĠComp et",
- "ger ies",
- "об од",
- "ł ×ķ",
- "ðŁ Ĵ",
- "Ġsen ators",
- "ĠBris bane",
- "ĠAl b",
- "utter ing",
- "ĠAll ow",
- "z ero",
- "Ġp ai",
- "ĠÐIJ лекÑģ",
- "ĠDis play",
- "ĠBl ade",
- "ĠApp s",
- "Ġp ä",
- "Ġд еÑģÑı",
- "Ġque lla",
- "ĠGa o",
- "ен нÑĭÑħ",
- "Ġspoil ers",
- "Ġgall ons",
- "ĠÙĦ ÙĬ",
- "ĠZ ion",
- "æľī ä¸Ģ",
- "on ie",
- "rag t",
- "ĠCh and",
- "Ġë³ ij",
- "Ġbl unt",
- "Ġus u",
- "ĠK ad",
- "ra kt",
- "Ġcin ematic",
- "Ġam munition",
- "re ne",
- "Ġfour teen",
- "ĠC arn",
- "c rit",
- "Ġten ure",
- "v u",
- "Ġprincipal mente",
- "Ġalle en",
- "éĢĻ ä¸Ģ",
- "Ġkompl ett",
- "Ġdü ny",
- "J ames",
- "Ġrecept or",
- "Ġones elf",
- "g uru",
- "Ġmerch ant",
- "l iness",
- "Ġover looked",
- "Ġharmon ic",
- "éķ ¿",
- "ies o",
- "×ķ× ŀ",
- "col m",
- "ĠпÑĢо екÑĤ",
- "ĠAd a",
- "ا س",
- "T im",
- "Ġrecur ring",
- "Ġproceed s",
- "ĠPart icularly",
- "ĠDown load",
- "et rical",
- "Ġmat rices",
- "Ġproyect o",
- "anc ies",
- "ĠUh m",
- "Ġc aves",
- "Ġìĸ´ë ł¤",
- "ĠLe af",
- "Ġоб ÑĭÑĩ",
- "ĠìĿ´ì ľł",
- "Euro pe",
- "Ġt Äħ",
- "Ġpul s",
- "Ġtak iego",
- "ÐĿ е",
- "G U",
- "Ġfor s",
- "Ïģ γ",
- "Ġfot os",
- "Ġ) )",
- "Ġë© ¤ë",
- "Ġaqu ilo",
- "ĠK urd",
- "ï¸ ı",
- "pt ic",
- "ĠD ort",
- "Ġmis ery",
- "aus o",
- "åĬ Ł",
- "chuck ling",
- "ĠR idge",
- "ĠíĸĪ ìĬµëĭĪëĭ¤",
- "Ġ* **",
- "å® ¢",
- "ĠHmm m",
- "Ġge ographic",
- "Ġany s",
- "Ġtal vez",
- "Ġske let",
- "Ġsign atures",
- "Ġlit ers",
- "IJë ©´",
- "ĠÑģво его",
- "Ġski ing",
- "ĠÐľ оÑģ",
- "Ġadop ting",
- "Ġha ft",
- "Ġsymm etric",
- "ĠL iqu",
- "Ġthy roid",
- "Ġmis in",
- "lud e",
- "Ġh ull",
- "ĠX D",
- "ĠG ust",
- "ze ich",
- "Ġvibr ations",
- "Ġes emp",
- "ĠвÑģ Ñİ",
- "ĠQu em",
- "Ġü brig",
- "ĠS ke",
- "ĠLyn ch",
- "room s",
- "art et",
- "f est",
- "Ġfr üher",
- "Ġl ure",
- "ä¸į好 æĦıæĢĿ",
- "ĠìķĮ ìķĦ",
- "ĠW IN",
- "ĠR YAN",
- "ĠкоÑĤоÑĢ ÑĥÑİ",
- "ĠK ash",
- "Ġ×Ķ× ŀ",
- "Ġsaf eg",
- "ĠHall elujah",
- "Ġдв ÑĥÑħ",
- "Ġstap le",
- "Ġsed iment",
- "ĠAct s",
- "Ġbl aming",
- "Ġmain land",
- "Ġsport ing",
- "Ġdecor ations",
- "Ġexecut ing",
- "Ġpar an",
- "ĠDoll ar",
- "Ġproject ions",
- "Ġcommission ed",
- "Ġb our",
- "ö m",
- "Ġste amed",
- "ĠëŃ ĺ",
- "Ġpet rol",
- "Ġcel ular",
- "å¸ ¶",
- "ĠHung ary",
- "Ġrent ed",
- "Ġв аÑĢи",
- "bb ie",
- "Ġsé cur",
- "ü ll",
- "Ġsw ings",
- "bet ween",
- "Ġи ÑĤ",
- "est ro",
- "Ġnie mand",
- "ĠìĤ ¼",
- "ĠP ardon",
- "ess es",
- "ĠM ID",
- "Ġcentral ized",
- "ĠAl ien",
- "cul os",
- "Ġcr ise",
- "裡 éĿ¢",
- "Ġcl asse",
- "beit et",
- "i ÄŁi",
- "Ġwh ales",
- "Ġper imeter",
- "Ġty ing",
- "Ġstr ony",
- "Ġlike wise",
- "ĠP unch",
- "D a",
- "ĠBapt ist",
- "Ġsort ing",
- "Ġ iv",
- "Ġíķ ©",
- "Ġre hab",
- "Ġet a",
- "ri ver",
- "Ġsa i",
- "ãģĦãģŁ ãģł",
- "od us",
- "ãģĬé¡ĺãģĦ ãģĹãģ¾ãģĻ",
- "Ġess ayer",
- "Ġtur tles",
- "ĠHaz rat",
- "Ġfab rics",
- "Ġcav ity",
- "Ġpon ieważ",
- "Ġschle cht",
- "Ġs alsa",
- "ÅŁ ekkür",
- "Ġse ating",
- "Ġeconom ists",
- "Ġman g",
- "Ġsegu inte",
- "Ġr ang",
- "Ġrat ios",
- "Ġconst ell",
- "Ġlong temps",
- "u ating",
- "Ġspo iled",
- "Ġrecip ients",
- "Ġsn iper",
- "ä¹ĭ åīį",
- "ìĬµ ëĭĪê¹Į",
- "Ġw p",
- "ĠLIN KE",
- "Ġfl are",
- "ĠAd ri",
- "ñ as",
- "Ġback l",
- "mä ÃŁ",
- "ĠB end",
- "Ġworkload s",
- "ĠÑģ Ñĥп",
- "Ġ197 5",
- "им ÑģÑı",
- "ан е",
- "Ġм он",
- "Ġaspir ations",
- "ĠA er",
- "ĠговоÑĢ иÑĤÑĮ",
- "ĠQ ian",
- "å¦ Ī",
- "Ġcomprom ised",
- "Ġyol k",
- "ла ÑģÑĤ",
- "Ġhe men",
- "ro ve",
- "d ens",
- "Ġком менÑĤ",
- "Ġ- --",
- "Ġflu ores",
- "но Ñģ",
- "ĠLiver pool",
- "ĠÑģоб ой",
- "ĠZ we",
- "Ġl umin",
- "ĠO G",
- "á ¸",
- "hol m",
- "pro fits",
- "S N",
- "Ġproport ions",
- "Ġm ica",
- "ĠB oh",
- "ĠAt las",
- "Ġuns ure",
- "Ġtour ing",
- "Ġn ied",
- "Ġt ÄĻ",
- "Ġimper ative",
- "Ġdem ek",
- "ĠSher iff",
- "r ance",
- "Ġhom eland",
- "ĠH ail",
- "ĠG anz",
- "y mm",
- "M on",
- "åĨ ·",
- "v ida",
- "Ġdesar roll",
- "æĬ Ģ",
- "Ġintrig uing",
- "ĠH ugo",
- "Ġ ãĤĤ",
- "é ¬",
- "а ÑĨ",
- "ĠWiÄĻ c",
- "att ed",
- "ĠìķĦëĭĪ ê³ł",
- "ĠV ari",
- "á d",
- "Ġsur real",
- "Ġdispar ities",
- "Ġm ó",
- "ull en",
- "ĠìŀĪ ëĭ¤ê³ł",
- "Ġп ожалÑĥйÑģÑĤа",
- "Ġma ins",
- "Ġe ject",
- "Ġmeth ane",
- "Ġmarginal ized",
- "Ġchill i",
- "r ès",
- "Ġy em",
- "ä½ł æĺ¯",
- "ĠCh un",
- "Ġdeb ts",
- "Ġdownload ing",
- "ĠAth ens",
- "is ierung",
- "ry n",
- "Ġte kn",
- "ĠQu indi",
- "éľ Ģ",
- "Ġtara f",
- "Ġh é",
- "Ġconscious ly",
- "Ġfix es",
- "uck le",
- "may ın",
- "Ġfre i",
- "Ġsp a",
- "Ġì§Ħ íĸī",
- "ĠاÙĦØ °",
- "ĠÑĥ к",
- "let t",
- "Ġolm uÅŁ",
- "Ġche esy",
- "า à¸ģ",
- "na ire",
- "Ġw iden",
- "Ġli en",
- "Ġesca ping",
- "igg s",
- "ĠBl ick",
- "c Äħ",
- "ĠìĦ ľë",
- "Ġ×Ķ× ¡",
- "Ġв пеÑĢ",
- "oph one",
- "ie ll",
- "ĠSU BSCRI",
- "Ġl ions",
- "Ġê·¸ ê²ĥ",
- "Ġinsp ires",
- "Ġguarante es",
- "Ġcome ça",
- "ĠGrow ing",
- "Ġneg lig",
- "ĠFrank f",
- "Ġge geben",
- "ĠÄij ầu",
- "Ġend lich",
- "Ġì į¨",
- "ĠT T",
- "ĠL ith",
- "ÏĢ α",
- "aster n",
- "ĠA zer",
- "Ġlun ar",
- "h ic",
- "Ġна ÑĢод",
- "Ġnen hum",
- "è· ij",
- "ĠSalv ador",
- "ĠPro gress",
- "Ġprivile ges",
- "ĠëıĻ ìķĪ",
- "Ġant agon",
- "ĠImp f",
- "Ġdesc ub",
- "ĠLe i",
- "ĠìĥĪë ¡ľ",
- "Ñĩ е",
- "Ġdó lares",
- "ĠMeg han",
- "ĠW ire",
- "to o",
- "ay ing",
- "us c",
- "Ġt ud",
- "Ġappe als",
- "ed uc",
- "Ġp ane",
- "Ġj i",
- "Ġde cks",
- "ĠAl ter",
- "Ġ å°±",
- "ìĦ ¤",
- "åĪĨ éIJĺ",
- "Ġproduct ions",
- "ĠWILL IAM",
- "Ġimpl ied",
- "Ġfulfill ment",
- "ĠA ah",
- "Ġsa ja",
- "x us",
- "ĠÎļ αι",
- "Ãł s",
- "uc ch",
- "ок о",
- "ĠDisc ord",
- "ĠS Y",
- "j sk",
- "ĠWall ace",
- "un ction",
- "Dan iel",
- "Ġk öt",
- "ij ah",
- "Ġmarch e",
- "Ġdis gr",
- "Ġm ungkin",
- "Ġal ma",
- "³ µ",
- "Ġextensive ly",
- "ĠFl oren",
- "ĠAll ison",
- "ãĤ ±",
- "ÙĬ Ùħ",
- "Ġju ven",
- "ĠRena issance",
- "Ġfundra ising",
- "ĠCha os",
- "Ġpar aly",
- "Ġnarr ator",
- "Ġecosystem s",
- "A sh",
- "Ġmitig ation",
- "ĠA ujourd",
- "ĠIde e",
- "! ,",
- "ĠÂ ½",
- "Ġland lord",
- "Ġdefect s",
- "Ġac re",
- "uls ive",
- "Ġalg ae",
- "pe k",
- "Ġem ba",
- "ĠR oc",
- "éĽ ¢",
- "ks om",
- "ä che",
- "Ġle uk",
- "Ġlever aging",
- "Ġê·¸ëłĩ ì§Ģ",
- "ĠPal m",
- "Ġä ven",
- "Ġl is",
- "ĠIn sp",
- "ĠR ita",
- "ĠAb b",
- "ith m",
- "Ġsuper vision",
- "Ġrevis it",
- "Ġpi ÄĻ",
- "Ġeu h",
- "Ġf ades",
- "Ġmot to",
- "åį ¡",
- "ез ж",
- "ĠSh im",
- "Ġrelev ance",
- "Ġo o",
- "Ġo stat",
- "n ica",
- "Ġcho ix",
- "ĠFac ulty",
- "Ġì¤ij ìĹIJ",
- "ĠAb ove",
- "Ġнеб олÑĮÑĪ",
- "Ġsequ encing",
- "Ġnutri ent",
- "Ġconqu ered",
- "Ġdigest ive",
- "Ġback drop",
- "ĠL ori",
- "ail able",
- "G ame",
- "Ġneglect ed",
- "om orph",
- "ill ah",
- "Ġkn e",
- "Ġsi itä",
- "Ġworks pace",
- "ĠVen ice",
- "ĠK ne",
- "Ñī о",
- "ħ Ģ",
- "ĠH ass",
- "Ġv ita",
- "Ŀ¼ë ©´",
- "Ġlay s",
- "ên cias",
- "é rica",
- "ĠL l",
- "æ± Ĥ",
- "ĠCo ca",
- "ĠWH Y",
- "èĪ ŀ",
- "Ġrout ing",
- "Ġperm issions",
- "Ġd ings",
- "pre nd",
- "pro gram",
- "Ġcro cod",
- "br al",
- "AAAA AAAA",
- "ag it",
- "ĠN ä",
- "Ġgek ommen",
- "at ten",
- "Ġrefer enced",
- "Ġpair ing",
- "ĠPart ner",
- "ĠCoron avirus",
- "Ñĸ Ñģ",
- "è½ ī",
- "Ġ×Ķ× ĵ",
- "Ġespec ÃŃfic",
- "ars i",
- "qu elle",
- "Ġspont aneous",
- "çĨ ±",
- "Ġê²ĥ ìĿĦ",
- "ĠÐŁÐ¾Ñģ ле",
- "ĠاÙĦ د",
- "ĠSh out",
- "Ġн ал",
- "Ġdisgu ise",
- "ĠJ ord",
- "Ġwe e",
- "Ġmiej sc",
- "Ġser um",
- "Ġplais ir",
- "Ġcred ible",
- "Ġb Ã¥",
- "ĠA J",
- "ma res",
- "Ġrod s",
- "Ġer an",
- "ãģ¾ ãģĤ",
- "Ġp ää",
- "ĠU A",
- "ĠUn known",
- "ĠÙĦ Ùħ",
- "ĠRab bi",
- "Ġla at",
- "Ġhairst yle",
- "ĠØ º",
- "éģ ĭ",
- "Ġc ach",
- "ĠWr iting",
- "оÑĩ ки",
- "ab ad",
- "Ġstraight en",
- "-- \"",
- "w ife",
- "Ġhott est",
- "Ġpun ya",
- "ĠF ashion",
- "gr iff",
- "ĠQ R",
- "ot ch",
- "ĠÐľ ожеÑĤ",
- "Cl oud",
- "ĠStri ke",
- "ĠHe in",
- "Ġ 羣çļĦ",
- "Ġle i",
- "ĠFl ow",
- "weg s",
- "Ġha br",
- "åīĽ åīĽ",
- "nah me",
- "Ì ģ",
- "Ġple asing",
- "op ping",
- "Ġ구ë ıħ",
- "Ġdr an",
- "Ġbang s",
- "Ġ7 9",
- "Ġsk et",
- "Ġcav al",
- "ĠMac ron",
- "Ġweight ed",
- "Ġm uted",
- "Ġnuest ras",
- "EE P",
- "Ġmath ematic",
- "ĠM RI",
- "ag us",
- "Ġtherap ies",
- "θ ε",
- "Ġun pl",
- "Ġcomm encer",
- "f ull",
- "Ġtow els",
- "Ġpr ue",
- "Ġlic enses",
- "׼ ×ķ׾",
- "ĠÐŁ оÑĩемÑĥ",
- "Ġpoint less",
- "B ye",
- "Ġelig ibility",
- "Ġscra pe",
- "Ġab usive",
- "ĠM ant",
- "Ġje unes",
- "t al",
- "ĠPrin cip",
- "ĠOrth odox",
- "Ġmel od",
- "ĠмаÑĤ еÑĢи",
- "Ġprosecut or",
- "Ġopio id",
- "ĠÑĥ веÑĢ",
- "ĠBe en",
- "Ġìłij ì¢ħ",
- "Ġd ynasty",
- "Ġajud a",
- "Ġent reg",
- "Ġweigh ed",
- "Ġe ure",
- "ĠB em",
- "Ġab normal",
- "8 2",
- "ĠJ R",
- "ĠA kt",
- "ĠB ri",
- "ú t",
- "Ġst agn",
- "! *",
- "Ġwe gen",
- "Ġle aking",
- "ĠW ords",
- "ĠM au",
- "Ġv ue",
- "ĠL iam",
- "ани ем",
- "Ġclin icians",
- "ĠP ump",
- "Ġför st",
- "? ...",
- "Ġautom otive",
- "ĠOw en",
- "zus agen",
- "ĠH undred",
- "Ġdecentral ized",
- "Ġbul bs",
- "Ġ×ľ× Ľ",
- "Ġprovin ces",
- "ĠMil an",
- "8 1",
- "k as",
- "Ġëĵ £",
- "Ġfor ça",
- "Ġright ly",
- "å³ ¶",
- "r Äħ",
- "Ġven ues",
- "Ġw ai",
- "Ġpred icting",
- "ĠWi Fi",
- "Ġê¶ģ ê¸Ī",
- "ر ÙĪ",
- "Ġ×Ķ× ĸ",
- "cent ury",
- "Ġgrad ual",
- "ĠProblem e",
- "ĠìĹ ħ",
- "Ġcop ing",
- "ĠBr us",
- "Ġpean uts",
- "irts chaft",
- "Ġз ал",
- "ĠT roy",
- "Ġsper m",
- "ĠM itar",
- "ĠTür kiye",
- "g rand",
- "¦ Ń",
- "Ġ×ŀ× ¡",
- "Ġp ans",
- "ĠKnow ledge",
- "ber ly",
- "ĠÐķ го",
- "Ġdan ced",
- "ĠFr ost",
- "ĠB urg",
- "Ġbit ing",
- "ìłķ ìĿĦ",
- "me al",
- "Ġhero ic",
- "Ġmother board",
- "ĠL icht",
- "ãģ£ ãģ",
- "ll an",
- "ай н",
- "ĠÑĢ Ñıд",
- "Ġ à¹Ģà¸",
- "on en",
- "ir ie",
- "Ar t",
- "r ang",
- "ν η",
- "Ġnew born",
- "Ġam is",
- "Ġا ÙĪر",
- "Ġsoph om",
- "ĠCare ful",
- "Ġprospect s",
- "ens en",
- "Ġthr ill",
- "ĠVi á»ĩt",
- "A dam",
- "r ition",
- "ent ric",
- "ud en",
- "Ġcertific ates",
- "Ġas hes",
- "èª ¿",
- "play ing",
- "Ġs adece",
- "Ġo st",
- "Ġairpl anes",
- "ÑĢ ок",
- "on er",
- "Ġmagnes ium",
- "Ġgod damn",
- "Ġ197 2",
- "ĠSch ule",
- "Ġtem at",
- "Ġpart out",
- "௠Ĥ",
- "Ġin ve",
- "ĠScient ists",
- "ĠHud son",
- "win ning",
- "ceks in",
- "Ġcongress ional",
- "or u",
- "Ġro pes",
- "в ед",
- "Ġmad re",
- "Ġf erry",
- "ĠCoh en",
- "ĠP red",
- "Ġvag y",
- "Ġб еÑģп",
- "Ġmult im",
- "Ġdrain age",
- "Ġsim ulator",
- "g iggles",
- "ĠSt adium",
- "об Ñī",
- "Ġnot ices",
- "Ġcraw ling",
- "Ġgr oupe",
- "åı ¸",
- "Ġkto ÅĽ",
- "ĠY oga",
- "Ġmed ida",
- "ĠÑħ ваÑĤ",
- "ĠL ite",
- "Ġr av",
- "or ama",
- "Ġdisc ord",
- "ĠDI RE",
- "Ġte h",
- "ĠN urs",
- "ç² ī",
- "Ġpitch ed",
- "Ġbark ing",
- "ĠC oke",
- "wi ad",
- "Ġpop ulated",
- "éĻ ¤",
- "pe lled",
- "Ġб ог",
- "Ġpe wno",
- "ĠC ube",
- "Ġrecru ited",
- "éĢĻ 種",
- "ĠC ara",
- "ıģ ını",
- "im ated",
- "ĠÑĪ кол",
- "ic ional",
- "ĠпÑĢо ÑĦ",
- "Ġcontam ination",
- "Ġúlt imos",
- "Ġfear ful",
- "Ġele phants",
- "us i",
- "ĠiT unes",
- "ĠSw ami",
- "ê ¼",
- "ĠìĦ¤ë ªħ",
- "ĠRich ards",
- "Ġmagn ets",
- "ĠRicht ung",
- "ĠLeg ion",
- "èı ľ",
- "Ġk itty",
- "Ġkiss ed",
- "Ġwater ing",
- "Ġcon o",
- "ĠPalest ine",
- "id ir",
- "Ġma ze",
- "Ġflu ids",
- "ĠProdu cer",
- "ĠKr sna",
- "好 åķ¦",
- "la f",
- "Ġ×IJ ×ķ",
- "Ġm iesz",
- "ĠX ing",
- "oint ed",
- "se in",
- "ĠF uk",
- "ĠDep ression",
- "ĠD uty",
- "ĠPan ther",
- "Ġsu nd",
- "Ġref ere",
- "Ġexc lusion",
- "Ġnav al",
- "ĠWin ston",
- "Ġsl ogan",
- "Ġhypoth etical",
- "Ġelev ate",
- "ë ł¹",
- "Ġcabe ça",
- "ĠGes und",
- "m eter",
- "ĠìķĦëĭĪë ©´",
- "Ġcloud y",
- "âĢ¦ ?",
- "ĠSch ritt",
- "ĠJ S",
- "ì į",
- "ĠSpr ings",
- "ĠB atter",
- "· °",
- "Ġtail or",
- "ĠPTS D",
- "ĠG ent",
- "Ġba ÄŁ",
- "Ġspat ula",
- "Ġcr ay",
- "ĠLeg isl",
- "Ġs ú",
- "Ġle ve",
- "า ม",
- "Ġer ad",
- "Ġdon g",
- "Ġd erm",
- "ĠBank s",
- "ich o",
- "åħĪ çĶŁ",
- "ĠFr anz",
- "ra vel",
- "éģ Ķ",
- "ол о",
- "Ġfl ute",
- "ĠE k",
- "Ġjoy ful",
- "Ġch ased",
- "ĠLar ge",
- "O ver",
- "Ġentrepreneur ial",
- "Ġcons iders",
- "Ñĥ ем",
- "op a",
- "Ġdorm ir",
- "ĠElement ary",
- "Ġprzy pad",
- "ÑĥÑģ ка",
- "ĠоÑĩ еÑĢ",
- "ug ene",
- "Ġten ido",
- "Ġlug ares",
- "ë ¥",
- "ĠÑĩ аÑģÑĤ",
- "Ġsa o",
- "Ġbra id",
- "ĠV ere",
- "ĠRe ich",
- "ĠP oss",
- "Ġin an",
- "w and",
- "re f",
- "Ġmont rer",
- "Ġ198 1",
- "çķ ª",
- "as ında",
- "Ġch rome",
- "ĠTr inity",
- "Ġexplo itation",
- "ĠS ense",
- "ĠC MS",
- "ĠNo ble",
- "ĠìĦł íĥĿ",
- "Ġswe lling",
- "elect ronic",
- "] ?",
- "Ġbr ushing",
- "Ġliquid ity",
- "ĠH ook",
- "ĠCon nor",
- "ĠAl um",
- "Ġgu cken",
- "su ite",
- "Ġwie le",
- "Ġbarrel s",
- "ĠReg el",
- "ĠM ent",
- "ĠT rip",
- "ĠBr ush",
- "ĠE rik",
- "ur ate",
- "ÉĻ r",
- "ĠC yr",
- "ou ble",
- "ĠBe cca",
- "Ġpass words",
- "Å ±",
- "bor g",
- "Ġv endo",
- "ĠCla us",
- "ĠF az",
- "ind est",
- "Ġdece ased",
- "Ġcompar isons",
- "ĠL CD",
- "ĠP ork",
- "Ġevent ual",
- "Ġpat reon",
- "Ġin ability",
- "Ġext inction",
- "Ġì¢ĭìķĦ íķĺëĬĶ",
- "ĠÑģ оÑģ",
- "aj u",
- "Ġ×ij× IJ×",
- "Ġso fort",
- "Ġdest ined",
- "ĠR in",
- "Ġmouth s",
- "ĠNat ürlich",
- "Ġpres erving",
- "Ġlim p",
- "é» ¨",
- "oc used",
- "ин г",
- "Ġexp osing",
- "ĠÎ ¾",
- "ë į",
- "la ugh",
- "Ġhis s",
- "ãģł ãģĭãĤī",
- "Ġind ie",
- "Ġdet al",
- "ÑĢав ÑģÑĤв",
- "Ġtr ên",
- "æķ °",
- "Ġog ni",
- "Ġsimple mente",
- "Ġ197 8",
- "Ġgo o",
- "Ġ196 7",
- "Ġgen ug",
- "h ö",
- "Ġhist ó",
- "å® Ł",
- "Ġlob ster",
- "c endo",
- "Ġte il",
- "Ġalle vi",
- "00 00",
- "OL D",
- "Ġpes os",
- "Ġbon uses",
- "Ġam i",
- "Ġrev ival",
- "ĠHor se",
- "Ġs ack",
- "T alk",
- "Ġmul her",
- "ĠпоÑģÑĤо Ñıн",
- "ĠH ood",
- "H uh",
- "Ġë¶ ģ",
- "Ġhy ung",
- "ĠMe eting",
- "Ġimport a",
- "Ġì°¾ ìķĦ",
- "ĠV ern",
- "Ġstri pped",
- "Ġref uses",
- "Ġqual ifications",
- "op l",
- "Ģë ıĦ",
- "ix ÃŃ",
- "Ġdi ab",
- "it ime",
- "fl ows",
- "Ġin ac",
- "ĠG ong",
- "Ġmeaning less",
- "Ġcourage ous",
- "Ġmicro bi",
- "az y",
- "h ist",
- "Ġvolunte ering",
- "V IE",
- "Ġviol ated",
- "Ġsymp athy",
- "ĠEd it",
- "好 åĥı",
- "elect ric",
- "produ ct",
- "Ġpand emia",
- "Ġgeomet ric",
- "ĠCon vers",
- "g re",
- "Ġgl ut",
- "ist ed",
- "ĠاÙĦ Ùĥ",
- "ĠCh ain",
- "ĠPres ent",
- "ĠY in",
- "ĠÑģ ог",
- "ĠV log",
- "Ġìĸ´ë ¨¸",
- "Ġdon n",
- "Ġh itch",
- "uck ing",
- "ãģĬ ãģĦ",
- "w ald",
- "ris k",
- "Ġhar i",
- "ĠK ens",
- "ĠId ol",
- "Ġвним ание",
- "Ġtod d",
- "Ġsm ashed",
- "Ġinv ari",
- "Ġкон ÑĤÑĢ",
- "Ġaut istic",
- "ìŀ¥ ëĭĺ",
- "R es",
- "д Ñĭ",
- "ch au",
- "Ġsel v",
- "Ġhät ten",
- "ठ¿",
- "Ġexpect s",
- "Ïģ η",
- "Ġaç ık",
- "ĠHT TP",
- "le ÅŁ",
- "Ġswe eping",
- "ĠBet a",
- "Ġcounterpart s",
- "ab ile",
- "ĠSim s",
- "C s",
- "Ġrep ar",
- "s qu",
- "Ġprovin cial",
- "Ġshare holders",
- "Ġrun ter",
- "Ġged acht",
- "ĠTe en",
- "Ġgrand s",
- "çĶ ¢",
- "ag les",
- "Ġrock y",
- "ven s",
- "Ġr ivals",
- "un al",
- "Ġreact s",
- "ë ©",
- "Ġmerc ury",
- "ĠLu igi",
- "Ġо г",
- "ĠJ UST",
- "Ġl od",
- "Ġcort ex",
- "w ig",
- "Ġl akh",
- "ì¤ij ìĹIJ",
- "ĠV ic",
- "ĠM und",
- "Ġma pped",
- "ĠD ell",
- "ĠD ruck",
- "Ġlif es",
- "алÑĮ ное",
- "ivid ual",
- "ad ım",
- "Ġat rav",
- "ĠFl ug",
- "ĠKle in",
- "ê±° ìķ¼",
- "ห à¸Ļ",
- "Ġapp li",
- "ா ?",
- "ü yorum",
- "ĠинÑĤеÑĢеÑģ но",
- "Ġdis infect",
- "> -",
- "Ġchamp agne",
- "Ġk la",
- "op ers",
- "Tr ans",
- "ĠDes ert",
- "Ġcultiv ate",
- "ĠFuck ing",
- "idel ity",
- "ĠÑĤ ан",
- "Ġinc ub",
- "Ġtem u",
- "Ġlearn er",
- "found er",
- "ĠSy l",
- "ãĤ Ģ",
- "Ġf ato",
- "z ier",
- "ĠìĹĨ ìĿ´",
- "ĠìĪ ¨",
- "Ġpsych o",
- "ĠÑĤел еÑĦ",
- "Ġregard e",
- "Ġrepresent ations",
- "Ġlit igation",
- "Ġsp ann",
- "ult s",
- "b ior",
- "è¦ĭ ãģ¦",
- "ä¸į å¤ļ",
- "ĠSur vey",
- "ĠLED s",
- "Ġtr ä",
- "Ġl ên",
- "Ġant ioxid",
- "еÑĢ ом",
- "Ġindu ction",
- "Ġfool ed",
- "ät zlich",
- "ĠговоÑĢ ÑıÑĤ",
- "ĠF act",
- "umb ai",
- "Ġw iggle",
- "NO UN",
- "Ġdévelop p",
- "ĠCl aro",
- "Ġì ¸",
- "ë ¬",
- "ãģªãĤĵ ãģł",
- "Ġaccum ulate",
- "Ġmaint ains",
- "ë Ħ",
- "ĠFight er",
- "íĨ ł",
- "Ġmat in",
- "Ġcoup on",
- "Ġst unt",
- "Ġdeb uted",
- "å¾ħ ãģ£ãģ¦",
- "Ġpra g",
- "ив аем",
- "7 3",
- "Ġexp res",
- "Ġìĺ¤ë ¹ł",
- "ĠпеÑĢ Ñģон",
- "Ġcalcul us",
- "Ġab rupt",
- "ĠInspect or",
- "our t",
- "æĸ Ļ",
- "ź niej",
- "int ense",
- "B a",
- "Ġl ounge",
- "Ġast hma",
- "ĠHi ç",
- "ª »",
- "Ġeditor ial",
- "Ġse ize",
- "Ġk ır",
- "Ġm ouve",
- "Ġtier ra",
- "Ġtestoster one",
- "Ġr h",
- "ĠKing ston",
- "EL LE",
- "ĠRepresent ative",
- "Ġ197 4",
- "Ġi ba",
- "T s",
- "Ġsort a",
- "Ġ( ?)",
- "Ġت ÙĪ",
- "ĠëĤ´ë ł¤",
- "Ġbek ommt",
- "Ġspirit ually",
- "Ġdist orted",
- "M ad",
- "Ġre im",
- "á nh",
- "ĠOtt oman",
- "ĠRel ig",
- "ĠEl s",
- "Ġret ained",
- "ĠLa ughs",
- "æĢ »",
- "ĠS AS",
- "ĠколиÑĩе ÑģÑĤво",
- "×ķת ר",
- "Ġinnov ate",
- "Ġk ork",
- "ĠÑĢаÑģÑģк азÑĭв",
- "ond ere",
- "iv i",
- "ay e",
- "ount y",
- "ĠполÑĥÑĩ аеÑĤÑģÑı",
- "Ġbun s",
- "åħ «",
- "Ġyüz den",
- "Ġsur geries",
- "Ø£ ÙĨ",
- "Ġbankrupt cy",
- "w elt",
- "Ġsi amo",
- "Ġdark est",
- "ĠH ann",
- "gg a",
- "Ġform as",
- "ĠD j",
- "n amed",
- "Ġshield s",
- "ue ller",
- "ĠF ew",
- "Ġl ace",
- "Ġfur ious",
- "ĠY U",
- "Ġsociet al",
- "Ġjudge ment",
- "ĠD os",
- "Ġj ab",
- "law s",
- "Ġrein vent",
- "ĠK atherine",
- "ĠCh oi",
- "ad ows",
- "Ġr ans",
- "od en",
- "ĠMid west",
- "n ın",
- "Ġdep ort",
- "ĠD ip",
- "ç´ ħ",
- "Ġaten ción",
- "ĠCourt ney",
- "ivid ad",
- "ĠÚ© Ûģ",
- "Ġeffic acy",
- "ĠBrook s",
- "Ġrefer ral",
- "Ġкон ÑĨ",
- "Ġmal icious",
- "Ġk ir",
- "ĠGod dess",
- "Ġfun ky",
- "Ġinter im",
- "ĠK örper",
- "Ġìĸ¼ë §",
- "k ur",
- "Ġк ли",
- "Ġtruc s",
- "ges etz",
- "Ġz ug",
- "ĠGl ück",
- "ĠMin ute",
- "Ġprest igious",
- "Ġnie z",
- "Ġconcent rations",
- "ла ÑģÑĤи",
- "ĠS is",
- "ĠVit amin",
- "ko v",
- "ĠP BS",
- "Ġне е",
- "Ġretail ers",
- "Ġcon ventions",
- "ĠSam antha",
- "Ġproud ly",
- "J ordan",
- "ĠJ ASON",
- "at k",
- "Ġtr iste",
- "Ġst är",
- "Ġreiter ate",
- "Ġpos terior",
- "Ġ197 3",
- "ĠP ine",
- "ĠJul iet",
- "Ġped ir",
- "k il",
- "Ġover lapping",
- "Ġexclud e",
- "Ġecon óm",
- "Ġaccept s",
- "ĠS ter",
- "æ± º",
- "Ġìļ ´ëıĻ",
- "est ab",
- "Ġt ug",
- "ar g",
- "Ġliv ro",
- "Ø§Ø µ",
- "Ġse ams",
- "Ġbur aya",
- "Ġe llo",
- "ĠT M",
- "ĠP aw",
- "ĠInd ex",
- "Ex c",
- "Ġinspir ational",
- "Ġd unk",
- "è° ģ",
- "ak ter",
- "Ġcondition er",
- "ĠSal ut",
- "ÅĤ ec",
- "Ġìī ½",
- "ĠÑĥз на",
- "ĠRome o",
- "f ruit",
- "ĠY O",
- "Ġchá» ī",
- "б Ñĥ",
- "b ons",
- "Ġreprodu ctive",
- "Ġor ada",
- "Ġíļ ¨",
- "Ġtent ar",
- "Ġma ñana",
- "ãĤ ¬",
- "Ġsol vent",
- "Jess ica",
- "ĠLeg al",
- "Ġtu a",
- "Ġs ic",
- "ĠE Q",
- "au kee",
- "ìĭľ ëĭ¤",
- "ĠÅŀ u",
- "Ġad here",
- "ĠT ul",
- "Ġà® Ĩ",
- "Ġtext books",
- "ĠFif th",
- "Ġexper i",
- "Ġch ic",
- "Ġhe ap",
- "in ely",
- "at ra",
- "T wo",
- "Ġhele maal",
- "Ġf ren",
- "æİ ¨",
- "Ġbis her",
- "Ø§Ø ´",
- "ĠìĦł ìĥĿ",
- "ĠT ages",
- "Ġs á»±",
- "Ġbull ied",
- "Ø ¤",
- "Ġbenef ited",
- "ĠPre viously",
- "ĠÑį ÑĦÑĦ",
- "Ù į",
- "Ġsen ate",
- "ĠM orm",
- "ij ke",
- "ĠF lu",
- "Ġincorpor ating",
- "j ack",
- "Ġп иÑĤ",
- "Ġimp ly",
- "Ġha cks",
- "ĠR ICH",
- "Ġк ваÑĢ",
- "ĠпÑĢек ÑĢаÑģ",
- "Ġdepend ency",
- "Ġìļ ©",
- "Ġì± ħ",
- "Ġwäh rend",
- "Ġsu lla",
- "ĠPitts burgh",
- "Ġesemp io",
- "¼ë ¡ľ",
- "pr ot",
- "ĠR osen",
- "ĠIndepend ence",
- "Ġpars ley",
- "ie gen",
- "Ġha w",
- "Ġaqu ell",
- "ĠC AP",
- "ĠÑĢабоÑĤ аÑĤÑĮ",
- "ĠCl iff",
- "ion ar",
- "Ġsec uring",
- "æĪijåĢij çļĦ",
- "ν ε",
- "Ġutil is",
- "Ġcou le",
- "ĠP ing",
- "Ġtre k",
- "Ġf ak",
- "Ġenorm e",
- "Ġìĭ «",
- "è® ©",
- "Ġdoub ling",
- "ĠнÑĢав иÑĤÑģÑı",
- "Ġh ed",
- "ho ven",
- "ĠStand ing",
- "Ġm ÃŃn",
- "ĠJ imin",
- "Ġmon arch",
- "Ġco ke",
- "Ġm r",
- "Ġcl ic",
- "Ã į",
- "Ġimpe achment",
- "Ġdur ability",
- "Ġvar ios",
- "Ġcommercial s",
- "Ġgreet ings",
- "ĠR i",
- "ĠApp reci",
- "ìŀĪ ëĬĶ",
- "Ġrés ult",
- "ér t",
- "Ġsal ute",
- "Ġpoder ia",
- "Ġsun rise",
- "ve ck",
- "Ġreluct ant",
- "Ġcommission er",
- "å¿ µ",
- "â te",
- "ĠKen ny",
- "ĠSir i",
- "ãĥĥ ãĥĹ",
- "ĠëĬ ĺ",
- "ĠE E",
- "Ġun ch",
- "к он",
- "ĠاÙĦØ ¥",
- "Ġbel ts",
- "Ġhas s",
- "Ġмо Ñı",
- "Ġdispl aced",
- "Ġab ra",
- "ÎŃ Î»",
- "Ġscratch es",
- "Ġcom et",
- "Ġauthor ization",
- "ĠL LC",
- "Ġprodu k",
- "Ġrehabil itation",
- "å ŀ",
- "Ñĸ Ñĩ",
- "ud ing",
- "ol it",
- "Ġ10 5",
- "Ġexp ands",
- "Ġalt ri",
- "ĠKom ment",
- "Ġan f",
- "P l",
- "ĠM ana",
- "f ed",
- "Ġb ri",
- "Ġor a",
- "G s",
- "ĠG ur",
- "uck land",
- "Ġjun ction",
- "Ġiron ic",
- "ĠFe ed",
- "Ġpra kt",
- "ĠHam mer",
- "Įë ıĦ",
- "ĠTr acy",
- "çµ ±",
- "ĠAs ide",
- "н его",
- "ĠиÑģполÑĮз оваÑĤÑĮ",
- "Ġz aj",
- "Ġequ itable",
- "Ġcur b",
- "Ġãģĵ ãĤĮ",
- "Ġderiv atives",
- "Ġpupp ies",
- "ĠKenn eth",
- "ĠCom pl",
- "ig ram",
- "ĠGar cia",
- ") \"",
- "ĠHar bor",
- "est ial",
- "Ġ ä¾Ĩ",
- "Ġ ers",
- "æ ¹",
- "Ġunw anted",
- "Ġbel ang",
- "аР³Ð¾",
- "em b",
- "d os",
- "ĠìĻ ľë",
- "ĠBud get",
- "Ġbatt ling",
- "ØŃ Øª",
- "k ok",
- "наÑĩ ала",
- "Ġpl ag",
- "Ġcant idad",
- "Ġgrup os",
- "Ġplug ins",
- "ler ini",
- "Ġиме еÑĤ",
- "Ġso zusagen",
- "ol ics",
- "Ġpue blo",
- "Ġrem inis",
- "r än",
- "ĠMor rison",
- "Ġl inha",
- "Ġbreath s",
- "ĠT aste",
- "Ġenf rent",
- "ĠDo cker",
- "Ġд ен",
- "Ġethnic ity",
- "Ġw ob",
- "Ġsuff ers",
- "Ġtransition ing",
- "ĠR ange",
- "ÄĻd zy",
- "Ġк аÑĤ",
- "Ġsy ner",
- "Ġdon ut",
- "Ġprob abilities",
- "ĠO mar",
- "Wh ich",
- "u ish",
- "is in",
- "Ġdem os",
- "ĠìłĢ 기",
- "Ġëĺij ê°Ļ",
- "Ġед ин",
- "Ġc erve",
- "Ġj oka",
- "I AN",
- "Ġkilomet er",
- "Ġhorizont ally",
- "ĠBh ag",
- "Ġ- >",
- "ĠMon itor",
- "Ġknowledge able",
- "Ġf av",
- "Ġpin ned",
- "Ġe Bay",
- "ick er",
- "Ġìŀłê¹ IJë§Į",
- "ĠXia omi",
- "Ġcap it",
- "Ġn p",
- "Ġ196 5",
- "ho e",
- "Ġn ok",
- "ĠS age",
- "Ġн елÑĮзÑı",
- "ĠT ow",
- "g am",
- "Ġdic en",
- "ĠSUBSCRI BE",
- "Ġrebo ot",
- "Ġp aj",
- "Ġë³´ìĹ ¬ë",
- "Ġth icken",
- "ĠRe ality",
- "id än",
- "N a",
- "Ġê²ĥ ìĿĢ",
- "!! )",
- "Ġrout ines",
- "Ġод ного",
- "Ġex ting",
- "Ġì¦ Ŀ",
- "Ġsulf ur",
- "Ġcar ve",
- "Ġastero id",
- "ĠWarri or",
- "Ġphotograph ers",
- "Ġpe ll",
- "Ġcros sover",
- "æĪij çŁ¥éģĵ",
- "Ġhace mos",
- "ĠNe j",
- "Ġsett ling",
- "Ġir m",
- "ĠBook s",
- "ient ôt",
- "Ġesp acio",
- "ĠSchol ars",
- "Ġdo omed",
- "ĠIR S",
- "w ohl",
- "Ġseg ue",
- "ĠëĪĦ ê°Ģ",
- "Ġpr atic",
- "B T",
- "ĠConsider ing",
- "ĠBuff alo",
- "Ġtrain ings",
- "Ġge bru",
- "ĠG leich",
- "Ġpir ates",
- "Ġen velop",
- "Ġre open",
- "im at",
- "Ġte e",
- "Ġsu ed",
- "fe h",
- "Ġ×Ķ× §",
- "Ġdi ets",
- "Ġjunt os",
- "ast o",
- "Ġmisunder stood",
- "Ġru im",
- "Ġclass ify",
- "ĠпÑĢод Ñĥк",
- "Ġin se",
- "Ġillust rated",
- "Ġcorros ion",
- "Ġacc red",
- "ĠAunt ie",
- "ĠпÑĢив еÑĤ",
- "ĠLI VE",
- "Ġre k",
- "Ġrece ipt",
- "åĪ° åºķ",
- "ĠBar bie",
- "ĠSn ake",
- "t urn",
- "Je ff",
- "ãģĬ ãģĬ",
- "ķ Ħ",
- "VO ICEOVER",
- "co ll",
- "Ġrun ners",
- "ìł ľë",
- "os os",
- "mo on",
- "Ġkey note",
- "ĠInst it",
- "S PEAK",
- "Ġplug s",
- "Ġcur v",
- "ĠY uri",
- "ĠTh eres",
- "ĠP s",
- "Ġμ ÏĢο",
- "Ġconver ter",
- "Ġref ine",
- "Ġbad ass",
- "Ġο ι",
- "Ġreg en",
- "az zi",
- "ÙĬ Ùģ",
- "Ġse ized",
- "Ġiç er",
- "ile e",
- "Ġup stream",
- "Ġbud s",
- "Ġp im",
- "Ġíķĺë £¨",
- "Ġall uded",
- "Ġthem ed",
- "Ġconsist ing",
- "Ġb ons",
- "un uz",
- "ĠпÑĢов од",
- "ĠLove ly",
- "ॠĭ",
- "Ġpar ach",
- "ĠSta ats",
- "éļ Ĭ",
- "Ġselect ive",
- "Ġf ase",
- "ĠGeor get",
- "Ġcoc aine",
- "Ġreprodu ction",
- "ĠL ara",
- "ĠL D",
- "Ġg h",
- "J on",
- "Ġl Ã¥",
- "Ġëij IJë",
- "Ġtyp ed",
- "ĠB ana",
- "ë ĵľë",
- "Ġsav ory",
- "ĠZ omb",
- "stand en",
- "Ġpedest rian",
- "Ġdifférent s",
- "Ġìĭ ¸",
- "èī ¯",
- "Ġcompl ained",
- "ç¦ ı",
- "ĠÐļ ÑĤо",
- "Ġ×ľ× ¤",
- "ali ÅĽmy",
- "Ġmort ar",
- "Ġverd ict",
- "Ġsu ficiente",
- "ĠMill ion",
- "mitt el",
- "in als",
- "ĠاÙĦØ ®",
- "аÑİ ÑģÑĮ",
- "Ġmi ÄĻdzy",
- "ĠO le",
- "Ġin vert",
- "czy Äĩ",
- "озм ожно",
- "star ter",
- "Ġaud itor",
- "ĠSc out",
- "ch ien",
- "ĠSver ige",
- "uff led",
- "Ġze hn",
- "ĠA uckland",
- "Ġarg ent",
- "Ġ197 6",
- "ĠHo e",
- "Ġboth ers",
- "Ġsocial ist",
- "Ġpl iers",
- "Ġemer gen",
- "ĠX P",
- "еÑĢ ов",
- "M ore",
- "ĠLe vi",
- "ĠAnd ers",
- "ibil idad",
- "ĠP arents",
- "Ġindu ced",
- "ìĸ´ì ¤",
- "Ġbal ances",
- "ĠвÑĭ ÑĪ",
- "Ġsubmar ine",
- "St art",
- "Ġdri es",
- "Ġvol ver",
- "Ġtick ing",
- "c ott",
- "Ġf aj",
- "pr és",
- "ĠS abb",
- "Ġза Ñĩ",
- "Ġпок Ñĥп",
- "Ġbapt ized",
- "ĠBrill iant",
- "ĠÐij ог",
- "Ġm ots",
- "b its",
- "Ġlatt ice",
- "æĪij è·Łä½ł",
- "Ġcor iander",
- "Ġresid ency",
- "yn c",
- "Ġpier wszy",
- "ĠKn ock",
- "ĠZ ap",
- "ĠÐķ в",
- "ê² ¬",
- "å°ı å¿ĥ",
- "Ġune ven",
- "ĠJ as",
- "od or",
- "ç¿ Ĵ",
- "7 4",
- "ĠS ite",
- "Ġacontece u",
- "ym pt",
- "Ġtril ogy",
- "Ġlan tern",
- "ĠZ ucker",
- "v ari",
- "we lling",
- "ĠPot ato",
- "gom ery",
- "Ġreact ed",
- "ĠChr on",
- "Ġj ede",
- "be eld",
- "Ġtw ent",
- "Ġl act",
- "æ¨ Ĥ",
- "Ġré se",
- "Ġrel ent",
- "Ġfurn ace",
- "Ġwid get",
- "Ġearthqu akes",
- "ĠAd just",
- "il it",
- "ĠØ£ ÙĪ",
- "Ġhear ings",
- "Ġdefend ant",
- "irs iniz",
- "Ġbas k",
- "c ja",
- "ľ ¨",
- "Ġrif les",
- "Ġinst al",
- "ĠFor give",
- "p ical",
- "ĠÐŀÑĩ енÑĮ",
- "Ġpet ites",
- "Ġh p",
- "Ġren owned",
- "ĠIn n",
- "Ġ주 ìĦ¸ìļĶ",
- "Ġemphas ized",
- "éĹ® é¢ĺ",
- "ĠìŀĪ ì£ł",
- "Ġê²ĥ ìľ¼ë¡ľ",
- "ãĤ Ĩ",
- "Å ĵ",
- "g ili",
- "D ave",
- "Ġexha usting",
- "ÅĤ ug",
- "Ġsch ema",
- "μ ά",
- "cy cl",
- "Ġaut ant",
- "Ġpar cel",
- "Ġmater ia",
- "ĠB erry",
- "ĠÑģ ами",
- "Ġextract ed",
- "ĠSay ing",
- "ism atic",
- "Ġпоп ÑĢоб",
- "Ġneur on",
- "g raph",
- "ľë ©´",
- "Ġencl osure",
- "ĠJoh ann",
- "Ġafter math",
- "ÑĤ об",
- "Ġu ży",
- "Ġs amp",
- "3 60",
- "ĠMe i",
- "Ġt aco",
- "Ġrecept ors",
- "Ġpunch es",
- "ĠHo je",
- "ĠÙĩ ÙĨا",
- "=\" #",
- "ĠAng ular",
- "Ġmus ique",
- "Ġro l",
- "ĠÃ ±",
- "ster reich",
- "Ġcl am",
- "ĠTre asury",
- "chem ical",
- "Ġap ar",
- "Ġapp end",
- "Ġforb id",
- "ĠHamb urg",
- "ак ов",
- "Ġê¸ Ī",
- "ild a",
- "Ġprepar ations",
- "Ġmog Äħ",
- "Ġcam ino",
- "E ric",
- "ĠBl ind",
- "èĪ ĩ",
- "å¹´ çļĦ",
- "ĠDis covery",
- "ì¸ ł",
- "çĪ ¶",
- "Ġinterpre ter",
- "Ġb red",
- "ĠPsal m",
- "Ġdef ended",
- "ìī ¬",
- "ĠEr fahr",
- "ĠPe ach",
- "Ġmo ons",
- "ĠO st",
- "Ġspé cial",
- "Ġarri ver",
- "ĠW is",
- "u ci",
- "Ġrobot ics",
- "I VE",
- "Ġsie ge",
- "ar la",
- "Ġsepar ates",
- "ĠT C",
- "íı °",
- "quis ite",
- "Ġparenth eses",
- "ик е",
- "ç« Ļ",
- "Ġtr ous",
- "å» º",
- "ĠÑģ илÑĮ",
- "Ġbe ers",
- "Ġпл аÑĤ",
- "ãģĻãģĶ ãģĦ",
- "Ġso la",
- "Ġd ès",
- "ming ham",
- "ik te",
- "Ġo ops",
- "Ġtw itch",
- "å° ĩ",
- "Ï Ī",
- "ĠShould n",
- "uv re",
- "Ġle er",
- "cript ions",
- "Ġeyes hadow",
- "ĠGu o",
- "ĠPow ell",
- "Ġsup uesto",
- "Ġan a",
- "r als",
- "ĠMont real",
- "Ġsurf ing",
- "ĠÐŁÐµÑĢ в",
- "×ŀ ×ķ",
- "Ġmillise conds",
- "Ġsubur bs",
- "Ġplanet a",
- "ÑĥÑĪ ка",
- "hr lich",
- "ĠH Y",
- "Ġس ÛĴ",
- "ĠM M",
- "ĠE ff",
- "åı¯ æĦĽ",
- "ĠH S",
- "ans on",
- "Ġì§ģ ìłij",
- "Ġsu o",
- "Ġdeploy ing",
- "Ġk unt",
- "ter ing",
- "Ġere ct",
- "ìŀ¥ ìĿ´",
- "ĠìĿĮ ìĭĿ",
- "Ġspec imen",
- "! ...",
- "æĪij 說",
- "Ġlig ne",
- "Ġk onst",
- "ade qu",
- "Ġìĥģ íĥľ",
- "Ġaccess ed",
- "ĠP ole",
- "k ill",
- "Ġë² Ħë",
- "Ġauthentic ity",
- "Ġapp elle",
- "ull e",
- "Ġrev ision",
- "Ġgo ats",
- "г ли",
- "Ġp au",
- "ĠR anger",
- "ĠIm ag",
- "aut hor",
- "Ġe ve",
- "ĠMess enger",
- "Ġn ay",
- "Ġwh oles",
- "ät te",
- "Ġon wards",
- "ĠDep ois",
- "Ġíijľ íĺĦ",
- "ĠSAR S",
- "Ġwszystk ich",
- "Ġdest ru",
- "umb ing",
- "Ġcompat ibility",
- "Ġmis information",
- "od ore",
- "ĠF avor",
- "ek o",
- "ı Į",
- "w aukee",
- "ĠTe aching",
- "ĠK O",
- "Ġbet ting",
- "Ġquest s",
- "Ġviv re",
- "ĠмÑĥз Ñĭ",
- "Ġs aga",
- "Ġswe ll",
- "Ġge he",
- "æĢİ麼 樣",
- "ĠоÑĢг аниз",
- "Ġg ide",
- "ĠG ross",
- "Ġdale j",
- "Ġcl aws",
- "á»Ļ c",
- "Ġprejud ice",
- "Ġins ign",
- "i hood",
- "Ġpl ed",
- "Ġdó nde",
- "ĠPolit ical",
- "Ġprem ises",
- "und ert",
- "ع ت",
- "on nen",
- "Ġespa ço",
- "Ġf é",
- "ĠHarr ison",
- "ĠC ensus",
- "Ġcard io",
- "Ġdi y",
- "Ġmil ieu",
- "Ġjourn ée",
- "ĠRe lease",
- "N IE",
- "ĠM uk",
- "id ée",
- "á»į i",
- "Ġiç inde",
- "ŀ Ļ",
- "Ġreson ate",
- "Ġm oles",
- "ĠF lying",
- "ĠGl oria",
- "ĠPast or",
- "ĠAre na",
- "好 ä¸į好",
- "N ON",
- "ол ов",
- "Ġall ÃŃ",
- "om at",
- "ìĸ´ë ıĦ",
- "Ġcaracter ÃŃst",
- "Ġdecl ining",
- "Ñĸ Ñı",
- "an co",
- "ĠIn form",
- "Ġbarg ain",
- "Ġbus hes",
- "ĠNat urally",
- "Ġre chts",
- "ĠT ensor",
- "ĠPat ricia",
- "Ġprincip io",
- "ĠM umbai",
- "Ġwom b",
- "Ġnost ra",
- "Ġdile mma",
- "Ġirgendw ann",
- "Ġ196 4",
- "Ġenerg ÃŃa",
- "Ġна ÑĢ",
- "Ġseg regation",
- "ĠA thlet",
- "Ġ» ,",
- "Ġy eni",
- "ĠSe it",
- "Ġven om",
- "Ġdak ika",
- "Ġëı Įë",
- "ĠÃī l",
- "Ġf us",
- "ĠM og",
- "¦½ ëĭĪëĭ¤",
- "Ġrem ar",
- "ĠTed dy",
- "Ġbreast s",
- "ic ans",
- "æĶ¶ çľĭ",
- "k ap",
- "Ġh Æ¡n",
- "ĠJ P",
- "ãĥ³ ãĤ¿",
- "Ġresur rect",
- "ĠìĿ ¸ë",
- "her ical",
- "Ġfot ograf",
- "ĠJos é",
- "Ġlivel ihood",
- "Ġbib li",
- "ter i",
- "Ġvor stellen",
- "ĠA AA",
- "Ġassess ing",
- "Y A",
- "Ġspl end",
- "Ġexca v",
- "Ġbapt ism",
- "y ll",
- "w ow",
- "M ac",
- "Ġpl astics",
- "teok bokki",
- "Ġintéress ant",
- "Ġcommand ed",
- "Ġfamous ly",
- "ĠÐĺ ли",
- "ĠMan uel",
- "Ġsouth west",
- "Ġde formation",
- "ÃŃcul o",
- "ĠнаÑħод иÑĤÑģÑı",
- "ĠP atter",
- "d egree",
- "ĠczÄĻ sto",
- "\" -",
- "Ġìħ ĭ",
- "Ġman ger",
- "ĠTrust ee",
- "Ģë ¦¬",
- "Ġpunt os",
- "iv able",
- "Ġvol atile",
- "ĠëĬ IJ",
- "Ġinst ability",
- "Ġc iel",
- "ci Äħ",
- "Ġpur ity",
- "но ÑģÑĤ",
- "S il",
- "ed ar",
- "åĻ ¨",
- "NOUN CER",
- "Ġspe lled",
- "G ER",
- "Ġsanct uary",
- "Ġacceler ating",
- "Ġsc out",
- "ĠпÑĢ ев",
- "f ahren",
- "ãģĵ ãģ¡ãĤī",
- "ĠëĤĺìĺ ¨",
- "Ġpocz Äħt",
- "ĠMe u",
- "ka ar",
- "³´ ê³ł",
- "ak ra",
- "D own",
- "ĠÃĦ r",
- "ĠEl ite",
- "Ġall ons",
- "Ġmay onnaise",
- "ĠS ustain",
- "prising ly",
- "Ġsuper vis",
- "Ġê·¸ëłĩ ì£ł",
- "Ġunemploy ed",
- "Ġfresh ly",
- "Ġ×ŀ× ¢",
- "ĠD h",
- "Ġtack ling",
- "Ġo gr",
- "Ġì´ Īë",
- "ãĤĪ ãĤį",
- "Ġlo ft",
- "ar ah",
- "ĠA irl",
- "ĠD ir",
- "ĠÐľ ожно",
- "Ġbook ing",
- "ĠC RA",
- "Ġhtt ps",
- "Ġcho ke",
- "Ġg own",
- "Ġno ite",
- "Ġz ac",
- "ist ol",
- "Ġsec re",
- "Ġresemb les",
- "Ġcu ad",
- "ìĤ¬ ê°Ģ",
- "sh ow",
- "Ġbl anc",
- "Ġag u",
- "ĠPr int",
- "ast ed",
- "ĠWe ather",
- "i pl",
- "Ġobsc ure",
- "Ġcont e",
- "ough s",
- ") ;",
- "ĠD ame",
- "ä¸Ģ 缴",
- "Ġclar ification",
- "Ġintim acy",
- "Ġup hold",
- "ĠMir ror",
- "Ġw agon",
- "x ide",
- "Ġcl og",
- "app er",
- "ĠImmedi ately",
- "ú de",
- "Ġtouch down",
- "Ġro oft",
- "аÑĪ а",
- "Ġç ıkt",
- "Ġla isser",
- "ĠUn real",
- "ens itive",
- "Ġ12 3",
- "Ġpl aster",
- "Ġduck s",
- "Ġet me",
- "Ġb ishop",
- "bre vi",
- "Ġb ic",
- "ä¸ĭ åİ»",
- "Ġrun time",
- "Ġamb itions",
- "м аÑĤ",
- "ĠWe in",
- "ĠMar i",
- "ĠíĬ ¸ë",
- "Ġresol ver",
- "Ġng Ãły",
- "ĠR ise",
- "ãĤĪãģĨ ãģ«",
- "ĠCr us",
- "Ġmerchand ise",
- "Ġel i",
- "Ġstate wide",
- "Ġow l",
- "éģ ł",
- "æĶ ¹",
- "Ġtwist ing",
- "Ġcontam inated",
- "ĠCom merce",
- "hy thm",
- "ĠÃ Ī",
- "Ġìĭ ¤ë",
- "Ġmus ste",
- "u ir",
- "Ġsum s",
- "ĠSome where",
- "ãĥ İ",
- "Ġk ami",
- "Ġa ired",
- "ĠAND REW",
- "Ġê º",
- "Ġv iendo",
- "Ġantib ody",
- "Ġabsol ument",
- "Ġprotest ers",
- "ĠQué bec",
- "st adt",
- "Sha un",
- "Ġcham bers",
- "ĠWe ar",
- "ĠEffect s",
- "Ġhaz ards",
- "Ġne i",
- "Ġcoraz ón",
- "Ġá ¼",
- "ĠS G",
- "Ķ ©",
- "ĠìĹŃ ìĭľ",
- "Ġcom fy",
- "ĠC ody",
- "Ġpens ando",
- "Ġg anska",
- "ĠAc ross",
- "öll ig",
- "aby te",
- "Ġwed ge",
- "Ġkal ian",
- "Ġsig ue",
- "end es",
- "ĠGro ÃŁ",
- "Ġutil iser",
- "Ġfl own",
- "ани Ñİ",
- "Ġle var",
- "rest rial",
- "Ġillust rations",
- "Ġas lında",
- "BLE EP",
- "Ġдо ÑģÑĤ",
- "Ġtur ret",
- "Ġsuit case",
- "ziÄĻ ki",
- "Ġsket ches",
- "Ġac red",
- "ĠRe i",
- "Ġt sun",
- "ĠS ag",
- "Ġthird s",
- "ĠKIR BY",
- "ra i",
- "Ġhuman os",
- "Ġrecomm ends",
- "Ġextraordin arily",
- "Ġcommence ment",
- "K N",
- "ope z",
- "Ġ×ij× ©",
- "Ġlet hal",
- "ĠEst amos",
- "Ġinspect or",
- "ĠSe ok",
- "e un",
- "Ġoff shore",
- "Ġget tin",
- "ye ars",
- "ĠSil ence",
- "ĠNat ur",
- "up un",
- "Ġtr zy",
- "Ġno get",
- "Ġhamb urger",
- "ĠPra ise",
- "é nd",
- "Ġ197 1",
- "yl ie",
- "k rit",
- "ĠìĥĿê°ģ ìĿ´",
- "çļ ®",
- "Ġmoment os",
- "Ġest é",
- "Ġdisse min",
- "Ġgig s",
- "Ġdes af",
- "Ġav is",
- "ĠZ oo",
- "ĠìķĬ ìĿĢ",
- "h äng",
- "åı ¥",
- "h ake",
- "ĠB ism",
- "Ġre think",
- "ĠMal colm",
- "Ġident ifies",
- "l ower",
- "ix el",
- "Ġtv Ã¥",
- "k ed",
- "ier z",
- "Ġö ffentlich",
- "Ġproc laim",
- "so on",
- "l ol",
- "Ġlo i",
- "Ġb itten",
- "ro llo",
- "Ġser mon",
- "Ġes qu",
- "Ġjack ets",
- "Ġgr áfic",
- "Ġпок азÑĭв",
- "Ġcabe za",
- "ch odzi",
- "Ġpel vis",
- "Ġnost algia",
- "Ġbre w",
- "Ġshort cuts",
- "ĠAd emás",
- "Ġsuperfic ial",
- "åħ© åĢĭ",
- "Ġbo ca",
- "ĠæĪij æĺ¯",
- "iment os",
- "åĽł 为",
- "Ġspr outs",
- "é£ Ľ",
- "ĠJon as",
- "ĠFloren ce",
- "st atic",
- "da ughter",
- "* )",
- "ÅĤ by",
- "f ashion",
- "ĠG inger",
- "Ġë§ ¤ë",
- "Ġhust le",
- "ut os",
- "ĠÑĤ Ñıж",
- "ĠL ös",
- "ש ×Ļ×Ŀ",
- "any ch",
- "tu ber",
- "Ġtid y",
- "Ġfront al",
- "Ġwhis key",
- "Ġhum id",
- "ĠÎ Ł",
- "Ġr idge",
- "Ġmar in",
- "Ġb ientôt",
- "ĠCarr ie",
- "ch w",
- "Ġtah un",
- "ĠEr geb",
- "F R",
- "Ġìłķ ë¶Ģ",
- "ĠSold ier",
- "Ġenlight enment",
- "Ġexam ining",
- "ĠNot re",
- "Ġer am",
- "ĠSun ny",
- "Ġlay ered",
- "ĠD azu",
- "r ades",
- "好 åIJĥ",
- "ĠнаÑĪ ей",
- "Ġtim ber",
- "Ġman ners",
- "ĠBir mingham",
- "Ġmini ature",
- "omet ers",
- "Ġfill er",
- "ĠR ip",
- "ĠK omb",
- "own er",
- "ì ¿",
- "id ian",
- "Ġdem ás",
- "ĠÙĪ ت",
- "Ġpreca utions",
- "Ġgovern o",
- "z elf",
- "ĠCom plete",
- "å¸ ĥ",
- "ĠPh antom",
- "ãģ¾ ãģļ",
- "Ġн ез",
- "ĠкаÑĢ ÑĤ",
- "ĠAnt wort",
- "ĠPf izer",
- "ĠFran co",
- "Ġw ÅĤ",
- "Ġfr ig",
- "es per",
- "Ġk ale",
- "Ġfilm maker",
- "Ġk urt",
- "Ġinv alid",
- "å± Ģ",
- "are lla",
- "Äĥ ng",
- "ram ento",
- "Ġnutr itional",
- "Ġdict ators",
- "Ġaf in",
- "Ġf uzzy",
- "ĠG ina",
- "ó t",
- "ĠExtrem adura",
- "Ġdemonst rations",
- "ĠMont gomery",
- "íķ´ì Ħ¤",
- "ĠGand hi",
- "ãĥ Ŀ",
- "ç½ ®",
- "Ġreun ion",
- "Ġjaki ÅĽ",
- "ĠZ ug",
- "OU GH",
- "l ifting",
- "Ġ à²",
- "á¹Ľ á¹£",
- "e b",
- "ĠW OW",
- "ĠSh iva",
- "omet ry",
- "Ġwild ly",
- "Ġt ended",
- "Ġmeg ap",
- "ì² ĺ",
- "Ġna use",
- "Ġg erek",
- "ãĥ ĭ",
- "ĠMar cel",
- "Ġn este",
- "خ ر",
- "Ġfe h",
- "åĨ ħ",
- "susp enseful",
- "ĠWrest le",
- "ĠPalestin ians",
- "ĠG ORD",
- "iy et",
- "ĠÑĢ ади",
- "Ġvers uchen",
- "Ġtrans istor",
- "ĠÐŁÑĢ оÑģÑĤо",
- "Ġпон ÑĢав",
- "Ġrhy me",
- "ĠVerm ont",
- "pl atz",
- "è® °",
- "ĠÄ°ÅŁ te",
- "ĠH ag",
- "ĠÐĺ м",
- "ĠÑĢаÑģÑģк аз",
- "Ġmet ros",
- "ĠInfin ity",
- "w olf",
- "ib al",
- "ft ig",
- "Ġ ÚĨ",
- "Ġíĺ¹ ìĭľ",
- "Ġo ggi",
- "Ġdisp osit",
- "ĠпÑĢ ил",
- "ĠвÑĭ пол",
- "Ġth ôi",
- "ĠK ENN",
- "Ġhand ing",
- "act us",
- "Ġtac os",
- "Ġformer ly",
- "ĠCorinth ians",
- "ãģ« ãģ¯",
- "ÑĨÑĸ ÑĹ",
- "Ġpad re",
- "Ġcongreg ation",
- "æ ij",
- "fer t",
- "Ġsub ir",
- "ais er",
- "qu a",
- "ara oh",
- "ĠCur ry",
- "ĠìķĬ ëĬĶ",
- "ел Ñİ",
- "Ġf uss",
- "Ġbo oty",
- "Ġl ows",
- "Ġh ommes",
- "ĠM H",
- "ĠDisney land",
- "w ent",
- "Ġresid ue",
- "Ġbe eping",
- "è¼ ķ",
- "ät ta",
- "Ġm ould",
- "ĠPro jekt",
- "st alk",
- "Ġartif act",
- "ĠAnt rag",
- "ĠAM D",
- "ĠCry pt",
- "Ġë© Ķ",
- "ĠFel ipe",
- "ĠCO B",
- "el u",
- "Ġself ies",
- "ĠS anti",
- "ch utz",
- "ĠУ кÑĢаÑĹ",
- "ges amt",
- "Ġflo ck",
- "j az",
- "pl ain",
- "Ġwr inkles",
- "Ġre ais",
- "Ġpal jon",
- "Ġempower ment",
- "Ġattend ees",
- "pp a",
- "Ġn eden",
- "он Ñĭ",
- "Ġtime frame",
- "ĠCher ry",
- "Ġid ée",
- "Ġg ag",
- "Ġdon key",
- "Ġô ng",
- "ĠH are",
- "éļ Ľ",
- "ĠK ara",
- "Ġacom pan",
- "pl aces",
- "im ientos",
- "ĠH amm",
- "б и",
- "ub en",
- "ili yor",
- "Ġth irst",
- "Ġk ry",
- "ĠGeorget own",
- "׳ ×Ķ",
- "Ġor ch",
- "Ġheart beat",
- "Ġtransform ations",
- "est ones",
- "ĠK H",
- "Ġcart oons",
- "Ġan ci",
- "Ġworth less",
- "Ġtail ored",
- "p u",
- "Americ ans",
- "Ġp iles",
- "ĠMon key",
- "Ġbas in",
- "ĠTem per",
- "ĠP aint",
- "Ġpunch ing",
- "Ġba ik",
- "ĠOak land",
- "v re",
- "ÅŁ allah",
- "yd d",
- "Ġcas ually",
- "od u",
- "Ġc oded",
- "ĠNorweg ian",
- "ĠV ince",
- "Ġprem ature",
- "ĠProm ise",
- "ек ÑģÑĤ",
- "Ġdevast ated",
- "ĠPrem ium",
- "ĠPar am",
- "ĠÃĸ yle",
- "um uz",
- "P O",
- "r ators",
- "Ġlamp s",
- "Ġterritor ial",
- "Ġback bone",
- "list ed",
- "D Y",
- "ĠاÙĦ ر",
- "Ġpurs ued",
- "ĠComm ons",
- "Ġê³ ¡",
- "lo cks",
- "ed or",
- "Ġconce ived",
- "g ere",
- "Ġdisappe aring",
- "ĠS ull",
- "ĠìĹ °ë",
- "Ġho ffe",
- "Ġdet ox",
- "íĶ Į",
- "Ġret ir",
- "ĠëģĿ ëĤ",
- "Ġper gunta",
- "ĠB OY",
- "ç² ¾",
- "Ġp enn",
- "æĿ¥ äºĨ",
- "h és",
- "h on",
- "Ġcatastroph ic",
- "Ġa ust",
- "Ġtor so",
- "Ġìĸ´ ëĬIJ",
- "ĠìĤ¬ëŀĮë ĵ¤ìĿ´",
- "Ġmarvel ous",
- "ĠHar ley",
- "ach ine",
- "Ġti ế",
- "itt o",
- "ĠI ÃŃm",
- "yl on",
- "Ġshut down",
- ".' '",
- "Ġap ologies",
- "ĠCommun ication",
- "ĠговоÑĢ Ñİ",
- "ãģĤ ãĥ¼",
- "âĦ ¢",
- "ÃŃ veis",
- "ac un",
- "Ġret aining",
- "Ġcontrad iction",
- "ĠAD AM",
- "C OM",
- "Bry an",
- "ĠM onsieur",
- "Ġadap ting",
- "Ш ÐIJ",
- "ĠSc r",
- "änd ert",
- "Ġpl aus",
- "ä»Ĭ天 çļĦ",
- "Ġon set",
- "Ġassist ants",
- "Ġval ves",
- "Ġsc atter",
- "ĠR ust",
- "aw ia",
- "Ġread iness",
- "Ġp ais",
- "Ġb ible",
- "Ġamb iente",
- "Ġа меÑĢик",
- "Ġunc ond",
- "Ġk alk",
- "åĬ ¨",
- "Ġmo c",
- "un n",
- "Ġact u",
- "Ġhum ming",
- "iss imo",
- "ĠPat rol",
- "g ow",
- "ãĥ ¤",
- "ĠTHE Y",
- "ĠBod en",
- "ĠB ie",
- "Ġre el",
- "ĠÑĥÑģл ов",
- "Ġende avor",
- "ĠPer iod",
- "ustom ed",
- "m als",
- "al on",
- "B ox",
- "ĠÏĥ αÏĤ",
- "Ġom dat",
- "Ġal tre",
- "ĠHe h",
- "k ad",
- "Ġprotect or",
- "Ġdomin ance",
- "odynam ic",
- "Ġcommunic ated",
- "k ö",
- "Ġprede cessor",
- "ĠL uk",
- "ĠFl ower",
- "Ġãģ ©",
- "po que",
- "ÑĤи ÑĢов",
- "Ġret rospect",
- "Ġdecis ive",
- "Ġexem pel",
- "{ \\",
- "ĠR ück",
- "r ite",
- "ĠZe us",
- "Ġcal orie",
- "Ġattract ions",
- "ĠH inter",
- "Ġuh m",
- "ĠíĮ IJ",
- "Ġrul ers",
- "Ġdiscour aged",
- "Ġaconte cer",
- "Ġacc ents",
- "ĠOpt im",
- "ĠAl g",
- "k ids",
- "20 21",
- "ĠLind say",
- "Ġfilm makers",
- "pr owad",
- "Ġter ug",
- "ëĭ ´",
- "ĠSom mer",
- "20 18",
- "Ġborrow ing",
- "ĠTrans fer",
- "н оп",
- "ari as",
- "Ġhead phone",
- "ì¼ ľ",
- "Ġtransl ating",
- "Ġauf ge",
- "ப à®Ł",
- "we is",
- "av ant",
- "pa id",
- "b aby",
- "Ġtough est",
- "Ġrepe ats",
- "ĠTer esa",
- "L ord",
- "Ġacab ar",
- "ĠR ide",
- "d ir",
- "Ġl eng",
- "Ġd wa",
- "Ġhead aches",
- "Ġn ữa",
- "ĠнаÑģ ÑĤоÑıÑī",
- "Ġbo ils",
- "Ġlong ing",
- "ri as",
- "ó rio",
- "ĠParad ise",
- "ĠSeñ or",
- "erd em",
- "Ġrein st",
- "Ġsal aries",
- "Ġinsec urity",
- "ÅĤo ÅĽci",
- "ĠабÑģолÑİÑĤ но",
- "ink en",
- "ĠEd dy",
- "ud os",
- "Ġd ummy",
- "Ðļ ак",
- "s ix",
- "Ġin box",
- "Ạ©",
- "Pe ople",
- "á»ĵ ng",
- "Ġorganiz ers",
- "f ind",
- "Ġü l",
- "ĠCO M",
- "ż a",
- "we ile",
- "Comment ary",
- "íĬ¸ë ¥¼",
- "ĠMitt el",
- "k us",
- "èĽ ĭ",
- "ठ¨",
- "ir al",
- "Ġgar ment",
- "ικ ά",
- "Ġst ool",
- "pay ers",
- "Ġsh immer",
- "ĠO llie",
- "ĠJe żeli",
- "è¿ĺ æľī",
- "Ġ197 7",
- "Ġje ux",
- "Ġext inct",
- "ĠTransport ation",
- "ĠM aker",
- "Ġj ohn",
- "Ġrich est",
- "Ġtraum at",
- "Ġli egen",
- "´ë ¥¼",
- "è¿Ļ éĩĮ",
- "Ġun rest",
- "ĠSt raw",
- "æĭľ æĭľ",
- "Ġcom a",
- "ĠKr isten",
- "ĠÐļон еÑĩно",
- "ĠBry ce",
- "ĠÑıк Ñĸ",
- "Ġpearl s",
- "Ġпоним аÑİ",
- "Ġadd itions",
- "Ġas ympt",
- "ĠменÑĮ ÑĪе",
- "Ġsc ans",
- "Ch ild",
- "ĠH ide",
- "к ÑĥÑİ",
- "et as",
- "Ġd ank",
- "Ġple as",
- "Ġess ays",
- "Ġj ets",
- "åħ Ĵ",
- "Ġв ед",
- "Ġposit ives",
- "ho f",
- "- )",
- "zz o",
- "Ġstar ters",
- "Ġsm iled",
- "Ġ194 4",
- "qu iera",
- "Ġro k",
- "Ġpu esto",
- "N ico",
- "Ġsim ulations",
- "Ġ à¶",
- "Ġintrig ued",
- "ĠOver watch",
- "åĸ Ĥ",
- "s igh",
- "b ai",
- "Ġë§IJ ê³ł",
- "id é",
- "Ġcra bs",
- "áºŃ p",
- "ĠIraq i",
- "ìĿ´ë ¥¼",
- "ÑĤ Ñı",
- "ĠSoph ia",
- "ĠDN S",
- "Ġönem li",
- "ĠLu o",
- "Ŀ ¤",
- "ĠCoun sel",
- "l igen",
- "анÑĮ ÑĪе",
- "Ġtrump et",
- "Ġd apat",
- "ĠJ M",
- "ĠEVER Y",
- "Ġå°į ä¸įå°į",
- "å¤ ¢",
- "ĠL ayer",
- "Ġc ô",
- "н ал",
- "ĠJ oo",
- "ĠH ack",
- "Ġs unt",
- "ĠLeon ard",
- "ĠFire base",
- "äng er",
- "Ġexpl oding",
- "v oy",
- "Ġì¦ IJ",
- "ĠÑģ еÑĢÑĮ",
- "Ġsever ity",
- "Ġbest imm",
- "çµIJ æŀľ",
- "Ġt iring",
- "Ġprocure ment",
- "Ġdiplom acy",
- "Ġdecor ative",
- "ĠÙĬ ا",
- "Ġpenet ration",
- "Õ «",
- "Ġout right",
- "EN E",
- "ĠUn i",
- "od les",
- "Ġz eros",
- "Ġdelight ful",
- "j m",
- "Ġdo po",
- "没 äºĭ",
- "Ġposit ivity",
- "ĠVIS TA",
- "ĠRes ource",
- "íĥ Ģë",
- "ÑĪ ие",
- "C arl",
- "Ġpip ing",
- "Ġchop ping",
- "ĠGan ze",
- "ü ss",
- "ĠA o",
- "Ġsh attered",
- "ĠDet ective",
- "Ġund oubtedly",
- "Ġhall uc",
- "Ġen ch",
- "Ñĭ Ñĩно",
- "ÑĥлÑı ÑĢ",
- "is esti",
- "Ġped als",
- "Ġdur um",
- "¤í Ķ",
- "la imer",
- "Ġprop re",
- "C u",
- "Ġtransl ator",
- "Ġca ÅĤ",
- "Ġê·¸ 걸",
- "Ġca ÅĤy",
- "U A",
- "Ġrev ised",
- "Ġпод об",
- "ĠArt icle",
- "ĠHait i",
- "ĠÃ ĵ",
- "ĠC trl",
- "Ġroz m",
- "la it",
- "Ġletz te",
- "is pering",
- "dis play",
- "Ġalumin ium",
- "Ġpalab ras",
- "Ġconoc er",
- "Ġz itten",
- "Ġdir ig",
- "åıª æľī",
- "Ġbrain storm",
- "Ġw ifi",
- "ĠPart icip",
- "Ġview point",
- "ĠQu an",
- "Ġhier arch",
- "W elcome",
- "å¯ ¾",
- "Ġoff en",
- "ĠRe covery",
- "gan o",
- "W ould",
- "Ġrep ro",
- "Ġper ceptions",
- "Ġdem asi",
- "ĠBangl adesh",
- "ĠIncred ible",
- "Ġlet zt",
- "Ġbehav ing",
- "Ġaston ishing",
- "Ġâ Ĩ",
- "ĠëĤ¨ ìŀIJ",
- "èµ° äºĨ",
- "ãĥ Ķ",
- "ĠGORD ON",
- "C AR",
- "? !\"",
- "ĠP rest",
- "Ġë§ŀ ìķĦìļĶ",
- "Ġt and",
- "Ġl ash",
- "ç Ĭ",
- "ific ant",
- "Ġint oler",
- "Ġг еÑĢо",
- "Ġte u",
- "as o",
- "ĠÑģов еÑĤ",
- "Ġtravel ers",
- "ĠSy nd",
- "ĠвеÑĢ Ñģ",
- "F onda",
- "ad ı",
- "Ġtrans cription",
- "Ġtit anium",
- "Ġtw ists",
- "Ġgear box",
- "ens ation",
- "f at",
- "C oll",
- "ĠCommon wealth",
- "z on",
- "ĠPolize i",
- "ĠAPP LAUSE",
- "f ry",
- "ĠJud a",
- "este em",
- "Ġso ck",
- "ĠJug end",
- "Ġк ÑģÑĤаÑĤи",
- "ĠD ro",
- "Ġproch aine",
- "ãĥ¼ ãĥ«",
- "Ġli ksom",
- "ĠEner gie",
- "ĠMar ina",
- "Ġ2 30",
- "Ġê°Ģ ìĦľ",
- "ump ing",
- "Ġl one",
- "ç´ ļ",
- "Ġfont s",
- "Ġbusiness man",
- "Ġp ly",
- "Ġdo e",
- "gr id",
- "ĠMil waukee",
- "ĠE den",
- "! \".",
- "ĠÛĮ Ûģ",
- "og ens",
- "Ġteas er",
- "Ġqui én",
- "Ġincent iv",
- "go vern",
- "Ġchild care",
- "Ġsneak ers",
- "Ġimprison ed",
- "Â ®",
- "иÑĤ еÑģÑĮ",
- "an bul",
- "Ġreg ain",
- "Ġtranqu il",
- "Red ner",
- "éĽ ¨",
- "IF A",
- "Ġide ological",
- "Ġmayor ÃŃa",
- "Ġb ureau",
- "et erm",
- "ĠD ID",
- "ìĬ ·",
- "Ġw aving",
- "Ġbe b",
- "Ġá r",
- "Ġк в",
- "Ġenv oy",
- "an ut",
- "ик Ñĥ",
- "ĠEnviron ment",
- "ĠAss ass",
- "ãĤĵ ãģ§",
- "ĠB read",
- "ĠТ ÑĥÑĤ",
- "Ġstair case",
- "ĠDise ase",
- "Ġauc un",
- "Ġëĭ Ī",
- "Ġconfront ation",
- "Ġ194 1",
- "Ġiron y",
- "Ġwor sh",
- "ãĤĮ ãĤĭ",
- "Ġf ick",
- "ĠNa omi",
- "Ġback side",
- "ie ux",
- "K ap",
- "Ġved ere",
- "Ġlength y",
- "Ġbreak er",
- "ĠRoll e",
- "Ġpred ator",
- "Ġnoss os",
- "Ġadvert ise",
- "è³ ĩ",
- "ÑĢод е",
- "Redner wechsel",
- "re ten",
- "Ġcollect ors",
- "ıģ ımız",
- "Ġtr ig",
- "Ġax es",
- "in ters",
- "Ġpen alties",
- "ĠOs man",
- "ĠJen na",
- "Ġfl akes",
- "Ġtrain ers",
- "Ġstun ned",
- "ĠSc roll",
- "ĠP ip",
- "Ġна ÑģÑĤ",
- "Ġnh Ãł",
- "ĠSm ack",
- "ẫ n",
- "rat os",
- "ĠÑĢабоÑĤ Ñĭ",
- "Ġu cz",
- "ĠLem on",
- "ĠS ind",
- "Ġpsych ic",
- "ĠAb g",
- "Ġmamm als",
- "Ġimmers ive",
- "Ġb ots",
- "Ġverschied ene",
- "Ġg eral",
- "Ġfoll ower",
- "Ġ ä»ĸ",
- "Ġsegur idad",
- "Ġimmers ed",
- "fe ito",
- "c ross",
- "Ġö ld",
- "íĥ Ħ",
- "Ġãģĵ ãģ®",
- "Ġ×Ķ ×Ļ×IJ",
- "ĠJ ian",
- "Ġbili yor",
- "are a",
- "Ġk af",
- "Ġgod t",
- "缸 ä¿¡",
- "Ġë°© ìĨ¡",
- "Ġdet riment",
- "æ¥ ļ",
- "Ñĸ л",
- "ĠÄij âu",
- "Ġchlor ide",
- "ø re",
- "le i",
- "Ġmont e",
- "Ġdifférent es",
- "à¯ģ .",
- "Ġcareg ivers",
- "Ġin adequ",
- "Ġfare well",
- "ĠÑĤип а",
- "ont ec",
- "ĠE ph",
- "HH H",
- "ĠTod os",
- "ĠС ШÐIJ",
- "Ġtro v",
- "Ġl ige",
- "Ġc ông",
- "ĠC iv",
- "Ġcap az",
- "ĠV allahi",
- "Ġquest e",
- "Ġrepl ica",
- "س ب",
- "z na",
- "ĠÑģл Ñĥж",
- "ĠP T",
- "w ave",
- "ien i",
- "Ġrel ied",
- "de velop",
- "Ġdem e",
- "ĠA man",
- "Ġ[ ...]",
- "Ġcompl iments",
- "u ais",
- "ĠíĮ ¨",
- "Ġsmell ing",
- "Ġdad urch",
- "ÙĪ ت",
- "Ġor anges",
- "Ġл ай",
- "Ġstabil ization",
- "åĢ į",
- "ãĤĮ ãģŁ",
- "æ¥ ½",
- "Ġappl iances",
- "Ġh m",
- "ĥ IJë©´",
- "odynam ics",
- "Ġc iÄĻ",
- "ĠC ott",
- "M ON",
- "ĠM ang",
- "æĶ¯ æĮģ",
- "Ġall erdings",
- "ικ ή",
- "sh ots",
- "Ġt s",
- "ĠG ör",
- "ĠCH AR",
- "Ġ: (",
- "Ġwr ath",
- "Ġf ique",
- "Ġfüh ren",
- "Ġtest ament",
- "Ġ^ ^",
- "á¹Ľá¹£ á¹ĩa",
- "AL D",
- "Ġtext o",
- "ĠDog s",
- "Ġs ib",
- "Ġpath etic",
- "ock s",
- "Ġrad ically",
- "ĠM ORE",
- "ĠJAM ES",
- "Ġing l",
- "ĠTechn ical",
- "Ġpor ch",
- "ĠU T",
- "ĠобÑıз аÑĤелÑĮно",
- "Ġrenew al",
- "Ġaesthet ics",
- "ik um",
- "Ġbe verage",
- "der n",
- "Ġpredict ive",
- "Ġch uy",
- "ĠRegard ing",
- "ĠFor ward",
- "ĠÙĪ ÙĦ",
- "Ġcontext ual",
- "Ġdwar f",
- "Ġpre he",
- "Ġgovern ed",
- "ħ Ħ",
- "Ġtrabal har",
- "Ġnegó cio",
- "ĠболÑĮÑĪ ой",
- "еÑĩ аÑĤ",
- "Ġд ÑĥÑħ",
- "Ġflood s",
- "Ġbow ling",
- "ĠO B",
- "ĠH är",
- "Ġgrad ing",
- "주 ëĬĶ",
- "Ġg ars",
- "d ling",
- "Ġr ak",
- "ë Ī",
- "c reat",
- "ĠÑī е",
- "Ġneighb ours",
- "f ood",
- "Qu ery",
- "Ġhero in",
- "ice ps",
- "ĠK inda",
- "N ET",
- "Ġmar i",
- "Ġim itate",
- "Ġach ter",
- "Ġsettle ments",
- "ra re",
- "cc iones",
- "Ġë ĵľ",
- "Ġf ik",
- "it ung",
- "Ġм акÑģим",
- "Ġel f",
- "Ġd alla",
- "ĠPol sce",
- "ĠP ul",
- "Ч ÑĤо",
- "ĠMor gen",
- "ØŃ Ùħ",
- "Ġsuprem acy",
- "Ġk ys",
- "ĠHur ricane",
- "ĠG TA",
- "ĠFe h",
- "Ġfinal mente",
- "m und",
- "ĠK rie",
- "é poque",
- "ĠT ucker",
- "IT T",
- "Ġl ur",
- "Ġdi pping",
- "ä v",
- "Ġeer ste",
- "ĠFl int",
- "bild ung",
- "ู à¹ī",
- "Ġto im",
- "Ġpr acy",
- "Ġtransform s",
- "Ġspeed ing",
- "Ġpresent er",
- "Ġfellow s",
- "f illed",
- "ie za",
- "Ġadv ising",
- "ĠInter view",
- "и гÑĢ",
- "we hr",
- "ĠD ante",
- "pt ure",
- "Īë¬ ¸",
- "¯ ¸ë",
- "IJ IJ",
- "ĠCoun ter",
- "Ġcr ist",
- "Ġì§ ľ",
- "Ġje une",
- "ĠÑģÑĤ ÑĢаÑĪ",
- "Ġmie Äĩ",
- "Ġtut or",
- "Ġmas ala",
- "Ġpowder ed",
- "Ġn au",
- "ĠFreder ick",
- "Ġbill ing",
- "ĠE isen",
- "Ġд обÑĢ",
- "Ġm est",
- "æ ½",
- "Ġsn ipp",
- "Ġmon o",
- "ĠA lo",
- "ĠMer cy",
- "éri ence",
- "Ġcasual ties",
- "ĠAN NOUNCER",
- "ä» İ",
- "Ġto car",
- "Ġbacter ial",
- "H o",
- "Ġstre ak",
- "ĠJ ENN",
- "Ġpl ast",
- "Ñģ лед",
- "Ġre app",
- "Ġpay check",
- "Ġmin ers",
- "hab t",
- "ĠJ ap",
- "н ÑĥÑĤ",
- "Ġred emption",
- "Ġqu ir",
- "hn lich",
- "Ġaccum ulation",
- "Ġsh ove",
- "Ġadrenal ine",
- "M ake",
- "ĠH ern",
- "oss ing",
- "ĠV il",
- "ub by",
- "her tz",
- "bre aks",
- "Ġsp ur",
- "ĠD aha",
- "US TIN",
- "Ġcontinu er",
- "ĠSa ul",
- "ãģ® ãģ¯",
- "Ġíı Ń",
- "ĠëIJĺë ©´",
- "Ġë§IJìĶ Ģ",
- "Ġо ж",
- "Ġsuspect s",
- "Ġla quelle",
- "ĠMuch as",
- "Ġv öllig",
- "ul en",
- "Ġimp res",
- "Ġlo bb",
- "ene e",
- "Ġн аж",
- "T a",
- "Ġréal ité",
- "ĠRe x",
- "Ġharvest ing",
- "Ġest r",
- "æ ¶",
- "osp ace",
- "OS S",
- "Ġdisturb ance",
- "ass ic",
- "ĠIs ab",
- "Ġdéc ouv",
- "ĠHamp shire",
- "Ġor nament",
- "Ġlu ôn",
- "ĠU W",
- "Ġj Äħ",
- "éĤ£ ä¹Ī",
- "Ġrespect o",
- "Ġcomun idad",
- "Ġcom igo",
- "ag na",
- "Ġintrins ic",
- "ĠAlum ni",
- "Ġses leri",
- "Ġestim ation",
- "âĢĶ âĢĶ",
- "Ġprodu it",
- "ãĢĤ ãĢį",
- "Ġв ÑĢ",
- "Ġwh irl",
- "Ġac ces",
- "ç u",
- "Ġvari ability",
- "Ġv odka",
- "its u",
- "Ġinternship s",
- "Ġalloc ate",
- "R R",
- "íĽ Ī",
- "Ġinstruction al",
- "t ant",
- "Ġà®ħ த",
- "Ġinv ites",
- "Ġha k",
- "Ġsca res",
- "Ġe clipse",
- "п ов",
- "к олÑĮ",
- "ativ as",
- "Ġstab bed",
- "ĠD OM",
- "ä¸į åĪ°",
- "ro ots",
- "ĠPict ure",
- "íĺ ¼",
- "ĠC HA",
- "ie c",
- "ı ı",
- "han ol",
- "Ġmisunder stand",
- "R ay",
- "Ġroad map",
- "ocument ed",
- "iz ione",
- "ĠOl ive",
- "r ift",
- "Ġ×Ķ× ł",
- "æ¯ į",
- "l est",
- "; ;",
- "ĠE A",
- "éľĢ è¦ģ",
- "од Ñĥ",
- "Ġhob bies",
- "Ġbur ial",
- "ãģ« ãģ¡ãģ¯",
- "Ð ¤",
- "le ge",
- "ĠH J",
- "Ġobject ion",
- "Ġãģ Ń",
- "ct ory",
- "Ġincre mental",
- "Ġgym n",
- "Ġepid emi",
- "Ñģ Ñĭл",
- "Ã ij",
- "Ġadvance ment",
- "Ġpar ch",
- "New s",
- "Ġa yr",
- "л ам",
- "Ġ×ľ× ©",
- "Ġdipl oma",
- "ãģ¡ãĤĥ ãĤĵ",
- "Ġrob bed",
- "On ly",
- "Ġinc ur",
- "Ġch anting",
- "Ġíķ´ë ıĦ",
- "Ġrich es",
- "ĠCar men",
- "Ġnost ro",
- "λ ÎŃ",
- "ĠPow der",
- "à¹Ģภ«",
- "ĠìŀĪ ìľ¼ë©´",
- "Ġgerçek ten",
- "ĠPik achu",
- "ем он",
- "OL L",
- "Ġplanet ary",
- "Ġsl ows",
- "Ġclock wise",
- "al ion",
- "Ġì Į",
- "Ġver n",
- "Ġh omme",
- "Ġend point",
- "Ġinnoc ence",
- "Ġelement os",
- "Ġsophom ore",
- "Ġnot ions",
- "ĠCould n",
- "p ur",
- "Ġz at",
- "Ġobs ess",
- "Ġmotiv o",
- "ĠK ub",
- "ĠDr ug",
- "A nt",
- "ĠPlay ers",
- "ĠHum ans",
- "Ġme lee",
- "ĠWild life",
- "ĠV P",
- "Ġvolcan ic",
- "Ġcom in",
- "ĠGu ang",
- "ĠÏĦι ÏĤ",
- "ĠоÑģоб енно",
- "ĠS ize",
- "L isten",
- "ĠA aa",
- "app ro",
- "Ġbar bar",
- "ĠPark inson",
- "нÑı ÑĤÑĮ",
- "å į°",
- "Ġunderest imate",
- "Ġsubst itution",
- "Ġcosm etic",
- "ä¸ĭ 次",
- "Ġwill en",
- "Ġbe ide",
- "ann i",
- "Ġcondition ed",
- "ĠDe bbie",
- "Ġis to",
- "ĠEd wards",
- "ìĽĮ ìļĶ",
- "ĠÑĤ ов",
- "Ġab brevi",
- "ĠM ün",
- "ĠPr inc",
- "ĠLi ang",
- "Ġst ink",
- "Ġradio active",
- "ãģĨ ãĤı",
- "Ġac ontec",
- "Ġun con",
- "ĠTur bo",
- "ãģ IJ",
- "Ġkiss es",
- "æĺ¯ ä»Ģ麼",
- "еÑĤ ÑĢов",
- "Ġfront ier",
- "ĠSp y",
- "ĠBel arus",
- "ĠC BS",
- "á» Ĺ",
- "am oto",
- "íķľë į°",
- "ĠÑģÑĤ ÑĢо",
- "ĠEn fin",
- "Ġbread th",
- "éĺ ²",
- "ĠCa fe",
- "ĠDaf ür",
- "ĠB our",
- "ar as",
- "Ġbl ueprint",
- "an ı",
- "Ġconst ants",
- "Ġattack er",
- "ĠForm ula",
- "za Äĩ",
- "Ġs owie",
- "Ġeyebr ow",
- "ob ook",
- "Ġset zen",
- "第 ä¸ī",
- "ons ider",
- "aw ning",
- "Ġsöyle ye",
- "Ġinv aded",
- "Ġpronoun s",
- "Ġdob ry",
- "S i",
- "ĠÐ¥ оÑĤ",
- "Ġvolley ball",
- "Ġl ament",
- "is ches",
- "ar me",
- "ap i",
- "ĠW iki",
- "ли ÑĪ",
- "Ġkas ih",
- "Ġp ess",
- "ĠÑĦ оÑĤ",
- "ĠS ul",
- "å¾ ·",
- "Ġpse udo",
- "Ġmem o",
- "ĠìĹ° ìĬµ",
- "ĠдоллаÑĢ ов",
- "ĠпеÑĢ ем",
- "ĠRe ach",
- "mir al",
- "alt ed",
- "Ġstat ut",
- "read ing",
- "Ġsöy led",
- "ĠLind sey",
- "ĠAh mad",
- "ë ¶Ģë",
- "ĠС егоднÑı",
- "Ġprzy got",
- "Ġhy ster",
- "U RE",
- "ĠNe igh",
- "Rep orter",
- "ĠB unu",
- "ĠTreat y",
- "ĠR ank",
- "ĠF ame",
- "in ished",
- "Ġge ared",
- "Ġcomp ose",
- "od ia",
- "ĠL on",
- "Ġjeste ÅĽmy",
- "ĠDIRE CTOR",
- "Ġel kaar",
- "ĠV iel",
- "×IJ× ©",
- "ynth ia",
- "ä¸ ¦",
- "Ġm ère",
- "ĠTom ato",
- "Ġex atamente",
- "ni ÄĻ",
- "ĠFre i",
- "ĠD if",
- "Ġopen ings",
- "Ġgraph ical",
- "ĠÑĥд об",
- "ĠвÑģ п",
- "ĠWeek ly",
- "ев а",
- "Ġhang s",
- "Ġuns afe",
- "Ġem blem",
- "ĠKolleg innen",
- "al ay",
- "Ġk si",
- "Ġh ides",
- "Ġol may",
- "Ġent ste",
- "Ġarth ritis",
- "ÃŁ erdem",
- "Ġbin nen",
- "Ġlist ens",
- "ĠH ess",
- "åĨį ä¾Ĩ",
- "ĠLou ise",
- "ld en",
- "ен Ñģ",
- "ĠVers ion",
- "ĠAgric ulture",
- "ìĬ¤ë ¥¼",
- "м ан",
- "ë Ħ¤ìļĶ",
- "Ġw ines",
- "ĠIN F",
- "r ul",
- "ĠJ K",
- "ıyor lar",
- "sh ield",
- "reat h",
- "Ġter us",
- "ĠL um",
- "Ġanticip ation",
- "Ġacc ustomed",
- "ĠM ina",
- "Ġw ield",
- "io è",
- "mer a",
- "Ġcount down",
- "Ġcl ing",
- "Ġcomm end",
- "Ġfakt iskt",
- "Ġdef enses",
- "Ġcock pit",
- "Ġком анд",
- "Ġdish was",
- "ĠThan os",
- "Ġkid neys",
- "Ġse he",
- "Ġmicro bes",
- "Ġc uff",
- "ĠвÑĭÑģ ок",
- "ĠSp icy",
- "çŃī çŃī",
- "வ ர",
- "cul us",
- "or c",
- "ç¾ ħ",
- "ix es",
- "ĠC redit",
- "Ġr aj",
- "Ġbring t",
- "ĠN iss",
- "Ġgr im",
- "ĠS OL",
- "Ġten im",
- "ĠSud an",
- "ĠSp art",
- "Ġpromot es",
- "ĠN ossa",
- "ĠÑģоÑģÑĤо Ñıни",
- "Ġì° ©",
- "Ġunc ont",
- "ĠLiber al",
- "ĠТ олÑĮко",
- "ĠV iele",
- "Ġktóre j",
- "Ġ* ***",
- "M ax",
- "ĠЧ ÑĤобÑĭ",
- "3 50",
- "Ġíĺ¼ ìŀIJ",
- "Ġë¶Ħë ĵ¤ìĿ´",
- "Ġwar p",
- "Ġteng a",
- "Ġsympath etic",
- "Ġbiz i",
- "ĠZ ack",
- "ied o",
- "Ġëī ´ì",
- "p iel",
- "ĠÑĤ ол",
- "Ġsc aled",
- "ĠPET ER",
- "ĠCO MM",
- "ĠC ame",
- "Ġcatast rophe",
- "Ġsweat y",
- "ig ration",
- "Ġstuff ing",
- "ĠÏĢολ Ïį",
- "ĠDri ver",
- "zy st",
- "T ech",
- "Ġassess ed",
- "ĠSur face",
- "ır ım",
- "s ur",
- "ler weile",
- "Ġд ог",
- "Ġshut ting",
- "Ġfr actions",
- "ĠÑģ ол",
- "every one",
- "Ġer n",
- "ĠÐĿ ов",
- "Ġdefend ers",
- "Ġvers ucht",
- "ãĥ³ãĥ Ģ",
- "Ġpol ity",
- "ĠÐŁ он",
- "ver ständ",
- "Ġbrows ers",
- "Ġtransform ative",
- "Ġdict ate",
- "ĠLE GO",
- "Ġning una",
- "ê´ ij",
- "Ġp izz",
- "ĠHar old",
- "ĠL opez",
- "Ú¾ ÛĮ",
- "an ız",
- "atch et",
- "ÙĬ ت",
- "Ġl ernen",
- "Ġê·Ģ ìŬ",
- "Ġhous ed",
- "Ġclean se",
- "ĠW AT",
- "lar ation",
- "Ġby tes",
- "Ġtuck ed",
- "Ġfault s",
- "д о",
- "F X",
- "Ġìĸ¼ë§ ĪëĤĺ",
- "Ġde form",
- "Ġcontract ing",
- "ĠTIM E",
- "ir se",
- "Ġne ben",
- "Ġc erc",
- "ĠArm strong",
- "Ġtest er",
- "Ġparf ait",
- "Ġjealous y",
- "Ġtox ins",
- "Ġdis bel",
- "ÑĥÑĢ Ñĭ",
- "imp ression",
- "Ġprost ate",
- "Ġfire wall",
- "Ġclass ics",
- "еÑĩ ÑĮ",
- "Ġsocial ism",
- "Ġgrac ious",
- "ĠÑģ нова",
- "Ġд нÑı",
- "Ġburn er",
- "ĠMin or",
- "Ġìļ°ë ¦¬ë",
- "Ġjed es",
- "Ġcontinu um",
- "Ġh ots",
- "Ġoccur rence",
- "Ġadminister ed",
- "Ġзам еÑĤ",
- "Ġhes itation",
- "Ġdr ills",
- "er ca",
- "ĠвÑĤоÑĢ ой",
- "Ġstead ily",
- "Ġinsan lar",
- "Ġi han",
- "í ij",
- "Ġhel per",
- "ĠSen in",
- "åģ ľ",
- "ов ание",
- "ĠER IC",
- "b la",
- "ĠAcad emic",
- "Ġhuman ities",
- "bl ack",
- "ump y",
- "ort ex",
- "Ġìł Īë",
- "ĠØ¥ ÙĨ",
- "Ġdiscl ose",
- "ĠEl ijah",
- "Ġλ ÎŃ",
- "ĠQu er",
- "ب ÙĦ",
- "ãĤ ¡",
- "T ell",
- "ar le",
- "Ñĸ ÑĢ",
- "Ġaug mented",
- "Ġë¹Ħ ìĬ·",
- "Ġand roid",
- "ठ¤",
- "ar ma",
- "Ġs zer",
- "ge ord",
- "Ġge ek",
- "Ġye ux",
- "Ġp ong",
- "ĠãģĿ ãģĨ",
- "Ġtort ured",
- "ĠB ath",
- "z ig",
- "ason able",
- "Ġn ets",
- "Ġbar u",
- "ĠFl at",
- "ĠV ater",
- "ĠTer ror",
- "ĠA vo",
- "Ġceremon ies",
- "ro e",
- "Ùģ س",
- "O ps",
- "Ġhy vin",
- "Ġap resent",
- "ol or",
- "ĠигÑĢ Ñĭ",
- "ort on",
- "Ġê·¸ëŀ ¬",
- "Ġlook in",
- "ĠT Y",
- "ĠM int",
- "Ad d",
- "Ġm ite",
- "ĠSm oke",
- "Ġnot a",
- "Ġm oss",
- "ĠAb end",
- "Ġì» ¨",
- "Ġexagger ated",
- "f ires",
- "Ġred ist",
- "ff iti",
- "Ġopen ness",
- "ê°IJ ìĿ´",
- "ende u",
- "ен ной",
- "W atch",
- "Ġav atar",
- "ĠP ey",
- "ur un",
- "Ġsen za",
- "Ġì§Ģ ìĹŃ",
- "ĠNat omiast",
- "Ġemer gence",
- "ray s",
- "Ġcraft ed",
- "g ary",
- "ãģł ãģij",
- "ü ng",
- "- \"",
- "Ġhack ed",
- "Ġstr ay",
- "en cie",
- "em o",
- "Ġcom en",
- "ĠK ız",
- "ĠJ asmine",
- "ĠH indi",
- "man as",
- "Ġinfin itely",
- "em on",
- "ìĿ¸ëį° ìļĶ",
- "j ak",
- "Ġro aring",
- "éri que",
- "s weise",
- "ĠRo lex",
- "åł± å°İ",
- "ĠStu art",
- "bn b",
- "Ġdiagn ose",
- "Ġcoher ent",
- "ĠM J",
- "æºĸ åĤĻ",
- "Ġp ike",
- "l av",
- "Ġorchest ral",
- "а ÑģÑĤи",
- "Ġterm inar",
- "Ġgather ings",
- "Ġcompl iant",
- "Ġupgrad ing",
- "Ġregul ator",
- "Ġlan ç",
- "éĢ £",
- "Ġmerch ants",
- "ta wa",
- "Ġmonit ored",
- "Ġrend re",
- "ä¸ ¤",
- "Ġunter wegs",
- "ang uard",
- "g ard",
- "ĠBel ow",
- "du ino",
- "ĠЦ е",
- "Ġimped ance",
- "ìľ ¡",
- "ä» ½",
- "Ġakt uell",
- "ĠV atic",
- "åŃ ©",
- "Ġste wards",
- "Ġbright est",
- "Ġk enn",
- "Ġk au",
- "ĠMat rix",
- "ĠB ark",
- "ĠðŁ ij",
- "Ġt aper",
- "Ġcas ino",
- "ר ×Ķ",
- "ys ical",
- "Ġbuild ers",
- "ĠczÅĤ owie",
- "ĠNep al",
- "Ġ! \"",
- "Ġterm e",
- "Ġin nych",
- "Ġmath s",
- "Ġdraft ed",
- "ĠB alk",
- "Ġhesit ant",
- "Ġvolt ar",
- "Ġrev ive",
- "ĠÑĦилÑĮ ма",
- "Ġassass in",
- "ĠS olutions",
- "Ġdu el",
- "Ġbear ings",
- "à¸Ħ ะ",
- "Ġrook ie",
- "ik at",
- "Ġbisc uits",
- "Ġc ords",
- "Ñĥв аÑĤи",
- "AR IN",
- "Ġprogress ing",
- "ĠG ir",
- "Ġpenet rate",
- "ĠSt orage",
- "e ight",
- "ĠÑĤ ÑĢÑĥ",
- "Ġdon ÃŃt",
- "Ġsiz in",
- "Ġout dated",
- "ĠнаÑĪ и",
- "Ġaff ir",
- "Ġspo ons",
- "Ġon i",
- "Ġfl ank",
- "ĠG ol",
- "h ã",
- "Ġp éri",
- "Ġhonor able",
- "ĠBreat he",
- "sc enes",
- "Ġob viamente",
- "ик Ñģ",
- "Ġש ×ŀ×",
- "Ġsmooth ie",
- "ŀ Īë",
- "Ġd ime",
- "ĠíĸĪ ìĸ´ìļĶ",
- "Ġapp el",
- "ĠCath olics",
- "Ġsing les",
- "Ġlat en",
- "Ġç ünkü",
- "ĠV ader",
- "æı Ľ",
- "Ġvard ı",
- "ĠIst anbul",
- "gr é",
- "ĠEl sa",
- "ë l",
- "Ġinve ce",
- "Ġcr ane",
- "Ġo be",
- "ĠSh ark",
- "Ġsm ack",
- "Ġrest oring",
- ". \\",
- "Ġë¹ łë",
- "Ġf aded",
- "um bers",
- "S inging",
- "Ġdep ressing",
- "th est",
- "ĠW ahr",
- "Ġmult itude",
- "ÑĢавÑģÑĤв ÑĥйÑĤе",
- "rij k",
- "ek a",
- "Ġcomplet es",
- "ĠWell s",
- "Ġro y",
- "ĠPr ay",
- "ĠKal au",
- "iz in",
- "iaÅĤ em",
- "Ġlo com",
- "ĠNash ville",
- "ĠPent agon",
- "ë ¯¸",
- "ĠNE W",
- "Äħ Äĩ",
- "ÃŃ ss",
- "Ġmarry ing",
- "Ġfe ud",
- "íĻ ķ",
- "æĢ ¥",
- ") !",
- "ĠOper ations",
- "Ñĥ ÑĶ",
- "Ġmo je",
- "Ġinstruct ed",
- "ĠëĪĦ 구",
- "Ġ×Ķ× Ĵ",
- "ĠпомоÑī ÑĮÑİ",
- "Ġsab ia",
- "ìķĺ ìĸ´ìļĶ",
- "pl ane",
- "p ri",
- "Ġпол ноÑģÑĤÑĮÑİ",
- "ĠK itty",
- "Ġpróp rio",
- "ed ere",
- "Ġinteres ante",
- "Ġд е",
- "Ġcond ensed",
- "Ġav ent",
- "T OR",
- "Ġgre asy",
- "AR K",
- "ort a",
- "A J",
- "Ġdis reg",
- "Ġcorrect ions",
- "Ġst ero",
- "Ġinfluen za",
- "Ġdess es",
- "Ġball ots",
- "Ġme get",
- "Ġma fia",
- "Ġb öl",
- "n ost",
- "ĠÑģÑĤ аÑĤÑĮ",
- "Ġrespond er",
- "Ġhint en",
- "g rav",
- "à¸Ń ะ",
- "yn chron",
- "Ġvi ens",
- "Ġsam o",
- "Ġd t",
- "pan nt",
- "ĠÅĽwi at",
- "Ġзап иÑģ",
- "Ġmer ged",
- "Ġke p",
- "Ġmis leading",
- "Ġdig amos",
- "Ġam mon",
- "è¾ Ľ",
- "ch et",
- "Ġê°Ģ ìł¸",
- "Ġun i",
- "ĠëIJĺ ëĬĶëį°",
- "Ġнап ÑĢав",
- "ĠкоÑĤоÑĢ ого",
- "Ġanim ate",
- "×ķ× IJ×",
- "еÑĢ в",
- "Ġmin ced",
- "Ġka um",
- "ãģĤ ãģģ",
- "ÏĢ ε",
- "л ег",
- "exist ing",
- "Ġplata form",
- "ĠK RIS",
- "ìĽ ł",
- "ĠFamil ien",
- "ĠLib ya",
- "Ġbiod iversity",
- "Ġidi ots",
- "ird i",
- "Ġszy b",
- "ĠRoll ing",
- "ü cht",
- "ĠÑĥд ив",
- "Ñģ Ñĥд",
- "Ġreal izar",
- "Ġcan ned",
- "ĠÑĢ ан",
- "Ġmet abolic",
- "ĠBe ef",
- "Ġkil ka",
- "лÑİ Ñģ",
- "Ġreg istry",
- "моÑĤÑĢ иÑĤе",
- "Ġviel ä",
- "Ġod c",
- "Ġcondem ned",
- "æ© ĭ",
- "f al",
- "ĠD il",
- "wo ÅĽci",
- "A w",
- "Ġstatist ically",
- "Ġso gen",
- "ĠB ETH",
- "Ġsh aving",
- "å¹ ¸",
- "oc al",
- "ĠFun ny",
- "Ġpeace fully",
- "Ġaddict ive",
- "ĠIns ert",
- "la uf",
- "Ġexperien cia",
- "é¦ĸ åħĪ",
- "иÑĤ елÑı",
- "ÃŃ gen",
- "ág ina",
- "Ġabdom en",
- "íķľ ëĭ¤",
- "ic us",
- "im ana",
- "ì į¨",
- "arch ing",
- "Ġkonk ret",
- "ìķ ĺë",
- "ек а",
- "ou fl",
- "ive l",
- "Ġn ude",
- "èt res",
- "Ġm onsieur",
- "Ġcl ash",
- "Ġtherap ists",
- "Ġcub ed",
- "Ġretrou ver",
- "Ġwave form",
- "Ġpot em",
- "ĠForm er",
- "is ión",
- "åº ľ",
- "Ġ×IJ× Ŀ",
- "und os",
- "ĠMein ung",
- "ص ÙĦ",
- "ĠJ ude",
- "Ġn Ã¥r",
- "ĠLeon ardo",
- "ĠCr isto",
- "ĠG OT",
- "ÑģÑĤÑĢÑĥ к",
- "L AN",
- "Ġg Ã¥ng",
- "Ġdé b",
- "ĠFrankf urt",
- "Ġcra ppy",
- "Ġli l",
- "ann ée",
- "ĠмеÑģÑĤ е",
- "RE T",
- "ĠN er",
- "ĠCO STA",
- "Ġjed em",
- "Ġcurt ains",
- "Ġiter ations",
- "Ġun av",
- "Ġpla que",
- "or um",
- "ĠÎ ¶",
- "Ġnúmer os",
- "Ġdes ap",
- "² ½",
- "Ġcomp iled",
- "Ġref le",
- "Ġrank ings",
- "Ġrep aired",
- "ĠÐĿап ÑĢ",
- "Ġdownload s",
- "Ġarm our",
- "Ġ×Ļ ×ķתר",
- "Ġlonge vity",
- "ĠTON ER",
- "ĠкомменÑĤ аÑĢ",
- "Ġcz ego",
- "Ġnot ify",
- "Ġairport s",
- "Ġend uring",
- "let te",
- "Ġapp arat",
- "Ġhab il",
- "á»ĩ c",
- "n ad",
- "IC O",
- "ĠBra h",
- "Ġseg ún",
- "Ġgovern ors",
- "k aha",
- "ĠSchl uss",
- "Ġodpow ied",
- "ir ting",
- "Ġrem pl",
- "ĠAb original",
- "ident ally",
- "Ġenhan cing",
- "lic ting",
- "ĠHawai ian",
- "Ġstri ving",
- "ĠN iet",
- "Ġzn aczy",
- "Ġobed ience",
- "ĠnÃ¥ got",
- "Ġexp ired",
- "Ġ19 18",
- "pres ented",
- "Ġpr owad",
- "ĠTer r",
- "ĠPrinc eton",
- "Ġmor gen",
- "Ġattract ing",
- "ĠS igma",
- "ign er",
- "ĠRe chts",
- "ĠP eki",
- "Ġmet hy",
- "Ġha mm",
- "Ġdire ito",
- "Ġdeleg ation",
- "ив аÑİÑĤ",
- "Ġg in",
- "You ng",
- "Ġdepend encies",
- "ĠBrad ley",
- "bud s",
- "Ġf is",
- "Ġpyt anie",
- "Ġinterconnect ed",
- "Ġemba ixo",
- "ĠS as",
- "Ġr uh",
- "ĠS icht",
- "S ur",
- "Ġsuper b",
- "ĠSabb ath",
- "ĠD anger",
- "k ol",
- "Ġh ou",
- "s upp",
- "ĠN acional",
- "Ġsuccess ion",
- "Ġv á",
- "ĠMaÃŁ nahmen",
- "ĠJess ie",
- "ĠId aho",
- "fore st",
- "ħ ĺ",
- "Ġ×ŀ× ĵ",
- "ĠØ£ ÙĬ",
- "Ġsweet heart",
- "Ġneat ly",
- "ĠEv angel",
- "ê³ ¡",
- "ĠSu ite",
- "úblic a",
- "ĠÑĥ ли",
- "ĠAnn ouncer",
- "l igh",
- "Ġsens ations",
- "Ġshel ters",
- "Ġh art",
- "Ġsqueez ing",
- "ĠR ivers",
- "ĠCook ing",
- "ì± ħ",
- "person al",
- "Ġman os",
- "ÑijÑĤ ÑģÑı",
- "w ij",
- "Ġgo gg",
- "ĠMill i",
- "ĠF P",
- "ün st",
- "ĠL S",
- "Ġspray ing",
- "Ġf aux",
- "Ġaut ograph",
- "olog ic",
- "Ġtor ment",
- "Ġencry pted",
- "á» ħ",
- "Ġest re",
- "ç¹ ¼",
- "à ±",
- "Ġst umbled",
- "Ġa ider",
- "Ġsab en",
- "x ter",
- "ĠC ities",
- "ĠTür k",
- "ëĭ ¥",
- "ch ine",
- "Ġto pping",
- "Ġpoison ed",
- "ĠRoman ia",
- "×ĵ ×Ļ",
- "Ģë ¡ľ",
- "ĠпоÑĢ Ñıд",
- "Ġchir ping",
- "ĠìĻ Ħë",
- "×ij× ¢",
- "Ġcu anto",
- "Ġdon ating",
- "ĠReg ent",
- "ĠBer uf",
- "Ġdistract ing",
- "Ġstam ina",
- "ĠDar ren",
- "Ġì¶ ķ",
- "l ists",
- "d al",
- "ch uss",
- "Ġeconom ist",
- "ãģĪ ãĥ¼",
- "org t",
- "Ġist iyorum",
- "è¿ Ľ",
- "ĠSur prise",
- "ĠHa o",
- "Ġìµľ ê³ł",
- "ĠG W",
- "ĠIn ner",
- "Ġqu ieren",
- "Ġmind ed",
- "Ġsupercom puter",
- "Ġdiagram s",
- "íĬ ľë",
- "ê²ł ìĸ´",
- "ĠобÑĬ ÑıÑģ",
- "Ġestab an",
- "Ġdestro ys",
- "ĠBre aking",
- "Ġkar Ä±ÅŁ",
- "Ġrebuild ing",
- "ľë ĮĢ",
- "ли во",
- "ĠSau ce",
- "ĠF usion",
- "×ķ× ŀ×",
- "ĠQu inn",
- "Ġga uche",
- "ĠÙĪ Ø£",
- "Ġ È",
- "ç ĵľ",
- "Ġtechn o",
- "Ġdisp atch",
- "ĠaÅŁ k",
- "Ġein zel",
- "ĠG mail",
- "ç ŀ",
- "Ġê°ľ ìĿ¸",
- "ĠÑģем ÑĮ",
- "Ġjour neys",
- "Ġi ht",
- "Ġfib re",
- "Ġdram as",
- "ouch ed",
- "Ġren ame",
- "Ġоп еÑĢ",
- "Ġpo o",
- "ĠD ru",
- "ĠиÑĤ ог",
- "Ġz ast",
- "Ġco z",
- "Ġz ucch",
- "Ġobt aining",
- "Ġcomm ute",
- "Ġsub mer",
- "ĠV ish",
- "ĠR abb",
- "og g",
- "Ġh ut",
- "íĸĪ ìĸ´",
- "æ¯Ķ å¦Ĥ",
- "ere mi",
- "Ġμ α",
- "Ġdisk ut",
- "Ġб Ñĥк",
- "Ġimp aired",
- "d epend",
- "ĠÙĪ ا",
- "ĠÑĢ Ñĥк",
- "Ġб аÑĢ",
- "Ġoxid ation",
- "Ġsitu ação",
- "ÉĻ n",
- "u ção",
- "Ġsag te",
- "ĠS ER",
- "ĠC ake",
- "Ġtur meric",
- "ĠK ak",
- "b ung",
- "ĠK á¹Ľá¹£á¹ĩa",
- "Ġpoison ing",
- "Ġsl ipping",
- "ĠS ays",
- "å°± åı¯ä»¥",
- "ò ng",
- "çŁ ³",
- "Â «",
- "ĠClaud ia",
- "ĠChar acter",
- "ни ÑĨ",
- "co at",
- "Ġprogress ed",
- "ĠFer gus",
- "Ġìĺ¤ ëĬ",
- "Ġo at",
- "ord able",
- "ĠLe y",
- "ĠHera us",
- "Ġresult ados",
- "ĠKay la",
- "Ġr iff",
- "Ġcheg ou",
- "Ġx i",
- "Ġsp acious",
- "Ġrecogn ised",
- "Ġe ch",
- "ĠT ie",
- "Ġlaunch er",
- "J im",
- "Ġsupp ression",
- "ĠImp ossible",
- "Ġguit ars",
- "ĠFour ier",
- "иÑĩеÑģ кий",
- "ĠTh erap",
- "ĠK af",
- "cent ered",
- "ĠÑģо оÑĤвеÑĤ",
- "Ġk lim",
- "Ġcarbohyd rates",
- "ign ant",
- "ĠAst ron",
- "Ġem ple",
- "Ġdr astic",
- "ĠмиÑĢ е",
- "в ин",
- "u w",
- "Ġpret tier",
- "Ġdon uts",
- "ĠAth ena",
- "Ġdiss ert",
- "Ġpl ante",
- "Ġur anium",
- "ìĿ Įë",
- "ar é",
- "Ġrze cz",
- "Ġdisplay ing",
- "æĪ ²",
- "Ġsar c",
- "r ão",
- "Ġtamp oco",
- "Ġphilosoph ers",
- "ĠRe cht",
- "æĵ ļ",
- "Ġcoment arios",
- "y se",
- "Ġìľ ¤",
- "Ġm ise",
- "ĠG in",
- "Ġн ом",
- "ĠFR OM",
- "l iner",
- "at if",
- "Ġspo ÅĤec",
- "x a",
- "ĠÑĤ ÑĢÑĥд",
- "Ġw ag",
- "기 ìĹIJ",
- "ĠM G",
- "Ġoff spring",
- "ĠUnder standing",
- "åıª æĺ¯",
- "OR A",
- "Ġwh irring",
- "Ġsur rend",
- "Ġpok er",
- "Ġmon uments",
- "ĠâĻ ©",
- "Ġorgan ised",
- "ĠSo zial",
- "ĠF actory",
- "Ñħ а",
- "Ġrese mble",
- "з д",
- "Ġexplos ions",
- "Ġpay roll",
- "Ġom n",
- "ĠJ orge",
- "ι Ïĥ",
- "Ġfract ure",
- "Ġpersec ution",
- "Ġdem ais",
- "E CH",
- ", )",
- "Ġcri ar",
- "ĠJ OSH",
- "Ġdem ographics",
- "Ġ16 00",
- "Ġcur rencies",
- "ĠT ips",
- "Ġ éĢĻåĢĭ",
- "ĠRe fer",
- "ĠDan cing",
- "Ġincons istent",
- "Ġde h",
- "Ġimm ens",
- "Ġme ist",
- "Ġimpat ient",
- "Ġbehav es",
- "æĿ ¾",
- "ĠëĤ´ì ļ©",
- "Ġback story",
- "Ġagree ing",
- "ĠÅ ģ",
- "ih in",
- "Ġtemper atura",
- "ĠBack ground",
- "Ġnut zen",
- "Ġëħ ¹",
- "ĠM änner",
- "Ġcollabor ations",
- "ĠK os",
- "éģİ åİ»",
- "Ġnight mares",
- "ë ĵ±",
- "ĠQueens land",
- "Ġassoci ates",
- "ĠK ok",
- "Ġfact orial",
- "ĠHy ung",
- "Ġê·¸ ëĭ¤ìĿĮ",
- "Ġfil ho",
- "Ġel ét",
- "Ġíĸī ë³µ",
- "° ±",
- "Ġgef unden",
- "Ġsemic ondu",
- "Ġcounsel ors",
- "ĠU pper",
- "ĠA ub",
- "ick ers",
- "V er",
- "Ġnorth west",
- "ĠMainten ant",
- "ĠL akes",
- "аÑı в",
- "int é",
- "ì° ½",
- "Ġг аз",
- "Ġgi orn",
- "Ġdigit ally",
- "ĠCirc uit",
- "ì¼ Ģ",
- "ãĤĬ ãģ¾ãģĹãģŁ",
- "Ġcheer ful",
- "ĠPet erson",
- "ĠDan ish",
- "ativ os",
- "Ġli ken",
- "Ġhar bor",
- "али ÑģÑĤ",
- "x e",
- "Ġcur ls",
- "ĠR hod",
- "E nd",
- "ĠE T",
- "Ġacqu aint",
- "ĠKel vin",
- "Ġtr if",
- "ĠA way",
- "ìŀIJ ëĬĶ",
- "v s",
- "Ġp ágina",
- "Ġin let",
- "ĠSant os",
- "Ġìļ° ìĻĢ",
- "Ġyap ıyorsun",
- "th eme",
- "Ġsou ff",
- "Ġinject ed",
- "Ġpó źniej",
- "iver so",
- "amp ed",
- "Ġda her",
- "Ġd agger",
- "ĠлÑİб им",
- "Ġt ummy",
- "Ġenlight ened",
- "c ents",
- "ĠD ah",
- "Ġcu est",
- "ä¾Ĩ 說",
- "IL Y",
- "Ġ×ij ר",
- "Ġbang ing",
- "ĠEm il",
- "ĠC ler",
- "ĠB order",
- "иж Ñĥ",
- "Ġpresent ers",
- "ĠST UD",
- "co ins",
- "ĠíĻ į",
- "Ġper ks",
- "Ġpar ap",
- "Ġcertain es",
- "ĠL ore",
- "ö st",
- "ĠMAR TIN",
- "Ġb ios",
- "Ġwhere by",
- "ver ts",
- "ĠMir anda",
- "Ġst ip",
- "æ¾ ¤",
- "and ez",
- "׼ ׾",
- "uj in",
- "Ġê ¾",
- "Ġaller gies",
- "pl ate",
- "Ġyap ıl",
- "Ġundert ake",
- "ĠëĤĺ ê°Ģ",
- "P art",
- "Ġkız ım",
- "h guru",
- "ãģĤ ãģ¨",
- "ĠJohn s",
- "Ġeyel ashes",
- "Ġdra ined",
- "Ġst Ã¥r",
- "ãģĤãĤĬ ãģ¾ãģĻ",
- "ĠJ ade",
- "Ġcal end",
- "fil m",
- "Ġmes a",
- "Ġlud zie",
- "Ġattract s",
- "Ġju ices",
- "Ġк ил",
- "Ġnieu we",
- "Ġmen cion",
- "Ġign ition",
- "Ġbl adder",
- "anda ag",
- "ĠExt ension",
- "íĤ ¨",
- "fe ed",
- "ĠÙĪ Ùĩ",
- "Ġsp un",
- "Ġt ät",
- "оÑĢ оÑĤ",
- "ty ard",
- "ron ics",
- "ĠH uge",
- "Ñĥж д",
- "st ring",
- "Ġun just",
- "Ġpra wn",
- "Ġfrost ing",
- "Ġdisappear ance",
- "ios a",
- "Ġcard i",
- "ĠPri est",
- "Ġcient ÃŃfic",
- "åĵª 裡",
- "ĠÐĴ аÑģ",
- "Ġë¶Ģ íĥģ",
- "Ġth ieves",
- "Ġphys ique",
- "ĠE ugene",
- "Ġбли з",
- "Ġmon opoly",
- "Ġbi ography",
- "Ġho ÅŁ",
- "Ġt ö",
- "m ac",
- "Ġshock s",
- "ìĦ ¸ë",
- "h it",
- "Ġsn ug",
- "Ġinc l",
- "Ġded ic",
- "Ġult ras",
- "Ġизв еÑģÑĤ",
- "Ġutil ization",
- "ĠÑģовеÑĢÑĪ енно",
- "Ġserv i",
- "st ag",
- "1 80",
- "Ġse wer",
- "ĠCh oice",
- "Ġdis charged",
- "ĠJ D",
- "ол еÑĤ",
- "ĠкваÑĢ ÑĤи",
- "Ġteles cop",
- "ĠJe ÅĽli",
- "ĠN ana",
- "c ale",
- "ĠÑĤ он",
- "mm m",
- "äºĨ åIJ§",
- "Ġge habt",
- "ëĤ ł",
- "æĬ ķ",
- "à¸Ļ à¸Ļ",
- "Ġet her",
- "Ġz en",
- "Ġresearch ed",
- "ĠCzy li",
- "å®Į åħ¨",
- "work ers",
- "Ġê²½ ì°°",
- "Ġsher iff",
- "all o",
- "Ġtip os",
- "Ġprosec ution",
- "Ġfrog s",
- "Ġf alt",
- "j d",
- "ĠíĮ Ķ",
- "Ġfilter ed",
- "ĠO ft",
- "Ġì į",
- "Ġdis fr",
- "ĠMust ang",
- "Ġwo ah",
- "ĠRE ALLY",
- "Ġмог ли",
- "Ġentr ada",
- "Ġиг ÑĢа",
- "Ġmix es",
- "ĠавÑĤом об",
- "Ð Ļ",
- "Ġsh in",
- "Ġparan ormal",
- "Ġsome place",
- "Ġdish on",
- "eta an",
- "Ġfu erte",
- "Ù ¹",
- "Ġdo om",
- "ìĪ ľ",
- "Ġexist ential",
- "Ġbu ld",
- "ĠSD K",
- "ĠпÑĢав да",
- "Ġturn over",
- "ĠìĹ¬ê¸° ìĹIJ",
- "Ġठ¹",
- "Ġmodel ed",
- "Ġbug ün",
- "Ġexperiment ation",
- "Ġmorning s",
- "Ġmed o",
- "Ste vie",
- "Ġplay able",
- "Ġairl ines",
- "g ments",
- "Ġê¸°ë ¶Ħ",
- "ĠT omb",
- "ĠMV P",
- "AUDI ENCE",
- "Ġcheck out",
- "Ġpas st",
- "Ġbe ispiel",
- "ĠLink s",
- "he avy",
- "Ġquestion able",
- "Ġìĵ °ë",
- "Ġs ill",
- "Ġmanip ulated",
- "ĠL oren",
- "Ġìľ ¼",
- "Ġver ge",
- "á k",
- "I ES",
- "Ġsab ot",
- "ĠCustom er",
- "ale ży",
- "Ġnom inee",
- "ĠG ad",
- "Ġnouve lles",
- "ĠS PE",
- "ist ling",
- "Ġo val",
- "обÑĢ аж",
- "if ty",
- "éĩ İ",
- "Ġbez el",
- "y et",
- "Ġfre ight",
- "ĠHan ım",
- "r ÃŃa",
- "Ġz oning",
- "Ġind em",
- "ĠB ü",
- "Ġfemin ism",
- "Ġvo ix",
- "Ġof icial",
- "Ġdi yorum",
- "» IJ",
- "Ġar ose",
- "Ġpar ar",
- "ìĿ¸ ì§Ģ",
- "ĠMart ine",
- "ĠL ect",
- "Ġrest er",
- "Ġdrown ing",
- "u ya",
- "c ida",
- "ĠAri el",
- "Ġ0 2",
- "Ġ×Ķ ×Ķ",
- "ç´ ł",
- "ĠW ert",
- "Т Ñĭ",
- "Ġwid ow",
- "Ġparch ment",
- "Ġcott age",
- "ĠX L",
- "ĠSl ack",
- "ĠN ES",
- "Ġro be",
- "Ġg imm",
- "Ġcam inho",
- "ĠHar per",
- "Ġcit rus",
- "Ġfirefight ers",
- "Ġdop amine",
- "el ets",
- "Ġdemocr at",
- "ìł ľë¡ľ",
- "Ġplay back",
- "o j",
- "ĠпÑĢ ок",
- "ĠSull ivan",
- "se mble",
- "ĠW orth",
- "ĠMust afa",
- "า ร",
- "Ġmet s",
- "éĸ Ģ",
- "л оÑģÑĮ",
- "Ġinert ia",
- "Ġuniform s",
- "è¶ ³",
- "é rio",
- "×ķר ×Ķ",
- "é nt",
- "Ġà® Ĵ",
- "ĠÑģам ÑĭÑħ",
- "Ġvou lais",
- "ĠZ immer",
- "ê² łë",
- "Ġн оÑģ",
- "en cias",
- "Ġrel ación",
- "Ġê± ¸ë",
- "Ġfact ion",
- "Ġg osp",
- "пол ож",
- "n ap",
- "h ak",
- "Ġproceed ings",
- "ĠìĨ Ķ",
- "ìķĦ ëĭĪ",
- "ĠìŀIJ 기",
- "Ġwer d",
- "Ġso f",
- "Ġsch lim",
- "Ġfl avored",
- "Ġquad ratic",
- "ĠBo ot",
- "Ġpublic ity",
- "ĠCar o",
- "Ġ ?\"",
- "ни ÑĨа",
- "man ia",
- "ĠS UR",
- "ĠB UR",
- "l ance",
- "ét ica",
- "Ġzob aczy",
- "Ġtri o",
- "s ama",
- "Ġta ÅŁ",
- "Ġas ymm",
- "ress er",
- "Ġت ع",
- "Ġп еÑģ",
- "Ġbeginning s",
- "lad ım",
- "ĠбÑĭ ÑģÑĤÑĢ",
- "Ġmo o",
- "ĠGene va",
- "Ġ åľ¨",
- "er us",
- "bor ah",
- "Ġref using",
- "b ull",
- "ĠWait ing",
- "ĠInd ividual",
- "Ġan onym",
- "im ens",
- "Ġmed idas",
- "Ġfragr ant",
- "Ġdirect ement",
- "ĠìķĦ ë§Ī",
- "ur ia",
- "Ġsp herical",
- "Ġab ge",
- "ĠVictor ian",
- "Ġspect acle",
- "ĠRodrig uez",
- "Ġoc up",
- "ĠN är",
- "mark s",
- "ng ulo",
- "ĠLu ci",
- "Ġshout ed",
- "Ġregul ators",
- "ÄŁ ini",
- "Ġdis ent",
- "ĠÑĢÑĭ н",
- "ëĤ ¨",
- "ĠìĤ ´ë",
- "Ġprobl èmes",
- "ĠF inger",
- "asse mble",
- "Ġpe ar",
- "Ġdro ite",
- "ĠEvery where",
- "t am",
- "оÑĤ ив",
- "в ой",
- "ordin ate",
- "ĠL ak",
- "Ġm Ỽi",
- "ĠTele vision",
- "Ġexpon entially",
- "av as",
- "Ġble v",
- "ĠM T",
- "ä¿ º",
- "Con nell",
- "ĠêµŃ 민",
- "ĠÑģво им",
- "Ġach a",
- "ĠD ynasty",
- "J in",
- "Ġto re",
- "Ġfl or",
- "Ġмног ие",
- "æ²Ĵ äºĭ",
- "ow an",
- "b ah",
- "Ġì£ Ħ",
- "ĠC ela",
- "Ġìµľ ê·¼",
- "Ġpermett re",
- "Ġab ras",
- "Ġverste hen",
- "Ġesc ort",
- "ĠThe m",
- "är ke",
- "por ter",
- "Ġkah kaha",
- "Ġhe ct",
- "Ġda u",
- "w ah",
- "ol ve",
- "ĠAg es",
- "s chaft",
- "ĠSt ell",
- "ne lle",
- "ĠEn suite",
- "ĠÐĴÑģ ем",
- "Ġcr éd",
- "ĠP P",
- "l ords",
- "gr unting",
- "Ġcontract ion",
- "G ot",
- "Ġacqu iring",
- "Ġso pr",
- "Ġpoison ous",
- "R NA",
- "Ġan ar",
- "ĠH of",
- "' )",
- "Ġremark ably",
- "Ġintern acional",
- "ü cke",
- "in qu",
- "Ġdu y",
- "Ġbeast s",
- "ĠL AN",
- "Ġpreced ent",
- "ĠRP M",
- "åij ¨",
- "Ġsel on",
- "Ġmort e",
- "Ġcomeç ou",
- "Ñı ла",
- "Ġinterpre ting",
- "ĠBur ke",
- "ÑĤ ÑĢа",
- "ĠìĿ´ë Ł¬",
- "Ġpess im",
- "ĠN ok",
- "íĮ Ŀ",
- "F emale",
- "Ġìĭ ¤í",
- "Ļ Ģ",
- "Ġstim ulation",
- "Ġsl ick",
- "Ġê°Ģ ëĬĶ",
- "Ġк аз",
- "ĠH BO",
- "Ġpap ier",
- "Ġkön nten",
- "Ñĥб ли",
- "ĠConst ant",
- "SPEAK ING",
- "Ġktó rÄħ",
- "Ġcos metics",
- "ĠT rend",
- "Ġrob bery",
- "Ġt itt",
- "Ġgj ort",
- "Ġdiet ary",
- "ł Į",
- "ĠKir by",
- "ĠпÑĢимеÑĢ но",
- "Ġqual ification",
- "Ġìķ ī",
- "Ġcabin ets",
- "Ġhtt p",
- "ĠEric a",
- "ç¾ ©",
- "Ġdisadvant ages",
- "Ġch attering",
- "y z",
- "fe it",
- "Ġgu ild",
- "ĠE TF",
- "ĠDrag ons",
- "ĠH ERE",
- "vent h",
- "ÙĦ اÙħ",
- "Ġmarch é",
- "D am",
- "Ġphot on",
- "Ġest able",
- "M ag",
- "Ġol har",
- "Ġcou pling",
- "ĠHil fe",
- "ĠW izard",
- "Ġм ало",
- "hel p",
- "ĠlÃŃ nea",
- "Ġì «",
- "Ġstand alone",
- "Ġmor ale",
- "Ġzwe ite",
- "ãĤĪãĤį ãģĹãģı",
- "ähr t",
- "Ġd otted",
- "Ġdri pping",
- "ĠFl ag",
- "éĿ Ĵ",
- "ro cket",
- "rate gy",
- "ir im",
- "Ġíķĺë ©´ìĦľ",
- "Ġsogen an",
- "ĠUn o",
- "ĠSch utz",
- "Ġest ilo",
- "ĠS ubs",
- "ĠDais y",
- "ÐĿ еÑĤ",
- "' ...",
- "Ġplat inum",
- "Ġb irl",
- "ĠSo vi",
- "Ġviol ate",
- "Ñĥ еÑĤÑģÑı",
- "r ill",
- "Ġtra z",
- "Ġsn ip",
- "Ġcum pl",
- "à¸Ń à¸ģ",
- "Ġc uk",
- "éħ Ĵ",
- "ĠParl ament",
- "Ġhyper t",
- "Ġpul p",
- "Ġtong ues",
- "at to",
- "Ġbus ca",
- "ih n",
- "ER O",
- "ĠÙĬ ع",
- "Ġvari as",
- "ĠMar ian",
- "Ġbound ed",
- "Ġpitch ing",
- "Ġdefic iency",
- "ĠBless ed",
- "ĠEx erc",
- "uch s",
- "ĠnhÆ° ng",
- "æľ¬ å½ĵ",
- "Ġrap ed",
- "h ales",
- "Ġmal a",
- "p ic",
- "Ġ40 1",
- "ÅĽ niej",
- "ar ina",
- "ëĵ¤ ìĿĦ",
- "ott i",
- "Ġдол го",
- "Ġtrack er",
- "ĠShel by",
- "Ġvan ished",
- "Ġbak ery",
- "Kap ı",
- "J esus",
- "ĠK R",
- "J O",
- "ħ ¸",
- "Ġdisc s",
- "ìĦ ¯",
- "ì§Ģ ë",
- "×Ļ× ¦",
- "em ary",
- "K endra",
- "Ġy ük",
- "ück t",
- "Ġv az",
- "Ġk up",
- "akt u",
- "ĠÑģп аÑģибо",
- "Ġa ik",
- "Ġnurs ery",
- "Ġendanger ed",
- "êm ement",
- "emat ics",
- "Ġrespond ers",
- "ĠRepresent atives",
- "Ġsculpt ures",
- "ig keiten",
- "Ġde pl",
- "Ġinterpret ations",
- "Ġdead lines",
- "Ġ194 2",
- "Ã Ĺ",
- "Ġsug ars",
- "em u",
- "l ively",
- "Ġrecre ational",
- "Ġdist ort",
- "Ġunders core",
- "Ġun quote",
- "Ġsaf est",
- "Ġsw ollen",
- "Ġanalys es",
- "Ġcommen cé",
- "å¦ ¹",
- "and in",
- "ĠÐ¥ оÑĢоÑĪо",
- "Ġdi arr",
- "ãģ¾ ãģģ",
- "zi est",
- "Ġtooth brush",
- "éł» éģĵ",
- "u ations",
- "Ġc ade",
- "Ġbackl ash",
- "h ind",
- "Ġris que",
- "z ess",
- "ĠìĿ´ìķ¼ 기",
- "Ġesper ar",
- "Ġtransl ations",
- "ion ed",
- "gro ans",
- "Ġп ÑĥÑĤ",
- "Ġgen etically",
- "éĢ ł",
- "Ġhapp iest",
- "Ġwer k",
- "ato on",
- "Ġmus i",
- "Ġfun ção",
- "Ġìŀħ ëĭĪëĭ¤",
- "ĠÑĢ ай",
- "Ġbe vor",
- "BL ANK",
- "Ġrepent ance",
- "P ut",
- "Ġpotrze b",
- "Ġsal a",
- "Ġcamp a",
- "W ER",
- "Ġdec ÃŃa",
- "Ġsécur ité",
- "ĠAppreci ate",
- "Ñĩ и",
- "ĠR andom",
- "ë³ Ħ",
- "k ah",
- "Ġmö j",
- "Ġsä ger",
- "Ġ×Ļ ׼×ķ׾",
- "Ġ19 0",
- "xt ures",
- "E u",
- "Ġg ä",
- "Ġ×ij× ª",
- "ĠC roat",
- "ap o",
- "P LE",
- "Ġpersist ence",
- "åĬ ©",
- "Ġbl ends",
- "Ġtre ffen",
- "ĠSanti ago",
- "yd ia",
- "al do",
- "ĠTensor Flow",
- "ĠD ual",
- "ãĥ ľ",
- "Ġch iff",
- "ìĹ ´",
- "Ġcontract ed",
- "Ġseg reg",
- "ĠFair y",
- "Ġwis ely",
- "Ġvulner abilities",
- "Ġhand held",
- "Ġgad gets",
- "Ġbo ÅŁ",
- "ĠPop ular",
- "Ġcurv ature",
- "ë ¬¸",
- "ĠMAR Y",
- "ìĿ´ì Ĭ",
- "Ġform ulation",
- "Ġcel ery",
- "Ġblur ry",
- "ĠT S",
- "ale z",
- "Ġw s",
- "Ġprogram m",
- "ĠSt ack",
- "ĠJ IM",
- "ов али",
- "ı ll",
- "Ġp ère",
- "ĠKan ye",
- "ĠDel aware",
- "Ġãģ ł",
- "Ġda unting",
- "Ġб еÑģ",
- "ĠSt upid",
- "b ig",
- "ffic ial",
- "Ġprecip itation",
- "Ġpl ung",
- "ụ c",
- "bur se",
- "Ġdar le",
- "Ġcri pp",
- "Ġpione er",
- "Ġdis put",
- "Ġse an",
- "ãģĵ ãĤĵãģª",
- "Ġresist or",
- "Ġalle in",
- "ipp les",
- "are l",
- "Ġend ors",
- "z ust",
- "ĠÑĢеб ÑıÑĤа",
- "ed ed",
- "Ġì¹´ë ©Ķë",
- "Ġlle va",
- "Ġken nt",
- "Ġб ал",
- "ĠDoc ument",
- "ĠKn ights",
- "Ġbuck le",
- "Ġìī ¬",
- "Ġal k",
- "ĠEvery day",
- "atter s",
- "Ġtoil ets",
- "Ġj ugar",
- "ĠìŀĪ ì§Ģ",
- "Ġgen auso",
- "ĠLandes regierung",
- "ãģ£ãģ ±",
- "ij e",
- "Ġtrail ers",
- "ĠT igers",
- "Ġg itti",
- "Ġforg iving",
- "Ġconcur rent",
- "ĠV u",
- "ĠíĬ¹ íŀĪ",
- "ĠBR OWN",
- "ound ed",
- "\" ;",
- "Ġtre mb",
- "Ġt iet",
- "ĠÑĢеж им",
- "Ġnuts hell",
- "ел иÑĩ",
- "Ġlos ers",
- "ric ting",
- "Ġrede em",
- "def ined",
- "N ice",
- "Ġbroad band",
- "K O",
- "Ġte asing",
- "Ġpart isan",
- "ı ma",
- "Ġìŀ¬ë ¯¸",
- "ĠJour ney",
- "Ġslop es",
- "un ing",
- "gr unts",
- "Ġt äll",
- "Ġuncover ed",
- "Ġmy ÅĽlÄĻ",
- "ĠEst her",
- "äº İ",
- "ĠHealth y",
- "Ġë° ij",
- "r ée",
- "Ġpolar ization",
- "Ġfl av",
- "Ġcambi ar",
- "Ġy r",
- "ĠR anch",
- "Ġspl its",
- "Ġtrou vé",
- "åľĭ 家",
- "Ġrecord er",
- "Ġdé part",
- "ÙĪ ب",
- "ĠK ry",
- "Ġinteress ant",
- "Ġeder im",
- "ÅĽ wiad",
- "il ateral",
- "w right",
- "Ġpour ra",
- "ê ter",
- "Ġcam el",
- "á ŀ",
- "Ġrapid ement",
- "Ġme j",
- "Ġstiff ness",
- "AD AS",
- "Ġdiff ers",
- "Ġal ot",
- "ĠS ig",
- "ÑıÑĤ елÑĮ",
- "Ġabstract ion",
- "åľ ĺ",
- "Ġke iner",
- "gr upp",
- "ĠSher lock",
- "íĺ Ķ",
- "Ġc ite",
- "Ġover flow",
- "Ġt ại",
- "ú car",
- "b ula",
- "Ġconjun to",
- "ĠC I",
- "Ġmoder ator",
- "Ġindirect ly",
- "Ġalle ine",
- "â Ĥ",
- "ÑĪ иб",
- "Ġб аб",
- "Ġdan ach",
- "Ġ19 39",
- "Ġpr omet",
- "Ġdest inations",
- "ĠIll ust",
- "ικ ÏĮ",
- "Ġsab es",
- "Ġhe h",
- "ĠGesetz ent",
- "ĠM iz",
- "ен ко",
- "ĠM ys",
- "Ð ¬",
- "ĠJuda ism",
- "Ġmust ache",
- "Ġst immt",
- "ĠG aza",
- "Ġvol te",
- "Ġnu o",
- "Ġm ón",
- "ĠCom put",
- "ู à¹Ī",
- "ĠR adi",
- "Ġexception ally",
- "Ġassum es",
- "éĸĭ å¿ĥ",
- "ãģĪ ãģ°",
- "in form",
- "Ġshr ine",
- "æĵ Ĭ",
- "Ġimplic ation",
- "ĠF itz",
- "æ²Ĵ éĹľä¿Ĥ",
- "! .",
- "Ġl t",
- "Ġall oy",
- "Ġeth ic",
- "Ġmonaster y",
- "ìĭľ ì£ł",
- "ica ção",
- "Ġcoordin ating",
- "ĠM oto",
- "Ġover look",
- "Ġcho is",
- "Ġantibiot ic",
- "ĠMin ne",
- "ĠB J",
- "ĠA pa",
- "or ian",
- "Ġsp illed",
- "J am",
- "Ġhus bands",
- "Ġcre ations",
- "Ġa ñ",
- "üs sel",
- "ĠìĿ´ì ļ©",
- "Ġanaly se",
- "r ose",
- "Ġpunch ed",
- "Ġpres que",
- "Ġastron omy",
- "Ġschwier ig",
- "ĠEb ola",
- "Ġc is",
- "Ġac et",
- "ĠF X",
- "end re",
- "ĠìĿĮ ìķħ",
- "Ġweb page",
- "Ġfre aked",
- "Ġlat te",
- "Ġì¿ ł",
- "Ġë¨ ¸ë",
- "N ever",
- "G ra",
- "íĻĶë ¥¼",
- "ey ed",
- "Ġë°ľë Ŀ¼",
- "Ġesper a",
- "Ġapare ce",
- "ra ção",
- "Ġdisrupt ive",
- "ĠJo int",
- "ur ous",
- "re as",
- "Ġquer ÃŃa",
- "Ġdistrib utions",
- "Ġexpon ent",
- "ì¹ ĺ를",
- "Ġd l",
- "z hou",
- "ĠHe aring",
- "å·® ä¸įå¤ļ",
- "ĠC raw",
- "Ġflo ats",
- "oun ced",
- "L ab",
- "W orld",
- "Ġbur dens",
- "Ġauthor itarian",
- "ĠB olt",
- "Ġод нÑĥ",
- "Ġpige on",
- "Ġdistract ions",
- "ĠHeraus forder",
- "Ġz est",
- "es c",
- "Ġsh akes",
- "at as",
- "ĠÙħ Ø´",
- "hol es",
- "Ġthink ers",
- "al ta",
- "Ġar che",
- "ĠS uk",
- "an ha",
- "Ġtempt ing",
- "Ġyou tuber",
- "Ġv ì",
- "Ġdz iaÅĤa",
- "ĠVatic an",
- "P ark",
- "Ġsup ers",
- "ĠNik ki",
- "ëĬ IJë",
- "or ang",
- "ram ient",
- "é ¬¼",
- "Ġê°ĸ ê³ł",
- "Ġdessert s",
- "Ġav ere",
- "ĠGreg ory",
- "Ġëĵ¤ìĸ´ì ĺ",
- "Ġcost ing",
- "ĠClin ic",
- "Ġreb els",
- "ĠM ob",
- "Ġbun lar",
- "ĠYour s",
- "ert ime",
- "Ġret ali",
- "m ara",
- "at us",
- "all es",
- "Ġд ÑĢ",
- "Ġд иÑģ",
- "Ġdiscount s",
- "ĠGU Y",
- "Ġкак ое",
- "ĠExper iment",
- "re ment",
- "ĠXi ang",
- "Ġb ate",
- "W E",
- "Ġspecial ize",
- "Ġde ity",
- "ĠL oki",
- "m ag",
- "ĠN it",
- "W est",
- "Ġmater nal",
- "Ġqu is",
- "åŁº æľ¬",
- "bro ken",
- "Ġlas ers",
- "Ġha kk",
- "ĠAng els",
- "Ġmaster y",
- "ant is",
- "T iffany",
- "ee e",
- "ç ij",
- "ore m",
- "Ġin acc",
- "Ġjurisd ictions",
- "ĠKard ash",
- "æľ º",
- "I l",
- "ĠS inn",
- "åĭķ çĶ»",
- "Ġathlet ics",
- "c ÄĻ",
- "Ġlo osely",
- "Ġdiet a",
- "A g",
- "Ġ? ?",
- "ĠëĮĢ íijľ",
- "Ġsuper v",
- "Ġnut rit",
- "Ġdr ifting",
- "ĠìĦłìĥĿ ëĭĺ",
- "Ġпон Ñıл",
- "ĠVict ory",
- "ÙĦ Ø©",
- "×ķ׳ ×Ķ",
- "Ġп иÑĪ",
- "Ġsh aved",
- "Ġmes ure",
- "ond en",
- "Ùĥ ر",
- "Ġex ile",
- "ĠDes de",
- "ĠP interest",
- "Ġattach ments",
- "Ġh ombres",
- "Ġfin es",
- "ĠìĦ¸ ìĥģ",
- "Ġsleep s",
- "ĠT aco",
- "ĠI RA",
- "ri os",
- "Ġo ll",
- "et es",
- "Ġun ut",
- "fashion ed",
- "Ġtre ball",
- "ĠNear ly",
- "ĠÑĢе алÑĮно",
- "Ġch il",
- "éĢ ±",
- "ÄŁ a",
- "ĠM EL",
- "ros cop",
- "ĠC G",
- "Ġv enge",
- "Ġdishwas her",
- "al gic",
- "Ġmod ifier",
- "Ġemb assy",
- "t imer",
- "em ics",
- "Ġintric ate",
- "Ġev et",
- "ĠëĮĢë °ķ",
- "Ġis ot",
- "Ġна ÑĥÑĩ",
- "ĠQu iz",
- "res o",
- "δ Ïİ",
- "Ġye lled",
- "Ġfed er",
- "ELL ER",
- "Ġexceed ed",
- "on as",
- "ic ano",
- "Ġжив оÑĤ",
- "ĠMa o",
- "ĠKaz uto",
- "Ġ ãħĭãħĭãħĭãħĭ",
- "Ġfront line",
- "ĠHung arian",
- "Ġüber all",
- "aw at",
- "Ġgri ps",
- "i ções",
- "arn ya",
- "ĠÍ ¡",
- "Ġse id",
- "Ġan ak",
- "Ġacab ou",
- "íķ ij",
- "Ġnot orious",
- "ĠGod zilla",
- "Ġover coming",
- "ĠP end",
- "Ġol abilir",
- "ül me",
- "Ġer halten",
- "ãĤī ãģĦ",
- "ê· ¹",
- "ĠM eter",
- "Ġsta an",
- "O l",
- "Ġch ats",
- "ĠBu enos",
- "ÃŃ ve",
- "alu able",
- "Ġstrateg ically",
- "Ġcompr ised",
- "ĠпеÑĢÑģон аж",
- "Ġw ann",
- "ĠC en",
- "н иÑĤе",
- "Ł ģ",
- "ĠÑĤоб ой",
- "i ad",
- "ĠkardeÅŁ im",
- "ĠCongress man",
- "ream ing",
- "h omme",
- "Ġcommun aut",
- "Ġalcohol ic",
- "Ġpick led",
- "Ġac ord",
- "p osition",
- "eg ól",
- "Ġtrou bling",
- "ĠMarch eg",
- "Ġzum indest",
- "Ġseam lessly",
- "Ġol un",
- "ĠTV s",
- "ĠпÑĢакÑĤи ÑĩеÑģки",
- "Ġback end",
- "ãģĵãĤĵ ãģ«ãģ¡ãģ¯",
- "id able",
- "Ġgad get",
- "Ġfa ço",
- "ĠMarcheg iani",
- "Ġë° ¤",
- "Ġaccident al",
- "ĠL P",
- "Ġeld est",
- "ĠAd miral",
- "Ġn Äĥm",
- "le ver",
- "Ġpast el",
- "Ġfond o",
- "Con nie",
- "Ġter cer",
- "Ġp act",
- "ĠMont e",
- "Ġme ats",
- "ĠS MS",
- "ĠAustral ians",
- "ç ¼",
- "Rh ett",
- "Ġexact ement",
- "Ġë¹ ¼",
- "ĠM OD",
- "ç ¡",
- "ĠR apt",
- "ĠNo ch",
- "Ġab ort",
- "ĠNav al",
- "ĠFu ji",
- "IN TER",
- "Ġнов Ñĭй",
- "Ġmiej sce",
- "ĠIC U",
- "ĠGrad uate",
- "ĠGl en",
- "ard i",
- "ĠÈ ĺ",
- "Ġsold er",
- "Ġprofess ions",
- "Ġorth og",
- "om n",
- "int rodu",
- "ĠDen ise",
- "ìŀIJë ¥¼",
- "Ġcorrespond ence",
- "AM A",
- "Ġinf lict",
- "Ġf and",
- "ĠG ü",
- "ĠÑĩ еÑĤ",
- "Ġtr aced",
- "Ġpat ents",
- "Ġamb ush",
- "Ġlot ta",
- "ff er",
- "ĠW agner",
- "Ġimp erson",
- "Ġextr êmement",
- "ÙĤ ت",
- "cond uct",
- "A tt",
- "ĠM ueller",
- "ĠAl icia",
- "Ġcy c",
- "Ġha cker",
- "Ġt ys",
- "Ġha il",
- "Ġз аÑıв",
- "Ġpas so",
- "Ġì¶ Ķê°Ģ",
- "ĠÎ Ī",
- "Ġpack aged",
- "ĠC ynthia",
- "he et",
- "ä¸Ń åĽ½",
- "ĠNiss an",
- "ĠQuest o",
- "é ¨",
- "d id",
- "Ġμ ια",
- "ĠEll is",
- "ĠAnal ysis",
- "ce mos",
- "Ġas eg",
- "ĠMy ster",
- "ĠCa o",
- "Ġtu v",
- "ĠIndust ry",
- "주 ê³ł",
- "ot al",
- "Ġpeque ño",
- "br as",
- "Ġcompreh end",
- "ĠSim pson",
- "ÑģÑĤв ие",
- "ocr acy",
- "иÑĩеÑģ ки",
- "ĠM ush",
- "ĠLaur ie",
- "Ġtriang ular",
- "ĠPres ents",
- "ĠK unden",
- "ç´ ¹",
- "æŃ ¦",
- "ĠIs s",
- "ĠDe ck",
- "á»ĥ n",
- "ĠDark ness",
- "Ġinflamm atory",
- "eremi ah",
- "Ġwar med",
- "vey ard",
- "ĠMem ory",
- "et ty",
- "Ġtax payers",
- "ภĵ",
- "Ø ¡",
- "Ġpract ise",
- "ëĭ ¬ë",
- "Ġdr illed",
- "m Ã¼ÅŁ",
- "log o",
- "ĠF ach",
- "¤ë ¡ľ",
- "Ġübrig ens",
- "Ġkon nten",
- "Ġnormal mente",
- "Ġarg ues",
- "iling ual",
- "°ë ¥¼",
- "eg al",
- "Ġtrava ill",
- "ov y",
- "а ÑĤо",
- "Ġr uth",
- "ĠL ights",
- "Ġconsist ed",
- "×ijר ×Ļ×Ŀ",
- "Ġstere otype",
- "Ġpay er",
- "ĠRe e",
- "ĠAir bnb",
- "Ġdr owned",
- "ĠZ oe",
- "Ġcan opy",
- "Ġbar r",
- "Ġн оÑĩ",
- "Ġpag an",
- "Ġj ars",
- "Ġr ê",
- "er ver",
- "æĪ ¿",
- "ie ben",
- "Ġes pect",
- "ĠF i",
- "Ġunw illing",
- "Ġtechn ician",
- "ặ t",
- "m ember",
- "ĠCan al",
- "س Ùħ",
- "Ġlie ber",
- "Ġin ference",
- "Ġhon oring",
- "åij µ",
- "ĠCamp aign",
- "Ġline age",
- "ĠSt ress",
- "Ġvict ories",
- "Ġde ja",
- "× £",
- "ê tes",
- "bl ick",
- "Ġмен ее",
- "oth s",
- "ĠCou ple",
- "J ason",
- "ĠNic olas",
- "ек Ñģ",
- "l ib",
- "Ġher ramient",
- "Ġ×IJ ×ķ×ŀר",
- "Ġвид им",
- "mill imeter",
- "Ġsil houette",
- "Ġdrive way",
- "Ġcher ish",
- "ãħł ãħł",
- "Ġrans om",
- "Ġinter disciplinary",
- "ĠPort al",
- "Ġtra g",
- "th ood",
- "Ġted ious",
- "Ġgloss y",
- "Ġpré par",
- "ĠC ay",
- "ĠT ook",
- "ĠBott om",
- "Ġz ig",
- "å «",
- "åį ±",
- "re presented",
- "à¹Ģล ย",
- "Ġdesar rollo",
- "ìĦ ľë",
- "Ġvis cos",
- "Ġmill igram",
- "ĠG und",
- "Ġfer ment",
- "d rum",
- "Ġdraw ers",
- "La ugh",
- "Ġpel os",
- "Ġpave ment",
- "Ġmem oir",
- "av ait",
- "Ġ20 50",
- "¤ë ¥¼",
- "Ġraz ón",
- "Ġflour ish",
- "Ġst ern",
- "ä¸ Ī",
- "ĠCh ung",
- "Ġser pent",
- "ĠGentle men",
- "羣çļĦ å¾Ī",
- "k ook",
- "Ġl ut",
- "import e",
- "p arent",
- "Ġw sz",
- "Ġsc ree",
- "ĠMitar beiter",
- "å· ´",
- "m ut",
- "Ġìĸĺ 기를",
- "Ġsem ble",
- "ĠO W",
- "Ġinvestig ator",
- "ĠCher yl",
- "ĠG erald",
- "Ġpr ere",
- "Ġcomp ares",
- "ny t",
- "Ġdiferen ça",
- "? -",
- "Ġqu á",
- "ר ×Ļ",
- "S en",
- "Ġhe ps",
- "Ġgrat uit",
- "Ġcons ort",
- "ĠST OP",
- "ĠProtest ant",
- "Ġelectro de",
- "â Ĺ",
- "Ġsecure ly",
- "иÑĩеÑģ кой",
- "Ġt ää",
- "Ġreg isters",
- "ĠHeaven ly",
- "og ly",
- "iss ä",
- "ĠPhys ics",
- "ĠMer kel",
- "Ġré v",
- "éĻ ¢",
- "Ġer ased",
- "ĠSac ramento",
- "Ġcoff in",
- "Ġex acer",
- "Ġl anz",
- "Ġpo ets",
- "ul if",
- "Ġì¹ ĺë",
- "ĠN erd",
- "ĠN CT",
- "ĠH our",
- "neh mer",
- "ŀ ĺëıĦ",
- "ĠPrin ci",
- "S w",
- "m ies",
- "ar med",
- "ĠBeat les",
- "Ġpropag ation",
- "Ġexch anged",
- "Ġcum ulative",
- "Ġì§ij ìĹIJ",
- "Ġdefe ating",
- "æĬ ±",
- "b els",
- "Ġw es",
- "ĠOdys sey",
- "ä½ł æĥ³",
- "av ior",
- "ĠìľĦ ìĹIJ",
- "Ġbr it",
- "Ġhij o",
- "D AY",
- "ĠاÙĦت ÙĬ",
- "ĠС еÑĢг",
- "Ñĥ ка",
- "eds iÄĻ",
- "Ġimp os",
- "Ġell as",
- "Ġfire arms",
- "ĠN R",
- "Ġ×ij× IJ",
- "ĠÐŁ ока",
- "aw i",
- "ĠìĦ± ê³µ",
- "Ġpup ils",
- "ĠT ack",
- "Ġfr ase",
- "ĠSh ip",
- "Ġst ad",
- "ä¸ ľ",
- "ĠGreat er",
- "un un",
- "imm ung",
- "gr own",
- "ĠN XT",
- "ĠAmeric as",
- "f ox",
- "Ġmant en",
- "éłIJ åĤĻ",
- "ĠÑģ ок",
- "Ġr ikt",
- "lect ric",
- "de ep",
- "Ġзна еÑĪÑĮ",
- "Ġben ut",
- "ĠInf rast",
- "ĠEm ir",
- "ĠоÑĤп ÑĢав",
- "ĠKim chi",
- "ĠFinn ish",
- "´ìł ģ",
- "ina ire",
- "Ġo ike",
- "æ¸ħ æ¥ļ",
- "Ġhost age",
- "ĠBut ton",
- "ÙĤ ÙĬ",
- "ek ing",
- "ĠKaz akh",
- "Ġcomfort ing",
- "Ġso g",
- "Ġgreet ed",
- "g uitar",
- "p ayer",
- "Ġrel ational",
- "Ġconstru ir",
- "çī¹ åĪ¥",
- "op ian",
- "ĠVol ume",
- "iet h",
- "ÑģÑĤв ом",
- "ur rection",
- "li ÅĽmy",
- "Ġhem isphere",
- "ĠBe an",
- "IG N",
- "Ġköt ü",
- "ĠFall out",
- "Ġbr ace",
- "ç¹¼ çºĮ",
- "ÏĢ ά",
- "ĠH AS",
- "Ġg é",
- "Ġcharacter ize",
- "ặ c",
- "ĠMil ky",
- "Ġtum ors",
- "Ġn uit",
- "ĠG az",
- "ĠìŀĪ ëĭ¤ëĬĶ",
- "Ġг аÑĢ",
- "ess ment",
- "ĠA be",
- "Ġë½ ij",
- "ĠEins atz",
- "J IN",
- "j ä",
- "C ry",
- "ĠProm ised",
- "ĠÑģеÑĢ д",
- "ok us",
- "Ġscal able",
- "ĠпоÑģмоÑĤÑĢ еÑĤÑĮ",
- "ück lich",
- "Ġreal ism",
- "Ġmay o",
- "Ġjuven ile",
- "Ġhead lights",
- "Ġgör Ã¼ÅŁ",
- "ĠRe form",
- "Ġhal ves",
- "cz ne",
- "Ġbreak up",
- "że j",
- "Ġr ätt",
- "D ay",
- "ĠìĿ¼ë ³¸",
- "Ġmu erte",
- "Ġtun es",
- "ĠSm ile",
- "rec ord",
- "Ġrecher che",
- "atisf ied",
- "Ġpo zi",
- "Ġcelebr ations",
- "ise xual",
- "ĠRO B",
- "third s",
- "ĠF ortune",
- "ĠÑĤ ой",
- "Ġbrand ed",
- "lo o",
- "Ġd ud",
- "Ġrandom ized",
- "Ġcomb in",
- "ä¸Ģ äºĽ",
- "ier an",
- "c zenia",
- "į ãĥ«",
- "Ġcur ator",
- "Ġar tery",
- "ĠÑĥ ÑĪ",
- "ĠÑĩ иÑĤ",
- "Ġsubsid ies",
- "Ġbloss om",
- "ĠTw ilight",
- "Ġhy vä",
- "ĠPom pe",
- "ĠC isco",
- "ĠÐŁÑĢ о",
- "Ġbir i",
- "Ġg ern",
- "Ġre built",
- "Ġw cze",
- "Ġbenefic i",
- "Ġdrum mer",
- "Ġsol ids",
- "Ġdi yorsun",
- "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĹãģŁ",
- "l ated",
- "Ġmud dy",
- "Ġh olog",
- "Ġcl aps",
- "ĠR ings",
- "ĠO key",
- "ĠBra ve",
- "Ġvalu ation",
- "Ġmig rant",
- "Ġinter mitt",
- "Ġeig ene",
- "ili ary",
- "ãĥ¼ ãĥĪ",
- "mark t",
- "k r",
- "ĠR ib",
- "á»Ļ i",
- "Ġaccus ations",
- "Ġa rab",
- "w ash",
- "ĠBard zo",
- "Ġu gh",
- "est ers",
- "oph ren",
- "Ġaliment os",
- "ĠU z",
- "Ö Ĥ",
- "Ġ6 50",
- "ĠпÑĢи еÑħ",
- "F I",
- "Ġsamp ai",
- "Ġparl é",
- "hes ion",
- "Ġs ır",
- "Ġapparat us",
- "Ġcor related",
- "ĠPrincip al",
- "Ġcor r",
- "ĠOffic ial",
- "иÑĩеÑģ кие",
- "Ġtermin als",
- "Sh ould",
- "Ġvac un",
- "Ġst ellt",
- "Ġmo oi",
- "etz ung",
- "Ġк ÑĢа",
- "Ġda i",
- "Ġп ож",
- "Te am",
- "ĠP PE",
- "ĠÐŀ Ñģ",
- "ĠLe ah",
- "ĠI vy",
- "y st",
- "Ġuh hh",
- "Ġnight time",
- "Ġtrend y",
- "Ġsec urities",
- "Ġcontin ents",
- "Ġfirst hand",
- "ĠVer on",
- "ĠëĤ ®",
- "Ġbrows ing",
- "ĠC ada",
- "t ro",
- "Ġtr amp",
- "re ib",
- "Ġerst mal",
- "irl er",
- "Ġps ic",
- "Ġget ir",
- "ĠN P",
- "Ġdzie ci",
- "об ÑĢаз",
- "Ġmagic ian",
- "Ġscrut iny",
- "Ġsl ab",
- "ĠO T",
- "ist y",
- "ir ies",
- "ore st",
- "Ġtask ed",
- "Ġmor ally",
- "ìķ¼ ì§Ģ",
- "ust ered",
- "Ġfool s",
- "Ġir respons",
- "Ġein f",
- "Ġvi á»ĩc",
- "Ġsc or",
- "Ġpill ows",
- "ĠG egen",
- "Ġtut te",
- "Ġquarter ly",
- "Ġdid nt",
- "ĠG ym",
- "ĠE ther",
- "ĠØ «",
- "лиÑĪ ком",
- "Ġsign aling",
- "ĠN ode",
- "ĠDonc s",
- "Ġy ah",
- "ĠKan al",
- "Ġf ading",
- "et in",
- "Ġinfluen cers",
- "Ġmed als",
- "Ġengine ered",
- "Ġfer mented",
- "ê²ł ì§Ģë§Į",
- "ĠBeet hoven",
- "×ŀ× ©",
- "inent al",
- "ĠìķĮë ł¤",
- "üt fen",
- "al nya",
- "Ġo vere",
- "Ġden kt",
- "ак ÑĤеÑĢ",
- "Ġâ ĺ",
- "Ġneces it",
- "Ġgener ators",
- "gr ass",
- "Ġпод Ñĥм",
- "lie ÃŁen",
- "B ar",
- "ľë ıĻ",
- "ĠдеÑĤ ей",
- "Ġsuck ing",
- "Ġsten cil",
- "Ġprim o",
- "ĠBreat h",
- "st rom",
- "Ġimmens ely",
- "Ġapp reh",
- "ìłķ ìĿ´",
- "P op",
- "Ġj ong",
- "ĠGi ul",
- "ĠAD HD",
- "Ġhö ren",
- "Ġe lo",
- "iv ent",
- "Ġr us",
- "Ġoutrage ous",
- "Ġmaster ed",
- "Ġì» ¤",
- "ÙĪ Ùģ",
- "ip es",
- "ĠRud y",
- "Jac ob",
- "Ġbull ish",
- "Ġt apped",
- "Ġfa ud",
- "iz ophren",
- "ĠÑģо Ñħ",
- "ĠDar ling",
- "Ġ196 3",
- "ĠPre vention",
- "² Ķ",
- "Ġabdom inal",
- "st ones",
- "Ġav aient",
- "á»ķ i",
- "m ake",
- "Ġs are",
- "ĠInst ant",
- "к ам",
- "Ġkeep er",
- "Ġblank ets",
- "ãģ§ ãģĹãĤĩãģĨ",
- "Ġswe ats",
- "ĠMinne apolis",
- "åħ¨ éĥ¨",
- "Ġgen ommen",
- "Ġfast en",
- "ĠBrus sels",
- "åij ¼",
- "Ġcaf eter",
- "Ġabsor bing",
- "Ġha go",
- "ĠEl mo",
- "Ġgust o",
- "ĠY ap",
- "M úsica",
- "Ġt ert",
- "Ġband a",
- "Ġm ily",
- "Ġthere after",
- "ĠStock holm",
- "ĠC arson",
- "Ġcalib ration",
- "ava ÅŁ",
- "ans a",
- "ik ke",
- "Ġfore see",
- "Ġqual che",
- "Ġdest e",
- "æ ¤",
- "ün üz",
- "Ġfor ge",
- "D is",
- "est en",
- "Ġδ ια",
- "Ġenca ps",
- "ĠGes pr",
- "Ġcher cher",
- "ick ets",
- "ÑĤоÑĢ Ñĭ",
- "C r",
- "ĠТак же",
- "Ġrabb its",
- "ĠD ot",
- "he iten",
- "Ġcaus al",
- "ĠF oster",
- "ajÄħ c",
- "Ġbere it",
- "Ġayud ar",
- "é« Ļ",
- "ãģ ³",
- "s ong",
- "com b",
- "Ġfr inge",
- "Ġcyber security",
- "Ġëľ ¨",
- "Ġk ier",
- "Ġbesch äft",
- "Ġкон ÑĨе",
- "Ġfacil it",
- "ĠNam en",
- "Ġbil ateral",
- "t x",
- "ĠW issenschaft",
- "Ġnu ances",
- "Ġr ipping",
- "Ġf y",
- "ĠSicher heit",
- "ĠGh ana",
- "ol on",
- "Ġto pped",
- "ĠMoroc co",
- "Ġrad ial",
- "ĠL EE",
- "ĠAndre as",
- "ed d",
- "ĠìĹ ´ë",
- "ĠAirl ines",
- "ãģĵ ãĤį",
- "Ġval ores",
- "ê· ľ",
- "H y",
- "Ġзад аÑĩ",
- "ĠKend all",
- "ĠÑħ аÑĢ",
- "ĠV amp",
- "Ġpy thon",
- "Ġmanage able",
- "ĠG ente",
- "o ise",
- "ici ary",
- "Ġimp oss",
- "ĠBun ny",
- "iest a",
- "And rew",
- "Ġser t",
- "ĠC ec",
- "zz arella",
- "Ġautom obile",
- "ĠT iere",
- "all ows",
- "åĨ Ĩ",
- "Ġë° Ģ",
- "ĠSc orp",
- "ĠJ elly",
- "ag ara",
- "ĠSt retch",
- "Ġrede f",
- "Ġexacer b",
- "ĠS HA",
- "é f",
- "ors a",
- "Ġflaw ed",
- "ĠNo el",
- "?! ?",
- "Ġpro cent",
- "Ġmen stru",
- "ĠпÑĢо Ñĩ",
- "Ġinf ants",
- "ðŁİ µ",
- "pa use",
- "ĠR acing",
- "Ġ194 8",
- "Ġsuper intendent",
- "id ores",
- "id y",
- "bra him",
- "Ġunl ucky",
- "Ġper k",
- "an ci",
- "Ġë§Įë Ĥĺ",
- "ĠÐľÐ¾Ñģ кв",
- "Ġfin ans",
- "Ġdiferen cia",
- "łĪ ìĿ´",
- "éħ į",
- "OR Y",
- "ĠT ac",
- "ÛĮ ا",
- "Ġdes em",
- "Ġваж но",
- "ĠJ U",
- "ĠìŀĪ ìŀĸìķĦìļĶ",
- "ĠÎ Ŀ",
- "Ġinform ations",
- "ĠH EL",
- "h st",
- "Ġпог овоÑĢ",
- "Ġvo iture",
- "Ġre us",
- "änd ig",
- "ĠпоÑħ ож",
- "j ing",
- "Ġd ru",
- "alt ra",
- "Ġprodu its",
- "Ġk ite",
- "Ġeye ball",
- "ĠB elt",
- "ĠRestaur ant",
- "Ġg amb",
- "Ġpor ridge",
- "it ters",
- "Ġconver ts",
- "Ġyard ım",
- "Ġmáxim o",
- "w irtschaft",
- "Ġíķĺë Ĥĺë",
- "Ġì¤ Ģ",
- "Ġice berg",
- "Ġvor bei",
- "Ġ25 6",
- "ocr atic",
- "Ġreck less",
- "on ner",
- "Ġm ús",
- "Ġlog ically",
- "ĠPr ison",
- "ĠNet z",
- "Ġvac ant",
- "Ġn immt",
- "ĠH ARR",
- "Ġз ов",
- "ĠDe e",
- "ring e",
- "ni est",
- "ĠR ules",
- "ìĬ¤ë Ł½",
- "cuss ions",
- "Ġfl oral",
- "Ġconstra ined",
- "Ġdifferent iation",
- "ĠQue bec",
- "ĠÛģ ÛĮÚº",
- "Ġpúblic a",
- "it el",
- "Ġaccommod ations",
- "ĠGr ü",
- "í ľ",
- "Ġpick les",
- "иÑĩеÑģ киÑħ",
- "Ġcomm issions",
- "ĠBa ek",
- "Ġçoc uÄŁ",
- "ĠMed ium",
- "Ġperiod ically",
- "Ġwonder fully",
- "Ġstaff ing",
- "ìĽ IJë",
- "ri re",
- "f le",
- "ĠMc L",
- "ĠÑĤ еп",
- "ĠпеÑĢ ек",
- "н олог",
- "Ġíģ¬ ê²Į",
- "çĻ¼ çı¾",
- "Ġprosper ous",
- "ĠSpirit ual",
- "ĠCh ick",
- "DI A",
- "ĠÐŁÑĢ ивеÑĤ",
- "Ġper ÃŃ",
- "ÑĮ ÑİÑĤ",
- "Ġconsult ants",
- "ĠEar l",
- "ä»Ĭ å¹´",
- "Ġru ining",
- "оÑĢ е",
- "Ġpens er",
- "Ġtak iej",
- "Ġstrength ened",
- "ĠLiqu id",
- "он еÑĨ",
- "ав аÑĤÑĮ",
- "Ġcam er",
- "Ġdisagre ement",
- "Ġbat hing",
- "ĠY osh",
- "a al",
- "pre chen",
- "RIS ADAS",
- "Ġsuper star",
- "æģ Ń",
- "лÑı ÑĤÑĮ",
- "Ġn ib",
- "ĠTh erm",
- "ĠDAN IEL",
- "Ġp aw",
- "Ġliqu ids",
- "Ġcapac it",
- "ark en",
- "Ġvag ina",
- "Ġm ashed",
- "Ġemer ges",
- "ys cy",
- "Ġun related",
- "ĠGu ild",
- "Ġin verted",
- "it ives",
- "T ra",
- "Ġbe gr",
- "Ġal te",
- "ì§ ķ",
- "ãĤģ ãģ¦",
- "ĠÑĢазÑĢ абоÑĤ",
- "f inder",
- "Ġдал ее",
- "Ġблаг одаÑĢ",
- "walk er",
- "Ġcr ater",
- "ass adors",
- "ren ces",
- "ins ki",
- "ĠK IM",
- "ĠEll iot",
- "20 17",
- "ĠS r",
- "ink a",
- "ano v",
- "Ġìŀĺë ª»",
- "Ġpropriet ary",
- "display style",
- "ĠÑģ им",
- "Ġиз б",
- "ĠPan el",
- "Ġinstinct s",
- "ĠCommun ications",
- "éº »",
- "mid t",
- "Ġë§Įëĵ¤ ìĸ´",
- "ĠÑģл ова",
- "ĠGil bert",
- "缮 åīį",
- "Т ак",
- "voor beeld",
- "е ÑİÑģÑĮ",
- "ary n",
- "que z",
- "Ġd art",
- "Ñĸ ÑĪ",
- "ĠH ut",
- "S al",
- "Ġs outheast",
- "Ġpestic ides",
- "Ġhelicop ters",
- "Ġend ured",
- "i ada",
- "Ġbre wing",
- "ìĹ ¬ë",
- "ĠÑģв обод",
- "ĠS aints",
- "ĠFr ançais",
- "ĠEconom ics",
- "Ġdis loc",
- "oph obia",
- "C amer",
- "Ġnegoti ated",
- "ĠÑģÑĤ али",
- "ìĬ¤í ģ",
- "og ie",
- "Ġtsun ami",
- "Ġpeel ed",
- "Ġmotiv ations",
- "è¨ Ń",
- "ost at",
- "fl an",
- "ĠD AC",
- "Ġk av",
- "' RE",
- "ĠPe arson",
- "b be",
- "c zenie",
- "Ġaten ção",
- "íĨµ ëł¹",
- "ãģ£ ãģ¡",
- "ĠÑĥд аÑĢ",
- "Ġintrodu ctory",
- "ĠI ci",
- "ë ĮĢë",
- "ak at",
- "Ġt rench",
- "Ġproceed ed",
- "ĠCo in",
- "Ġdere cho",
- "ĠRed e",
- "æ¯ Ľ",
- "ан нÑĭй",
- "Ġincarcer ated",
- "ĠRich mond",
- "R ock",
- "ĠP av",
- "ĠKar ma",
- "ug es",
- "Ġconte ú",
- "ë ¹Ħ",
- "Ġê·¸ë §Į",
- "ĠG one",
- "Ġwsp óÅĤ",
- "ĠRah men",
- "un ken",
- "Ġì¤ijìļĶ íķľ",
- "Ġi b",
- "Ġatt aching",
- "H ay",
- "Ġsu ka",
- "ìį ¹",
- "Ġpivot al",
- "ĠRes pect",
- "ÃŃ da",
- "I B",
- "ĠVer antwort",
- "w iet",
- "Ġforens ic",
- "ÑĢи ÑģÑĤ",
- "ĠпÑĢинÑĨип е",
- "Ġmark ings",
- "Ġk ettle",
- "ĠOper a",
- "ĠDo ctors",
- "Ġshred ded",
- "Ġrec uer",
- "Ġvig il",
- "ĠF ail",
- "Ġentre v",
- "Ġд ÑĥÑĪ",
- "Ġout breaks",
- "èµ° åIJ§",
- "ĠÏĢ ο",
- "Ġro gue",
- "ang led",
- "Ġyear ly",
- "ĠCre ed",
- "Ġw am",
- "Ġlot us",
- "ê³ ¼ë",
- "ãĢģ ãĢģ",
- "ĠSp it",
- "ĠIt u",
- "Ġstra ins",
- "Ġstamp ed",
- "Ġpl aint",
- "Ġpot ion",
- "Ġconsolid ation",
- "è© ķ",
- "оÑĩ кÑĥ",
- "Ġvlog ging",
- "Ġsl ate",
- "ĠAu ft",
- "ĠInc or",
- "ừ ng",
- "§ IJ",
- "en h",
- "Ġhe iÃŁ",
- "Ġdom est",
- "ĠSt rom",
- "åį ³",
- "ak is",
- "Ġfra gen",
- "Ġfin er",
- "ĠS ug",
- "Ġup hill",
- "Ġé én",
- "âĢ¦ )",
- "ĠÑģ оп",
- "ĠCore y",
- "Ġsie bie",
- "Ġm use",
- "Ġclo ves",
- "Ġp ous",
- "ĠFin anz",
- "ĠR oute",
- "am at",
- "Ġmut ually",
- "ĠвнÑĥÑĤ ÑĢи",
- "ĠSel ena",
- "ë Ķ",
- "ĠGa ussian",
- "ë ¶ĢíĦ°",
- "Ġ×ij× Ľ",
- "Ġej erc",
- "å¾ ®",
- "ke a",
- "ĠG erry",
- "ĠS ic",
- "大 çļĦ",
- "Ġ196 6",
- "ies e",
- "Ġfoss ils",
- "Ġest ad",
- "ĠK ane",
- "ci Äĩ",
- "Ġìľł íĬľë",
- "Ġп ам",
- "ĠCru ise",
- "int érieur",
- "Ġbe kannt",
- "ĠP ode",
- "Ġdem ander",
- "R em",
- "Ġinv ade",
- "Ġdecor ating",
- "rop ic",
- "Ġcow boy",
- "ĠPh oto",
- "opol it",
- "Ġì»¬ë Ł¬ë",
- "Ġre ap",
- "Ġhand writing",
- "à¹Ħ ร",
- "Ġë ļ",
- "Ġب عد",
- "ĠM t",
- "Ù Ģ",
- "Ġspaces hip",
- "Ġnational ism",
- "Ġcouncil s",
- "ĠGriff in",
- "ĠAh med",
- "Ġcl ich",
- "ĠO L",
- "w l",
- "ĠPil ot",
- "å® ®",
- "Ġacron ym",
- "Ġg els",
- "Ġelectro ly",
- "è ĵ",
- "Ġм ной",
- "Ġepis od",
- "ĠDies es",
- "ĠAT P",
- "Ġed iyorum",
- "Ġexpress es",
- "Ġexhib its",
- "C omm",
- "Ġк ÑĢÑĥп",
- "Ġmat ar",
- "Ġ20 25",
- "ĠArt em",
- "vas ive",
- "r Ãł",
- "Ġbe ÅŁ",
- "é» ĥ",
- "Ġliz ard",
- "Ġfill e",
- "Ġì§ Ī문",
- "Ġмо Ñī",
- "Ġt ür",
- "Ġcul prit",
- "Ġwo ven",
- "ĠAN Y",
- "n im",
- "Ġt ay",
- "Ġprom in",
- "Ġacom pa",
- "Ġid é",
- "Ġbo iler",
- "ĠThe men",
- "Ġaven ue",
- "ĠM ud",
- "Ġнов Ñĭе",
- "Ġwitness ing",
- "Ġl ance",
- "ĠCH AN",
- "ĠBe ver",
- "ت Ùħ",
- "Ġchem otherapy",
- "K ing",
- "ĠbÄĻd ÄĻ",
- "Ġat ual",
- "Ġt ive",
- "Ġtalk in",
- "Ġqued ar",
- "ie ÃŁ",
- "ed el",
- "Ġìĸ´ì łľ",
- "Ġjog ar",
- "Ġö r",
- "Ġundert aking",
- "ĠStre ngth",
- "Ġmil hões",
- "ĠW ine",
- "ĠM olt",
- "è® ²",
- "ãģij ãĤĮ",
- "Ġunderm ine",
- "ĠArch ives",
- "v ana",
- "mer cial",
- "M C",
- "Ġcast e",
- "п ÑĢ",
- "Ġlegisl ators",
- "ul ators",
- "ên io",
- "Ġëį °ë",
- "ĠÑħоÑĤ иÑĤе",
- "Ġн ек",
- "Ġs urn",
- "Ġcons ci",
- "ĠP OW",
- "Ġcul inary",
- "ĠK AT",
- "ĠFol ks",
- "Ñĭв аем",
- "Ġв ок",
- "ãģij ãĤĭ",
- "s ervice",
- "pt s",
- "Ġпоб ед",
- "æĺ¯ åķĬ",
- "Ġt ents",
- "Ġn ord",
- "ST E",
- "Ġrepublic an",
- "Ġwy k",
- "Ġmin ions",
- "èĻ ķ",
- "Ġmem ang",
- "j est",
- "Ġcompar ative",
- "Ġty le",
- "car bon",
- "bed ingt",
- "ks en",
- "Ġneg ativity",
- "Ġsjäl v",
- "Ġd ú",
- "æīĢ æľī",
- "Ġrec alled",
- "c ra",
- "ĠT ada",
- "ĠÑĢÑĥ ки",
- "ĠопÑĢед ел",
- "Ġproc rast",
- "Ġjog os",
- "ĠO o",
- "ĠHe arts",
- "Ġé ch",
- "Ġksi Äħż",
- "Ġco arse",
- "ĠT ube",
- "ĠG reens",
- "Ġé n",
- "Ġdumb bell",
- "ĠÑĤ и",
- "Ġquer er",
- "ا ØŃ",
- "Ïĥ ει",
- "ĠпÑĢав илÑĮно",
- "Ġп ап",
- "Ġcomp ra",
- "Ġt ér",
- "ĠAnt es",
- "Ġoptim um",
- "Ġbisc uit",
- "κ ι",
- "acz ego",
- "Ġìĭľê°Ħ ìĿ´",
- "ĠMar ines",
- "ver o",
- "Ġvacc inations",
- "Ġpet ty",
- "rit ers",
- "Ġа л",
- "count ry",
- "Ġcoun ters",
- "Ġattend ant",
- "ĠH ui",
- "ãģ¨ãģĦãģĨãģĵãģ¨ ãģ§",
- "ck a",
- "ÑģÑĤвен нÑĭй",
- "gu y",
- "Ġtrick ed",
- "ĠR ED",
- "Ġthr illing",
- "ÏĢο ι",
- "Ġpig gy",
- "Ġan unci",
- "OR TER",
- "ĠVal ue",
- "Ġr ond",
- "ĠA DA",
- "Ġpos er",
- "h ores",
- "ĠR oland",
- "ĵ ¯",
- "Ġno ir",
- "Ġש ×IJ×",
- "ë° ľ",
- "iem and",
- "ĠпоÑĤ еÑĢ",
- "ê³ ³",
- "Ġê± ±",
- "Ġformat ting",
- "ĠL ed",
- "è§Ģ çľ¾",
- "Ġkill ers",
- "ĠÄij ấy",
- "Ġha ar",
- "ag ain",
- "! > [",
- "min ster",
- "Ġв ли",
- "Ġident ifier",
- "ĠLamb da",
- "Ġtr os",
- "Ġflaw less",
- "Ġdetriment al",
- "Ġbun ları",
- "W ar",
- "Ġreg ião",
- "羣çļĦ æĺ¯",
- "ĠB ike",
- "cess ors",
- "Ġc ùng",
- "ĠR N",
- "Ġê½ ĥ",
- "Ġküç ük",
- "ĠBegin ning",
- "íĺ ¸ë",
- "Ġge we",
- "Ġden ote",
- "ĠAlber to",
- "Ġprob iot",
- "Ġo de",
- "Ġmol ar",
- "Ġburst ing",
- "ass umed",
- "Ġfoot prints",
- "ved a",
- "Ġstero ids",
- "Ġfl aming",
- "ĠE ller",
- "Ġerk ennen",
- "ät zen",
- "Ġlife cycle",
- "ĠD OU",
- "ĠK arena",
- "ĠGuer ra",
- "è¿ĺ æĺ¯",
- "Ġsin ister",
- "Ġpod éis",
- "Ġpar ab",
- "Ġok o",
- "Ġmat éri",
- "Ġcar ic",
- "son aro",
- "Ġpratic amente",
- "ÑĥÑģ а",
- "Ġcomun que",
- "Ġvig ilant",
- "Ġreg imes",
- "ĠShoot ing",
- "Ġra ids",
- "ĠN ora",
- "ĠW ieder",
- "m ens",
- "ĠÑģ од",
- "Ġê²½ìļ° ìĹIJëĬĶ",
- "Ġв Ñħод",
- "Ġaut obi",
- "ĠS chn",
- "ĠRob bie",
- "ĠF itness",
- "Ġкон ÑĦ",
- "Ġpeng uin",
- "моÑĤÑĢ Ñı",
- "Ġми ним",
- "play s",
- "Ġdeleg ates",
- "M er",
- "Ġsist em",
- "ĠMicha els",
- "m ale",
- "ا ع",
- "Ġcá ch",
- "ĠH ä",
- "Ġ×Ļ ×ķ×ĵ×¢",
- "Ġsuper power",
- "Ġstr on",
- "Ġro ver",
- "Ġdé pend",
- "éĻ ³",
- "Ġret iring",
- "Ġvamp ires",
- "Ġmer de",
- "ĠCh anging",
- "Ġt ame",
- "Ġspokes person",
- "Ġc ay",
- "Ġfl irting",
- "ĠGr ö",
- "Ġw är",
- "Ġwy b",
- "Ġcoe ur",
- "ạ nh",
- "ĠìĻĢ ìĦľ",
- "Ġconna is",
- "ĠHundred s",
- "ĠBe a",
- "Ġα ÏĢ",
- "pr uch",
- "Ġsocied ade",
- "ĠWh ilst",
- "ĠK ait",
- "esp ace",
- "Ġch ia",
- "ĠEr m",
- "Ġë°Ķ ê¿",
- "Ġf ences",
- "ĠM ortal",
- "ê² ģ",
- "Ġг ÑĢаÑĦ",
- "ĠHom eland",
- "ĠJ UN",
- "is st",
- "Ġpar lar",
- "Ġsport y",
- "é o",
- "Ġdeep en",
- "ĠBeh avior",
- "éĢ ı",
- "åĵĪåĵĪ åĵĪ",
- "Ġer rand",
- "Ġrot ary",
- "ĠWell ington",
- "W ind",
- "Ġmes ela",
- "ả ng",
- "iend e",
- "Ġex cell",
- "ĠGen ius",
- "ĠEdu ardo",
- "æľī 人",
- "ĠÅŁ unu",
- "ĠÄ° stanbul",
- "Ġprod uto",
- "Ġ ãħİãħİ",
- "O FF",
- "Ġwoll t",
- "çĪ Ĩ",
- "Ġëī´ì Ĭ¤",
- "Ġl ass",
- "Ġher tz",
- "Ġar omatic",
- "Ġзв он",
- "Ġaut oc",
- "ĠL ust",
- "Ġ11 2",
- "ĠÎ Ĺ",
- "Ġreview ers",
- "Ġrecept ive",
- "å°į äºĨ",
- "â nd",
- "og lo",
- "ĠìķĦëĭ Ļ",
- "Ġn go",
- "Ñĸ ÑĤи",
- "Ã¥ t",
- "con o",
- "Ġtek rar",
- "Ġ주 ê³ł",
- "Ġgel miÅŁ",
- "Ġbed time",
- "ĠAr gh",
- "AD A",
- "ĠгоÑĢод а",
- "ĠÄ ĩ",
- "Ġall iances",
- "g iggling",
- "Ġyer de",
- "Ġsp ies",
- "Ġg utes",
- "ç i",
- "Ġallt id",
- "ĠL ah",
- "ŀ IJë",
- "Ġdo kÅĤad",
- "ÙĪ ÙĬ",
- "Ġtoxic ity",
- "Ġcancell ation",
- "Ġ195 8",
- "d ro",
- "Ġìŀij ìĿĢ",
- "ĠMotor ola",
- "Ġmult in",
- "Ġenthusi asts",
- "ĠM ighty",
- "ĠCoc onut",
- ": ãĢĮ",
- "ĠPict ures",
- "Ġsang re",
- "Ġbl inking",
- "ol esome",
- "ĠìĬ¤íĥĢ ìĿ¼",
- "F P",
- "Ġboom ing",
- "ĠдеÑģÑı ÑĤ",
- "Ġr atchet",
- "Ġtim elines",
- "len ess",
- "Ġc ages",
- "ĠGood night",
- "omet imes",
- "Ġc unning",
- "ĠR isk",
- "ul ed",
- "d ade",
- "Ġpr ata",
- "Ġgust arÃŃa",
- "am us",
- "ĠJin ping",
- "Ġest rut",
- "Ġdescob rir",
- "ĠM Äģ",
- "ĠAll an",
- "Ġ åĪĨ",
- "Ġ×ľ× §",
- "Ġpres erv",
- "ĠStraw berry",
- "Ä ı",
- "L u",
- "Ġk ro",
- "ĠRep orts",
- "ìħĶ ìķ¼",
- "Ġval t",
- "Ġpouv ait",
- "Ġapp ar",
- "ĠB one",
- "Ġprefer ably",
- "ĠRep ública",
- "å°± åĪ°",
- "Ġher zlich",
- "Ġchim ney",
- "Ġç ev",
- "Ġvis as",
- "Ġver r",
- "Ġcultiv ation",
- "ĠArmen ia",
- "Ġвд ÑĢÑĥг",
- "Ġcock ro",
- "retch ed",
- "art z",
- "ĠлÑİд Ñıм",
- "ĠpolÃŃt icas",
- "ĠP anz",
- "ĠA KA",
- "ĠëĪ Į룬",
- "Ġer ro",
- "Ġcam per",
- "Ġ10 2",
- "ठ¸",
- "d one",
- "Ġho ard",
- "ĠÐŁÐ¾ÑĤ ом",
- "je ong",
- "Ġdest a",
- "p ak",
- "Ġin im",
- "Ġgrow ers",
- "ĠMess age",
- "Ġele ctor",
- "eng age",
- "ĠFor bes",
- "ĠCincinn ati",
- "Ġdiffé rence",
- "d f",
- "Ġsp ar",
- "Ġawait s",
- "ĠUSS R",
- "ĠR ising",
- "ĠHo ÅŁ",
- "Ġfoot ing",
- "Ġcond iciones",
- "ÑĤоÑĢ ов",
- "Ġclin ician",
- "ĠDisk uss",
- "å£ ĵ",
- "ר ×Ĵ",
- "× ¥",
- "ite it",
- "g ren",
- "Ġchar isma",
- "Ġle uke",
- "Ġirrit ating",
- "Ġcir ca",
- "ĠRhod es",
- "Ġp ior",
- "Ġhandic ap",
- "roy able",
- "Ġv ull",
- "O G",
- "Ġin ÃŃcio",
- "ier i",
- "Ġspl ashing",
- "Ġdem ise",
- "Ġassist ir",
- "Ñĩ ÑĤо",
- "Ġcover t",
- "ĠG ud",
- "ภī",
- "kl är",
- "ĠìŀIJ 꾸",
- "Ġver ändert",
- "ĠR EM",
- "ĠCon ven",
- "at ge",
- "Ġpierws ze",
- "Ġcler gy",
- "ling ton",
- "l iv",
- "V PN",
- "ĠÑģ ожал",
- "ĠH ate",
- "ãģ¨ ãģĵãĤį",
- "ÏĨ ο",
- "ĠResp ons",
- "оз д",
- "Ġet mek",
- "Ġchem in",
- "Ùħ Ø©",
- "Ġê°Ģ 족",
- "T re",
- "Ġum as",
- "ĠBur ton",
- "Ġpatri arch",
- "ĠSmithson ian",
- "¥ ĺ",
- "M oon",
- "A ir",
- "Ġmed ios",
- "Ġer aser",
- "Ġwoll ten",
- "Ġpare il",
- "ĠBill ie",
- "æĬ ½",
- "еÑĢÑĤ в",
- "Ġparl ament",
- "Ġag ony",
- "ĠQU E",
- "sequ ently",
- "An other",
- "ĠWh ew",
- "ĠAnn ual",
- "Ġse ben",
- "ìĥģ ìĿĦ",
- "val ues",
- "ŀľë §Į",
- "Ġsin on",
- "ere al",
- "ĠEn light",
- "ĠChem istry",
- "ĠCatal unya",
- "Ġdoct r",
- "ant on",
- "Ġst uk",
- "ĠPl ate",
- "ĠKardash ian",
- "Ġfil os",
- "ĠW et",
- "Ġпоп ÑĭÑĤ",
- "Ġunknown s",
- "ĠSch on",
- "ĠBald win",
- "Ġtelescop es",
- "ĠG ucci",
- "ox ide",
- "ĠConserv ative",
- "ìĦ± ìĿĦ",
- "Ġhina us",
- "P ower",
- "Ġê±´ ê°ķ",
- "Ġprev ail",
- "orm an",
- "m achine",
- "Ġ194 6",
- "Ġun bel",
- "Ġsch aut",
- "Ġp iel",
- "e enth",
- "Ġobject ively",
- "Ġch akra",
- "aud io",
- "Ġch icos",
- "ĠV ault",
- "å° Ī",
- "Ġmedic inal",
- "ĠT ail",
- "Wh ile",
- "Ġas phalt",
- "Ġfro ze",
- "ĠE K",
- "unch ing",
- "n osis",
- "20 15",
- "ĠG ri",
- "Ġodd ly",
- "ĠM är",
- "ĠA eg",
- "c olo",
- "P ar",
- "Ġëĵ¤ ìĸ´ë",
- "Ġv inden",
- "ĠO VER",
- "Ġ iced",
- "Ġsc orp",
- "Ġha c",
- "qual ified",
- "ĠÑĥвид еÑĤÑĮ",
- "erm o",
- "H EN",
- "Ġso i",
- "Ġmulti ples",
- "Ġlay outs",
- "Ġblind ness",
- "ĠB owser",
- "Ġпод ÑĤ",
- "ĠÃ İ",
- "vention al",
- "Ġm ata",
- "mad ı",
- "Ġge ez",
- "Ġcad ence",
- "Ġważ ne",
- "ĠChrist ie",
- "ven ge",
- "C all",
- "Ġturn around",
- "Ġblo b",
- "ĠЯ к",
- "ĠVoice over",
- "Ġper il",
- "ĠJa ime",
- "ĠH OY",
- "l ane",
- "Ġse bel",
- "ĠDu o",
- "ĠHistor ical",
- "Ġd ni",
- "Ġg ema",
- "y k",
- "Ġsab em",
- "ắ ng",
- "Ġv ars",
- "ĠRon nie",
- "ĠRon aldo",
- "ĠPer què",
- "ns inn",
- "h air",
- "Ġrelent less",
- "Ġl yn",
- "Ġtravel er",
- "æĢİ麼 äºĨ",
- "n ine",
- "Ġant im",
- "Ġì¼ Ģ",
- "Ġsnow ball",
- "ĠÑħаÑĢ акÑĤеÑĢ",
- "Ġintern s",
- "Ġconstitu ency",
- "ĠÐĿ ам",
- "׾ ׾",
- "V EL",
- "Ġvikt igt",
- "Ġap oyo",
- "ÙĦ ب",
- "Ġj ard",
- "Ġheight ened",
- "ÑĢо ÑģÑĤ",
- "ĠSM ITH",
- "Ġдел а",
- "Ġrepair ing",
- "Ġr igt",
- "ĠShe ikh",
- "ĠBrit ney",
- "Ġevery time",
- "Ġadvent urous",
- "oc key",
- "er nt",
- "Ġat aque",
- "ĠAltern atively",
- "e ffect",
- "Ġpalav ras",
- "ĠElli ott",
- "Ġréuss i",
- "Ġhypert ension",
- "ĠMan ual",
- "Ġproph etic",
- "Ġhand c",
- "ÑĮ е",
- "Ġref rain",
- "ĠSqu id",
- "ìŀ ¡",
- "Ġком ан",
- "äll en",
- "Ġlleg ó",
- "Ġbas h",
- "ion y",
- "ĠÑģк лад",
- "Ġк аб",
- "Ġcare less",
- "ĠP ool",
- "Ġtr ás",
- "Ġfil s",
- "ĠSch r",
- "Ġsp rawd",
- "ĠMon aten",
- "Ġunfor gettable",
- "ĠCott on",
- "Ġinconven ient",
- "ĠR X",
- "or is",
- "Ġhum bled",
- "ת ×Ĺ",
- "ĠØ¢ Ù¾",
- "Ġincre ÃŃ",
- "ĠKomment are",
- "èĪ Ĵ",
- "r ación",
- "Ġv antage",
- "ĠSe al",
- "ĠìĿ´ 거를",
- "Ġjou e",
- "ãģĿãģĨ ãģ§ãģĻãģŃ",
- "Ġìĺ¤ë ŀĺ",
- "ĠиÑģп ÑĭÑĤ",
- "ob en",
- "Ġgr ate",
- "Ġcontro le",
- "ĠPer cy",
- "ÅĤ ada",
- "Ġsimult aneous",
- "Ġprot oty",
- "ĠgroÃŁ er",
- "Ġbew usst",
- "iniz i",
- "Ġpass ieren",
- "ĠHapp iness",
- "åī ĩ",
- "sh i",
- "ge ht",
- "Ġstation ed",
- "ĠErgeb nis",
- "Ġdirect amente",
- "Ġsurv ives",
- "Ġperson es",
- "BER G",
- "Ġvom iting",
- "Ġconhe cer",
- "Ġad jour",
- "ĠCiv ic",
- "pe i",
- "bur st",
- "Ġëĭ¤ ëĭĪ",
- "é ı",
- "Ġsl ed",
- "Ġplataform a",
- "ĠS ect",
- "ĠDe fin",
- "çĻ» éĮ²",
- "én om",
- "chn et",
- "Ġprofit ability",
- "Ġerre icht",
- "á»ı i",
- "c ation",
- "Ġì§Ģ ê¸",
- "Ġperd re",
- "Ġfel ony",
- "Ġ195 7",
- "æĪij å¾Ī",
- "Ġunsuccess ful",
- "Ġnag yon",
- "Ġelastic ity",
- "Ġfac ade",
- "Ġearth ly",
- "ĠамеÑĢик ан",
- "Ġcon n",
- "c la",
- "D u",
- "Ġpolit iques",
- "Ġhal o",
- "iant es",
- "Ġмо ей",
- "ãĥ³ ãĥī",
- "ton es",
- "el ier",
- "è® ļ",
- "ht aking",
- "Ġwicht ige",
- "Ġan no",
- "ĠL ok",
- "ill ions",
- "Ġv iver",
- "Ġsol chen",
- "Ġsu f",
- "ĠSal z",
- "ĠN vidia",
- "z uge",
- "ĠSp ike",
- "V ideo",
- "Ġtw or",
- "ĠA la",
- "èij ī",
- "Ġh anya",
- "ĠAd m",
- "ìĿ µ",
- "ĠPatient en",
- "ĠOn ion",
- "ĠKo be",
- "ĠSc ene",
- "ĠR ash",
- "æ¨ Ļ",
- "ÑĢа ÑģÑĤ",
- "ist ani",
- "Gen eral",
- "le ye",
- "imb ap",
- "Ġconce aled",
- "ĠFr idays",
- "ĠW ool",
- "Ġнов ÑĭÑħ",
- "ش ر",
- "Ġê²° ê³¼",
- "Ġjed och",
- "´ìĭ ľ",
- "ĵ¤ ëıĦ",
- "Ġìŀ¥ ëĤľ",
- "uk t",
- "L ou",
- "Ġ먹 ìĸ´",
- "ĠEx pect",
- "Ġдом ой",
- "Ġirrespons ible",
- "Ġac erca",
- "ĠZ ust",
- "ר ×ĺ",
- "U I",
- "Ġyout ubers",
- "ĠPos itive",
- "Ġsoci oe",
- "Ġsn atch",
- "èĥ Į",
- "Ġrefresh ed",
- "Ġnom inations",
- "ĠP att",
- "Ġobsol ete",
- "Ġdem iÅŁ",
- "åı ¤",
- "orm uÅŁ",
- "ĠìĨĶì§ģ íŀĪ",
- "Ġf la",
- "Ġcra ziest",
- "ĠZ ie",
- "ĠT ú",
- "z ep",
- "ic em",
- "Ġë©ĭ ìŀĪ",
- "Ġcyn ical",
- "ãģĿ ãĤĵãģª",
- "Ġt resp",
- "Ġcra z",
- "Õ¥ Õ",
- "Ġne lle",
- "Ġm ph",
- "ĠN ered",
- "ĠK ob",
- "ĠE ck",
- "¨¸ ëĭĪ",
- "J an",
- "ĠТ огда",
- "Ġde ci",
- "ĠV og",
- "Ġbubb ling",
- "éĢ Ģ",
- "ú a",
- "Ġproduct os",
- "iber al",
- "Ġrepl icated",
- "ĠImp rove",
- "ill ary",
- "C ha",
- "Ġré du",
- "ĥIJ íķĺë©´",
- "Ġcon not",
- "ĠK rit",
- "ĠдÑĥÑħ ов",
- "Ġtread mill",
- "ĠP W",
- "Ġзов ÑĥÑĤ",
- "Ġcl ams",
- "Ġdra fting",
- "Ġ195 6",
- "un ta",
- "Ġexpend itures",
- "ĠHoo ver",
- "W OO",
- "ÑĪе е",
- "Ġded uction",
- "mon ary",
- "Ġreci b",
- "Ġpo vo",
- "Ġëį Ķë",
- "ĠP AL",
- "ĠBl ow",
- "Ġwy p",
- "Ġdest ac",
- "de al",
- "Gra eme",
- "Ġnécess aire",
- "Ġdamn ed",
- "Ġ19 38",
- "Ġìĭ¤ ìłľë¡ľ",
- "Ġtro op",
- "Ġinsight ful",
- "ĠT J",
- "ĠоÑģ в",
- "Ġf idelity",
- "ĠSk ip",
- "ĠMay o",
- "ë§ Ŀ",
- "app e",
- "Ġbl as",
- "ĠW Y",
- "ĠG N",
- "ct ar",
- "S u",
- "Ġcu ent",
- "he ws",
- "Ġcorps es",
- "A bs",
- "Ġwaste water",
- "Ġc iek",
- "ĠOn u",
- "Ġexplos ives",
- "Ġar ma",
- "ĠSTEP HAN",
- "polit ik",
- "ĠOs aka",
- "ta ÅĤ",
- "Ġyap ıyor",
- "Ġiz quier",
- "Ġbele za",
- "ĠWy att",
- "åIJ ¸",
- "Ġsu k",
- "Ġspec jal",
- "Ġdan ke",
- "wh istle",
- "ĠfÃŃs ica",
- "ĠHar riet",
- "ĠìķĦ íĮĮ",
- "Ġwill kommen",
- "ip ing",
- "ĠÑģмоÑĤÑĢ иÑĤе",
- "Ġмож еÑĪÑĮ",
- "Ġinacc urate",
- "Ġarrog ance",
- "ĠRem o",
- "γ ά",
- "ass ed",
- "Ġdeliver ies",
- "Ġst inky",
- "ĠпеÑĢ еж",
- "j ay",
- "Ġtrans itional",
- "Ġr ere",
- "ĠNGO s",
- "ĠAT M",
- "خ ت",
- "i ology",
- "Ġв лад",
- "Ġsch me",
- "ĠSh ine",
- "ìķ ¡",
- "p ants",
- "Ġser ge",
- "Ġsen hor",
- "Ġab duct",
- "ĠBry ant",
- "V ES",
- "Ġawak ened",
- "ĠL az",
- "rop olis",
- "ĠLa o",
- "è¾Ľ èĭ¦",
- "Ġvill a",
- "Ġsumm ers",
- "Ġent hal",
- "Ġ194 9",
- "V ia",
- "Ġìĸ´ì ¨",
- "Ġtend on",
- "Ġviol et",
- "Ġintellect ually",
- "Ġboun ced",
- "ara us",
- "Ġ19 19",
- "Ġvra ag",
- "Ġsp el",
- "ĠSch war",
- "Sc ott",
- "ĠInd o",
- "Ġë§ Ŀ",
- "Ġcanon ical",
- "ĠI KE",
- "Ġthat ÃŃs",
- "Ġme llan",
- "æ¯ Ĵ",
- "ig mat",
- "C ould",
- "... ?)",
- "Ġfo arte",
- "ĠKum ar",
- "rend o",
- "Ġél é",
- "à ´",
- "val uation",
- "c ases",
- "Ġintuit ively",
- "h ong",
- "ett ed",
- "Ġsou ven",
- "Ġmor b",
- "Ġc ors",
- "ĠN V",
- "ĠHas an",
- "æĥħ åĨµ",
- "ie ved",
- "Ġì§Ģê¸Ī ìĿĢ",
- "Ġdum pling",
- "Ġcontr ôle",
- "Ġambigu ity",
- "æ©Ł æľĥ",
- "Ġco g",
- "ĠScript ures",
- "Ġc ai",
- "Ġbe ver",
- "大家 éĥ½",
- "Ġhu is",
- "Ġa ime",
- "Ġerkl ären",
- "ĠL M",
- "ĠF ey",
- "éļ ¾",
- "ற த",
- "Ġsuper vised",
- "Ġje we",
- "s pl",
- "ĠÑĨенÑĤ ÑĢ",
- "Ġcoll isions",
- "ÙĦ Ùģ",
- "ĠHog warts",
- "ĠDur ham",
- "×ķ× £",
- "Ġphosph ate",
- "Ġoverse e",
- "Ġinspect ions",
- "Ġbr inc",
- "ĠZ ak",
- "Ġpay off",
- "Ġch aud",
- "ĠHung er",
- "ã os",
- "v ir",
- "Ġf iance",
- "Ġb oug",
- "l ived",
- "c ry",
- "åĽŀ ä¾Ĩ",
- "Ġjoint ly",
- "Ġgirl friends",
- "ĠNe xus",
- "¦¬ ê²łìĬµëĭĪëĭ¤",
- "ĠK wang",
- "åĵĪ åĽī",
- "å§ ij",
- "ÅĤ ÄĻ",
- "ĠN eden",
- "ie ce",
- "Ġins erting",
- "æŁ ĵ",
- "ĠM ummy",
- "ĠGlo be",
- "Ġle e",
- "Ġg erman",
- "Ġcre ams",
- "ach o",
- "Ġch Æ°a",
- "ĠGal ile",
- "Ġfür s",
- "Ġest iver",
- "c idos",
- "Christ ian",
- "Ġlors qu",
- "Ġcut est",
- "v ale",
- "ĠкÑĢ еп",
- "Ġw ary",
- "Ġslic ing",
- "Ġesper ando",
- "ĠV ander",
- "ĠDe ixa",
- "Ġ195 4",
- "Ġmów iÄħ",
- "Ñĸ ÑĶ",
- "Ġtool ing",
- "Ġrest or",
- "Ġpos ición",
- "Ġintent ar",
- "ĠAp ache",
- "OU L",
- "ĠÙĪ ب",
- "Ġmat ière",
- "ãĥ¼ ãĤĵ",
- "Ġl inen",
- "Ġestrat ég",
- "ĠMut ta",
- "é¡ ¯",
- "è¡Į äºĨ",
- "Ġpart ing",
- "Ġminim izing",
- "Ġapp rendre",
- "æľ Ŀ",
- "Ġан глий",
- "ĠDo o",
- "ĠFire fox",
- "c ómo",
- "Ġge opolit",
- "Ġmak an",
- "Ġmog elijk",
- "ĠÏĢε Ïģι",
- "Ġcá» ©",
- "Ġinstall er",
- "Ġdib uj",
- "ĠHe ath",
- "lo op",
- "ĠBro ken",
- "HY UN",
- "sh elf",
- "Ġf izer",
- "Ġenh ances",
- "ä¾ĭ ãģĪãģ°",
- "Ġдо ÑģÑĤи",
- "ĠP UB",
- "ĠKolleg in",
- "Ġatt ained",
- "Ä ¾",
- "Ġmist ress",
- "ĠOft entimes",
- "×ŀ ×Ļ×Ŀ",
- "Ġbe we",
- "ĠS ora",
- "ra uen",
- "ba um",
- "Ġroll ers",
- "Ġm ering",
- "ĠP AC",
- "Ġн Ñĸ",
- "ĠRép ublique",
- "ĠÑĤ ÑĢав",
- "ĠV anguard",
- "uc iones",
- "Ġ무ë ĮĢ",
- "Ġg our",
- "¯ ¤",
- "ĠÏ ī",
- "Ġsa una",
- "Ġpe ine",
- "ĠVal erie",
- "ĠS ikh",
- "fend imiz",
- "ber o",
- "ĠÑĩ и",
- "Ġdo ÅĽwiad",
- "ĠE uros",
- "Ġcomment aires",
- "Ġtwe aks",
- "ĠF aster",
- "ĠÑĢаÑģ к",
- "Ġprogress ively",
- "ĠE uch",
- "bor o",
- "ĠIng red",
- "C ap",
- "Ġun check",
- "Ġìĺ¤ë ¥¸",
- "Ġw re",
- "ĠF T",
- "ör ung",
- "Ġmemor ized",
- "ĠD inner",
- "ĠP hew",
- "ou bl",
- "Ġput a",
- "Ġadm its",
- "ез де",
- "op od",
- "Ġpand a",
- "Ġhing es",
- "ci pe",
- "Ġtrans act",
- "Ġpod ia",
- "Ġp ics",
- "Ġcriter ion",
- "ĠOrchest ra",
- "ĠBl og",
- "Ġsolem n",
- "ĠPix ar",
- "Th ree",
- "Ġв низ",
- "ĠVol unte",
- "ĠSav age",
- "ĠPV C",
- "ĠC af",
- "Ġwy kon",
- "Ġgrad ers",
- "Ġcr ouch",
- "Ġcl iche",
- "Ġsoy beans",
- "ĠM UR",
- "ĠGonz alez",
- "ĠM imi",
- "ĠBol sonaro",
- "Ġdi aphrag",
- "Ġbil ang",
- "ëIJĺ ëĬĶ",
- "éĤ£ æĪijåĢij",
- "Ġregul ating",
- "M c",
- "J udge",
- "Ġн ож",
- "Ġjak Äħ",
- "ites se",
- "ĠW ij",
- "Ġl ata",
- "gro aning",
- "POS ING",
- "Ġ×IJ×ķת ×ķ",
- "Ġha ga",
- "Ġground ing",
- "Ġviol ently",
- "Ġt ills",
- "Ġeng ag",
- "ĠHo llow",
- "Ġпоп ÑĥлÑıÑĢ",
- "Ġw prowad",
- "Ġrepl aces",
- "Ġfluores cent",
- "urg ical",
- "igg ly",
- "ĠTrad itional",
- "t te",
- "ĠÙĦ Ùĩ",
- "Ġphosph orus",
- "Ġapr on",
- "ĠWat ers",
- "ĠK ultur",
- "ав ай",
- "Ġol ives",
- "Ġ×Ķ×IJ× ľ",
- "Ġteil weise",
- "Ġsen cill",
- "Ġprend s",
- "Ġnarr ower",
- "Ġj ätte",
- "ĠInformation en",
- "ìĥģ ìĿ´",
- "Ġstar ve",
- "Ġfr ick",
- "ĠBe weg",
- "ठ²",
- "Ġdolph in",
- "ĠLAUGH TER",
- "ĠINTER VIE",
- "åĶ ī",
- "Ġyan lÄ±ÅŁ",
- "Ġtor pedo",
- "Ġshort ages",
- "ìĿ´ë ĵľ",
- "ıld ı",
- "Ġp aws",
- "Ġo zone",
- "Ġcultiv ated",
- "ĠF ot",
- "Ġnot or",
- "н оз",
- "Ġко ÑĪ",
- "Ġtouch screen",
- "ĠAll y",
- "æľĢ è¿ij",
- "Ġ맼ìŀĪ ìĸ´ìļĶ",
- "ĠС еÑĢ",
- "Ġв полне",
- "Ġpap rika",
- "ĠDust in",
- "Ġefect o",
- "Ġop ini",
- "Ġmu ut",
- "Ġhá»į c",
- "Ġinter ject",
- "ÄĻ t",
- "Ġbut ts",
- "ure z",
- "ĠP ike",
- "ĠH ok",
- "ĠGu inea",
- "ĠCath edral",
- "Ġ14 00",
- "C ra",
- "+ ,",
- "ë§ Ľ",
- "³´ë ıĦë¡Ŀ",
- "aby rin",
- "Ġvide og",
- "Ġо ÑĢÑĥж",
- "Ġu ž",
- "Ġbus cando",
- "ĠAss istance",
- "éĻ ½",
- "Ġmel hores",
- "ì¡ ´",
- "Ġëģ ¼",
- "ĠR J",
- "Ġت Ùħ",
- "Ġo min",
- "Ġmotor cycles",
- "ĠS app",
- "Ġsupply ing",
- "ĠAl gun",
- "Ġaer ospace",
- "×¢ ׾",
- "oc cup",
- "le ist",
- "Ġê±° ëĬĶ",
- "Ġcomplet a",
- "b res",
- "! (",
- "ĠÐŁÑĢ ед",
- "Ġdisadvant aged",
- "ĠAtt end",
- "ĠJud ah",
- "á»ĭ ch",
- "yl ene",
- "act ly",
- "Ġset ups",
- "Ġammon ia",
- "ĠSchwe iz",
- "ĠSh ame",
- "Ġband e",
- "ĠF uel",
- "Ġtroubles ome",
- "Ġnum ero",
- "ĠM OM",
- "ĠпÑĢед лаг",
- "ment ioned",
- "ĠболÑĮÑĪ ое",
- "ĠVikt or",
- "ĠSty les",
- "Ġcruc ified",
- "ructure d",
- "en viron",
- "Ġmor als",
- "Ġmed itating",
- "Ġax ial",
- "is ance",
- "ĠAb st",
- "G reen",
- "Ġê± ´ì",
- "Ġquad rant",
- "Ġper gi",
- "Ġcamer aman",
- "ĠSe qu",
- "Ġpa used",
- "ĠLa ughing",
- "ê· Ģ",
- "? ..",
- "ĠÅ» e",
- "Ġpermit ir",
- "Ġdetect ors",
- "ĠH UD",
- "av al",
- "ĠìĹ¬ê¸° ê¹Įì§Ģ",
- "Ġh ubs",
- "Ġbest immt",
- "ĠбÑĥдеÑĤ е",
- "INTER POSING",
- "Ġten gan",
- "Ġcra ve",
- "ĠBundes regierung",
- "ĠBlo ody",
- "Ġus ability",
- "ĠE as",
- "ĠÄijá»Ļ ng",
- "Ġ195 5",
- "Ġkrie gen",
- "Ġhabit ual",
- "Ġessential s",
- "rim inal",
- "Ġroomm ates",
- "éĤ£ å°±",
- "ĠпеÑĢе Ñħод",
- "Ġng hi",
- "Ġmen ing",
- "ĠSym phony",
- "ĠH ug",
- "ag gi",
- "Ġw ied",
- "Ġmit ad",
- "ãģ£ãģ¦ ãģĦãģĨ",
- "te enth",
- "ida Äĩ",
- "S ave",
- "Ġrob iÄĩ",
- "Ġboun ces",
- "° ĸìĹIJ",
- "st ars",
- "Ġprag matic",
- "Ġcogn ition",
- "Ġwra pper",
- "Ġw arten",
- "ad h",
- "Ġpens a",
- "ĠHert z",
- "Ġn ÄĽ",
- "ĠRe id",
- "ĠPC s",
- "ĠMo le",
- "Ġ.. ...",
- "Ġpre cio",
- "ĠChampions hips",
- "ê°Ģë Ŀ½",
- "Ġv ér",
- "Ġcorrid ors",
- "ĠElect ronic",
- "S l",
- "Ġа ле",
- "Ġoverth row",
- "Ġk abul",
- "ĠR ES",
- "ĠCyber punk",
- "ог од",
- "ĠÐĿ ав",
- "Ġw an",
- "Ġmanifest ations",
- "Ġcual es",
- "ĠW ise",
- "ĠLös ung",
- "Ġex fol",
- "Ġearn s",
- "ÑĥÑģÑĤ иÑĤÑĮ",
- "Ġsa pp",
- "ĠBra un",
- "ĠBRAND ON",
- "ì¹ Ļ",
- "Ġs ano",
- "ĠF EL",
- "Ñĭв айÑĤеÑģÑĮ",
- "ожд ениÑı",
- "Ġse wn",
- "F un",
- "Ġrecipro cal",
- "Ġexpans ive",
- "ĠTra ffic",
- "Ġktóre go",
- "ĠÙĪ س",
- "æĺ ¥",
- "Ġë¹ ¨",
- "pro ve",
- "ig are",
- "Ġlo h",
- "Ø§Ø ¶",
- "H ope",
- "Ġdevote es",
- "ĠG om",
- "Ġste als",
- "ĠU ms",
- "ĠTw ice",
- "ãĤ ²",
- "iy im",
- "Ġrhythm ic",
- "ĠV orte",
- "Ġpref ix",
- "om ination",
- "Ġdat o",
- "Ġcust ard",
- "ĠVO ICE",
- "å· ŀ",
- "Ġmen y",
- "ist ors",
- "Ġíĺ ij",
- "ĠìĤ´ì ķĦ",
- "Ġíĥ Ħ",
- "Ġk ort",
- "Ġab a",
- "ĠV era",
- "ep y",
- "Ġì¹´ë©Ķë Ŀ¼",
- "Ġsubmer ged",
- "ĠC lock",
- "Ġthumbna ils",
- "Ġbo ast",
- "ĠF are",
- "!! ]",
- "ĠÅĽ m",
- "Ġkaik ki",
- "ĠTechn ologies",
- "ìĻ ¸",
- "ãĥ Ĵ",
- "иÑĤ ай",
- "å°ı æĻĤ",
- "Ġа ÑĤ",
- "Ġkn obs",
- "Ġre icht",
- "ượ ng",
- "gl io",
- "Ġ맼 ìĿ´",
- "ê°IJ ìĿĦ",
- "Ġjot ka",
- "ĠHand y",
- "ĠHab en",
- "n ous",
- "Ġin land",
- "Ġam azon",
- "ho oting",
- "S L",
- "Ġle isten",
- "~ \"",
- "Ġprov oke",
- "ĠTw ist",
- "Ġ×ij× Ĺ",
- "Ġdepart ed",
- "ê° ľë¥¼",
- "Ġk onse",
- "ĠCar wyn",
- "íķĺ ìĭł",
- "ident al",
- "ES CO",
- "Ġt teokbokki",
- "Ġdiz endo",
- "ç· ´",
- "ınd aki",
- "imas u",
- "af ar",
- "Ġland fill",
- "Ġcorrect ing",
- "Ġcle ars",
- "ĠNum mer",
- "H AM",
- "Ġcart ridges",
- "ĠDies el",
- "p aced",
- "Ġobl iv",
- "Ġmoy ens",
- "ĠSin ne",
- "ĠPre is",
- "il iz",
- "ĠÑģм ож",
- "Ġbroad en",
- "ä»ĸ æĺ¯",
- "x es",
- "Ġcarbohyd rate",
- "íĺ ¹",
- "se ok",
- "Ġecho es",
- "Ġc ess",
- "ë° Ķ",
- "Ġб изнеÑģ",
- "Ġllam ado",
- "Ġess ent",
- "ĠìĿ¼ë °ĺ",
- "ĠA ires",
- "ph en",
- "Ġze bra",
- "Ġsymbol ism",
- "On ce",
- "Ġr acks",
- "ĠKaf ka",
- "ĠÑģеÑĢÑĮ ез",
- "Ġsin n",
- "p icious",
- "ka a",
- "Ġmotherf ucker",
- "Ġapprentices hip",
- "Ġr pm",
- "Ġtax ation",
- "Ġfur ry",
- "ĠSac red",
- "ĠÑĢаз м",
- "por a",
- "eng es",
- "ĠíĹ Īë",
- "ĠÑģ ин",
- "Ġsanit izer",
- "Ġcr inge",
- "ĠS ca",
- "оÑĩ но",
- "Ġof ere",
- "Ġmel odies",
- "ĠVel vet",
- "ĠIhr er",
- "ĠHy brid",
- "ĠG iov",
- "Ġirgend was",
- "Ġdep ende",
- "ĠUs ers",
- "Ġh ump",
- "dri ving",
- "Ġs f",
- "Ġruth less",
- "à¹ĢภĦ",
- "Ġlem ons",
- "Ġfö ret",
- "ĠO j",
- "Ġм ама",
- "Ġinter personal",
- "Ġge v",
- "Ġab norm",
- "иÑģ л",
- "Ġин д",
- "Ġkont roll",
- "Ġreg res",
- "Ġled ge",
- "Ġerzäh lt",
- "ĠT act",
- "Ġarri vé",
- "Ġsubstant ive",
- "Ġspoon ful",
- "zw ischen",
- "oooo o",
- "Ġconten ido",
- "Ġbes l",
- "á»ĥ m",
- "k ten",
- "Jam ie",
- "Ġsand y",
- "ä¸į åIJĮ",
- "â ĭ",
- "Ġp ase",
- "Ġdet te",
- "ĠBelg ian",
- "ê° ľë",
- "ula res",
- "r ud",
- "ig or",
- "ĠíĮ ¬ë",
- "Ġremed ies",
- "Ġblast ing",
- "ĠS ich",
- "Ġож ид",
- "Ġmon str",
- "Ġmanif old",
- "Ġglaub en",
- "ĠE ST",
- "Ġstream line",
- "Ġlobb ying",
- "ĠGoth ic",
- "to ire",
- ".. '",
- "Ġdém ocr",
- "Ġнаб лÑİд",
- "Ġwsp ól",
- "ĠczÄĻ ÅĽÄĩ",
- "ä¸ĭ éĿ¢",
- "is és",
- "g angen",
- "Ġbez pie",
- "rem lin",
- "ê° Ŀ",
- "St ill",
- "Ġres ides",
- "Ġgele cek",
- "Ġtélé phone",
- "Ġpe wn",
- "Ġle opard",
- "Ġcompliment ary",
- "Ġc rib",
- "ĠAnim als",
- "Ġge il",
- "ess el",
- "Ġgard er",
- "Ġcatch y",
- "æ¨ ¹",
- "ĠE ts",
- "ĠCom mercial",
- "ĠD ENNIS",
- "ĠCoordin ator",
- "ĠAb igail",
- "ffff ff",
- "ấ p",
- "Ġpeque ña",
- "Ġinject ions",
- "ce kt",
- "Ġphilanthrop y",
- "Ġp uck",
- "Ġcelebr ates",
- "ĠD unk",
- "ĠD latego",
- "ãģ¾ ãģł",
- "δ ή",
- "grad uate",
- "ĠM obil",
- "t ill",
- "ac am",
- "Ġyol ks",
- "Ġtang led",
- "Ġman iac",
- "Ġoblig ed",
- "ĠLa ink",
- "Ġver der",
- "ĠDam on",
- "Ġmut ant",
- "Ġhop ping",
- "Ġre ins",
- "Ġinver ter",
- "Ġcont empt",
- "׳ ס",
- "le arning",
- "M iss",
- "ĠÐĵ оÑģ",
- "ĠMe yer",
- "ê»ĺ ìĦľ",
- "é£ İ",
- "×ķ׳ ×Ļ×Ŀ",
- "ask ing",
- "Ġtrim ming",
- "Ġtre asury",
- "Ġs ente",
- "A ust",
- "ĠUnterstüt zung",
- "ĠCom edy",
- "ĠAn akin",
- "é ¹",
- "ÑĢÑĥ ÑĤ",
- "ĠH ari",
- "ograph ers",
- "Ġoat meal",
- "ĠB ots",
- "ä¸į äºĨ",
- "Ġп алÑĮ",
- "Ġacknowledge ment",
- "x ic",
- "Ġê´Ģ ìĭ¬",
- "gas ping",
- "Ġãģ ķ",
- "Ġterr ace",
- "Ġor naments",
- "ĠM ER",
- "comm ittee",
- "ĠìĹĨ ìĬµëĭĪëĭ¤",
- "Ġr ij",
- "é ³",
- "צ ×Ŀ",
- "le me",
- "Ġlibert ies",
- "Ġfell as",
- "ĠCop per",
- "ben ch",
- "ĠIde a",
- "á»į n",
- "ÑĪ а",
- "Ġvers ión",
- "ÏĦο Ïį",
- "ĠÐľ и",
- "ĠпÑĢил ож",
- "Ġbox er",
- "ĠT anner",
- "ĠM oy",
- "ì¹ĺ ëĬĶ",
- "T hr",
- "Ġtin ham",
- "Ġpol ishing",
- "Ġconsequ ently",
- "Ġamen ities",
- "ĠK I",
- "ĠGRE EN",
- "ĠFrank ie",
- "н иÑĤ",
- "itt el",
- "Ñģ кое",
- "urs ed",
- "Ġup bringing",
- "Ġth ứ",
- "ĠìĭĿ ìľ¼ë¡ľ",
- "Ġwh im",
- "Ġchin ese",
- "conf idence",
- "ĠJ eder",
- "ãģª ãģ®ãģ§",
- "aj cie",
- "ĠT ous",
- "ĠPow ers",
- "ừ a",
- "other mal",
- "ĠвÑĭ ÑĪе",
- "r ale",
- "Ø§Ø ®",
- "Ġì§Ģ ìĽIJ",
- "Ġép isode",
- "Ġsul ph",
- "Ġenc ara",
- "k raft",
- "alar ı",
- "ĠCom es",
- "Ġdiv ul",
- "ĠRud olph",
- "ĠM use",
- "Ġut ens",
- "ĠìŀIJ 주",
- "Ġp ana",
- "ĠVeget a",
- "ĠPH P",
- "ĠN SA",
- "ent in",
- "ĠCarne gie",
- "ا ÙĬ",
- "iÄĻ cy",
- "H arry",
- "Ġf ır",
- "С п",
- "Ġglad ly",
- "Ġaver aging",
- "íķĺ ê²łìĬµëĭĪëĭ¤",
- "лÑı ÑİÑĤÑģÑı",
- "ĠÐľ енÑı",
- "Ġquot ation",
- "ri res",
- "itch ens",
- "ay ed",
- "Ġun att",
- "ĠP erez",
- "ĠоÑĤ меÑĤ",
- "Ġtact ile",
- "ĠEu h",
- "is ini",
- "b uh",
- "Ġhat ır",
- "ĠìŀĪ ìľ¼",
- "Ġpolicy makers",
- "³´ì Ħ¸ìļĶ",
- "ac ı",
- "Ġκ ι",
- "Ġregister ing",
- "re to",
- "ĠSpr inkle",
- "ĠGram my",
- "ax ter",
- "Ġб и",
- "Ġsit ter",
- "Ġpred ic",
- "Ġthin ly",
- "Ġstr um",
- "Ġag grav",
- "Ġa ha",
- "ر ج",
- "m ellow",
- "Ġconst ante",
- "ĠL aut",
- "ist on",
- "Ġtransition ed",
- "ĠCamb odia",
- "ãģĦ ãģįãģ¾ãģĻ",
- "è·Ł 大家",
- "art ed",
- "Ġmis f",
- "ĠPunk te",
- "Įë ĵł",
- "Ġtremb ling",
- "Ġges pannt",
- "ĠعÙĦÙĬ Ùĩ",
- "Ġникак иÑħ",
- "Ġë¶Ģë ĵľë",
- "ĠÑĢазв иÑĤ",
- "Ġit chy",
- "Ġc iento",
- "Ġpl ains",
- "Ġk ittens",
- "Ġback log",
- "ĠPres iding",
- "pt a",
- "Ġha voc",
- "ĠDarr in",
- "ĠÐĽÑİ Ð±",
- "Ġsegreg ated",
- "Ġg hetto",
- "Ġerle bt",
- "Ġdrug iej",
- "ĠSi xt",
- "åı ĥ",
- "ร ะ",
- "uen cia",
- "Ġíķĺ 기",
- "ĠëĨ į",
- "Ġrob i",
- "Ġpione ers",
- "Ġmilli ards",
- "ĠWitch er",
- "Ġ무ìĹ ĩ",
- "or ro",
- "m ass",
- "Ġdiver gence",
- "ĠRiver a",
- "ĠNo odles",
- "Ġend roit",
- "ĠK osten",
- "ĠдÑĢÑĥг а",
- "ĠmÃŃn imo",
- "ĠKazakh stan",
- "ت Ùĩ",
- "Ġвоз дÑĥ",
- "Ġgesch rieben",
- "ĠN il",
- "Ñģ ки",
- "ĠFr üh",
- "Ġbever ages",
- "æº IJ",
- "ĠG on",
- "æĺ ¨",
- "Ar in",
- "ĠInt ro",
- "ocaly ptic",
- "Ġexhaust ion",
- "ĠStat us",
- "ĠBatter y",
- "és z",
- "£ ¼ë",
- "air y",
- "Ġë³´ìŬë ĵľë",
- "Ġdispar ity",
- "Ù Į",
- "ĠTuc son",
- "Ġbright ly",
- "pro blem",
- "Ġbiom ass",
- "éĻ į",
- "§ ī",
- "Ġhur dle",
- "Ġwavelength s",
- "Ġ< <",
- "Ġteam ed",
- "FF FF",
- "ĠS lim",
- "om ial",
- "Ġunve iled",
- "ĠVere in",
- "ÙĤ Ø·",
- "est ry",
- "Ġcl ás",
- "Ġch eddar",
- "Ġaccus ing",
- "ĠScient ific",
- "ĠбÑĥд е",
- "ĠCyr us",
- "ε ÏĦε",
- "Ĩĵ ê³ł",
- "Ġë³ Ħ",
- "Ġcur d",
- "Ġrefer rals",
- "sh ift",
- "åį ķ",
- "nik ów",
- "Ġm ier",
- "Ġconf ronting",
- "ê²ĥ ëıĦ",
- "aw l",
- "Ġtry in",
- "Ġê·¸ëŀĺ ìļĶ",
- "Ġch iar",
- "Ġìĺ¤ëĬ ĺëıĦ",
- "æĶ¿ æ²»",
- "es que",
- "Ġmism os",
- "ĠSh ak",
- "Ġsoci aux",
- "Ġpi ÅŁ",
- "ĠkiÅŁ i",
- "Ġcy an",
- "h ay",
- "be w",
- "b od",
- "ĠÎ ¹",
- "ĠMain ly",
- "Ñİ ÑĤÑĮ",
- "hab itude",
- "ĠÑģп окой",
- "è·Ł æĪij",
- "Ġpre con",
- "ĠM andy",
- "ðŁ¤ £",
- "ill os",
- "Ġgr upp",
- "Ġcr umble",
- "Ġconstru ctor",
- "erv ices",
- "Ġlight house",
- "ĠCon cept",
- "ан ÑĤи",
- "alt ro",
- "h ope",
- "ĠAll eg",
- "ìĸ´ë ¥¼",
- "pie ces",
- "oun ter",
- "Ġíķĺ ëĭĪê¹Į",
- "ĠìĿ¸ íĦ°ë",
- "Ġvérit able",
- "Ġthread ed",
- "bl ind",
- "Ĥĺë Ŀ¼",
- "Ġtr ays",
- "ĠEd ison",
- "ĠÃĸ z",
- "ĠSte vie",
- "Ġl ender",
- "Ġbrig ade",
- "Ġdeuts che",
- "m uffled",
- "b art",
- "Ġinsan ity",
- "Ġsav vy",
- "Ġsens ational",
- "Ġdere chos",
- "ĠM X",
- "ĠпÑĢ еп",
- "Ġthreat ens",
- "Ġrealt Ãł",
- "Ġindic ative",
- "Ġch ops",
- "Ġbenef iting",
- "ĠVern on",
- "ĠSt rand",
- "n un",
- "qu ently",
- "10 1",
- "Ġe el",
- "ìĪ Ļ",
- "r ints",
- "ĠÙħ س",
- "Ġب د",
- "Ġпо ÑģÑĤÑĢо",
- "Ġyap mÄ±ÅŁ",
- "Ġol ması",
- "Ġi edereen",
- "ol é",
- "ke f",
- "Ġë°ľ ìĥĿ",
- "Ġr ained",
- "Ġalm ighty",
- "ĠвÑĭ д",
- "ĠC PR",
- "F re",
- "Ġinhab ited",
- "Ġarb ets",
- "Ġa kin",
- "а ÑģÑĤв",
- "v ania",
- "Ġhäuf ig",
- "ĠMat te",
- "s orry",
- "Jen ny",
- "ĠгÑĢ ад",
- "Ġwh it",
- "Ġbro kers",
- "å¯ Ł",
- "Ġh ine",
- "ast en",
- "Ġг ÑĢÑĥ",
- "M B",
- "ĠP RI",
- "S ab",
- "Ġwrest ler",
- "Ġfacil itating",
- "Ġeh kä",
- "ĠC red",
- "Ġ12 7",
- "Ġnot hin",
- "Ġmand ated",
- "å¯ Į",
- "ÑĥÑĤ ÑģÑĤв",
- "F rank",
- "Ġwor s",
- "Ġdzie ÅĦ",
- "ĠUnder ground",
- "Ġznaj du",
- "ĠB ä",
- "ĠPrin zip",
- "аÑĤ елей",
- "Ġveter inar",
- "Ġsplend id",
- "Ġroz p",
- "Ġpsych opath",
- "ig on",
- "Ġh ops",
- "Ġc ần",
- "ĠX ian",
- "Ġtro isième",
- "Ġproduct o",
- "ĠdeÄŁ er",
- "ĠContin uing",
- "ив ал",
- "c ık",
- "Ġmoistur izer",
- "Wh ite",
- "Ġsi is",
- "ĠEver est",
- "ien ced",
- "Ġcả m",
- "ĠJ apon",
- "´ìł Ħ",
- "Ġten ÃŃan",
- "Ġenc anta",
- "M m",
- "Ġdrop down",
- "ĠI ya",
- "³´ë ©´",
- "Ġword ing",
- "ĠSque eze",
- "ĠMap le",
- "Ġclar ified",
- "ĠMun icip",
- "ĠRou ge",
- "ĠNick i",
- "ĠGo o",
- "v olt",
- "t ek",
- "fect ure",
- "f red",
- "ar rive",
- "ãĥ¼ ãģĦ",
- "te z",
- "E p",
- "Ġob ras",
- "ĠV ID",
- "ĠR iv",
- "ĠMod i",
- "i be",
- "Ġacontec endo",
- "Ġim itation",
- "Ġcamoufl age",
- "Ġspan ning",
- "ĠSEC RET",
- "ĠOre o",
- "ìĨĮë ¦¬",
- "Ġh unch",
- "Ġca ÅĤe",
- "Ġspont aneously",
- "ĠPer d",
- "Ġet ap",
- "ĠHo le",
- "ĠDis ability",
- "Ġafter life",
- "æģ ©",
- "Ġtest ified",
- "Ġpres up",
- "Ġpet roleum",
- "Ġcontr ario",
- "ĠAss essment",
- "ÄŁ lu",
- "Ġp ests",
- "Ġdil ig",
- "ĠвÑģÑĤÑĢ еÑĤ",
- "Ġcons équ",
- "Ġcann ons",
- "Ġcan oe",
- "ĠM ile",
- "Ġcit oy",
- "Ġbe gged",
- "ĠMin nie",
- "ÅĤy ch",
- "Ġprinci pe",
- "ÏĢÏĮ ν",
- "m niej",
- "Ġw ert",
- "Ġëĭ¤ë ĵ¤",
- "an se",
- "Ġunc les",
- "Ġprovoc ative",
- "Ġinter sections",
- "Ġdemocr ats",
- "ĠJul ius",
- "ин ки",
- "yg usal",
- "Ġ׾ ×ķ",
- "Ġgj orde",
- "Ġg asket",
- "ĠB ock",
- "ĠÄ° n",
- "b reat",
- "ĠEqu ity",
- "ard ı",
- "Ġкан але",
- "Ġд ней",
- "Ġt Ỽi",
- "Ġfi xture",
- "Ġab uses",
- "Ġv aya",
- "Ġou vert",
- "Ġmultic ultural",
- "Ġcontext o",
- "ĠSes ame",
- "Ġdé pl",
- "Ġcons omm",
- "ĠPart e",
- "Ġp em",
- "ĠCon an",
- "Ġб ÑĸлÑĮ",
- "Ġpersu aded",
- "Ġdra ins",
- "M oo",
- "F ORE",
- "Ġб аÑĤ",
- "Ġf od",
- "ĠProduct s",
- "ì§Ħ ì§ľ",
- "Ġ\" [",
- "ĠW ick",
- "ĠNar uto",
- "н али",
- "ry w",
- "Ġl odge",
- "Ġin h",
- "Ġvont ade",
- "Ġdi j",
- "ĠJes ús",
- "Look ing",
- "Ġfore arm",
- "ĠIntegr ation",
- "ĠHARR IS",
- "Ġtool bar",
- "le ader",
- "Ġsel dom",
- "Ġб ÑĢоÑģ",
- "ĠK ook",
- "он д",
- "Ġmon opol",
- "Ġmill et",
- "Ġl ira",
- "ĠAs ians",
- "Ġ18 90",
- "ci ÄŁim",
- "Ġed en",
- "ĠIKE A",
- "ĠNeigh bor",
- "ĠKazu ya",
- "ü d",
- "Ġpsych edel",
- "Ġenvision ed",
- "åĿ Ĺ",
- "Ġï· »",
- "Ġw under",
- "ĠBulgar ia",
- "B rid",
- "Ġmar row",
- "Ġdep iction",
- "ĠT in",
- "ĠPhar ise",
- "Ġeinz ige",
- "Ġblind ly",
- "ãģĽ ãģ¦",
- "Ġdef ens",
- "D ire",
- "Ġvibr ating",
- "Ġtroll s",
- "Ġdisrespect ful",
- "Ġw od",
- "Ġstimul i",
- "Ġcreep ing",
- "Ġcla irement",
- "Ġsc ariest",
- "Ġdécouv rir",
- "Ġ10 4",
- "ĠвеÑĢ Ñħ",
- "ĠÅĤ at",
- "Ġróż ne",
- "Ġbar ley",
- "ĠRe pl",
- "ĠT we",
- "k ke",
- "ĠãģĿ ãĤĮ",
- "ĠRed mi",
- "ĠMet roid",
- "Ġή ÏĦαν",
- "Che ck",
- "ĠS EN",
- "Ġ ido",
- "ÑĤоÑĢ ии",
- "ó p",
- "UN KNOWN",
- "Ġänd ern",
- "ĠJu ice",
- "ĠGes icht",
- "å°± æľĥ",
- "ĠнаÑģÑĤ олÑĮко",
- "íĥ ķ",
- "Â Ń",
- "ex hales",
- "Ġì´ ī",
- "Ġj sem",
- "ÏĢ ÏīÏĤ",
- "Ġit t",
- "ëªħ ìĿ´",
- "Ġrem ix",
- "Ġbloss oms",
- "ĠR enee",
- "is ations",
- "ìĬ¤í Ħ°",
- "Ġë³´ ìĿ´ëĬĶ",
- "uest as",
- "op edia",
- "ĠA im",
- "ìĿ´ì¦ Ī",
- "sc ene",
- "Ġleak age",
- "uck t",
- "S ad",
- "A sk",
- "Ġsusp ense",
- "Ġimp ost",
- "ĠStrateg ic",
- "ĠIt ÃŃs",
- "âĢ Į",
- "Ġkey boards",
- "Ġam using",
- "og r",
- "id erman",
- "ŀ ĸ",
- "Ġв ижÑĥ",
- "Ġd ips",
- "Ġapolog ized",
- "ĠST AR",
- "Ġesc uela",
- "ĠC hing",
- "н ениÑı",
- "Ġë¶Ģë¶Ħ ìĿ´",
- "ĠFle et",
- "Ġs amb",
- "Ġentsprech end",
- "Ġelectrod es",
- "ĠFrei heit",
- "æĪij ä¸įçŁ¥éģĵ",
- "ĠSh rim",
- "iÃŁ e",
- "Ġselect ions",
- "Ġfor di",
- "Ġd oss",
- "Ñı Ñĩ",
- "Ġdiscrimin ate",
- "ĠAu ÃŁerdem",
- "Ġdesenvol v",
- "ĠIntern al",
- "ĠBened ict",
- "å¯ Ĩ",
- "ĠSh iv",
- "M issy",
- "Ġоб наÑĢÑĥж",
- "Ġна ÑģÑĤÑĢо",
- "Ġcontrol ar",
- "ĠL ia",
- "Ġopio ids",
- "ant u",
- "Ġcup board",
- "æģ IJ",
- "г е",
- "acht s",
- "Ġcur ated",
- "Ġx em",
- "Ġwe ary",
- "Ġbre thren",
- "Ġbudget ing",
- "Ġpour tant",
- "éļ »",
- "ais ia",
- "ĠоÑĤв еÑĩ",
- "ĠG IS",
- "μ αι",
- "Ġש×Ķ ×ķ×IJ",
- "Ġsa ud",
- "Ġl Ỽ",
- "Ðķ Т",
- "ub ine",
- "ĠнÑĥж ен",
- "Ġkidna pping",
- "Ġbr at",
- "ĠTer re",
- "ĠMon et",
- "Ġë§Ī ìĬ¤íģ",
- "Ġflash y",
- "ĠIS BN",
- "Ġfreel ance",
- "i age",
- "Ġjun ge",
- "ì¶ ©",
- "cer al",
- "ĠÑĤоÑĩ ки",
- "Ġform ulate",
- "ĠF ER",
- "ĠDart mouth",
- "ìľ¼ë ©´ìĦľ",
- "å¢ ĥ",
- "ow iÄħ",
- "ĠëĶĶ ìŀIJ",
- "Ġreg iment",
- "Ġmetabol ismo",
- "ĠP arr",
- "Ġ충 ë¶Ħ",
- "Ġsan ity",
- "ĠL al",
- "ĠG ö",
- "ĠG la",
- "Ġprot o",
- "Ġmicroscop ic",
- "Ġk ang",
- "ĠSc alia",
- "Ġp ug",
- "ĠSc ore",
- "ĠSav annah",
- "Ġgard e",
- "ĠN OR",
- "å°į åIJ§",
- "Ġsche int",
- "Ġp óÅĤ",
- "Ġcor ri",
- "Ġbr ute",
- "Ġ ÅĤad",
- "ä»ĸ 们",
- "Ġsucceed ing",
- "Ġbicy cles",
- "N on",
- "Ġseek ers",
- "Ġuncond itional",
- "Ġrhy mes",
- "ĠGar age",
- "Ġinv oice",
- "Ġcan vi",
- "ne ck",
- "Ġcustom izable",
- "irit ual",
- "Que en",
- "íķĺ ìĭľëĬĶ",
- "Ġpower less",
- "Ġcs ak",
- "ä¸į ä¼ļ",
- "is oft",
- "Ġìłķ íĻķ",
- "Ġnh ân",
- "ĠM AND",
- "ĠH af",
- "Ġrevol ves",
- "ä¹Ł åı¯ä»¥",
- "ov an",
- "ar oo",
- "ĠGr ind",
- "éĽ ª",
- "Ġindispens able",
- "Ġconsult ed",
- "ĠClin ical",
- "A cc",
- "Ġol hos",
- "Ġmon ter",
- "ĠH ana",
- "et ah",
- "Ġva an",
- "Ġt igers",
- "Ġcau cus",
- "ðŁĺ Ĥ",
- "³´ì ŀIJ",
- "pow ers",
- "ium s",
- "ĠíĨ łë",
- "Ġtrad icional",
- "Ġreson ated",
- "Ġìĭł 기",
- "th em",
- "Ro bert",
- "Ġelement o",
- "Ġant id",
- "Ġоб Ñģ",
- "Ġnat ives",
- "Ġlo ca",
- "ow ment",
- "ĠT ight",
- "Ġ æĢĿ",
- "Ġmel an",
- "ĠN ue",
- "am is",
- "Ġsor gen",
- "as ına",
- "H ome",
- "ĠPUB G",
- "Ġaw fully",
- "ĠSh ore",
- "ĠPer ché",
- "ĠL au",
- "ĠCind erella",
- "ĠCh est",
- "Ġsem antic",
- "Ġdesert ed",
- "ĠMom o",
- "ĠHern andez",
- "gen es",
- "ĠAd ult",
- "иÑĩеÑģ кого",
- "osh ima",
- "ĠcaracterÃŃst icas",
- "ĠK L",
- "´ìŀ ¥",
- "oc ar",
- "Ġfeh lt",
- "Ġd ruk",
- "ĠPop py",
- "EN GLISH",
- "ĠVerg leich",
- "B rien",
- "Ġrec omp",
- "ĠÑģ д",
- "Ġmer ger",
- "Ġmarket ers",
- "Ġhoney moon",
- "Ġpen so",
- "Ġbell i",
- "еÑĤ Ñĥ",
- "Ġbank er",
- "Cam era",
- "ĠSt all",
- "ĠSt amp",
- "ĠB ite",
- "еж де",
- "Ġs ür",
- "Ġgü ç",
- "ĠPas sover",
- "ĠBug ün",
- "ĠÑģожал ениÑİ",
- "Ġн из",
- "Ġman ure",
- "Ġglac ier",
- "è« ĩ",
- "RA Y",
- "ter ror",
- "Ġsal ads",
- "Ġhur ricanes",
- "ĠDesign er",
- "ator io",
- "Ġfact ual",
- "ĠTam my",
- "Ġзв ÑĥÑĩ",
- "Ġintrodu ctions",
- "Ġhouse keeping",
- "Ġh anger",
- "ëĭ ĺë",
- "ak te",
- "ĠCol a",
- "' ]",
- "ĠG ender",
- "оÑĢ он",
- "ip se",
- "ic ias",
- "Ġsuccess ive",
- "Ġpolit ic",
- "Ġhö her",
- "ĠQ iao",
- "ĠG imme",
- "Ġл ож",
- "Ġse b",
- "ĠWe iter",
- "ĠSak ura",
- "ĠB oulder",
- "ĠAm érica",
- "peÅĤ nie",
- "Ġtecn ologÃŃa",
- "ish ops",
- "f ur",
- "Ġmoon light",
- "Ġdispers ed",
- "Ġre z",
- "ен ное",
- "алÑĮ нÑĥÑİ",
- "ĠTw elve",
- "ĠH OR",
- "ìĭ¤í ŀĪ",
- "il age",
- "Ġshad ed",
- "Ġres umes",
- "ĠPe anut",
- "ĠM ILL",
- "ap ons",
- "ĠU FC",
- "ĠSo le",
- "Ġjoy stick",
- "ĠOliv ier",
- "war ming",
- "Ġsyll abus",
- "Ġоб Ñīе",
- "Ġhi á»ĩn",
- "Ġfest a",
- "Ġcr adle",
- "ĠZ ac",
- "Ġremem brance",
- "Ġê°Ļ ìķĦìĦľ",
- "ĠpiÄĻ k",
- "Ġco exist",
- "ĠV II",
- "Ġá reas",
- "Ġu waż",
- "Ġobser vers",
- "Ġmännisk or",
- "co on",
- "ĠD AM",
- "Ġnas zym",
- "Ġall igator",
- "ĠFree ze",
- "ĠEst ate",
- "ĠÑĤÑĢ ади",
- "Ġunder cover",
- "Ġn ies",
- "ĠFeh ler",
- "pl in",
- "ĠK abul",
- "il ate",
- "Ġê³ł ìĸij",
- "Ġm op",
- "ìĦ ¼",
- "Ġand erer",
- "ĠK ELL",
- "ок и",
- "Ġж еÑģÑĤ",
- "Ġgra zing",
- "Ġda ÃŃ",
- "Ġcapital ize",
- "Ġa pex",
- "Ġnurt uring",
- "Ġcort ar",
- "Ġcontr ac",
- "ımız ı",
- "Ġtand em",
- "éĥ½ æľī",
- "ge ment",
- "ĠÑģиÑģÑĤем а",
- "Ġman que",
- "ia jÄħ",
- "W OR",
- "Ġا ب",
- "Ġcart s",
- "AN O",
- "Ġë°Ľ ê³ł",
- "ĠC ena",
- "ĠBi ology",
- "id ar",
- "Ġa ż",
- "er ne",
- "an u",
- "Ġthank ed",
- "Ġsubmar ines",
- "Ġman ic",
- "Ġм оз",
- "ä¼ Ĭ",
- "inst ant",
- "ess ential",
- "Ġsam urai",
- "Ġpast i",
- "Ġal an",
- "Ġbro ch",
- "Ġb aker",
- "ĠGu ill",
- "¨ ¼",
- "Ġwithd rawn",
- "ëĭ Ŀ",
- "Per fect",
- "qu ency",
- "Ġstream lined",
- "Ġ13 00",
- "´ë ıĦ",
- "Ġëĸ łë",
- "Ġãģ¯ ãģĦ",
- "Ġh vad",
- "ä¸Ģå®ļ è¦ģ",
- "Ġverb ally",
- "ĠK ons",
- "Ġì¡° ìĭ¬",
- "Ġdie z",
- "æİ° æİ°",
- "Ġchuck ling",
- "ĠM ih",
- "Ġrall ies",
- "Ġman ter",
- "Ġearn est",
- "s uper",
- "Ġge ce",
- "ĠR end",
- "ĠGer ade",
- "jen igen",
- "ĠV all",
- "Ġìŀ ĪëĤĺ",
- "ĠÑģказ ала",
- "Ġtrabal h",
- "ĠнаÑĪ ем",
- "Ġм еÑħ",
- "ik it",
- "Ġnoun s",
- "Ġneurolog ical",
- "Ġmotiv ational",
- "ĠMcM ahon",
- "ĠFin ished",
- "Ġë³´ ìĿ´",
- "ĠField s",
- "Ġadoles cents",
- "ĠT isch",
- "ĠNe ben",
- "ĠFl owers",
- "ĠEner g",
- "Ġdire t",
- "ĠTh i",
- "ĠP icas",
- "æĥ ľ",
- "æĢİä¹Ī æł·",
- "Ġav ete",
- "ĠF ors",
- "ĠChap el",
- "N ão",
- "E t",
- "ĠÑģод еÑĢж",
- "ren o",
- "Ġs ven",
- "Ġdost ÄĻp",
- "ne e",
- "ĠSnap dragon",
- "ĠID s",
- "ìķĺ ëĬĶëį°",
- "ר ×ļ",
- "Ġsun flower",
- "Ġperpet ual",
- "ç³ ĸ",
- "Ġkn ights",
- "Ġg ird",
- "ĠTo ld",
- "Ġvolcano es",
- "Ġadvers ary",
- "ĠEconom y",
- "Ġextra pol",
- "Ġbl uetooth",
- "Ġzoom ing",
- "Ġsk ys",
- "Ġgen ial",
- "ÃŃcul os",
- "amb re",
- "Ġм еÑĢ",
- "Ġteen y",
- "Ġstress ing",
- "ìķ Į",
- "ON Y",
- "Ġtransluc ent",
- "Ġround ing",
- "Ġgr ues",
- "×Ļ׳ ×Ķ",
- "ap rès",
- "Ġprue ba",
- "Ġpoly gon",
- "Ġblue berry",
- "ĠProgram m",
- "Ġtren ches",
- "Ġse bagai",
- "Ġpal ate",
- "Ġla ude",
- "Ġbehav ed",
- "Ġlongitud inal",
- "ĠMod ule",
- "Ġadm ir",
- "λ ι",
- "G reg",
- "Ġwy st",
- "Ġpropag ate",
- "Ġmold s",
- "ĠT ub",
- "ĠL oud",
- "ust o",
- "Ġun stoppable",
- "Ġreinfor cing",
- "éĿŀ常 çļĦ",
- "ĠпÑĢоблем а",
- "Ġpot encial",
- "Ġhe mp",
- "ìŀ Ķ",
- "ठ¯",
- "Ġopt ic",
- "Ġerfolg reich",
- "Ñģ Ñĭ",
- "олÑĮ ÑĪе",
- "ur st",
- "ĠPo is",
- "Ġrespond ents",
- "Ġneh me",
- "ĠEx ternal",
- "ol ate",
- "H yun",
- "Ġquart z",
- "Ġmathematic ian",
- "Ġbás icamente",
- "Ġa il",
- "ìł ľë¥¼",
- "att utto",
- "Ġno oit",
- "Ġaff lict",
- "ĠOl ga",
- "èŃ ·",
- "Ġна ÑĤ",
- "Ġd ites",
- "Ġreal idade",
- "Ġk än",
- "Ġuniqu eness",
- "Ġpad res",
- "Ġsubs idi",
- "Ġpige ons",
- "β α",
- "st ad",
- "Ġder en",
- "ĠС лед",
- "d oo",
- "ĠопиÑģ ании",
- "Ġam ber",
- "Ġgoose bumps",
- "ĠfrÃ¥ gor",
- "ĠV ital",
- "ĠIsrael ites",
- "w asser",
- "Is n",
- "Ġcomm its",
- "ĠSTE VEN",
- "ĠBev ölker",
- "uit ive",
- "Ġleg en",
- "Ġbr uk",
- "иÑĢов ан",
- "yn en",
- "hel m",
- "Ġgener ational",
- "ĠL ändern",
- "οι ÏĢÏĮν",
- "uz u",
- "Ġcall er",
- "он ÑĮ",
- "üm ü",
- "Ġbes ar",
- "Ġpl ats",
- "Ġmig rated",
- "Ġj ap",
- "ĠW AR",
- "Ġdis sect",
- "ĠZus ch",
- "ĠZe iten",
- "ĠL ions",
- "ĠD F",
- "â Ķ",
- "ки в",
- "Ġpedest rians",
- "ĠMar ilyn",
- "d ock",
- "Ġy ht",
- "Ġre incarn",
- "ĠSon o",
- "ĠGrow th",
- "ÑĥÑģ ов",
- "Ġdun geons",
- "Ġbag us",
- "k ich",
- "ĠÑĥ кÑĢаÑĹ",
- "éĨ «",
- "ĠK eller",
- "chem istry",
- "J apanese",
- "Ġwill st",
- "Ġdecomp osition",
- "ĠÑģÑĤ ен",
- "Ġrev ived",
- "íķĻ êµIJ",
- "ĠÅ ĵ",
- "ä½ IJ",
- "ìĭ ¸",
- "ipp y",
- "Ġhour ly",
- "j än",
- "ĠWork shop",
- "Ŀ¼ ìĦľ",
- "Ġcu arto",
- "Ġpat rim",
- "ĠB urch",
- "ĠìŀĪ 기",
- "Ġhe pat",
- "Ġh Ãłng",
- "ĠëĮĢ íķ´",
- "ĠваÑĪ и",
- "Ġre work",
- "Ġpar se",
- "Ġçıkt ı",
- "ĠS ax",
- "ĠMong o",
- "ĠAa ah",
- "ram ble",
- "D J",
- "Ġstabil ized",
- "ĠSpe ech",
- "Book s",
- "Ġhur dles",
- "ĠW O",
- "ĠLamb org",
- "Ġ19 33",
- "Ġvor bere",
- "Ġclin ically",
- "Ġbreat htaking",
- "ĠGate way",
- "пеÑĢв ÑĭÑħ",
- "ut ers",
- "Ġë¹ µ",
- "Ġyet er",
- "Ġpull ey",
- "Ġmuff in",
- "ĠPre fer",
- "ĠP ence",
- "Ġinform ação",
- "ìĬ¤í Ĭ¸ë",
- "ãĤ¸ ãĥ£",
- "ĠTur tle",
- "ĠReg ina",
- "ĠLo ad",
- "do es",
- "pan ze",
- "¸ Ķ",
- "Ġmin a",
- "ĠLatin os",
- "amm ers",
- "ĠT ort",
- "ĠBey once",
- "имо ÑģÑĤи",
- "ĠвопÑĢоÑģ Ñĭ",
- "Ġbul un",
- "èĢĮ å·²",
- "ine k",
- "bere ich",
- "Ġpast ure",
- "ĠO A",
- "ĠM elt",
- "ĠEt t",
- "ĠD Y",
- "Ġob wohl",
- "Ġle agues",
- "ÑĤ еÑģÑĮ",
- "Ġк ÑĥÑģ",
- "Ġv ors",
- "Ġto pp",
- "ograph ical",
- "as st",
- "Ġl indo",
- "Ġë°Ŀ íĺĶ",
- "Ġré fl",
- "Ġclim bs",
- "Ġv arsa",
- "Ġmethy l",
- "ĠKar ere",
- "Æ°á» Ł",
- "R ad",
- "Ġprepared ness",
- "он Ñĩ",
- "ĠO D",
- "ĠC GI",
- "Ġठ®",
- "Ġspeech less",
- "Ġlas ci",
- "Ġbol ag",
- "ĠÑħоÑĩ еÑĤÑģÑı",
- "Ġgr ieving",
- "ĠJohann es",
- "ĠCar roll",
- "ad aki",
- "Ī ¬ë",
- "ĠsÅĤ u",
- "Ġinner halb",
- "Ġgymn astics",
- "п ÑĢи",
- "if iques",
- "Ġkar ate",
- "Ġdom u",
- "ãģĿãĤĮ ãģ§",
- "OTH ER",
- "Ġdemand é",
- "Ġbook let",
- "ĠKy oto",
- "Ġw oh",
- "ĠMar ÃŃa",
- "viol ent",
- "J E",
- "Ġl óg",
- "Ġbrut ally",
- "c ot",
- "ĠÙħ ÛĮ",
- "ĠWars z",
- "å® Ī",
- "w ol",
- "Ġmik ä",
- "ĠPron ounce",
- "ĠBrend an",
- "Ġr oup",
- "Ġital iano",
- "å¦Ĥ æѤ",
- "Ġкомп ÑĮÑİÑĤ",
- "Ġur ging",
- "ed es",
- "Ġcarbon o",
- "ĠRichards on",
- "ĠÐĿ аÑĩ",
- "ĠTra iner",
- "ĠCrime a",
- "Ġdi apers",
- "Ġco vet",
- "ĠMah ar",
- "ĠH utch",
- "ĠAus w",
- "ber ty",
- "Ġind ifferent",
- "кÑĢ еÑĤ",
- "uld ade",
- "Ġhar ms",
- "¢ ÙĨ",
- "les ia",
- "Ġg io",
- "ĠMist ress",
- "ĠK nox",
- "ĠFRE E",
- "Ġë £¨ë",
- "ĠнаÑĪ а",
- "Ġinvinci ble",
- "Ġma iden",
- "ĠJ eez",
- "Ġbre ve",
- "po le",
- "Ġcritic isms",
- "ĠRus ia",
- "ठ®",
- "ph in",
- "ĠComp are",
- "ĠB ON",
- "Ġsne aking",
- "ĠR ails",
- "ĠG eral",
- "Ġ195 3",
- "H ola",
- "Ġоп ÑĭÑĤ",
- "Ġrain forest",
- "Ġbel um",
- "ĠOb i",
- "ĠIS S",
- "ãĤĮ ãģªãģĦ",
- "ĠС в",
- "Ġbl ond",
- "Ġwz gl",
- "Ġpowiedz iaÅĤ",
- "Ġch oking",
- "ĠSong s",
- "ĠBir az",
- "Ġyell s",
- "Ġstyl ist",
- "ÏĮ ÏĦε",
- "Ġsch reiben",
- "ĠJ aw",
- "ĠEle ven",
- "ĠR if",
- "/ .",
- "Ġìĺ¤ë ŀľë§Į",
- "Ġtreat ies",
- "uff ed",
- "ĠâĪ Ĵ",
- "Ġroof s",
- "à¹Ģภª",
- "Ġë »",
- "Ġspark le",
- "ĠK iev",
- "ĠAr gu",
- "ere cht",
- "ĠÐĿад о",
- "ĠF IL",
- "Ġmol ta",
- "ĠDe vi",
- "Ġcam pe",
- "Ġbene vol",
- "ĠT ough",
- "Ġmo im",
- "Ġevac uate",
- "Ġer rado",
- "å© Ĩ",
- "ÑĢÑĥ го",
- "Ġíİ ĺ",
- "ĠÎĵ ια",
- "Ġweak en",
- "Ġillum inated",
- "Ġsig lo",
- "ĠV acc",
- "и ей",
- "al is",
- "ĠÑĥ ÑģÑĤÑĢой",
- "Ġdon a",
- "ÅĤ os",
- "ü man",
- "Ġprodu cción",
- "Ġcl ot",
- "ĠM ango",
- "Ġune asy",
- "Ġsh uts",
- "ĠExam ples",
- "ve ll",
- "e be",
- "Ġprompt ly",
- "ĠT eles",
- "ĠпÑĢоÑĪ л",
- "Ġpu erta",
- "Ġüber zeug",
- "Ġco ch",
- "so cial",
- "ĠB enson",
- "ĠM eth",
- "ĠEx ped",
- "Ġsupplement al",
- "Ġconce ive",
- "Ġ×ĺ ×ķ×ij",
- "Ġcapt ivity",
- "ıĻ ìķĪ",
- "ĠÑħ Ñĥд",
- "form ing",
- "Ġupload s",
- "Ġturbul ence",
- "j oint",
- "Ġsatisf actory",
- "ĠAn ime",
- "Ġwash es",
- "Ġliber als",
- "ĠSun shine",
- "ĠRE AL",
- "ub lik",
- "b inary",
- "T ony",
- "Ġpolar ized",
- "Ġenrich ed",
- "t aking",
- "ĠëģĿ ëĤĺ",
- "Ġple asures",
- "Ġex termin",
- "in ese",
- "at l",
- "v är",
- "аÑĢ Ñĭ",
- "Ġmy ÅĽ",
- "n arrator",
- "Ġод ном",
- "Ġnaj wiÄĻ",
- "Ġmobil ize",
- "Ġmill or",
- "Ġat a",
- "æ· ·",
- "ĠpolÃŃt ico",
- "Ġple ad",
- "Ġpain ters",
- "ĠS ow",
- "о ÑĦ",
- "ĠìĺĽ ëĤł",
- "ĠÑĩ ÑĤоб",
- "Ġs abor",
- "ĠUnd ert",
- "ĠJER RY",
- "Å¡ ÃŃ",
- "Ġë° ĸìĹIJ",
- "Ġpréc éd",
- "Ġannot ation",
- "ĠI naudible",
- "Ġtext ured",
- "Ġfisher man",
- "v ordan",
- "icher ung",
- "Ġìłģ ìĿ´",
- "Ġge zeigt",
- "Ġmand ates",
- "Ġbe ak",
- "ĠTW O",
- "ĠAk bar",
- "il ian",
- "Ġtiế p",
- "Ġsuperior ity",
- "ink u",
- "Ġl ys",
- "ĠF CC",
- "ĠC PA",
- "ust ering",
- "nic os",
- "an ja",
- "Ġch ills",
- "ĠC age",
- "Ġse aling",
- "Ġsa ç",
- "Ġded ans",
- "ĠAl ger",
- "Ġspe zie",
- "Ġcol oss",
- "ıy ı",
- "clock wise",
- "Ġexact amente",
- "Ġ iemand",
- "am ı",
- "Ġmand ar",
- "ra j",
- "f aced",
- "ag ua",
- "Ġê¹ Ķë",
- "Ġins besondere",
- "Ġdri zzle",
- "Ġdimin ish",
- "ĠY oda",
- "A I",
- "Ġbil miyorum",
- "ĠM MA",
- "ateg ory",
- "ĠпеÑĢ еп",
- "Ġparticip ar",
- "Ġnormal ized",
- "Ġcomplex ities",
- "æ´ ²",
- "æİ §",
- "аÑĢ ов",
- "m ist",
- "ich a",
- "Gr oup",
- "Ġresil iency",
- "Ġnog le",
- "ĠCN C",
- "pr ü",
- "Ġphysic ists",
- "н ок",
- "L I",
- "Ġstuff s",
- "Ġsist emas",
- "Ġinterfer ing",
- "ĠMar vin",
- "ér cito",
- "ĠìĹĨ ê³ł",
- "Ġson ic",
- "Ġequ iv",
- "Ġab ord",
- "ĠRam en",
- "Ġ0 9",
- "med im",
- "at iques",
- "Ġдел аÑİÑĤ",
- "Ġunanim ously",
- "Ġsk irts",
- "ĠíĬ¹ ë³Ħ",
- "ĠP rix",
- "k ami",
- "Ġfr uition",
- "Ġbirthday s",
- "ик ом",
- "Ġinaug ural",
- "Ġcorrel ate",
- "ĠT ory",
- "ĠëĤĺ ìģ",
- "Ġde w",
- "ĠPre cis",
- "ih i",
- "Ġë¬¸ìłľ ê°Ģ",
- "Ġc iting",
- "ĠL ana",
- "ĠK ag",
- "Ġplay through",
- "ĠProt ocol",
- "fr ist",
- "hov ah",
- "Ġmerc iful",
- "Ġb ilingual",
- "ĠG uitar",
- "r h",
- "Ġglam orous",
- "ĠVik ings",
- "ĠOoo oh",
- "íķĺ ëĬĶëį°",
- "ĠUg anda",
- "Ġcollaps es",
- "ent ry",
- "Ġantioxid ants",
- "ëĤ ĺë",
- "ÑĪ аÑı",
- "Ġtri via",
- "Ġgä ller",
- "Ġfun gi",
- "Ġmil ks",
- "Ġd icht",
- "μ η",
- "po ke",
- "ĠвÑĭп ÑĥÑģк",
- "Ġfeed er",
- "ĠAl cohol",
- "h ower",
- "Ġdes erving",
- "ĠRe bel",
- "ios is",
- "Ġ10 3",
- "Ġhand out",
- "Ġen m",
- "Ġland lords",
- "Ġge ology",
- "r ils",
- "Ġco bra",
- "ĠV old",
- "ĠP anch",
- "ĠGRE G",
- "Ġpr oss",
- "Ġbrac elets",
- "ĠV ega",
- "Ġroz um",
- "æ¬ ¾",
- "аз д",
- "ĠLy nd",
- "ĠHon ors",
- "Ġsurrend ered",
- "Ġlibr arians",
- "12 5",
- "ĠÑģ иг",
- "Ġuniform ly",
- "ĠE agles",
- "ìķ Ļ",
- "иÑĤ ан",
- "and id",
- "ĠìłĪë ĮĢ",
- "ĠØ ¶",
- "Ġarrest s",
- "ĠCS V",
- "ĠAzerbai jan",
- "ort ic",
- "ĠD X",
- "ĠAdvent ures",
- "Ġab us",
- "ĠF au",
- "Ġschlim m",
- "Ġratt ling",
- "Ġconsum es",
- "ĠTol kien",
- "Ġresurrect ed",
- "ĠX Y",
- "íĬ¸ ê°Ģ",
- "ĠвÑĭ ÑģÑĤÑĥп",
- "ĠAng ie",
- "żen ia",
- "M ic",
- "ĠShe ila",
- "acht et",
- "Ġover st",
- "Ġl â",
- "Ġine ffective",
- "æĿ ¡",
- "æĢİä¹Ī äºĨ",
- "å¿ Ļ",
- "Ġwicht iger",
- "Ġv ino",
- "Ġp um",
- "Ġang led",
- "ĠP ione",
- "ĠM ỹ",
- "ãģĿãĤĮ ãģ¯",
- "wo ÅĽÄĩ",
- "d raw",
- "ั à¹Ī",
- "mark ets",
- "Ġcaf es",
- "ĠC em",
- "â Ŀ¤",
- "ĠS uit",
- "M K",
- "Ġemphas izes",
- "Ġtort illa",
- "Ġmejor ar",
- "ĠSur viv",
- "cast ing",
- "Ġeduc ación",
- "ĠG um",
- "u ely",
- "ĠìĹ¬ê¸° ëĬĶ",
- "Ġstretch y",
- "en ça",
- "Ġwith hold",
- "Ġex iting",
- "Ġenthal py",
- "ĠTrans it",
- "ıl mÄ±ÅŁ",
- "al ies",
- "Ġsal var",
- "Ġlean ed",
- "ĠgroÃŁ es",
- "Ġf itt",
- "ак и",
- "S arah",
- "Ġhost el",
- "Ġfinger na",
- "Ġnadzie jÄĻ",
- "w ives",
- "R ec",
- "Ġsp ool",
- "аÑĤ ов",
- "ĠEn emy",
- "Ġf ury",
- "Ġdet ta",
- "ĠF ay",
- "éļ ¨",
- "Ñı ÑİÑĤ",
- "Ġaproxim adamente",
- "Ġsil os",
- "Ġmag ist",
- "Ġc ree",
- "ĠKr ank",
- "ĠD OWN",
- "Ġstart led",
- "Ġre born",
- "ĠUm welt",
- "ĠSuz anne",
- "ни ÑĨÑĭ",
- "out ez",
- "ĠJ AC",
- "y ards",
- "rad as",
- "ra u",
- "ip ts",
- "h ail",
- "Ġparagraph s",
- "Ġme glio",
- "Ġisol ating",
- "Ġace ite",
- "ĠH arsh",
- "Ġcy st",
- "ĠBlock chain",
- "ĠÑħоÑĢоÑĪ ий",
- "Ġvirt uous",
- "Ġinvestig ación",
- "Ġdev oir",
- "Ġmast urb",
- "ĠS ale",
- "ÙĬر Ø©",
- "ĠÎ §",
- "ĠStra ÃŁen",
- "Ġdi kk",
- "Ġa fore",
- "ĠJung kook",
- "Ġcho ciaż",
- "ĠDebat te",
- "Ġweird ly",
- "Ġvia je",
- "reg ist",
- "H elp",
- "Ġkind eren",
- "Ġform ulated",
- "Ġenf im",
- "ĠTow ards",
- "ко ÑĹ",
- "iver ing",
- "ĠдеÑĤ и",
- "char ger",
- "Ġpur l",
- "Ġacadem ically",
- "ĠNur se",
- "Ġdel eting",
- "ay o",
- "Ġref usal",
- "Ġdepict s",
- "ĠDr acula",
- "Ġtoast ed",
- "ĠZomb ie",
- "ĠSuper ior",
- "ĠB old",
- "Ġquizz es",
- "Ġg le",
- "4 50",
- "Ġcome ço",
- "yn n",
- "Ġver st",
- "ĠO laf",
- "Ġpom oc",
- "ĠS ask",
- "ë ĺ",
- "ĠT CP",
- "ĠProper ty",
- "íķĺ ì£ł",
- "à¸ľ ม",
- "bo om",
- "ar os",
- "ĠÑĢоÑģÑģ ий",
- "ĠбÑĭв аеÑĤ",
- "åĩº åİ»",
- "ĠìĿ´ìķ¼ 기를",
- "Ġcomb ien",
- "v acc",
- "Ġeben falls",
- "par a",
- "Ġз м",
- "Ġdesper ation",
- "ord re",
- "Ġש׾ ×Ļ",
- "Ġgener ously",
- "ĠÐŀ к",
- "Ġorb iting",
- "> ",
- "?",
- "@",
- "A",
- "B",
- "C",
- "D",
- "E",
- "F",
- "G",
- "H",
- "I",
- "J",
- "K",
- "L",
- "M",
- "N",
- "O",
- "P",
- "Q",
- "R",
- "S",
- "T",
- "U",
- "V",
- "W",
- "X",
- "Y",
- "Z",
- "[",
- "\\",
- "]",
- "^",
- "_",
- "`",
- "a",
- "b",
- "c",
- "d",
- "e",
- "f",
- "g",
- "h",
- "i",
- "j",
- "k",
- "l",
- "m",
- "n",
- "o",
- "p",
- "q",
- "r",
- "s",
- "t",
- "u",
- "v",
- "w",
- "x",
- "y",
- "z",
- "{",
- "|",
- "}",
- "~",
- "\u00a1",
- "\u00a2",
- "\u00a3",
- "\u00a4",
- "\u00a5",
- "\u00a6",
- "\u00a7",
- "\u00a8",
- "\u00a9",
- "\u00aa",
- "\u00ab",
- "\u00ac",
- "\u00ae",
- "\u00af",
- "\u00b0",
- "\u00b1",
- "\u00b2",
- "\u00b3",
- "\u00b4",
- "\u00b5",
- "\u00b6",
- "\u00b7",
- "\u00b8",
- "\u00b9",
- "\u00ba",
- "\u00bb",
- "\u00bc",
- "\u00bd",
- "\u00be",
- "\u00bf",
- "\u00c0",
- "\u00c1",
- "\u00c2",
- "\u00c3",
- "\u00c4",
- "\u00c5",
- "\u00c6",
- "\u00c7",
- "\u00c8",
- "\u00c9",
- "\u00ca",
- "\u00cb",
- "\u00cc",
- "\u00cd",
- "\u00ce",
- "\u00cf",
- "\u00d0",
- "\u00d1",
- "\u00d2",
- "\u00d3",
- "\u00d4",
- "\u00d5",
- "\u00d6",
- "\u00d7",
- "\u00d8",
- "\u00d9",
- "\u00da",
- "\u00db",
- "\u00dc",
- "\u00dd",
- "\u00de",
- "\u00df",
- "\u00e0",
- "\u00e1",
- "\u00e2",
- "\u00e3",
- "\u00e4",
- "\u00e5",
- "\u00e6",
- "\u00e7",
- "\u00e8",
- "\u00e9",
- "\u00ea",
- "\u00eb",
- "\u00ec",
- "\u00ed",
- "\u00ee",
- "\u00ef",
- "\u00f0",
- "\u00f1",
- "\u00f2",
- "\u00f3",
- "\u00f4",
- "\u00f5",
- "\u00f6",
- "\u00f7",
- "\u00f8",
- "\u00f9",
- "\u00fa",
- "\u00fb",
- "\u00fc",
- "\u00fd",
- "\u00fe",
- "\u00ff",
- "\u0100",
- "\u0101",
- "\u0102",
- "\u0103",
- "\u0104",
- "\u0105",
- "\u0106",
- "\u0107",
- "\u0108",
- "\u0109",
- "\u010a",
- "\u010b",
- "\u010c",
- "\u010d",
- "\u010e",
- "\u010f",
- "\u0110",
- "\u0111",
- "\u0112",
- "\u0113",
- "\u0114",
- "\u0115",
- "\u0116",
- "\u0117",
- "\u0118",
- "\u0119",
- "\u011a",
- "\u011b",
- "\u011c",
- "\u011d",
- "\u011e",
- "\u011f",
- "\u0120",
- "\u0121",
- "\u0122",
- "\u0123",
- "\u0124",
- "\u0125",
- "\u0126",
- "\u0127",
- "\u0128",
- "\u0129",
- "\u012a",
- "\u012b",
- "\u012c",
- "\u012d",
- "\u012e",
- "\u012f",
- "\u0130",
- "\u0131",
- "\u0132",
- "\u0133",
- "\u0134",
- "\u0135",
- "\u0136",
- "\u0137",
- "\u0138",
- "\u0139",
- "\u013a",
- "\u013b",
- "\u013c",
- "\u013d",
- "\u013e",
- "\u013f",
- "\u0140",
- "\u0141",
- "\u0142",
- "\u0143",
- "\u0120t",
- "\u0120a",
- "\u0120th",
- "in",
- "er",
- "\u0120w",
- "\u0120s",
- "ou",
- "\u0120the",
- "re",
- "on",
- "at",
- "en",
- "\u0120c",
- "it",
- "is",
- "\u0120b",
- "nd",
- "\u0120d",
- "\u0120m",
- "\u0120h",
- "\u0120o",
- "ing",
- "es",
- "\u0120p",
- "\u0120to",
- "an",
- "\u0120f",
- "or",
- "ll",
- "\u0120I",
- "\u0120l",
- "\u0120y",
- "ar",
- "\u0120g",
- "\u0120you",
- "ed",
- "\u0120and",
- "\u0120in",
- "\u0120of",
- "as",
- "\u0120n",
- "om",
- "ic",
- "\u0120that",
- "us",
- "et",
- "ve",
- "al",
- "ow",
- "le",
- "\u0120is",
- "\u0120e",
- "\u0120it",
- "ot",
- "'s",
- "\u0120be",
- "ion",
- "\u0120T",
- "\u0120wh",
- "\u0120A",
- "ent",
- "\u0120S",
- "\u0120re",
- "ay",
- "\u0120we",
- "\u0120on",
- "ere",
- "\u0120ha",
- "ut",
- "ac",
- "id",
- "ig",
- "os",
- "ke",
- "ver",
- "im",
- "\u0120\u00d0",
- "\u0120Th",
- "am",
- "all",
- "\u0120for",
- "el",
- "ch",
- "ro",
- "\u0120this",
- "\u0120st",
- "\u0120W",
- "\u0120u",
- "ad",
- "out",
- "ir",
- "ld",
- "ct",
- "\u0120k",
- "if",
- "\u0120go",
- "..",
- "\u00d0\u00be",
- "ith",
- "ly",
- "ht",
- "qu",
- "\u0120-",
- "\u0120do",
- "\u0120j",
- "\u0120have",
- "\u0120B",
- "\u0120an",
- "\u0120with",
- "\u0120are",
- "\u0120r",
- "\u0120de",
- "\u0120se",
- "\u0120so",
- "\u0120v",
- "st",
- "ill",
- "ur",
- "\u0120li",
- "\u0120M",
- "est",
- "od",
- "ally",
- "'t",
- "ust",
- "\u0120as",
- "\u0120C",
- "ce",
- "\u0120me",
- "\u00d0\u00b0",
- "\u00d0\u00b5",
- "il",
- "\u0120H",
- "\u0120was",
- "ter",
- "th",
- "\u0120can",
- "ant",
- "\u0120com",
- "our",
- "ight",
- "\u0120Y",
- "ation",
- "\u0120And",
- "ol",
- "\u0120sh",
- "\u00d1\u0124",
- "op",
- "se",
- "\u0120not",
- "\u0120So",
- "\u0120ne",
- "un",
- "\u0120ab",
- "\u0120like",
- "\u0120at",
- "\u0120D",
- "ie",
- "\u0120he",
- "\u0120con",
- "\u0120ch",
- "ore",
- "\u0120al",
- "\u0120or",
- "\u0120qu",
- "\u0120O",
- "ome",
- "ra",
- "ul",
- "\u0120N",
- "pp",
- "\u0120your",
- "ould",
- "\u0120P",
- "\u0120fr",
- "ge",
- "ers",
- "'re",
- "\u00d0\u00b8",
- "\u0120they",
- "\u0120what",
- "use",
- "\u0120all",
- "\u0120The",
- "\u0120L",
- "ess",
- "em",
- "\u0120kn",
- "\u0120just",
- "art",
- "\u0120pro",
- "very",
- "um",
- "\u0120lo",
- "\u0120\u00ec",
- "\u0120my",
- "ok",
- "\u0120ex",
- "ab",
- "\u0120there",
- "\u0120but",
- "\u0120know",
- "\u0120su",
- "\u0120G",
- "\u00d1\u0123",
- "\u0120E",
- "\u0120ma",
- "\u00d0\u00be\u00d0",
- "\u0120en",
- "\u0120about",
- "\u0120It",
- "ist",
- "\u0120wor",
- "ri",
- "ind",
- "\u0120one",
- "ate",
- "and",
- "ink",
- "\u0120le",
- "ort",
- "'m",
- "\u0120F",
- "ich",
- "\u00d1\u0122",
- "ide",
- "\u0120get",
- "\u0120out",
- "...",
- "\u0120will",
- "\u00e3\u0123",
- "ive",
- "\u00d0\u00bd",
- "\u0120from",
- "ain",
- "\u0120We",
- "\u0120up",
- "pe",
- "res",
- "ca",
- "\u0120R",
- "\u0120if",
- "\u0120pl",
- "\u0120don",
- "ack",
- "\u01201",
- "\u0120\"",
- "\u0120tr",
- "\u0120us",
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- "\u0120sen",
- "\u0120parents",
- "\u0120Wow",
- "\u0120feed",
- "\u0120save",
- "\u0120serious",
- "\u0120init",
- "EL",
- "undred",
- "AS",
- "\u0120van",
- "orrow",
- "\u0120worth",
- "\u0120search",
- "\u012016",
- "\u0120parts",
- "\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120compan",
- "\u0120movie",
- "\u0120method",
- "\u0120ill",
- "\u0120wish",
- "dy",
- "\u0120item",
- "\u0120minus",
- "anger",
- "\u0120voice",
- "\u0120skin",
- "\u0120areas",
- "\u0120eight",
- "\u0120obs",
- "\u0120,",
- "\u00d0\u00b0\u00d0\u00b9",
- "\u0120oil",
- "\u0120cy",
- "\u0120baby",
- "sy",
- "\u0120employ",
- "\u0120Ke",
- "\u0120places",
- "\u0120fix",
- "\u0120est\u00c3\u00a1",
- "\u00e3\u0123\u00a8",
- "ived",
- "\u0120lots",
- "\u0120season",
- "unk",
- "alt",
- "\u0120table",
- "\u0120\u00d0\u00a2",
- "\u00c3\u00a2",
- "\u0120attention",
- "\u00e3\u0123\u00aa",
- "\u0120Her",
- "\u0120age",
- "\u0120pra",
- "back",
- "cil",
- "\u0120network",
- "rit",
- "\u0120doc",
- "\u0120aren",
- "igen",
- "\u0120\u00eb\u0126",
- "\u00d8\u00af",
- "ender",
- "\u0120total",
- "\u0120price",
- "\u0120crazy",
- "\u00ec\u013c",
- "iqu",
- "though",
- "You",
- "\u00d9\u0129",
- "\u00e3\u0124\u0135",
- "\u00cf\u0127",
- "\u0120sat",
- "\u0120bi",
- "\u0120Die",
- "\u0120sha",
- "\u0120thanks",
- "uh",
- "\u0120stage",
- "\u00d0\u00b0\u00d0\u00b6",
- "\u0120Fl",
- "\u0120leav",
- "\u0120boy",
- "\u0120af",
- "\u00c3\u00b6n",
- "\u0120Get",
- "\u0120accept",
- "\u0120enter",
- "\u0120tur",
- "\u0120si\u00c4\u013b",
- "\u0120honest",
- "\u00e3\u0122\u012e",
- "\u0120sam",
- "\u0120repl",
- "ging",
- "\u0120development",
- "\u0120Act",
- "ora",
- "\u00e3\u0122\u012f",
- "\u00e4\u00be",
- "\u0120knows",
- "\u0120image",
- "\u0120Lord",
- "\u00d0\u00b8\u00d1\u0124\u00d1\u012e",
- "\u0120weeks",
- "\u0120sex",
- "\u0136\u00eb",
- "\u0120hundred",
- "\u0120sounds",
- "\u0120learned",
- "\u0120bud",
- "\u0120\u00d1\u0123\u00d1\u0124",
- "\u0120incred",
- "\u00e2\u013b",
- "\u0120nos",
- "\u0120drop",
- "\u0120ben",
- "\u0120\u00d0\u013a",
- "\u0120safe",
- "ata",
- "\u0120fuck",
- "soci",
- "\u0120dan",
- "\u0120cross",
- "10",
- "mo",
- "vert",
- "\u012017",
- "zie",
- "\u00e5\u0137",
- "\u0120dom",
- "\u0120Bo",
- "\u0120setting",
- "\u0120involved",
- "arily",
- "\u0120sind",
- "\u0120sus",
- "\u0120worry",
- "eth",
- "\u00ea\u00b9\u012e",
- "\u0120sun",
- "\u0120hier",
- "\u0120certainly",
- "oul",
- "orts",
- "\u0120Er",
- "\u0120Um",
- "\u0120caus",
- "\u0120natural",
- "\u0120\u00c3\u00bc",
- "\u0120cry",
- "\u0120Sec",
- "\u0120som",
- "\u00e6\u00b2",
- "\u0120education",
- "\u00d0\u00b0\u00d0\u00b5\u00d1\u0124",
- "\u0120multip",
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- "\u0120eye",
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- "mon",
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- "ites",
- "\u0120Af",
- "reci",
- "attle",
- "\u0120services",
- "\u0120Google",
- "\u00e9\u0123",
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- "\u0120drive",
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- "uz",
- "\u0120Mo",
- "\u00ec\u013e\u00bc\u00eb",
- "val",
- "\u00e5\u0122\u012d",
- "\u0120fol",
- "\u0120\u00ec\u00a2",
- "ffic",
- "\u0120ra",
- "\u0120sin",
- "\u0120blue",
- "\u0120affect",
- "\u0120mis",
- "\u0120shot",
- "\u0120\u00d0\u00be\u00d0\u00b1",
- "asing",
- "\u0120signific",
- "\u0120Che",
- "\u0120\u00ea\u00b3",
- "\u0120positive",
- "\u00ec\u00a3",
- "\u0120wie",
- "\u012040",
- "ording",
- "\u0120From",
- "\u00ea\u00b5",
- "\u0120brand",
- "\u0120trust",
- "\u0120ple",
- "\u0120communic",
- "\u0120weight",
- "\u0120asking",
- "\u0120tax",
- "\u0120Japan",
- "\u00e3\u0123\u0141",
- "\u0120\u00ed\u0137\u013a",
- "ops",
- "\u00cf\u0124",
- "\u0120putting",
- "\u0120roll",
- "\u0120America",
- "reg",
- "\u0140\u00d7",
- "atures",
- "ension",
- "\u0120Somet",
- "\u0120original",
- "ping",
- "\u0120\u00c5\u0141",
- "\u0120products",
- "\u00e3\u0125\u00bc",
- "\u0120contact",
- "olution",
- "\u0120goal",
- "\u0120pow",
- "\u0120performance",
- "\u0120blood",
- "ators",
- "\u0120Mich",
- "\u0120temper",
- "\u0120Dan",
- "\u0120sugg",
- "\u00d1\u0124\u00d0\u00b8",
- "\u0120imm",
- "\u0120office",
- "\u0120arri",
- "\u0120comfort",
- "\u0120\u00d0\u0136",
- "\u0120suggest",
- "\u0120plat",
- "\u0124\u013a",
- "19",
- "\u0120om",
- "\u0120seven",
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- "ille",
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- "\u0120bag",
- "\u00c3\u00bcn",
- "ively",
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- "mos",
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- "\u0120ir",
- "akes",
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- "\u0120\u00d0\u013c",
- "fl",
- "just",
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- "AL",
- "\u0120gotta",
- "\u0120fan",
- "\u0120challenge",
- "\u0120companies",
- "\u0120People",
- "",
- "\u0120heroes",
- "\u0120Boston",
- "\u0120dependent",
- "\u0120motivation",
- "flix",
- "\u0120seam",
- "\u00d0\u00ba\u00d0\u00b8\u00d0\u00b5",
- "\u0120drain",
- "oded",
- "\u0120guilty",
- "\u0120Jenn",
- "ingen",
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- "\u0120Uncle",
- "\u0120Honestly",
- "ELI",
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- "\u0120\u00d1\u0129\u00d0\u00b5\u00d1\u0122",
- "\u0120cann",
- "\u0120tier",
- "\u0120statements",
- "\u00ea\u00b3\u0142\u00ec\u013c\u0136",
- "\u0120\u00eb\u0137\u012e\u00eb\u00ac\u00b8\u00ec\u0139\u0132",
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- "an\u00c3\u00a7",
- "\u0120Kenn",
- "\u0120Ni",
- "\u0120Fight",
- "organ",
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- "\u0120S\u00c3\u0143",
- "\u00e3\u0125\u00aa",
- "\u0120slic",
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- "\u0120Federal",
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- "\u0120daar",
- "\u0120prompt",
- "\u0120pursue",
- "\u0120entertainment",
- "\u0120throat",
- "\u0120problema",
- "\u0120mart",
- "\u00ec\u00bc",
- "\u0120provider",
- "\u00d8\u012e",
- "\u0120\u00d7\u0139",
- "inte",
- "making",
- "\u0120stroke",
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- "Un",
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- "\u0120\u00d0\u0140\u00d0\u00bd",
- "\u0120\u00d0\u00b8\u00d1\u0123",
- "nah",
- "\u0120\u00d0\u0137\u00d1\u0123\u00d0\u00bb\u00d0\u00b8",
- "\u0120corners",
- "\u0120tricky",
- "inch",
- "lijk",
- "\u0120pressing",
- "level",
- "ANG",
- "\u0120radiation",
- "\u00ec\u0126\u0142",
- "\u0120confront",
- "\u0120vet",
- "\u0120representative",
- "\u0120propag",
- "\u0120crap",
- "\u0120Dec",
- "\u0120ramp",
- "\u00d0\u00b5\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d1\u012e",
- "u\u00c3\u00a9s",
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- "cription",
- "\u0120bills",
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- "\u00e1\u00ba\u00a5t",
- "\u0120lowest",
- "has",
- "screen",
- "ograp",
- "\u00d0\u00b0\u00d0\u00bb\u00d0\u00be",
- "inton",
- "\u0120Jah",
- "\u00e8\u0122\u0127",
- "it\u00c3\u0142",
- "\u0120kay",
- "\u0120rotation",
- "\u0120Were",
- "abei",
- "\u0120trials",
- "\u0120lever",
- "ighty",
- "\u0120spoon",
- "\u0120hunt",
- "cling",
- "\u0120dism",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a",
- "\u0120assault",
- "\u0120\u00ed\u013a\u0137",
- "\u0120weekly",
- "\u0120mismo",
- "\u0120genetic",
- "ulpt",
- "\u0120Student",
- "\u0120realistic",
- "\u0120authentic",
- "\u00e6\u012b\u0135",
- "asta",
- "\u0120arrested",
- "\u0120guidelines",
- "\u0120\u00d7\u013e\u00d7\u0132",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00b2",
- "\u0120Coming",
- "f\u00c3\u00bcr",
- "\u0120requests",
- "\u0125\u0132",
- "\u0120analyze",
- "\u0120interess",
- "\u0120halt",
- "\u0120Oper",
- "onom",
- "\u0120duck",
- "\u0120withd",
- "ser",
- "\u0120\u00cf\u012e",
- "\u0120History",
- "\u0120youtube",
- "\u00e3\u0124\u012f",
- "\u0120saber",
- "walk",
- "font",
- "\u0120overview",
- "39",
- "\u00c3\u00bcy",
- "etti",
- "\u0120frozen",
- "\u0120flesh",
- "\u00c4\u0141i",
- "\u0120PM",
- "\u0120\u00ec\u013b\u0122",
- "\u00e9\u00a2",
- "\u00d1\u0128\u00d0\u00b8\u00d0\u00b8",
- "\u0120\u00ea\u00b8\u00b0\u00eb",
- "\u00ed\u0123\u00ac",
- "\u0120prose",
- "oooo",
- "rates",
- "WS",
- "\u0120automatic",
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- "\u00c5\u0133",
- "\u0120neighbors",
- "\u00c2\u00bb.",
- "\u0120Expl",
- "\u0120circul",
- "cover",
- "weg",
- "\u0120sticks",
- "\u0120eller",
- "\u0120www",
- "\u0120dorm",
- "\u0120Exper",
- "\u0120statistics",
- "\u0120emails",
- "\u0120grave",
- "imiz",
- "HS",
- "\u0120uit",
- ",'",
- "\u0120laser",
- "\u00e8\u012b",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00bc",
- "\u00d1\u012d\u00d1\u012a",
- "\u00d1\u012b\u00d1\u0133",
- "\u0120genau",
- "\u0120tienen",
- "\u0120meditation",
- "\u0120Organ",
- "\u0120estimate",
- "\u0120\u00eb\u00ac\u00b4\u00ec",
- "lets",
- "\u0120n\u00c3\u0142y",
- "\u0120mindset",
- "\u0120reson",
- "\u0120m\u00c3\u00a9s",
- "\u0120numerous",
- "\u0120vielleicht",
- "\u0120Third",
- "uous",
- "\u0120Dead",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b4",
- "HN",
- "\u0120racing",
- "\u0120agents",
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- "\u0120tear",
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- "\u0120survival",
- "\u00e6\u0138\u00b0",
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- "\u0120;",
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- "\u0120ES",
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- "300",
- "\u0120ense",
- "\u0120\u00ec\u00b5",
- "\u0120dict",
- "GA",
- "\u0120ah\u00c3\u0143",
- "\u00e5\u012d\u0137",
- "\u0120tej",
- "\u0120\u00d0\u00be\u00d1\u0123\u00d1\u0124",
- "\u0120Elect",
- "\u0120intellectual",
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- "\u0120burden",
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- "\u0120\u00ec\u0138\u00b4\u00eb\u0138\u00bb",
- "\u0120cheer",
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- "\u0120Jetzt",
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- "\u0120brut",
- "\u0120salad",
- "\u00d0\u00be\u00d0\u00b1\u00d1\u012b\u00d0\u00b5",
- "\u0120solving",
- "\u0120floating",
- "\u00e7\u00b7",
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- "\u00d9\u012a\u00d9\u0126",
- "\u0120perd",
- "iffer",
- "\u0120sculpt",
- "hhh",
- "\u0120Week",
- "\u0120enthus",
- "\u0120nad",
- "\u0120merch",
- "\u0120\u00ed\u013b\u0137",
- "\u0120mile",
- "\u00e5\u00a5\u00bd\u00e4\u00ba\u0128",
- "\u0120\u00ce\u00b8",
- "\u0120\u00eb\u0124\u013a\u00eb",
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- "38",
- "\u0120chains",
- "\u0120Almost",
- "\u0120tickets",
- "rin",
- "\u0120CC",
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- "uno",
- "\u0120entrepreneurs",
- "\u0120Network",
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- "\u0120prev",
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- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00b1\u00d1\u0131",
- "\u0120Poke",
- "\u0120Pod",
- "\u0120Turkey",
- "\u00e7\u0131\u00be\u00e5\u013e\u00a8",
- "\u0120abstract",
- "\u0120snake",
- "\u0120Amy",
- "\u0120\u00eb\u012c\u0132\u00eb\u0124\u012e",
- "\u0120brave",
- "\u0120\u00ec\u0140\u012a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120Kal",
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- "\u00c3\u00a1rio",
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- "gines",
- "\u0120alloc",
- "ONG",
- "\u0120scientist",
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- "\u0120per\u00c3\u00b2",
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- "\u0120Deutsch",
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- "\u0120\u00eb\u00b0\u0136",
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- "dri",
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- "aland",
- "\u012034",
- "06",
- "\u0120Bru",
- "\u0120\u00ec\u0142\u0137\u00eb\u00a7\u0132",
- "Just",
- "boy",
- "\u0120shoe",
- "\u0120creature",
- "\u0120headed",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00ba",
- "\u00e6\u00b1",
- "\u0120essence",
- "\u0120remarkable",
- "\u0120n\u00c3\u00bamer",
- "\u0120drew",
- "\u0120puzzle",
- "\u0120Library",
- "\u0120Fu",
- "ashes",
- "kk",
- "\u0120Ist",
- "\u00a6\u00b0",
- "\u0120Bry",
- "\u0120ceremony",
- "\u0120\u00e0\u00ae\u0130",
- "\u0120cri",
- "equ",
- "\u00e3\u0124\u00a2",
- "\u0120prize",
- "\u0120dimensions",
- "ogram",
- "\u0120leather",
- "\u0120populations",
- "uum",
- "\u0120vegan",
- "\u00d1\u0131\u00d0\u00b4",
- "\u0120c\u00c3\u00b3mo",
- "\u00e5\u0126",
- "\u0120strip",
- "\u00e5\u00a3",
- "\u0120vacation",
- "\u0127\u0137",
- "\u0120meals",
- "ilipp",
- "\u0120ents",
- "aram",
- "richt",
- "\u0120grain",
- "\u0120Spain",
- "\u0120cheek",
- "\u0120Aff",
- "ION",
- "\u0120Bring",
- "\u012038",
- "ielen",
- "ulu",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a\u00d0\u00b5",
- "\u0120announcement",
- "\u0120\u00d1\u0124\u00d1\u0125\u00d1\u0124",
- "\u0120Prophet",
- "ardo",
- "37",
- "\u0120woke",
- "\u0120translation",
- "\u0120NOT",
- "\u0120CL",
- "\u0120d\u00c3\u00bc\u00c5\u0141",
- "\u00d1\u0128\u00d1\u0138",
- "acer",
- "\u0120Loc",
- "\u0120perception",
- "NO",
- "\u0120diesen",
- "Look",
- "heart",
- "aved",
- "\u0120boundary",
- "\u0120flows",
- "\u00d1\u0133\u00d0\u00bc",
- "\u0120arguments",
- "\u0120elections",
- "\u00c4\u00b1s",
- "\u0120heck",
- "\u0120suitable",
- "\u0120fiber",
- "\u0120Stra",
- "xy",
- "\u0120Hum",
- "\u0120monthly",
- "uper",
- "\u0120golf",
- "\u0120lately",
- "\u0120Gard",
- "\u0120Ren",
- "\u0120Ast",
- "\u0120Fant",
- "\u00d0\u00b0\u00d1\u0123\u00d1\u0123",
- "\u0120obser",
- "\u00eb\u00a1\u013e",
- "\u0120easiest",
- "\u012f\u0136\u00eb",
- "\u0120websites",
- "pol",
- "\u0120cocon",
- "\u0120\u00e0\u00ae\u0129",
- "\u0120Veg",
- "\u0120walks",
- "\u0120intro",
- "\u0120directed",
- "\u0120Anna",
- "\u0120\u00eb\u0135\u00a4\u00ec\u0138\u00b4",
- "\u0120Eastern",
- "\u0120Saint",
- "\u0120Bow",
- "\u0120roast",
- "\u0120URL",
- "\u0120jeden",
- "uras",
- "aja",
- "\u0120semi",
- "\u0120rapidly",
- "\u0120targets",
- "\u0120Control",
- "\u0120bah",
- "\u0120reflection",
- "\u0120creativity",
- "holders",
- "\u0120\u00ec\u013a\u00ac\u00eb",
- "\u0120amongst",
- "\u0120feeding",
- "\u00d1\u012f\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d1\u0125",
- "\u0120\u00d0\u00b2\u00d0\u00b8\u00d0\u00b4\u00d0\u00b5",
- "\u0120\u00eb\u00a7\u012e\u00eb\u0135\u00a4",
- "\u0120Smart",
- "\u0120reliable",
- "\u0120vezes",
- "\u0120\u00d7\u00a8",
- "chuckles",
- "azione",
- "\u0120Williams",
- "\u0120a\u00c3\u00a7",
- "\u0120slee",
- "\u00d0\u00b5\u00d1\u012b",
- "\u0120timeline",
- "\u0120thorough",
- "\u00e1\u00bb\u012f",
- "\u0120Ot",
- "\u00e1\u00ba\u00a1n",
- "\u0120imagination",
- "\u0120mechanics",
- "rist",
- "\u0120claimed",
- "\u00cf\u0126\u00ce\u00b7",
- "\u00c3\u00aate",
- "\u0120Hurry",
- "\u0120iPad",
- "\u0120constru",
- "\u0120Cla",
- "\u0120Als",
- "\u00e4\u00bc\u013c",
- "utz",
- "\u0120cultures",
- "\u0120\u00ec\u0138\u00b4\u00eb\u0138\u00bb\u00ea\u00b2\u012e",
- "\u0120belongs",
- "\u0120yer",
- "\u0120Doesn",
- "\u0120geomet",
- "\u0120bid",
- "\u0120foam",
- "\u0120hob",
- "\u0120Britain",
- "\u0120substance",
- "\u0120anniversary",
- "\u0120\u00eb\u0126\u012a",
- "\u0120noted",
- "\u0120governor",
- "\u0120stocks",
- "31",
- "\u0120diye",
- "\u00ec\u012c\u00a4\u00eb",
- "\u0120reb",
- "zel",
- "\u0120multiply",
- "\u0120operator",
- "\u0126\u00a4\u00ec\u013c\u0136",
- "\u0120waters",
- "\u0120d\u00c3\u00a4r",
- "\u0120unser",
- "\u0120Elizabeth",
- "\u00e9\u00ab\u013a",
- "\u0120increasingly",
- "\u0120Gro",
- "\u0120engines",
- "irs",
- "\u00d8\u00ab",
- "\u0120treasure",
- "PC",
- "inction",
- "iri",
- "\u0120accum",
- "\u0120variation",
- "\u0120pom",
- "\u0120titles",
- "\u0120Fest",
- "\u00c3\u00b3s",
- "\u0120elder",
- "nym",
- "run",
- "\u00d1\u0131\u00d0\u00b2",
- "\u0120innovative",
- "\u0120nombre",
- "\u0120coinc",
- "\u0120franch",
- "\u0120entonces",
- "\u0120nichts",
- "\u0120exclusive",
- "\u0120Cheers",
- "\u0120Bi",
- "uje",
- "\u00e6\u0143\u00a1",
- "\u0120pok",
- "\u0120Prem",
- "\u0120rocket",
- "ELIPE",
- "\u0120hospitals",
- "rium",
- "\u0120juste",
- "\u0120hammer",
- "\u0120quantum",
- "\u0120responses",
- "lly",
- "endi",
- "\u0120actively",
- "\u0120fridge",
- "iate",
- "long",
- "\u0120quem",
- "\u0120deaths",
- "\u0120superior",
- "cken",
- "\u00ec\u013f\u00b4\u00ec\u0139\u0132",
- "ktop",
- "\u0120gathered",
- "\u00a3\u00a8",
- "\u0120dazu",
- "\u0120recipes",
- "\u0120buzz",
- "cen",
- "\u0120anytime",
- "onsense",
- "\u0120circles",
- "\u0120solved",
- "\u0120\u00ec\u012d\u0142",
- "\u0120coronavirus",
- "\u0120Luke",
- "\u0120bubb",
- "\u0120contempor",
- "rzy",
- "\u0120Jane",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bc",
- "\u0120screws",
- "\u0120hybrid",
- "\u0120casual",
- "\u0120selbst",
- "being",
- "\u0120\u00c4\u0132",
- "\u0120Columb",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0129",
- "\u0120bucket",
- "\u0120evaluate",
- "\u0120idol",
- "\u0120reputation",
- "\u0120\u00ec\u0128\u012e\u00eb",
- "\u00d9\u012a\u00d8\u00b1",
- "\u0120hecho",
- "\u0120poem",
- "\u0120subjects",
- "plant",
- "\u0120Beh",
- "\u0120Speaking",
- "\u0120batteries",
- "\u0120followers",
- "\u00c3\u00b6l",
- "\u0120gently",
- "\u0120sixt",
- "\u0120parameter",
- "\u0120ikke",
- "\u0120Tour",
- "\u0120DJ",
- "otte",
- "\u0120Jahren",
- "\u0120preparation",
- "\u0120\u00d0\u00b4\u00d1\u0125\u00d0\u00bc",
- "\u0120800",
- "cop",
- "iking",
- "\u0120\u00eb\u00ac\u00b8",
- "\u0120\u00d0\u00bd\u00d1\u0125",
- "\u0120\u00d0\u00bb\u00d0\u00b5\u00d1\u0124",
- "\u00e5\u0132\u012e",
- "\u0120Ide",
- "\u0120\u00ec\u00a1\u00b0\u00ea\u00b8\u012a",
- "\u0120laughter",
- "\u0120molecules",
- "\u0120Rest",
- "\u0120observed",
- "dzie",
- "\u0120advertising",
- "erto",
- "\u0120moins",
- "\u0120MIT",
- "\u0120excit",
- "\u0120tum",
- "\u0120tyl",
- "\u0120invested",
- "\u0120pharm",
- "\u0120unexpected",
- "\u0120phi",
- "otype",
- "weise",
- "\u0120ge\u00c3\u00a7",
- "jourd",
- "\u0120horses",
- "n\u00c4\u0127",
- "=\"",
- "\u0120SM",
- "\u0120fib",
- "\u0120clips",
- "\u00e7\u0137\u00b6",
- "\u00e5\u00a6\u0124\u00e6\u0140\u013e",
- "\u0120regime",
- "\u0120rotate",
- "rou",
- "nik",
- "\u0120armor",
- "\u00f0\u0141\u013a",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00b0",
- "\u00e5\u00ba\u00a6",
- "\u0120Och",
- "\u0120richtig",
- "\u00c3\u00bczel",
- "aneously",
- "mek",
- "\u00e9\u012e\u00af",
- "\u0120Xiao",
- "\u0120existed",
- "worth",
- "\u00e3\u0123\u00a3\u00e3\u0123\u00a8",
- "\u0120naught",
- "\u0120hei\u00c3\u0141t",
- "\u0120Bal",
- "\u0120resid",
- "ivot",
- "omatic",
- "\u0120hired",
- "\u0120gradually",
- "\u0120onions",
- "\u0120compat",
- "\u0120intim",
- "\u0120jew",
- "\u0120contribution",
- "\u0120Ire",
- "acji",
- "\u0120slice",
- "\u0120immun",
- "\u0120Rus",
- "\u0120grows",
- "\u0120Similarly",
- "\u0120hardest",
- "\u0120struck",
- "\u0120measurement",
- "...]",
- "they",
- "\u0120\u00ec\u0142\u0122\u00eb",
- "\u0120sneak",
- "\u0120applies",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00bc",
- "\u00e6\u0135",
- "\u00d7\u0133\u00d7\u00a8",
- "\u0120\u00d0\u00a7\u00d1\u0124\u00d0\u00be",
- "\u0120outro",
- "\u0120innocent",
- "\u0120mog",
- "\u0120Samsung",
- "\u0120mercy",
- "\u0120handling",
- "\u0120intervention",
- "idays",
- "got",
- "\u0120curric",
- "\u0120boundaries",
- "\u0120confusing",
- "\u013f\u00bc\u00eb\u012c\u0136",
- "\u00e6\u0129",
- "\u0120stitches",
- "\u00c3\u0143vel",
- "\u0120tunnel",
- "it\u00c3\u00a4",
- "\u0120gost",
- "imy",
- "\u0120czas",
- "\u0120m\u00c3\u00a9",
- "\u0120catal",
- "\u0120Simon",
- "\u0120LIAM",
- "mic",
- "\u0120\u00d0\u00a4",
- "\u0120eyel",
- "isas",
- "\u0120CPU",
- "\u0120Dou",
- "\u0120n\u00c3\u00a4ch",
- "\u0120infinity",
- "\u0120rif",
- "\u0120Peace",
- "\u0120Cu",
- "\u0120minimal",
- "\u0120listened",
- "\u0120pole",
- "halb",
- "\u0120loaded",
- "\u0120steady",
- "\u0120Besides",
- "\u00c3\u00aam",
- "\u0120lap",
- "\u0120coop",
- "\u0120friendship",
- "world",
- "\u0120geh",
- "\u0120tylko",
- "\u0120Laura",
- "\u0120surrounded",
- "\u0120Event",
- "\u0120chap",
- "\u0120Wonder",
- "break",
- "\u0120drove",
- "\u0120broader",
- "\u0120chi",
- "Fi",
- "\u0120gehen",
- "\u0120western",
- "\u0120intelligent",
- "\u0120persist",
- "\u0120founded",
- "\u00e3\u0123\u0135\u00e3\u0123\u00a8",
- "\u0120historic",
- "\u0120fr\u00c3\u00a5",
- "cks\u00c3\u00a5",
- "\u0120handy",
- "\u0120symp",
- "\u0120rows",
- "\u0120nutri",
- "bur",
- "\u0120Leon",
- "\u0120sistema",
- "\u0120extensive",
- "\u0120\u00d1\u0125\u00d0\u00b2",
- "\u00ed\u0131",
- "\u0120nights",
- "\u0120c\u00c3\u00a1c",
- "\u0120counting",
- "\u0120Must",
- "allow",
- "\u00d0\u00b5\u00d1\u0123\u00d1\u0123",
- "Mom",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b4\u00d0\u00be",
- "\u0120barrel",
- "\u00e3\u0125\u0140",
- "ARD",
- "\u0120installation",
- "\u0120insect",
- "\u0120\u00eb\u0127\u00b8\u00eb",
- "uj\u00c4\u0127",
- "\u0120\u00c4\u0133i",
- "\u0120packed",
- "\u0120fiction",
- "Now",
- "\u0120Yay",
- "\u0120pert",
- "rons",
- "unde",
- "aches",
- "\u0120styles",
- "\u0120apr\u00c3\u00a8s",
- "oku",
- "\u0120Vice",
- "\u00c4\u00b1n\u00c4\u00b1z",
- "comm",
- "\u0120assigned",
- "\u0120interactions",
- "\u0120acab",
- "FELIPE",
- "\u0120rescue",
- "\u0120industries",
- "\u0120Andy",
- "\u0120praise",
- "\u0120flame",
- "\u0120snack",
- "\u00ed\u0124",
- "\u00e7\u0123",
- "\u0120swo",
- "render",
- "\u0120boards",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00bc",
- "enne",
- "\u0120pasta",
- "\u0120devil",
- "\u0120Fel",
- "\u0120hatte",
- "\u0120colleg",
- "eh",
- "\u00ec\u00bb",
- "\u00e3\u0123\u0135\u00e3\u0123\u00ae",
- "\u0120productive",
- "forward",
- "\u00d0\u00b8\u00d0\u00bf",
- "\u0120smartphone",
- "\u0120invis",
- "\u0120bum",
- "\u0120whoa",
- "\u00ec\u0140\u0126",
- "\u0120ocks\u00c3\u00a5",
- "\u0120Lang",
- "\u0120Syria",
- "\u0120sesi",
- "\u00ce\u00af\u00ce\u00b1",
- "\u0120approval",
- "48",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00b8\u00d0\u00bd",
- "\u0120\u00eb\u0138",
- "\u0120Harr",
- "\u0120Administ",
- "\u0120\u00d7\u00a4",
- "\u0120Dean",
- "fi",
- "\u0120citizen",
- "\u0120shark",
- "05",
- "\u0120boil",
- "\u0120indicate",
- "\u00e5\u00a1",
- "Are",
- "\u0120layout",
- "\u0120refr",
- "\u0120Pacific",
- "AAAA",
- "\u0120Australian",
- "gression",
- "Voice",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u0123\u00d1\u0131",
- "\u0120shelter",
- "To",
- "aupt",
- "\u0120evaluation",
- "apor",
- "\u0120currency",
- "\u0120\u00d0\u00bc\u00d0\u00bd\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "igos",
- "\u00e3\u0123\u00b0",
- "\u0120oct",
- "\u0120royal",
- "\u00e8\u00b3",
- "asil",
- "\u0120Children",
- "\u0120rien",
- "\u0120\u00eb\u0135\u013e\u00eb",
- "\u0120barrier",
- "\u0120ejemplo",
- "\u0120ek",
- "ND",
- "esp",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00b0",
- "\u0120pic",
- "\u0120killer",
- "\u0120integrate",
- "\u0120fewer",
- "\u0120disabilities",
- "\u0120....",
- "\u0120triangle",
- "\u0120fees",
- "\u0120widely",
- "emi",
- "\u0120overwhelming",
- "\u0120zomb",
- "\u0120bere",
- "\u0120hood",
- "\u0120Aye",
- "\u0120Harvard",
- "ev",
- "\u0120\u00cf\u0126\u00ce\u00bf\u00cf\u0127",
- "\u0120cups",
- "\u0120Auch",
- "zona",
- "\u01201990",
- "\u0120wei\u00c3\u0141",
- "\u0120crunch",
- "\u00e6\u00a5",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00b2",
- "\u0120measuring",
- "\u0120stations",
- "\u0120Stephen",
- "\u0120shortly",
- "\u0120signing",
- "\u0120comedy",
- "omo",
- "\u0120suggestions",
- "\u0120signature",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00b2",
- "\u0120disorder",
- "aska",
- "\u0120worlds",
- "\u0120precisely",
- "norm",
- "rav",
- "\u0120Civil",
- "Inter",
- "\u0120Certain",
- "\u0120injured",
- "\u0120suggests",
- "\u0120Golden",
- "\u0120cyber",
- "\u0120\u00d8\u00b4",
- "\u0120temporary",
- "\u0120cooper",
- "\u0120voted",
- "\u0120ought",
- "\u00e1\u00ba\u00a5y",
- "xual",
- "\u0120panels",
- "\u012095",
- "\u0120handsome",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120permit",
- "\u0120kein",
- "\u0120badly",
- "\u0120notifications",
- "iza",
- "\u0120Notice",
- "\u0120inclusive",
- "\u0120answering",
- "\u0120\u00ed\u0139",
- "uld",
- "\u00ed\u0127\u012e",
- "\u0120nowadays",
- "\u012037",
- "\u0120bolt",
- "\u0120static",
- "\u0120Hop",
- "\u0120avant",
- "ajo",
- "\u0120\u00eb\u00a7\u013d\u00ec\u0140\u012a",
- "\u0120fifty",
- "\u0120Final",
- "\u0120scores",
- "\u0120Tap",
- "\u0120cyl",
- "\u0120convince",
- "\u0120anyways",
- "oda",
- "\u0120\u00ec\u0137\u00bc",
- "\u0120serves",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b9",
- "\u0120Zoom",
- "\u0120savings",
- "ulo",
- "\u0120southern",
- "viewer",
- "\u0120hoje",
- "\u0120seja",
- "\u0120representing",
- "\u012a\u00eb\u012f\u013a",
- "lik",
- "\u0120Somebody",
- "\u0120beast",
- "\u0120sticking",
- "\u0120insist",
- "\u0120talented",
- "\u0120explaining",
- "\u0120attorney",
- "\u00e9\u0125\u00a8",
- "\u0120stairs",
- "\u0120Dog",
- "\u00ed\u012d",
- "\u0120cig",
- "\u0120shaped",
- "\u0120sons",
- "\u00cf\u0123\u00ce\u00b9",
- "utt",
- "\u0120\u00ec\u0136",
- "\u0120parad",
- "\u00ec\u013f\u00b8\u00eb\u012f\u00b0",
- "\u0120horn",
- "\u0120Jour",
- "anno",
- "\u0120worldwide",
- "\u00e5\u012c\u013d",
- "\u0120participation",
- "\u00a6\u0126",
- "\u0120m\u00c3\u00b3w",
- "\u0120burned",
- "\u0120writers",
- "allah",
- "\u0120Fund",
- "\u0120clever",
- "\u0120Leute",
- "bin",
- "\u0120beating",
- "foot",
- "\u0120\u00ec\u013d\u0132",
- "\u0120Studio",
- "\u0120vag",
- "bey",
- "rze",
- "\u0120opposition",
- "\u0120\u00d0\u00b6\u00d0\u00b8\u00d0\u00b7",
- "who",
- "\u0120\u00ea\u00b1\u00b4",
- "\u0120trace",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bd\u00d1\u012e",
- "\u0120epid",
- "\u0120gesch",
- "\u0120Nar",
- "\u0120BE",
- "\u00d1\u0125\u00d0\u00b9",
- "\u0120Sign",
- "edly",
- "\u0120clay",
- "\u0120instantly",
- "\u0120gathering",
- "\u0120Galaxy",
- "\u0120bored",
- "\u0120Buddh",
- "c\u00c3\u00a9",
- "\u0120mam",
- "\u0120slope",
- "\u0120\u00eb\u012d\u00a4\u00ec\u013f\u012e",
- "\u0120sch\u00c3\u00b6n",
- "\u0120pir",
- "gef",
- "amer",
- "\u0120h\u00c3\u00b6",
- "\u0120colleague",
- "\u0120presents",
- "adium",
- "\u0120\u00e0\u00ae\u00b5",
- "\u0120falar",
- "beep",
- "\u0120dried",
- "isms",
- "\u0120rope",
- "\u0120workshop",
- "\u0120estud",
- "\u0120bands",
- "\u0120themes",
- "\u00e5\u0127\u00ac",
- "\u00d9\u012c\u00d8\u00b1",
- "\u00e5\u0132\u0130",
- "\u0120reminder",
- "\u00d1\u0124\u00d1\u0125",
- "\u0120Bh",
- "\u0120coconut",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u0120Channel",
- "\u0120immigration",
- "\u00c3\u00a4s",
- ".....",
- "\u00e4\u00b8\u00bb",
- "\u00e7\u013b\u00bd",
- "stop",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d1\u0122",
- "\u0120coins",
- "\u0120\u00d1\u0129\u00d0\u00b0\u00d1\u0123",
- "\u0120destruction",
- "lined",
- "\u0120barriers",
- "antine",
- "\u0120printed",
- "\u0120congratulations",
- "\u0120Heart",
- "\u0120inqu",
- "tha",
- "\u0120hardly",
- "\u0120Aven",
- "\u0120tinha",
- "\u0120Sony",
- "\u0120NF",
- "\u0120graduates",
- "\u0120squeeze",
- "eremy",
- "\u00cf\u0126\u00ce\u00b9",
- "\u0120epic",
- "\u0120Ju",
- "\u0120olm",
- "\u0120Laughter",
- "\u0120beliefs",
- "\u0120Cru",
- "\u0120True",
- "\u0120Soul",
- "oween",
- "\u0120romantic",
- "\u0120\u00d0\u00b7\u00d0\u00b2",
- "\u0120anos",
- "\u0120Yup",
- "\u00e9\u013a\u00bf",
- "dim",
- "\u0120infer",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00bc",
- "\u0120soc",
- "uka",
- "\u0120precise",
- "\u0120dropping",
- "\u0120clue",
- "\u0120errors",
- "charge",
- "\u0120Pu",
- "ometer",
- "\u0120lambda",
- "acional",
- "\u0120Dong",
- "\u0120chamber",
- "\u0120thankful",
- "\u0120Nu",
- "\u0120Hawai",
- "\u0120info",
- "\u0120activate",
- "\u0120Qual",
- "\u0120qued",
- "\u00d1\u0125\u00d0\u00bb\u00d1\u012e",
- "\u0120cloth",
- "\u00e5\u0138\u013e",
- "\u0120wichtig",
- "55",
- "\u0120otra",
- "ographer",
- "\u0120curios",
- "\u01201980",
- "\u0120empres",
- "dess",
- "eur",
- "\u0120cluster",
- "arter",
- "obile",
- "\u0120Yan",
- "\u0120Adv",
- "\u0120discipline",
- "\u0120\u00ec\u0142\u0137\u00eb\u0131\u0126",
- "\u0120Place",
- "\u0120Select",
- "TE",
- "\u0120\u00d0\u00b1\u00d1\u012d\u00d0\u00bb\u00d0\u00b0",
- "\u0120whis",
- "\u0120bay",
- "\u0120Dor",
- "encing",
- "\u0120repet",
- "\u0120ficar",
- "pad",
- "\u0120fog",
- "uyor",
- "\u0120snap",
- "ibt",
- "\u0120sobie",
- "\u0120appointment",
- "\u0120Ry",
- "\u0120ceiling",
- "ourse",
- "\u0120writes",
- "\u0120Afghanistan",
- "\u0120mos",
- "aze",
- "\u0120penal",
- "\u0120crystal",
- "ICE",
- "\u00ea\u00b0\u0132",
- "\u00e9\u0141",
- "\u0120Tesla",
- "\u0120theories",
- "\u0120appeal",
- "\u0120newspaper",
- "\u0120cookies",
- "\u00e6\u00a9",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d9\u0126",
- "\u0120maj",
- "\u0120Getting",
- "kommen",
- "\u0120Heaven",
- "ells",
- "\u0120divine",
- "\u00c4\u00ab",
- "\u0120akt",
- "\u0120hopes",
- "\u0120Chen",
- "wegen",
- "***",
- "\u0120Frage",
- "\u0120\u00d0\u00bd\u00d0\u00b8",
- "\u00e0\u00b8\u00b9",
- "minister",
- "nesota",
- "which",
- "\u0120explicit",
- "\u0120verdad",
- "\u0120graduated",
- "\u0120Philipp",
- "QL",
- "\u0120MI",
- "\u0120devot",
- "\u0120cure",
- "\u0120closest",
- "\u0120\u00c3\u0126",
- "\u0120sexy",
- "\u00e3\u0123\u013d",
- "\u0120Death",
- "oko",
- "ugu",
- "\u0120Anne",
- "itarian",
- "esa",
- "\u00d0\u00b5\u00d0\u00b3\u00d0\u00be\u00d0\u00b4",
- "\u0120Dur",
- "\u0120000",
- "zeit",
- "\u0120tournament",
- "\u0120melhor",
- "\u00e0\u00b8\u00aa",
- "\u0120indu",
- "\u0120flaw",
- "\u0120wars",
- "\u0120Mind",
- "\u0120Iron",
- "\u00d1\u0124\u00d0\u00b0\u00d0\u00ba",
- "\u0120VR",
- "\u0120siz",
- "\u0120Southern",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0141\u00ac\u00eb",
- "\u0120awak",
- "\u0120\u00ec\u0137\u0140",
- "\u0120cube",
- "believable",
- "ifall",
- "dis",
- "\u0120abandoned",
- "mind",
- "\u0120parl",
- "\u0120classical",
- "\u00e8\u012d",
- "\u00e1\u00bb\u013bt",
- "\u0120Auto",
- "\u0120Bor",
- "\u00e7\u00a9",
- "400",
- "\u0120Society",
- "\u0120subtle",
- "\u0120missions",
- "\u0120remembered",
- "\u0120Either",
- "\u0120daf\u00c3\u00bcr",
- "ORD",
- "\u0120intensity",
- "ESIN",
- "\u0120Cup",
- "\u0120rarely",
- "\u0120toys",
- "\u0120Charlie",
- "\u00e1\u00bb\u0141",
- "\u0120glaube",
- "\u0120rounds",
- "TIN",
- "\u0120capability",
- "\u0120derivative",
- "\u0120referring",
- "\u0120d\u00c3\u00a5",
- "\u0120TALI",
- "\u0120cotton",
- "\u0120confer",
- "\u0120columns",
- "\u0120liberal",
- "\u0120nunca",
- "\u0120\u00ce\u00bc\u00ce\u00b5",
- "\u0120indo",
- "iben",
- "\u0120Beispiel",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0142\u0129",
- "\u0120\u00d1\u0125\u00d1\u0129",
- "\u0120hoy",
- "\u0120fry",
- "\u0120Scottish",
- "\u00e8\u012c",
- "\u0120civ",
- "\u0120conservative",
- "\u0120airpl",
- "\u0120sar",
- "rus",
- "\u0120investments",
- "\u0120infinite",
- "\u0120\u00e0\u00ae\u0137",
- "\u0120TALIESIN",
- "\u0120Gary",
- "uell",
- "\u0120\u00d0\u00b0\u00d0\u00ba",
- "\u0120Cir",
- "\u0120ritual",
- "\u0120>>>",
- "\u0120tempt",
- "\u0120Tech",
- "\u0120Pokemon",
- "\u0120improvements",
- "\u0120spare",
- "\u0120translate",
- "\u0120sonra",
- "\u0120Film",
- "wort",
- "\u0120\u00d0\u00bc\u00d0\u00b8",
- "\u0120periods",
- "\u0120jealous",
- "\u00e3\u0123\u0126\u00e3\u0123\u0126",
- "\u0120tir",
- "MI",
- "\u0120conducted",
- "\u0120\u00ec\u0137\u012a\u00eb\u0127\u0137",
- "09",
- "\u0120Polit",
- "\u0120Whereas",
- "\u0120moisture",
- "\u0120sins",
- "\u0120kap",
- "\u0120\u00d1\u012f\u00d0\u00ba",
- "\u0120benim",
- "\u0120eliminate",
- "\u0120athletes",
- "\u0120Manager",
- "\u0120featured",
- "apore",
- "\u00e4\u00ba\u013d",
- "\u0120\u00eb\u00b0\u013e",
- "\u0120perf",
- "\u0120Thus",
- "\u0120debut",
- "\u00d0\u00be\u00d0\u00b1\u00d1\u0122",
- "\u0120se\u00c3\u00b1",
- "\u0120mysterious",
- "words",
- "\u0136\u00ea\u00b0\u0122",
- "\u0120checks",
- "\u0120volunteer",
- "\u0120washing",
- "\u0120Marvel",
- "\u0120AB",
- "issors",
- "!'",
- "\u0120Full",
- "yeon",
- "\u0120weigh",
- "\u0120JOHN",
- "\u0120vos",
- "\u0120procedures",
- "\u0120addressed",
- "\u0120Berlin",
- "puter",
- "\u0120Ban",
- "\u0120medication",
- "\u0120drone",
- "\u0120\u00d1\u0125\u00d0\u00b1",
- "\u0120Jean",
- "\u0120caps",
- "\u0120disappointed",
- "\u0120wore",
- "\u0120\u00ea\u00b5\u0143",
- "\u0120organize",
- "\u0120Halloween",
- "\u0120fantasy",
- "yard",
- "\u0120nosotros",
- "\u0120jumped",
- "\u0120photography",
- "\u0120Name",
- "rec",
- "AB",
- "\u0120blessing",
- "\u0120Shut",
- "\u0120bitter",
- "pop",
- "\u00e3\u0123\u013f\u00e3\u0124\u012e",
- "\u0120dei",
- "\u0120fulfill",
- "\u00e7\u0132\u0128",
- "\u0120dengan",
- "\u0120belo",
- "\u0120Meanwhile",
- "\u0120depois",
- "\u0120diabetes",
- "\u0120bund",
- "\u0120Zealand",
- "\u0120digest",
- "\u0120tires",
- "\u0120dod",
- "agne",
- "\u00e1\u00ba\u00bft",
- "\u0120peel",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00b1",
- "\u0120nodes",
- "\u0120trends",
- "\u0120Switch",
- "\u0120Award",
- "\u0120Orig",
- "\u0120Hal",
- "\u0120estas",
- "\u0120360",
- "\u0120simult",
- "\u0120comic",
- "\u0120m\u00c3\u0142",
- "\u0120balanced",
- "\u0120Princess",
- "\u0120kilometers",
- "\u00e1\u00bb\u00a9",
- "\u0120partir",
- "\u00ec\u00a4\u0133",
- "soft",
- "\u0120View",
- "\u0120biological",
- "inst",
- "44",
- "\u0120manera",
- "\u0120comprehensive",
- "\u0120Sab",
- "\u0120crimes",
- "yers",
- "\u0120Company",
- "\u0120Phot",
- "\u0120pouco",
- "iac",
- "\u0120beim",
- "inate",
- "\u0120subsequ",
- "\u0120Mayor",
- "\u0120centuries",
- "\u00c3\u00a8res",
- "\u00ec\u0140\u0138\u00ec\u0137\u0126\u00ec\u013c\u0136",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0141\u00bc",
- "\u0120Frau",
- "\u0120OH",
- "\u0120\u00eb\u0123\u013f",
- "\u0120Nah",
- "\u0120Series",
- "\u0120overnight",
- "\u00ed\u0134\u012a",
- "\u0120\u00e2\u0122\u00a2",
- "\u0120trave",
- "attered",
- "\u0120warri",
- "\u0120Grund",
- "\u0120Indones",
- "\u0120scra",
- "oby",
- "\u0120Brook",
- "\u0120curs",
- "\u0120\u00eb\u00b8",
- "\u0120explains",
- "ramatic",
- "\u0120participating",
- "\u0120minut",
- "\u0120contracts",
- "\u0120gegen",
- "\u0120disappeared",
- "\u0120SN",
- "\u0120robust",
- "aph",
- "\u0120shrim",
- "\u0120devast",
- "cope",
- "\u0120meets",
- "\u0120peaceful",
- "mate",
- "\u0120weld",
- "\u0120\u00d7\u00aa",
- "don",
- "\u00d1\u0125\u00d1\u0124\u00d1\u012e",
- "\u0120registered",
- "\u0120Nik",
- "jin",
- "\u0120cav",
- "\u0120echt",
- "iox",
- "\u0120flowing",
- "\u00d0\u00bd\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00b8",
- "\u0120toe",
- "\u0120entity",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0",
- "fits",
- "\u0120Patrick",
- "\u00d1\u0124\u00d1\u0122",
- "\u0120leverage",
- "\u0120correl",
- "iah",
- "\u0120strings",
- "istinct",
- "\u0120gue",
- "archy",
- "\u0120tengo",
- "\u00c4\u00b1m\u00c4\u00b1z",
- "\u0120orbit",
- "\u00e4\u00b8\u00ba",
- "\u0120\u00d0\u00b5\u00d1\u012b\u00d1\u0133",
- "cake",
- "\u0120\u00d7\u013e\u00d7\u0136",
- "\u0120Minnesota",
- "\u0120brake",
- "owie",
- "\u0120craw",
- "\u00ea\u00b8\u00b0\u00eb\u00a5\u00bc",
- "\u0120programme",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d1\u0125\u00d1\u0129",
- "\u00e5\u0131\u00aa",
- "iences",
- "\u0120Oui",
- "\u0120Pers",
- "imiento",
- "\u0120Invest",
- "\u0120slower",
- "\u00e6\u013b\u0124\u00e5\u0122\u013b",
- "\u0120Beth",
- "\u0120nurse",
- "\u0120Spring",
- "Sp",
- "\u0120unemploy",
- "\u00d0\u00b4\u00d0\u00b8",
- "\u0120genius",
- "\u0120Aaron",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0141\u00ac",
- "\u0120ei",
- "\u00e3\u0123\u0139\u00e3\u0124\u0129",
- "\u0120tanks",
- "\u0120aujourd",
- "\u0120complexity",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d1\u012a",
- "\u0120oldest",
- "\u0120letz",
- "\u00e5\u0127\u00a5",
- "\u0120phenomenon",
- "print",
- "\u0120Bundes",
- "itat",
- "\u00ea\u00bb\u013a",
- "\u012042",
- "\u0120Wi",
- "\u0120incom",
- "\u0120gek",
- "\u0120embrace",
- "\u0120ties",
- "oute",
- "\u0120dose",
- "\u0120Friends",
- "\u00d1\u012d\u00d1\u0124",
- "\u00d0\u00b5\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d1\u0131",
- "\u0120org",
- "\u0126\u00eb\u00a1\u013e",
- "\u00c3\u00b3g",
- "\u0120exceed",
- "\u0120gods",
- "\u0120\u00ea\u00b1\u00b0\u00ec\u013a\u012a\u00ec\u013c\u0136",
- "\u0120societ",
- "\u0120Univers",
- "it\u00c3\u00a4t",
- "\u0120worden",
- "\u0120smoking",
- "\u0120intens",
- "abul",
- "emia",
- "\u00e8\u0133",
- "47",
- "fly",
- "\u01202006",
- "\u0120Seriously",
- "\u0120przez",
- "\u00e6\u00bc",
- "cre",
- "\u0120nan",
- "\u0120modes",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120Hang",
- "emen",
- "\u0120beneficial",
- "\u0120voters",
- "\u0120Broad",
- "\u0120bent",
- "Wow",
- "\u0120mul",
- "\u00e5\u0135\u00a5",
- "\u0120UC",
- "\u0120damaged",
- "\u0120Ukraine",
- "\u0120wipe",
- "\u0120stones",
- "\u0120managers",
- "\u0120rab",
- "\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be",
- "lat",
- "\u0120dece",
- "\u0120graphic",
- "\u0120foss",
- "\u0120disagree",
- "\u0120Amen",
- "\u0120secrets",
- "hole",
- "inkle",
- "\u0120fortunate",
- "\u0120\u00ec\u00b1",
- "\u00ec\u013e\u0126",
- "\u00e8\u0132\u00ac",
- "\u0120habits",
- "\u0120buried",
- "\u0120hin",
- "\u0120virtually",
- "olas",
- "\u0120RP",
- "\u0120Tab",
- "low",
- "\u0120sacrific",
- "\u0120estimated",
- "oln",
- "\u00d9\u012d",
- "cur",
- "\u0120Feel",
- "\u0120castle",
- "\u0120useless",
- "\u0120disg",
- "\u0120Jacob",
- "\u0120gaan",
- "\u0120upside",
- "\u0120parece",
- "\u00e3\u0125\u00b3\u00e3\u0125",
- "\u0120shipping",
- "\u0120CR",
- "\u0120disrupt",
- "acter",
- "UND",
- "fu",
- "\u00e5\u00ae\u012e",
- "\u0120Pick",
- "\u0120Charl",
- "\u0120Bull",
- "\u0120enterprise",
- "\u0120punishment",
- "acking",
- "\u0120fraction",
- "\u0120tablet",
- "\u0120chord",
- "\u0120similarly",
- "\u00e5\u0127\u00b6\u00e5\u00af\u00a6",
- "\u0120Toronto",
- "\u0120courts",
- "\u00c4\u0141l",
- "eszcze",
- "\u0120pronoun",
- "\u0120Sister",
- "\u0120MP",
- "\u0120greatly",
- "\u0120Dank",
- "icop",
- "\u0120garbage",
- "\u0120resolve",
- "\u0120Saf",
- "\u0120Gun",
- "\u0120compound",
- "\u0120\u00eb\u00b0\u00b0",
- "\u0120Musik",
- "\u00e2\u013b\u00ab",
- "\u0120chaos",
- "\u0120Whenever",
- "\u0120euros",
- "\u0120orchest",
- "\u0120refriger",
- "alan",
- "\u00e0\u00b8\u00b7",
- "\u0120Amazing",
- "\u0120pud",
- "agan",
- "\u0120jeszcze",
- "isy",
- "\u0120accuracy",
- "\u0120Ama",
- "isode",
- "\u00eb\u012e\u0122",
- "\u0120interpretation",
- "\u0120Liber",
- "\u00e6\u00b7",
- "cam",
- "\u0120evolved",
- "\u0120Kay",
- "\u00d1\u0128\u00d1\u012d",
- "\u0120creator",
- "itas",
- "\u0120alarm",
- "\u0120celebration",
- "zent",
- "\u0120funcion",
- "\u0120ov",
- "umbling",
- "\u0120%",
- "\u00e0\u00b8\u012a",
- "\u0120restrictions",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b2",
- "\u0120Kinder",
- "\u0120banana",
- "\u00d1\u012e\u00d1\u0131",
- "\u0120diameter",
- "\u0120northern",
- "urers",
- "\u0120Pas",
- "\u00e6\u012a\u0133\u00e7\u013c\u0126",
- "\u0120workforce",
- "\u0120jung",
- "\u0120guarante",
- "\u0120equilib",
- "\u0120suite",
- "\u0120euro",
- "\u0120deliber",
- "Ste",
- "\u0120downtown",
- "\u0120chin",
- "\u0120codes",
- "edia",
- "\u0120sheep",
- "reshold",
- "wnie",
- "\u00c3\u00b3b",
- "\u0120underlying",
- "lia",
- "jer",
- "\u00cf\u0122\u00cf\u012e",
- "\u00e7\u013f",
- "throp",
- "\u0120zap",
- "\u0120vacuum",
- "\u0120Hab",
- "\u0120wrapped",
- "\u00ec\u00a2",
- "\u0120inventory",
- "\u00d0\u00bc\u00d0\u00b0",
- "\u0120coord",
- "\u0120plates",
- "\u0120symm",
- "Te",
- "\u0120w\u00c5\u0124a\u00c5\u013dnie",
- "\u0120reaches",
- "\u0120lonely",
- "Script",
- "lee",
- "esser",
- "\u0120\u00ea\u00b1\u00b8",
- "\u0120Gesch",
- "\u0120Moving",
- "\u0120r\u00c3\u00a9p",
- "\u0120Vill",
- "\u00e5\u0132\u012a",
- "\u0120Rachel",
- "\u0120temos",
- "ONE",
- "\u0120strain",
- "\u0120angel",
- "\u0120f\u00c3\u00a5",
- "Tr",
- "\u0120acho",
- "\u0120highlights",
- "\u0120Wer",
- "\u0120Carl",
- "\u0120blur",
- "\u0120regards",
- "\u00c2\u00b7",
- "\u00d0\u00b8\u00d0\u00bb\u00d1\u0123\u00d1\u0131",
- "\u0120recre",
- "\u0120Yani",
- "UCK",
- "\u0142\u00b8",
- "\u0120electrons",
- "\u0120Spiel",
- "\u0120ved",
- "\u00da\u00be",
- "\u0120beam",
- "\u0120idiot",
- "\u00eb\u0135\u00a4",
- "\u00d0\u00bd\u00d0\u00b0\u00d1\u0129",
- "idd",
- "\u0120ski",
- "itative",
- "\u0120hypothes",
- "\u00e3\u0123\u00a7\u00e3\u0123\u013b\u00e3\u0123\u0143",
- "enter",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u012a\u00eb",
- "\u0120ihre",
- "\u0120preview",
- "angel",
- "\u0120demon",
- "\u0120dus",
- "\u0120dic",
- "\u0120Kom",
- "LEY",
- "...!",
- "\u0120sieht",
- "\u0120Sonic",
- "\u0120tenho",
- "anas",
- "\u0120digit",
- "\u0120Maar",
- "\u0120undergrad",
- "ouncer",
- "uffy",
- "\u0120conversion",
- "\u0120disconnect",
- "\u0120echo",
- "omer",
- "\u0120curriculum",
- "\u0120perch\u00c3\u00a9",
- "\u0120wand",
- "..?",
- "\u0120rolled",
- "\u0120entrepreneur",
- "\u0120theoret",
- "\u0120\u00d1\u012b\u00d0\u00be",
- "\u0120insights",
- "\u0120zusammen",
- "oin",
- "rett",
- "produ",
- "\u0120visitors",
- "eous",
- "\u0120grandmother",
- "\u0120humor",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d1\u0127",
- "zenia",
- "inson",
- "\u0120reset",
- "\u0120baseball",
- "\u0120matching",
- "\u00eb\u012d\u00a4\u00ea\u00b0\u0122",
- "\u0120punto",
- "\u00ec\u00a1",
- "\u0120rede",
- "\u0120addressing",
- "\u0120forecast",
- "\u0120Bol",
- "\u0120colored",
- "\u0120documentation",
- "\u0120expectation",
- "\u0120Northern",
- "\u0120creo",
- "\u0120\u00e0\u00ae\u013c",
- "fon",
- "\u0120unsere",
- "UM",
- "\u0120copies",
- "\u0120expanded",
- "\u0120veterans",
- "\u0120Alm",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00be\u00d0\u00b1\u00d1\u012b\u00d0\u00b5",
- "\u0120psychological",
- "\u0120nosso",
- "\u0120payments",
- "imeters",
- "\u0120-->",
- "\u0120Jennifer",
- "\u0120volunteers",
- "osse",
- "orious",
- "\u0120\u00d0\u00b1\u00d1\u012d\u00d0\u00bb\u00d0\u00b8",
- "\u00e8\u0124",
- "\u0120Ess",
- "ws",
- "\u0120BC",
- "\u0120IC",
- "Woman",
- "\u0120vont",
- "\u0120ethnic",
- "ENN",
- "\u00d0\u00b8\u00d0\u00bc\u00d0\u00be",
- "\u0120lob",
- "\u0120oui",
- "cs",
- "\u0120rehe",
- "\u0120\u00ec\u0142\u0123",
- "\u0120chick",
- "\u00c3\u00basica",
- "\u0120kont",
- "\u0120District",
- "\u0120pile",
- "\u0120\u00d0\u00b0\u00d0\u00b2",
- "\u00d0\u00b5\u00d0\u00b9\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "\u0120\u00c2\u00a3",
- "\u0120issued",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00bf",
- "\u0120prosper",
- "\u0120profound",
- "\u0120Dear",
- "\u0120\u00e3\u0123\u0135",
- "\u0120funded",
- "\u0120bisa",
- "\u0140\u013a\u00eb",
- "\u00d7\u0141",
- "\u0120\u00ec\u013f\u013a",
- "\u0120twelve",
- "\u0120Champions",
- "\u00e9\u013f\u0140\u00e5\u00b8\u00b8",
- "\u00d1\u0123\u00d0\u00bb",
- "\u01202005",
- "pm",
- "\u0120onde",
- "\u0120diff\u00c3\u00a9",
- "\u0120Chall",
- "\u0120difficulties",
- "\u0120garage",
- "\u0120d\u00c3\u00a1",
- "\u00c3\u00bcnk",
- "\u0120\u00eb\u00ac\u00bc",
- "\u0120tran",
- "\u0120submitted",
- "zw",
- "\u00d9\u012a\u00d8\u00a7",
- "\u0120ark",
- "\u0120\u00ec\u0126\u00b1",
- "\u0120grocery",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00b0",
- "iere",
- "\u0120aest",
- "\u0120exhibition",
- "\u0120r\u00c3\u00a9s",
- "\u0120consistency",
- "\u0120cookie",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00b9",
- "\u0120replacement",
- "\u00e6\u00b2\u00b9",
- "\u0120Sem",
- "\u0120\u00ec\u0124\u00ac\u00ec\u013c\u00a9",
- "800",
- "\u0120genes",
- "\u0120transaction",
- "\u0120EL",
- "\u0120durante",
- "ibles",
- "\u0120Eat",
- "tail",
- "issance",
- "\u0120toss",
- "\u0120survived",
- "\u0120offices",
- "\u0120supportive",
- "Where",
- "\u0120toutes",
- "\u0120\u00eb\u00a7\u012b",
- "\u0120jokes",
- "ieron",
- "apers",
- "\u0120mature",
- "\u0120Marsh",
- "\u0120sido",
- "kind",
- "\u0120realmente",
- "\u0120Chef",
- "\u0120quelque",
- "\u0120judges",
- "eft",
- "ERS",
- "\u0120jet",
- "\u0120persons",
- "\u00e8\u00bb",
- "izations",
- "rik",
- "\u0120shops",
- "\u0120Wy",
- "\u0120eleg",
- "qu\u00c3\u00a8",
- "quoi",
- "\u0120juga",
- "\u0120\u00ed\u0137\u013e\u00eb\u00b2\u012a",
- "\u0120Question",
- "\u0120Global",
- "\u0120\u00ec\u0137\u00bd\u00ea\u00b0\u0126",
- "\u0120Station",
- "\u00e6\u0130\u00a5",
- "\u0120Ohio",
- "\u0120sticky",
- "\u0120stressed",
- "\u0120g\u00c3\u00bcn",
- "\u0120\u00ed\u013f",
- "\u00d1\u0123\u00d1\u0124\u00d1\u0125\u00d0\u00bf",
- "\u00e9\u00a1\u012e",
- "\u0120PhD",
- "immer",
- "\u0120mentor",
- "\u0120invented",
- "\u0120reun",
- "\u0120inevit",
- "\u0120pol\u00c3\u0143t",
- "\u0120execute",
- "\u0120Story",
- "\u0120outstanding",
- "\u0120guer",
- "\u0120Rain",
- "\u0120choses",
- "\u0120Tit",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d1\u0122",
- "\u0120Singapore",
- "\u0120None",
- "\u0120chronic",
- "\u00b0\u00eb\u012f\u00b0",
- "\u0120ego",
- "\u00e6\u0142\u00b7",
- "EST",
- "\u00e3\u0123\u0124\u00e3\u0124\u012c",
- "\u0120Wang",
- "\u0120NAT",
- "\u0120aug",
- "\u0120desktop",
- "\u0120eternal",
- "\u0120\u00ec\u0124\u00ac\u00ec\u012d\u00a4",
- "\u0120Constitution",
- "\u00ec\u0124\u00ac\u00eb",
- "\u00d7\u013b\u00d7\u013e",
- "pres",
- "\u0120\u00d0\u00a2\u00d1\u012d",
- "\u0120interf",
- "\u0120lists",
- "\u0120fights",
- "ften",
- "\u0120Iowa",
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- "\u0120Hosp",
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- "\u0120paths",
- "\u0120instances",
- "Bl",
- "range",
- "\u00e1\u00bb\u00b1",
- "\u0120Sit",
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- "\u0120\u00ec\u012d\u013e\u00ec\u0140\u0133",
- "\u0120m\u00c3\u00acnh",
- "ansas",
- "\u0120sna",
- "\u0120philosoph",
- "\u0120passe",
- "\u00c6\u00b0\u00e1\u00bb\u013fi",
- "akh",
- "ental",
- "\u0120ihn",
- "ructor",
- "\u0120\u00d0\u00b2\u00d0\u00b0\u00d1\u012a",
- "\u0120generous",
- "\u0120pivot",
- "\u00d0\u00bf\u00d0\u00be\u00d0\u00bb",
- "\u0120jamais",
- "\u0120coment",
- "\u0120Lew",
- "odzi",
- "\u0120Xbox",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b4",
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- "\u012b\u00ec\u0140\u00a5",
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- "lass",
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- "Beifall",
- "\u0120\u00ea\u00b0\u013e",
- "\u0120beloved",
- "\u00d7\u0142\u00d7\u0137",
- "sell",
- "\u0120honored",
- "leh",
- "\u0120w\u00c3\u00a4re",
- "unting",
- "\u0120fraud",
- "\u0120RAM",
- "\u00ea\u00b1\u00b8",
- "\u0120kills",
- "\u0120economics",
- "04",
- "\u00d0\u00bf\u00d0\u00b5\u00d1\u0122",
- "\u0120coisas",
- "\u0120\u00d0\u00b8\u00d0\u00b3\u00d1\u0122",
- "\u00c3\u0143m",
- "\u0120m\u00c3\u00b6chte",
- "\u0120\u00ec\u00b5\u013e",
- "\u0120stimul",
- "\u0120fastest",
- "lv",
- "\u0120g\u00c3\u00a9n",
- "\u0120Sounds",
- "\u01201970",
- "\u0120homework",
- "speaking",
- "\u0120encouraging",
- "\u0120query",
- "\u0120revers",
- "profit",
- "\u0120dy",
- "\u0120\u00ec\u0140\u0133",
- "\u00eb\u012c\u0136\u00eb\u012f\u00b0\u00ec\u013c\u0136",
- "\u0120soap",
- "\u0120Gall",
- "\u0120CN",
- "\u0120Ans",
- "\u0120fic",
- "anks",
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- "\u0120\u00ec\u0142\u0122\u00ed\u013f\u00ac",
- "\u0120Making",
- "\u0120come\u00c3\u00a7",
- "\u00ea\u00b3\u0126",
- "\u0120association",
- "Dad",
- "hee",
- "\u0120hogy",
- "\u0120apro",
- "\u0120invisible",
- "American",
- "\u00ed\u0130",
- "\u0120vibe",
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- "\u0120advocate",
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- "\u0120vel",
- "\u0120summar",
- "\u0120freaking",
- "chron",
- "\u0120pinch",
- "\u0120wszystk",
- "iscal",
- "\u0120proved",
- "\u0120mindful",
- "\u0120t\u00c3\u00a4",
- "\u0120noises",
- "\u0120isolated",
- "\u0120crossed",
- "\u0120\u00ea\u00b0\u0137",
- "\u0120voil\u00c3\u0142",
- "\u0120chore",
- "\u0120RA",
- "Com",
- "\u0120relaxed",
- "atro",
- "\u0120prevention",
- "Voiceover",
- "OD",
- "\u0120Covid",
- "\u0120separation",
- "\u0120-[",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u00e7\u013b\u00bc",
- "\u0120SD",
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- "\u0120partial",
- "\u0120algorithms",
- "\u0120Anyone",
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- "\u0120b\u00e1\u00ba\u00a1n",
- "\u0120battles",
- "Good",
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- "\u0120bastante",
- "\u0120advant",
- "\u0120Sweet",
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- "\u00e3\u0124\u00b8",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00b1\u00d0\u00b5",
- "plet",
- "\u0120encouraged",
- "\u00e5\u0135\u00a6",
- "\u0120miracle",
- "\u0120Bun",
- "\u0120Var",
- "rimination",
- "elect",
- "\u0120Mult",
- "\u0120delivering",
- "eing",
- "\u0120cm",
- "nehmen",
- "\u0120Line",
- "\u0120\u00eb\u00a7\u012e",
- "enced",
- "\u0120Sound",
- "\u0120Contin",
- "ijd",
- "UNG",
- "kle",
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- "\u0120Pur",
- "owned",
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- "\u0120basics",
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- "\u0120Guy",
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- "\u0120m\u00c3\u0143",
- "won",
- "\u00e5\u00ae\u00b3",
- "\u012085",
- "\u00e6\u0124",
- "\u0120mum",
- "\u0120ignor",
- "\u0120printing",
- "acular",
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- "\u0120\u00ec\u0139\u00ac\u00eb\u0141\u00ac\u00eb\u00b6\u0126",
- "\u0120angles",
- "\u0120terminal",
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- "\u0120Beautiful",
- "\u0120produces",
- "\u0120nuestro",
- "ted",
- "\u0120eligible",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b7",
- "icial",
- "\u0120Hero",
- "\u0120consume",
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- "cci\u00c3\u00b3n",
- "\u0120iz",
- "\u00c6\u00b0\u00e1\u00bb\u00a3c",
- "\u00ce\u00af\u00ce\u00bd\u00ce\u00b1\u00ce\u00b9",
- "\u0120\u00d8\u00a3\u00d9\u0128",
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- "\u0120Media",
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- "..!",
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- "\u0120\u00d1\u0131\u00d0\u00ba",
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- "jan",
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- "\u00e5\u0131\u012d",
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- "agem",
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- "\u00e5\u0132\u0139",
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- "\u0120\u00d5",
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- "\u0120bor",
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- "\u0120c\u00c3\u00a1i",
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- "\u0120\u00ce\u00ba\u00ce\u00b1",
- "dog",
- "\u0120Karen",
- "illes",
- "\u00ce\u00b2",
- "\u0120agriculture",
- "\u00d7\u0137\u00d7\u0141",
- "\u0120Sean",
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- "agh",
- "\u0120publicly",
- "\u0120peux",
- "\u0120Alexander",
- "\u0120priorit",
- "\u0120lazy",
- "ardon",
- "attering",
- "\u0120costume",
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- "\u00e8\u00bf\u013a",
- "\u0120unw",
- "\u00d0\u013d",
- "\u0120thickness",
- "quito",
- "gunt",
- "istas",
- "neys",
- "\u0120\u00eb\u0132\u013a\u00ea\u00b2\u012e",
- "\u0120Brasil",
- "\u0120token",
- "\u0120affili",
- "lon",
- "\u0120f\u00c3\u00a5r",
- "\u0120Beach",
- "\u0120witch",
- "\u0120Seven",
- "\u0120pant",
- "\u00ce\u00bb\u00ce\u00bb",
- "\u0120captain",
- "\u00e5\u013f",
- "\u0120veut",
- "\u0120pouvoir",
- "acz",
- "\u0120Barb",
- "\u0120utility",
- "\u0120contemporary",
- "\u0120obtained",
- "\u0120paintings",
- "ear",
- "\u0120pean",
- "\u0120Og",
- "\u0120cust",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00bc",
- "\u0124\u013a\u00eb",
- "\u0120Isso",
- "\u0120aconte",
- "\u0120Tele",
- "\u0120Assistant",
- "\u00c3\u012b",
- "\u00ed\u0138\u012a\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120counts",
- "\u0120buck",
- "\u0120Deep",
- "\u0120tackle",
- "\u0120harsh",
- "\u0120decides",
- "\u00e9\u0139\u013e",
- ".\u00e2\u0122\u012d",
- "\u00e9\u0124\u012c",
- "\u0120Angel",
- "\u0120laying",
- "\u0120calories",
- "\u0120controlling",
- "\u0120advantages",
- "\u0120\u00d1\u012f\u00d1\u0124\u00d0\u00be\u00d0\u00b9",
- "\u0120approaching",
- "\u0120threats",
- "akan",
- "ematic",
- "mann",
- "\u00ea\u00b3\u00b5",
- "mumbles",
- "aci\u00c3\u00b3",
- "\u0120maintaining",
- "\u0120founder",
- "lah",
- "fight",
- "\u0120admitted",
- "\u00e2\u0122\u00a6.",
- "\u0137\u012e",
- "abol",
- "\u0120usage",
- "\u0120nonsense",
- "\u0120Palest",
- "\u0120contre",
- "\u0120Democratic",
- "\u0120ER",
- "jekt",
- "\u0120arbit",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00bb",
- "\u0120Michelle",
- "icher",
- "esh",
- "\u0120Pho",
- "\u00d0\u00ba\u00d0\u00be\u00d0\u00bc",
- "49",
- "\u0120Energy",
- "\u00ce\u00bf\u00cf\u012f",
- "\u0120cents",
- "\u0120refers",
- "\u0120gospel",
- "\u0120Sha",
- "\u0120Share",
- "\u00d7\u013b\u00d7\u0142",
- "\u0120clinic",
- "\u0120\u00eb\u0126\u00a3",
- "\u0120equality",
- "ugs",
- "\u0120shed",
- "\u0120planes",
- "\u0120toute",
- "reck",
- "\u0120strand",
- "\u0120biology",
- "\u0120league",
- "\u0120Pok",
- "\u0120n\u00c3\u00bamero",
- "\u0120Coast",
- "\u0120consistently",
- "\u0120nucle",
- "OOOO",
- "\u0120objet",
- "\u0120chor",
- "\u0120ginger",
- "\u0120dabei",
- "\u0120cooperation",
- "\u00e0\u00af\u012f.",
- "nten",
- "\u00e7\u00a4",
- "l\u00c3\u0142",
- "\u00ec\u0138\u0133",
- "rado",
- "\u0120passive",
- "\u0120gloves",
- "\u0120underground",
- "\u0120logical",
- "\u0120ket",
- "\u0120functionality",
- "\u00b8\u00eb\u00a6\u00ac",
- "\u0120portal",
- "eller",
- "\u00d7\u013b\u00d7\u00a8",
- "\u0120Ted",
- "\u0120Gre",
- "\u0132\u013e",
- "\u0120personnel",
- "\u0120emerging",
- "\u0120F\u00c3\u00bcr",
- "\u0120meantime",
- "usalem",
- "\u0120Clear",
- "\u0120trapped",
- "\u0120\u00ec\u013c\u00b0",
- "\u0120displ",
- "\u0120mettre",
- "\u0120municip",
- "\u0120withdraw",
- "\u0120spat",
- "unes",
- "\u0120accessibility",
- "\u00e6\u012a\u0133\u00e4\u00bb\u00ac",
- "\u0120apare",
- "\u0120prospect",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b7",
- "\u0120copper",
- "\u0120PRO",
- "\u00cf\u0127\u00cf\u0126",
- "\u0120attacking",
- "\u0120Vin",
- "\u0120Stone",
- "\u0120investigate",
- "style",
- "\u0120\u00ce\u00bb",
- "\u00eb\u00a1\u013f",
- "\u00eb\u00a7\u012a",
- "\u0120inspect",
- "\u0120liver",
- "\u00d0\u00b0\u00d0\u00bb\u00d0\u00b8\u00d1\u0123\u00d1\u012e",
- "\u0120sera",
- "halten",
- "eman",
- "\u0120ministry",
- "''",
- "\u0120dots",
- "\u00e3\u0127\u012d\u00e3\u0127\u012d\u00e3\u0127\u012d\u00e3\u0127\u012d",
- "\u00d1\u0125\u00d1\u0123\u00d1\u0124",
- "\u0120Jak",
- "AKE",
- "\u0120gaps",
- "ucker",
- "\u0120\u00d0\u00b8\u00d0\u00bd\u00d1\u0124\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d1\u0123",
- "\u0120Emily",
- "\u0120interval",
- "\u0120tender",
- "\u0120Technology",
- "game",
- "\u0120trib",
- "\u00d9\u0126\u00d8\u00a7",
- "\u0120Development",
- "\u00d9\u0127\u00d8\u00a7",
- "\u0120wrist",
- "\u0120fires",
- "\u0120targeted",
- "\u00ec\u0142\u0132",
- "\u0120sod",
- "\u00ed\u013c\u012e",
- "\u0120oldu\u00c4\u0141",
- "\u0120seasons",
- "ventions",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u0120sometime",
- "\u00d0\u00bb\u00d0\u00b8\u00d0\u00b2",
- "n\u00c3\u00a9",
- "\u0120t\u00c3\u00ba",
- "\u0120Deus",
- "\u0120execution",
- "\u00c3\u00a1p",
- "\u0120Change",
- "\u0120Indeed",
- "\u0120regulation",
- "\u0120Hung",
- "\u00c3\u00a9is",
- "\u0120wishes",
- "\u0120jazz",
- "\u0120structural",
- "\u0120blowing",
- "\u0120by\u00c4\u0129",
- "\u0120thermal",
- "phant",
- "\u00d1\u0122\u00d1\u0125\u00d0\u00b7",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u0124",
- "\u0120Pull",
- "\u0120confusion",
- "\u00d0\u00bd\u00d1\u012d\u00d0\u00bc\u00d0\u00b8",
- "\u0120scenarios",
- "\u00ec\u0142\u0123\u00ec\u013e\u00bc\u00eb\u00a1\u013e",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d1\u0124",
- "\u0120tattoo",
- "\u0120autre",
- "\u0120heating",
- "\u0120treating",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc",
- "\u0120exclus",
- "\u0120LOL",
- "wear",
- "agle",
- "\u0120zur\u00c3\u00bcck",
- "\u0120rational",
- "su",
- "\u0120deter",
- "\u0120Native",
- "\u00e0\u00ae\u0137\u00e0\u00ae\u00b3",
- "ached",
- "\u0120\u00e3\u0125",
- "\u0120Entonces",
- "\u0120hora",
- "\u00ec\u013f\u00b4\u00ec\u0139\u0132\u00ec\u013c\u0136",
- "\u0120lite",
- "\u00c3\u00ab",
- "\u0120sixth",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d0\u00b5\u00d0\u00b5",
- "actor",
- "\u0120psychology",
- "\u00e7\u013d\u00b8",
- "\u0120demands",
- "\u0120peer",
- "\u0120newly",
- "\u0120WWE",
- "Donald",
- "\u0120Box",
- "\u0120pine",
- "\u0120loading",
- "\u0120Nico",
- "\u0120s\u00c5\u0124",
- "omme",
- "ART",
- "\u0120recruit",
- "\u0120bugs",
- "arents",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b1",
- "\u0120Inside",
- "ipper",
- "dramatic",
- "\u0120planets",
- "orde",
- "\u0120yoga",
- "child",
- "\u0120Marie",
- "\u0120\u00e3\u0123\u0124",
- "\u0120BL",
- "\u0120filmed",
- "\u0120refresh",
- "\u0120tomatoes",
- "\u0120fet",
- "Qu\u00c3\u00a9",
- "\u0120!!",
- "\u0120\u00eb\u0124\u00b4\u00eb",
- "rine",
- "\u0120interactive",
- "sal",
- "annah",
- "pez",
- "\u00e7\u00b6\u0135",
- "\u0120understands",
- "\u0120Tokyo",
- "\u0120libraries",
- "\u0120reader",
- "\u0133\u0132",
- "oz",
- "\u0120Ende",
- "\u0120Flo",
- "\u0120mild",
- "\u0120poetry",
- "\u0120\u00d0\u00b6\u00d0\u00b8\u00d0\u00b2",
- "\u00e6\u0126\u013d",
- "\u0120behave",
- "\u0120doen",
- "\u0120Susan",
- "page",
- "raham",
- "\u0120communications",
- "\u0120tuning",
- "\u0120pac",
- "\u0120anxious",
- "IO",
- "Mark",
- "\u0120hi\u00c3\u00a7",
- "books",
- "\u0120piss",
- "\u0120enabled",
- "achelor",
- "\u0120FOR",
- "\u0120\u00c3\u00a9c",
- "\u0120TR",
- "ilst",
- "hat",
- "\u0120\u00ec\u013f\u012e",
- "\u0120tych",
- "\u0120jar",
- "\u0120builds",
- "\u0120Argent",
- "\u0120intermedi",
- "\u0120lou",
- "\u0120ara",
- "\u0120assignment",
- "\u0120cabinet",
- "\u0120retirement",
- "\u00e3\u0123\u00bb",
- "\u0120disabled",
- "rica",
- "\u0120awards",
- "\u0120boots",
- "\u0120acknowled",
- "\u0120thy",
- "\u0120\u00ea\u00b5\u00ac",
- "\u0120synd",
- "\u00d0\u00bd\u00d0\u00b8\u00d0\u00b9",
- "ilton",
- "\u0120probl",
- "\u0120Fal",
- "\u0120verdade",
- "\u0120700",
- "\u0120Learning",
- "ocus",
- "\u0120palace",
- "Not",
- "tain",
- "cm",
- "\u0120magnet",
- "incoln",
- "\u0120figuring",
- "\u0120Lyn",
- "\u0120Boss",
- "\u0120VO",
- "\u0120diagnosis",
- "\u0120equipped",
- "watch",
- "inos",
- "aders",
- "\u0120shelf",
- "\u0120organis",
- "\u0120nod",
- "\u0120k\u00c4\u00b1z",
- "ppers",
- "\u0120restore",
- "\u0120artic",
- "\u0120Voice",
- "\u00c4\u00b1yorum",
- "\u00ea\u00b2\u00a9",
- "\u0120spreading",
- "\u0120hips",
- "\u0120ward",
- "ureau",
- "\u0120intersection",
- "66",
- "\u012039",
- "\u00e7\u00b3",
- "\u0120waited",
- "\u00ec\u00b4",
- "hhhh",
- "\u0120dys",
- "\u0120EN",
- "\u0120batch",
- "\u0120caf",
- "\u0120marker",
- "\u00e5\u00a4\u00a7\u00e5\u00ae\u00b6\u00e5\u00a5\u00bd",
- "orable",
- "\u00c3\u00b3ria",
- "\u0120stepped",
- "\u0120celebrating",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b0",
- "\u0120worn",
- "\u0120Fol",
- "\u0120pla",
- "\u0120attempts",
- "\u0120tweet",
- "\u0120rust",
- "gence",
- "\u00ed\u0128\u00b5",
- "\u0120revel",
- "\u0120recept",
- "eness",
- "\u0120((",
- "\u00e3\u0125\u00bc\u00e3\u0125",
- "!\u00e2\u0122\u012d",
- "\u0120\u00ec\u0128\u0132",
- "\u0120influenced",
- "\u00d0\u00b8\u00d0\u00b6",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bd\u00d0\u00b5\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120colleges",
- "ioni",
- "\u0120sag",
- "Ann",
- "olar",
- "\u0120expressions",
- "\u0120suits",
- "\u0120ownership",
- "eland",
- "piece",
- "\u00e6\u0122\u0130\u00e4\u00b9\u012a",
- "\u0120despu\u00c3\u00a9s",
- "\u0120tel",
- "\u0120insult",
- "\u0120\u00ea\u00b5\u012b\u00ec\u0140\u00a5",
- "\u0120Small",
- "\u0120FR",
- "oka",
- "berries",
- "\u0120Anton",
- "\u00d0\u00b5\u00d0\u00bb\u00d1\u0131",
- "\u00d1\u0131\u00d1\u0123",
- "\u0120valve",
- "acts",
- "\u0120woods",
- "\u00e0\u00ae\u00a3",
- "\u0120cultiv",
- "\u0120f\u00c3\u00a1",
- "\u00e3\u0123\u00a8\u00e3\u0123\u0126\u00e3\u0123\u0128",
- "\u0120cheers",
- "\u0120assumption",
- "\u0120fitness",
- "\u00c3\u0143cul",
- "\u0120podr",
- "\u0120weit",
- "\u0120Hind",
- "\u0120dign",
- "\u0120\u00d0\u00b7\u00d0\u00bd",
- "\u0120squad",
- "\u0120destro",
- "cere",
- "shirt",
- "immt",
- "engers",
- "\u0120s\u00c3\u00a4",
- "k\u00c5\u0124ad",
- "\u0120\u00c8\u013b",
- "\u0120occas",
- "\u0120\u00ec\u00a4\u0126",
- "\u0120processor",
- "\u0120DM",
- "\u0120Daddy",
- "\u0120sooner",
- "\u0120straightforward",
- "\u0120departments",
- "\u0120Chrome",
- "\u0120workplace",
- "\u0120Python",
- "\u0120meng",
- "\u0120DAN",
- "\u0120Ice",
- "\u0120\u00eb\u012a\u012a",
- "\u0120Gi",
- "\u0120hiring",
- "\u0120landed",
- "\u0120democratic",
- "iedz",
- "\u00e3\u0123\u013a\u00e3\u0124\u0125",
- "\u0120sev",
- "icia",
- "\u0120especial",
- "\u0120Nous",
- "\u0120h\u00c3\u00a4t",
- "\u0120bou",
- "pert",
- "iesz",
- "\u00e5\u0133\u0122",
- "\u0120vil",
- "\u00c5\u013dli",
- "\u0120\u00c3\u00aen",
- "\u0120losses",
- "\u00e9\u0137\u00b7",
- "\u0120toast",
- "\u0120realm",
- "\u0120Austin",
- "\u0120Information",
- "\u0120resume",
- "\u0120chase",
- "\u0120salary",
- "\u0120\u00eb\u00b6\u0126",
- "\u00d0\u00bb\u00d0\u00b8\u00d1\u0129",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d0\u00b5\u00d0\u00b4",
- "\u0120Further",
- "\u0120caring",
- "\u0120vig",
- "\u0120valor",
- "\u00e8\u00bf\u013b\u00e4\u00b8\u00aa",
- "\u0120\u00d1\u0129\u00d0\u00b0",
- "\u0120analytics",
- "\u0120globe",
- "\u0120MAN",
- "\u0120nel",
- "\u00ec\u013f\u00b4\u00ec\u0137\u00bc",
- "\u0141\u00bc",
- "\u0120oy",
- "\u00ed\u0137\u013a\u00ec\u0126\u00b8\u00ec\u013c\u0136",
- "jen",
- "\u0120troubles",
- "ahaha",
- "\u0120churches",
- "uet",
- "\u0120measurements",
- "bil",
- "\u00ec\u00bd",
- "ifully",
- "\u00d0\u00b8\u00d0\u00bd\u00d1\u0125",
- "\u0120Wilson",
- "\u00a6\u00b4",
- "\u0120\u00ed\u012e\u012e",
- "\u0120\u00ec\u00b0\u00a8",
- "\u0120p\u00c3\u00bablic",
- "\u0120Jerusalem",
- "\u0120nails",
- "\u0120spine",
- "\u0120hemos",
- "\u0120zn",
- "quis",
- "\u0120Leben",
- "\u0120references",
- "ITH",
- "iper",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d0\u00b1\u00d1\u0131",
- "\u00ec\u0123",
- "\u0120Wa",
- "state",
- "\u00a7\u013f",
- "\u00e5\u0127\u00b1",
- "\u0120Gener",
- "\u0120actress",
- "\u0120Enjoy",
- "\u00e0\u00b9\u0125",
- "\u0120\u00d7\u0134",
- "\u0120infected",
- "\u0120shaking",
- "\u0120nick",
- "\u00e0\u00b8\u00b8",
- "\u0120fot",
- "\u0120accomplished",
- "uke",
- "\u0120sheets",
- "\u0120fence",
- "\u0120nursing",
- "\u0120introducing",
- "\u0120feat",
- "One",
- "TO",
- "\u0120clubs",
- "\u0120Bruce",
- "onge",
- "change",
- "\u0120Batman",
- "\u00e5\u0131\u00b0",
- "\u0120Officer",
- "\u0120hydro",
- "\u0120supplement",
- "\u0120cela",
- "\u0120longest",
- "\u0120competing",
- "\u0120conhe",
- "giving",
- "\u0120brains",
- "\u0120loans",
- "\u0120wage",
- "\u0120Clinton",
- "\u0120s\u00c4\u0125",
- "aneous",
- "\u0120lord",
- "\u00d1\u0122\u00d1\u0125\u00d0\u00b6",
- "\u0120quiz",
- "\u0120stiff",
- "\u0120LGB",
- "sz",
- "ME",
- "mare",
- "there",
- "\u0120n\u00c3\u00a4r",
- "\u0120Mand",
- "last",
- "\u0120dag",
- "\u0120halfway",
- "\u0120Band",
- "\u0120\u00eb\u012d\u00a4\u00ec\u012d\u013e",
- "\u0120Aren",
- "\u0120ile",
- "PN",
- "ento",
- "\u0120algum",
- "\u0120soccer",
- "\u0120blocked",
- "\u0120Jonathan",
- "\u0120sew",
- "\u0120Testament",
- "\u0120vale",
- "\u0120behavi",
- "\u00e5\u00a7\u012d",
- "\u0120conna",
- "ICH",
- "\u0120audiences",
- "ml",
- "ammad",
- "\u0120\u00ec\u0124\u00b4\u00ec",
- "IGH",
- "\u0120races",
- "emed",
- "\u0120m\u00e1\u00bb\u013bt",
- "\u00c3\u00af",
- "\u0120overs",
- "\u0120declared",
- "\u0120sana",
- "\u0120Una",
- "\u0120\u00d1\u0122\u00d0\u00b5",
- "ucks",
- "\u0120pairs",
- "\u0120ange",
- "Ne",
- "\u0120ups",
- "avy",
- "\u00c3\u00b8r",
- "reek",
- "\u0120behaviors",
- "\u0120reflected",
- "\u0120priorities",
- "\u0120condu",
- "\u0120retreat",
- "\u0120expenses",
- "\u0120\u00eb\u00b4\u0132",
- "\u0120triple",
- "\u0120\u00ea\u00b5\u012b\u00ec\u0140\u00a5\u00ed\u0140\u012a",
- "\u00c3\u00a4lt",
- "\u0120indigenous",
- "\u0120mining",
- "\u0120acceptable",
- "\u0120ruin",
- "CA",
- "uine",
- "\u0120pipeline",
- "ctic",
- "\u00c3\u00aat",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u0120boun",
- "\u0120Digital",
- "\u0120Boom",
- "\u00d1\u0128\u00d0\u00b5",
- "\u0120\u00d0\u00bb\u00d1\u0125\u00d1\u0129",
- "\u0120asc",
- "\u012e\u0122\u00eb\u00a1\u013e",
- "\u0120Goodbye",
- "\u0120render",
- "enez",
- "arre",
- "\u0120THAT",
- "bour",
- "ici\u00c3\u00b3n",
- "\u00e3\u0124\u0143",
- "Every",
- "\u0120wires",
- "\u0120Parliament",
- "nung",
- "ateur",
- "\u0120Save",
- "\u0120Phys",
- "\u0120amor",
- "\u0120Eve",
- "\u0120fright",
- "\u0120gamma",
- "\u0120micros",
- "mitt",
- "\u0120Code",
- "\u0120Bey",
- "pled",
- "\u0120\u00d0\u00b8\u00d1\u0123\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00b7",
- "\u00e7\u0139",
- "\u00ec\u0125\u012b",
- "\u00e5\u00a5\u00b9",
- "\u0120monet",
- "\u0120Jahre",
- "\u0120luxury",
- "\u0120deaf",
- "\u0120betray",
- "\u0120\u00ea\u00b2\u00b0",
- "\u00d0\u00b8\u00d0\u00ba\u00d0\u00b8",
- "\u0120defeated",
- "\u0120undert",
- "\u0120weg",
- "\u0120cooler",
- "\u00e3\u0123\u0137\u00e3\u0124\u0135",
- "iami",
- "\u00e9\u0124\u0126\u00e6\u013e\u012b",
- "\u0120Jessica",
- "\u0120Joy",
- "\u0120sophistic",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d0\u00b8",
- "\u00f0\u013f\u013a",
- "\u0120chili",
- "\u0120Type",
- "\u0120proteins",
- "\u0120presenting",
- "alia",
- "\u00ec\u013c\u00b8",
- "\u0120Major",
- "\u0120molecule",
- "umer",
- "\u0120collapse",
- "\u0120Anyways",
- "\u0120Mountain",
- "anted",
- "\u00e3\u0122\u0132",
- "\u0120\u00d0\u00b2\u00d0\u00b8\u00d0\u00b4\u00d0\u00b5\u00d0\u00be",
- "\u00e6\u00b0\u00b4",
- "Aud",
- "\u0120conqu",
- "\u0120voll",
- "\u0120knit",
- "\u0120membr",
- "\u0120Market",
- "\u0120dari",
- "\u0120calculated",
- "\u00d0\u00b3\u00d0\u00b8",
- "\u0120shrimp",
- "\u0120Mu",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0124",
- "\u0120\u00ec\u013a\u0123\u00ec\u0125\u0123",
- "\u0120productivity",
- "\u0120cognitive",
- "\u0120Heb",
- "ictions",
- "\u00ea\u00b2\u00bd",
- "\u0120cr\u00c3\u00a9",
- "f\u00c3\u00b6r",
- "\u0120praying",
- "ashi",
- "\u0120Tik",
- "\u00c3\u00b3r",
- "wen",
- "\u00d1\u012e\u00d1\u0130",
- "ixo",
- "\u0120(\"",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00bb",
- "\u0120\u00ec\u0138\u00b4\u00eb\u0138\u00a4",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00b4",
- "\u0120Drive",
- "\u00e3\u0122\u0133",
- "\u0120Equ",
- "\u0120equilibrium",
- "\u0120describes",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00b5",
- "42",
- "\u0120Current",
- "yy",
- "\u0120absorb",
- "\u0120soldier",
- "ders",
- "\u0120testimony",
- "\u0120decline",
- "\u013e\u00eb\u00a1\u013e",
- "gage",
- "\u0120inspire",
- "lapping",
- "\u0120spinning",
- "\u0120slavery",
- "\u0120facial",
- "\u0120traditions",
- "\u00c3\u00a1rios",
- "\u0120Hospital",
- "\u0120nest",
- "\u0120\u00eb\u012a\u0126",
- "\u0120toi",
- "\u0120fears",
- "\u00ec\u0127\u00a8",
- "\u0120Muh",
- "\u0120graduation",
- "\u0120impacted",
- "\u0120aunt",
- "\u0120Lets",
- "\u0120aluminum",
- "\u0120dominant",
- "\u0120Davis",
- "\u0120Navy",
- "\u0120compt",
- "oples",
- "\u0120estava",
- "\u00e8\u00a5",
- "\u0120scal",
- "\u0120preserve",
- "\u0120Opp",
- "\u0120practically",
- "\u0120magnitude",
- "\u0120fitting",
- "\u0120coordinate",
- "\u0120furniture",
- "\u0120Famil",
- "\u0120explosion",
- "\u0120documentary",
- "\u0120Script",
- "\u0120portray",
- "mat",
- "\u0120scheduled",
- "\u0120dynamics",
- "phy",
- "aky",
- "\u0120UI",
- "Che",
- "\u0120continuously",
- "\u0120Prov",
- "\u00e5\u00b0\u0133",
- "\u00d1\u0125\u00d0\u00b7",
- "rah",
- "\u0120gerne",
- "proof",
- "\u0120secretary",
- "\u0120Patreon",
- "scream",
- "\u0120Kids",
- "\u00e1\u00bb\u0135i",
- "\u0120kg",
- "\u0120uncertainty",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00b6\u00d0\u00b4",
- "\u0120mitig",
- "\u0120reads",
- "\u00e5\u00b7\u00b2",
- "\u0120Ru",
- "\u0120priest",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b4",
- "\u0120limitations",
- "\u0120float",
- "600",
- "\u0120Toy",
- "\u0120Jimmy",
- "\u0120offensive",
- "eni",
- "\u0120Xi",
- "\u0120eyebr",
- "\u0120Turk",
- "\u0120accidentally",
- "\u0120ohne",
- "\u0120Saud",
- "95",
- "\u0120Dutch",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u0123",
- "\u0120Seattle",
- "\u0120\u00eb\u0135\u00b1",
- "check",
- "k\u00c4\u013b",
- "\u0120contributions",
- "\u0120beside",
- "\u0120quindi",
- "\u0120flew",
- "\u00e6\u0139\u00b6",
- "\u00d8\u00b0\u00d8\u00a7",
- "\u0120LO",
- "\u0120waist",
- "\u0120EV",
- "\u0120holidays",
- "jon",
- "\u0120misunder",
- "\u00d1\u0131\u00d0\u00bd",
- "\u0120bout",
- "\u0120dimin",
- "\u00e1\u00ba\u00bd",
- "\u00c3\u00b3l",
- "\u0120Grace",
- "\u0120inputs",
- "\u0120deny",
- "\u0120forming",
- "\u0120Bild",
- "\u0120adequ",
- "\u0120folk",
- "\u0120rejected",
- "semb",
- "\u0120frustrated",
- "open",
- "\u0120Better",
- "ilon",
- "\u0120towel",
- "\u0120differential",
- "\u0120sacred",
- "\u0120sail",
- "\u00e9\u0129\u012e",
- "entimes",
- "\u0120gentleman",
- "\u0120iconic",
- "\u0120comparing",
- "\u0120sagt",
- "\u0120texts",
- "\u0120grandma",
- "\u0120rolls",
- "\u0120contents",
- "\u00e4\u00b8\u012f\u00e5\u00a5\u00bd",
- "\u00d0\u00be\u00d1\u0123\u00d1\u0123",
- "\u0120suspension",
- "roit",
- "\u00a6\u00bc",
- "\u0120assez",
- "\u0120dort",
- "\u0120Math",
- "\u0120Victor",
- "\u0120JavaScript",
- "\u00e4\u00b8\u012f\u00e5\u00b0\u012f",
- "\u0120enhan",
- "\u00c5\u013b",
- "\u0120Bush",
- "\u0120promotion",
- "\u0120kin",
- "\u0120monsters",
- "\u0120Colorado",
- "\u0120\u00ce\u00b2",
- "\u00ed\u0137\u00b4\u00ec\u013c\u0136",
- "\u00e6\u0143\u00a3",
- "ifferent",
- "\u0120naked",
- "\u0120prod",
- "etics",
- "\u0120Woman",
- "\u0120treatments",
- "\u0120estoy",
- "v\u00c3\u00a9",
- "\u0120lifting",
- "\u0120yapt",
- "\u0120Rober",
- "\u0120\u00ec\u00b9\u013e",
- "\u0120substitute",
- "aku",
- "ridge",
- "\u0120\u00ea\u00b1\u00b0\u00eb",
- "\u0120responded",
- "\u0120b\u00c3\u00a9",
- "\u0120Engineer",
- "\u0120transferred",
- "\u00eb\u00b2",
- "\u0120haber",
- "oop",
- "\u0120WE",
- "\u0120vest",
- "\u0120forty",
- "\u0120DS",
- "\u01202004",
- "\u0120coaching",
- "nom",
- "\u0120Bab",
- "\u0120nossa",
- "\u0120Jake",
- "\u0120gy",
- "\u0120deleg",
- "\u0120\u00ec\u0140\u0142",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0123",
- "\u0120standpoint",
- "\u0120disad",
- "\u0120artwork",
- "Ad",
- "illo",
- "\u0120\u00c4\u0133\u00c6\u00b0\u00e1\u00bb\u00a3c",
- "\u0120Prom",
- "\u0120Lib",
- "\u0120criticism",
- "\u0120contacts",
- "\u00d1\u0122\u00d0\u00b0\u00d0\u00bc",
- "\u0120achievement",
- "\u00d0\u0136\u00d0\u00b0",
- "\u0120dissol",
- "\u0120Vegas",
- "\u0120streams",
- "\u0120Kent",
- "\u0120\u00d8\u00b9\u00d9\u0126\u00d9\u012b",
- "\u0120radius",
- "\u0120sucks",
- "\u0120Ach",
- "\u0120fi",
- "oust",
- "\u0120\u00d0\u00bb\u00d1\u0130\u00d0\u00b4\u00d0\u00b8",
- "\u0120palette",
- "\u0120Haz",
- "\u0120Anthony",
- "\u0120tema",
- "\u0120Cos",
- "\u0120safer",
- "\u00ce\u00b1\u00cf\u0124",
- "\u0120contrad",
- "\u0120maior",
- "\u0120inflation",
- "\u0120Silver",
- "\u0120attending",
- "\u00ed\u0137\u013e\u00ed\u0127\u012e",
- "arto",
- "\u0120applauding",
- "\u0120computing",
- "\u0120Hat",
- "\u00e6\u00bb",
- "know",
- "makers",
- "\u0120conoc",
- "\u0120educated",
- "\u0120modified",
- "\u0120inclusion",
- "mental",
- "\u0140\u0132",
- "isia",
- "\u0120\u00cf\u0122\u00ce\u00bf\u00cf\u0127",
- "\u0120aun",
- "\u0120Ireland",
- "\u0120k\u00c3\u00b6",
- "\u0120compliance",
- "\u0120inspiring",
- "\u00d0\u00b8\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120dispos",
- "\u00ec\u00b0\u00a8",
- "\u0120wip",
- "rical",
- "rawd",
- "\u0120tres",
- "\u0120mobil",
- "olutions",
- "BO",
- "\u0120bounce",
- "\u0120assumed",
- "\u0120Medical",
- "\u0120fiscal",
- "\u0120ng\u00c6\u00b0\u00e1\u00bb\u013fi",
- "itionally",
- "\u0120stolen",
- "\u0120BM",
- "\u0120mechanisms",
- "\u00ce\u00b5\u00ce\u00af",
- "\u0120qualified",
- "\u0120\u00ec\u0140\u0132\u00eb",
- "ughters",
- "\u0120HIV",
- "\u0120Lots",
- "\u0120servers",
- "\u0120carr",
- "\u0120Together",
- "\u0120attracted",
- "\u0120kr",
- "\u00e6\u012a\u0133\u00e6\u013a\u00af",
- "thur",
- "inin",
- "\u0120Half",
- "\u00c8\u013d",
- "\u0120Pap",
- "\u0120reminded",
- "ALL",
- "\u0120helmet",
- "\u0120bottles",
- "\u0120professors",
- "\u0120seine",
- "\u00c5\u0124\u00c4\u0127",
- "\u00e3\u0125\u0131",
- "\u0120\u00ea\u00b1\u00b0\u00ec\u0137\u00bc",
- "\u0120\u00d7\u00a2\u00d7\u013e",
- "fun",
- "\u0120Bird",
- "\u0120fighter",
- "\u0120\u00eb\u0136\u00b0\u00eb",
- "\u0120Tool",
- "\u0120tin",
- "inois",
- "\u00eb\u00b6\u0126",
- "\u00d7\u013b\u00d7\u0141",
- "\u0120CAR",
- "\u00e5\u0132\u012f",
- "irsty",
- "\u0120outdoor",
- "\u0120NS",
- "\u00e3\u0127\u0130",
- "ffen",
- "\u0120lud",
- "Hello",
- "\u0120roller",
- "iele",
- "\u0120Poland",
- "\u0120apa",
- "exp",
- "\u0120certificate",
- "\u0120Town",
- "\u00d0\u00b0\u00d1\u0130\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "ilde",
- "\u0120determin",
- "PR",
- "\u0120freeze",
- "\u0120mainstream",
- "\u0120objectives",
- "blo",
- "\u0120takie",
- "\u00e5\u0135\u012a\u00e5\u0135\u012a",
- "\u0120\u00eb\u00b0\u0136\u00eb\u00a1\u013e",
- "elet",
- "\u0120IV",
- "\u0120Fast",
- "\u0120dere",
- "emp",
- "\u0120Dra",
- "\u0120\u00ec\u0140\u012a\u00ec\u0139\u012a",
- "\u0120discrimination",
- "\u0120\u00ce\u00b5\u00ce\u00af\u00ce\u00bd\u00ce\u00b1\u00ce\u00b9",
- "necess",
- "\u00e6\u00ae",
- "\u00c4\u00b1\u00c4\u0141\u00c4\u00b1",
- "\u0120posting",
- "wi\u00c5\u013dcie",
- "\u0120lub",
- "\u0120olive",
- "\u0120rim",
- "\u0120modeling",
- "\u0120a\u00c3\u00b1o",
- "\u0120Pakistan",
- "\u0120overl",
- "\u0120inflam",
- "NE",
- "\u00ec\u0139\u0132\u00ea\u00b2\u012e",
- "\u0120attended",
- "\u0120dealt",
- "\u0120Alt",
- "\u0120Lincoln",
- "\u0120awake",
- "\u0120filters",
- "\u0120Within",
- "czywi\u00c5\u013dcie",
- "\u0120s\u00c3\u00bb",
- "\u0120Johnny",
- "\u0120integrity",
- "\u0120isolation",
- "\u0120Easy",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bd",
- "\u0120Alice",
- "\u0120smiling",
- "enix",
- ",...",
- "\u00ce\u00b6",
- "\u0120begun",
- "\u0120jewel",
- "\u0120conventional",
- "\u0120statist",
- "\u0120handed",
- "\u0120irre",
- "\u0120prohib",
- "\u0120satellite",
- "\u00e9\u00a6\u013b",
- "\u0120Indust",
- "\u0120traged",
- "\u0120trava",
- "\u0120ihm",
- "\u0120cruel",
- "\u0120Agora",
- "\u0120Doc",
- "\u0120zones",
- "\u0120mall",
- "\u0120tray",
- "\u00d7\u0137\u00d7\u0142",
- "\u0120irrit",
- "\u0120kans",
- "\u0120Beat",
- "udge",
- "ielle",
- "\u0120trusted",
- "\u0120bikes",
- "\u0120\u00d1\u0125\u00d0\u00bf",
- "\u0120Member",
- "wick",
- "\u0120creators",
- "\u0120heritage",
- "indistinct",
- "\u0120resur",
- "ennen",
- "Come",
- "\u0120firing",
- "\u0120Bueno",
- "\u0120\u00d0\u00a2\u00d0\u00be",
- "ikan",
- "ettes",
- "\u0120kes",
- "\u0120trips",
- "\u0120divorce",
- "\u0120Kl",
- "\u0120consol",
- "keep",
- "\u00ea\u00b8\u00b0\u00ea\u00b0\u0122",
- "\u0120Report",
- "\u0120hosting",
- "\u0120diamond",
- "\u0120complic",
- "\u0120helicop",
- "\u0120depuis",
- "ds",
- "\u0120Chan",
- "\u00d1\u0131\u00d0\u00bb",
- "\u0120scissors",
- "ilation",
- "\u0120proportion",
- "ERE",
- "\u0120\u00d9\u012a\u00d8\u00a7\u00d9\u0126",
- "inta",
- "\u0120muchas",
- "uation",
- "itis",
- "\u00e6\u012c\u012c",
- "\u00d1\u0131\u00d1\u012b",
- "\u0120niin",
- "\u0120emphasize",
- "uela",
- "\u0120producers",
- "\u0120rze",
- "\u00c3\u00a4nder",
- "ETH",
- "\u00e6\u00ba",
- "\u0120constitu",
- "\u00e5\u013d\u00bd",
- "\u0120performances",
- "istle",
- "gov",
- "\u0120Liter",
- "\u0120incorporate",
- "\u0120educate",
- "\u0120Nin",
- "\u00ec\u00aa\u00bd",
- "\u00d9\u0129\u00d9\u0127",
- "eleration",
- "\u00d7\u0137\u00d7\u0133",
- "\u0120ya\u00c5\u0141",
- "orous",
- "\u0120Cas",
- "\u0120grants",
- "\u00eb\u012c\u00a5",
- "amel",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0142\u0129\u00ea\u00b2\u012e",
- "\u0120Este",
- "\u00d1\u0127\u00d0\u00be\u00d0\u00b4\u00d0\u00b8\u00d1\u0124",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d0\u00bb\u00d0\u00b5",
- "\u0120gent",
- "\u0120focuses",
- "alities",
- "\u0120Rh",
- "\u00eb\u00b3\u00b4",
- "\u00e6\u00b0\u0133",
- "\u0120Dance",
- "rr",
- "\u0120amer",
- "\u0120utilize",
- "\u0120l\u00c3\u0143",
- "\u0120Among",
- "\u0120pregnancy",
- "\u0120loops",
- "\u00d0\u00b0\u00d0\u00bb\u00d0\u00be\u00d1\u0123\u00d1\u012e",
- "\u0120Moh",
- "\u0120catching",
- "\u0120glob",
- "\u0120ajud",
- "\u0120[?",
- "\u0120Anal",
- "looking",
- "\u0120surfaces",
- "\u0120progressive",
- "\u0120viral",
- "08",
- "\u00ce\u00be",
- "KA",
- "\u0120\u00c5\u00bcy",
- "\u0120picks",
- "annon",
- "\u0120bulk",
- "\u0120Ross",
- "\u0120describing",
- "\u0120Gel",
- "\u0120locally",
- "\u0120endless",
- "\u0120massage",
- "\u0120cleaned",
- "\u0120traveled",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u012d",
- "\u0120sentiment",
- "igma",
- "\u0120Nas",
- "\u0120chemicals",
- "\u0120righteous",
- "\u0120Magic",
- "\u0120relates",
- "\u0120trucks",
- "\u01201960",
- "\u00e5\u012a\u00a5",
- "\u0120appet",
- "\u0120snacks",
- "\u0120Summer",
- "\u0120y\u00c3\u00bcz",
- "\u0120pris",
- "\u0120Mexican",
- "\u0120transparen",
- "\u0120minority",
- "\u0120verte",
- "\u0120lassen",
- "46",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00ba",
- "\u00c3\u00a9p",
- "\u0120\u00d1\u0126\u00d0\u00b8\u00d0\u00bb\u00d1\u012e",
- "\u0120iyi",
- "\u0120span",
- "\u00ed\u0137\u013a\u00ec\u00a7\u0122",
- "\u0120indicated",
- "quar",
- "\u0120scholarship",
- "\u0120LGBT",
- "\u0120historically",
- "\u00c3\u00b3\u00c5\u0124",
- "\u0120minist",
- "\u0120penet",
- "\u0120Rap",
- "\u0120conservation",
- "\u00e7\u013d\u00b4",
- "\u0120Honey",
- "\u0120Bei",
- "idel",
- "\u0120responsibilities",
- "\u0120messy",
- "\u0120Except",
- "ORE",
- "\u0120initiatives",
- "\u0120junior",
- "\u0120designers",
- "\u0120exploration",
- "\u0120sponsor",
- "\u0120mobility",
- "\u0120integ",
- "lando",
- "\u0120bark",
- "\u0120indicates",
- "\u00e0\u00b6",
- "\u0120employer",
- "\u00e5\u00ae\u012b",
- "\u0120cousin",
- "\u0120boiling",
- "\u0120chrom",
- "\u0120\u00c3\u00a7al",
- "\u0120perpet",
- "\u0120contained",
- "\u0120parks",
- "\u00d0\u00ab",
- "\u0120Engineering",
- "Please",
- "\u0120Starting",
- "hero",
- "\u0120lawyers",
- "\u00e8\u00a5\u00bf",
- "\u0120zd",
- "\u0120franchise",
- "rage",
- "\u0120intuit",
- "\u0120GL",
- "reach",
- "\u0120Elle",
- "\u0120nh\u00c6\u00b0",
- "\u0120Nord",
- "\u0120bean",
- "07",
- "\u0120pleasant",
- "\u00e5\u00bd\u0135",
- "viron",
- "\u0120gradient",
- "zus",
- "\u0120EM",
- "\u0120essay",
- "\u00ec\u0139\u0132\u00ec\u013c\u0136",
- "\u00e1\u00ba\u00bfn",
- "nu",
- "\u00e1\u00bb\u00ab",
- "\u0120\u00c3\u012bs",
- "\u0120denomin",
- "\u0120Girls",
- "\u0120personnes",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00a3",
- "bild",
- "\u0120Stat",
- "\u0120compliment",
- "\u0120Kate",
- "\u0120optimal",
- "\u0120hid",
- "\u00d8\u00af\u00d9\u012c",
- "\u0120quicker",
- "wall",
- "En",
- "INE",
- "???",
- "\u00ec\u00b2\u00b4",
- "\u0120Action",
- "\u00e5\u0141",
- "\u0120penalty",
- "\u0120Kaz",
- "'?",
- "\u0120cried",
- "\u0120canvas",
- "fte",
- "\u0120exclud",
- "\u00b8\u00eb\u00a1\u013e",
- "\u0120emphasis",
- "\u0120enzy",
- "\u0120Hou",
- "\u0120overseas",
- "\u00c3\u0143amos",
- "\u00e5\u00b8\u00ab",
- "\u00c3\u00b6glich",
- "\u0120headphones",
- "cn",
- "\u0120Age",
- "\u0120akan",
- "\u0120characteristic",
- "\u00ed\u0137\u013a\u00eb\u00a9\u00b4",
- "gets",
- "\u0120\u00eb\u00b6\u012a",
- "\u0120rival",
- "\u0120borders",
- "emente",
- "em\u00c3\u00a1s",
- "\u0120yol",
- "\u0120compe",
- "enders",
- "\u00c4\u00b1ndan",
- "\u0120m\u00c3\u00b6glich",
- "\u0120bubbles",
- "natural",
- "\u0120armed",
- "\u0120elabor",
- "\u0120\u00ec\u013f\u00b4\u00eb\u00b2\u012a",
- "\u0120washed",
- "\u00ce\u00bf\u00cf\u0127\u00ce\u00bc\u00ce\u00b5",
- "\u00e8\u00ab\u012d",
- "\u0120flavors",
- "\u0120existe",
- "\u0120prest",
- "\u0120Thema",
- "\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0123",
- "eron",
- "UE",
- "eri",
- "\u0120concer",
- "\u0120aix\u00c3\u00b2",
- "\u00e5\u0127\u00a9",
- "\u0120protective",
- "\u0120\u00d0\u00b7\u00d0\u00bd\u00d0\u00b0\u00d1\u0130",
- "\u0120\u00eb\u0124\u0142",
- "\u0120III",
- "\u0120meer",
- "\u0120Shop",
- "lli",
- "\u0120Order",
- "\u0120MY",
- "\u0120Ghost",
- "\u00e3\u0124\u0124\u00e3\u0123\u0128",
- "adel",
- "\u0120stole",
- "\u0120releasing",
- "\u0120Comment",
- "\u0120trains",
- "\u00eb\u00aa\u0127",
- "\u0120wissen",
- "ensed",
- "\u0120descend",
- "\u0120fier",
- "\u0120radi",
- "\u0120persu",
- "\u00e7\u00a2",
- "\u0120\u00d0\u00bc\u00d0\u00bd",
- "\u0120Dest",
- "\u0120worries",
- "itet",
- "bas",
- "\u0120stab",
- "name",
- "oric",
- "\u0120Close",
- "\u0120alumni",
- "\u0120Self",
- "ffe",
- "itating",
- "atherine",
- "\u0120Rights",
- "\u0120ellos",
- "\u0120warrant",
- "\u0120nerve",
- "\u0120vegetable",
- "\u0120Teil",
- "\u0120\u00ea\u00b0\u013b\u00ec\u013f\u00b4",
- "RY",
- "\u0120sustainability",
- "\u0120steht",
- "\u0120brid",
- "ada\u00c5\u0141",
- "\u0120tv",
- "\u0120duration",
- "\u0120pessoa",
- "\u0120metrics",
- "\u0120adam",
- "cas",
- "\u00d0\u00b0\u00d1\u0122\u00d0\u00b8",
- "\u0120evident",
- "\u0120displayed",
- "\u00d8\u00a7\u00d8\u00a6",
- "\u0120reck",
- "\u0120Buddha",
- "\u0120dele",
- "\u0120Diego",
- "osph",
- "\u0120bla",
- "\u0120Mik",
- "ulator",
- "\u01202001",
- "\u0120promoting",
- "ych",
- "\u0120EX",
- "\u0120lastly",
- "\u0120outline",
- "\u0120spirits",
- "\u0120veux",
- "\u0120subtract",
- "\u0120\u00c5\u0141imdi",
- "\u0120pins",
- "\u0120burger",
- "\u0120molto",
- "\u0120hab\u00c3\u0143a",
- "\u0120\u00eb\u00b0\u013a",
- "igu",
- "erst",
- "\u0120nen",
- "\u0120bacon",
- "itious",
- "\u0120carries",
- "\u0120promises",
- "nde",
- "\u0120Left",
- "\u0120Lim",
- "\u00e6\u00a3",
- "\u012044",
- "\u0120careers",
- "\u0120\u00ec\u00a3\u00bc\u00eb",
- "\u0120speeds",
- "qu\u00c3\u00a9",
- "mad",
- "market",
- "isme",
- "\u01202003",
- "\u0120recess",
- "\u0120JUD",
- "\u0120racist",
- "\u0120Schl",
- "\u0120parler",
- "\u0120otros",
- "ishes",
- "\u0120converted",
- "aaaa",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b8",
- "\u0120Ark",
- "\u0120Chance",
- "\u0120elementary",
- "\u00ce\u00b5\u00ce\u00bd",
- "inks",
- "Interviewer",
- "\u0120freely",
- "alah",
- "\u0120\u00eb\u012d\u00a4\u00eb\u00a5\u00b8",
- "\u0120requested",
- "\u0120torque",
- "no\u00c5\u013dci",
- "oured",
- "\u0120Staff",
- "\u0120stain",
- "\u0120Alan",
- "\u0120vere",
- "\u0120Winter",
- "\u0120defect",
- "iedy",
- "\u0120beats",
- "\u0120h\u00c3\u00a1",
- "umn",
- "oons",
- "itudes",
- "\u0120seit",
- "oly",
- "\u0120reserv",
- "\u0120extr",
- "\u0120physician",
- "visor",
- "\u0120handful",
- "\u0120Nations",
- "\u0120\u00ec\u00a2\u012d\u00ec\u013f\u0122",
- "uccess",
- "\u0120upstairs",
- "\u0120Square",
- "\u0120hein",
- "\u0120Season",
- "olis",
- "\u0120prince",
- "\u0120defensive",
- "\u00e7\u00bd",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d1\u0123\u00d1\u0124",
- "\u00d1\u0138\u00d0\u00b9",
- "\u0120\u00d8\u00a7\u00d9\u0128",
- "umble",
- "\u00ea\u00b9\u012e\u00ec\u013c\u0136",
- "\u0120assass",
- "\u0120circular",
- "\u0120qualities",
- "\u0120hmm",
- "\u0120blown",
- "\u0120Liz",
- "\u0120Kur",
- "\u0120SA",
- "\u0120findings",
- "\u0120colours",
- "\u0120delle",
- "\u0120IR",
- "\u0120Ath",
- "\u0120Dub",
- "\u0120Ox",
- "\u0120\u00d8\u00ae",
- "\u0120pockets",
- "\u0120grill",
- "\u0120switching",
- "\u0120preferred",
- "\u0120Wales",
- "\u0120exemplo",
- "\u0120chopped",
- "\u0120vaccination",
- "\u0120neuro",
- "\u0120specify",
- "ivos",
- "\u0120ser\u00c3\u00a1",
- "\u0120zie",
- "\u0120\u00e0\u00ae\u00ae",
- "\u0120resulting",
- "\u0120Ugh",
- "\u0120messed",
- "CD",
- "\u0120paar",
- "\u0120comer",
- "\u0120couch",
- "\u0120Festival",
- "\u012049",
- "vous",
- "zens",
- "\u00e7\u00a8\u00ae",
- "\u0120Kennedy",
- "\u0120Ts",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u0139",
- "\u0120demonstration",
- "\u0120unto",
- "\u0120frustrating",
- "\u0120laboratory",
- "\u0120egy",
- "\u0120beautifully",
- "\u0120\u00ec\u0140\u00ac\u00eb",
- "\u0120algu",
- "\u0120\u00c3\u00b6yle",
- "\u00e4\u00bd\u0142\u00e7\u013e\u012d",
- "\u0120PH",
- "\u0120fortune",
- "\u0120cleaner",
- "\u0120Robin",
- "\u0120saus",
- "\u0120Geld",
- "\u0120kat",
- "obs",
- "\u0120olur",
- "\u0120matt",
- "\u0120questa",
- "\u0120suggestion",
- "encer",
- "\u00d0\u00be\u00d1\u0123\u00d1\u0124",
- "\u0120radar",
- "\u0120\u00ec\u0140\u00a1",
- "isha",
- "\u00e0\u00ae\u00a8",
- "\u00e3\u0124\u0135\u00e3\u0123\u00aa",
- "jes",
- "\u0120veel",
- "\u00ec\u0124\u00b0",
- "\u0120authors",
- "\u00e3\u0122\u0130",
- "plan",
- "\u0120collaborative",
- "\u0120instinct",
- "\u0120farming",
- "auge",
- "Edu",
- "\u0120membership",
- "\u0120simultaneously",
- "\u0120bake",
- "\u0120k\u00c3\u00a4",
- "\u0120lectures",
- "\u00d1\u0129\u00d0\u00b5\u00d1\u0123",
- "\u0120prendre",
- "\u0120collaps",
- "\u0120Saya",
- "\u0120Fut",
- "\u0120yog",
- "\u0120Rather",
- "\u00d8\u00b1\u00d9\u012c",
- "\u0120camps",
- "\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b4",
- "\u0120simulation",
- "\u0120Mak",
- "Laughs",
- "\u0120grey",
- "\u0120sentences",
- "yen",
- "\u0120Unless",
- "Je",
- "\u0120Satan",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00b6\u00d0\u00b5",
- "\u0120NA",
- "\u0120bron",
- "\u0120?]",
- "\u0120souls",
- "\u0120lightning",
- "\u0120imagined",
- "\u0120czyli",
- "psilon",
- "etta",
- "\u0120believing",
- "\u0120strongest",
- "\u0120CON",
- "\u0120quelques",
- "\u0120immigrants",
- "\u0120wallet",
- "\u00e9\u0122\u013b\u00e6\u013a\u00af",
- "\u0120Jersey",
- "\u0120implications",
- "\u0120forb",
- "\u00e3\u0122\u0131",
- "\u0120unbelievable",
- "\u00d8\u00a7\u00d8\u00a1",
- "\u0120operational",
- "\u00c3\u00bcs",
- "\u0120GM",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0141\u00b0\u00eb\u012f\u00b0",
- "\u0120gracias",
- "\u0120entend",
- "\u0120Regard",
- "rob",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d1\u0127",
- "\u00e8\u0131",
- "\u0120Revolution",
- "\u0120waar",
- "\u0120Biz",
- "theless",
- "\u0120sponsored",
- "quier",
- "\u0120\u00ec\u013f\u00bc\u00eb",
- "\u0120tek",
- "\u0120\u00eb\u0132\u0142",
- "igkeit",
- "\u0120Luck",
- "\u0120Certainly",
- "\u0120toll",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u0120Money",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d1\u0122",
- "\u0120Double",
- "\u0120Wolf",
- "\u0120chunk",
- "\u00ce\u00ac\u00ce\u00bd",
- "it\u00c3\u00a9s",
- "oning",
- "Mar",
- "\u0120grandes",
- "\u0120collections",
- "\u0120Europa",
- "\u0120\u00d0\u00b0\u00d1\u0122",
- "\u0120\u00e2\u0122\u012d\u00e2\u0122\u012d\u00e2\u0122\u012d",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0141\u00ac\u00eb\u00a9\u00b4",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012c",
- "\u0120\u00e3\u0123\u00aa",
- "\u0120\u00ec\u012d\u013e\u00ea\u00b0\u0126",
- "\u0120Custom",
- "\u0120\u00ec\u00b2\u013a",
- "\u00d1\u0138\u00d0\u00bb\u00d1\u012e",
- "\u0120individually",
- "\u00ed\u0139",
- "\u0120dozen",
- "\u0120owe",
- "\u0120Victoria",
- "\u00e5\u0131\u00af\u00e8\u0125\u00bd",
- "\u0120beet",
- "urb",
- "\u0120analog",
- "i\u00c3\u00a7\u00c3\u00a3o",
- "\u0124\u013e",
- "soever",
- "\u0120modo",
- "\u0120subscribed",
- "\u00ec\u0140\u00ac",
- "\u0120entities",
- "\u00e7\u012b\u0129",
- "\u0120closet",
- "\u0120responding",
- "\u0120printer",
- "\u0120Stephan",
- "\u0120by\u00c5\u0124",
- "\u0120Dom",
- "\u0120Fern",
- "\u0120Pier",
- "\u0120wi\u00c4\u013bc",
- "\u0120hence",
- "\u0120modules",
- "\u00e3\u0125\u00ac",
- "\u0120\u00eb\u0136\u00b1",
- "\u0120Danny",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d0\u00b1\u00d0\u00b5",
- "\u0120vad",
- "\u0120\u00ec\u0139\u0126",
- "\u0120sous",
- "\u0120sphere",
- "BY",
- "\u0120Ped",
- "igned",
- "\u0120wheat",
- "\u0120unders",
- "\u0120evolve",
- "\u0120declar",
- "\u0120lightly",
- "\u0120identifying",
- "\u00e6\u0126\u0131\u00e6\u0122\u013f",
- "\u0120legendary",
- "\u0120genuine",
- "\u0120grind",
- "\u0120Une",
- "geben",
- "\u0120bicy",
- "\u0120jumps",
- "\u0120province",
- "zi\u00c4\u013b",
- "\u0120\u00d7\u0132\u00d7\u0142\u00d7\u013b",
- "\u0120hoc",
- "\u0120\u00d0\u00b1\u00d0\u00bb",
- "\u0120Grad",
- "\u0120revenge",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00aa",
- "ooh",
- "\u00e6\u012d\u013e",
- "\u00d0\u00b0\u00d1\u0128\u00d0\u00b8\u00d0\u00b8",
- "\u00e5\u00b9\u00b3",
- "\u0120electro",
- "\u0120\u00eb\u0132\u0132",
- "\u00e3\u0123\u00a7\u00e3\u0123\u00af",
- "\u0120fals",
- "riel",
- "oker",
- "\u0120Excellent",
- "\u0120Morgan",
- "\u0120brick",
- "\u0120substantial",
- "\u0120pollution",
- "\u0120T\u00c3\u00bcr",
- "\u0120Evet",
- "\u0120lung",
- "\u00e3\u0123\u0138",
- "\u00d7\u013b\u00d7\u00a9",
- "ommes",
- "\u0120realizing",
- "\u0120humble",
- "\u0120Lock",
- "\u0120bod",
- "\u0120\u00ec\u0138\u00b8",
- "\u0120peers",
- "uzz",
- "\u0120embedded",
- "\u0120claro",
- "\u0120aggreg",
- "\u0120employers",
- "\u0120Raj",
- "\u0120\u00e3\u0123\u00a8",
- "\u0120Yi",
- "\u0120jeu",
- "aters",
- "\u0120strikes",
- "nos",
- "autres",
- "dr",
- "opher",
- "\u0120Apparently",
- "\u00ed\u013a\u0126",
- "\u0120infant",
- "\u00d8\u00a7\u00d8\u00a8",
- "\u00d1\u0124\u00d1\u012d",
- "\u00ed\u013d",
- "\u00da\u00af",
- "\u0120redes",
- "aca\u00c4\u0141\u00c4\u00b1m",
- "\u0120DAVID",
- "\u0120Chicken",
- "\u0120perspectives",
- "\u0120viewer",
- "\u0120shar",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b8\u00d0\u00b7",
- "ligt",
- "eros",
- "itable",
- "\u00d0\u00b8\u00d0\u00bb\u00d0\u00be\u00d1\u0123\u00d1\u012e",
- "\u0120dif\u00c3\u0143",
- "\u00b4\u00eb\u012f\u00b0",
- "\u0120retired",
- "\u0120thats",
- "zenie",
- "beiten",
- "\u0120mycket",
- "\u0120Rab",
- "\u0120inflamm",
- "\u00ec\u00b0\u00ae",
- "\u0120dum",
- "\u0120daddy",
- "\u00e6\u013e\u0141",
- "\u0120immers",
- "\u0120playlist",
- "\u00e0\u00af\u0128",
- "\u0120traum",
- "\u0120refuse",
- "step",
- "\u00e0\u00ae\u013c",
- "cup",
- "\u0120pops",
- "rimin",
- "ay\u00c4\u00b1m",
- "\u0120ald",
- "\u0120unnecess",
- "\u0120dah",
- "\u0120Irish",
- "\u0120compr",
- "la\u00c5\u0141",
- "TP",
- "\u0120translated",
- "Sc",
- "ce\u00c4\u0141im",
- "\u00b4\u0132",
- "\u0120drei",
- "\u0120\u00d0\u00bb\u00d1\u0130\u00d0\u00b4\u00d0\u00b5\u00d0\u00b9",
- "\u0120quiero",
- "\u0120hele",
- "zlich",
- "\u0120apples",
- "\u0120districts",
- "\u0120credits",
- "\u0120asp",
- "\u0120\u00eb\u012d\u00a8",
- "oral",
- "\u00e5\u00bd\u00b1",
- "\u0120stepping",
- "\u0120Va",
- "\u0120gains",
- "65",
- "\u0120nuestra",
- "eday",
- "assador",
- "\u0120Lind",
- "\u0120crops",
- "ciendo",
- "igue",
- "\u0120bana",
- "Am",
- "\u0120pent",
- "\u0120addiction",
- "\u0120packaging",
- "\u00c3\u00a4d",
- "\u00aa\u00a8",
- "\u0120perqu\u00c3\u00a8",
- "\u0120campaigns",
- "\u0120steep",
- "\u0120neue",
- "\u0120embarrassed",
- "\u0120distinction",
- "itzer",
- "\u00e5\u0133\u012c",
- "\u0120registration",
- "\u0120llam",
- "\u0120Almighty",
- "liest",
- "\u0120uz",
- "nak",
- "\u00e7\u00ba",
- "\u0120teraz",
- "iamente",
- "\u0120transactions",
- "\u0120c\u00c3\u00b4t",
- "\u0120switched",
- "\u0120combo",
- "\u0120prayers",
- "\u0120internship",
- "\u0120addresses",
- "\u0120charity",
- "\u0120WOO",
- "\u0120bait",
- "\u00e8\u00bf\u0129",
- "\u0120\u00ef\u00bf\u00bd",
- "\u0120fica",
- "\u0120Tyler",
- "aru",
- "\u0120atoms",
- "\u0120Level",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d0\u00bc",
- "\u0120fame",
- "ulk",
- "\u0120teaches",
- "\u0120rebuild",
- "\u00d0\u00b5\u00d0\u00b4\u00d1\u012e",
- "\u0120Indonesia",
- "ushi",
- "\u0120Short",
- "\u0120ensuring",
- "fs",
- "ele",
- "\u0120marginal",
- "\u0120conclude",
- "amt",
- "\u0120verify",
- "\u0120McDonald",
- "\u0120skal",
- "\u0120reconst",
- "\u0120Mann",
- "\u0120basement",
- "\u0120transformed",
- "\u0120occasionally",
- "zone",
- "\u0120Dans",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b9",
- "\u0120diagnosed",
- "\u0120\u00cf\u0126\u00ce\u00b1",
- "\u0120commands",
- "\u0120presidential",
- "\u0120abb",
- "\u0120bracket",
- "\u0120Lem",
- "\u00c3\u00a5ng",
- "\u0120favorites",
- "\u0120revol",
- "\u0120\u00ed\u012c\u00b9",
- "\u0120harass",
- "\u00e9\u0127",
- "\u0120cleans",
- "st\u00c3\u00a4nd",
- "\u0120knocked",
- "\u0120peoples",
- "\u0120musicians",
- "\u0120mutual",
- "\u0120Cold",
- "88",
- "zej",
- "atie",
- "\u0120Honor",
- "\u0120obsessed",
- "\u0120MUSIC",
- "\u0120Break",
- "\u00c3\u00bang",
- "\u0120modify",
- "\u0120s\u00c3\u00b6yle",
- "\u0120\u00d7\u0140\u00d7\u0136",
- "\u0120Online",
- "fo",
- "\u0120Miller",
- "\u0120liking",
- "\u0120inhab",
- "\u0120gratitude",
- "\u0120Journal",
- "arness",
- "John",
- "\u0120Git",
- "\u00e5\u012b\u013d",
- "\u0120sincere",
- "\u0120Sci",
- "\u0120Eli",
- "\u0120symbols",
- "\u0120manually",
- "\u00ce\u00b5\u00cf\u0124",
- "\u0120\u00d0\u00b2\u00d1\u0138\u00d0\u00b4",
- "\u0120Fat",
- "\u0120labels",
- "\u0120sophisticated",
- "umps",
- "\u0120releases",
- "\u012047",
- "\u0120OM",
- "\u00ea\u00b0\u0122\u00eb",
- "\u0120Bien",
- "\u0120Ref",
- "\u00e8\u00a8\u013a",
- "\u0120Sta",
- "\u0120Egg",
- "\u0120indicator",
- "pson",
- "\u0120nas\u00c4\u00b1l",
- "Right",
- "\u0120convey",
- "\u0120knot",
- "\u0120connects",
- "ulas",
- "\u0120preced",
- "\u0120inequality",
- "amiento",
- "\u0120reply",
- "OY",
- "\u0120dismiss",
- "\u0120\u00eb\u0132\u013e",
- "\u00e7\u0126\u00a1",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d1\u012a\u00d0\u00be",
- "\u0120m\u00c3\u00a9d",
- "\u0120randomly",
- "\u0120Ont",
- "uard",
- "\u0120pulls",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d1\u012e",
- "\u0120Need",
- "\u0120Soft",
- "\u0120strengths",
- "\u0120goed",
- "umen",
- "\u00e6\u0143\u00bb",
- "\u0120\u00ed\u0130\u00b8",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00b1",
- "\u0120clarity",
- "\u0120Ai",
- "\u0120balloon",
- "\u0120Pand",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d",
- "\u0120shiny",
- "\u0120smallest",
- "onia",
- "hill",
- "oting",
- "\u0120eing",
- "\u0120merely",
- "\u0120seus",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00bf",
- "\u0120\u00ed\u0128\u00b5",
- "\u0120guides",
- "\u0120specialist",
- "\u0120steak",
- "\u00e3\u0124\u012a\u00e3\u0123\u0128",
- "\u0120migration",
- "quele",
- "\u0120ruined",
- "\u0120pupp",
- "\u00e5\u00a5\u00b3",
- "\u0120kend",
- "angan",
- "\u0120palm",
- "\u0120unfair",
- "\u0120zm",
- "\u0120DV",
- "chester",
- "\u00d0\u00b8\u00d1\u0130",
- "\u0120ooh",
- "erg",
- "ATH",
- "\u00b0\u00a9",
- "\u00e5\u0135\u00aa",
- "rison",
- "\u0120involving",
- "\u0120partly",
- "an\u00c3\u00a7ais",
- "\u0120vow",
- "\u0120prominent",
- "\u0120cryst",
- "iba",
- "\u0120deserves",
- "\u0120overt",
- "\u0120sensit",
- "\u0120Whe",
- "\u0120tighten",
- "\u0120intimid",
- "\u0120aliment",
- "will",
- "\u0120strengthen",
- "\u0120Tan",
- "\u00e5\u0131\u012a",
- "\u00e3\u0123\u0139\u00e3\u0123\u00be\u00e3\u0123\u013b",
- "oni",
- "\u0120Mun",
- "\u0120proph",
- "\u0120rehears",
- "\u0120Kle",
- "\u0120veces",
- "\u0120wondered",
- "oki",
- "\u0120senses",
- "\u00b4\u00ec\u012d",
- "\u00c6\u00b0\u00e1\u00bb\u013d",
- "\u0120\u00c8\u013bi",
- "\u0120muchos",
- "\u0120watches",
- "ortunate",
- "\u0120Juan",
- "\u00ec\u0140\u0138\u00ec\u0137\u0126",
- "\u00d1\u0122\u00d0\u00b5",
- "ei",
- "ionen",
- "\u0120experimental",
- "\u0120daughters",
- "\u00e0\u00b8\u013d",
- "\u0120mentally",
- "becca",
- "aware",
- "\u00ec\u0126\u013f",
- "\u0120whatsoever",
- "\u0120enables",
- "\u0120Low",
- "oid",
- "\u00e0\u00b8\u012c",
- "\u00c3\u00b3d",
- "\u00d8\u00ba",
- "\u0120constructed",
- "\u0120Ladies",
- "\u0120accused",
- "\u0120\u00d0\u00b0\u00d0\u00bd",
- "Dan",
- "\u0120spawn",
- "\u0120containers",
- "\u0120artistic",
- "\u00c4\u00b1p",
- "\u0120discl",
- "\u0120autres",
- "inas",
- "\u0120Nation",
- "\u0120nag",
- "bean",
- "whe",
- "\u013e\u00eb\u0131\u0126",
- "\u0120Seoul",
- "\u0120\u00ed\u0131\u00ac",
- "\u0120Nich",
- "\u0120complement",
- "\u0120interven",
- "\u0120Model",
- "\u0120Orange",
- "namon",
- "\u0120calculation",
- "see",
- "\u0120ustedes",
- "\u0120leb",
- "\u0120doct",
- "\u00d1\u0138\u00d0\u00bd",
- "\u0120foster",
- "\u0120elastic",
- "\u0120Ahh",
- "\u0120ace",
- "\u0120Pink",
- "\u0120Jeg",
- "\u0120deer",
- "\u00e3\u0123\u0139\u00e3\u0123\u0126",
- "sis",
- "\u0120jako",
- "\u0120Emma",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d0\u00be",
- "\u0120portrait",
- "\u0120maker",
- "\u0120aument",
- "\u00d1\u0122\u00d0\u00be\u00d0\u00b1",
- "\u0120airplane",
- "\u0120transparency",
- "\u0120adjustment",
- "\u0120CDC",
- "\u00c3\u00a7on",
- "\u0120uploaded",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00b9\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d0\u00b2",
- "\u0120iter",
- "\u0120curse",
- "\u00c3\u00b4n",
- "merce",
- "aran",
- "\u0120leak",
- "\u00e7\u00b5\u0132",
- "\u0120absence",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d0\u00b9",
- "\u0120readers",
- "aler",
- "\u0120beneath",
- "ango",
- "hetic",
- "\u0120finns",
- "\u0120poop",
- "\u0120duplic",
- "Hi",
- "igs",
- "ologically",
- "opp",
- "\u0120dizer",
- "\u0120Allen",
- "\u0120gli",
- "\u0120acceleration",
- "\u0120vitamin",
- "\u00e3\u0125\u0143",
- "v\u00c3\u00a4",
- "\u0120Access",
- "\u00e0\u00ae\u013b",
- "r\u00c3\u00a1s",
- "\u0120appreciated",
- "\u0120nah",
- "\u0120poster",
- "\u0120tale",
- "\u0120highlighted",
- "\u00e6\u0138\u0129",
- "\u00c5\u00bceli",
- "\u0120blockchain",
- "\u0120microw",
- "\u0120cinema",
- "\u0120Chang",
- "\u0120Search",
- "usters",
- "\u0120Zero",
- "\u0120Division",
- "\u00d1\u0122\u00d0\u00b0\u00d1\u0123",
- "\u0120scare",
- "\u0120jelly",
- "\u0120Administration",
- "SO",
- "\u0120lined",
- "\u0120\u00ea\u00b0\u0126",
- "\u0120geben",
- "\u0120soda",
- "\u0120winners",
- "\u00b3\u00bc",
- "\u00d9\u0134",
- "\u0120Amb",
- "\u00e5\u0137\u0131\u00e9\u00a1\u012e",
- "\u00e5\u0136",
- "\u0120peg",
- "\u00e5\u00b7\u00b1",
- "43",
- "\u0120raus",
- "\u0120rewards",
- "\u0120inclus",
- "\u0120highway",
- "\u0120hah",
- "\u0120multiplied",
- "\u0120s\u00e1\u00ba\u00bd",
- "\u0120disciples",
- "\u0120ning",
- "\u0120dressing",
- "\u0120attributes",
- "\u0120Mosc",
- "\u0120Greece",
- "\u0120sek",
- "\u0120Learn",
- "\u0120jus",
- "rendre",
- "\u0120personne",
- "plete",
- "\u0120placing",
- "\u0120luego",
- "illance",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012b",
- "\u0120provision",
- "\u0120lion",
- "tra",
- "boards",
- "\u0120behaviour",
- "hey",
- "\u0120subscription",
- "\u0120protagon",
- "\u00e3\u0125\u00a3",
- "\u0120vara",
- "\u0120\u00c5\u0141u",
- "\u0120haha",
- "\u0120teaspoon",
- "\u00e6\u0141",
- "avoir",
- "\u0120crypto",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d1\u0122",
- "\u0120Store",
- "abs",
- "\u0120Students",
- "\u0120laund",
- "into",
- "\u0120approached",
- "\u00b0\u013e",
- "\u00d1\u0125\u00d1\u0130\u00d1\u012b",
- "\u0120Labor",
- "otes",
- "iatric",
- "\u0120gro\u00c3\u0141",
- "utive",
- "\u0120\u00d0\u00b8\u00d0\u00b4",
- "\u0120Gib",
- "\u0120placement",
- "\u0120dif\u00c3\u0143cil",
- "\u0120frog",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d0\u00b5\u00d1\u0127",
- "\u0120Jr",
- "azed",
- "\u00d1\u0125\u00d1\u012b",
- "\u0120\u00ea\u00bc",
- "frame",
- "\u00d0\u00b0\u00d0\u00b5\u00d1\u012a\u00d1\u012e",
- "\u0120lockdown",
- "\u00e5\u0133\u00b3",
- "\u0120medi",
- "\u0120\u00d7\u0136\u00d7\u0140\u00d7",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d0\u00b9",
- "emale",
- "\u00ec\u00a2\u0127",
- "ateral",
- "\u0120distant",
- "\u0120bears",
- "\u0120journalist",
- "\u00e8\u00a7\u00a3",
- "\u0120Marshall",
- "\u0120Ihnen",
- "uetooth",
- "bag",
- "\u0120\u00c4\u0133\u00c3\u00a3",
- "\u0120Highness",
- "\u0120\u00ec\u00b0\u012f",
- "\u00d0\u00b8\u00d0\u00ba\u00d0\u00b0",
- "\u0120Wu",
- "\u0120Fran",
- "\u0120peng",
- "\u0120fon",
- "\u0120hypothesis",
- "\u0120\u00d1\u0122\u00d1\u0125",
- "\u0120ly",
- "\u00d7\u013c",
- "\u00ec\u013d\u0136",
- "\u0120Radio",
- "\u00e0\u00b8\u0140",
- "Dav",
- "\u0120embarrassing",
- "\u0120\u00ec\u0140\u012a\u00ec\u0138\u00b4",
- "\u0120casting",
- "\u0120cage",
- "\u0120Psych",
- "\u0120\u00ec\u013f\u00bc\u00eb\u012d\u00a8",
- "\u0120\u00c5\u00be",
- "imb",
- "\u0120directors",
- "SH",
- "\u0120\u00cf\u0126\u00ce\u00b7\u00ce\u00bd",
- "\u00e1\u00bb\u0123u",
- "\u0120konu\u00c5\u0141",
- "\u0120optional",
- "quarters",
- "iker",
- "\u0120Sant",
- "\u0120verses",
- "\u00eb\u00b6\u0122",
- "\u0120olar",
- "\u0120\u00cf\u0129",
- "\u00e3\u0125\u0137",
- "\u0120\u00ce\u00b3\u00ce\u00b9\u00ce\u00b1",
- "\u0120Imm",
- "\u0120controversial",
- "\u0120ersten",
- "\u0120recip",
- "\u0120Christianity",
- "\u0120\u00ea\u00b4\u013e",
- "ordon",
- "\u00d7\u0137\u00d7\u00a9",
- "\u0120slash",
- "\u0120Pf",
- "\u00d1\u0125\u00d0\u00b4\u00d1\u012e",
- "\u00d7\u0137\u00d7\u013f",
- "\u0120Perry",
- "\u0120mamy",
- "\u0120backgrounds",
- "\u0120\u00e0\u00ae\u0130\u00e0\u00ae\u00a9",
- "\u0120pendant",
- "\u0120Columbia",
- "\u0120inverse",
- "\u0120\u00d1\u0129\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00b7",
- "\u0120sv",
- "\u0120digging",
- "41",
- "chem",
- "\u0120navigation",
- "\u0120Shin",
- "\u0120Front",
- "PD",
- "\u0120bearing",
- "\u0120Wasser",
- "\u0120wax",
- "\u0120CHRIS",
- "ching",
- "\u0120pressed",
- "El",
- "\u0120Dal",
- "onsin",
- "\u0120binding",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00b9",
- "poons",
- "\u0120mock",
- "arest",
- "\u00d0\u00ba\u00d1\u0122\u00d0\u00b0",
- "MM",
- "\u0120corrupt",
- "storm",
- "\u0120refres",
- "\u0120Coach",
- "ll\u00c3\u00a4",
- "\u0120THIS",
- "\u0120parag",
- "\u0120\u00ec\u0135\u00b0",
- "pool",
- "\u0120billions",
- "\u0120\u00ea\u00b9\u0122",
- "group",
- "\u0120welcoming",
- "cellence",
- "\u0120Duke",
- "\u00ea\u00b8\u00b4",
- "\u0120primera",
- "\u00ec\u0142\u00b8",
- "\u0120pond",
- "\u0120statue",
- "\u0120\u00ea\u00b5\u00ac\u00eb",
- "\u0120hatch",
- "\u0120instrumental",
- "\u0120residential",
- "\u00ec\u00bb\u00a4",
- "\u0120accepting",
- "oshi",
- "date",
- "\u0120\u00ec\u0136\u00a8",
- "\u0120planted",
- "\u0120joking",
- "\u0120\u00ec\u0126\u013e",
- "\u0120hated",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0123\u00d0\u00ba",
- "\u0120slept",
- "\u0120packages",
- "\u0120islands",
- "esen",
- "\u00c4\u0141\u00c4\u00b1",
- "\u0120diagon",
- "\u0120Osc",
- "\u0120mesh",
- "\u0120scales",
- "arity",
- "\u0120Defense",
- "\u00e3\u0123\u00a1\u00e3\u0124\u0129",
- "\u0120Lewis",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d1\u0131",
- "\u0120flies",
- "uinely",
- "\u0120Consider",
- "\u0120stark",
- "hew",
- "\u0120As\u00c3\u0143",
- "\u00b3\u00b4\u00eb",
- "\u0120propose",
- "\u0120\u00ed\u0137\u013a\u00eb\u00a9\u00b4",
- "odo",
- "\u0120Normally",
- "\u0120heeft",
- "\u0120Harris",
- "gro",
- "\u0120Blood",
- "base",
- "\u0120iOS",
- "\u0120touches",
- "\u0120inspir",
- "\u0120\u00d7\u0135",
- "\u0120binary",
- "\u0120\u00ec\u00b6\u0136",
- "\u0120serial",
- "\u0120ion",
- "\u0120unemployment",
- "\u0120odds",
- "\u0120Fab",
- "\u0120FBI",
- "BRUN",
- "\u0120weights",
- "\u00ce\u00bd\u00ce\u00bf",
- "atile",
- "\u0120nurses",
- "\u0120involvement",
- "\u0120\u00ed\u0136\u00bc",
- "\u0120governance",
- "\u0120\u00e2\u0124\u00ac",
- "\u00d1\u0122\u00d1\u0125\u00d0\u00bf",
- "ierra",
- "\u00ed\u013a\u0137",
- "\u0120Jerry",
- "\u0120beard",
- "\u0120salvation",
- "\u0120Along",
- "gentle",
- "\u0120Ki",
- "bol",
- "\u0120Plat",
- "\u0120hasht",
- "\u00e8\u00bf\u0133",
- "\u0120ware",
- "\u0120partie",
- "ycz",
- "\u0120intr",
- "Fih",
- "nent",
- "\u0120cheat",
- "ilen",
- "\u0120\u00eb\u00af",
- "orie",
- "\u0120f\u00c3\u00a1cil",
- "etric",
- "\u0120affecting",
- "unciation",
- "\u0120affairs",
- "\u0120bee",
- "\u0120viewing",
- "\u0120orang",
- "\u0120Lan",
- "\u0120\u00d0\u00a1\u00d1\u0124",
- "\u00e4\u00b8\u0138",
- "\u0120Mes",
- "\u0125\u0123",
- "erie",
- "\u0120espa",
- "\u0120interpre",
- "\u0120possess",
- "\u0120purely",
- "rito",
- "found",
- "asma",
- "\u00ec\u0142\u0123\u00ec\u013f\u00b8",
- "\u0120examine",
- "\u0120\u00d1\u0125\u00d0\u00bc",
- "\u0120besch",
- "\u0120Tomorrow",
- "\u0120Block",
- "\u0120variant",
- "\u0120preference",
- "\u0120coaches",
- "\u0120medications",
- "\u0120\u00ed\u013a\u0126",
- "\u0120empire",
- "\u00eb\u0126\u00a4",
- "\u0120Illinois",
- "\u0120crispy",
- "\u0120th\u00c3\u00ac",
- "\u0120bees",
- "77",
- "\u0120glow",
- "\u00e8\u00ba",
- "\u0120Studies",
- "\u00e5\u0132\u0126",
- "\u0120Challenge",
- "\u0120unlikely",
- "\u00d0\u00a7",
- "\u00c4\u00b1yorsun",
- "DIE",
- "\u0120minimize",
- "izard",
- "\u0120\u00c3\u00ban",
- "\u0120encontrar",
- "\u0120Kill",
- "\u00e5\u00bb",
- "\u0120vanilla",
- "\u0120Grant",
- "\u0120GT",
- "sea",
- "\u0120sought",
- "\u00d0\u00b2\u00d0\u00be\u00d0\u00b4",
- "\u0120n\u00c3\u00a4m",
- "\u0120Aunt",
- "OWN",
- "\u0120pumpkin",
- "stellen",
- "\u0120rag",
- "\u00d0\u00b5\u00d0\u00b3\u00d0\u00b4\u00d0\u00b0",
- "\u0120storyt",
- "\u0120forum",
- "\u00e6\u00a9\u0141",
- "\u0120estaba",
- "uche",
- "\u0120congress",
- "\u0120Rey",
- "\u0120dramatically",
- "\u0120Sport",
- "\u0120Yellow",
- "\u0120\u00ea\u00b3\u0126\u00ec\u0128\u012f",
- "\u0120disgusting",
- "\u0120Recent",
- "\u0120acquired",
- "\u0120cables",
- "\u00e7\u0136\u013c",
- "din",
- "\u0120visto",
- "\u0120communicating",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00b2\u00d0\u00bb\u00d1\u0131",
- "\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u00e3\u0125\u00bb\u00e3\u0125\u00bb\u00e3\u0125\u00bb",
- "\u0120r\u00c3\u00a9g",
- "\u0120socks",
- "\u0120proces",
- "because",
- "\u0120utter",
- "\u0120colocar",
- "\u0120newest",
- "\u0120gramm",
- "\u00e8\u00a1\u00a8",
- "\u00e4\u00b8\u012f\u00e7\u0141\u00a5\u00e9\u0123\u0135",
- "\u0120shifting",
- "\u0120carrier",
- "\u0120\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d1\u0122",
- "\u0120Schw",
- "\u0120executed",
- "\u0120maintained",
- "\u0120\u00cf\u0128",
- "\u0120Moses",
- "\u0120disse",
- "\u0120horr",
- "\u00e3\u0122\u013e",
- "\u0120rally",
- "\u0120allem",
- "\u0120Eventually",
- "\u0120diyor",
- "lvania",
- "\u0120schnell",
- "\u0120\u00ea\u00b3\u00bc",
- "\u0120\u00eb\u00a7\u00a4",
- "\u0120struggles",
- "late",
- "\u0120clarify",
- "\u00c3\u00a9ment",
- "\u0120multiplic",
- "\u00d0\u00b8\u00d0\u00b1\u00d0\u00be",
- "\u0120journ",
- "\u0120fragr",
- "\u0120surprisingly",
- "\u0120desperate",
- "52",
- "\u0120sul",
- "\u0120Read",
- "\u0120Fried",
- "\u0120mond",
- "woo",
- "\u0120organizing",
- "\u00e3\u0123\u0139\u00e3\u0124\u0129\u00e3\u0123\u0128",
- "\u0120Soon",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0123",
- "\u0120Nur",
- "\u0120\u00d0\u0139\u00d0\u00b4",
- "\u0120spider",
- "\u00d0\u00b5\u00d1\u0123\u00d1\u0131",
- "\u0120tutorials",
- "\u0120nutrients",
- "orer",
- "\u0120coefficient",
- "\u0120arrangement",
- "\u0120pricing",
- "nan",
- "yu",
- "BL",
- "\u0120tribe",
- "\u0120Howard",
- "unks",
- "\u0120newer",
- "\u0120provin",
- "\u0120prediction",
- "hos",
- "\u0120olsun",
- "\u0120Around",
- "\u0120vier",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00bd",
- "\u0120valley",
- "\u0120Ela",
- "ifi",
- "\u0120galaxy",
- "\u0120tranqu",
- "\u0120advers",
- "\u0120Temple",
- "iffs",
- "igence",
- "\u00e8\u0129\u00aa\u00e5\u00b7\u00b1",
- "\u0120k\u00c3\u00b6nnte",
- "\u0120\u00c4\u0133\u00c3\u00b3",
- "Did",
- "\u0120photographs",
- "\u0120AWS",
- "\u00d1\u0128\u00d0\u00b8\u00d1\u0131",
- "\u0120guards",
- "\u0120appointed",
- "\u0120Gil",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00bc",
- "\u0120cod",
- "\u0120Unlike",
- "\u0120evenly",
- "isconsin",
- "\u0120estou",
- "\u0120mnie",
- "\u0120Exec",
- "\u0120MV",
- "\u0120Eine",
- "\u00e4\u00bf\u00a1",
- "\u0120Roger",
- "\u0120Fac",
- "\u0120List",
- "\u0120fuer",
- "\u00d0\u00b0\u00d0\u00b5\u00d1\u0124\u00d0\u00b5",
- "omed",
- "\u0120attraction",
- "\u00e8\u012b\u00b2",
- "\u0120terrain",
- "\u0120Drop",
- "\u0120corporations",
- "\u0120sciences",
- "\u0120throne",
- "\u00e3\u0123\u0126\u00e3\u0123\u0141",
- "\u0120aj",
- "\u0120Rot",
- "\u00e7\u012b\u00b9",
- "\u0120supporters",
- "\u0120Bere",
- "Here",
- "\u0120diferentes",
- "\u0120significance",
- "\u00cf\u0125\u00ce\u00b7",
- "\u00e6\u012a\u0133\u00e8\u00a6\u00ba\u00e5\u00be\u0139",
- "\u0120clamp",
- "\u0120\u00eb\u012e\u0122\u00eb",
- "\u0120fabulous",
- "rez",
- "\u00e6\u012e\u0123",
- "\u0120assumptions",
- "uther",
- "wid",
- "pot",
- "\u00e8\u00bf\u0130",
- "\u0120yan",
- "ulin",
- "\u00d1\u0122\u00d1\u012d\u00d0\u00b2",
- "\u0120Slow",
- "\u0120Pennsy",
- "\u0120\u00ed\u0137\u00b4\u00ec\u0126\u013e",
- "\u0120meio",
- "\u0120wealthy",
- "\u0120Eight",
- "\u0120pulse",
- "\u0120friction",
- "idity",
- "\u0120Holl",
- "iyorum",
- "\u0120sounded",
- "\u0120Carr",
- "\u0120fork",
- "\u00e2\u013a",
- "\u0120PA",
- "\u0120conspir",
- "\u0120coding",
- "rt",
- "\u0120Typ",
- "\u0120\u00ec\u0138\u0133",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b3",
- "\u0120miser",
- "\u0120\u00d1\u0123\u00d0\u00bc\u00d0\u00be\u00d1\u0124\u00d1\u0122",
- "\u0120Sweden",
- "\u0120olarak",
- "\u0120Zhang",
- "\u0120Chi",
- "\u0120Titan",
- "\u0120screening",
- "\u0120Spider",
- "\u0120\u00c5\u0140imdi",
- "\u0120obstacles",
- "lara",
- "\u0120challenged",
- "pse",
- "TON",
- "\u00e1\u00bb\u00a5",
- "\u0120Pi",
- "\u0120lagi",
- "ieurs",
- "\u0120hurting",
- "\u0120neglect",
- "\u0120generating",
- "\u0120youngest",
- "\u0120audit",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00b7",
- "\u00cf\u0123\u00ce\u00ac",
- "\u0120donate",
- "\u0120PDF",
- "\u0120visits",
- "\u0120cruise",
- "PP",
- "aser",
- "\u0120wsp",
- "backs",
- "ivals",
- "\u00e3\u0123\u0128\u00e3\u0124\u0135",
- "\u0120deve",
- "\u0120proport",
- "\u0120cath",
- "\u0120Effect",
- "\u0120winds",
- "\u0120\u00ec\u013b\u0136",
- "\u0120charts",
- "\u0120sama",
- "\u0120automation",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00ba\u00d0\u00b0",
- "\u0120olan",
- "\u0120boats",
- "\u0120cafe",
- "\u0120denied",
- "\u0120Mama",
- "\u0120blocking",
- "\u0120Thor",
- "\u0120phenomenal",
- "\u0120stakeholders",
- "\u0120unos",
- "\u00d1\u0125\u00d0\u00b5\u00d1\u0124",
- "\u0120Abraham",
- "\u00e3\u0123\u00a7\u00e3\u0124\u0124",
- "\u0120detection",
- "\u0120juris",
- "\u0120powered",
- "zial",
- "\u0120welfare",
- "\u0120upgrad",
- "\u0120mo\u00c5\u00bcna",
- "\u0120Case",
- "cular",
- "\u0136\u00ec\u013f\u00b4",
- "\u00e3\u0125\u0123",
- "\u0120Guess",
- "\u0120cycles",
- "\u00e4\u00be\u012d",
- "\u00e7\u00b5\u00a6",
- "rock",
- "umi",
- "\u0120elite",
- "\u0120qu\u00c3\u00a8",
- "\u00e5\u0142\u00b1",
- "\u00d1\u0124\u00d0\u00be\u00d0\u00bc",
- "\u0120shore",
- "gunta",
- "\u0120ku",
- "\u0120faithful",
- "\u0120Jeremy",
- "aid",
- "\u00e0\u00b7",
- "ugal",
- "\u00e5\u00b0\u012f\u00e5\u0137\u012c",
- "\u0120Vel",
- "\u0120vrai",
- "stell",
- "\u00a8\u00b8",
- "\u0120kol",
- "\u00e8\u00bd",
- "\u0120quanto",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d1\u0122",
- "\u01202002",
- "esy",
- "\u0120reserve",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d1\u0124",
- "\u0120deployed",
- "\u0120defining",
- "\u0120sau",
- "\u0120gaat",
- "\")",
- "\u0120transmit",
- "\u0120publishing",
- "\u0120ranking",
- "\u0120offense",
- "\u012046",
- "pin",
- "\u0120Taking",
- "\u0120entitled",
- "\u0120genuinely",
- "\u0120variations",
- "\u0120finde",
- "\u0120tau",
- "\u0120unfortunate",
- "\u0120Rah",
- "ports",
- "\u0120c\u00c5",
- "\u0120monkey",
- "\u0120brac",
- "wei",
- "lung",
- "\u0120artif",
- "\u0120syrup",
- "\u0120\u00d0\u0136\u00d0\u00b0\u00d0\u00b2",
- "\u0120lifted",
- "\u0120chez",
- "\u0120Advent",
- "\u0120Stock",
- "\u0120dol",
- "\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd",
- "\u00d0\u00b8\u00d1\u012a\u00d1\u012e",
- "\u0120yn",
- "gio",
- "det",
- "\u0120desse",
- "\u0120gri",
- "\u0120Chairman",
- "\u00e7\u0127",
- "\u0120cuenta",
- "anim",
- "\u0120crab",
- "\u0120escal",
- "\u0120premi\u00c3\u00a8re",
- "\u0120Gef",
- "\u0120dining",
- "\u0120seventh",
- "\u0120chasing",
- "\u0120Tower",
- "\u0120brutal",
- "\u0120fundamentally",
- "\u00e3\u0123\u00a8\u00e3\u0123\u0128",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d1\u0131",
- "stage",
- "\u0120acquis",
- "\u0120cylinder",
- "\u0120commander",
- "mem",
- "\u0120UV",
- "happy",
- "\u0120epsilon",
- "\u0120invitation",
- "\u0120farmer",
- "chair",
- "\u0120destiny",
- "\u0120sovere",
- "\u0120Hebrew",
- "\u0120servant",
- "\u0120bew",
- "\u0120gast",
- "uties",
- "\u0120administrative",
- "\u0120Command",
- "\u00c3\u00a9ta",
- "\u0120nitrogen",
- "\u00ea\u00b7\u00bc",
- "\u0120abi",
- "\u0120villain",
- "\u0120blanket",
- "\u0120Send",
- "\u0120beaten",
- "\u00b2\u0126",
- "\u0120volunt",
- "\u0120scholar",
- "\u0120Emperor",
- "\u012043",
- "vable",
- "\u0120Dus",
- "\u0120GU",
- "\u0120targeting",
- "www",
- "\u0120amendment",
- "\u00ec\u0128\u012e\u00eb",
- "\u0120ting",
- "\u0120nasty",
- "\u0120gauge",
- "\u0120\u00d1\u0122\u00d0\u00be\u00d0\u00b4",
- "\u0120Hans",
- "Your",
- "\u00ce\u00b1\u00ce\u00bd",
- "\u0120projet",
- "\u0120Hawaii",
- "\u0120suspicious",
- "\u0120schw",
- "\u0120removal",
- "\u0120intrig",
- "\u0120MU",
- "\u0120ponto",
- "\u00e0\u00a4\u00be",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u0122\u00d0\u00b0\u00d0\u00b7",
- "\u0120guessing",
- "pace",
- "\u0120mothers",
- "\u0120millimeter",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d0\u00b5",
- "\u00e6\u00b2\u00a1\u00e6\u013e\u012b",
- "\u0120availability",
- "icz",
- "\u00e6\u0143\u00a4",
- "\u0120fract",
- "\u0120bases",
- "km",
- "\u0120BTS",
- "\u0120Field",
- "\u0120dzie",
- "\u0120segundo",
- "\u0120\u00eb\u0124\u013a\u00eb\u012c\u0136",
- "\u0120legitimate",
- "imas",
- "\u0120\u00d0\u00b2\u00d0\u00bd",
- "\u0120corruption",
- "\u0120smash",
- "\u0120Valent",
- "\u0120aligned",
- "\u0120Pennsylvania",
- "\u0120gab",
- "\u0120Eun",
- "enth",
- "\u0120Morning",
- "\u0120candle",
- "\u0120backpack",
- "\u0120Islamic",
- "a\u00c3\u00a7\u00c3\u00b5es",
- "\u0120encry",
- "\u0120mushrooms",
- "\u00ed\u012e\u012e",
- "dit",
- "\u0120transit",
- "\u0120Wisconsin",
- "\u0120participated",
- "\u0120Ils",
- "\u0120unfold",
- "\u00b6\u0122\u00eb",
- "\u0120profits",
- "\u0120warming",
- "\u0120Gang",
- "\u0120networking",
- "\u0120mega",
- "\u0120thoroughly",
- "lements",
- "\u0120Hm",
- "\u0120deciding",
- "\u0120emotionally",
- "\u0120exhausted",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u0124",
- "cido",
- "\u0120HTML",
- "\u0120copyright",
- "\u0120melody",
- "yim",
- "\u0120anders",
- "oshop",
- "\u0120\u00eb\u00b3\u00bc",
- "\u0120athlete",
- "\u0120GE",
- "\u0120frequent",
- "\u0120desires",
- "\u0120needing",
- "\u0120Yun",
- "\u0120rifle",
- "\u0120lover",
- "'T",
- "\u0120dense",
- "\u0120t\u00c3\u00a3o",
- "\u0120notified",
- "\u0120idi",
- "\u00ec\u0139\u0143",
- "\u00ed\u0128",
- "\u0120interacting",
- "\u0120rapport",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00b8",
- "ski",
- "\u0120besser",
- "\u0120manufacturer",
- "\u0120Kyle",
- "\u0120accountable",
- "\u0120Sak",
- "\u0120Pil",
- "\u0120Domin",
- "\u0120presum",
- "\u0120\u00d0\u0134\u00d1\u0123\u00d0\u00b5",
- "\u0120vinegar",
- "\u0120guaranteed",
- "\u00e7\u013e\u012d\u00e5\u012a\u00b0",
- "\u0120handled",
- "\u00e9\u0141\u00b3",
- "cat",
- "\u0120civilization",
- "\u0120accomp",
- "\u0120VM",
- "\u00c3\u00a9mon",
- "\u0120deze",
- "\u0120grades",
- "\u0120sollte",
- "\u0120staring",
- "\u00d7\u0132\u00d7\u00aa",
- "arnt",
- "\u0120horizon",
- "\u0120travail",
- "hour",
- "\u00e7\u00ac\u00ac\u00e4\u00b8\u0122",
- "\u0120ED",
- "\u0120Dak",
- "\u0120ny",
- "\u0120conve",
- "\u0120Cham",
- "\u0120firms",
- "\u0120Liu",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00b0\u00d0\u00bd",
- "\u0120libert",
- "\u0120lenses",
- "\u0120intake",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00b1",
- "\u0120mensen",
- "hel",
- "\u0120practition",
- "\u0120350",
- "\u00e3\u0124\u00b3",
- "FO",
- "\u0120beds",
- "\u0120ancestors",
- "\u0120\u00ec\u0139\u0126\u00ec\u00b2\u0143",
- "\u0120disturb",
- "\u0120Lastly",
- "\u0120Support",
- "\u00e0\u00b8\u00b5\u00e0\u00b9\u012b",
- "\u0120Corona",
- "\u0120enthusi",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b7\u00d0\u00bc",
- "\u0120\u00ec\u0124\u00ac\u00eb\u0140\u012e\u00eb",
- "\u012052",
- "bird",
- "\u0120reduces",
- "\u0120\u00ec\u0140\u012a\u00ec\u013f\u0126",
- "\u0120Gene",
- "\u00ea\u00b5\u0132",
- "\u00c4\u013bp",
- "\u0120\u00c3\u013eber",
- "\u0120concerning",
- "user",
- "\u0120concentrate",
- "\u0120WHAT",
- "ishop",
- "onymous",
- "nold",
- "\u0120suggesting",
- "\u00a9\u00b0",
- "\u0120Fish",
- "........",
- "\u0120vessel",
- "\u0120trabajo",
- "\u00e3\u0123\u00b5",
- "\u0120Ocean",
- "\u00e5\u00a7\u0132",
- "yg",
- "\u0120towns",
- "del",
- "\u0120terrifying",
- "\u0120\u00c3\u00a7al\u00c4\u00b1\u00c5\u0141",
- "\u0120sino",
- "\u0120eats",
- "\u0120gez",
- "\u0120geme",
- "\u0120\u00ec\u013b\u0126",
- "\u0120compart",
- "\u0120implementing",
- "\u0120Potter",
- "\u0120Germans",
- "\u0120g\u00c5\u0124",
- "\u0120tennis",
- "\u0120carpet",
- "auer",
- "\u0120Saudi",
- "yeong",
- "\u0120curry",
- "\u0120Forest",
- "\u00d1\u012d\u00d0\u00bb",
- "\u0120fifteen",
- "\u0120bolts",
- "\u0120{\\",
- "\u00ac\u00b4",
- "\u0120settlement",
- "\u0120lange",
- "\u0120bam",
- "Get",
- "\u00ed\u0137\u013b",
- "\u0120swap",
- "\u0120Khan",
- "\u0120commence",
- "\u0120quarantine",
- "\u0120scored",
- "\u00e7\u0138",
- "\u01201950",
- "\u0120thicker",
- "\u0120s\u00c3\u00bbr",
- "\u00e5\u0131\u00a3",
- "\u0120Larry",
- "\u0120allez",
- "\u00ec\u012d\u013e\u00eb\u012c\u0136",
- "\u0120g\u00c3\u00bc",
- "\u0120spectacular",
- "//",
- "both",
- "\u0120stats",
- "\u00e5\u00a6\u00b3",
- "\u0120Nancy",
- "\u0120bunu",
- "\u0120crust",
- "\u0120activated",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0140",
- "outhe",
- "\u0120ports",
- "\u0120neural",
- "\u0120jaw",
- "\u0120observations",
- "\u0120voit",
- "aban",
- "\u00e1\u00ba\u00a3i",
- "\u00a6\u00ac\u00eb\u00a5\u00bc",
- "omes",
- "\u00e0\u00af\u012d",
- "qui",
- "\u0120kindness",
- "\u00d0\u0133",
- "\u012041",
- "\u0120moderate",
- "\u0120angels",
- "\u0120Tamb",
- "\u00c3\u00a8t",
- "\u0120chlor",
- "\u0120Billy",
- "\u00ec\u00b2\u013a\u00eb",
- "acon",
- "\u0120selecting",
- "\u0120Delta",
- "\u0120null",
- "denly",
- "\u0120ciud",
- "\u0120tendency",
- "\u0120breakdown",
- "\u0120mint",
- "\u00d1\u0126\u00d0\u00be\u00d1\u0122\u00d0\u00bc",
- "orph",
- "\u0120dawn",
- "spr",
- "\u0120WILL",
- "\u00c3\u00a4chlich",
- "\u0120puppy",
- "700",
- "\u0120\u00e0\u00ae\u00a4",
- "\u0120fails",
- "\u0120Conc",
- "\u0120relatives",
- "\u0120inviting",
- "\u0120autonom",
- "\u0120composed",
- "\u0120unity",
- "\u0120decis",
- "\u0120accessories",
- "\u0120Cass",
- "\u0120bist",
- "\u0120Tip",
- "\u00ec\u00a7\u00b8",
- "\u0120punt",
- "\u0120r\u00c3\u00a1p",
- "\u00e9\u0122\u00b2",
- "ANK",
- "\u00e3\u0123\u013c",
- "exist",
- "\u0120compatible",
- "\u0120ner",
- "\u0120\u00d0\u00b5\u00d0\u00bc\u00d1\u0125",
- "\u0120aplic",
- "\u0120bapt",
- "\u0120failing",
- "\u0120Tamam",
- "\u0120oscill",
- "\u0120letzten",
- "\u0120repeatedly",
- "\u0120jungle",
- "\u0120Push",
- "hai",
- "\u0120\u00ce\u00b7",
- "\u0120deadly",
- "\u00d1\u0131\u00d0\u00b6",
- "wi\u00c4\u0127",
- "\u0120Common",
- "\u0120\u00ce\u0137",
- "\u0120skate",
- "TC",
- "\u0120Mini",
- "\u0120hobby",
- "\u00e1\u00ba\u00a7n",
- "\u0120routes",
- "\u0120amigos",
- "\u0120conjun",
- "\u0120partnerships",
- "\u0120novo",
- "\u0120aver",
- "\u0120pouvez",
- "bridge",
- "\u0120preoc",
- "him",
- "\u0120turb",
- "\u0120sob",
- "\u0120Snap",
- "\u0120\u00ec\u00b0\u00b8",
- "minute",
- "\u0120traject",
- "uj\u00c4\u013b",
- "\u0120eager",
- "\u0120regulatory",
- "\u0120banking",
- "bling",
- "\u00d1\u012a\u00d1\u012e",
- "a\u00c5\u00bc",
- "\u0120bizarre",
- "itated",
- "dire",
- "\u0120threatened",
- "\u0120shining",
- "\u0120nesse",
- "\u0120corps",
- "\u0120\u00d1\u0123\u00d1\u0125",
- "\u0120teles",
- "\u0120temp",
- "tem",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00bd",
- "\u0120fever",
- "New",
- "\u0120heavier",
- "\u0120Sah",
- "bud",
- "\u0120outros",
- "\u0120\u00ec\u00b0\u00be",
- "\u0120\u00eb\u00aa\u0127",
- "arring",
- "\u0120\u00ea\u00b4\u013e\u00ec\u00b0\u00ae",
- "\u0120Nap",
- "\u0120semin",
- "\u0120Than",
- "ifs",
- "\u0120desen",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b5",
- "\u0120loses",
- "\u0120Balt",
- "kon",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00bf\u00d1\u0122",
- "\u0120vois",
- "\u0120Moscow",
- "\u0120chairs",
- "his",
- "\u0120refugees",
- "kg",
- "\u0120kole",
- "\u012f\u00a8",
- "\u00d0\u00b0\u00d1\u0123\u00d0\u00b8\u00d0\u00b1\u00d0\u00be",
- "\u00a6\u00bd",
- "\u0120Universe",
- "\u0120Direct",
- "\u0120cheating",
- "\u0120Cin",
- "\u0120patri",
- "\u0120advise",
- "\u0120Nether",
- "\u0120primeiro",
- "\u0120mentioning",
- "nut",
- "56",
- "ar\u00c4\u00b1",
- "\u0120petite",
- "bled",
- "\u0120pensar",
- "icio",
- "IND",
- "\u0120veteran",
- "\u0120ladder",
- "\u0120consequence",
- "\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb",
- "\u0120Burn",
- "\u0120rug",
- "\u0120Made",
- "\u0120git",
- "\"...",
- "\u0120competitors",
- "\u0120przed",
- "\u0120apparent",
- "\u0120Argentina",
- "\u0120Working",
- "\u0120collaborate",
- "woman",
- "\u0120retain",
- "\u0120leurs",
- "\u0120dashboard",
- "\u00d7\u013b\u00d7\u0135",
- "\u0120Early",
- "BM",
- "\u0120\u00d0\u00b5\u00d1\u0133",
- "\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b3",
- "\u0120satisfying",
- "\u0120oftentimes",
- "\u0120mapping",
- "\u00c3\u00bcnk\u00c3\u00bc",
- "arth",
- "fold",
- "\u0120launching",
- "\u0120aura",
- "\u0120precision",
- "works",
- "God",
- "\u0120strap",
- "\u0120Imper",
- "\u0120rivers",
- "\u0120|",
- "\u0120cuer",
- "regon",
- "\u0120arrival",
- "\u00d0\u00ba\u00d0\u00b0\u00d1\u0127",
- "\u0120Miami",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u012d",
- "\u0120survivors",
- "\u0120Senior",
- "David",
- "\u0120estado",
- "\u0120sectors",
- "\u0120popping",
- "\u0120chim",
- "ay\u00c4\u00b1",
- "\u0120kunnen",
- "\u0120gallery",
- "\u0120sunlight",
- "esehen",
- "\u0120yelling",
- "\u0120Mein",
- "\u0120Phoenix",
- "\u0120mano",
- "\u0120historia",
- "\u0120occurring",
- "\u00e6\u00ac\u00b8",
- "\u00ec\u00b8",
- "\u00d0\u00b0\u00d0\u00b4\u00d0\u00b8",
- "\u00e5\u00be\u0127",
- "\u0120institutional",
- "\u0120Tut",
- "\u00e7\u00b2",
- "\u0120slaves",
- "\u00e3\u0123\u00a9\u00e3\u0123\u0128",
- "\u0120forgiveness",
- "\u0120twin",
- "\u0120Hyun",
- "\u00d0\u00bd\u00d1\u012e",
- "\u0120Komm",
- "andra",
- "shot",
- "ss\u00c3\u00a4",
- "\u0120\u00d1\u0128\u00d0\u00b5",
- "atta",
- "\u0120expense",
- "\u0120GPU",
- "\u0120Past",
- "ribly",
- "\u0120\u00eb\u0143\u0132\u00ec\u0137\u00bc",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d0\u00b0",
- "\u0120respir",
- "\u00e6\u013f\u00b1",
- "\u0120Queens",
- "hops",
- "\u0120s\u00c3\u00a9rie",
- "\u0120pref",
- "\u0120comed",
- "\u0120plut",
- "\u0120Overall",
- "\u0120\u00e3\u0123\u013f",
- "\u0120cush",
- "\u0120ringing",
- "\u0120incorrect",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d1\u0122",
- "\u0120geometry",
- "\u0120advertis",
- "\u0120\u00d0\u00a8",
- "\u0120reviewed",
- "\u00e3\u0123\u0124\u00e3\u0123\u0124",
- "\u0120dozens",
- "\u0120determination",
- "\u0120Phill",
- "\u0120contributed",
- "\u0120Cit",
- "\u0120passengers",
- "\u0120c\u00c3\u00b4t\u00c3\u00a9",
- "\u0120rever",
- "\u0120technological",
- "\u0120allen",
- "\u0120raining",
- "avi",
- "\u0120salty",
- "\u0120typing",
- "\u0120\u00d1\u0124\u00d0\u00b5",
- "\u0120tilt",
- "\u0120\u00ec\u00b9\u013a",
- "\u0120\u00d0\u00be\u00d1\u0122",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d1\u0131\u00d0\u00bc",
- "\u0120rou",
- "\u0120arena",
- "arat",
- "\u00e5\u012a\u00ab",
- "HHHH",
- "\u0120manufacturers",
- "\u0120Edward",
- "\u0120tuck",
- "\u0120blows",
- "ingo",
- "\u0120Marc",
- "\u00ec\u0137\u0126\u00ec\u0126\u013e",
- "Mich",
- "\u0120Clean",
- "\u00e8\u00b4",
- "esto",
- "\u0120Pack",
- "\u0120shaft",
- "BRUNO",
- "\u0120aven",
- "uur",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00ba\u00d0\u00be",
- "\u00ea\u00b4\u0122",
- "\u0120automated",
- "\u0120venture",
- "\u0120surveillance",
- "\u0120Grow",
- "\u0120Emer",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d1\u0122",
- "\u0120investor",
- "\u0120Yok",
- "\u0120latter",
- "\u0120NI",
- "\u0120functioning",
- "\u0120Hamilton",
- "\u012051",
- "\u0120murdered",
- "\u0120anchor",
- "\u0120cuc",
- "\u0120SCP",
- "\u0120Madam",
- "\u0120constraints",
- "\u0120barn",
- "anken",
- "\u0120\u00eb\u00a7\u0130\u00ec\u013f\u0122",
- "\u0120Motor",
- "\u0120Doing",
- "\u0120amen",
- "etts",
- "\u0120instructor",
- "egt",
- "ako",
- "\u0120posture",
- "ivia",
- "\u0120Polish",
- "\u0120\u00d0\u00b4\u00d0\u00b2\u00d0\u00b0",
- "\u0120colorful",
- "\u0120elbow",
- "\u0120parle",
- "\u0120passer",
- "\u0120condem",
- "ortal",
- "\u0120fertil",
- "\u00d8\u00a7\u00d8\u00af",
- "\u0120Colomb",
- "\u0120alignment",
- "\u0120astronaut",
- "\u0120Mut",
- "\u0120salmon",
- "\u0120structured",
- "\u0140\u00d7\u00a8",
- "\u0120clicks",
- "\u0120miej",
- "\u00e6\u0136\u00bf",
- "\u00e3\u0123\u0126\u00e3\u0124\u0126",
- "\u0120Round",
- "\u0120rainbow",
- "\u0120VA",
- "\u00e3\u0123\u0136\u00e3\u0123\u0138",
- "\u00ec\u00a7\u012a",
- "otz",
- ",",
- "\u0120chords",
- "\u0120Sanders",
- "\u0120\u00eb\u00b6\u0126\u00eb",
- "Ben",
- "\u0120dar\u00c3\u00bcber",
- "ilians",
- "\u0120ordering",
- "\u0120Manh",
- "\u0120kilogram",
- "\u0120kar\u00c5\u0141",
- "\u0120grasp",
- "\u0120ghosts",
- "alen",
- "\u0120Jedi",
- "\u0120\u00d0\u00b1\u00d0\u00bb\u00d0\u00b8",
- "\u0120downloaded",
- "\u0120conducting",
- "\u0120Hak",
- "\u0120researcher",
- "ilan",
- "good",
- "\u0120Hannah",
- "\u0120d\u00c3\u00bc\u00c5\u0141\u00c3\u00bcn",
- "\u0120Messiah",
- "uity",
- "iona",
- "\u0120probable",
- "\u0120YE",
- "\u0120independently",
- "\u0120buffer",
- "burn",
- "ourd",
- "\u0120McK",
- "\u0120lingu",
- "ujemy",
- "\u00d0\u00b5\u00d1\u0122\u00d1\u0124",
- "\u0120intuitive",
- "\u0120cracks",
- "appropri",
- "nty",
- "\u0120geen",
- "\u0120lend",
- "\u0120certification",
- "IDS",
- "unter",
- "pees",
- "\u0120trump",
- "\u0120bankrupt",
- "\u0120feas",
- "\u00e8\u0139",
- "\u0120du\u00c5\u00bc",
- "\u00e6\u00b8\u0127",
- "\u0120viruses",
- "\u012058",
- "god",
- "\u0120\u00d0\u00b6\u00d0\u00b5\u00d0\u00bb",
- "\u0120stalk",
- "Ind",
- "achi",
- "\u0120CF",
- "\u0120Cond",
- "\u0120sanct",
- "\u0120conten",
- "\u0120freed",
- "\u0120RT",
- "\u0120mentors",
- "\u00ec\u00a1\u00b1",
- "\u0120portable",
- "\u0120Paulo",
- "rane",
- "HAHA",
- "\u0120Section",
- "\u00e7\u0128",
- "hyun",
- "\u0120\u00ce\u0143\u00cf\u0129",
- "\u0120Pub",
- "\u0120Independ",
- "\u0120compounds",
- "\u0120\u00d1\u0123\u00d1\u012d",
- "\u0120messaging",
- "\u0120dedication",
- "\u0120noticing",
- "\u0120devoted",
- "\u00d1\u0130\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120snakes",
- "\u0120battlefield",
- "pers",
- "\u0120dela",
- "92",
- "\u0120hai",
- "ill\u00c3\u00a4",
- "\u00c3\u00a9rer",
- "every",
- "\u0120responsive",
- "\u00d7\u013b\u00d7\u0137",
- "opf",
- "\u00e9\u012b",
- "\u012c\u00b8",
- "Because",
- "\u0120tourism",
- "\u0120\u00ea\u00b7\u00b8\u00ea\u00b2\u012e",
- "\u00d7\u0137\u00d7\u00a6",
- "\u0120cans",
- "st\u00c3\u00bct",
- "\u0120donne",
- "\u0120Dios",
- "\u0120Uber",
- "actory",
- "\u0120oriented",
- "\u0120Herm",
- "\u0120patron",
- "urf",
- "bei",
- "\u0120programa",
- "\u0120Ohh",
- "gener",
- "\u0120fist",
- "\u0120Wendy",
- "\u0120anda",
- "\u0120guessed",
- "\u0120freak",
- "\u00e4\u00b8\u0143\u00e5\u013e\u012d",
- "\u0120Kings",
- "chool",
- "\u0120offline",
- "\u0120Indiana",
- "\u0120Alliance",
- "\u012053",
- "\u0120particul",
- "\u0120Focus",
- "\u0120inhabit",
- "\u0120\u00ea\u00b0\u013b\u00ec\u013f\u0122\u00eb\u012f\u00b0",
- "\u0120McG",
- "owski",
- "\u0120\u00ec\u013f\u00b4\u00ea\u00b1\u00b4",
- "\u0120pa\u00c5\u0126st",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00b8",
- "itta",
- "\u0120confirmation",
- "\u0120Brooklyn",
- "\u0120noodle",
- "fund",
- "itud",
- "\u0120grandparents",
- "\u0120barbecue",
- "\u00ce\u00b5\u00ce\u00b9\u00cf\u0124",
- "\u0120\u00e1",
- "\u0120ballot",
- "\u0120Veter",
- "\u0120pipes",
- "igious",
- "\u0120Graph",
- "ested",
- "\u0120\u00eb\u00b8\u012e\u00eb",
- "\u0120KE",
- "\u00e3\u0123\u00a1\u00e3\u0124\u0129\u00e3\u0123\u00a3\u00e3\u0123\u00a8",
- "\u0120eins",
- "\u0120hatred",
- "\u00e3\u0123\u0133\u00e3\u0123\u00a9",
- "\u0120dang",
- "eeee",
- "\u0120archae",
- "\u0120Jesse",
- "\u0120detected",
- "\u0120seni",
- "burgh",
- "\u0120displacement",
- "\u0120dop",
- "\u0120conditioning",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00ba\u00d0\u00be",
- "\u0120disturbing",
- "PH",
- "\u0120thinner",
- "\u0120wounded",
- "\u0120Cuando",
- "\u0120cushion",
- "\u0120whites",
- "\u0120preferences",
- "\u0120\u00ec\u00a4\u0122\u00eb\u00b9\u0126",
- "\u0120ka\u00c5\u00bc",
- "\u0120Gate",
- "\u0120Path",
- "dles",
- "\u00e0\u00b8\u0126\u00e0\u00b8\u00a3",
- "imore",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u0139\u00ac",
- "\u0120disciplines",
- "\u00e1\u00bb\u0131",
- "\u0120mesma",
- "\u0120\u00ec\u0125\u012a\u00eb",
- "\u0120\u00ec\u012d\u00ac",
- "\u0120ging",
- "\u0120umbrella",
- "IGHT",
- "\u0120pension",
- "\u0120combining",
- "SS",
- "\u0120rectangle",
- "\u00e1\u00bb\u0129t",
- "\u0120proxim",
- "\u0120Cow",
- "\u00b8\u012e",
- "\u0120intentional",
- "\u00e6\u0137\u013b",
- "\u0120decid",
- "\u0120\u00d1\u0123\u00d0\u00ba\u00d0\u00b0\u00d0\u00b6",
- "\u0120Uma",
- "iasm",
- "buz",
- "\u0120debris",
- "\u0120cass",
- "\u0120Prop",
- "iska",
- "\u00eb\u0142\u00a5",
- "esterol",
- "ussian",
- "\u00ec\u013f\u00b4\u00eb\u0140\u0133",
- "\u0120unlimited",
- "\u0120admire",
- "\u0120tightly",
- "\u0120genome",
- "\u0120Junior",
- "venir",
- "gus",
- "\u0120c\u00c4\u0125",
- "\u0120Vlad",
- "\u0120\u00ed\u0124",
- "\u0120relativ",
- "inci",
- "\u0120aunque",
- "\u0120Boys",
- "\u00d1\u0128\u00d0\u00b8\u00d0\u00be\u00d0\u00bd",
- "\u0120Swiss",
- "\u0120physicians",
- "\u0120\u00ed\u0131\u012b",
- "\u0120PET",
- "\u0120wounds",
- "about",
- "\u00c3\u0142i",
- "onz",
- "urities",
- "\u0120\u00d1\u0125\u00d0\u00b2\u00d0\u00b8\u00d0\u00b4",
- "\u00e5\u00b7\u00a6",
- "\u0120mentality",
- "\u0120variance",
- "\u0120segunda",
- "\u0120volcano",
- "alie",
- "\u00e0\u00a5\u0129",
- "\u0120tiles",
- "\u0120Terry",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d9\u0126\u00d9\u0129",
- "\u0120canon",
- "\u0120scattered",
- "pton",
- "\u0120definitions",
- "\u0120algebra",
- "oten",
- "ablo",
- "ijuana",
- "\u0120wrapping",
- "\u0120sesame",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0129\u00d0\u00b8\u00d0\u00bd\u00d0\u00b0",
- "\u0120Alf",
- "\u0120\u00d0\u0142\u00d0\u00be\u00d1\u0123\u00d1\u0123",
- "orno",
- "\u0120ankle",
- "\u0120specialty",
- "\u0120attempting",
- "iliation",
- "\u01201920",
- "\u0120phenomena",
- "\u0120Product",
- "\u0120Buck",
- "\u0120Aww",
- "seen",
- "\u0120void",
- "\u0120Franklin",
- "\u0120advocacy",
- "\u0120Sep",
- "\u0120coolest",
- "\u0120\u00d1\u0123\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d1\u0125",
- "\u0120Quand",
- "\u0120900",
- "\u0120Trad",
- "dies",
- "\u0120hash",
- "\u00e6\u012a\u0133\u00e5\u00b0\u00b1",
- "\u00e4\u00b9\u0141\u00e6\u013a\u00af",
- "\u0120pots",
- "\u0120sadly",
- "\u0120viable",
- "\u0120Tiger",
- "\u0120ONE",
- "\u0120neurons",
- "owanie",
- "\u00c4\u0139",
- "\u0120Shar",
- "\u0120Landes",
- "\u0120conferences",
- "\u00e8\u00a9\u00b2",
- "\u0120credential",
- "\u0120lime",
- "inee",
- "xit",
- "pay",
- "\u0120incons",
- "\u0120>>:",
- "\u00e8\u00aa\u012f",
- "\u0120\u00ed\u0140\u013a\u00eb",
- "\u0120lesser",
- "\u0120spill",
- "\u0120premise",
- "\u0120365",
- "\u0120Host",
- "\u0120tomar",
- "\u00d7\u0132\u00d7\u013e",
- "\u00eb\u00b2\u012a",
- "\u0120Whats",
- "\u0120lightweight",
- "\u0120Map",
- "fia",
- "ellschaft",
- "\u0120vendors",
- "uesto",
- "\u0120Mister",
- "\u0120\u00d0\u0141\u00d1\u0122\u00d0\u00b8",
- "\u00e5\u0131\u00b3",
- "hma",
- "\u0120intentionally",
- "\u0120Tang",
- "\u00e9\u0139\u00ae",
- "\u0120identification",
- "\u0120etcetera",
- "\u0120Nee",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d0\u00b8",
- "\u00ea\u00b7\u00b8",
- "\u0120cryptocur",
- "\u0120inhale",
- "\u0120addict",
- "\u00e5\u0132\u0126\u00e4\u00bd\u012f",
- "\u0120mau",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00b0\u00d1\u0131",
- "\u0120\u00eb\u00b2\u0126",
- "\u0120comprar",
- "iedzie\u00c4\u0129",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bd\u00d0\u00be",
- "\u0120beginner",
- "\u0120\u00d0\u00bc\u00d1\u0125\u00d0\u00b6",
- "\u0120obsc",
- "\u0120limiting",
- "ascular",
- "\u0120inspection",
- "aci",
- "\u0120rejo",
- "Mus",
- "\u0120zaten",
- "\u0120szcz",
- "\u0120Madrid",
- "\u0120varieties",
- "\u0120est\u00c3\u0142",
- "\u0120Shakes",
- "\u0120kits",
- "\u0120administer",
- "\u0120lava",
- "\u0120g\u00c3\u00a5",
- "\u00e8\u00a9\u00a6",
- "\u00d7\u00aa\u00d7\u013b",
- "\u0120Wayne",
- "\u0120instagram",
- "\u0120rated",
- "paper",
- "\u0120bild",
- "\u0120pretending",
- "\u0120observing",
- "\u0120\u00d1\u0123\u00d0\u00b0\u00d0\u00bc\u00d0\u00be\u00d0\u00bc",
- "\u0120tror",
- "\u0120organisms",
- "\u0120falta",
- "\u0120hometown",
- "\u00e7\u00b1",
- "\u0120\u00ed\u012d",
- "\u0120cheg",
- "\u0120\u00ec\u00a1",
- "\u0120comma",
- "is\u00c3\u00a9",
- "\u0120likelihood",
- "avored",
- "\u0120geldi",
- "\u00d0\u00bd\u00d0\u00b8\u00d0\u00ba\u00d0\u00be\u00d0\u00b2",
- "\u0120medio",
- "\u0120jakie",
- "\u0120Jup",
- "\u0120greenhouse",
- "\u0120spit",
- "\u00d0\u00ba\u00d0\u00be\u00d0\u00b5",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00b6",
- "\u0120Gram",
- "\u0120Conference",
- "\u0120deficit",
- "s\u00c4\u00b1n",
- "inse",
- "u\u00c4\u0141",
- "\u0120richt",
- "\u0120coincidence",
- "\u00e5\u0131\u012f",
- "\u0120europ",
- "\u0120butterfly",
- "pread",
- "\u0120\u00ec\u0138\u00bc",
- "\u00e8\u0122\u00b6",
- "\u0120wavel",
- "\u0120Infin",
- "\u0120Planet",
- "\u0120selfie",
- "ientras",
- "\u0120arrog",
- "oser",
- "idal",
- "\u0142\u00d7\u0139\u00d7\u0142\u00d7\u0137",
- "\u00c3\u00bct\u00c3\u00bcn",
- "\u0120freshman",
- "\u0120Machine",
- "\u00cf\u0125\u00cf\u0126",
- "\u0120Dia",
- "\u00ec\u013f\u00b4\u00eb\u012d\u00a4",
- "\u00e3\u0123\u0135\u00e3\u0123\u0128",
- "nea",
- "\u0120listing",
- "\u0120configure",
- "utor",
- "Up",
- "tschaft",
- "ri\u00c3\u00a8re",
- "\u0120upwards",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0129\u00d1\u0125",
- "\u0120sweep",
- "Br",
- "\u0120expressing",
- "\u0120unhappy",
- "\u0120mandatory",
- "gender",
- "\u0120A\u00c3\u0143",
- "\u0120indicators",
- "\u0120oils",
- "note",
- "\u0120segur",
- "\u00d0\u00be\u00d0\u00b6\u00d0\u00b5\u00d1\u0124",
- "ynasty",
- "\u0120distances",
- "\u0120merge",
- "BERT",
- "\u0120surrender",
- "\u0120buat",
- "\u0120Awards",
- "\u0120se\u00c3\u00b1or",
- "odox",
- "\u0120flavour",
- "\u0120abdom",
- "\u0120configur",
- "86",
- "\u0120DIY",
- "\u0120rigid",
- "\u00b0\u013a",
- "\u0120corporation",
- "\u0120groom",
- "jaw",
- "\u0120Near",
- "\u00d0\u00b8\u00d0\u00bb\u00d0\u00be",
- "\u0120opera",
- "\u0120Innov",
- "\u00d0\u00b8\u00d1\u0122\u00d0\u00b0",
- "\u0135\u00b1",
- "\u0120specified",
- "\u0120cosm",
- "\u0120Freedom",
- "\u0120clown",
- "\u0120Nem",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00bb",
- "\u00d1\u0133\u00d0\u00bd",
- "\u0120charger",
- "\u00e0\u00b9\u0123\u00e0\u00b8\u00a5",
- "\u0120influential",
- "\u00c3\u00a4sident",
- "\u00e9\u00a4",
- "\u0120\u00ec\u0126\u0142\u00eb",
- "\u0120volumes",
- "\u00e6\u0132",
- "\u0120outras",
- "\u0120Twitch",
- "\u0120founding",
- "\u0120awhile",
- "\u0120coil",
- "\u00ea\u00b0\u013b",
- "\u0120c\u00e1\u00ba\u00a3",
- "\u0120Throw",
- "\u0120Hence",
- "ommt",
- "\u0120Benjamin",
- "\u00d0\u00b3\u00d0\u00bb\u00d1\u0131\u00d0\u00b4",
- "Time",
- "obic",
- "\u0120mour",
- "\u0120dread",
- "\u0120L\u00c3\u0142",
- "\u0120Chile",
- "\u0120preval",
- "\u0120vain",
- "\u0120art\u00c4\u00b1k",
- "\u0120preserved",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00b4",
- "\u0120warehouse",
- "\u0120beste",
- "\u0120Several",
- "\u0120Situation",
- "\u0120cardboard",
- "Tod",
- "erna",
- "\u0120garant",
- "\u0120gesture",
- "\u0120hen",
- "\u0120spelling",
- "osexual",
- "\u0120anne",
- "\u0120mice",
- "\u0120Meine",
- "card",
- "\u0120rebell",
- "\u0120certo",
- "\u0120\u00ec\u013e\u0142\u00eb",
- "\u0120verschied",
- "\u0120Bos",
- "\u0120invention",
- "\u0120trze",
- "\u0120mani\u00c3\u00a8re",
- "\u0120Chad",
- "\u0120spre",
- "\u0120organisations",
- "\u0120poorly",
- "\u0120anterior",
- "\u0120stair",
- "\u00d0\u00ba\u00d1\u0122",
- "\u0120atomic",
- "\u0120sympath",
- "\u0120continually",
- "\u0120kleine",
- "\u00c3\u00a8te",
- "\u00d0\u00b8\u00d1\u012b",
- "\u00ce\u00bf\u00cf\u0124",
- "peut",
- "\u0120reposit",
- "\u0120entra",
- "Em",
- "\u0120financing",
- "\u0120\u00d0\u00bc\u00d0\u00bd\u00d0\u00be\u00d0\u00b3",
- "\u0120thesis",
- "\u0120Computer",
- "eau",
- "\u0120Tree",
- "\u0120bride",
- "onsieur",
- "shire",
- "wic",
- "DE",
- "\u0120\u00ec\u012a\u013a\u00eb",
- "\u0120acom",
- "\u0120PO",
- "ersch",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bc\u00d0\u00be\u00d1\u012b",
- "\u0120Armen",
- "\u0120\u00ec\u00a3\u00bd",
- "\u0120zor",
- "\u0120prints",
- "\u0120Dass",
- "\u00e6\u00b8\u00af",
- "\u0120durable",
- "\u0120Transport",
- "\u00ec\u0140\u0132\u00ea\u00b0\u0122",
- "\u0120\u00d0\u00bb\u00d0\u00b5\u00d0\u00b3",
- "\u0120d\u00c3\u00a9t",
- "\u00c3\u00b4le",
- "amous",
- "YN",
- "\u0120cliff",
- "\u0120grammar",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u012f\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d1\u0125",
- "\u0120l\u00c3\u0142m",
- "esch",
- "\u0120miserable",
- "\u0120volts",
- "\u0120Cad",
- "ukan",
- "\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "rust",
- "\u0120\u00ec\u013a\u00ac\u00eb\u013f\u00bc",
- "\u0120verk",
- "\u0120chickens",
- "\u0120Yoo",
- "\u0120outfits",
- "code",
- "\u0120hierarchy",
- "netes",
- "\u0120counterpart",
- "\u0120t\u00c3\u00b4i",
- "\u0120ted",
- "\u0120Bart",
- "\u0120\u00eb\u013f\u00bc",
- "\u0120Genau",
- "\u0120incoming",
- "\u0120ABC",
- "rique",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bf",
- "qual",
- "\u0120incentive",
- "\u0120ihren",
- "\u00d7\u0142\u00d7\u013b",
- "loe",
- "\u01201930",
- "\u0120barg",
- "\u0120diction",
- "\u0120\u00c3\u00b6nce",
- "INS",
- "\u0120reh",
- "isiaj",
- "mouth",
- "\u0120scoring",
- "l\u00c4\u00b1k",
- "\u0120\u00ec\u0137\u0126\u00ec\u00a3\u00bc",
- "ORIA",
- "\u0120Estados",
- "\u0120companion",
- "\u0120assemble",
- "\u0120punished",
- "\u0120ital",
- "\u0120prevents",
- "istes",
- "\u0120Kentucky",
- "\u0120locate",
- "\u0120fasting",
- "\u00e3\u0123\u00a8\u00e6\u0122\u013f",
- "\u0125\u0122",
- "\u0120Seb",
- "\u0120Crown",
- "opia",
- "\u0120whip",
- "usz",
- "\u00d0\u00ba\u00d0\u00b0\u00d0\u00bc\u00d0\u00b8",
- "\u0120databases",
- "\u00e5\u0143\u0139",
- "\u0120prosec",
- "\u01201997",
- "\u0120\u00ec\u0124\u00b4\u00ec\u00a7\u013f",
- "\u0120Solar",
- "\u0120Pues",
- "\u0120Zen",
- "ollo",
- "\u0120Guru",
- "\u0120squeez",
- "\u0120\u00d0\u0139\u00d0\u00b0",
- "\u0120\u00c4\u012f",
- "ceptions",
- "cca",
- "izable",
- "mand",
- "\u0120breakthrough",
- "\u0120tablespoon",
- "\u0120SEC",
- "ikh",
- "\u0120S\u00c3\u00a3o",
- "\u0120\u00d0\u00bf\u00d0\u00bb\u00d0\u00be",
- "amen",
- "\u0120prac",
- "\u0120darling",
- "\u0120taller",
- "\u0120rendering",
- "\u0120\u00ec\u013c\u00b0\u00eb\u00a6\u00ac\u00ea\u00b0\u0122",
- "\u0120\u00cf\u0126\u00ce\u00b7\u00cf\u0124",
- "\u0120m\u00c3\u00a3",
- "\u0120esos",
- "uerdo",
- "\u0120\u00d1\u0123\u00d1\u0129\u00d0\u00b8\u00d1\u0124",
- "aller",
- "\u00ec\u0139\u012a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120millones",
- "lerin",
- "\u0120pegar",
- "onne",
- "\u0120enrollment",
- "\u0120liegt",
- "\u0120boa",
- "wi\u00c4\u013b",
- "bsp",
- "\u0120cycling",
- "\u0120Bernie",
- "\u01201989",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00bb\u00d1\u012e",
- "\u0120Dakota",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d1\u0131\u00d0\u00b7",
- "\u0120CP",
- "\u0120stare",
- "\u00ed\u0124\u00a4",
- "\u0120prosperity",
- "\u0120arrangements",
- "\u0120arriving",
- "m\u00c3\u00a4",
- "\u0120kayak",
- "ipt",
- "\u0120pardon",
- "\u0120relat",
- "\u0120verste",
- "\u0120Fig",
- "\u0120foil",
- "\u0120Talking",
- "peare",
- "\u0120noi",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d1\u012a",
- "\u0120hockey",
- "\u0120ado",
- "\u0120OUT",
- "67",
- "\u0120hormones",
- "\u0120Avenue",
- "\u0120Superman",
- "\u0120prescription",
- "ubernetes",
- "CL",
- "otive",
- "NIS",
- "ienen",
- "\u0120sadness",
- "\u0120Vit",
- "Ty",
- "\u0120starter",
- "\u0120bede",
- "\u0120foundations",
- "\u0120sore",
- "\u00e5\u00ba\u0139",
- "\u00d1\u012b\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "\u00ec\u013c\u00b0\u00eb",
- "\u0120\u00d1\u0129\u00d1\u0125\u00d0\u00b2",
- "link",
- "\u0120maneu",
- "working",
- "\u00c3\u0142n",
- "\u0120Attack",
- "\u0120Cart",
- "veis",
- "\u0120Resp",
- "ensing",
- "\u0120\u00ec\u00a2\u012d\u00ec\u0137\u0126\u00ec\u013c\u0136",
- "\u0120escuch",
- "\u0120RNA",
- "\u0124\u00b4",
- "\u0120adop",
- "\u0120bending",
- "\u00d8\u00b9\u00d8\u00af",
- "\u0120manages",
- "usp",
- "\u0120tart",
- "\u0120router",
- "Bo",
- "\u0120establishing",
- "\u0120balancing",
- "\u0120athletic",
- "\u0120Slo",
- "\u0120fills",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b1",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00bb",
- "\u0120posso",
- "\u0120Vielen",
- "\u0120critics",
- "\u0120lawsuit",
- "\u0120Isaac",
- "\u0120\u00d1\u0126\u00d0\u00b8\u00d0\u00bb\u00d1\u012e\u00d0\u00bc",
- "\u0120tras",
- "\u0120praw",
- "\u0120Crazy",
- "\u0120neu",
- "\u0120kull",
- "\u0120tumor",
- "\u0120APP",
- "gate",
- "\u0120ARE",
- "98",
- "\u0120Steam",
- "\u0120fucked",
- "lage",
- "\u0120\u00e2\u013b\u00ac",
- "\u0120MD",
- "fy",
- "\u0120shells",
- "\u0120Seems",
- "izers",
- "\u0120ranges",
- "\u0120Antonio",
- "ATION",
- "\u0120Baba",
- "\u0120\u00ec\u0125\u012b",
- "kun",
- "\u0120prayed",
- "\u00d1\u0122\u00d1\u0131",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "\u0120seas",
- "bury",
- "\u0120\u00d7\u0136\u00d7\u00a9",
- "\u0120trait",
- "\u0120Depending",
- "\u0120dre",
- "\u0120k\u00c3\u00b6nnt",
- "\u00d1\u0128\u00d1\u0125",
- "\u0120lipstick",
- "eez",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bc\u00d0\u00b5\u00d1\u0122",
- "\u0120assignments",
- "Bob",
- "\u0120metals",
- "\u0120specially",
- "\u00e5\u00b0\u012f\u00e4\u00b8\u012f\u00e5\u00b0\u012f",
- "\u0120\u00ec\u013a\u012a\u00eb",
- "\u0120\u00c5\u00a1",
- "\u0120vista",
- "\u0120\u00ce\u00ac",
- "\u0120twins",
- "\u0120notable",
- "\u0120Sau",
- "\u0120d\u00c3\u00a9velop",
- "\u0120\u00c3\u00a7ek",
- "\u0120polynom",
- "avam",
- "\u0120tamb\u00c3\u00a9",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00be\u00d0\u00bc",
- "\u0120plasma",
- "\u0120efect",
- "\u0120l\u00c3\u00a4ng",
- "\u0120casi",
- "\u00d1\u0123\u00d0\u00b0",
- "\u00c4\u00b1m\u00c4\u00b1",
- "\u00e3\u0123\u013b\u00e3\u0124\u012d",
- "\u0135\u00a4\u00ec\u013f\u0122",
- "\u0120labour",
- "ossen",
- "\u0120Pun",
- "rif",
- "\u0120doses",
- "\u0120operates",
- "\u00d0\u00b8\u00d0\u00bb\u00d0\u00bb\u00d0\u00b8",
- "\u0120jaar",
- "staw",
- "\u0120\u00ec\u0124\u00ac\u00eb\u0140\u0133",
- "\u0120atm",
- "\u0120protects",
- "\u0120imped",
- "HO",
- "\u0120cima",
- "\u0120toch",
- "abis",
- "\u0120sendo",
- "laus",
- "\u0120curl",
- "\u0120Num",
- "\u0120sponsors",
- "\u0120d\u00c3\u00a9but",
- "\u0120Alexa",
- "\u0120B\u00c3\u00bcr",
- "\u0120Amer",
- "\u0120cope",
- "\u0120\u00d0\u00b8\u00d0\u00b7\u00d0\u00b2",
- "jal",
- "\u01201995",
- "apat",
- "resse",
- "\u0120Prize",
- "\u0120Claire",
- "\u0120Brandon",
- "\u0120wszystko",
- "\u0120valued",
- "\u00e0\u00b8\u013b\u00e0\u00b8\u00b0",
- "\u0120sect",
- "\u0120secretly",
- "\u0120diamonds",
- "\u0120Evan",
- "\u0120RPG",
- "\u00e3\u0123\u00ab\u00e3\u0123\u00aa",
- "\u012a\u00eb\u0131\u0126",
- "\u0120Universal",
- "\u0120doubts",
- "\u0120Pin",
- "wi\u00c4\u0127z",
- "\u013c\u00a9",
- "\u0120albo",
- "\u0120braucht",
- "AUL",
- "\u0120Mobile",
- "grades",
- "\u0120schem",
- "why",
- "\u0120Nicht",
- "pi",
- "gle",
- "\u0120chorus",
- "\u0120gly",
- "\u0120reinforce",
- "\u0120muff",
- "\u0120Shen",
- "\u0120Hola",
- "\u00d1\u0125\u00d0\u00b3",
- "videmment",
- "vial",
- "acious",
- "laimed",
- "\u0120Rico",
- "\u0120vegg",
- "\u0120illustration",
- "\u0120Butter",
- "owad",
- "\u0120eux",
- "\u0120enfants",
- "\u0120Leader",
- "\u0120Village",
- "etically",
- "\u00d9\u0128\u00d9\u012c",
- "\u0120stew",
- "\u0120surprises",
- "\u0120cue",
- "\u0120Grandma",
- "\u0120Celsius",
- "\u0120Richt",
- "enc",
- "\u0120petition",
- "\u0120herb",
- "\u0120wicked",
- "\u0120schle",
- "ocaly",
- "\u0120transf",
- "\u0120tokens",
- "\u0120Gray",
- "\u0120BBC",
- "IK",
- "\u01201500",
- "zn",
- "\u0120Nev",
- "\u0120koy",
- "\u0120zar",
- "\u0120bullshit",
- "\u0120Colombia",
- "ulative",
- "\u0120widespread",
- "yect",
- "kit",
- "\u0120empresa",
- "\u0120nour",
- "\u0120burns",
- "atin",
- "aired",
- "\u0120revolutionary",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d1\u0125",
- "\u0120Logan",
- "\u01201996",
- "\u0120Graham",
- "reb",
- "\u0120NHS",
- "\u00e6\u013e\u013d",
- "\u0120costumes",
- "\u0120nawet",
- "\u0120lovers",
- "\u0120Lucy",
- "\u0120Indigenous",
- "\u00ed\u0137\u013a\u00ea\u00b8\u00b0",
- "\u0120immunity",
- "\u00a5\u00b4\u00eb",
- "uito",
- "\u0120excessive",
- "\u0120donations",
- "\u0120\u00d7\u0136\u00d7\u00a8",
- "\u0120\u00ec\u00b2\u00ab",
- "\u00e9\u012b\u0126",
- "\u0120drying",
- "melon",
- "\u0120surveys",
- "\u0120\u00eb\u00ac\u00b4\u00ec\u012c\u00a8",
- "\u00e9\u00a2\u00a8",
- "aaa",
- "\u0120probe",
- "ancial",
- "\u0120louder",
- "\u0120hotels",
- "\u00c3\u00bc\u00c4\u0141",
- "agner",
- "\u0120origins",
- "\u0120\u00eb\u00a7\u012a\u00ec\u00a7\u0122\u00eb\u00a7\u012b",
- "\u0120**",
- "\u0120strangers",
- "\u0120Haus",
- "comed",
- "\u0120anthrop",
- "\u0120uso",
- "\u0120\u00ec\u0137\u0126\u00ec\u00a7\u0123",
- "\u0120Yuan",
- "\u0120\u00ed\u0137\u0126\u00ec\u013c\u0136",
- "pler",
- "ressive",
- "\u0120spraw",
- "\u0120Stew",
- "\u01201994",
- "\u0120elders",
- "\u0120meinen",
- "\u0120junt",
- "\u0120acoust",
- "\u0120Wohn",
- "\u0120bananas",
- "\u0120projection",
- "\u0120Stick",
- "legt",
- "speed",
- "\u0120c\u00c5\u00a9ng",
- "\u0120Wort",
- "\u0120Baltimore",
- "\u0120\u00d1\u0128\u00d0\u00b5\u00d0\u00bb",
- "\u0120dunno",
- "\u00e5\u00bc\u00b7",
- "?,",
- "\u00e3\u0125\u012b\u00e3\u0125\u00b3",
- "\u0120Local",
- "osto",
- "\u00d0\u0143",
- "\u00d0\u00be\u00d0\u00b4\u00d0\u00b0",
- "\u0120Portuguese",
- "\u0120theirs",
- "\u0120d\u00c3\u00a9m",
- "\u00e5\u0131\u00a6",
- "\u0120drauf",
- "\u0120Buddhist",
- "erta",
- "Ge",
- "\u0120carrot",
- "\u0120Wonderful",
- "\u0120soak",
- "\u0120chairman",
- "ggi",
- "ICA",
- "fried",
- "\u0120flick",
- "\u0120Throughout",
- "\u0120\u00ec\u013c\u00b0\u00eb",
- "\u0120cough",
- "\u0120fluffy",
- "school",
- "\u0120ripped",
- "--------",
- "\u0120Zukunft",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b1",
- "\u0120sto",
- "\u0120BO",
- "pent",
- "\u0120Lawrence",
- "\u00cf\u012b\u00cf\u0124",
- "sticks",
- "\u0120Eins",
- "\u0120\u00d1\u0122\u00d1\u012d",
- "\u0120Strong",
- "\u0120caramel",
- "\u0120spite",
- "azar",
- "\u00e9\u0125\u00bd\u00e6\u013a\u00af",
- "\u0120critically",
- "\u0120obra",
- "owitz",
- "\u0120Zone",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00ba",
- "\u0120sug",
- "arded",
- "\u0120g\u00c3\u00ac",
- "ffentlich",
- "anche",
- "\u00d8\u0141",
- "astically",
- "\u00ec\u013f\u00bc\u00eb",
- "\u00d0\u00bb\u00d0\u00b0\u00d0\u00b2",
- "\u0120simplest",
- "\u0120Friend",
- "\u0120quello",
- "\u0120ambition",
- "\u0120abbiamo",
- "\u00e5\u00ba\u0137",
- "\u0120\u00d1\u0126\u00d0\u00be\u00d1\u0122\u00d0\u00bc",
- "\u0120Essa",
- "\u0120educators",
- "\u0120statistical",
- "\u00e9\u0122\u013b\u00e9\u0124\u012c",
- "\u0120changer",
- "\u0120atau",
- "\u00c3\u00a9tais",
- "\u0120Shakespeare",
- "\u00eb\u0132\u013a",
- "\u0120triggers",
- "\u0120realiz",
- "\u0120celui",
- "wheel",
- "\u0120loyalty",
- "\u0120screams",
- "kehr",
- "\u0120Mega",
- "east",
- "\u0120tops",
- "\u0120Totally",
- "ountain",
- "lord",
- "\u0120violation",
- "\u0120GA",
- "\u0120nicer",
- "\u0120Fresh",
- "\u0120Melissa",
- "function",
- "\u0120rape",
- "\u0120exceptions",
- "\u0120silicon",
- "\u0120liberty",
- "\u0120households",
- "\u00e3\u0123\u012f\u00e3\u0123\u00be\u00e3\u0123\u013b",
- "\u0120CA",
- "\u0120\u00d0\u0140\u00d0\u00b1",
- "\u0120lib",
- "\u0140\u012e",
- "cific",
- "\u0120tropical",
- "\u0120investigating",
- "HD",
- "\u0120adapter",
- "\u0120Pitt",
- "ancia",
- "\u0120Shell",
- "friendly",
- "\u0120conclusions",
- "\u0120turtle",
- "\u0120decomp",
- "\u0120animations",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d0\u00ba",
- "insi",
- "\u0120retention",
- "kie",
- "\u0120injection",
- "\u0120Madison",
- "\u00ec\u00b0\u00b0",
- "\u0120vient",
- "\u0120varied",
- "\u0120violin",
- "\u0120Bil",
- "\u0120luckily",
- "\u0120htt",
- "l\u00c3\u00a4",
- "\u0120ranch",
- "\u00e7\u013e\u012d\u00e7\u013e\u012d",
- "\u0120s\u00c3\u00b3lo",
- "\u00ec\u0137\u0127",
- "\u0120Derek",
- "\u0120Scripture",
- "\u00d0\u00be\u00d1\u0122\u00d0\u00b0",
- "\u0120classrooms",
- "avil",
- "formed",
- "\u0120beforehand",
- "\u0120Gem",
- "prech",
- "\u0120lin",
- "\u0120greens",
- "\u00d1\u0128\u00d0\u00b5\u00d0\u00b2",
- "\u0120Mercedes",
- "\u0120drought",
- "gasps",
- "\u0120abortion",
- "\u0120terribly",
- "\u0120spos\u00c3\u00b3b",
- "\u0120secured",
- "\u0120atr\u00c3\u00a1s",
- "\u0120wavelength",
- "\u0120grains",
- "ective",
- "\u0120spacecraft",
- "\u0120tours",
- "\u0120profes",
- "\u0120surgeon",
- "\u0120Pie",
- "\u0120ideally",
- "arner",
- "UP",
- "opard",
- "sce",
- "\u0120immense",
- "\u0120Ort",
- "roller",
- "\u0120Dallas",
- "\u0120Nicholas",
- "\u0120sulf",
- "\u0120Toyota",
- "\u0120quantities",
- "ceans",
- "\u0120cui",
- "an\u00c3\u00a7a",
- "\u0120CAN",
- "itzerland",
- "\u00e5\u0126\u00bf",
- "\u0120zou",
- "\u0120Cyber",
- "legen",
- "\u0120Init",
- "edu",
- "\u0120apert",
- "\u0120adjac",
- "ouv",
- "\u00e8\u0122\u012e\u00e4\u00b8\u0136",
- "rs",
- "\u0120cabbage",
- "\u0120wheelchair",
- "inyl",
- "\u0120Dynam",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u012a\u00eb\u013f\u00bc",
- "\u0120ling",
- "hl",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b3\u00d1\u0125",
- "\u0120crisp",
- "\u0120mij",
- "\u0120dug",
- "nin",
- "\u0120bloss",
- "\u0120belonging",
- "\u0120loudly",
- "\u0120minerals",
- "\u0120concluded",
- "\u0120searched",
- "96",
- "\u0120Meet",
- "\u0120SEO",
- "\u0120\u00d0\u00a1\u00d0\u00ba",
- "\u0120Hob",
- "otta",
- "\u0120propaganda",
- "\u0120cinnamon",
- "\u0120hunter",
- "\u0120gemeins",
- "\u0120sculpture",
- "ulsion",
- "\u0120v\u00c3\u00a4l",
- "\u0120magazines",
- "\u0120controversy",
- "\u00e4\u00b8\u0122\u00e6\u00a8\u00a3",
- "\u0120sequences",
- "\u00e3\u0123\u0126\u00e3\u0124\u012d",
- "\u0120\u00ed\u013c\u012e",
- "\u0120deleted",
- "\u00e4\u00bd\u00bf",
- "\u0132\u00eb\u0131\u0126",
- "\u0120varying",
- "\u00e3\u0125\u0128",
- "\u0120mounting",
- "\u0120affair",
- "\u0120pathways",
- "\u00e6\u00a6",
- "\u0120digo",
- "\u00e4\u00ba\u00ae",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00ba",
- "Alex",
- "\u0120tobacco",
- "\u0120CV",
- "\u0120bothered",
- "\u0120ambient",
- "inky",
- "\u0120SL",
- "\u0120hates",
- "\u0120je\u00c5\u00bceli",
- "\u0120congreg",
- "\u0120elas",
- "\u0120deuts",
- "\u0120Studios",
- "ch\u00c4\u013b",
- "\u0120documented",
- "\u0120Cruz",
- "\u0120Len",
- "\u0120Douglas",
- "\u0120Portugal",
- "enti",
- "\u0120spouse",
- "\u0120analys",
- "avia",
- "\u0120edited",
- "\u0120l\u00e1\u00ba\u00a1i",
- "built",
- "\u0120ville",
- "adora",
- "\u0120bracelet",
- "\u0120sushi",
- "\u0120pm",
- "\u0120trails",
- "\u0120lug",
- "\u0120\u00c3\u00b6ver",
- "\u0120sorrow",
- "\u0120colony",
- "adox",
- "\u0120serie",
- "anyak",
- "\u0120\u00d8\u00b7",
- "\u0120Gulf",
- "\u00e6\u013a\u00af\u00e4\u00b8\u012f\u00e6\u013a\u00af",
- "\u0120PV",
- "\u0120Samuel",
- "\u0120Kit",
- "\u0120Ral",
- "ontin",
- "expl",
- "\u0120entries",
- "\u0120activists",
- "Ps",
- "\u0120sant",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d1\u0129",
- "\u0120Bruno",
- "keley",
- "\u0120tutto",
- "\u00e9\u0136",
- "\u0120vintage",
- "\u0120terrified",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0127",
- "usive",
- "owers",
- "\u00d0\u00b0\u00d0\u00b9\u00d1\u0124",
- "\u00eb\u0131\u013b",
- "\u0120twisted",
- "\u0120Thought",
- "\u0120tah",
- "\u0120shrink",
- "\u0120sheer",
- "lit",
- "\u0120dalam",
- "\u0120dib",
- "\u0120vard",
- "owane",
- "\u0120dobr",
- "\u0120Rena",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d1\u0130",
- "\u0120pa\u00c3\u0143ses",
- "\u0120Era",
- "\u00e3\u0123\u00ae\u00e3\u0123\u00a7",
- "\u0120BUT",
- "sighs",
- "\u0120\u00ea\u00b7\u00b8\u00ea\u00b1\u00b0",
- "\u0120gro\u00c3\u0141en",
- "\u0120\u00eb\u00b9\u00a8\u00eb\u00a6\u00ac",
- "\u0120nerves",
- "\u0120constit",
- "\u0120preocup",
- "\u0120Gay",
- "\u0120Xu",
- "keeper",
- "heure",
- "..)",
- "\u0120Calm",
- "\u0120Unidos",
- "\u0120\u00ec\u013f\u00b4\u00ea\u00b2\u0125",
- "\u0120Aqui",
- "\u0120\u00ec\u0142\u013e\u00ec\u013f\u00bc",
- "d\u00c4\u00b1r",
- "\u00ec\u00a6\u013a",
- "your",
- "\u0120\u00d1\u012f\u00d1\u0124\u00d0\u00b8\u00d0\u00bc",
- "2020",
- "\u0120rund",
- "\u0120HO",
- "\u0120Catherine",
- "ieli",
- "\u0120fusion",
- "\u0120ideology",
- "\u0120foram",
- "shaped",
- "\u0120\u00ed\u013d\u0126\u00eb",
- "\u0120wt",
- "\u0120retr",
- "\u0120pr\u00c3\u00a9c",
- "\u0120\u00ea\u00b0\u0133",
- "\u0120openly",
- "vity",
- "\u00ea\u00b5\u00ac\u00ec\u013c\u0136",
- "\u0120obstacle",
- "\u0120boo",
- "\u0120seiner",
- "icorn",
- "\u0120eigenlijk",
- "\u0120header",
- "aremos",
- "\u0120softer",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d0\u00b4",
- "\u0120prejud",
- "\u0120defines",
- "ierte",
- "\u0120blending",
- "\u0120believers",
- "\u0120Wochen",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d0\u00ba\u00d0\u00b0\u00d0\u00ba",
- "\u0120\u00d0\u013c\u00d0\u00be\u00d0\u00b3\u00d0\u00b4\u00d0\u00b0",
- "\u0120Typically",
- "\u0120\u00ed\u0123\u00ac",
- "\u00e7\u00ae\u00a1",
- "cios",
- "\u0120missiles",
- "\u0120sponge",
- "\u0120Kitchen",
- "\u0120tren",
- "ningen",
- "\u0120scrap",
- "\u0120serait",
- "\u00b4\u00ec\u0142",
- "\u00e7\u00b9",
- "\u0120\u00eb\u00b0\u013a\u00eb",
- "\u0120restored",
- "\u0120przyk\u00c5\u0124ad",
- "\u0120Kubernetes",
- "\u0120sait",
- "\u0120uw",
- "\u0120enabling",
- "\u0120travers",
- "amps",
- "\u00e5\u0131\u0139",
- "\u0120OMG",
- "ensor",
- "\u0120zosta",
- "\u0120pronounced",
- "Ang",
- "normal",
- "\u0120economies",
- "tin",
- "\u0120Champion",
- "izen",
- "\u0120arbeiten",
- "\u0120Gospel",
- "\u0120Zu",
- "nga",
- "\u0120literacy",
- "\u0120Mans",
- "\u0120circulation",
- "\u0120adap",
- "\u0120Total",
- "\u0120mereka",
- "\u0120olacak",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d1\u0124\u00d0\u00b8",
- "Jack",
- "\u0120mund",
- "\u0120thief",
- "bies",
- "\u0120\u00ea\u00b2\u0123",
- "aque",
- "\u0120\u00da\u00a9\u00db\u012e",
- "\u0120Scar",
- "\u00e5\u00b2",
- "\u0120abol",
- "\u0120devote",
- "\u012001",
- "\u0120sitten",
- "\u0120Visual",
- "week",
- "some",
- "ingt",
- "\u0120journalism",
- "\u0120Hir",
- "\u0120Bachelor",
- "inery",
- "\u00c3\u013eND",
- "\u00e3\u0125\u0141",
- "\u00e7\u00bb\u013b",
- "\u0120coloring",
- "\u0120Crist",
- "\u0120celebrities",
- "\u0120\u00d1\u0129\u00d0\u00b8\u00d1\u0123",
- "\u0120Crit",
- "\u0120differentiate",
- "\u0120\u00d0\u013e\u00d0\u00bd\u00d0\u00b5",
- "elim",
- "\u0120seafood",
- "\u0120algumas",
- "otherapy",
- "\u00e6\u012a\u00b0",
- "\u0120glaub",
- "\u0120arbitrary",
- "gens",
- "\u0120\u00d0\u00b1\u00d1\u0125\u00d0\u00b4\u00d0\u00b5\u00d0\u00bc",
- "\u0120tav",
- "\u0120creamy",
- "\u0120Country",
- "a\u00c3\u00b1",
- "\u00d0\u00bc\u00d0\u00b5\u00d1\u0124",
- "\u0120hinter",
- "\u0120mism",
- "\u0120illustrate",
- "\u00c3\u013eNDNIS",
- "\u0120decreasing",
- "\u0120weniger",
- "AKI",
- "ixon",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b9",
- "\u0120fatto",
- "\u0120nerd",
- "\u00e7\u0142",
- "\u0120bitte",
- "Per",
- "\u0120tane",
- "\u0120g\u00c3\u00b6z",
- "\u0120forte",
- "\u0120Ey",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b2\u00d0\u00b5\u00d1\u0122",
- "\u00e8\u00a2\u00ab",
- "\u0120WordPress",
- "\u0120Mis",
- "\u00c5\u00af",
- "z\u00c3\u00a4h",
- "\u0120int\u00c3\u00a9ress",
- "osaurs",
- "\u0120Falls",
- "\u0120nessa",
- "97",
- "\u0120museums",
- "\u0120corresponds",
- "\u0120sings",
- "four",
- "\u0120eder",
- "\u0120Communist",
- "oa",
- "nek",
- "\u0120WHO",
- "\u0120corpo",
- "\u0120messing",
- "\u00cf\u0126\u00ce\u00b1\u00ce\u00b9",
- "\u0120brushes",
- "\u0120bisc",
- "\u0120Arbeits",
- "\u0120Tax",
- "\u0120sele",
- "\u0120flags",
- "oupe",
- "\u0120anticipated",
- "\u00e3\u0125\u0133",
- "\u0120Nad",
- "\u0120poured",
- "\u0120ml",
- "\u0120llama",
- "\u0120visualize",
- "\u0120listeners",
- "\u00d9\u0126\u00d9\u0125",
- "alten",
- "Michael",
- "\u0120cos\u00c3\u00ac",
- "\u00d5\u00a1\u00d5",
- "opus",
- "\u0120\u00ed\u0137\u00b4\u00ec\u00a3\u00bc",
- "\u0120hike",
- "\u0120Attorney",
- "\u0120Hillary",
- "uded",
- "\u0120\u00ed\u0137\u013a\u00ec\u00a7\u0122\u00eb\u00a7\u012e",
- "\u0120dove",
- "\u0120storms",
- "\u00d0\u00b0\u00d0\u00ba\u00d1\u0123",
- "\u0120doctrine",
- "\u0120hex",
- "iks",
- "no\u00c5\u013d\u00c4\u0129",
- "\u0120scripts",
- "\u0120\u00ce\u00b4\u00ce\u00b5\u00ce\u00bd",
- "\u0120\u00d1\u012f\u00d1\u0124\u00d0\u00b8\u00d1\u0127",
- "\u0120\u00d0\u0128",
- "aber",
- "\u0120Vas",
- "\u0120centimeters",
- "\u00d7\u0140\u00d7\u0136",
- "\u00d0\u00bd\u00d0\u00b8\u00d0\u00b1",
- "\u0120riders",
- "\u0120Trib",
- "\u00e5\u012e\u0127",
- "\u0120tak\u00c5\u00bce",
- "\u0120noun",
- "\u0120icons",
- "\u0120solely",
- "minded",
- "\u0120dispon",
- "\u0120Switzerland",
- "\u0120clusters",
- "\u0120queda",
- "ailing",
- "\u0120manga",
- "\u012068",
- "\u0126\u012a",
- "\u0120tet",
- "gins",
- "haus",
- "\u00e7\u00a9\u00ba",
- "\u00e5\u00b7\u00a5",
- "\u0120OP",
- "oted",
- "\u0120nouveau",
- "ALLY",
- "\u00d9\u012a\u00d8\u00af",
- "\u00c3\u00b2n",
- "\u0120mortality",
- "\u0120GitHub",
- "drop",
- "\u0120disgu",
- "\u0120recom",
- "\u0120locals",
- "\u0120homemade",
- "amba",
- "\u0120pronunciation",
- "\u0120alphabet",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u012e",
- "owany",
- "iras",
- "idency",
- "OME",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0123",
- "arak",
- "viamente",
- "\u0120nonprofit",
- "\u0120YouTuber",
- "\u0120parenth",
- "\u0120Boo",
- "vat",
- "\u0120Stir",
- "\u0120precip",
- "\u0120ants",
- "\u0120ally",
- "\u0120Maori",
- "\u0120\u00eb\u012e\u0122\u00ed\u0137\u013e",
- "\u00e5\u0131\u00af\u00e6\u013a\u00af",
- "ogene",
- "\u0120Labour",
- "arette",
- "\u0120recycling",
- "ensa",
- "\u0120pursuit",
- "\u0120sak",
- "\u0120\u00d0\u0139\u00d0\u00b4\u00d0\u00b5\u00d1\u0123\u00d1\u012e",
- "\u0120tolerance",
- "\u0120saat",
- "\u0120clicked",
- "\u00e2\u013b\u00a5",
- "\u0120facebook",
- "\u0120Into",
- "\u0120incentives",
- "\u00ea\u00b8\u00b0\u00eb\u012c\u0136",
- "\u0120Dennis",
- "\u0120Wik",
- "gesch",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u013d",
- "\u0120\u00cf\u0122\u00ce\u00b1",
- "\u0120Whoo",
- "\u0120rounded",
- "\u0120dope",
- "\u0120capturing",
- "\u0120Warri",
- "\u0120civilian",
- "\u0120charming",
- "\u0120esas",
- "\u0120sustained",
- "\u0120leaning",
- "\u0120abundance",
- "\u00c3\u0143lia",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d1\u012d\u00d0\u00b9",
- "\u0120ph\u00e1\u00ba\u00a3i",
- "acja",
- "\u0120\u00ea\u00b0\u013b\u00ec\u0137\u0126",
- "activ",
- "\u00e0\u00b8\u00b2\u00e0\u00b8\u00a2",
- "\u012097",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b9",
- "cro",
- "\u0120Jackie",
- "ittees",
- "bracht",
- "ulent",
- "\u0120\u00ec\u0142\u013e\u00eb",
- "\u0120plugin",
- "vantage",
- "party",
- "\u0120suas",
- "\u0120ante",
- "\u00d1\u0125\u00d0\u00bb",
- "\u00d0\u013f\u00d0\u0132",
- "\u00e6\u0124\u00a8",
- "\u0120\u00cf\u0125\u00cf\u0127",
- "\u0120meth",
- "\u0120enthusiasm",
- "\u00d1\u0131\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u00ed\u013b\u0136\u00eb",
- "\u0120synthetic",
- "\u0120seasoning",
- "\u0120Lost",
- "onomy",
- "\u0120Spark",
- "\u0120bure",
- "\u0120assured",
- "\u0120imagin",
- "\u0120carro",
- "Sha",
- "\u00c4\u0127t",
- "\u00d0\u00bd\u00d1\u0125\u00d1\u0124\u00d1\u012e",
- "\u00c3\u00a1tica",
- "TY",
- "\u0120kern",
- "\u0120Brazilian",
- "\u00c3\u00b0",
- "\u0120suspended",
- "\u0120Carib",
- "\u0120bizim",
- "\u0120Oliver",
- "\u00e3\u0123\u00b6",
- "Tom",
- "\u0120\u00d0\u00bf\u00d0\u00bb\u00d0\u00b0\u00d0\u00bd",
- "\u0120nope",
- "omething",
- "\u0120beiden",
- "\u00d1\u0128\u00d0\u00b5\u00d0\u00bd",
- "\u0120fluct",
- "\u0120\u00ce\u00bc\u00ce\u00bf\u00cf\u0127",
- "\u0120fathers",
- "\u0120Blake",
- "\u0120upward",
- "\u0120Dash",
- "\u0120Lil",
- "\u0120\u00ec\u012a\u013a\u00eb\u0131\u0126",
- "\u0120revelation",
- "\u0120elevated",
- "\u0120Jiang",
- "LED",
- "\u0120Thompson",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b3\u00d1\u0125\u00d1\u0124",
- "\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d1\u0125",
- "ifiers",
- "\u0120comeback",
- "\u0120buyers",
- "\u00ea\u00b2\u00b0",
- "\u0120Sales",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5",
- "ciones",
- "\u0120whistle",
- "\u0120dull",
- "LEX",
- "\u0120\u00ed\u0137\u013a\u00ea\u00b2\u0142\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120criminals",
- "\u0120descent",
- "ipple",
- "mas\u00c4\u00b1",
- "\u0120foolish",
- "\u0120\u00d0\u00b4\u00d1\u0125\u00d0\u00bc\u00d0\u00b0\u00d1\u0130",
- "tar",
- "\u0120mango",
- "\u0120choreography",
- "Matt",
- "\u0120territor",
- "\u0120acaba",
- "\u0120Einstein",
- "\u0120IBM",
- "\u0120Metal",
- "\u0120Crystal",
- "\u0120rah",
- "\u0120foul",
- "\u0120Islands",
- "\u0120intact",
- "\u0120Rail",
- ".:",
- "\u0120ac\u00c3\u00a1",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00bf",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00b5",
- "\u0120Write",
- "hehe",
- "\u0120FO",
- "\u0120\u00cf\u0125\u00cf\u0126\u00ce\u00b7",
- "\u0120doin",
- "held",
- "\u0120appropriately",
- "\u0120deliberately",
- "\u0120archive",
- "\u0120giveaway",
- "\u00e3\u0123\u0135\u00e3\u0123\u0135",
- "\u0120finale",
- "\u00d0\u00bb\u00d0\u00b0\u00d1\u0123",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00be",
- "\u00c6\u00a1n",
- "\u00e6\u00a3\u0134",
- "ogo",
- "\u00e7\u012b\u00a9",
- "\u0120Audience",
- "\u00e3\u0127\u0142",
- "\u0120subur",
- "\u0120headache",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00bd\u00d1\u0131",
- "\u0120Witch",
- "\u0120Swedish",
- "\u0120BI",
- "\u0120erase",
- "\u0120khi",
- "\u0120commentary",
- "\u0120Sultan",
- "\u00ed\u0125\u013f",
- "\u0120Leban",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u012d",
- "\u0120Pam",
- "pekt",
- "month",
- "\u0120grounded",
- "\u00ea\u00be",
- "\u0120\u00c5\u0141ekilde",
- "250",
- "\u0120SCH",
- "ioso",
- "\u0120inaug",
- "heimer",
- "\u0120reflecting",
- "\u0120Ruth",
- "\u0120Oil",
- "\u0120trouver",
- "uep",
- "..]",
- "\u0120\u00ec\u0140\u012a\u00eb",
- "\u0120olha",
- "\u0120reasonably",
- "\u0120glitch",
- "UB",
- "\u0120Gran",
- "\u0120adalah",
- "\u0120lent",
- "\u00d8\u00b1\u00d8\u00a7",
- "\u0120traction",
- "\u0120adjusting",
- "\u00b4\u00a4",
- "\u00d0\u00bd\u00d0\u00b8\u00d0\u00b1\u00d1\u0125\u00d0\u00b4\u00d1\u012e",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bf",
- "\u0120stretched",
- "\u0120ort",
- "\u0120cosine",
- "viol",
- "\u0120\u00ec\u0127",
- "cir",
- "\u0120bastard",
- "\u00e4\u00b8\u0129",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u0120quier",
- "\u0120pressures",
- "\u0120Anh",
- "\u00e5\u00b9\u00be",
- "\u0120elles",
- "\u0120\u00d0\u00b4\u00d1\u0122\u00d1\u0125\u00d0\u00b7",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b6\u00d0\u00b5\u00d1\u0124\u00d0\u00b5",
- "\u0120ch\u00e1\u00bb",
- "\u0120M\u00c3\u00a9",
- "\u00c3\u00b6k",
- "\u00e1\u00ba\u00a7u",
- "\u00ec\u0142\u012a",
- "zin",
- "\u0120caution",
- "iban",
- "\u0120judging",
- "\u00d1\u0125\u00d1\u0130\u00d1\u0124",
- "\u0120baj",
- "\u0120\u00d0\u00a1\u00d0\u00b5\u00d0\u00b9\u00d1\u0129\u00d0\u00b0\u00d1\u0123",
- "\u0120Poor",
- "\u0120Nazi",
- "\u0120upbeat",
- "yang",
- "\u0120weekends",
- "\u0120Essentially",
- "\u0120oluyor",
- "\u0120spatial",
- "acker",
- "\u0120seller",
- "\u0120\u00d7\u0132\u00d7\u0137\u00d7\u00aa",
- "\u0133\u00d7\u013e",
- "\u0120vivid",
- "\u0120Bond",
- "\u00ea\u00b6\u012e",
- "iskt",
- "\u00e3\u0124\u00b5",
- "\u0120goat",
- "driver",
- "\u0120mug",
- "ictional",
- "\u0120allt",
- "\u0120Initi",
- "\u0120Rand",
- "\u0120finishes",
- "\u0120\u00ea\u00b0\u012a",
- "\u0120vitam",
- "\u0120teenagers",
- "\u0120Morris",
- "\u00ec\u00a4\u0126",
- "\u0120Ori",
- "iya",
- "\u0120my\u00c3\u00b6s",
- "Step",
- "\u0120Kre",
- "\u00e8\u00be\u00a6",
- "\u0120dinosaur",
- "\u0120\u00eb\u00aa\u0129",
- "affe",
- "\u0120\u00eb\u0132\u00a9\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120zeg",
- "\u00e5\u012a\u0129",
- "\u0120Manhattan",
- "\u0120sujet",
- "uelle",
- "stoff",
- "\u0120d\u00c3\u00bcr",
- "\u0120submar",
- "eses",
- "\u0120aquele",
- "\u0120nou",
- "\u0120Faith",
- "tz",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d1\u0125",
- "aceut",
- "liers",
- "\u0120bandwidth",
- "\u00c6\u00b0\u00e1\u00bb\u013f",
- "\u0120respective",
- "\u0120Ave",
- "\u0120spreadshe",
- "\u0120Sent",
- "icamente",
- "\u0120infra",
- "\u0120learners",
- "\u0120\u00e0\u00ae\u012b",
- "aiah",
- "renal",
- "\u0120mustard",
- "\u0120habt",
- "\u00e7\u0125",
- "\u0120Qu\u00c3\u00a9",
- "\u0120analyzing",
- "\u00e6\u00af\u0131",
- "\u0120solic",
- "\u0120\u00d7\u0136\u00d7\u0137\u00d7\u0132",
- "\u0120causa",
- "\u0120welcomed",
- "\u0120Success",
- "\u0120facile",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d1\u0125",
- "schein",
- "\u0120fetch",
- "\u0120strat",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d0\u00b8\u00d1\u0124",
- "\u00ec\u0139\u0132\u00ec\u0126\u013e\u00eb\u012c\u0136",
- "\u0120\u00d1\u0123\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d0\u00be\u00d0\u00b1",
- "mam",
- "\u0120ser\u00c3\u0143a",
- "naments",
- "writer",
- "\u0120consulting",
- "\u00ed\u013a\u0122",
- "\u0120Berkeley",
- "eu",
- "asive",
- "UU",
- "\u0120Analyt",
- "\u0120submission",
- "\u0120magnificent",
- "enza",
- "\u0120econ",
- "\u0120profiles",
- "\u0120incar",
- "Ab",
- "\u0120Nun",
- "\u0120hic",
- "screaming",
- "\u0120resilient",
- "\u00e5\u012a\u00a9",
- "grund",
- "\u0120concur",
- "\u0120bereits",
- "LD",
- "\u0120nurt",
- "\u00ec\u012b",
- "\u0120feast",
- "\u0120encuent",
- "\u0120Michel",
- "\u0120suprem",
- "\"]",
- "\u0120feeds",
- "\u0120Kollegen",
- "isser",
- "\u0120Feng",
- "\u0120Wen",
- "mun",
- "\u0120ten\u00c3\u0143a",
- "\u0120Wrest",
- "\u0120\u00ec\u013a\u00a4\u00eb\u012c\u013a\u00ec\u013f\u0122",
- "\u0120stead",
- "\u0120restoration",
- "\u0120donated",
- "\u0120dels",
- "\u0120census",
- "\u0120desperately",
- "worthy",
- "HE",
- "\u0120Spa",
- "\u0120Bryan",
- "\u0120hj",
- "\u0120Raw",
- "\u00ec\u0137\u0126\u00eb",
- "\u0120Camera",
- "\u0120zien",
- "\u0120styl",
- "\u0120TW",
- "\u0120Cheese",
- "borne",
- "\u0120obl",
- "\u0120Already",
- "\u0120unstable",
- "\u0120flames",
- "post",
- "Ha",
- "romagn",
- "\u0120\u00ec\u0139\u0126\u00eb\u00a7\u012a",
- "dest",
- "\u0120kolej",
- "\u0120temporarily",
- "\u0120determining",
- "\u0120Glass",
- "\u00d1\u0122\u00d0\u00be\u00d0\u00bd",
- "olan",
- "\u0120dominated",
- "\u00e5\u012e\u0138",
- "____",
- "\u0120\u00d9\u0129\u00d8\u00b0\u00d8\u00a7",
- "\u0120Dana",
- "\u0120dinheiro",
- "aqu",
- "\u00eb\u00af\u00bc",
- "\u0120\u00c3\u0142s",
- "\u0120Joey",
- "\u0120Griff",
- "\u0120attain",
- "\u0120transitions",
- "\u0120Literally",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00b4",
- "\u0120Haven",
- "\u0120grabbing",
- "\u0120crystals",
- "\u0120Fourth",
- "\u0120candles",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d1\u0125\u00d1\u0129\u00d0\u00b0",
- "rico",
- "\u01205000",
- "etto",
- "\u0120undo",
- "\u0120kto",
- "\u0120divert",
- "\u0120chir",
- "\u0120persec",
- "\u0120hiking",
- "\u0120announcements",
- "\u00e7\u0136\u00b1",
- "\u00d0\u00b7\u00d1\u012d",
- "\u0120auc",
- "\u0120systemic",
- "\u0120RM",
- "\u00cf\u0125\u00ce\u00b1",
- "\u0120\u00d0\u0136\u00d0\u00b6",
- "\u0120yar",
- "\u0120Ward",
- "\u0120pissed",
- "\u0120carn",
- "\u0120autonomous",
- "\u00e3\u0127\u0130\u00e3\u0127\u0130",
- "sover",
- "\u00e6\u00b2\u0134\u00e9\u012e\u00af",
- "\u00e5\u00be\u012a\u00e5\u00a5\u00bd",
- "\u0120reflex",
- "\u0120gardens",
- "\u0120dated",
- "\u00ec\u00b1",
- "ami\u00c4\u013b",
- "\u0120continuity",
- "\u0120citizenship",
- "\u0120schwer",
- "\u0120zak",
- "table",
- "\u0120\u00d1\u0123\u00d1\u0129",
- "\u00e8\u00a7\u0123",
- "\u0120\u00cf\u0125\u00ce\u00b5",
- "\u0120generates",
- "\u00ea\u00b5\u00ac\u00eb\u0124\u013a",
- "\u00c3\u00b6h",
- "\u00c3\u00b3m",
- "alam",
- "\u0120JUDY",
- "\u0120Bug",
- "\u0120\u00e3\u0123\u00a6",
- "\u0120drones",
- "\u0120\u00c3\u00a1gua",
- "acaks",
- "\u00e6\u013c",
- "\u0120\u00d0\u013c\u00d0\u00be\u00d0\u00bd",
- "\u00d7\u0138\u00d7\u0136",
- "\u0120strive",
- "\u0120Altern",
- "\u0120nearest",
- "\u0120proyect",
- "tera",
- "\u0120ASHLEY",
- "\u0120worm",
- "\u0120replay",
- "\u0120tara",
- "\u0120Indians",
- "\u00e3\u0124\u00b0",
- "icaid",
- "\u0120\u00ec\u012a\u013e",
- "\u0120appealing",
- "\u0120Wes",
- "\u0120mentions",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bb\u00d0\u00b5",
- "\u0120kw",
- "\u0120fragile",
- "isz",
- "k\u00c3\u00b3w",
- "hang",
- "color",
- "\u0120presidente",
- "87",
- "\u00d0\u00b5\u00d1\u0126",
- "\u00e7\u012a\u00b8",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00b1\u00d0\u00b0\u00d0\u00b2",
- "\u0120Nelson",
- "\u00c3\u00a1fic",
- "\u0120MICHAEL",
- "\u0120mechanic",
- "\u0120metres",
- "\u0120oczywi\u00c5\u013dcie",
- "\u0120Cind",
- "\u0120ogs\u00c3\u00a5",
- "\u0120landsca",
- "ACE",
- "\u0120headlines",
- "\u0120catalyst",
- "\u0120Catch",
- "inkles",
- "\u0120pills",
- "ordo",
- "\u0120immigrant",
- "\u0120examination",
- "\u0120accidents",
- "z\u00c4\u0127d",
- "\u0120quiere",
- "\u0120nella",
- "\u012067",
- "\u0120passa",
- "\u0120superfic",
- "istor",
- "\u0120nov",
- "\u00eb\u012d\u00b5",
- "\u0120mandate",
- "isons",
- "\u0120Virtual",
- "\u0120selber",
- "\u0120counseling",
- "\u0120NBA",
- "\u0120sept",
- "\u0120believer",
- "\u0120marvel",
- "\u0120Integr",
- "\u0120\u00d0\u00bc\u00d1\u0138",
- "\u0120orph",
- "\u0120backward",
- "\u0120Generation",
- "\u0120Pict",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d1\u0124",
- "\u0120tapi",
- "prochen",
- "\u0120hallway",
- "hte",
- "\u0120\u00db\u0123\u00db\u0134",
- "\u0120Zum",
- "\u00e8\u0122\u0123\u00e5\u00b8\u00ab",
- "achment",
- "iquer",
- "folg",
- "\u0120Eddie",
- "\u0120Kil",
- "\u0120wellness",
- "stock",
- "\u00e8\u00bc\u0125",
- "\u0120ka\u00c3\u00a7",
- "\u0120terrorism",
- "\u0120pointer",
- "Of",
- "heric",
- "\u0120Ultimately",
- "\u0120meses",
- "\u0120Trade",
- "\u0120pint",
- "\u0120tuition",
- "\u0120disagre",
- "\u0120\u00ea\u00b2\u012e\u00ec\u0140\u0126",
- "\u0120manuscript",
- "\u0120roomm",
- "\u0120outputs",
- "\u00d0\u00b5\u00d1\u0128\u00d0\u00b8",
- "\u0120ries",
- "\u0120salud",
- "otzdem",
- "\u0120masses",
- "\u0120by\u00c5\u0124a",
- "\u0120clearing",
- "\u0120discourse",
- "atson",
- "\u0120folded",
- "\u0120Jar",
- "\u00d9\u0126\u00d9\u012b",
- "900",
- "\u0120\u00d1\u0125\u00d1\u0123\u00d0\u00bf",
- "\u0120prophecy",
- "\u0120interfere",
- "\u00d0\u00b8\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u00e0\u00b9\u012e",
- "\u0120thri",
- "\u0120\u00d7\u0140\u00d7\u00a9",
- "\u0120laz\u00c4\u00b1m",
- "\u01201992",
- "\u0120futuro",
- "\u0120locking",
- "\u0120embargo",
- "\u0120Neither",
- "ivamente",
- "\u0120m\u00c3\u00a5ste",
- "\u0120mik",
- "\u0120collector",
- "\u00d0\u00b5\u00d0\u00ba\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d1\u0122",
- "\u0120Gand",
- "\u0120sentir",
- "\u0120Might",
- "\u00e5\u00a1\u0136",
- "\u0120ganzen",
- "UC",
- "\u0120relating",
- "SD",
- "\u0120mosquito",
- "GR",
- "\u0120hollow",
- "\u00e2\u013a\u0127",
- "\u0120Walker",
- "\u0120affiliate",
- "\u0120duplicate",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00bc",
- "\u0120grape",
- "\u0120Organization",
- "\u0120synt",
- "Joe",
- "\u0120geg",
- "\u0120revealing",
- "\u0120Ethan",
- "outer",
- "\u0120yay",
- "\u00e9\u00ab\u0136",
- "\u00d0\u00bb\u00d0\u00b0\u00d1\u0122",
- "\u0120reportedly",
- "\u0120ihrer",
- "\u0120recognise",
- "\u0120bumper",
- "\u0120Randy",
- "\u0120Venus",
- "tles",
- "\u0120appetite",
- "\u0120glucose",
- "\u0120chodzi",
- "\u0120Furthermore",
- "tir",
- "\u0120conta",
- "\u0120intuition",
- "\u0120altitude",
- "\u0120chunks",
- "\u0120Joshua",
- "\u00c4\u00b1\u00c4\u0141\u00c4\u00b1m",
- "rylic",
- "leans",
- "\u0120\u00ed\u0136\u00bc\u00eb",
- "LL",
- "Que",
- "\u0120gor",
- "\u0120\u00d0\u00b7\u00d0\u00bd\u00d0\u00b0\u00d1\u0129\u00d0\u00b8\u00d1\u0124",
- "\u0120poems",
- "\u0120excel",
- "\u0120explored",
- "\u0120popul",
- "\u0120incluso",
- "st\u00c3\u00a4",
- "\u0120Gavin",
- "alling",
- "\u0120\u00cf\u0126\u00ce\u00bf\u00ce\u00bd",
- "\u00e9\u00a9",
- "arbeit",
- "\u0120Gas",
- "\u0120glorious",
- "rieben",
- "\u0120spam",
- "\u0120indoor",
- "\u0120thrust",
- "\u0120Ald",
- "\u0120Prior",
- "\u0120onboard",
- "\u00e3\u0123\u0142\u00e3\u0123\u0137\u00e3\u0123\u0126",
- "oca",
- "ASH",
- "\u00a3\u0142",
- "\u0120Christine",
- "\u0120drawer",
- "\u0120noon",
- "\u0120\u00ec\u0140\u013a\u00eb",
- "\u0120permanently",
- "\u00e6\u00b7\u00b1",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bc\u00d0\u00b5\u00d1\u0122",
- "\u0120podcasts",
- "erapeut",
- "prit",
- "\u0120stainless",
- "\u0120\u00da\u00a9\u00db\u0134",
- "\u0120familia",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d1\u0122",
- "unto",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d0\u00bb",
- "\u0120h\u00c3\u00a4",
- "\u0120Hai",
- "\u0120PB",
- "izon",
- "\u0120konnte",
- "\u0120b\u00c3\u00bcy\u00c3\u00bck",
- "\u0120utilizar",
- "\u00da\u0128",
- "\u0120aquesta",
- "\u0120mixer",
- "udent",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00ba\u00d1\u0123",
- "\u00c5\u0124u",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d1\u0123\u00d1\u0124\u00d0\u00b5\u00d0\u00bc",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d1\u0122\u00d0\u00bc",
- "\u0120fatal",
- "\u0120considerations",
- "\u0120validation",
- "\u0120oli",
- "\u0120karde\u00c5\u0141",
- "\u0120GLORIA",
- "\u0120pall",
- "\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b5",
- "\u0120rectang",
- "\u0120medieval",
- "allahi",
- "asti",
- "\u0120Syrian",
- "\u0120shear",
- "\u0120debug",
- "\u0120Mai",
- "\u0120knocking",
- "\u0120Lex",
- "ardan",
- "rov",
- "\u0120memorial",
- "\u00e6\u00b0\u00a3",
- "ooky",
- "\u0120stuffed",
- "\u0120pass\u00c3\u00a9",
- "\u0120wig",
- "\u0124\u0142",
- "\u0120pr\u00c3\u00b3xima",
- "\u01201991",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d0\u00b6\u00d0\u00b4\u00d1\u0125",
- "\u0120nuestros",
- "\u0120Beast",
- "\u0120smo",
- "atched",
- "ologia",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b4",
- "\u0120gee",
- "\u0120conceptual",
- "\u0120\u00c3\u00b4",
- "\u0120decreases",
- "\u0120queries",
- "\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a",
- "\u0120Apart",
- "\u0120exempl",
- "\u00e5\u00b1\u00b1",
- "\u0120fled",
- "\u0120OFF",
- "ggak",
- "\u0120bead",
- "hir",
- "lies",
- "\u0120Clearly",
- "\u00c4\u00b1lar",
- "\u0120chess",
- "\u0120whichever",
- "\u012096",
- "\u00e1\u00ba\u00b1",
- "\u0120respects",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d1\u0122",
- "\u0120organism",
- "\u0120grandpa",
- "\u0120Vie",
- "\u00e8\u00b7\u0141\u00e4\u00bd\u0142",
- "\u0120flooding",
- "\u0120upgraded",
- "\u00d1\u0133\u00d1\u0122",
- "\u0120cheeks",
- "\u0120conquer",
- "\u0120stubborn",
- "\u0120puzzles",
- "\u0120auction",
- "\u0120relying",
- "\u0120PROF",
- "\u0120Esper",
- "\u0120\u00d0\u013e\u00d0\u00a3",
- "\u0120hype",
- "\u0120possibil",
- "\u0120imprison",
- "\u0120Ern",
- "\u00ec\u0139\u012a\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120envie",
- "\u0120resurrection",
- "\u00e4\u00b8\u012f\u00e8\u00a1\u012e",
- "\u0120sper",
- "\u0120Venezuela",
- "som",
- "\u0120\u00ec\u0140\u0142\u00ea\u00b9",
- "\u0120nouvelle",
- "\u0120closes",
- "\u01201940",
- "\u0120qua",
- "\u0120Jared",
- "\u0120Pir",
- "\u0120inde",
- "\u0120scrub",
- "uku",
- "\u0120requiring",
- "\u0120\u00d0\u00b2\u00d0\u00b0\u00d0\u00bc\u00d0\u00b8",
- "\u0120considerable",
- "\u00e5\u0132\u013d",
- "ilia",
- "\u0120inne",
- "\u0120meinem",
- "\u0120hardship",
- "\u0120traps",
- "roc",
- "\u0120\u00ec\u0126\u00a4\u00eb",
- "\u0120researching",
- "\u0120Margaret",
- "\u0120penny",
- "\u0120b\u00c4\u00b1rak",
- "\u00d1\u0133\u00d0\u00bb",
- "\u0120wool",
- "\u0120rhet",
- "\u0120flatten",
- "\u00e7\u0129",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u00a3",
- "\u0120pied",
- "\u0120Chap",
- "\u0120underm",
- "\u0120fret",
- "\u0120crashed",
- "\u0120Frauen",
- "\u00d8\u00b0\u00d9\u0129",
- "ivan",
- "\u0120literary",
- "latego",
- "\u0120sp\u00c3\u00a4ter",
- "\u0120similarities",
- "\u00e2\u0128",
- "\u0120Coron",
- "\u0120Creek",
- "\u0120bosses",
- "\u0120accompanied",
- "\u0120debates",
- "\u0120assembled",
- "\u0120\u00c3\u0123",
- "\u0120Vai",
- "\u0120tract",
- "\u0120simplement",
- "\u0120Arin",
- "\u0120vulnerability",
- "\u0120hormone",
- "IEL",
- "OOK",
- "\u0120relay",
- "\u0120Andrea",
- "ril",
- "\u0120necessity",
- "aceutical",
- "\u00d1\u0130\u00d1\u012b",
- "ousing",
- "nahmen",
- "\u0120footprint",
- "map",
- "\u0120Tier",
- "annya",
- "intend",
- "\u00e5\u0138\u00ae",
- "\u00e5\u00a2",
- "\u0120decorate",
- "\u0120zombies",
- "\u0120Hyd",
- "\u0120Suz",
- "\u0120campuses",
- "\u0120Emb",
- "\u0120throttle",
- "\u0120admin",
- "\u0120oportun",
- "\u0120mirrors",
- "\u0120identities",
- "\u0120Clin",
- "\u0120\u00eb\u00b9\u0126\u00eb",
- "\u00e1\u00b9\u00a3",
- "\u0120Ott",
- "\u0120blues",
- "\u0120impressions",
- "-,",
- "\u0120vague",
- "afe",
- "\u0120inferior",
- "erald",
- "\u0120medicines",
- "\u0120pregunta",
- "osely",
- "\u0120t\u00c3\u00a9l\u00c3\u00a9",
- "\u0120Month",
- "\u0120Leaders",
- "\u0120Egyptian",
- "\u0120ration",
- "kers",
- "heits",
- "\u0120recht",
- "Play",
- "\u0120eg",
- "\u0120polls",
- "\u0120WOODR",
- "\u0120slots",
- "jam",
- "Both",
- "\u0120Rat",
- "\u00d1\u0122\u00d0\u00b0\u00d0\u00b6",
- "\u0120Bright",
- "\u00e4\u00b8\u0122\u00e5\u00ae\u013c",
- "\u00e1\u00bb\u0133i",
- "urious",
- "\u0120singers",
- "\u0120login",
- "\u0120t\u00c3\u00aam",
- "lation",
- "\u0120Mum",
- "\u00c6\u00b0\u00e1\u00bb\u013fng",
- "\u0120Editor",
- "\u00e5\u0132\u0133",
- "\u0120innovations",
- "have",
- "\u0120Sek",
- "\u0120weaker",
- "\u0120Gob",
- "After",
- "\u00b4\u00ec\u00a7\u0122",
- "\u0120\u00eb\u00ac\u00b8\u00ec\u0142\u013e",
- "\u00e3\u0125\u00bc\u00e3\u0125\u00bc",
- "\u0120disadvantage",
- "\u00e7\u00a2\u00ba",
- "\u0120gaze",
- "\u0120Mack",
- "\u00cf\u0123\u00ce\u00af",
- "\u0120Kiss",
- "\u0120Holo",
- "\u0120Birth",
- "izi",
- "bab",
- "\u00e4\u00bf\u013f",
- "\u00ec\u012d\u013e\u00ea\u00b3\u0142",
- "\u00d0\u00b4\u00d0\u00b5\u00d1\u0122\u00d0\u00b6",
- "\u0120squat",
- "\u00d0\u00ba\u00d1\u0125\u00d1\u0123",
- "uni",
- "\u0120Comme",
- "\u0120WOODRUFF",
- "\u0120Championship",
- "\u0120welche",
- "\u0120Youth",
- "zem",
- "\u0120odpow",
- "\u0120persistent",
- "rut",
- "\u00ec\u0136\u00a9",
- "\u00ed\u0138\u00a5",
- "lair",
- "iku",
- "\u0120vendor",
- "\u0120ch\u00c3\u00bang",
- "\u0120financi",
- "\u0120overly",
- "\u00c3\u00a2u",
- "\u0120gluten",
- "\u01201800",
- "\u0120divisions",
- "\u0120ciudad",
- "\u0120obed",
- "\u0120warum",
- "\u0120eher",
- "\u0120elim",
- "\u0120\u00d0\u0134\u00d0\u00be",
- "\u0120peuvent",
- "\u0120Wanna",
- "\u0120attendance",
- "\u0120assessments",
- "\u0120Bog",
- "\u0120imagery",
- "\u0120collectively",
- "\u0120informal",
- "\u0120Schwe",
- "\u0120deutlich",
- "\u0120Chel",
- "\u0120PE",
- "owed",
- "\u0120banner",
- "\u0120shelves",
- "\u0120Return",
- "\u00e6\u012d\u00bf",
- "LAUGHS",
- "\u0120congratulate",
- "\u0120Norway",
- "\u0120dwell",
- "\u0120Caribbean",
- "\u0120norms",
- "\u0120Animal",
- "\u0120Valentine",
- "\u0120extending",
- "\u0120Vou",
- "orr",
- "\u0120Cheng",
- "\u00c2\u00a1",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b3",
- "\u0120veg",
- "\u0120h\u00c3\u00a5",
- "\u0120Xin",
- "\u0120\u00ec\u00b9\u00b4\u00eb",
- "emet",
- "\u0120hypoth",
- "\u0120interessante",
- "rices",
- "IZ",
- "\u0120USD",
- "\u0120runner",
- "\u0120Bag",
- "\u0120\u00ea\u00bd",
- "\u0120come\u00c3\u00a7ar",
- "\u0120pigs",
- "\u0120weaknesses",
- "Ph",
- "\u0120Viol",
- "\u00e4\u00b8\u012f\u00e7\u0136\u00a8",
- "\u0120dragging",
- "\u0120Aqu\u00c3\u0143",
- "\u0120CSS",
- "\u0120millimeters",
- "\u0120est\u00c3\u00a1s",
- "\u0120acute",
- "\u0120dejar",
- "i\u00c4\u0141",
- "obra",
- "Love",
- "\u0120silk",
- "****",
- "\u0120joins",
- "\u0120prol",
- "\u0120\u00ea\u00b0\u0132\u00ec\u0124\u00ac\u00ed\u0137\u00a9\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u00e6\u0136\u00af",
- "\u00d8\u0143\u00d8\u00af",
- "aghetti",
- "\u00c3\u00a4nner",
- "\u0120strang",
- "\u0120doubled",
- "\u0120descriptions",
- "\u0120stellen",
- "\u0120parti",
- "\u00e7\u00ab\u012d",
- "\u00b2\u0126\u00eb",
- "\u0120\u00c3\u00b6\u00c4\u0141",
- "ighing",
- "\u0120angular",
- "\u0120natuur",
- "\u0120Shel",
- "\u00c6\u00b0\u00c6\u00a1",
- "\u0120rays",
- "\u0120seper",
- "start",
- "vised",
- "\u0120rushed",
- "\u0120internationally",
- "\u0120nivel",
- "\u0120boxing",
- "fallen",
- "\u00e1\u00bb\u0133c",
- "\u0120seinen",
- "plicity",
- "\u0120carboh",
- "\u0120Travis",
- "uso",
- "\u0120Phase",
- "\u0120activation",
- "\u0120opio",
- "\u00b7\u00a8",
- "\u0120decreased",
- "Car",
- "\u0120bundle",
- "\u0120expend",
- "ormal",
- "\u0120adjacent",
- "\u0120mee",
- "\u0120\u00d0\u00be\u00d1\u0122\u00d0\u00b3",
- "\u0120transcript",
- "\u0120Language",
- "GS",
- "\u00e8\u00a7\u012b",
- "\u0120seul",
- "\u00c3\u0142nh",
- "\u0120nya",
- "nings",
- "\u0120\u00ec\u012d\u013e\u00eb",
- "\u0120\u00eb\u0136\u00b0\u00eb\u013f\u00bc",
- "\u0120Agr",
- "\u00c3\u0143d",
- "\u00e7\u0137\u013b",
- "\u0120aby",
- "\u0120Neo",
- "\u00c4\u00b1yoruz",
- "\u0120Thinking",
- "aime",
- "\u0120vite",
- "\u0120trav\u00c3\u00a9s",
- "\u0120\u00d7\u0133\u00d7\u00a2",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d0\u00b4",
- "Our",
- "hoot",
- "\u0120liner",
- "\u0120Pizza",
- "\u0120hyg",
- "flies",
- "\u0120Continue",
- "\u0120dental",
- "\u0120Tib",
- "\u0120regulate",
- "lie\u00c3\u0141",
- "ALK",
- "\u0120Tae",
- "\u00ea\u00b8\u00b8",
- "\u0120Brexit",
- "\u0120Gut",
- "\u0120occupation",
- "\u0120zrobi",
- "\u00c3\u00a2m",
- "\u0120whisk",
- "\u00e4\u00b8\u0138\u00e7\u0137\u012e",
- "\u0120kanske",
- "omon",
- "robe",
- "\u0120warfare",
- "\u0120th\u00e1\u00bb\u0125",
- "\u0120jaki",
- "\u0120strokes",
- "\u0120peas",
- "\u0120Damit",
- "HAN",
- "\u0120interference",
- "\u0120\u00d0\u00bc\u00d0\u00b8\u00d0\u00bd\u00d1\u0125\u00d1\u0124",
- "NER",
- "outing",
- "\u0120textures",
- "\u0141\u012b",
- "owi",
- "\u0120\u00ed\u0137\u013b",
- "\u0120dens",
- "\u0120protagonist",
- "\u00c3\u00a4nn",
- "\u0120goddess",
- "\u0120wollte",
- "ijo",
- "\u0120Woche",
- "\u0120VPN",
- "story",
- "\u0120kinderg",
- "\u0120funnel",
- "\u0120distress",
- "\u00d0\u00bd\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d1\u012e\u00d1\u0130",
- "\u0120noisy",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b4\u00d0\u00be\u00d0\u00bb\u00d0\u00b6",
- "\u0120daran",
- "\u0120enzyme",
- "\u00d0\u00bb\u00d0\u00be\u00d0\u00b6",
- "\u0120mute",
- "\u0120dwar",
- "\u0120\u00d8\u00a7\u00d8\u00b3",
- "\u0120kompl",
- "\u0120merit",
- "\u0120fosse",
- "\u0120Drink",
- "\u0120fora",
- "\u0120wohl",
- "\u0120breeze",
- "\u0120sanit",
- "\u0120drin",
- "\u0120\u00ec\u013f\u00b4\u00ea\u00b1\u00b0\u00eb\u012c\u0136",
- "\u012062",
- "\u0120\u00ec\u00b0\u00a8\u00eb",
- "abytes",
- "\u0120deeds",
- "\u0120\u00d0\u00b9",
- "i\u00c3\u00a8me",
- "iggling",
- "\u0120\"'",
- "\u0120\u00d1\u0129\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120Answer",
- "\u0120evangel",
- "\u01201080",
- "\u0120Visit",
- "icient",
- "\u0120reliability",
- "\u00d1\u0130\u00d1\u0123\u00d1\u012e",
- "\u0120Earlier",
- "\u0120fid",
- "\u00e7\u0143\u012b\u00e4\u00b8\u0122\u00e4\u00b8\u012d",
- "\u0120sleeves",
- "iyorsun",
- "\u0120bib",
- "\u0120Account",
- "\u00d1\u0131\u00d0\u00bb\u00d0\u00b8",
- "ciplinary",
- "zas",
- "\u0120\u00d0\u00b1\u00d0\u00b5\u00d1\u0122",
- "\u0120necklace",
- "\u0120blender",
- "\u0120Phillips",
- "eti",
- "\u0120Jupiter",
- "\u0120provoc",
- "\u0120Years",
- "entre",
- "acio",
- "\u0120k\u00c3\u00bc",
- "\u0120antenna",
- "\u0120novels",
- "\u0120fart",
- "\u0120Sugar",
- "\u0120Judy",
- "\u0120collapsed",
- "\u00e7\u00b0",
- "ritis",
- "\u0120\u00ec\u0125\u0123\u00ed\u013b\u00a9",
- "\u00d0\u0139\u00d0\u00ab",
- "\u0120Verf",
- "ranean",
- "ereum",
- "\u0120Target",
- "\u012088",
- "\u0120\u00d0\u013a\u00d0\u00b7",
- "ideo",
- "\u0120regression",
- "\u00ec\u00b6\u013e",
- "\u0120m\u00c3\u00b3wi",
- "\u0120studios",
- "iens",
- "iph",
- "\u0120frying",
- "\u0120fascinated",
- "\u0120Wah",
- "bucks",
- "maya",
- "\u0120Saturn",
- "\u0120Mommy",
- "\u0120ratings",
- "\u0120autumn",
- "\u00c6\u00b0\u00c6\u00a1ng",
- "\u0120loser",
- "\u0120centro",
- "\u00c3\u00a9rieur",
- "\u0120Fold",
- "\u0120supervisor",
- "\u0120Nobel",
- "\u0120underest",
- "obia",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d1\u0131",
- "\u0120verw",
- "\u0120fuels",
- "\u0120artifacts",
- "\u0120\u00eb\u00b6\u013b",
- "\u0120Autom",
- "\u00e7\u013c\u0126\u00e6\u013a\u00af",
- "\u00db\u0136",
- "\u00d7\u0137\u00d7\u00a1",
- "\u0120ihnen",
- "\u012059",
- "ounding",
- "\u00d0\u00b5\u00d1\u0122\u00d1\u012d",
- "inars",
- "chant",
- "\u0120addicted",
- "\u0120explosive",
- "\u0120dispers",
- "\u00e2\u0138\u012a",
- "axis",
- "ARY",
- "\u0120lum",
- "\u0120\u00d1\u0125\u00d1\u0123\u00d0\u00bb",
- "\u0120\u00d8\u012e",
- "\u0120rupees",
- "\u0120Pearl",
- "camp",
- "tv",
- "oya",
- "\u0120concludes",
- "\u0120collision",
- "\u0120buyer",
- "\u0120playground",
- "\u0120springs",
- "\u0120feminine",
- "\u0120Ras",
- "\u0120incarcer",
- "\u00ed\u0139\u013a",
- "\u0120dialect",
- "\u0120closure",
- "\u0120chatting",
- "\u0120babe",
- "\u0120spotlight",
- "\u0120notation",
- "\u00e8\u00b7\u00af",
- "Star",
- "i\u00c3\u00a3o",
- "\u0120t\u00c3\u00aate",
- "\u0120tide",
- "\u0120junto",
- "\u0120senator",
- "\u00d0\u00a5",
- "\u0120excuses",
- "\u0120blink",
- "\u0120admission",
- "\u0120Lily",
- "\u00d1\u012d\u00d0\u00bc\u00d0\u00b8",
- "\u0120amigo",
- "\u0120lust",
- "\u00eb\u012d\u00ac",
- "\u0120amino",
- "\u00e4\u00ba\u012d\u00e6\u0125\u0127",
- "\u0120consultant",
- "\u0120Electric",
- "\u0120\u00eb\u0127\u00b8\u00eb\u0140\u013a",
- "ujah",
- "\u0120shooter",
- "ichten",
- "\u0120Ukrainian",
- "\u0120aims",
- "\u0120Entertain",
- "\u0120miracles",
- "\u00e8\u0143\u00b0",
- "\u0120zeigen",
- "\u0120lam",
- "\u0120ress",
- "\u0120Jill",
- "ylan",
- "\u0120rook",
- "\u0120haya",
- "\u0120passport",
- "adata",
- "\u0120juicy",
- "conf",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00b9",
- "\u0120Sz",
- "\u0120intercept",
- "\u00e3\u0123\u0124\u00e3\u0124\u012c\u00e3\u0123\u012e\u00e3\u0123\u00a8\u00e3\u0123\u0128\u00e3\u0123\u0136\u00e3\u0123\u0138",
- "\u0120Teams",
- "\u0120maken",
- "irrel",
- "\u0120LIKE",
- "\u00e1\u00ba\u0143y",
- "\u00ea\u00b5\u00b0",
- "\u0120shortage",
- "\u0120paradigm",
- "\u0120papel",
- "\u0120astero",
- "\u00e3\u0123\u00be\u00e3\u0123\u0141",
- "\u0120sollen",
- "\u0120Mickey",
- "\u0120Orleans",
- "\u0120cholesterol",
- "\u0120goose",
- "\u00d1\u0128\u00d0\u00b8\u00d1\u0130",
- "\u00e3\u0123\u0124\u00e3\u0124\u012d",
- "\u0120FL",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b2",
- "\u0120tribute",
- "\u0120Gam",
- "\u0120\u00c3\u00a9videmment",
- "\u00d1\u0131\u00d1\u0127",
- "\u00e5\u00ae\u0140",
- "\u00e7\u0136\u00b0",
- "\u0120inappropri",
- "uhan",
- "\u0120organizational",
- "ailed",
- "\u0120endure",
- "\u012076",
- "\u0120shotgun",
- "\u0120livre",
- "\u0120suited",
- "\u0120warmth",
- "\u0120SIM",
- "\u0120envision",
- "\u0120degrad",
- "\u00c3\u00aene",
- "Laughing",
- "\u0120Whoever",
- "\u0120Buddhism",
- "\u0120sprinkle",
- "ce\u00c4\u0141iz",
- "\u0120ruins",
- "\u0120starch",
- "\u0120Herz",
- "\u0120injustice",
- "\u0120humidity",
- "\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb\u00d1\u0125\u00d0\u00b9",
- "\u0120Object",
- "\u0120Ign",
- "\u0120Exam",
- "igers",
- "\u0120thou",
- "\u0120Soy",
- "ivas",
- "\u0120poles",
- "math",
- "\u0120\u00d0\u00b2\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc",
- "INGING",
- "edral",
- "\u0120explor",
- "\u0120roasted",
- "\u0120crawl",
- "\u0120coff",
- "\u0120anom",
- "\u0120wij",
- "\u0120improves",
- "\u0120treaty",
- "\u0120discovering",
- "\u0120statute",
- "\u0120mercado",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00bb",
- "\u0120intel",
- "\u0120Chancellor",
- "\u0120Medicaid",
- "ugi",
- "\u0120verbal",
- "\u0120d\u00c3\u00b6n",
- "\u0120scripture",
- "\u0120iteration",
- "eks",
- "\u0120Oxford",
- "\u0120w\u00c3\u00a4h",
- "\u0120Vad",
- "\u0120AK",
- "\u0120\u00ec\u0137\u0126\u00ec\u013f\u00b4\u00eb",
- "\u0120iets",
- "\u0120needles",
- "\u00d9\u0125\u00d9\u0127",
- "\u0120pasado",
- "\u0120albums",
- "\u0120yea",
- "etzen",
- "\u0126\u00eb\u0131\u0126",
- "\u0120determines",
- "\u0120thee",
- "\u0120Playing",
- "\u00c3\u00a4rt",
- "\u0120\u00d7\u00a6",
- "cled",
- "\u0120downward",
- "alone",
- "\u0120solu",
- "\u0120partition",
- "\u0120wz",
- "dd",
- "\u0120pessoal",
- "\u00e5\u00aa\u00bd",
- "\u0120factories",
- "\u0120bleibt",
- "\u00e0\u00b8\u00a1\u00e0\u00b8\u00b2",
- "alsa",
- "\u0120NFL",
- "\u0120fuera",
- "\u0120reserved",
- "\u0120Earn",
- "\u0120helt",
- "\u0120shortcut",
- "\u0120convincing",
- "space",
- "\u0120enforce",
- "\u0120cores",
- "\u0120efter",
- "\u0120recession",
- "xico",
- "\u0120proposition",
- "arians",
- "ropol",
- "\u0120\u00eb\u00aa\u00b0\u00eb",
- "\u0120\u00ce\u013e",
- "\u0120\u00ec\u013c\u0136\u00ec\u00a6\u013a",
- "\u0120activist",
- "\u0120conviction",
- "\u0120zab",
- "\u0120canceled",
- "\u00d1\u0124\u00d0\u00be\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120\u00ce\u00ae",
- "\u00e9\u0122\u013b\u00e6\u00a8\u00a3\u00e5\u0143\u0132",
- "nite",
- "\u0120fundra",
- "buzzer",
- "\u00d0\u00b5\u00d0\u00bb\u00d0\u00be",
- "ications",
- "\u0120zona",
- "\u0120teens",
- "\u0120methodology",
- "\u0120\u00ec\u00a4\u0133\u00ec\u013c\u0136",
- "than",
- "\u0120Ul",
- "\u0120Grey",
- "\u0120hog",
- "INK",
- "\u0120Sung",
- "\u0120Claud",
- "\u0120CNN",
- "\u0120delivers",
- "alin",
- "\u0120Adobe",
- "othe",
- "\u0120Deswegen",
- "\u00e0\u00b8\u00b3",
- "\u0120werde",
- "\u0120grease",
- "\u0120upgrades",
- "\u0120Finland",
- "accept",
- "\u0120interrog",
- "bee",
- "\u0120\u00e3\u0123\u00ab",
- "\u0120prede",
- "\u0120Nep",
- "\u0120Cambridge",
- "\u0120graphs",
- "\u0120haunted",
- "\u00d1\u0123\u00d0\u00b5\u00d0\u00bc",
- "\u00e6\u00a7",
- "\u00e5\u0127\u012d",
- "Some",
- "\u0120Mall",
- "\u0120rehearsal",
- "\u0120Urban",
- "\u0120Lag",
- "\u0120nim",
- "\u00ea\u00b0\u0137",
- "\u0120positioned",
- "\u0120avoided",
- "EMA",
- "\u0120llegar",
- "\u0120r\u00c3\u00a1pido",
- "\u0120gouvern",
- "\u0120hing",
- "\u0120dealer",
- "\u0120reforms",
- "\u0120fatty",
- "\u00d0\u00ba\u00d0\u00be\u00d0\u00bb",
- "\u0120Ace",
- "\u0120nep",
- "\u0120\u00ec\u00b2\u0143",
- "\u0120computation",
- "\u0120Stream",
- "bourne",
- "tur",
- "Por",
- "\u0120sleepy",
- "\u0120banget",
- "\u00e3\u0123\u0124\u00e3\u0123\u00ae",
- "\u0120weighs",
- "\u0120bleiben",
- "\u0120Gren",
- "\u0120unions",
- "\u0120\u00ea\u00b5\u0132",
- "\u0120aprender",
- "uitar",
- "\u0120Jest",
- "uming",
- "\u0120Player",
- "\u0120Extrem",
- "\u0120integer",
- "\u00d0\u00b0\u00d1\u0129\u00d0\u00b5",
- "\u0120concerts",
- "\u00d7\u0137\u00d7\u013d",
- "\u0120troch\u00c4\u013b",
- "\u0120Repe",
- "\u00e9\u0129\u012f\u00e8\u00a6\u0123",
- "\u00e0\u00b9\u0124",
- "\u00c5\u00bcen",
- "\u0120sounding",
- "\u0120anonymous",
- "\u0120exca",
- "\u0120Iranian",
- "\u0120energetic",
- "\u0120wives",
- "\u0120\u00d1\u0128\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120ais",
- "\u00e3\u0123\u012d\u00e3\u0123\u00aa",
- "\u0120sudah",
- "\u0120underwear",
- "\u0120crunchy",
- "\u0120Pain",
- "\u0120ger\u00c3\u00a7ek",
- "redict",
- "\u0120misma",
- "\u00d1\u0138\u00d1\u0124",
- "\u0120surviving",
- "\u00ce\u0143\u00cf\u0124",
- "\u0120participant",
- "\u0120Hessen",
- "\u00c3\u00a1rias",
- "\u0120subway",
- "ist\u00c3\u00a4",
- "\u0120coral",
- "\u0120marijuana",
- "\u0120Memorial",
- "\u00d1\u012a\u00d0\u00b8\u00d0\u00b9",
- "riz",
- "\u0120satellites",
- "\u0120lease",
- "\u0120Cameron",
- "umph",
- "\u0120classmates",
- "\u00c3\u00a4h\u00c3\u00a4n",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00b5",
- "\u0120hue",
- "\u0135\u00a4\u00ec\u013f\u0126",
- "\u0120proportional",
- "\u0120noss",
- "\u0120laps",
- "r\u00c3\u00a5",
- "\u0120bitcoin",
- "\u00d0\u0139\u00d0\u00ab\u00d0\u013c\u00d0\u0132",
- "\u0120\u00ec\u00b6\u00a9",
- "\u0120\u00d9\u0126\u00d9\u0126",
- "\u0120Mort",
- "\u0120Esp",
- "arnos",
- "\u0120\u00d1\u0123\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7\u00d0\u00b0\u00d0\u00bb",
- "\u0120\u00c3\u00a4nd",
- "\u00e5\u0127\u0126",
- "\u00d7\u013b\u00d7\u013b\u00d7\u013f",
- "\u0120Geb",
- "gehen",
- "Inaudible",
- "borough",
- "\u00d1\u0126\u00d1\u0126",
- "\u0120fellowship",
- "\u0120Paper",
- "\u0120curved",
- "\u0120GEOR",
- "\u0120calculator",
- "\u0120Catal",
- "\u0120v\u00c3\u0142o",
- "\u0120bypass",
- "\u00d0\u00bb\u00d0\u00b5\u00d1\u0124",
- "\u00e0\u00b3",
- "trans",
- "rencies",
- "\u00ec\u00a1\u012e",
- "igent",
- "\u0120tasted",
- "\u0120oceans",
- "uft",
- "ervice",
- "\u0120\u00d0\u013e\u00d0\u00a3\u00d0\u0139\u00d0\u00ab\u00d0\u013c\u00d0\u0132",
- "\u0120Classic",
- "\u0120respectively",
- "~)",
- "\u00c3\u00aetre",
- "\u0120Nash",
- "\u0120zit",
- "\u0120\u00ec\u013d\u0125",
- "\u0120\u00eb\u0128\u0134",
- "quote",
- "\u0120Uns",
- "\u0120tac",
- "\u0120proves",
- "\u0120Portland",
- "bly",
- "\u0120ere",
- "\u00ec\u00b6\u0136",
- "\u0120\u00c3\u00a9poca",
- "\u0120\u00d1\u0124\u00d1\u012d\u00d1\u0123\u00d1\u0131\u00d1\u0129",
- "76",
- "\u0120hade",
- "\u0120Fro",
- "\u0120pol\u00c3\u0143tica",
- "tag",
- "\u0120\u00ed\u0137\u0143",
- "\u0120sch\u00c3\u00b6",
- "arett",
- "\u0120provisions",
- "\u0120motors",
- "\u0120imaging",
- "\u0120dok",
- "ulously",
- "\u0120meille",
- "\u00e7\u0130\u00b0\u00e5\u013e\u00a8",
- "\u00eb\u0132",
- "\u0120ISO",
- "\u0120STEM",
- "\u0120Bowl",
- "\u0120towers",
- "\u0120Ee",
- "\u0120Performance",
- "\u0120loin",
- "cussion",
- "\u0120coastal",
- "iale",
- "compass",
- "\u0120spells",
- "\u0120disappointing",
- "\u0120\u00eb\u00b2\u012a\u00ec\u00a7\u00b8",
- "EER",
- "\u0120versatile",
- "asury",
- "\u0120enfin",
- "\u0120downside",
- "\u0120guiding",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d9\u0124",
- "\u0120ninety",
- "charged",
- "\u0120Fans",
- "\u0120philosophical",
- "\u0120garn",
- "\u0120m\u00c3\u00a5nga",
- "\u0120willingness",
- "\u0120portions",
- "aben",
- "\u0120\u00ef",
- "\u00c2\u00bf",
- "raul",
- "\u0120sprint",
- "ifen",
- "\u00c4\u00b1yla",
- "\u0120\u00d0\u00ba\u00d1\u0125\u00d0\u00bf",
- "\u00e3\u0123\u0131\u00e3\u0123\u0142\u00e3\u0123\u0137\u00e3\u0123\u0126",
- "\u0120ensuite",
- "\u0120Capitol",
- "\u012063",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d1\u0122\u00d0\u00b8\u00d1\u0124",
- "\u0120appointments",
- "\u00e6\u012b\u00be",
- "omiast",
- "\u0120careg",
- "\u0120publisher",
- "\u0120heraus",
- "\u0120\u00ce\u00b5\u00ce\u00af",
- "\u0120VS",
- "\u00e3\u0123\u013f\u00e3\u0123\u0139\u00e3\u0123\u00a6",
- "\u00e4\u00b8\u0143\u00e5\u0127\u00b1",
- "\u0120sacrifices",
- "third",
- "\u0120humanitarian",
- "\u0120\u00eb\u0124\u00b4\u00ec",
- "imon",
- "\u0120inequ",
- "\u0120zob",
- "\u0120comfortably",
- "\u0120Dinge",
- "\u0120cancelled",
- "\u0120PSAKI",
- "\u0120Robinson",
- "\u0120fins",
- ")?",
- "\u0120Histor",
- "\u0120\u00d1\u0129\u00d0\u00b5\u00d0\u00bb\u00d0\u00be\u00d0\u00b2\u00d0\u00b5\u00d0\u00ba\u00d0\u00b0",
- "\u0120tbsp",
- "text",
- "kim",
- "\u0120updating",
- "\u0120geld",
- "feld",
- "\u0131\u00bc",
- "\u0120m\u00c3\u00a4",
- "\u0120caf\u00c3\u00a9",
- "\u00d6\u0122",
- "\u0120Sri",
- "\u0120Region",
- "\u0120Hahaha",
- "\u0120finances",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00b4",
- "\u0120bunk",
- "ruk",
- "haft",
- "\u0120lateral",
- "\u0120extensions",
- "\u0120\u00ec\u0137\u0126\u00ec\u013f\u00b4",
- "\u0120definite",
- "\u0120Zhao",
- "\u0120Luis",
- "sty",
- "\u0120casos",
- "\u0120Klim",
- "\u01201993",
- "\u0120realization",
- "\u0120historian",
- "\u0120cracked",
- "\u00eb\u0124\u00b4",
- "\u0120syst\u00c3\u00a8me",
- "\u0120CIA",
- "\u0120\u00d1\u0124\u00d0\u00b2\u00d0\u00be",
- "ospheric",
- "\u0120flee",
- "\u0120r\u00e1\u00ba\u00a5t",
- "\u0120Regardless",
- "\u0120reluct",
- "\u0120timely",
- "\u0120Julian",
- "GM",
- "\u00e9\u0134",
- "adura",
- "\u00e9\u00a3\u0141",
- "\u0120dresses",
- "\u00e7\u0123\u00a3",
- "\u0120\u00eb\u0136\u0136",
- "\u0120nominated",
- "\u0120advocates",
- "ymph",
- "\u0120recordings",
- "\u0120deviation",
- "\u0120prioritize",
- "\u0120spiral",
- "\u0120YOUR",
- "\u0120transpose",
- "ampoo",
- "\u0120\u00ec\u013d\u0132\u00eb\u0140\u013a",
- "\u0120Vision",
- "\u0120polite",
- "\u0120hamb",
- "\u0120Patient",
- "\u00e6\u00af\u0136\u00e8\u00bc\u0125",
- "\u00ed\u0123\u00ac\u00eb",
- "\u0120sia",
- "\u0120\u00ea\u00b3\u00b3",
- "\u0120\u00c5\u00bee",
- "\u00e8\u00a7\u0122",
- "\u0120supermarket",
- "\u00eb\u00b9",
- "\u0120Sierra",
- "\u0120grilled",
- "\u0120Upon",
- "\u0120absent",
- "\u0120mec",
- "\u0120Apollo",
- "\u0120punk",
- "\u0120Pa\u00c5\u0126st",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b9",
- "\u0120\u00ea\u00b1\u00b0\u00ea\u00b8\u00b0",
- "Girl",
- "\u0120skinny",
- "\u0120Premier",
- "\u0120territories",
- "\u0120liability",
- "\u0120jerk",
- "ratic",
- "\u0120dancers",
- "\u0120\u00d1\u0125\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120\u00ea\u00b4\u0122\u00eb",
- "only",
- "\u0120Stu",
- "\u0120skeleton",
- "\u0120\u00eb\u0143\u0132\u00eb",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00bd",
- "\u00c4\u00b1kt",
- "\u0120MIKE",
- "\u0120l\u00c3\u00b6",
- "mie",
- "\u0120reiter",
- "\u00e3\u0123\u0135\u00e3\u0124\u012e\u00e3\u0123\u00af",
- "\u0120Kolleg",
- "\u0120Adams",
- "licher",
- "\u0120\u00c3\u00a7ocuk",
- "\u00d1\u0131\u00d0\u00b3",
- "\u0120blush",
- "\u0120sunshine",
- "\u0120ez",
- "\u0120Devil",
- "\u0120\u00ea\u00b8\u00b8",
- "\u0120\u00e3\u0123\u012c",
- "add",
- "\u0120licensed",
- "\u0120vinyl",
- "\u0120Czech",
- "imag",
- "\u0120cracking",
- "\u0120\u00ec\u00ba",
- "\u0120udah",
- "\u0120sommes",
- "\u0120\u00ec\u0138\u00bc\u00ea\u00b5",
- "wa\u00c4\u0129",
- "\u0120fres",
- "\u00e5\u0133\u00bd",
- "\u0120Walmart",
- "\u0120\u00d0\u00a2\u00d0\u00b5\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d1\u012e",
- "atisf",
- "CI",
- "lang",
- "\u0120diffusion",
- "\u00e7\u0136\u00b7",
- "\u0120somos",
- "\u0120Makes",
- "\u00e6\u012a\u0133\u00e6\u0125\u00b3",
- "\u0120Ricky",
- "\u0120mucha",
- "\u00ed\u0137\u00a8",
- "\u0120horsepower",
- "asia",
- "\u0120fibers",
- "\u0120erm",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d0\u00b5",
- "\u0120jeste",
- "\u0120firefight",
- "\u0120cuisine",
- "\u0120besonders",
- "dig",
- "\u0120\u00ec\u00a2\u0127",
- "\u0120\u00d1\u0125\u00d0\u00b6",
- "\u0120tracing",
- "\u0120certains",
- "\u0120Apply",
- "\u00d1\u012d\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u00e7\u012e",
- "\u0120bru",
- "\u0120YES",
- "\u0120Bai",
- "\u0120Dit",
- "\u0120Bis",
- "\u0120unle",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d1\u0124\u00d0\u00be\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120Awak",
- "..\"",
- "\u0120125",
- "\u0120rooted",
- "\u0120cautious",
- "const",
- "\u0120orchestra",
- "\u00e7\u013e\u00bc",
- "\u0120\u00d0\u00b2\u00d0\u00bd\u00d1\u0125\u00d1\u0124",
- "\u0120quelqu",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120Method",
- "\u00ec\u00b9\u013e",
- "\u0120\u00ce\u00bc\u00ce\u00b1\u00cf\u0124",
- "l\u00c3\u00bc",
- "\u0120\u00ec\u0137\u0126\u00ea\u00b9\u012e",
- "\u0120naming",
- "Char",
- "\u0120Sicher",
- "\u0120privileged",
- "\u0120Fly",
- "\u0120\u00e3\u0123\u012d",
- "\u00e1\u00ba\u0143t",
- "\u0120advances",
- "\u0120Zelda",
- "\u0120andra",
- "\u0120grinding",
- "\u0120Edition",
- "pf",
- "\u0120warriors",
- "\u0120hedge",
- "\u0120unseren",
- "\u0120\u00d1\u0123\u00d1\u0130\u00d0\u00b4\u00d0\u00b0",
- "eliness",
- "\u0120personalities",
- "\u0120f\u00c3\u00b6",
- "'M",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120shipped",
- "\u0120meteor",
- "\u0120surroundings",
- "\u0120Fill",
- "uesta",
- "\u0120Personal",
- "\u0120Alle",
- "ORT",
- "\u00e4\u00b9\u0127",
- "\u0120Sche",
- "VI",
- "\u0120comparable",
- "damn",
- "\u0120ditch",
- "YAN",
- "ismus",
- "\u0120pickup",
- "\u0120dak",
- "\u0120EP",
- "best",
- "\u0120Sue",
- "\u00c3\u00a4llt",
- "\u0120popcorn",
- "\u0120folding",
- "home",
- "\u00d0\u00b8\u00d0\u00b2\u00d0\u00b0\u00d0\u00b5\u00d1\u0124",
- "\u00e5\u00b7\u00b2\u00e7\u00b6\u0135",
- "\u0120annot",
- "chuck",
- "\u0120fierce",
- "\u0120damaging",
- "\u0120flop",
- "\u0120pasar",
- "\u0120reef",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b5\u00d0\u00b9",
- "\u0120zoo",
- "overs",
- "jets",
- "\u0120pr\u00c3\u00a8s",
- "\u0120Silicon",
- "teok",
- "\u0120Seth",
- "atamente",
- "\u0120transmitted",
- "\u0120replicate",
- "\u0120slim",
- "\u0120Cream",
- "\u00e6\u0126\u0141\u00e3\u0123\u013a",
- "\u0120sidewalk",
- "\u00ec\u012a\u013a\u00eb",
- "\u0120\u00d0\u00b6\u00d0\u00b8\u00d0\u00b7\u00d0\u00bd\u00d1\u012e",
- "\u0120Monica",
- "\u00e4\u00be\u0128\u00e4\u00ba\u0128",
- "\u0120copied",
- "\u0120Terra",
- "istent",
- "\u00e7\u00b3\u00bb",
- "\u0120\u00d0\u00be\u00d0\u00bd\u00d0\u00be",
- "\u0120whale",
- "\u0120WITH",
- "\u00d0\u00bb\u00d1\u0125\u00d1\u012a",
- "\u00e5\u00bd\u00b1\u00e7\u012b\u0129",
- "\u0120Een",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b8",
- "\u0120ordin",
- "\u0120plural",
- "\u0120spokes",
- "\u0120dispute",
- "\u0120sensible",
- "\u0120preaching",
- "\u0120kt\u00c3\u00b3rzy",
- "pted",
- "avier",
- "\u0120pistol",
- "\u0120Tapi",
- "\u0120\u00c5\u0124",
- "ffff",
- "\u0120acrylic",
- "\u0120ignorance",
- "\u0120Ziel",
- "rans",
- "\u0120welding",
- "mid",
- "\u00e6\u012a\u0133\u00e4\u00b8\u012f",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc",
- "\u0120lanes",
- "\u0120mines",
- "\u0120moms",
- "\u00d7\u0137\u00d7\u0139",
- "\u0120Chamber",
- "tier",
- "\u0120modest",
- "\u0120\u00ec\u0139\u00ac\u00ea\u00b8\u00b0\u00ec\u0126\u013e",
- "\u0120unas",
- "\u0120wrench",
- "handed",
- "\u0120saturated",
- "\u0120Fang",
- "\u0120Commissioner",
- "\u00e0\u00a4\u00b0",
- "\u0120\u00d7\u0138",
- "\u0120Louisiana",
- "\u0120Mask",
- "\u0120cubes",
- "\u00ec\u0136\u00a8",
- "\u0120vid\u00c3\u00a9os",
- "\u0120n\u00c3\u00a5gon",
- "\u0120rider",
- "\u0120\u00ec\u00b6\u013e",
- "\u0120s\u00c3\u00b3n",
- "\u0120Latino",
- "bank",
- "\u00ed\u0137\u00b4\u00ec\u00a3\u00bc",
- "\u0120Brend",
- "\u0120sexuality",
- "...,",
- "\u0120forgetting",
- "\u0120\u00db\u012e",
- "\u0120Avengers",
- "\u0120Bonjour",
- "cessor",
- "\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0139",
- "cence",
- "\u0120geograph",
- "culo",
- "\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120sweating",
- "\u00ed\u0125\u0122",
- "\u0120symmetry",
- "ts\u00c3\u00a5",
- "\u0120jan",
- "\u0120Ferr",
- "\u00e9\u00a6\u0138",
- "\u0120ambassador",
- "zi\u00c4\u013bk",
- "\u0120musun",
- "\u0120\u00d1\u0125\u00d1\u0124",
- "\u0120LG",
- "issent",
- "commun",
- "\u0120cours",
- "\u0120develops",
- "\u0120bronze",
- "\u0120substances",
- "driven",
- "\u00ec\u00a3\u00bc\u00ec\u0126\u00b8\u00ec\u013c\u0136",
- "\u0120aos",
- "\u00e5\u0126\u0126",
- "\u0120PROFESS",
- "half",
- "\u0120sorted",
- "\u0120Bomb",
- "\u00d0\u00bb\u00d0\u00b0\u00d0\u00b3",
- "\u0120Malaysia",
- "\u0120Christina",
- "\u0120teammate",
- "\u00e8\u0123\u0140",
- "FT",
- "\u0120k\u00c4\u00b1",
- "hearted",
- "++",
- "ogenic",
- "\u0120bells",
- "\u0120Ouais",
- "\u0120specialists",
- "\u00d0\u00b1\u00d1\u012d",
- "depth",
- "lasses",
- "gies",
- "\u0120Coffee",
- "\u0120marking",
- "\u0120foll",
- "uli",
- "\u0120adhesive",
- "\u0120Bot",
- "\u0120Punkt",
- "eye",
- "\u0120Bub",
- "elong",
- "\u00e5\u012a\u00b6",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00ba",
- "\u0120donor",
- "84",
- "\u0120enfor",
- "\u0120catches",
- "\u0120bricks",
- "\u0120knitting",
- "\u0120Knowing",
- "oks",
- "HY",
- "ride",
- "\u0120Fantasy",
- "iman",
- "\u0120pse",
- "\u0120\u00ec\u013a\u00a8",
- "\u0120\u00d0\u00b2\u00d0\u00b4",
- "\u0120restra",
- "\u0120evaluated",
- "\u00d1\u0122\u00d0\u00b5\u00d0\u00b2",
- "\u0120fortunately",
- "\u0120chegar",
- "\u00d8\u00b1\u00d8\u00a8",
- "\u0120domains",
- "ibi",
- "arry",
- "\u0120shutter",
- "\u0120ficou",
- "Mike",
- "\u0120inclu",
- "\u0120donors",
- "\u0120apl",
- "\u0120Lower",
- "\u0120imported",
- "\u0120academy",
- "\u0120finals",
- "\u0120disappears",
- "\u00d9\u012c\u00d8\u00a7",
- "\u0120administrator",
- "js",
- "\u0120cutter",
- "\u0120ranging",
- "\u00c3\u00b6rper",
- "\u0120constraint",
- "\u0120Table",
- "\u0120Shan",
- "vic",
- "\u0120Fix",
- "\u0120Swift",
- "ounces",
- "\u0120Warum",
- "\u0120lettuce",
- "appelle",
- "\u0120shave",
- "\u0120b\u00c3\u00a1s",
- "\u012077",
- "\u0120Ooo",
- "ao",
- "\u0120McM",
- "\u0120Drew",
- "\u0120lump",
- "\u0120lashes",
- "scheinlich",
- "Rep",
- "inis",
- "\u0120Cette",
- "\u0120composite",
- "emetery",
- "\u0120sorte",
- "\u0120Financial",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00b5",
- "rones",
- "\u0120Voy",
- "\u0120t\u00c3\u00a9c",
- "\u0142\u00b9",
- "\u0120Ninja",
- "\u0120Corin",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d1\u0131",
- "\u00ec\u013f\u00b4\u00ec\u0139\u012a",
- "\u0120nich",
- "\u0120detective",
- "\u00e2\u0122\u00a6\"",
- "\u00cf\u0125\u00ce\u00b5",
- "\u013f\u00bc\u00eb\u0131\u0126",
- "\u0120\u00eb\u00b3\u0122",
- "\u0120\u00eb\u00b8\u0136\u00eb",
- "\u0120prope",
- "\u0120Wright",
- "\u0120\u00d7\u0136\u00d7\u00aa",
- "\u0120Shi",
- "\u0120\u00e3\u0123\u0141",
- "\u0120investigations",
- "\u00e9\u0124\u0126\u00e6\u013a\u00af",
- "\u0120PowerPoint",
- "\u0120Chu",
- "\u0120\u00ec\u013a\u00a4\u00ed",
- "\u0120\u00ec\u013b\u0126\u00ec\u0142\u0126",
- "\u0120Fragen",
- "unning",
- "\u0120pourrait",
- "\u0120textbook",
- "\u00d0\u00bc\u00d1\u012d",
- "\u0120fahren",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d1\u0122",
- "\u0120lakes",
- "\u00c3\u00bcnde",
- "Int",
- "\u0120Metro",
- "\u0120mansion",
- "\u0120\u00d0\u00b0\u00d0\u00b1",
- "\u0120Zhou",
- "\u0120corridor",
- "\u0120escol",
- "\u0120indicating",
- "ia\u00c5\u0124a",
- "\u0120mommy",
- "\u0120archives",
- "\u0120founders",
- "engine",
- "\u0120Dieu",
- "\u0120sickness",
- "\u0120\u00eb\u00b3\u00b4\u00eb\u012d\u012a\u00ea\u00b9\u012e",
- "\u0120arb",
- "\u0120ned",
- "\u0120Chop",
- "\u0120covid",
- "\u0120slam",
- "\u0120publications",
- "DC",
- "\u0120spends",
- "\u00e6\u00be",
- "\u0120refugee",
- "\u0120dile",
- "\u0120\u00d7\u0132\u00d7\u0138",
- "ificar",
- "\u0120Sach",
- "Gu",
- "\u0120reload",
- "????",
- "\u0120je\u00c5\u013dli",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u0120simplicity",
- "\u0120bullying",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00bb",
- "\u0120realidad",
- "\u0120unclear",
- "appa",
- "levant",
- "\u0120ISIS",
- "\u0120Watson",
- "\u0120dein",
- "\u0120Micro",
- "\u00ed\u0137\u013e\u00eb",
- "\u00c3\u00bcg",
- "\u0120devam",
- "\u0120tweeted",
- "\u00e5\u00b0\u0130",
- "\u0120understandable",
- "atan",
- "\u0120versa",
- "\u0120preca",
- "\u0120v\u00e1\u00bb\u0123",
- "\u0120Copy",
- "\u0120Oracle",
- "\u0120mindfulness",
- "\u0120discret",
- "ernen",
- "\u0120Ple",
- "Have",
- "\u0120isolate",
- "\u0120deu",
- "\u0120seventy",
- "\u0120Hills",
- "\u0120arcade",
- "\u0120\u00d1\u0123\u00d0\u00bf\u00d0\u00b5\u00d1\u0128\u00d0\u00b8",
- "\u0120siguiente",
- "\u0120B\u00c3\u013eNDNIS",
- "liga",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00b5\u00d1\u0129",
- "\u00c3\u00b4m",
- "\u0120tweets",
- "\u0120schauen",
- "\u0120critique",
- "\u0120\u00f0\u0141\u0130\u00b5",
- "\u0120statt",
- "\u0120\u00d1\u0123\u00d0\u00b0\u00d0\u00bc\u00d0\u00be\u00d0\u00b5",
- "\u00c3\u00a2ncia",
- "\u0120supernatural",
- "\u0120plugged",
- "Fl",
- "yn\u00c4\u00b1",
- "\u0120Tambi\u00c3\u00a9n",
- "\u0120encouragement",
- "\u0120Server",
- "\u00eb\u0124\u013e",
- "upa",
- "\u0120aston",
- "\u0120hears",
- "\u00d1\u0122\u00d0\u00b0\u00d1\u0127",
- "\u0120sche",
- "\u0120rats",
- "\u0120recuper",
- "\u0120unten",
- "\u0120Fighting",
- "\u0120academics",
- "\u00e7\u00a4\u00ba",
- "\u0120S\u00c3\u00bc",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d1\u0127",
- "\u0120paired",
- "\u0122\u00ec\u013f\u0126",
- "\u0120\u00c3\u00a1rea",
- "\u0120sweetness",
- "\u00e5\u0131\u012c",
- "\u0120defer",
- "\u0120muitas",
- "\u0120Audio",
- "\u0120locker",
- "\u00d9\u012c\u00d8\u00af",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00b2",
- "\u0120buena",
- "ANS",
- "\u0120detector",
- "avo",
- "bek",
- "\u0120\u00ce\u00b1\u00ce\u00bd",
- "\u00ed\u0130\u00b8",
- "\u0120dragged",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bb\u00d0\u00b6\u00d0\u00b5\u00d0\u00bd",
- "\u00c3\u0138",
- "\u00d8\u00b1\u00d8\u00a9",
- "\u00ec\u013f\u00b4\u00ec\u00a7\u0122",
- "\u0120celle",
- "cking",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00ac",
- "\u0120Canvas",
- "\u0120espa\u00c3\u00b1",
- "\u0120glimp",
- "\u0120spreads",
- "ongo",
- "\u0120Mason",
- "\u0120Ing",
- "\u0120\u00ea\u00b0\u0122\u00eb\u012c\u00a5",
- "\u00cf\u0126\u00ce\u00b9\u00ce\u00ba",
- "\u0120secular",
- "\u0120bater",
- "\u0120inquiry",
- "\u0120energies",
- "\u0120manufactured",
- "\u0120vegetarian",
- "\u0120pineapple",
- "\u00d1\u0131\u00d1\u0124\u00d0\u00b0",
- "\u0120practitioners",
- "2000",
- "\u0120\u00ed\u0137\u00b4\u00ec\u013c\u0136",
- "\u0120\u00ec\u0139\u00ac\u00eb\u0141\u00ac\u00eb\u00b6\u0126\u00eb\u0135\u00a4",
- "\u0120\u00eb\u00b6\u012a\u00eb",
- "\u0120Jefferson",
- "\u0120Joan",
- "\u0120tram",
- "\u00e5\u00ae\u00b9",
- "chmal",
- "\u0120Hait",
- "\u00e1\u00b9\u0129",
- "\u0120unreal",
- "\u0120symbolic",
- "\u0120stealth",
- "\u0120splash",
- "\u0120Entertainment",
- "\u0120metallic",
- "?\".",
- "\u00e8\u00b6\u012c",
- "around",
- "\u0120despair",
- "\u0120Nevada",
- "\u0120Finance",
- "\u0120krie",
- "\u0120Lux",
- "\u0120Smash",
- "keeping",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00b3",
- "\u0120narciss",
- "\u0120dzisiaj",
- "\u0120tolerate",
- "oard",
- "\u0120linking",
- "\u0120Economic",
- "\u0120\u00ec\u00bc",
- "\u0120morph",
- "\u0120Nak",
- "\u0120Baker",
- "aton",
- "rings",
- "\u0120Peng",
- "\u0120Airport",
- "\u00e3\u0123\u012d\u00e3\u0123\u00a3\u00e3\u0123\u0141",
- "\u00ed\u0137\u013a\u00eb\u012d\u00a4",
- "\u00a7\u0123",
- "prints",
- "\u0120hadi",
- "\u0120empir",
- "\u0120Lives",
- "anners",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc",
- "\u0120PROFESSOR",
- "\u0120positively",
- "antom",
- "\u0120badge",
- "kelt",
- "\u0120interfer",
- "\u0120fulfilling",
- "\u0120visualization",
- "\u00e9\u0139\u013e\u00e4\u00bf\u0124",
- "\u0120Price",
- "\u00ef\u00bf\u00bd\u00ef\u00bf\u00bd",
- "\u0120scenery",
- "\u0120prone",
- "\u0120wizard",
- "\u0120banyak",
- "verb",
- "sky",
- "\u0120wished",
- "\u0120railway",
- "\u0120\u00c3\u00bczer",
- "\u0120alguien",
- "\u0120AW",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bb\u00d0\u00b8\u00d1\u0129\u00d0\u00b5",
- "\u0120reacting",
- "\u0120Buch",
- "\u00e0\u00b8\u00b6",
- "\u0120anth",
- "\u0120sih",
- "\u0120hust",
- "\u0120Screen",
- "ilant",
- "aho",
- "\u0120fragrance",
- "\u0120elevation",
- "\u0120Mediter",
- "\u0120\u00eb\u00bf",
- "\u0120\u00c3\u00a9qu",
- "\u0120wraps",
- "\u0120inert",
- "\u0120recreate",
- "\u00d0\u00bb\u00d0\u00b0\u00d1\u0124",
- "\u0120boleh",
- "\u0120harassment",
- "unky",
- "\u0120glimpse",
- "regierung",
- "\u0120futur",
- "\u0120repository",
- "\u0120engra",
- "\u0120trafficking",
- "assis",
- "\u0120Trek",
- "\u0120\u00eb\u00b2\u012e",
- "\u0120\u00eb\u00a7\u012a\u00eb",
- "\u0120Kab",
- "aniu",
- "give",
- "\u0120dinosaurs",
- "\u0120feather",
- "\u0120attitudes",
- "\u0120plum",
- "\u0120RS",
- "\u0120Anfang",
- "illery",
- "\u0120\u00ec\u012c\u00a4",
- "MY",
- "\u0120trzeba",
- "\u0120skies",
- "\u0120Aj",
- "urable",
- "CU",
- "\u0120Shane",
- "\u0120departure",
- "\u0120TON",
- "ieten",
- "rats",
- "\u00e6\u00b0\u0139",
- "isu",
- "\u0120bord",
- "\u0120interestingly",
- "\u00e7\u013b\u00bb",
- "oughing",
- "\u0120rushing",
- "\u0120volatility",
- "\u0120pyt",
- "\u0120formats",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d1\u0124",
- "\u0120\u00ea\u00bc\u0143",
- "\u0120whatnot",
- "\u0120comport",
- "sw",
- "orean",
- "\u0120Relax",
- "\u0120clan",
- "\u0120AH",
- "\u0120pew",
- "\u0120dictionary",
- "Take",
- "shirts",
- "\u0120Hugh",
- "\u0120\u00d8\u00b9\u00d9\u0126\u00d9\u012c",
- "\u0120Pic",
- "\u0120enrolled",
- "\u0120jednak",
- "\u0120offerings",
- "\u0120coraz",
- "Life",
- "\u0120!!!",
- "\u0120cler",
- "\u0120Videos",
- "\u0120Rodrig",
- "\u0120Ident",
- "\u0120Pos",
- "\u0120Stage",
- "\u0120Race",
- "\u0120enact",
- "\u00e3\u0123\u0126\u00e3\u0123\u00be\u00e3\u0123\u0139\u00e3\u0123\u0141",
- "\u0120Gy",
- "\u0120Hispan",
- "\u0120defence",
- "\u0120Campbell",
- "matic",
- "\u0120relev",
- "\u0120peach",
- "\u0126\u00b8\u00ec\u013c\u0136",
- "\u0120paradise",
- "\u0120ceremon",
- "\u0120annoyed",
- "\u00e6\u012e\u0129",
- "lax",
- "\u0120exploit",
- "\u0120clause",
- "eker",
- "\u0120Bloom",
- "nant",
- "ateurs",
- "\u0120heights",
- "Even",
- "\u00d1\u0123\u00d0\u00be\u00d0\u00bd",
- "\u0120outrage",
- "\u0120Vietnamese",
- "\u00e3\u0123\u00af\u00e3\u0123\u00af",
- "TR",
- "\u0120eer",
- "\u0120cannon",
- "\u0120Comb",
- "\u0132\u00eb\u00a7\u012e",
- "\u00e8\u00bb\u012c",
- "\u0120\u00ea\u00b2\u0125\u00eb\u0131\u0126",
- "\u0120accomplishments",
- "\u0120Analytics",
- "\u0120shaping",
- "reiben",
- "\u0120bachelor",
- "\u0120fingert",
- "acked",
- "\u0120pyramid",
- "\u0120Stewart",
- "\u00c3\u00a1st",
- "\u0120survivor",
- "\u0120duct",
- "\u0120dealers",
- "\u00e6\u00b4\u00bb",
- "\u00d8\u00b9\u00d9\u0127",
- "\u00d0\u00bb\u00d0\u00b8\u00d0\u00bd",
- "\u0120ede",
- "\u00d7\u0137\u00d7\u00a2",
- "\u0120\u00d9\u0125\u00d8\u00a7\u00d9\u0128",
- "\u0120\u00cf\u0126\u00ce\u00b9",
- "\u0120chooses",
- "\u0120Own",
- "\u00d0\u00b3\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d0\u00b2",
- "hire",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d1\u012d\u00d0\u00b5",
- "\u0120\u00d0\u013d\u00d1\u0130",
- "\u0120\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00b2",
- "tech",
- "\u0120droit",
- "\u0120subjective",
- "enes",
- "\u0120divis",
- "avez",
- "\u0120maneuver",
- "\u00e0\u00b9\u0126\u00e0\u00b8\u0136",
- "adece",
- "\u0120Ens",
- "acial",
- "\u0120Protection",
- "\u0138\u00b4",
- "\u0120formally",
- "\u0120wyd",
- "ingu\u00c3\u00a9m",
- "\u0120ziem",
- "\u0120recruiting",
- "\u00d7\u013b\u00d7\u013c",
- "nem",
- "\u0120forbidden",
- "\u0120Bapt",
- "\u00d7\u0132\u00d7\u0142\u00d7\u013b",
- "\u0120subset",
- "\u0120Magaz",
- "nement",
- "\u0120aquela",
- "ragon",
- "\u0120committees",
- "\u0120\u00c3\u00a9taient",
- "udi",
- "\u0120Dawn",
- "\u0120bore",
- "\u0120composer",
- "\u0120wi\u00c4\u013bcej",
- "anga",
- "\u0120dislike",
- "\u0120Days",
- "\u00e5\u0141\u00ba",
- "\u0120paral",
- "\u0120mientras",
- "\u0120heavens",
- "\u00e3\u0123\u0134",
- "heid",
- "\u0120traders",
- "once",
- "\u0120mascara",
- "\u0120\u00cf\u0122\u00cf\u0123\u00ce\u00bf",
- "\u0120whisper",
- "\u0120Musk",
- "\u00e9\u013d\u0128",
- "\u0120Familie",
- "Allah",
- "\u0120Olivia",
- "\u0120Pros",
- "\u0120olika",
- "ilim",
- "\u0120r\u00c3\u00a9pond",
- "\u0120Peters",
- "\u0120\u00e5\u00be\u012a",
- "\u0120bites",
- "\u0120vic",
- "\u0120NY",
- "emption",
- "\u0120450",
- "\u0120visuals",
- "\u0120lieu",
- "\u00c3\u00bccken",
- "\u0120Steel",
- "\u0120GP",
- "wait",
- "\u0120noticeable",
- "ucha",
- "\u0120rehabil",
- "\u0120rejection",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d0\u00b5\u00d0\u00b4\u00d1\u0125\u00d1\u0130\u00d1\u012b",
- "\u0120slider",
- "\u0120regarded",
- "\u0120gravit",
- "\u0120Reserve",
- "count",
- "\u0120breeding",
- "\u0120longe",
- "aleb",
- "\u0120knight",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b9",
- "\u0120pr\u00c3\u00a9sent",
- "\u0124\u013a\u00ec\u013c\u0136",
- "\u0120Specifically",
- "\u0120poses",
- "\u0120veure",
- "okay",
- "emas",
- "\u0120\u00e3\u0123\u00a7\u00e3\u0123\u013b",
- "\u0120maj\u00c4\u0127",
- "\u0120webinars",
- "\u0120cannabis",
- "\u0120damals",
- "\u0120Northwest",
- "\u0120pada",
- "\u0120crowds",
- "\u0120futures",
- "\u0120\u00c3\u00a4n",
- "\u0120civilians",
- "\u0120Sachen",
- "\u00e6\u012f",
- "\u0120traces",
- "\u0120\u00eb\u00a8\u00b9\u00ea\u00b3\u0142",
- "QU",
- "\u00e9\u00a1\u013a\u00e3\u0123\u0126",
- "\u0120IF",
- "an\u00c4\u00b1n",
- "\u00ec\u0124\u00b4",
- "\u0120biblical",
- "\u0120Ved",
- "\u0120storing",
- "\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d0\u00bb\u00d1\u0131",
- "\u00e6\u0129\u012b\u00e8\u00a9\u00b2",
- "\u0120nast",
- "\u0120d\u00c3\u00b6",
- "\u00d1\u0122\u00d0\u00be\u00d0\u00bf",
- "elia",
- "\u0120sideways",
- "\u0120Understand",
- "\u0120Qur",
- "\u0120perpend",
- "\u0120Millionen",
- "\u0120watermelon",
- "\u0120Divine",
- "ultur",
- "abord",
- "\u0120successes",
- "\u0120hombre",
- "\u0120carp",
- "\u0120suscept",
- "ungkin",
- "\u0120kij",
- "ulus",
- "\u00d8\u00a7\u00d8\u00ac",
- "\u0120notch",
- "\u0120polynomial",
- "\u00e5\u00b9\u00b2",
- "\u00e5\u00a9",
- "\u0120\u00c3\u00banico",
- "\u0120telescope",
- "\u0120politique",
- "kiem",
- "\u0120\u00ce\u0143\u00ce\u00bd\u00ce\u00b1",
- "\u0120aggregate",
- "\u0120Geoff",
- "\u0120tril",
- "\u0120GRA",
- "\u0120subscriber",
- "imet",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bb\u00d0\u00bb\u00d0\u00b0\u00d1\u0122",
- "oping",
- "\u0120therapeut",
- "\u0120Cancer",
- "\u0120parade",
- "\u0120irrig",
- "\u00e2\u013b\u00aa\u00e2\u013b\u00aa",
- "\u0120clearer",
- "\u0120bog",
- "\u0120Maur",
- "\u00e0\u00b8\u00b2\u00e0\u00b8\u0129",
- "\u0120Shanghai",
- "achte",
- "\u0120Kol",
- "elujah",
- "\u0120hav",
- "\u0120Crime",
- "sek",
- "\u0120\u00eb\u00a1\u013e",
- "ienna",
- "\u0120Gor",
- "\u00e8\u013d",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0124\u00d1\u0122",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00b6\u00d0\u00b5\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120Lift",
- "\u0120Sort",
- "\u0120Psal",
- "\u0120ping",
- "\u0135\u013f",
- "phis",
- "\u0120FUCK",
- "\u0120Syn",
- "\u0120bamboo",
- "\u00ac\u00ec\u013a\u0123",
- "cuts",
- "\u0120mmm",
- "\u0120funktioniert",
- "\u0120_",
- "\u00c3\u0143cio",
- "Stop",
- "\u0120imaginary",
- "\u0120notamment",
- "\u0120Initiative",
- "\u00e3\u0125\u00a5",
- "\u0120Kurt",
- "\u0120loosen",
- "\u0120buscar",
- "\u00e7\u0123\u00ab",
- "\u0120zelf",
- "\u0120props",
- "\u00e5\u013d\u012b",
- "\u0120moeten",
- "\u0120milli",
- "\u0120halls",
- "\u0120Match",
- "\u0120brackets",
- "\u0120Cou",
- "\u00e6\u00a6\u0124",
- "\u0120\u00d0\u013e\u00d0\u00b0\u00d1\u0122",
- "ISA",
- "\u0120cigarette",
- "\u0120competitions",
- "\u0120MIN",
- "\u0120beh\u00c3\u00b6",
- "voor",
- "\u0120ust",
- "\u0120Zi",
- "\u0120Occ",
- "ulates",
- "\u0120balloons",
- "\u0120pronto",
- "\u0120Miy",
- "\u0120File",
- "\u0120\u00d0\u00ba\u00d0\u00bb\u00d0\u00b0\u00d1\u0123\u00d1\u0123",
- "\u00d0\u00bd\u00d1\u0125\u00d0\u00bb",
- "\u0120cereal",
- "\u0120increment",
- "\u0120refined",
- "\u00e5\u0131\u00a6\u00e5\u00a4\u0138",
- "prising",
- "\u0120RF",
- "\u0120respectful",
- "\u0120loot",
- "asket",
- "\u0120deixa",
- "ingle",
- "\u0120funciona",
- "\u0120Revel",
- "\u0120sober",
- "\u0120performs",
- "\u0120Gentle",
- "\u00e3\u0124\u00a8",
- "\u0120recipient",
- "\u0120Hause",
- "\u0120\u00eb\u0125",
- "From",
- "\u0120ministers",
- "\u0120paradox",
- "\u00e5\u00b0\u00b1\u00e6\u013a\u00af\u00e8\u00aa\u00aa",
- "\u0120tasting",
- "\u0120\u00d7\u0136\u00d7\u0139",
- "\u0120reuse",
- "\u0120Lane",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b2\u00d0\u00b5\u00d1\u0122\u00d1\u012a",
- "\u0120remembers",
- "\u0120feminist",
- "\u0120commitments",
- "\u0120projected",
- "\u0120gaz",
- "iyoruz",
- "\u0120obligations",
- "Ro",
- "zar",
- "\u0120chw",
- "\u0120JAM",
- "\u0120b\u00c4\u013bd\u00c4\u0127",
- "aspberry",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u00eb\u00b2\u0137",
- "\u0120regulated",
- "\u0120wicht",
- "\u0120Trevor",
- "\u0120secondly",
- "\u0120Ihre",
- "elsh",
- "\u0120reporters",
- "\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00b0",
- "oyo",
- "GI",
- "\u0120interconnect",
- "\u00e9\u0132\u013a",
- "OSH",
- "\u00e6\u0143\u00b2",
- "\u0120brass",
- "\u0120ignoring",
- "\u00e4\u00bb\u012c\u00e6\u0139\u00a5",
- "infect",
- "\u0120projekt",
- "oret",
- "\u00cf\u0126\u00ce\u00b1\u00ce\u00bd",
- "\u0120\u00d1\u0124\u00d0\u00b8\u00d0\u00bf",
- "\u0120mutta",
- "\u0120unboxing",
- "\u0126\u00b0",
- "\u00e5\u00a1\u012c",
- "\u0120advised",
- "\u0120Denver",
- "\u0120severely",
- "\u0120Mhm",
- "\u0120flipped",
- "\u0120pien",
- "\u0120kommun",
- "\u0120FRE",
- "\u0120\u00e0\u00ae\u0129\u00e0\u00ae\u00b0",
- "ainted",
- "\u0120knives",
- "\u0120habl",
- "\u0120geworden",
- "arettes",
- "CS",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d0\u00bb\u00d0\u00b5\u00d0\u00bd\u00d1\u012e",
- "\u0120galax",
- "\u0120ninete",
- "\u00ea\u00b1\u00b0\u00eb\u0124\u013a",
- "\u0120sis",
- "\u0120advisory",
- "\u0120drilling",
- "\u0120Wouldn",
- "\u00c3\u00bcnf",
- "gestellt",
- "\u0120Helen",
- "\u0120\u00d7\u0140\u00d7\u0132",
- "apolis",
- "\u0120rzeczy",
- "\u0120terra",
- "\u0120hep",
- "\u0120alg\u00c3\u00ban",
- "ikk",
- "\u0120astronom",
- "\u0120Starbucks",
- "k\u00c4\u0127",
- "\u0120patrol",
- "\u0120\u00ec\u00bd\u0136",
- "\u0120gon",
- "\u0120\u00e3\u0122\u0132",
- "\u0120sonst",
- "\u0120encounters",
- "\u0120retrou",
- "\u0120sharks",
- "\u0120dor",
- "\u0120Rever",
- "\u0120evapor",
- "\u0120reservoir",
- "\u0120alleged",
- "uler",
- "\u0120verm",
- "\u0120commerce",
- "\u0120fitted",
- "gem",
- "\u0120tactical",
- "\u0120lith",
- "\u00e9\u012b\u0126\u00e5\u00a1\u0136",
- "had",
- "\u00e8\u00ae\u012c",
- "\u0120carbohyd",
- "\u0120lengths",
- "\u00ce\u00b9\u00ce\u00bf",
- "\u0120demographic",
- "Rob",
- "\u0120Skin",
- "ccoli",
- "\u0120simplified",
- "\u0120readily",
- "\u0120Cum",
- "adesh",
- "\u0120D\u00c3\u00a5",
- "usst",
- "igne",
- "eton",
- "\u0120menor",
- "qi",
- "OOM",
- "\u00e0\u00b8\u0143\u00e0\u00b8\u013b",
- "\u0120psychiat",
- "\u0120eighty",
- "\u0120\u00d0\u00bc\u00d0\u00b8\u00d0\u00bb\u00d0\u00bb\u00d0\u00b8",
- "\u0120Tob",
- "edo",
- "\u00e7\u00b6\u00b2",
- "\u0120\u00c4\u0133\u00e1\u00ba\u00bfn",
- "\u0120circuits",
- "\u0120LAUGH",
- "icism",
- "emor",
- "\u0120regener",
- "egree",
- "\u0120bureauc",
- "\u0120Alber",
- "\u00e4\u00b9\u012d\u00e5\u00be\u012e",
- "\u0120Wor",
- "\u00e5\u00a4\u00ab",
- "\u0120resin",
- "\u0120by\u00c5\u0124y",
- "\u0120IG",
- "\u00e0\u00af\u012f,",
- "\u012078",
- "\u0120weeds",
- "\u0120Myth",
- "93",
- "\u00e6\u00bf",
- "\u0120\u00eb\u0124\u013a\u00ec\u013b\u0136",
- "\u00c3\u00a9v",
- "\u00e1\u00bd",
- "\u00c3\u00b6ren",
- "\u00c3\u00a7ar",
- "\u0120PAUL",
- "\u0120disadvant",
- "\u0120positioning",
- "\u0120cocktail",
- "\u0120agrees",
- "nn",
- "\u0120Sally",
- "Ms",
- "\u0120inherent",
- "\u0120monetary",
- "\u0120natur",
- "\u0120Nh",
- "\u0120Import",
- "\u0120leben",
- "\u0120wi",
- "ussy",
- "\u0120obes",
- "\u0120wandering",
- "\u0120\u00ec\u012d\u0142\u00eb",
- "\u00c4\u0127da",
- "etchup",
- "\u0120disposal",
- "\u0120JA",
- "\u0120Cer",
- "zilla",
- "\u0120virgin",
- "\u0120Slide",
- "andel",
- "\u0120righteousness",
- "\u0120\u00ce\u00a3",
- "\u0120ideia",
- "\u00e4\u00bd\u0142\u00e5\u00a5\u00bd",
- "\u00d0\u00b8\u00d1\u0122\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u00d7\u00a8\u00d7\u0132",
- "Comment",
- "\u0120prelim",
- "\u0120Vale",
- "\u0120\u00ec\u00a7\u0122\u00eb\u0124\u013e",
- "\u0120Vanc",
- "OMAN",
- "\u0120\u00d0\u00bf\u00d1\u0138\u00d0\u00b4",
- "\u0120yum",
- "stre",
- "cem",
- "\u0120pocz",
- "\u0120fragment",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d1\u0125\u00d1\u0129\u00d0\u00b0\u00d0\u00b5",
- "\u0120undergo",
- "\u0120Hank",
- "ceks",
- "\u0120FPS",
- "\u0120ocur",
- "\u0120deterior",
- "\u00e6\u00b3\u00a8",
- "\u0120empresas",
- "Paul",
- "\u0120)))",
- "\u0120\u00d0\u00b2\u00d1\u0122\u00d0\u00b5\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8",
- "\u0120scold",
- "\u00d7\u013b\u00d7\u00a2",
- "\u0120suspected",
- "\u0120accessing",
- "\u0120substit",
- "\u0120historians",
- "\u00e4\u00bb\u00bb",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bb\u00d0\u00be",
- "\u0120socied",
- "rone",
- "\u0120reden",
- "\u0120extends",
- "epherd",
- "\u0120balcon",
- "\u00e4\u00b8\u012f\u00e8\u00b5\u00b7",
- "\u0120Solo",
- "\u0120politician",
- "\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120irgendw",
- "\u0120traumatic",
- "\u0120rapper",
- "\u0120ROBERT",
- "Really",
- "\u00e6\u0123\u00af",
- "\u0120lineup",
- "ASE",
- "\u0120contractor",
- "\u0120Corporation",
- "gor",
- "\u0120Todo",
- "\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be\u00d0\u00b9",
- "FBE",
- "\u0120newsletter",
- "\u0120ko\u00c5\u0126",
- "alties",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d1\u0129",
- "\u0120Heavy",
- "\u0120swords",
- "\u0120manipulation",
- "\u0120funk",
- "\u0120v\u00c3\u00a5r",
- "\u0120Taliban",
- "\u0120\u00eb\u00b0\u00a5",
- "\u0120acne",
- "\u00c3\u00bcr\u00c3\u00bc",
- "\u0120deswegen",
- "\u0120Dust",
- "\u0120silic",
- "\u0120hooks",
- "\u0120blij",
- "\u0120petits",
- "\u0120filme",
- "\u0120Bereich",
- "\u0120Said",
- "\u0120imposed",
- "\u0120diary",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d1\u0122",
- "\u0120Gates",
- "\u0120alta",
- "\u00e5\u00b8\u012e",
- "\u0120chcia",
- "pleasant",
- "\u0120\u00eb\u00b0\u013f",
- "\u0120mo\u00c5\u00bcemy",
- "\u0120Austria",
- "\u0120broker",
- "\u0120sucked",
- "\u00e8\u0122\u0125",
- "\u0120compartment",
- "\u0120clone",
- "\u0120\u00d7\u0136\u00d7\u00a2",
- "\u0120Danke",
- "\u0120nochmal",
- "\u00d0\u00b5\u00d0\u00b7\u00d0\u00b4",
- "\u0120adrenal",
- "\u0120kleinen",
- "\u00e3\u0123\u00be\u00e3\u0123\u0139\u00e3\u0124\u0129\u00e3\u0123\u0128",
- "\u0120subsequently",
- "\u0120decentral",
- "\u0120genetics",
- "\u0120\u00ea\u00b4\u0133",
- "\u0120monitors",
- "\u0120Applic",
- "\u0120Reporter",
- "wert",
- "\u0120wiem",
- "\u0120Movement",
- "\u0120interviewing",
- "\u0120hairs",
- "\u0120pu\u00c3\u00b2",
- "\u0120Chelsea",
- "\u0120coher",
- "\u0120cot",
- "\u0120zas",
- "\u0120patches",
- "\u0120lah",
- "\u00d1\u0125\u00d0\u00bd\u00d0\u00ba",
- "\u0120Reagan",
- "\u0120Marco",
- "city",
- "\u0120defender",
- "\u0120decoration",
- "iji",
- "\u0120litter",
- "\u00d0\u00a8",
- "\u0120jego",
- "REW",
- "\u0120Pik",
- "\u0120Hee",
- "\u0120Iv",
- "\u0120\u00d0\u00b8\u00d0\u00b4\u00d0\u00b5",
- "\u0120Theater",
- "\u0120\u00d1\u0129\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u0120sweater",
- "\u0120highlighting",
- "\u0120ainsi",
- "\u0120diplomatic",
- "\u0120Nevertheless",
- "\u00e5\u00b3",
- "ASON",
- "\u0120p\u00c3\u00bablico",
- "\u0120ferm",
- "reated",
- "cod",
- "\u0120\u00eb\u00ac\u00bc\u00eb",
- "\u0120mister",
- "\u0120Vancouver",
- "\u0120recognizes",
- "ecd",
- "\u0120complications",
- "encial",
- "\u00e3\u0123\u0139\u00e3\u0123\u0131",
- "\u0120\u00ea\u00b0\u0122\u00ec\u00a7\u0122",
- "\u0120Ultimate",
- "\u0120vaig",
- "\u0120Merry",
- "\u00d7\u0137\u00d7\u0134",
- "\u0120Marcus",
- "\u00e7\u00b8\u00bd",
- "owego",
- "\u0120mente",
- "Sm",
- "\u0120aja",
- "\u0120Tao",
- "\u0120judicial",
- "\u0120entrepreneurship",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00bc\u00d0\u00bd\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "\u0120pis",
- "\u0120erg",
- "\u0120christ",
- "\u0120Curt",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d0\u00bf",
- "\u00ce\u00bb\u00ce\u00b5",
- "ensch",
- "\u00c3\u0143re",
- "\u0120focal",
- "\u0120Diamond",
- "av\u00c3\u0143a",
- "\u0120hanno",
- "\u0120Squad",
- "\u0120associations",
- "\u0120Creative",
- "\u0120messenger",
- "\u0120begging",
- "\u0120decimal",
- "\u0120d\u00c4\u00b1\u00c5\u0141",
- "\u0120metadata",
- "sels",
- "\u0120\u00c4\u00b0\u00c5\u0141",
- "\u00e1\u00bb\u00afa",
- "\u0120difficile",
- "d\u00c4\u00b1",
- "\u0120slaughter",
- "\u0120Verg",
- "\u0120\u00d7\u0134\u00d7\u013f",
- "\u00e7\u00b0\u00a1",
- "\u00e6\u012e\u012b",
- "\u0120Tea",
- "asses",
- "Ok",
- "\u0120synthes",
- "otiation",
- "\u0120painter",
- "\u0120elbows",
- "\u0120architectural",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b4",
- "\u0120glor",
- "image",
- "ampa",
- "culiar",
- "\u0142\u00a8",
- "\u0120teve",
- "\u0120Stelle",
- "\u0120Bam",
- "\u0120\u00ec\u00b4\u012a",
- "asis",
- "ipedia",
- "\u0120GI",
- "\u0120Active",
- "\u00e7\u0126\u00b6\u00e5\u0132\u0130",
- "azi",
- "\u00e3\u0124\u012e\u00e3\u0123\u00a6",
- "\u0120Lucky",
- "\u00ed\u0137\u00a9",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u0120runway",
- "\u0120authentication",
- "\u0120posible",
- "\u0120supplements",
- "\u0120surgical",
- "Gen",
- "\u0120feasible",
- "DO",
- "\u0120outlook",
- "\u0120intervals",
- "\u0120anecd",
- "\u00c3\u0142ng",
- "\u0120straps",
- "\u0120Shu",
- "udd",
- "issenschaft",
- "\u0120porte",
- "\u0120committing",
- "\u0120alley",
- "\u0120covenant",
- "\u0120Pedro",
- "lessness",
- "\u0120Solid",
- "\u0120Molly",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00ba\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d1\u0122",
- "\u0120cooperate",
- "\u00e5\u012e\u0139",
- "ollen",
- "\u0120tuna",
- "\u0120kindergarten",
- "\u0120Siz",
- "\u0120du\u00c5\u00bco",
- "\u0120MBA",
- "\u0120GEORGE",
- "\u0120Fisher",
- "\u00e5\u00bf\u013a",
- "\u0120Caesar",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d0\u00b8\u00d0\u00b2",
- "\u0120Delhi",
- "zym",
- "\u0120explicar",
- "\u00ea\u00b0\u0122\u00ec\u00a7\u0122",
- "uns",
- "grow",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d1\u0123",
- "\u012086",
- "\u0120stating",
- "\u0120massa",
- "chter",
- "\u0120\u00ec\u00bb\u00ac\u00eb\u0141\u00ac",
- "\u0120deputy",
- "SM",
- "noc",
- "\u0120geography",
- "\u0120Enterprise",
- "\u0120Cant",
- "\u00c3\u00b6z",
- "\u0120unpack",
- "\u0120\u00ed\u013b\u0136\u00eb",
- "\u0120searches",
- "\u0120presidency",
- "\u0120trivial",
- "\u0120pige",
- "oubt",
- "\u00e3\u0124\u013c",
- "\u00ec\u00bc\u0122\u00ec\u013f\u00b4",
- "\u0120budgets",
- "\u0120ub",
- "\u0120pne",
- "\u0120Yale",
- "\u0120\u00c5\u0141\u00c3\u00b6yle",
- "regular",
- "\u0120imperfect",
- "ARA",
- "\u0120fam\u00c3\u0143lia",
- "urm",
- "\u0120Adventure",
- "\u00e3\u0125\u012c",
- "cis",
- "emark",
- "\u0120nego",
- "\u0120inappropriate",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00b7",
- "\u0120\u00d1\u0122\u00d0\u00be\u00d0\u00bb",
- "\u0120dreamed",
- "Bry",
- "\u0120shuttle",
- "\u0120pillars",
- "\u0120bik",
- "inum",
- "\u0120\u00d1\u0125\u00d1\u0123",
- "\u0120Nebr",
- "\u0120perpendicular",
- "\u0120booked",
- "bery",
- "\u0120vikt",
- "bear",
- "esus",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b7\u00d0\u00bc\u00d0\u00be\u00d0\u00b6\u00d0\u00bd\u00d0\u00be",
- "\u00a8\u00b9",
- "\u0120presumably",
- "\u0120Memphis",
- "\u0120ambulance",
- "\u00d7\u0137\u00d7\u0140\u00d7\u00a8",
- "\u0120thumbnail",
- "\u0120modification",
- "\u00e9\u0129\u0131",
- "\u0120interpreted",
- "\u0120promo",
- "\u0120\u00ce\u00ba\u00ce\u00ac",
- "\u0120\u00ce\u00b5\u00cf\u0122",
- "\u0120acoustic",
- "\u0120DB",
- "\u00e5\u0135\u0130",
- "\u0120nonetheless",
- "oule",
- "\u0120pequ",
- "\u0120knob",
- "\u00e3\u0124\u00a3",
- "\u0120\u00eb\u0131\u012e\u00ec\u0137\u0126",
- "\u0120purchases",
- "\u0120\u00c3\u0129\u00c3\u00bcnk\u00c3\u00bc",
- "\u0120dividing",
- "perform",
- "raction",
- "healthy",
- "\u0120Title",
- "\u0120uk",
- "\u0120cerca",
- "\u0120arguably",
- "\u0120fale",
- "\u00eb\u00b3\u00b5",
- "\u0120gamers",
- "\u0120utilizing",
- "\u0120offended",
- "\u0120tava",
- "al\u00c4\u00b1",
- "\u0120median",
- "\u0120infectious",
- "\u0120Annie",
- "\u0120smartphones",
- "\u0120parole",
- "\u00e5\u0138\u013f",
- "\u0120Epic",
- "zza",
- "\u0120unified",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0137\u012e",
- "\u0120curtain",
- "\u0120\u00c4\u0125",
- "\u0120sexually",
- "\u0120unserem",
- "\u0120Convention",
- "\u0120allegedly",
- "Ya",
- "\u0120Hoo",
- "enment",
- "\u00e6\u0122\u00aa",
- "\u00ed\u013d\u0126",
- "\u0120gigantic",
- "\u0120noting",
- "\u0120rebo",
- "\u0120Jama",
- "\u0120Alz",
- "\u0120borrowed",
- "\u00ec\u00b9\u00a8",
- "\u0120peripher",
- "\u00d0\u00be\u00d1\u0124\u00d0\u00b0",
- "\u0120GB",
- "\u0120Gear",
- "\u0120economically",
- "\u0120telefon",
- "\u0120queremos",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d1\u012a\u00d0\u00b5",
- "\u0120ras",
- "\u0120Teach",
- "icios",
- "atos",
- "\u0120pledge",
- "bau",
- "\u0120Himself",
- "Link",
- "\u0120espero",
- "\u0120chromos",
- "\u0120PER",
- "\u0120erle",
- "\u0120podium",
- "\u00c3\u00a7os",
- "\u0120nieu",
- "\u0120fen",
- "\u0120GOD",
- "\u0120Chocolate",
- "werk",
- "\u0120t\u00e1\u00bb\u00ab",
- "\u0120suppress",
- "\u00ce\u00bb\u00ce\u00b7",
- "\u0120240",
- "\u0120sit\u00c3\u00a4",
- "\u0120honesty",
- "\u0120Bio",
- "\u0120Bard",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012b\u00d0\u00b5\u00d0\u00bc",
- "\u0120\u00d0\u00bc\u00d1\u0125\u00d0\u00b7",
- "\u0120marble",
- "\u0120\u00d1\u0128\u00d0\u00b5\u00d0\u00bd\u00d1\u0124",
- "\u0120procure",
- "\u0120rotor",
- "bern",
- "\u0120tuh",
- "\u0120headset",
- "atem",
- "\u0120warranty",
- "\u00e0\u00ae\u00b4",
- "\u0120filing",
- "\u00ce\u00b9\u00ce\u00ac",
- "\u0120comprendre",
- "\u0120impulse",
- "\u0120salv",
- "written",
- "\u0120institute",
- "Kim",
- "\u0120LGBTQ",
- "ficiente",
- "His",
- "\u0120\u00ce\u00b1\u00cf\u0127\u00cf\u0126\u00cf\u012e",
- "\u0120teenage",
- "orus",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d0\u00b1",
- "See",
- "\u0120Conserv",
- "\u00e1\u00bb\u0123n",
- "fulness",
- "\u0120strawberries",
- "\u0120Abu",
- "\u00d0\u00b8\u00d0\u00be\u00d0\u00bd",
- "\u0120olla",
- "NOISE",
- "\u0120Employ",
- "\u0120wiped",
- "urger",
- "\u0120modifications",
- "\u0120\u00ed\u0137\u013a\u00ec\u00a7\u0122",
- "\u0120footsteps",
- "\u0120honors",
- "\u0120adul",
- "\u0120flipping",
- "\u0120HU",
- "ZY",
- "\u0120integrating",
- "\u00d8\u00a8\u00d8\u00b1",
- "ulla",
- "\u0120natuurlijk",
- "\u0120\u00ed\u0139\u012a",
- "\u0120Ethereum",
- "\u00d9\u012c\u00d9\u0126",
- "wed",
- "\u0120peaks",
- "\u0120Kes",
- "\u0120bloom",
- "\u0120crashing",
- "\u0120911",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bb\u00d0\u00b8\u00d1\u0129",
- "\u0120controllers",
- "\u0120Dod",
- "\u0120\u00d0\u00b2\u00d0\u00bc\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b5",
- "\u0120sortir",
- "\u00e5\u00a5\u0129",
- "\u0120Straight",
- "\u0120Gracias",
- "\u0120groove",
- "\u0120togg",
- "\u0120\u00ec\u012d\u00b6\u00ec\u013f\u0122",
- "\u00c3\u00a9ro",
- "\u0120outward",
- "\u0120WA",
- "\u0120Rocky",
- "\u0120scam",
- "\u0120hayat",
- "ignty",
- "\u00e2\u0126",
- "plings",
- "\u0120antibiotics",
- "\u0120\u00e4\u00b8\u0122",
- "\u0120nevertheless",
- "jang",
- "commerce",
- "\u0120spoiler",
- "\u0120glove",
- "\u0120chatter",
- "\u0120BY",
- "~?",
- "\u0120\u00ed\u013a\u00b8",
- "\u0120demol",
- "wechsel",
- "imir",
- "\u0120raid",
- "\u00d0\u00b5\u00d1\u0122\u00d1\u0127",
- "\u00ec\u0140\u0132\u00ea\u00b8\u00b0",
- "enf",
- "\u0120commented",
- "\u0120optimized",
- "\u0120convicted",
- "\u0120bats",
- "\u0120SB",
- "\u0120Aur",
- "\u0120Tong",
- "\u0120implicit",
- "\u0120Janet",
- "\u0120reag",
- "\u00e3\u0123\u00b2",
- "\u0120Advanced",
- "\u0120impose",
- "\u00d7\u00a9\u00d7\u0136",
- "\u0120schemes",
- "ougher",
- "abolic",
- "\u0120\u00ea\u00b1\u00b0\u00ec\u00a3\u0142",
- "\u0120slowing",
- "\u0120wtedy",
- "\u0120destructive",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00b4",
- "\u0120landmark",
- "\u0120\u00eb\u0131\u012a",
- "\u0120Walking",
- "\u00e1\u00ba\u00b9",
- "\u0120tijd",
- "\u0120KN",
- "\u0120Quant",
- "\u00ec\u013a\u00a4\u00eb",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d1\u0125",
- "\u0120perder",
- "\u0120nove",
- "\u00c3\u00a4nde",
- "\u0120\u00e3\u0123\u0139",
- "bia",
- "\u0120custody",
- "\u0120biod",
- "\u00e6\u013f\u00b1\u00e8\u00a5\u00bf",
- "\u0120directing",
- "...\u00e2\u0122\u012d",
- "\u0120reloc",
- "\u0120demande",
- "\u00e3\u0124\u0135\u00e3\u0123\u0142",
- "\u0120o\u00c4\u0141lum",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d0\u00b0",
- "\u0120Milk",
- "\u00e5\u0131\u00b7",
- "\u0120Kra",
- "\u0120Honda",
- "\u0120pue",
- "\u0120elekt",
- "\u0120beginners",
- "\u0120spear",
- "\u00c3\u0143nh",
- "\u0120Luft",
- "\u0120nig",
- "\u0120Schools",
- "\u0120forums",
- "\u0120Qin",
- "ppo",
- "\u0120zag",
- "\u0120\u00d0\u00ae",
- "\u0120toothp",
- "\u0120Style",
- "\u00ec\u00b4\u012a",
- "\u0120punct",
- "\u0120reps",
- "\u0120Aly",
- "\u0120amendments",
- "\u0120\u00c3\u00b6z",
- "\u0120digits",
- "urai",
- "\u0120chaotic",
- "\u0120Masters",
- "eon",
- "\u0120Cash",
- "\u0120Cuz",
- "\u0120bedeutet",
- "\u0120scanning",
- "\u0120\u00d0\u00b6\u00d0\u00b4",
- "\u00d0\u00bd\u00d0\u00b5\u00d1\u0124",
- "\u0120certainty",
- "jek",
- "\u0120dijo",
- "\u0120Climate",
- "\u0120rinse",
- "\u0120krij",
- "veland",
- "\u0120soundtrack",
- "\u0120Safe",
- "\u0120Nova",
- "94",
- "\u0120athe",
- "\u0120Verb",
- "oler",
- "\u00ec\u013f\u00b4\u00ec\u00a3\u0142",
- "\u0120vin",
- "\u0120respiratory",
- "\u0120Study",
- "\u0120CAM",
- "\u0120avocado",
- "\u0120Zhen",
- "\u0120latency",
- "\u0120feathers",
- "\u0120contar",
- "\u0120\u00d0\u00b2\u00d0\u00b5\u00d1\u012b",
- "\u0120fark",
- "\u0120blended",
- "\u0120exploded",
- "\u0120XX",
- "\u0120Benim",
- "\u0120algu\u00c3\u00a9m",
- "istoire",
- "\u0120confidential",
- "\u0120mast",
- "\u0120\u00ec\u00bf",
- "geh",
- "\u0120disrespect",
- "\u0120Systems",
- "\u00c6\u00b0a",
- "Ed",
- "\u0120wys",
- "\u0120exotic",
- "\u0120glowing",
- "\u00c3\u00b9ng",
- "ounge",
- "\u00e8\u0126",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b7",
- "\u0120palav",
- "\u0120Sword",
- "\u0120gim",
- "\u0120Crow",
- "\u0120potent",
- "bish",
- "\u0120abused",
- "\u0120Jed",
- "\u0120gambling",
- "\u0120Spect",
- "\u0120investigators",
- "\u00e6\u013b\u013c",
- "\u0120ratt",
- "\u0120dob",
- "\u0120DES",
- "hog",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00ba\u00d1\u0122\u00d1\u012d",
- "\u00ed\u012e\u0127",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bd\u00d1\u012e\u00d0\u00b3\u00d0\u00b8",
- "\u0120\u00ed\u013a\u00b9",
- "\u0120\u00eb\u00a8\u00b8\u00eb\u00a6\u00ac",
- "\u0120saturation",
- "\u0120inherited",
- "\u0120Innovation",
- "\u00ec\u0139\u012a\u00eb\u012f\u013a",
- "\u0120tangible",
- "\u0120depri",
- "hed",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bc\u00d0\u00be\u00d0\u00b3",
- "\u0120sliced",
- "\u00e0\u00a5\u012f",
- "\u0120th\u00e1\u00ba\u00bf",
- "\u00c5\u00a5",
- "68",
- "\u0120corona",
- "\u0120gifted",
- "\u0120soir",
- "\u0120humility",
- "\u0120\u00ec\u013f\u00b4\u00ea\u00b1\u00b8",
- "\u0120flaws",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00ba\u00d1\u0124\u00d0\u00b8",
- "\u0120kald",
- "wa\u00c5\u00bc",
- "yw",
- "\u00e3\u0124\u0135\u00e3\u0123\u00a7\u00e3\u0123\u013b",
- "irteen",
- "\u0120crochets",
- "\u00a6\u00ac\u00ea\u00b0\u0122",
- "\u0120\u00ec\u0142\u0126\u00ec\u0139\u0132",
- "\u0120dese",
- "\u00e6\u00a5\u0143",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d0\u00b3",
- "\u0120dzia\u00c5\u0124",
- "\u0120l\u00c3\u00a9g",
- "changing",
- "\u0120llev",
- "\u00c5\u0126sk",
- "\u00e7\u0136\u00bb",
- "\u01201984",
- "orns",
- "\u0120Welsh",
- "\u0120pharmaceutical",
- "\u0120pumping",
- "\u0120Shaw",
- "punk",
- "\u0120vault",
- "\u0120kinetic",
- "\u0120hurricane",
- "\u0120Including",
- "\u00e1\u00bb\u00a9c",
- "\u0120Grandpa",
- "anship",
- "\u00e9\u00a6\u013b\u00e6\u00b8\u00af",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00b6",
- "\u013e\u0142",
- "utta",
- "\u0120\u00ea\u00b2\u0123\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120baz",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u012a",
- "\u0120peculiar",
- "zy\u00c4\u0129",
- "\u0120Ellie",
- "\u0120learns",
- "\u0120Krishna",
- "\u0120consecut",
- "\u0120empath",
- "\u0120Din",
- "\u0120traded",
- "\u0120Boris",
- "uggage",
- "olla",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b7\u00d0\u00b2",
- "\u0120eternity",
- "\u0120\u00d0\u00b2\u00d0\u00bf",
- "\u00c3\u00a8mes",
- "\u0120grapp",
- "b\u00c3\u00a9",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00b4\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00b2",
- "\u0120FC",
- "\u012f\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "even",
- "\u0120Nebraska",
- "ortune",
- "\u0120karena",
- "\u0120Agent",
- "\u0120sting",
- "\u0120PI",
- "\u0120municipal",
- "powered",
- "\u0120consegue",
- "\u0120Manchester",
- "\u0120rainy",
- "\u0120bli",
- "\u0120kost",
- "\u0120halten",
- "\u0120Ahhh",
- "insula",
- "erting",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d9\u0123",
- "\u0120relacion",
- "\u0120komen",
- "\u0120dome",
- "\u0120priests",
- "\u0120Introdu",
- "rophe",
- "shore",
- "velt",
- "clipse",
- "\u0120\u00d1\u0122\u00d1\u0125\u00d1\u0123",
- "\u00d7\u013b\u00d7\u00a1",
- "\u0120sabemos",
- "\u0120Holland",
- "ogi",
- "anki",
- "\u0120Mats",
- "\u0120smoked",
- "ullie",
- "\u0120europe",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00b9\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00b8\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120bardziej",
- "\u0120transforming",
- "\u0120Ez",
- "opath",
- "\u0120\u00ec\u0138\u00b8\u00eb\u012d\u012a",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00bd",
- "\u00e1\u00ba\u00b1ng",
- "\u00e0\u00b8\u00b1\u00e0\u00b9\u012b",
- "\u0120Ouch",
- "\u0120clearance",
- "ustain",
- "\u0120solidarity",
- "\u0120proving",
- "\u0120\u00d0\u013a\u00d0\u00bd",
- "\u0120\u00d1\u0123\u00d1\u012c",
- "\u0120prolong",
- "\u00d0\u00b0\u00d0\u00b4\u00d0\u00bd\u00d0\u00be",
- "\u0120sos",
- "\u0120Deal",
- "\u0120170",
- "mons",
- "\u0120\u00d0\u00b7\u00d0\u00b5\u00d0\u00bc",
- "\u0120logged",
- "\u0120lifelong",
- "\u0120sensory",
- "\u0120behold",
- "\u0120FAR",
- "\u00c3\u00a8tement",
- "\u0120Federation",
- "\u0120dodge",
- "\u0120Shir",
- "\u0120dragons",
- "\u0120Arctic",
- "\u00c4\u0127\u00c5\u00bc",
- "\u00c5\u012f",
- "\u00c2\u00ba",
- "\u0120denke",
- "\u0120podr\u00c3\u0143a",
- "cole",
- "\u00d1\u0125\u00d0\u00bb\u00d1\u012e\u00d1\u0124\u00d0\u00b0\u00d1\u0124",
- "\u0120systematic",
- "\u00d0\u00b0\u00d0\u00bc\u00d0\u00b0",
- "chos",
- "\u0120clinics",
- "\u0120BS",
- "\u0120tales",
- "usions",
- "\u0120\u00ed\u012a\u00ac",
- "\u0120preservation",
- "\u0120lore",
- "\u0120Protest",
- "\u00e1\u00bb\u013d",
- "\u00e5\u00b8\u0124",
- "\u0120acknowledged",
- "\u0120Isaiah",
- "\u0120\u00eb\u0137\u012e\u00eb\u012c\u0136",
- "\u0120\u00d7\u013a",
- "\u0120competitor",
- "\u0120advancing",
- "zip",
- "\u0120tenth",
- "\u0120Laure",
- "\u0120hints",
- "\u0120exercising",
- "\u0140\u013e\u00eb",
- "\u0120Intelligence",
- "uated",
- "OUT",
- "oped",
- "\u0120autonomy",
- "\u0120branding",
- "\u0120Mediterranean",
- "\u00d1\u0138\u00d0\u00ba",
- "\u0120screwdriver",
- "\u0120supre",
- "\u0120stap",
- "\u0120jurisdiction",
- "\u0120Settings",
- "\u0120forefront",
- "\u0120Female",
- "comfort",
- "\u0120multiplication",
- "\u0120Murray",
- "\u0120bob",
- "\u0120Tas",
- "\u0120tahu",
- "\u0120onun",
- "etter",
- "\u0120prophets",
- "lag",
- "\u0120revenues",
- "\u0120pr\u00c3\u00a1",
- "\u0120uploading",
- "\u0120machinery",
- "ascal",
- "\u0120Est\u00c3\u00a1",
- "\u0120Goth",
- "\u0120Bald",
- "\u0120Saw",
- "\u0120stripes",
- "\u00ec\u0142\u0133",
- "\u0120powin",
- "\u00e6\u0139\u00a5\u00e6\u013e\u00ac",
- "\u0120hostile",
- "\u0120darum",
- "\u0120prevented",
- "\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb\u00d1\u0125\u00d0\u00b9\u00d1\u0123\u00d1\u0124\u00d0\u00b0",
- "\u0120algunas",
- "\u0120hopeless",
- "\u0120znaj",
- "\u0120readings",
- "\u0120craving",
- "tat",
- "\u0120Pig",
- "\u0120liar",
- "\u00e7\u012a\u00b1",
- "\u0120multiplayer",
- "\u0120dale",
- "\u0120Course",
- "\u00ed\u0123\u00bc",
- "\u0120Kita",
- "\u0120customs",
- "\u0120responds",
- "endra",
- "\u00e8\u00a6\u0138",
- "\u0120metro",
- "\u00d1\u0123\u00d0\u00be\u00d0\u00bb",
- "\u0120mitigate",
- "\u0120oppression",
- "\u0120\u00e6\u012a\u0133\u00e5\u0122\u0133",
- "quinho",
- "\u0120ammo",
- "\u0120enfer",
- "\u0120pony",
- "\u0120ounces",
- "\u00b0\u0136",
- "\u0120\u00ec\u012a\u013a\u00ea\u00b0\u0122",
- "\u0120dicho",
- "\u0120Deb",
- "\u0120wonders",
- "\u0120Roose",
- "\u0120prizes",
- "\u0120ALEX",
- "\u0120thankfully",
- "\u0120tissues",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d0\u00bd\u00d0\u00be",
- "\u0120Luna",
- "intelligible",
- "\u0120\u00ec\u013b\u00b8",
- "\u00ea\u00b0\u0133",
- "\u0120Heat",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00b4",
- "\u0120Qui",
- "\u0120ions",
- "\u0120accommodation",
- "\u00e4\u00be\u00bf",
- "\u0120Kart",
- "ienst",
- "\u0120tarde",
- "\u0120soaked",
- "\u0120Casey",
- "\u0120\u00ec\u00b4\u013f",
- "\u0120\u00d1\u0122\u00d1\u0125\u00d0\u00b1",
- "\u0120differenti",
- "\u0120leftover",
- "\u0120exchanges",
- "second",
- "\u0120firstly",
- "\u0120builder",
- "rien",
- "\u0120dw",
- "\u0120bouncing",
- "?<",
- "olog\u00c3\u0143a",
- "wealth",
- "\u0120meditate",
- "\u0135\u00a4\u00ec\u013f\u013a",
- "\u0120Craft",
- "\u00e8\u00a7\u012b\u00e5\u00be\u0139",
- "\u00e6\u013b\u00ae",
- "riv",
- "\u0120Against",
- "\u0120ceramic",
- "esp\u00c3\u00a8re",
- "\u0120competent",
- "\u0120Hopkins",
- "\u0120kilos",
- "\u0120gravel",
- "\u0120piston",
- "\u0120friendships",
- "\u0120escre",
- "\u0120voz",
- "\u0120Gesellschaft",
- "\u0120unterst\u00c3\u00bct",
- "\u0120muj",
- "\u0120warnings",
- "pos",
- "\u0120Professional",
- "wszy",
- "odle",
- "bands",
- "\u0120teamwork",
- "stellung",
- "\u0120dx",
- "\u00e5\u012f\u012c",
- "\u0120attorneys",
- "\u0120weitere",
- "\u00e3\u0127\u012d\u00e3\u0127\u012d\u00e3\u0127\u012d",
- "\u0120Original",
- "\u00d7\u013b\u00d7\u0139",
- "\u0120broadcasting",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b2\u00d1\u012d\u00d0\u00b9",
- "uchi",
- "\u0120heure",
- "\u0120grabs",
- "\u0120WOR",
- "\u0120Plaid",
- "Min",
- "\u0120paz",
- "\u0120Puis",
- "umu",
- "itates",
- "\u0120coats",
- "\u0120buen",
- "\u0120heir",
- "\u0120pneum",
- "\u00d7\u00a9\u00d7\u00a8",
- "enser",
- "\u0120JUDGE",
- "\u0120blonde",
- "\u00e1\u00b9\u013d",
- "\u0120gak",
- "\u0120s\u00c4\u00b1k",
- "\u0120quoted",
- "\u0120equipo",
- "\u0120wishing",
- "\u00c3\u0143cia",
- "\u0120verbs",
- "\u00e7\u00b5\u0126",
- "\u0120Canadians",
- "\u0120governing",
- "\u0120Evans",
- "Euro",
- "\u0120genres",
- "\u0120unterschied",
- "\u0120Becky",
- "\u00b3\u00bc\u00ea\u00b2\u012e\u00ec\u013c\u0136",
- "\u0120einge",
- "\u0120Raise",
- "oland",
- "\u0120Strateg",
- "\u0120eres",
- "\u0120Veterans",
- "\u0120breakout",
- "\u0120sant\u00c3\u00a9",
- "\u0120adel",
- "\u0120investigated",
- "\u0120peur",
- "\u0120agile",
- "\u0120railroad",
- "anska",
- "\u0120\u00d0\u00b5\u00d0\u00b9",
- "\u0120expos",
- "atories",
- "\u0120Content",
- "\u0120truths",
- "\u0120Trail",
- "\u0120gua",
- "\u0120pores",
- "\u0120writings",
- "\u0120Uhr",
- "\u0120Thats",
- "\u0120icing",
- "OC",
- "\u0120Production",
- "\u0120carne",
- "ISS",
- "\u0120ningu\u00c3\u00a9m",
- "non",
- "\u0120vicious",
- "\u00d7\u0137\u00d7\u0136",
- "\u0120reconnect",
- "\u0120centres",
- "\u0120Kem",
- "\u0120crease",
- "\u0120\u00ec\u013f\u00b4\u00eb\u00af\u00b8",
- "\u00d0\u00b0\u00d0\u00b9\u00d1\u0124\u00d0\u00b5\u00d1\u0123\u00d1\u012e",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d1\u0122",
- "\u0120Hay\u00c4\u00b1r",
- "\u0120\u00d1\u0123\u00d1\u0125\u00d0\u00b4",
- "\u0120\u00c3\u00banica",
- "owa\u00c5\u0124",
- "\u0120adher",
- "hua",
- "ZZ",
- "\u0120preciso",
- "\u0120currents",
- "\u0120seasoned",
- "\u0120IoT",
- "\u0120Bishop",
- "\u00e8\u00a8\u012a",
- "sted",
- "\u0120Bernard",
- "\u00ec\u00a4\u013a",
- "\u00e6\u00b2\u00bb",
- "\u0120Glenn",
- "\u0120kt\u00c3\u00b3rym",
- "\u00e0\u00b8\u00b7\u00e0\u00b9\u012a",
- "\u0120astrolog",
- "\u0120Kot",
- "\u00e5\u00a4\u013e",
- "\u0120parfois",
- "\u0120forwards",
- "\u0120Wi\u00c4\u013b",
- "\u0120\u00ce\u013a",
- "\u0120nano",
- "\u00e8\u00bb\u012f",
- "sub",
- "\u0120Brill",
- "\u0120grit",
- "\u0120cited",
- "gado",
- "\u0120melts",
- "\u0120forc\u00c3\u00a9",
- "\u00e2\u0138\u012a\u00e2\u0138\u012a",
- "\u0120bajo",
- "\u0120discretion",
- "\u00b0\u00b0",
- "ativity",
- "\u0120situated",
- "\u00e3\u0125\u00ab\u00e3\u0124\u00af",
- "\u00d1\u012b\u00d0\u00b5\u00d0\u00b5",
- "\u00e5\u013e\u00b0\u00e6\u0138\u00b9",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bd\u00d1\u0128\u00d0\u00b8\u00d0\u00bf",
- "amaz",
- "\u0120aquarium",
- "\u0120dissolve",
- "\u0120Gods",
- "Super",
- "\u0120amid",
- "zk",
- "\u0120\u00e3\u0123\u0126",
- "\u00e9\u0142\u0132",
- "ampf",
- "\u0120hela",
- "'!",
- "\u0120developmental",
- "\u0120Dise",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b1\u00d0\u00be\u00d1\u0124\u00d0\u00b0\u00d0\u00b5\u00d1\u0124",
- "\u0120snapshot",
- "\u00e5\u00a5\u00bd\u00e5\u00a5\u00bd",
- "\u00d5\u00b8",
- "\u0120Yue",
- "\u0120Hulk",
- "\u0120Doom",
- "\u0120Felix",
- "\u0120r\u00c3\u00a9f",
- "Male",
- "\u00e7\u00b7\u012c",
- "phants",
- "ENS",
- "\u0120Mechan",
- "\u0120Golf",
- "\u00e5\u0128\u012f\u00e8\u00a6\u012d",
- "\u0120generosity",
- "\u00c3\u00a4tze",
- "\u0120unlocked",
- "\u0120\u00e3\u0124\u0134",
- "\u00ed\u0125\u0123",
- "ocalypse",
- "Alright",
- "\u0120\u00ea\u00b0\u013e\u00eb",
- "\u0120\u00d7\u0132\u00d7\u0133\u00d7\u013e",
- "\u0120Keeping",
- "\u0120collaborating",
- "chief",
- "\u0120Fernando",
- "\u0120chefs",
- "\u0120\u00ed\u0136\u00bc\u00eb\u00b6\u0122",
- "\u0120skipped",
- "\u0120personn",
- "\u0120axe",
- "chez",
- "\u0120extraction",
- "\u0120AV",
- "\u0120Gibbs",
- "\u0120\u00ed\u013e",
- "\u0120s\u00c4\u00b1",
- "IAM",
- "View",
- "\u0120GRANT",
- "\u0120\u00eb\u00aa\u00b8",
- "\u0120verification",
- "\u0120depicted",
- "\u0120Moz",
- "oux",
- "\u0120tul",
- "\u0120scanner",
- "\u0120comedian",
- "\u0120Volks",
- "\u0120JEFF",
- "\u00e8\u00a8\u0124\u00e9\u0138\u00b1",
- "\u00a7\u0126",
- "\u0120distraction",
- "r\u00c3\u00a1",
- "\u0120INTER",
- "\u0120sincer",
- "\u0120\u00d7\u0140\u00d7\u00aa",
- "\u0120\u00d7\u00a9\u00d7\u0142",
- "\u0120constructive",
- "arf",
- "\u0120\u00eb\u012a\u0126\u00eb",
- "\u0120eco",
- "ramos",
- "\u0120renewed",
- "inement",
- "\u0120Ub",
- "\u0120Pepper",
- "\u00ec\u00a7\u0122\u00ea\u00b0\u0122",
- "\u0120Darwin",
- "\u0120merchand",
- "\u0120v\u00c3\u00a1rias",
- "\u00c3\u00a8ce",
- "NG",
- "\u0120\u00ec\u013e\u0126\u00ed\u0137\u00b4\u00ec\u0126\u013e",
- "\u0120\u00d0\u00b0\u00d0\u00ba\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "\u0120Unters",
- "\u00d8\u00b9\u00d9\u0126",
- "\u0120intric",
- "omma",
- "ieving",
- "\u0120Caroline",
- "\u00e5\u0135\u0123",
- "\u0120PRES",
- "\u0120performer",
- "\u0120autour",
- "\u00e3\u0123\u00be\u00e3\u0123\u013d\u00e3\u0124\u0135",
- "\u0120utterly",
- "\u0120synthesis",
- "\u0120lesbian",
- "\u0120retrieve",
- "\u0120maneira",
- "\u0120impair",
- "\u0120mentoring",
- "\u0120Souls",
- "\u0120GoPro",
- "\u00d1\u0122\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120cose",
- "\u0120SSD",
- "IRE",
- "\u0120upfront",
- "\u0120Aun",
- "\u0120gamer",
- "\u0120litt",
- "\u0120aggression",
- "\u0120Likewise",
- "\u0120Betty",
- "\u0120Dart",
- "\u0120DLC",
- "ishment",
- "\u00ec\u0140\u00a5\u00ec\u013f\u0126",
- "\u0120\u00e5\u00af\u00b9",
- "\u00e7\u00bb\u0131",
- "cream",
- "\u0120Babylon",
- "\u0120nug",
- "brar",
- "\u0120ayn\u00c4\u00b1",
- "amily",
- "bike",
- "ahahaha",
- "loyd",
- "\u0120mira",
- "\u0120perme",
- "\u0120Gaming",
- "\u0120firmware",
- "Ma",
- "\u0120assisted",
- "atics",
- "\u0120\u00ec\u0137\u0140\u00ec\u013e\u00bc\u00eb\u00a1\u013e",
- "\u0120Mental",
- "niejs",
- "\u0120Iz",
- "ow\u00c4\u0127",
- "\u0120tougher",
- "\u0120deed",
- "\u00e8\u012d\u00a6",
- "\u0120stylish",
- "\u0120Tools",
- "\u0120Hamp",
- "\u0120sunscreen",
- "\u0120articulate",
- "iye",
- "\u00d0\u00b8\u00d1\u0126",
- "\u0120Spread",
- "\u0120HAVE",
- "\u0120swirl",
- "\u0120sponsoring",
- "\u00e4\u00bb\u012d",
- "iovascular",
- "mesi",
- "\u0120relaxation",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b8\u00d1\u0127",
- "\u0120margins",
- "\u0120sa\u00c4\u0141",
- "\u0120Pride",
- "\u0120\u00cf\u0126\u00ce\u00bf\u00cf\u0127\u00cf\u0124",
- "\u00d0\u00b8\u00d1\u0128\u00d0\u00b8",
- "enci",
- "Does",
- "\u0120corpse",
- "\u0120endurance",
- "\u0120\u00ed\u0140\u013a",
- "\u00ec\u00b9\u00b4",
- "\u0120haircut",
- "\u0120interrupted",
- "\u0120windy",
- "\u0120Caleb",
- "\u00cf\u0123\u00cf\u0129",
- "\u0120Pourquoi",
- "\u0120holistic",
- "uclear",
- "\u0120Whole",
- "\u00e5\u00a3\u00ab",
- "Act",
- "\u0120gallon",
- "cade",
- "\u0120Regional",
- "roads",
- "\u0120Schne",
- "\u00c3\u00a1ng",
- "\u0120\u00d0\u00b8\u00d0\u00b7\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd",
- "\u00e3\u0124\u012a\u00e3\u0123\u0143",
- "\u0120menus",
- "\u0120splitting",
- "\u0120priced",
- "\u0120\u00ce\u0135",
- "\u0120username",
- "\u0120\u00d0\u0140\u00d1\u0129",
- "\u0120compressed",
- "yin",
- "\u0120guardian",
- "\u0120goof",
- "\u0120checklist",
- "\u0120interchange",
- "\u0120expedition",
- "\u0120extern",
- "\u0120infrared",
- "engo",
- "\u0120denying",
- "\u0120packets",
- "onent",
- "BB",
- "\u0120Incre",
- "\u0120sini",
- "\u00c3\u0141er",
- "\u00c3\u00a8g",
- "maal",
- "generation",
- "\u0120minorities",
- "\u0120llevar",
- "\u0120nomination",
- "\u0120consid",
- "\u0120\u00d7\u013e\u00d7\u00a2",
- "mu\u00c5\u0141",
- "\u0120Esc",
- "\u0120numerator",
- "\u0120kaik",
- "\u0120kt\u00c3\u00b3rych",
- "iesen",
- "\u0120v\u00c3\u00aa",
- "\u0120USS",
- "\u0120Private",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d0\u00be",
- "\u0120al\u00c3\u00a9m",
- "\u00c3\u0143tulo",
- "\u0120limb",
- "\u0120forgiven",
- "\u0120disclosure",
- "\u00cf\u0126\u00ce\u00af",
- "\u0120ning\u00c3\u00ban",
- "\u0120therapeutic",
- "\u0120negotiating",
- "\u0120Nike",
- "enseful",
- "\u0120incap",
- "\u0120flagship",
- "town",
- "\u00e2\u012a",
- "\u0120\u00cf\u0122\u00ce\u00bf\u00ce\u00bb",
- "\u0120wolves",
- "\u0120violations",
- "\u0120Arnold",
- "\u0120intervene",
- "\u0120heater",
- "\u0120recursos",
- "\u0120maid",
- "\u00ea\u00b2\u00bc",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00b2\u00d0\u00b0\u00d0\u00b9\u00d1\u0124\u00d0\u00b5",
- "\u0120Celebr",
- "\u0120cape",
- "\u0120Sty",
- "ainen",
- "site",
- "bij",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00b7",
- "\u0120framed",
- "\u0120publishers",
- "\u0120\u00d1\u0129\u00d1\u0125\u00d1\u0124\u00d1\u012e",
- "\u0120temptation",
- "\u0120certeza",
- "\u0120exempt",
- "\u00ec\u012c\u00b9",
- "selling",
- "\u0120Task",
- "hoon",
- "\u0120Coc",
- "\u0120Parks",
- "\u0120repetition",
- "\u0120\u00d1\u0124\u00d1\u0125\u00d0\u00b4\u00d0\u00b0",
- "\u0120ensl",
- "\u0120de\u00c4\u0141i\u00c5\u0141",
- "\u0120Orlando",
- "\u0120Mainten",
- "\u00e6\u0143\u00a2",
- "ocument",
- "\u0120HC",
- "\u0120scooter",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00bf\u00d0\u00b8\u00d1\u0123",
- "\u0120tighter",
- "\u0120tease",
- "\u0120removes",
- "\u0120kijken",
- "\u0120\u00d1\u0123\u00d1\u0125\u00d1\u012b\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "\u0120th\u00c3\u00a9",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00b3\u00d0\u00bb\u00d1\u0131\u00d0\u00b4",
- "\u0120relieve",
- "\u0120mit\u00c3\u00a4",
- "\u0120stationary",
- "\u00c3\u00b6ff",
- "pable",
- "\u0120arter",
- "\u0120d\u00c3\u00a9f",
- "rative",
- "\u0120conect",
- "\u0120saddle",
- "\u0120Diane",
- "\u0120commemor",
- "fendim",
- "S\u00c3\u0143",
- "\u0120\u00ed\u0123\u00b4\u00eb",
- "\u0120mange",
- "atte",
- "\u0120arrogant",
- "\u0120robotic",
- "\u0120gi\u00c3\u0142",
- "\u00e6\u013a\u00af\u00e7\u013c\u0126",
- "\u0120neighbourhood",
- "isson",
- "\u0120\u00d0\u00b4\u00d0\u00b2\u00d0\u00b8\u00d0\u00b6",
- "\u0120RI",
- "\u0120Norman",
- "brand",
- "amation",
- "\u0120razor",
- "\u0120murders",
- "\u0120\u00d1\u0124\u00d1\u0125",
- "\u0120wszystkim",
- "\u0120utilities",
- "\u0120microscop",
- "\u00ea\u00bf",
- "\u0120daqui",
- "ollar",
- "\u0120\u00d0\u0136\u00d0\u00b0\u00d0\u00b2\u00d0\u00b0\u00d0\u00b9\u00d1\u0124\u00d0\u00b5",
- "\u0120ann\u00c3\u00a9e",
- "\u0120kilometres",
- "\u0120homosexual",
- "\u0120architects",
- "\u00e3\u0123\u00a1\u00e3\u0123\u00af",
- "\u0120niye",
- "LER",
- "\u0120microphones",
- "\u0120Stunden",
- "\u0120consecutive",
- "ienda",
- "v\u00c3\u00a4nd",
- "DER",
- "\u0120lifts",
- "\u0120Meat",
- "\u0120savez",
- "\u00ed\u0138\u012a\u00eb\u012f\u013a",
- "Men",
- "\u0120dismant",
- "\u00ea\u00b1\u00b0\u00eb\u00a5\u00bc",
- "\u0120insulation",
- "\u0120scall",
- "\u0120spooky",
- "\u0120parc",
- "\u0120ballet",
- "\u0120WhatsApp",
- "\u0120franc",
- "\u0120deliberate",
- "\u0120\u00ed\u0127\u012e",
- "\u0120mars",
- "\u0120Zur",
- "Pr",
- "disciplinary",
- "\u0120obsession",
- "\u00d0\u00bc\u00d0\u00b5",
- "\u0120marching",
- "\u0120Emergency",
- "iguous",
- "\u0120szy",
- "\u0120Lands",
- "\u0120boarding",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0129\u00d1\u0124\u00d0\u00b8",
- "\u0120envy",
- "\u0120compassionate",
- "\u0120merci",
- "\u0120desirable",
- "dale",
- "\u0120can\u00c4\u00b1m",
- "\u0120Antar",
- "temps",
- "\u0120configured",
- "\u0120Compared",
- "neh",
- "icating",
- "\u0120nickel",
- "\u00d9\u012a\u00d9\u0124",
- "\u00d9\u0125\u00d9\u012a\u00d9\u0128",
- "opes",
- "\u0120formulas",
- "\u0120\u00d0\u0137\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120pobl",
- "\u0120PJ",
- "\u0120Lud",
- "\u00e4\u00bb\u012c\u00e5\u013d\u0140",
- "\u0120Brid",
- "\u0120Hog",
- "\u0120Bris",
- "Jen",
- "\u0120shading",
- "\u0120Yas",
- "\u0120disturbed",
- "\u0120recommending",
- "\u0120c\u00c3\u00a9",
- "\u0120HOW",
- "\u00ec\u0139\u012a\u00ec\u0138\u00b4",
- "\u0120reversed",
- "\u0120Interestingly",
- "ioxid",
- "\u00e5\u0127\u0143",
- "\u0120\u00ec\u013a\u00a4\u00ec\u00bc\u0122\u00ec\u013f\u00b4",
- "\u00e1\u00ba\u00bfu",
- "xx",
- "\u0120ouais",
- "\u0120YouTubers",
- "\u0120Rosa",
- "\u0120Haupt",
- "jadi",
- "\u0120vlogs",
- "\u0120cultura",
- "\u0120Leadership",
- "\u0120Hep",
- "\u0120illum",
- "\u00b4\u00eb\u0131\u013b",
- "\u0120customized",
- "\u0120marca",
- "\u0120quatro",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b3",
- "\u0120SpaceX",
- "\u0120Eigen",
- "asting",
- "\u0120oldu\u00c4\u0141u",
- "\u0120forts",
- "\u00e3\u0123\u012b",
- "riment",
- "iencia",
- "\u0120tenir",
- "roffen",
- "\u01201979",
- "\u0120cie",
- "\u0120\u00eb\u0132\u013a\u00ea\u00b3\u0142",
- "\u0120escri",
- "\u00cf\u012e\u00cf\u0124",
- "\u00ed\u0131\u00ac",
- "uzzy",
- "Cong",
- "\u00ec\u013f\u00b8\u00ec\u013f\u00b4",
- "Great",
- "sil",
- "\u00c3\u00a9ch",
- "\u00e3\u0123\u00a8\u00e3\u0123\u012d",
- "\u0120multic",
- "\u0120Disk",
- "\u00b2\u0137",
- "\u0120fazla",
- "\u0120levant",
- "\u0120abajo",
- "urry",
- "stru",
- "\u0120\u00eb\u00a8\u00b9\u00eb\u012c\u0136",
- "\u0120accessory",
- "\u0120\u00d0\u00b4\u00d0\u00b2\u00d0\u00b8\u00d0\u00b3",
- "\u0120Rid",
- "2019",
- "\u0120downstream",
- "\u00e6\u0137\u00b8",
- "\u0120kaz",
- "utan",
- "\u0120charcoal",
- "\u0120afect",
- "wu",
- "\u0120contexts",
- "\u0120feared",
- "\u0120\u00ec\u0126\u00a4",
- "\u0120histories",
- "\u0120fas",
- "ensible",
- "\u0120cocoa",
- "illar",
- "geons",
- "\u0120spirituality",
- "\u0120Pew",
- "\u0120pharmacy",
- "\u0120passions",
- "\u0120bos",
- "\u0120all\u00c3\u00a1",
- "\u0120thriving",
- "\u0120React",
- "\u0120occupy",
- "\u0120withdrawal",
- "\u0120allowance",
- "\u0120Fraktion",
- "\u0120buddies",
- "\u0120idle",
- "\u0120dissolved",
- "\u0120prevalent",
- "\u0120militar",
- "\u0120sensing",
- "\u0120pojaw",
- "\u0120ancora",
- "\u0120abundant",
- "\u0120hairst",
- "\u00e3\u0123\u0124\u00e3\u0124\u012e",
- "\u0120twee",
- "\u0120n\u00c3\u00a4chste",
- "\u0120M\u00c3\u00b6glichkeit",
- "\u0120hoo",
- "ufficient",
- "\u0120fantast",
- "\u0120edible",
- "\u0120\u00eb\u0138\u00a8\u00ec\u0138\u00b4\u00ec",
- "\u00ec\u013d\u0125",
- "\u0120vein",
- "ucci",
- "\u0120devotion",
- "\u0120concealer",
- "income",
- "\u0120recycled",
- "\u0120\u00ec\u012c\u00a4\u00ed\u0125\u0122",
- "\u0120pontos",
- "\u0120dessus",
- "\u0120v\u00c3\u00a9rit",
- "\u0120reflections",
- "\u0120AA",
- "\u0120takeaway",
- "bare",
- "\u0120Contact",
- "eil",
- "\u0120Hear",
- "\u0120mirac",
- "\u0120Gerilim",
- "\u0120\u00d1\u0123\u00d0\u00b0\u00d0\u00bc\u00d1\u012d\u00d0\u00b9",
- "\u0120vivo",
- "\u0120kilograms",
- "\u0120Crim",
- "\u00c3\u00bbt",
- "78",
- "\u0120sincerely",
- "raz",
- "\u0120\u00eb\u00b3\u00b5",
- "\u0120arriv",
- "\u0120conception",
- "\u0120Persian",
- "\u0120sj\u00c3\u00a4l",
- "\u0120starring",
- "\u0120\u00ec\u0137\u0126\u00eb\u00ac\u00b4",
- "\u0120Forever",
- "\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120veil",
- "\u0120subtit",
- "odka",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bd\u00d0\u00be\u00d1\u012a",
- "\u0120cooks",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u0131",
- "Kay",
- "\u0120ni\u00c3\u00b1os",
- "\u0120Phone",
- "\u0120stitching",
- "\u0120fingerprint",
- "\u00e9\u00a2\u013a",
- "\u00ce\u00bb\u00ce\u00ac",
- "\u0120dedicate",
- "\u0120Lob",
- "\u0120blacks",
- "\u0120Ble",
- "bout",
- "\u0120\u00c4\u0133ang",
- "\u0120eks",
- "\u0120squash",
- "\u0120K\u00c3\u00bc",
- "odi",
- "\u0120n\u00c6\u00b0\u00e1\u00bb\u013dc",
- "\u0120voyage",
- "\u0120playful",
- "\u0120\u00d8\u00a5\u00d9\u0126\u00d9\u012b",
- "anic",
- "\u0120condemn",
- "\u0120B\u00c3\u00b6yle",
- "\u0120Polize",
- "\u00e3\u0124\u00bf\u00e3\u0125\u00bc",
- "\u0120ayuda",
- "\u0120pam",
- "\u00e0\u00b9\u0126\u00e0\u00b8\u013d",
- "\u0120Kathy",
- "\u00d0\u00b5\u00d0\u00b4\u00d0\u00b8\u00d0\u00bd",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00b2\u00d0\u00b0",
- "\u0120brig",
- "eger",
- "\u0120eagle",
- "\u0120visions",
- "\u0120\u00ed\u0137\u0143\u00ec\u0125\u0123",
- "\u0120shitty",
- "\u0120hott",
- "\u0120Britt",
- "utors",
- "ENTE",
- "\u00e6\u013d\u00b2",
- "\u0120phon",
- "\u0120Bing",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b4\u00d0\u00b4\u00d0\u00b5\u00d1\u0122\u00d0\u00b6",
- "spring",
- "\u00e6\u0138\u00af",
- "etten",
- "\u0120pilgr",
- "\u0120ediyor",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u0124\u00d1\u012d",
- "aggio",
- "\u0120jul",
- "\u0120comprend",
- "teil",
- "\u0120\u00d8\u00b2",
- "\u0120performers",
- "\u0120infamous",
- "\u0120MK",
- "\u00e7\u00aa",
- "\u00e6\u00b3\u0123",
- "otle",
- "eff",
- "\u0120Hash",
- "\u0120coward",
- "\u0120BRA",
- "\u0120DD",
- "\u0120comida",
- "\u0120plata",
- "\u0120flap",
- "\u0120Mehr",
- "ribution",
- "\u0120Yemen",
- "\u0120mysteries",
- "\u0120\u00c4\u00b0yi",
- "\u0120stell",
- "\u0120eyeliner",
- "\u0120deles",
- "\u0120nailed",
- "\u0120illnesses",
- "\u0120stacks",
- "\u0120trabajar",
- "flower",
- "ciu",
- "\u0120crude",
- "\u0120substantially",
- "\u0120homem",
- "\u0120nephew",
- "\u0120stamps",
- "\u0120carbs",
- "\u00d1\u012e\u00d1\u0124\u00d0\u00b5",
- "mooth",
- "\u0120tunnels",
- "acie",
- "\u00e6\u00b3\u00a2",
- "\u0120Se\u00c3\u00b1",
- "\u0120Hera",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u012a\u00ec\u0139\u0132\u00ec\u013c\u0136",
- "\u0120Wyoming",
- "\u0120HDMI",
- "\u0120Lis",
- "uci\u00c3\u00b3n",
- "\u0120steer",
- "\u00d0\u00be\u00d1\u0130",
- "\u00d0\u00b8\u00d1\u0124\u00d0\u00b0",
- "NT",
- "\u0120\u00ec\u0138\u00bc\u00ea\u00b5\u00b4",
- "\u0120palms",
- "\u0120neon",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d1\u0131",
- "\u0120filtering",
- "\u0120jouer",
- "\u0120H\u00c3\u00b6",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d1\u0123",
- "\u00ea\u00b2\u0142\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u012081",
- "\u0120storyline",
- "\u0120przep",
- "\u0120thanking",
- "\u0120Boeing",
- "\u0120softly",
- "jem",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d1\u012d\u00d1\u0127",
- "\u0120flashlight",
- "\u0120\u00d0\u00bf\u00d1\u0125",
- "\u0120WOMAN",
- "\u00e1\u00ba\u00afc",
- "\u00c3\u0143ch",
- "\u0120luxurious",
- "\u0120w\u00c3\u00bcn",
- "\u0120impactful",
- "\u0120conson",
- "reu",
- "irring",
- "ifter",
- "\u0120constituents",
- "\u00e8\u0132\u00bd",
- "\u012094",
- "\u0120Tou",
- "gom",
- "\u0120\u00ec\u0125\u013f\u00ea\u00b0\u0123\u00ec\u013f\u0126",
- "\u0120stereotypes",
- "\u0120mo\u00c5\u00bcli",
- "\u00e5\u012a\u0128\u00e4\u00ba\u00ab",
- "\u0124\u00a8",
- "\u0120pencils",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d0\u00be\u00d0\u00b6",
- "\u0120ihrem",
- "\u0120Besch",
- "\u0120Koh",
- "\u0120Entscheid",
- "\u0120lek",
- "\u0120f\u00c3\u00b6rs",
- "\u0120totalmente",
- "\u0120lively",
- "\u0120entropy",
- "\u0120discern",
- "\u0120\u00d0\u0139\u00d0\u00bd\u00d0\u00b0",
- "\u0120dov",
- "\u0120mythology",
- "\u00e8\u00a8\u013a\u00e5\u00be\u0139",
- "apanese",
- "\u0120approximate",
- "\u00d0\u00b0\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "ifiable",
- "\u0120Seo",
- "\u00e5\u0122\u0134",
- "\u00b4\u00ec\u012d\u00ac\u00ed\u0140\u012a",
- "\u0120\u00ec\u013a\u00b7",
- "\u0120temporal",
- "\u0120iT",
- "\u0120estat",
- "\u00d0\u00ba\u00d0\u00b8\u00d0\u00bc",
- "\u0120sprink",
- "\u0120grund",
- "\u0120infantry",
- "\u0120schaffen",
- "\u00e7\u00b4\u0126",
- "\u0120ank",
- "riages",
- "\u0120Yeon",
- "\u0120Moroc",
- "\u0120invasive",
- "\u0123\u0136",
- "\u0120parenting",
- "\u0120Ris",
- "ibile",
- "\u0120mods",
- "\u00e5\u00bd\u00a2",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b2\u00d0\u00b5\u00d1\u0122",
- "\u0120Thing",
- "\u0120Wherever",
- "\u0120acknowledging",
- "\u0120pawn",
- "ummer",
- "orb",
- "69",
- "\u0120retrouve",
- "\u0120relies",
- "\u0120Highway",
- "\u0120awe",
- "\u00e3\u0123\u00a7\u00e3\u0123\u013b\u00e3\u0123\u012d",
- "itaire",
- "\u0120applicant",
- "\u0120aisle",
- "worm",
- "\u0120payload",
- "\u0120carre",
- "\u0120Bach",
- "\u00e6\u0142\u00bc",
- "\u0120\u00ec\u00b9\u013e\u00ea\u00b5\u00ac\u00eb",
- "\u00d0\u00bd\u00d0\u00b8\u00d0\u00b5",
- "\u0120it\u00c3\u0143s",
- "onnaise",
- "sol",
- "\u00e8\u0131\u00af",
- "algia",
- "\u0120rocking",
- "\u0120besten",
- "rites",
- "^^",
- "\u00d0\u00b8\u00d0\u00bd\u00d0\u00be\u00d0\u00b9",
- "\u0120baixo",
- "\u0120\u00ea\u00b8\u00b0\u00ec\u0138\u00b5",
- "\u00d0\u00be\u00d1\u0124\u00d1\u0122\u00d0\u00b8",
- "sim",
- "\u0120incarn",
- "\u00eb\u012d\u00a4\u00ec\u013f\u012e",
- "\u0120lick",
- "sided",
- "\u012071",
- "forder",
- "\u0120resonance",
- "\u0120tegen",
- "\u0120metaph",
- "owser",
- "\u0120\u00d7\u0132\u00d7\u0142\u00d7\u0139\u00d7\u0142\u00d7\u0137",
- "?\u00e3\u0122\u012f",
- "\u0120spielen",
- "\u0120volley",
- "\u0136\u00ec\u013f\u00b4\u00ed\u0123\u00ac\u00ec\u0139\u0127",
- "looked",
- "\u0120sentenced",
- "\u0120multiplying",
- "\u0120ideals",
- "\u0120wahrscheinlich",
- "\u0120deposits",
- "bilir",
- "\u0120effet",
- "illon",
- "\u012a\u00eb\u00a7\u012e",
- "\u0120testimon",
- "\u0120zawsze",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0128\u00d0\u00b5\u00d1\u0123\u00d1\u0123",
- "\u0120Lav",
- "\u00e4\u00b8\u012f\u00e9\u012e\u00af",
- "\u0120travailler",
- "\u0120laisse",
- "\u0120Mountains",
- "\u0120\u00d1\u0122\u00d0\u00be\u00d0\u00b1",
- "\u0120examined",
- "itus",
- "Was",
- "\u00d0\u00bb\u00d1\u012d",
- "\u0120attributed",
- "\u0120\u00ec\u012c\u00b9",
- "\u0120Baron",
- "\u0120gep",
- "\u0120attent",
- "\u0120Collection",
- "\u0120theat",
- "\u0120Cai",
- "\u0120wells",
- "\u0120humano",
- "\u00e7\u0139\u0127",
- "\u0120Hast",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0124\u00d1\u0131",
- "czas",
- "\u0120permits",
- "\u0120legg",
- "\u0120epo",
- "\u0120Fen",
- "\u0120thi",
- "\u0120Foi",
- "\u0120\u00c3\u00a9lect",
- "\u012083",
- "\u0120overth",
- "\u0120\u00e8\u00ac\u013f\u00e8\u00ac\u013f",
- "\u0120tenant",
- "\u00e8\u00b2\u00b7",
- "Next",
- "\u0120praised",
- "security",
- "\u0120Impact",
- "\u00e4\u00b8\u00ba\u00e4\u00bb\u0122\u00e4\u00b9\u012a",
- "\u0120vouch",
- "\u0120neg\u00c3\u00b3",
- "\u0120unve",
- "\u0120criticize",
- "\u0120Kenya",
- "\u0120tactic",
- "\u0120logr",
- "\u0120pois",
- "\u0120papa",
- "speaks",
- "\u00f0\u0141\u0133",
- "ispers",
- "\u0120surplus",
- "\u0120colder",
- "\u00e5\u012f\u0139",
- "\u00e5\u0132\u00ac",
- "plets",
- "\u0120Vienna",
- "\u0120Lead",
- "\u0120aerial",
- "\u0120Tah",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u0124\u00d0\u00be\u00d0\u00b2",
- "\u0120Greeks",
- "Cam",
- "\u0120m\u00c3\u00a1xim",
- "\u0120kuin",
- "chio",
- "\u0120demonstrates",
- "anos",
- "\u0120Cert",
- "\u0120\u00d1\u012f\u00d0\u00bd",
- "\u0120blogs",
- "\u0120\u00ec\u0126\u013e\u00ec\u013c\u00b8",
- "\u0120beams",
- "\u00d0\u00b8\u00d0\u00ba\u00d0\u00be\u00d0\u00b2",
- "\u0120prompted",
- "\u0120frightening",
- "\u0120Porsche",
- "\u00e3\u0123\u012a\u00e3\u0123\u00a6",
- "lar\u00c4\u00b1n\u00c4\u00b1",
- "\u0120chilling",
- "isphere",
- "\u0120flashing",
- "\u0120Kard",
- "bread",
- "\u0120exh",
- "\u0120tycker",
- "\u0120ecological",
- "\u0120Mae",
- "\u0120\u00d7\u0140\u00d7\u0132\u00d7\u0137\u00d7\u0135",
- "\u0120\u00eb\u0124\u013a\u00eb\u0131\u0126",
- "\u00d0\u00bb\u00d0\u00be\u00d0\u00bd",
- "yss",
- "\u0120pergunt",
- "\u0120prix",
- "izzard",
- "\u0120cancers",
- "\u012091",
- "susp",
- "\u0120Item",
- "\u00c5\u0141a",
- "\u0120pest",
- "\u0120tak\u00c4\u0127",
- "\u0120lymph",
- "\u0120Patri",
- "fill",
- "\u0120reconna",
- "\u0120optimism",
- "\u0120mimic",
- "\u0120\u00ec\u00b2\u013e",
- "\u0120Madame",
- "ocy",
- "lining",
- "\u00e5\u0133\u012c\u00e8\u00a8\u00b4",
- "erme",
- "\u0120folders",
- "\u0120cz\u00c5\u0124",
- "uchar",
- "\u0120curso",
- "\u0120breach",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0124\u00d1\u012e",
- "\u0120pami\u00c4\u013b",
- "\u0120elig",
- "\u0120autop",
- "Flow",
- "\u0120programmed",
- "\u0120Process",
- "\u0120figur",
- "\u0120SF",
- "\u0120Eles",
- "\u0120programmes",
- "\u0120dizzy",
- "\u00ec\u012d\u013e\u00ea\u00b0\u0126",
- "\u0120\u00d0\u00bb\u00d0\u00b8\u00d0\u00b1\u00d0\u00be",
- "\u0120sniff",
- "\u0120Sebastian",
- "\u0120Hye",
- "\u01204000",
- "\u0120permite",
- "\u00e6\u00a2\u013f",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d1\u012b",
- "\u0120guit",
- "\u0120Dais",
- "\u0120accordance",
- "\u0120modular",
- "ogeneous",
- "\u00e6\u012d\u012f",
- "\u0120pouquinho",
- "\u0120artillery",
- "\u0120lubric",
- "\u0120volcan",
- "\u0120NH",
- "\u00f0\u0141\u00a4",
- "\u0120dean",
- "Rh",
- "\u0120ministre",
- "\u00e5\u013f\u0132",
- "\u0120Inv",
- "\u0120Bulgar",
- "\u0120Daten",
- "\u00e8\u0130",
- "Im",
- "\u0120originated",
- "\u0120Nixon",
- "integr",
- "\u0120lacks",
- "\u0120Nacht",
- "\u00ec\u0138\u00b4\u00eb\u0124\u013a",
- "camera",
- "\u0120radish",
- "kiye",
- "\u0120anges",
- "\u0120pr\u00c3\u00a9f",
- "juk",
- "\u0120Bee",
- "\u0120BU",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d1\u0123\u00d0\u00bf",
- "\u0120BT",
- "\u00c3\u00aames",
- "\u0120St\u00c3\u00bcck",
- "\u0120Ink",
- "\u00e6\u012a\u0138\u00e8\u0122\u0127",
- "\u0120Sergeant",
- "\u0120Multip",
- "\u0120hi\u00c3\u00a7bir",
- "\u0120\u00d0\u00a1\u00d0\u00b0\u00d0\u00bc",
- "\u0120D\u00c3\u00a9",
- "olph",
- "\u00ec\u0138\u00b8",
- "\u0120impat",
- "\u0120\u00ec\u0137\u012c\u00ea\u00b3\u0142",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b2\u00d0\u00b5\u00d1\u0122\u00d0\u00bd\u00d0\u00be\u00d0\u00b5",
- "\u0120unpredictable",
- "\u0120mend",
- "\u0120\u00ec\u0139\u0128\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120jakie\u00c5\u013d",
- "\u0120anni",
- "\u0120donn\u00c3\u00a9",
- "\u0120Kirsty",
- "\u0120rectangular",
- "\u0120empezar",
- "\u0120Exchange",
- "\u00ea\u00b0\u0136",
- "\u0120\u00c3\u00a9conom",
- "\u00e3\u0123\u0135\u00e3\u0124\u0135",
- "elin",
- "reibt",
- "\u0120\u00d7\u0136\u00d7\u00a4",
- "\u0120cemetery",
- "\u0120espa\u00c3\u00b1ol",
- "olin",
- "\u00d0\u00bb\u00d1\u0130\u00d0\u00b4",
- "\u0120gr\u00c3\u00a2ce",
- "allen",
- "\u0120Philos",
- "\u0120Erst",
- "\u0120\u00ec\u0125\u012a",
- "\u0120Vid",
- "Give",
- "OH",
- "\u00ce\u00bc\u00ce\u00bf",
- "\u0120Pare",
- "\u0120metabolism",
- "\u0120maple",
- "\u0120axle",
- "\u0120Dy",
- "\u0120komme",
- "\u00cf\u0130\u00ce\u00bd",
- "\u0120greatness",
- "\u0120verified",
- "\u0120sp\u00c3\u00a9",
- "\u0120Fahrenheit",
- "\u0120Bren",
- "\u0120Confeder",
- "\u0120histoire",
- "\u0120eliminating",
- "\u0120Adding",
- "\u0120Abi",
- "\u00e6\u013f\u0130",
- "\u0120hospitality",
- "tim",
- "\u0120bonito",
- "\u0120partes",
- "\u0120\u00d0\u00b4\u00d1\u0122\u00d1\u0125\u00d0\u00b3\u00d0\u00b8\u00d1\u0127",
- "\u0120Shay",
- "\u0120Sed",
- "\u0120regrets",
- "\u00d1\u0131\u00d0\u00bc\u00d0\u00b8",
- "\u0120tenants",
- "\u00e9\u0122\u0141",
- "\u0120PTS",
- "\u0120devi",
- "\u0120Late",
- "uez",
- "\u0120s\u00c3\u00b6yl",
- "\u00e3\u0124\u00bb",
- "\u0120\u00ec\u0140\u00ac\u00eb\u00b0\u012e",
- "\u0120toggle",
- "\u0120masking",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "\u0120pers\u00c3\u00b6n",
- "\u0120american",
- "fik",
- "\u0120RGB",
- "enson",
- "\u0120KA",
- "wwww",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00b3",
- "metics",
- "\u0120educator",
- "\u00e3\u0124\u00b7\u00e3\u0125\u00ab\u00e3\u0124\u00af",
- "park",
- "\u00d0\u00b5\u00d0\u00bb\u00d1\u012e\u00d0\u00b7\u00d1\u0131",
- "arus",
- "\u00d1\u0122\u00d0\u00b5\u00d1\u0124",
- "\u0120feito",
- "\u0120choir",
- "\u0120largo",
- "\u0120eens",
- "\u0120watts",
- "\u0120Single",
- "\u0120susceptible",
- "icer",
- "\u0120\u00d0\u00b2\u00d0\u00ba\u00d0\u00bb\u00d1\u0130\u00d1\u0129",
- "\u0120pus",
- "\u00ed\u013b\u013a",
- "Eng",
- "\u0120fantas",
- "\u0120specification",
- "\u0120confronted",
- "\u0120Columbus",
- "\u00d0\u00b8\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "ar\u00c4\u00b1m",
- "\u0120caffeine",
- "munition",
- "\u0120migrants",
- "lide",
- "itations",
- "\u0120Geme",
- "\u00e1\u00ba\u00ab",
- "\u0120planner",
- "\u0120stimulate",
- "\u0120aproxim",
- "ceu",
- "\u0120Nom",
- "\u0120vog",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0124",
- "\u0120ense\u00c3\u00b1",
- "\u0120sellers",
- "\u0120guten",
- "zd",
- "Cal",
- "\u0120descript",
- "\u0120reconciliation",
- "zinho",
- "\u00e1\u00b9\u0129a",
- "\u00e3\u0123\u013a\u00e3\u0124\u0125\u00e3\u0123\u0124",
- "acyj",
- "\u0120COL",
- "saw",
- "\u0120\u00ed\u013b\u0137\u00ec\u013f\u00b8",
- "\u0120varit",
- "\u0120partnering",
- "\u0120detention",
- "\u0120bombing",
- "clapping",
- "iencies",
- "ondu",
- "AME",
- "\u0120\u00ea\u00b0\u013b\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "c\u00c3\u0143a",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u0120ASMR",
- "\u0120homepage",
- "\u0120si\u00c3\u00a8",
- "antha",
- "\u0120Poll",
- "\u0120igen",
- "cych",
- "\u0120\u00ea\u00b0\u0133\u00ec\u0140\u0132\u00ea\u00b8\u00b0",
- "\u0120considerably",
- "\u00e4\u00bb\u0138\u00e7\u013c\u0126",
- "\u0120Arist",
- "\u0120withstand",
- "\u0120qualitative",
- "\u0120Kraft",
- "\u0120\u00d1\u012f\u00d0\u00bb\u00d0\u00b5\u00d0\u00ba\u00d1\u0124",
- "\u0120Bead",
- "\u00d0\u00b5\u00d0\u00ba\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "\u0120crushing",
- "\u00ec\u00b3\u0132",
- "\u0120navy",
- "\u00d9\u012a\u00da\u00ba",
- "sho",
- "\u0120oak",
- "ippers",
- "\u0120soils",
- "\u0120pigment",
- "\u0120evitar",
- "\u00e3\u0125\u0129",
- "\u0120fuse",
- "\u0120Dale",
- ":\"",
- "\u0120compl\u00c3\u00a8tement",
- "\u0120kel",
- "\u00e0\u00b9\u0128",
- "\u0120quatre",
- "\u0120UM",
- "\u0120\u00eb\u00a7\u0132\u00eb",
- "\u00e6\u0142\u00b9",
- "\u00c3\u0143r",
- "\u0120leisure",
- "\u0120Housing",
- "\u0120folds",
- "estion",
- "ARS",
- "\u0120mash",
- "urpose",
- "\u0120accumulated",
- "\u0120Stuff",
- "\u00e8\u00aa\u0140",
- "\u0120tapes",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120LOVE",
- "\u01201982",
- "\u0120scars",
- "\u0120capitalist",
- "\u0120Ned",
- "\u0120soften",
- "\u0120notably",
- "\u0120forc\u00c3\u00a9ment",
- "\u0120Raum",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00be\u00d0\u00b1\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u0120trademark",
- "\u0120fertig",
- "\u0120?!",
- "\u00e6\u0139\u0142",
- "\u0120reinforced",
- "\u0120recharge",
- "\u0120Putting",
- "\u0120villains",
- "\u0120handic",
- "\u0120advertisement",
- "\u00d8\u00aa\u00d9\u012c",
- "\u0120\u00d1\u0123\u00d1\u0125\u00d0\u00bc",
- "\u0120Riley",
- "\u00d7\u0137\u00d7\u0133\u00d7",
- "\u00e4\u00ba\u00ac",
- "Os",
- "\u00d8\u00a7\u00d8\u00b2",
- "Boy",
- "\u0120squish",
- "ocket",
- "\u0120testify",
- "\u00e6\u00bc\u0136",
- "\u0120\u00d7\u013e\u00d7\u0140\u00d7",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d1\u0123\u00d1\u0123",
- "manuel",
- "\u0120Arkansas",
- "iffe",
- "\u0120analysts",
- "\u0120Deaf",
- "\u0120j\u00c3\u00b3",
- "\u0120groceries",
- "\u0120Wheel",
- "\u0120\u00d1\u0122\u00d0\u00b8\u00d1\u0123",
- "\u0120c\u00c3\u00b2n",
- "\u0120Cob",
- "\u0120prisons",
- "\u00c3\u00a8ve",
- "\u0120Cabinet",
- "\u0120posed",
- "\u0120guerre",
- "\u0120Lloyd",
- "\u0120clerk",
- "\u0120crises",
- "\u0120Sho",
- "\u0120Ore",
- "\u0120Football",
- "\u0120Advis",
- "\u0120Zheng",
- "\u00e8\u012f",
- "\u0120AMY",
- "\u0120unfor",
- "\u0120monaster",
- "\u0120compile",
- "\u0120immortal",
- "atable",
- "\u0120parano",
- "\u0120tiver",
- "\u0120Steph",
- "\u0120Fu\u00c3\u0141",
- "\u0120discontin",
- "\u0120ripe",
- "\u0120hacking",
- "\u0120siendo",
- "\u0120seguro",
- "altres",
- "\u0120anderes",
- "\u0120\u00eb\u00a6\u00ac\u00eb",
- "\u0120exports",
- "\u00e6\u0143\u00a5",
- "\u0120tabii",
- "\u0120\u00ea\u00b8\u00b0\u00eb\u012d\u00a4\u00eb",
- "\u0120bothering",
- "\u0120pickle",
- "\u0120BRIAN",
- "\u0120altar",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00b1",
- "\u0120transferring",
- "\u0120Vors",
- "\u0120\u00d9\u0129\u00d9\u012a",
- "\u0120Za",
- "\u0120Frances",
- "\u0120browse",
- "emit",
- "\u0120chewing",
- "\u0120Freddy",
- "\u0120editors",
- "\u00c3\u00a4lle",
- "\u0120\u00ed\u012e\u0122",
- "\u0120Sque",
- "\u0120Cultural",
- "awk",
- "\u0120Sache",
- "\u0120Carbon",
- "\u00e1\u00ba\u00aft",
- "FL",
- "\u0120NGO",
- "pe\u00c5\u0124",
- "\u0120Sou",
- "\u0120hvor",
- "unintelligible",
- "\u0120\u00eb\u00b2\u0137",
- "\u0120\u00c2\u00b0",
- "iin",
- "\u0120\u00d7\u00a2\u00d7\u013f",
- "\u0120derri\u00c3\u00a8re",
- "\u0120czym",
- "\u0120Apost",
- "\u0120regarder",
- "\u0120agrade",
- "\u0120Candy",
- "\u0120mare",
- "\u0120introduces",
- "birds",
- "\u0120uniquely",
- "\u0120muk",
- "\u0120cooker",
- "\u0120crews",
- "\u0120jeito",
- "ERT",
- "\u00b6\u0126\u00eb",
- "nisse",
- "\u0120ef",
- "\u0120carte",
- "\u0120Yak",
- "\u0120PAT",
- "\u00d0\u00b8\u00d0\u00bd\u00d0\u00be",
- "bokki",
- "\u0120mates",
- "\u0120distint",
- "\u0120\u00ec\u00bd\u0136\u00eb\u00a1\u013e\u00eb\u0124\u013a",
- "\u0120y\u00c4\u00b1l",
- "\u0120\u00ce\u00ba\u00ce\u00ac\u00ce\u00bd",
- "\u0120configurations",
- "enga",
- "recht",
- "Happy",
- "\u00e3\u0124\u0126\u00e3\u0123\u00a3\u00e3\u0123\u00a6",
- "invest",
- "\u0120reconstruct",
- "\u0120\u00d1\u012f\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d1\u0125",
- "\u0120mosque",
- "raum",
- "\u0120voyez",
- "\u0120NBC",
- "\u0120\u00ec\u0140\u0132\u00ec\u012d\u0142",
- "\u0120sturdy",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00bf",
- "\u0120ansch",
- "alid",
- "\u0120masih",
- "\u0120REP",
- "\u0120\u00ec\u00bd\u0136\u00eb",
- "\u0120deduct",
- "\u0120salir",
- "wurf",
- "ilot",
- "\u0120Mutter",
- "olds",
- "\u0120FEMA",
- "\u0120Bib",
- "\u0120neighboring",
- "\u0120bliss",
- "\u0120\u00ed\u013a\u00bc",
- "\u00d0\u00bb\u00d0\u00b8\u00d1\u0123\u00d1\u012e",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d0\u00b5\u00d0\u00b1",
- "\u0120\u00e5\u00b0\u00b1\u00e6\u013a\u00af",
- "\u0120grenade",
- "\u0120egal",
- "\u0120finely",
- "\u0120petals",
- "\u0120keer",
- "\u0120chyba",
- "\u0120skipping",
- "\u0120thirteen",
- "\u0120gravy",
- "\u0120SAT",
- "61",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00b3",
- "\u0120mins",
- "ITE",
- "\u0120sozial",
- "\u00ed\u0137\u013a\u00eb\u00a9\u00b4\u00ec\u0126\u013e",
- "ruktur",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b7\u00d0\u00bc\u00d0\u00be\u00d0\u00b6",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d1\u0131\u00d1\u0124\u00d1\u012e",
- "\u0120arth",
- "\u0120Cuban",
- "\u0120treasures",
- "\u0120fertilizer",
- "\u0120awakening",
- "\u0120\u00eb\u00b0\u00b1\u00ec\u012d\u0142",
- "\u0120rall",
- "\u0120depict",
- "\u0120Pablo",
- "\u0120nineteen",
- "\u0120watt",
- "\u0120entirety",
- "KS",
- "\u0120Woods",
- "Sch",
- "\u0120\u00da\u00a9\u00d9\u012a",
- "\u0120Dry",
- "\u00e3\u0123\u0140",
- "uve",
- "\u0120reconstruction",
- "\u0120anatomy",
- "\u012a\u00eb\u00a5\u00bc",
- "\u0120baba",
- "\u0120listener",
- "\u0120sharpen",
- "\u0120Peru",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00b7",
- "\u0120recreation",
- "\u0120initiate",
- "\u0120calor",
- "\u0120Naj",
- "gee",
- "\u0120Feels",
- "\u0120Snapchat",
- "\u0120Tet",
- "\u0120Nest",
- "\u0120Daf",
- "\u0120Finish",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00ba\u00d0\u00b8\u00d0\u00bc",
- "\u00c3\u00bac",
- "izens",
- "\u0120spins",
- "\u0120embry",
- "\u0120passages",
- "\u0120cient",
- "\u0120justification",
- "\u00e4\u00bb\u0138\u00e8\u00aa\u00aa",
- "\u0120olmaz",
- "\u0120flooded",
- "\u0120emoji",
- "\u0120embracing",
- "\u0120discard",
- "\u0120Basic",
- "agog",
- "\u0120\u00ec\u013e\u0126\u00ed\u0137\u00b4",
- "\u0120asylum",
- "erin",
- "\u0120fim",
- "\u0120ninja",
- "\u0120automate",
- "\u0120allergic",
- "\u00c3\u00bf\u00c3\u00bf\u00c3\u00bf\u00c3\u00bf",
- "amam",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d1\u0122",
- "\u0120Oi",
- "\u00c3\u00a4us",
- "\u0120induct",
- "\u0120BEN",
- "\u0120z\u00c5\u0124",
- "\u0120ka\u00c5\u00bcdy",
- "\u0120AMP",
- "n\u00c4\u013d",
- "Sure",
- "\u0120quil",
- "\u0120espec",
- "rok",
- "BSCRI",
- "\u0120liebe",
- "pus",
- "achsen",
- "\u0120cricket",
- "\u00eb\u012c\u0132",
- "\u0120Frame",
- "ekk\u00c3\u00bcr",
- "arb",
- "\u0120p\u00c5\u013b",
- "\u00d0\u00b8\u00d1\u0123\u00d1\u0123",
- "\u0120zeggen",
- "\u0120doubles",
- "\u0120Dre",
- "test",
- "insp",
- "boys",
- "\u0120m\u00c3\u00a3o",
- "\u0120Verse",
- "\u0120muscular",
- "\u0120MALE",
- "\u0120dulu",
- "\u0120occasional",
- "Lo",
- "conomic",
- "\u0120vak",
- "\u0120remedy",
- "\u00e5\u00a4\u0142",
- "\u0120\u00e2\u013b\u00aa\u00e2\u013b\u00aa\u00e2\u013b\u00aa",
- "vem",
- "\u0120\u00c3\u00b6nem",
- "\u0120kar\u00c5\u0141\u00c4\u00b1",
- "\u0120Sharp",
- "hur",
- "\u0120\u00eb\u00b0\u00a9\u00eb\u00b2\u0137",
- "\u0120grandson",
- "\u0120aktiv",
- "\u0120Thrones",
- "\u0120\u00ec\u0137\u012a\u00ec\u0139\u0132",
- "\u0120tots",
- "\u0120subd",
- "\u0120Paula",
- "\u0120graves",
- "\u0120Brent",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d0\u00ba\u00d1\u0124\u00d0\u00be",
- "\u0120s\u00c3\u00b6z",
- "\u0120crec",
- "\u0120Vladimir",
- "\u00e7\u0138\u00ab",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b9",
- "\u0120\"-",
- "\u0120psy",
- "atri",
- "idan",
- "\u0120a\u00c3\u00ban",
- "\u0120standardized",
- "\u00ec\u00b9\u013a\u00eb",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120Zhu",
- "something",
- "\u0120750",
- "\u0120mujeres",
- "\u0120ait",
- "\u00e9\u0139\u00b4",
- "agu",
- "\u0120corrected",
- "ikka",
- "eled",
- "\u0120Career",
- "owym",
- "\u0120roommate",
- "\u0120descendants",
- "\u0120Napoleon",
- "\u0120\u00d0\u0136\u00d0\u00be",
- "\u00ed\u0138\u012a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120bunun",
- "\u0120Micha",
- "\u00e7\u00b7\u013c",
- "\u0120descob",
- "PI",
- "\u0120palabra",
- "\u0120tracked",
- "\u0120dependence",
- "\u0120Barack",
- "\u00e5\u0123\u0129",
- "\u0120fertility",
- "\u0120Southwest",
- "\u0120incomplete",
- "\u0120comunic",
- "\u0120compris",
- "\u0120Restaur",
- "\u0120acron",
- "\u00ce\u00ba\u00ce\u00b1",
- "\u0120apprentices",
- "\u0120musst",
- "\u0120Abr",
- "\u0120pentru",
- "\u0120Consort",
- "\u0120Avec",
- "\u0120dumplings",
- "LR",
- "\u0120wszystkie",
- "\u0120swamp",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00b2",
- "uggle",
- "\u0120watercolor",
- "\u0120proton",
- "\u0120Espa\u00c3\u00b1a",
- "ocking",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d0\u00bb",
- "\u0120takim",
- "Very",
- "\u0120dementia",
- "\u0120\u00c5\u0141eyi",
- "Jac",
- "\u0120MacBook",
- "\u0120Liv",
- "fficients",
- "\u0120Hunt",
- "\u0120overlay",
- "\u00e6\u0126\u0141\u00e8\u00a6\u00ba",
- "\u0120Skype",
- "punkt",
- "\u0120confined",
- "\u0120Adrian",
- "\u00d8\u00b1\u00d9\u0125",
- "\u0120Jeep",
- "\u0120enquanto",
- "\u0120anest",
- "\u00d0\u00be\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d1\u012e",
- "\u0120irrigation",
- "\u00e1\u00bb\u0133n",
- "\u0120eighteen",
- "\u0120Pon",
- "\u0120rescued",
- "\u01201983",
- "r\u00c3\u00bc",
- "jae",
- "\u0120Jeong",
- "\u0120amazingly",
- "\u0120FDP",
- "\u0120backstage",
- "cue",
- "\u0120\u00cf\u0125\u00cf\u0126\u00ce\u00b7\u00ce\u00bd",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00b5",
- "\u0120livestock",
- "\u0120Warner",
- "\u0120majors",
- "\u00e3\u0125\u0123\u00e3\u0125\u00a3",
- "\u0120cooperative",
- "\u0120Brady",
- "rained",
- "rieb",
- "\u0120\u00d7\u0133\u00d7\u0140\u00d7",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120FE",
- "\u0120leaked",
- "\u0120Mercury",
- "\u0120persuade",
- "\u0120transformer",
- "\u0120Norweg",
- "\u0120\u00ec\u0139\u00ac\u00eb\u0141\u00ac",
- "\u0120zrobi\u00c4\u0129",
- "\u0120cardiovascular",
- "\u0120Crash",
- "\u0120gossip",
- "\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d1\u012e",
- "\u0120\u00ec\u00aa\u00bd",
- "\u0120swept",
- "\u0120Horn",
- "\u0120At\u00c3\u00a9",
- "\u0120bukan",
- "\u0120Kaw",
- "KY",
- "\u0120Stories",
- "Gary",
- "\u0120gardening",
- "\u0120Quickly",
- "\u0120Falcon",
- "\u0120ovat",
- "c\u00c4\u00b1",
- "\u0120Complet",
- "\u0120Date",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bc",
- "\u0120l\u00c3\u00a4uft",
- "\u0120Audrey",
- "\u0120Went",
- "\u0120pel\u00c3\u0143cul",
- "\u0120carriage",
- "\u0120unacceptable",
- "nymi",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d1\u012d\u00d1\u012a",
- "\u0120terre",
- "uellement",
- "EEEE",
- "\u0120pharmac",
- "h\u00c3\u00b5es",
- "\u0120zich",
- "\u0120migrate",
- "\u0120Fry",
- "\u00c3\u00b1ana",
- "\u0120Muito",
- "EOVER",
- "\u0120fortress",
- "\u0120Compan",
- "\u0120JSON",
- "ordnung",
- "\u0120warto",
- "\u0120ungef",
- "\u00ec\u0127\u0136\u00ec\u0126\u013e",
- "\u0120\u00d1\u0122\u00d0\u00be\u00d0\u00ba",
- "\u0120paddle",
- "Jared",
- "\u0120submitting",
- "\u0120latch",
- "\u0120fug",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d1\u0123",
- "\u0120Ef",
- "\u0120launches",
- "\u0120ft",
- "otechn",
- "\u0120travelled",
- "\u00d8\u00a7\u00d9\u0123",
- "\u00e9\u0123\u0137",
- "\u0120proch",
- "\u0120dedim",
- "83",
- "\u0120rebound",
- "\u0120LU",
- "path",
- "\u0120\u00d1\u0123\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00b2",
- "\u0120\u00c3\u00b6l",
- "\u0120\u00ed\u0124\u00a4",
- "\u0120privat",
- "\u0120tractor",
- "\u0120Attention",
- "Ser",
- "\u0120coses",
- "\u00c3\u00a1ria",
- "pal",
- "\u0120\u00ec\u013f\u0122",
- "\u0120successor",
- "\u0120connectors",
- "\u0120\u00d1\u0125\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00bd\u00d0\u00be\u00d0\u00b2",
- "\u0120genocide",
- "\u0120sufficiently",
- "\u0120Aix\u00c3\u00b2",
- "\u0120stabilize",
- "\u0120congest",
- "\u0120carving",
- "\u0120zost",
- "\u0120\u00d0\u00b1\u00d1\u012d\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be",
- "\u0120shortest",
- "\u0120livel",
- "\u012089",
- "\u00e9\u0123\u012c",
- "\u0120erk",
- "\u0120portraits",
- "\u00e0\u00a5\u0122",
- "\u00e8\u013a",
- "boat",
- "llah",
- "ANC",
- "\u0120empirical",
- "\u0120Echo",
- "\u0120Nederland",
- "\u00e8\u00bf\u013b\u00e4\u00b9\u012a",
- "Net",
- "\u0120cuidado",
- "\u0120Roma",
- "\u0120calf",
- "\u0120giants",
- "\u0120Explorer",
- "\u0120Collect",
- "alition",
- "\u0120Destiny",
- "\u0120ausge",
- "\u0120Edu",
- "\u0120Clo",
- "\u0120earrings",
- "\u0120Track",
- "\u0120ROS",
- "\u0120Belle",
- "\u00e7\u013b\u00be",
- "\u0120pueda",
- "\u0120daytime",
- "\u0120supplier",
- "\u0120SV",
- "\u0120Exhale",
- "\u0120galera",
- "course",
- "\u0120centimeter",
- "\u0120Bast",
- "mud",
- "\u0120sangat",
- "\u0120Physical",
- "\u0120privately",
- "\u0120trata",
- "lynn",
- "illi",
- "\u0120\u00eb\u00a9\u0136\u00ec\u013f\u00b4\u00ed\u0123\u00ac\u00ec\u0139\u0127",
- "\u0120crystall",
- "\u0120pods",
- "\u00e1\u00ba\u00a3n",
- "inator",
- "\u0120Records",
- "\u00e5\u00ae\u013a",
- "\u00c4\u0141imiz",
- "issement",
- "hare",
- "hadow",
- "\u0120DK",
- "\u0120\u00ec\u0137\u012e\u00ea\u00b3\u0142",
- "\u0120wyn",
- "\u0120requesting",
- "\u0120Donna",
- "\u0120\u00ec\u0139\u00b4\u00ec\u012d\u00ac\u00ed\u0140\u012a",
- "inea",
- "\u0120exert",
- "\u0120Duncan",
- "\u0120\u00d0\u00b2\u00d0\u00b5\u00d1\u0129",
- "\u0120Hah",
- "\u00e0\u00a4\u0124",
- "\u0120Lif",
- "\u0120Finding",
- "\u0120Nov",
- "\u0120\u00d0\u00b7\u00d0\u00bd\u00d0\u00b0\u00d0\u00ba",
- "\u0120\u00d0\u00be\u00d1\u0126",
- "\u0120Qu\u00c3\u00a8",
- "\u0120quarterback",
- "\u0120\u00d1\u0126\u00d0\u00b0\u00d0\u00ba",
- "\u0120bipartisan",
- "\u00c4\u0141in",
- "\u0120n\u00c3\u00a9cess",
- "\u0120referendum",
- "\u0120compiler",
- "\u0120probabil",
- "\u00d0\u00b5\u00d0\u00b4\u00d0\u00b8",
- "\u0120trader",
- "\u00e6\u013a\u0135",
- "\u0120Rum",
- "geme",
- "\u0120dio",
- "\u0120b\u00c4\u013bdziemy",
- "\u0120\u00cf\u0122\u00ce\u00ac",
- "\u00ea\u00be\u00b8",
- "\u00d7\u0137\u00d7\u013a",
- "\u0120\u00e0\u00a4\u0137",
- "\u0120\u00d0\u00b1\u00d0\u00bb\u00d0\u00b0\u00d0\u00b3",
- "\u0120scalp",
- "\u0120Pause",
- "\u0120caption",
- "\u0120endanger",
- "\u0120enlar",
- "\u0120rotten",
- "\u00e3\u0125\u0125\u00e3\u0125\u012a",
- "\u0120wah",
- "\u00e8\u0124\u012b",
- "\u0120dzi",
- "\u0120Install",
- "Ay",
- "\u0120crear",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u0124\u00d0\u00b0",
- "\u0120weighing",
- "\u0120butterflies",
- "\u0120Gast",
- "\u00e4\u00ba\u0137",
- "horn",
- "warz",
- "ICEOVER",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b9\u00d1\u0124\u00d0\u00b8",
- "\u0120coefficients",
- "\u00e7\u00b0\u00a1\u00e5\u0138\u00ae",
- "\u0120Spencer",
- "\u0120Higher",
- "\u0120cowork",
- "\u00e5\u00a8\u013a",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b5",
- "\u0120monit",
- "\u0120dysfunction",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00bd\u00d0\u00be\u00d0\u00b2",
- "\u0120tournaments",
- "\u0120oyster",
- "BN",
- "\u0120trud",
- "slow",
- "\u0120Penny",
- "\u0120Odys",
- "\u00c3\u00a6r",
- "\u0120fou",
- "\u0120enjoyment",
- "\u00d0\u00b0\u00d1\u0124\u00d1\u012d",
- "\u0120wygl\u00c4\u0127da",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00b0\u00d1\u0131",
- "\u0120Protect",
- "\u0120moy",
- "\u0120claw",
- "\u0120suspicion",
- "\u0120sacrificed",
- "\u0120gosto",
- "Big",
- "\u0120aggressively",
- "\u0120vorne",
- "\u00e3\u0125\u0142",
- "\u0120blamed",
- "\u0120Sehr",
- "\u00d7\u00a4\u00d7\u00a8",
- "cito",
- "\u0120seals",
- "\u0120mujer",
- "\u0120Weird",
- "\u0120forens",
- "\u0120contributes",
- "estra",
- "\u0120pog",
- "LOL",
- "\u0120hacerlo",
- "\u00d0\u00be\u00d1\u0124\u00d1\u012e",
- "fiction",
- "79",
- "\u00ce\u00bb\u00ce\u00bf",
- "\u00e5\u00a4\u00a7\u00e6\u00a6\u0124",
- "\u00e5\u00a3\u00b0",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00b1",
- "\u0120GS",
- "\u0120Clara",
- "itez",
- "\u0120advocating",
- "\u0120\u00ed\u0136\u0126\u00eb",
- "sung",
- "\u0120vertices",
- "\u0120navigating",
- "\u0120europ\u00c3\u00a9",
- "\u00e7\u013c\u0128",
- "\u0120slowed",
- "\u0120foreground",
- "\u0120Industrial",
- "\u0120adore",
- "\u00ec\u012d\u0143",
- "\u0120cr\u00c3\u00a9er",
- "\u00e6\u0140\u0139",
- "chnitt",
- "\u0120unaware",
- "\u0120curly",
- "entar",
- "\u0120ler",
- "\u0120prohibited",
- "\u0120Heroes",
- "\u0120Reed",
- "uca",
- "\u0120smok",
- "\u0120kunna",
- "zeitig",
- "immen",
- "\u0120Lun",
- "\u0120\u00d0\u00b0\u00d0\u00b1\u00d1\u0123\u00d0\u00be\u00d0\u00bb\u00d1\u0130\u00d1\u0124",
- "\u0120degli",
- "\u0120villagers",
- "\u0120preset",
- "zept",
- "uds",
- "\u0120emit",
- "\u00e4\u00bd\u0142\u00e8\u00a6\u0123",
- "\u0120\u00eb\u012b",
- "\u00eb\u012c\u0136\u00ec\u00a7\u0122",
- "\u00d0\u00bd\u00d0\u00b0\u00d0\u00ba\u00d0\u00be",
- "\u0120os\u00c3\u00b3b",
- "\u01201969",
- "\u0120\u00d0\u0132\u00d1\u0122",
- "\u0120manchmal",
- "\u0120Brock",
- "\u0120mantra",
- "\u0120WIL",
- "bach",
- "in\u00c3\u00a4",
- "elas",
- "keln",
- "\u0120disciple",
- "\u0120qualc",
- "\u0120dehyd",
- "\u00ec\u013f\u00b4\u00eb\u013f\u00bc\u00eb\u012c\u0136",
- "Af",
- "\u00ec\u0126\u00b1\u00ec\u013f\u00b4",
- "Ryan",
- "\u0120puppet",
- "\u0120\u00d0\u00b4\u00d1\u0122\u00d1\u0125\u00d0\u00b3\u00d0\u00b8\u00d0\u00b5",
- "\u0120rud",
- "\u0120pending",
- "Plus",
- "\u0120\u00ec\u0137\u012c\u00ec\u013f\u0126",
- "\u0120b\u00e1\u00bb\u012d",
- "\u0120Sega",
- "\u00c3\u00a7e",
- "\u0120programmer",
- "bli",
- "\u0120unl",
- "\u0120enslaved",
- "\u0120soci\u00c3\u00a9t\u00c3\u00a9",
- "\u00c4\u0123h",
- "\u0120inheritance",
- "\u0120Bangl",
- "ermaid",
- "\u0120practitioner",
- "\u0120Stalin",
- "\u0120User",
- "cible",
- "\u0120cardiac",
- "\u0120Koreans",
- "\u0120dumped",
- "\u0120\u00d7\u0136\u00d7\u013b\u00d7\u0136",
- "\u00c3\u00a1is",
- "\u0120hydraulic",
- "oubtedly",
- "\u0120Pit",
- "\u0120picnic",
- "\u0120beh\u00c3\u00b6ver",
- "\u0120\u00d1\u0123\u00d0\u00bc\u00d0\u00be\u00d0\u00b3",
- "\u0120braking",
- "\u00e9\u00bb\u0133",
- "utar",
- "\u0120\u00ec\u0126\u00b8\u00eb",
- "ubl",
- "\u0120\u00c3\u00bcz",
- "\u0120majesty",
- "\u0120bers",
- "utable",
- "\u0120hotter",
- "\u00e7\u0127\u00a7",
- "\u00db\u012e\u00d9\u0128",
- "\u0120biases",
- "\u0120subjected",
- "\u0120naughty",
- "\u0120circus",
- "\u00e3\u0123\u0139\u00e3\u0123\u012d",
- "\u0120Immedi",
- "\u0120Stefan",
- "\u0120Triple",
- "enk",
- "\u0120wit",
- "\u0120recycle",
- "emie",
- "dated",
- "\u0120unload",
- "\u0120popula",
- "chin",
- "\u0120yields",
- "\u0120english",
- "\u0120Bonnie",
- "\u0120spiders",
- "\u00c3\u0123",
- "\u0120erosion",
- "\u00e9\u0125\u00a8\u00e5\u012a\u0128",
- "\u0120NICK",
- "\u00d0\u00b8\u00d1\u0131\u00d1\u0127",
- "\u0120impart",
- "\u0120\u00d0\u00ba\u00d0\u00bd\u00d0\u00b8",
- "\u0120resolutions",
- "\u0120lithium",
- "\u0120convergence",
- "\u0120Tara",
- "\u0120\u00d0\u00b4\u00d0\u00b2\u00d0\u00b5",
- "ths",
- "\u0120Cindy",
- "\u00e6\u012a\u0133\u00e8\u00a6\u0123",
- "\u00e5\u00b9\u00ab",
- "\u0120DIE",
- "\u0120assurance",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d0\u00b8\u00d1\u0123",
- "\u0120buckets",
- "\u0120cues",
- "\u0120Quiet",
- "\u0120similarity",
- "\u0120foundational",
- "\u0120Minist",
- "\u00e6\u00bb\u00bf",
- "\u0120pian",
- "\u0120centr",
- "\u0120numb",
- "\u0120monks",
- "ujourd",
- "enzie",
- "\u0120skateboard",
- "\u0120dlatego",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d1\u0124",
- "\u0120AE",
- "\u0120masterpiece",
- "\u0120Solomon",
- "\u0120Reddit",
- "\u0120riot",
- "abl",
- "\u0120Jazz",
- "\u0120electromagnetic",
- "\u0120insecure",
- "\u0120Compet",
- "geries",
- "\u00d0\u00be\u00d0\u00b1\u00d0\u00be\u00d0\u00b4",
- "\u0142\u00d7\u0137",
- "\u00f0\u0141\u0134",
- "\u0120senators",
- "\u0120Brisbane",
- "\u0120Alb",
- "uttering",
- "\u0120Allow",
- "zero",
- "\u0120pai",
- "\u0120\u00d0\u0132\u00d0\u00bb\u00d0\u00b5\u00d0\u00ba\u00d1\u0123",
- "\u0120Display",
- "\u0120Blade",
- "\u0120Apps",
- "\u0120p\u00c3\u00a4",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d1\u0123\u00d1\u0131",
- "\u0120quella",
- "\u0120Gao",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d1\u012d\u00d1\u0127",
- "\u0120spoilers",
- "\u0120gallons",
- "\u0120\u00d9\u0126\u00d9\u012c",
- "\u0120Zion",
- "\u00e6\u013e\u012b\u00e4\u00b8\u0122",
- "onie",
- "ragt",
- "\u0120Chand",
- "\u0120\u00eb\u00b3\u0133",
- "\u0120blunt",
- "\u0120usu",
- "\u0120Kad",
- "rakt",
- "\u0120cinematic",
- "\u0120ammunition",
- "rene",
- "\u0120fourteen",
- "\u0120Carn",
- "crit",
- "\u0120tenure",
- "vu",
- "\u0120principalmente",
- "\u0120alleen",
- "\u00e9\u0122\u013b\u00e4\u00b8\u0122",
- "\u0120komplett",
- "\u0120d\u00c3\u00bcny",
- "James",
- "\u0120receptor",
- "\u0120oneself",
- "guru",
- "\u0120merchant",
- "liness",
- "\u0120overlooked",
- "\u0120harmonic",
- "\u00e9\u0137\u00bf",
- "ieso",
- "\u00d7\u0137\u00d7\u0140",
- "colm",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b5\u00d0\u00ba\u00d1\u0124",
- "\u0120Ada",
- "\u00d8\u00a7\u00d8\u00b3",
- "Tim",
- "\u0120recurring",
- "\u0120proceeds",
- "\u0120Particularly",
- "\u0120Download",
- "etrical",
- "\u0120matrices",
- "\u0120proyecto",
- "ancies",
- "\u0120Uhm",
- "\u0120caves",
- "\u0120\u00ec\u0138\u00b4\u00eb\u0142\u00a4",
- "\u0120Leaf",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012d\u00d1\u0129",
- "\u0120\u00ec\u013f\u00b4\u00ec\u013e\u0142",
- "Europe",
- "\u0120t\u00c4\u0127",
- "\u0120puls",
- "\u0120takiego",
- "\u00d0\u013f\u00d0\u00b5",
- "GU",
- "\u0120fors",
- "\u00cf\u0123\u00ce\u00b3",
- "\u0120fotos",
- "\u0120))",
- "\u0120\u00eb\u00a9\u00a4\u00eb",
- "\u0120aquilo",
- "\u0120Kurd",
- "\u00ef\u00b8\u0131",
- "ptic",
- "\u0120Dort",
- "\u0120misery",
- "auso",
- "\u00e5\u012c\u0141",
- "chuckling",
- "\u0120Ridge",
- "\u0120\u00ed\u0138\u012a\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120***",
- "\u00e5\u00ae\u00a2",
- "\u0120Hmmm",
- "\u0120geographic",
- "\u0120anys",
- "\u0120talvez",
- "\u0120skelet",
- "\u0120signatures",
- "\u0120liters",
- "\u0132\u00eb\u00a9\u00b4",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u0120skiing",
- "\u0120\u00d0\u013e\u00d0\u00be\u00d1\u0123",
- "\u0120adopting",
- "\u0120haft",
- "\u0120symmetric",
- "\u0120Liqu",
- "\u0120thyroid",
- "\u0120misin",
- "lude",
- "\u0120hull",
- "\u0120XD",
- "\u0120Gust",
- "zeich",
- "\u0120vibrations",
- "\u0120esemp",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d1\u0130",
- "\u0120Quem",
- "\u0120\u00c3\u00bcbrig",
- "\u0120Ske",
- "\u0120Lynch",
- "rooms",
- "artet",
- "fest",
- "\u0120fr\u00c3\u00bcher",
- "\u0120lure",
- "\u00e4\u00b8\u012f\u00e5\u00a5\u00bd\u00e6\u0126\u0131\u00e6\u0122\u013f",
- "\u0120\u00ec\u0137\u012e\u00ec\u0137\u0126",
- "\u0120WIN",
- "\u0120RYAN",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d1\u0125\u00d1\u0130",
- "\u0120Kash",
- "\u0120\u00d7\u0136\u00d7\u0140",
- "\u0120safeg",
- "\u0120Hallelujah",
- "\u0120\u00d0\u00b4\u00d0\u00b2\u00d1\u0125\u00d1\u0127",
- "\u0120staple",
- "\u0120sediment",
- "\u0120Acts",
- "\u0120blaming",
- "\u0120mainland",
- "\u0120sporting",
- "\u0120decorations",
- "\u0120executing",
- "\u0120paran",
- "\u0120Dollar",
- "\u0120projections",
- "\u0120commissioned",
- "\u0120bour",
- "\u00c3\u00b6m",
- "\u0120steamed",
- "\u0120\u00eb\u0143\u013a",
- "\u0120petrol",
- "\u0120celular",
- "\u00e5\u00b8\u00b6",
- "\u0120Hungary",
- "\u0120rented",
- "\u0120\u00d0\u00b2\u00d0\u00b0\u00d1\u0122\u00d0\u00b8",
- "bbie",
- "\u0120s\u00c3\u00a9cur",
- "\u00c3\u00bcll",
- "\u0120swings",
- "between",
- "\u0120\u00d0\u00b8\u00d1\u0124",
- "estro",
- "\u0120niemand",
- "\u0120\u00ec\u0124\u00bc",
- "\u0120Pardon",
- "esses",
- "\u0120MID",
- "\u0120centralized",
- "\u0120Alien",
- "culos",
- "\u0120crise",
- "\u00e8\u00a3\u00a1\u00e9\u013f\u00a2",
- "\u0120classe",
- "beitet",
- "i\u00c4\u0141i",
- "\u0120whales",
- "\u0120perimeter",
- "\u0120tying",
- "\u0120strony",
- "\u0120likewise",
- "\u0120Punch",
- "Da",
- "\u0120Baptist",
- "\u0120sorting",
- "\u0120iv",
- "\u0120\u00ed\u0137\u00a9",
- "\u0120rehab",
- "\u0120eta",
- "river",
- "\u0120sai",
- "\u00e3\u0123\u0126\u00e3\u0123\u0141\u00e3\u0123\u0142",
- "odus",
- "\u00e3\u0123\u012c\u00e9\u00a1\u013a\u00e3\u0123\u0126\u00e3\u0123\u0139\u00e3\u0123\u00be\u00e3\u0123\u013b",
- "\u0120essayer",
- "\u0120turtles",
- "\u0120Hazrat",
- "\u0120fabrics",
- "\u0120cavity",
- "\u0120poniewa\u00c5\u00bc",
- "\u0120schlecht",
- "\u0120salsa",
- "\u00c5\u0141ekk\u00c3\u00bcr",
- "\u0120seating",
- "\u0120economists",
- "\u0120mang",
- "\u0120seguinte",
- "\u0120rang",
- "\u0120ratios",
- "\u0120constell",
- "\u0120longtemps",
- "uating",
- "\u0120spoiled",
- "\u0120recipients",
- "\u0120sniper",
- "\u00e4\u00b9\u012d\u00e5\u012b\u012f",
- "\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00ea\u00b9\u012e",
- "\u0120wp",
- "\u0120LINKE",
- "\u0120flare",
- "\u0120Adri",
- "\u00c3\u00b1as",
- "\u0120backl",
- "m\u00c3\u00a4\u00c3\u0141",
- "\u0120Bend",
- "\u0120workloads",
- "\u0120\u00d1\u0123\u00d1\u0125\u00d0\u00bf",
- "\u01201975",
- "\u00d0\u00b8\u00d0\u00bc\u00d1\u0123\u00d1\u0131",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b5",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00bd",
- "\u0120aspirations",
- "\u0120Aer",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d1\u0122\u00d0\u00b8\u00d1\u0124\u00d1\u012e",
- "\u0120Qian",
- "\u00e5\u00a6\u012a",
- "\u0120compromised",
- "\u0120yolk",
- "\u00d0\u00bb\u00d0\u00b0\u00d1\u0123\u00d1\u0124",
- "\u0120hemen",
- "rove",
- "dens",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d1\u0124",
- "\u0120---",
- "\u0120fluores",
- "\u00d0\u00bd\u00d0\u00be\u00d1\u0123",
- "\u0120Liverpool",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b1\u00d0\u00be\u00d0\u00b9",
- "\u0120Zwe",
- "\u0120lumin",
- "\u0120OG",
- "\u00e1\u00b8",
- "holm",
- "profits",
- "SN",
- "\u0120proportions",
- "\u0120mica",
- "\u0120Boh",
- "\u0120Atlas",
- "\u0120unsure",
- "\u0120touring",
- "\u0120nied",
- "\u0120t\u00c4\u013b",
- "\u0120imperative",
- "\u0120demek",
- "\u0120Sheriff",
- "rance",
- "\u0120homeland",
- "\u0120Hail",
- "\u0120Ganz",
- "ymm",
- "Mon",
- "\u00e5\u0128\u00b7",
- "vida",
- "\u0120desarroll",
- "\u00e6\u012c\u0122",
- "\u0120intriguing",
- "\u0120Hugo",
- "\u0120\u00e3\u0124\u0124",
- "\u00e9\u00ac",
- "\u00d0\u00b0\u00d1\u0128",
- "\u0120Wi\u00c4\u013bc",
- "atted",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u012a\u00ea\u00b3\u0142",
- "\u0120Vari",
- "\u00c3\u00a1d",
- "\u0120surreal",
- "\u0120disparities",
- "\u0120m\u00c3\u00b3",
- "ullen",
- "\u0120\u00ec\u0140\u012a\u00eb\u012d\u00a4\u00ea\u00b3\u0142",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb\u00d1\u0125\u00d0\u00b9\u00d1\u0123\u00d1\u0124\u00d0\u00b0",
- "\u0120mains",
- "\u0120eject",
- "\u0120methane",
- "\u0120marginalized",
- "\u0120chilli",
- "r\u00c3\u00a8s",
- "\u0120yem",
- "\u00e4\u00bd\u0142\u00e6\u013a\u00af",
- "\u0120Chun",
- "\u0120debts",
- "\u0120downloading",
- "\u0120Athens",
- "isierung",
- "ryn",
- "\u0120tekn",
- "\u0120Quindi",
- "\u00e9\u013e\u0122",
- "\u0120taraf",
- "\u0120h\u00c3\u00a9",
- "\u0120consciously",
- "\u0120fixes",
- "uckle",
- "may\u00c4\u00b1n",
- "\u0120frei",
- "\u0120spa",
- "\u0120\u00ec\u00a7\u0126\u00ed\u0138\u012b",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00b0",
- "\u0120\u00d1\u0125\u00d0\u00ba",
- "lett",
- "\u0120olmu\u00c5\u0141",
- "\u0120cheesy",
- "\u00e0\u00b8\u00b2\u00e0\u00b8\u0123",
- "naire",
- "\u0120widen",
- "\u0120lien",
- "\u0120escaping",
- "iggs",
- "\u0120Blick",
- "c\u00c4\u0127",
- "\u0120\u00ec\u0126\u013e\u00eb",
- "\u0120\u00d7\u0136\u00d7\u00a1",
- "\u0120\u00d0\u00b2\u00d0\u00bf\u00d0\u00b5\u00d1\u0122",
- "ophone",
- "iell",
- "\u0120SUBSCRI",
- "\u0120lions",
- "\u0120\u00ea\u00b7\u00b8\u00ea\u00b2\u0125",
- "\u0120inspires",
- "\u0120guarantees",
- "\u0120come\u00c3\u00a7a",
- "\u0120Growing",
- "\u0120neglig",
- "\u0120Frankf",
- "\u0120gegeben",
- "\u0120\u00c4\u0133\u00e1\u00ba\u00a7u",
- "\u0120endlich",
- "\u0120\u00ec\u012f\u00a8",
- "\u0120TT",
- "\u0120Lith",
- "\u00cf\u0122\u00ce\u00b1",
- "astern",
- "\u0120Azer",
- "\u0120lunar",
- "hic",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0122\u00d0\u00be\u00d0\u00b4",
- "\u0120nenhum",
- "\u00e8\u00b7\u0133",
- "\u0120Salvador",
- "\u0120Progress",
- "\u0120privileges",
- "\u0120\u00eb\u0131\u013b\u00ec\u0137\u012a",
- "\u0120antagon",
- "\u0120Impf",
- "\u0120descub",
- "\u0120Lei",
- "\u0120\u00ec\u0125\u012a\u00eb\u00a1\u013e",
- "\u00d1\u0129\u00d0\u00b5",
- "\u0120d\u00c3\u00b3lares",
- "\u0120Meghan",
- "\u0120Wire",
- "too",
- "aying",
- "usc",
- "\u0120tud",
- "\u0120appeals",
- "educ",
- "\u0120pane",
- "\u0120ji",
- "\u0120decks",
- "\u0120Alter",
- "\u0120\u00e5\u00b0\u00b1",
- "\u00ec\u0126\u00a4",
- "\u00e5\u012a\u0128\u00e9\u0132\u013a",
- "\u0120productions",
- "\u0120WILLIAM",
- "\u0120implied",
- "\u0120fulfillment",
- "\u0120Aah",
- "\u0120saja",
- "xus",
- "\u0120\u00ce\u013c\u00ce\u00b1\u00ce\u00b9",
- "\u00c3\u0142s",
- "ucch",
- "\u00d0\u00be\u00d0\u00ba\u00d0\u00be",
- "\u0120Discord",
- "\u0120SY",
- "jsk",
- "\u0120Wallace",
- "unction",
- "Daniel",
- "\u0120k\u00c3\u00b6t",
- "ijah",
- "\u0120marche",
- "\u0120disgr",
- "\u0120mungkin",
- "\u0120alma",
- "\u00b3\u00b5",
- "\u0120extensively",
- "\u0120Floren",
- "\u0120Allison",
- "\u00e3\u0124\u00b1",
- "\u00d9\u012c\u00d9\u0127",
- "\u0120juven",
- "\u0120Renaissance",
- "\u0120fundraising",
- "\u0120Chaos",
- "\u0120paraly",
- "\u0120narrator",
- "\u0120ecosystems",
- "Ash",
- "\u0120mitigation",
- "\u0120Aujourd",
- "\u0120Idee",
- "!,",
- "\u0120\u00c2\u00bd",
- "\u0120landlord",
- "\u0120defects",
- "\u0120acre",
- "ulsive",
- "\u0120algae",
- "pek",
- "\u0120emba",
- "\u0120Roc",
- "\u00e9\u013d\u00a2",
- "ksom",
- "\u00c3\u00a4che",
- "\u0120leuk",
- "\u0120leveraging",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0142\u0129\u00ec\u00a7\u0122",
- "\u0120Palm",
- "\u0120\u00c3\u00a4ven",
- "\u0120lis",
- "\u0120Insp",
- "\u0120Rita",
- "\u0120Abb",
- "ithm",
- "\u0120supervision",
- "\u0120revisit",
- "\u0120pi\u00c4\u013b",
- "\u0120euh",
- "\u0120fades",
- "\u0120motto",
- "\u00e5\u012f\u00a1",
- "\u00d0\u00b5\u00d0\u00b7\u00d0\u00b6",
- "\u0120Shim",
- "\u0120relevance",
- "\u0120oo",
- "\u0120ostat",
- "nica",
- "\u0120choix",
- "\u0120Faculty",
- "\u0120\u00ec\u00a4\u0133\u00ec\u0139\u0132",
- "\u0120Above",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a",
- "\u0120sequencing",
- "\u0120nutrient",
- "\u0120conquered",
- "\u0120digestive",
- "\u0120backdrop",
- "\u0120Lori",
- "ailable",
- "Game",
- "\u0120neglected",
- "omorph",
- "illah",
- "\u0120kne",
- "\u0120siit\u00c3\u00a4",
- "\u0120workspace",
- "\u0120Venice",
- "\u0120Kne",
- "\u00d1\u012b\u00d0\u00be",
- "\u0127\u0122",
- "\u0120Hass",
- "\u0120vita",
- "\u013f\u00bc\u00eb\u00a9\u00b4",
- "\u0120lays",
- "\u00c3\u00aancias",
- "\u00c3\u00a9rica",
- "\u0120Ll",
- "\u00e6\u00b1\u0124",
- "\u0120Coca",
- "\u0120WHY",
- "\u00e8\u012a\u0140",
- "\u0120routing",
- "\u0120permissions",
- "\u0120dings",
- "prend",
- "program",
- "\u0120crocod",
- "bral",
- "AAAAAAAA",
- "agit",
- "\u0120N\u00c3\u00a4",
- "\u0120gekommen",
- "atten",
- "\u0120referenced",
- "\u0120pairing",
- "\u0120Partner",
- "\u0120Coronavirus",
- "\u00d1\u0138\u00d1\u0123",
- "\u00e8\u00bd\u012b",
- "\u0120\u00d7\u0136\u00d7\u0135",
- "\u0120espec\u00c3\u0143fic",
- "arsi",
- "quelle",
- "\u0120spontaneous",
- "\u00e7\u0128\u00b1",
- "\u0120\u00ea\u00b2\u0125\u00ec\u013f\u0126",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u0123\u00d0\u00bb\u00d0\u00b5",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00af",
- "\u0120Shout",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00bb",
- "\u0120disguise",
- "\u0120Jord",
- "\u0120wee",
- "\u0120miejsc",
- "\u0120serum",
- "\u0120plaisir",
- "\u0120credible",
- "\u0120b\u00c3\u00a5",
- "\u0120AJ",
- "mares",
- "\u0120rods",
- "\u0120eran",
- "\u00e3\u0123\u00be\u00e3\u0123\u0124",
- "\u0120p\u00c3\u00a4\u00c3\u00a4",
- "\u0120UA",
- "\u0120Unknown",
- "\u0120\u00d9\u0126\u00d9\u0127",
- "\u0120Rabbi",
- "\u0120laat",
- "\u0120hairstyle",
- "\u0120\u00d8\u00ba",
- "\u00e9\u0123\u012d",
- "\u0120cach",
- "\u0120Writing",
- "\u00d0\u00be\u00d1\u0129\u00d0\u00ba\u00d0\u00b8",
- "abad",
- "\u0120straighten",
- "--\"",
- "wife",
- "\u0120hottest",
- "\u0120punya",
- "\u0120Fashion",
- "griff",
- "\u0120QR",
- "otch",
- "\u0120\u00d0\u013e\u00d0\u00be\u00d0\u00b6\u00d0\u00b5\u00d1\u0124",
- "Cloud",
- "\u0120Strike",
- "\u0120Hein",
- "\u0120\u00e7\u013e\u0141\u00e7\u013c\u0126",
- "\u0120lei",
- "\u0120Flow",
- "wegs",
- "\u0120habr",
- "\u00e5\u012b\u013d\u00e5\u012b\u013d",
- "nahme",
- "\u00cc\u0123",
- "\u0120pleasing",
- "opping",
- "\u0120\u00ea\u00b5\u00ac\u00eb\u0131\u0127",
- "\u0120dran",
- "\u0120bangs",
- "\u012079",
- "\u0120sket",
- "\u0120caval",
- "\u0120Macron",
- "\u0120weighted",
- "\u0120muted",
- "\u0120nuestras",
- "EEP",
- "\u0120mathematic",
- "\u0120MRI",
- "agus",
- "\u0120therapies",
- "\u00ce\u00b8\u00ce\u00b5",
- "\u0120unpl",
- "\u0120commencer",
- "full",
- "\u0120towels",
- "\u0120prue",
- "\u0120licenses",
- "\u00d7\u013d\u00d7\u0137\u00d7\u013e",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u0129\u00d0\u00b5\u00d0\u00bc\u00d1\u0125",
- "\u0120pointless",
- "Bye",
- "\u0120eligibility",
- "\u0120scrape",
- "\u0120abusive",
- "\u0120Mant",
- "\u0120jeunes",
- "tal",
- "\u0120Princip",
- "\u0120Orthodox",
- "\u0120melod",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d1\u0124\u00d0\u00b5\u00d1\u0122\u00d0\u00b8",
- "\u0120prosecutor",
- "\u0120opioid",
- "\u0120\u00d1\u0125\u00d0\u00b2\u00d0\u00b5\u00d1\u0122",
- "\u0120Been",
- "\u0120\u00ec\u0142\u0133\u00ec\u00a2\u0127",
- "\u0120dynasty",
- "\u0120ajuda",
- "\u0120entreg",
- "\u0120weighed",
- "\u0120eure",
- "\u0120Bem",
- "\u0120abnormal",
- "82",
- "\u0120JR",
- "\u0120Akt",
- "\u0120Bri",
- "\u00c3\u00bat",
- "\u0120stagn",
- "!*",
- "\u0120wegen",
- "\u0120leaking",
- "\u0120Words",
- "\u0120Mau",
- "\u0120vue",
- "\u0120Liam",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b5\u00d0\u00bc",
- "\u0120clinicians",
- "\u0120Pump",
- "\u0120f\u00c3\u00b6rst",
- "?...",
- "\u0120automotive",
- "\u0120Owen",
- "zusagen",
- "\u0120Hundred",
- "\u0120decentralized",
- "\u0120bulbs",
- "\u0120\u00d7\u013e\u00d7\u013d",
- "\u0120provinces",
- "\u0120Milan",
- "81",
- "kas",
- "\u0120\u00eb\u0135\u00a3",
- "\u0120for\u00c3\u00a7a",
- "\u0120rightly",
- "\u00e5\u00b3\u00b6",
- "r\u00c4\u0127",
- "\u0120venues",
- "\u0120wai",
- "\u0120predicting",
- "\u0120WiFi",
- "\u0120\u00ea\u00b6\u0123\u00ea\u00b8\u012a",
- "\u00d8\u00b1\u00d9\u012a",
- "\u0120\u00d7\u0136\u00d7\u0138",
- "century",
- "\u0120gradual",
- "\u0120Probleme",
- "\u0120\u00ec\u0139\u0127",
- "\u0120coping",
- "\u0120Brus",
- "\u0120peanuts",
- "irtschaft",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00bb",
- "\u0120Troy",
- "\u0120sperm",
- "\u0120Mitar",
- "\u0120T\u00c3\u00bcrkiye",
- "grand",
- "\u00a6\u0143",
- "\u0120\u00d7\u0140\u00d7\u00a1",
- "\u0120pans",
- "\u0120Knowledge",
- "berly",
- "\u0120\u00d0\u0137\u00d0\u00b3\u00d0\u00be",
- "\u0120danced",
- "\u0120Frost",
- "\u0120Burg",
- "\u0120biting",
- "\u00ec\u0142\u0137\u00ec\u013f\u0126",
- "meal",
- "\u0120heroic",
- "\u0120motherboard",
- "\u0120Licht",
- "\u00e3\u0123\u00a3\u00e3\u0123",
- "llan",
- "\u00d0\u00b0\u00d0\u00b9\u00d0\u00bd",
- "\u0120\u00d1\u0122\u00d1\u0131\u00d0\u00b4",
- "\u0120\u00e0\u00b9\u0122\u00e0\u00b8",
- "onen",
- "irie",
- "Art",
- "rang",
- "\u00ce\u00bd\u00ce\u00b7",
- "\u0120newborn",
- "\u0120amis",
- "\u0120\u00d8\u00a7\u00d9\u012a\u00d8\u00b1",
- "\u0120sophom",
- "\u0120Careful",
- "\u0120prospects",
- "ensen",
- "\u0120thrill",
- "\u0120Vi\u00e1\u00bb\u0129t",
- "Adam",
- "rition",
- "entric",
- "uden",
- "\u0120certificates",
- "\u0120ashes",
- "\u00e8\u00aa\u00bf",
- "playing",
- "\u0120sadece",
- "\u0120ost",
- "\u0120airplanes",
- "\u00d1\u0122\u00d0\u00be\u00d0\u00ba",
- "oner",
- "\u0120magnesium",
- "\u0120goddamn",
- "\u01201972",
- "\u0120Schule",
- "\u0120temat",
- "\u0120partout",
- "\u00e0\u00af\u0124",
- "\u0120inve",
- "\u0120Scientists",
- "\u0120Hudson",
- "winning",
- "ceksin",
- "\u0120congressional",
- "oru",
- "\u0120ropes",
- "\u00d0\u00b2\u00d0\u00b5\u00d0\u00b4",
- "\u0120madre",
- "\u0120ferry",
- "\u0120Cohen",
- "\u0120Pred",
- "\u0120vagy",
- "\u0120\u00d0\u00b1\u00d0\u00b5\u00d1\u0123\u00d0\u00bf",
- "\u0120multim",
- "\u0120drainage",
- "\u0120simulator",
- "giggles",
- "\u0120Stadium",
- "\u00d0\u00be\u00d0\u00b1\u00d1\u012b",
- "\u0120notices",
- "\u0120crawling",
- "\u0120groupe",
- "\u00e5\u0131\u00b8",
- "\u0120kto\u00c5\u013d",
- "\u0120Yoga",
- "\u0120medida",
- "\u0120\u00d1\u0127\u00d0\u00b2\u00d0\u00b0\u00d1\u0124",
- "\u0120Lite",
- "\u0120rav",
- "orama",
- "\u0120discord",
- "\u0120DIRE",
- "\u0120teh",
- "\u0120Nurs",
- "\u00e7\u00b2\u012b",
- "\u0120pitched",
- "\u0120barking",
- "\u0120Coke",
- "wiad",
- "\u0120populated",
- "\u00e9\u013b\u00a4",
- "pelled",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00b3",
- "\u0120pewno",
- "\u0120Cube",
- "\u0120recruited",
- "\u00e9\u0122\u013b\u00e7\u00a8\u00ae",
- "\u0120Cara",
- "\u00c4\u00b1\u00c4\u0141\u00c4\u00b1n\u00c4\u00b1",
- "imated",
- "\u0120\u00d1\u012a\u00d0\u00ba\u00d0\u00be\u00d0\u00bb",
- "icional",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0126",
- "\u0120contamination",
- "\u0120\u00c3\u00baltimos",
- "\u0120fearful",
- "\u0120elephants",
- "usi",
- "\u0120iTunes",
- "\u0120Swami",
- "\u00ea\u00bc",
- "\u0120\u00ec\u0126\u00a4\u00eb\u00aa\u0127",
- "\u0120Richards",
- "\u0120magnets",
- "\u0120Richtung",
- "\u0120Legion",
- "\u00e8\u0131\u013e",
- "\u0120kitty",
- "\u0120kissed",
- "\u0120watering",
- "\u0120cono",
- "\u0120Palestine",
- "idir",
- "\u0120maze",
- "\u0120fluids",
- "\u0120Producer",
- "\u0120Krsna",
- "\u00e5\u00a5\u00bd\u00e5\u0137\u00a6",
- "laf",
- "\u0120\u00d7\u0132\u00d7\u0137",
- "\u0120miesz",
- "\u0120Xing",
- "ointed",
- "sein",
- "\u0120Fuk",
- "\u0120Depression",
- "\u0120Duty",
- "\u0120Panther",
- "\u0120sund",
- "\u0120refere",
- "\u0120exclusion",
- "\u0120naval",
- "\u0120Winston",
- "\u0120slogan",
- "\u0120hypothetical",
- "\u0120elevate",
- "\u00eb\u0142\u00b9",
- "\u0120cabe\u00c3\u00a7a",
- "\u0120Gesund",
- "meter",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u012a\u00eb\u00a9\u00b4",
- "\u0120cloudy",
- "\u00e2\u0122\u00a6?",
- "\u0120Schritt",
- "\u0120JS",
- "\u00ec\u012f",
- "\u0120Springs",
- "\u0120Batter",
- "\u00b7\u00b0",
- "\u0120tailor",
- "\u0120PTSD",
- "\u0120Gent",
- "\u0120ba\u00c4\u0141",
- "\u0120spatula",
- "\u0120cray",
- "\u0120Legisl",
- "\u0120s\u00c3\u00ba",
- "\u0120leve",
- "\u00e0\u00b8\u00b2\u00e0\u00b8\u00a1",
- "\u0120erad",
- "\u0120dong",
- "\u0120derm",
- "\u0120Banks",
- "icho",
- "\u00e5\u0127\u012a\u00e7\u0136\u0141",
- "\u0120Franz",
- "ravel",
- "\u00e9\u0123\u0136",
- "\u00d0\u00be\u00d0\u00bb\u00d0\u00be",
- "\u0120flute",
- "\u0120Ek",
- "\u0120joyful",
- "\u0120chased",
- "\u0120Large",
- "Over",
- "\u0120entrepreneurial",
- "\u0120considers",
- "\u00d1\u0125\u00d0\u00b5\u00d0\u00bc",
- "opa",
- "\u0120dormir",
- "\u0120Elementary",
- "\u0120przypad",
- "\u00d1\u0125\u00d1\u0123\u00d0\u00ba\u00d0\u00b0",
- "\u0120\u00d0\u00be\u00d1\u0129\u00d0\u00b5\u00d1\u0122",
- "ugene",
- "\u0120tenido",
- "\u0120lugares",
- "\u00eb\u00a5",
- "\u0120\u00d1\u0129\u00d0\u00b0\u00d1\u0123\u00d1\u0124",
- "\u0120sao",
- "\u0120braid",
- "\u0120Vere",
- "\u0120Reich",
- "\u0120Poss",
- "\u0120inan",
- "wand",
- "ref",
- "\u0120montrer",
- "\u01201981",
- "\u00e7\u0137\u00aa",
- "as\u00c4\u00b1nda",
- "\u0120chrome",
- "\u0120Trinity",
- "\u0120exploitation",
- "\u0120Sense",
- "\u0120CMS",
- "\u0120Noble",
- "\u0120\u00ec\u0126\u0142\u00ed\u0125\u013f",
- "\u0120swelling",
- "electronic",
- "]?",
- "\u0120brushing",
- "\u0120liquidity",
- "\u0120Hook",
- "\u0120Connor",
- "\u0120Alum",
- "\u0120gucken",
- "suite",
- "\u0120wiele",
- "\u0120barrels",
- "\u0120Regel",
- "\u0120Ment",
- "\u0120Trip",
- "\u0120Brush",
- "\u0120Erik",
- "urate",
- "\u00c9\u013br",
- "\u0120Cyr",
- "ouble",
- "\u0120Becca",
- "\u0120passwords",
- "\u00c5\u00b1",
- "borg",
- "\u0120vendo",
- "\u0120Claus",
- "\u0120Faz",
- "indest",
- "\u0120deceased",
- "\u0120comparisons",
- "\u0120LCD",
- "\u0120Pork",
- "\u0120eventual",
- "\u0120patreon",
- "\u0120inability",
- "\u0120extinction",
- "\u0120\u00ec\u00a2\u012d\u00ec\u0137\u0126\u00ed\u0137\u013a\u00eb\u012c\u0136",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d1\u0123",
- "aju",
- "\u0120\u00d7\u0133\u00d7\u0132\u00d7",
- "\u0120sofort",
- "\u0120destined",
- "\u0120Rin",
- "\u0120mouths",
- "\u0120Nat\u00c3\u00bcrlich",
- "\u0120preserving",
- "\u0120limp",
- "\u00e9\u00bb\u00a8",
- "ocused",
- "\u00d0\u00b8\u00d0\u00bd\u00d0\u00b3",
- "\u0120exposing",
- "\u0120\u00ce\u00be",
- "\u00eb\u012f",
- "laugh",
- "\u0120hiss",
- "\u00e3\u0123\u0142\u00e3\u0123\u012d\u00e3\u0124\u012b",
- "\u0120indie",
- "\u0120detal",
- "\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "\u0120tr\u00c3\u00aan",
- "\u00e6\u0137\u00b0",
- "\u0120ogni",
- "\u0120simplemente",
- "\u01201978",
- "\u0120goo",
- "\u01201967",
- "\u0120genug",
- "h\u00c3\u00b6",
- "\u0120hist\u00c3\u00b3",
- "\u00e5\u00ae\u0141",
- "\u0120lobster",
- "cendo",
- "\u0120teil",
- "\u0120allevi",
- "0000",
- "OLD",
- "\u0120pesos",
- "\u0120bonuses",
- "\u0120ami",
- "\u0120revival",
- "\u0120Horse",
- "\u0120sack",
- "Talk",
- "\u0120mulher",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d1\u0131\u00d0\u00bd",
- "\u0120Hood",
- "Huh",
- "\u0120\u00eb\u00b6\u0123",
- "\u0120hyung",
- "\u0120Meeting",
- "\u0120importa",
- "\u0120\u00ec\u00b0\u00be\u00ec\u0137\u0126",
- "\u0120Vern",
- "\u0120stripped",
- "\u0120refuses",
- "\u0120qualifications",
- "opl",
- "\u0122\u00eb\u0131\u0126",
- "ix\u00c3\u0143",
- "\u0120diab",
- "itime",
- "flows",
- "\u0120inac",
- "\u0120Gong",
- "\u0120meaningless",
- "\u0120courageous",
- "\u0120microbi",
- "azy",
- "hist",
- "\u0120volunteering",
- "VIE",
- "\u0120violated",
- "\u0120sympathy",
- "\u0120Edit",
- "\u00e5\u00a5\u00bd\u00e5\u0125\u0131",
- "electric",
- "product",
- "\u0120pandemia",
- "\u0120geometric",
- "\u0120Convers",
- "gre",
- "\u0120glut",
- "isted",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d9\u0125",
- "\u0120Chain",
- "\u0120Present",
- "\u0120Yin",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b3",
- "\u0120Vlog",
- "\u0120\u00ec\u0138\u00b4\u00eb\u00a8\u00b8",
- "\u0120donn",
- "\u0120hitch",
- "ucking",
- "\u00e3\u0123\u012c\u00e3\u0123\u0126",
- "wald",
- "risk",
- "\u0120hari",
- "\u0120Kens",
- "\u0120Idol",
- "\u0120\u00d0\u00b2\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b5",
- "\u0120todd",
- "\u0120smashed",
- "\u0120invari",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bd\u00d1\u0124\u00d1\u0122",
- "\u0120autistic",
- "\u00ec\u0140\u00a5\u00eb\u012d\u013a",
- "Res",
- "\u00d0\u00b4\u00d1\u012d",
- "chau",
- "\u0120selv",
- "\u0120h\u00c3\u00a4tten",
- "\u00e0\u00a4\u00bf",
- "\u0120expects",
- "\u00cf\u0123\u00ce\u00b7",
- "\u0120a\u00c3\u00a7\u00c4\u00b1k",
- "\u0120HTTP",
- "le\u00c5\u0141",
- "\u0120sweeping",
- "\u0120Beta",
- "\u0120counterparts",
- "abile",
- "\u0120Sims",
- "Cs",
- "\u0120repar",
- "squ",
- "\u0120provincial",
- "\u0120shareholders",
- "\u0120runter",
- "\u0120gedacht",
- "\u0120Teen",
- "\u0120grands",
- "\u00e7\u0136\u00a2",
- "agles",
- "\u0120rocky",
- "vens",
- "\u0120rivals",
- "unal",
- "\u0120reacts",
- "\u00eb\u00a9",
- "\u0120mercury",
- "\u0120Luigi",
- "\u0120\u00d0\u00be\u00d0\u00b3",
- "\u0120JUST",
- "\u0120lod",
- "\u0120cortex",
- "wig",
- "\u0120lakh",
- "\u00ec\u00a4\u0133\u00ec\u0139\u0132",
- "\u0120Vic",
- "\u0120Mund",
- "\u0120mapped",
- "\u0120Dell",
- "\u0120Druck",
- "\u0120lifes",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be\u00d0\u00b5",
- "ividual",
- "ad\u00c4\u00b1m",
- "\u0120atrav",
- "\u0120Flug",
- "\u0120Klein",
- "\u00ea\u00b1\u00b0\u00ec\u0137\u00bc",
- "\u00e0\u00b8\u00ab\u00e0\u00b8\u013b",
- "\u0120appli",
- "\u00e0\u00ae\u00be?",
- "\u00c3\u00bcyorum",
- "\u0120\u00d0\u00b8\u00d0\u00bd\u00d1\u0124\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d1\u0123\u00d0\u00bd\u00d0\u00be",
- "\u0120disinfect",
- ">-",
- "\u0120champagne",
- "\u0120kla",
- "opers",
- "Trans",
- "\u0120Desert",
- "\u0120cultivate",
- "\u0120Fucking",
- "idelity",
- "\u0120\u00d1\u0124\u00d0\u00b0\u00d0\u00bd",
- "\u0120incub",
- "\u0120temu",
- "\u0120learner",
- "founder",
- "\u0120Syl",
- "\u00e3\u0124\u0122",
- "\u0120fato",
- "zier",
- "\u0120\u00ec\u0139\u0128\u00ec\u013f\u00b4",
- "\u0120\u00ec\u012a\u00a8",
- "\u0120psycho",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d0\u00b5\u00d1\u0126",
- "\u0120regarde",
- "\u0120representations",
- "\u0120litigation",
- "\u0120spann",
- "ults",
- "bior",
- "\u00e8\u00a6\u012d\u00e3\u0123\u00a6",
- "\u00e4\u00b8\u012f\u00e5\u00a4\u013c",
- "\u0120Survey",
- "\u0120LEDs",
- "\u0120tr\u00c3\u00a4",
- "\u0120l\u00c3\u00aan",
- "\u0120antioxid",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00be\u00d0\u00bc",
- "\u0120induction",
- "\u0120fooled",
- "\u00c3\u00a4tzlich",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d1\u0122\u00d1\u0131\u00d1\u0124",
- "\u0120Fact",
- "umbai",
- "\u0120wiggle",
- "NOUN",
- "\u0120d\u00c3\u00a9velopp",
- "\u0120Claro",
- "\u0120\u00ec\u00b8",
- "\u00eb\u00ac",
- "\u00e3\u0123\u00aa\u00e3\u0124\u0135\u00e3\u0123\u0142",
- "\u0120accumulate",
- "\u0120maintains",
- "\u00eb\u0126",
- "\u0120Fighter",
- "\u00ed\u0128\u0142",
- "\u0120matin",
- "\u0120coupon",
- "\u0120stunt",
- "\u0120debuted",
- "\u00e5\u00be\u0127\u00e3\u0123\u00a3\u00e3\u0123\u00a6",
- "\u0120prag",
- "\u00d0\u00b8\u00d0\u00b2\u00d0\u00b0\u00d0\u00b5\u00d0\u00bc",
- "73",
- "\u0120expres",
- "\u0120\u00ec\u013a\u00a4\u00eb\u00b9\u0142",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d1\u0123\u00d0\u00be\u00d0\u00bd",
- "\u0120calculus",
- "\u0120abrupt",
- "\u0120Inspector",
- "ourt",
- "\u00e6\u0138\u013b",
- "\u00c5\u00baniej",
- "intense",
- "Ba",
- "\u0120lounge",
- "\u0120asthma",
- "\u0120Hi\u00c3\u00a7",
- "\u00aa\u00bb",
- "\u0120editorial",
- "\u0120seize",
- "\u0120k\u00c4\u00b1r",
- "\u0120mouve",
- "\u0120tierra",
- "\u0120testosterone",
- "\u0120rh",
- "\u0120Kingston",
- "ELLE",
- "\u0120Representative",
- "\u01201974",
- "\u0120iba",
- "Ts",
- "\u0120sorta",
- "\u0120(?)",
- "\u0120\u00d8\u00aa\u00d9\u012a",
- "\u0120\u00eb\u0124\u00b4\u00eb\u0142\u00a4",
- "\u0120bekommt",
- "\u0120spiritually",
- "\u0120distorted",
- "Mad",
- "\u0120reim",
- "\u00c3\u00a1nh",
- "\u0120Ottoman",
- "\u0120Relig",
- "\u0120Els",
- "\u0120retained",
- "\u0120Laughs",
- "\u00e6\u0122\u00bb",
- "\u0120SAS",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bb\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00be",
- "\u00d7\u0137\u00d7\u00aa\u00d7\u00a8",
- "\u0120innovate",
- "\u0120kork",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0123\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7\u00d1\u012d\u00d0\u00b2",
- "ondere",
- "ivi",
- "aye",
- "ounty",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d1\u0125\u00d1\u0129\u00d0\u00b0\u00d0\u00b5\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120buns",
- "\u00e5\u0127\u00ab",
- "\u0120y\u00c3\u00bczden",
- "\u0120surgeries",
- "\u00d8\u00a3\u00d9\u0128",
- "\u0120bankruptcy",
- "welt",
- "\u0120siamo",
- "\u0120darkest",
- "\u0120Hann",
- "gga",
- "\u0120formas",
- "\u0120Dj",
- "named",
- "\u0120shields",
- "ueller",
- "\u0120Few",
- "\u0120lace",
- "\u0120furious",
- "\u0120YU",
- "\u0120societal",
- "\u0120judgement",
- "\u0120Dos",
- "\u0120jab",
- "laws",
- "\u0120reinvent",
- "\u0120Katherine",
- "\u0120Choi",
- "adows",
- "\u0120rans",
- "oden",
- "\u0120Midwest",
- "n\u00c4\u00b1n",
- "\u0120deport",
- "\u0120Dip",
- "\u00e7\u00b4\u0127",
- "\u0120atenci\u00c3\u00b3n",
- "\u0120Courtney",
- "ividad",
- "\u0120\u00da\u00a9\u00db\u0123",
- "\u0120efficacy",
- "\u0120Brooks",
- "\u0120referral",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bd\u00d1\u0128",
- "\u0120malicious",
- "\u0120kir",
- "\u0120Goddess",
- "\u0120funky",
- "\u0120interim",
- "\u0120K\u00c3\u00b6rper",
- "\u0120\u00ec\u0138\u00bc\u00eb\u00a7",
- "kur",
- "\u0120\u00d0\u00ba\u00d0\u00bb\u00d0\u00b8",
- "\u0120trucs",
- "gesetz",
- "\u0120zug",
- "\u0120Gl\u00c3\u00bcck",
- "\u0120Minute",
- "\u0120prestigious",
- "\u0120niez",
- "\u0120concentrations",
- "\u00d0\u00bb\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00b8",
- "\u0120Sis",
- "\u0120Vitamin",
- "kov",
- "\u0120PBS",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b5",
- "\u0120retailers",
- "\u0120conventions",
- "\u0120Samantha",
- "\u0120proudly",
- "Jordan",
- "\u0120JASON",
- "atk",
- "\u0120triste",
- "\u0120st\u00c3\u00a4r",
- "\u0120reiterate",
- "\u0120posterior",
- "\u01201973",
- "\u0120Pine",
- "\u0120Juliet",
- "\u0120pedir",
- "kil",
- "\u0120overlapping",
- "\u0120exclude",
- "\u0120econ\u00c3\u00b3m",
- "\u0120accepts",
- "\u0120Ster",
- "\u00e6\u00b1\u00ba",
- "\u0120\u00ec\u013c\u00b4\u00eb\u0131\u013b",
- "estab",
- "\u0120tug",
- "arg",
- "\u0120livro",
- "\u00d8\u00a7\u00d8\u00b5",
- "\u0120seams",
- "\u0120buraya",
- "\u0120ello",
- "\u0120TM",
- "\u0120Paw",
- "\u0120Index",
- "Exc",
- "\u0120inspirational",
- "\u0120dunk",
- "\u00e8\u00b0\u0123",
- "akter",
- "\u0120conditioner",
- "\u0120Salut",
- "\u00c5\u0124ec",
- "\u0120\u00ec\u012b\u00bd",
- "\u0120\u00d1\u0125\u00d0\u00b7\u00d0\u00bd\u00d0\u00b0",
- "\u0120Romeo",
- "fruit",
- "\u0120YO",
- "\u0120ch\u00e1\u00bb\u012b",
- "\u00d0\u00b1\u00d1\u0125",
- "bons",
- "\u0120reproductive",
- "\u0120orada",
- "\u0120\u00ed\u013c\u00a8",
- "\u0120tentar",
- "\u0120ma\u00c3\u00b1ana",
- "\u00e3\u0124\u00ac",
- "\u0120solvent",
- "Jessica",
- "\u0120Legal",
- "\u0120tua",
- "\u0120sic",
- "\u0120EQ",
- "aukee",
- "\u00ec\u012d\u013e\u00eb\u012d\u00a4",
- "\u0120\u00c5\u0140u",
- "\u0120adhere",
- "\u0120Tul",
- "\u0120\u00e0\u00ae\u0128",
- "\u0120textbooks",
- "\u0120Fifth",
- "\u0120experi",
- "\u0120chic",
- "\u0120heap",
- "inely",
- "atra",
- "Two",
- "\u0120helemaal",
- "\u0120fren",
- "\u00e6\u0130\u00a8",
- "\u0120bisher",
- "\u00d8\u00a7\u00d8\u00b4",
- "\u0120\u00ec\u0126\u0142\u00ec\u0125\u013f",
- "\u0120Tages",
- "\u0120s\u00e1\u00bb\u00b1",
- "\u0120bullied",
- "\u00d8\u00a4",
- "\u0120benefited",
- "\u0120Previously",
- "\u0120\u00d1\u012f\u00d1\u0126\u00d1\u0126",
- "\u00d9\u012f",
- "\u0120senate",
- "\u0120Morm",
- "ijke",
- "\u0120Flu",
- "\u0120incorporating",
- "jack",
- "\u0120\u00d0\u00bf\u00d0\u00b8\u00d1\u0124",
- "\u0120imply",
- "\u0120hacks",
- "\u0120RICH",
- "\u0120\u00d0\u00ba\u00d0\u00b2\u00d0\u00b0\u00d1\u0122",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0123",
- "\u0120dependency",
- "\u0120\u00ec\u013c\u00a9",
- "\u0120\u00ec\u00b1\u0127",
- "\u0120w\u00c3\u00a4hrend",
- "\u0120sulla",
- "\u0120Pittsburgh",
- "\u0120esempio",
- "\u00bc\u00eb\u00a1\u013e",
- "prot",
- "\u0120Rosen",
- "\u0120Independence",
- "\u0120parsley",
- "iegen",
- "\u0120haw",
- "\u0120aquell",
- "\u0120CAP",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b1\u00d0\u00be\u00d1\u0124\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120Cliff",
- "ionar",
- "\u0120securing",
- "\u00e6\u012a\u0133\u00e5\u0122\u0133\u00e7\u013c\u0126",
- "\u00ce\u00bd\u00ce\u00b5",
- "\u0120utilis",
- "\u0120coule",
- "\u0120Ping",
- "\u0120trek",
- "\u0120fak",
- "\u0120enorme",
- "\u0120\u00ec\u012d\u00ab",
- "\u00e8\u00ae\u00a9",
- "\u0120doubling",
- "\u0120\u00d0\u00bd\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d0\u00b8\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120hed",
- "hoven",
- "\u0120Standing",
- "\u0120m\u00c3\u0143n",
- "\u0120Jimin",
- "\u0120monarch",
- "\u0120coke",
- "\u0120mr",
- "\u0120clic",
- "\u00c3\u012f",
- "\u0120impeachment",
- "\u0120durability",
- "\u0120varios",
- "\u0120commercials",
- "\u0120greetings",
- "\u0120Ri",
- "\u0120Appreci",
- "\u00ec\u0140\u012a\u00eb\u012c\u0136",
- "\u0120r\u00c3\u00a9sult",
- "\u00c3\u00a9rt",
- "\u0120salute",
- "\u0120poderia",
- "\u0120sunrise",
- "veck",
- "\u0120reluctant",
- "\u0120commissioner",
- "\u00e5\u00bf\u00b5",
- "\u00c3\u00a2te",
- "\u0120Kenny",
- "\u0120Siri",
- "\u00e3\u0125\u0125\u00e3\u0125\u0139",
- "\u0120\u00eb\u012c\u013a",
- "\u0120EE",
- "\u0120unch",
- "\u00d0\u00ba\u00d0\u00be\u00d0\u00bd",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00a5",
- "\u0120belts",
- "\u0120hass",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d1\u0131",
- "\u0120displaced",
- "\u0120abra",
- "\u00ce\u0143\u00ce\u00bb",
- "\u0120scratches",
- "\u0120comet",
- "\u0120authorization",
- "\u0120LLC",
- "\u0120produk",
- "\u0120rehabilitation",
- "\u00e5\u0140",
- "\u00d1\u0138\u00d1\u0129",
- "uding",
- "olit",
- "\u0120105",
- "\u0120expands",
- "\u0120altri",
- "\u0120Komment",
- "\u0120anf",
- "Pl",
- "\u0120Mana",
- "fed",
- "\u0120bri",
- "\u0120ora",
- "Gs",
- "\u0120Gur",
- "uckland",
- "\u0120junction",
- "\u0120ironic",
- "\u0120Feed",
- "\u0120prakt",
- "\u0120Hammer",
- "\u012e\u00eb\u0131\u0126",
- "\u0120Tracy",
- "\u00e7\u00b5\u00b1",
- "\u0120Aside",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00b3\u00d0\u00be",
- "\u0120\u00d0\u00b8\u00d1\u0123\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00b7\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120zaj",
- "\u0120equitable",
- "\u0120curb",
- "\u0120\u00e3\u0123\u0135\u00e3\u0124\u012e",
- "\u0120derivatives",
- "\u0120puppies",
- "\u0120Kenneth",
- "\u0120Compl",
- "igram",
- "\u0120Garcia",
- ")\"",
- "\u0120Harbor",
- "estial",
- "\u0120\u00e4\u00be\u0128",
- "\u0120ers",
- "\u00e6\u00b9",
- "\u0120unwanted",
- "\u0120belang",
- "\u00d0\u00b0\u00d0\u00b3\u00d0\u00be",
- "emb",
- "dos",
- "\u0120\u00ec\u013b\u013e\u00eb",
- "\u0120Budget",
- "\u0120battling",
- "\u00d8\u0143\u00d8\u00aa",
- "kok",
- "\u00d0\u00bd\u00d0\u00b0\u00d1\u0129\u00d0\u00b0\u00d0\u00bb\u00d0\u00b0",
- "\u0120plag",
- "\u0120cantidad",
- "\u0120grupos",
- "\u0120plugins",
- "lerini",
- "\u0120\u00d0\u00b8\u00d0\u00bc\u00d0\u00b5\u00d0\u00b5\u00d1\u0124",
- "\u0120sozusagen",
- "olics",
- "\u0120pueblo",
- "\u0120reminis",
- "r\u00c3\u00a4n",
- "\u0120Morrison",
- "\u0120linha",
- "\u0120breaths",
- "\u0120Taste",
- "\u0120enfrent",
- "\u0120Docker",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bd",
- "\u0120ethnicity",
- "\u0120wob",
- "\u0120suffers",
- "\u0120transitioning",
- "\u0120Range",
- "\u00c4\u013bdzy",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d1\u0124",
- "\u0120syner",
- "\u0120donut",
- "\u0120probabilities",
- "\u0120Omar",
- "Which",
- "uish",
- "isin",
- "\u0120demos",
- "\u0120\u00ec\u0142\u0122\u00ea\u00b8\u00b0",
- "\u0120\u00eb\u013a\u0133\u00ea\u00b0\u013b",
- "\u0120\u00d0\u00b5\u00d0\u00b4\u00d0\u00b8\u00d0\u00bd",
- "\u0120cerve",
- "\u0120joka",
- "IAN",
- "\u0120kilometer",
- "\u0120horizontally",
- "\u0120Bhag",
- "\u0120->",
- "\u0120Monitor",
- "\u0120knowledgeable",
- "\u0120fav",
- "\u0120pinned",
- "\u0120eBay",
- "icker",
- "\u0120\u00ec\u0140\u0142\u00ea\u00b9\u0132\u00eb\u00a7\u012e",
- "\u0120Xiaomi",
- "\u0120capit",
- "\u0120np",
- "\u01201965",
- "hoe",
- "\u0120nok",
- "\u0120Sage",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00bb\u00d1\u012e\u00d0\u00b7\u00d1\u0131",
- "\u0120Tow",
- "gam",
- "\u0120dicen",
- "\u0120SUBSCRIBE",
- "\u0120reboot",
- "\u0120paj",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u0139\u00ac\u00eb",
- "\u0120thicken",
- "\u0120Reality",
- "id\u00c3\u00a4n",
- "Na",
- "\u0120\u00ea\u00b2\u0125\u00ec\u013f\u0122",
- "!!)",
- "\u0120routines",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "\u0120exting",
- "\u0120\u00ec\u00a6\u013f",
- "\u0120sulfur",
- "\u0120carve",
- "\u0120asteroid",
- "\u0120Warrior",
- "\u0120photographers",
- "\u0120pell",
- "\u0120crossover",
- "\u00e6\u012a\u0133\u00e7\u0141\u00a5\u00e9\u0123\u0135",
- "\u0120hacemos",
- "\u0120Nej",
- "\u0120settling",
- "\u0120irm",
- "\u0120Books",
- "ient\u00c3\u00b4t",
- "\u0120espacio",
- "\u0120Scholars",
- "\u0120doomed",
- "\u0120IRS",
- "wohl",
- "\u0120segue",
- "\u0120\u00eb\u012a\u0126\u00ea\u00b0\u0122",
- "\u0120pratic",
- "BT",
- "\u0120Considering",
- "\u0120Buffalo",
- "\u0120trainings",
- "\u0120gebru",
- "\u0120Gleich",
- "\u0120pirates",
- "\u0120envelop",
- "\u0120reopen",
- "imat",
- "\u0120tee",
- "\u0120sued",
- "feh",
- "\u0120\u00d7\u0136\u00d7\u00a7",
- "\u0120diets",
- "\u0120juntos",
- "asto",
- "\u0120misunderstood",
- "\u0120ruim",
- "\u0120classify",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b4\u00d1\u0125\u00d0\u00ba",
- "\u0120inse",
- "\u0120illustrated",
- "\u0120corrosion",
- "\u0120accred",
- "\u0120Auntie",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120LIVE",
- "\u0120rek",
- "\u0120receipt",
- "\u00e5\u012a\u00b0\u00e5\u00ba\u0137",
- "\u0120Barbie",
- "\u0120Snake",
- "turn",
- "Jeff",
- "\u00e3\u0123\u012c\u00e3\u0123\u012c",
- "\u0137\u0126",
- "VOICEOVER",
- "coll",
- "\u0120runners",
- "\u00ec\u0142\u013e\u00eb",
- "osos",
- "moon",
- "\u0120keynote",
- "\u0120Instit",
- "SPEAK",
- "\u0120plugs",
- "\u0120curv",
- "\u0120Yuri",
- "\u0120Theres",
- "\u0120Ps",
- "\u0120\u00ce\u00bc\u00cf\u0122\u00ce\u00bf",
- "\u0120converter",
- "\u0120refine",
- "\u0120badass",
- "\u0120\u00ce\u00bf\u00ce\u00b9",
- "\u0120regen",
- "azzi",
- "\u00d9\u012c\u00d9\u0123",
- "\u0120seized",
- "\u0120i\u00c3\u00a7er",
- "ilee",
- "\u0120upstream",
- "\u0120buds",
- "\u0120pim",
- "\u0120\u00ed\u0137\u013a\u00eb\u00a3\u00a8",
- "\u0120alluded",
- "\u0120themed",
- "\u0120consisting",
- "\u0120bons",
- "unuz",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d0\u00b4",
- "\u0120Lovely",
- "\u00e0\u00a5\u012d",
- "\u0120parach",
- "\u0120Staats",
- "\u00e9\u013c\u012c",
- "\u0120selective",
- "\u0120fase",
- "\u0120Georget",
- "\u0120cocaine",
- "\u0120reproduction",
- "\u0120Lara",
- "\u0120LD",
- "\u0120gh",
- "Jon",
- "\u0120l\u00c3\u00a5",
- "\u0120\u00eb\u0133\u0132\u00eb",
- "\u0120typed",
- "\u0120Bana",
- "\u00eb\u0135\u013e\u00eb",
- "\u0120savory",
- "\u0120Zomb",
- "standen",
- "\u0120pedestrian",
- "\u0120diff\u00c3\u00a9rents",
- "\u0120\u00ec\u012d\u00b8",
- "\u00e8\u012b\u00af",
- "\u0120complained",
- "\u00e7\u00a6\u0131",
- "\u0120\u00d0\u013c\u00d1\u0124\u00d0\u00be",
- "\u0120\u00d7\u013e\u00d7\u00a4",
- "ali\u00c5\u013dmy",
- "\u0120mortar",
- "\u0120verdict",
- "\u0120suficiente",
- "\u0120Million",
- "mittel",
- "inals",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00ae",
- "\u00d0\u00b0\u00d1\u0130\u00d1\u0123\u00d1\u012e",
- "\u0120mi\u00c4\u013bdzy",
- "\u0120Ole",
- "\u0120invert",
- "czy\u00c4\u0129",
- "\u00d0\u00be\u00d0\u00b7\u00d0\u00bc\u00d0\u00be\u00d0\u00b6\u00d0\u00bd\u00d0\u00be",
- "starter",
- "\u0120auditor",
- "\u0120Scout",
- "chien",
- "\u0120Sverige",
- "uffled",
- "\u0120zehn",
- "\u0120Auckland",
- "\u0120argent",
- "\u01201976",
- "\u0120Hoe",
- "\u0120bothers",
- "\u0120socialist",
- "\u0120pliers",
- "\u0120emergen",
- "\u0120XP",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "More",
- "\u0120Levi",
- "\u0120Anders",
- "ibilidad",
- "\u0120Parents",
- "\u0120induced",
- "\u00ec\u0138\u00b4\u00ec\u00a4",
- "\u0120balances",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d1\u012a",
- "\u0120submarine",
- "Start",
- "\u0120dries",
- "\u0120volver",
- "\u0120ticking",
- "cott",
- "\u0120faj",
- "pr\u00c3\u00a9s",
- "\u0120Sabb",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d1\u0129",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00ba\u00d1\u0125\u00d0\u00bf",
- "\u0120baptized",
- "\u0120Brilliant",
- "\u0120\u00d0\u0133\u00d0\u00be\u00d0\u00b3",
- "\u0120mots",
- "bits",
- "\u0120lattice",
- "\u00e6\u012a\u0133\u00e8\u00b7\u0141\u00e4\u00bd\u0142",
- "\u0120coriander",
- "\u0120residency",
- "ync",
- "\u0120pierwszy",
- "\u0120Knock",
- "\u0120Zap",
- "\u0120\u00d0\u0137\u00d0\u00b2",
- "\u00ea\u00b2\u00ac",
- "\u00e5\u00b0\u0131\u00e5\u00bf\u0125",
- "\u0120uneven",
- "\u0120Jas",
- "odor",
- "\u00e7\u00bf\u0134",
- "74",
- "\u0120Site",
- "\u0120aconteceu",
- "ympt",
- "\u0120trilogy",
- "\u0120lantern",
- "\u0120Zucker",
- "vari",
- "welling",
- "\u0120Potato",
- "gomery",
- "\u0120reacted",
- "\u0120Chron",
- "\u0120jede",
- "beeld",
- "\u0120twent",
- "\u0120lact",
- "\u00e6\u00a8\u0124",
- "\u0120r\u00c3\u00a9se",
- "\u0120relent",
- "\u0120furnace",
- "\u0120widget",
- "\u0120earthquakes",
- "\u0120Adjust",
- "ilit",
- "\u0120\u00d8\u00a3\u00d9\u012a",
- "\u0120hearings",
- "\u0120defendant",
- "irsiniz",
- "\u0120bask",
- "cja",
- "\u013e\u00a8",
- "\u0120rifles",
- "\u0120instal",
- "\u0120Forgive",
- "pical",
- "\u0120\u00d0\u0140\u00d1\u0129\u00d0\u00b5\u00d0\u00bd\u00d1\u012e",
- "\u0120petites",
- "\u0120hp",
- "\u0120renowned",
- "\u0120Inn",
- "\u0120\u00ec\u00a3\u00bc\u00ec\u0126\u00b8\u00ec\u013c\u0136",
- "\u0120emphasized",
- "\u00e9\u0139\u00ae\u00e9\u00a2\u013a",
- "\u0120\u00ec\u0140\u012a\u00ec\u00a3\u0142",
- "\u0120\u00ea\u00b2\u0125\u00ec\u013e\u00bc\u00eb\u00a1\u013e",
- "\u00e3\u0124\u0128",
- "\u00c5\u0135",
- "gili",
- "Dave",
- "\u0120exhausting",
- "\u00c5\u0124ug",
- "\u0120schema",
- "\u00ce\u00bc\u00ce\u00ac",
- "cycl",
- "\u0120autant",
- "\u0120parcel",
- "\u0120materia",
- "\u0120Berry",
- "\u0120\u00d1\u0123\u00d0\u00b0\u00d0\u00bc\u00d0\u00b8",
- "\u0120extracted",
- "\u0120Saying",
- "ismatic",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b1",
- "\u0120neuron",
- "graph",
- "\u013e\u00eb\u00a9\u00b4",
- "\u0120enclosure",
- "\u0120Johann",
- "\u0120aftermath",
- "\u00d1\u0124\u00d0\u00be\u00d0\u00b1",
- "\u0120u\u00c5\u00bcy",
- "\u0120samp",
- "360",
- "\u0120Mei",
- "\u0120taco",
- "\u0120receptors",
- "\u0120punches",
- "\u0120Hoje",
- "\u0120\u00d9\u0129\u00d9\u0128\u00d8\u00a7",
- "=\"#",
- "\u0120Angular",
- "\u0120musique",
- "\u0120rol",
- "\u0120\u00c3\u00b1",
- "sterreich",
- "\u0120clam",
- "\u0120Treasury",
- "chemical",
- "\u0120apar",
- "\u0120append",
- "\u0120forbid",
- "\u0120Hamburg",
- "\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b2",
- "\u0120\u00ea\u00b8\u012a",
- "ilda",
- "\u0120preparations",
- "\u0120mog\u00c4\u0127",
- "\u0120camino",
- "Eric",
- "\u0120Blind",
- "\u00e8\u012a\u0129",
- "\u00e5\u00b9\u00b4\u00e7\u013c\u0126",
- "\u0120Discovery",
- "\u00ec\u00b8\u0142",
- "\u00e7\u012a\u00b6",
- "\u0120interpreter",
- "\u0120bred",
- "\u0120Psalm",
- "\u0120defended",
- "\u00ec\u012b\u00ac",
- "\u0120Erfahr",
- "\u0120Peach",
- "\u0120moons",
- "\u0120Ost",
- "\u0120sp\u00c3\u00a9cial",
- "\u0120arriver",
- "\u0120Wis",
- "uci",
- "\u0120robotics",
- "IVE",
- "\u0120siege",
- "arla",
- "\u0120separates",
- "\u0120TC",
- "\u00ed\u0131\u00b0",
- "quisite",
- "\u0120parentheses",
- "\u00d0\u00b8\u00d0\u00ba\u00d0\u00b5",
- "\u00e7\u00ab\u013b",
- "\u0120trous",
- "\u00e5\u00bb\u00ba",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00bb\u00d1\u012e",
- "\u0120beers",
- "\u0120\u00d0\u00bf\u00d0\u00bb\u00d0\u00b0\u00d1\u0124",
- "\u00e3\u0123\u013b\u00e3\u0123\u0136\u00e3\u0123\u0126",
- "\u0120sola",
- "\u0120d\u00c3\u00a8s",
- "mingham",
- "ikte",
- "\u0120oops",
- "\u0120twitch",
- "\u00e5\u00b0\u0129",
- "\u00cf\u012a",
- "\u0120Shouldn",
- "uvre",
- "\u0120leer",
- "criptions",
- "\u0120eyeshadow",
- "\u0120Guo",
- "\u0120Powell",
- "\u0120supuesto",
- "\u0120ana",
- "rals",
- "\u0120Montreal",
- "\u0120surfing",
- "\u0120\u00d0\u0141\u00d0\u00b5\u00d1\u0122\u00d0\u00b2",
- "\u00d7\u0140\u00d7\u0137",
- "\u0120milliseconds",
- "\u0120suburbs",
- "\u0120planeta",
- "\u00d1\u0125\u00d1\u012a\u00d0\u00ba\u00d0\u00b0",
- "hrlich",
- "\u0120HY",
- "\u0120\u00d8\u00b3\u00db\u0134",
- "\u0120MM",
- "\u0120Eff",
- "\u00e5\u0131\u00af\u00e6\u0126\u013d",
- "\u0120HS",
- "anson",
- "\u0120\u00ec\u00a7\u0123\u00ec\u0142\u0133",
- "\u0120suo",
- "\u0120deploying",
- "\u0120kunt",
- "tering",
- "\u0120erect",
- "\u00ec\u0140\u00a5\u00ec\u013f\u00b4",
- "\u0120\u00ec\u013f\u012e\u00ec\u012d\u013f",
- "\u0120specimen",
- "!...",
- "\u00e6\u012a\u0133\u00e8\u00aa\u00aa",
- "\u0120ligne",
- "\u0120konst",
- "adequ",
- "\u0120\u00ec\u0125\u0123\u00ed\u0125\u013e",
- "\u0120accessed",
- "\u0120Pole",
- "kill",
- "\u0120\u00eb\u00b2\u0126\u00eb",
- "\u0120authenticity",
- "\u0120appelle",
- "ulle",
- "\u0120revision",
- "\u0120goats",
- "\u00d0\u00b3\u00d0\u00bb\u00d0\u00b8",
- "\u0120pau",
- "\u0120Ranger",
- "\u0120Imag",
- "author",
- "\u0120eve",
- "\u0120Messenger",
- "\u0120nay",
- "\u0120wholes",
- "\u00c3\u00a4tte",
- "\u0120onwards",
- "\u0120Depois",
- "\u0120\u00ed\u0133\u013e\u00ed\u013a\u0126",
- "\u0120SARS",
- "\u0120wszystkich",
- "\u0120destru",
- "umbing",
- "\u0120compatibility",
- "\u0120misinformation",
- "odore",
- "\u0120Favor",
- "eko",
- "\u0131\u012e",
- "waukee",
- "\u0120Teaching",
- "\u0120KO",
- "\u0120betting",
- "\u0120quests",
- "\u0120vivre",
- "\u0120\u00d0\u00bc\u00d1\u0125\u00d0\u00b7\u00d1\u012d",
- "\u0120saga",
- "\u0120swell",
- "\u0120gehe",
- "\u00e6\u0122\u0130\u00e9\u00ba\u00bc\u00e6\u00a8\u00a3",
- "\u0120\u00d0\u00be\u00d1\u0122\u00d0\u00b3\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b7",
- "\u0120gide",
- "\u0120Gross",
- "\u0120dalej",
- "\u0120claws",
- "\u00e1\u00bb\u013bc",
- "\u0120prejudice",
- "\u0120insign",
- "ihood",
- "\u0120pled",
- "\u0120d\u00c3\u00b3nde",
- "\u0120Political",
- "\u0120premises",
- "undert",
- "\u00d8\u00b9\u00d8\u00aa",
- "onnen",
- "\u0120espa\u00c3\u00a7o",
- "\u0120f\u00c3\u00a9",
- "\u0120Harrison",
- "\u0120Census",
- "\u0120cardio",
- "\u0120diy",
- "\u0120milieu",
- "\u0120journ\u00c3\u00a9e",
- "\u0120Release",
- "NIE",
- "\u0120Muk",
- "id\u00c3\u00a9e",
- "\u00e1\u00bb\u012fi",
- "\u0120i\u00c3\u00a7inde",
- "\u0140\u013b",
- "\u0120resonate",
- "\u0120moles",
- "\u0120Flying",
- "\u0120Gloria",
- "\u0120Pastor",
- "\u0120Arena",
- "\u00e5\u00a5\u00bd\u00e4\u00b8\u012f\u00e5\u00a5\u00bd",
- "NON",
- "\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b2",
- "\u0120all\u00c3\u0143",
- "omat",
- "\u00ec\u0138\u00b4\u00eb\u0131\u0126",
- "\u0120caracter\u00c3\u0143st",
- "\u0120declining",
- "\u00d1\u0138\u00d1\u0131",
- "anco",
- "\u0120Inform",
- "\u0120bargain",
- "\u0120bushes",
- "\u0120Naturally",
- "\u0120rechts",
- "\u0120Tensor",
- "\u0120Patricia",
- "\u0120principio",
- "\u0120Mumbai",
- "\u0120womb",
- "\u0120nostra",
- "\u0120dilemma",
- "\u0120irgendwann",
- "\u01201964",
- "\u0120energ\u00c3\u0143a",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0122",
- "\u0120segregation",
- "\u0120Athlet",
- "\u0120\u00c2\u00bb,",
- "\u0120yeni",
- "\u0120Seit",
- "\u0120venom",
- "\u0120dakika",
- "\u0120\u00eb\u0131\u012e\u00eb",
- "\u0120\u00c3\u012bl",
- "\u0120fus",
- "\u0120Mog",
- "\u00a6\u00bd\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120remar",
- "\u0120Teddy",
- "\u0120breasts",
- "icans",
- "\u00e6\u0136\u00b6\u00e7\u013e\u012d",
- "kap",
- "\u0120h\u00c6\u00a1n",
- "\u0120JP",
- "\u00e3\u0125\u00b3\u00e3\u0124\u00bf",
- "\u0120resurrect",
- "\u0120\u00ec\u013f\u00b8\u00eb",
- "herical",
- "\u0120fotograf",
- "\u0120Jos\u00c3\u00a9",
- "\u0120livelihood",
- "\u0120bibli",
- "teri",
- "\u0120vorstellen",
- "\u0120AAA",
- "\u0120assessing",
- "YA",
- "\u0120splend",
- "\u0120excav",
- "\u0120baptism",
- "yll",
- "wow",
- "Mac",
- "\u0120plastics",
- "teokbokki",
- "\u0120int\u00c3\u00a9ressant",
- "\u0120commanded",
- "\u0120famously",
- "\u0120\u00d0\u013a\u00d0\u00bb\u00d0\u00b8",
- "\u0120Manuel",
- "\u0120southwest",
- "\u0120deformation",
- "\u00c3\u0143culo",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0127\u00d0\u00be\u00d0\u00b4\u00d0\u00b8\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120Patter",
- "degree",
- "\u0120cz\u00c4\u013bsto",
- "\"-",
- "\u0120\u00ec\u0127\u012d",
- "\u0120manger",
- "\u0120Trustee",
- "\u0122\u00eb\u00a6\u00ac",
- "\u0120puntos",
- "ivable",
- "\u0120volatile",
- "\u0120\u00eb\u012c\u0132",
- "\u0120instability",
- "\u0120ciel",
- "ci\u00c4\u0127",
- "\u0120purity",
- "\u00d0\u00bd\u00d0\u00be\u00d1\u0123\u00d1\u0124",
- "Sil",
- "edar",
- "\u00e5\u013b\u00a8",
- "NOUNCER",
- "\u0120spelled",
- "GER",
- "\u0120sanctuary",
- "\u0120accelerating",
- "\u0120scout",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00b2",
- "fahren",
- "\u00e3\u0123\u0135\u00e3\u0123\u00a1\u00e3\u0124\u012b",
- "\u0120\u00eb\u0124\u013a\u00ec\u013a\u00a8",
- "\u0120pocz\u00c4\u0127t",
- "\u0120Meu",
- "kaar",
- "\u00b3\u00b4\u00ea\u00b3\u0142",
- "akra",
- "Down",
- "\u0120\u00c3\u0126r",
- "\u0120Elite",
- "\u0120allons",
- "\u0120mayonnaise",
- "\u0120Sustain",
- "prisingly",
- "\u0120supervis",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0142\u0129\u00ec\u00a3\u0142",
- "\u0120unemployed",
- "\u0120freshly",
- "\u0120\u00d7\u0140\u00d7\u00a2",
- "\u0120Dh",
- "\u0120tackling",
- "\u0120ogr",
- "\u0120\u00ec\u00b4\u012a\u00eb",
- "\u00e3\u0124\u012a\u00e3\u0124\u012f",
- "\u0120loft",
- "arah",
- "\u0120Airl",
- "\u0120Dir",
- "\u0120\u00d0\u013e\u00d0\u00be\u00d0\u00b6\u00d0\u00bd\u00d0\u00be",
- "\u0120booking",
- "\u0120CRA",
- "\u0120https",
- "\u0120choke",
- "\u0120gown",
- "\u0120noite",
- "\u0120zac",
- "istol",
- "\u0120secre",
- "\u0120resembles",
- "\u0120cuad",
- "\u00ec\u0124\u00ac\u00ea\u00b0\u0122",
- "show",
- "\u0120blanc",
- "\u0120agu",
- "\u0120Print",
- "asted",
- "\u0120Weather",
- "ipl",
- "\u0120obscure",
- "\u0120conte",
- "oughs",
- ");",
- "\u0120Dame",
- "\u00e4\u00b8\u0122\u00e7\u013d\u00b4",
- "\u0120clarification",
- "\u0120intimacy",
- "\u0120uphold",
- "\u0120Mirror",
- "\u0120wagon",
- "xide",
- "\u0120clog",
- "apper",
- "\u0120Immediately",
- "\u00c3\u00bade",
- "\u0120touchdown",
- "\u0120rooft",
- "\u00d0\u00b0\u00d1\u012a\u00d0\u00b0",
- "\u0120\u00c3\u00a7\u00c4\u00b1kt",
- "\u0120laisser",
- "\u0120Unreal",
- "ensitive",
- "\u0120123",
- "\u0120plaster",
- "\u0120ducks",
- "\u0120etme",
- "\u0120bishop",
- "brevi",
- "\u0120bic",
- "\u00e4\u00b8\u012d\u00e5\u0130\u00bb",
- "\u0120runtime",
- "\u0120ambitions",
- "\u00d0\u00bc\u00d0\u00b0\u00d1\u0124",
- "\u0120Wein",
- "\u0120Mari",
- "\u0120\u00ed\u012c\u00b8\u00eb",
- "\u0120resolver",
- "\u0120ng\u00c3\u0142y",
- "\u0120Rise",
- "\u00e3\u0124\u012a\u00e3\u0123\u0128\u00e3\u0123\u00ab",
- "\u0120Crus",
- "\u0120merchandise",
- "\u0120eli",
- "\u0120statewide",
- "\u0120owl",
- "\u00e9\u0123\u0142",
- "\u00e6\u0136\u00b9",
- "\u0120twisting",
- "\u0120contaminated",
- "\u0120Commerce",
- "hythm",
- "\u0120\u00c3\u012a",
- "\u0120\u00ec\u012d\u00a4\u00eb",
- "\u0120musste",
- "uir",
- "\u0120sums",
- "\u0120Somewhere",
- "\u00e3\u0125\u0130",
- "\u0120kami",
- "\u0120aired",
- "\u0120ANDREW",
- "\u0120\u00ea\u00ba",
- "\u0120viendo",
- "\u0120antibody",
- "\u0120absolument",
- "\u0120protesters",
- "\u0120Qu\u00c3\u00a9bec",
- "stadt",
- "Shaun",
- "\u0120chambers",
- "\u0120Wear",
- "\u0120Effects",
- "\u0120hazards",
- "\u0120nei",
- "\u0120coraz\u00c3\u00b3n",
- "\u0120\u00e1\u00bc",
- "\u0120SG",
- "\u0136\u00a9",
- "\u0120\u00ec\u0139\u0143\u00ec\u012d\u013e",
- "\u0120comfy",
- "\u0120Cody",
- "\u0120pensando",
- "\u0120ganska",
- "\u0120Across",
- "\u00c3\u00b6llig",
- "abyte",
- "\u0120wedge",
- "\u0120kalian",
- "\u0120sigue",
- "endes",
- "\u0120Gro\u00c3\u0141",
- "\u0120utiliser",
- "\u0120flown",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d1\u0130",
- "\u0120levar",
- "restrial",
- "\u0120illustrations",
- "\u0120asl\u00c4\u00b1nda",
- "BLEEP",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d1\u0123\u00d1\u0124",
- "\u0120turret",
- "\u0120suitcase",
- "zi\u00c4\u013bki",
- "\u0120sketches",
- "\u0120acred",
- "\u0120Rei",
- "\u0120tsun",
- "\u0120Sag",
- "\u0120thirds",
- "\u0120KIRBY",
- "rai",
- "\u0120humanos",
- "\u0120recommends",
- "\u0120extraordinarily",
- "\u0120commencement",
- "KN",
- "opez",
- "\u0120\u00d7\u0133\u00d7\u00a9",
- "\u0120lethal",
- "\u0120Estamos",
- "\u0120inspector",
- "\u0120Seok",
- "eun",
- "\u0120offshore",
- "\u0120gettin",
- "years",
- "\u0120Silence",
- "\u0120Natur",
- "upun",
- "\u0120trzy",
- "\u0120noget",
- "\u0120hamburger",
- "\u0120Praise",
- "\u00c3\u00a9nd",
- "\u01201971",
- "ylie",
- "krit",
- "\u0120\u00ec\u0125\u013f\u00ea\u00b0\u0123\u00ec\u013f\u00b4",
- "\u00e7\u013c\u00ae",
- "\u0120momentos",
- "\u0120est\u00c3\u00a9",
- "\u0120dissemin",
- "\u0120gigs",
- "\u0120desaf",
- "\u0120avis",
- "\u0120Zoo",
- "\u0120\u00ec\u0137\u012c\u00ec\u013f\u0122",
- "h\u00c3\u00a4ng",
- "\u00e5\u0131\u00a5",
- "hake",
- "\u0120Bism",
- "\u0120rethink",
- "\u0120Malcolm",
- "\u0120identifies",
- "lower",
- "ixel",
- "\u0120tv\u00c3\u00a5",
- "ked",
- "ierz",
- "\u0120\u00c3\u00b6ffentlich",
- "\u0120proclaim",
- "soon",
- "lol",
- "\u0120loi",
- "\u0120bitten",
- "rollo",
- "\u0120sermon",
- "\u0120esqu",
- "\u0120jackets",
- "\u0120gr\u00c3\u00a1fic",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7\u00d1\u012d\u00d0\u00b2",
- "\u0120cabeza",
- "chodzi",
- "\u0120pelvis",
- "\u0120nostalgia",
- "\u0120brew",
- "\u0120shortcuts",
- "\u0120Adem\u00c3\u00a1s",
- "\u0120superficial",
- "\u00e5\u0127\u00a9\u00e5\u0122\u012d",
- "\u0120boca",
- "\u0120\u00e6\u012a\u0133\u00e6\u013a\u00af",
- "imentos",
- "\u00e5\u013d\u0142\u00e4\u00b8\u00ba",
- "\u0120sprouts",
- "\u00e9\u00a3\u013d",
- "\u0120Jonas",
- "\u0120Florence",
- "static",
- "daughter",
- "*)",
- "\u00c5\u0124by",
- "fashion",
- "\u0120Ginger",
- "\u0120\u00eb\u00a7\u00a4\u00eb",
- "\u0120hustle",
- "utos",
- "\u0120\u00d1\u0124\u00d1\u0131\u00d0\u00b6",
- "\u0120L\u00c3\u00b6s",
- "\u00d7\u00a9\u00d7\u013b\u00d7\u013f",
- "anych",
- "tuber",
- "\u0120tidy",
- "\u0120frontal",
- "\u0120whiskey",
- "\u0120humid",
- "\u0120\u00ce\u0141",
- "\u0120ridge",
- "\u0120marin",
- "\u0120bient\u00c3\u00b4t",
- "\u0120Carrie",
- "chw",
- "\u0120tahun",
- "\u0120Ergeb",
- "FR",
- "\u0120\u00ec\u0142\u0137\u00eb\u00b6\u0122",
- "\u0120Soldier",
- "\u0120enlightenment",
- "\u0120examining",
- "\u0120Notre",
- "\u0120eram",
- "\u0120Sunny",
- "\u0120layered",
- "\u0120Dazu",
- "rades",
- "\u00e5\u00a5\u00bd\u00e5\u0132\u0125",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u012a\u00d0\u00b5\u00d0\u00b9",
- "\u0120timber",
- "\u0120manners",
- "\u0120Birmingham",
- "\u0120miniature",
- "ometers",
- "\u0120filler",
- "\u0120Rip",
- "\u0120Komb",
- "owner",
- "\u00ec\u00bf",
- "idian",
- "\u0120dem\u00c3\u00a1s",
- "\u0120\u00d9\u012a\u00d8\u00aa",
- "\u0120precautions",
- "\u0120governo",
- "zelf",
- "\u0120Complete",
- "\u00e5\u00b8\u0125",
- "\u0120Phantom",
- "\u00e3\u0123\u00be\u00e3\u0123\u013c",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00b7",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d1\u0122\u00d1\u0124",
- "\u0120Antwort",
- "\u0120Pfizer",
- "\u0120Franco",
- "\u0120w\u00c5\u0124",
- "\u0120frig",
- "esper",
- "\u0120kale",
- "\u0120filmmaker",
- "\u0120kurt",
- "\u0120invalid",
- "\u00e5\u00b1\u0122",
- "arella",
- "\u00c4\u0125ng",
- "ramento",
- "\u0120nutritional",
- "\u0120dictators",
- "\u0120afin",
- "\u0120fuzzy",
- "\u0120Gina",
- "\u00c3\u00b3t",
- "\u0120Extremadura",
- "\u0120demonstrations",
- "\u0120Montgomery",
- "\u00ed\u0137\u00b4\u00ec\u0126\u00a4",
- "\u0120Gandhi",
- "\u00e3\u0125\u013f",
- "\u00e7\u00bd\u00ae",
- "\u0120reunion",
- "\u0120jaki\u00c5\u013d",
- "\u0120Zug",
- "OUGH",
- "lifting",
- "\u0120\u00e0\u00b2",
- "\u00e1\u00b9\u013d\u00e1\u00b9\u00a3",
- "eb",
- "\u0120WOW",
- "\u0120Shiva",
- "ometry",
- "\u0120wildly",
- "\u0120tended",
- "\u0120megap",
- "\u00ec\u00b2\u013a",
- "\u0120nause",
- "\u0120gerek",
- "\u00e3\u0125\u012d",
- "\u0120Marcel",
- "\u0120neste",
- "\u00d8\u00ae\u00d8\u00b1",
- "\u0120feh",
- "\u00e5\u0128\u0127",
- "suspenseful",
- "\u0120Wrestle",
- "\u0120Palestinians",
- "\u0120GORD",
- "iyet",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b4\u00d0\u00b8",
- "\u0120versuchen",
- "\u0120transistor",
- "\u0120\u00d0\u0141\u00d1\u0122\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00be",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bd\u00d1\u0122\u00d0\u00b0\u00d0\u00b2",
- "\u0120rhyme",
- "\u0120Vermont",
- "platz",
- "\u00e8\u00ae\u00b0",
- "\u0120\u00c4\u00b0\u00c5\u0141te",
- "\u0120Hag",
- "\u0120\u00d0\u013a\u00d0\u00bc",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0123\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7",
- "\u0120metros",
- "\u0120Infinity",
- "wolf",
- "ibal",
- "ftig",
- "\u0120\u00da\u0128",
- "\u0120\u00ed\u013a\u00b9\u00ec\u012d\u013e",
- "\u0120oggi",
- "\u0120disposit",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bb",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00bf\u00d0\u00be\u00d0\u00bb",
- "\u0120th\u00c3\u00b4i",
- "\u0120KENN",
- "\u0120handing",
- "actus",
- "\u0120tacos",
- "\u0120formerly",
- "\u0120Corinthians",
- "\u00e3\u0123\u00ab\u00e3\u0123\u00af",
- "\u00d1\u0128\u00d1\u0138\u00d1\u0139",
- "\u0120padre",
- "\u0120congregation",
- "\u00e6\u0133",
- "fert",
- "\u0120subir",
- "aiser",
- "qua",
- "araoh",
- "\u0120Curry",
- "\u0120\u00ec\u0137\u012c\u00eb\u012c\u0136",
- "\u00d0\u00b5\u00d0\u00bb\u00d1\u0130",
- "\u0120fuss",
- "\u0120booty",
- "\u0120lows",
- "\u0120hommes",
- "\u0120MH",
- "\u0120Disneyland",
- "went",
- "\u0120residue",
- "\u0120beeping",
- "\u00e8\u00bc\u0137",
- "\u00c3\u00a4tta",
- "\u0120mould",
- "\u0120Projekt",
- "stalk",
- "\u0120artifact",
- "\u0120Antrag",
- "\u0120AMD",
- "\u0120Crypt",
- "\u0120\u00eb\u00a9\u0136",
- "\u0120Felipe",
- "\u0120COB",
- "elu",
- "\u0120selfies",
- "\u0120Santi",
- "chutz",
- "\u0120\u00d0\u00a3\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0139",
- "gesamt",
- "\u0120flock",
- "jaz",
- "plain",
- "\u0120wrinkles",
- "\u0120reais",
- "\u0120paljon",
- "\u0120empowerment",
- "\u0120attendees",
- "ppa",
- "\u0120neden",
- "\u00d0\u00be\u00d0\u00bd\u00d1\u012d",
- "\u0120timeframe",
- "\u0120Cherry",
- "\u0120id\u00c3\u00a9e",
- "\u0120gag",
- "\u0120donkey",
- "\u0120\u00c3\u00b4ng",
- "\u0120Hare",
- "\u00e9\u013c\u013d",
- "\u0120Kara",
- "\u0120acompan",
- "places",
- "imientos",
- "\u0120Hamm",
- "\u00d0\u00b1\u00d0\u00b8",
- "uben",
- "iliyor",
- "\u0120thirst",
- "\u0120kry",
- "\u0120Georgetown",
- "\u00d7\u0142\u00d7\u0136",
- "\u0120orch",
- "\u0120heartbeat",
- "\u0120transformations",
- "estones",
- "\u0120KH",
- "\u0120cartoons",
- "\u0120anci",
- "\u0120worthless",
- "\u0120tailored",
- "pu",
- "Americans",
- "\u0120piles",
- "\u0120Monkey",
- "\u0120basin",
- "\u0120Temper",
- "\u0120Paint",
- "\u0120punching",
- "\u0120baik",
- "\u0120Oakland",
- "vre",
- "\u00c5\u0141allah",
- "ydd",
- "\u0120casually",
- "odu",
- "\u0120coded",
- "\u0120Norwegian",
- "\u0120Vince",
- "\u0120premature",
- "\u0120Promise",
- "\u00d0\u00b5\u00d0\u00ba\u00d1\u0123\u00d1\u0124",
- "\u0120devastated",
- "\u0120Premium",
- "\u0120Param",
- "\u0120\u00c3\u0138yle",
- "umuz",
- "PO",
- "rators",
- "\u0120lamps",
- "\u0120territorial",
- "\u0120backbone",
- "listed",
- "DY",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00b1",
- "\u0120pursued",
- "\u0120Commons",
- "\u0120\u00ea\u00b3\u00a1",
- "locks",
- "edor",
- "\u0120conceived",
- "gere",
- "\u0120disappearing",
- "\u0120Sull",
- "\u0120\u00ec\u0139\u00b0\u00eb",
- "\u0120hoffe",
- "\u0120detox",
- "\u00ed\u0136\u012e",
- "\u0120retir",
- "\u0120\u00eb\u0123\u013f\u00eb\u0124",
- "\u0120pergunta",
- "\u0120BOY",
- "\u00e7\u00b2\u00be",
- "\u0120penn",
- "\u00e6\u013f\u00a5\u00e4\u00ba\u0128",
- "h\u00c3\u00a9s",
- "hon",
- "\u0120catastrophic",
- "\u0120aust",
- "\u0120torso",
- "\u0120\u00ec\u0138\u00b4\u00eb\u012c\u0132",
- "\u0120\u00ec\u0124\u00ac\u00eb\u0140\u012e\u00eb\u0135\u00a4\u00ec\u013f\u00b4",
- "\u0120marvelous",
- "\u0120Harley",
- "achine",
- "\u0120ti\u00e1\u00ba\u00bf",
- "itto",
- "\u0120I\u00c3\u0143m",
- "ylon",
- "\u0120shutdown",
- ".''",
- "\u0120apologies",
- "\u0120Communication",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d1\u0122\u00d1\u0130",
- "\u00e3\u0123\u0124\u00e3\u0125\u00bc",
- "\u00e2\u0126\u00a2",
- "\u00c3\u0143veis",
- "acun",
- "\u0120retaining",
- "\u0120contradiction",
- "\u0120ADAM",
- "COM",
- "Bryan",
- "\u0120Monsieur",
- "\u0120adapting",
- "\u00d0\u00a8\u00d0\u0132",
- "\u0120Scr",
- "\u00c3\u00a4ndert",
- "\u0120plaus",
- "\u00e4\u00bb\u012c\u00e5\u00a4\u00a9\u00e7\u013c\u0126",
- "\u0120onset",
- "\u0120assistants",
- "\u0120valves",
- "\u0120scatter",
- "\u0120Rust",
- "awia",
- "\u0120readiness",
- "\u0120pais",
- "\u0120bible",
- "\u0120ambiente",
- "\u0120\u00d0\u00b0\u00d0\u00bc\u00d0\u00b5\u00d1\u0122\u00d0\u00b8\u00d0\u00ba",
- "\u0120uncond",
- "\u0120kalk",
- "\u00e5\u012c\u00a8",
- "\u0120moc",
- "unn",
- "\u0120actu",
- "\u0120humming",
- "issimo",
- "\u0120Patrol",
- "gow",
- "\u00e3\u0125\u00a4",
- "\u0120THEY",
- "\u0120Boden",
- "\u0120Bie",
- "\u0120reel",
- "\u0120\u00d1\u0125\u00d1\u0123\u00d0\u00bb\u00d0\u00be\u00d0\u00b2",
- "\u0120endeavor",
- "\u0120Period",
- "ustomed",
- "mals",
- "alon",
- "Box",
- "\u0120\u00cf\u0125\u00ce\u00b1\u00cf\u0124",
- "\u0120omdat",
- "\u0120altre",
- "\u0120Heh",
- "kad",
- "\u0120protector",
- "\u0120dominance",
- "odynamic",
- "\u0120communicated",
- "k\u00c3\u00b6",
- "\u0120predecessor",
- "\u0120Luk",
- "\u0120Flower",
- "\u0120\u00e3\u0123\u00a9",
- "poque",
- "\u00d1\u0124\u00d0\u00b8\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120retrospect",
- "\u0120decisive",
- "\u0120exempel",
- "{\\",
- "\u0120R\u00c3\u00bcck",
- "rite",
- "\u0120Zeus",
- "\u0120calorie",
- "\u0120attractions",
- "\u0120Hinter",
- "\u0120uhm",
- "\u0120\u00ed\u012e\u0132",
- "\u0120rulers",
- "\u0120discouraged",
- "\u0120acontecer",
- "\u0120accents",
- "\u0120Optim",
- "\u0120Alg",
- "kids",
- "2021",
- "\u0120Lindsay",
- "\u0120filmmakers",
- "prowad",
- "\u0120terug",
- "\u00eb\u012d\u00b4",
- "\u0120Sommer",
- "2018",
- "\u0120borrowing",
- "\u0120Transfer",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00bf",
- "arias",
- "\u0120headphone",
- "\u00ec\u00bc\u013e",
- "\u0120translating",
- "\u0120aufge",
- "\u00e0\u00ae\u00aa\u00e0\u00ae\u0141",
- "weis",
- "avant",
- "paid",
- "baby",
- "\u0120toughest",
- "\u0120repeats",
- "\u0120Teresa",
- "Lord",
- "\u0120acabar",
- "\u0120Ride",
- "dir",
- "\u0120leng",
- "\u0120dwa",
- "\u0120headaches",
- "\u0120n\u00e1\u00bb\u00afa",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d1\u0131\u00d1\u012b",
- "\u0120boils",
- "\u0120longing",
- "rias",
- "\u00c3\u00b3rio",
- "\u0120Paradise",
- "\u0120Se\u00c3\u00b1or",
- "erdem",
- "\u0120reinst",
- "\u0120salaries",
- "\u0120insecurity",
- "\u00c5\u0124o\u00c5\u013dci",
- "\u0120\u00d0\u00b0\u00d0\u00b1\u00d1\u0123\u00d0\u00be\u00d0\u00bb\u00d1\u0130\u00d1\u0124\u00d0\u00bd\u00d0\u00be",
- "inken",
- "\u0120Eddy",
- "udos",
- "\u0120dummy",
- "\u00d0\u013c\u00d0\u00b0\u00d0\u00ba",
- "six",
- "\u0120inbox",
- "\u00e1\u00ba\u00a9",
- "People",
- "\u00e1\u00bb\u0135ng",
- "\u0120organizers",
- "find",
- "\u0120\u00c3\u00bcl",
- "\u0120COM",
- "\u00c5\u00bca",
- "weile",
- "Commentary",
- "\u00ed\u012c\u00b8\u00eb\u00a5\u00bc",
- "\u0120Mittel",
- "kus",
- "\u00e8\u013d\u012d",
- "\u00e0\u00a4\u00a8",
- "iral",
- "\u0120garment",
- "\u00ce\u00b9\u00ce\u00ba\u00ce\u00ac",
- "\u0120stool",
- "payers",
- "\u0120shimmer",
- "\u0120Ollie",
- "\u0120Je\u00c5\u00bceli",
- "\u00e8\u00bf\u013a\u00e6\u013e\u012b",
- "\u01201977",
- "\u0120jeux",
- "\u0120extinct",
- "\u0120Transportation",
- "\u0120Maker",
- "\u0120john",
- "\u0120richest",
- "\u0120traumat",
- "\u0120liegen",
- "\u00b4\u00eb\u00a5\u00bc",
- "\u00e8\u00bf\u013b\u00e9\u0129\u012e",
- "\u0120unrest",
- "\u0120Straw",
- "\u00e6\u012d\u013e\u00e6\u012d\u013e",
- "\u0120coma",
- "\u0120Kristen",
- "\u0120\u00d0\u013c\u00d0\u00be\u00d0\u00bd\u00d0\u00b5\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120Bryce",
- "\u0120\u00d1\u0131\u00d0\u00ba\u00d1\u0138",
- "\u0120pearls",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc\u00d0\u00b0\u00d1\u0130",
- "\u0120additions",
- "\u0120asympt",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d1\u012e\u00d1\u012a\u00d0\u00b5",
- "\u0120scans",
- "Child",
- "\u0120Hide",
- "\u00d0\u00ba\u00d1\u0125\u00d1\u0130",
- "etas",
- "\u0120dank",
- "\u0120pleas",
- "\u0120essays",
- "\u0120jets",
- "\u00e5\u0127\u0134",
- "\u0120\u00d0\u00b2\u00d0\u00b5\u00d0\u00b4",
- "\u0120positives",
- "hof",
- "-)",
- "zzo",
- "\u0120starters",
- "\u0120smiled",
- "\u01201944",
- "quiera",
- "\u0120rok",
- "\u0120puesto",
- "Nico",
- "\u0120simulations",
- "\u0120\u00e0\u00b6",
- "\u0120intrigued",
- "\u0120Overwatch",
- "\u00e5\u0138\u0124",
- "sigh",
- "bai",
- "\u0120\u00eb\u00a7\u0132\u00ea\u00b3\u0142",
- "id\u00c3\u00a9",
- "\u0120crabs",
- "\u00e1\u00ba\u0143p",
- "\u0120Iraqi",
- "\u00ec\u013f\u00b4\u00eb\u00a5\u00bc",
- "\u00d1\u0124\u00d1\u0131",
- "\u0120Sophia",
- "\u0120DNS",
- "\u0120\u00c3\u00b6nemli",
- "\u0120Luo",
- "\u013f\u00a4",
- "\u0120Counsel",
- "ligen",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u012e\u00d1\u012a\u00d0\u00b5",
- "\u0120trumpet",
- "\u0120dapat",
- "\u0120JM",
- "\u0120EVERY",
- "\u0120\u00e5\u00b0\u012f\u00e4\u00b8\u012f\u00e5\u00b0\u012f",
- "\u00e5\u00a4\u00a2",
- "\u0120Layer",
- "\u0120c\u00c3\u00b4",
- "\u00d0\u00bd\u00d0\u00b0\u00d0\u00bb",
- "\u0120Joo",
- "\u0120Hack",
- "\u0120sunt",
- "\u0120Leonard",
- "\u0120Firebase",
- "\u00c3\u00a4nger",
- "\u0120exploding",
- "voy",
- "\u0120\u00ec\u00a6\u0132",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d1\u0122\u00d1\u012e",
- "\u0120severity",
- "\u0120bestimm",
- "\u00e7\u00b5\u0132\u00e6\u0140\u013e",
- "\u0120tiring",
- "\u0120procurement",
- "\u0120diplomacy",
- "\u0120decorative",
- "\u0120\u00d9\u012c\u00d8\u00a7",
- "\u0120penetration",
- "\u00d5\u00ab",
- "\u0120outright",
- "ENE",
- "\u0120Uni",
- "odles",
- "\u0120zeros",
- "\u0120delightful",
- "jm",
- "\u0120dopo",
- "\u00e6\u00b2\u00a1\u00e4\u00ba\u012d",
- "\u0120positivity",
- "\u0120VISTA",
- "\u0120Resource",
- "\u00ed\u0125\u0122\u00eb",
- "\u00d1\u012a\u00d0\u00b8\u00d0\u00b5",
- "Carl",
- "\u0120piping",
- "\u0120chopping",
- "\u0120Ganze",
- "\u00c3\u00bcss",
- "\u0120Ao",
- "\u0120shattered",
- "\u0120Detective",
- "\u0120undoubtedly",
- "\u0120halluc",
- "\u0120ench",
- "\u00d1\u012d\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u00d1\u0125\u00d0\u00bb\u00d1\u0131\u00d1\u0122",
- "isesti",
- "\u0120pedals",
- "\u0120durum",
- "\u00a4\u00ed\u0136",
- "laimer",
- "\u0120propre",
- "Cu",
- "\u0120translator",
- "\u0120ca\u00c5\u0124",
- "\u0120\u00ea\u00b7\u00b8\u00ea\u00b1\u00b8",
- "\u0120ca\u00c5\u0124y",
- "UA",
- "\u0120revised",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b4\u00d0\u00be\u00d0\u00b1",
- "\u0120Article",
- "\u0120Haiti",
- "\u0120\u00c3\u0135",
- "\u0120Ctrl",
- "\u0120rozm",
- "lait",
- "\u0120letzte",
- "ispering",
- "display",
- "\u0120aluminium",
- "\u0120palabras",
- "\u0120conocer",
- "\u0120zitten",
- "\u0120dirig",
- "\u00e5\u0131\u00aa\u00e6\u013e\u012b",
- "\u0120brainstorm",
- "\u0120wifi",
- "\u0120Particip",
- "\u0120viewpoint",
- "\u0120Quan",
- "\u0120hierarch",
- "Welcome",
- "\u00e5\u00af\u00be",
- "\u0120offen",
- "\u0120Recovery",
- "gano",
- "Would",
- "\u0120repro",
- "\u0120perceptions",
- "\u0120demasi",
- "\u0120Bangladesh",
- "\u0120Incredible",
- "\u0120letzt",
- "\u0120behaving",
- "\u0120astonishing",
- "\u0120\u00e2\u0128",
- "\u0120\u00eb\u0124\u00a8\u00ec\u0140\u0132",
- "\u00e8\u00b5\u00b0\u00e4\u00ba\u0128",
- "\u00e3\u0125\u0136",
- "\u0120GORDON",
- "CAR",
- "?!\"",
- "\u0120Prest",
- "\u0120\u00eb\u00a7\u0140\u00ec\u0137\u0126\u00ec\u013c\u0136",
- "\u0120tand",
- "\u0120lash",
- "\u00e7\u012c",
- "ificant",
- "\u0120intoler",
- "\u0120\u00d0\u00b3\u00d0\u00b5\u00d1\u0122\u00d0\u00be",
- "\u0120teu",
- "aso",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120travelers",
- "\u0120Synd",
- "\u0120\u00d0\u00b2\u00d0\u00b5\u00d1\u0122\u00d1\u0123",
- "Fonda",
- "ad\u00c4\u00b1",
- "\u0120transcription",
- "\u0120titanium",
- "\u0120twists",
- "\u0120gearbox",
- "ensation",
- "fat",
- "Coll",
- "\u0120Commonwealth",
- "zon",
- "\u0120Polizei",
- "\u0120APPLAUSE",
- "fry",
- "\u0120Juda",
- "esteem",
- "\u0120sock",
- "\u0120Jugend",
- "\u0120\u00d0\u00ba\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d1\u0124\u00d0\u00b8",
- "\u0120Dro",
- "\u0120prochaine",
- "\u00e3\u0125\u00bc\u00e3\u0125\u00ab",
- "\u0120liksom",
- "\u0120Energie",
- "\u0120Marina",
- "\u0120230",
- "\u0120\u00ea\u00b0\u0122\u00ec\u0126\u013e",
- "umping",
- "\u0120lone",
- "\u00e7\u00b4\u013c",
- "\u0120fonts",
- "\u0120businessman",
- "\u0120ply",
- "\u0120doe",
- "grid",
- "\u0120Milwaukee",
- "\u0120Eden",
- "!\".",
- "\u0120\u00db\u012e\u00db\u0123",
- "ogens",
- "\u0120teaser",
- "\u0120qui\u00c3\u00a9n",
- "\u0120incentiv",
- "govern",
- "\u0120childcare",
- "\u0120sneakers",
- "\u0120imprisoned",
- "\u00c2\u00ae",
- "\u00d0\u00b8\u00d1\u0124\u00d0\u00b5\u00d1\u0123\u00d1\u012e",
- "anbul",
- "\u0120regain",
- "\u0120tranquil",
- "Redner",
- "\u00e9\u013d\u00a8",
- "IFA",
- "\u0120ideological",
- "\u0120mayor\u00c3\u0143a",
- "\u0120bureau",
- "eterm",
- "\u0120DID",
- "\u00ec\u012c\u00b7",
- "\u0120waving",
- "\u0120beb",
- "\u0120\u00c3\u00a1r",
- "\u0120\u00d0\u00ba\u00d0\u00b2",
- "\u0120envoy",
- "anut",
- "\u00d0\u00b8\u00d0\u00ba\u00d1\u0125",
- "\u0120Environment",
- "\u0120Assass",
- "\u00e3\u0124\u0135\u00e3\u0123\u00a7",
- "\u0120Bread",
- "\u0120\u00d0\u00a2\u00d1\u0125\u00d1\u0124",
- "\u0120staircase",
- "\u0120Disease",
- "\u0120aucun",
- "\u0120\u00eb\u012d\u012a",
- "\u0120confrontation",
- "\u01201941",
- "\u0120irony",
- "\u0120worsh",
- "\u00e3\u0124\u012e\u00e3\u0124\u012d",
- "\u0120fick",
- "\u0120Naomi",
- "\u0120backside",
- "ieux",
- "Kap",
- "\u0120vedere",
- "\u0120lengthy",
- "\u0120breaker",
- "\u0120Rolle",
- "\u0120predator",
- "\u0120nossos",
- "\u0120advertise",
- "\u00e8\u00b3\u0129",
- "\u00d1\u0122\u00d0\u00be\u00d0\u00b4\u00d0\u00b5",
- "Rednerwechsel",
- "reten",
- "\u0120collectors",
- "\u00c4\u00b1\u00c4\u0141\u00c4\u00b1m\u00c4\u00b1z",
- "\u0120trig",
- "\u0120axes",
- "inters",
- "\u0120penalties",
- "\u0120Osman",
- "\u0120Jenna",
- "\u0120flakes",
- "\u0120trainers",
- "\u0120stunned",
- "\u0120Scroll",
- "\u0120Pip",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0123\u00d1\u0124",
- "\u0120nh\u00c3\u0142",
- "\u0120Smack",
- "\u00e1\u00ba\u00abn",
- "ratos",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b1\u00d0\u00be\u00d1\u0124\u00d1\u012d",
- "\u0120ucz",
- "\u0120Lemon",
- "\u0120Sind",
- "\u0120psychic",
- "\u0120Abg",
- "\u0120mammals",
- "\u0120immersive",
- "\u0120bots",
- "\u0120verschiedene",
- "\u0120geral",
- "\u0120follower",
- "\u0120\u00e4\u00bb\u0138",
- "\u0120seguridad",
- "\u0120immersed",
- "feito",
- "cross",
- "\u0120\u00c3\u00b6ld",
- "\u00ed\u0125\u0126",
- "\u0120\u00e3\u0123\u0135\u00e3\u0123\u00ae",
- "\u0120\u00d7\u0136\u00d7\u013b\u00d7\u0132",
- "\u0120Jian",
- "\u0120biliyor",
- "area",
- "\u0120kaf",
- "\u0120godt",
- "\u00e7\u013d\u00b8\u00e4\u00bf\u00a1",
- "\u0120\u00eb\u00b0\u00a9\u00ec\u0128\u00a1",
- "\u0120detriment",
- "\u00e6\u00a5\u013c",
- "\u00d1\u0138\u00d0\u00bb",
- "\u0120\u00c4\u0133\u00c3\u00a2u",
- "\u0120chloride",
- "\u00c3\u00b8re",
- "lei",
- "\u0120monte",
- "\u0120diff\u00c3\u00a9rentes",
- "\u00e0\u00af\u0123.",
- "\u0120caregivers",
- "\u0120inadequ",
- "\u0120farewell",
- "\u0120\u00d1\u0124\u00d0\u00b8\u00d0\u00bf\u00d0\u00b0",
- "ontec",
- "\u0120Eph",
- "HHH",
- "\u0120Todos",
- "\u0120\u00d0\u00a1\u00d0\u00a8\u00d0\u0132",
- "\u0120trov",
- "\u0120lige",
- "\u0120c\u00c3\u00b4ng",
- "\u0120Civ",
- "\u0120capaz",
- "\u0120Vallahi",
- "\u0120queste",
- "\u0120replica",
- "\u00d8\u00b3\u00d8\u00a8",
- "zna",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d1\u0125\u00d0\u00b6",
- "\u0120PT",
- "wave",
- "ieni",
- "\u0120relied",
- "develop",
- "\u0120deme",
- "\u0120Aman",
- "\u0120[...]",
- "\u0120compliments",
- "uais",
- "\u0120\u00ed\u012e\u00a8",
- "\u0120smelling",
- "\u0120dadurch",
- "\u00d9\u012a\u00d8\u00aa",
- "\u0120oranges",
- "\u0120\u00d0\u00bb\u00d0\u00b0\u00d0\u00b9",
- "\u0120stabilization",
- "\u00e5\u0122\u012f",
- "\u00e3\u0124\u012e\u00e3\u0123\u0141",
- "\u00e6\u00a5\u00bd",
- "\u0120appliances",
- "\u0120hm",
- "\u0125\u0132\u00eb\u00a9\u00b4",
- "odynamics",
- "\u0120ci\u00c4\u013b",
- "\u0120Cott",
- "MON",
- "\u0120Mang",
- "\u00e6\u0136\u00af\u00e6\u012e\u0123",
- "\u0120allerdings",
- "\u00ce\u00b9\u00ce\u00ba\u00ce\u00ae",
- "shots",
- "\u0120ts",
- "\u0120G\u00c3\u00b6r",
- "\u0120CHAR",
- "\u0120:(",
- "\u0120wrath",
- "\u0120fique",
- "\u0120f\u00c3\u00bchren",
- "\u0120testament",
- "\u0120^^",
- "\u00e1\u00b9\u013d\u00e1\u00b9\u00a3\u00e1\u00b9\u0129a",
- "ALD",
- "\u0120texto",
- "\u0120Dogs",
- "\u0120sib",
- "\u0120pathetic",
- "ocks",
- "\u0120radically",
- "\u0120MORE",
- "\u0120JAMES",
- "\u0120ingl",
- "\u0120Technical",
- "\u0120porch",
- "\u0120UT",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u0131\u00d0\u00b7\u00d0\u00b0\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120renewal",
- "\u0120aesthetics",
- "ikum",
- "\u0120beverage",
- "dern",
- "\u0120predictive",
- "\u0120chuy",
- "\u0120Regarding",
- "\u0120Forward",
- "\u0120\u00d9\u012a\u00d9\u0126",
- "\u0120contextual",
- "\u0120dwarf",
- "\u0120prehe",
- "\u0120governed",
- "\u0127\u0126",
- "\u0120trabalhar",
- "\u0120neg\u00c3\u00b3cio",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a\u00d0\u00be\u00d0\u00b9",
- "\u00d0\u00b5\u00d1\u0129\u00d0\u00b0\u00d1\u0124",
- "\u0120\u00d0\u00b4\u00d1\u0125\u00d1\u0127",
- "\u0120floods",
- "\u0120bowling",
- "\u0120OB",
- "\u0120H\u00c3\u00a4r",
- "\u0120grading",
- "\u00ec\u00a3\u00bc\u00eb\u012c\u0136",
- "\u0120gars",
- "dling",
- "\u0120rak",
- "\u00eb\u012a",
- "creat",
- "\u0120\u00d1\u012b\u00d0\u00b5",
- "\u0120neighbours",
- "food",
- "Query",
- "\u0120heroin",
- "iceps",
- "\u0120Kinda",
- "NET",
- "\u0120mari",
- "\u0120imitate",
- "\u0120achter",
- "\u0120settlements",
- "rare",
- "cciones",
- "\u0120\u00eb\u0135\u013e",
- "\u0120fik",
- "itung",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d0\u00ba\u00d1\u0123\u00d0\u00b8\u00d0\u00bc",
- "\u0120elf",
- "\u0120dalla",
- "\u0120Polsce",
- "\u0120Pul",
- "\u00d0\u00a7\u00d1\u0124\u00d0\u00be",
- "\u0120Morgen",
- "\u00d8\u0143\u00d9\u0127",
- "\u0120supremacy",
- "\u0120kys",
- "\u0120Hurricane",
- "\u0120GTA",
- "\u0120Feh",
- "\u0120finalmente",
- "mund",
- "\u0120Krie",
- "\u00c3\u00a9poque",
- "\u0120Tucker",
- "ITT",
- "\u0120lur",
- "\u0120dipping",
- "\u00c3\u00a4v",
- "\u0120eerste",
- "\u0120Flint",
- "bildung",
- "\u00e0\u00b8\u00b9\u00e0\u00b9\u012b",
- "\u0120toim",
- "\u0120pracy",
- "\u0120transforms",
- "\u0120speeding",
- "\u0120presenter",
- "\u0120fellows",
- "filled",
- "ieza",
- "\u0120advising",
- "\u0120Interview",
- "\u00d0\u00b8\u00d0\u00b3\u00d1\u0122",
- "wehr",
- "\u0120Dante",
- "pture",
- "\u012a\u00eb\u00ac\u00b8",
- "\u00af\u00b8\u00eb",
- "\u0132\u0132",
- "\u0120Counter",
- "\u0120crist",
- "\u0120\u00ec\u00a7\u013e",
- "\u0120jeune",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00b0\u00d1\u012a",
- "\u0120mie\u00c4\u0129",
- "\u0120tutor",
- "\u0120masala",
- "\u0120powdered",
- "\u0120nau",
- "\u0120Frederick",
- "\u0120billing",
- "\u0120Eisen",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00b1\u00d1\u0122",
- "\u0120mest",
- "\u00e6\u00bd",
- "\u0120snipp",
- "\u0120mono",
- "\u0120Alo",
- "\u0120Mercy",
- "\u00c3\u00a9rience",
- "\u0120casualties",
- "\u0120ANNOUNCER",
- "\u00e4\u00bb\u0130",
- "\u0120tocar",
- "\u0120bacterial",
- "Ho",
- "\u0120streak",
- "\u0120JENN",
- "\u0120plast",
- "\u00d1\u0123\u00d0\u00bb\u00d0\u00b5\u00d0\u00b4",
- "\u0120reapp",
- "\u0120paycheck",
- "\u0120miners",
- "habt",
- "\u0120Jap",
- "\u00d0\u00bd\u00d1\u0125\u00d1\u0124",
- "\u0120redemption",
- "\u0120quir",
- "hnlich",
- "\u0120accumulation",
- "\u0120shove",
- "\u0120adrenaline",
- "Make",
- "\u0120Hern",
- "ossing",
- "\u0120Vil",
- "ubby",
- "hertz",
- "breaks",
- "\u0120spur",
- "\u0120Daha",
- "USTIN",
- "\u0120continuer",
- "\u0120Saul",
- "\u00e3\u0123\u00ae\u00e3\u0123\u00af",
- "\u0120\u00ed\u0131\u0143",
- "\u0120\u00eb\u0132\u013a\u00eb\u00a9\u00b4",
- "\u0120\u00eb\u00a7\u0132\u00ec\u0136\u0122",
- "\u0120\u00d0\u00be\u00d0\u00b6",
- "\u0120suspects",
- "\u0120laquelle",
- "\u0120Muchas",
- "\u0120v\u00c3\u00b6llig",
- "ulen",
- "\u0120impres",
- "\u0120lobb",
- "enee",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b6",
- "Ta",
- "\u0120r\u00c3\u00a9alit\u00c3\u00a9",
- "\u0120Rex",
- "\u0120harvesting",
- "\u0120estr",
- "\u00e6\u00b6",
- "ospace",
- "OSS",
- "\u0120disturbance",
- "assic",
- "\u0120Isab",
- "\u0120d\u00c3\u00a9couv",
- "\u0120Hampshire",
- "\u0120ornament",
- "\u0120lu\u00c3\u00b4n",
- "\u0120UW",
- "\u0120j\u00c4\u0127",
- "\u00e9\u0124\u00a3\u00e4\u00b9\u012a",
- "\u0120respecto",
- "\u0120comunidad",
- "\u0120comigo",
- "agna",
- "\u0120intrinsic",
- "\u0120Alumni",
- "\u0120sesleri",
- "\u0120estimation",
- "\u00e2\u0122\u0136\u00e2\u0122\u0136",
- "\u0120produit",
- "\u00e3\u0122\u0124\u00e3\u0122\u012f",
- "\u0120\u00d0\u00b2\u00d1\u0122",
- "\u0120whirl",
- "\u0120acces",
- "\u00c3\u00a7u",
- "\u0120variability",
- "\u0120vodka",
- "itsu",
- "\u0120internships",
- "\u0120allocate",
- "RR",
- "\u00ed\u013d\u012a",
- "\u0120instructional",
- "tant",
- "\u0120\u00e0\u00ae\u0127\u00e0\u00ae\u00a4",
- "\u0120invites",
- "\u0120hak",
- "\u0120scares",
- "\u0120eclipse",
- "\u00d0\u00bf\u00d0\u00be\u00d0\u00b2",
- "\u00d0\u00ba\u00d0\u00be\u00d0\u00bb\u00d1\u012e",
- "ativas",
- "\u0120stabbed",
- "\u0120DOM",
- "\u00e4\u00b8\u012f\u00e5\u012a\u00b0",
- "roots",
- "\u0120Picture",
- "\u00ed\u013a\u00bc",
- "\u0120CHA",
- "iec",
- "\u00c4\u00b1\u00c4\u00b1",
- "hanol",
- "\u0120misunderstand",
- "Ray",
- "\u0120roadmap",
- "ocumented",
- "izione",
- "\u0120Olive",
- "rift",
- "\u0120\u00d7\u0136\u00d7\u0142",
- "\u00e6\u00af\u012f",
- "lest",
- ";;",
- "\u0120EA",
- "\u00e9\u013e\u0122\u00e8\u00a6\u0123",
- "\u00d0\u00be\u00d0\u00b4\u00d1\u0125",
- "\u0120hobbies",
- "\u0120burial",
- "\u00e3\u0123\u00ab\u00e3\u0123\u00a1\u00e3\u0123\u00af",
- "\u00d0\u00a4",
- "lege",
- "\u0120HJ",
- "\u0120objection",
- "\u0120\u00e3\u0123\u0143",
- "ctory",
- "\u0120incremental",
- "\u0120gymn",
- "\u0120epidemi",
- "\u00d1\u0123\u00d1\u012d\u00d0\u00bb",
- "\u00c3\u0133",
- "\u0120advancement",
- "\u0120parch",
- "News",
- "\u0120ayr",
- "\u00d0\u00bb\u00d0\u00b0\u00d0\u00bc",
- "\u0120\u00d7\u013e\u00d7\u00a9",
- "\u0120diploma",
- "\u00e3\u0123\u00a1\u00e3\u0124\u0125\u00e3\u0124\u0135",
- "\u0120robbed",
- "Only",
- "\u0120incur",
- "\u0120chanting",
- "\u0120\u00ed\u0137\u00b4\u00eb\u0131\u0126",
- "\u0120riches",
- "\u0120Carmen",
- "\u0120nostro",
- "\u00ce\u00bb\u00ce\u0143",
- "\u0120Powder",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u00ab",
- "\u0120\u00ec\u0140\u012a\u00ec\u013e\u00bc\u00eb\u00a9\u00b4",
- "\u0120ger\u00c3\u00a7ekten",
- "\u0120Pikachu",
- "\u00d0\u00b5\u00d0\u00bc\u00d0\u00be\u00d0\u00bd",
- "OLL",
- "\u0120planetary",
- "\u0120slows",
- "\u0120clockwise",
- "alion",
- "\u0120\u00ec\u012e",
- "\u0120vern",
- "\u0120homme",
- "\u0120endpoint",
- "\u0120innocence",
- "\u0120elementos",
- "\u0120sophomore",
- "\u0120notions",
- "\u0120Couldn",
- "pur",
- "\u0120zat",
- "\u0120obsess",
- "\u0120motivo",
- "\u0120Kub",
- "\u0120Drug",
- "Ant",
- "\u0120Players",
- "\u0120Humans",
- "\u0120melee",
- "\u0120Wildlife",
- "\u0120VP",
- "\u0120volcanic",
- "\u0120comin",
- "\u0120Guang",
- "\u0120\u00cf\u0126\u00ce\u00b9\u00cf\u0124",
- "\u0120\u00d0\u00be\u00d1\u0123\u00d0\u00be\u00d0\u00b1\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d0\u00be",
- "\u0120Size",
- "Listen",
- "\u0120Aaa",
- "appro",
- "\u0120barbar",
- "\u0120Parkinson",
- "\u00d0\u00bd\u00d1\u0131\u00d1\u0124\u00d1\u012e",
- "\u00e5\u012f\u00b0",
- "\u0120underestimate",
- "\u0120substitution",
- "\u0120cosmetic",
- "\u00e4\u00b8\u012d\u00e6\u00ac\u00a1",
- "\u0120willen",
- "\u0120beide",
- "anni",
- "\u0120conditioned",
- "\u0120Debbie",
- "\u0120isto",
- "\u0120Edwards",
- "\u00ec\u013d\u012e\u00ec\u013c\u0136",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00b2",
- "\u0120abbrevi",
- "\u0120M\u00c3\u00bcn",
- "\u0120Princ",
- "\u0120Liang",
- "\u0120stink",
- "\u0120radioactive",
- "\u00e3\u0123\u0128\u00e3\u0124\u0131",
- "\u0120acontec",
- "\u0120uncon",
- "\u0120Turbo",
- "\u00e3\u0123\u0132",
- "\u0120kisses",
- "\u00e6\u013a\u00af\u00e4\u00bb\u0122\u00e9\u00ba\u00bc",
- "\u00d0\u00b5\u00d1\u0124\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120frontier",
- "\u0120Spy",
- "\u0120Belarus",
- "\u0120CBS",
- "\u00e1\u00bb\u0139",
- "amoto",
- "\u00ed\u0137\u013e\u00eb\u012f\u00b0",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be",
- "\u0120Enfin",
- "\u0120breadth",
- "\u00e9\u013a\u00b2",
- "\u0120Cafe",
- "\u0120Daf\u00c3\u00bcr",
- "\u0120Bour",
- "aras",
- "\u0120blueprint",
- "an\u00c4\u00b1",
- "\u0120constants",
- "\u0120attacker",
- "\u0120Formula",
- "za\u00c4\u0129",
- "\u0120sowie",
- "\u0120eyebrow",
- "obook",
- "\u0120setzen",
- "\u00e7\u00ac\u00ac\u00e4\u00b8\u012b",
- "onsider",
- "awning",
- "\u0120s\u00c3\u00b6yleye",
- "\u0120invaded",
- "\u0120pronouns",
- "\u0120dobry",
- "Si",
- "\u0120\u00d0\u00a5\u00d0\u00be\u00d1\u0124",
- "\u0120volleyball",
- "\u0120lament",
- "isches",
- "arme",
- "api",
- "\u0120Wiki",
- "\u00d0\u00bb\u00d0\u00b8\u00d1\u012a",
- "\u0120kasih",
- "\u0120pess",
- "\u0120\u00d1\u0126\u00d0\u00be\u00d1\u0124",
- "\u0120Sul",
- "\u00e5\u00be\u00b7",
- "\u0120pseudo",
- "\u0120memo",
- "\u0120\u00ec\u0139\u00b0\u00ec\u012c\u00b5",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bb\u00d0\u00bb\u00d0\u00b0\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00bc",
- "\u0120Reach",
- "miral",
- "alted",
- "\u0120statut",
- "reading",
- "\u0120s\u00c3\u00b6yled",
- "\u0120Lindsey",
- "\u0120Ahmad",
- "\u00eb\u00b6\u0122\u00eb",
- "\u0120\u00d0\u00a1\u00d0\u00b5\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d1\u0131",
- "\u0120przygot",
- "\u0120hyster",
- "URE",
- "\u0120Neigh",
- "Reporter",
- "\u0120Bunu",
- "\u0120Treaty",
- "\u0120Rank",
- "\u0120Fame",
- "inished",
- "\u0120geared",
- "\u0120compose",
- "odia",
- "\u0120Lon",
- "\u0120jeste\u00c5\u013dmy",
- "\u0120DIRECTOR",
- "\u0120elkaar",
- "\u0120Viel",
- "\u00d7\u0132\u00d7\u00a9",
- "ynthia",
- "\u00e4\u00b8\u00a6",
- "\u0120m\u00c3\u00a8re",
- "\u0120Tomato",
- "\u0120exatamente",
- "ni\u00c4\u013b",
- "\u0120Frei",
- "\u0120Dif",
- "\u0120openings",
- "\u0120graphical",
- "\u0120\u00d1\u0125\u00d0\u00b4\u00d0\u00be\u00d0\u00b1",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d0\u00bf",
- "\u0120Weekly",
- "\u00d0\u00b5\u00d0\u00b2\u00d0\u00b0",
- "\u0120hangs",
- "\u0120unsafe",
- "\u0120emblem",
- "\u0120Kolleginnen",
- "alay",
- "\u0120ksi",
- "\u0120hides",
- "\u0120olmay",
- "\u0120entste",
- "\u0120arthritis",
- "\u00c3\u0141erdem",
- "\u0120binnen",
- "\u0120listens",
- "\u0120Hess",
- "\u00e5\u0128\u012f\u00e4\u00be\u0128",
- "\u0120Louise",
- "lden",
- "\u00d0\u00b5\u00d0\u00bd\u00d1\u0123",
- "\u0120Version",
- "\u0120Agriculture",
- "\u00ec\u012c\u00a4\u00eb\u00a5\u00bc",
- "\u00d0\u00bc\u00d0\u00b0\u00d0\u00bd",
- "\u00eb\u0126\u00a4\u00ec\u013c\u0136",
- "\u0120wines",
- "\u0120INF",
- "rul",
- "\u0120JK",
- "\u00c4\u00b1yorlar",
- "shield",
- "reath",
- "\u0120terus",
- "\u0120Lum",
- "\u0120anticipation",
- "\u0120accustomed",
- "\u0120Mina",
- "\u0120wield",
- "io\u00c3\u00a8",
- "mera",
- "\u0120countdown",
- "\u0120cling",
- "\u0120commend",
- "\u0120faktiskt",
- "\u0120defenses",
- "\u0120cockpit",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00b0\u00d0\u00bd\u00d0\u00b4",
- "\u0120dishwas",
- "\u0120Thanos",
- "\u0120kidneys",
- "\u0120sehe",
- "\u0120microbes",
- "\u0120cuff",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d1\u0123\u00d0\u00be\u00d0\u00ba",
- "\u0120Spicy",
- "\u00e7\u0143\u012b\u00e7\u0143\u012b",
- "\u00e0\u00ae\u00b5\u00e0\u00ae\u00b0",
- "culus",
- "orc",
- "\u00e7\u00be\u0127",
- "ixes",
- "\u0120Credit",
- "\u0120raj",
- "\u0120bringt",
- "\u0120Niss",
- "\u0120grim",
- "\u0120SOL",
- "\u0120tenim",
- "\u0120Sudan",
- "\u0120Spart",
- "\u0120promotes",
- "\u0120Nossa",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d1\u0131\u00d0\u00bd\u00d0\u00b8",
- "\u0120\u00ec\u00b0\u00a9",
- "\u0120uncont",
- "\u0120Liberal",
- "\u0120\u00d0\u00a2\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00ba\u00d0\u00be",
- "\u0120Viele",
- "\u0120kt\u00c3\u00b3rej",
- "\u0120****",
- "Max",
- "\u0120\u00d0\u00a7\u00d1\u0124\u00d0\u00be\u00d0\u00b1\u00d1\u012d",
- "350",
- "\u0120\u00ed\u013a\u00bc\u00ec\u0140\u0132",
- "\u0120\u00eb\u00b6\u0126\u00eb\u0135\u00a4\u00ec\u013f\u00b4",
- "\u0120warp",
- "\u0120tenga",
- "\u0120sympathetic",
- "\u0120bizi",
- "\u0120Zack",
- "iedo",
- "\u0120\u00eb\u012b\u00b4\u00ec",
- "piel",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00bb",
- "\u0120scaled",
- "\u0120PETER",
- "\u0120COMM",
- "\u0120Came",
- "\u0120catastrophe",
- "\u0120sweaty",
- "igration",
- "\u0120stuffing",
- "\u0120\u00cf\u0122\u00ce\u00bf\u00ce\u00bb\u00cf\u012f",
- "\u0120Driver",
- "zyst",
- "Tech",
- "\u0120assessed",
- "\u0120Surface",
- "\u00c4\u00b1r\u00c4\u00b1m",
- "sur",
- "lerweile",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00b3",
- "\u0120shutting",
- "\u0120fractions",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00bb",
- "everyone",
- "\u0120ern",
- "\u0120\u00d0\u013f\u00d0\u00be\u00d0\u00b2",
- "\u0120defenders",
- "\u0120versucht",
- "\u00e3\u0125\u00b3\u00e3\u0125\u0122",
- "\u0120polity",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d0\u00bd",
- "verst\u00c3\u00a4nd",
- "\u0120browsers",
- "\u0120transformative",
- "\u0120dictate",
- "\u0120LEGO",
- "\u0120ninguna",
- "\u00ea\u00b4\u0133",
- "\u0120pizz",
- "\u0120Harold",
- "\u0120Lopez",
- "\u00da\u00be\u00db\u012e",
- "an\u00c4\u00b1z",
- "atchet",
- "\u00d9\u012c\u00d8\u00aa",
- "\u0120lernen",
- "\u0120\u00ea\u00b7\u0122\u00ec\u0139\u00ac",
- "\u0120housed",
- "\u0120cleanse",
- "\u0120WAT",
- "laration",
- "\u0120bytes",
- "\u0120tucked",
- "\u0120faults",
- "\u00d0\u00b4\u00d0\u00be",
- "FX",
- "\u0120\u00ec\u0138\u00bc\u00eb\u00a7\u012a\u00eb\u0124\u013a",
- "\u0120deform",
- "\u0120contracting",
- "\u0120TIME",
- "irse",
- "\u0120neben",
- "\u0120cerc",
- "\u0120Armstrong",
- "\u0120tester",
- "\u0120parfait",
- "\u0120jealousy",
- "\u0120toxins",
- "\u0120disbel",
- "\u00d1\u0125\u00d1\u0122\u00d1\u012d",
- "impression",
- "\u0120prostate",
- "\u0120firewall",
- "\u0120classics",
- "\u00d0\u00b5\u00d1\u0129\u00d1\u012e",
- "\u0120socialism",
- "\u0120gracious",
- "\u0120\u00d1\u0123\u00d0\u00bd\u00d0\u00be\u00d0\u00b2\u00d0\u00b0",
- "\u0120\u00d0\u00b4\u00d0\u00bd\u00d1\u0131",
- "\u0120burner",
- "\u0120Minor",
- "\u0120\u00ec\u013c\u00b0\u00eb\u00a6\u00ac\u00eb",
- "\u0120jedes",
- "\u0120continuum",
- "\u0120hots",
- "\u0120occurrence",
- "\u0120administered",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00bc\u00d0\u00b5\u00d1\u0124",
- "\u0120hesitation",
- "\u0120drills",
- "erca",
- "\u0120\u00d0\u00b2\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b9",
- "\u0120steadily",
- "\u0120insanlar",
- "\u0120ihan",
- "\u00ed\u0133",
- "\u0120helper",
- "\u0120Senin",
- "\u00e5\u0123\u013e",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b5",
- "\u0120ERIC",
- "bla",
- "\u0120Academic",
- "\u0120humanities",
- "black",
- "umpy",
- "ortex",
- "\u0120\u00ec\u0142\u012a\u00eb",
- "\u0120\u00d8\u00a5\u00d9\u0128",
- "\u0120disclose",
- "\u0120Elijah",
- "\u0120\u00ce\u00bb\u00ce\u0143",
- "\u0120Quer",
- "\u00d8\u00a8\u00d9\u0126",
- "\u00e3\u0124\u00a1",
- "Tell",
- "arle",
- "\u00d1\u0138\u00d1\u0122",
- "\u0120augmented",
- "\u0120\u00eb\u00b9\u0126\u00ec\u012c\u00b7",
- "\u0120android",
- "\u00e0\u00a4\u00a4",
- "arma",
- "\u0120szer",
- "geord",
- "\u0120geek",
- "\u0120yeux",
- "\u0120pong",
- "\u0120\u00e3\u0123\u013f\u00e3\u0123\u0128",
- "\u0120tortured",
- "\u0120Bath",
- "zig",
- "asonable",
- "\u0120nets",
- "\u0120baru",
- "\u0120Flat",
- "\u0120Vater",
- "\u0120Terror",
- "\u0120Avo",
- "\u0120ceremonies",
- "roe",
- "\u00d9\u0123\u00d8\u00b3",
- "Ops",
- "\u0120hyvin",
- "\u0120apresent",
- "olor",
- "\u0120\u00d0\u00b8\u00d0\u00b3\u00d1\u0122\u00d1\u012d",
- "orton",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0140\u00ac",
- "\u0120lookin",
- "\u0120TY",
- "\u0120Mint",
- "Add",
- "\u0120mite",
- "\u0120Smoke",
- "\u0120nota",
- "\u0120moss",
- "\u0120Abend",
- "\u0120\u00ec\u00bb\u00a8",
- "\u0120exaggerated",
- "fires",
- "\u0120redist",
- "ffiti",
- "\u0120openness",
- "\u00ea\u00b0\u0132\u00ec\u013f\u00b4",
- "endeu",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d0\u00be\u00d0\u00b9",
- "Watch",
- "\u0120avatar",
- "\u0120Pey",
- "urun",
- "\u0120senza",
- "\u0120\u00ec\u00a7\u0122\u00ec\u0139\u0143",
- "\u0120Natomiast",
- "\u0120emergence",
- "rays",
- "\u0120crafted",
- "gary",
- "\u00e3\u0123\u0142\u00e3\u0123\u0133",
- "\u00c3\u00bcng",
- "-\"",
- "\u0120hacked",
- "\u0120stray",
- "encie",
- "emo",
- "\u0120comen",
- "\u0120K\u00c4\u00b1z",
- "\u0120Jasmine",
- "\u0120Hindi",
- "manas",
- "\u0120infinitely",
- "emon",
- "\u00ec\u013f\u00b8\u00eb\u012f\u00b0\u00ec\u013c\u0136",
- "jak",
- "\u0120roaring",
- "\u00c3\u00a9rique",
- "sweise",
- "\u0120Rolex",
- "\u00e5\u0142\u00b1\u00e5\u00b0\u0130",
- "\u0120Stuart",
- "bnb",
- "\u0120diagnose",
- "\u0120coherent",
- "\u0120MJ",
- "\u00e6\u00ba\u0138\u00e5\u0124\u013b",
- "\u0120pike",
- "lav",
- "\u0120orchestral",
- "\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00b8",
- "\u0120terminar",
- "\u0120gatherings",
- "\u0120compliant",
- "\u0120upgrading",
- "\u0120regulator",
- "\u0120lan\u00c3\u00a7",
- "\u00e9\u0122\u00a3",
- "\u0120merchants",
- "tawa",
- "\u0120monitored",
- "\u0120rendre",
- "\u00e4\u00b8\u00a4",
- "\u0120unterwegs",
- "anguard",
- "gard",
- "\u0120Below",
- "duino",
- "\u0120\u00d0\u00a6\u00d0\u00b5",
- "\u0120impedance",
- "\u00ec\u013e\u00a1",
- "\u00e4\u00bb\u00bd",
- "\u0120aktuell",
- "\u0120Vatic",
- "\u00e5\u0143\u00a9",
- "\u0120stewards",
- "\u0120brightest",
- "\u0120kenn",
- "\u0120kau",
- "\u0120Matrix",
- "\u0120Bark",
- "\u0120\u00f0\u0141\u0133",
- "\u0120taper",
- "\u0120casino",
- "\u00d7\u00a8\u00d7\u0136",
- "ysical",
- "\u0120builders",
- "\u0120cz\u00c5\u0124owie",
- "\u0120Nepal",
- "\u0120!\"",
- "\u0120terme",
- "\u0120innych",
- "\u0120maths",
- "\u0120drafted",
- "\u0120Balk",
- "\u0120hesitant",
- "\u0120voltar",
- "\u0120revive",
- "\u0120\u00d1\u0126\u00d0\u00b8\u00d0\u00bb\u00d1\u012e\u00d0\u00bc\u00d0\u00b0",
- "\u0120assassin",
- "\u0120Solutions",
- "\u0120duel",
- "\u0120bearings",
- "\u00e0\u00b8\u0126\u00e0\u00b8\u00b0",
- "\u0120rookie",
- "ikat",
- "\u0120biscuits",
- "\u0120cords",
- "\u00d1\u0125\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d0\u00b8",
- "ARIN",
- "\u0120progressing",
- "\u0120Gir",
- "\u0120penetrate",
- "\u0120Storage",
- "eight",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d1\u0125",
- "\u0120don\u00c3\u0143t",
- "\u0120sizin",
- "\u0120outdated",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u012a\u00d0\u00b8",
- "\u0120affir",
- "\u0120spoons",
- "\u0120oni",
- "\u0120flank",
- "\u0120Gol",
- "h\u00c3\u00a3",
- "\u0120p\u00c3\u00a9ri",
- "\u0120honorable",
- "\u0120Breathe",
- "scenes",
- "\u0120obviamente",
- "\u00d0\u00b8\u00d0\u00ba\u00d1\u0123",
- "\u0120\u00d7\u00a9\u00d7\u0140\u00d7",
- "\u0120smoothie",
- "\u0140\u012a\u00eb",
- "\u0120dime",
- "\u0120\u00ed\u0138\u012a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120appel",
- "\u0120Catholics",
- "\u0120singles",
- "\u0120laten",
- "\u0120\u00c3\u00a7\u00c3\u00bcnk\u00c3\u00bc",
- "\u0120Vader",
- "\u00e6\u0131\u013d",
- "\u0120vard\u00c4\u00b1",
- "\u0120Istanbul",
- "gr\u00c3\u00a9",
- "\u0120Elsa",
- "\u00c3\u00abl",
- "\u0120invece",
- "\u0120crane",
- "\u0120obe",
- "\u0120Shark",
- "\u0120smack",
- "\u0120restoring",
- ".\\",
- "\u0120\u00eb\u00b9\u0142\u00eb",
- "\u0120faded",
- "umbers",
- "Singing",
- "\u0120depressing",
- "thest",
- "\u0120Wahr",
- "\u0120multitude",
- "\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d1\u0125\u00d0\u00b9\u00d1\u0124\u00d0\u00b5",
- "rijk",
- "eka",
- "\u0120completes",
- "\u0120Wells",
- "\u0120roy",
- "\u0120Pray",
- "\u0120Kalau",
- "izin",
- "ia\u00c5\u0124em",
- "\u0120locom",
- "\u0120Nashville",
- "\u0120Pentagon",
- "\u00eb\u00af\u00b8",
- "\u0120NEW",
- "\u00c4\u0127\u00c4\u0129",
- "\u00c3\u0143ss",
- "\u0120marrying",
- "\u0120feud",
- "\u00ed\u013b\u0137",
- "\u00e6\u0122\u00a5",
- ")!",
- "\u0120Operations",
- "\u00d1\u0125\u00d1\u0136",
- "\u0120moje",
- "\u0120instructed",
- "\u0120\u00eb\u012a\u0126\u00ea\u00b5\u00ac",
- "\u0120\u00d7\u0136\u00d7\u0134",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bc\u00d0\u00be\u00d1\u012b\u00d1\u012e\u00d1\u0130",
- "\u0120sabia",
- "\u00ec\u0137\u013a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "plane",
- "pri",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d0\u00bd\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d1\u012e\u00d1\u0130",
- "\u0120Kitty",
- "\u0120pr\u00c3\u00b3prio",
- "edere",
- "\u0120interesante",
- "\u0120\u00d0\u00b4\u00d0\u00b5",
- "\u0120condensed",
- "\u0120avent",
- "TOR",
- "\u0120greasy",
- "ARK",
- "orta",
- "AJ",
- "\u0120disreg",
- "\u0120corrections",
- "\u0120stero",
- "\u0120influenza",
- "\u0120desses",
- "\u0120ballots",
- "\u0120meget",
- "\u0120mafia",
- "\u0120b\u00c3\u00b6l",
- "nost",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120responder",
- "\u0120hinten",
- "grav",
- "\u00e0\u00b8\u0143\u00e0\u00b8\u00b0",
- "ynchron",
- "\u0120viens",
- "\u0120samo",
- "\u0120dt",
- "pannt",
- "\u0120\u00c5\u013dwiat",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00bf\u00d0\u00b8\u00d1\u0123",
- "\u0120merged",
- "\u0120kep",
- "\u0120misleading",
- "\u0120digamos",
- "\u0120ammon",
- "\u00e8\u00be\u013d",
- "chet",
- "\u0120\u00ea\u00b0\u0122\u00ec\u0142\u00b8",
- "\u0120uni",
- "\u0120\u00eb\u0132\u013a\u00eb\u012c\u0136\u00eb\u012f\u00b0",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00b2",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "\u0120animate",
- "\u00d7\u0137\u00d7\u0132\u00d7",
- "\u00d0\u00b5\u00d1\u0122\u00d0\u00b2",
- "\u0120minced",
- "\u0120kaum",
- "\u00e3\u0123\u0124\u00e3\u0123\u0123",
- "\u00cf\u0122\u00ce\u00b5",
- "\u00d0\u00bb\u00d0\u00b5\u00d0\u00b3",
- "existing",
- "\u0120plataform",
- "\u0120KRIS",
- "\u00ec\u013d\u0142",
- "\u0120Familien",
- "\u0120Libya",
- "\u0120biodiversity",
- "\u0120idiots",
- "irdi",
- "\u0120szyb",
- "\u0120Rolling",
- "\u00c3\u00bccht",
- "\u0120\u00d1\u0125\u00d0\u00b4\u00d0\u00b8\u00d0\u00b2",
- "\u00d1\u0123\u00d1\u0125\u00d0\u00b4",
- "\u0120realizar",
- "\u0120canned",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00bd",
- "\u0120metabolic",
- "\u0120Beef",
- "\u0120kilka",
- "\u00d0\u00bb\u00d1\u0130\u00d1\u0123",
- "\u0120registry",
- "\u00d0\u00bc\u00d0\u00be\u00d1\u0124\u00d1\u0122\u00d0\u00b8\u00d1\u0124\u00d0\u00b5",
- "\u0120viel\u00c3\u00a4",
- "\u0120odc",
- "\u0120condemned",
- "\u00e6\u00a9\u012d",
- "fal",
- "\u0120Dil",
- "wo\u00c5\u013dci",
- "Aw",
- "\u0120statistically",
- "\u0120sogen",
- "\u0120BETH",
- "\u0120shaving",
- "\u00e5\u00b9\u00b8",
- "ocal",
- "\u0120Funny",
- "\u0120peacefully",
- "\u0120addictive",
- "\u0120Insert",
- "lauf",
- "\u0120experiencia",
- "\u00e9\u00a6\u0138\u00e5\u0127\u012a",
- "\u00d0\u00b8\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d1\u0131",
- "\u00c3\u0143gen",
- "\u00c3\u00a1gina",
- "\u0120abdomen",
- "\u00ed\u0137\u013e\u00eb\u012d\u00a4",
- "icus",
- "imana",
- "\u00ec\u012f\u00a8",
- "arching",
- "\u0120konkret",
- "\u00ec\u0137\u013a\u00eb",
- "\u00d0\u00b5\u00d0\u00ba\u00d0\u00b0",
- "oufl",
- "ivel",
- "\u0120nude",
- "\u00c3\u00a8tres",
- "\u0120monsieur",
- "\u0120clash",
- "\u0120therapists",
- "\u0120cubed",
- "\u0120retrouver",
- "\u0120waveform",
- "\u0120potem",
- "\u0120Former",
- "isi\u00c3\u00b3n",
- "\u00e5\u00ba\u013e",
- "\u0120\u00d7\u0132\u00d7\u013f",
- "undos",
- "\u0120Meinung",
- "\u00d8\u00b5\u00d9\u0126",
- "\u0120Jude",
- "\u0120n\u00c3\u00a5r",
- "\u0120Leonardo",
- "\u0120Cristo",
- "\u0120GOT",
- "\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d1\u0125\u00d0\u00ba",
- "LAN",
- "\u0120g\u00c3\u00a5ng",
- "\u0120d\u00c3\u00a9b",
- "\u0120Frankfurt",
- "\u0120crappy",
- "\u0120lil",
- "ann\u00c3\u00a9e",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d1\u0123\u00d1\u0124\u00d0\u00b5",
- "RET",
- "\u0120Ner",
- "\u0120COSTA",
- "\u0120jedem",
- "\u0120curtains",
- "\u0120iterations",
- "\u0120unav",
- "\u0120plaque",
- "orum",
- "\u0120\u00ce\u00b6",
- "\u0120n\u00c3\u00bameros",
- "\u0120desap",
- "\u00b2\u00bd",
- "\u0120compiled",
- "\u0120refle",
- "\u0120rankings",
- "\u0120repaired",
- "\u0120\u00d0\u013f\u00d0\u00b0\u00d0\u00bf\u00d1\u0122",
- "\u0120downloads",
- "\u0120armour",
- "\u0120\u00d7\u013b\u00d7\u0137\u00d7\u00aa\u00d7\u00a8",
- "\u0120longevity",
- "\u0120TONER",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d1\u0124\u00d0\u00b0\u00d1\u0122",
- "\u0120czego",
- "\u0120notify",
- "\u0120airports",
- "\u0120enduring",
- "lette",
- "\u0120apparat",
- "\u0120habil",
- "\u00e1\u00bb\u0129c",
- "nad",
- "ICO",
- "\u0120Brah",
- "\u0120seg\u00c3\u00ban",
- "\u0120governors",
- "kaha",
- "\u0120Schluss",
- "\u0120odpowied",
- "irting",
- "\u0120rempl",
- "\u0120Aboriginal",
- "identally",
- "\u0120enhancing",
- "licting",
- "\u0120Hawaiian",
- "\u0120striving",
- "\u0120Niet",
- "\u0120znaczy",
- "\u0120obedience",
- "\u0120n\u00c3\u00a5got",
- "\u0120expired",
- "\u01201918",
- "presented",
- "\u0120prowad",
- "\u0120Terr",
- "\u0120Princeton",
- "\u0120morgen",
- "\u0120attracting",
- "\u0120Sigma",
- "igner",
- "\u0120Rechts",
- "\u0120Peki",
- "\u0120methy",
- "\u0120hamm",
- "\u0120direito",
- "\u0120delegation",
- "\u00d0\u00b8\u00d0\u00b2\u00d0\u00b0\u00d1\u0130\u00d1\u0124",
- "\u0120gin",
- "Young",
- "\u0120dependencies",
- "\u0120Bradley",
- "buds",
- "\u0120fis",
- "\u0120pytanie",
- "\u0120interconnected",
- "\u0120embaixo",
- "\u0120Sas",
- "\u0120ruh",
- "\u0120Sicht",
- "Sur",
- "\u0120superb",
- "\u0120Sabbath",
- "\u0120Danger",
- "kol",
- "\u0120hou",
- "supp",
- "\u0120Nacional",
- "\u0120succession",
- "\u0120v\u00c3\u00a1",
- "\u0120Ma\u00c3\u0141nahmen",
- "\u0120Jessie",
- "\u0120Idaho",
- "forest",
- "\u0127\u013a",
- "\u0120\u00d7\u0140\u00d7\u0135",
- "\u0120\u00d8\u00a3\u00d9\u012c",
- "\u0120sweetheart",
- "\u0120neatly",
- "\u0120Evangel",
- "\u00ea\u00b3\u00a1",
- "\u0120Suite",
- "\u00c3\u00bablica",
- "\u0120\u00d1\u0125\u00d0\u00bb\u00d0\u00b8",
- "\u0120Announcer",
- "ligh",
- "\u0120sensations",
- "\u0120shelters",
- "\u0120hart",
- "\u0120squeezing",
- "\u0120Rivers",
- "\u0120Cooking",
- "\u00ec\u00b1\u0127",
- "personal",
- "\u0120manos",
- "\u00d1\u0133\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "wij",
- "\u0120gogg",
- "\u0120Milli",
- "\u0120FP",
- "\u00c3\u00bcnst",
- "\u0120LS",
- "\u0120spraying",
- "\u0120faux",
- "\u0120autograph",
- "ologic",
- "\u0120torment",
- "\u0120encrypted",
- "\u00e1\u00bb\u0127",
- "\u0120estre",
- "\u00e7\u00b9\u00bc",
- "\u00e0\u00b1",
- "\u0120stumbled",
- "\u0120aider",
- "\u0120saben",
- "xter",
- "\u0120Cities",
- "\u0120T\u00c3\u00bcrk",
- "\u00eb\u012d\u00a5",
- "chine",
- "\u0120topping",
- "\u0120poisoned",
- "\u0120Romania",
- "\u00d7\u0135\u00d7\u013b",
- "\u0122\u00eb\u00a1\u013e",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0122\u00d1\u0131\u00d0\u00b4",
- "\u0120chirping",
- "\u0120\u00ec\u013b\u0126\u00eb",
- "\u00d7\u0133\u00d7\u00a2",
- "\u0120cuanto",
- "\u0120donating",
- "\u0120Regent",
- "\u0120Beruf",
- "\u0120distracting",
- "\u0120stamina",
- "\u0120Darren",
- "\u0120\u00ec\u00b6\u0137",
- "lists",
- "dal",
- "chuss",
- "\u0120economist",
- "\u00e3\u0123\u012a\u00e3\u0125\u00bc",
- "orgt",
- "\u0120istiyorum",
- "\u00e8\u00bf\u013d",
- "\u0120Surprise",
- "\u0120Hao",
- "\u0120\u00ec\u00b5\u013e\u00ea\u00b3\u0142",
- "\u0120GW",
- "\u0120Inner",
- "\u0120quieren",
- "\u0120minded",
- "\u0120supercomputer",
- "\u0120diagrams",
- "\u00ed\u012c\u013e\u00eb",
- "\u00ea\u00b2\u0142\u00ec\u0138\u00b4",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012c\u00d1\u0131\u00d1\u0123",
- "\u0120estaban",
- "\u0120destroys",
- "\u0120Breaking",
- "\u0120kar\u00c4\u00b1\u00c5\u0141",
- "\u0120rebuilding",
- "\u013e\u00eb\u012e\u0122",
- "\u00d0\u00bb\u00d0\u00b8\u00d0\u00b2\u00d0\u00be",
- "\u0120Sauce",
- "\u0120Fusion",
- "\u00d7\u0137\u00d7\u0140\u00d7",
- "\u0120Quinn",
- "\u0120gauche",
- "\u0120\u00d9\u012a\u00d8\u00a3",
- "\u0120\u00c8",
- "\u00e7\u0135\u013e",
- "\u0120techno",
- "\u0120dispatch",
- "\u0120a\u00c5\u0141k",
- "\u0120einzel",
- "\u0120Gmail",
- "\u00e7\u0140",
- "\u0120\u00ea\u00b0\u013e\u00ec\u013f\u00b8",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d0\u00bc\u00d1\u012e",
- "\u0120journeys",
- "\u0120iht",
- "\u0120fibre",
- "\u0120dramas",
- "ouched",
- "\u0120rename",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d0\u00b5\u00d1\u0122",
- "\u0120poo",
- "\u0120Dru",
- "\u0120\u00d0\u00b8\u00d1\u0124\u00d0\u00be\u00d0\u00b3",
- "\u0120zast",
- "\u0120coz",
- "\u0120zucch",
- "\u0120obtaining",
- "\u0120commute",
- "\u0120submer",
- "\u0120Vish",
- "\u0120Rabb",
- "ogg",
- "\u0120hut",
- "\u00ed\u0138\u012a\u00ec\u0138\u00b4",
- "\u00e6\u00af\u0136\u00e5\u00a6\u0124",
- "eremi",
- "\u0120\u00ce\u00bc\u00ce\u00b1",
- "\u0120diskut",
- "\u0120\u00d0\u00b1\u00d1\u0125\u00d0\u00ba",
- "\u0120impaired",
- "depend",
- "\u0120\u00d9\u012a\u00d8\u00a7",
- "\u0120\u00d1\u0122\u00d1\u0125\u00d0\u00ba",
- "\u0120\u00d0\u00b1\u00d0\u00b0\u00d1\u0122",
- "\u0120oxidation",
- "\u0120situa\u00c3\u00a7\u00c3\u00a3o",
- "\u00c9\u013bn",
- "u\u00c3\u00a7\u00c3\u00a3o",
- "\u0120sagte",
- "\u0120SER",
- "\u0120Cake",
- "\u0120turmeric",
- "\u0120Kak",
- "bung",
- "\u0120K\u00e1\u00b9\u013d\u00e1\u00b9\u00a3\u00e1\u00b9\u0129a",
- "\u0120poisoning",
- "\u0120slipping",
- "\u0120Says",
- "\u00e5\u00b0\u00b1\u00e5\u0131\u00af\u00e4\u00bb\u00a5",
- "\u00c3\u00b2ng",
- "\u00e7\u0141\u00b3",
- "\u00c2\u00ab",
- "\u0120Claudia",
- "\u0120Character",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0128",
- "coat",
- "\u0120progressed",
- "\u0120Fergus",
- "\u0120\u00ec\u013a\u00a4\u00eb\u012c",
- "\u0120oat",
- "ordable",
- "\u0120Ley",
- "\u0120Heraus",
- "\u0120resultados",
- "\u0120Kayla",
- "\u0120riff",
- "\u0120chegou",
- "\u0120xi",
- "\u0120spacious",
- "\u0120recognised",
- "\u0120ech",
- "\u0120Tie",
- "\u0120launcher",
- "Jim",
- "\u0120suppression",
- "\u0120Impossible",
- "\u0120guitars",
- "\u0120Fourier",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d0\u00b9",
- "\u0120Therap",
- "\u0120Kaf",
- "centered",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00be\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120klim",
- "\u0120carbohydrates",
- "ignant",
- "\u0120Astron",
- "\u0120emple",
- "\u0120drastic",
- "\u0120\u00d0\u00bc\u00d0\u00b8\u00d1\u0122\u00d0\u00b5",
- "\u00d0\u00b2\u00d0\u00b8\u00d0\u00bd",
- "uw",
- "\u0120prettier",
- "\u0120donuts",
- "\u0120Athena",
- "\u0120dissert",
- "\u0120plante",
- "\u0120uranium",
- "\u00ec\u013f\u012e\u00eb",
- "ar\u00c3\u00a9",
- "\u0120rzecz",
- "\u0120displaying",
- "\u00e6\u012a\u00b2",
- "\u0120sarc",
- "r\u00c3\u00a3o",
- "\u0120tampoco",
- "\u0120philosophers",
- "\u0120Recht",
- "\u00e6\u0135\u013c",
- "\u0120comentarios",
- "yse",
- "\u0120\u00ec\u013e\u00a4",
- "\u0120mise",
- "\u0120Gin",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00bc",
- "\u0120FROM",
- "liner",
- "atif",
- "\u0120spo\u00c5\u0124ec",
- "xa",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d1\u0125\u00d0\u00b4",
- "\u0120wag",
- "\u00ea\u00b8\u00b0\u00ec\u0139\u0132",
- "\u0120MG",
- "\u0120offspring",
- "\u0120Understanding",
- "\u00e5\u0131\u00aa\u00e6\u013a\u00af",
- "ORA",
- "\u0120whirring",
- "\u0120surrend",
- "\u0120poker",
- "\u0120monuments",
- "\u0120\u00e2\u013b\u00a9",
- "\u0120organised",
- "\u0120Sozial",
- "\u0120Factory",
- "\u00d1\u0127\u00d0\u00b0",
- "\u0120resemble",
- "\u00d0\u00b7\u00d0\u00b4",
- "\u0120explosions",
- "\u0120payroll",
- "\u0120omn",
- "\u0120Jorge",
- "\u00ce\u00b9\u00cf\u0125",
- "\u0120fracture",
- "\u0120persecution",
- "\u0120demais",
- "ECH",
- ",)",
- "\u0120criar",
- "\u0120JOSH",
- "\u0120demographics",
- "\u01201600",
- "\u0120currencies",
- "\u0120Tips",
- "\u0120\u00e9\u0122\u013b\u00e5\u0122\u012d",
- "\u0120Refer",
- "\u0120Dancing",
- "\u0120inconsistent",
- "\u0120deh",
- "\u0120immens",
- "\u0120meist",
- "\u0120impatient",
- "\u0120behaves",
- "\u00e6\u013f\u00be",
- "\u0120\u00eb\u0124\u00b4\u00ec\u013c\u00a9",
- "\u0120backstory",
- "\u0120agreeing",
- "\u0120\u00c5\u0123",
- "ihin",
- "\u0120temperatura",
- "\u0120Background",
- "\u0120nutzen",
- "\u0120\u00eb\u0127\u00b9",
- "\u0120M\u00c3\u00a4nner",
- "\u0120collaborations",
- "\u0120Kos",
- "\u00e9\u0123\u0130\u00e5\u0130\u00bb",
- "\u0120nightmares",
- "\u00eb\u0135\u00b1",
- "\u0120Queensland",
- "\u0120associates",
- "\u0120Kok",
- "\u0120factorial",
- "\u0120Hyung",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u012d\u00a4\u00ec\u013f\u012e",
- "\u0120filho",
- "\u0120el\u00c3\u00a9t",
- "\u0120\u00ed\u0138\u012b\u00eb\u00b3\u00b5",
- "\u00b0\u00b1",
- "\u0120gefunden",
- "\u0120semicondu",
- "\u0120counselors",
- "\u0120Upper",
- "\u0120Aub",
- "ickers",
- "Ver",
- "\u0120northwest",
- "\u0120Maintenant",
- "\u0120Lakes",
- "\u00d0\u00b0\u00d1\u0131\u00d0\u00b2",
- "int\u00c3\u00a9",
- "\u00ec\u00b0\u00bd",
- "\u0120\u00d0\u00b3\u00d0\u00b0\u00d0\u00b7",
- "\u0120giorn",
- "\u0120digitally",
- "\u0120Circuit",
- "\u00ec\u00bc\u0122",
- "\u00e3\u0124\u012c\u00e3\u0123\u00be\u00e3\u0123\u0139\u00e3\u0123\u0141",
- "\u0120cheerful",
- "\u0120Peterson",
- "\u0120Danish",
- "ativos",
- "\u0120liken",
- "\u0120harbor",
- "\u00d0\u00b0\u00d0\u00bb\u00d0\u00b8\u00d1\u0123\u00d1\u0124",
- "xe",
- "\u0120curls",
- "\u0120Rhod",
- "End",
- "\u0120ET",
- "\u0120acquaint",
- "\u0120Kelvin",
- "\u0120trif",
- "\u0120Away",
- "\u00ec\u0140\u0132\u00eb\u012c\u0136",
- "vs",
- "\u0120p\u00c3\u00a1gina",
- "\u0120inlet",
- "\u0120Santos",
- "\u0120\u00ec\u013c\u00b0\u00ec\u013b\u0122",
- "\u0120yap\u00c4\u00b1yorsun",
- "theme",
- "\u0120souff",
- "\u0120injected",
- "\u0120p\u00c3\u00b3\u00c5\u00baniej",
- "iverso",
- "amped",
- "\u0120daher",
- "\u0120dagger",
- "\u0120\u00d0\u00bb\u00d1\u0130\u00d0\u00b1\u00d0\u00b8\u00d0\u00bc",
- "\u0120tummy",
- "\u0120enlightened",
- "cents",
- "\u0120Dah",
- "\u0120cuest",
- "\u00e4\u00be\u0128\u00e8\u00aa\u00aa",
- "ILY",
- "\u0120\u00d7\u0133\u00d7\u00a8",
- "\u0120banging",
- "\u0120Emil",
- "\u0120Cler",
- "\u0120Border",
- "\u00d0\u00b8\u00d0\u00b6\u00d1\u0125",
- "\u0120presenters",
- "\u0120STUD",
- "coins",
- "\u0120\u00ed\u013b\u012f",
- "\u0120perks",
- "\u0120parap",
- "\u0120certaines",
- "\u0120Lore",
- "\u00c3\u00b6st",
- "\u0120MARTIN",
- "\u0120bios",
- "\u0120whereby",
- "verts",
- "\u0120Miranda",
- "\u0120stip",
- "\u00e6\u00be\u00a4",
- "andez",
- "\u00d7\u013d\u00d7\u013e",
- "ujin",
- "\u0120\u00ea\u00be",
- "\u0120allergies",
- "plate",
- "\u0120yap\u00c4\u00b1l",
- "\u0120undertake",
- "\u0120\u00eb\u0124\u013a\u00ea\u00b0\u0122",
- "Part",
- "\u0120k\u00c4\u00b1z\u00c4\u00b1m",
- "hguru",
- "\u00e3\u0123\u0124\u00e3\u0123\u00a8",
- "\u0120Johns",
- "\u0120eyelashes",
- "\u0120drained",
- "\u0120st\u00c3\u00a5r",
- "\u00e3\u0123\u0124\u00e3\u0124\u012c\u00e3\u0123\u00be\u00e3\u0123\u013b",
- "\u0120Jade",
- "\u0120calend",
- "film",
- "\u0120mesa",
- "\u0120ludzie",
- "\u0120attracts",
- "\u0120juices",
- "\u0120\u00d0\u00ba\u00d0\u00b8\u00d0\u00bb",
- "\u0120nieuwe",
- "\u0120mencion",
- "\u0120ignition",
- "\u0120bladder",
- "andaag",
- "\u0120Extension",
- "\u00ed\u0124\u00a8",
- "feed",
- "\u0120\u00d9\u012a\u00d9\u0129",
- "\u0120spun",
- "\u0120t\u00c3\u00a4t",
- "\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d1\u0124",
- "tyard",
- "ronics",
- "\u0120Huge",
- "\u00d1\u0125\u00d0\u00b6\u00d0\u00b4",
- "string",
- "\u0120unjust",
- "\u0120prawn",
- "\u0120frosting",
- "\u0120disappearance",
- "iosa",
- "\u0120cardi",
- "\u0120Priest",
- "\u0120cient\u00c3\u0143fic",
- "\u00e5\u0135\u00aa\u00e8\u00a3\u00a1",
- "\u0120\u00d0\u0134\u00d0\u00b0\u00d1\u0123",
- "\u0120\u00eb\u00b6\u0122\u00ed\u0125\u0123",
- "\u0120thieves",
- "\u0120physique",
- "\u0120Eugene",
- "\u0120\u00d0\u00b1\u00d0\u00bb\u00d0\u00b8\u00d0\u00b7",
- "\u0120monopoly",
- "\u0120biography",
- "\u0120ho\u00c5\u0141",
- "\u0120t\u00c3\u00b6",
- "mac",
- "\u0120shocks",
- "\u00ec\u0126\u00b8\u00eb",
- "hit",
- "\u0120snug",
- "\u0120incl",
- "\u0120dedic",
- "\u0120ultras",
- "\u0120\u00d0\u00b8\u00d0\u00b7\u00d0\u00b2\u00d0\u00b5\u00d1\u0123\u00d1\u0124",
- "\u0120utilization",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b2\u00d0\u00b5\u00d1\u0122\u00d1\u012a\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d0\u00be",
- "\u0120servi",
- "stag",
- "180",
- "\u0120sewer",
- "\u0120Choice",
- "\u0120discharged",
- "\u0120JD",
- "\u00d0\u00be\u00d0\u00bb\u00d0\u00b5\u00d1\u0124",
- "\u0120\u00d0\u00ba\u00d0\u00b2\u00d0\u00b0\u00d1\u0122\u00d1\u0124\u00d0\u00b8",
- "\u0120telescop",
- "\u0120Je\u00c5\u013dli",
- "\u0120Nana",
- "cale",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00bd",
- "mmm",
- "\u00e4\u00ba\u0128\u00e5\u0132\u00a7",
- "\u0120gehabt",
- "\u00eb\u0124\u0142",
- "\u00e6\u012c\u0137",
- "\u00e0\u00b8\u013b\u00e0\u00b8\u013b",
- "\u0120ether",
- "\u0120zen",
- "\u0120researched",
- "\u0120Czyli",
- "\u00e5\u00ae\u012e\u00e5\u0127\u00a8",
- "workers",
- "\u0120\u00ea\u00b2\u00bd\u00ec\u00b0\u00b0",
- "\u0120sheriff",
- "allo",
- "\u0120tipos",
- "\u0120prosecution",
- "\u0120frogs",
- "\u0120falt",
- "jd",
- "\u0120\u00ed\u012e\u0136",
- "\u0120filtered",
- "\u0120Oft",
- "\u0120\u00ec\u012f",
- "\u0120disfr",
- "\u0120Mustang",
- "\u0120woah",
- "\u0120REALLY",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b3\u00d0\u00bb\u00d0\u00b8",
- "\u0120entrada",
- "\u0120\u00d0\u00b8\u00d0\u00b3\u00d1\u0122\u00d0\u00b0",
- "\u0120mixes",
- "\u0120\u00d0\u00b0\u00d0\u00b2\u00d1\u0124\u00d0\u00be\u00d0\u00bc\u00d0\u00be\u00d0\u00b1",
- "\u00d0\u013b",
- "\u0120shin",
- "\u0120paranormal",
- "\u0120someplace",
- "\u0120dishon",
- "etaan",
- "\u0120fuerte",
- "\u00d9\u00b9",
- "\u0120doom",
- "\u00ec\u012a\u013e",
- "\u0120existential",
- "\u0120buld",
- "\u0120SDK",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d0\u00b4\u00d0\u00b0",
- "\u0120turnover",
- "\u0120\u00ec\u0139\u00ac\u00ea\u00b8\u00b0\u00ec\u0139\u0132",
- "\u0120\u00e0\u00a4\u00b9",
- "\u0120modeled",
- "\u0120bug\u00c3\u00bcn",
- "\u0120experimentation",
- "\u0120mornings",
- "\u0120medo",
- "Stevie",
- "\u0120playable",
- "\u0120airlines",
- "gments",
- "\u0120\u00ea\u00b8\u00b0\u00eb\u00b6\u0126",
- "\u0120Tomb",
- "\u0120MVP",
- "AUDIENCE",
- "\u0120checkout",
- "\u0120passt",
- "\u0120beispiel",
- "\u0120Links",
- "heavy",
- "\u0120questionable",
- "\u0120\u00ec\u0135\u00b0\u00eb",
- "\u0120sill",
- "\u0120manipulated",
- "\u0120Loren",
- "\u0120\u00ec\u013e\u00bc",
- "\u0120verge",
- "\u00c3\u00a1k",
- "IES",
- "\u0120sabot",
- "\u0120Customer",
- "ale\u00c5\u00bcy",
- "\u0120nominee",
- "\u0120Gad",
- "\u0120nouvelles",
- "\u0120SPE",
- "istling",
- "\u0120oval",
- "\u00d0\u00be\u00d0\u00b1\u00d1\u0122\u00d0\u00b0\u00d0\u00b6",
- "ifty",
- "\u00e9\u0129\u0130",
- "\u0120bezel",
- "yet",
- "\u0120freight",
- "\u0120Han\u00c4\u00b1m",
- "r\u00c3\u0143a",
- "\u0120zoning",
- "\u0120indem",
- "\u0120B\u00c3\u00bc",
- "\u0120feminism",
- "\u0120voix",
- "\u0120oficial",
- "\u0120diyorum",
- "\u00bb\u0132",
- "\u0120arose",
- "\u0120parar",
- "\u00ec\u013f\u00b8\u00ec\u00a7\u0122",
- "\u0120Martine",
- "\u0120Lect",
- "\u0120rester",
- "\u0120drowning",
- "uya",
- "cida",
- "\u0120Ariel",
- "\u012002",
- "\u0120\u00d7\u0136\u00d7\u0136",
- "\u00e7\u00b4\u0142",
- "\u0120Wert",
- "\u00d0\u00a2\u00d1\u012d",
- "\u0120widow",
- "\u0120parchment",
- "\u0120cottage",
- "\u0120XL",
- "\u0120Slack",
- "\u0120NES",
- "\u0120robe",
- "\u0120gimm",
- "\u0120caminho",
- "\u0120Harper",
- "\u0120citrus",
- "\u0120firefighters",
- "\u0120dopamine",
- "elets",
- "\u0120democrat",
- "\u00ec\u0142\u013e\u00eb\u00a1\u013e",
- "\u0120playback",
- "oj",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00ba",
- "\u0120Sullivan",
- "semble",
- "\u0120Worth",
- "\u0120Mustafa",
- "\u00e0\u00b8\u00b2\u00e0\u00b8\u00a3",
- "\u0120mets",
- "\u00e9\u0138\u0122",
- "\u00d0\u00bb\u00d0\u00be\u00d1\u0123\u00d1\u012e",
- "\u0120inertia",
- "\u0120uniforms",
- "\u00e8\u00b6\u00b3",
- "\u00c3\u00a9rio",
- "\u00d7\u0137\u00d7\u00a8\u00d7\u0136",
- "\u00c3\u00a9nt",
- "\u0120\u00e0\u00ae\u0134",
- "\u0120\u00d1\u0123\u00d0\u00b0\u00d0\u00bc\u00d1\u012d\u00d1\u0127",
- "\u0120voulais",
- "\u0120Zimmer",
- "\u00ea\u00b2\u0142\u00eb",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d1\u0123",
- "encias",
- "\u0120relaci\u00c3\u00b3n",
- "\u0120\u00ea\u00b1\u00b8\u00eb",
- "\u0120faction",
- "\u0120gosp",
- "\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b6",
- "nap",
- "hak",
- "\u0120proceedings",
- "\u0120\u00ec\u0128\u0136",
- "\u00ec\u0137\u0126\u00eb\u012d\u012a",
- "\u0120\u00ec\u0140\u0132\u00ea\u00b8\u00b0",
- "\u0120werd",
- "\u0120sof",
- "\u0120schlim",
- "\u0120flavored",
- "\u0120quadratic",
- "\u0120Boot",
- "\u0120publicity",
- "\u0120Caro",
- "\u0120?\"",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0128\u00d0\u00b0",
- "mania",
- "\u0120SUR",
- "\u0120BUR",
- "lance",
- "\u00c3\u00a9tica",
- "\u0120zobaczy",
- "\u0120trio",
- "sama",
- "\u0120ta\u00c5\u0141",
- "\u0120asymm",
- "resser",
- "\u0120\u00d8\u00aa\u00d8\u00b9",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0123",
- "\u0120beginnings",
- "lad\u00c4\u00b1m",
- "\u0120\u00d0\u00b1\u00d1\u012d\u00d1\u0123\u00d1\u0124\u00d1\u0122",
- "\u0120moo",
- "\u0120Geneva",
- "\u0120\u00e5\u013e\u00a8",
- "erus",
- "borah",
- "\u0120refusing",
- "bull",
- "\u0120Waiting",
- "\u0120Individual",
- "\u0120anonym",
- "imens",
- "\u0120medidas",
- "\u0120fragrant",
- "\u0120directement",
- "\u0120\u00ec\u0137\u0126\u00eb\u00a7\u012a",
- "uria",
- "\u0120spherical",
- "\u0120abge",
- "\u0120Victorian",
- "\u0120spectacle",
- "\u0120Rodriguez",
- "\u0120ocup",
- "\u0120N\u00c3\u00a4r",
- "marks",
- "ngulo",
- "\u0120Luci",
- "\u0120shouted",
- "\u0120regulators",
- "\u00c4\u0141ini",
- "\u0120disent",
- "\u0120\u00d1\u0122\u00d1\u012d\u00d0\u00bd",
- "\u00eb\u0124\u00a8",
- "\u0120\u00ec\u0124\u00b4\u00eb",
- "\u0120probl\u00c3\u00a8mes",
- "\u0120Finger",
- "assemble",
- "\u0120pear",
- "\u0120droite",
- "\u0120Everywhere",
- "tam",
- "\u00d0\u00be\u00d1\u0124\u00d0\u00b8\u00d0\u00b2",
- "\u00d0\u00b2\u00d0\u00be\u00d0\u00b9",
- "ordinate",
- "\u0120Lak",
- "\u0120m\u00e1\u00bb\u013di",
- "\u0120Television",
- "\u0120exponentially",
- "avas",
- "\u0120blev",
- "\u0120MT",
- "\u00e4\u00bf\u00ba",
- "Connell",
- "\u0120\u00ea\u00b5\u0143\u00eb\u00af\u00bc",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b8\u00d0\u00bc",
- "\u0120acha",
- "\u0120Dynasty",
- "Jin",
- "\u0120tore",
- "\u0120flor",
- "\u0120\u00d0\u00bc\u00d0\u00bd\u00d0\u00be\u00d0\u00b3\u00d0\u00b8\u00d0\u00b5",
- "\u00e6\u00b2\u0134\u00e4\u00ba\u012d",
- "owan",
- "bah",
- "\u0120\u00ec\u00a3\u0126",
- "\u0120Cela",
- "\u0120\u00ec\u00b5\u013e\u00ea\u00b7\u00bc",
- "\u0120permettre",
- "\u0120abras",
- "\u0120verstehen",
- "\u0120escort",
- "\u0120Them",
- "\u00c3\u00a4rke",
- "porter",
- "\u0120kahkaha",
- "\u0120hect",
- "\u0120dau",
- "wah",
- "olve",
- "\u0120Ages",
- "schaft",
- "\u0120Stell",
- "nelle",
- "\u0120Ensuite",
- "\u0120\u00d0\u0134\u00d1\u0123\u00d0\u00b5\u00d0\u00bc",
- "\u0120cr\u00c3\u00a9d",
- "\u0120PP",
- "lords",
- "grunting",
- "\u0120contraction",
- "Got",
- "\u0120acquiring",
- "\u0120sopr",
- "\u0120poisonous",
- "RNA",
- "\u0120anar",
- "\u0120Hof",
- "')",
- "\u0120remarkably",
- "\u0120internacional",
- "\u00c3\u00bccke",
- "inqu",
- "\u0120duy",
- "\u0120beasts",
- "\u0120LAN",
- "\u0120precedent",
- "\u0120RPM",
- "\u00e5\u0133\u00a8",
- "\u0120selon",
- "\u0120morte",
- "\u0120come\u00c3\u00a7ou",
- "\u00d1\u0131\u00d0\u00bb\u00d0\u00b0",
- "\u0120interpreting",
- "\u0120Burke",
- "\u00d1\u0124\u00d1\u0122\u00d0\u00b0",
- "\u0120\u00ec\u013f\u00b4\u00eb\u0141\u00ac",
- "\u0120pessim",
- "\u0120Nok",
- "\u00ed\u012e\u013f",
- "Female",
- "\u0120\u00ec\u012d\u00a4\u00ed",
- "\u013b\u0122",
- "\u0120stimulation",
- "\u0120slick",
- "\u0120\u00ea\u00b0\u0122\u00eb\u012c\u0136",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7",
- "\u0120HBO",
- "\u0120papier",
- "\u0120k\u00c3\u00b6nnten",
- "\u00d1\u0125\u00d0\u00b1\u00d0\u00bb\u00d0\u00b8",
- "\u0120Constant",
- "SPEAKING",
- "\u0120kt\u00c3\u00b3r\u00c4\u0127",
- "\u0120cosmetics",
- "\u0120Trend",
- "\u0120robbery",
- "\u0120titt",
- "\u0120gjort",
- "\u0120dietary",
- "\u0142\u012e",
- "\u0120Kirby",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bc\u00d0\u00b5\u00d1\u0122\u00d0\u00bd\u00d0\u00be",
- "\u0120qualification",
- "\u0120\u00ec\u0137\u012b",
- "\u0120cabinets",
- "\u0120http",
- "\u0120Erica",
- "\u00e7\u00be\u00a9",
- "\u0120disadvantages",
- "\u0120chattering",
- "yz",
- "feit",
- "\u0120guild",
- "\u0120ETF",
- "\u0120Dragons",
- "\u0120HERE",
- "venth",
- "\u00d9\u0126\u00d8\u00a7\u00d9\u0127",
- "\u0120march\u00c3\u00a9",
- "Dam",
- "\u0120photon",
- "\u0120estable",
- "Mag",
- "\u0120olhar",
- "\u0120coupling",
- "\u0120Hilfe",
- "\u0120Wizard",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d0\u00bb\u00d0\u00be",
- "help",
- "\u0120l\u00c3\u0143nea",
- "\u0120\u00ec\u00ab",
- "\u0120standalone",
- "\u0120morale",
- "\u0120zweite",
- "\u00e3\u0124\u012a\u00e3\u0124\u012f\u00e3\u0123\u0139\u00e3\u0123\u0131",
- "\u00c3\u00a4hrt",
- "\u0120dotted",
- "\u0120dripping",
- "\u0120Flag",
- "\u00e9\u013f\u0134",
- "rocket",
- "rategy",
- "irim",
- "\u0120\u00ed\u0137\u013a\u00eb\u00a9\u00b4\u00ec\u0126\u013e",
- "\u0120sogenan",
- "\u0120Uno",
- "\u0120Schutz",
- "\u0120estilo",
- "\u0120Subs",
- "\u0120Daisy",
- "\u00d0\u013f\u00d0\u00b5\u00d1\u0124",
- "'...",
- "\u0120platinum",
- "\u0120birl",
- "\u0120Sovi",
- "\u0120violate",
- "\u00d1\u0125\u00d0\u00b5\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "rill",
- "\u0120traz",
- "\u0120snip",
- "\u0120cumpl",
- "\u00e0\u00b8\u0143\u00e0\u00b8\u0123",
- "\u0120cuk",
- "\u00e9\u0127\u0134",
- "\u0120Parlament",
- "\u0120hypert",
- "\u0120pulp",
- "\u0120tongues",
- "atto",
- "\u0120busca",
- "ihn",
- "ERO",
- "\u0120\u00d9\u012c\u00d8\u00b9",
- "\u0120varias",
- "\u0120Marian",
- "\u0120bounded",
- "\u0120pitching",
- "\u0120deficiency",
- "\u0120Blessed",
- "\u0120Exerc",
- "uchs",
- "\u0120nh\u00c6\u00b0ng",
- "\u00e6\u013e\u00ac\u00e5\u00bd\u0135",
- "\u0120raped",
- "hales",
- "\u0120mala",
- "pic",
- "\u0120401",
- "\u00c5\u013dniej",
- "arina",
- "\u00eb\u0135\u00a4\u00ec\u013f\u0126",
- "otti",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bb\u00d0\u00b3\u00d0\u00be",
- "\u0120tracker",
- "\u0120Shelby",
- "\u0120vanished",
- "\u0120bakery",
- "Kap\u00c4\u00b1",
- "Jesus",
- "\u0120KR",
- "JO",
- "\u0127\u00b8",
- "\u0120discs",
- "\u00ec\u0126\u00af",
- "\u00ec\u00a7\u0122\u00eb",
- "\u00d7\u013b\u00d7\u00a6",
- "emary",
- "Kendra",
- "\u0120y\u00c3\u00bck",
- "\u00c3\u00bcckt",
- "\u0120vaz",
- "\u0120kup",
- "aktu",
- "\u0120\u00d1\u0123\u00d0\u00bf\u00d0\u00b0\u00d1\u0123\u00d0\u00b8\u00d0\u00b1\u00d0\u00be",
- "\u0120aik",
- "\u0120nursery",
- "\u0120endangered",
- "\u00c3\u00aamement",
- "ematics",
- "\u0120responders",
- "\u0120Representatives",
- "\u0120sculptures",
- "igkeiten",
- "\u0120depl",
- "\u0120interpretations",
- "\u0120deadlines",
- "\u01201942",
- "\u00c3\u0139",
- "\u0120sugars",
- "emu",
- "lively",
- "\u0120recreational",
- "\u0120distort",
- "\u0120underscore",
- "\u0120unquote",
- "\u0120safest",
- "\u0120swollen",
- "\u0120analyses",
- "\u0120commenc\u00c3\u00a9",
- "\u00e5\u00a6\u00b9",
- "andin",
- "\u0120\u00d0\u00a5\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d1\u012a\u00d0\u00be",
- "\u0120diarr",
- "\u00e3\u0123\u00be\u00e3\u0123\u0123",
- "ziest",
- "\u0120toothbrush",
- "\u00e9\u0142\u00bb\u00e9\u0123\u0135",
- "uations",
- "\u0120cade",
- "\u0120backlash",
- "hind",
- "\u0120risque",
- "zess",
- "\u0120\u00ec\u013f\u00b4\u00ec\u0137\u00bc\u00ea\u00b8\u00b0",
- "\u0120esperar",
- "\u0120translations",
- "ioned",
- "groans",
- "\u0120\u00d0\u00bf\u00d1\u0125\u00d1\u0124",
- "\u0120genetically",
- "\u00e9\u0122\u0142",
- "\u0120happiest",
- "\u0120werk",
- "atoon",
- "\u0120musi",
- "\u0120fun\u00c3\u00a7\u00c3\u00a3o",
- "\u0120\u00ec\u0140\u0127\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b9",
- "\u0120bevor",
- "BLANK",
- "\u0120repentance",
- "Put",
- "\u0120potrzeb",
- "\u0120sala",
- "\u0120campa",
- "WER",
- "\u0120dec\u00c3\u0143a",
- "\u0120s\u00c3\u00a9curit\u00c3\u00a9",
- "\u0120Appreciate",
- "\u00d1\u0129\u00d0\u00b8",
- "\u0120Random",
- "\u00eb\u00b3\u0126",
- "kah",
- "\u0120m\u00c3\u00b6j",
- "\u0120s\u00c3\u00a4ger",
- "\u0120\u00d7\u013b\u00d7\u013d\u00d7\u0137\u00d7\u013e",
- "\u0120190",
- "xtures",
- "Eu",
- "\u0120g\u00c3\u00a4",
- "\u0120\u00d7\u0133\u00d7\u00aa",
- "\u0120Croat",
- "apo",
- "PLE",
- "\u0120persistence",
- "\u00e5\u012c\u00a9",
- "\u0120blends",
- "\u0120treffen",
- "\u0120Santiago",
- "ydia",
- "aldo",
- "\u0120TensorFlow",
- "\u0120Dual",
- "\u00e3\u0125\u013e",
- "\u0120chiff",
- "\u00ec\u0139\u00b4",
- "\u0120contracted",
- "\u0120segreg",
- "\u0120Fairy",
- "\u0120wisely",
- "\u0120vulnerabilities",
- "\u0120handheld",
- "\u0120gadgets",
- "\u0120bo\u00c5\u0141",
- "\u0120Popular",
- "\u0120curvature",
- "\u00eb\u00ac\u00b8",
- "\u0120MARY",
- "\u00ec\u013f\u00b4\u00ec\u012c",
- "\u0120formulation",
- "\u0120celery",
- "\u0120blurry",
- "\u0120TS",
- "alez",
- "\u0120ws",
- "\u0120programm",
- "\u0120Stack",
- "\u0120JIM",
- "\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d0\u00bb\u00d0\u00b8",
- "\u00c4\u00b1ll",
- "\u0120p\u00c3\u00a8re",
- "\u0120Kanye",
- "\u0120Delaware",
- "\u0120\u00e3\u0123\u0142",
- "\u0120daunting",
- "\u0120\u00d0\u00b1\u00d0\u00b5\u00d1\u0123",
- "\u0120Stupid",
- "big",
- "fficial",
- "\u0120precipitation",
- "\u0120plung",
- "\u00e1\u00bb\u00a5c",
- "burse",
- "\u0120darle",
- "\u0120cripp",
- "\u0120pioneer",
- "\u0120disput",
- "\u0120sean",
- "\u00e3\u0123\u0135\u00e3\u0124\u0135\u00e3\u0123\u00aa",
- "\u0120resistor",
- "\u0120allein",
- "ipples",
- "arel",
- "\u0120endors",
- "zust",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00b1\u00d1\u0131\u00d1\u0124\u00d0\u00b0",
- "eded",
- "\u0120\u00ec\u00b9\u00b4\u00eb\u00a9\u0136\u00eb",
- "\u0120lleva",
- "\u0120kennt",
- "\u0120\u00d0\u00b1\u00d0\u00b0\u00d0\u00bb",
- "\u0120Document",
- "\u0120Knights",
- "\u0120buckle",
- "\u0120\u00ec\u012b\u00ac",
- "\u0120alk",
- "\u0120Everyday",
- "atters",
- "\u0120toilets",
- "\u0120jugar",
- "\u0120\u00ec\u0140\u012a\u00ec\u00a7\u0122",
- "\u0120genauso",
- "\u0120Landesregierung",
- "\u00e3\u0123\u00a3\u00e3\u0123\u00b1",
- "ije",
- "\u0120trailers",
- "\u0120Tigers",
- "\u0120gitti",
- "\u0120forgiving",
- "\u0120concurrent",
- "\u0120Vu",
- "\u0120\u00ed\u012c\u00b9\u00ed\u0140\u012a",
- "\u0120BROWN",
- "ounded",
- "\";",
- "\u0120tremb",
- "\u0120tiet",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00b6\u00d0\u00b8\u00d0\u00bc",
- "\u0120nutshell",
- "\u00d0\u00b5\u00d0\u00bb\u00d0\u00b8\u00d1\u0129",
- "\u0120losers",
- "ricting",
- "\u0120redeem",
- "defined",
- "Nice",
- "\u0120broadband",
- "KO",
- "\u0120teasing",
- "\u0120partisan",
- "\u00c4\u00b1ma",
- "\u0120\u00ec\u0140\u00ac\u00eb\u00af\u00b8",
- "\u0120Journey",
- "\u0120slopes",
- "uning",
- "grunts",
- "\u0120t\u00c3\u00a4ll",
- "\u0120uncovered",
- "\u0120my\u00c5\u013dl\u00c4\u013b",
- "\u0120Esther",
- "\u00e4\u00ba\u0130",
- "\u0120Healthy",
- "\u0120\u00eb\u00b0\u0133",
- "r\u00c3\u00a9e",
- "\u0120polarization",
- "\u0120flav",
- "\u0120cambiar",
- "\u0120yr",
- "\u0120Ranch",
- "\u0120splits",
- "\u0120trouv\u00c3\u00a9",
- "\u00e5\u013e\u012d\u00e5\u00ae\u00b6",
- "\u0120recorder",
- "\u0120d\u00c3\u00a9part",
- "\u00d9\u012a\u00d8\u00a8",
- "\u0120Kry",
- "\u0120interessant",
- "\u0120ederim",
- "\u00c5\u013dwiad",
- "ilateral",
- "wright",
- "\u0120pourra",
- "\u00c3\u00aater",
- "\u0120camel",
- "\u00e1\u0140",
- "\u0120rapidement",
- "\u0120mej",
- "\u0120stiffness",
- "ADAS",
- "\u0120differs",
- "\u0120alot",
- "\u0120Sig",
- "\u00d1\u0131\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d1\u012e",
- "\u0120abstraction",
- "\u00e5\u013e\u013a",
- "\u0120keiner",
- "grupp",
- "\u0120Sherlock",
- "\u00ed\u013a\u0136",
- "\u0120cite",
- "\u0120overflow",
- "\u0120t\u00e1\u00ba\u00a1i",
- "\u00c3\u00bacar",
- "bula",
- "\u0120conjunto",
- "\u0120CI",
- "\u0120moderator",
- "\u0120indirectly",
- "\u0120alleine",
- "\u00e2\u0124",
- "\u00d1\u012a\u00d0\u00b8\u00d0\u00b1",
- "\u0120\u00d0\u00b1\u00d0\u00b0\u00d0\u00b1",
- "\u0120danach",
- "\u01201939",
- "\u0120promet",
- "\u0120destinations",
- "\u0120Illust",
- "\u00ce\u00b9\u00ce\u00ba\u00cf\u012e",
- "\u0120sabes",
- "\u0120heh",
- "\u0120Gesetzent",
- "\u0120Miz",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00ba\u00d0\u00be",
- "\u0120Mys",
- "\u00d0\u00ac",
- "\u0120Judaism",
- "\u0120mustache",
- "\u0120stimmt",
- "\u0120Gaza",
- "\u0120volte",
- "\u0120nuo",
- "\u0120m\u00c3\u00b3n",
- "\u0120Comput",
- "\u00e0\u00b8\u00b9\u00e0\u00b9\u012a",
- "\u0120Radi",
- "\u0120exceptionally",
- "\u0120assumes",
- "\u00e9\u0138\u012d\u00e5\u00bf\u0125",
- "\u00e3\u0123\u012a\u00e3\u0123\u00b0",
- "inform",
- "\u0120shrine",
- "\u00e6\u0135\u012c",
- "\u0120implication",
- "\u0120Fitz",
- "\u00e6\u00b2\u0134\u00e9\u0139\u013e\u00e4\u00bf\u0124",
- "!.",
- "\u0120lt",
- "\u0120alloy",
- "\u0120ethic",
- "\u0120monastery",
- "\u00ec\u012d\u013e\u00ec\u00a3\u0142",
- "ica\u00c3\u00a7\u00c3\u00a3o",
- "\u0120coordinating",
- "\u0120Moto",
- "\u0120overlook",
- "\u0120chois",
- "\u0120antibiotic",
- "\u0120Minne",
- "\u0120BJ",
- "\u0120Apa",
- "orian",
- "\u0120spilled",
- "Jam",
- "\u0120husbands",
- "\u0120creations",
- "\u0120a\u00c3\u00b1",
- "\u00c3\u00bcssel",
- "\u0120\u00ec\u013f\u00b4\u00ec\u013c\u00a9",
- "\u0120analyse",
- "rose",
- "\u0120punched",
- "\u0120presque",
- "\u0120astronomy",
- "\u0120schwierig",
- "\u0120Ebola",
- "\u0120cis",
- "\u0120acet",
- "\u0120FX",
- "endre",
- "\u0120\u00ec\u013f\u012e\u00ec\u0137\u0127",
- "\u0120webpage",
- "\u0120freaked",
- "\u0120latte",
- "\u0120\u00ec\u00bf\u0142",
- "\u0120\u00eb\u00a8\u00b8\u00eb",
- "Never",
- "Gra",
- "\u00ed\u013b\u0136\u00eb\u00a5\u00bc",
- "eyed",
- "\u0120\u00eb\u00b0\u013e\u00eb\u013f\u00bc",
- "\u0120espera",
- "\u0120aparece",
- "ra\u00c3\u00a7\u00c3\u00a3o",
- "\u0120disruptive",
- "\u0120Joint",
- "urous",
- "reas",
- "\u0120quer\u00c3\u0143a",
- "\u0120distributions",
- "\u0120exponent",
- "\u00ec\u00b9\u013a\u00eb\u00a5\u00bc",
- "\u0120dl",
- "zhou",
- "\u0120Hearing",
- "\u00e5\u00b7\u00ae\u00e4\u00b8\u012f\u00e5\u00a4\u013c",
- "\u0120Craw",
- "\u0120floats",
- "ounced",
- "Lab",
- "World",
- "\u0120burdens",
- "\u0120authoritarian",
- "\u0120Bolt",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d1\u0125",
- "\u0120pigeon",
- "\u0120distractions",
- "\u0120Herausforder",
- "\u0120zest",
- "esc",
- "\u0120shakes",
- "atas",
- "\u0120\u00d9\u0127\u00d8\u00b4",
- "holes",
- "\u0120thinkers",
- "alta",
- "\u0120arche",
- "\u0120Suk",
- "anha",
- "\u0120tempting",
- "\u0120youtuber",
- "\u0120v\u00c3\u00ac",
- "\u0120dzia\u00c5\u0124a",
- "\u0120Vatican",
- "Park",
- "\u0120supers",
- "\u0120Nikki",
- "\u00eb\u012c\u0132\u00eb",
- "orang",
- "ramient",
- "\u00e9\u00ac\u00bc",
- "\u0120\u00ea\u00b0\u0138\u00ea\u00b3\u0142",
- "\u0120desserts",
- "\u0120avere",
- "\u0120Gregory",
- "\u0120\u00eb\u0135\u00a4\u00ec\u0138\u00b4\u00ec\u013a",
- "\u0120costing",
- "\u0120Clinic",
- "\u0120rebels",
- "\u0120Mob",
- "\u0120bunlar",
- "\u0120Yours",
- "ertime",
- "\u0120retali",
- "mara",
- "atus",
- "alles",
- "\u0120\u00d0\u00b4\u00d1\u0122",
- "\u0120\u00d0\u00b4\u00d0\u00b8\u00d1\u0123",
- "\u0120discounts",
- "\u0120GUY",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00ba\u00d0\u00be\u00d0\u00b5",
- "\u0120Experiment",
- "rement",
- "\u0120Xiang",
- "\u0120bate",
- "WE",
- "\u0120specialize",
- "\u0120deity",
- "\u0120Loki",
- "mag",
- "\u0120Nit",
- "West",
- "\u0120maternal",
- "\u0120quis",
- "\u00e5\u0141\u00ba\u00e6\u013e\u00ac",
- "broken",
- "\u0120lasers",
- "\u0120hakk",
- "\u0120Angels",
- "\u0120mastery",
- "antis",
- "Tiffany",
- "eee",
- "\u00e7\u0133",
- "orem",
- "\u0120inacc",
- "\u0120jurisdictions",
- "\u0120Kardash",
- "\u00e6\u013e\u00ba",
- "Il",
- "\u0120Sinn",
- "\u00e5\u012d\u0137\u00e7\u0136\u00bb",
- "\u0120athletics",
- "c\u00c4\u013b",
- "\u0120loosely",
- "\u0120dieta",
- "Ag",
- "\u0120??",
- "\u0120\u00eb\u012e\u0122\u00ed\u0133\u013e",
- "\u0120superv",
- "\u0120nutrit",
- "\u0120drifting",
- "\u0120\u00ec\u0126\u0142\u00ec\u0125\u013f\u00eb\u012d\u013a",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bd\u00d1\u0131\u00d0\u00bb",
- "\u0120Victory",
- "\u00d9\u0126\u00d8\u00a9",
- "\u00d7\u0137\u00d7\u0142\u00d7\u0136",
- "\u0120\u00d0\u00bf\u00d0\u00b8\u00d1\u012a",
- "\u0120shaved",
- "\u0120mesure",
- "onden",
- "\u00d9\u0125\u00d8\u00b1",
- "\u0120exile",
- "\u0120Desde",
- "\u0120Pinterest",
- "\u0120attachments",
- "\u0120hombres",
- "\u0120fines",
- "\u0120\u00ec\u0126\u00b8\u00ec\u0125\u0123",
- "\u0120sleeps",
- "\u0120Taco",
- "\u0120IRA",
- "rios",
- "\u0120oll",
- "etes",
- "\u0120unut",
- "fashioned",
- "\u0120treball",
- "\u0120Nearly",
- "\u0120\u00d1\u0122\u00d0\u00b5\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120chil",
- "\u00e9\u0122\u00b1",
- "\u00c4\u0141a",
- "\u0120MEL",
- "roscop",
- "\u0120CG",
- "\u0120venge",
- "\u0120dishwasher",
- "algic",
- "\u0120modifier",
- "\u0120embassy",
- "timer",
- "emics",
- "\u0120intricate",
- "\u0120evet",
- "\u0120\u00eb\u012e\u0122\u00eb\u00b0\u0137",
- "\u0120isot",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0125\u00d1\u0129",
- "\u0120Quiz",
- "reso",
- "\u00ce\u00b4\u00cf\u0130",
- "\u0120yelled",
- "\u0120feder",
- "ELLER",
- "\u0120exceeded",
- "onas",
- "icano",
- "\u0120\u00d0\u00b6\u00d0\u00b8\u00d0\u00b2\u00d0\u00be\u00d1\u0124",
- "\u0120Mao",
- "\u0120Kazuto",
- "\u0120\u00e3\u0127\u012d\u00e3\u0127\u012d\u00e3\u0127\u012d\u00e3\u0127\u012d",
- "\u0120frontline",
- "\u0120Hungarian",
- "\u0120\u00c3\u00bcberall",
- "awat",
- "\u0120grips",
- "i\u00c3\u00a7\u00c3\u00b5es",
- "arnya",
- "\u0120\u00cd\u00a1",
- "\u0120seid",
- "\u0120anak",
- "\u0120acabou",
- "\u00ed\u0137\u0133",
- "\u0120notorious",
- "\u0120Godzilla",
- "\u0120overcoming",
- "\u0120Pend",
- "\u0120olabilir",
- "\u00c3\u00bclme",
- "\u0120erhalten",
- "\u00e3\u0124\u012b\u00e3\u0123\u0126",
- "\u00ea\u00b7\u00b9",
- "\u0120Meter",
- "\u0120staan",
- "Ol",
- "\u0120chats",
- "\u0120Buenos",
- "\u00c3\u0143ve",
- "aluable",
- "\u0120strategically",
- "\u0120comprised",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d1\u0123\u00d0\u00be\u00d0\u00bd\u00d0\u00b0\u00d0\u00b6",
- "\u0120wann",
- "\u0120Cen",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0124\u00d0\u00b5",
- "\u0141\u0123",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00b1\u00d0\u00be\u00d0\u00b9",
- "iad",
- "\u0120karde\u00c5\u0141im",
- "\u0120Congressman",
- "reaming",
- "homme",
- "\u0120communaut",
- "\u0120alcoholic",
- "\u0120pickled",
- "\u0120acord",
- "position",
- "eg\u00c3\u00b3l",
- "\u0120troubling",
- "\u0120Marcheg",
- "\u0120zumindest",
- "\u0120seamlessly",
- "\u0120olun",
- "\u0120TVs",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00ba\u00d1\u0124\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00b8",
- "\u0120backend",
- "\u00e3\u0123\u0135\u00e3\u0124\u0135\u00e3\u0123\u00ab\u00e3\u0123\u00a1\u00e3\u0123\u00af",
- "idable",
- "\u0120gadget",
- "\u0120fa\u00c3\u00a7o",
- "\u0120Marchegiani",
- "\u0120\u00eb\u00b0\u00a4",
- "\u0120accidental",
- "\u0120LP",
- "\u0120eldest",
- "\u0120Admiral",
- "\u0120n\u00c4\u0125m",
- "lever",
- "\u0120pastel",
- "\u0120fondo",
- "Connie",
- "\u0120tercer",
- "\u0120pact",
- "\u0120Monte",
- "\u0120meats",
- "\u0120SMS",
- "\u0120Australians",
- "\u00e7\u00bc",
- "Rhett",
- "\u0120exactement",
- "\u0120\u00eb\u00b9\u00bc",
- "\u0120MOD",
- "\u00e7\u00a1",
- "\u0120Rapt",
- "\u0120Noch",
- "\u0120abort",
- "\u0120Naval",
- "\u0120Fuji",
- "INTER",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00b2\u00d1\u012d\u00d0\u00b9",
- "\u0120miejsce",
- "\u0120ICU",
- "\u0120Graduate",
- "\u0120Glen",
- "ardi",
- "\u0120\u00c8\u013a",
- "\u0120solder",
- "\u0120professions",
- "\u0120orthog",
- "omn",
- "introdu",
- "\u0120Denise",
- "\u00ec\u0140\u0132\u00eb\u00a5\u00bc",
- "\u0120correspondence",
- "AMA",
- "\u0120inflict",
- "\u0120fand",
- "\u0120G\u00c3\u00bc",
- "\u0120\u00d1\u0129\u00d0\u00b5\u00d1\u0124",
- "\u0120traced",
- "\u0120patents",
- "\u0120ambush",
- "\u0120lotta",
- "ffer",
- "\u0120Wagner",
- "\u0120imperson",
- "\u0120extr\u00c3\u00aamement",
- "\u00d9\u0124\u00d8\u00aa",
- "conduct",
- "Att",
- "\u0120Mueller",
- "\u0120Alicia",
- "\u0120cyc",
- "\u0120hacker",
- "\u0120tys",
- "\u0120hail",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d1\u0131\u00d0\u00b2",
- "\u0120passo",
- "\u0120\u00ec\u00b6\u0136\u00ea\u00b0\u0122",
- "\u0120\u00ce\u012a",
- "\u0120packaged",
- "\u0120Cynthia",
- "heet",
- "\u00e4\u00b8\u0143\u00e5\u013d\u00bd",
- "\u0120Nissan",
- "\u0120Questo",
- "\u00e9\u00a8",
- "did",
- "\u0120\u00ce\u00bc\u00ce\u00b9\u00ce\u00b1",
- "\u0120Ellis",
- "\u0120Analysis",
- "cemos",
- "\u0120aseg",
- "\u0120Myster",
- "\u0120Cao",
- "\u0120tuv",
- "\u0120Industry",
- "\u00ec\u00a3\u00bc\u00ea\u00b3\u0142",
- "otal",
- "\u0120peque\u00c3\u00b1o",
- "bras",
- "\u0120comprehend",
- "\u0120Simpson",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00b8\u00d0\u00b5",
- "ocracy",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00b8",
- "\u0120Mush",
- "\u0120Laurie",
- "\u0120triangular",
- "\u0120Presents",
- "\u0120Kunden",
- "\u00e7\u00b4\u00b9",
- "\u00e6\u0143\u00a6",
- "\u0120Iss",
- "\u0120Deck",
- "\u00e1\u00bb\u0125n",
- "\u0120Darkness",
- "\u0120inflammatory",
- "eremiah",
- "\u0120warmed",
- "veyard",
- "\u0120Memory",
- "etty",
- "\u0120taxpayers",
- "\u00e0\u00b8\u0135",
- "\u00d8\u00a1",
- "\u0120practise",
- "\u00eb\u012d\u00ac\u00eb",
- "\u0120drilled",
- "m\u00c3\u00bc\u00c5\u0141",
- "logo",
- "\u0120Fach",
- "\u00a4\u00eb\u00a1\u013e",
- "\u0120\u00c3\u00bcbrigens",
- "\u0120konnten",
- "\u0120normalmente",
- "\u0120argues",
- "ilingual",
- "\u00b0\u00eb\u00a5\u00bc",
- "egal",
- "\u0120travaill",
- "ovy",
- "\u00d0\u00b0\u00d1\u0124\u00d0\u00be",
- "\u0120ruth",
- "\u0120Lights",
- "\u0120consisted",
- "\u00d7\u0133\u00d7\u00a8\u00d7\u013b\u00d7\u013f",
- "\u0120stereotype",
- "\u0120payer",
- "\u0120Ree",
- "\u0120Airbnb",
- "\u0120drowned",
- "\u0120Zoe",
- "\u0120canopy",
- "\u0120barr",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d1\u0129",
- "\u0120pagan",
- "\u0120jars",
- "\u0120r\u00c3\u00aa",
- "erver",
- "\u00e6\u012a\u00bf",
- "ieben",
- "\u0120espect",
- "\u0120Fi",
- "\u0120unwilling",
- "\u0120technician",
- "\u00e1\u00ba\u00b7t",
- "member",
- "\u0120Canal",
- "\u00d8\u00b3\u00d9\u0127",
- "\u0120lieber",
- "\u0120inference",
- "\u0120honoring",
- "\u00e5\u0133\u00b5",
- "\u0120Campaign",
- "\u0120lineage",
- "\u0120Stress",
- "\u0120victories",
- "\u0120deja",
- "\u00d7\u00a3",
- "\u00c3\u00aates",
- "blick",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d0\u00bd\u00d0\u00b5\u00d0\u00b5",
- "oths",
- "\u0120Couple",
- "Jason",
- "\u0120Nicolas",
- "\u00d0\u00b5\u00d0\u00ba\u00d1\u0123",
- "lib",
- "\u0120herramient",
- "\u0120\u00d7\u0132\u00d7\u0137\u00d7\u0140\u00d7\u00a8",
- "\u0120\u00d0\u00b2\u00d0\u00b8\u00d0\u00b4\u00d0\u00b8\u00d0\u00bc",
- "millimeter",
- "\u0120silhouette",
- "\u0120driveway",
- "\u0120cherish",
- "\u00e3\u0127\u0142\u00e3\u0127\u0142",
- "\u0120ransom",
- "\u0120interdisciplinary",
- "\u0120Portal",
- "\u0120trag",
- "thood",
- "\u0120tedious",
- "\u0120glossy",
- "\u0120pr\u00c3\u00a9par",
- "\u0120Cay",
- "\u0120Took",
- "\u0120Bottom",
- "\u0120zig",
- "\u00e5\u00ab",
- "\u00e5\u012f\u00b1",
- "represented",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u00a5\u00e0\u00b8\u00a2",
- "\u0120desarrollo",
- "\u00ec\u0126\u013e\u00eb",
- "\u0120viscos",
- "\u0120milligram",
- "\u0120Gund",
- "\u0120ferment",
- "drum",
- "\u0120drawers",
- "Laugh",
- "\u0120pelos",
- "\u0120pavement",
- "\u0120memoir",
- "avait",
- "\u01202050",
- "\u00a4\u00eb\u00a5\u00bc",
- "\u0120raz\u00c3\u00b3n",
- "\u0120flourish",
- "\u0120stern",
- "\u00e4\u00b8\u012a",
- "\u0120Chung",
- "\u0120serpent",
- "\u0120Gentlemen",
- "\u00e7\u013e\u0141\u00e7\u013c\u0126\u00e5\u00be\u012a",
- "kook",
- "\u0120lut",
- "importe",
- "parent",
- "\u0120wsz",
- "\u0120scree",
- "\u0120Mitarbeiter",
- "\u00e5\u00b7\u00b4",
- "mut",
- "\u0120\u00ec\u0138\u013a\u00ea\u00b8\u00b0\u00eb\u00a5\u00bc",
- "\u0120semble",
- "\u0120OW",
- "\u0120investigator",
- "\u0120Cheryl",
- "\u0120Gerald",
- "\u0120prere",
- "\u0120compares",
- "nyt",
- "\u0120diferen\u00c3\u00a7a",
- "?-",
- "\u0120qu\u00c3\u00a1",
- "\u00d7\u00a8\u00d7\u013b",
- "Sen",
- "\u0120heps",
- "\u0120gratuit",
- "\u0120consort",
- "\u0120STOP",
- "\u0120Protestant",
- "\u0120electrode",
- "\u00e2\u0139",
- "\u0120securely",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00b9",
- "\u0120t\u00c3\u00a4\u00c3\u00a4",
- "\u0120registers",
- "\u0120Heavenly",
- "ogly",
- "iss\u00c3\u00a4",
- "\u0120Physics",
- "\u0120Merkel",
- "\u0120r\u00c3\u00a9v",
- "\u00e9\u013b\u00a2",
- "\u0120erased",
- "\u0120Sacramento",
- "\u0120coffin",
- "\u0120exacer",
- "\u0120lanz",
- "\u0120poets",
- "ulif",
- "\u0120\u00ec\u00b9\u013a\u00eb",
- "\u0120Nerd",
- "\u0120NCT",
- "\u0120Hour",
- "nehmer",
- "\u0140\u013a\u00eb\u0131\u0126",
- "\u0120Princi",
- "Sw",
- "mies",
- "armed",
- "\u0120Beatles",
- "\u0120propagation",
- "\u0120exchanged",
- "\u0120cumulative",
- "\u0120\u00ec\u00a7\u0133\u00ec\u0139\u0132",
- "\u0120defeating",
- "\u00e6\u012c\u00b1",
- "bels",
- "\u0120wes",
- "\u0120Odyssey",
- "\u00e4\u00bd\u0142\u00e6\u0125\u00b3",
- "avior",
- "\u0120\u00ec\u013e\u0126\u00ec\u0139\u0132",
- "\u0120brit",
- "\u0120hijo",
- "DAY",
- "\u0120\u00d8\u00a7\u00d9\u0126\u00d8\u00aa\u00d9\u012c",
- "\u0120\u00d0\u00a1\u00d0\u00b5\u00d1\u0122\u00d0\u00b3",
- "\u00d1\u0125\u00d0\u00ba\u00d0\u00b0",
- "edsi\u00c4\u013b",
- "\u0120impos",
- "\u0120ellas",
- "\u0120firearms",
- "\u0120NR",
- "\u0120\u00d7\u0133\u00d7\u0132",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d0\u00ba\u00d0\u00b0",
- "awi",
- "\u0120\u00ec\u0126\u00b1\u00ea\u00b3\u00b5",
- "\u0120pupils",
- "\u0120Tack",
- "\u0120frase",
- "\u0120Ship",
- "\u0120stad",
- "\u00e4\u00b8\u013e",
- "\u0120Greater",
- "unun",
- "immung",
- "grown",
- "\u0120NXT",
- "\u0120Americas",
- "fox",
- "\u0120manten",
- "\u00e9\u0142\u0132\u00e5\u0124\u013b",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00ba",
- "\u0120rikt",
- "lectric",
- "deep",
- "\u0120\u00d0\u00b7\u00d0\u00bd\u00d0\u00b0\u00d0\u00b5\u00d1\u012a\u00d1\u012e",
- "\u0120benut",
- "\u0120Infrast",
- "\u0120Emir",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00b2",
- "\u0120Kimchi",
- "\u0120Finnish",
- "\u00b4\u00ec\u0142\u0123",
- "inaire",
- "\u0120oike",
- "\u00e6\u00b8\u0127\u00e6\u00a5\u013c",
- "\u0120hostage",
- "\u0120Button",
- "\u00d9\u0124\u00d9\u012c",
- "eking",
- "\u0120Kazakh",
- "\u0120comforting",
- "\u0120sog",
- "\u0120greeted",
- "guitar",
- "payer",
- "\u0120relational",
- "\u0120construir",
- "\u00e7\u012b\u00b9\u00e5\u012a\u00a5",
- "opian",
- "\u0120Volume",
- "ieth",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00be\u00d0\u00bc",
- "urrection",
- "li\u00c5\u013dmy",
- "\u0120hemisphere",
- "\u0120Bean",
- "IGN",
- "\u0120k\u00c3\u00b6t\u00c3\u00bc",
- "\u0120Fallout",
- "\u0120brace",
- "\u00e7\u00b9\u00bc\u00e7\u00ba\u012e",
- "\u00cf\u0122\u00ce\u00ac",
- "\u0120HAS",
- "\u0120g\u00c3\u00a9",
- "\u0120characterize",
- "\u00e1\u00ba\u00b7c",
- "\u0120Milky",
- "\u0120tumors",
- "\u0120nuit",
- "\u0120Gaz",
- "\u0120\u00ec\u0140\u012a\u00eb\u012d\u00a4\u00eb\u012c\u0136",
- "\u0120\u00d0\u00b3\u00d0\u00b0\u00d1\u0122",
- "essment",
- "\u0120Abe",
- "\u0120\u00eb\u00bd\u0133",
- "\u0120Einsatz",
- "JIN",
- "j\u00c3\u00a4",
- "Cry",
- "\u0120Promised",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d1\u0122\u00d0\u00b4",
- "okus",
- "\u0120scalable",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d0\u00bc\u00d0\u00be\u00d1\u0124\u00d1\u0122\u00d0\u00b5\u00d1\u0124\u00d1\u012e",
- "\u00c3\u00bccklich",
- "\u0120realism",
- "\u0120mayo",
- "\u0120juvenile",
- "\u0120headlights",
- "\u0120g\u00c3\u00b6r\u00c3\u00bc\u00c5\u0141",
- "\u0120Reform",
- "\u0120halves",
- "czne",
- "\u0120breakup",
- "\u00c5\u00bcej",
- "\u0120r\u00c3\u00a4tt",
- "Day",
- "\u0120\u00ec\u013f\u00bc\u00eb\u00b3\u00b8",
- "\u0120muerte",
- "\u0120tunes",
- "\u0120Smile",
- "record",
- "\u0120recherche",
- "atisfied",
- "\u0120pozi",
- "\u0120celebrations",
- "isexual",
- "\u0120ROB",
- "thirds",
- "\u0120Fortune",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d0\u00b9",
- "\u0120branded",
- "loo",
- "\u0120dud",
- "\u0120randomized",
- "\u0120combin",
- "\u00e4\u00b8\u0122\u00e4\u00ba\u013d",
- "ieran",
- "czenia",
- "\u012f\u00e3\u0125\u00ab",
- "\u0120curator",
- "\u0120artery",
- "\u0120\u00d1\u0125\u00d1\u012a",
- "\u0120\u00d1\u0129\u00d0\u00b8\u00d1\u0124",
- "\u0120subsidies",
- "\u0120blossom",
- "\u0120Twilight",
- "\u0120hyv\u00c3\u00a4",
- "\u0120Pompe",
- "\u0120Cisco",
- "\u0120\u00d0\u0141\u00d1\u0122\u00d0\u00be",
- "\u0120biri",
- "\u0120gern",
- "\u0120rebuilt",
- "\u0120wcze",
- "\u0120benefici",
- "\u0120drummer",
- "\u0120solids",
- "\u0120diyorsun",
- "\u00e3\u0123\u0124\u00e3\u0124\u012c\u00e3\u0123\u012e\u00e3\u0123\u00a8\u00e3\u0123\u0128\u00e3\u0123\u0136\u00e3\u0123\u0138\u00e3\u0123\u0126\u00e3\u0123\u00be\u00e3\u0123\u0139\u00e3\u0123\u0141",
- "lated",
- "\u0120muddy",
- "\u0120holog",
- "\u0120claps",
- "\u0120Rings",
- "\u0120Okey",
- "\u0120Brave",
- "\u0120valuation",
- "\u0120migrant",
- "\u0120intermitt",
- "\u0120eigene",
- "iliary",
- "\u00e3\u0125\u00bc\u00e3\u0125\u012a",
- "markt",
- "kr",
- "\u0120Rib",
- "\u00e1\u00bb\u013bi",
- "\u0120accusations",
- "\u0120arab",
- "wash",
- "\u0120Bardzo",
- "\u0120ugh",
- "esters",
- "ophren",
- "\u0120alimentos",
- "\u0120Uz",
- "\u00d6\u0124",
- "\u0120650",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00b5\u00d1\u0127",
- "FI",
- "\u0120sampai",
- "\u0120parl\u00c3\u00a9",
- "hesion",
- "\u0120s\u00c4\u00b1r",
- "\u0120apparatus",
- "\u0120correlated",
- "\u0120Principal",
- "\u0120corr",
- "\u0120Official",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d0\u00b5",
- "\u0120terminals",
- "Should",
- "\u0120vacun",
- "\u0120stellt",
- "\u0120mooi",
- "etzung",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d0\u00b0",
- "\u0120dai",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b6",
- "Team",
- "\u0120PPE",
- "\u0120\u00d0\u0140\u00d1\u0123",
- "\u0120Leah",
- "\u0120Ivy",
- "yst",
- "\u0120uhhh",
- "\u0120nighttime",
- "\u0120trendy",
- "\u0120securities",
- "\u0120continents",
- "\u0120firsthand",
- "\u0120Veron",
- "\u0120\u00eb\u0124\u00ae",
- "\u0120browsing",
- "\u0120Cada",
- "tro",
- "\u0120tramp",
- "reib",
- "\u0120erstmal",
- "irler",
- "\u0120psic",
- "\u0120getir",
- "\u0120NP",
- "\u0120dzieci",
- "\u00d0\u00be\u00d0\u00b1\u00d1\u0122\u00d0\u00b0\u00d0\u00b7",
- "\u0120magician",
- "\u0120scrutiny",
- "\u0120slab",
- "\u0120OT",
- "isty",
- "iries",
- "orest",
- "\u0120tasked",
- "\u0120morally",
- "\u00ec\u0137\u00bc\u00ec\u00a7\u0122",
- "ustered",
- "\u0120fools",
- "\u0120irrespons",
- "\u0120einf",
- "\u0120vi\u00e1\u00bb\u0129c",
- "\u0120scor",
- "\u0120pillows",
- "\u0120Gegen",
- "\u0120tutte",
- "\u0120quarterly",
- "\u0120didnt",
- "\u0120Gym",
- "\u0120Ether",
- "\u0120\u00d8\u00ab",
- "\u00d0\u00bb\u00d0\u00b8\u00d1\u012a\u00d0\u00ba\u00d0\u00be\u00d0\u00bc",
- "\u0120signaling",
- "\u0120Node",
- "\u0120Doncs",
- "\u0120yah",
- "\u0120Kanal",
- "\u0120fading",
- "etin",
- "\u0120influencers",
- "\u0120medals",
- "\u0120engineered",
- "\u0120fermented",
- "\u00ea\u00b2\u0142\u00ec\u00a7\u0122\u00eb\u00a7\u012e",
- "\u0120Beethoven",
- "\u00d7\u0140\u00d7\u00a9",
- "inental",
- "\u0120\u00ec\u0137\u012e\u00eb\u0142\u00a4",
- "\u00c3\u00bctfen",
- "alnya",
- "\u0120overe",
- "\u0120denkt",
- "\u00d0\u00b0\u00d0\u00ba\u00d1\u0124\u00d0\u00b5\u00d1\u0122",
- "\u0120\u00e2\u013a",
- "\u0120necesit",
- "\u0120generators",
- "grass",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b4\u00d1\u0125\u00d0\u00bc",
- "lie\u00c3\u0141en",
- "Bar",
- "\u013e\u00eb\u0131\u013b",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d1\u0124\u00d0\u00b5\u00d0\u00b9",
- "\u0120sucking",
- "\u0120stencil",
- "\u0120primo",
- "\u0120Breath",
- "strom",
- "\u0120immensely",
- "\u0120appreh",
- "\u00ec\u0142\u0137\u00ec\u013f\u00b4",
- "Pop",
- "\u0120jong",
- "\u0120Giul",
- "\u0120ADHD",
- "\u0120h\u00c3\u00b6ren",
- "\u0120elo",
- "ivent",
- "\u0120rus",
- "\u0120outrageous",
- "\u0120mastered",
- "\u0120\u00ec\u00bb\u00a4",
- "\u00d9\u012a\u00d9\u0123",
- "ipes",
- "\u0120Rudy",
- "Jacob",
- "\u0120bullish",
- "\u0120tapped",
- "\u0120faud",
- "izophren",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d1\u0127",
- "\u0120Darling",
- "\u01201963",
- "\u0120Prevention",
- "\u00b2\u0136",
- "\u0120abdominal",
- "stones",
- "\u0120avaient",
- "\u00e1\u00bb\u0137i",
- "make",
- "\u0120sare",
- "\u0120Instant",
- "\u00d0\u00ba\u00d0\u00b0\u00d0\u00bc",
- "\u0120keeper",
- "\u0120blankets",
- "\u00e3\u0123\u00a7\u00e3\u0123\u0139\u00e3\u0124\u0129\u00e3\u0123\u0128",
- "\u0120sweats",
- "\u0120Minneapolis",
- "\u00e5\u0127\u00a8\u00e9\u0125\u00a8",
- "\u0120genommen",
- "\u0120fasten",
- "\u0120Brussels",
- "\u00e5\u0133\u00bc",
- "\u0120cafeter",
- "\u0120absorbing",
- "\u0120hago",
- "\u0120Elmo",
- "\u0120gusto",
- "\u0120Yap",
- "M\u00c3\u00basica",
- "\u0120tert",
- "\u0120banda",
- "\u0120mily",
- "\u0120thereafter",
- "\u0120Stockholm",
- "\u0120Carson",
- "\u0120calibration",
- "ava\u00c5\u0141",
- "ansa",
- "ikke",
- "\u0120foresee",
- "\u0120qualche",
- "\u0120deste",
- "\u00e6\u00a4",
- "\u00c3\u00bcn\u00c3\u00bcz",
- "\u0120forge",
- "Dis",
- "esten",
- "\u0120\u00ce\u00b4\u00ce\u00b9\u00ce\u00b1",
- "\u0120encaps",
- "\u0120Gespr",
- "\u0120chercher",
- "ickets",
- "\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d1\u012d",
- "Cr",
- "\u0120\u00d0\u00a2\u00d0\u00b0\u00d0\u00ba\u00d0\u00b6\u00d0\u00b5",
- "\u0120rabbits",
- "\u0120Dot",
- "heiten",
- "\u0120causal",
- "\u0120Foster",
- "aj\u00c4\u0127c",
- "\u0120bereit",
- "\u0120ayudar",
- "\u00e9\u00ab\u013b",
- "\u00e3\u0123\u00b3",
- "song",
- "comb",
- "\u0120fringe",
- "\u0120cybersecurity",
- "\u0120\u00eb\u013e\u00a8",
- "\u0120kier",
- "\u0120besch\u00c3\u00a4ft",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bd\u00d1\u0128\u00d0\u00b5",
- "\u0120facilit",
- "\u0120Namen",
- "\u0120bilateral",
- "tx",
- "\u0120Wissenschaft",
- "\u0120nuances",
- "\u0120ripping",
- "\u0120fy",
- "\u0120Sicherheit",
- "\u0120Ghana",
- "olon",
- "\u0120topped",
- "\u0120Morocco",
- "\u0120radial",
- "\u0120LEE",
- "\u0120Andreas",
- "edd",
- "\u0120\u00ec\u0139\u00b4\u00eb",
- "\u0120Airlines",
- "\u00e3\u0123\u0135\u00e3\u0124\u012f",
- "\u0120valores",
- "\u00ea\u00b7\u013e",
- "Hy",
- "\u0120\u00d0\u00b7\u00d0\u00b0\u00d0\u00b4\u00d0\u00b0\u00d1\u0129",
- "\u0120Kendall",
- "\u0120\u00d1\u0127\u00d0\u00b0\u00d1\u0122",
- "\u0120Vamp",
- "\u0120python",
- "\u0120manageable",
- "\u0120Gente",
- "oise",
- "iciary",
- "\u0120imposs",
- "\u0120Bunny",
- "iesta",
- "Andrew",
- "\u0120sert",
- "\u0120Cec",
- "zzarella",
- "\u0120automobile",
- "\u0120Tiere",
- "allows",
- "\u00e5\u0128\u0128",
- "\u0120\u00eb\u00b0\u0122",
- "\u0120Scorp",
- "\u0120Jelly",
- "agara",
- "\u0120Stretch",
- "\u0120redef",
- "\u0120exacerb",
- "\u0120SHA",
- "\u00c3\u00a9f",
- "orsa",
- "\u0120flawed",
- "\u0120Noel",
- "?!?",
- "\u0120procent",
- "\u0120menstru",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0129",
- "\u0120infants",
- "\u00f0\u0141\u0130\u00b5",
- "pause",
- "\u0120Racing",
- "\u01201948",
- "\u0120superintendent",
- "idores",
- "idy",
- "brahim",
- "\u0120unlucky",
- "\u0120perk",
- "anci",
- "\u0120\u00eb\u00a7\u012e\u00eb\u0124\u013a",
- "\u0120\u00d0\u013e\u00d0\u00be\u00d1\u0123\u00d0\u00ba\u00d0\u00b2",
- "\u0120finans",
- "\u0120diferencia",
- "\u0142\u012a\u00ec\u013f\u00b4",
- "\u00e9\u0127\u012f",
- "ORY",
- "\u0120Tac",
- "\u00db\u012e\u00d8\u00a7",
- "\u0120desem",
- "\u0120\u00d0\u00b2\u00d0\u00b0\u00d0\u00b6\u00d0\u00bd\u00d0\u00be",
- "\u0120JU",
- "\u0120\u00ec\u0140\u012a\u00ec\u0140\u0138\u00ec\u0137\u0126\u00ec\u013c\u0136",
- "\u0120\u00ce\u013f",
- "\u0120informations",
- "\u0120HEL",
- "hst",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b3\u00d0\u00be\u00d0\u00b2\u00d0\u00be\u00d1\u0122",
- "\u0120voiture",
- "\u0120reus",
- "\u00c3\u00a4ndig",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0127\u00d0\u00be\u00d0\u00b6",
- "jing",
- "\u0120dru",
- "altra",
- "\u0120produits",
- "\u0120kite",
- "\u0120eyeball",
- "\u0120Belt",
- "\u0120Restaurant",
- "\u0120gamb",
- "\u0120porridge",
- "itters",
- "\u0120converts",
- "\u0120yard\u00c4\u00b1m",
- "\u0120m\u00c3\u00a1ximo",
- "wirtschaft",
- "\u0120\u00ed\u0137\u013a\u00eb\u0124\u013a\u00eb",
- "\u0120\u00ec\u00a4\u0122",
- "\u0120iceberg",
- "\u0120vorbei",
- "\u0120256",
- "ocratic",
- "\u0120reckless",
- "onner",
- "\u0120m\u00c3\u00bas",
- "\u0120logically",
- "\u0120Prison",
- "\u0120Netz",
- "\u0120vacant",
- "\u0120nimmt",
- "\u0120HARR",
- "\u0120\u00d0\u00b7\u00d0\u00be\u00d0\u00b2",
- "\u0120Dee",
- "ringe",
- "niest",
- "\u0120Rules",
- "\u00ec\u012c\u00a4\u00eb\u0141\u00bd",
- "cussions",
- "\u0120floral",
- "\u0120constrained",
- "\u0120differentiation",
- "\u0120Quebec",
- "\u0120\u00db\u0123\u00db\u012e\u00da\u00ba",
- "\u0120p\u00c3\u00bablica",
- "itel",
- "\u0120accommodations",
- "\u0120Gr\u00c3\u00bc",
- "\u00ed\u013e",
- "\u0120pickles",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00b8\u00d1\u0127",
- "\u0120commissions",
- "\u0120Baek",
- "\u0120\u00c3\u00a7ocu\u00c4\u0141",
- "\u0120Medium",
- "\u0120periodically",
- "\u0120wonderfully",
- "\u0120staffing",
- "\u00ec\u013d\u0132\u00eb",
- "rire",
- "fle",
- "\u0120McL",
- "\u0120\u00d1\u0124\u00d0\u00b5\u00d0\u00bf",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00ba",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00bb\u00d0\u00be\u00d0\u00b3",
- "\u0120\u00ed\u0123\u00ac\u00ea\u00b2\u012e",
- "\u00e7\u013b\u00bc\u00e7\u0131\u00be",
- "\u0120prosperous",
- "\u0120Spiritual",
- "\u0120Chick",
- "DIA",
- "\u0120\u00d0\u0141\u00d1\u0122\u00d0\u00b8\u00d0\u00b2\u00d0\u00b5\u00d1\u0124",
- "\u0120per\u00c3\u0143",
- "\u00d1\u012e\u00d1\u0130\u00d1\u0124",
- "\u0120consultants",
- "\u0120Earl",
- "\u00e4\u00bb\u012c\u00e5\u00b9\u00b4",
- "\u0120ruining",
- "\u00d0\u00be\u00d1\u0122\u00d0\u00b5",
- "\u0120penser",
- "\u0120takiej",
- "\u0120strengthened",
- "\u0120Liquid",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00b5\u00d1\u0128",
- "\u00d0\u00b0\u00d0\u00b2\u00d0\u00b0\u00d1\u0124\u00d1\u012e",
- "\u0120camer",
- "\u0120disagreement",
- "\u0120bathing",
- "\u0120Yosh",
- "aal",
- "prechen",
- "RISADAS",
- "\u0120superstar",
- "\u00e6\u0123\u0143",
- "\u00d0\u00bb\u00d1\u0131\u00d1\u0124\u00d1\u012e",
- "\u0120nib",
- "\u0120Therm",
- "\u0120DANIEL",
- "\u0120paw",
- "\u0120liquids",
- "\u0120capacit",
- "arken",
- "\u0120vagina",
- "\u0120mashed",
- "\u0120emerges",
- "yscy",
- "\u0120unrelated",
- "\u0120Guild",
- "\u0120inverted",
- "itives",
- "Tra",
- "\u0120begr",
- "\u0120alte",
- "\u00ec\u00a7\u0137",
- "\u00e3\u0124\u0123\u00e3\u0123\u00a6",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d1\u0122\u00d0\u00b0\u00d0\u00b1\u00d0\u00be\u00d1\u0124",
- "finder",
- "\u0120\u00d0\u00b4\u00d0\u00b0\u00d0\u00bb\u00d0\u00b5\u00d0\u00b5",
- "\u0120\u00d0\u00b1\u00d0\u00bb\u00d0\u00b0\u00d0\u00b3\u00d0\u00be\u00d0\u00b4\u00d0\u00b0\u00d1\u0122",
- "walker",
- "\u0120crater",
- "assadors",
- "rences",
- "inski",
- "\u0120KIM",
- "\u0120Elliot",
- "2017",
- "\u0120Sr",
- "inka",
- "anov",
- "\u0120\u00ec\u0140\u013a\u00eb\u00aa\u00bb",
- "\u0120proprietary",
- "displaystyle",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00bc",
- "\u0120\u00d0\u00b8\u00d0\u00b7\u00d0\u00b1",
- "\u0120Panel",
- "\u0120instincts",
- "\u0120Communications",
- "\u00e9\u00ba\u00bb",
- "midt",
- "\u0120\u00eb\u00a7\u012e\u00eb\u0135\u00a4\u00ec\u0138\u00b4",
- "\u0120\u00d1\u0123\u00d0\u00bb\u00d0\u00be\u00d0\u00b2\u00d0\u00b0",
- "\u0120Gilbert",
- "\u00e7\u013d\u00ae\u00e5\u012b\u012f",
- "\u00d0\u00a2\u00d0\u00b0\u00d0\u00ba",
- "voorbeeld",
- "\u00d0\u00b5\u00d1\u0130\u00d1\u0123\u00d1\u012e",
- "aryn",
- "quez",
- "\u0120dart",
- "\u00d1\u0138\u00d1\u012a",
- "\u0120Hut",
- "Sal",
- "\u0120southeast",
- "\u0120pesticides",
- "\u0120helicopters",
- "\u0120endured",
- "iada",
- "\u0120brewing",
- "\u00ec\u0139\u00ac\u00eb",
- "\u0120\u00d1\u0123\u00d0\u00b2\u00d0\u00be\u00d0\u00b1\u00d0\u00be\u00d0\u00b4",
- "\u0120Saints",
- "\u0120Fran\u00c3\u00a7ais",
- "\u0120Economics",
- "\u0120disloc",
- "ophobia",
- "Camer",
- "\u0120negotiated",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b0\u00d0\u00bb\u00d0\u00b8",
- "\u00ec\u012c\u00a4\u00ed\u0123",
- "ogie",
- "\u0120tsunami",
- "\u0120peeled",
- "\u0120motivations",
- "\u00e8\u00a8\u0143",
- "ostat",
- "flan",
- "\u0120DAC",
- "\u0120kav",
- "'RE",
- "\u0120Pearson",
- "bbe",
- "czenie",
- "\u0120aten\u00c3\u00a7\u00c3\u00a3o",
- "\u00ed\u0128\u00b5\u00eb\u0142\u00b9",
- "\u00e3\u0123\u00a3\u00e3\u0123\u00a1",
- "\u0120\u00d1\u0125\u00d0\u00b4\u00d0\u00b0\u00d1\u0122",
- "\u0120introductory",
- "\u0120Ici",
- "\u00eb\u012e\u0122\u00eb",
- "akat",
- "\u0120trench",
- "\u0120proceeded",
- "\u0120Coin",
- "\u0120derecho",
- "\u0120Rede",
- "\u00e6\u00af\u013d",
- "\u00d0\u00b0\u00d0\u00bd\u00d0\u00bd\u00d1\u012d\u00d0\u00b9",
- "\u0120incarcerated",
- "\u0120Richmond",
- "Rock",
- "\u0120Pav",
- "\u0120Karma",
- "uges",
- "\u0120conte\u00c3\u00ba",
- "\u00eb\u00b9\u0126",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u00a7\u012e",
- "\u0120Gone",
- "\u0120wsp\u00c3\u00b3\u00c5\u0124",
- "\u0120Rahmen",
- "unken",
- "\u0120\u00ec\u00a4\u0133\u00ec\u013c\u0136\u00ed\u0137\u013e",
- "\u0120ib",
- "\u0120attaching",
- "Hay",
- "\u0120suka",
- "\u00ec\u012f\u00b9",
- "\u0120pivotal",
- "\u0120Respect",
- "\u00c3\u0143da",
- "IB",
- "\u0120Verantwort",
- "wiet",
- "\u0120forensic",
- "\u00d1\u0122\u00d0\u00b8\u00d1\u0123\u00d1\u0124",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bd\u00d1\u0128\u00d0\u00b8\u00d0\u00bf\u00d0\u00b5",
- "\u0120markings",
- "\u0120kettle",
- "\u0120Opera",
- "\u0120Doctors",
- "\u0120shredded",
- "\u0120recuer",
- "\u0120vigil",
- "\u0120Fail",
- "\u0120entrev",
- "\u0120\u00d0\u00b4\u00d1\u0125\u00d1\u012a",
- "\u0120outbreaks",
- "\u00e8\u00b5\u00b0\u00e5\u0132\u00a7",
- "\u0120\u00cf\u0122\u00ce\u00bf",
- "\u0120rogue",
- "angled",
- "\u0120yearly",
- "\u0120Creed",
- "\u0120wam",
- "\u0120lotus",
- "\u00ea\u00b3\u00bc\u00eb",
- "\u00e3\u0122\u0123\u00e3\u0122\u0123",
- "\u0120Spit",
- "\u0120Itu",
- "\u0120strains",
- "\u0120stamped",
- "\u0120plaint",
- "\u0120potion",
- "\u0120consolidation",
- "\u00e8\u00a9\u0137",
- "\u00d0\u00be\u00d1\u0129\u00d0\u00ba\u00d1\u0125",
- "\u0120vlogging",
- "\u0120slate",
- "\u0120Auft",
- "\u0120Incor",
- "\u00e1\u00bb\u00abng",
- "\u00a7\u0132",
- "enh",
- "\u0120hei\u00c3\u0141",
- "\u0120domest",
- "\u0120Strom",
- "\u00e5\u012f\u00b3",
- "akis",
- "\u0120fragen",
- "\u0120finer",
- "\u0120Sug",
- "\u0120uphill",
- "\u0120\u00c3\u00a9\u00c3\u00a9n",
- "\u00e2\u0122\u00a6)",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00bf",
- "\u0120Corey",
- "\u0120siebie",
- "\u0120muse",
- "\u0120cloves",
- "\u0120pous",
- "\u0120Finanz",
- "\u0120Route",
- "amat",
- "\u0120mutually",
- "\u0120\u00d0\u00b2\u00d0\u00bd\u00d1\u0125\u00d1\u0124\u00d1\u0122\u00d0\u00b8",
- "\u0120Selena",
- "\u00eb\u0136",
- "\u0120Gaussian",
- "\u00eb\u00b6\u0122\u00ed\u0126\u00b0",
- "\u0120\u00d7\u0133\u00d7\u013d",
- "\u0120ejerc",
- "\u00e5\u00be\u00ae",
- "kea",
- "\u0120Gerry",
- "\u0120Sic",
- "\u00e5\u00a4\u00a7\u00e7\u013c\u0126",
- "\u01201966",
- "iese",
- "\u0120fossils",
- "\u0120estad",
- "\u0120Kane",
- "ci\u00c4\u0129",
- "\u0120\u00ec\u013e\u0142\u00ed\u012c\u013e\u00eb",
- "\u0120\u00d0\u00bf\u00d0\u00b0\u00d0\u00bc",
- "\u0120Cruise",
- "int\u00c3\u00a9rieur",
- "\u0120bekannt",
- "\u0120Pode",
- "\u0120demander",
- "Rem",
- "\u0120invade",
- "\u0120decorating",
- "ropic",
- "\u0120cowboy",
- "\u0120Photo",
- "opolit",
- "\u0120\u00ec\u00bb\u00ac\u00eb\u0141\u00ac\u00eb",
- "\u0120reap",
- "\u0120handwriting",
- "\u00e0\u00b9\u0126\u00e0\u00b8\u00a3",
- "\u0120\u00eb\u013c",
- "\u0120\u00d8\u00a8\u00d8\u00b9\u00d8\u00af",
- "\u0120Mt",
- "\u00d9\u0122",
- "\u0120spaceship",
- "\u0120nationalism",
- "\u0120councils",
- "\u0120Griffin",
- "\u0120Ahmed",
- "\u0120clich",
- "\u0120OL",
- "wl",
- "\u0120Pilot",
- "\u00e5\u00ae\u00ae",
- "\u0120acronym",
- "\u0120gels",
- "\u0120electroly",
- "\u00e8\u0135",
- "\u0120\u00d0\u00bc\u00d0\u00bd\u00d0\u00be\u00d0\u00b9",
- "\u0120episod",
- "\u0120Dieses",
- "\u0120ATP",
- "\u0120ediyorum",
- "\u0120expresses",
- "\u0120exhibits",
- "Comm",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d1\u0125\u00d0\u00bf",
- "\u0120matar",
- "\u01202025",
- "\u0120Artem",
- "vasive",
- "r\u00c3\u0142",
- "\u0120be\u00c5\u0141",
- "\u00e9\u00bb\u0125",
- "\u0120lizard",
- "\u0120fille",
- "\u0120\u00ec\u00a7\u012a\u00eb\u00ac\u00b8",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d1\u012b",
- "\u0120t\u00c3\u00bcr",
- "\u0120culprit",
- "\u0120woven",
- "\u0120ANY",
- "nim",
- "\u0120tay",
- "\u0120promin",
- "\u0120acompa",
- "\u0120id\u00c3\u00a9",
- "\u0120boiler",
- "\u0120Themen",
- "\u0120avenue",
- "\u0120Mud",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00b2\u00d1\u012d\u00d0\u00b5",
- "\u0120witnessing",
- "\u0120lance",
- "\u0120CHAN",
- "\u0120Bever",
- "\u00d8\u00aa\u00d9\u0127",
- "\u0120chemotherapy",
- "King",
- "\u0120b\u00c4\u013bd\u00c4\u013b",
- "\u0120atual",
- "\u0120tive",
- "\u0120talkin",
- "\u0120quedar",
- "ie\u00c3\u0141",
- "edel",
- "\u0120\u00ec\u0138\u00b4\u00ec\u0142\u013e",
- "\u0120jogar",
- "\u0120\u00c3\u00b6r",
- "\u0120undertaking",
- "\u0120Strength",
- "\u0120milh\u00c3\u00b5es",
- "\u0120Wine",
- "\u0120Molt",
- "\u00e8\u00ae\u00b2",
- "\u00e3\u0123\u0133\u00e3\u0124\u012e",
- "\u0120undermine",
- "\u0120Archives",
- "vana",
- "mercial",
- "MC",
- "\u0120caste",
- "\u00d0\u00bf\u00d1\u0122",
- "\u0120legislators",
- "ulators",
- "\u00c3\u00aanio",
- "\u0120\u00eb\u012f\u00b0\u00eb",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0124\u00d0\u00b8\u00d1\u0124\u00d0\u00b5",
- "\u0120\u00d0\u00bd\u00d0\u00b5\u00d0\u00ba",
- "\u0120surn",
- "\u0120consci",
- "\u0120POW",
- "\u0120culinary",
- "\u0120KAT",
- "\u0120Folks",
- "\u00d1\u012d\u00d0\u00b2\u00d0\u00b0\u00d0\u00b5\u00d0\u00bc",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00ba",
- "\u00e3\u0123\u0133\u00e3\u0124\u012d",
- "service",
- "pts",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b1\u00d0\u00b5\u00d0\u00b4",
- "\u00e6\u013a\u00af\u00e5\u0137\u012c",
- "\u0120tents",
- "\u0120nord",
- "STE",
- "\u0120republican",
- "\u0120wyk",
- "\u0120minions",
- "\u00e8\u013b\u0137",
- "\u0120memang",
- "jest",
- "\u0120comparative",
- "\u0120tyle",
- "carbon",
- "bedingt",
- "ksen",
- "\u0120negativity",
- "\u0120sj\u00c3\u00a4lv",
- "\u0120d\u00c3\u00ba",
- "\u00e6\u012b\u0122\u00e6\u013e\u012b",
- "\u0120recalled",
- "cra",
- "\u0120Tada",
- "\u0120\u00d1\u0122\u00d1\u0125\u00d0\u00ba\u00d0\u00b8",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00b4\u00d0\u00b5\u00d0\u00bb",
- "\u0120procrast",
- "\u0120jogos",
- "\u0120Oo",
- "\u0120Hearts",
- "\u0120\u00c3\u00a9ch",
- "\u0120ksi\u00c4\u0127\u00c5\u00bc",
- "\u0120coarse",
- "\u0120Tube",
- "\u0120Greens",
- "\u0120\u00c3\u00a9n",
- "\u0120dumbbell",
- "\u0120\u00d1\u0124\u00d0\u00b8",
- "\u0120querer",
- "\u00d8\u00a7\u00d8\u0143",
- "\u00cf\u0125\u00ce\u00b5\u00ce\u00b9",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b0\u00d0\u00b2\u00d0\u00b8\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d0\u00be",
- "\u0120\u00d0\u00bf\u00d0\u00b0\u00d0\u00bf",
- "\u0120compra",
- "\u0120t\u00c3\u00a9r",
- "\u0120Antes",
- "\u0120optimum",
- "\u0120biscuit",
- "\u00ce\u00ba\u00ce\u00b9",
- "aczego",
- "\u0120\u00ec\u012d\u013e\u00ea\u00b0\u0126\u00ec\u013f\u00b4",
- "\u0120Marines",
- "vero",
- "\u0120vaccinations",
- "\u0120petty",
- "riters",
- "\u0120\u00d0\u00b0\u00d0\u00bb",
- "country",
- "\u0120counters",
- "\u0120attendant",
- "\u0120Hui",
- "\u00e3\u0123\u00a8\u00e3\u0123\u0126\u00e3\u0123\u0128\u00e3\u0123\u0135\u00e3\u0123\u00a8\u00e3\u0123\u00a7",
- "cka",
- "\u00d1\u0123\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d1\u012d\u00d0\u00b9",
- "guy",
- "\u0120tricked",
- "\u0120RED",
- "\u0120thrilling",
- "\u00cf\u0122\u00ce\u00bf\u00ce\u00b9",
- "\u0120piggy",
- "\u0120anunci",
- "ORTER",
- "\u0120Value",
- "\u0120rond",
- "\u0120ADA",
- "\u0120poser",
- "hores",
- "\u0120Roland",
- "\u0135\u00af",
- "\u0120noir",
- "\u0120\u00d7\u00a9\u00d7\u0132\u00d7",
- "\u00eb\u00b0\u013e",
- "iemand",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0124\u00d0\u00b5\u00d1\u0122",
- "\u00ea\u00b3\u00b3",
- "\u0120\u00ea\u00b1\u00b1",
- "\u0120formatting",
- "\u0120Led",
- "\u00e8\u00a7\u0122\u00e7\u013e\u00be",
- "\u0120killers",
- "\u0120\u00c4\u0133\u00e1\u00ba\u00a5y",
- "\u0120haar",
- "again",
- "!>[",
- "minster",
- "\u0120\u00d0\u00b2\u00d0\u00bb\u00d0\u00b8",
- "\u0120identifier",
- "\u0120Lambda",
- "\u0120tros",
- "\u0120flawless",
- "\u0120detrimental",
- "\u0120bunlar\u00c4\u00b1",
- "War",
- "\u0120regi\u00c3\u00a3o",
- "\u00e7\u013e\u0141\u00e7\u013c\u0126\u00e6\u013a\u00af",
- "\u0120Bike",
- "cessors",
- "\u0120c\u00c3\u00b9ng",
- "\u0120RN",
- "\u0120\u00ea\u00bd\u0125",
- "\u0120k\u00c3\u00bc\u00c3\u00a7\u00c3\u00bck",
- "\u0120Beginning",
- "\u00ed\u013a\u00b8\u00eb",
- "\u0120gewe",
- "\u0120denote",
- "\u0120Alberto",
- "\u0120probiot",
- "\u0120ode",
- "\u0120molar",
- "\u0120bursting",
- "assumed",
- "\u0120footprints",
- "veda",
- "\u0120steroids",
- "\u0120flaming",
- "\u0120Eller",
- "\u0120erkennen",
- "\u00c3\u00a4tzen",
- "\u0120lifecycle",
- "\u0120DOU",
- "\u0120Karena",
- "\u0120Guerra",
- "\u00e8\u00bf\u013a\u00e6\u013a\u00af",
- "\u0120sinister",
- "\u0120pod\u00c3\u00a9is",
- "\u0120parab",
- "\u0120oko",
- "\u0120mat\u00c3\u00a9ri",
- "\u0120caric",
- "sonaro",
- "\u0120praticamente",
- "\u00d1\u0125\u00d1\u0123\u00d0\u00b0",
- "\u0120comunque",
- "\u0120vigilant",
- "\u0120regimes",
- "\u0120Shooting",
- "\u0120raids",
- "\u0120Nora",
- "\u0120Wieder",
- "mens",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b4",
- "\u0120\u00ea\u00b2\u00bd\u00ec\u013c\u00b0\u00ec\u0139\u0132\u00eb\u012c\u0136",
- "\u0120\u00d0\u00b2\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u0120autobi",
- "\u0120Schn",
- "\u0120Robbie",
- "\u0120Fitness",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bd\u00d1\u0126",
- "\u0120penguin",
- "\u00d0\u00bc\u00d0\u00be\u00d1\u0124\u00d1\u0122\u00d1\u0131",
- "\u0120\u00d0\u00bc\u00d0\u00b8\u00d0\u00bd\u00d0\u00b8\u00d0\u00bc",
- "plays",
- "\u0120delegates",
- "Mer",
- "\u0120sistem",
- "\u0120Michaels",
- "male",
- "\u00d8\u00a7\u00d8\u00b9",
- "\u0120c\u00c3\u00a1ch",
- "\u0120H\u00c3\u00a4",
- "\u0120\u00d7\u013b\u00d7\u0137\u00d7\u0135\u00d7\u00a2",
- "\u0120superpower",
- "\u0120stron",
- "\u0120rover",
- "\u0120d\u00c3\u00a9pend",
- "\u00e9\u013b\u00b3",
- "\u0120retiring",
- "\u0120vampires",
- "\u0120merde",
- "\u0120Changing",
- "\u0120tame",
- "\u0120spokesperson",
- "\u0120cay",
- "\u0120flirting",
- "\u0120Gr\u00c3\u00b6",
- "\u0120w\u00c3\u00a4r",
- "\u0120wyb",
- "\u0120coeur",
- "\u00e1\u00ba\u00a1nh",
- "\u0120\u00ec\u013b\u0122\u00ec\u0126\u013e",
- "\u0120connais",
- "\u0120Hundreds",
- "\u0120Bea",
- "\u0120\u00ce\u00b1\u00cf\u0122",
- "pruch",
- "\u0120sociedade",
- "\u0120Whilst",
- "\u0120Kait",
- "espace",
- "\u0120chia",
- "\u0120Erm",
- "\u0120\u00eb\u00b0\u0136\u00ea\u00bf",
- "\u0120fences",
- "\u0120Mortal",
- "\u00ea\u00b2\u0123",
- "\u0120\u00d0\u00b3\u00d1\u0122\u00d0\u00b0\u00d1\u0126",
- "\u0120Homeland",
- "\u0120JUN",
- "isst",
- "\u0120parlar",
- "\u0120sporty",
- "\u00c3\u00a9o",
- "\u0120deepen",
- "\u0120Behavior",
- "\u00e9\u0122\u0131",
- "\u00e5\u0135\u012a\u00e5\u0135\u012a\u00e5\u0135\u012a",
- "\u0120errand",
- "\u0120rotary",
- "\u0120Wellington",
- "Wind",
- "\u0120mesela",
- "\u00e1\u00ba\u00a3ng",
- "iende",
- "\u0120excell",
- "\u0120Genius",
- "\u0120Eduardo",
- "\u00e6\u013e\u012b\u00e4\u00ba\u00ba",
- "\u0120\u00c5\u0141unu",
- "\u0120\u00c4\u00b0stanbul",
- "\u0120produto",
- "\u0120\u00e3\u0127\u0130\u00e3\u0127\u0130",
- "OFF",
- "\u0120wollt",
- "\u00e7\u012a\u0128",
- "\u0120\u00eb\u012b\u00b4\u00ec\u012c\u00a4",
- "\u0120lass",
- "\u0120hertz",
- "\u0120aromatic",
- "\u0120\u00d0\u00b7\u00d0\u00b2\u00d0\u00be\u00d0\u00bd",
- "\u0120autoc",
- "\u0120Lust",
- "\u0120112",
- "\u0120\u00ce\u0139",
- "\u0120reviewers",
- "\u0120receptive",
- "\u00e5\u00b0\u012f\u00e4\u00ba\u0128",
- "\u00c3\u00a2nd",
- "oglo",
- "\u0120\u00ec\u0137\u0126\u00eb\u012d\u013b",
- "\u0120ngo",
- "\u00d1\u0138\u00d1\u0124\u00d0\u00b8",
- "\u00c3\u00a5t",
- "cono",
- "\u0120tekrar",
- "\u0120\u00ec\u00a3\u00bc\u00ea\u00b3\u0142",
- "\u0120gelmi\u00c5\u0141",
- "\u0120bedtime",
- "\u0120Argh",
- "ADA",
- "\u0120\u00d0\u00b3\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b4\u00d0\u00b0",
- "\u0120\u00c4\u0129",
- "\u0120alliances",
- "giggling",
- "\u0120yerde",
- "\u0120spies",
- "\u0120gutes",
- "\u00c3\u00a7i",
- "\u0120alltid",
- "\u0120Lah",
- "\u0140\u0132\u00eb",
- "\u0120dok\u00c5\u0124ad",
- "\u00d9\u012a\u00d9\u012c",
- "\u0120toxicity",
- "\u0120cancellation",
- "\u01201958",
- "dro",
- "\u0120\u00ec\u0140\u0133\u00ec\u013f\u0122",
- "\u0120Motorola",
- "\u0120multin",
- "\u0120enthusiasts",
- "\u0120Mighty",
- "\u0120Coconut",
- ":\u00e3\u0122\u012e",
- "\u0120Pictures",
- "\u0120sangre",
- "\u0120blinking",
- "olesome",
- "\u0120\u00ec\u012c\u00a4\u00ed\u0125\u0122\u00ec\u013f\u00bc",
- "FP",
- "\u0120booming",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d1\u0123\u00d1\u0131\u00d1\u0124",
- "\u0120ratchet",
- "\u0120timelines",
- "leness",
- "\u0120cages",
- "\u0120Goodnight",
- "ometimes",
- "\u0120cunning",
- "\u0120Risk",
- "uled",
- "dade",
- "\u0120prata",
- "\u0120gustar\u00c3\u0143a",
- "amus",
- "\u0120Jinping",
- "\u0120estrut",
- "\u0120descobrir",
- "\u0120M\u00c4\u0123",
- "\u0120Allan",
- "\u0120\u00e5\u012a\u0128",
- "\u0120\u00d7\u013e\u00d7\u00a7",
- "\u0120preserv",
- "\u0120Strawberry",
- "\u00c4\u0131",
- "Lu",
- "\u0120kro",
- "\u0120Reports",
- "\u00ec\u0127\u0136\u00ec\u0137\u00bc",
- "\u0120valt",
- "\u0120pouvait",
- "\u0120appar",
- "\u0120Bone",
- "\u0120preferably",
- "\u0120Rep\u00c3\u00bablica",
- "\u00e5\u00b0\u00b1\u00e5\u012a\u00b0",
- "\u0120herzlich",
- "\u0120chimney",
- "\u0120\u00c3\u00a7ev",
- "\u0120visas",
- "\u0120verr",
- "\u0120cultivation",
- "\u0120Armenia",
- "\u0120\u00d0\u00b2\u00d0\u00b4\u00d1\u0122\u00d1\u0125\u00d0\u00b3",
- "\u0120cockro",
- "retched",
- "artz",
- "\u0120\u00d0\u00bb\u00d1\u0130\u00d0\u00b4\u00d1\u0131\u00d0\u00bc",
- "\u0120pol\u00c3\u0143ticas",
- "\u0120Panz",
- "\u0120AKA",
- "\u0120\u00eb\u012a\u012e\u00eb\u0141\u00ac",
- "\u0120erro",
- "\u0120camper",
- "\u0120102",
- "\u00e0\u00a4\u00b8",
- "done",
- "\u0120hoard",
- "\u0120\u00d0\u0141\u00d0\u00be\u00d1\u0124\u00d0\u00be\u00d0\u00bc",
- "jeong",
- "\u0120desta",
- "pak",
- "\u0120inim",
- "\u0120growers",
- "\u0120Message",
- "\u0120elector",
- "engage",
- "\u0120Forbes",
- "\u0120Cincinnati",
- "\u0120diff\u00c3\u00a9rence",
- "df",
- "\u0120spar",
- "\u0120awaits",
- "\u0120USSR",
- "\u0120Rising",
- "\u0120Ho\u00c5\u0141",
- "\u0120footing",
- "\u0120condiciones",
- "\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "\u0120clinician",
- "\u0120Diskuss",
- "\u00e5\u00a3\u0135",
- "\u00d7\u00a8\u00d7\u0134",
- "\u00d7\u00a5",
- "iteit",
- "gren",
- "\u0120charisma",
- "\u0120leuke",
- "\u0120irritating",
- "\u0120circa",
- "\u0120Rhodes",
- "\u0120pior",
- "\u0120handicap",
- "royable",
- "\u0120vull",
- "OG",
- "\u0120in\u00c3\u0143cio",
- "ieri",
- "\u0120splashing",
- "\u0120demise",
- "\u0120assistir",
- "\u00d1\u0129\u00d1\u0124\u00d0\u00be",
- "\u0120covert",
- "\u0120Gud",
- "\u00e0\u00b8\u012b",
- "kl\u00c3\u00a4r",
- "\u0120\u00ec\u0140\u0132\u00ea\u00be\u00b8",
- "\u0120ver\u00c3\u00a4ndert",
- "\u0120REM",
- "\u0120Conven",
- "atge",
- "\u0120pierwsze",
- "\u0120clergy",
- "lington",
- "liv",
- "VPN",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb",
- "\u0120Hate",
- "\u00e3\u0123\u00a8\u00e3\u0123\u0135\u00e3\u0124\u012f",
- "\u00cf\u0128\u00ce\u00bf",
- "\u0120Respons",
- "\u00d0\u00be\u00d0\u00b7\u00d0\u00b4",
- "\u0120etmek",
- "\u0120chemin",
- "\u00d9\u0127\u00d8\u00a9",
- "\u0120\u00ea\u00b0\u0122\u00ec\u00a1\u00b1",
- "Tre",
- "\u0120umas",
- "\u0120Burton",
- "\u0120patriarch",
- "\u0120Smithsonian",
- "\u00a5\u013a",
- "Moon",
- "Air",
- "\u0120medios",
- "\u0120eraser",
- "\u0120wollten",
- "\u0120pareil",
- "\u0120Billie",
- "\u00e6\u012c\u00bd",
- "\u00d0\u00b5\u00d1\u0122\u00d1\u0124\u00d0\u00b2",
- "\u0120parlament",
- "\u0120agony",
- "\u0120QUE",
- "sequently",
- "Another",
- "\u0120Whew",
- "\u0120Annual",
- "\u0120seben",
- "\u00ec\u0125\u0123\u00ec\u013f\u0126",
- "values",
- "\u0140\u013e\u00eb\u00a7\u012e",
- "\u0120sinon",
- "ereal",
- "\u0120Enlight",
- "\u0120Chemistry",
- "\u0120Catalunya",
- "\u0120doctr",
- "anton",
- "\u0120stuk",
- "\u0120Plate",
- "\u0120Kardashian",
- "\u0120filos",
- "\u0120Wet",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bf\u00d1\u012d\u00d1\u0124",
- "\u0120unknowns",
- "\u0120Schon",
- "\u0120Baldwin",
- "\u0120telescopes",
- "\u0120Gucci",
- "oxide",
- "\u0120Conservative",
- "\u00ec\u0126\u00b1\u00ec\u013f\u0126",
- "\u0120hinaus",
- "Power",
- "\u0120\u00ea\u00b1\u00b4\u00ea\u00b0\u0137",
- "\u0120prevail",
- "orman",
- "machine",
- "\u01201946",
- "\u0120unbel",
- "\u0120schaut",
- "\u0120piel",
- "eenth",
- "\u0120objectively",
- "\u0120chakra",
- "audio",
- "\u0120chicos",
- "\u0120Vault",
- "\u00e5\u00b0\u012a",
- "\u0120medicinal",
- "\u0120Tail",
- "While",
- "\u0120asphalt",
- "\u0120froze",
- "\u0120EK",
- "unching",
- "nosis",
- "2015",
- "\u0120Gri",
- "\u0120oddly",
- "\u0120M\u00c3\u00a4r",
- "\u0120Aeg",
- "colo",
- "Par",
- "\u0120\u00eb\u0135\u00a4\u00ec\u0138\u00b4\u00eb",
- "\u0120vinden",
- "\u0120OVER",
- "\u0120iced",
- "\u0120scorp",
- "\u0120hac",
- "qualified",
- "\u0120\u00d1\u0125\u00d0\u00b2\u00d0\u00b8\u00d0\u00b4\u00d0\u00b5\u00d1\u0124\u00d1\u012e",
- "ermo",
- "HEN",
- "\u0120soi",
- "\u0120multiples",
- "\u0120layouts",
- "\u0120blindness",
- "\u0120Bowser",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00b4\u00d1\u0124",
- "\u0120\u00c3\u0130",
- "ventional",
- "\u0120mata",
- "mad\u00c4\u00b1",
- "\u0120geez",
- "\u0120cadence",
- "\u0120wa\u00c5\u00bcne",
- "\u0120Christie",
- "venge",
- "Call",
- "\u0120turnaround",
- "\u0120blob",
- "\u0120\u00d0\u00af\u00d0\u00ba",
- "\u0120Voiceover",
- "\u0120peril",
- "\u0120Jaime",
- "\u0120HOY",
- "lane",
- "\u0120sebel",
- "\u0120Duo",
- "\u0120Historical",
- "\u0120dni",
- "\u0120gema",
- "yk",
- "\u0120sabem",
- "\u00e1\u00ba\u00afng",
- "\u0120vars",
- "\u0120Ronnie",
- "\u0120Ronaldo",
- "\u0120Perqu\u00c3\u00a8",
- "nsinn",
- "hair",
- "\u0120relentless",
- "\u0120lyn",
- "\u0120traveler",
- "\u00e6\u0122\u0130\u00e9\u00ba\u00bc\u00e4\u00ba\u0128",
- "nine",
- "\u0120antim",
- "\u0120\u00ec\u00bc\u0122",
- "\u0120snowball",
- "\u0120\u00d1\u0127\u00d0\u00b0\u00d1\u0122\u00d0\u00b0\u00d0\u00ba\u00d1\u0124\u00d0\u00b5\u00d1\u0122",
- "\u0120interns",
- "\u0120constituency",
- "\u0120\u00d0\u013f\u00d0\u00b0\u00d0\u00bc",
- "\u00d7\u013e\u00d7\u013e",
- "VEL",
- "\u0120viktigt",
- "\u0120apoyo",
- "\u00d9\u0126\u00d8\u00a8",
- "\u0120jard",
- "\u0120heightened",
- "\u00d1\u0122\u00d0\u00be\u00d1\u0123\u00d1\u0124",
- "\u0120SMITH",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bb\u00d0\u00b0",
- "\u0120repairing",
- "\u0120rigt",
- "\u0120Sheikh",
- "\u0120Britney",
- "\u0120everytime",
- "\u0120adventurous",
- "ockey",
- "ernt",
- "\u0120ataque",
- "\u0120Alternatively",
- "effect",
- "\u0120palavras",
- "\u0120Elliott",
- "\u0120r\u00c3\u00a9ussi",
- "\u0120hypertension",
- "\u0120Manual",
- "\u0120prophetic",
- "\u0120handc",
- "\u00d1\u012e\u00d0\u00b5",
- "\u0120refrain",
- "\u0120Squid",
- "\u00ec\u0140\u00a1",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00b0\u00d0\u00bd",
- "\u00c3\u00a4llen",
- "\u0120lleg\u00c3\u00b3",
- "\u0120bash",
- "iony",
- "\u0120\u00d1\u0123\u00d0\u00ba\u00d0\u00bb\u00d0\u00b0\u00d0\u00b4",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00b1",
- "\u0120careless",
- "\u0120Pool",
- "\u0120tr\u00c3\u00a1s",
- "\u0120fils",
- "\u0120Schr",
- "\u0120sprawd",
- "\u0120Monaten",
- "\u0120unforgettable",
- "\u0120Cotton",
- "\u0120inconvenient",
- "\u0120RX",
- "oris",
- "\u0120humbled",
- "\u00d7\u00aa\u00d7\u0139",
- "\u0120\u00d8\u00a2\u00d9\u00be",
- "\u0120incre\u00c3\u0143",
- "\u0120Kommentare",
- "\u00e8\u012a\u0134",
- "raci\u00c3\u00b3n",
- "\u0120vantage",
- "\u0120Seal",
- "\u0120\u00ec\u013f\u00b4\u00ea\u00b1\u00b0\u00eb\u00a5\u00bc",
- "\u0120joue",
- "\u00e3\u0123\u013f\u00e3\u0123\u0128\u00e3\u0123\u00a7\u00e3\u0123\u013b\u00e3\u0123\u0143",
- "\u0120\u00ec\u013a\u00a4\u00eb\u0140\u013a",
- "\u0120\u00d0\u00b8\u00d1\u0123\u00d0\u00bf\u00d1\u012d\u00d1\u0124",
- "oben",
- "\u0120grate",
- "\u0120controle",
- "\u0120Percy",
- "\u00c5\u0124ada",
- "\u0120simultaneous",
- "\u0120prototy",
- "\u0120gro\u00c3\u0141er",
- "\u0120bewusst",
- "inizi",
- "\u0120passieren",
- "\u0120Happiness",
- "\u00e5\u012b\u0129",
- "shi",
- "geht",
- "\u0120stationed",
- "\u0120Ergebnis",
- "\u0120directamente",
- "\u0120survives",
- "\u0120persones",
- "BERG",
- "\u0120vomiting",
- "\u0120conhecer",
- "\u0120adjour",
- "\u0120Civic",
- "pei",
- "burst",
- "\u0120\u00eb\u012d\u00a4\u00eb\u012d\u012a",
- "\u00e9\u0131",
- "\u0120sled",
- "\u0120plataforma",
- "\u0120Sect",
- "\u0120Defin",
- "\u00e7\u013b\u00bb\u00e9\u012e\u00b2",
- "\u00c3\u00a9nom",
- "chnet",
- "\u0120profitability",
- "\u0120erreicht",
- "\u00e1\u00bb\u0131i",
- "cation",
- "\u0120\u00ec\u00a7\u0122\u00ea\u00b8",
- "\u0120perdre",
- "\u0120felony",
- "\u01201957",
- "\u00e6\u012a\u0133\u00e5\u00be\u012a",
- "\u0120unsuccessful",
- "\u0120nagyon",
- "\u0120elasticity",
- "\u0120facade",
- "\u0120earthly",
- "\u0120\u00d0\u00b0\u00d0\u00bc\u00d0\u00b5\u00d1\u0122\u00d0\u00b8\u00d0\u00ba\u00d0\u00b0\u00d0\u00bd",
- "\u0120conn",
- "cla",
- "Du",
- "\u0120politiques",
- "\u0120halo",
- "iantes",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b5\u00d0\u00b9",
- "\u00e3\u0125\u00b3\u00e3\u0125\u012b",
- "tones",
- "elier",
- "\u00e8\u00ae\u013c",
- "htaking",
- "\u0120wichtige",
- "\u0120anno",
- "\u0120Lok",
- "illions",
- "\u0120viver",
- "\u0120solchen",
- "\u0120suf",
- "\u0120Salz",
- "\u0120Nvidia",
- "zuge",
- "\u0120Spike",
- "Video",
- "\u0120twor",
- "\u0120Ala",
- "\u00e8\u0133\u012b",
- "\u0120hanya",
- "\u0120Adm",
- "\u00ec\u013f\u00b5",
- "\u0120Patienten",
- "\u0120Onion",
- "\u0120Kobe",
- "\u0120Scene",
- "\u0120Rash",
- "\u00e6\u00a8\u013b",
- "\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d1\u0124",
- "istani",
- "General",
- "leye",
- "imbap",
- "\u0120concealed",
- "\u0120Fridays",
- "\u0120Wool",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00b2\u00d1\u012d\u00d1\u0127",
- "\u00d8\u00b4\u00d8\u00b1",
- "\u0120\u00ea\u00b2\u00b0\u00ea\u00b3\u00bc",
- "\u0120jedoch",
- "\u00b4\u00ec\u012d\u013e",
- "\u0135\u00a4\u00eb\u0131\u0126",
- "\u0120\u00ec\u0140\u00a5\u00eb\u0124\u013e",
- "ukt",
- "Lou",
- "\u0120\u00eb\u00a8\u00b9\u00ec\u0138\u00b4",
- "\u0120Expect",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d0\u00bc\u00d0\u00be\u00d0\u00b9",
- "\u0120irresponsible",
- "\u0120acerca",
- "\u0120Zust",
- "\u00d7\u00a8\u00d7\u013a",
- "UI",
- "\u0120youtubers",
- "\u0120Positive",
- "\u0120socioe",
- "\u0120snatch",
- "\u00e8\u0125\u012e",
- "\u0120refreshed",
- "\u0120nominations",
- "\u0120Patt",
- "\u0120obsolete",
- "\u0120demi\u00c5\u0141",
- "\u00e5\u0131\u00a4",
- "ormu\u00c5\u0141",
- "\u0120\u00ec\u0128\u0136\u00ec\u00a7\u0123\u00ed\u0140\u012a",
- "\u0120fla",
- "\u0120craziest",
- "\u0120Zie",
- "\u0120T\u00c3\u00ba",
- "zep",
- "icem",
- "\u0120\u00eb\u00a9\u012d\u00ec\u0140\u012a",
- "\u0120cynical",
- "\u00e3\u0123\u013f\u00e3\u0124\u0135\u00e3\u0123\u00aa",
- "\u0120tresp",
- "\u0120craz",
- "\u00d5\u00a5\u00d5",
- "\u0120nelle",
- "\u0120mph",
- "\u0120Nered",
- "\u0120Kob",
- "\u0120Eck",
- "\u00a8\u00b8\u00eb\u012d\u012a",
- "Jan",
- "\u0120\u00d0\u00a2\u00d0\u00be\u00d0\u00b3\u00d0\u00b4\u00d0\u00b0",
- "\u0120deci",
- "\u0120Vog",
- "\u0120bubbling",
- "\u00e9\u0122\u0122",
- "\u00c3\u00baa",
- "\u0120productos",
- "iberal",
- "\u0120replicated",
- "\u0120Improve",
- "illary",
- "Cha",
- "\u0120r\u00c3\u00a9du",
- "\u0125\u0132\u00ed\u0137\u013a\u00eb\u00a9\u00b4",
- "\u0120connot",
- "\u0120Krit",
- "\u0120\u00d0\u00b4\u00d1\u0125\u00d1\u0127\u00d0\u00be\u00d0\u00b2",
- "\u0120treadmill",
- "\u0120PW",
- "\u0120\u00d0\u00b7\u00d0\u00be\u00d0\u00b2\u00d1\u0125\u00d1\u0124",
- "\u0120clams",
- "\u0120drafting",
- "\u01201956",
- "unta",
- "\u0120expenditures",
- "\u0120Hoover",
- "WOO",
- "\u00d1\u012a\u00d0\u00b5\u00d0\u00b5",
- "\u0120deduction",
- "monary",
- "\u0120recib",
- "\u0120povo",
- "\u0120\u00eb\u012f\u0136\u00eb",
- "\u0120PAL",
- "\u0120Blow",
- "\u0120wyp",
- "\u0120destac",
- "deal",
- "Graeme",
- "\u0120n\u00c3\u00a9cessaire",
- "\u0120damned",
- "\u01201938",
- "\u0120\u00ec\u012d\u00a4\u00ec\u0142\u013e\u00eb\u00a1\u013e",
- "\u0120troop",
- "\u0120insightful",
- "\u0120TJ",
- "\u0120\u00d0\u00be\u00d1\u0123\u00d0\u00b2",
- "\u0120fidelity",
- "\u0120Skip",
- "\u0120Mayo",
- "\u00eb\u00a7\u013f",
- "appe",
- "\u0120blas",
- "\u0120WY",
- "\u0120GN",
- "ctar",
- "Su",
- "\u0120cuent",
- "hews",
- "\u0120corpses",
- "Abs",
- "\u0120wastewater",
- "\u0120ciek",
- "\u0120Onu",
- "\u0120explosives",
- "\u0120arma",
- "\u0120STEPHAN",
- "politik",
- "\u0120Osaka",
- "ta\u00c5\u0124",
- "\u0120yap\u00c4\u00b1yor",
- "\u0120izquier",
- "\u0120beleza",
- "\u0120Wyatt",
- "\u00e5\u0132\u00b8",
- "\u0120suk",
- "\u0120specjal",
- "\u0120danke",
- "whistle",
- "\u0120f\u00c3\u0143sica",
- "\u0120Harriet",
- "\u0120\u00ec\u0137\u0126\u00ed\u012e\u012e",
- "\u0120willkommen",
- "iping",
- "\u0120\u00d1\u0123\u00d0\u00bc\u00d0\u00be\u00d1\u0124\u00d1\u0122\u00d0\u00b8\u00d1\u0124\u00d0\u00b5",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b6\u00d0\u00b5\u00d1\u012a\u00d1\u012e",
- "\u0120inaccurate",
- "\u0120arrogance",
- "\u0120Remo",
- "\u00ce\u00b3\u00ce\u00ac",
- "assed",
- "\u0120deliveries",
- "\u0120stinky",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00b6",
- "jay",
- "\u0120transitional",
- "\u0120rere",
- "\u0120NGOs",
- "\u0120ATM",
- "\u00d8\u00ae\u00d8\u00aa",
- "iology",
- "\u0120\u00d0\u00b2\u00d0\u00bb\u00d0\u00b0\u00d0\u00b4",
- "\u0120schme",
- "\u0120Shine",
- "\u00ec\u0137\u00a1",
- "pants",
- "\u0120serge",
- "\u0120senhor",
- "\u0120abduct",
- "\u0120Bryant",
- "VES",
- "\u0120awakened",
- "\u0120Laz",
- "ropolis",
- "\u0120Lao",
- "\u00e8\u00be\u013d\u00e8\u012d\u00a6",
- "\u0120villa",
- "\u0120summers",
- "\u0120enthal",
- "\u01201949",
- "Via",
- "\u0120\u00ec\u0138\u00b4\u00ec\u00a8",
- "\u0120tendon",
- "\u0120violet",
- "\u0120intellectually",
- "\u0120bounced",
- "araus",
- "\u01201919",
- "\u0120vraag",
- "\u0120spel",
- "\u0120Schwar",
- "Scott",
- "\u0120Indo",
- "\u0120\u00eb\u00a7\u013f",
- "\u0120canonical",
- "\u0120IKE",
- "\u0120that\u00c3\u0143s",
- "\u0120mellan",
- "\u00e6\u00af\u0134",
- "igmat",
- "Could",
- "...?)",
- "\u0120foarte",
- "\u0120Kumar",
- "rendo",
- "\u0120\u00c3\u00a9l\u00c3\u00a9",
- "\u00e0\u00b4",
- "valuation",
- "cases",
- "\u0120intuitively",
- "hong",
- "etted",
- "\u0120souven",
- "\u0120morb",
- "\u0120cors",
- "\u0120NV",
- "\u0120Hasan",
- "\u00e6\u0125\u0127\u00e5\u0128\u00b5",
- "ieved",
- "\u0120\u00ec\u00a7\u0122\u00ea\u00b8\u012a\u00ec\u013f\u0122",
- "\u0120dumpling",
- "\u0120contr\u00c3\u00b4le",
- "\u0120ambiguity",
- "\u00e6\u00a9\u0141\u00e6\u013e\u0125",
- "\u0120cog",
- "\u0120Scriptures",
- "\u0120cai",
- "\u0120bever",
- "\u00e5\u00a4\u00a7\u00e5\u00ae\u00b6\u00e9\u0125\u00bd",
- "\u0120huis",
- "\u0120aime",
- "\u0120erkl\u00c3\u00a4ren",
- "\u0120LM",
- "\u0120Fey",
- "\u00e9\u013c\u00be",
- "\u00e0\u00ae\u00b1\u00e0\u00ae\u00a4",
- "\u0120supervised",
- "\u0120jewe",
- "spl",
- "\u0120\u00d1\u0128\u00d0\u00b5\u00d0\u00bd\u00d1\u0124\u00d1\u0122",
- "\u0120collisions",
- "\u00d9\u0126\u00d9\u0123",
- "\u0120Hogwarts",
- "\u0120Durham",
- "\u00d7\u0137\u00d7\u00a3",
- "\u0120phosphate",
- "\u0120oversee",
- "\u0120inspections",
- "\u0120brinc",
- "\u0120Zak",
- "\u0120payoff",
- "\u0120chaud",
- "\u0120Hunger",
- "\u00c3\u00a3os",
- "vir",
- "\u0120fiance",
- "\u0120boug",
- "lived",
- "cry",
- "\u00e5\u013d\u0140\u00e4\u00be\u0128",
- "\u0120jointly",
- "\u0120girlfriends",
- "\u0120Nexus",
- "\u00a6\u00ac\u00ea\u00b2\u0142\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120Kwang",
- "\u00e5\u0135\u012a\u00e5\u013d\u012b",
- "\u00e5\u00a7\u0133",
- "\u00c5\u0124\u00c4\u013b",
- "\u0120Neden",
- "iece",
- "\u0120inserting",
- "\u00e6\u0141\u0135",
- "\u0120Mummy",
- "\u0120Globe",
- "\u0120lee",
- "\u0120german",
- "\u0120creams",
- "acho",
- "\u0120ch\u00c6\u00b0a",
- "\u0120Galile",
- "\u0120f\u00c3\u00bcrs",
- "\u0120estiver",
- "cidos",
- "Christian",
- "\u0120lorsqu",
- "\u0120cutest",
- "vale",
- "\u0120\u00d0\u00ba\u00d1\u0122\u00d0\u00b5\u00d0\u00bf",
- "\u0120wary",
- "\u0120slicing",
- "\u0120esperando",
- "\u0120Vander",
- "\u0120Deixa",
- "\u01201954",
- "\u0120m\u00c3\u00b3wi\u00c4\u0127",
- "\u00d1\u0138\u00d1\u0136",
- "\u0120tooling",
- "\u0120restor",
- "\u0120posici\u00c3\u00b3n",
- "\u0120intentar",
- "\u0120Apache",
- "OUL",
- "\u0120\u00d9\u012a\u00d8\u00a8",
- "\u0120mati\u00c3\u00a8re",
- "\u00e3\u0125\u00bc\u00e3\u0124\u0135",
- "\u0120linen",
- "\u0120estrat\u00c3\u00a9g",
- "\u0120Mutta",
- "\u00e9\u00a1\u00af",
- "\u00e8\u00a1\u012e\u00e4\u00ba\u0128",
- "\u0120parting",
- "\u0120minimizing",
- "\u0120apprendre",
- "\u00e6\u013e\u013f",
- "\u0120\u00d0\u00b0\u00d0\u00bd\u00d0\u00b3\u00d0\u00bb\u00d0\u00b8\u00d0\u00b9",
- "\u0120Doo",
- "\u0120Firefox",
- "c\u00c3\u00b3mo",
- "\u0120geopolit",
- "\u0120makan",
- "\u0120mogelijk",
- "\u0120\u00cf\u0122\u00ce\u00b5\u00cf\u0123\u00ce\u00b9",
- "\u0120c\u00e1\u00bb\u00a9",
- "\u0120installer",
- "\u0120dibuj",
- "\u0120Heath",
- "loop",
- "\u0120Broken",
- "HYUN",
- "shelf",
- "\u0120fizer",
- "\u0120enhances",
- "\u00e4\u00be\u012d\u00e3\u0123\u012a\u00e3\u0123\u00b0",
- "\u0120\u00d0\u00b4\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00b8",
- "\u0120PUB",
- "\u0120Kollegin",
- "\u0120attained",
- "\u00c4\u00be",
- "\u0120mistress",
- "\u0120Oftentimes",
- "\u00d7\u0140\u00d7\u013b\u00d7\u013f",
- "\u0120bewe",
- "\u0120Sora",
- "rauen",
- "baum",
- "\u0120rollers",
- "\u0120mering",
- "\u0120PAC",
- "\u0120\u00d0\u00bd\u00d1\u0138",
- "\u0120R\u00c3\u00a9publique",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d0\u00b0\u00d0\u00b2",
- "\u0120Vanguard",
- "uciones",
- "\u0120\u00eb\u00ac\u00b4\u00eb\u012e\u0122",
- "\u0120gour",
- "\u00af\u00a4",
- "\u0120\u00cf\u012b",
- "\u0120sauna",
- "\u0120peine",
- "\u0120Valerie",
- "\u0120Sikh",
- "fendimiz",
- "bero",
- "\u0120\u00d1\u0129\u00d0\u00b8",
- "\u0120do\u00c5\u013dwiad",
- "\u0120Euros",
- "\u0120commentaires",
- "\u0120tweaks",
- "\u0120Faster",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d1\u0123\u00d0\u00ba",
- "\u0120progressively",
- "\u0120Euch",
- "boro",
- "\u0120Ingred",
- "Cap",
- "\u0120uncheck",
- "\u0120\u00ec\u013a\u00a4\u00eb\u00a5\u00b8",
- "\u0120wre",
- "\u0120FT",
- "\u00c3\u00b6rung",
- "\u0120memorized",
- "\u0120Dinner",
- "\u0120Phew",
- "oubl",
- "\u0120puta",
- "\u0120admits",
- "\u00d0\u00b5\u00d0\u00b7\u00d0\u00b4\u00d0\u00b5",
- "opod",
- "\u0120panda",
- "\u0120hinges",
- "cipe",
- "\u0120transact",
- "\u0120podia",
- "\u0120pics",
- "\u0120criterion",
- "\u0120Orchestra",
- "\u0120Blog",
- "\u0120solemn",
- "\u0120Pixar",
- "Three",
- "\u0120\u00d0\u00b2\u00d0\u00bd\u00d0\u00b8\u00d0\u00b7",
- "\u0120Volunte",
- "\u0120Savage",
- "\u0120PVC",
- "\u0120Caf",
- "\u0120wykon",
- "\u0120graders",
- "\u0120crouch",
- "\u0120cliche",
- "\u0120soybeans",
- "\u0120MUR",
- "\u0120Gonzalez",
- "\u0120Mimi",
- "\u0120Bolsonaro",
- "\u0120diaphrag",
- "\u0120bilang",
- "\u00eb\u0132\u013a\u00eb\u012c\u0136",
- "\u00e9\u0124\u00a3\u00e6\u012a\u0133\u00e5\u0122\u0133",
- "\u0120regulating",
- "Mc",
- "Judge",
- "\u0120\u00d0\u00bd\u00d0\u00be\u00d0\u00b6",
- "\u0120jak\u00c4\u0127",
- "itesse",
- "\u0120Wij",
- "\u0120lata",
- "groaning",
- "POSING",
- "\u0120\u00d7\u0132\u00d7\u0137\u00d7\u00aa\u00d7\u0137",
- "\u0120haga",
- "\u0120grounding",
- "\u0120violently",
- "\u0120tills",
- "\u0120engag",
- "\u0120Hollow",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d0\u00bf\u00d1\u0125\u00d0\u00bb\u00d1\u0131\u00d1\u0122",
- "\u0120wprowad",
- "\u0120replaces",
- "\u0120fluorescent",
- "urgical",
- "iggly",
- "\u0120Traditional",
- "tte",
- "\u0120\u00d9\u0126\u00d9\u0129",
- "\u0120phosphorus",
- "\u0120apron",
- "\u0120Waters",
- "\u0120Kultur",
- "\u00d0\u00b0\u00d0\u00b2\u00d0\u00b0\u00d0\u00b9",
- "\u0120olives",
- "\u0120\u00d7\u0136\u00d7\u0132\u00d7\u013e",
- "\u0120teilweise",
- "\u0120sencill",
- "\u0120prends",
- "\u0120narrower",
- "\u0120j\u00c3\u00a4tte",
- "\u0120Informationen",
- "\u00ec\u0125\u0123\u00ec\u013f\u00b4",
- "\u0120starve",
- "\u0120frick",
- "\u0120Beweg",
- "\u00e0\u00a4\u00b2",
- "\u0120dolphin",
- "\u0120LAUGHTER",
- "\u0120INTERVIE",
- "\u00e5\u0136\u012b",
- "\u0120yanl\u00c4\u00b1\u00c5\u0141",
- "\u0120torpedo",
- "\u0120shortages",
- "\u00ec\u013f\u00b4\u00eb\u0135\u013e",
- "\u00c4\u00b1ld\u00c4\u00b1",
- "\u0120paws",
- "\u0120ozone",
- "\u0120cultivated",
- "\u0120Fot",
- "\u0120notor",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00b7",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d1\u012a",
- "\u0120touchscreen",
- "\u0120Ally",
- "\u00e6\u013e\u0122\u00e8\u00bf\u0133",
- "\u0120\u00eb\u00a7\u013d\u00ec\u0140\u012a\u00ec\u0138\u00b4\u00ec\u013c\u0136",
- "\u0120\u00d0\u00a1\u00d0\u00b5\u00d1\u0122",
- "\u0120\u00d0\u00b2\u00d0\u00bf\u00d0\u00be\u00d0\u00bb\u00d0\u00bd\u00d0\u00b5",
- "\u0120paprika",
- "\u0120Dustin",
- "\u0120efecto",
- "\u0120opini",
- "\u0120muut",
- "\u0120h\u00e1\u00bb\u012fc",
- "\u0120interject",
- "\u00c4\u013bt",
- "\u0120butts",
- "urez",
- "\u0120Pike",
- "\u0120Hok",
- "\u0120Guinea",
- "\u0120Cathedral",
- "\u01201400",
- "Cra",
- "+,",
- "\u00eb\u00a7\u013d",
- "\u00b3\u00b4\u00eb\u0131\u0126\u00eb\u00a1\u013f",
- "abyrin",
- "\u0120videog",
- "\u0120\u00d0\u00be\u00d1\u0122\u00d1\u0125\u00d0\u00b6",
- "\u0120u\u00c5\u00be",
- "\u0120buscando",
- "\u0120Assistance",
- "\u00e9\u013b\u00bd",
- "\u0120melhores",
- "\u00ec\u00a1\u00b4",
- "\u0120\u00eb\u0123\u00bc",
- "\u0120RJ",
- "\u0120\u00d8\u00aa\u00d9\u0127",
- "\u0120omin",
- "\u0120motorcycles",
- "\u0120Sapp",
- "\u0120supplying",
- "\u0120Algun",
- "\u0120aerospace",
- "\u00d7\u00a2\u00d7\u013e",
- "occup",
- "leist",
- "\u0120\u00ea\u00b1\u00b0\u00eb\u012c\u0136",
- "\u0120completa",
- "bres",
- "!(",
- "\u0120\u00d0\u0141\u00d1\u0122\u00d0\u00b5\u00d0\u00b4",
- "\u0120disadvantaged",
- "\u0120Attend",
- "\u0120Judah",
- "\u00e1\u00bb\u012dch",
- "ylene",
- "actly",
- "\u0120setups",
- "\u0120ammonia",
- "\u0120Schweiz",
- "\u0120Shame",
- "\u0120bande",
- "\u0120Fuel",
- "\u0120troublesome",
- "\u0120numero",
- "\u0120MOM",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00b4\u00d0\u00bb\u00d0\u00b0\u00d0\u00b3",
- "mentioned",
- "\u0120\u00d0\u00b1\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a\u00d0\u00be\u00d0\u00b5",
- "\u0120Viktor",
- "\u0120Styles",
- "\u0120crucified",
- "ructured",
- "environ",
- "\u0120morals",
- "\u0120meditating",
- "\u0120axial",
- "isance",
- "\u0120Abst",
- "Green",
- "\u0120\u00ea\u00b1\u00b4\u00ec",
- "\u0120quadrant",
- "\u0120pergi",
- "\u0120cameraman",
- "\u0120Sequ",
- "\u0120paused",
- "\u0120Laughing",
- "\u00ea\u00b7\u0122",
- "?..",
- "\u0120\u00c5\u00bbe",
- "\u0120permitir",
- "\u0120detectors",
- "\u0120HUD",
- "aval",
- "\u0120\u00ec\u0139\u00ac\u00ea\u00b8\u00b0\u00ea\u00b9\u012e\u00ec\u00a7\u0122",
- "\u0120hubs",
- "\u0120bestimmt",
- "\u0120\u00d0\u00b1\u00d1\u0125\u00d0\u00b4\u00d0\u00b5\u00d1\u0124\u00d0\u00b5",
- "INTERPOSING",
- "\u0120tengan",
- "\u0120crave",
- "\u0120Bundesregierung",
- "\u0120Bloody",
- "\u0120usability",
- "\u0120Eas",
- "\u0120\u00c4\u0133\u00e1\u00bb\u013bng",
- "\u01201955",
- "\u0120kriegen",
- "\u0120habitual",
- "\u0120essentials",
- "riminal",
- "\u0120roommates",
- "\u00e9\u0124\u00a3\u00e5\u00b0\u00b1",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d1\u0127\u00d0\u00be\u00d0\u00b4",
- "\u0120nghi",
- "\u0120mening",
- "\u0120Symphony",
- "\u0120Hug",
- "aggi",
- "\u0120wied",
- "\u0120mitad",
- "\u00e3\u0123\u00a3\u00e3\u0123\u00a6\u00e3\u0123\u0126\u00e3\u0123\u0128",
- "teenth",
- "ida\u00c4\u0129",
- "Save",
- "\u0120robi\u00c4\u0129",
- "\u0120bounces",
- "\u00b0\u0138\u00ec\u0139\u0132",
- "stars",
- "\u0120pragmatic",
- "\u0120cognition",
- "\u0120wrapper",
- "\u0120warten",
- "adh",
- "\u0120pensa",
- "\u0120Hertz",
- "\u0120n\u00c4\u013d",
- "\u0120Reid",
- "\u0120PCs",
- "\u0120Mole",
- "\u0120.....",
- "\u0120precio",
- "\u0120Championships",
- "\u00ea\u00b0\u0122\u00eb\u013f\u00bd",
- "\u0120v\u00c3\u00a9r",
- "\u0120corridors",
- "\u0120Electronic",
- "Sl",
- "\u0120\u00d0\u00b0\u00d0\u00bb\u00d0\u00b5",
- "\u0120overthrow",
- "\u0120kabul",
- "\u0120RES",
- "\u0120Cyberpunk",
- "\u00d0\u00be\u00d0\u00b3\u00d0\u00be\u00d0\u00b4",
- "\u0120\u00d0\u013f\u00d0\u00b0\u00d0\u00b2",
- "\u0120wan",
- "\u0120manifestations",
- "\u0120cuales",
- "\u0120Wise",
- "\u0120L\u00c3\u00b6sung",
- "\u0120exfol",
- "\u0120earns",
- "\u00d1\u0125\u00d1\u0123\u00d1\u0124\u00d0\u00b8\u00d1\u0124\u00d1\u012e",
- "\u0120sapp",
- "\u0120Braun",
- "\u0120BRANDON",
- "\u00ec\u00b9\u013b",
- "\u0120sano",
- "\u0120FEL",
- "\u00d1\u012d\u00d0\u00b2\u00d0\u00b0\u00d0\u00b9\u00d1\u0124\u00d0\u00b5\u00d1\u0123\u00d1\u012e",
- "\u00d0\u00be\u00d0\u00b6\u00d0\u00b4\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d1\u0131",
- "\u0120sewn",
- "Fun",
- "\u0120reciprocal",
- "\u0120expansive",
- "\u0120Traffic",
- "\u0120kt\u00c3\u00b3rego",
- "\u0120\u00d9\u012a\u00d8\u00b3",
- "\u00e6\u013a\u00a5",
- "\u0120\u00eb\u00b9\u00a8",
- "prove",
- "igare",
- "\u0120loh",
- "\u00d8\u00a7\u00d8\u00b6",
- "Hope",
- "\u0120devotees",
- "\u0120Gom",
- "\u0120steals",
- "\u0120Ums",
- "\u0120Twice",
- "\u00e3\u0124\u00b2",
- "iyim",
- "\u0120rhythmic",
- "\u0120Vorte",
- "\u0120prefix",
- "omination",
- "\u0120dato",
- "\u0120custard",
- "\u0120VOICE",
- "\u00e5\u00b7\u0140",
- "\u0120meny",
- "istors",
- "\u0120\u00ed\u013a\u0133",
- "\u0120\u00ec\u0124\u00b4\u00ec\u0137\u0126",
- "\u0120\u00ed\u0125\u0126",
- "\u0120kort",
- "\u0120aba",
- "\u0120Vera",
- "epy",
- "\u0120\u00ec\u00b9\u00b4\u00eb\u00a9\u0136\u00eb\u013f\u00bc",
- "\u0120submerged",
- "\u0120Clock",
- "\u0120thumbnails",
- "\u0120boast",
- "\u0120Fare",
- "!!]",
- "\u0120\u00c5\u013dm",
- "\u0120kaikki",
- "\u0120Technologies",
- "\u00ec\u013b\u00b8",
- "\u00e3\u0125\u0134",
- "\u00d0\u00b8\u00d1\u0124\u00d0\u00b0\u00d0\u00b9",
- "\u00e5\u00b0\u0131\u00e6\u013b\u0124",
- "\u0120\u00d0\u00b0\u00d1\u0124",
- "\u0120knobs",
- "\u0120reicht",
- "\u00c6\u00b0\u00e1\u00bb\u00a3ng",
- "glio",
- "\u0120\u00eb\u00a7\u013d\u00ec\u013f\u00b4",
- "\u00ea\u00b0\u0132\u00ec\u013f\u0126",
- "\u0120jotka",
- "\u0120Handy",
- "\u0120Haben",
- "nous",
- "\u0120inland",
- "\u0120amazon",
- "hooting",
- "SL",
- "\u0120leisten",
- "~\"",
- "\u0120provoke",
- "\u0120Twist",
- "\u0120\u00d7\u0133\u00d7\u0139",
- "\u0120departed",
- "\u00ea\u00b0\u013e\u00eb\u00a5\u00bc",
- "\u0120konse",
- "\u0120Carwyn",
- "\u00ed\u0137\u013a\u00ec\u012d\u0142",
- "idental",
- "ESCO",
- "\u0120tteokbokki",
- "\u0120dizendo",
- "\u00e7\u00b7\u00b4",
- "\u00c4\u00b1ndaki",
- "imasu",
- "afar",
- "\u0120landfill",
- "\u0120correcting",
- "\u0120clears",
- "\u0120Nummer",
- "HAM",
- "\u0120cartridges",
- "\u0120Diesel",
- "paced",
- "\u0120obliv",
- "\u0120moyens",
- "\u0120Sinne",
- "\u0120Preis",
- "iliz",
- "\u0120\u00d1\u0123\u00d0\u00bc\u00d0\u00be\u00d0\u00b6",
- "\u0120broaden",
- "\u00e4\u00bb\u0138\u00e6\u013a\u00af",
- "xes",
- "\u0120carbohydrate",
- "\u00ed\u013a\u00b9",
- "seok",
- "\u0120echoes",
- "\u0120cess",
- "\u00eb\u00b0\u0136",
- "\u0120\u00d0\u00b1\u00d0\u00b8\u00d0\u00b7\u00d0\u00bd\u00d0\u00b5\u00d1\u0123",
- "\u0120llamado",
- "\u0120essent",
- "\u0120\u00ec\u013f\u00bc\u00eb\u00b0\u013a",
- "\u0120Aires",
- "phen",
- "\u0120zebra",
- "\u0120symbolism",
- "Once",
- "\u0120racks",
- "\u0120Kafka",
- "\u0120\u00d1\u0123\u00d0\u00b5\u00d1\u0122\u00d1\u012e\u00d0\u00b5\u00d0\u00b7",
- "\u0120sinn",
- "picious",
- "kaa",
- "\u0120motherfucker",
- "\u0120apprenticeship",
- "\u0120rpm",
- "\u0120taxation",
- "\u0120furry",
- "\u0120Sacred",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d0\u00bc",
- "pora",
- "enges",
- "\u0120\u00ed\u0139\u012a\u00eb",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d0\u00bd",
- "\u0120sanitizer",
- "\u0120cringe",
- "\u0120Sca",
- "\u00d0\u00be\u00d1\u0129\u00d0\u00bd\u00d0\u00be",
- "\u0120ofere",
- "\u0120melodies",
- "\u0120Velvet",
- "\u0120Ihrer",
- "\u0120Hybrid",
- "\u0120Giov",
- "\u0120irgendwas",
- "\u0120depende",
- "\u0120Users",
- "\u0120hump",
- "driving",
- "\u0120sf",
- "\u0120ruthless",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u0126",
- "\u0120lemons",
- "\u0120f\u00c3\u00b6ret",
- "\u0120Oj",
- "\u0120\u00d0\u00bc\u00d0\u00b0\u00d0\u00bc\u00d0\u00b0",
- "\u0120interpersonal",
- "\u0120gev",
- "\u0120abnorm",
- "\u00d0\u00b8\u00d1\u0123\u00d0\u00bb",
- "\u0120\u00d0\u00b8\u00d0\u00bd\u00d0\u00b4",
- "\u0120kontroll",
- "\u0120regres",
- "\u0120ledge",
- "\u0120erz\u00c3\u00a4hlt",
- "\u0120Tact",
- "\u0120arriv\u00c3\u00a9",
- "\u0120substantive",
- "\u0120spoonful",
- "zwischen",
- "ooooo",
- "\u0120contenido",
- "\u0120besl",
- "\u00e1\u00bb\u0125m",
- "kten",
- "Jamie",
- "\u0120sandy",
- "\u00e4\u00b8\u012f\u00e5\u0132\u012e",
- "\u00e2\u012d",
- "\u0120pase",
- "\u0120dette",
- "\u0120Belgian",
- "\u00ea\u00b0\u013e\u00eb",
- "ulares",
- "rud",
- "igor",
- "\u0120\u00ed\u012e\u00ac\u00eb",
- "\u0120remedies",
- "\u0120blasting",
- "\u0120Sich",
- "\u0120\u00d0\u00be\u00d0\u00b6\u00d0\u00b8\u00d0\u00b4",
- "\u0120monstr",
- "\u0120manifold",
- "\u0120glauben",
- "\u0120EST",
- "\u0120streamline",
- "\u0120lobbying",
- "\u0120Gothic",
- "toire",
- "..'",
- "\u0120d\u00c3\u00a9mocr",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d0\u00b1\u00d0\u00bb\u00d1\u0130\u00d0\u00b4",
- "\u0120wsp\u00c3\u00b3l",
- "\u0120cz\u00c4\u013b\u00c5\u013d\u00c4\u0129",
- "\u00e4\u00b8\u012d\u00e9\u013f\u00a2",
- "is\u00c3\u00a9s",
- "gangen",
- "\u0120bezpie",
- "remlin",
- "\u00ea\u00b0\u013f",
- "Still",
- "\u0120resides",
- "\u0120gelecek",
- "\u0120t\u00c3\u00a9l\u00c3\u00a9phone",
- "\u0120pewn",
- "\u0120leopard",
- "\u0120complimentary",
- "\u0120crib",
- "\u0120Animals",
- "\u0120geil",
- "essel",
- "\u0120garder",
- "\u0120catchy",
- "\u00e6\u00a8\u00b9",
- "\u0120Ets",
- "\u0120Commercial",
- "\u0120DENNIS",
- "\u0120Coordinator",
- "\u0120Abigail",
- "ffffff",
- "\u00e1\u00ba\u00a5p",
- "\u0120peque\u00c3\u00b1a",
- "\u0120injections",
- "cekt",
- "\u0120philanthropy",
- "\u0120puck",
- "\u0120celebrates",
- "\u0120Dunk",
- "\u0120Dlatego",
- "\u00e3\u0123\u00be\u00e3\u0123\u0142",
- "\u00ce\u00b4\u00ce\u00ae",
- "graduate",
- "\u0120Mobil",
- "till",
- "acam",
- "\u0120yolks",
- "\u0120tangled",
- "\u0120maniac",
- "\u0120obliged",
- "\u0120Laink",
- "\u0120verder",
- "\u0120Damon",
- "\u0120mutant",
- "\u0120hopping",
- "\u0120reins",
- "\u0120inverter",
- "\u0120contempt",
- "\u00d7\u0142\u00d7\u00a1",
- "learning",
- "Miss",
- "\u0120\u00d0\u0135\u00d0\u00be\u00d1\u0123",
- "\u0120Meyer",
- "\u00ea\u00bb\u013a\u00ec\u0126\u013e",
- "\u00e9\u00a3\u0130",
- "\u00d7\u0137\u00d7\u0142\u00d7\u013b\u00d7\u013f",
- "asking",
- "\u0120trimming",
- "\u0120treasury",
- "\u0120sente",
- "Aust",
- "\u0120Unterst\u00c3\u00bctzung",
- "\u0120Comedy",
- "\u0120Anakin",
- "\u00e9\u00b9",
- "\u00d1\u0122\u00d1\u0125\u00d1\u0124",
- "\u0120Hari",
- "ographers",
- "\u0120oatmeal",
- "\u0120Bots",
- "\u00e4\u00b8\u012f\u00e4\u00ba\u0128",
- "\u0120\u00d0\u00bf\u00d0\u00b0\u00d0\u00bb\u00d1\u012e",
- "\u0120acknowledgement",
- "xic",
- "\u0120\u00ea\u00b4\u0122\u00ec\u012d\u00ac",
- "gasping",
- "\u0120\u00e3\u0123\u0137",
- "\u0120terrace",
- "\u0120ornaments",
- "\u0120MER",
- "committee",
- "\u0120\u00ec\u0139\u0128\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u0120rij",
- "\u00e9\u00b3",
- "\u00d7\u00a6\u00d7\u013f",
- "leme",
- "\u0120liberties",
- "\u0120fellas",
- "\u0120Copper",
- "bench",
- "\u0120Idea",
- "\u00e1\u00bb\u012fn",
- "\u00d1\u012a\u00d0\u00b0",
- "\u0120versi\u00c3\u00b3n",
- "\u00cf\u0126\u00ce\u00bf\u00cf\u012f",
- "\u0120\u00d0\u013e\u00d0\u00b8",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b8\u00d0\u00bb\u00d0\u00be\u00d0\u00b6",
- "\u0120boxer",
- "\u0120Tanner",
- "\u0120Moy",
- "\u00ec\u00b9\u013a\u00eb\u012c\u0136",
- "Thr",
- "\u0120tinham",
- "\u0120polishing",
- "\u0120consequently",
- "\u0120amenities",
- "\u0120KI",
- "\u0120GREEN",
- "\u0120Frankie",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0124",
- "ittel",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00b5",
- "ursed",
- "\u0120upbringing",
- "\u0120th\u00e1\u00bb\u00a9",
- "\u0120\u00ec\u012d\u013f\u00ec\u013e\u00bc\u00eb\u00a1\u013e",
- "\u0120whim",
- "\u0120chinese",
- "confidence",
- "\u0120Jeder",
- "\u00e3\u0123\u00aa\u00e3\u0123\u00ae\u00e3\u0123\u00a7",
- "ajcie",
- "\u0120Tous",
- "\u0120Powers",
- "\u00e1\u00bb\u00aba",
- "othermal",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d1\u012a\u00d0\u00b5",
- "rale",
- "\u00d8\u00a7\u00d8\u00ae",
- "\u0120\u00ec\u00a7\u0122\u00ec\u013d\u0132",
- "\u0120\u00c3\u00a9pisode",
- "\u0120sulph",
- "\u0120encara",
- "kraft",
- "alar\u00c4\u00b1",
- "\u0120Comes",
- "\u0120divul",
- "\u0120Rudolph",
- "\u0120Muse",
- "\u0120utens",
- "\u0120\u00ec\u0140\u0132\u00ec\u00a3\u00bc",
- "\u0120pana",
- "\u0120Vegeta",
- "\u0120PHP",
- "\u0120NSA",
- "entin",
- "\u0120Carnegie",
- "\u00d8\u00a7\u00d9\u012c",
- "i\u00c4\u013bcy",
- "Harry",
- "\u0120f\u00c4\u00b1r",
- "\u00d0\u00a1\u00d0\u00bf",
- "\u0120gladly",
- "\u0120averaging",
- "\u00ed\u0137\u013a\u00ea\u00b2\u0142\u00ec\u012c\u00b5\u00eb\u012d\u012a\u00eb\u012d\u00a4",
- "\u00d0\u00bb\u00d1\u0131\u00d1\u0130\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120\u00d0\u013e\u00d0\u00b5\u00d0\u00bd\u00d1\u0131",
- "\u0120quotation",
- "rires",
- "itchens",
- "ayed",
- "\u0120unatt",
- "\u0120Perez",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00bc\u00d0\u00b5\u00d1\u0124",
- "\u0120tactile",
- "\u0120Euh",
- "isini",
- "buh",
- "\u0120hat\u00c4\u00b1r",
- "\u0120\u00ec\u0140\u012a\u00ec\u013e\u00bc",
- "\u0120policymakers",
- "\u00b3\u00b4\u00ec\u0126\u00b8\u00ec\u013c\u0136",
- "ac\u00c4\u00b1",
- "\u0120\u00ce\u00ba\u00ce\u00b9",
- "\u0120registering",
- "reto",
- "\u0120Sprinkle",
- "\u0120Grammy",
- "axter",
- "\u0120\u00d0\u00b1\u00d0\u00b8",
- "\u0120sitter",
- "\u0120predic",
- "\u0120thinly",
- "\u0120strum",
- "\u0120aggrav",
- "\u0120aha",
- "\u00d8\u00b1\u00d8\u00ac",
- "mellow",
- "\u0120constante",
- "\u0120Laut",
- "iston",
- "\u0120transitioned",
- "\u0120Cambodia",
- "\u00e3\u0123\u0126\u00e3\u0123\u012f\u00e3\u0123\u00be\u00e3\u0123\u013b",
- "\u00e8\u00b7\u0141\u00e5\u00a4\u00a7\u00e5\u00ae\u00b6",
- "arted",
- "\u0120misf",
- "\u0120Punkte",
- "\u012e\u00eb\u0135\u0142",
- "\u0120trembling",
- "\u0120gespannt",
- "\u0120\u00d8\u00b9\u00d9\u0126\u00d9\u012c\u00d9\u0129",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d0\u00ba\u00d0\u00b0\u00d0\u00ba\u00d0\u00b8\u00d1\u0127",
- "\u0120\u00eb\u00b6\u0122\u00eb\u0135\u013e\u00eb",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7\u00d0\u00b2\u00d0\u00b8\u00d1\u0124",
- "\u0120itchy",
- "\u0120ciento",
- "\u0120plains",
- "\u0120kittens",
- "\u0120backlog",
- "\u0120Presiding",
- "pta",
- "\u0120havoc",
- "\u0120Darrin",
- "\u0120\u00d0\u013d\u00d1\u0130\u00d0\u00b1",
- "\u0120segregated",
- "\u0120ghetto",
- "\u0120erlebt",
- "\u0120drugiej",
- "\u0120Sixt",
- "\u00e5\u0131\u0125",
- "\u00e0\u00b8\u00a3\u00e0\u00b8\u00b0",
- "uencia",
- "\u0120\u00ed\u0137\u013a\u00ea\u00b8\u00b0",
- "\u0120\u00eb\u0128\u012f",
- "\u0120robi",
- "\u0120pioneers",
- "\u0120milliards",
- "\u0120Witcher",
- "\u0120\u00eb\u00ac\u00b4\u00ec\u0139\u0129",
- "orro",
- "mass",
- "\u0120divergence",
- "\u0120Rivera",
- "\u0120Noodles",
- "\u0120endroit",
- "\u0120Kosten",
- "\u0120\u00d0\u00b4\u00d1\u0122\u00d1\u0125\u00d0\u00b3\u00d0\u00b0",
- "\u0120m\u00c3\u0143nimo",
- "\u0120Kazakhstan",
- "\u00d8\u00aa\u00d9\u0129",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00b7\u00d0\u00b4\u00d1\u0125",
- "\u0120geschrieben",
- "\u0120Nil",
- "\u00d1\u0123\u00d0\u00ba\u00d0\u00b8",
- "\u0120Fr\u00c3\u00bch",
- "\u0120beverages",
- "\u00e6\u00ba\u0132",
- "\u0120Gon",
- "\u00e6\u013a\u00a8",
- "Arin",
- "\u0120Intro",
- "ocalyptic",
- "\u0120exhaustion",
- "\u0120Status",
- "\u0120Battery",
- "\u00c3\u00a9sz",
- "\u00a3\u00bc\u00eb",
- "airy",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u0139\u00ac\u00eb\u0135\u013e\u00eb",
- "\u0120disparity",
- "\u00d9\u012e",
- "\u0120Tucson",
- "\u0120brightly",
- "problem",
- "\u0120biomass",
- "\u00e9\u013b\u012f",
- "\u00a7\u012b",
- "\u0120hurdle",
- "\u0120wavelengths",
- "\u0120<<",
- "\u0120teamed",
- "FFFF",
- "\u0120Slim",
- "omial",
- "\u0120unveiled",
- "\u0120Verein",
- "\u00d9\u0124\u00d8\u00b7",
- "estry",
- "\u0120cl\u00c3\u00a1s",
- "\u0120cheddar",
- "\u0120accusing",
- "\u0120Scientific",
- "\u0120\u00d0\u00b1\u00d1\u0125\u00d0\u00b4\u00d0\u00b5",
- "\u0120Cyrus",
- "\u00ce\u00b5\u00cf\u0126\u00ce\u00b5",
- "\u0128\u0135\u00ea\u00b3\u0142",
- "\u0120\u00eb\u00b3\u0126",
- "\u0120curd",
- "\u0120referrals",
- "shift",
- "\u00e5\u012f\u0137",
- "nik\u00c3\u00b3w",
- "\u0120mier",
- "\u0120confronting",
- "\u00ea\u00b2\u0125\u00eb\u0131\u0126",
- "awl",
- "\u0120tryin",
- "\u0120\u00ea\u00b7\u00b8\u00eb\u0140\u013a\u00ec\u013c\u0136",
- "\u0120chiar",
- "\u0120\u00ec\u013a\u00a4\u00eb\u012c\u013a\u00eb\u0131\u0126",
- "\u00e6\u0136\u00bf\u00e6\u00b2\u00bb",
- "esque",
- "\u0120mismos",
- "\u0120Shak",
- "\u0120sociaux",
- "\u0120pi\u00c5\u0141",
- "\u0120ki\u00c5\u0141i",
- "\u0120cyan",
- "hay",
- "bew",
- "bod",
- "\u0120\u00ce\u00b9",
- "\u0120Mainly",
- "\u00d1\u0130\u00d1\u0124\u00d1\u012e",
- "habitude",
- "\u0120\u00d1\u0123\u00d0\u00bf\u00d0\u00be\u00d0\u00ba\u00d0\u00be\u00d0\u00b9",
- "\u00e8\u00b7\u0141\u00e6\u012a\u0133",
- "\u0120precon",
- "\u0120Mandy",
- "\u00f0\u0141\u00a4\u00a3",
- "illos",
- "\u0120grupp",
- "\u0120crumble",
- "\u0120constructor",
- "ervices",
- "\u0120lighthouse",
- "\u0120Concept",
- "\u00d0\u00b0\u00d0\u00bd\u00d1\u0124\u00d0\u00b8",
- "altro",
- "hope",
- "\u0120Alleg",
- "\u00ec\u0138\u00b4\u00eb\u00a5\u00bc",
- "pieces",
- "ounter",
- "\u0120\u00ed\u0137\u013a\u00eb\u012d\u012a\u00ea\u00b9\u012e",
- "\u0120\u00ec\u013f\u00b8\u00ed\u0126\u00b0\u00eb",
- "\u0120v\u00c3\u00a9ritable",
- "\u0120threaded",
- "blind",
- "\u0124\u013a\u00eb\u013f\u00bc",
- "\u0120trays",
- "\u0120Edison",
- "\u0120\u00c3\u0138z",
- "\u0120Stevie",
- "\u0120lender",
- "\u0120brigade",
- "\u0120deutsche",
- "muffled",
- "bart",
- "\u0120insanity",
- "\u0120savvy",
- "\u0120sensational",
- "\u0120derechos",
- "\u0120MX",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00b5\u00d0\u00bf",
- "\u0120threatens",
- "\u0120realt\u00c3\u0142",
- "\u0120indicative",
- "\u0120chops",
- "\u0120benefiting",
- "\u0120Vernon",
- "\u0120Strand",
- "nun",
- "quently",
- "101",
- "\u0120eel",
- "\u00ec\u012a\u013b",
- "rints",
- "\u0120\u00d9\u0127\u00d8\u00b3",
- "\u0120\u00d8\u00a8\u00d8\u00af",
- "\u0120\u00d0\u00bf\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be",
- "\u0120yapm\u00c4\u00b1\u00c5\u0141",
- "\u0120olmas\u00c4\u00b1",
- "\u0120iedereen",
- "ol\u00c3\u00a9",
- "kef",
- "\u0120\u00eb\u00b0\u013e\u00ec\u0125\u013f",
- "\u0120rained",
- "\u0120almighty",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00b4",
- "\u0120CPR",
- "Fre",
- "\u0120inhabited",
- "\u0120arbets",
- "\u0120akin",
- "\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "vania",
- "\u0120h\u00c3\u00a4ufig",
- "\u0120Matte",
- "sorry",
- "Jenny",
- "\u0120\u00d0\u00b3\u00d1\u0122\u00d0\u00b0\u00d0\u00b4",
- "\u0120whit",
- "\u0120brokers",
- "\u00e5\u00af\u0141",
- "\u0120hine",
- "asten",
- "\u0120\u00d0\u00b3\u00d1\u0122\u00d1\u0125",
- "MB",
- "\u0120PRI",
- "Sab",
- "\u0120wrestler",
- "\u0120facilitating",
- "\u0120ehk\u00c3\u00a4",
- "\u0120Cred",
- "\u0120127",
- "\u0120nothin",
- "\u0120mandated",
- "\u00e5\u00af\u012e",
- "\u00d1\u0125\u00d1\u0124\u00d1\u0123\u00d1\u0124\u00d0\u00b2",
- "Frank",
- "\u0120wors",
- "\u0120dzie\u00c5\u0126",
- "\u0120Underground",
- "\u0120znajdu",
- "\u0120B\u00c3\u00a4",
- "\u0120Prinzip",
- "\u00d0\u00b0\u00d1\u0124\u00d0\u00b5\u00d0\u00bb\u00d0\u00b5\u00d0\u00b9",
- "\u0120veterinar",
- "\u0120splendid",
- "\u0120rozp",
- "\u0120psychopath",
- "igon",
- "\u0120hops",
- "\u0120c\u00e1\u00ba\u00a7n",
- "\u0120Xian",
- "\u0120troisi\u00c3\u00a8me",
- "\u0120producto",
- "\u0120de\u00c4\u0141er",
- "\u0120Continuing",
- "\u00d0\u00b8\u00d0\u00b2\u00d0\u00b0\u00d0\u00bb",
- "c\u00c4\u00b1k",
- "\u0120moisturizer",
- "White",
- "\u0120siis",
- "\u0120Everest",
- "ienced",
- "\u0120c\u00e1\u00ba\u00a3m",
- "\u0120Japon",
- "\u00b4\u00ec\u0142\u0126",
- "\u0120ten\u00c3\u0143an",
- "\u0120encanta",
- "Mm",
- "\u0120dropdown",
- "\u0120Iya",
- "\u00b3\u00b4\u00eb\u00a9\u00b4",
- "\u0120wording",
- "\u0120Squeeze",
- "\u0120Maple",
- "\u0120clarified",
- "\u0120Municip",
- "\u0120Rouge",
- "\u0120Nicki",
- "\u0120Goo",
- "volt",
- "tek",
- "fecture",
- "fred",
- "arrive",
- "\u00e3\u0125\u00bc\u00e3\u0123\u0126",
- "tez",
- "Ep",
- "\u0120obras",
- "\u0120VID",
- "\u0120Riv",
- "\u0120Modi",
- "ibe",
- "\u0120acontecendo",
- "\u0120imitation",
- "\u0120camouflage",
- "\u0120spanning",
- "\u0120SECRET",
- "\u0120Oreo",
- "\u00ec\u0128\u012e\u00eb\u00a6\u00ac",
- "\u0120hunch",
- "\u0120ca\u00c5\u0124e",
- "\u0120spontaneously",
- "\u0120Perd",
- "\u0120etap",
- "\u0120Hole",
- "\u0120Disability",
- "\u0120afterlife",
- "\u00e6\u0123\u00a9",
- "\u0120testified",
- "\u0120presup",
- "\u0120petroleum",
- "\u0120contrario",
- "\u0120Assessment",
- "\u00c4\u0141lu",
- "\u0120pests",
- "\u0120dilig",
- "\u0120\u00d0\u00b2\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00b5\u00d1\u0124",
- "\u0120cons\u00c3\u00a9qu",
- "\u0120cannons",
- "\u0120canoe",
- "\u0120Mile",
- "\u0120citoy",
- "\u0120begged",
- "\u0120Minnie",
- "\u00c5\u0124ych",
- "\u0120principe",
- "\u00cf\u0122\u00cf\u012e\u00ce\u00bd",
- "mniej",
- "\u0120wert",
- "\u0120\u00eb\u012d\u00a4\u00eb\u0135\u00a4",
- "anse",
- "\u0120uncles",
- "\u0120provocative",
- "\u0120intersections",
- "\u0120democrats",
- "\u0120Julius",
- "\u00d0\u00b8\u00d0\u00bd\u00d0\u00ba\u00d0\u00b8",
- "ygusal",
- "\u0120\u00d7\u013e\u00d7\u0137",
- "\u0120gjorde",
- "\u0120gasket",
- "\u0120Bock",
- "\u0120\u00c4\u00b0n",
- "breat",
- "\u0120Equity",
- "ard\u00c4\u00b1",
- "\u0120\u00d0\u00ba\u00d0\u00b0\u00d0\u00bd\u00d0\u00b0\u00d0\u00bb\u00d0\u00b5",
- "\u0120\u00d0\u00b4\u00d0\u00bd\u00d0\u00b5\u00d0\u00b9",
- "\u0120t\u00e1\u00bb\u013di",
- "\u0120fixture",
- "\u0120abuses",
- "\u0120vaya",
- "\u0120ouvert",
- "\u0120multicultural",
- "\u0120contexto",
- "\u0120Sesame",
- "\u0120d\u00c3\u00a9pl",
- "\u0120consomm",
- "\u0120Parte",
- "\u0120pem",
- "\u0120Conan",
- "\u0120\u00d0\u00b1\u00d1\u0138\u00d0\u00bb\u00d1\u012e",
- "\u0120persuaded",
- "\u0120drains",
- "Moo",
- "FORE",
- "\u0120\u00d0\u00b1\u00d0\u00b0\u00d1\u0124",
- "\u0120fod",
- "\u0120Products",
- "\u00ec\u00a7\u0126\u00ec\u00a7\u013e",
- "\u0120\"[",
- "\u0120Wick",
- "\u0120Naruto",
- "\u00d0\u00bd\u00d0\u00b0\u00d0\u00bb\u00d0\u00b8",
- "ryw",
- "\u0120lodge",
- "\u0120inh",
- "\u0120vontade",
- "\u0120dij",
- "\u0120Jes\u00c3\u00bas",
- "Looking",
- "\u0120forearm",
- "\u0120Integration",
- "\u0120HARRIS",
- "\u0120toolbar",
- "leader",
- "\u0120seldom",
- "\u0120\u00d0\u00b1\u00d1\u0122\u00d0\u00be\u00d1\u0123",
- "\u0120Kook",
- "\u00d0\u00be\u00d0\u00bd\u00d0\u00b4",
- "\u0120monopol",
- "\u0120millet",
- "\u0120lira",
- "\u0120Asians",
- "\u01201890",
- "ci\u00c4\u0141im",
- "\u0120eden",
- "\u0120IKEA",
- "\u0120Neighbor",
- "\u0120Kazuya",
- "\u00c3\u00bcd",
- "\u0120psychedel",
- "\u0120envisioned",
- "\u00e5\u013f\u0139",
- "\u0120\u00ef\u00b7\u00bb",
- "\u0120wunder",
- "\u0120Bulgaria",
- "Brid",
- "\u0120marrow",
- "\u0120depiction",
- "\u0120Tin",
- "\u0120Pharise",
- "\u0120einzige",
- "\u0120blindly",
- "\u00e3\u0123\u013d\u00e3\u0123\u00a6",
- "\u0120defens",
- "Dire",
- "\u0120vibrating",
- "\u0120trolls",
- "\u0120disrespectful",
- "\u0120wod",
- "\u0120stimuli",
- "\u0120creeping",
- "\u0120clairement",
- "\u0120scariest",
- "\u0120d\u00c3\u00a9couvrir",
- "\u0120104",
- "\u0120\u00d0\u00b2\u00d0\u00b5\u00d1\u0122\u00d1\u0127",
- "\u0120\u00c5\u0124at",
- "\u0120r\u00c3\u00b3\u00c5\u00bcne",
- "\u0120barley",
- "\u0120Repl",
- "\u0120Twe",
- "kke",
- "\u0120\u00e3\u0123\u013f\u00e3\u0124\u012e",
- "\u0120Redmi",
- "\u0120Metroid",
- "\u0120\u00ce\u00ae\u00cf\u0126\u00ce\u00b1\u00ce\u00bd",
- "Check",
- "\u0120SEN",
- "\u0120ido",
- "\u00d1\u0124\u00d0\u00be\u00d1\u0122\u00d0\u00b8\u00d0\u00b8",
- "\u00c3\u00b3p",
- "UNKNOWN",
- "\u0120\u00c3\u00a4ndern",
- "\u0120Juice",
- "\u0120Gesicht",
- "\u00e5\u00b0\u00b1\u00e6\u013e\u0125",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d0\u00ba\u00d0\u00be",
- "\u00ed\u0125\u0137",
- "\u00c2\u0143",
- "exhales",
- "\u0120\u00ec\u00b4\u012b",
- "\u0120jsem",
- "\u00cf\u0122\u00cf\u012b\u00cf\u0124",
- "\u0120itt",
- "\u00eb\u00aa\u0127\u00ec\u013f\u00b4",
- "\u0120remix",
- "\u0120blossoms",
- "\u0120Renee",
- "isations",
- "\u00ec\u012c\u00a4\u00ed\u0126\u00b0",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u013f\u00b4\u00eb\u012c\u0136",
- "uestas",
- "opedia",
- "\u0120Aim",
- "\u00ec\u013f\u00b4\u00ec\u00a6\u012a",
- "scene",
- "\u0120leakage",
- "uckt",
- "Sad",
- "Ask",
- "\u0120suspense",
- "\u0120impost",
- "\u0120Strategic",
- "\u0120It\u00c3\u0143s",
- "\u00e2\u0122\u012e",
- "\u0120keyboards",
- "\u0120amusing",
- "ogr",
- "iderman",
- "\u0140\u0138",
- "\u0120\u00d0\u00b2\u00d0\u00b8\u00d0\u00b6\u00d1\u0125",
- "\u0120dips",
- "\u0120apologized",
- "\u0120STAR",
- "\u0120escuela",
- "\u0120Ching",
- "\u00d0\u00bd\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d1\u0131",
- "\u0120\u00eb\u00b6\u0122\u00eb\u00b6\u0126\u00ec\u013f\u00b4",
- "\u0120Fleet",
- "\u0120samb",
- "\u0120entsprechend",
- "\u0120electrodes",
- "\u0120Freiheit",
- "\u00e6\u012a\u0133\u00e4\u00b8\u012f\u00e7\u0141\u00a5\u00e9\u0123\u0135",
- "\u0120Shrim",
- "i\u00c3\u0141e",
- "\u0120selections",
- "\u0120fordi",
- "\u0120doss",
- "\u00d1\u0131\u00d1\u0129",
- "\u0120discriminate",
- "\u0120Au\u00c3\u0141erdem",
- "\u0120desenvolv",
- "\u0120Internal",
- "\u0120Benedict",
- "\u00e5\u00af\u0128",
- "\u0120Shiv",
- "Missy",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d0\u00bd\u00d0\u00b0\u00d1\u0122\u00d1\u0125\u00d0\u00b6",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be",
- "\u0120controlar",
- "\u0120Lia",
- "\u0120opioids",
- "antu",
- "\u0120cupboard",
- "\u00e6\u0123\u0132",
- "\u00d0\u00b3\u00d0\u00b5",
- "achts",
- "\u0120curated",
- "\u0120xem",
- "\u0120weary",
- "\u0120brethren",
- "\u0120budgeting",
- "\u0120pourtant",
- "\u00e9\u013c\u00bb",
- "aisia",
- "\u0120\u00d0\u00be\u00d1\u0124\u00d0\u00b2\u00d0\u00b5\u00d1\u0129",
- "\u0120GIS",
- "\u00ce\u00bc\u00ce\u00b1\u00ce\u00b9",
- "\u0120\u00d7\u00a9\u00d7\u0136\u00d7\u0137\u00d7\u0132",
- "\u0120saud",
- "\u0120l\u00e1\u00bb\u013d",
- "\u00d0\u0137\u00d0\u00a2",
- "ubine",
- "\u0120\u00d0\u00bd\u00d1\u0125\u00d0\u00b6\u00d0\u00b5\u00d0\u00bd",
- "\u0120kidnapping",
- "\u0120brat",
- "\u0120Terre",
- "\u0120Monet",
- "\u0120\u00eb\u00a7\u012a\u00ec\u012c\u00a4\u00ed\u0123",
- "\u0120flashy",
- "\u0120ISBN",
- "\u0120freelance",
- "iage",
- "\u0120junge",
- "\u00ec\u00b6\u00a9",
- "ceral",
- "\u0120\u00d1\u0124\u00d0\u00be\u00d1\u0129\u00d0\u00ba\u00d0\u00b8",
- "\u0120formulate",
- "\u0120FER",
- "\u0120Dartmouth",
- "\u00ec\u013e\u00bc\u00eb\u00a9\u00b4\u00ec\u0126\u013e",
- "\u00e5\u00a2\u0125",
- "owi\u00c4\u0127",
- "\u0120\u00eb\u0136\u0136\u00ec\u0140\u0132",
- "\u0120regiment",
- "\u0120metabolismo",
- "\u0120Parr",
- "\u0120\u00ec\u00b6\u00a9\u00eb\u00b6\u0126",
- "\u0120sanity",
- "\u0120Lal",
- "\u0120G\u00c3\u00b6",
- "\u0120Gla",
- "\u0120proto",
- "\u0120microscopic",
- "\u0120kang",
- "\u0120Scalia",
- "\u0120pug",
- "\u0120Score",
- "\u0120Savannah",
- "\u0120garde",
- "\u0120NOR",
- "\u00e5\u00b0\u012f\u00e5\u0132\u00a7",
- "\u0120scheint",
- "\u0120p\u00c3\u00b3\u00c5\u0124",
- "\u0120corri",
- "\u0120brute",
- "\u0120\u00c5\u0124ad",
- "\u00e4\u00bb\u0138\u00e4\u00bb\u00ac",
- "\u0120succeeding",
- "\u0120bicycles",
- "Non",
- "\u0120seekers",
- "\u0120unconditional",
- "\u0120rhymes",
- "\u0120Garage",
- "\u0120invoice",
- "\u0120canvi",
- "neck",
- "\u0120customizable",
- "iritual",
- "Queen",
- "\u00ed\u0137\u013a\u00ec\u012d\u013e\u00eb\u012c\u0136",
- "\u0120powerless",
- "\u0120csak",
- "\u00e4\u00b8\u012f\u00e4\u00bc\u013c",
- "isoft",
- "\u0120\u00ec\u0142\u0137\u00ed\u013b\u0137",
- "\u0120nh\u00c3\u00a2n",
- "\u0120MAND",
- "\u0120Haf",
- "\u0120revolves",
- "\u00e4\u00b9\u0141\u00e5\u0131\u00af\u00e4\u00bb\u00a5",
- "ovan",
- "aroo",
- "\u0120Grind",
- "\u00e9\u013d\u00aa",
- "\u0120indispensable",
- "\u0120consulted",
- "\u0120Clinical",
- "Acc",
- "\u0120olhos",
- "\u0120monter",
- "\u0120Hana",
- "etah",
- "\u0120vaan",
- "\u0120tigers",
- "\u0120caucus",
- "\u00f0\u0141\u013a\u0124",
- "\u00b3\u00b4\u00ec\u0140\u0132",
- "powers",
- "iums",
- "\u0120\u00ed\u0128\u0142\u00eb",
- "\u0120tradicional",
- "\u0120resonated",
- "\u0120\u00ec\u012d\u0142\u00ea\u00b8\u00b0",
- "them",
- "Robert",
- "\u0120elemento",
- "\u0120antid",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u0123",
- "\u0120natives",
- "\u0120loca",
- "owment",
- "\u0120Tight",
- "\u0120\u00e6\u0122\u013f",
- "\u0120melan",
- "\u0120Nue",
- "amis",
- "\u0120sorgen",
- "as\u00c4\u00b1na",
- "Home",
- "\u0120PUBG",
- "\u0120awfully",
- "\u0120Shore",
- "\u0120Perch\u00c3\u00a9",
- "\u0120Lau",
- "\u0120Cinderella",
- "\u0120Chest",
- "\u0120semantic",
- "\u0120deserted",
- "\u0120Momo",
- "\u0120Hernandez",
- "genes",
- "\u0120Adult",
- "\u00d0\u00b8\u00d1\u0129\u00d0\u00b5\u00d1\u0123\u00d0\u00ba\u00d0\u00be\u00d0\u00b3\u00d0\u00be",
- "oshima",
- "\u0120caracter\u00c3\u0143sticas",
- "\u0120KL",
- "\u00b4\u00ec\u0140\u00a5",
- "ocar",
- "\u0120fehlt",
- "\u0120druk",
- "\u0120Poppy",
- "ENGLISH",
- "\u0120Vergleich",
- "Brien",
- "\u0120recomp",
- "\u0120\u00d1\u0123\u00d0\u00b4",
- "\u0120merger",
- "\u0120marketers",
- "\u0120honeymoon",
- "\u0120penso",
- "\u0120belli",
- "\u00d0\u00b5\u00d1\u0124\u00d1\u0125",
- "\u0120banker",
- "Camera",
- "\u0120Stall",
- "\u0120Stamp",
- "\u0120Bite",
- "\u00d0\u00b5\u00d0\u00b6\u00d0\u00b4\u00d0\u00b5",
- "\u0120s\u00c3\u00bcr",
- "\u0120g\u00c3\u00bc\u00c3\u00a7",
- "\u0120Passover",
- "\u0120Bug\u00c3\u00bcn",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b6\u00d0\u00b0\u00d0\u00bb\u00d0\u00b5\u00d0\u00bd\u00d0\u00b8\u00d1\u0130",
- "\u0120\u00d0\u00bd\u00d0\u00b8\u00d0\u00b7",
- "\u0120manure",
- "\u0120glacier",
- "\u00e8\u00ab\u0129",
- "RAY",
- "terror",
- "\u0120salads",
- "\u0120hurricanes",
- "\u0120Designer",
- "atorio",
- "\u0120factual",
- "\u0120Tammy",
- "\u0120\u00d0\u00b7\u00d0\u00b2\u00d1\u0125\u00d1\u0129",
- "\u0120introductions",
- "\u0120housekeeping",
- "\u0120hanger",
- "\u00eb\u012d\u013a\u00eb",
- "akte",
- "\u0120Cola",
- "']",
- "\u0120Gender",
- "\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d0\u00bd",
- "ipse",
- "icias",
- "\u0120successive",
- "\u0120politic",
- "\u0120h\u00c3\u00b6her",
- "\u0120Qiao",
- "\u0120Gimme",
- "\u0120\u00d0\u00bb\u00d0\u00be\u00d0\u00b6",
- "\u0120seb",
- "\u0120Weiter",
- "\u0120Sakura",
- "\u0120Boulder",
- "\u0120Am\u00c3\u00a9rica",
- "pe\u00c5\u0124nie",
- "\u0120tecnolog\u00c3\u0143a",
- "ishops",
- "fur",
- "\u0120moonlight",
- "\u0120dispersed",
- "\u0120rez",
- "\u00d0\u00b5\u00d0\u00bd\u00d0\u00bd\u00d0\u00be\u00d0\u00b5",
- "\u00d0\u00b0\u00d0\u00bb\u00d1\u012e\u00d0\u00bd\u00d1\u0125\u00d1\u0130",
- "\u0120Twelve",
- "\u0120HOR",
- "\u00ec\u012d\u00a4\u00ed\u0140\u012a",
- "ilage",
- "\u0120shaded",
- "\u0120resumes",
- "\u0120Peanut",
- "\u0120MILL",
- "apons",
- "\u0120UFC",
- "\u0120Sole",
- "\u0120joystick",
- "\u0120Olivier",
- "warming",
- "\u0120syllabus",
- "\u0120\u00d0\u00be\u00d0\u00b1\u00d1\u012b\u00d0\u00b5",
- "\u0120hi\u00e1\u00bb\u0129n",
- "\u0120festa",
- "\u0120cradle",
- "\u0120Zac",
- "\u0120remembrance",
- "\u0120\u00ea\u00b0\u013b\u00ec\u0137\u0126\u00ec\u0126\u013e",
- "\u0120pi\u00c4\u013bk",
- "\u0120coexist",
- "\u0120VII",
- "\u0120\u00c3\u00a1reas",
- "\u0120uwa\u00c5\u00bc",
- "\u0120observers",
- "\u0120m\u00c3\u00a4nniskor",
- "coon",
- "\u0120DAM",
- "\u0120naszym",
- "\u0120alligator",
- "\u0120Freeze",
- "\u0120Estate",
- "\u0120\u00d1\u0124\u00d1\u0122\u00d0\u00b0\u00d0\u00b4\u00d0\u00b8",
- "\u0120undercover",
- "\u0120nies",
- "\u0120Fehler",
- "plin",
- "\u0120Kabul",
- "ilate",
- "\u0120\u00ea\u00b3\u0142\u00ec\u0138\u0133",
- "\u0120mop",
- "\u00ec\u0126\u00bc",
- "\u0120anderer",
- "\u0120KELL",
- "\u00d0\u00be\u00d0\u00ba\u00d0\u00b8",
- "\u0120\u00d0\u00b6\u00d0\u00b5\u00d1\u0123\u00d1\u0124",
- "\u0120grazing",
- "\u0120da\u00c3\u0143",
- "\u0120capitalize",
- "\u0120apex",
- "\u0120nurturing",
- "\u0120cortar",
- "\u0120contrac",
- "\u00c4\u00b1m\u00c4\u00b1z\u00c4\u00b1",
- "\u0120tandem",
- "\u00e9\u0125\u00bd\u00e6\u013e\u012b",
- "gement",
- "\u0120\u00d1\u0123\u00d0\u00b8\u00d1\u0123\u00d1\u0124\u00d0\u00b5\u00d0\u00bc\u00d0\u00b0",
- "\u0120manque",
- "iaj\u00c4\u0127",
- "WOR",
- "\u0120\u00d8\u00a7\u00d8\u00a8",
- "\u0120carts",
- "ANO",
- "\u0120\u00eb\u00b0\u013d\u00ea\u00b3\u0142",
- "\u0120Cena",
- "\u0120Biology",
- "idar",
- "\u0120a\u00c5\u00bc",
- "erne",
- "anu",
- "\u0120thanked",
- "\u0120submarines",
- "\u0120manic",
- "\u0120\u00d0\u00bc\u00d0\u00be\u00d0\u00b7",
- "\u00e4\u00bc\u012c",
- "instant",
- "essential",
- "\u0120samurai",
- "\u0120pasti",
- "\u0120alan",
- "\u0120broch",
- "\u0120baker",
- "\u0120Guill",
- "\u00a8\u00bc",
- "\u0120withdrawn",
- "\u00eb\u012d\u013f",
- "Perfect",
- "quency",
- "\u0120streamlined",
- "\u01201300",
- "\u00b4\u00eb\u0131\u0126",
- "\u0120\u00eb\u0138\u0142\u00eb",
- "\u0120\u00e3\u0123\u00af\u00e3\u0123\u0126",
- "\u0120hvad",
- "\u00e4\u00b8\u0122\u00e5\u00ae\u013c\u00e8\u00a6\u0123",
- "\u0120verbally",
- "\u0120Kons",
- "\u0120\u00ec\u00a1\u00b0\u00ec\u012d\u00ac",
- "\u0120diez",
- "\u00e6\u0130\u00b0\u00e6\u0130\u00b0",
- "\u0120chuckling",
- "\u0120Mih",
- "\u0120rallies",
- "\u0120manter",
- "\u0120earnest",
- "super",
- "\u0120gece",
- "\u0120Rend",
- "\u0120Gerade",
- "jenigen",
- "\u0120Vall",
- "\u0120\u00ec\u0140\u012a\u00eb\u0124\u013a",
- "\u0120\u00d1\u0123\u00d0\u00ba\u00d0\u00b0\u00d0\u00b7\u00d0\u00b0\u00d0\u00bb\u00d0\u00b0",
- "\u0120trabalh",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u012a\u00d0\u00b5\u00d0\u00bc",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d1\u0127",
- "ikit",
- "\u0120nouns",
- "\u0120neurological",
- "\u0120motivational",
- "\u0120McMahon",
- "\u0120Finished",
- "\u0120\u00eb\u00b3\u00b4\u00ec\u013f\u00b4",
- "\u0120Fields",
- "\u0120adolescents",
- "\u0120Tisch",
- "\u0120Neben",
- "\u0120Flowers",
- "\u0120Energ",
- "\u0120diret",
- "\u0120Thi",
- "\u0120Picas",
- "\u00e6\u0125\u013e",
- "\u00e6\u0122\u0130\u00e4\u00b9\u012a\u00e6\u0142\u00b7",
- "\u0120avete",
- "\u0120Fors",
- "\u0120Chapel",
- "N\u00c3\u00a3o",
- "Et",
- "\u0120\u00d1\u0123\u00d0\u00be\u00d0\u00b4\u00d0\u00b5\u00d1\u0122\u00d0\u00b6",
- "reno",
- "\u0120sven",
- "\u0120dost\u00c4\u013bp",
- "nee",
- "\u0120Snapdragon",
- "\u0120IDs",
- "\u00ec\u0137\u013a\u00eb\u012c\u0136\u00eb\u012f\u00b0",
- "\u00d7\u00a8\u00d7\u013c",
- "\u0120sunflower",
- "\u0120perpetual",
- "\u00e7\u00b3\u0138",
- "\u0120knights",
- "\u0120gird",
- "\u0120Told",
- "\u0120volcanoes",
- "\u0120adversary",
- "\u0120Economy",
- "\u0120extrapol",
- "\u0120bluetooth",
- "\u0120zooming",
- "\u0120skys",
- "\u0120genial",
- "\u00c3\u0143culos",
- "ambre",
- "\u0120\u00d0\u00bc\u00d0\u00b5\u00d1\u0122",
- "\u0120teeny",
- "\u0120stressing",
- "\u00ec\u0137\u012e",
- "ONY",
- "\u0120translucent",
- "\u0120rounding",
- "\u0120grues",
- "\u00d7\u013b\u00d7\u0142\u00d7\u0136",
- "apr\u00c3\u00a8s",
- "\u0120prueba",
- "\u0120polygon",
- "\u0120blueberry",
- "\u0120Programm",
- "\u0120trenches",
- "\u0120sebagai",
- "\u0120palate",
- "\u0120laude",
- "\u0120behaved",
- "\u0120longitudinal",
- "\u0120Module",
- "\u0120admir",
- "\u00ce\u00bb\u00ce\u00b9",
- "Greg",
- "\u0120wyst",
- "\u0120propagate",
- "\u0120molds",
- "\u0120Tub",
- "\u0120Loud",
- "usto",
- "\u0120unstoppable",
- "\u0120reinforcing",
- "\u00e9\u013f\u0140\u00e5\u00b8\u00b8\u00e7\u013c\u0126",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d0\u00b1\u00d0\u00bb\u00d0\u00b5\u00d0\u00bc\u00d0\u00b0",
- "\u0120potencial",
- "\u0120hemp",
- "\u00ec\u0140\u0136",
- "\u00e0\u00a4\u00af",
- "\u0120optic",
- "\u0120erfolgreich",
- "\u00d1\u0123\u00d1\u012d",
- "\u00d0\u00be\u00d0\u00bb\u00d1\u012e\u00d1\u012a\u00d0\u00b5",
- "urst",
- "\u0120Pois",
- "\u0120respondents",
- "\u0120nehme",
- "\u0120External",
- "olate",
- "Hyun",
- "\u0120quartz",
- "\u0120mathematician",
- "\u0120b\u00c3\u00a1sicamente",
- "\u0120ail",
- "\u00ec\u0142\u013e\u00eb\u00a5\u00bc",
- "attutto",
- "\u0120nooit",
- "\u0120afflict",
- "\u0120Olga",
- "\u00e8\u0143\u00b7",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u0124",
- "\u0120dites",
- "\u0120realidade",
- "\u0120k\u00c3\u00a4n",
- "\u0120uniqueness",
- "\u0120padres",
- "\u0120subsidi",
- "\u0120pigeons",
- "\u00ce\u00b2\u00ce\u00b1",
- "stad",
- "\u0120deren",
- "\u0120\u00d0\u00a1\u00d0\u00bb\u00d0\u00b5\u00d0\u00b4",
- "doo",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d0\u00b8\u00d1\u0123\u00d0\u00b0\u00d0\u00bd\u00d0\u00b8\u00d0\u00b8",
- "\u0120amber",
- "\u0120goosebumps",
- "\u0120fr\u00c3\u00a5gor",
- "\u0120Vital",
- "\u0120Israelites",
- "wasser",
- "Isn",
- "\u0120commits",
- "\u0120STEVEN",
- "\u0120Bev\u00c3\u00b6lker",
- "uitive",
- "\u0120legen",
- "\u0120bruk",
- "\u00d0\u00b8\u00d1\u0122\u00d0\u00be\u00d0\u00b2\u00d0\u00b0\u00d0\u00bd",
- "ynen",
- "helm",
- "\u0120generational",
- "\u0120L\u00c3\u00a4ndern",
- "\u00ce\u00bf\u00ce\u00b9\u00cf\u0122\u00cf\u012e\u00ce\u00bd",
- "uzu",
- "\u0120caller",
- "\u00d0\u00be\u00d0\u00bd\u00d1\u012e",
- "\u00c3\u00bcm\u00c3\u00bc",
- "\u0120besar",
- "\u0120plats",
- "\u0120migrated",
- "\u0120jap",
- "\u0120WAR",
- "\u0120dissect",
- "\u0120Zusch",
- "\u0120Zeiten",
- "\u0120Lions",
- "\u0120DF",
- "\u00e2\u0136",
- "\u00d0\u00ba\u00d0\u00b8\u00d0\u00b2",
- "\u0120pedestrians",
- "\u0120Marilyn",
- "dock",
- "\u0120yht",
- "\u0120reincarn",
- "\u0120Sono",
- "\u0120Growth",
- "\u00d1\u0125\u00d1\u0123\u00d0\u00be\u00d0\u00b2",
- "\u0120dungeons",
- "\u0120bagus",
- "kich",
- "\u0120\u00d1\u0125\u00d0\u00ba\u00d1\u0122\u00d0\u00b0\u00d1\u0139",
- "\u00e9\u0128\u00ab",
- "\u0120Keller",
- "chemistry",
- "Japanese",
- "\u0120willst",
- "\u0120decomposition",
- "\u0120\u00d1\u0123\u00d1\u0124\u00d0\u00b5\u00d0\u00bd",
- "\u0120revived",
- "\u00ed\u0137\u013b\u00ea\u00b5\u0132",
- "\u0120\u00c5\u0135",
- "\u00e4\u00bd\u0132",
- "\u00ec\u012d\u00b8",
- "ippy",
- "\u0120hourly",
- "j\u00c3\u00a4n",
- "\u0120Workshop",
- "\u013f\u00bc\u00ec\u0126\u013e",
- "\u0120cuarto",
- "\u0120patrim",
- "\u0120Burch",
- "\u0120\u00ec\u0140\u012a\u00ea\u00b8\u00b0",
- "\u0120hepat",
- "\u0120h\u00c3\u0142ng",
- "\u0120\u00eb\u012e\u0122\u00ed\u0137\u00b4",
- "\u0120\u00d0\u00b2\u00d0\u00b0\u00d1\u012a\u00d0\u00b8",
- "\u0120rework",
- "\u0120parse",
- "\u0120\u00c3\u00a7\u00c4\u00b1kt\u00c4\u00b1",
- "\u0120Sax",
- "\u0120Mongo",
- "\u0120Aaah",
- "ramble",
- "DJ",
- "\u0120stabilized",
- "\u0120Speech",
- "Books",
- "\u0120hurdles",
- "\u0120WO",
- "\u0120Lamborg",
- "\u01201933",
- "\u0120vorbere",
- "\u0120clinically",
- "\u0120breathtaking",
- "\u0120Gateway",
- "\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b2\u00d1\u012d\u00d1\u0127",
- "uters",
- "\u0120\u00eb\u00b9\u00b5",
- "\u0120yeter",
- "\u0120pulley",
- "\u0120muffin",
- "\u0120Prefer",
- "\u0120Pence",
- "\u0120informa\u00c3\u00a7\u00c3\u00a3o",
- "\u00ec\u012c\u00a4\u00ed\u012c\u00b8\u00eb",
- "\u00e3\u0124\u00b8\u00e3\u0125\u00a3",
- "\u0120Turtle",
- "\u0120Regina",
- "\u0120Load",
- "does",
- "panze",
- "\u00b8\u0136",
- "\u0120mina",
- "\u0120Latinos",
- "ammers",
- "\u0120Tort",
- "\u0120Beyonce",
- "\u00d0\u00b8\u00d0\u00bc\u00d0\u00be\u00d1\u0123\u00d1\u0124\u00d0\u00b8",
- "\u0120\u00d0\u00b2\u00d0\u00be\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u0123\u00d1\u012d",
- "\u0120bulun",
- "\u00e8\u0122\u012e\u00e5\u00b7\u00b2",
- "inek",
- "bereich",
- "\u0120pasture",
- "\u0120OA",
- "\u0120Melt",
- "\u0120Ett",
- "\u0120DY",
- "\u0120obwohl",
- "\u0120leagues",
- "\u00d1\u0124\u00d0\u00b5\u00d1\u0123\u00d1\u012e",
- "\u0120\u00d0\u00ba\u00d1\u0125\u00d1\u0123",
- "\u0120vors",
- "\u0120topp",
- "ographical",
- "asst",
- "\u0120lindo",
- "\u0120\u00eb\u00b0\u013f\u00ed\u013a\u0136",
- "\u0120r\u00c3\u00a9fl",
- "\u0120climbs",
- "\u0120varsa",
- "\u0120methyl",
- "\u0120Karere",
- "\u00c6\u00b0\u00e1\u00bb\u0141",
- "Rad",
- "\u0120preparedness",
- "\u00d0\u00be\u00d0\u00bd\u00d1\u0129",
- "\u0120OD",
- "\u0120CGI",
- "\u0120\u00e0\u00a4\u00ae",
- "\u0120speechless",
- "\u0120lasci",
- "\u0120bolag",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0129\u00d0\u00b5\u00d1\u0124\u00d1\u0123\u00d1\u0131",
- "\u0120grieving",
- "\u0120Johannes",
- "\u0120Carroll",
- "adaki",
- "\u012a\u00ac\u00eb",
- "\u0120s\u00c5\u0124u",
- "\u0120innerhalb",
- "\u0120gymnastics",
- "\u00d0\u00bf\u00d1\u0122\u00d0\u00b8",
- "ifiques",
- "\u0120karate",
- "\u0120domu",
- "\u00e3\u0123\u013f\u00e3\u0124\u012e\u00e3\u0123\u00a7",
- "OTHER",
- "\u0120demand\u00c3\u00a9",
- "\u0120booklet",
- "\u0120Kyoto",
- "\u0120woh",
- "\u0120Mar\u00c3\u0143a",
- "violent",
- "JE",
- "\u0120l\u00c3\u00b3g",
- "\u0120brutally",
- "cot",
- "\u0120\u00d9\u0127\u00db\u012e",
- "\u0120Warsz",
- "\u00e5\u00ae\u012a",
- "wol",
- "\u0120mik\u00c3\u00a4",
- "\u0120Pronounce",
- "\u0120Brendan",
- "\u0120roup",
- "\u0120italiano",
- "\u00e5\u00a6\u0124\u00e6\u0143\u00a4",
- "\u0120\u00d0\u00ba\u00d0\u00be\u00d0\u00bc\u00d0\u00bf\u00d1\u012e\u00d1\u0130\u00d1\u0124",
- "\u0120urging",
- "edes",
- "\u0120carbono",
- "\u0120Richardson",
- "\u0120\u00d0\u013f\u00d0\u00b0\u00d1\u0129",
- "\u0120Trainer",
- "\u0120Crimea",
- "\u0120diapers",
- "\u0120covet",
- "\u0120Mahar",
- "\u0120Hutch",
- "\u0120Ausw",
- "berty",
- "\u0120indifferent",
- "\u00d0\u00ba\u00d1\u0122\u00d0\u00b5\u00d1\u0124",
- "uldade",
- "\u0120harms",
- "\u00a2\u00d9\u0128",
- "lesia",
- "\u0120gio",
- "\u0120Mistress",
- "\u0120Knox",
- "\u0120FREE",
- "\u0120\u00eb\u00a3\u00a8\u00eb",
- "\u0120\u00d0\u00bd\u00d0\u00b0\u00d1\u012a\u00d0\u00b0",
- "\u0120invincible",
- "\u0120maiden",
- "\u0120Jeez",
- "\u0120breve",
- "pole",
- "\u0120criticisms",
- "\u0120Rusia",
- "\u00e0\u00a4\u00ae",
- "phin",
- "\u0120Compare",
- "\u0120BON",
- "\u0120sneaking",
- "\u0120Rails",
- "\u0120Geral",
- "\u01201953",
- "Hola",
- "\u0120\u00d0\u00be\u00d0\u00bf\u00d1\u012d\u00d1\u0124",
- "\u0120rainforest",
- "\u0120belum",
- "\u0120Obi",
- "\u0120ISS",
- "\u00e3\u0124\u012e\u00e3\u0123\u00aa\u00e3\u0123\u0126",
- "\u0120\u00d0\u00a1\u00d0\u00b2",
- "\u0120blond",
- "\u0120wzgl",
- "\u0120powiedzia\u00c5\u0124",
- "\u0120choking",
- "\u0120Songs",
- "\u0120Biraz",
- "\u0120yells",
- "\u0120stylist",
- "\u00cf\u012e\u00cf\u0126\u00ce\u00b5",
- "\u0120schreiben",
- "\u0120Jaw",
- "\u0120Eleven",
- "\u0120Rif",
- "/.",
- "\u0120\u00ec\u013a\u00a4\u00eb\u0140\u013e\u00eb\u00a7\u012e",
- "\u0120treaties",
- "uffed",
- "\u0120\u00e2\u012a\u0134",
- "\u0120roofs",
- "\u00e0\u00b9\u0122\u00e0\u00b8\u00aa",
- "\u0120\u00eb\u00bb",
- "\u0120sparkle",
- "\u0120Kiev",
- "\u0120Argu",
- "erecht",
- "\u0120\u00d0\u013f\u00d0\u00b0\u00d0\u00b4\u00d0\u00be",
- "\u0120FIL",
- "\u0120molta",
- "\u0120Devi",
- "\u0120campe",
- "\u0120benevol",
- "\u0120Tough",
- "\u0120moim",
- "\u0120evacuate",
- "\u0120errado",
- "\u00e5\u00a9\u0128",
- "\u00d1\u0122\u00d1\u0125\u00d0\u00b3\u00d0\u00be",
- "\u0120\u00ed\u0130\u013a",
- "\u0120\u00ce\u0135\u00ce\u00b9\u00ce\u00b1",
- "\u0120weaken",
- "\u0120illuminated",
- "\u0120siglo",
- "\u0120Vacc",
- "\u00d0\u00b8\u00d0\u00b5\u00d0\u00b9",
- "alis",
- "\u0120\u00d1\u0125\u00d1\u0123\u00d1\u0124\u00d1\u0122\u00d0\u00be\u00d0\u00b9",
- "\u0120dona",
- "\u00c5\u0124os",
- "\u00c3\u00bcman",
- "\u0120producci\u00c3\u00b3n",
- "\u0120clot",
- "\u0120Mango",
- "\u0120uneasy",
- "\u0120shuts",
- "\u0120Examples",
- "vell",
- "ebe",
- "\u0120promptly",
- "\u0120Teles",
- "\u0120\u00d0\u00bf\u00d1\u0122\u00d0\u00be\u00d1\u012a\u00d0\u00bb",
- "\u0120puerta",
- "\u0120\u00c3\u00bcberzeug",
- "\u0120coch",
- "social",
- "\u0120Benson",
- "\u0120Meth",
- "\u0120Exped",
- "\u0120supplemental",
- "\u0120conceive",
- "\u0120\u00d7\u013a\u00d7\u0137\u00d7\u0133",
- "\u0120captivity",
- "\u0131\u013b\u00ec\u0137\u012a",
- "\u0120\u00d1\u0127\u00d1\u0125\u00d0\u00b4",
- "forming",
- "\u0120uploads",
- "\u0120turbulence",
- "joint",
- "\u0120satisfactory",
- "\u0120Anime",
- "\u0120washes",
- "\u0120liberals",
- "\u0120Sunshine",
- "\u0120REAL",
- "ublik",
- "binary",
- "Tony",
- "\u0120polarized",
- "\u0120enriched",
- "taking",
- "\u0120\u00eb\u0123\u013f\u00eb\u0124\u013a",
- "\u0120pleasures",
- "\u0120extermin",
- "inese",
- "atl",
- "v\u00c3\u00a4r",
- "\u00d0\u00b0\u00d1\u0122\u00d1\u012d",
- "\u0120my\u00c5\u013d",
- "narrator",
- "\u0120\u00d0\u00be\u00d0\u00b4\u00d0\u00bd\u00d0\u00be\u00d0\u00bc",
- "\u0120najwi\u00c4\u013b",
- "\u0120mobilize",
- "\u0120millor",
- "\u0120ata",
- "\u00e6\u00b7\u00b7",
- "\u0120pol\u00c3\u0143tico",
- "\u0120plead",
- "\u0120painters",
- "\u0120Sow",
- "\u00d0\u00be\u00d1\u0126",
- "\u0120\u00ec\u013a\u013d\u00eb\u0124\u0142",
- "\u0120\u00d1\u0129\u00d1\u0124\u00d0\u00be\u00d0\u00b1",
- "\u0120sabor",
- "\u0120Undert",
- "\u0120JERRY",
- "\u00c5\u00a1\u00c3\u0143",
- "\u0120\u00eb\u00b0\u0138\u00ec\u0139\u0132",
- "\u0120pr\u00c3\u00a9c\u00c3\u00a9d",
- "\u0120annotation",
- "\u0120Inaudible",
- "\u0120textured",
- "\u0120fisherman",
- "vordan",
- "icherung",
- "\u0120\u00ec\u0142\u0123\u00ec\u013f\u00b4",
- "\u0120gezeigt",
- "\u0120mandates",
- "\u0120beak",
- "\u0120TWO",
- "\u0120Akbar",
- "ilian",
- "\u0120ti\u00e1\u00ba\u00bfp",
- "\u0120superiority",
- "inku",
- "\u0120lys",
- "\u0120FCC",
- "\u0120CPA",
- "ustering",
- "nicos",
- "anja",
- "\u0120chills",
- "\u0120Cage",
- "\u0120sealing",
- "\u0120sa\u00c3\u00a7",
- "\u0120dedans",
- "\u0120Alger",
- "\u0120spezie",
- "\u0120coloss",
- "\u00c4\u00b1y\u00c4\u00b1",
- "clockwise",
- "\u0120exactamente",
- "\u0120iemand",
- "am\u00c4\u00b1",
- "\u0120mandar",
- "raj",
- "faced",
- "agua",
- "\u0120\u00ea\u00b9\u0136\u00eb",
- "\u0120insbesondere",
- "\u0120drizzle",
- "\u0120diminish",
- "\u0120Yoda",
- "AI",
- "\u0120bilmiyorum",
- "\u0120MMA",
- "ategory",
- "\u0120\u00d0\u00bf\u00d0\u00b5\u00d1\u0122\u00d0\u00b5\u00d0\u00bf",
- "\u0120participar",
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- "\u0120complexities",
- "\u00e6\u00b4\u00b2",
- "\u00e6\u0130\u00a7",
- "\u00d0\u00b0\u00d1\u0122\u00d0\u00be\u00d0\u00b2",
- "mist",
- "icha",
- "Group",
- "\u0120resiliency",
- "\u0120nogle",
- "\u0120CNC",
- "pr\u00c3\u00bc",
- "\u0120physicists",
- "\u00d0\u00bd\u00d0\u00be\u00d0\u00ba",
- "LI",
- "\u0120stuffs",
- "\u0120sistemas",
- "\u0120interfering",
- "\u0120Marvin",
- "\u00c3\u00a9rcito",
- "\u0120\u00ec\u0139\u0128\u00ea\u00b3\u0142",
- "\u0120sonic",
- "\u0120equiv",
- "\u0120abord",
- "\u0120Ramen",
- "\u012009",
- "medim",
- "atiques",
- "\u0120\u00d0\u00b4\u00d0\u00b5\u00d0\u00bb\u00d0\u00b0\u00d1\u0130\u00d1\u0124",
- "\u0120unanimously",
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- "\u0120\u00ed\u012c\u00b9\u00eb\u00b3\u0126",
- "\u0120Prix",
- "kami",
- "\u0120fruition",
- "\u0120birthdays",
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- "\u0120inaugural",
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- "\u0120\u00eb\u0124\u013a\u00ec\u0123",
- "\u0120dew",
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- "ihi",
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- "\u0120citing",
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- "frist",
- "hovah",
- "\u0120merciful",
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- "rh",
- "\u0120glamorous",
- "\u0120Vikings",
- "\u0120Ooooh",
- "\u00ed\u0137\u013a\u00eb\u012c\u0136\u00eb\u012f\u00b0",
- "\u0120Uganda",
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- "entry",
- "\u0120antioxidants",
- "\u00eb\u0124\u013a\u00eb",
- "\u00d1\u012a\u00d0\u00b0\u00d1\u0131",
- "\u0120trivia",
- "\u0120g\u00c3\u00a4ller",
- "\u0120fungi",
- "\u0120milks",
- "\u0120dicht",
- "\u00ce\u00bc\u00ce\u00b7",
- "poke",
- "\u0120\u00d0\u00b2\u00d1\u012d\u00d0\u00bf\u00d1\u0125\u00d1\u0123\u00d0\u00ba",
- "\u0120feeder",
- "\u0120Alcohol",
- "hower",
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- "\u0120Rebel",
- "iosis",
- "\u0120103",
- "\u0120handout",
- "\u0120enm",
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- "\u0120geology",
- "rils",
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- "\u0120Vold",
- "\u0120Panch",
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- "\u0120pross",
- "\u0120bracelets",
- "\u0120Vega",
- "\u0120rozum",
- "\u00e6\u00ac\u00be",
- "\u00d0\u00b0\u00d0\u00b7\u00d0\u00b4",
- "\u0120Lynd",
- "\u0120Honors",
- "\u0120surrendered",
- "\u0120librarians",
- "125",
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- "andid",
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- "\u0120\u00d8\u00b6",
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- "ortic",
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- "\u0120Angie",
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- "Mic",
- "\u0120Sheila",
- "achtet",
- "\u0120overst",
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- "\u00e6\u013f\u00a1",
- "\u00e6\u0122\u0130\u00e4\u00b9\u012a\u00e4\u00ba\u0128",
- "\u00e5\u00bf\u013b",
- "\u0120wichtiger",
- "\u0120vino",
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- "wo\u00c5\u013d\u00c4\u0129",
- "draw",
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- "markets",
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- "\u0120withhold",
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- "\u0120enthalpy",
- "\u0120Transit",
- "\u00c4\u00b1lm\u00c4\u00b1\u00c5\u0141",
- "alies",
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- "\u0120gro\u00c3\u0141es",
- "\u0120fitt",
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- "Sarah",
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- "\u0120nadziej\u00c4\u013b",
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- "\u0120spool",
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- "\u0120Enemy",
- "\u0120fury",
- "\u0120detta",
- "\u0120Fay",
- "\u00e9\u013c\u00a8",
- "\u00d1\u0131\u00d1\u0130\u00d1\u0124",
- "\u0120aproximadamente",
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- "\u0120cree",
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- "\u0120Umwelt",
- "\u0120Suzanne",
- "\u00d0\u00bd\u00d0\u00b8\u00d1\u0128\u00d1\u012d",
- "outez",
- "\u0120JAC",
- "yards",
- "radas",
- "rau",
- "ipts",
- "hail",
- "\u0120paragraphs",
- "\u0120meglio",
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- "\u0120aceite",
- "\u0120Harsh",
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- "\u0120Blockchain",
- "\u0120\u00d1\u0127\u00d0\u00be\u00d1\u0122\u00d0\u00be\u00d1\u012a\u00d0\u00b8\u00d0\u00b9",
- "\u0120virtuous",
- "\u0120investigaci\u00c3\u00b3n",
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- "\u0120\u00ce\u00a7",
- "\u0120Stra\u00c3\u0141en",
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- "Help",
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- "ivering",
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- "450",
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- "ynn",
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- "\u00ed\u0137\u013a\u00ec\u00a3\u0142",
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- "boom",
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- "\u0120desperation",
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- "<|21.76|>",
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- "<|21.84|>",
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- "<|21.88|>",
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- "<|22.04|>",
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- "<|22.10|>",
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- "<|22.26|>",
- "<|22.28|>",
- "<|22.30|>",
- "<|22.32|>",
- "<|22.34|>",
- "<|22.36|>",
- "<|22.38|>",
- "<|22.40|>",
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- "<|22.48|>",
- "<|22.50|>",
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- "<|24.62|>",
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- "<|29.98|>",
- "<|30.00|>"
-]
\ No newline at end of file
diff --git a/whisper_pipeline/requirements.txt b/whisper_pipeline/requirements.txt
deleted file mode 100644
index 3a88f890aa0a1fa1acace63d18350ea60b227786..0000000000000000000000000000000000000000
--- a/whisper_pipeline/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-fastapi==0.111.0
-uvicorn==0.22.0
-pynini
-transformers
\ No newline at end of file
diff --git a/whisper_pipeline/start.sh b/whisper_pipeline/start.sh
deleted file mode 100644
index 1369f36a16b6df729218617de367426c44d39f51..0000000000000000000000000000000000000000
--- a/whisper_pipeline/start.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/bash
-
-# Start the FastAPI server with uvicorn
-exec uvicorn api:app --host $HOST --port $PORT --forwarded-allow-ips '*'
diff --git a/whisper_pipeline/text_processing/__init__.py b/whisper_pipeline/text_processing/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/whisper_pipeline/text_processing/__pycache__/__init__.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index c49c51bc9e07f85e90e21479948eff89e6eb4551..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/__pycache__/data_loader_utils.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/data_loader_utils.cpython-310.pyc
deleted file mode 100644
index 566af41625903a4938267c342a951c9ee4f0fe2f..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/__pycache__/inverse_normalize.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/inverse_normalize.cpython-310.pyc
deleted file mode 100644
index dd05920ee8722f7776e856aeaed630d4ddbbb0db..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/__pycache__/moses_tokenizers.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/moses_tokenizers.cpython-310.pyc
deleted file mode 100644
index ae5518d9e3718fa63078e1e94e2f7be098c07c96..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/__pycache__/normalize.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/normalize.cpython-310.pyc
deleted file mode 100644
index 46fbbe86fc118164d0d45308fba4c5087b10d617..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/__pycache__/token_parser.cpython-310.pyc b/whisper_pipeline/text_processing/__pycache__/token_parser.cpython-310.pyc
deleted file mode 100644
index 8b79fad405c0a6351323f3b6f5bc30a321ab2665..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/data_loader_utils.py b/whisper_pipeline/text_processing/data_loader_utils.py
deleted file mode 100644
index 31a9d68c94ae7be63120983e9832c36e6331d473..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/data_loader_utils.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import json
-import re
-from collections import defaultdict, namedtuple
-from typing import Dict, List, Optional, Set, Tuple
-
-EOS_TYPE = "EOS"
-PUNCT_TYPE = "PUNCT"
-PLAIN_TYPE = "PLAIN"
-Instance = namedtuple('Instance', 'token_type un_normalized normalized')
-known_types = [
- "PLAIN",
- "DATE",
- "CARDINAL",
- "LETTERS",
- "VERBATIM",
- "MEASURE",
- "DECIMAL",
- "ORDINAL",
- "DIGIT",
- "MONEY",
- "TELEPHONE",
- "ELECTRONIC",
- "FRACTION",
- "TIME",
- "ADDRESS",
-]
-
-
-def load_kaggle_text_norm_file(file_path: str) -> List[Instance]:
- """
- https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish
- Loads text file in the Kaggle Google text normalization file format: \t\t<`self` if trivial class or normalized text>
- E.g.
- PLAIN Brillantaisia
- PLAIN is
- PLAIN a
- PLAIN genus
- PLAIN of
- PLAIN plant
- PLAIN in
- PLAIN family
- PLAIN Acanthaceae
- PUNCT . sil
-
-
- Args:
- file_path: file path to text file
-
- Returns: flat list of instances
- """
- res = []
- with open(file_path, 'r') as fp:
- for line in fp:
- parts = line.strip().split("\t")
- if parts[0] == "":
- res.append(Instance(token_type=EOS_TYPE, un_normalized="", normalized=""))
- else:
- l_type, l_token, l_normalized = parts
- l_token = l_token.lower()
- l_normalized = l_normalized.lower()
-
- if l_type == PLAIN_TYPE:
- res.append(Instance(token_type=l_type, un_normalized=l_token, normalized=l_token))
- elif l_type != PUNCT_TYPE:
- res.append(Instance(token_type=l_type, un_normalized=l_token, normalized=l_normalized))
- return res
-
-
-def load_files(file_paths: List[str], load_func=load_kaggle_text_norm_file) -> List[Instance]:
- """
- Load given list of text files using the `load_func` function.
-
- Args:
- file_paths: list of file paths
- load_func: loading function
-
- Returns: flat list of instances
- """
- res = []
- for file_path in file_paths:
- res.extend(load_func(file_path=file_path))
- return res
-
-
-def clean_generic(text: str) -> str:
- """
- Cleans text without affecting semiotic classes.
-
- Args:
- text: string
-
- Returns: cleaned string
- """
- text = text.strip()
- text = text.lower()
- return text
-
-
-def evaluate(preds: List[str], labels: List[str], input: Optional[List[str]] = None, verbose: bool = True) -> float:
- """
- Evaluates accuracy given predictions and labels.
-
- Args:
- preds: predictions
- labels: labels
- input: optional, only needed for verbosity
- verbose: if true prints [input], golden labels and predictions
-
- Returns accuracy
- """
- acc = 0
- nums = len(preds)
- for i in range(nums):
- pred_norm = clean_generic(preds[i])
- label_norm = clean_generic(labels[i])
- if pred_norm == label_norm:
- acc = acc + 1
- else:
- if input:
- print(f"inpu: {json.dumps(input[i])}")
- print(f"gold: {json.dumps(label_norm)}")
- print(f"pred: {json.dumps(pred_norm)}")
- return acc / nums
-
-
-def training_data_to_tokens(
- data: List[Instance], category: Optional[str] = None
-) -> Dict[str, Tuple[List[str], List[str]]]:
- """
- Filters the instance list by category if provided and converts it into a map from token type to list of un_normalized and normalized strings
-
- Args:
- data: list of instances
- category: optional semiotic class category name
-
- Returns Dict: token type -> (list of un_normalized strings, list of normalized strings)
- """
- result = defaultdict(lambda: ([], []))
- for instance in data:
- if instance.token_type != EOS_TYPE:
- if category is None or instance.token_type == category:
- result[instance.token_type][0].append(instance.un_normalized)
- result[instance.token_type][1].append(instance.normalized)
- return result
-
-
-def training_data_to_sentences(data: List[Instance]) -> Tuple[List[str], List[str], List[Set[str]]]:
- """
- Takes instance list, creates list of sentences split by EOS_Token
- Args:
- data: list of instances
- Returns (list of unnormalized sentences, list of normalized sentences, list of sets of categories in a sentence)
- """
- # split data at EOS boundaries
- sentences = []
- sentence = []
- categories = []
- sentence_categories = set()
-
- for instance in data:
- if instance.token_type == EOS_TYPE:
- sentences.append(sentence)
- sentence = []
- categories.append(sentence_categories)
- sentence_categories = set()
- else:
- sentence.append(instance)
- sentence_categories.update([instance.token_type])
- un_normalized = [" ".join([instance.un_normalized for instance in sentence]) for sentence in sentences]
- normalized = [" ".join([instance.normalized for instance in sentence]) for sentence in sentences]
- return un_normalized, normalized, categories
-
-
-def post_process_punctuation(text: str) -> str:
- """
- Normalized quotes and spaces
-
- Args:
- text: text
-
- Returns: text with normalized spaces and quotes
- """
- text = (
- text.replace('( ', '(')
- .replace(' )', ')')
- .replace('{ ', '{')
- .replace(' }', '}')
- .replace('[ ', '[')
- .replace(' ]', ']')
- .replace(' ', ' ')
- .replace('”', '"')
- .replace("’", "'")
- .replace("»", '"')
- .replace("«", '"')
- .replace("\\", "")
- .replace("„", '"')
- .replace("´", "'")
- .replace("’", "'")
- .replace('“', '"')
- .replace("‘", "'")
- .replace('`', "'")
- .replace('- -', "--")
- )
-
- for punct in "!,.:;?":
- text = text.replace(f' {punct}', punct)
- return text.strip()
-
-
-def pre_process(text: str) -> str:
- """
- Adds space around punctuation marks
-
- Args:
- text: string that may include semiotic classes
-
- Returns: text with spaces around punctuation marks
- """
- space_both = '*<=>^[]{}'
- for punct in space_both:
- text = text.replace(punct, ' ' + punct + ' ')
-
- text = text.replace('--', ' ' + '--' + ' ')
- # remove extra space
- text = re.sub(r' +', ' ', text)
- return text
diff --git a/whisper_pipeline/text_processing/inverse_normalize.py b/whisper_pipeline/text_processing/inverse_normalize.py
deleted file mode 100644
index 0c4938213badf6fe97cdfad1614f8e9d06d81560..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/inverse_normalize.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from argparse import ArgumentParser
-from time import perf_counter
-from typing import List
-from difflib import SequenceMatcher
-
-from text_processing.normalize import Normalizer
-from text_processing.token_parser import TokenParser
-
-
-class InverseNormalizer(Normalizer):
- """
- Inverse normalizer that converts text from spoken to written form. Useful for ASR postprocessing.
- Input is expected to have no punctuation outside of approstrophe (') and dash (-) and be lower cased.
-
- Args:
- lang: language specifying the ITN
- cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
- overwrite_cache: set to True to overwrite .far files
- """
-
- def __init__(self, lang: str = 'en', cache_dir: str = None, overwrite_cache: bool = False):
- if lang == 'vi':
- from text_processing.vi.taggers.tokenize_and_classify import ClassifyFst
- from text_processing.vi.verbalizers.verbalize_final import VerbalizeFinalFst
- else:
- raise NotImplementedError
-
- self.tagger = ClassifyFst(cache_dir=cache_dir, overwrite_cache=overwrite_cache)
- self.verbalizer = VerbalizeFinalFst()
- self.parser = TokenParser()
-
- def inverse_normalize_list(self, texts: List[str], verbose=False) -> List[str]:
- """
- NeMo inverse text normalizer
-
- Args:
- texts: list of input strings
- verbose: whether to print intermediate meta information
-
- Returns converted list of input strings
- """
- return self.normalize_list(texts=texts, verbose=verbose)
-
- def inverse_normalize(self, text: str, verbose: bool = False) -> str:
- """
- Main function. Inverse normalizes tokens from spoken to written form
- e.g. twelve kilograms -> 12 kg
-
- Args:
- text: string that may include semiotic classes
- verbose: whether to print intermediate meta information
-
- Returns: written form
- """
- chunk_size = 512
- tokens = text.split()
- if len(tokens) <= chunk_size:
- return self.normalize(text=text, verbose=verbose)
- else:
- result = ""
- for i in range(0, len(tokens), chunk_size):
- sub_text = " ".join(tokens[i: i + chunk_size])
- result += self.normalize(text=sub_text, verbose=verbose) + " "
- return result.strip()
-
- def inverse_normalize_list_with_metadata(self, text_metas: List, verbose=False) -> List[str]:
- """
- NeMo inverse text normalizer
-
- Args:
- texts: list of input strings
- verbose: whether to print intermediate meta information
-
- Returns converted list of input strings
- """
- res = []
- for input in text_metas:
- try:
- text = self.inverse_normalize_with_metadata(input, verbose=verbose)
- except:
- print(input)
- raise Exception
- res.append(text)
- return res
-
- def inverse_normalize_with_metadata_text(self, text_meta: str, verbose: bool = False):
- """
- Main function. Inverse normalizes tokens from spoken to written form
- e.g. twelve kilograms -> 12 kg
-
- Args:
- text_meta: list of tokens include text, start time, end time and score for each token
- verbose: whether to print intermediate meta information
-
- Returns: written form
- """
- # text = " ".join([token['text'] for token in text_meta])
- normalize_text = self.inverse_normalize(text_meta, verbose=verbose)
- # print(normalize_text)
- # If no changes are made, return original
- if text_meta == normalize_text:
- return text_meta
-
-
- return normalize_text
- def inverse_normalize_with_metadata(self, text_meta: List, verbose: bool = False):
- """
- Main function. Inverse normalizes tokens from spoken to written form
- e.g. twelve kilograms -> 12 kg
-
- Args:
- text_meta: list of tokens include text, start time, end time and score for each token
- verbose: whether to print intermediate meta information
-
- Returns: written form
- """
- text = " ".join([token['text'] for token in text_meta])
- normalize_text = self.inverse_normalize(text, verbose=verbose)
-
- # If no changes are made, return original
- if text == normalize_text:
- return text_meta
-
- normalize_text_meta = []
- source_tokens = text.split()
- target_tokens = normalize_text.split()
- matcher = SequenceMatcher(None, source_tokens, target_tokens)
- diffs = list(matcher.get_opcodes())
-
- for diff in diffs:
- tag, i1, i2, j1, j2 = diff
-
- if tag == "equal":
- normalize_text_meta.extend(text_meta[i1:i2])
- else:
- start = text_meta[i1]['start']
- end = text_meta[i2 - 1]['end']
- num_target_tokens = j2 - j1
- time_step = (end - start) / num_target_tokens
- for c in range(num_target_tokens):
- normalize_text_meta.append(
- {
- 'text': target_tokens[j1 + c],
- 'start': start,
- 'end': start + time_step,
- }
- )
- start += time_step
-
- return normalize_text_meta
-
-
-def parse_args():
- parser = ArgumentParser()
- parser.add_argument("input_string", help="input string", type=str)
- parser.add_argument("--language", help="language", choices=['vi'], default="en", type=str)
- parser.add_argument("--verbose", help="print info for debugging", action='store_true')
- parser.add_argument("--overwrite_cache", help="set to True to re-create .far grammar files", action="store_true")
- parser.add_argument(
- "--cache_dir",
- help="path to a dir with .far grammar file. Set to None to avoid using cache",
- default=None,
- type=str,
- )
- return parser.parse_args()
-
-
-if __name__ == "__main__":
- args = parse_args()
- start_time = perf_counter()
- inverse_normalizer = InverseNormalizer(
- lang=args.language, cache_dir=args.cache_dir, overwrite_cache=args.overwrite_cache
- )
- print(f'Time to generate graph: {round(perf_counter() - start_time, 2)} sec')
- start_time = perf_counter()
- print(inverse_normalizer.inverse_normalize(args.input_string, verbose=args.verbose))
- print(f'Execution time: {round(perf_counter() - start_time, 2)} sec')
diff --git a/whisper_pipeline/text_processing/moses_tokenizers.py b/whisper_pipeline/text_processing/moses_tokenizers.py
deleted file mode 100644
index 27e91e6c5262490842365f7b07728f61ea5bb8e6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/moses_tokenizers.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List
-
-from sacremoses import MosesDetokenizer, MosesPunctNormalizer, MosesTokenizer
-
-
-class MosesProcessor:
- """
- Tokenizer, Detokenizer and Normalizer utilities in Moses
- """
-
- def __init__(self, lang_id: str):
- self.moses_tokenizer = MosesTokenizer(lang=lang_id)
- self.moses_detokenizer = MosesDetokenizer(lang=lang_id)
- self.normalizer = MosesPunctNormalizer(lang=lang_id)
-
- def detokenize(self, tokens: List[str]) -> str:
- """
- Detokenizes a list of tokens
- Args:
- tokens: list of strings as tokens
- Returns:
- detokenized string
- """
- return self.moses_detokenizer.detokenize(tokens)
-
- def tokenize(self, text: str):
- """
- Tokenizes text using Moses -> Sentencepiece.
- """
- return self.moses_tokenizer.tokenize(text, escape=False, return_str=True)
-
- def normalize(self, text: str):
- return self.normalizer.normalize(text)
diff --git a/whisper_pipeline/text_processing/normalize.py b/whisper_pipeline/text_processing/normalize.py
deleted file mode 100644
index cbfd2a6b06524713e18c7e54bbc8adbc03a7ff8b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/normalize.py
+++ /dev/null
@@ -1,295 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import itertools
-import os
-from argparse import ArgumentParser
-from collections import OrderedDict
-from typing import List
-
-from text_processing.data_loader_utils import post_process_punctuation, pre_process
-from text_processing.token_parser import PRESERVE_ORDER_KEY, TokenParser
-from tqdm import tqdm
-
-try:
- import pynini
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-try:
- from text_processing.moses_tokenizers import MosesProcessor
-
- NLP_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- NLP_AVAILABLE = False
-
-
-class Normalizer:
- """
- Normalizer class that converts text from written to spoken form.
- Useful for TTS preprocessing.
-
- Args:
- input_case: expected input capitalization
- lang: language specifying the TN rules, by default: English
- cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
- overwrite_cache: set to True to overwrite .far files
- whitelist: path to a file with whitelist replacements
- """
-
- def __init__(
- self,
- input_case: str,
- lang: str = 'en',
- deterministic: bool = True,
- cache_dir: str = None,
- overwrite_cache: bool = False,
- whitelist: str = None,
- ):
- raise NotImplementedError
-
- # self.tagger = ClassifyFst(
- # input_case=input_case,
- # deterministic=deterministic,
- # cache_dir=cache_dir,
- # overwrite_cache=overwrite_cache,
- # whitelist=whitelist,
- # )
- # self.verbalizer = VerbalizeFinalFst(deterministic=deterministic)
- # self.parser = TokenParser()
- # self.lang = lang
-
- # if NLP_AVAILABLE:
- # self.processor = MosesProcessor(lang_id=lang)
- # else:
- # self.processor = None
- # print("NeMo NLP is not available. Moses de-tokenization will be skipped.")
-
- def normalize_list(self, texts: List[str], verbose=False) -> List[str]:
- """
- NeMo text normalizer
-
- Args:
- texts: list of input strings
- verbose: whether to print intermediate meta information
-
- Returns converted list input strings
- """
- res = []
- for input in tqdm(texts):
- try:
- text = self.normalize(input, verbose=verbose)
- except:
- print(input)
- raise Exception
- res.append(text)
- return res
-
- def normalize(
- self, text: str, verbose: bool = False, punct_pre_process: bool = False, punct_post_process: bool = False
- ) -> str:
- """
- Main function. Normalizes tokens from written to spoken form
- e.g. 12 kg -> twelve kilograms
-
- Args:
- text: string that may include semiotic classes
- verbose: whether to print intermediate meta information
- punct_pre_process: whether to perform punctuation pre-processing, for example, [25] -> [ 25 ]
- punct_post_process: whether to normalize punctuation
-
- Returns: spoken form
- """
- if punct_pre_process:
- text = pre_process(text)
- text = text.strip()
- if not text:
- if verbose:
- print(text)
- return text
- text = pynini.escape(text)
- tagged_lattice = self.find_tags(text)
- # if verbose:
- # print(tagged_lattice)
- tagged_text = self.select_tag(tagged_lattice)
- if verbose:
- print(tagged_text)
- self.parser(tagged_text)
- tokens = self.parser.parse()
- tags_reordered = self.generate_permutations(tokens)
- for tagged_text in tags_reordered:
- tagged_text = pynini.escape(tagged_text)
-
- verbalizer_lattice = self.find_verbalizer(tagged_text)
- if verbalizer_lattice.num_states() == 0:
- continue
- output = self.select_verbalizer(verbalizer_lattice)
- if punct_post_process:
- output = post_process_punctuation(output)
- # do post-processing based on Moses detokenizer
- if self.processor:
- output = self.processor.detokenize([output])
- return output
- raise ValueError()
-
- def _permute(self, d: OrderedDict) -> List[str]:
- """
- Creates reorderings of dictionary elements and serializes as strings
-
- Args:
- d: (nested) dictionary of key value pairs
-
- Return permutations of different string serializations of key value pairs
- """
- l = []
- if PRESERVE_ORDER_KEY in d.keys():
- d_permutations = [d.items()]
- else:
- d_permutations = itertools.permutations(d.items())
- for perm in d_permutations:
- subl = [""]
- for k, v in perm:
- if isinstance(v, str):
- subl = ["".join(x) for x in itertools.product(subl, [f"{k}: \"{v}\" "])]
- elif isinstance(v, OrderedDict):
- rec = self._permute(v)
- subl = ["".join(x) for x in itertools.product(subl, [f" {k} {{ "], rec, [f" }} "])]
- elif isinstance(v, bool):
- subl = ["".join(x) for x in itertools.product(subl, [f"{k}: true "])]
- else:
- raise ValueError()
- l.extend(subl)
- return l
-
- def generate_permutations(self, tokens: List[dict]):
- """
- Generates permutations of string serializations of list of dictionaries
-
- Args:
- tokens: list of dictionaries
-
- Returns string serialization of list of dictionaries
- """
-
- def _helper(prefix: str, tokens: List[dict], idx: int):
- """
- Generates permutations of string serializations of given dictionary
-
- Args:
- tokens: list of dictionaries
- prefix: prefix string
- idx: index of next dictionary
-
- Returns string serialization of dictionary
- """
- if idx == len(tokens):
- yield prefix
- return
- token_options = self._permute(tokens[idx])
- for token_option in token_options:
- yield from _helper(prefix + token_option, tokens, idx + 1)
-
- return _helper("", tokens, 0)
-
- def find_tags(self, text: str) -> 'pynini.FstLike':
- """
- Given text use tagger Fst to tag text
-
- Args:
- text: sentence
-
- Returns: tagged lattice
- """
- lattice = text @ self.tagger.fst
- return lattice
-
- def select_tag(self, lattice: 'pynini.FstLike') -> str:
- """
- Given tagged lattice return shortest path
-
- Args:
- tagged_text: tagged text
-
- Returns: shortest path
- """
- tagged_text = pynini.shortestpath(lattice, nshortest=1, unique=True).string()
- return tagged_text
-
- def find_verbalizer(self, tagged_text: str) -> 'pynini.FstLike':
- """
- Given tagged text creates verbalization lattice
- This is context-independent.
-
- Args:
- tagged_text: input text
-
- Returns: verbalized lattice
- """
- lattice = tagged_text @ self.verbalizer.fst
- return lattice
-
- def select_verbalizer(self, lattice: 'pynini.FstLike') -> str:
- """
- Given verbalized lattice return shortest path
-
- Args:
- lattice: verbalization lattice
-
- Returns: shortest path
- """
- output = pynini.shortestpath(lattice, nshortest=1, unique=True).string()
- return output
-
-
-def parse_args():
- parser = ArgumentParser()
- parser.add_argument("input_string", help="input string", type=str)
- parser.add_argument("--language", help="language", choices=["en"], default="en", type=str)
- parser.add_argument(
- "--input_case", help="input capitalization", choices=["lower_cased", "cased"], default="cased", type=str
- )
- parser.add_argument("--verbose", help="print info for debugging", action='store_true')
- parser.add_argument(
- "--punct_post_process", help="set to True to enable punctuation post processing", action="store_true"
- )
- parser.add_argument(
- "--punct_pre_process", help="set to True to enable punctuation pre processing", action="store_true"
- )
- parser.add_argument("--overwrite_cache", help="set to True to re-create .far grammar files", action="store_true")
- parser.add_argument("--whitelist", help="path to a file with with whitelist", default=None, type=str)
- parser.add_argument(
- "--cache_dir",
- help="path to a dir with .far grammar file. Set to None to avoid using cache",
- default=None,
- type=str,
- )
- return parser.parse_args()
-
-
-if __name__ == "__main__":
- args = parse_args()
- whitelist = os.path.abspath(args.whitelist) if args.whitelist else None
- normalizer = Normalizer(
- input_case=args.input_case, cache_dir=args.cache_dir, overwrite_cache=args.overwrite_cache, whitelist=whitelist
- )
- print(
- normalizer.normalize(
- args.input_string,
- verbose=args.verbose,
- punct_pre_process=args.punct_pre_process,
- punct_post_process=args.punct_post_process,
- )
- )
diff --git a/whisper_pipeline/text_processing/token_parser.py b/whisper_pipeline/text_processing/token_parser.py
deleted file mode 100644
index 3a05d4df5b4af36b66297342e9f091079fa360a1..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/token_parser.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import string
-from collections import OrderedDict
-from typing import Dict, List, Union
-
-PRESERVE_ORDER_KEY = "preserve_order"
-EOS = ""
-
-
-class TokenParser:
- """
- Parses tokenized/classified text, e.g. 'tokens { money { integer: "20" currency: "$" } } tokens { name: "left"}'
-
- Args
- text: tokenized text
- """
-
- def __call__(self, text):
- """
- Setup function
-
- Args:
- text: text to be parsed
-
- """
- self.text = text
- self.len_text = len(text)
- self.char = text[0] # cannot handle empty string
- self.index = 0
-
- def parse(self) -> List[dict]:
- """
- Main function. Implements grammar:
- A -> space F space F space F ... space
-
- Returns list of dictionaries
- """
- l = list()
- while self.parse_ws():
- token = self.parse_token()
- if not token:
- break
- l.append(token)
- return l
-
- def parse_token(self) -> Dict[str, Union[str, dict]]:
- """
- Implements grammar:
- F-> no_space KG no_space
-
- Returns: K, G as dictionary values
- """
- d = OrderedDict()
- key = self.parse_string_key()
- if key is None:
- return None
- self.parse_ws()
- if key == PRESERVE_ORDER_KEY:
- self.parse_char(":")
- self.parse_ws()
- value = self.parse_chars("true")
- else:
- value = self.parse_token_value()
-
- d[key] = value
- return d
-
- def parse_token_value(self) -> Union[str, dict]:
- """
- Implements grammar:
- G-> no_space :"VALUE" no_space | no_space {A} no_space
-
- Returns: string or dictionary
- """
- if self.char == ":":
- self.parse_char(":")
- self.parse_ws()
- self.parse_char("\"")
- value_string = self.parse_string_value()
- self.parse_char("\"")
- return value_string
- elif self.char == "{":
- d = OrderedDict()
- self.parse_char("{")
- list_token_dicts = self.parse()
- # flatten tokens
- for tok_dict in list_token_dicts:
- for k, v in tok_dict.items():
- d[k] = v
- self.parse_char("}")
- return d
- else:
- raise ValueError()
-
- def parse_char(self, exp) -> bool:
- """
- Parses character
-
- Args:
- exp: character to read in
-
- Returns true if successful
- """
- assert self.char == exp
- self.read()
- return True
-
- def parse_chars(self, exp) -> bool:
- """
- Parses characters
-
- Args:
- exp: characters to read in
-
- Returns true if successful
- """
- ok = False
- for x in exp:
- ok |= self.parse_char(x)
- return ok
-
- def parse_string_key(self) -> str:
- """
- Parses string key, can only contain ascii and '_' characters
-
- Returns parsed string key
- """
- assert self.char not in string.whitespace and self.char != EOS
- incl_criterium = string.ascii_letters + "_"
- l = []
- while self.char in incl_criterium:
- l.append(self.char)
- if not self.read():
- raise ValueError()
-
- if not l:
- return None
- return "".join(l)
-
- def parse_string_value(self) -> str:
- """
- Parses string value, ends with quote followed by space
-
- Returns parsed string value
- """
- assert self.char not in string.whitespace and self.char != EOS
- l = []
- while self.char != "\"" or self.text[self.index + 1] != " ":
- l.append(self.char)
- if not self.read():
- raise ValueError()
-
- if not l:
- return None
- return "".join(l)
-
- def parse_ws(self):
- """
- Deletes whitespaces.
-
- Returns true if not EOS after parsing
- """
- not_eos = self.char != EOS
- while not_eos and self.char == " ":
- not_eos = self.read()
- return not_eos
-
- def read(self):
- """
- Reads in next char.
-
- Returns true if not EOS
- """
- if self.index < self.len_text - 1: # should be unique
- self.index += 1
- self.char = self.text[self.index]
- return True
- self.char = EOS
- return False
diff --git a/whisper_pipeline/text_processing/vi/__init__.py b/whisper_pipeline/text_processing/vi/__init__.py
deleted file mode 100644
index 071d53b10845a7d8f158398f1bf9607d33c706bd..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.taggers.tokenize_and_classify import ClassifyFst
-from text_processing.vi.verbalizers.verbalize import VerbalizeFst
-from text_processing.vi.verbalizers.verbalize_final import VerbalizeFinalFst
-
-import logging
-
-try:
- import pynini
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- logging.warning(
- "`pynini` is not installed ! \n"
- "Please run the `text_processing/setup.sh` script"
- "prior to usage of this toolkit."
- )
-
- PYNINI_AVAILABLE = False
diff --git a/whisper_pipeline/text_processing/vi/__pycache__/__init__.cpython-310.pyc b/whisper_pipeline/text_processing/vi/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index c0b96002a3bf3cf2f2f91e9e1f1ac68639cac847..0000000000000000000000000000000000000000
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deleted file mode 100644
index 65430c5860a9c6d98734c502efc4f75260ac9ffa..0000000000000000000000000000000000000000
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deleted file mode 100644
index eec5a6914bbfb51dab6e3da50e0cb8d7b78babf3..0000000000000000000000000000000000000000
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deleted file mode 100644
index 4fe36dda3e227727a4d770c5b9e5f5010431daa8..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/vi/__pycache__/utils.cpython-310.pyc b/whisper_pipeline/text_processing/vi/__pycache__/utils.cpython-310.pyc
deleted file mode 100644
index f42e3bf9b5a5b16224b56ed6a8d29ad47a39ddd2..0000000000000000000000000000000000000000
Binary files a/whisper_pipeline/text_processing/vi/__pycache__/utils.cpython-310.pyc and /dev/null differ
diff --git a/whisper_pipeline/text_processing/vi/__pycache__/utils.cpython-38.pyc b/whisper_pipeline/text_processing/vi/__pycache__/utils.cpython-38.pyc
deleted file mode 100644
index 2f75cde60ed613dca57792be0d705f71e8214216..0000000000000000000000000000000000000000
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diff --git a/whisper_pipeline/text_processing/vi/data/__init__.py b/whisper_pipeline/text_processing/vi/data/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/data/currency.tsv b/whisper_pipeline/text_processing/vi/data/currency.tsv
deleted file mode 100644
index 7346ffdc4c8ce057c1e405df07afcaa1c4713de9..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/currency.tsv
+++ /dev/null
@@ -1,11 +0,0 @@
-$ usd
-$ đô la
-$ đô la mỹ
-€ euro
-€ ơ rô
-₩ won
-₩ uôn
-RM ringgit
-RM ring gít
-RM rinh gít
-VND việt nam đồng
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/electronic/__init__.py b/whisper_pipeline/text_processing/vi/data/electronic/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/electronic/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/data/electronic/domain.tsv b/whisper_pipeline/text_processing/vi/data/electronic/domain.tsv
deleted file mode 100644
index c56c252adbea808bdc263c5752ad20d364af95a0..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/electronic/domain.tsv
+++ /dev/null
@@ -1,11 +0,0 @@
-com
-uk
-fr
-net
-br
-in
-ru
-de
-it
-vn
-edu
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/electronic/server_name.tsv b/whisper_pipeline/text_processing/vi/data/electronic/server_name.tsv
deleted file mode 100644
index 4eadcaf5915a7546c523f9bdc62bcec47295513c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/electronic/server_name.tsv
+++ /dev/null
@@ -1,16 +0,0 @@
-g mail gmail
-gmail
-n vidia nvidia
-nvidia
-outlook
-hotmail
-yahoo
-aol
-gmx
-msn
-live
-yandex
-orange
-wanadoo
-web
-comcast
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/electronic/symbols.tsv b/whisper_pipeline/text_processing/vi/data/electronic/symbols.tsv
deleted file mode 100644
index eccbe3d475124e813edd9571d04fd0e608ffdbbd..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/electronic/symbols.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-. chấm
-- gạch
-_ gạch dưới
-_ shift gạch
-_ síp gạch
-! chấm than
-# thăng
-$ đô
-% phần trăm
-& và
-* sao
-: hai chấm
-+ cộng
-/ sẹc
-= bằng
-? chấm hỏi
-? hỏi chấm
-^ mũ
-| hoặc
-, phẩy
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/magnitudes.tsv b/whisper_pipeline/text_processing/vi/data/magnitudes.tsv
deleted file mode 100644
index 727dc567011cbd36724dbdf7a7f91629a95ff5d3..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/magnitudes.tsv
+++ /dev/null
@@ -1,5 +0,0 @@
-k nghìn
-k ngàn
-m triệu
-b tỉ
-b tỷ
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/math/symbols.tsv b/whisper_pipeline/text_processing/vi/data/math/symbols.tsv
deleted file mode 100644
index 2475c6ec39b8f27ce0b30da16c68b7cc257cee27..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/math/symbols.tsv
+++ /dev/null
@@ -1,10 +0,0 @@
-% phần trăm
-& và
-* nhân
-* sao
-+ cộng
-- trừ
-/ chia
-= bằng
-^ mũ
-| hoặc
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/measurements.tsv b/whisper_pipeline/text_processing/vi/data/measurements.tsv
deleted file mode 100644
index fc62b107cedc6456fe0ea2313201b06172fb7166..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/measurements.tsv
+++ /dev/null
@@ -1,184 +0,0 @@
-°f fahrenheit
-°f độ f
-°c celsius
-°c độ c
-km kilomet
-km ki lô met
-km ki lô mét
-km kilô mét
-km kilo mét
-m met
-m mét
-cm centimet
-cm cen ti mét
-cm xen ti mét
-cm xăng ti mét
-mm millimet
-mm mi li mét
-mm mili mét
-ha hecta
-ha héc ta
-mi mile
-m² met vuông
-m² mét vuông
-km² kilomet vuông
-km² ki lô met vuông
-km² kilo mét vuông
-km² ki lô mét vuông
-ft foot
-% phần trăm
-hz héc
-hz hẹc
-kw kilowatt
-kw kilo watt
-kw ki lô watt
-kw ki lô oát
-kw ki lô goát
-hp mã lực
-mg milligram
-mg milli gram
-mg mi li gram
-mg mi li gam
-mg mili gam
-kg kilogram
-kg kilo gram
-kg ki lô gram
-kg kilo gam
-kg ki lô gam
-kg kí lô
-ghz giga hẹc
-ghz giga héc
-ghz gi ga héc
-ghz gi ga hẹc
-khz kilo hẹc
-khz kilo héc
-khz ki lô héc
-khz ki lô hẹc
-mhz mega héc
-mhz mega hẹc
-mhz mê ga hẹc
-mhz mê ga héc
-v vôn
-h giờ
-phút phút
-giây giây
-nm nanomet
-nm nano mét
-nm na nô mét
-mA milli ampe
-mA mi li ampe
-mA mi li am pe
-kwh kilowatt giờ
-kwh kilo watt giờ
-kwh ki lô oát giờ
-kwh ki lô goát giờ
-m³ met khối
-m³ mét khối
-cm³ centimet khối
-cm³ cen ti mét khối
-cm³ xen ti mét khối
-cm³ xăng ti mét khối
-tw tera watt
-tw terawatt
-tw te ra watt
-tw tê ra watt
-tw tê ra oát
-tw tê ra goát
-mv milli vôn
-mv mi li vôn
-mw megawatt
-mw mega watt
-mw mê ga watt
-mw mê ga oát
-mw mê ga goát
-μm micromet
-μm micro mét
-μm muy crô mét
-μm mi crô mét
-μm muy cờ rô mét
-μm mi cờ rô mét
-inch inch
-tb terabyte
-tb tera byte
-tb te ra byte
-tb te ra bai
-tb tê ra byte
-tb tê ra bai
-g gram
-g gam
-ω ohm
-ω ôm
-db decibel
-db deci ben
-db đề xi ben
-μg microgram
-μg micro gram
-μg muy crô gram
-μg micro gam
-μg muy crô gam
-μg muy cờ rô gam
-pg petagram
-pg peta gam
-pg pê ta gam
-gb gigabyte
-gb giga byte
-gb giga bai
-gb gi ga byte
-gb gi ga bai
-mb megabyte
-mb mega byte
-mb mega bai
-mb me ga byte
-mb me ga bai
-mb mê ga byte
-mb mê ga bai
-kb kilobyte
-kb kilo byte
-kb kilo bai
-kb ki lô byte
-kb ki lô bai
-kbps kilobit trên giây
-mbps megabit trên giây
-kv kilovolt
-kv kilo vôn
-kv ki lô vôn
-mv megavolt
-mv mega vôn
-mv me ga vôn
-mv mê ga vôn
-yd yard
-rad radian
-gpa giga pascal
-gpa gi ga pascal
-gw gigawatt
-gw giga watt
-gw gi ga watt
-gw gi ga oát
-ns nano giây
-ns na nô giây
-μs micro giây
-μs muy crô giây
-μs muy cờ rô giây
-pa pascal
-ms milli giây
-ms mi li giây
-dm decimet
-dm đề xi mét
-dm³ đề xi mét khối
-dm³ decimet khối
-mm² millimet vuông
-mm² mi li mét vuông
-cm² centimet vuông
-cm² centi mét vuông
-cm² xăng ti mét vuông
-cm² xen ti mét vuông
-pb peta byte
-pb petabyte
-pb pê ta byte
-kcal kilocalories
-kcal kilocalo
-kcal ki lô calo
-l lít
-ml millilit
-ml mili lít
-ml mi li lít
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/months.tsv b/whisper_pipeline/text_processing/vi/data/months.tsv
deleted file mode 100644
index 93da6892e2ba87903e4769bc9c83f68b9a6aed0d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/months.tsv
+++ /dev/null
@@ -1,14 +0,0 @@
-một 1
-hai 2
-ba 3
-tư 4
-bốn 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
-mười 10
-mười một 11
-mười hai 12
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/__init__.py b/whisper_pipeline/text_processing/vi/data/numbers/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/digit.tsv b/whisper_pipeline/text_processing/vi/data/numbers/digit.tsv
deleted file mode 100644
index bdac5ded18ff400993bf25b2593c8f91074ab54b..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/digit.tsv
+++ /dev/null
@@ -1,10 +0,0 @@
-một 1
-hai 2
-ba 3
-bốn 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/hundred.tsv b/whisper_pipeline/text_processing/vi/data/numbers/hundred.tsv
deleted file mode 100644
index b2f35ae9fbf3dd092bd26e5f8208fb0fe105f2e8..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/hundred.tsv
+++ /dev/null
@@ -1 +0,0 @@
-trăm
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/teen.tsv b/whisper_pipeline/text_processing/vi/data/numbers/teen.tsv
deleted file mode 100644
index 722dc678d4eb0e34f771231e6a188bdee24f0aae..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/teen.tsv
+++ /dev/null
@@ -1,11 +0,0 @@
-mười 10
-mười một 11
-mười hai 12
-mười ba 13
-mười bốn 14
-mười lăm 15
-mười sáu 16
-mười bảy 17
-mười bẩy 17
-mười tám 18
-mười chín 19
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/thousands.tsv b/whisper_pipeline/text_processing/vi/data/numbers/thousands.tsv
deleted file mode 100644
index d2ca259e8be8724ef3691ac07364d7a82dbf16a2..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/thousands.tsv
+++ /dev/null
@@ -1,6 +0,0 @@
-nghìn
-triệu
-tỉ
-nghìn tỉ
-triệu tỉ
-tỉ tỉ
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/ties.tsv b/whisper_pipeline/text_processing/vi/data/numbers/ties.tsv
deleted file mode 100644
index cca1f8008036ae6ae6984222c62b2704882d667c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/ties.tsv
+++ /dev/null
@@ -1,9 +0,0 @@
-hai 2
-ba 3
-bốn 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/numbers/zero.tsv b/whisper_pipeline/text_processing/vi/data/numbers/zero.tsv
deleted file mode 100644
index 70d6dcf26ae2f51b2696500e4b86259bee198e21..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/numbers/zero.tsv
+++ /dev/null
@@ -1 +0,0 @@
-không 0
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/ordinals/__init__.py b/whisper_pipeline/text_processing/vi/data/ordinals/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/ordinals/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/data/ordinals/digit.tsv b/whisper_pipeline/text_processing/vi/data/ordinals/digit.tsv
deleted file mode 100644
index a1403b7ceb4b9fef31bed0962dd90cbbff72bfdd..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/ordinals/digit.tsv
+++ /dev/null
@@ -1,12 +0,0 @@
-nhất 1
-nhì 2
-hai 2
-ba 3
-bốn 4
-tư 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/__init__.py b/whisper_pipeline/text_processing/vi/data/time/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/data/time/hours.tsv b/whisper_pipeline/text_processing/vi/data/time/hours.tsv
deleted file mode 100644
index 48e35fc901305e1249ce2327d2cc05a3df3d5c47..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/hours.tsv
+++ /dev/null
@@ -1,29 +0,0 @@
-không 0
-một 1
-hai 2
-ba 3
-bốn 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
-mười 10
-mười một 11
-mười hai 12
-mười ba 13
-mười bốn 14
-mười lăm 15
-mười sáu 16
-mười bảy 17
-mười bẩy 17
-mười tám 18
-mười chín 19
-hai mươi 20
-hai mươi mốt 21
-hai mốt 21
-hai mươi hai 22
-hai hai 22
-hai mươi ba 23
-hai ba 23
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/hours_to.tsv b/whisper_pipeline/text_processing/vi/data/time/hours_to.tsv
deleted file mode 100644
index a5621957997573ffd0b4e9159c6ecb4906abded3..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/hours_to.tsv
+++ /dev/null
@@ -1,25 +0,0 @@
-1 0
-2 1
-3 2
-4 3
-5 4
-6 5
-7 6
-8 7
-9 8
-10 9
-11 10
-12 11
-13 12
-14 13
-15 14
-16 15
-17 16
-18 17
-19 18
-20 19
-21 20
-22 21
-23 22
-24 23
-0 23
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/hours_to_night.tsv b/whisper_pipeline/text_processing/vi/data/time/hours_to_night.tsv
deleted file mode 100644
index 1374f45c0f5b0b4bb194074b8e52e96d99cb6bdb..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/hours_to_night.tsv
+++ /dev/null
@@ -1,12 +0,0 @@
-1 13
-2 14
-3 15
-4 16
-5 17
-6 18
-7 19
-8 20
-9 21
-10 22
-11 23
-12 0
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/minutes.tsv b/whisper_pipeline/text_processing/vi/data/time/minutes.tsv
deleted file mode 100644
index 0d7872b948f46a295b27d11ff5f023a6c38b92d1..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/minutes.tsv
+++ /dev/null
@@ -1,118 +0,0 @@
-không 0
-một 1
-hai 2
-ba 3
-bốn 4
-năm 5
-sáu 6
-bảy 7
-bẩy 7
-tám 8
-chín 9
-mười 10
-mười một 11
-mười hai 12
-mười ba 13
-mười bốn 14
-mười lăm 15
-mười sáu 16
-mười bảy 17
-mười bẩy 17
-mười tám 18
-mười chín 19
-hai mươi 20
-hai mươi mốt 21
-hai mươi hai 22
-hai mươi ba 23
-hai mươi tư 24
-hai mươi bốn 24
-hai mươi lăm 25
-hai mươi sáu 26
-hai mươi bảy 27
-hai mươi bẩy 27
-hai mươi tám 28
-hai mươi chín 29
-ba mươi 30
-ba mươi mốt 21
-ba mươi hai 22
-ba mươi ba 33
-ba mươi tư 34
-ba mươi bốn 34
-ba mươi lăm 35
-ba mươi sáu 36
-ba mươi bảy 37
-ba mươi bẩy 37
-ba mươi tám 38
-ba mươi chín 39
-bốn mươi 40
-bốn mươi mốt 41
-bốn mươi hai 42
-bốn mươi ba 43
-bốn mươi tư 44
-bốn mươi bốn 44
-bốn mươi lăm 45
-bốn mươi sáu 46
-bốn mươi bảy 47
-bốn mươi bẩy 47
-bốn mươi tám 48
-bốn mươi chín 49
-năm mươi 50
-năm mươi mốt 51
-năm mươi hai 52
-năm mươi ba 53
-năm mươi tư 54
-năm mươi bốn 54
-năm mươi lăm 55
-năm mươi sáu 56
-năm mươi bảy 57
-năm mươi bẩy 57
-năm mươi tám 58
-năm mươi chín 59
-hai mốt 21
-hai hai 22
-hai ba 23
-hai tư 24
-hai bốn 24
-hai lăm 25
-hai năm 25
-hai sáu 26
-hai bảy 27
-hai bẩy 27
-hai tám 28
-hai chín 29
-ba mốt 21
-ba hai 22
-ba ba 33
-ba tư 34
-ba bốn 34
-ba lăm 35
-ba năm 35
-ba sáu 36
-ba bảy 37
-ba bẩy 37
-ba tám 38
-ba chín 39
-bốn mốt 41
-bốn hai 42
-bốn ba 43
-bốn tư 44
-bốn bốn 44
-bốn lăm 45
-bốn năm 45
-bốn sáu 46
-bốn bảy 47
-bốn bẩy 47
-bốn tám 48
-bốn chín 49
-năm mốt 51
-năm hai 52
-năm ba 53
-năm tư 54
-năm bốn 54
-năm lăm 55
-năm năm 55
-năm sáu 56
-năm bảy 57
-năm bẩy 57
-năm tám 58
-năm chín 59
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/minutes_to.tsv b/whisper_pipeline/text_processing/vi/data/time/minutes_to.tsv
deleted file mode 100644
index bcb3283651c49b4de67cda126be014c2713ab4c2..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/minutes_to.tsv
+++ /dev/null
@@ -1,59 +0,0 @@
-1 59
-2 58
-3 57
-4 56
-5 55
-6 54
-7 53
-8 52
-9 51
-10 50
-11 49
-12 48
-13 47
-14 46
-15 45
-16 44
-17 43
-18 42
-19 41
-20 40
-21 39
-22 38
-23 37
-24 36
-25 35
-26 34
-27 33
-28 32
-29 31
-30 30
-31 29
-32 28
-33 27
-34 26
-35 25
-36 24
-37 23
-38 22
-39 21
-40 20
-41 19
-42 18
-43 17
-44 16
-45 15
-46 14
-47 13
-48 12
-49 11
-50 10
-51 9
-52 8
-53 7
-54 6
-55 5
-56 4
-57 3
-58 2
-59 1
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/time_suffix.tsv b/whisper_pipeline/text_processing/vi/data/time/time_suffix.tsv
deleted file mode 100644
index b39a9ef9a92ccb3e54434abe6bd88a802dc6c900..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/time_suffix.tsv
+++ /dev/null
@@ -1,8 +0,0 @@
-p m p.m.
-pm p.m.
-p.m.
-p.m p.m.
-am a.m.
-a.m.
-a.m a.m.
-a m a.m.
\ No newline at end of file
diff --git a/whisper_pipeline/text_processing/vi/data/time/time_zone.tsv b/whisper_pipeline/text_processing/vi/data/time/time_zone.tsv
deleted file mode 100644
index 3e0ade467a844e4f040d51c3334341b729432174..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/data/time/time_zone.tsv
+++ /dev/null
@@ -1,7 +0,0 @@
-cst c s t
-cet c e t
-pst p s t
-est e s t
-pt p t
-et e t
-gmt g m t
diff --git a/whisper_pipeline/text_processing/vi/data/whitelist.tsv b/whisper_pipeline/text_processing/vi/data/whitelist.tsv
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/whisper_pipeline/text_processing/vi/graph_utils.py b/whisper_pipeline/text_processing/vi/graph_utils.py
deleted file mode 100644
index 0b27f03f51f115dcbdcd7d8c3a518568e000f607..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/graph_utils.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 os
-import string
-from pathlib import Path
-from typing import Dict
-
-try:
- import pynini
- from pynini import Far
- from pynini.export import export
- from pynini.lib import byte, pynutil, utf8
-
- NEMO_CHAR = utf8.VALID_UTF8_CHAR
-
- NEMO_DIGIT = byte.DIGIT
- NEMO_LOWER = pynini.union(*string.ascii_lowercase).optimize()
- NEMO_UPPER = pynini.union(*string.ascii_uppercase).optimize()
- NEMO_ALPHA = pynini.union(NEMO_LOWER, NEMO_UPPER).optimize()
- NEMO_ALNUM = pynini.union(NEMO_DIGIT, NEMO_ALPHA).optimize()
- NEMO_HEX = pynini.union(*string.hexdigits).optimize()
- NEMO_NON_BREAKING_SPACE = "\u00A0"
- NEMO_SPACE = " "
- NEMO_WHITE_SPACE = pynini.union(" ", "\t", "\n", "\r", "\u00A0").optimize()
- NEMO_NOT_SPACE = pynini.difference(NEMO_CHAR, NEMO_WHITE_SPACE).optimize()
- NEMO_NOT_QUOTE = pynini.difference(NEMO_CHAR, r'"').optimize()
-
- NEMO_PUNCT = pynini.union(*map(pynini.escape, string.punctuation)).optimize()
- NEMO_GRAPH = pynini.union(NEMO_ALNUM, NEMO_PUNCT).optimize()
-
- NEMO_SIGMA = pynini.closure(NEMO_CHAR)
-
- delete_space = pynutil.delete(pynini.closure(NEMO_WHITE_SPACE))
- insert_space = pynutil.insert(" ")
- delete_extra_space = pynini.cross(pynini.closure(NEMO_WHITE_SPACE, 1), " ")
-
- # French frequently compounds numbers with hyphen.
- delete_hyphen = pynutil.delete(pynini.closure("-", 0, 1))
- insert_hyphen = pynutil.insert("-")
-
- TO_LOWER = pynini.union(*[pynini.cross(x, y) for x, y in zip(string.ascii_uppercase, string.ascii_lowercase)])
- TO_UPPER = pynini.invert(TO_LOWER)
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- # Create placeholders
- NEMO_CHAR = None
-
- NEMO_DIGIT = None
- NEMO_LOWER = None
- NEMO_UPPER = None
- NEMO_ALPHA = None
- NEMO_ALNUM = None
- NEMO_HEX = None
- NEMO_NON_BREAKING_SPACE = "\u00A0"
- NEMO_SPACE = " "
- NEMO_WHITE_SPACE = None
- NEMO_NOT_SPACE = None
- NEMO_NOT_QUOTE = None
-
- NEMO_PUNCT = None
- NEMO_GRAPH = None
-
- NEMO_SIGMA = None
-
- delete_space = None
- insert_space = None
- delete_extra_space = None
-
- delete_hyphen = None
- insert_hyphen = None
-
- TO_LOWER = None
- TO_UPPER = None
-
- PYNINI_AVAILABLE = False
-
-
-def generator_main(file_name: str, graphs: Dict[str, pynini.FstLike]):
- """
- Exports graph as OpenFst finite state archive (FAR) file with given file name and rule name.
-
- Args:
- file_name: exported file name
- graphs: Mapping of a rule name and Pynini WFST graph to be exported
- """
- exporter = export.Exporter(file_name)
- for rule, graph in graphs.items():
- exporter[rule] = graph.optimize()
- exporter.close()
- print(f"Created {file_name}")
-
-
-def convert_space(fst) -> "pynini.FstLike":
- """
- Converts space to nonbreaking space.
- Used only in tagger grammars for transducing token values within quotes, e.g. name: "hello kitty"
- This is making transducer significantly slower, so only use when there could be potential spaces within quotes, otherwise leave it.
-
- Args:
- fst: input fst
-
- Returns output fst where breaking spaces are converted to non breaking spaces
- """
- return fst @ pynini.cdrewrite(pynini.cross(NEMO_SPACE, NEMO_NON_BREAKING_SPACE), "", "", NEMO_SIGMA)
-
-
-class GraphFst:
- """
- Base class for all grammar fsts.
-
- Args:
- name: name of grammar class
- kind: either 'classify' or 'verbalize'
- deterministic: if True will provide a single transduction option,
- for False multiple transduction are generated (used for audio-based normalization)
- """
-
- def __init__(self, name: str, kind: str, deterministic: bool = True):
- self.name = name
- self.kind = str
- self._fst = None
- self.deterministic = deterministic
-
- self.far_path = Path(os.path.dirname(__file__) + "/grammars/" + kind + "/" + name + ".far")
- if self.far_exist():
- self._fst = Far(self.far_path, mode="r", arc_type="standard", far_type="default").get_fst()
-
- def far_exist(self) -> bool:
- """
- Returns true if FAR can be loaded
- """
- return self.far_path.exists()
-
- @property
- def fst(self) -> "pynini.FstLike":
- return self._fst
-
- @fst.setter
- def fst(self, fst):
- self._fst = fst
-
- def add_tokens(self, fst) -> "pynini.FstLike":
- """
- Wraps class name around to given fst
-
- Args:
- fst: input fst
-
- Returns:
- Fst: fst
- """
- return pynutil.insert(f"{self.name} {{ ") + fst + pynutil.insert(" }")
-
- def delete_tokens(self, fst) -> "pynini.FstLike":
- """
- Deletes class name wrap around output of given fst
-
- Args:
- fst: input fst
-
- Returns:
- Fst: fst
- """
- res = (
- pynutil.delete(f"{self.name}")
- + delete_space
- + pynutil.delete("{")
- + delete_space
- + fst
- + delete_space
- + pynutil.delete("}")
- )
- return res @ pynini.cdrewrite(pynini.cross("\u00A0", " "), "", "", NEMO_SIGMA)
diff --git a/whisper_pipeline/text_processing/vi/taggers/__init__.py b/whisper_pipeline/text_processing/vi/taggers/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/whisper_pipeline/text_processing/vi/taggers/__pycache__/__init__.cpython-310.pyc b/whisper_pipeline/text_processing/vi/taggers/__pycache__/__init__.cpython-310.pyc
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diff --git a/whisper_pipeline/text_processing/vi/taggers/address.py b/whisper_pipeline/text_processing/vi/taggers/address.py
deleted file mode 100644
index 5591eb755fe2b0413c1bc8f5c9a29d89d753be94..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/address.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- GraphFst,
- delete_space,
-)
-
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class AddressFst(GraphFst):
- """
- Finite state transducer for classifying address
- e.g. hai sẹc ba -> tokens { address { value: "2/3" } }
- e.g. hai sẹc mười sẹc năm -> tokens { address{ value: "2/10/5" } }
-
- Args:
- cardinal: OrdinalFst
- """
-
- def __init__(self, cardinal: GraphFst):
- super().__init__(name="address", kind="classify")
-
- graph_cardinal = cardinal.graph_no_exception
-
- split_component = pynini.cross(pynini.union("sẹc", "sẹt"), "/")
- graph_address = (
- pynini.closure(graph_cardinal + delete_space + split_component + delete_space, 1) + graph_cardinal
- )
- graph = pynutil.insert('value: "') + graph_address + pynutil.insert('"')
-
- final_graph = self.add_tokens(graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/cardinal.py b/whisper_pipeline/text_processing/vi/taggers/cardinal.py
deleted file mode 100644
index 6fc6f1bdd1a9c3029ad76c3def5863e33b97ac86..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/cardinal.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- NEMO_DIGIT,
- NEMO_SPACE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class CardinalFst(GraphFst):
- """
- Finite state transducer for classifying cardinals
- e.g. trừ hai mươi ba -> cardinal { integer: "23" negative: "-" } }
- e.g. hai nghìn lẻ chín -> cardinal { integer: "2009"} }
- Numbers below ten are not converted.
- """
-
- def __init__(self):
- super().__init__(name="cardinal", kind="classify")
- graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
- graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
- graph_ties = pynini.string_file(get_abs_path("data/numbers/ties.tsv"))
- graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv"))
-
- graph_one = pynini.cross("mốt", "1")
- graph_four = pynini.cross("tư", "4")
- graph_five = pynini.cross("lăm", "5")
- graph_half = pynini.cross("rưỡi", "5")
- graph_hundred = pynini.cross("trăm", "")
- graph_ten = pynini.cross("mươi", "")
- zero = pynini.cross(pynini.union("linh", "lẻ"), "0")
-
- optional_ten = pynini.closure(delete_space + graph_ten, 0, 1)
- last_digit_exception = pynini.project(pynini.cross("năm", "5"), "input")
- last_digit = pynini.union(
- (pynini.project(graph_digit, "input") - last_digit_exception.arcsort()) @ graph_digit,
- graph_one,
- graph_four,
- graph_five,
- )
-
- graph_hundred_ties_component = (graph_digit | graph_zero) + delete_space + graph_hundred
- graph_hundred_ties_component += delete_space
- graph_hundred_ties_component += pynini.union(
- graph_teen,
- (graph_half | graph_four | graph_one) + pynutil.insert("0"),
- graph_ties + optional_ten + ((delete_space + last_digit) | pynutil.insert("0")),
- zero + delete_space + (graph_digit | graph_four),
- pynutil.insert("00"),
- )
- graph_hundred_ties_component |= (
- pynutil.insert("0")
- + delete_space
- + pynini.union(
- graph_teen,
- graph_ties + delete_space + graph_ten + delete_space + last_digit,
- graph_ties + delete_space + pynini.union(graph_one, graph_four, graph_five),
- graph_ties + delete_space + graph_ten + pynutil.insert("0"),
- zero + delete_space + (graph_digit | graph_four),
- )
- )
- graph_hundred_component = pynini.union(
- graph_hundred_ties_component,
- graph_ties + delete_space + graph_digit,
- pynutil.insert("00") + delete_space + graph_digit
- )
-
- graph_hundred_component_at_least_one_none_zero_digit = graph_hundred_component @ (
- pynini.closure(NEMO_DIGIT) + (NEMO_DIGIT - "0") + pynini.closure(NEMO_DIGIT)
- )
- self.graph_hundred_component_at_least_one_none_zero_digit = graph_hundred_component_at_least_one_none_zero_digit
- graph_hundred_ties_zero = graph_hundred_ties_component | pynutil.insert("000")
-
- graph_thousands = pynini.union(
- graph_hundred_component_at_least_one_none_zero_digit
- + delete_space
- + pynutil.delete(pynini.union("nghìn", "ngàn")),
- pynutil.insert("000", weight=0.1),
- )
-
- graph_ten_thousand = pynini.union(
- graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete("vạn"),
- pynutil.insert("0000", weight=0.1),
- )
-
- graph_ten_thousand_suffix = pynini.union(
- graph_digit + delete_space + pynutil.delete(pynini.union("nghìn", "ngàn")),
- pynutil.insert("0", weight=0.1),
- )
-
- graph_million = pynini.union(
- graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete("triệu"),
- pynutil.insert("000", weight=0.1),
- )
- graph_billion = pynini.union(
- graph_hundred_component_at_least_one_none_zero_digit
- + delete_space
- + pynutil.delete(pynini.union("tỉ", "tỷ")),
- pynutil.insert("000", weight=0.1),
- )
-
- graph = pynini.union(
- graph_billion
- + delete_space
- + graph_million
- + delete_space
- + graph_thousands
- + delete_space
- + graph_hundred_ties_zero,
- graph_ten_thousand + delete_space + graph_ten_thousand_suffix + delete_space + graph_hundred_ties_zero,
- graph_hundred_component_at_least_one_none_zero_digit
- + delete_space
- + pynutil.delete(pynini.union("nghìn", "ngàn"))
- + delete_space
- + (((last_digit | graph_half) + pynutil.insert("00")) | graph_hundred_ties_zero),
- graph_digit,
- graph_zero,
- )
-
- graph = graph @ pynini.union(
- pynutil.delete(pynini.closure("0")) + pynini.difference(NEMO_DIGIT, "0") + pynini.closure(NEMO_DIGIT),
- "0",
- )
-
- # don't convert cardinals from zero to nine inclusive
- graph_exception = pynini.project(pynini.union(graph_digit, graph_zero), "input")
-
- self.graph_no_exception = graph
-
- self.graph = (pynini.project(graph, "input") - graph_exception.arcsort()) @ graph
-
- optional_minus_graph = pynini.closure(
- pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"-"') + NEMO_SPACE,
- 0,
- 1,
- )
-
- final_graph = optional_minus_graph + pynutil.insert('integer: "') + self.graph + pynutil.insert('"')
-
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/date.py b/whisper_pipeline/text_processing/vi/taggers/date.py
deleted file mode 100644
index 1785346f5093384eaec9040ba7361d628eb97b3a..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/date.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- delete_extra_space,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv")).optimize()
- graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")).optimize()
- graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv")).optimize()
- ties_graph = pynini.string_file(get_abs_path("data/numbers/ties.tsv")).optimize()
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- graph_teen = None
- graph_digit = None
- graph_zero = None
- ties_graph = None
-
- PYNINI_AVAILABLE = True
-
-
-def _get_month_graph():
- """
- Transducer for month, e.g. march -> march
- """
- month_graph = pynini.string_file(get_abs_path("data/months.tsv")).optimize()
- return month_graph
-
-
-def _get_ties_graph():
- """
- Transducer for 20-99 e.g
- hai ba -> 23
- """
- graph_one = pynini.cross("mốt", "1")
- graph_four = pynini.cross("tư", "4")
- graph_five = pynini.cross("lăm", "5")
- graph_ten = pynini.cross("mươi", "")
- optional_ten = pynini.closure(delete_space + graph_ten, 0, 1)
-
- graph = pynini.union(
- ties_graph + optional_ten + delete_space + (graph_digit | graph_one | graph_four | graph_five),
- ties_graph + delete_space + graph_ten + pynutil.insert("0"),
- )
- return graph
-
-
-def _get_year_graph():
- """
- Transducer for year, e.g. hai không hai mươi -> 2020
- """
-
- def _get_digits_graph():
- zero = pynini.cross((pynini.union("linh", "lẻ")), "0")
- four = pynini.cross("tư", "4")
- graph = pynini.union(
- zero + delete_space + (graph_digit | four),
- graph_zero + delete_space + graph_digit,
- )
- graph.optimize()
- return graph
-
- def _get_hundreds_graph(graph_ties, graph_digits):
- graph = (
- graph_digit
- + delete_space
- + pynutil.delete("trăm")
- + delete_space
- + (graph_teen | graph_ties | graph_digits)
- )
- return graph
-
- def _get_thousands_graph(graph_ties, graph_digits):
- graph_hundred_component = ((graph_digit | graph_zero) + delete_space + pynutil.delete("trăm")) | pynutil.insert(
- "0"
- )
- graph = (
- graph_digit
- + delete_space
- + pynutil.delete(pynini.union("nghìn", "ngàn"))
- + delete_space
- + graph_hundred_component
- + delete_space
- + (graph_teen | graph_ties | graph_digits)
- )
- return graph
-
- graph_ties = _get_ties_graph()
- graph_digits = _get_digits_graph()
- graph_hundreds = _get_hundreds_graph(graph_ties, graph_digits)
- graph_thousands = _get_thousands_graph(graph_ties, graph_digits)
- year_graph = (
- # 20 19, 40 12, 2012, 2 0 0 5, 2 0 17, 938 - assuming no limit on the year
- graph_digit
- + delete_space
- + (graph_digit | graph_zero)
- + delete_space
- + (graph_teen | graph_ties | graph_digits)
- | graph_thousands
- | graph_hundreds
- | (graph_digit + pynutil.insert("0") + delete_space + (graph_ties | graph_digits | graph_teen))
- )
- year_graph.optimize()
- return year_graph
-
-
-class DateFst(GraphFst):
- """
- Finite state transducer for classifying date,
- e.g. mười lăm tháng một năm hai nghìn mười hai -> date { day: "15" month: "1" year: "2012" preserve_order: true }
- e.g. ngày ba mốt tháng mười hai năm một chín chín chín -> date { day: "31" month: "12" year: "2012" preserve_order: true }
- e.g. năm hai không hai mốt -> date { year: "2021" preserve_order: true }
-
- Args:
- cardinal: CardinalFst
- """
-
- def __init__(self, cardinal: GraphFst):
- super().__init__(name="date", kind="classify")
-
- cardinal_graph = cardinal.graph_no_exception
- year_graph = _get_year_graph()
- YEAR_WEIGHT = 0.001
- year_graph = pynutil.add_weight(year_graph, YEAR_WEIGHT)
- month_graph = _get_month_graph()
-
- month_graph = pynutil.insert('month: "') + month_graph + pynutil.insert('"')
- month_exception = pynini.project(pynini.cross("năm", "5"), "input")
- month_graph_exception = (pynini.project(month_graph, "input") - month_exception.arcsort()) @ month_graph
-
- day_graph = pynutil.insert('day: "') + cardinal_graph + pynutil.insert('"')
- # day_suffix = pynini.union("ngày", "mùng")
- # optional_day = pynini.closure(day_suffix + delete_space, 0, 1)
-
- graph_month = pynutil.delete("tháng") + delete_space + month_graph_exception
- graph_year = (
- delete_extra_space
- + pynutil.delete("năm")
- + delete_extra_space
- + pynutil.insert('year: "')
- + pynutil.add_weight(year_graph, -YEAR_WEIGHT)
- + pynutil.insert('"')
- )
- optional_graph_year = pynini.closure(graph_year, 0, 1)
- graph_my = pynutil.delete("tháng") + delete_space + month_graph + graph_year
- graph_dmy = (
- day_graph + delete_space + pynutil.delete("tháng") + delete_extra_space + month_graph + optional_graph_year
- )
- graph_year = (
- pynutil.delete("năm") + delete_extra_space + pynutil.insert('year: "') + year_graph + pynutil.insert('"')
- )
-
- final_graph = (graph_dmy | graph_my | graph_month | graph_year) + pynutil.insert(" preserve_order: true")
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/decimal.py b/whisper_pipeline/text_processing/vi/taggers/decimal.py
deleted file mode 100644
index b9f7ce9bf95ab0422a6408da63c8355343f12a65..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/decimal.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- NEMO_DIGIT,
- GraphFst,
- delete_extra_space,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
-
-
-def get_quantity(decimal: "pynini.FstLike", cardinal_up_to_hundred: "pynini.FstLike") -> "pynini.FstLike":
- """
- Returns FST that transforms either a cardinal or decimal followed by a quantity into a numeral,
- e.g. một triệu -> integer_part: "1" quantity: "triệu"
- e.g. một tỷ rưỡi -> integer_part: "1" fractional_part: "5" quantity: "tỷ"
-
- Args:
- decimal: decimal FST
- cardinal_up_to_hundred: cardinal FST
- """
- numbers = cardinal_up_to_hundred @ (
- pynutil.delete(pynini.closure("0")) + pynini.difference(NEMO_DIGIT, "0") + pynini.closure(NEMO_DIGIT)
- )
- suffix = pynini.union("triệu", "tỉ", "tỷ", "vạn")
- graph_four = pynini.cross("tư", "4")
- graph_one = pynini.cross("mốt", "1")
- graph_half = pynini.cross("rưỡi", "5")
- last_digit_exception = pynini.project(pynini.cross("năm", "5"), "input")
- last_digit = pynini.union(
- (pynini.project(graph_digit, "input") - last_digit_exception.arcsort()) @ graph_digit,
- graph_one,
- graph_four,
- graph_half,
- )
- optional_fraction_graph = pynini.closure(
- delete_extra_space
- + pynutil.insert('fractional_part: "')
- + (last_digit | graph_half | graph_one | graph_four)
- + pynutil.insert('"'),
- 0,
- 1,
- )
-
- res = (
- pynutil.insert('integer_part: "')
- + numbers
- + pynutil.insert('"')
- + delete_extra_space
- + pynutil.insert('quantity: "')
- + suffix
- + pynutil.insert('"')
- + optional_fraction_graph
- )
- res |= (
- decimal + delete_extra_space + pynutil.insert('quantity: "') + (suffix | "ngàn" | "nghìn") + pynutil.insert('"')
- )
- return res
-
-
-class DecimalFst(GraphFst):
- """
- Finite state transducer for classifying decimal
- e.g. âm hai hai phẩy không năm tư năm tỉ -> decimal { negative: "true" integer_part: "22" fractional_part: "054" quantity: "tỉ" }
- e.g. không chấm ba lăm -> decimal { integer_part: "0" fractional_part: "35" }
- e.g. một triệu rưỡi -> decimal { integer_part: "1" quantity: "triệu" fractional_part: "5" }
- Args:
- cardinal: CardinalFst
- """
-
- def __init__(self, cardinal: GraphFst):
- super().__init__(name="decimal", kind="classify")
-
- cardinal_graph = cardinal.graph_no_exception
-
- graph_decimal = graph_digit | pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
- graph_one = pynini.cross("mốt", "1")
- graph_four = pynini.cross("tư", "4")
- graph_five = pynini.cross("lăm", "5")
-
- graph_decimal = pynini.union(
- graph_decimal,
- graph_four,
- pynini.closure(graph_decimal + delete_space, 1) + (graph_decimal | graph_four | graph_five | graph_one),
- )
- self.graph = graph_decimal
-
- point = pynutil.delete("chấm") | pynutil.delete("phẩy")
-
- optional_graph_negative = pynini.closure(
- pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space,
- 0,
- 1,
- )
-
- graph_fractional = pynutil.insert('fractional_part: "') + graph_decimal + pynutil.insert('"')
- graph_integer = pynutil.insert('integer_part: "') + cardinal_graph + pynutil.insert('"')
- final_graph_wo_sign = (
- pynini.closure(graph_integer + delete_extra_space, 0, 1) + point + delete_extra_space + graph_fractional
- )
- final_graph = optional_graph_negative + final_graph_wo_sign
-
- self.final_graph_wo_negative = final_graph_wo_sign | get_quantity(
- final_graph_wo_sign,
- cardinal.graph_hundred_component_at_least_one_none_zero_digit,
- )
- final_graph |= optional_graph_negative + get_quantity(
- final_graph_wo_sign,
- cardinal.graph_hundred_component_at_least_one_none_zero_digit,
- )
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/electronic.py b/whisper_pipeline/text_processing/vi/taggers/electronic.py
deleted file mode 100644
index 7d359b68b2a2bd350fdecfd612e8ad3f669c067c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/electronic.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- NEMO_ALPHA,
- GraphFst,
- insert_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class ElectronicFst(GraphFst):
- """
- Finite state transducer for classifying electronic: as URLs, email addresses, etc.
- e.g. c d f một a còng a b c dot e d u -> tokens { electronic { username: "cdf1" domain: "abc.edu" } }
- """
-
- def __init__(self):
- super().__init__(name="electronic", kind="classify")
-
- delete_extra_space = pynutil.delete(" ")
- alpha_num = (
- NEMO_ALPHA
- | pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
- | pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
- )
-
- symbols = pynini.string_file(get_abs_path("data/electronic/symbols.tsv")).invert()
-
- accepted_username = alpha_num | symbols
- process_dot = pynini.cross("chấm", ".")
- username = (
- pynutil.insert('username: "')
- + alpha_num
- + pynini.closure(delete_extra_space + accepted_username)
- + pynutil.insert('"')
- )
- single_alphanum = pynini.closure(alpha_num + delete_extra_space) + alpha_num
- server = single_alphanum | pynini.string_file(get_abs_path("data/electronic/server_name.tsv"))
- domain = single_alphanum | pynini.string_file(get_abs_path("data/electronic/domain.tsv"))
- multi_domain = (
- pynini.closure(process_dot + delete_extra_space + domain + delete_extra_space)
- + process_dot
- + delete_extra_space
- + domain
- )
- domain_graph = pynutil.insert('domain: "') + server + delete_extra_space + multi_domain + pynutil.insert('"')
- graph = (
- username
- + delete_extra_space
- + pynutil.delete(pynini.union("a còng", "a móc", "a vòng"))
- + insert_space
- + delete_extra_space
- + domain_graph
- )
-
- ############# url ###
- protocol_end = pynini.cross(pynini.union("w w w", "www"), "www")
- protocol_start = (pynini.cross("h t t p", "http") | pynini.cross("h t t p s", "https")) + pynini.cross(
- " hai chấm sẹc sẹc ", "://"
- )
- # .com,
- ending = (
- delete_extra_space
- + symbols
- + delete_extra_space
- + (domain | pynini.closure(accepted_username + delete_extra_space) + accepted_username)
- )
-
- protocol = (
- pynini.closure(protocol_start, 0, 1)
- + protocol_end
- + delete_extra_space
- + process_dot
- + pynini.closure(delete_extra_space + accepted_username, 1)
- + pynini.closure(ending, 1, 2)
- )
- protocol = pynutil.insert('protocol: "') + protocol + pynutil.insert('"')
- graph |= protocol
- ########
-
- final_graph = self.add_tokens(graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/fraction.py b/whisper_pipeline/text_processing/vi/taggers/fraction.py
deleted file mode 100644
index 1ad7826827e671acf81119ecbbe74add5b923b4a..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/fraction.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- GraphFst,
- delete_space,
- delete_extra_space,
-)
-
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class FractionFst(GraphFst):
- """
- Finite state transducer for classifying fraction
- e.g. 2 phần 3 -> tokens { fraction { numerator: "2" denominator: "3" } }
- e.g. 2 trên 3 -> tokens { fraction { numerator: "2" denominator: "3" } }
- e.g. 2 chia 3 -> tokens { fraction { numerator: "2" denominator: "3" } }
-
- Args:
- cardinal: OrdinalFst
- """
-
- def __init__(self, cardinal: GraphFst):
- super().__init__(name="fraction", kind="classify")
- # integer_part # numerator # denominator
-
- graph_cardinal = cardinal.graph_no_exception
- graph_four = pynini.cross("tư", "4")
-
- numerator = pynutil.insert('numerator: "') + graph_cardinal + pynutil.insert('"')
- fraction_component = pynutil.delete(pynini.union("phần", "trên", "chia"))
- denominator = pynutil.insert('denominator: "') + (graph_cardinal | graph_four) + pynutil.insert('"')
-
- graph_fraction_component = numerator + delete_space + fraction_component + delete_extra_space + denominator
- self.graph_fraction_component = graph_fraction_component
-
- graph = graph_fraction_component
- graph = graph.optimize()
- self.final_graph_wo_negative = graph
-
- optional_graph_negative = pynini.closure(
- pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space,
- 0,
- 1,
- )
- graph = optional_graph_negative + graph
- final_graph = self.add_tokens(graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/math.py b/whisper_pipeline/text_processing/vi/taggers/math.py
deleted file mode 100644
index f1cd3a36b4f436e81d7772ab3c0f7494c9b64389..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/math.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- delete_space,
- delete_extra_space,
- NEMO_ALPHA,
-)
-
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class MathFst(GraphFst):
- """
- Finite state transducer for classifying math equation
- e.g. x cộng y cộng z -> tokens { math { equation: "x + y + z" } }
- e.g. hai bình phương cộng trừ năm -> tokens { math { equation: "2² + -5" } }
-
- Args:
- cardinal: OrdinalFst
- """
-
- def __init__(self, cardinal: GraphFst):
- super().__init__(name="math", kind="classify")
- # integer_part # numerator # denominator
-
- graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
- graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
- graph_symbols = pynini.string_file(get_abs_path("data/math/symbols.tsv")).invert()
- graph_one = pynini.cross("mốt", "1")
- graph_four = pynini.cross("tư", "4")
- graph_five = pynini.cross("lăm", "5")
-
- graph_cardinal = cardinal.graph_no_exception
- optional_graph_negative = pynini.closure(pynini.cross(pynini.union("âm", "trừ"), "-") + delete_space, 0, 1)
- optional_graph_power = pynini.closure(
- delete_space + pynini.cross("bình phương", "²") | delete_space + pynini.cross("lập phương", "³"),
- 0,
- 1,
- )
-
- graph_digit = graph_digit | graph_zero
- graph_fraction = pynini.union(
- graph_digit,
- graph_four,
- pynini.closure(graph_digit + delete_space, 1) + (graph_digit | graph_four | graph_five | graph_one),
- )
- optional_graph_fraction = pynini.closure(
- delete_space + pynini.cross(pynini.union("chấm", "phẩy"), ".") + delete_space + graph_fraction,
- 0,
- 1,
- )
- graph_decimal = graph_cardinal + optional_graph_fraction
-
- alpha_num = NEMO_ALPHA | graph_decimal
- graph_equation = (
- pynini.closure(
- optional_graph_negative
- + alpha_num
- + optional_graph_power
- + delete_extra_space
- + graph_symbols
- + delete_extra_space,
- 1,
- )
- + optional_graph_negative
- + alpha_num
- + optional_graph_power
- )
-
- graph = pynutil.insert('equation: "') + graph_equation + pynutil.insert('"')
- final_graph = self.add_tokens(graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/measure.py b/whisper_pipeline/text_processing/vi/taggers/measure.py
deleted file mode 100644
index 690f77d086d86523d9a3aa28215a0a09663bb985..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/measure.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- convert_space,
- delete_extra_space,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class MeasureFst(GraphFst):
- """
- Finite state transducer for classifying measure
- e.g. trừ mười hai ki lô gam -> measure { negative: "true" cardinal { integer: "12" } units: "kg" }
-
- Args:
- cardinal: CardinalFst
- decimal: DecimalFst
- """
-
- def __init__(self, cardinal: GraphFst, decimal: GraphFst):
- super().__init__(name="measure", kind="classify")
-
- cardinal_graph = cardinal.graph_no_exception
-
- graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
- graph_four = pynini.cross("tư", "4")
- graph_one = pynini.cross("mốt", "1")
- graph_half = pynini.cross("rưỡi", "5")
-
- graph_unit = pynini.string_file(get_abs_path("data/measurements.tsv"))
- graph_unit_singular = pynini.invert(graph_unit) # singular -> abbr
-
- optional_graph_negative = pynini.closure(
- pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space,
- 0,
- 1,
- )
-
- unit_singular = convert_space(graph_unit_singular)
- unit_misc = pynutil.insert("/") + pynutil.delete("trên") + delete_space + convert_space(graph_unit_singular)
-
- unit_singular = (
- pynutil.insert('units: "')
- + (unit_singular | unit_misc | pynutil.add_weight(unit_singular + delete_space + unit_misc, 0.01))
- + pynutil.insert('"')
- )
-
- subgraph_decimal = (
- pynutil.insert("decimal { ")
- + optional_graph_negative
- + decimal.final_graph_wo_negative
- + pynutil.insert(" }")
- + delete_extra_space
- + unit_singular
- )
-
- subgraph_cardinal = (
- pynutil.insert("cardinal { ")
- + optional_graph_negative
- + pynutil.insert('integer: "')
- + cardinal_graph
- + pynutil.insert('"')
- + pynutil.insert(" }")
- + delete_extra_space
- + unit_singular
- )
- fraction_graph = (
- delete_extra_space
- + pynutil.insert('fractional_part: "')
- + (graph_digit | graph_half | graph_one | graph_four)
- + pynutil.insert('"')
- )
-
- subgraph_cardinal |= (
- pynutil.insert("cardinal { ")
- + optional_graph_negative
- + pynutil.insert('integer: "')
- + cardinal_graph
- + pynutil.insert('" }')
- + delete_extra_space
- + unit_singular
- + fraction_graph
- )
- final_graph = subgraph_decimal | subgraph_cardinal
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/money.py b/whisper_pipeline/text_processing/vi/taggers/money.py
deleted file mode 100644
index 60792e6060218a317cc56b187d1a1744fc39c4b7..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/money.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- NEMO_DIGIT,
- GraphFst,
- convert_space,
- delete_extra_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class MoneyFst(GraphFst):
- """
- Finite state transducer for classifying money
- e.g. mười hai đô la mỹ -> money { integer_part: "12" currency: "$" }
- e.g. mười phẩy chín đồng -> money { integer_part: "10.9" currency: "đ" }
-
- Args:
- cardinal: CardinalFst
- decimal: DecimalFst
- """
-
- def __init__(self, cardinal: GraphFst, decimal: GraphFst):
- super().__init__(name="money", kind="classify")
- # quantity, integer_part, fractional_part, currency
-
- cardinal_graph = cardinal.graph_no_exception
- graph_decimal_final = decimal.final_graph_wo_negative
- graph_half = pynini.cross("rưỡi", "5")
-
- unit = pynini.string_file(get_abs_path("data/currency.tsv"))
- unit_singular = pynini.invert(unit)
-
- graph_unit_singular = pynutil.insert('currency: "') + convert_space(unit_singular) + pynutil.insert('"')
-
- add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT)
-
- # twelve dollars fifty, only after integer
- optional_cents_suffix = pynini.closure(
- delete_extra_space
- + pynutil.insert('fractional_part: "')
- + (pynutil.add_weight(cardinal_graph @ add_leading_zero_to_double_digit, -0.7) | graph_half)
- + pynutil.insert('"'),
- 0,
- 1,
- )
-
- graph_integer = (
- pynutil.insert('integer_part: "')
- + cardinal_graph
- + pynutil.insert('"')
- + delete_extra_space
- + graph_unit_singular
- + optional_cents_suffix
- )
-
- graph_decimal = graph_decimal_final + delete_extra_space + graph_unit_singular + optional_cents_suffix
- final_graph = graph_integer | graph_decimal
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/ordinal.py b/whisper_pipeline/text_processing/vi/taggers/ordinal.py
deleted file mode 100644
index d37c55a9a1dbaddc9635ea56d8f04f47808182f6..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/ordinal.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- delete_space,
- GraphFst,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ImportError, ModuleNotFoundError):
- PYNINI_AVAILABLE = False
-
-
-class OrdinalFst(GraphFst):
- """
- Finite state transducer for classifying ordinal
- e.g. thứ nhất -> ordinal { integer: "1" }
- """
-
- def __init__(self):
- super().__init__(name="ordinal", kind="classify")
-
- graph_digit = pynini.string_file(get_abs_path("data/ordinals/digit.tsv"))
- graph_ordinal = pynini.cross("thứ", "")
- graph = graph_digit
-
- self.graph = graph
- final_graph = pynutil.insert('integer: "') + graph_ordinal + delete_space + self.graph + pynutil.insert('"')
- final_graph = self.add_tokens(final_graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/punctuation.py b/whisper_pipeline/text_processing/vi/taggers/punctuation.py
deleted file mode 100644
index 99ebcab416bcdb9d73af4d42f4cd731744c42418..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/punctuation.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import GraphFst
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = False
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class PunctuationFst(GraphFst):
- """
- Finite state transducer for classifying punctuation
- e.g. a, -> tokens { name: "a" } tokens { name: "," }
- """
-
- def __init__(self):
- super().__init__(name="punctuation", kind="classify")
-
- s = "!#$%&'()*+,-./:;<=>?@^_`{|}~"
- punct = pynini.union(*s)
-
- graph = pynutil.insert('name: "') + punct + pynutil.insert('"')
-
- self.fst = graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/telephone.py b/whisper_pipeline/text_processing/vi/taggers/telephone.py
deleted file mode 100644
index f7a332dfa6dd7f021a3fe333a0a569ba0ffb0507..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/telephone.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class TelephoneFst(GraphFst):
- """
- Finite state transducer for classifying telephone numbers, e.g.
- một hai ba một hai ba năm sáu bảy tám -> { number_part: "1231235678" }
- """
-
- def __init__(self):
- super().__init__(name="telephone", kind="classify")
- graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
- graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
- graph_one = pynini.cross("mốt", "1")
- graph_four = pynini.cross("tư", "4")
- graph_five = pynini.cross("lăm", "5")
- digit = graph_digit | graph_zero
- last_digit_exception = pynini.project(pynini.cross("năm", "5"), "input")
- last_digit_with_exception = pynini.union(
- (pynini.project(digit, "input") - last_digit_exception.arcsort()) @ digit,
- graph_one,
- graph_four,
- graph_five,
- )
- last_digit = digit | graph_one | graph_four | graph_five
-
- graph_number_part = pynini.union(
- pynini.closure(digit + delete_space, 2, 3) + last_digit_with_exception,
- pynini.closure(digit + delete_space, 3) + last_digit,
- )
- number_part = pynutil.insert('number_part: "') + graph_number_part + pynutil.insert('"')
-
- graph = number_part
- final_graph = self.add_tokens(graph)
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/time.py b/whisper_pipeline/text_processing/vi/taggers/time.py
deleted file mode 100644
index 5f6d550a5be051cb2db6606316c4add05c49d8c3..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/time.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- convert_space,
- delete_extra_space,
- delete_space,
- insert_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class TimeFst(GraphFst):
- """
- Finite state transducer for classifying time
- e.g. hai rưỡi -> time { hours: "2" minutes: "30" }
- e.g. chín giờ kém hai mươi -> time { hours: "8" minutes: "40" }
- e.g. ba phút hai giây -> time { minutes: "3" seconds: "2" }
- e.g. mười giờ chín phút bốn mươi lăm giây -> time { hours: "10" minutes: "9" seconds: "45" }
- """
-
- def __init__(self):
- super().__init__(name="time", kind="classify")
- # hours, minutes, seconds, suffix, zone, style, speak_period
-
- graph_hours_to = pynini.string_file(get_abs_path("data/time/hours_to.tsv"))
- graph_minutes_to = pynini.string_file(get_abs_path("data/time/minutes_to.tsv"))
- graph_hours = pynini.string_file(get_abs_path("data/time/hours.tsv"))
- graph_minutes = pynini.string_file(get_abs_path("data/time/minutes.tsv"))
- time_zone_graph = pynini.invert(pynini.string_file(get_abs_path("data/time/time_zone.tsv")))
-
- graph_half = pynini.cross("rưỡi", "30")
- oclock = pynini.cross("giờ", "")
- minute = pynini.cross("phút", "")
- optional_minute = pynini.closure(delete_space + minute, 0, 1)
- second = pynini.cross("giây", "")
-
- final_graph_hour = pynutil.insert('hours: "') + graph_hours + pynutil.insert('"') + delete_space + oclock
- graph_minute = graph_minutes + optional_minute
- graph_second = graph_minutes + delete_space + second
- final_time_zone_optional = pynini.closure(
- delete_space
- + insert_space
- + pynutil.insert('zone: "')
- + convert_space(time_zone_graph)
- + pynutil.insert('"'),
- 0,
- 1,
- )
-
- graph_hm = (
- final_graph_hour
- + delete_extra_space
- + pynutil.insert('minutes: "')
- + (graph_minute | graph_half)
- + pynutil.insert('"')
- )
-
- graph_hms = (
- final_graph_hour
- + delete_extra_space
- + pynutil.insert('minutes: "')
- + graph_minutes
- + delete_space
- + minute
- + pynutil.insert('"')
- + delete_extra_space
- + pynutil.insert('seconds: "')
- + graph_second
- + pynutil.insert('"')
- )
-
- graph_ms = (
- pynutil.insert('minutes: "')
- + graph_minutes
- + delete_space
- + minute
- + pynutil.insert('"')
- + delete_extra_space
- + pynutil.insert('seconds: "')
- + (graph_second | graph_half)
- + pynutil.insert('"')
- )
-
- graph_hours_to_component = graph_hours @ graph_hours_to
- graph_minutes_to_component = graph_minutes @ graph_minutes_to
-
- graph_time_to = (
- pynutil.insert('hours: "')
- + graph_hours_to_component
- + pynutil.insert('"')
- + delete_space
- + oclock
- + delete_space
- + pynutil.delete("kém")
- + delete_extra_space
- + pynutil.insert('minutes: "')
- + graph_minutes_to_component
- + pynutil.insert('"')
- + optional_minute
- )
-
- final_graph = (final_graph_hour | graph_hm | graph_hms) + final_time_zone_optional
- final_graph |= graph_ms
- final_graph |= graph_time_to
-
- final_graph = self.add_tokens(final_graph)
-
- self.fst = final_graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/tokenize_and_classify.py b/whisper_pipeline/text_processing/vi/taggers/tokenize_and_classify.py
deleted file mode 100644
index f4da5b6dbe13bc467c64bf19f9c3a1bb747a8709..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/tokenize_and_classify.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 os
-
-from text_processing.vi.taggers.address import AddressFst
-from text_processing.vi.taggers.cardinal import CardinalFst
-from text_processing.vi.taggers.date import DateFst
-from text_processing.vi.taggers.decimal import DecimalFst
-from text_processing.vi.taggers.electronic import ElectronicFst
-from text_processing.vi.taggers.fraction import FractionFst
-from text_processing.vi.taggers.measure import MeasureFst
-from text_processing.vi.taggers.math import MathFst
-from text_processing.vi.taggers.money import MoneyFst
-from text_processing.vi.taggers.ordinal import OrdinalFst
-from text_processing.vi.taggers.punctuation import PunctuationFst
-from text_processing.vi.taggers.telephone import TelephoneFst
-from text_processing.vi.taggers.time import TimeFst
-from text_processing.vi.taggers.whitelist import WhiteListFst
-from text_processing.vi.taggers.word import WordFst
-from text_processing.vi.graph_utils import (
- GraphFst,
- delete_extra_space,
- delete_space,
- generator_main,
-)
-
-import logging
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class ClassifyFst(GraphFst):
- """
- Final class that composes all other classification grammars. This class can process an entire sentence, that is lower cased.
- For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File.
- More details to deployment at NeMo/tools/text_processing_deployment.
-
- Args:
- cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
- overwrite_cache: set to True to overwrite .far files
- """
-
- def __init__(self, cache_dir: str = None, overwrite_cache: bool = False):
- super().__init__(name="tokenize_and_classify", kind="classify")
-
- far_file = None
- if cache_dir is not None and cache_dir != "None":
- os.makedirs(cache_dir, exist_ok=True)
- far_file = os.path.join(cache_dir, "_en_itn.far")
- if not overwrite_cache and far_file and os.path.exists(far_file):
- self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"]
- logging.info(f"ClassifyFst.fst was restored from {far_file}.")
- else:
- logging.info(f"Creating ClassifyFst grammars.")
- cardinal = CardinalFst()
- cardinal_graph = cardinal.fst
-
- fraction = FractionFst(cardinal)
- fraction_graph = fraction.fst
-
- ordinal = OrdinalFst()
- ordinal_graph = ordinal.fst
-
- decimal = DecimalFst(cardinal)
- decimal_graph = decimal.fst
-
- address_graph = AddressFst(cardinal=cardinal).fst
- math_graph = MathFst(cardinal=cardinal).fst
- measure_graph = MeasureFst(cardinal=cardinal, decimal=decimal).fst
- date_graph = DateFst(cardinal=cardinal).fst
- word_graph = WordFst().fst
- time_graph = TimeFst().fst
- money_graph = MoneyFst(cardinal=cardinal, decimal=decimal).fst
- whitelist_graph = WhiteListFst().fst
- punct_graph = PunctuationFst().fst
- electronic_graph = ElectronicFst().fst
- telephone_graph = TelephoneFst().fst
-
- classify = (
- pynutil.add_weight(whitelist_graph, 1.01)
- | pynutil.add_weight(time_graph, 1.05)
- | pynutil.add_weight(date_graph, 1.09)
- | pynutil.add_weight(decimal_graph, 1.08)
- | pynutil.add_weight(measure_graph, 1.1)
- | pynutil.add_weight(cardinal_graph, 1.1)
- | pynutil.add_weight(ordinal_graph, 1.1)
- | pynutil.add_weight(math_graph, 1.1)
- | pynutil.add_weight(fraction_graph, 1.09)
- | pynutil.add_weight(address_graph, 1.09)
- | pynutil.add_weight(money_graph, 1.07)
- | pynutil.add_weight(telephone_graph, 1.1)
- | pynutil.add_weight(electronic_graph, 1.1)
- | pynutil.add_weight(word_graph, 100)
- )
-
- punct = pynutil.insert("tokens { ") + pynutil.add_weight(punct_graph, weight=1.1) + pynutil.insert(" }")
- token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }")
- token_plus_punct = (
- pynini.closure(punct + pynutil.insert(" ")) + token + pynini.closure(pynutil.insert(" ") + punct)
- )
-
- graph = token_plus_punct + pynini.closure(delete_extra_space + token_plus_punct)
- graph = delete_space + graph + delete_space
-
- self.fst = graph.optimize()
-
- if far_file:
- generator_main(far_file, {"tokenize_and_classify": self.fst})
- logging.info(f"ClassifyFst grammars are saved to {far_file}.")
diff --git a/whisper_pipeline/text_processing/vi/taggers/whitelist.py b/whisper_pipeline/text_processing/vi/taggers/whitelist.py
deleted file mode 100644
index 07e54cdba7e7cf0f4814989d5600f0bdc6ddb027..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/whitelist.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.utils import get_abs_path
-from text_processing.vi.graph_utils import (
- GraphFst,
- convert_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class WhiteListFst(GraphFst):
- """
- Finite state transducer for classifying whitelisted tokens
- e.g. misses -> tokens { name: "mrs." }
- This class has highest priority among all classifier grammars. Whitelisted tokens are defined and loaded from "data/whitelist.tsv".
- """
-
- def __init__(self):
- super().__init__(name="whitelist", kind="classify")
-
- whitelist = pynini.string_file(get_abs_path("data/whitelist.tsv")).invert()
- graph = pynutil.insert('name: "') + convert_space(whitelist) + pynutil.insert('"')
- self.fst = graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/taggers/word.py b/whisper_pipeline/text_processing/vi/taggers/word.py
deleted file mode 100644
index 3b4ec8d51b4b25fce22a5e854380a26ea8fb5830..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/taggers/word.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_NOT_SPACE,
- GraphFst,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class WordFst(GraphFst):
- """
- Finite state transducer for classifying plain tokens, that do not belong to any special class. This can be considered as the default class.
- e.g. sleep -> tokens { name: "sleep" }
- """
-
- def __init__(self):
- super().__init__(name="word", kind="classify")
- word = pynutil.insert('name: "') + pynini.closure(NEMO_NOT_SPACE, 1) + pynutil.insert('"')
- self.fst = word.optimize()
diff --git a/whisper_pipeline/text_processing/vi/utils.py b/whisper_pipeline/text_processing/vi/utils.py
deleted file mode 100644
index 436f274135085271c389db8a57d6b90264489c4e..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/utils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-
-def get_abs_path(rel_path):
- """
- Get absolute path
-
- Args:
- rel_path: relative path to this file
-
- Returns absolute path
- """
- return os.path.dirname(os.path.abspath(__file__)) + "/" + rel_path
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/__init__.py b/whisper_pipeline/text_processing/vi/verbalizers/__init__.py
deleted file mode 100644
index bc443be41c4c3fcf0764eb9fd3e1828d63f8438c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
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diff --git a/whisper_pipeline/text_processing/vi/verbalizers/address.py b/whisper_pipeline/text_processing/vi/verbalizers/address.py
deleted file mode 100644
index fa7333acfe6cf576bdf0307865a20f5067c54435..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/address.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class AddressFst(GraphFst):
- """
- Finite state transducer for verbalizing address
- e.g. address { value: "2/3"} -> 2/3
- """
-
- def __init__(self):
- super().__init__(name="address", kind="verbalize")
- value_part = pynutil.delete('value: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"')
- delete_tokens = self.delete_tokens(value_part)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/cardinal.py b/whisper_pipeline/text_processing/vi/verbalizers/cardinal.py
deleted file mode 100644
index 3286b50f6158f2689e0b6e829161b8713c1e80de..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/cardinal.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class CardinalFst(GraphFst):
- """
- Finite state transducer for verbalizing cardinal
- e.g. cardinal { integer: "23" negative: "-" } -> -23
- """
-
- def __init__(self):
- super().__init__(name="cardinal", kind="verbalize")
- optional_sign = pynini.closure(
- pynutil.delete("negative:")
- + delete_space
- + pynutil.delete('"')
- + NEMO_NOT_QUOTE
- + pynutil.delete('"')
- + delete_space,
- 0,
- 1,
- )
- graph = (
- pynutil.delete("integer:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- self.numbers = graph
- graph = optional_sign + graph
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/date.py b/whisper_pipeline/text_processing/vi/verbalizers/date.py
deleted file mode 100644
index 95e94f574f1946614beb92077aeb70a01ea307f1..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/date.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class DateFst(GraphFst):
- """
- Finite state transducer for verbalizing date, e.g.
- date { month: "1" year: "2012"} -> tháng 1 năm 2012
- date { day: "5" month: "10" year: "2021" preserve_order: true } -> 5 tháng 10 năm 2021
- """
-
- def __init__(self):
- super().__init__(name="date", kind="verbalize")
- day = (
- pynutil.delete("day:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- month = (
- pynutil.delete("month:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- year = (
- pynutil.delete("year:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + delete_space
- + pynutil.delete('"')
- )
-
- # (day) month year
- # day month
- graph_dm = day + delete_space + pynutil.insert(" tháng ") + month
- graph_dmy = graph_dm + delete_space + pynutil.insert(" năm ") + year
- graph_m = pynutil.insert("tháng ") + month
- graph_my = pynutil.insert("tháng ") + month + delete_space + pynutil.insert(" năm ") + year
- graph_y = pynutil.insert("năm ") + year
-
- optional_preserve_order = pynini.closure(
- pynutil.delete("preserve_order:") + delete_space + pynutil.delete("true") + delete_space
- | pynutil.delete("field_order:")
- + delete_space
- + pynutil.delete('"')
- + NEMO_NOT_QUOTE
- + pynutil.delete('"')
- + delete_space
- )
-
- final_graph = (graph_y | graph_m | graph_dm | graph_dmy | graph_my) + delete_space + optional_preserve_order
-
- delete_tokens = self.delete_tokens(final_graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/decimal.py b/whisper_pipeline/text_processing/vi/verbalizers/decimal.py
deleted file mode 100644
index c484af85e88bd473cbce717adfcc2bf677b349e5..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/decimal.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class DecimalFst(GraphFst):
- """
- Finite state transducer for verbalizing decimal, e.g.
- decimal { negative: "true" integer_part: "12" fractional_part: "5006" quantity: "tỷ" } -> -12.5006 tỷ
- """
-
- def __init__(self):
- super().__init__(name="decimal", kind="verbalize")
- optionl_sign = pynini.closure(pynini.cross('negative: "true"', "-") + delete_space, 0, 1)
- integer = (
- pynutil.delete("integer_part:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- optional_integer = pynini.closure(integer + delete_space, 0, 1)
- fractional = (
- pynutil.insert(".")
- + pynutil.delete("fractional_part:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- optional_fractional = pynini.closure(fractional + delete_space, 0, 1)
- quantity = (
- pynutil.delete("quantity:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- optional_quantity = pynini.closure(pynutil.insert(" ") + quantity + delete_space, 0, 1)
- graph = optional_integer + optional_fractional + optional_quantity
- self.numbers = graph
- graph = optionl_sign + graph
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/electronic.py b/whisper_pipeline/text_processing/vi/verbalizers/electronic.py
deleted file mode 100644
index cdd746c47f5da83c2c1b4502499f561d71adaa39..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/electronic.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class ElectronicFst(GraphFst):
- """
- Finite state transducer for verbalizing electronic
- e.g. tokens { electronic { username: "cdf1" domain: "abc.edu" } } -> cdf1@abc.edu
- """
-
- def __init__(self):
- super().__init__(name="electronic", kind="verbalize")
- user_name = (
- pynutil.delete("username:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- domain = (
- pynutil.delete("domain:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
-
- protocol = (
- pynutil.delete("protocol:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
-
- graph = user_name + delete_space + pynutil.insert("@") + domain
- graph |= protocol
-
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/fraction.py b/whisper_pipeline/text_processing/vi/verbalizers/fraction.py
deleted file mode 100644
index c752931e1bf6d2aee0c3babeed8789144889af20..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/fraction.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class FractionFst(GraphFst):
- """
- Finite state transducer for verbalizing fraction,
- e.g. fraction { numerator: "2" denominator: "3" } } -> 2/3
- e.g. fraction { numerator: "20" denominator: "3" negative: "true"} } -> 2/3
- """
-
- def __init__(self):
- super().__init__(name="fraction", kind="verbalize")
- optional_sign = pynini.closure(pynini.cross('negative: "true"', "-") + delete_space, 0, 1)
- numerator = pynutil.delete('numerator: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"')
-
- denominator = (
- pynutil.insert("/")
- + pynutil.delete('denominator: "')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
-
- graph = (numerator + delete_space + denominator).optimize()
- self.numbers = graph
- delete_tokens = self.delete_tokens(optional_sign + graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/math.py b/whisper_pipeline/text_processing/vi/verbalizers/math.py
deleted file mode 100644
index 8e14fc822d9f6c3deeedfcc638d6bcd3b3776bd8..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/math.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class MathFst(GraphFst):
- """
- Finite state transducer for verbalizing cardinal
- e.g. math { equation: "2.5 * 2 = 5"} -> 2.5 * 2 = 5
- """
-
- def __init__(self):
- super().__init__(name="math", kind="verbalize")
- equation_part = pynutil.delete('equation: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"')
- delete_tokens = self.delete_tokens(equation_part)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/measure.py b/whisper_pipeline/text_processing/vi/verbalizers/measure.py
deleted file mode 100644
index 662f63a1d55eef4d2beb6ec282735fb8a79cf17d..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/measure.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_CHAR,
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
-
- PYNINI_AVAILABLE = False
-
-
-class MeasureFst(GraphFst):
- """
- Finite state transducer for verbalizing measure, e.g.
- measure { negative: "true" cardinal { integer: "12" } units: "kg" } -> -12 kg
-
- Args:
- decimal: DecimalFst
- cardinal: CardinalFst
- """
-
- def __init__(self, decimal: GraphFst, cardinal: GraphFst):
- super().__init__(name="measure", kind="verbalize")
- optional_sign = pynini.closure(pynini.cross('negative: "true"', "-"), 0, 1)
- unit = (
- pynutil.delete("units:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_CHAR - " ", 1)
- + pynutil.delete('"')
- + delete_space
- )
- graph_decimal = (
- pynutil.delete("decimal {")
- + delete_space
- + optional_sign
- + delete_space
- + decimal.numbers
- + delete_space
- + pynutil.delete("}")
- )
- graph_cardinal = (
- pynutil.delete("cardinal {")
- + delete_space
- + optional_sign
- + delete_space
- + cardinal.numbers
- + delete_space
- + pynutil.delete("}")
- )
- fractional = (
- pynutil.insert(".")
- + pynutil.delete("fractional_part:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
- optional_fractional = pynini.closure(fractional + delete_space, 0, 1)
- graph = (
- (graph_cardinal | graph_decimal)
- + delete_space
- + optional_fractional
- + pynutil.insert(" ")
- + unit
- + delete_space
- )
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/money.py b/whisper_pipeline/text_processing/vi/verbalizers/money.py
deleted file mode 100644
index fe62e71109ddc8aa0f8fd8b0c8633884d2e55ff5..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/money.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_CHAR,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
-
- PYNINI_AVAILABLE = False
-
-
-class MoneyFst(GraphFst):
- """
- Finite state transducer for verbalizing money, e.g.
- money { integer_part: "12" fractional_part: "05" currency: "$" } -> 12.05$
-
- Args:
- decimal: DecimalFst
- """
-
- def __init__(self, decimal: GraphFst):
- super().__init__(name="money", kind="verbalize")
- unit = (
- pynutil.delete("currency:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_CHAR - " ", 1)
- + pynutil.delete('"')
- )
- graph = decimal.numbers + delete_space + unit
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/ordinal.py b/whisper_pipeline/text_processing/vi/verbalizers/ordinal.py
deleted file mode 100644
index 44c607cbb2761234afb15cf897194348818bced9..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/ordinal.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class OrdinalFst(GraphFst):
- """
- Finite state transducer for verbalizing ordinal, e.g.
- ordinal { integer: "2" } -> thứ 2
- """
-
- def __init__(self):
- super().__init__(name="ordinal", kind="verbalize")
- graph = (
- pynutil.delete("integer:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_NOT_QUOTE, 1)
- + pynutil.delete('"')
- )
-
- graph = pynutil.insert("thứ ") + graph
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/telephone.py b/whisper_pipeline/text_processing/vi/verbalizers/telephone.py
deleted file mode 100644
index c3bdb1c64fbf098b87e8532ddcaa8af966568f1e..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/telephone.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_NOT_QUOTE,
- GraphFst,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class TelephoneFst(GraphFst):
- """
- Finite state transducer for verbalizing telephone, e.g.
- telephone { number_part: "1231235678" }
- -> 1231235678
- """
-
- def __init__(self):
- super().__init__(name="telephone", kind="verbalize")
-
- number_part = pynutil.delete('number_part: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"')
- delete_tokens = self.delete_tokens(number_part)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/time.py b/whisper_pipeline/text_processing/vi/verbalizers/time.py
deleted file mode 100644
index c4547ffbaa67e2c017e3585ce21015cfb736a18c..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/time.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_CHAR,
- NEMO_DIGIT,
- GraphFst,
- delete_space,
- insert_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class TimeFst(GraphFst):
- """
- Finite state transducer for verbalizing time, e.g.
- time { hours: "3" } -> 3h
- time { hours: "12" minutes: "30" } -> 12:30
- time { hours: "1" minutes: "12" second: "22"} -> 1:12:22
- time { minutes: "36" second: "45"} -> 36p45s
- time { hours: "2" zone: "gmt" } -> 2h gmt
- """
-
- def __init__(self):
- super().__init__(name="time", kind="verbalize")
- add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT)
- hour = (
- pynutil.delete("hours:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_DIGIT, 1)
- + pynutil.delete('"')
- )
- minute = (
- pynutil.delete("minutes:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_DIGIT, 1)
- + pynutil.delete('"')
- )
- second = (
- pynutil.delete("seconds:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_DIGIT, 1)
- + pynutil.delete('"')
- )
- zone = (
- delete_space
- + insert_space
- + pynutil.delete("zone:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_CHAR - " ", 1)
- + pynutil.delete('"')
- )
- optional_zone = pynini.closure(zone, 0, 1)
- optional_second = pynini.closure(
- delete_space + pynutil.insert(":") + (second @ add_leading_zero_to_double_digit),
- 0,
- 1,
- )
-
- graph_h = hour + pynutil.insert("h")
- graph_hms = (
- hour + delete_space + pynutil.insert(":") + (minute @ add_leading_zero_to_double_digit) + optional_second
- )
- graph_ms = (
- minute
- + delete_space
- + pynutil.insert("p")
- + (second @ add_leading_zero_to_double_digit)
- + pynutil.insert("s")
- )
-
- graph = (graph_h | graph_ms | graph_hms) + optional_zone
- delete_tokens = self.delete_tokens(graph)
- self.fst = delete_tokens.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/verbalize.py b/whisper_pipeline/text_processing/vi/verbalizers/verbalize.py
deleted file mode 100644
index f3ee65c3c90011d4a9a7037785aba20a316a8c74..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/verbalize.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import GraphFst
-from text_processing.vi.verbalizers.address import AddressFst
-from text_processing.vi.verbalizers.cardinal import CardinalFst
-from text_processing.vi.verbalizers.date import DateFst
-from text_processing.vi.verbalizers.decimal import DecimalFst
-from text_processing.vi.verbalizers.electronic import ElectronicFst
-from text_processing.vi.verbalizers.fraction import FractionFst
-from text_processing.vi.verbalizers.measure import MeasureFst
-from text_processing.vi.verbalizers.math import MathFst
-from text_processing.vi.verbalizers.money import MoneyFst
-from text_processing.vi.verbalizers.ordinal import OrdinalFst
-from text_processing.vi.verbalizers.telephone import TelephoneFst
-from text_processing.vi.verbalizers.time import TimeFst
-from text_processing.vi.verbalizers.whitelist import WhiteListFst
-
-
-class VerbalizeFst(GraphFst):
- """
- Composes other verbalizer grammars.
- For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File.
- More details to deployment at NeMo/tools/text_processing_deployment.
- """
-
- def __init__(self):
- super().__init__(name="verbalize", kind="verbalize")
- cardinal = CardinalFst()
- cardinal_graph = cardinal.fst
- ordinal_graph = OrdinalFst().fst
- decimal = DecimalFst()
- decimal_graph = decimal.fst
- fraction = FractionFst()
- fraction_graph = fraction.fst
- address_graph = AddressFst().fst
- math_graph = MathFst().fst
- measure_graph = MeasureFst(decimal=decimal, cardinal=cardinal).fst
- money_graph = MoneyFst(decimal=decimal).fst
- time_graph = TimeFst().fst
- date_graph = DateFst().fst
- whitelist_graph = WhiteListFst().fst
- telephone_graph = TelephoneFst().fst
- electronic_graph = ElectronicFst().fst
- graph = (
- time_graph
- | date_graph
- | money_graph
- | measure_graph
- | ordinal_graph
- | fraction_graph
- | math_graph
- | address_graph
- | decimal_graph
- | cardinal_graph
- | whitelist_graph
- | telephone_graph
- | electronic_graph
- )
- self.fst = graph
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/verbalize_final.py b/whisper_pipeline/text_processing/vi/verbalizers/verbalize_final.py
deleted file mode 100644
index 1dae7cf29c62eee5750e206fb00ebfbdd18d6f23..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/verbalize_final.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.verbalizers.verbalize import (
- VerbalizeFst,
-)
-from text_processing.vi.verbalizers.word import WordFst
-from text_processing.vi.graph_utils import (
- GraphFst,
- delete_extra_space,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class VerbalizeFinalFst(GraphFst):
- """
- Finite state transducer that verbalizes an entire sentence, e.g.
- tokens { name: "its" } tokens { time { hours: "12" minutes: "30" } } tokens { name: "now" } -> its 12:30 now
- """
-
- def __init__(self):
- super().__init__(name="verbalize_final", kind="verbalize")
- verbalize = VerbalizeFst().fst
- word = WordFst().fst
- types = verbalize | word
- graph = (
- pynutil.delete("tokens")
- + delete_space
- + pynutil.delete("{")
- + delete_space
- + types
- + delete_space
- + pynutil.delete("}")
- )
- graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space
- self.fst = graph
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/whitelist.py b/whisper_pipeline/text_processing/vi/verbalizers/whitelist.py
deleted file mode 100644
index a48a39bb2e3debbab260ed9e8658026905d24117..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/whitelist.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_CHAR,
- NEMO_SIGMA,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class WhiteListFst(GraphFst):
- """
- Finite state transducer for verbalizing whitelist
- e.g. tokens { name: "mrs." } -> mrs.
- """
-
- def __init__(self):
- super().__init__(name="whitelist", kind="verbalize")
- graph = (
- pynutil.delete("name:")
- + delete_space
- + pynutil.delete('"')
- + pynini.closure(NEMO_CHAR - " ", 1)
- + pynutil.delete('"')
- )
- graph = graph @ pynini.cdrewrite(pynini.cross(u"\u00A0", " "), "", "", NEMO_SIGMA)
- self.fst = graph.optimize()
diff --git a/whisper_pipeline/text_processing/vi/verbalizers/word.py b/whisper_pipeline/text_processing/vi/verbalizers/word.py
deleted file mode 100644
index 6d68122aa525ee197757fa150f633fcfaf1136ad..0000000000000000000000000000000000000000
--- a/whisper_pipeline/text_processing/vi/verbalizers/word.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-# Copyright 2015 and onwards Google, Inc.
-#
-# 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 text_processing.vi.graph_utils import (
- NEMO_CHAR,
- NEMO_SIGMA,
- GraphFst,
- delete_space,
-)
-
-try:
- import pynini
- from pynini.lib import pynutil
-
- PYNINI_AVAILABLE = True
-except (ModuleNotFoundError, ImportError):
- PYNINI_AVAILABLE = False
-
-
-class WordFst(GraphFst):
- """
- Finite state transducer for verbalizing plain tokens
- e.g. tokens { name: "sleep" } -> sleep
- """
-
- def __init__(self):
- super().__init__(name="word", kind="verbalize")
- chars = pynini.closure(NEMO_CHAR - " ", 1)
- char = pynutil.delete("name:") + delete_space + pynutil.delete('"') + chars + pynutil.delete('"')
- graph = char @ pynini.cdrewrite(pynini.cross(u"\u00A0", " "), "", "", NEMO_SIGMA)
-
- self.fst = graph.optimize()