diff --git a/.gitattributes b/.gitattributes
index 3f31f83755cb78f78f338004a52cbea749d0a3b5..e21b3166f3b267f4b297b1edd4f381232d276201 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -35,3 +35,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 trained_30_percents/kafka.wav filter=lfs diff=lfs merge=lfs -text
 trained_30_percents/output.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/kafka.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_0.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_1.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_10.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_2.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_3.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_4.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_5.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_6.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_7.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_8.wav filter=lfs diff=lfs merge=lfs -text
+trained_50_percents/output_9.wav filter=lfs diff=lfs merge=lfs -text
diff --git a/trained_50_percents/.gitignore b/trained_50_percents/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..f36a175e777c9a599d458d1735e004e4c0243c14
--- /dev/null
+++ b/trained_50_percents/.gitignore
@@ -0,0 +1,16 @@
+# Python build artifacts
+__pycache__/
+*.pyc
+
+# Environment variables
+.env
+
+# Virtual environment
+venv/
+
+# Model backups and outputs
+model.fp32.safetensors
+output.wav
+
+# Temporary scripts
+check_dtype.py
diff --git a/trained_50_percents/.vscode/launch.json b/trained_50_percents/.vscode/launch.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb807233bf650bd9af82519d18d6da466706b9c2
--- /dev/null
+++ b/trained_50_percents/.vscode/launch.json
@@ -0,0 +1,17 @@
+{
+    // 使用 IntelliSense 了解相关属性。 
+    // 悬停以查看现有属性的描述。
+    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+
+        {
+            "name": "Python 调试程序: 当前文件",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": false
+        }
+    ]
+}
\ No newline at end of file
diff --git a/trained_50_percents/BATCH_INFERENCE_README.md b/trained_50_percents/BATCH_INFERENCE_README.md
new file mode 100644
index 0000000000000000000000000000000000000000..754fe185953c5c0731049cd553651490b641004d
--- /dev/null
+++ b/trained_50_percents/BATCH_INFERENCE_README.md
@@ -0,0 +1,30 @@
+# 批量推理功能说明
+
+本文档介绍了 ReSpark TTS 模型的批量推理功能，该功能可以显著提高多个文本的语音合成效率。
+
+## 使用方法
+
+### 基本批量推理
+```python
+from utilities import generate_embeddings_batch
+from tts_batch_infer import generate_speech_batch
+
+# 准备文本列表
+texts = [
+    "第一个要合成的文本。",
+    "第二个要合成的文本。",
+    "第三个要合成的文本。"
+]
+
+# 批量生成语音
+wavs = generate_speech_batch(
+    model, tokenizer, texts, audio_tokenizer,
+    prompt_text="提示文本",
+    prompt_audio=prompt_audio,
+    device=device
+)
+
+# 保存音频文件
+for i, wav in enumerate(wavs):
+    sf.write(f'output_{i}.wav', wav, sample_rate)
+``` 
\ No newline at end of file
diff --git a/trained_50_percents/BiCodec/config.yaml b/trained_50_percents/BiCodec/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cfe1d559cf895597649dc30d53af27c7909deb6f
--- /dev/null
+++ b/trained_50_percents/BiCodec/config.yaml
@@ -0,0 +1,60 @@
+audio_tokenizer:
+  mel_params:
+    sample_rate: 16000
+    n_fft: 1024
+    win_length: 640
+    hop_length: 320
+    mel_fmin: 10 
+    mel_fmax: null
+    num_mels: 128
+
+  encoder:
+    input_channels: 1024
+    vocos_dim: 384
+    vocos_intermediate_dim: 2048
+    vocos_num_layers: 12
+    out_channels: 1024
+    sample_ratios: [1,1]
+
+  decoder:
+    input_channel: 1024
+    channels: 1536
+    rates: [8, 5, 4, 2]
+    kernel_sizes: [16,11,8,4]
+
+  quantizer:
+    input_dim: 1024
+    codebook_size: 8192
+    codebook_dim: 8
+    commitment: 0.25
+    codebook_loss_weight: 2.0
+    use_l2_normlize: True
+    threshold_ema_dead_code: 0.2
+  
+  speaker_encoder:
+    input_dim: 128
+    out_dim: 1024
+    latent_dim: 128
+    token_num: 32
+    fsq_levels: [4, 4, 4, 4, 4, 4]
+    fsq_num_quantizers: 1
+
+  prenet:
+    input_channels: 1024
+    vocos_dim: 384
+    vocos_intermediate_dim: 2048
+    vocos_num_layers: 12
+    out_channels: 1024
+    condition_dim: 1024
+    sample_ratios: [1,1]
+    use_tanh_at_final: False
+
+  postnet: 
+    input_channels: 1024
+    vocos_dim: 384
+    vocos_intermediate_dim: 2048
+    vocos_num_layers: 6
+    out_channels: 1024
+    use_tanh_at_final: False
+
+
diff --git a/trained_50_percents/BiCodec/model.safetensors b/trained_50_percents/BiCodec/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..2668beb3777b36bd26473ac2a7b6e5bde15d2750
--- /dev/null
+++ b/trained_50_percents/BiCodec/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9940cd48d4446e4340ced82d234bf5618350dd9f5db900ebe47a4fdb03867ec
+size 625518756
diff --git a/trained_50_percents/Readme.md b/trained_50_percents/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..cf690a96e8ab1f647ef58d3a0afce9615c83b3e2
--- /dev/null
+++ b/trained_50_percents/Readme.md
@@ -0,0 +1,130 @@
+---
+license: apache-2.0
+---
+
+# ReSpark TTS Model
+
+This repository contains the ReSpark Text-to-Speech (TTS) model, a powerful and efficient model for generating high-quality speech from text. It is based on the RWKV architecture and utilizes the BiCodec tokenizer for audio processing.
+
+## Installation
+
+First, install the required dependencies:
+
+```bash
+pip install transformers rwkv-fla torch torchaudio torchvision transformers soundfile numpy librosa omegaconf soxr soundfile einx librosa
+```
+
+## Usage
+
+The `tts.py` script provides a complete example of how to use this model for text-to-speech synthesis with voice cloning.
+
+### Running the Test Script
+
+To generate speech, simply run the script:
+
+```bash
+python tts.py
+```
+
+### How it Works
+
+The script performs the following steps:
+1.  Loads the pre-trained `AutoModelForCausalLM` and `AutoTokenizer` from the current directory.
+2.  Initializes the `BiCodecTokenizer` for audio encoding and decoding.
+3.  Loads a reference audio file (`kafka.wav`) and its corresponding transcript (`prompt_text`) to provide a voice prompt.
+4.  Resamples the reference audio to match the model's expected sample rate (24000 Hz).
+5.  Takes a target text (`text`) to be synthesized.
+6.  Calls the `generate_speech` function, which generates audio based on the target text and the voice from the reference audio.
+7.  Saves the generated audio to `output.wav`.
+
+You can modify the `prompt_text`, `prompt_audio_file`, and `text` variables in `tts.py` to synthesize different text with different voices.
+
+### Example Code (`tts.py`)
+
+```python
+import os
+import sys
+current_dir = os.path.dirname(os.path.abspath(__file__))
+print('add current dir to sys.path', current_dir)
+sys.path.append(current_dir)
+from sparktts.models.audio_tokenizer import BiCodecTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import soundfile as sf
+import numpy as np
+import torch
+from utilities import generate_embeddings
+
+def generate_speech(model, tokenizer, text, bicodec, prompt_text=None, prompt_audio=None, 
+                   max_new_tokens=3000, do_sample=True, top_k=50, top_p=0.95, 
+                   temperature=1.0, device="cuda:0"):
+    """
+    Function to generate speech.
+    """
+    eos_token_id = model.config.vocab_size - 1
+    
+    embeddings = generate_embeddings(
+        model=model,
+        tokenizer=tokenizer,
+        text=text,
+        bicodec=bicodec,
+        prompt_text=prompt_text,
+        prompt_audio=prompt_audio
+    )
+    
+    global_tokens = embeddings['global_tokens'].unsqueeze(0)
+    model.eval()
+    
+    with torch.no_grad():
+        generated_outputs = model.generate(
+            inputs_embeds=embeddings['input_embs'],
+            attention_mask=torch.ones((1, embeddings['input_embs'].shape[1]),dtype=torch.long,device=device),
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=eos_token_id,
+            pad_token_id=tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else tokenizer.eos_token_id,
+            use_cache=True
+        )
+    
+    semantic_tokens_tensor = generated_outputs[:,:-1]
+    
+    with torch.no_grad():
+        wav = bicodec.detokenize(global_tokens, semantic_tokens_tensor)
+    
+    return wav
+
+# --- Main execution ---
+device = 'cuda:0'
+
+# Initialize tokenizers and model
+audio_tokenizer = BiCodecTokenizer(model_dir=current_dir, device=device)
+tokenizer = AutoTokenizer.from_pretrained(current_dir, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(current_dir, trust_remote_code=True)
+
+model = model.bfloat16().to(device)
+model.eval()
+
+# Prepare prompt audio and text for voice cloning
+prompt_text = "我们并不是通过物理移动手段找到星河的。"
+prompt_audio_file = os.path.join(current_dir, 'kafka.wav')
+prompt_audio, sampling_rate = sf.read(prompt_audio_file)
+
+# Resample audio if necessary
+target_sample_rate = audio_tokenizer.config['sample_rate']
+if sampling_rate != target_sample_rate:
+    from librosa import resample
+    prompt_audio = resample(prompt_audio, orig_sr=sampling_rate, target_sr=target_sample_rate)
+    prompt_audio = np.array(prompt_audio, dtype=np.float32)
+
+# Text to synthesize
+text = "科学技术是第一生产力，最近 AI的迅猛发展让我们看到了迈向星辰大海的希望。"
+
+# Generate speech
+wav = generate_speech(model, tokenizer, text, audio_tokenizer, prompt_audio=prompt_audio, device=device)
+
+# Save the output
+sf.write('output.wav', wav, target_sample_rate)
+print("Generated audio saved to output.wav")
+```
diff --git a/trained_50_percents/Readme_zh.md b/trained_50_percents/Readme_zh.md
new file mode 100644
index 0000000000000000000000000000000000000000..5ef2567209e6d6f7e936cff2eff3e79c8179c2cf
--- /dev/null
+++ b/trained_50_percents/Readme_zh.md
@@ -0,0 +1,130 @@
+---
+license: apache-2.0
+---
+
+# ReSpark TTS 模型
+
+本仓库包含 ReSpark 文本转语音 (TTS) 模型，这是一个强大而高效的模型，可以从文本生成高质量的语音。它基于 RWKV 架构，并利用 BiCodec-Tokenizer 进行音频处理。
+
+## 安装
+
+首先，请安装所需的依赖库：
+
+```bash
+pip install transformers rwkv-fla torch torchaudio torchvision transformers soundfile numpy librosa omegaconf soxr soundfile einx librosa
+```
+
+## 使用方法
+
+`tts.py` 脚本提供了一个完整的使用该模型进行文本转语音合成（带声音克隆功能）的示例。
+
+### 运行测试脚本
+
+要生成语音，只需运行以下脚本：
+
+```bash
+python tts.py
+```
+
+### 工作原理
+
+该脚本执行以下步骤：
+1.  从当前目录加载预训练的 `AutoModelForCausalLM` 和 `AutoTokenizer`。
+2.  初始化用于音频编码和解码的 `BiCodecTokenizer`。
+3.  加载一个参考音频文件 (`kafka.wav`) 及其对应的文本 (`prompt_text`) 以提供声音提示（voice prompt）。
+4.  如果需要，将参考音频重采样以匹配模型期望的采样率 (24000 Hz)。
+5.  指定一个需要被合成的目标文本 (`text`)。
+6.  调用 `generate_speech` 函数，该函数会根据目标文本和参考音频中的声音生成音频。
+7.  将生成的音频保存到 `output.wav`。
+
+您可以修改 `tts.py` 文件中的 `prompt_text`、`prompt_audio_file` 和 `text` 变量，以使用不同的声音合成不同的文本。
+
+### 示例代码 (`tts.py`)
+
+```python
+import os
+import sys
+current_dir = os.path.dirname(os.path.abspath(__file__))
+print('add current dir to sys.path', current_dir)
+sys.path.append(current_dir)
+from sparktts.models.audio_tokenizer import BiCodecTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import soundfile as sf
+import numpy as np
+import torch
+from utilities import generate_embeddings
+
+def generate_speech(model, tokenizer, text, bicodec, prompt_text=None, prompt_audio=None, 
+                   max_new_tokens=3000, do_sample=True, top_k=50, top_p=0.95, 
+                   temperature=1.0, device="cuda:0"):
+    """
+    生成语音的函数
+    """
+    eos_token_id = model.config.vocab_size - 1
+    
+    embeddings = generate_embeddings(
+        model=model,
+        tokenizer=tokenizer,
+        text=text,
+        bicodec=bicodec,
+        prompt_text=prompt_text,
+        prompt_audio=prompt_audio
+    )
+    
+    global_tokens = embeddings['global_tokens'].unsqueeze(0)
+    model.eval()
+    
+    with torch.no_grad():
+        generated_outputs = model.generate(
+            inputs_embeds=embeddings['input_embs'],
+            attention_mask=torch.ones((1, embeddings['input_embs'].shape[1]),dtype=torch.long,device=device),
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=eos_token_id,
+            pad_token_id=tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else tokenizer.eos_token_id,
+            use_cache=True
+        )
+    
+    semantic_tokens_tensor = generated_outputs[:,:-1]
+    
+    with torch.no_grad():
+        wav = bicodec.detokenize(global_tokens, semantic_tokens_tensor)
+    
+    return wav
+
+# --- 主程序 ---
+device = 'cuda:0'
+
+# 初始化分词器和模型
+audio_tokenizer = BiCodecTokenizer(model_dir=current_dir, device=device)
+tokenizer = AutoTokenizer.from_pretrained(current_dir, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(current_dir, trust_remote_code=True)
+
+model = model.bfloat16().to(device)
+model.eval()
+
+# 准备用于声音克隆的提示音频和文本
+prompt_text = "我们并不是通过物理移动手段找到星河的。"
+prompt_audio_file = os.path.join(current_dir, 'kafka.wav')
+prompt_audio, sampling_rate = sf.read(prompt_audio_file)
+
+# 如果需要，重采样音频
+target_sample_rate = audio_tokenizer.config['sample_rate']
+if sampling_rate != target_sample_rate:
+    from librosa import resample
+    prompt_audio = resample(prompt_audio, orig_sr=sampling_rate, target_sr=target_sample_rate)
+    prompt_audio = np.array(prompt_audio, dtype=np.float32)
+
+# 要合成的文本
+text = "科学技术是第一生产力，最近 AI的迅猛发展让我们看到了迈向星辰大海的希望。"
+
+# 生成语音
+wav = generate_speech(model, tokenizer, text, audio_tokenizer, prompt_audio=prompt_audio, device=device)
+
+# 保存输出
+sf.write('output.wav', wav, target_sample_rate)
+print("生成的音频已保存到 output.wav")
+```
diff --git a/trained_50_percents/__init__.py b/trained_50_percents/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/trained_50_percents/__pycache__/spark_llm.cpython-311.pyc b/trained_50_percents/__pycache__/spark_llm.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a10686b38d4f660347776ca9da9a11dc40f74785
Binary files /dev/null and b/trained_50_percents/__pycache__/spark_llm.cpython-311.pyc differ
diff --git a/trained_50_percents/__pycache__/utilities.cpython-311.pyc b/trained_50_percents/__pycache__/utilities.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a6a8cc8a34e10a5f8033a88ac9bff7ae3e797153
Binary files /dev/null and b/trained_50_percents/__pycache__/utilities.cpython-311.pyc differ
diff --git a/trained_50_percents/added_tokens.json b/trained_50_percents/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..01936f7f079d12dd5d05c8208cb6edeaacaf7baa
--- /dev/null
+++ b/trained_50_percents/added_tokens.json
@@ -0,0 +1,3 @@
+{
+  "<|rwkv_tokenizer_end_of_text|>": 0
+}
diff --git a/trained_50_percents/config.json b/trained_50_percents/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b2ae81d707ee5a2d7c86bd1bd16f2341b90e0b6d
--- /dev/null
+++ b/trained_50_percents/config.json
@@ -0,0 +1,66 @@
+{
+  "a_low_rank_dim": 64,
+  "architectures": [
+    "RWKV7ForSpeech"
+  ],
+  "attn": null,
+  "attn_mode": "chunk",
+  "audio_global_vocab_size": 4096,
+  "auto_map": {
+    "AutoConfig": "modeling_rwkvspeech.RWKV7SpeechConfig",
+    "AutoModel": "modeling_rwkvspeech.RWKV7Model",
+    "AutoModelForCausalLM": "modeling_rwkvspeech.RWKV7ForSpeech"
+  },
+  "bos_token_id": 0,
+  "decay_low_rank_dim": 64,
+  "eos_token_id": 0,
+  "fuse_cross_entropy": true,
+  "fuse_norm": false,
+  "gate_low_rank_dim": 128,
+  "head_dim": 64,
+  "hidden_act": "sqrelu",
+  "hidden_ratio": 4.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.006,
+  "intermediate_size": 4096,
+  "max_position_embeddings": 2048,
+  "model_type": "rwkv7",
+  "norm_bias": true,
+  "norm_eps": 1e-05,
+  "norm_first": true,
+  "num_heads": 32,
+  "num_hidden_layers": 24,
+  "text_vocab_size": 65536,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "v_low_rank_dim": 32,
+  "value_dim": [
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024
+  ],
+  "vocab_size": 8193
+}
diff --git a/trained_50_percents/config.yaml b/trained_50_percents/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0010b4159b48e19f609e6dda5d9ea718ffafba85
--- /dev/null
+++ b/trained_50_percents/config.yaml
@@ -0,0 +1,7 @@
+highpass_cutoff_freq: 40
+sample_rate: 16000 
+segment_duration: 2.4 # (s)
+max_val_duration: 12 # (s)
+latent_hop_length: 320
+ref_segment_duration: 6
+volume_normalize: true
\ No newline at end of file
diff --git a/trained_50_percents/configuration_rwkv7.py b/trained_50_percents/configuration_rwkv7.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1691f38bc8600aa96bc338be825fea907704ace
--- /dev/null
+++ b/trained_50_percents/configuration_rwkv7.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+
+from typing import Dict, Optional
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class RWKV7Config(PretrainedConfig):
+
+    model_type = 'rwkv7'
+    keys_to_ignore_at_inference = ['past_key_values']
+
+    def __init__(
+        self,
+        attn_mode: str = "chunk",
+        hidden_size: int = 2048,
+        hidden_ratio: Optional[int] = 4,
+        intermediate_size: Optional[int] = None,
+        num_hidden_layers: int = 24,
+        head_dim: Optional[int] = 64,
+        num_heads: Optional[int] = None,
+        decay_low_rank_dim: int = 64,
+        gate_low_rank_dim: int = 128,
+        a_low_rank_dim: int = 64,
+        v_low_rank_dim: int = 16,
+        hidden_act: str = "sqrelu",
+        max_position_embeddings: int = 2048,
+        norm_first: bool = True,
+        norm_bias: bool = True,
+        norm_eps: float = 1e-5,
+        attn: Optional[Dict] = None,
+        use_cache: bool = True,
+        pad_token_id: int = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_word_embeddings: bool = False,
+        initializer_range: float = 0.006,
+        fuse_norm: bool = True,
+        fuse_cross_entropy: bool = True,
+        vocab_size: int = 32000,
+        **kwargs
+    ):
+        self.attn_mode = attn_mode
+        self.hidden_size = hidden_size
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+        self.norm_first = norm_first
+        self.num_hidden_layers = num_hidden_layers
+
+        if head_dim is None and num_heads is not None:
+            head_dim = int(hidden_size // num_heads)
+        elif head_dim is not None and num_heads is None:
+            num_heads = int(hidden_size // head_dim)
+
+        self.head_dim = head_dim
+        self.num_heads = num_heads
+
+        self.decay_low_rank_dim = decay_low_rank_dim
+        self.gate_low_rank_dim = gate_low_rank_dim
+        self.a_low_rank_dim = a_low_rank_dim
+        self.v_low_rank_dim = v_low_rank_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.norm_bias = norm_bias
+        self.norm_eps = norm_eps
+        self.attn = attn
+        self.use_cache = use_cache
+        self.initializer_range = initializer_range
+        self.fuse_norm = fuse_norm
+        self.fuse_cross_entropy = fuse_cross_entropy
+        self.vocab_size = vocab_size
+
+        if attn is not None:
+            if not isinstance(attn, Dict):
+                raise ValueError("attn must be a dictionary")
+            if 'layers' not in attn:
+                raise ValueError("Layer indices must be provided to initialize hybrid attention layers")
+            if 'num_heads' not in attn:
+                raise ValueError("Number of heads must be provided to initialize hybrid attention layers")
+            attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads'])
+            attn['qkv_bias'] = attn.get('qkv_bias', False)
+            attn['window_size'] = attn.get('window_size', None)
+            attn['rope_theta'] = attn.get('rope_theta', 10000.)
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
diff --git a/trained_50_percents/generation_config.json b/trained_50_percents/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e55d754ffc52c3e15c40c8da31ca3976a475c66
--- /dev/null
+++ b/trained_50_percents/generation_config.json
@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.52.4"
+}
diff --git a/trained_50_percents/hf_rwkv_tokenizer.py b/trained_50_percents/hf_rwkv_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..054c31812cfa7084a708486328fef78d44427a82
--- /dev/null
+++ b/trained_50_percents/hf_rwkv_tokenizer.py
@@ -0,0 +1,280 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for RWKV."""
+
+import os
+import re
+from typing import TYPE_CHECKING, List, Optional, Tuple
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "rwkv_vocab_v20230424.txt",
+}
+
+class TRIE:
+    __slots__ = tuple("ch,to,values,front".split(","))
+    to: list
+    values: set
+
+    def __init__(self, front=None, ch=None):
+        self.ch = ch
+        self.to = [None for ch in range(256)]
+        self.values = set()
+        self.front = front
+
+    def __repr__(self):
+        fr = self
+        ret = []
+        while fr != None:
+            if fr.ch != None:
+                ret.append(fr.ch)
+            fr = fr.front
+        return "<TRIE %s %s>" % (ret[::-1], self.values)
+
+    def add(self, key: bytes, idx: int = 0, val=None):
+        if idx == len(key):
+            if val is None:
+                val = key
+            self.values.add(val)
+            return self
+        ch = key[idx]
+        if self.to[ch] is None:
+            self.to[ch] = TRIE(front=self, ch=ch)
+        return self.to[ch].add(key, idx=idx + 1, val=val)
+
+    def find_longest(self, key: bytes, idx: int = 0):
+        u: TRIE = self
+        ch: int = key[idx]
+
+        while u.to[ch] is not None:
+            u = u.to[ch]
+            idx += 1
+            if u.values:
+                ret = idx, u, u.values
+            if idx == len(key):
+                break
+            ch = key[idx]
+        return ret
+
+
+class RWKV_TOKENIZER:
+    def __init__(self, file_name):
+        self.idx2token = {}
+        sorted = []  # must be already sorted
+        with open(file_name, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        for l in lines:
+            idx = int(l[: l.index(" ")])
+            x = eval(l[l.index(" ") : l.rindex(" ")])
+            x = x.encode("utf-8") if isinstance(x, str) else x
+            assert isinstance(x, bytes)
+
+            assert len(x) == int(l[l.rindex(" ") :])
+            sorted += [x]
+            self.idx2token[idx] = x
+
+        self.token2idx = {}
+        for k, v in self.idx2token.items():
+            self.token2idx[v] = int(k)
+
+        self.root = TRIE()
+        for t, i in self.token2idx.items():
+            _ = self.root.add(t, val=(t, i))
+
+    def encodeBytes(self, src: bytes):
+        idx: int = 0
+        tokens = []
+        while idx < len(src):
+            _idx: int = idx
+            idx, _, values = self.root.find_longest(src, idx)
+            assert idx != _idx
+            _, token = next(iter(values))
+            tokens.append(token)
+        return tokens
+
+    def decodeBytes(self, tokens):
+        return b"".join(map(lambda i: self.idx2token[i], tokens))
+
+    def encode(self, src):
+        if isinstance(src, str):
+            return [self.encodeBytes(src.encode("utf-8"))]
+        elif isinstance(src, list):
+            return [self.encodeBytes(s.encode("utf-8")) for s in src]
+
+    def decode(self, tokens):
+        return [self.decodeBytes(batch).decode("utf-8") for batch in tokens]
+        # try:
+        #     return self.decodeBytes(tokens).decode('utf-8')
+        # except:
+        #     return '\ufffd' # bad utf-8
+
+    def printTokens(self, tokens):
+        for i in tokens:
+            s = self.idx2token[i]
+            try:
+                s = s.decode("utf-8")
+            except:
+                pass
+            print(f"{repr(s)}{i}", end=" ")
+        print()
+
+
+class RwkvTokenizer(PreTrainedTokenizer):
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self, vocab_file, bos_token="<|rwkv_tokenizer_end_of_text|>", eos_token="<|rwkv_tokenizer_end_of_text|>", unk_token="<|rwkv_tokenizer_end_of_text|>", **kwargs
+    ):
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'."
+            )
+
+        with open(vocab_file, "r", encoding="utf-8") as reader:
+            tokens = reader.readlines()
+
+        if "add_bos_token" in kwargs:
+            self.add_bos_token = kwargs["add_bos_token"]
+        else:
+            self.add_bos_token = False
+        self.trie_tokenizer = RWKV_TOKENIZER(vocab_file)
+        vocab = self.trie_tokenizer.token2idx
+        self.encoder = vocab
+        self.decoder = {v: k for k, v in vocab.items()}
+        self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
+        super().__init__(
+            bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs
+        )
+
+    @property
+    def vocab_size(self):
+        return len(self.encoder)
+
+    def get_vocab(self):
+        vocab = self.encoder
+        vocab.update(self.added_tokens_encoder)
+        vocab = dict(sorted(vocab.items(), key=lambda item: item[1]))
+        return vocab
+
+    def _tokenize(self, text, split_special_tokens=False):
+        # return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
+        return self.trie_tokenizer.encode(text)[0]
+
+    def _convert_token_to_id(self, token):
+        return token
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (byte) using the vocab."""
+        token = self.decoder.get(index, self.unk_token)
+        if isinstance(token, (bytes)):
+            token = token.decode("utf-8", errors="replace")
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
+        out_string = b"".join(
+            [k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]
+        ).decode("utf-8")
+        return out_string
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory,
+                (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
+            )
+        else:
+            vocab_file = (
+                filename_prefix + "-" if filename_prefix else ""
+            ) + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(
+                self.encoder.items(), key=lambda kv: kv[1]
+            ):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(str(token) + "\n")
+                index += 1
+        return (vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is None:
+            return output
+
+        return output + bos_token_ids + token_ids_1
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+
+        if not self.add_bos_token:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=False,
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0))
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
+        
\ No newline at end of file
diff --git a/trained_50_percents/kafka.wav b/trained_50_percents/kafka.wav
new file mode 100644
index 0000000000000000000000000000000000000000..486fc4b0c9e99e920511a65a3e66f07a57f2e8cc
--- /dev/null
+++ b/trained_50_percents/kafka.wav
@@ -0,0 +1,3 @@
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diff --git a/trained_50_percents/model.safetensors b/trained_50_percents/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..a9865cf754cb4b7a4f4ccecea48ff9263b26667e
--- /dev/null
+++ b/trained_50_percents/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 809355976
diff --git a/trained_50_percents/modeling_rwkvspeech.py b/trained_50_percents/modeling_rwkvspeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e7a941c84c51ad49f8530fc9d99b6330b342e1
--- /dev/null
+++ b/trained_50_percents/modeling_rwkvspeech.py
@@ -0,0 +1,6 @@
+from spark_llm import RWKV7SpeechConfig,RWKV7ForSpeech
+from rwkvfla.models.rwkv7 import RWKV7Model
+
+RWKV7ForCausalLM = RWKV7ForSpeech
+RWKV7Model = RWKV7Model
+RWKV7Config = RWKV7SpeechConfig
diff --git a/trained_50_percents/output.wav b/trained_50_percents/output.wav
new file mode 100644
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--- /dev/null
+++ b/trained_50_percents/output.wav
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
+++ b/trained_50_percents/output_0.wav
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
+++ b/trained_50_percents/output_1.wav
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
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new file mode 100644
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--- /dev/null
+++ b/trained_50_percents/output_2.wav
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
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new file mode 100644
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new file mode 100644
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--- /dev/null
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diff --git a/trained_50_percents/rwkv_vocab_v20230424.txt b/trained_50_percents/rwkv_vocab_v20230424.txt
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,65529 @@
+1 '\x00' 1
+2 '\x01' 1
+3 '\x02' 1
+4 '\x03' 1
+5 '\x04' 1
+6 '\x05' 1
+7 '\x06' 1
+8 '\x07' 1
+9 '\x08' 1
+10 '\t' 1
+11 '\n' 1
+12 '\x0b' 1
+13 '\x0c' 1
+14 '\r' 1
+15 '\x0e' 1
+16 '\x0f' 1
+17 '\x10' 1
+18 '\x11' 1
+19 '\x12' 1
+20 '\x13' 1
+21 '\x14' 1
+22 '\x15' 1
+23 '\x16' 1
+24 '\x17' 1
+25 '\x18' 1
+26 '\x19' 1
+27 '\x1a' 1
+28 '\x1b' 1
+29 '\x1c' 1
+30 '\x1d' 1
+31 '\x1e' 1
+32 '\x1f' 1
+33 ' ' 1
+34 '!' 1
+35 '"' 1
+36 '#' 1
+37 '$' 1
+38 '%' 1
+39 '&' 1
+40 "'" 1
+41 '(' 1
+42 ')' 1
+43 '*' 1
+44 '+' 1
+45 ',' 1
+46 '-' 1
+47 '.' 1
+48 '/' 1
+49 '0' 1
+50 '1' 1
+51 '2' 1
+52 '3' 1
+53 '4' 1
+54 '5' 1
+55 '6' 1
+56 '7' 1
+57 '8' 1
+58 '9' 1
+59 ':' 1
+60 ';' 1
+61 '<' 1
+62 '=' 1
+63 '>' 1
+64 '?' 1
+65 '@' 1
+66 'A' 1
+67 'B' 1
+68 'C' 1
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+29794 ' Slot' 5
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+29797 ' Snap' 5
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+29800 ' Soft' 5
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+29803 ' Solo' 5
+29804 ' Some' 5
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+29807 ' Sony' 5
+29808 ' Soon' 5
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+29810 ' Sort' 5
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+29812 ' Soup' 5
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+29817 ' Spot' 5
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+29819 ' Stan' 5
+29820 ' Star' 5
+29821 ' Stat' 5
+29822 ' Stay' 5
+29823 ' Stef' 5
+29824 ' Step' 5
+29825 ' Ster' 5
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+29827 ' Stir' 5
+29828 ' Stop' 5
+29829 ' Stra' 5
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+29832 ' Stro' 5
+29833 ' Stud' 5
+29834 ' Such' 5
+29835 ' Suff' 5
+29836 ' Suit' 5
+29837 ' Sund' 5
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+36019 'would' 5
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+36025 'xpath' 5
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+36027 'xygen' 5
+36028 'yahoo' 5
+36029 'yards' 5
+36030 'ycler' 5
+36031 'years' 5
+36032 'yield' 5
+36033 'ylene' 5
+36034 'ylvan' 5
+36035 'ymbol' 5
+36036 'yntax' 5
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+36044 'ział' 5
+36045 'zilla' 5
+36046 'zione' 5
+36047 'zsche' 5
+36048 '}}_{\\' 5
+36049 'ÇÃO' 5
+36050 'État' 5
+36051 'ában' 5
+36052 'ácil' 5
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+36054 'ális' 5
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+36074 'äler' 5
+36075 'älle' 5
+36076 'ällt' 5
+36077 'ämä' 5
+36078 'ände' 5
+36079 'änge' 5
+36080 'ären' 5
+36081 'ässt' 5
+36082 'äter' 5
+36083 'ätte' 5
+36084 'ätze' 5
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+36087 'ædia' 5
+36088 'çais' 5
+36089 'çois' 5
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+36091 'ção' 5
+36092 'èces' 5
+36093 'èles' 5
+36094 'èmes' 5
+36095 'ènes' 5
+36096 'èque' 5
+36097 'ères' 5
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+36099 'ètre' 5
+36100 'èves' 5
+36101 'ébec' 5
+36102 'ében' 5
+36103 'écur' 5
+36104 'éder' 5
+36105 'édia' 5
+36106 'édie' 5
+36107 'édé' 5
+36108 'élé' 5
+36109 'émet' 5
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+36112 'ének' 5
+36113 'énez' 5
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+36117 'érer' 5
+36118 'érez' 5
+36119 'éric' 5
+36120 'érie' 5
+36121 'ério' 5
+36122 'éré' 5
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+36124 'éső' 5
+36125 'état' 5
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+36130 'êque' 5
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+36132 'être' 5
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+36134 'ício' 5
+36135 'ícul' 5
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+36137 'ígen' 5
+36138 'ília' 5
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+36142 'ître' 5
+36143 'ñana' 5
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+36145 'ództ' 5
+36146 'ópez' 5
+36147 'ória' 5
+36148 'ório' 5
+36149 'ôtel' 5
+36150 'öder' 5
+36151 'önig' 5
+36152 'öße' 5
+36153 'úmer' 5
+36154 'über' 5
+36155 'ücke' 5
+36156 'ügel' 5
+36157 'ügen' 5
+36158 'ühle' 5
+36159 'ührt' 5
+36160 'üler' 5
+36161 'ület' 5
+36162 'ünst' 5
+36163 'ční' 5
+36164 'ędzy' 5
+36165 'ění' 5
+36166 'ılı' 5
+36167 'ında' 5
+36168 'ını' 5
+36169 'łoż' 5
+36170 'łuż' 5
+36171 'łów' 5
+36172 'ńczy' 5
+36173 'ńska' 5
+36174 'ński' 5
+36175 'ństw' 5
+36176 'ście' 5
+36177 'śnie' 5
+36178 'ště' 5
+36179 'ším' 5
+36180 'ướ' 5
+36181 'ườ' 5
+36182 'ưở' 5
+36183 'ượ' 5
+36184 'ảng' 5
+36185 'ằng' 5
+36186 'ịch' 5
+36187 'ống' 5
+36188 'ồng' 5
+36189 'ụng' 5
+36190 'ứng' 5
+36191 'ững' 5
+36192 '’il' 5
+36193 '’ll' 5
+36194 '’re' 5
+36195 '’ve' 5
+36196 '“No' 5
+36197 '”),' 5
+36198 '”).' 5
+36199 '…..' 5
+36200 '！",' 5
+36201 '：</' 5
+36202 '\t\t\t\t\t\t' 6
+36203 '\n\t\t\t\t\t' 6
+36204 '\n\t\t   ' 6
+36205 '\n\n\t\t\t\t' 6
+36206 '\n\n\n\n\n\n' 6
+36207 '\n\n\n   ' 6
+36208 '\n\n    ' 6
+36209 '\n    \n' 6
+36210 '\n     ' 6
+36211 '\r\n\t\t\t\t' 6
+36212 '\r\n\r\n\r\n' 6
+36213 '\r\n    ' 6
+36214 ' \n    ' 6
+36215 '  \n   ' 6
+36216 '      ' 6
+36217 ' #####' 6
+36218 ' (...)' 6
+36219 ' (…)' 6
+36220 ' *****' 6
+36221 ' -----' 6
+36222 ' ABOUT' 6
+36223 ' AFTER' 6
+36224 ' ALTER' 6
+36225 ' ASCII' 6
+36226 ' Aaron' 6
+36227 ' Abbas' 6
+36228 ' Abbey' 6
+36229 ' Abdel' 6
+36230 ' Abdul' 6
+36231 ' About' 6
+36232 ' Above' 6
+36233 ' Abram' 6
+36234 ' Abuse' 6
+36235 ' Accum' 6
+36236 ' Achie' 6
+36237 ' Activ' 6
+36238 ' Actor' 6
+36239 ' Adams' 6
+36240 ' Adapt' 6
+36241 ' Added' 6
+36242 ' Admin' 6
+36243 ' Admir' 6
+36244 ' Adobe' 6
+36245 ' Adolf' 6
+36246 ' Adult' 6
+36247 ' Advis' 6
+36248 ' Advoc' 6
+36249 ' Afric' 6
+36250 ' After' 6
+36251 ' Again' 6
+36252 ' Agent' 6
+36253 ' Agric' 6
+36254 ' Ahmad' 6
+36255 ' Ahmed' 6
+36256 ' Aires' 6
+36257 ' Alban' 6
+36258 ' Alber' 6
+36259 ' Album' 6
+36260 ' Aleks' 6
+36261 ' Alert' 6
+36262 ' Aless' 6
+36263 ' Alexa' 6
+36264 ' Alger' 6
+36265 ' Alice' 6
+36266 ' Alien' 6
+36267 ' Align' 6
+36268 ' Allah' 6
+36269 ' Allan' 6
+36270 ' Alleg' 6
+36271 ' Allen' 6
+36272 ' Allow' 6
+36273 ' Along' 6
+36274 ' Alpha' 6
+36275 ' Alter' 6
+36276 ' Além' 6
+36277 ' Amber' 6
+36278 ' Amend' 6
+36279 ' Among' 6
+36280 ' Analy' 6
+36281 ' Andre' 6
+36282 ' Angel' 6
+36283 ' Angle' 6
+36284 ' Anglo' 6
+36285 ' Anita' 6
+36286 ' Annex' 6
+36287 ' Annie' 6
+36288 ' Antar' 6
+36289 ' Anton' 6
+36290 ' Apart' 6
+36291 ' Apost' 6
+36292 ' Apple' 6
+36293 ' Apply' 6
+36294 ' Appro' 6
+36295 ' April' 6
+36296 ' Arabs' 6
+36297 ' Arbit' 6
+36298 ' Arbor' 6
+36299 ' Areas' 6
+36300 ' Arena' 6
+36301 ' Arist' 6
+36302 ' Armed' 6
+36303 ' Armen' 6
+36304 ' Array' 6
+36305 ' Arrow' 6
+36306 ' Asian' 6
+36307 ' Aside' 6
+36308 ' Assad' 6
+36309 ' Asset' 6
+36310 ' Aster' 6
+36311 ' Aston' 6
+36312 ' Async' 6
+36313 ' Athen' 6
+36314 ' Atlas' 6
+36315 ' Audio' 6
+36316 ' Audit' 6
+36317 ' Autom' 6
+36318 ' Autor' 6
+36319 ' Avoid' 6
+36320 ' Award' 6
+36321 ' Azure' 6
+36322 ' BASIS' 6
+36323 ' BEGIN' 6
+36324 ' BLACK' 6
+36325 ' BLOCK' 6
+36326 ' BOOST' 6
+36327 ' Bacon' 6
+36328 ' Baker' 6
+36329 ' Banks' 6
+36330 ' Barcl' 6
+36331 ' Baron' 6
+36332 ' Barry' 6
+36333 ' Barth' 6
+36334 ' Based' 6
+36335 ' Basel' 6
+36336 ' Basic' 6
+36337 ' Basil' 6
+36338 ' Basin' 6
+36339 ' Basis' 6
+36340 ' Batch' 6
+36341 ' Bates' 6
+36342 ' Bauer' 6
+36343 ' Bayer' 6
+36344 ' Beach' 6
+36345 ' Bears' 6
+36346 ' Beast' 6
+36347 ' Beaut' 6
+36348 ' Begin' 6
+36349 ' Being' 6
+36350 ' Bella' 6
+36351 ' Belle' 6
+36352 ' Below' 6
+36353 ' Bench' 6
+36354 ' Bened' 6
+36355 ' Benef' 6
+36356 ' Benny' 6
+36357 ' Berry' 6
+36358 ' Besch' 6
+36359 ' Betty' 6
+36360 ' Bever' 6
+36361 ' Bible' 6
+36362 ' Bibli' 6
+36363 ' Biden' 6
+36364 ' Bills' 6
+36365 ' Billy' 6
+36366 ' Birds' 6
+36367 ' Birth' 6
+36368 ' Black' 6
+36369 ' Blair' 6
+36370 ' Blake' 6
+36371 ' Blanc' 6
+36372 ' Blank' 6
+36373 ' Blast' 6
+36374 ' Bless' 6
+36375 ' Blind' 6
+36376 ' Block' 6
+36377 ' Blood' 6
+36378 ' Bloom' 6
+36379 ' Blues' 6
+36380 ' Board' 6
+36381 ' Bobby' 6
+36382 ' Books' 6
+36383 ' Boost' 6
+36384 ' Booth' 6
+36385 ' Boris' 6
+36386 ' Bound' 6
+36387 ' Brady' 6
+36388 ' Brain' 6
+36389 ' Brand' 6
+36390 ' Brass' 6
+36391 ' Braun' 6
+36392 ' Bravo' 6
+36393 ' Bread' 6
+36394 ' Break' 6
+36395 ' Brent' 6
+36396 ' Brett' 6
+36397 ' Brian' 6
+36398 ' Brief' 6
+36399 ' Brill' 6
+36400 ' Bring' 6
+36401 ' Britt' 6
+36402 ' Broad' 6
+36403 ' Brock' 6
+36404 ' Bronx' 6
+36405 ' Brook' 6
+36406 ' Brown' 6
+36407 ' Bruce' 6
+36408 ' Bruno' 6
+36409 ' Bryan' 6
+36410 ' Buddh' 6
+36411 ' Buddy' 6
+36412 ' Build' 6
+36413 ' Built' 6
+36414 ' Bulls' 6
+36415 ' Bunny' 6
+36416 ' Burke' 6
+36417 ' Burns' 6
+36418 ' Byron' 6
+36419 ' Bytes' 6
+36420 ' CHECK' 6
+36421 ' CLAIM' 6
+36422 ' CLASS' 6
+36423 ' CONST' 6
+36424 ' CONTR' 6
+36425 ' COUNT' 6
+36426 ' COURT' 6
+36427 ' COVID' 6
+36428 ' Cable' 6
+36429 ' Cache' 6
+36430 ' Café' 6
+36431 ' Cairo' 6
+36432 ' Calif' 6
+36433 ' Calls' 6
+36434 ' Canad' 6
+36435 ' Canal' 6
+36436 ' Candy' 6
+36437 ' Canon' 6
+36438 ' Cards' 6
+36439 ' Carey' 6
+36440 ' Carib' 6
+36441 ' Carlo' 6
+36442 ' Carol' 6
+36443 ' Cases' 6
+36444 ' Casey' 6
+36445 ' Catal' 6
+36446 ' Catch' 6
+36447 ' Cause' 6
+36448 ' Caval' 6
+36449 ' Cecil' 6
+36450 ' Cells' 6
+36451 ' Cette' 6
+36452 ' Chain' 6
+36453 ' Chair' 6
+36454 ' Chall' 6
+36455 ' Champ' 6
+36456 ' Chand' 6
+36457 ' Chang' 6
+36458 ' Chaos' 6
+36459 ' Charg' 6
+36460 ' Charl' 6
+36461 ' Chart' 6
+36462 ' Chase' 6
+36463 ' Check' 6
+36464 ' Cheng' 6
+36465 ' Chern' 6
+36466 ' Chess' 6
+36467 ' Chest' 6
+36468 ' Chick' 6
+36469 ' Chief' 6
+36470 ' Child' 6
+36471 ' Chile' 6
+36472 ' Chili' 6
+36473 ' China' 6
+36474 ' Chloe' 6
+36475 ' Chris' 6
+36476 ' Chrom' 6
+36477 ' Chron' 6
+36478 ' Chrys' 6
+36479 ' Chuck' 6
+36480 ' Chung' 6
+36481 ' Cindy' 6
+36482 ' Cisco' 6
+36483 ' Civic' 6
+36484 ' Civil' 6
+36485 ' Claim' 6
+36486 ' Clara' 6
+36487 ' Clare' 6
+36488 ' Clark' 6
+36489 ' Class' 6
+36490 ' Claud' 6
+36491 ' Claus' 6
+36492 ' Clean' 6
+36493 ' Clear' 6
+36494 ' Clerk' 6
+36495 ' Click' 6
+36496 ' Cliff' 6
+36497 ' Clint' 6
+36498 ' Clock' 6
+36499 ' Clone' 6
+36500 ' Close' 6
+36501 ' Cloud' 6
+36502 ' Clubs' 6
+36503 ' Coach' 6
+36504 ' Coast' 6
+36505 ' Cohen' 6
+36506 ' Colin' 6
+36507 ' Colon' 6
+36508 ' Color' 6
+36509 ' Combo' 6
+36510 ' Comey' 6
+36511 ' Comic' 6
+36512 ' Compl' 6
+36513 ' Congo' 6
+36514 ' Conse' 6
+36515 ' Const' 6
+36516 ' Contr' 6
+36517 ' Coord' 6
+36518 ' Coral' 6
+36519 ' Coron' 6
+36520 ' Corps' 6
+36521 ' Costa' 6
+36522 ' Costs' 6
+36523 ' Could' 6
+36524 ' Count' 6
+36525 ' Coupe' 6
+36526 ' Court' 6
+36527 ' Cover' 6
+36528 ' Covid' 6
+36529 ' Crack' 6
+36530 ' Craft' 6
+36531 ' Craig' 6
+36532 ' Crash' 6
+36533 ' Crazy' 6
+36534 ' Cream' 6
+36535 ' Creat' 6
+36536 ' Creek' 6
+36537 ' Crime' 6
+36538 ' Crist' 6
+36539 ' Cross' 6
+36540 ' Crown' 6
+36541 ' Crypt' 6
+36542 ' Cuban' 6
+36543 ' Curry' 6
+36544 ' Curve' 6
+36545 ' Cyber' 6
+36546 ' Cycle' 6
+36547 ' Czech' 6
+36548 ' DEBUG' 6
+36549 ' DWORD' 6
+36550 ' Daddy' 6
+36551 ' Daily' 6
+36552 ' Daisy' 6
+36553 ' Dance' 6
+36554 ' Danny' 6
+36555 ' Dante' 6
+36556 ' Daten' 6
+36557 ' Dates' 6
+36558 ' David' 6
+36559 ' Davis' 6
+36560 ' Death' 6
+36561 ' Debug' 6
+36562 ' Decor' 6
+36563 ' Decre' 6
+36564 ' Delay' 6
+36565 ' Delhi' 6
+36566 ' Delta' 6
+36567 ' Denis' 6
+36568 ' Dense' 6
+36569 ' Depth' 6
+36570 ' Derby' 6
+36571 ' Derek' 6
+36572 ' Desde' 6
+36573 ' Devil' 6
+36574 ' Devon' 6
+36575 ' Dhabi' 6
+36576 ' Diagn' 6
+36577 ' Diana' 6
+36578 ' Diane' 6
+36579 ' Diego' 6
+36580 ' Diese' 6
+36581 ' Dirac' 6
+36582 ' Dixon' 6
+36583 ' Dodge' 6
+36584 ' Doesn' 6
+36585 ' Doing' 6
+36586 ' Domin' 6
+36587 ' Donna' 6
+36588 ' Dover' 6
+36589 ' Doyle' 6
+36590 ' Draft' 6
+36591 ' Drain' 6
+36592 ' Drake' 6
+36593 ' Drama' 6
+36594 ' Dream' 6
+36595 ' Dress' 6
+36596 ' Drive' 6
+36597 ' Drugs' 6
+36598 ' Dubai' 6
+36599 ' Dutch' 6
+36600 ' Dylan' 6
+36601 ' Dynam' 6
+36602 ' Díaz' 6
+36603 ' ERROR' 6
+36604 ' EVENT' 6
+36605 ' EVERY' 6
+36606 ' Eagle' 6
+36607 ' Early' 6
+36608 ' Earth' 6
+36609 ' Ebola' 6
+36610 ' Eddie' 6
+36611 ' Edgar' 6
+36612 ' Edwin' 6
+36613 ' Egypt' 6
+36614 ' Eigen' 6
+36615 ' Eight' 6
+36616 ' Eisen' 6
+36617 ' Elder' 6
+36618 ' Elect' 6
+36619 ' Elena' 6
+36620 ' Elias' 6
+36621 ' Elite' 6
+36622 ' Ellen' 6
+36623 ' Ellis' 6
+36624 ' Elvis' 6
+36625 ' Email' 6
+36626 ' Embed' 6
+36627 ' Ember' 6
+36628 ' Emily' 6
+36629 ' Empty' 6
+36630 ' Engel' 6
+36631 ' Enjoy' 6
+36632 ' Enter' 6
+36633 ' Entre' 6
+36634 ' Entry' 6
+36635 ' Equal' 6
+36636 ' Equip' 6
+36637 ' Ernst' 6
+36638 ' Error' 6
+36639 ' Esper' 6
+36640 ' Essay' 6
+36641 ' Essex' 6
+36642 ' Eston' 6
+36643 ' Ethan' 6
+36644 ' Ether' 6
+36645 ' Euler' 6
+36646 ' Europ' 6
+36647 ' Evans' 6
+36648 ' Event' 6
+36649 ' Every' 6
+36650 ' Exact' 6
+36651 ' Excel' 6
+36652 ' Exped' 6
+36653 ' Exper' 6
+36654 ' Extra' 6
+36655 ' FALSE' 6
+36656 ' FIRST' 6
+36657 ' FIXME' 6
+36658 ' FLAGS' 6
+36659 ' Facts' 6
+36660 ' Faith' 6
+36661 ' Falls' 6
+36662 ' False' 6
+36663 ' Famil' 6
+36664 ' Fargo' 6
+36665 ' Fault' 6
+36666 ' Favor' 6
+36667 ' Feder' 6
+36668 ' Felix' 6
+36669 ' Femin' 6
+36670 ' Ferry' 6
+36671 ' Fetch' 6
+36672 ' Fiber' 6
+36673 ' Field' 6
+36674 ' Fifth' 6
+36675 ' Fifty' 6
+36676 ' Fight' 6
+36677 ' Filed' 6
+36678 ' Files' 6
+36679 ' Filip' 6
+36680 ' Films' 6
+36681 ' Final' 6
+36682 ' First' 6
+36683 ' Fixed' 6
+36684 ' Flags' 6
+36685 ' Flash' 6
+36686 ' Fleet' 6
+36687 ' Flint' 6
+36688 ' Float' 6
+36689 ' Flood' 6
+36690 ' Floor' 6
+36691 ' Flora' 6
+36692 ' Floyd' 6
+36693 ' Flynn' 6
+36694 ' Focus' 6
+36695 ' Foods' 6
+36696 ' Force' 6
+36697 ' Forms' 6
+36698 ' Forty' 6
+36699 ' Forum' 6
+36700 ' Found' 6
+36701 ' Frame' 6
+36702 ' Franc' 6
+36703 ' Frank' 6
+36704 ' Franz' 6
+36705 ' Fresh' 6
+36706 ' Freud' 6
+36707 ' Fried' 6
+36708 ' Fritz' 6
+36709 ' Front' 6
+36710 ' Frost' 6
+36711 ' Fruit' 6
+36712 ' Fully' 6
+36713 ' Funds' 6
+36714 ' GENER' 6
+36715 ' GNOME' 6
+36716 ' GOODS' 6
+36717 ' GROUP' 6
+36718 ' Games' 6
+36719 ' Gamma' 6
+36720 ' Gates' 6
+36721 ' Gauss' 6
+36722 ' Gavin' 6
+36723 ' Gener' 6
+36724 ' Geoff' 6
+36725 ' Georg' 6
+36726 ' Gesch' 6
+36727 ' Getty' 6
+36728 ' Ghana' 6
+36729 ' Ghost' 6
+36730 ' Giant' 6
+36731 ' Gibbs' 6
+36732 ' Girls' 6
+36733 ' Given' 6
+36734 ' Glass' 6
+36735 ' Glenn' 6
+36736 ' Globe' 6
+36737 ' Glory' 6
+36738 ' Gloss' 6
+36739 ' Gmail' 6
+36740 ' Goals' 6
+36741 ' Going' 6
+36742 ' Gomez' 6
+36743 ' Goods' 6
+36744 ' Gould' 6
+36745 ' Grace' 6
+36746 ' Grade' 6
+36747 ' Grand' 6
+36748 ' Grant' 6
+36749 ' Graph' 6
+36750 ' Grass' 6
+36751 ' Great' 6
+36752 ' Greek' 6
+36753 ' Green' 6
+36754 ' Griff' 6
+36755 ' Gross' 6
+36756 ' Group' 6
+36757 ' Grove' 6
+36758 ' Groß' 6
+36759 ' Grund' 6
+36760 ' Guang' 6
+36761 ' Guard' 6
+36762 ' Guest' 6
+36763 ' Guide' 6
+36764 ' Guild' 6
+36765 ' Guill' 6
+36766 ' Gupta' 6
+36767 ' Gött' 6
+36768 ' HTTPS' 6
+36769 ' Haiti' 6
+36770 ' Hamas' 6
+36771 ' Hands' 6
+36772 ' Happy' 6
+36773 ' Hardy' 6
+36774 ' Harry' 6
+36775 ' Hasan' 6
+36776 ' Haupt' 6
+36777 ' Haven' 6
+36778 ' Hawai' 6
+36779 ' Hawks' 6
+36780 ' Hayes' 6
+36781 ' Heads' 6
+36782 ' Heart' 6
+36783 ' Heath' 6
+36784 ' Heavy' 6
+36785 ' Helen' 6
+36786 ' Hello' 6
+36787 ' Hence' 6
+36788 ' Henri' 6
+36789 ' Henry' 6
+36790 ' Hicks' 6
+36791 ' Higgs' 6
+36792 ' Hills' 6
+36793 ' Himal' 6
+36794 ' Hindi' 6
+36795 ' Hindu' 6
+36796 ' Hitch' 6
+36797 ' Hogan' 6
+36798 ' Holly' 6
+36799 ' Homer' 6
+36800 ' Homes' 6
+36801 ' Honda' 6
+36802 ' Honey' 6
+36803 ' Honor' 6
+36804 ' Horse' 6
+36805 ' Hotel' 6
+36806 ' Hours' 6
+36807 ' House' 6
+36808 ' Huang' 6
+36809 ' Human' 6
+36810 ' Humph' 6
+36811 ' Hutch' 6
+36812 ' Hydro' 6
+36813 ' Hyper' 6
+36814 ' Höhe' 6
+36815 ' INDEX' 6
+36816 ' INNER' 6
+36817 ' INPUT' 6
+36818 ' INTER' 6
+36819 ' Idaho' 6
+36820 ' Ideal' 6
+36821 ' Ideas' 6
+36822 ' Ident' 6
+36823 ' Image' 6
+36824 ' Immun' 6
+36825 ' Imper' 6
+36826 ' Impro' 6
+36827 ' Incre' 6
+36828 ' Index' 6
+36829 ' India' 6
+36830 ' Indic' 6
+36831 ' Infer' 6
+36832 ' Influ' 6
+36833 ' Initi' 6
+36834 ' Inner' 6
+36835 ' Innov' 6
+36836 ' Input' 6
+36837 ' Instr' 6
+36838 ' Intel' 6
+36839 ' Inter' 6
+36840 ' Intro' 6
+36841 ' Iraqi' 6
+36842 ' Irene' 6
+36843 ' Irish' 6
+36844 ' Isaac' 6
+36845 ' Islam' 6
+36846 ' Issue' 6
+36847 ' Italy' 6
+36848 ' Items' 6
+36849 ' Jacob' 6
+36850 ' Jahre' 6
+36851 ' James' 6
+36852 ' Jamie' 6
+36853 ' Janet' 6
+36854 ' Japan' 6
+36855 ' Jared' 6
+36856 ' Jason' 6
+36857 ' Jenny' 6
+36858 ' Jerry' 6
+36859 ' Jesse' 6
+36860 ' Jesus' 6
+36861 ' Jiang' 6
+36862 ' Jimmy' 6
+36863 ' Johan' 6
+36864 ' Johns' 6
+36865 ' Joint' 6
+36866 ' Joker' 6
+36867 ' Jonas' 6
+36868 ' Jones' 6
+36869 ' Jorge' 6
+36870 ' Josef' 6
+36871 ' José' 6
+36872 ' Joyce' 6
+36873 ' João' 6
+36874 ' Judge' 6
+36875 ' Julia' 6
+36876 ' Julie' 6
+36877 ' Kabul' 6
+36878 ' Kafka' 6
+36879 ' Kanye' 6
+36880 ' Karen' 6
+36881 ' Kathy' 6
+36882 ' Katie' 6
+36883 ' Keith' 6
+36884 ' Kelly' 6
+36885 ' Kenny' 6
+36886 ' Kenya' 6
+36887 ' Kerry' 6
+36888 ' Kevin' 6
+36889 ' Kings' 6
+36890 ' Kirby' 6
+36891 ' Kitty' 6
+36892 ' Klaus' 6
+36893 ' Klein' 6
+36894 ' Knock' 6
+36895 ' Known' 6
+36896 ' Korea' 6
+36897 ' Kraft' 6
+36898 ' Krish' 6
+36899 ' Krist' 6
+36900 ' Kumar' 6
+36901 ' Kunst' 6
+36902 ' Kyoto' 6
+36903 ' Köln' 6
+36904 ' LIMIT' 6
+36905 ' LOCAL' 6
+36906 ' LaTeX' 6
+36907 ' Label' 6
+36908 ' Labor' 6
+36909 ' Laden' 6
+36910 ' Lakes' 6
+36911 ' Lance' 6
+36912 ' Lands' 6
+36913 ' Lange' 6
+36914 ' Lanka' 6
+36915 ' Large' 6
+36916 ' Larry' 6
+36917 ' Laser' 6
+36918 ' Later' 6
+36919 ' Latin' 6
+36920 ' Laura' 6
+36921 ' Laure' 6
+36922 ' Layer' 6
+36923 ' Learn' 6
+36924 ' Leave' 6
+36925 ' Leban' 6
+36926 ' Leeds' 6
+36927 ' Legal' 6
+36928 ' Leigh' 6
+36929 ' Lemma' 6
+36930 ' Lemon' 6
+36931 ' Lenin' 6
+36932 ' Leone' 6
+36933 ' Level' 6
+36934 ' Lever' 6
+36935 ' Levin' 6
+36936 ' Lewis' 6
+36937 ' León' 6
+36938 ' Liber' 6
+36939 ' Libre' 6
+36940 ' Libya' 6
+36941 ' Light' 6
+36942 ' Limit' 6
+36943 ' Linda' 6
+36944 ' Lines' 6
+36945 ' Links' 6
+36946 ' Linux' 6
+36947 ' Lions' 6
+36948 ' Lista' 6
+36949 ' Liste' 6
+36950 ' Lists' 6
+36951 ' Liter' 6
+36952 ' Liver' 6
+36953 ' Lives' 6
+36954 ' Lloyd' 6
+36955 ' Local' 6
+36956 ' Locke' 6
+36957 ' Lodge' 6
+36958 ' Logan' 6
+36959 ' Logic' 6
+36960 ' Login' 6
+36961 ' Looks' 6
+36962 ' Lopez' 6
+36963 ' Lords' 6
+36964 ' Loren' 6
+36965 ' Lotus' 6
+36966 ' Louis' 6
+36967 ' Lower' 6
+36968 ' Lucas' 6
+36969 ' Lucia' 6
+36970 ' Lucky' 6
+36971 ' Luigi' 6
+36972 ' Lynch' 6
+36973 ' MODEL' 6
+36974 ' Maced' 6
+36975 ' Macro' 6
+36976 ' Magic' 6
+36977 ' Maine' 6
+36978 ' Maint' 6
+36979 ' Major' 6
+36980 ' Maker' 6
+36981 ' Makes' 6
+36982 ' Malay' 6
+36983 ' Malik' 6
+36984 ' Malta' 6
+36985 ' Manit' 6
+36986 ' Manor' 6
+36987 ' Maple' 6
+36988 ' March' 6
+36989 ' Marco' 6
+36990 ' Maria' 6
+36991 ' Marie' 6
+36992 ' Marin' 6
+36993 ' Mario' 6
+36994 ' Marks' 6
+36995 ' Marsh' 6
+36996 ' Marty' 6
+36997 ' Mason' 6
+36998 ' Match' 6
+36999 ' Mater' 6
+37000 ' Matth' 6
+37001 ' Maven' 6
+37002 ' Maver' 6
+37003 ' Maxim' 6
+37004 ' Maybe' 6
+37005 ' Mayer' 6
+37006 ' Mayor' 6
+37007 ' Mazda' 6
+37008 ' McCoy' 6
+37009 ' McKin' 6
+37010 ' Means' 6
+37011 ' Medal' 6
+37012 ' Media' 6
+37013 ' Medic' 6
+37014 ' Megan' 6
+37015 ' Memor' 6
+37016 ' Mercy' 6
+37017 ' Merge' 6
+37018 ' Messi' 6
+37019 ' Metal' 6
+37020 ' Meter' 6
+37021 ' Metro' 6
+37022 ' Meyer' 6
+37023 ' Miami' 6
+37024 ' Micha' 6
+37025 ' Miche' 6
+37026 ' Micro' 6
+37027 ' Might' 6
+37028 ' Milan' 6
+37029 ' Miles' 6
+37030 ' Milky' 6
+37031 ' Mills' 6
+37032 ' Minor' 6
+37033 ' Mitch' 6
+37034 ' Mixed' 6
+37035 ' Mobil' 6
+37036 ' Modal' 6
+37037 ' Model' 6
+37038 ' Modes' 6
+37039 ' Molly' 6
+37040 ' Money' 6
+37041 ' Mongo' 6
+37042 ' Monte' 6
+37043 ' Month' 6
+37044 ' Moody' 6
+37045 ' Moore' 6
+37046 ' Moral' 6
+37047 ' Moran' 6
+37048 ' Morph' 6
+37049 ' Morse' 6
+37050 ' Moses' 6
+37051 ' Motor' 6
+37052 ' Mount' 6
+37053 ' Mouse' 6
+37054 ' Movie' 6
+37055 ' Multi' 6
+37056 ' Mundo' 6
+37057 ' Music' 6
+37058 ' Musik' 6
+37059 ' MySQL' 6
+37060 ' Myers' 6
+37061 ' März' 6
+37062 ' NEVER' 6
+37063 ' NSLog' 6
+37064 ' Named' 6
+37065 ' Names' 6
+37066 ' Nancy' 6
+37067 ' Natal' 6
+37068 ' Natur' 6
+37069 ' Naval' 6
+37070 ' Nazis' 6
+37071 ' Needs' 6
+37072 ' Negro' 6
+37073 ' Nepal' 6
+37074 ' Neuro' 6
+37075 ' Never' 6
+37076 ' Newsp' 6
+37077 ' Nexus' 6
+37078 ' Nicol' 6
+37079 ' Nigel' 6
+37080 ' Niger' 6
+37081 ' Night' 6
+37082 ' Nikol' 6
+37083 ' Ninja' 6
+37084 ' Ninth' 6
+37085 ' Nixon' 6
+37086 ' Niño' 6
+37087 ' Nobel' 6
+37088 ' Noble' 6
+37089 ' Nodes' 6
+37090 ' Noise' 6
+37091 ' Nokia' 6
+37092 ' Nolan' 6
+37093 ' Norte' 6
+37094 ' North' 6
+37095 ' Notes' 6
+37096 ' Notre' 6
+37097 ' Novel' 6
+37098 ' Numer' 6
+37099 ' Nurse' 6
+37100 ' OAuth' 6
+37101 ' ORDER' 6
+37102 ' OTHER' 6
+37103 ' Obama' 6
+37104 ' Occup' 6
+37105 ' Ocean' 6
+37106 ' Offer' 6
+37107 ' Offic' 6
+37108 ' Often' 6
+37109 ' Olive' 6
+37110 ' Olson' 6
+37111 ' Olymp' 6
+37112 ' Omaha' 6
+37113 ' Omega' 6
+37114 ' Opera' 6
+37115 ' Optim' 6
+37116 ' Order' 6
+37117 ' Organ' 6
+37118 ' Orion' 6
+37119 ' Osaka' 6
+37120 ' Oscar' 6
+37121 ' Other' 6
+37122 ' Outer' 6
+37123 ' Owens' 6
+37124 ' Owner' 6
+37125 ' PHOTO' 6
+37126 ' Pablo' 6
+37127 ' Pages' 6
+37128 ' Paint' 6
+37129 ' Palin' 6
+37130 ' Panda' 6
+37131 ' Panel' 6
+37132 ' Paolo' 6
+37133 ' Paper' 6
+37134 ' Papua' 6
+37135 ' Parad' 6
+37136 ' Param' 6
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+37139 ' Parks' 6
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+37141 ' Parts' 6
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+37143 ' Patch' 6
+37144 ' Patel' 6
+37145 ' Patri' 6
+37146 ' Paula' 6
+37147 ' Paulo' 6
+37148 ' Pavel' 6
+37149 ' Payne' 6
+37150 ' País' 6
+37151 ' Peace' 6
+37152 ' Pearl' 6
+37153 ' Pedro' 6
+37154 ' Penal' 6
+37155 ' Pence' 6
+37156 ' Penny' 6
+37157 ' Percy' 6
+37158 ' Perez' 6
+37159 ' Perry' 6
+37160 ' Perth' 6
+37161 ' Perú' 6
+37162 ' Peter' 6
+37163 ' Petro' 6
+37164 ' Pharm' 6
+37165 ' Phase' 6
+37166 ' Phill' 6
+37167 ' Phone' 6
+37168 ' Photo' 6
+37169 ' Piece' 6
+37170 ' Pilot' 6
+37171 ' Piper' 6
+37172 ' Pitts' 6
+37173 ' Pixel' 6
+37174 ' Pizza' 6
+37175 ' Place' 6
+37176 ' Plain' 6
+37177 ' Plane' 6
+37178 ' Plans' 6
+37179 ' Plant' 6
+37180 ' Plate' 6
+37181 ' Plato' 6
+37182 ' Plaza' 6
+37183 ' Pluto' 6
+37184 ' Point' 6
+37185 ' Polar' 6
+37186 ' Polic' 6
+37187 ' Polit' 6
+37188 ' Pompe' 6
+37189 ' Porto' 6
+37190 ' Ports' 6
+37191 ' Posts' 6
+37192 ' Power' 6
+37193 ' Pract' 6
+37194 ' Preis' 6
+37195 ' Press' 6
+37196 ' Prest' 6
+37197 ' Price' 6
+37198 ' Pride' 6
+37199 ' Prime' 6
+37200 ' Princ' 6
+37201 ' Print' 6
+37202 ' Prior' 6
+37203 ' Prism' 6
+37204 ' Prize' 6
+37205 ' Produ' 6
+37206 ' Proof' 6
+37207 ' Prote' 6
+37208 ' Proto' 6
+37209 ' Proxy' 6
+37210 ' Prés' 6
+37211 ' Pseud' 6
+37212 ' Psych' 6
+37213 ' Putin' 6
+37214 ' Qaeda' 6
+37215 ' Qatar' 6
+37216 ' QtGui' 6
+37217 ' Quant' 6
+37218 ' Quart' 6
+37219 ' Queen' 6
+37220 ' Query' 6
+37221 ' Quest' 6
+37222 ' Queue' 6
+37223 ' Quick' 6
+37224 ' Quinn' 6
+37225 ' Quint' 6
+37226 ' Quite' 6
+37227 ' Quote' 6
+37228 ' Quran' 6
+37229 ' RIGHT' 6
+37230 ' Rabbi' 6
+37231 ' Radio' 6
+37232 ' Rails' 6
+37233 ' Rally' 6
+37234 ' Ralph' 6
+37235 ' Raman' 6
+37236 ' Ramos' 6
+37237 ' Ranch' 6
+37238 ' Randy' 6
+37239 ' Range' 6
+37240 ' Rapid' 6
+37241 ' Rates' 6
+37242 ' Ratio' 6
+37243 ' Raven' 6
+37244 ' React' 6
+37245 ' Ready' 6
+37246 ' Rebel' 6
+37247 ' Recon' 6
+37248 ' Redis' 6
+37249 ' Refer' 6
+37250 ' Regex' 6
+37251 ' Reich' 6
+37252 ' Renew' 6
+37253 ' René' 6
+37254 ' Reply' 6
+37255 ' Reset' 6
+37256 ' Retro' 6
+37257 ' Revel' 6
+37258 ' Rever' 6
+37259 ' Rhode' 6
+37260 ' Ricky' 6
+37261 ' Rider' 6
+37262 ' Ridge' 6
+37263 ' Right' 6
+37264 ' Riley' 6
+37265 ' Rings' 6
+37266 ' River' 6
+37267 ' Robin' 6
+37268 ' Robot' 6
+37269 ' Roche' 6
+37270 ' Rocky' 6
+37271 ' Roger' 6
+37272 ' Roman' 6
+37273 ' Romeo' 6
+37274 ' Româ' 6
+37275 ' Rosen' 6
+37276 ' Rossi' 6
+37277 ' Rouge' 6
+37278 ' Rough' 6
+37279 ' Round' 6
+37280 ' Route' 6
+37281 ' Rover' 6
+37282 ' Royal' 6
+37283 ' Rubio' 6
+37284 ' Rugby' 6
+37285 ' Rules' 6
+37286 ' Rural' 6
+37287 ' Russo' 6
+37288 ' Rück' 6
+37289 ' SHALL' 6
+37290 ' SMALL' 6
+37291 ' SPACE' 6
+37292 ' START' 6
+37293 ' STATE' 6
+37294 ' Sabha' 6
+37295 ' Sachs' 6
+37296 ' Sadly' 6
+37297 ' Saint' 6
+37298 ' Salad' 6
+37299 ' Salem' 6
+37300 ' Sales' 6
+37301 ' Sally' 6
+37302 ' Salon' 6
+37303 ' Samoa' 6
+37304 ' Sanct' 6
+37305 ' Sandy' 6
+37306 ' Santa' 6
+37307 ' Santo' 6
+37308 ' Sarah' 6
+37309 ' Satan' 6
+37310 ' Sauce' 6
+37311 ' Saudi' 6
+37312 ' Scala' 6
+37313 ' Scale' 6
+37314 ' Scene' 6
+37315 ' Sched' 6
+37316 ' Schne' 6
+37317 ' Schul' 6
+37318 ' Schwe' 6
+37319 ' Score' 6
+37320 ' Scots' 6
+37321 ' Scott' 6
+37322 ' Scout' 6
+37323 ' Seems' 6
+37324 ' Seite' 6
+37325 ' Sense' 6
+37326 ' Seoul' 6
+37327 ' Separ' 6
+37328 ' Serge' 6
+37329 ' Serie' 6
+37330 ' Serum' 6
+37331 ' Serve' 6
+37332 ' Setup' 6
+37333 ' Seven' 6
+37334 ' Sever' 6
+37335 ' Shall' 6
+37336 ' Shane' 6
+37337 ' Shang' 6
+37338 ' Shape' 6
+37339 ' Share' 6
+37340 ' Sharp' 6
+37341 ' Shawn' 6
+37342 ' Sheet' 6
+37343 ' Shell' 6
+37344 ' Shift' 6
+37345 ' Shock' 6
+37346 ' Shore' 6
+37347 ' Short' 6
+37348 ' Shows' 6
+37349 ' Siber' 6
+37350 ' Sigma' 6
+37351 ' Silva' 6
+37352 ' Simon' 6
+37353 ' Simpl' 6
+37354 ' Since' 6
+37355 ' Singh' 6
+37356 ' Sites' 6
+37357 ' Sixth' 6
+37358 ' Skill' 6
+37359 ' Skype' 6
+37360 ' Sleep' 6
+37361 ' Small' 6
+37362 ' Smart' 6
+37363 ' Smith' 6
+37364 ' Snake' 6
+37365 ' Solar' 6
+37366 ' Solid' 6
+37367 ' Solve' 6
+37368 ' Songs' 6
+37369 ' Sorry' 6
+37370 ' Sound' 6
+37371 ' South' 6
+37372 ' Space' 6
+37373 ' Spain' 6
+37374 ' Spark' 6
+37375 ' Spart' 6
+37376 ' Spect' 6
+37377 ' Speed' 6
+37378 ' Spell' 6
+37379 ' Spiel' 6
+37380 ' Split' 6
+37381 ' Sport' 6
+37382 ' Spurs' 6
+37383 ' Squad' 6
+37384 ' Stack' 6
+37385 ' Stadt' 6
+37386 ' Staff' 6
+37387 ' Stage' 6
+37388 ' Stall' 6
+37389 ' Stand' 6
+37390 ' Stark' 6
+37391 ' Stars' 6
+37392 ' Start' 6
+37393 ' State' 6
+37394 ' Stats' 6
+37395 ' Steam' 6
+37396 ' Steel' 6
+37397 ' Stein' 6
+37398 ' Steph' 6
+37399 ' Steps' 6
+37400 ' Stern' 6
+37401 ' Steve' 6
+37402 ' Stick' 6
+37403 ' Still' 6
+37404 ' Stock' 6
+37405 ' Stone' 6
+37406 ' Store' 6
+37407 ' Storm' 6
+37408 ' Story' 6
+37409 ' Strat' 6
+37410 ' Strip' 6
+37411 ' Study' 6
+37412 ' Style' 6
+37413 ' Städ' 6
+37414 ' Sudan' 6
+37415 ' Sugar' 6
+37416 ' Suite' 6
+37417 ' Sunni' 6
+37418 ' Sunny' 6
+37419 ' Super' 6
+37420 ' Susan' 6
+37421 ' Sweet' 6
+37422 ' Swift' 6
+37423 ' Swing' 6
+37424 ' Swiss' 6
+37425 ' Sword' 6
+37426 ' Syria' 6
+37427 ' TABLE' 6
+37428 ' THREE' 6
+37429 ' TODAY' 6
+37430 ' TOTAL' 6
+37431 ' TRANS' 6
+37432 ' Table' 6
+37433 ' Taken' 6
+37434 ' Takes' 6
+37435 ' Tales' 6
+37436 ' Tamil' 6
+37437 ' Tampa' 6
+37438 ' Tasks' 6
+37439 ' Teams' 6
+37440 ' Techn' 6
+37441 ' Teddy' 6
+37442 ' Terms' 6
+37443 ' Terra' 6
+37444 ' Terry' 6
+37445 ' Tesla' 6
+37446 ' Tests' 6
+37447 ' Texas' 6
+37448 ' Thank' 6
+37449 ' Their' 6
+37450 ' Theme' 6
+37451 ' There' 6
+37452 ' Therm' 6
+37453 ' These' 6
+37454 ' Thing' 6
+37455 ' Think' 6
+37456 ' Third' 6
+37457 ' Thorn' 6
+37458 ' Those' 6
+37459 ' Three' 6
+37460 ' Throw' 6
+37461 ' Tibet' 6
+37462 ' Tiger' 6
+37463 ' Timer' 6
+37464 ' Times' 6
+37465 ' Titan' 6
+37466 ' Title' 6
+37467 ' Toast' 6
+37468 ' Today' 6
+37469 ' Token' 6
+37470 ' Tokyo' 6
+37471 ' Tomas' 6
+37472 ' Tommy' 6
+37473 ' Tools' 6
+37474 ' Topic' 6
+37475 ' Torah' 6
+37476 ' Total' 6
+37477 ' Touch' 6
+37478 ' Tours' 6
+37479 ' Tová' 6
+37480 ' Tower' 6
+37481 ' Trace' 6
+37482 ' Track' 6
+37483 ' Tracy' 6
+37484 ' Trade' 6
+37485 ' Trail' 6
+37486 ' Train' 6
+37487 ' Trans' 6
+37488 ' Treat' 6
+37489 ' Trees' 6
+37490 ' Trent' 6
+37491 ' Trial' 6
+37492 ' Tribe' 6
+37493 ' Truck' 6
+37494 ' Trump' 6
+37495 ' Trust' 6
+37496 ' Truth' 6
+37497 ' Tunis' 6
+37498 ' Tuple' 6
+37499 ' Turbo' 6
+37500 ' Turks' 6
+37501 ' Tweet' 6
+37502 ' Twist' 6
+37503 ' Tyler' 6
+37504 ' Types' 6
+37505 ' Tyson' 6
+37506 ' UNION' 6
+37507 ' Ultra' 6
+37508 ' Uncle' 6
+37509 ' Under' 6
+37510 ' Union' 6
+37511 ' Units' 6
+37512 ' Unity' 6
+37513 ' Unter' 6
+37514 ' Until' 6
+37515 ' Upper' 6
+37516 ' Urban' 6
+37517 ' Usage' 6
+37518 ' Users' 6
+37519 ' Using' 6
+37520 ' Utils' 6
+37521 ' VALUE' 6
+37522 ' VIDEO' 6
+37523 ' Valid' 6
+37524 ' Valle' 6
+37525 ' Value' 6
+37526 ' Vaugh' 6
+37527 ' Vegas' 6
+37528 ' Veget' 6
+37529 ' Venez' 6
+37530 ' Venus' 6
+37531 ' Verde' 6
+37532 ' Veter' 6
+37533 ' Video' 6
+37534 ' Views' 6
+37535 ' Villa' 6
+37536 ' Ville' 6
+37537 ' Vince' 6
+37538 ' Virus' 6
+37539 ' Visit' 6
+37540 ' Vista' 6
+37541 ' Vital' 6
+37542 ' Você' 6
+37543 ' Voice' 6
+37544 ' Volks' 6
+37545 ' WHERE' 6
+37546 ' Wales' 6
+37547 ' Walsh' 6
+37548 ' Waste' 6
+37549 ' Watch' 6
+37550 ' Water' 6
+37551 ' Watts' 6
+37552 ' Wayne' 6
+37553 ' Weber' 6
+37554 ' Weiss' 6
+37555 ' Welch' 6
+37556 ' Wells' 6
+37557 ' Welsh' 6
+37558 ' Wendy' 6
+37559 ' Whats' 6
+37560 ' Wheat' 6
+37561 ' Wheel' 6
+37562 ' Where' 6
+37563 ' Which' 6
+37564 ' While' 6
+37565 ' White' 6
+37566 ' Whole' 6
+37567 ' Width' 6
+37568 ' Wiley' 6
+37569 ' Wings' 6
+37570 ' Witch' 6
+37571 ' Woman' 6
+37572 ' Women' 6
+37573 ' Woods' 6
+37574 ' Words' 6
+37575 ' Works' 6
+37576 ' World' 6
+37577 ' Worth' 6
+37578 ' Would' 6
+37579 ' Wrest' 6
+37580 ' Write' 6
+37581 ' Wrong' 6
+37582 ' Yahoo' 6
+37583 ' Years' 6
+37584 ' Yemen' 6
+37585 ' Yield' 6
+37586 ' Young' 6
+37587 ' Youth' 6
+37588 ' Zhang' 6
+37589 ' Zheng' 6
+37590 ' [...]' 6
+37591 ' […]' 6
+37592 ' _____' 6
+37593 ' abbre' 6
+37594 ' abdom' 6
+37595 ' aberr' 6
+37596 ' abide' 6
+37597 ' abort' 6
+37598 ' about' 6
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+37611 ' accus' 6
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+37626 ' adopt' 6
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+37628 ' adult' 6
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+37631 ' aeros' 6
+37632 ' afirm' 6
+37633 ' afore' 6
+37634 ' after' 6
+37635 ' again' 6
+37636 ' agent' 6
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+37638 ' agony' 6
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+37643 ' ahora' 6
+37644 ' aided' 6
+37645 ' aides' 6
+37646 ' aimed' 6
+37647 ' ainda' 6
+37648 ' ainsi' 6
+37649 ' aired' 6
+37650 ' aisle' 6
+37651 ' aktiv' 6
+37652 ' alarm' 6
+37653 ' album' 6
+37654 ' alcan' 6
+37655 ' alert' 6
+37656 ' algae' 6
+37657 ' algun' 6
+37658 ' alias' 6
+37659 ' alien' 6
+37660 ' align' 6
+37661 ' alike' 6
+37662 ' aliqu' 6
+37663 ' alive' 6
+37664 ' alkal' 6
+37665 ' alkyl' 6
+37666 ' alleg' 6
+37667 ' allem' 6
+37668 ' allen' 6
+37669 ' aller' 6
+37670 ' alles' 6
+37671 ' allev' 6
+37672 ' alley' 6
+37673 ' alloc' 6
+37674 ' allot' 6
+37675 ' allow' 6
+37676 ' alloy' 6
+37677 ' alone' 6
+37678 ' along' 6
+37679 ' alors' 6
+37680 ' aloud' 6
+37681 ' alpha' 6
+37682 ' altar' 6
+37683 ' alter' 6
+37684 ' altre' 6
+37685 ' altru' 6
+37686 ' além' 6
+37687 ' amalg' 6
+37688 ' ambit' 6
+37689 ' ambos' 6
+37690 ' ambul' 6
+37691 ' amend' 6
+37692 ' amino' 6
+37693 ' ammon' 6
+37694 ' among' 6
+37695 ' ample' 6
+37696 ' analy' 6
+37697 ' anche' 6
+37698 ' andra' 6
+37699 ' anecd' 6
+37700 ' anest' 6
+37701 ' angel' 6
+37702 ' anger' 6
+37703 ' angle' 6
+37704 ' angry' 6
+37705 ' anime' 6
+37706 ' ankle' 6
+37707 ' annex' 6
+37708 ' annot' 6
+37709 ' annoy' 6
+37710 ' antes' 6
+37711 ' antib' 6
+37712 ' antic' 6
+37713 ' antid' 6
+37714 ' antim' 6
+37715 ' antip' 6
+37716 ' antis' 6
+37717 ' antit' 6
+37718 ' antiv' 6
+37719 ' anál' 6
+37720 ' août' 6
+37721 ' apare' 6
+37722 ' apart' 6
+37723 ' aplic' 6
+37724 ' apopt' 6
+37725 ' apost' 6
+37726 ' appar' 6
+37727 ' appel' 6
+37728 ' appet' 6
+37729 ' apple' 6
+37730 ' apply' 6
+37731 ' appro' 6
+37732 ' april' 6
+37733 ' após' 6
+37734 ' aquí' 6
+37735 ' arXiv' 6
+37736 ' arbit' 6
+37737 ' arche' 6
+37738 ' areas' 6
+37739 ' arena' 6
+37740 ' argue' 6
+37741 ' arise' 6
+37742 ' arist' 6
+37743 ' armed' 6
+37744 ' armor' 6
+37745 ' aroma' 6
+37746 ' arose' 6
+37747 ' array' 6
+37748 ' arriv' 6
+37749 ' arrog' 6
+37750 ' arrow' 6
+37751 ' arsen' 6
+37752 ' arter' 6
+37753 ' artic' 6
+37754 ' ashes' 6
+37755 ' aside' 6
+37756 ' asked' 6
+37757 ' aspir' 6
+37758 ' assay' 6
+37759 ' asset' 6
+37760 ' assez' 6
+37761 ' assim' 6
+37762 ' assum' 6
+37763 ' aster' 6
+37764 ' aston' 6
+37765 ' astro' 6
+37766 ' async' 6
+37767 ' atlas' 6
+37768 ' atmos' 6
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+40249 ' égal' 6
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+40254 ' êtes' 6
+40255 ' être' 6
+40256 ' över' 6
+40257 ' über' 6
+40258 ' Česk' 6
+40259 ' česk' 6
+40260 ' để' 6
+40261 ' đị' 6
+40262 ' độ' 6
+40263 '":"","' 6
+40264 '######' 6
+40265 '******' 6
+40266 '------' 6
+40267 '-même' 6
+40268 '-être' 6
+40269 '......' 6
+40270 '../../' 6
+40271 '======' 6
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+40273 'ACHINE' 6
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+40393 'Contin' 6
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+40401 'Cursor' 6
+40402 'Custom' 6
+40403 'DEFINE' 6
+40404 'DELETE' 6
+40405 'DEVICE' 6
+40406 'DIRECT' 6
+40407 'DITION' 6
+40408 'DOMAIN' 6
+40409 'DOUBLE' 6
+40410 'Damage' 6
+40411 'Daniel' 6
+40412 'Decode' 6
+40413 'Define' 6
+40414 'Degree' 6
+40415 'Delete' 6
+40416 'Depart' 6
+40417 'Depend' 6
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+40423 'Dialog' 6
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+40430 'Double' 6
+40431 'Dragon' 6
+40432 'Drawer' 6
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+43778 '이러' 6
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+43784 '이션' 6
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+43799 '일부' 6
+43800 '일에' 6
+43801 '입니' 6
+43802 '있는' 6
+43803 '있다' 6
+43804 '있습' 6
+43805 '있어' 6
+43806 '있었' 6
+43807 '있으' 6
+43808 '자가' 6
+43809 '자는' 6
+43810 '자들' 6
+43811 '자료' 6
+43812 '자신' 6
+43813 '자의' 6
+43814 '작품' 6
+43815 '장마' 6
+43816 '장샵' 6
+43817 '적으' 6
+43818 '적인' 6
+43819 '전남' 6
+43820 '전에' 6
+43821 '전자' 6
+43822 '전쟁' 6
+43823 '전화' 6
+43824 '정보' 6
+43825 '정부' 6
+43826 '정치' 6
+43827 '제공' 6
+43828 '졌다' 6
+43829 '조선' 6
+43830 '좋아' 6
+43831 '좋은' 6
+43832 '주의' 6
+43833 '중국' 6
+43834 '지난' 6
+43835 '지노' 6
+43836 '지는' 6
+43837 '지를' 6
+43838 '지만' 6
+43839 '지역' 6
+43840 '지원' 6
+43841 '지하' 6
+43842 '처리' 6
+43843 '최신' 6
+43844 '추천' 6
+43845 '축구' 6
+43846 '출신' 6
+43847 '출장' 6
+43848 '충남' 6
+43849 '카지' 6
+43850 '케어' 6
+43851 '코로' 6
+43852 '코스' 6
+43853 '타이' 6
+43854 '테라' 6
+43855 '통해' 6
+43856 '특별' 6
+43857 '페이' 6
+43858 '포함' 6
+43859 '프랑' 6
+43860 '프로' 6
+43861 '프리' 6
+43862 '필요' 6
+43863 '하게' 6
+43864 '하고' 6
+43865 '하기' 6
+43866 '하나' 6
+43867 '하는' 6
+43868 '하다' 6
+43869 '하며' 6
+43870 '하면' 6
+43871 '하여' 6
+43872 '하였' 6
+43873 '하지' 6
+43874 '학교' 6
+43875 '한국' 6
+43876 '한다' 6
+43877 '한민' 6
+43878 '함께' 6
+43879 '합니' 6
+43880 '해서' 6
+43881 '해야' 6
+43882 '했고' 6
+43883 '했다' 6
+43884 '했습' 6
+43885 '했으' 6
+43886 '현재' 6
+43887 '홈케' 6
+43888 '홈타' 6
+43889 '확인' 6
+43890 '활동' 6
+43891 '회사' 6
+43892 '회원' 6
+43893 '회의' 6
+43894 '후기' 6
+43895 '！”' 6
+43896 '！」' 6
+43897 '！！' 6
+43898 '），' 6
+43899 '：“' 6
+43900 '？”' 6
+43901 '？」' 6
+43902 '��' 6
+43903 '\t\t\t\t\t\t\t' 7
+43904 '\n\t\t\t\t\t\t' 7
+43905 '\n\t\t\t   ' 7
+43906 '\n\t     ' 7
+43907 '\n\n\n\n\n\n\n' 7
+43908 '\n\n     ' 7
+43909 '\n      ' 7
+43910 '\r\n\t\t\t\t\t' 7
+43911 '\r\n\r\n   ' 7
+43912 '\r\n     ' 7
+43913 ' \n     ' 7
+43914 '       ' 7
+43915 ' ------' 7
+43916 ' ACCESS' 7
+43917 ' ACTION' 7
+43918 ' ASSERT' 7
+43919 ' AUTHOR' 7
+43920 ' Abbott' 7
+43921 ' Abrams' 7
+43922 ' Academ' 7
+43923 ' Accept' 7
+43924 ' Access' 7
+43925 ' Across' 7
+43926 ' Acting' 7
+43927 ' Action' 7
+43928 ' Active' 7
+43929 ' Actual' 7
+43930 ' Adding' 7
+43931 ' Adjust' 7
+43932 ' Adrian' 7
+43933 ' Advent' 7
+43934 ' Affero' 7
+43935 ' Afghan' 7
+43936 ' Africa' 7
+43937 ' Agency' 7
+43938 ' Agenda' 7
+43939 ' Agents' 7
+43940 ' Alaska' 7
+43941 ' Albany' 7
+43942 ' Albert' 7
+43943 ' Alexis' 7
+43944 ' Alfred' 7
+43945 ' Allied' 7
+43946 ' Allies' 7
+43947 ' Almost' 7
+43948 ' Alonso' 7
+43949 ' Altern' 7
+43950 ' Alumni' 7
+43951 ' Always' 7
+43952 ' Amanda' 7
+43953 ' Amazon' 7
+43954 ' Americ' 7
+43955 ' Amount' 7
+43956 ' Analog' 7
+43957 ' Anders' 7
+43958 ' Andrea' 7
+43959 ' Andrew' 7
+43960 ' André' 7
+43961 ' Angela' 7
+43962 ' Angels' 7
+43963 ' Animal' 7
+43964 ' Annual' 7
+43965 ' Answer' 7
+43966 ' Anyone' 7
+43967 ' Anyway' 7
+43968 ' Apache' 7
+43969 ' Apollo' 7
+43970 ' Appeal' 7
+43971 ' Append' 7
+43972 ' Après' 7
+43973 ' Arabia' 7
+43974 ' Arabic' 7
+43975 ' Archae' 7
+43976 ' Archer' 7
+43977 ' Archie' 7
+43978 ' Arctic' 7
+43979 ' Argent' 7
+43980 ' Arnold' 7
+43981 ' Around' 7
+43982 ' Arrays' 7
+43983 ' Arthur' 7
+43984 ' Artist' 7
+43985 ' Ashley' 7
+43986 ' Assert' 7
+43987 ' Assign' 7
+43988 ' Assist' 7
+43989 ' Associ' 7
+43990 ' Assume' 7
+43991 ' Athens' 7
+43992 ' Athlet' 7
+43993 ' Atomic' 7
+43994 ' Attack' 7
+43995 ' Auburn' 7
+43996 ' August' 7
+43997 ' Aurora' 7
+43998 ' Austin' 7
+43999 ' Author' 7
+44000 ' Außer' 7
+44001 ' Avatar' 7
+44002 ' Avenue' 7
+44003 ' Awards' 7
+44004 ' Backup' 7
+44005 ' Bailey' 7
+44006 ' Baltic' 7
+44007 ' Barack' 7
+44008 ' Barber' 7
+44009 ' Barker' 7
+44010 ' Barnes' 7
+44011 ' Barton' 7
+44012 ' Basket' 7
+44013 ' Batman' 7
+44014 ' Battle' 7
+44015 ' Baxter' 7
+44016 ' Bayern' 7
+44017 ' Beauty' 7
+44018 ' Becker' 7
+44019 ' Before' 7
+44020 ' Behind' 7
+44021 ' Bengal' 7
+44022 ' Benson' 7
+44023 ' Bergen' 7
+44024 ' Berger' 7
+44025 ' Berlin' 7
+44026 ' Bernie' 7
+44027 ' Better' 7
+44028 ' Beyond' 7
+44029 ' Bieber' 7
+44030 ' Binary' 7
+44031 ' Bishop' 7
+44032 ' Bitmap' 7
+44033 ' Blacks' 7
+44034 ' Blocks' 7
+44035 ' Boeing' 7
+44036 ' Bolton' 7
+44037 ' Bonnie' 7
+44038 ' Booker' 7
+44039 ' Border' 7
+44040 ' Bosnia' 7
+44041 ' Boston' 7
+44042 ' Bottom' 7
+44043 ' Branch' 7
+44044 ' Brasil' 7
+44045 ' Brazil' 7
+44046 ' Breast' 7
+44047 ' Brexit' 7
+44048 ' Bridge' 7
+44049 ' Bright' 7
+44050 ' Broker' 7
+44051 ' Bronze' 7
+44052 ' Brooks' 7
+44053 ' Browns' 7
+44054 ' Bryant' 7
+44055 ' Bubble' 7
+44056 ' Buddha' 7
+44057 ' Budget' 7
+44058 ' Buenos' 7
+44059 ' Buffer' 7
+44060 ' Bulgar' 7
+44061 ' Bullet' 7
+44062 ' Bundes' 7
+44063 ' Bundle' 7
+44064 ' Bureau' 7
+44065 ' Burger' 7
+44066 ' Burton' 7
+44067 ' Butler' 7
+44068 ' Butter' 7
+44069 ' Button' 7
+44070 ' CGRect' 7
+44071 ' COLUMN' 7
+44072 ' CONFIG' 7
+44073 ' CREATE' 7
+44074 ' Caesar' 7
+44075 ' Calcul' 7
+44076 ' Calder' 7
+44077 ' Called' 7
+44078 ' Calvin' 7
+44079 ' Camera' 7
+44080 ' Campus' 7
+44081 ' Canada' 7
+44082 ' Cancel' 7
+44083 ' Cancer' 7
+44084 ' Candid' 7
+44085 ' Cannon' 7
+44086 ' Cannot' 7
+44087 ' Canvas' 7
+44088 ' Canyon' 7
+44089 ' Carbon' 7
+44090 ' Career' 7
+44091 ' Carlos' 7
+44092 ' Carmen' 7
+44093 ' Carrie' 7
+44094 ' Carson' 7
+44095 ' Carter' 7
+44096 ' Casino' 7
+44097 ' Castle' 7
+44098 ' Castro' 7
+44099 ' Casual' 7
+44100 ' Celebr' 7
+44101 ' Celtic' 7
+44102 ' Census' 7
+44103 ' Center' 7
+44104 ' Centre' 7
+44105 ' Centro' 7
+44106 ' Chance' 7
+44107 ' Change' 7
+44108 ' Chapel' 7
+44109 ' Charge' 7
+44110 ' Checks' 7
+44111 ' Cheese' 7
+44112 ' Cherry' 7
+44113 ' Chiefs' 7
+44114 ' Choice' 7
+44115 ' Choose' 7
+44116 ' Christ' 7
+44117 ' Chrome' 7
+44118 ' Church' 7
+44119 ' Cinema' 7
+44120 ' Circle' 7
+44121 ' Cities' 7
+44122 ' Ciudad' 7
+44123 ' Claims' 7
+44124 ' Claire' 7
+44125 ' Clarke' 7
+44126 ' Claude' 7
+44127 ' Clause' 7
+44128 ' Client' 7
+44129 ' Clinic' 7
+44130 ' Closed' 7
+44131 ' Coffee' 7
+44132 ' Colleg' 7
+44133 ' Colomb' 7
+44134 ' Colors' 7
+44135 ' Colour' 7
+44136 ' Columb' 7
+44137 ' Column' 7
+44138 ' Combat' 7
+44139 ' Combin' 7
+44140 ' Comedy' 7
+44141 ' Comics' 7
+44142 ' Coming' 7
+44143 ' Commit' 7
+44144 ' Common' 7
+44145 ' Commun' 7
+44146 ' Compan' 7
+44147 ' Compar' 7
+44148 ' Compet' 7
+44149 ' Comput' 7
+44150 ' Config' 7
+44151 ' Connor' 7
+44152 ' Conrad' 7
+44153 ' Contin' 7
+44154 ' Conway' 7
+44155 ' Cookie' 7
+44156 ' Cooper' 7
+44157 ' Copper' 7
+44158 ' Corbyn' 7
+44159 ' Corner' 7
+44160 ' Corona' 7
+44161 ' Corpor' 7
+44162 ' Corpus' 7
+44163 ' Cotton' 7
+44164 ' County' 7
+44165 ' Course' 7
+44166 ' Courts' 7
+44167 ' Create' 7
+44168 ' Credit' 7
+44169 ' Crimea' 7
+44170 ' Crisis' 7
+44171 ' Cruise' 7
+44172 ' Crypto' 7
+44173 ' Cuando' 7
+44174 ' Cursor' 7
+44175 ' Curtis' 7
+44176 ' Custom' 7
+44177 ' Cyprus' 7
+44178 ' César' 7
+44179 ' DAMAGE' 7
+44180 ' DELETE' 7
+44181 ' DIRECT' 7
+44182 ' Dakota' 7
+44183 ' Dallas' 7
+44184 ' Dalton' 7
+44185 ' Damage' 7
+44186 ' Danger' 7
+44187 ' Daniel' 7
+44188 ' Danish' 7
+44189 ' Darwin' 7
+44190 ' Davies' 7
+44191 ' Dawson' 7
+44192 ' Dayton' 7
+44193 ' Debate' 7
+44194 ' Debian' 7
+44195 ' Define' 7
+44196 ' Degree' 7
+44197 ' Delete' 7
+44198 ' Democr' 7
+44199 ' Denise' 7
+44200 ' Dennis' 7
+44201 ' Dental' 7
+44202 ' Denver' 7
+44203 ' Depart' 7
+44204 ' Depend' 7
+44205 ' Deputy' 7
+44206 ' Desert' 7
+44207 ' Design' 7
+44208 ' Detail' 7
+44209 ' Detect' 7
+44210 ' Device' 7
+44211 ' Dexter' 7
+44212 ' Dialog' 7
+44213 ' Diesel' 7
+44214 ' Digest' 7
+44215 ' Dillon' 7
+44216 ' Direct' 7
+44217 ' Disney' 7
+44218 ' Divine' 7
+44219 ' Django' 7
+44220 ' Docker' 7
+44221 ' Doctor' 7
+44222 ' Dollar' 7
+44223 ' Domain' 7
+44224 ' Donald' 7
+44225 ' Double' 7
+44226 ' Dragon' 7
+44227 ' Dreams' 7
+44228 ' Driver' 7
+44229 ' Drupal' 7
+44230 ' Dublin' 7
+44231 ' Dudley' 7
+44232 ' Duncan' 7
+44233 ' Durant' 7
+44234 ' Durham' 7
+44235 ' During' 7
+44236 ' EXISTS' 7
+44237 ' EXPECT' 7
+44238 ' Eagles' 7
+44239 ' Easter' 7
+44240 ' Eating' 7
+44241 ' Econom' 7
+44242 ' Edison' 7
+44243 ' Edited' 7
+44244 ' Editor' 7
+44245 ' Edmund' 7
+44246 ' Edward' 7
+44247 ' Effect' 7
+44248 ' Eighth' 7
+44249 ' Either' 7
+44250 ' Elaine' 7
+44251 ' Eleven' 7
+44252 ' Elliot' 7
+44253 ' Empire' 7
+44254 ' Employ' 7
+44255 ' Enable' 7
+44256 ' Energy' 7
+44257 ' Engine' 7
+44258 ' Ensure' 7
+44259 ' Entity' 7
+44260 ' Ernest' 7
+44261 ' Errors' 7
+44262 ' Escape' 7
+44263 ' Españ' 7
+44264 ' Essays' 7
+44265 ' Establ' 7
+44266 ' Estado' 7
+44267 ' Estate' 7
+44268 ' Esther' 7
+44269 ' Ethics' 7
+44270 ' Eugene' 7
+44271 ' Europa' 7
+44272 ' Europe' 7
+44273 ' Events' 7
+44274 ' Excell' 7
+44275 ' Except' 7
+44276 ' Expand' 7
+44277 ' Expect' 7
+44278 ' Expert' 7
+44279 ' Export' 7
+44280 ' Fabric' 7
+44281 ' Factor' 7
+44282 ' Failed' 7
+44283 ' Falcon' 7
+44284 ' Family' 7
+44285 ' Farmer' 7
+44286 ' Father' 7
+44287 ' Fellow' 7
+44288 ' Female' 7
+44289 ' Fields' 7
+44290 ' Figure' 7
+44291 ' Filter' 7
+44292 ' Finals' 7
+44293 ' Finish' 7
+44294 ' Finite' 7
+44295 ' Fiscal' 7
+44296 ' Fisher' 7
+44297 ' Flickr' 7
+44298 ' Flight' 7
+44299 ' Flores' 7
+44300 ' Flower' 7
+44301 ' Flying' 7
+44302 ' Folder' 7
+44303 ' Follow' 7
+44304 ' Forbes' 7
+44305 ' Forces' 7
+44306 ' Forest' 7
+44307 ' Format' 7
+44308 ' Former' 7
+44309 ' Foster' 7
+44310 ' Fourth' 7
+44311 ' Fowler' 7
+44312 ' France' 7
+44313 ' Franco' 7
+44314 ' Franç' 7
+44315 ' Fraser' 7
+44316 ' Freder' 7
+44317 ' French' 7
+44318 ' Freund' 7
+44319 ' Friday' 7
+44320 ' Friend' 7
+44321 ' Frozen' 7
+44322 ' Fuller' 7
+44323 ' Fusion' 7
+44324 ' Future' 7
+44325 ' GLOBAL' 7
+44326 ' Galaxy' 7
+44327 ' Galile' 7
+44328 ' Gaming' 7
+44329 ' Gandhi' 7
+44330 ' Garcia' 7
+44331 ' Garden' 7
+44332 ' Gareth' 7
+44333 ' Gemini' 7
+44334 ' Gender' 7
+44335 ' Geneva' 7
+44336 ' Genome' 7
+44337 ' George' 7
+44338 ' Gerald' 7
+44339 ' Gerard' 7
+44340 ' German' 7
+44341 ' Giants' 7
+44342 ' Gibson' 7
+44343 ' GitHub' 7
+44344 ' Github' 7
+44345 ' Global' 7
+44346 ' Gloria' 7
+44347 ' Golden' 7
+44348 ' Google' 7
+44349 ' Gordon' 7
+44350 ' Gospel' 7
+44351 ' Gothic' 7
+44352 ' Govern' 7
+44353 ' Graham' 7
+44354 ' Grammy' 7
+44355 ' Grande' 7
+44356 ' Graves' 7
+44357 ' Greece' 7
+44358 ' Greeks' 7
+44359 ' Greene' 7
+44360 ' Greens' 7
+44361 ' Ground' 7
+44362 ' Groups' 7
+44363 ' Growth' 7
+44364 ' Gründ' 7
+44365 ' Guards' 7
+44366 ' Guinea' 7
+44367 ' Gustav' 7
+44368 ' Gómez' 7
+44369 ' HEADER' 7
+44370 ' Hammer' 7
+44371 ' Handle' 7
+44372 ' Hannah' 7
+44373 ' Hansen' 7
+44374 ' Harbor' 7
+44375 ' Harlem' 7
+44376 ' Harley' 7
+44377 ' Harmon' 7
+44378 ' Harold' 7
+44379 ' Harper' 7
+44380 ' Harris' 7
+44381 ' Harvey' 7
+44382 ' Hassan' 7
+44383 ' Having' 7
+44384 ' Hawaii' 7
+44385 ' Hazard' 7
+44386 ' Header' 7
+44387 ' Health' 7
+44388 ' Heaven' 7
+44389 ' Hebrew' 7
+44390 ' Height' 7
+44391 ' Helena' 7
+44392 ' Helper' 7
+44393 ' Herald' 7
+44394 ' Herman' 7
+44395 ' Heroes' 7
+44396 ' Hidden' 7
+44397 ' Higher' 7
+44398 ' Hilton' 7
+44399 ' Hindus' 7
+44400 ' Histor' 7
+44401 ' Hitler' 7
+44402 ' Hockey' 7
+44403 ' Holder' 7
+44404 ' Hollow' 7
+44405 ' Holmes' 7
+44406 ' Hoover' 7
+44407 ' Howard' 7
+44408 ' Huawei' 7
+44409 ' Hubble' 7
+44410 ' Hudson' 7
+44411 ' Hughes' 7
+44412 ' Humans' 7
+44413 ' Hunter' 7
+44414 ' Hybrid' 7
+44415 ' INSERT' 7
+44416 ' Ignore' 7
+44417 ' Images' 7
+44418 ' Impact' 7
+44419 ' Import' 7
+44420 ' Income' 7
+44421 ' Indeed' 7
+44422 ' Indian' 7
+44423 ' Indies' 7
+44424 ' Indust' 7
+44425 ' Infant' 7
+44426 ' Infect' 7
+44427 ' Inform' 7
+44428 ' Injury' 7
+44429 ' Inputs' 7
+44430 ' Insert' 7
+44431 ' Inside' 7
+44432 ' Instit' 7
+44433 ' Integr' 7
+44434 ' Intent' 7
+44435 ' Intern' 7
+44436 ' Invent' 7
+44437 ' Invest' 7
+44438 ' Irving' 7
+44439 ' Isabel' 7
+44440 ' Isaiah' 7
+44441 ' Island' 7
+44442 ' Israel' 7
+44443 ' Issues' 7
+44444 ' Italia' 7
+44445 ' JQuery' 7
+44446 ' Jackie' 7
+44447 ' Jacobs' 7
+44448 ' Jahren' 7
+44449 ' Jensen' 7
+44450 ' Jeremy' 7
+44451 ' Jerome' 7
+44452 ' Jersey' 7
+44453 ' Jewish' 7
+44454 ' Johann' 7
+44455 ' Johnny' 7
+44456 ' Jordan' 7
+44457 ' Joseph' 7
+44458 ' Joshua' 7
+44459 ' Judges' 7
+44460 ' Judith' 7
+44461 ' Julian' 7
+44462 ' Juliet' 7
+44463 ' Julius' 7
+44464 ' Junior' 7
+44465 ' Justin' 7
+44466 ' Kaiser' 7
+44467 ' Kansas' 7
+44468 ' Kaplan' 7
+44469 ' Kazakh' 7
+44470 ' Keller' 7
+44471 ' Kelley' 7
+44472 ' Kelvin' 7
+44473 ' Kepler' 7
+44474 ' Kerala' 7
+44475 ' Kernel' 7
+44476 ' Kimber' 7
+44477 ' Kinder' 7
+44478 ' Kindle' 7
+44479 ' Knight' 7
+44480 ' Korean' 7
+44481 ' Kosovo' 7
+44482 ' Kultur' 7
+44483 ' Kuwait' 7
+44484 ' König' 7
+44485 ' LETTER' 7
+44486 ' LIABLE' 7
+44487 ' Labels' 7
+44488 ' Labour' 7
+44489 ' Ladies' 7
+44490 ' Lakers' 7
+44491 ' Lambda' 7
+44492 ' Lastly' 7
+44493 ' Latino' 7
+44494 ' Latvia' 7
+44495 ' Launch' 7
+44496 ' Lauren' 7
+44497 ' Lawson' 7
+44498 ' Layout' 7
+44499 ' Leader' 7
+44500 ' League' 7
+44501 ' Lebens' 7
+44502 ' Legacy' 7
+44503 ' Legend' 7
+44504 ' Legion' 7
+44505 ' Legisl' 7
+44506 ' Length' 7
+44507 ' Lennon' 7
+44508 ' Lenovo' 7
+44509 ' Leslie' 7
+44510 ' Lesser' 7
+44511 ' Letter' 7
+44512 ' Levels' 7
+44513 ' Levine' 7
+44514 ' Libert' 7
+44515 ' Libyan' 7
+44516 ' Lights' 7
+44517 ' Linear' 7
+44518 ' Linked' 7
+44519 ' Lionel' 7
+44520 ' Liquid' 7
+44521 ' Lisbon' 7
+44522 ' Listen' 7
+44523 ' Little' 7
+44524 ' Living' 7
+44525 ' Locale' 7
+44526 ' Logger' 7
+44527 ' London' 7
+44528 ' Louise' 7
+44529 ' Ludwig' 7
+44530 ' Luther' 7
+44531 ' López' 7
+44532 ' MATLAB' 7
+44533 ' METHOD' 7
+44534 ' MODULE' 7
+44535 ' Macron' 7
+44536 ' Madame' 7
+44537 ' Madrid' 7
+44538 ' Maduro' 7
+44539 ' Maggie' 7
+44540 ' Magnet' 7
+44541 ' Making' 7
+44542 ' Malays' 7
+44543 ' Manage' 7
+44544 ' Manila' 7
+44545 ' Manual' 7
+44546 ' Manuel' 7
+44547 ' Marcel' 7
+44548 ' Marcos' 7
+44549 ' Marcus' 7
+44550 ' Marian' 7
+44551 ' Marina' 7
+44552 ' Marine' 7
+44553 ' Marino' 7
+44554 ' Marion' 7
+44555 ' Marker' 7
+44556 ' Market' 7
+44557 ' Markov' 7
+44558 ' Markus' 7
+44559 ' Martha' 7
+44560 ' Martin' 7
+44561 ' Martí' 7
+44562 ' Marvel' 7
+44563 ' Marvin' 7
+44564 ' María' 7
+44565 ' Master' 7
+44566 ' Matrix' 7
+44567 ' Matter' 7
+44568 ' McCain' 7
+44569 ' Mechan' 7
+44570 ' Median' 7
+44571 ' Medium' 7
+44572 ' Meghan' 7
+44573 ' Member' 7
+44574 ' Memory' 7
+44575 ' Mental' 7
+44576 ' Merkel' 7
+44577 ' Meteor' 7
+44578 ' Method' 7
+44579 ' Metric' 7
+44580 ' Mexico' 7
+44581 ' Michel' 7
+44582 ' Mickey' 7
+44583 ' Middle' 7
+44584 ' Miguel' 7
+44585 ' Milano' 7
+44586 ' Miller' 7
+44587 ' Milton' 7
+44588 ' Mining' 7
+44589 ' Minist' 7
+44590 ' Mirror' 7
+44591 ' Mobile' 7
+44592 ' Models' 7
+44593 ' Modern' 7
+44594 ' Module' 7
+44595 ' Moment' 7
+44596 ' Monaco' 7
+44597 ' Monday' 7
+44598 ' Monica' 7
+44599 ' Monkey' 7
+44600 ' Monroe' 7
+44601 ' Moreno' 7
+44602 ' Morgan' 7
+44603 ' Mormon' 7
+44604 ' Morris' 7
+44605 ' Morton' 7
+44606 ' Moscow' 7
+44607 ' Mother' 7
+44608 ' Motion' 7
+44609 ' Motors' 7
+44610 ' Moving' 7
+44611 ' Mumbai' 7
+44612 ' Munich' 7
+44613 ' Murder' 7
+44614 ' Murphy' 7
+44615 ' Murray' 7
+44616 ' Museum' 7
+44617 ' Muslim' 7
+44618 ' Musée' 7
+44619 ' Mutual' 7
+44620 ' NOTICE' 7
+44621 ' NUMBER' 7
+44622 ' Naples' 7
+44623 ' Napole' 7
+44624 ' Napoli' 7
+44625 ' Nathan' 7
+44626 ' Nation' 7
+44627 ' Native' 7
+44628 ' Nature' 7
+44629 ' Nearly' 7
+44630 ' Nelson' 7
+44631 ' Nested' 7
+44632 ' Nether' 7
+44633 ' Neural' 7
+44634 ' Nevada' 7
+44635 ' Newark' 7
+44636 ' Newman' 7
+44637 ' Newton' 7
+44638 ' Nguyen' 7
+44639 ' Nickel' 7
+44640 ' Nicola' 7
+44641 ' Nicole' 7
+44642 ' Nissan' 7
+44643 ' Nobody' 7
+44644 ' Normal' 7
+44645 ' Norman' 7
+44646 ' Norris' 7
+44647 ' Norton' 7
+44648 ' Norway' 7
+44649 ' Notice' 7
+44650 ' Notify' 7
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+44652 ' OBJECT' 7
+44653 ' OPTION' 7
+44654 ' OUTPUT' 7
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+44666 ' Orange' 7
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+44669 ' Orient' 7
+44670 ' Origin' 7
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+44673 ' Output' 7
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+44675 ' PARTIC' 7
+44676 ' PROVID' 7
+44677 ' PUBLIC' 7
+44678 ' Packet' 7
+44679 ' Palace' 7
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+44681 ' Palmer' 7
+44682 ' Panama' 7
+44683 ' Papers' 7
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+44690 ' Pascal' 7
+44691 ' Pastor' 7
+44692 ' Patent' 7
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+44702 ' Philos' 7
+44703 ' Photos' 7
+44704 ' Pierce' 7
+44705 ' Pierre' 7
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+44708 ' Planet' 7
+44709 ' Plants' 7
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+44711 ' Player' 7
+44712 ' Please' 7
+44713 ' Plugin' 7
+44714 ' Pocket' 7
+44715 ' Poetry' 7
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+44718 ' Police' 7
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+44720 ' Polish' 7
+44721 ' Portal' 7
+44722 ' Porter' 7
+44723 ' Portug' 7
+44724 ' Postal' 7
+44725 ' Posted' 7
+44726 ' Potter' 7
+44727 ' Powell' 7
+44728 ' Powers' 7
+44729 ' Prague' 7
+44730 ' Prayer' 7
+44731 ' Pretty' 7
+44732 ' Priest' 7
+44733 ' Primer' 7
+44734 ' Prince' 7
+44735 ' Prison' 7
+44736 ' Proced' 7
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+44738 ' Public' 7
+44739 ' Puerto' 7
+44740 ' Punjab' 7
+44741 ' Purple' 7
+44742 ' Python' 7
+44743 ' Pérez' 7
+44744 ' Quando' 7
+44745 ' Quebec' 7
+44746 ' Queens' 7
+44747 ' RETURN' 7
+44748 ' Rabbit' 7
+44749 ' Rachel' 7
+44750 ' Racing' 7
+44751 ' Rafael' 7
+44752 ' Rahman' 7
+44753 ' Ramsey' 7
+44754 ' Random' 7
+44755 ' Ranger' 7
+44756 ' Rapids' 7
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+44758 ' Rating' 7
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+44760 ' Reader' 7
+44761 ' Reagan' 7
+44762 ' Really' 7
+44763 ' Reason' 7
+44764 ' Recall' 7
+44765 ' Recent' 7
+44766 ' Recipe' 7
+44767 ' Recogn' 7
+44768 ' Recomm' 7
+44769 ' Record' 7
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+44771 ' Reduce' 7
+44772 ' Reform' 7
+44773 ' Refuge' 7
+44774 ' Regina' 7
+44775 ' Region' 7
+44776 ' Relief' 7
+44777 ' Remark' 7
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+44780 ' Render' 7
+44781 ' Repeat' 7
+44782 ' Report' 7
+44783 ' Rescue' 7
+44784 ' Resort' 7
+44785 ' Result' 7
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+44804 ' Ronald' 7
+44805 ' Rousse' 7
+44806 ' Router' 7
+44807 ' Runner' 7
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+44812 ' STATES' 7
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+44842 ' Script' 7
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+44847 ' Second' 7
+44848 ' Secret' 7
+44849 ' Sector' 7
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+44852 ' Según' 7
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+44894 ' Sophie' 7
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+44950 ' TARGET' 7
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+44954 ' Taking' 7
+44955 ' Talent' 7
+44956 ' Target' 7
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+44959 ' Temper' 7
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+44961 ' Tennis' 7
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+44982 ' Toyota' 7
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+44988 ' Trevor' 7
+44989 ' Trials' 7
+44990 ' Triple' 7
+44991 ' Trophy' 7
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+44994 ' Tucker' 7
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+44996 ' Turkey' 7
+44997 ' Turner' 7
+44998 ' Twelve' 7
+44999 ' Twenty' 7
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+45001 ' UIView' 7
+45002 ' UNESCO' 7
+45003 ' UPDATE' 7
+45004 ' Ubuntu' 7
+45005 ' Uganda' 7
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+45009 ' Unique' 7
+45010 ' United' 7
+45011 ' União' 7
+45012 ' Unless' 7
+45013 ' Unlike' 7
+45014 ' Update' 7
+45015 ' Upload' 7
+45016 ' VALUES' 7
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+45018 ' Valley' 7
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+47334 ' solver' 7
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+47376 ' sprite' 7
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+47379 ' square' 7
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+47385 ' stages' 7
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+47392 ' stands' 7
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+47398 ' states' 7
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+47403 ' stderr' 7
+47404 ' stdout' 7
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+47407 ' steril' 7
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+47409 ' sticky' 7
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+47414 ' stones' 7
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+47418 ' strain' 7
+47419 ' strand' 7
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+47427 ' stride' 7
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+47430 ' stripe' 7
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+47442 ' sturdy' 7
+47443 ' styled' 7
+47444 ' styles' 7
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+47446 ' submar' 7
+47447 ' submit' 7
+47448 ' subset' 7
+47449 ' subsid' 7
+47450 ' substr' 7
+47451 ' subtle' 7
+47452 ' suburb' 7
+47453 ' subway' 7
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+47455 ' sucked' 7
+47456 ' sudden' 7
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+47458 ' suffix' 7
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+47464 ' summed' 7
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+47467 ' summon' 7
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+47471 ' supern' 7
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+47474 ' supper' 7
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+47481 ' switch' 7
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+47483 ' symbol' 7
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+47486 ' syntax' 7
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+47489 ' série' 7
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+47491 ' tablet' 7
+47492 ' tackle' 7
+47493 ' tactic' 7
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+47496 ' tailor' 7
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+47499 ' talent' 7
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+47502 ' talál' 7
+47503 ' també' 7
+47504 ' tandem' 7
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+47526 ' termin' 7
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+47556 ' thrust' 7
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+47560 ' thông' 7
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+47568 ' timing' 7
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+47599 ' tragic' 7
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+47604 ' transc' 7
+47605 ' transf' 7
+47606 ' transl' 7
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+47608 ' trauma' 7
+47609 ' travel' 7
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+47641 ' twelve' 7
+47642 ' twenty' 7
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+47644 ' typeof' 7
+47645 ' typing' 7
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+47647 ' télé' 7
+47648 ' ubuntu' 7
+47649 ' ultras' 7
+47650 ' unable' 7
+47651 ' unanim' 7
+47652 ' uncomp' 7
+47653 ' unders' 7
+47654 ' undert' 7
+47655 ' uneasy' 7
+47656 ' uneven' 7
+47657 ' unfair' 7
+47658 ' unfold' 7
+47659 ' unions' 7
+47660 ' unique' 7
+47661 ' united' 7
+47662 ' unjust' 7
+47663 ' unless' 7
+47664 ' unlike' 7
+47665 ' unlock' 7
+47666 ' unpack' 7
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+47668 ' unreal' 7
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+47670 ' unsafe' 7
+47671 ' unseen' 7
+47672 ' unsett' 7
+47673 ' unsure' 7
+47674 ' unused' 7
+47675 ' update' 7
+47676 ' upgrad' 7
+47677 ' upheld' 7
+47678 ' uphold' 7
+47679 ' upload' 7
+47680 ' upside' 7
+47681 ' uptake' 7
+47682 ' upward' 7
+47683 ' urgent' 7
+47684 ' urging' 7
+47685 ' urllib' 7
+47686 ' usable' 7
+47687 ' useful' 7
+47688 ' userId' 7
+47689 ' uterus' 7
+47690 ' utilis' 7
+47691 ' utiliz' 7
+47692 ' utmost' 7
+47693 ' vacant' 7
+47694 ' vacuum' 7
+47695 ' vagina' 7
+47696 ' valeur' 7
+47697 ' valign' 7
+47698 ' valley' 7
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+47701 ' valves' 7
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+47703 ' vanish' 7
+47704 ' varias' 7
+47705 ' varied' 7
+47706 ' varies' 7
+47707 ' varios' 7
+47708 ' vastly' 7
+47709 ' vector' 7
+47710 ' vendor' 7
+47711 ' ventil' 7
+47712 ' venues' 7
+47713 ' verbal' 7
+47714 ' verify' 7
+47715 ' versch' 7
+47716 ' verses' 7
+47717 ' versus' 7
+47718 ' vertex' 7
+47719 ' vessel' 7
+47720 ' viable' 7
+47721 ' victim' 7
+47722 ' videos' 7
+47723 ' vidéo' 7
+47724 ' viewed' 7
+47725 ' viewer' 7
+47726 ' világ' 7
+47727 ' violet' 7
+47728 ' violin' 7
+47729 ' virgin' 7
+47730 ' virtue' 7
+47731 ' viscos' 7
+47732 ' vision' 7
+47733 ' visits' 7
+47734 ' visual' 7
+47735 ' việc' 7
+47736 ' vocals' 7
+47737 ' voiced' 7
+47738 ' voices' 7
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+47740 ' volley' 7
+47741 ' volont' 7
+47742 ' volume' 7
+47743 ' volunt' 7
+47744 ' vortex' 7
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+47746 ' voting' 7
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+47749 ' vulner' 7
+47750 ' válto' 7
+47751 ' város' 7
+47752 ' véhic' 7
+47753 ' vérit' 7
+47754 ' vídeo' 7
+47755 ' waited' 7
+47756 ' waiter' 7
+47757 ' waived' 7
+47758 ' waiver' 7
+47759 ' waking' 7
+47760 ' walked' 7
+47761 ' wallet' 7
+47762 ' wander' 7
+47763 ' wanted' 7
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+47774 ' weakly' 7
+47775 ' wealth' 7
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+47779 ' weight' 7
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+47782 ' werden' 7
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+47784 ' wheels' 7
+47785 ' whence' 7
+47786 ' whilst' 7
+47787 ' whisky' 7
+47788 ' whites' 7
+47789 ' wholes' 7
+47790 ' wholly' 7
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+47792 ' widely' 7
+47793 ' widget' 7
+47794 ' widths' 7
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+47796 ' wildly' 7
+47797 ' window' 7
+47798 ' winner' 7
+47799 ' winter' 7
+47800 ' wiping' 7
+47801 ' wiring' 7
+47802 ' wisdom' 7
+47803 ' wisely' 7
+47804 ' wished' 7
+47805 ' wishes' 7
+47806 ' within' 7
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+47808 ' wolves' 7
+47809 ' wonder' 7
+47810 ' wooden' 7
+47811 ' worden' 7
+47812 ' worked' 7
+47813 ' worker' 7
+47814 ' worlds' 7
+47815 ' worthy' 7
+47816 ' wouldn' 7
+47817 ' wounds' 7
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+47819 ' writes' 7
+47820 ' wrześ' 7
+47821 ' wurden' 7
+47822 ' würde' 7
+47823 ' yearly' 7
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+47825 ' yellow' 7
+47826 ' yields' 7
+47827 ' yogurt' 7
+47828 ' youths' 7
+47829 ' zombie' 7
+47830 ' zoning' 7
+47831 ' związ' 7
+47832 ' États' 7
+47833 ' Übers' 7
+47834 ' álbum' 7
+47835 ' áreas' 7
+47836 ' écrit' 7
+47837 ' élect' 7
+47838 ' época' 7
+47839 ' équip' 7
+47840 ' établ' 7
+47841 ' était' 7
+47842 ' étant' 7
+47843 ' étudi' 7
+47844 ' évén' 7
+47845 ' éxito' 7
+47846 ' única' 7
+47847 ' único' 7
+47848 ' člán' 7
+47849 ' část' 7
+47850 ' đầu' 7
+47851 ' đến' 7
+47852 ' œuvre' 7
+47853 ' świat' 7
+47854 ' από' 7
+47855 ' για' 7
+47856 ' και' 7
+47857 ' την' 7
+47858 ' της' 7
+47859 ' του' 7
+47860 ' Аль' 7
+47861 ' Ана' 7
+47862 ' Анд' 7
+47863 ' Бар' 7
+47864 ' Бер' 7
+47865 ' Бра' 7
+47866 ' Бри' 7
+47867 ' Вар' 7
+47868 ' Вер' 7
+47869 ' Вла' 7
+47870 ' Вол' 7
+47871 ' Все' 7
+47872 ' Від' 7
+47873 ' Він' 7
+47874 ' Ген' 7
+47875 ' Гер' 7
+47876 ' Гор' 7
+47877 ' Гра' 7
+47878 ' Гре' 7
+47879 ' Гри' 7
+47880 ' Дже' 7
+47881 ' Див' 7
+47882 ' Для' 7
+47883 ' Дми' 7
+47884 ' Дру' 7
+47885 ' Его' 7
+47886 ' Кар' 7
+47887 ' Ком' 7
+47888 ' Кон' 7
+47889 ' Кор' 7
+47890 ' Кра' 7
+47891 ' Кри' 7
+47892 ' Кур' 7
+47893 ' Лон' 7
+47894 ' Льв' 7
+47895 ' Май' 7
+47896 ' Мак' 7
+47897 ' Мар' 7
+47898 ' Мон' 7
+47899 ' Мор' 7
+47900 ' Мос' 7
+47901 ' Нау' 7
+47902 ' Нов' 7
+47903 ' Нор' 7
+47904 ' Нью' 7
+47905 ' Оте' 7
+47906 ' Пав' 7
+47907 ' Пар' 7
+47908 ' Пер' 7
+47909 ' Пет' 7
+47910 ' Под' 7
+47911 ' Пор' 7
+47912 ' Пра' 7
+47913 ' Пре' 7
+47914 ' При' 7
+47915 ' Про' 7
+47916 ' Пів' 7
+47917 ' Під' 7
+47918 ' Раз' 7
+47919 ' Рас' 7
+47920 ' Рес' 7
+47921 ' Роб' 7
+47922 ' Роз' 7
+47923 ' Рос' 7
+47924 ' Рус' 7
+47925 ' США' 7
+47926 ' Сан' 7
+47927 ' Свя' 7
+47928 ' Сер' 7
+47929 ' Сов' 7
+47930 ' Спо' 7
+47931 ' Ста' 7
+47932 ' Сте' 7
+47933 ' Так' 7
+47934 ' Тер' 7
+47935 ' Тре' 7
+47936 ' Три' 7
+47937 ' Тур' 7
+47938 ' Хар' 7
+47939 ' Хро' 7
+47940 ' Чем' 7
+47941 ' Чер' 7
+47942 ' Это' 7
+47943 ' або' 7
+47944 ' акт' 7
+47945 ' але' 7
+47946 ' аль' 7
+47947 ' ана' 7
+47948 ' анг' 7
+47949 ' бан' 7
+47950 ' без' 7
+47951 ' био' 7
+47952 ' бли' 7
+47953 ' бор' 7
+47954 ' бра' 7
+47955 ' бри' 7
+47956 ' бро' 7
+47957 ' був' 7
+47958 ' буд' 7
+47959 ' был' 7
+47960 ' важ' 7
+47961 ' вер' 7
+47962 ' взя' 7
+47963 ' вид' 7
+47964 ' вла' 7
+47965 ' вме' 7
+47966 ' вне' 7
+47967 ' воз' 7
+47968 ' вой' 7
+47969 ' вос' 7
+47970 ' вра' 7
+47971 ' вре' 7
+47972 ' все' 7
+47973 ' всі' 7
+47974 ' вто' 7
+47975 ' від' 7
+47976 ' вій' 7
+47977 ' він' 7
+47978 ' где' 7
+47979 ' гер' 7
+47980 ' гла' 7
+47981 ' год' 7
+47982 ' гор' 7
+47983 ' гра' 7
+47984 ' гре' 7
+47985 ' гру' 7
+47986 ' дан' 7
+47987 ' два' 7
+47988 ' две' 7
+47989 ' дви' 7
+47990 ' дво' 7
+47991 ' дву' 7
+47992 ' дей' 7
+47993 ' дет' 7
+47994 ' дея' 7
+47995 ' диа' 7
+47996 ' для' 7
+47997 ' дов' 7
+47998 ' дол' 7
+47999 ' дра' 7
+48000 ' дру' 7
+48001 ' дія' 7
+48002 ' его' 7
+48003 ' еди' 7
+48004 ' ему' 7
+48005 ' ещё' 7
+48006 ' жен' 7
+48007 ' жов' 7
+48008 ' зав' 7
+48009 ' зад' 7
+48010 ' зай' 7
+48011 ' зак' 7
+48012 ' зан' 7
+48013 ' зап' 7
+48014 ' зву' 7
+48015 ' зда' 7
+48016 ' зем' 7
+48017 ' змі' 7
+48018 ' зна' 7
+48019 ' иде' 7
+48020 ' изу' 7
+48021 ' или' 7
+48022 ' има' 7
+48023 ' име' 7
+48024 ' исп' 7
+48025 ' как' 7
+48026 ' кам' 7
+48027 ' кан' 7
+48028 ' као' 7
+48029 ' кар' 7
+48030 ' кла' 7
+48031 ' кли' 7
+48032 ' клу' 7
+48033 ' кни' 7
+48034 ' кня' 7
+48035 ' кол' 7
+48036 ' ком' 7
+48037 ' кон' 7
+48038 ' кор' 7
+48039 ' кра' 7
+48040 ' кре' 7
+48041 ' кри' 7
+48042 ' кур' 7
+48043 ' лет' 7
+48044 ' луч' 7
+48045 ' май' 7
+48046 ' мар' 7
+48047 ' мат' 7
+48048 ' мая' 7
+48049 ' має' 7
+48050 ' мен' 7
+48051 ' мет' 7
+48052 ' мно' 7
+48053 ' мож' 7
+48054 ' мор' 7
+48055 ' муж' 7
+48056 ' між' 7
+48057 ' міс' 7
+48058 ' нав' 7
+48059 ' над' 7
+48060 ' наи' 7
+48061 ' най' 7
+48062 ' нап' 7
+48063 ' нау' 7
+48064 ' нај' 7
+48065 ' ней' 7
+48066 ' них' 7
+48067 ' нов' 7
+48068 ' нор' 7
+48069 ' нуж' 7
+48070 ' оби' 7
+48071 ' обо' 7
+48072 ' обу' 7
+48073 ' объ' 7
+48074 ' обы' 7
+48075 ' окт' 7
+48076 ' она' 7
+48077 ' они' 7
+48078 ' ори' 7
+48079 ' ору' 7
+48080 ' осо' 7
+48081 ' ост' 7
+48082 ' осу' 7
+48083 ' още' 7
+48084 ' пар' 7
+48085 ' пер' 7
+48086 ' пес' 7
+48087 ' пла' 7
+48088 ' пло' 7
+48089 ' пов' 7
+48090 ' под' 7
+48091 ' пол' 7
+48092 ' пом' 7
+48093 ' пор' 7
+48094 ' пос' 7
+48095 ' пот' 7
+48096 ' поч' 7
+48097 ' поэ' 7
+48098 ' поя' 7
+48099 ' пра' 7
+48100 ' пре' 7
+48101 ' при' 7
+48102 ' про' 7
+48103 ' пря' 7
+48104 ' пси' 7
+48105 ' пун' 7
+48106 ' пів' 7
+48107 ' під' 7
+48108 ' раз' 7
+48109 ' рай' 7
+48110 ' ран' 7
+48111 ' рас' 7
+48112 ' реа' 7
+48113 ' ред' 7
+48114 ' род' 7
+48115 ' роз' 7
+48116 ' рос' 7
+48117 ' рус' 7
+48118 ' рів' 7
+48119 ' різ' 7
+48120 ' рік' 7
+48121 ' сай' 7
+48122 ' сам' 7
+48123 ' све' 7
+48124 ' сви' 7
+48125 ' сво' 7
+48126 ' свя' 7
+48127 ' сві' 7
+48128 ' сез' 7
+48129 ' сем' 7
+48130 ' сер' 7
+48131 ' сим' 7
+48132 ' син' 7
+48133 ' ско' 7
+48134 ' сла' 7
+48135 ' сле' 7
+48136 ' сло' 7
+48137 ' слу' 7
+48138 ' соб' 7
+48139 ' сов' 7
+48140 ' сот' 7
+48141 ' спе' 7
+48142 ' спи' 7
+48143 ' спо' 7
+48144 ' сра' 7
+48145 ' сре' 7
+48146 ' ста' 7
+48147 ' сте' 7
+48148 ' сти' 7
+48149 ' сто' 7
+48150 ' сту' 7
+48151 ' так' 7
+48152 ' там' 7
+48153 ' тан' 7
+48154 ' теа' 7
+48155 ' тек' 7
+48156 ' тем' 7
+48157 ' тео' 7
+48158 ' тер' 7
+48159 ' тех' 7
+48160 ' той' 7
+48161 ' том' 7
+48162 ' тор' 7
+48163 ' тра' 7
+48164 ' тре' 7
+48165 ' три' 7
+48166 ' тро' 7
+48167 ' тру' 7
+48168 ' тур' 7
+48169 ' уби' 7
+48170 ' уда' 7
+48171 ' уез' 7
+48172 ' уже' 7
+48173 ' ули' 7
+48174 ' уни' 7
+48175 ' уні' 7
+48176 ' упо' 7
+48177 ' уча' 7
+48178 ' уче' 7
+48179 ' учи' 7
+48180 ' учё' 7
+48181 ' фай' 7
+48182 ' фев' 7
+48183 ' фло' 7
+48184 ' фон' 7
+48185 ' фор' 7
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+48187 ' цар' 7
+48188 ' цер' 7
+48189 ' час' 7
+48190 ' чем' 7
+48191 ' чер' 7
+48192 ' чис' 7
+48193 ' что' 7
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+48195 ' шта' 7
+48196 ' што' 7
+48197 ' экс' 7
+48198 ' эта' 7
+48199 ' эти' 7
+48200 ' это' 7
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+48202 ' язы' 7
+48203 ' яка' 7
+48204 ' які' 7
+48205 ' још' 7
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+48207 ' الأ' 7
+48208 ' الت' 7
+48209 ' الع' 7
+48210 ' الم' 7
+48211 ' بال' 7
+48212 ' وال' 7
+48213 ' ‘‘' 7
+48214 ' “…' 7
+48215 ' ′′' 7
+48216 ' −→' 7
+48217 ' 가능' 7
+48218 ' 가장' 7
+48219 ' 가지' 7
+48220 ' 감독' 7
+48221 ' 강원' 7
+48222 ' 강추' 7
+48223 ' 같은' 7
+48224 ' 개인' 7
+48225 ' 것으' 7
+48226 ' 것은' 7
+48227 ' 것을' 7
+48228 ' 것이' 7
+48229 ' 게임' 7
+48230 ' 경기' 7
+48231 ' 경남' 7
+48232 ' 경북' 7
+48233 ' 경우' 7
+48234 ' 관련' 7
+48235 ' 국가' 7
+48236 ' 그는' 7
+48237 ' 그러' 7
+48238 ' 그리' 7
+48239 ' 그의' 7
+48240 ' 기록' 7
+48241 ' 내용' 7
+48242 ' 다른' 7
+48243 ' 다시' 7
+48244 ' 다음' 7
+48245 ' 당시' 7
+48246 ' 대학' 7
+48247 ' 대한' 7
+48248 ' 대해' 7
+48249 ' 덤프' 7
+48250 ' 되었' 7
+48251 ' 된다' 7
+48252 ' 들어' 7
+48253 ' 따라' 7
+48254 ' 때문' 7
+48255 ' 또는' 7
+48256 ' 또한' 7
+48257 ' 리뷰' 7
+48258 ' 링크' 7
+48259 ' 만들' 7
+48260 ' 많은' 7
+48261 ' 모두' 7
+48262 ' 모든' 7
+48263 ' 문제' 7
+48264 ' 미국' 7
+48265 ' 방송' 7
+48266 ' 부산' 7
+48267 ' 사람' 7
+48268 ' 사용' 7
+48269 ' 사이' 7
+48270 ' 사회' 7
+48271 ' 생각' 7
+48272 ' 서비' 7
+48273 ' 서울' 7
+48274 ' 선수' 7
+48275 ' 세계' 7
+48276 ' 시간' 7
+48277 ' 시작' 7
+48278 ' 시즌' 7
+48279 ' 아니' 7
+48280 ' 아이' 7
+48281 ' 없는' 7
+48282 ' 연구' 7
+48283 ' 영화' 7
+48284 ' 우리' 7
+48285 ' 위한' 7
+48286 ' 위해' 7
+48287 ' 의해' 7
+48288 ' 이름' 7
+48289 ' 이상' 7
+48290 ' 이용' 7
+48291 ' 이후' 7
+48292 ' 인천' 7
+48293 ' 일본' 7
+48294 ' 일부' 7
+48295 ' 있는' 7
+48296 ' 있다' 7
+48297 ' 있습' 7
+48298 ' 있었' 7
+48299 ' 있으' 7
+48300 ' 자신' 7
+48301 ' 작품' 7
+48302 ' 전남' 7
+48303 ' 정보' 7
+48304 ' 제공' 7
+48305 ' 조선' 7
+48306 ' 좋아' 7
+48307 ' 좋은' 7
+48308 ' 지난' 7
+48309 ' 지역' 7
+48310 ' 추천' 7
+48311 ' 축구' 7
+48312 ' 출장' 7
+48313 ' 충남' 7
+48314 ' 통해' 7
+48315 ' 포함' 7
+48316 ' 프랑' 7
+48317 ' 프로' 7
+48318 ' 필요' 7
+48319 ' 하고' 7
+48320 ' 하나' 7
+48321 ' 하는' 7
+48322 ' 하였' 7
+48323 ' 하지' 7
+48324 ' 한국' 7
+48325 ' 한다' 7
+48326 ' 함께' 7
+48327 ' 합니' 7
+48328 ' 했다' 7
+48329 ' 현재' 7
+48330 ' 확인' 7
+48331 ' 활동' 7
+48332 ' 회사' 7
+48333 ' 회원' 7
+48334 ' 후기' 7
+48335 '#######' 7
+48336 "'était" 7
+48337 '*******' 7
+48338 ',’’' 7
+48339 ',’”' 7
+48340 '------+' 7
+48341 '-------' 7
+48342 '.......' 7
+48343 '.’”' 7
+48344 '=======' 7
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+48346 'ADDRESS' 7
+48347 'ATIONAL' 7
+48348 'Account' 7
+48349 'Actions' 7
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+48361 'Archive' 7
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+49885 'ßerdem' 7
+49886 'ágenes' 7
+49887 'ánchez' 7
+49888 'ándose' 7
+49889 'ástica' 7
+49890 'áticas' 7
+49891 'ázquez' 7
+49892 'ädchen' 7
+49893 'ährend' 7
+49894 'ències' 7
+49895 'économ' 7
+49896 'église' 7
+49897 'éments' 7
+49898 'énario' 7
+49899 'époque' 7
+49900 'équipe' 7
+49901 'ército' 7
+49902 'érieur' 7
+49903 'érique' 7
+49904 'étique' 7
+49905 'évrier' 7
+49906 'ências' 7
+49907 'ículos' 7
+49908 'ística' 7
+49909 'öffent' 7
+49910 'öglich' 7
+49911 'ública' 7
+49912 'ührung' 7
+49913 'üsseld' 7
+49914 'ńskiej' 7
+49915 'œuvres' 7
+49916 'ździer' 7
+49917 'ường' 7
+49918 'ượng' 7
+49919 '\t\t\t\t\t\t\t\t' 8
+49920 '\n\t\t\t\t\t\t\t' 8
+49921 '\n\n\n\n\n\n\n\n' 8
+49922 '\n\n      ' 8
+49923 '\n       ' 8
+49924 '\r\n\r\n\r\n\r\n' 8
+49925 '\r\n      ' 8
+49926 ' \n      ' 8
+49927 '        ' 8
+49928 ' -------' 8
+49929 ' ARISING' 8
+49930 ' Ability' 8
+49931 ' Abraham' 8
+49932 ' Academy' 8
+49933 ' Account' 8
+49934 ' Actions' 8
+49935 ' Adapter' 8
+49936 ' Address' 8
+49937 ' Además' 8
+49938 ' Admiral' 8
+49939 ' Advance' 8
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+49943 ' Airport' 8
+49944 ' Airways' 8
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+49946 ' Alberta' 8
+49947 ' Alberto' 8
+49948 ' Alcohol' 8
+49949 ' Alexand' 8
+49950 ' Algebra' 8
+49951 ' Algeria' 8
+49952 ' Allison' 8
+49953 ' Already' 8
+49954 ' Alvarez' 8
+49955 ' Amazing' 8
+49956 ' America' 8
+49957 ' Ancient' 8
+49958 ' Andreas' 8
+49959 ' Andrews' 8
+49960 ' Android' 8
+49961 ' Angeles' 8
+49962 ' Angular' 8
+49963 ' Animals' 8
+49964 ' Another' 8
+49965 ' Anthony' 8
+49966 ' Anthrop' 8
+49967 ' Antoine' 8
+49968 ' Antonio' 8
+49969 ' Anxiety' 8
+49970 ' Appeals' 8
+49971 ' Applied' 8
+49972 ' Arabian' 8
+49973 ' Archive' 8
+49974 ' Arduino' 8
+49975 ' Arizona' 8
+49976 ' Armenia' 8
+49977 ' Arsenal' 8
+49978 ' Article' 8
+49979 ' Artikel' 8
+49980 ' Artists' 8
+49981 ' Assange' 8
+49982 ' Atlanta' 8
+49983 ' Attempt' 8
+49984 ' Auditor' 8
+49985 ' Austral' 8
+49986 ' Austria' 8
+49987 ' Authors' 8
+49988 ' Average' 8
+49989 ' Awesome' 8
+49990 ' BETWEEN' 8
+49991 ' Babylon' 8
+49992 ' Baghdad' 8
+49993 ' Bahamas' 8
+49994 ' Bahrain' 8
+49995 ' Balance' 8
+49996 ' Baldwin' 8
+49997 ' Bangkok' 8
+49998 ' Baptist' 8
+49999 ' Barbara' 8
+50000 ' Barrett' 8
+50001 ' Battery' 8
+50002 ' Beatles' 8
+50003 ' Because' 8
+50004 ' Bedford' 8
+50005 ' Beijing' 8
+50006 ' Belarus' 8
+50007 ' Belfast' 8
+50008 ' Belgian' 8
+50009 ' Belgium' 8
+50010 ' Believe' 8
+50011 ' Bennett' 8
+50012 ' Bernard' 8
+50013 ' Besides' 8
+50014 ' Between' 8
+50015 ' Beverly' 8
+50016 ' Binding' 8
+50017 ' Biology' 8
+50018 ' Bitcoin' 8
+50019 ' Bolivia' 8
+50020 ' Boolean' 8
+50021 ' Borough' 8
+50022 ' Bradley' 8
+50023 ' Brandon' 8
+50024 ' Brennan' 8
+50025 ' Brewing' 8
+50026 ' Brigade' 8
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+50028 ' Britain' 8
+50029 ' Britann' 8
+50030 ' British' 8
+50031 ' Broncos' 8
+50032 ' Brother' 8
+50033 ' Browser' 8
+50034 ' Buffalo' 8
+50035 ' Builder' 8
+50036 ' Bürger' 8
+50037 ' CHAPTER' 8
+50038 ' COMMAND' 8
+50039 ' COPYING' 8
+50040 ' Cabinet' 8
+50041 ' Calgary' 8
+50042 ' Calling' 8
+50043 ' Cameron' 8
+50044 ' Capital' 8
+50045 ' Capitol' 8
+50046 ' Captain' 8
+50047 ' Caption' 8
+50048 ' Capture' 8
+50049 ' Cardiff' 8
+50050 ' Carroll' 8
+50051 ' Catalan' 8
+50052 ' Catalog' 8
+50053 ' Celsius' 8
+50054 ' Centers' 8
+50055 ' Central' 8
+50056 ' Century' 8
+50057 ' Certain' 8
+50058 ' Chamber' 8
+50059 ' Changed' 8
+50060 ' Changes' 8
+50061 ' Channel' 8
+50062 ' Chapman' 8
+50063 ' Chapter' 8
+50064 ' Charles' 8
+50065 ' Charlie' 8
+50066 ' Charter' 8
+50067 ' Chelsea' 8
+50068 ' Chennai' 8
+50069 ' Chester' 8
+50070 ' Chicago' 8
+50071 ' Chicken' 8
+50072 ' Chinese' 8
+50073 ' Chronic' 8
+50074 ' Circuit' 8
+50075 ' Citizen' 8
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+50077 ' Classic' 8
+50078 ' Claudia' 8
+50079 ' Clayton' 8
+50080 ' Clearly' 8
+50081 ' Clement' 8
+50082 ' Climate' 8
+50083 ' Clinton' 8
+50084 ' Cluster' 8
+50085 ' Coleman' 8
+50086 ' Collect' 8
+50087 ' College' 8
+50088 ' Collins' 8
+50089 ' Cologne' 8
+50090 ' Colonel' 8
+50091 ' Columns' 8
+50092 ' Combine' 8
+50093 ' Comité' 8
+50094 ' Command' 8
+50095 ' Comment' 8
+50096 ' Commons' 8
+50097 ' Compact' 8
+50098 ' Company' 8
+50099 ' Compare' 8
+50100 ' Complex' 8
+50101 ' Compute' 8
+50102 ' Concent' 8
+50103 ' Concept' 8
+50104 ' Concert' 8
+50105 ' Concord' 8
+50106 ' Conduct' 8
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+50108 ' Connect' 8
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+50110 ' Console' 8
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+50112 ' Consult' 8
+50113 ' Contact' 8
+50114 ' Content' 8
+50115 ' Contest' 8
+50116 ' Context' 8
+50117 ' Contrib' 8
+50118 ' Control' 8
+50119 ' Convers' 8
+50120 ' Convert' 8
+50121 ' Cookies' 8
+50122 ' Corinth' 8
+50123 ' Cornell' 8
+50124 ' Correct' 8
+50125 ' Council' 8
+50126 ' Counsel' 8
+50127 ' Counter' 8
+50128 ' Country' 8
+50129 ' Cowboys' 8
+50130 ' Created' 8
+50131 ' Creates' 8
+50132 ' Creator' 8
+50133 ' Cricket' 8
+50134 ' Critics' 8
+50135 ' Croatia' 8
+50136 ' Crystal' 8
+50137 ' Culture' 8
+50138 ' Current' 8
+50139 ' Customs' 8
+50140 ' Cynthia' 8
+50141 ' DAMAGES' 8
+50142 ' DEFAULT' 8
+50143 ' Daniels' 8
+50144 ' Dataset' 8
+50145 ' Deborah' 8
+50146 ' Decimal' 8
+50147 ' Default' 8
+50148 ' Defence' 8
+50149 ' Defense' 8
+50150 ' Denmark' 8
+50151 ' Derived' 8
+50152 ' Desktop' 8
+50153 ' Despite' 8
+50154 ' Destroy' 8
+50155 ' Details' 8
+50156 ' Detroit' 8
+50157 ' Deutsch' 8
+50158 ' Develop' 8
+50159 ' Devices' 8
+50160 ' Diagram' 8
+50161 ' Diamond' 8
+50162 ' Digital' 8
+50163 ' Disable' 8
+50164 ' Disease' 8
+50165 ' Display' 8
+50166 ' Doctors' 8
+50167 ' Dodgers' 8
+50168 ' Dominic' 8
+50169 ' Donovan' 8
+50170 ' Dorothy' 8
+50171 ' Douglas' 8
+50172 ' Dragons' 8
+50173 ' Drawing' 8
+50174 ' Dropbox' 8
+50175 ' Duchess' 8
+50176 ' Duterte' 8
+50177 ' Dynamic' 8
+50178 ' Dynasty' 8
+50179 ' EXPRESS' 8
+50180 ' Earlier' 8
+50181 ' Eastern' 8
+50182 ' Eclipse' 8
+50183 ' Economy' 8
+50184 ' Ecuador' 8
+50185 ' Edition' 8
+50186 ' Edwards' 8
+50187 ' Effects' 8
+50188 ' Elastic' 8
+50189 ' Eleanor' 8
+50190 ' Electro' 8
+50191 ' Element' 8
+50192 ' Elliott' 8
+50193 ' Emanuel' 8
+50194 ' Embassy' 8
+50195 ' Emerson' 8
+50196 ' Emperor' 8
+50197 ' Encoder' 8
+50198 ' England' 8
+50199 ' English' 8
+50200 ' Enrique' 8
+50201 ' Episode' 8
+50202 ' Epstein' 8
+50203 ' Erdogan' 8
+50204 ' España' 8
+50205 ' Estados' 8
+50206 ' Estonia' 8
+50207 ' Evangel' 8
+50208 ' Evening' 8
+50209 ' Everton' 8
+50210 ' Example' 8
+50211 ' Execute' 8
+50212 ' Experts' 8
+50213 ' Express' 8
+50214 ' Extract' 8
+50215 ' Extreme' 8
+50216 ' FITNESS' 8
+50217 ' Factors' 8
+50218 ' Factory' 8
+50219 ' Faculty' 8
+50220 ' Failure' 8
+50221 ' Falcons' 8
+50222 ' Fantasy' 8
+50223 ' Farmers' 8
+50224 ' Fashion' 8
+50225 ' Feature' 8
+50226 ' Federal' 8
+50227 ' Ferrari' 8
+50228 ' Fiction' 8
+50229 ' Fighter' 8
+50230 ' Figures' 8
+50231 ' Filters' 8
+50232 ' Finally' 8
+50233 ' Finance' 8
+50234 ' Finding' 8
+50235 ' Finland' 8
+50236 ' Finnish' 8
+50237 ' Firefox' 8
+50238 ' Firstly' 8
+50239 ' Fischer' 8
+50240 ' Fleming' 8
+50241 ' Florida' 8
+50242 ' Flowers' 8
+50243 ' Foreign' 8
+50244 ' Forever' 8
+50245 ' Formula' 8
+50246 ' Forrest' 8
+50247 ' Fortune' 8
+50248 ' Forward' 8
+50249 ' Fourier' 8
+50250 ' Frances' 8
+50251 ' Francis' 8
+50252 ' França' 8
+50253 ' Freddie' 8
+50254 ' Freedom' 8
+50255 ' Freeman' 8
+50256 ' Friends' 8
+50257 ' Funding' 8
+50258 ' Further' 8
+50259 ' GENERAL' 8
+50260 ' Gabriel' 8
+50261 ' Gallery' 8
+50262 ' García' 8
+50263 ' Gardens' 8
+50264 ' Gardner' 8
+50265 ' Garrett' 8
+50266 ' Gateway' 8
+50267 ' Gazette' 8
+50268 ' General' 8
+50269 ' Generic' 8
+50270 ' Genesis' 8
+50271 ' Genetic' 8
+50272 ' Georges' 8
+50273 ' Georgia' 8
+50274 ' Germans' 8
+50275 ' Germany' 8
+50276 ' Getting' 8
+50277 ' Gilbert' 8
+50278 ' Glasgow' 8
+50279 ' Goldman' 8
+50280 ' Goodman' 8
+50281 ' Grammar' 8
+50282 ' Gravity' 8
+50283 ' Greater' 8
+50284 ' Gregory' 8
+50285 ' Griffin' 8
+50286 ' Growing' 8
+50287 ' HOLDERS' 8
+50288 ' HOWEVER' 8
+50289 ' Hamburg' 8
+50290 ' Hammond' 8
+50291 ' Hampton' 8
+50292 ' Handler' 8
+50293 ' Handles' 8
+50294 ' Harbour' 8
+50295 ' Harvard' 8
+50296 ' Harvest' 8
+50297 ' HashMap' 8
+50298 ' Haskell' 8
+50299 ' Hawkins' 8
+50300 ' Healthy' 8
+50301 ' Hearing' 8
+50302 ' Heather' 8
+50303 ' Heights' 8
+50304 ' Herbert' 8
+50305 ' Highway' 8
+50306 ' Hilbert' 8
+50307 ' Hillary' 8
+50308 ' Himself' 8
+50309 ' History' 8
+50310 ' Hoffman' 8
+50311 ' Holding' 8
+50312 ' Holiday' 8
+50313 ' Holland' 8
+50314 ' Hopkins' 8
+50315 ' Horizon' 8
+50316 ' Housing' 8
+50317 ' Houston' 8
+50318 ' However' 8
+50319 ' Hubbard' 8
+50320 ' Hundred' 8
+50321 ' Hungary' 8
+50322 ' Hunting' 8
+50323 ' Hussein' 8
+50324 ' Hyundai' 8
+50325 ' IMPLIED' 8
+50326 ' INTEGER' 8
+50327 ' Ibrahim' 8
+50328 ' Iceland' 8
+50329 ' Ideally' 8
+50330 ' Illegal' 8
+50331 ' Illustr' 8
+50332 ' Imagine' 8
+50333 ' Imaging' 8
+50334 ' Improve' 8
+50335 ' Include' 8
+50336 ' Indiana' 8
+50337 ' Indians' 8
+50338 ' Indones' 8
+50339 ' Initial' 8
+50340 ' Install' 8
+50341 ' Instant' 8
+50342 ' Instead' 8
+50343 ' Integer' 8
+50344 ' Invalid' 8
+50345 ' Invoice' 8
+50346 ' Iranian' 8
+50347 ' Ireland' 8
+50348 ' Islamic' 8
+50349 ' Islands' 8
+50350 ' Israeli' 8
+50351 ' Italian' 8
+50352 ' Jackson' 8
+50353 ' Jacques' 8
+50354 ' Jakarta' 8
+50355 ' Jamaica' 8
+50356 ' Janeiro' 8
+50357 ' January' 8
+50358 ' Jeffrey' 8
+50359 ' Jenkins' 8
+50360 ' Jessica' 8
+50361 ' Johnson' 8
+50362 ' Journal' 8
+50363 ' Journey' 8
+50364 ' Judaism' 8
+50365 ' Jupiter' 8
+50366 ' Justice' 8
+50367 ' Kashmir' 8
+50368 ' Katrina' 8
+50369 ' Keeping' 8
+50370 ' Kennedy' 8
+50371 ' Kenneth' 8
+50372 ' Killing' 8
+50373 ' Kingdom' 8
+50374 ' Kitchen' 8
+50375 ' Knights' 8
+50376 ' Knowing' 8
+50377 ' Kremlin' 8
+50378 ' Krishna' 8
+50379 ' Kristen' 8
+50380 ' Kurdish' 8
+50381 ' Källor' 8
+50382 ' LICENSE' 8
+50383 ' LIMITED' 8
+50384 ' Lambert' 8
+50385 ' Laplace' 8
+50386 ' Laravel' 8
+50387 ' Laurent' 8
+50388 ' Lebanon' 8
+50389 ' Lecture' 8
+50390 ' Leipzig' 8
+50391 ' Leonard' 8
+50392 ' Letters' 8
+50393 ' Liberal' 8
+50394 ' Liberia' 8
+50395 ' Liberty' 8
+50396 ' Library' 8
+50397 ' License' 8
+50398 ' Limited' 8
+50399 ' Lincoln' 8
+50400 ' Lindsay' 8
+50401 ' Lindsey' 8
+50402 ' Lithuan' 8
+50403 ' Loading' 8
+50404 ' Located' 8
+50405 ' Looking' 8
+50406 ' Lorenzo' 8
+50407 ' Luckily' 8
+50408 ' METHODS' 8
+50409 ' Machine' 8
+50410 ' Madison' 8
+50411 ' Madonna' 8
+50412 ' Majesty' 8
+50413 ' Malcolm' 8
+50414 ' Manager' 8
+50415 ' Manning' 8
+50416 ' Mapping' 8
+50417 ' Marines' 8
+50418 ' Markets' 8
+50419 ' Marshal' 8
+50420 ' Martín' 8
+50421 ' Marxist' 8
+50422 ' Masters' 8
+50423 ' Matches' 8
+50424 ' Matthew' 8
+50425 ' Maurice' 8
+50426 ' Maximum' 8
+50427 ' Maxwell' 8
+50428 ' McLaren' 8
+50429 ' Meaning' 8
+50430 ' Measure' 8
+50431 ' Medical' 8
+50432 ' Meeting' 8
+50433 ' Melissa' 8
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+50436 ' Mercury' 8
+50437 ' Message' 8
+50438 ' Methods' 8
+50439 ' Metrics' 8
+50440 ' Mexican' 8
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+50442 ' Midwest' 8
+50443 ' Millenn' 8
+50444 ' Million' 8
+50445 ' Mineral' 8
+50446 ' Minimal' 8
+50447 ' Minimum' 8
+50448 ' Minutes' 8
+50449 ' Miranda' 8
+50450 ' Missing' 8
+50451 ' Mission' 8
+50452 ' Modules' 8
+50453 ' Mohamed' 8
+50454 ' MongoDB' 8
+50455 ' Monitor' 8
+50456 ' Monster' 8
+50457 ' Montana' 8
+50458 ' Monthly' 8
+50459 ' Morales' 8
+50460 ' Morning' 8
+50461 ' Morocco' 8
+50462 ' Mozilla' 8
+50463 ' Mueller' 8
+50464 ' Municip' 8
+50465 ' Musical' 8
+50466 ' Muslims' 8
+50467 ' MyClass' 8
+50468 ' Myanmar' 8
+50469 ' Mystery' 8
+50470 ' Männer' 8
+50471 ' México' 8
+50472 ' Müller' 8
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+50475 ' Natural' 8
+50476 ' Neither' 8
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+50478 ' Network' 8
+50479 ' Neumann' 8
+50480 ' Neville' 8
+50481 ' Newport' 8
+50482 ' Nichols' 8
+50483 ' Nicolas' 8
+50484 ' Nielsen' 8
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+50486 ' Norfolk' 8
+50487 ' Nothing' 8
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+50489 ' Nuclear' 8
+50490 ' Numbers' 8
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+50493 ' Objects' 8
+50494 ' October' 8
+50495 ' Officer' 8
+50496 ' Oktober' 8
+50497 ' Olivier' 8
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+50499 ' Ontario' 8
+50500 ' Opening' 8
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+50502 ' Optical' 8
+50503 ' Options' 8
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+50505 ' Organic' 8
+50506 ' Orlando' 8
+50507 ' Orleans' 8
+50508 ' Ottoman' 8
+50509 ' Outlook' 8
+50510 ' Outside' 8
+50511 ' Overall' 8
+50512 ' PRIMARY' 8
+50513 ' PROFITS' 8
+50514 ' PURPOSE' 8
+50515 ' Pacific' 8
+50516 ' Package' 8
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+50519 ' Parents' 8
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+50521 ' Parkway' 8
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+50528 ' Pattern' 8
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+50541 ' Pharmac' 8
+50542 ' Philipp' 8
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+50544 ' Phillip' 8
+50545 ' Phoenix' 8
+50546 ' Physics' 8
+50547 ' Picture' 8
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+50551 ' Playing' 8
+50552 ' Plugins' 8
+50553 ' Pointer' 8
+50554 ' Poisson' 8
+50555 ' Pokemon' 8
+50556 ' Popular' 8
+50557 ' Porsche' 8
+50558 ' Pradesh' 8
+50559 ' Predict' 8
+50560 ' Premier' 8
+50561 ' Premium' 8
+50562 ' Prepare' 8
+50563 ' Present' 8
+50564 ' Preston' 8
+50565 ' Prevent' 8
+50566 ' Preview' 8
+50567 ' Primary' 8
+50568 ' Privacy' 8
+50569 ' Private' 8
+50570 ' Problem' 8
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+50572 ' Process' 8
+50573 ' Product' 8
+50574 ' Profile' 8
+50575 ' Program' 8
+50576 ' Project' 8
+50577 ' Promise' 8
+50578 ' Prophet' 8
+50579 ' Protect' 8
+50580 ' Protein' 8
+50581 ' Protest' 8
+50582 ' Provide' 8
+50583 ' Publish' 8
+50584 ' Purpose' 8
+50585 ' Públic' 8
+50586 ' QString' 8
+50587 ' QWidget' 8
+50588 ' Quality' 8
+50589 ' Quantum' 8
+50590 ' Quarter' 8
+50591 ' Québec' 8
+50592 ' REUTERS' 8
+50593 ' Radical' 8
+50594 ' Raiders' 8
+50595 ' Railway' 8
+50596 ' Rainbow' 8
+50597 ' Raleigh' 8
+50598 ' Ramirez' 8
+50599 ' Randall' 8
+50600 ' Rangers' 8
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+50603 ' Reality' 8
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+50607 ' Refresh' 8
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+50609 ' Related' 8
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+50612 ' Replace' 8
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+50615 ' Require' 8
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+50617 ' Respond' 8
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+50619 ' Results' 8
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+50625 ' Revised' 8
+50626 ' Rewrite' 8
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+50629 ' Riemann' 8
+50630 ' Roberto' 8
+50631 ' Roberts' 8
+50632 ' Rodgers' 8
+50633 ' Rolling' 8
+50634 ' Romance' 8
+50635 ' Romania' 8
+50636 ' Ronaldo' 8
+50637 ' Running' 8
+50638 ' Runtime' 8
+50639 ' Russell' 8
+50640 ' Russian' 8
+50641 ' SERVICE' 8
+50642 ' SPECIAL' 8
+50643 ' SUPPORT' 8
+50644 ' Samples' 8
+50645 ' Samsung' 8
+50646 ' Sanchez' 8
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+50671 ' Shirley' 8
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+50674 ' Silicon' 8
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+50679 ' Sisters' 8
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+50681 ' Society' 8
+50682 ' Solomon' 8
+50683 ' Somalia' 8
+50684 ' Somehow' 8
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+50691 ' Species' 8
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+50701 ' Stevens' 8
+50702 ' Stewart' 8
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+50712 ' Subject' 8
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+50714 ' Summary' 8
+50715 ' Sundays' 8
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+50719 ' Surface' 8
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+50722 ' Symbols' 8
+50723 ' Systems' 8
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+50735 ' Thought' 8
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+50737 ' Through' 8
+50738 ' Thunder' 8
+50739 ' Tibetan' 8
+50740 ' Tickets' 8
+50741 ' Tiffany' 8
+50742 ' Timothy' 8
+50743 ' Tobacco' 8
+50744 ' Tonight' 8
+50745 ' Toolkit' 8
+50746 ' Toronto' 8
+50747 ' Tourism' 8
+50748 ' Trading' 8
+50749 ' Traffic' 8
+50750 ' Trainer' 8
+50751 ' Transit' 8
+50752 ' Tribune' 8
+50753 ' Trigger' 8
+50754 ' Trinity' 8
+50755 ' Trudeau' 8
+50756 ' Tuesday' 8
+50757 ' Tunisia' 8
+50758 ' Turkish' 8
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+54804 '\n\t\t\t\t\t\t\t\t' 9
+54805 '\n\n       ' 9
+54806 '\n    \n   ' 9
+54807 '\n        ' 9
+54808 '\r\n       ' 9
+54809 ' \n       ' 9
+54810 '         ' 9
+54811 ' --------' 9
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+58588 '것이다' 9
+58589 '것입니' 9
+58590 '경우에' 9
+58591 '그러나' 9
+58592 '그리고' 9
+58593 '대학교' 9
+58594 '대한민' 9
+58595 '되었다' 9
+58596 '됩니다' 9
+58597 '두리코' 9
+58598 '드라마' 9
+58599 '등학교' 9
+58600 '때문에' 9
+58601 '로마테' 9
+58602 '로미로' 9
+58603 '로부터' 9
+58604 '리코스' 9
+58605 '마사지' 9
+58606 '마테라' 9
+58607 '미로미' 9
+58608 '민국의' 9
+58609 '사이트' 9
+58610 '서비스' 9
+58611 '스웨디' 9
+58612 '습니다' 9
+58613 '아로마' 9
+58614 '았습니' 9
+58615 '었습니' 9
+58616 '었으며' 9
+58617 '에서는' 9
+58618 '웨디시' 9
+58619 '이라고' 9
+58620 '이라는' 9
+58621 '이마사' 9
+58622 '이었다' 9
+58623 '이용자' 9
+58624 '인정보' 9
+58625 '입니다' 9
+58626 '있습니' 9
+58627 '있었다' 9
+58628 '있으며' 9
+58629 '장마사' 9
+58630 '적으로' 9
+58631 '정보를' 9
+58632 '좋아요' 9
+58633 '좋은글' 9
+58634 '출장마' 9
+58635 '출장샵' 9
+58636 '코로나' 9
+58637 '타이마' 9
+58638 '테라피' 9
+58639 '프랑스' 9
+58640 '하면서' 9
+58641 '하였다' 9
+58642 '하지만' 9
+58643 '한민국' 9
+58644 '합니다' 9
+58645 '했습니' 9
+58646 '홈케어' 9
+58647 '홈타이' 9
+58648 '���' 9
+58649 '\n\t\t\t\t\t\t\t\t\t' 10
+58650 '\n\n\n       ' 10
+58651 '\n\n        ' 10
+58652 '\n         ' 10
+58653 '\r\n        ' 10
+58654 ' \n        ' 10
+58655 '          ' 10
+58656 ' ---------' 10
+58657 ' According' 10
+58658 ' Addiction' 10
+58659 ' Administr' 10
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+61222 'стову' 10
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+61261 'щение' 10
+61262 'щения' 10
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+61264 'ються' 10
+61265 'ющего' 10
+61266 'ється' 10
+61267 'ільки' 10
+61268 'љашње' 10
+61269 '\n\n         ' 11
+61270 '\n          ' 11
+61271 '\r\n\r\n       ' 11
+61272 '\r\n         ' 11
+61273 ' \n         ' 11
+61274 '           ' 11
+61275 ' ----------' 11
+61276 ' Aboriginal' 11
+61277 ' Activation' 11
+61278 ' Activities' 11
+61279 ' Additional' 11
+61280 ' Adventures' 11
+61281 ' Affordable' 11
+61282 ' Afterwards' 11
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+61284 ' Ambassador' 11
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+61292 ' Assignment' 11
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+61295 ' Associates' 11
+61296 ' Attachment' 11
+61297 ' Attributes' 11
+61298 ' Australian' 11
+61299 ' Azerbaijan' 11
+61300 ' BACKGROUND' 11
+61301 ' Background' 11
+61302 ' Bangladesh' 11
+61303 ' Basketball' 11
+61304 ' Biological' 11
+61305 ' Birmingham' 11
+61306 ' BlackBerry' 11
+61307 ' Blockchain' 11
+61308 ' Buckingham' 11
+61309 ' CONDITIONS' 11
+61310 ' CONNECTION' 11
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+61313 ' Categories' 11
+61314 ' Chancellor' 11
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+61322 ' Comparison' 11
+61323 ' Components' 11
+61324 ' Conclusion' 11
+61325 ' Concurrent' 11
+61326 ' Conditions' 11
+61327 ' Conference' 11
+61328 ' Connection' 11
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+61331 ' Constraint' 11
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+61343 ' Definition' 11
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+61349 ' Developing' 11
+61350 ' Diagnostic' 11
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+61352 ' Difference' 11
+61353 ' Dimensions' 11
+61354 ' Directions' 11
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+61358 ' Electronic' 11
+61359 ' Elementary' 11
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+61370 ' Experiment' 11
+61371 ' Expression' 11
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+61376 ' Fitzgerald' 11
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+61380 ' Geographic' 11
+61381 ' Geological' 11
+61382 ' Government' 11
+61383 ' Guidelines' 11
+61384 ' Healthcare' 11
+61385 ' Hernández' 11
+61386 ' Historical' 11
+61387 ' Horizontal' 11
+61388 ' Huntington' 11
+61389 ' Identifier' 11
+61390 ' Increasing' 11
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+61392 ' Individual' 11
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+61395 ' Industries' 11
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+61397 ' Initiative' 11
+61398 ' Innovation' 11
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+61401 ' Integrated' 11
+61402 ' Interstate' 11
+61403 ' Investment' 11
+61404 ' JavaScript' 11
+61405 ' Javascript' 11
+61406 ' Kazakhstan' 11
+61407 ' Kubernetes' 11
+61408 ' Laboratory' 11
+61409 ' Leadership' 11
+61410 ' Liberation' 11
+61411 ' Lieutenant' 11
+61412 ' Ligações' 11
+61413 ' Literature' 11
+61414 ' Louisville' 11
+61415 ' Luxembourg' 11
+61416 ' Madagascar' 11
+61417 ' MainWindow' 11
+61418 ' Management' 11
+61419 ' Manchester' 11
+61420 ' Mechanical' 11
+61421 ' Membership' 11
+61422 ' Millennium' 11
+61423 ' Monitoring' 11
+61424 ' Montgomery' 11
+61425 ' NEGLIGENCE' 11
+61426 ' Navigation' 11
+61427 ' Nottingham' 11
+61428 ' Observable' 11
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+61493 ' Technology' 11
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+61495 ' TensorFlow' 11
+61496 ' Throughout' 11
+61497 ' Tournament' 11
+61498 ' Transcript' 11
+61499 ' Transition' 11
+61500 ' Ultimately' 11
+61501 ' Unexpected' 11
+61502 ' University' 11
+61503 ' Validation' 11
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+61505 ' Verfügung' 11
+61506 ' Vietnamese' 11
+61507 ' Volkswagen' 11
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+61512 ' Williamson' 11
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+62747 ' Михай' 11
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+65239 ' элемент' 15
+65240 '---------------' 15
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+65242 'OnClickListener' 15
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+65244 'ValidationError' 15
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+65248 '\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n' 16
+65249 '\n               ' 16
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+65311 '\n                ' 17
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+65358 '\n                 ' 18
+65359 '                  ' 18
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+65361 'ственного' 18
+65362 '\n                  ' 19
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+65364 ' telecommunications' 19
+65365 ' Александр' 19
+65366 ' Биография' 19
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+65388 '\n                   ' 20
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+65395 '\n\n                   ' 21
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+65400 ' Литература' 21
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+65414 '\n                     ' 22
+65415 '                      ' 22
+65416 'министратив' 22
+65417 '\n                      ' 23
+65418 '                       ' 23
+65419 ' Хронологија' 23
+65420 ' використову' 23
+65421 ' энциклопеди' 23
+65422 '\n                       ' 24
+65423 '                        ' 24
+65424 '************************' 24
+65425 '------------------------' 24
+65426 '––––––––' 24
+65427 '————————' 24
+65428 '……………………' 24
+65429 '────────' 24
+65430 '════════' 24
+65431 '████████' 24
+65432 '\n\n                       ' 25
+65433 '\n            \n           ' 25
+65434 '\n                        ' 25
+65435 '\r\n                       ' 25
+65436 '                         ' 25
+65437 ' Становништво' 25
+65438 ' университета' 25
+65439 '\n                         ' 26
+65440 '                          ' 26
+65441 '\n                          ' 27
+65442 '                           ' 27
+65443 ' административ' 27
+65444 '\n                           ' 28
+65445 '                            ' 28
+65446 '\n\n                           ' 29
+65447 '\n                            ' 29
+65448 '                             ' 29
+65449 '\n                             ' 30
+65450 '                              ' 30
+65451 '------------------------------' 30
+65452 '\n                              ' 31
+65453 '                               ' 31
+65454 '\n                               ' 32
+65455 '                                ' 32
+65456 '################################' 32
+65457 '********************************' 32
+65458 '--------------------------------' 32
+65459 '................................' 32
+65460 '////////////////////////////////' 32
+65461 '================================' 32
+65462 '________________________________' 32
+65463 'ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ' 32
+65464 '\n\n                               ' 33
+65465 '\n                                ' 33
+65466 '                                 ' 33
+65467 ' --------------------------------' 33
+65468 '\n                                 ' 34
+65469 '                                  ' 34
+65470 '\n                                  ' 35
+65471 '                                   ' 35
+65472 '\n                                   ' 36
+65473 '                                    ' 36
+65474 '\n                                    ' 37
+65475 '                                     ' 37
+65476 '\n                                     ' 38
+65477 '                                      ' 38
+65478 '\n                                      ' 39
+65479 '                                       ' 39
+65480 '\n                                       ' 40
+65481 '                                        ' 40
+65482 '\n                                        ' 41
+65483 '                                         ' 41
+65484 '                                          ' 42
+65485 '                                           ' 43
+65486 '\n                                           ' 44
+65487 '                                            ' 44
+65488 '                                             ' 45
+65489 '                                              ' 46
+65490 '                                               ' 47
+65491 '\n                                               ' 48
+65492 '                                                ' 48
+65493 '################################################' 48
+65494 '************************************************' 48
+65495 '------------------------------------------------' 48
+65496 '================================================' 48
+65497 '————————————————' 48
+65498 '────────────────' 48
+65499 '                                                 ' 49
+65500 ' ------------------------------------------------' 49
+65501 '                                                  ' 50
+65502 '                                                   ' 51
+65503 '\n                                                   ' 52
+65504 '                                                    ' 52
+65505 '                                                     ' 53
+65506 '                                                      ' 54
+65507 '                                                       ' 55
+65508 '                                                        ' 56
+65509 '                                                         ' 57
+65510 '                                                          ' 58
+65511 '                                                           ' 59
+65512 '                                                              ' 62
+65513 '                                                                ' 64
+65514 '################################################################' 64
+65515 '****************************************************************' 64
+65516 '----------------------------------------------------------------' 64
+65517 '////////////////////////////////////////////////////////////////' 64
+65518 '================================================================' 64
+65519 ' ----------------------------------------------------------------' 65
+65520 ' =================================================================' 66
+65521 '//----------------------------------------------------------------' 66
+65522 '                                                                        ' 72
+65523 '************************************************************************' 72
+65524 '                                                                          ' 74
+65525 '############################################################################' 76
+65526 '****************************************************************************' 76
+65527 '################################################################################' 80
+65528 '--------------------------------------------------------------------------------' 80
+65529 '                                                                                                                                ' 128
diff --git a/trained_50_percents/spark_llm.py b/trained_50_percents/spark_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b2d22f31e6704afd0058f7ea1b46f7eebdf91ef
--- /dev/null
+++ b/trained_50_percents/spark_llm.py
@@ -0,0 +1,202 @@
+import torch
+import torch.nn as nn
+from typing import Optional, Union, Tuple, Dict, Unpack
+from transformers.modeling_utils import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.utils.deprecation import deprecate_kwarg
+from rwkvfla.models.rwkv7.modeling_rwkv7 import RWKV7Model, RWKV7PreTrainedModel, Cache,RWKV7ForCausalLM
+from rwkvfla.models.rwkv7.modeling_rwkv7 import FusedLinearCrossEntropyLoss, FusedCrossEntropyLoss
+from transformers.generation.utils import GenerationMixin
+
+from rwkvfla.models.rwkv7.configuration_rwkv7 import RWKV7Config
+
+class RWKV7SpeechConfig(RWKV7Config):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.text_vocab_size = kwargs.get("text_vocab_size", kwargs.get("text_vocab_size"))
+        self.audio_global_vocab_size = kwargs.get("audio_global_vocab_size", kwargs.get("audio_global_vocab_size"))
+
+
+class RWKV7ForSpeech(RWKV7ForCausalLM):
+    config_class = RWKV7SpeechConfig
+    def __init__(self, config: RWKV7SpeechConfig):
+        super().__init__(config)
+        self.model = RWKV7Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)#Spark 0.5B vocab size is 8192 + 1 for eos resulting in 8193
+        self.criterion = None
+        self.text_embedder = nn.Embedding(config.text_vocab_size, config.hidden_size)
+        self.global_embedder = nn.Embedding(config.audio_global_vocab_size, config.hidden_size)#Spark 0.5B global token size is 4096
+        #TTS Tag includes GLOBAL=0, SEMANTIC=1,START_TTS=2
+        self.tts_tag_embedder = nn.Embedding(3, config.hidden_size)
+        # Initialize weights and apply final processing
+        self.post_init()
+        self.dropout = torch.nn.Dropout(0.02)
+
+    def get_input_embeddings(self):
+        return self.model.embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def generate(self, *args, **kwargs):
+        try:
+            return super().generate(*args, **kwargs)
+        except AttributeError as exception:
+            if 'past_key_values' in str(exception):
+                raise AttributeError(
+                    f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
+                    f"which is not supported for {self.__class__.__name__}. "
+                    f"Try another generation strategy instead. "
+                    f"For the available generation strategies, check this doc: "
+                    f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
+                )
+            else:
+                raise exception
+
+    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor = None,
+        past_key_values: Optional[Cache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: bool = True,
+        logits_to_keep: Optional[int] = None,
+        **kwargs
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is not empty.
+        if past_key_values is not None and len(past_key_values) > 0:
+            input_ids = input_ids[:, -1:]
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and len(past_key_values) == 0:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {'input_ids': input_ids.contiguous()}
+
+        if logits_to_keep is not None:
+            model_inputs['logits_to_keep'] = logits_to_keep
+
+        model_inputs.update({
+            'past_key_values': past_key_values,
+            'use_cache': use_cache,
+            'attention_mask': attention_mask,
+            'logits_to_keep': logits_to_keep,
+        })
+        return model_inputs
+
+    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        logits_to_keep: Optional[int] = 0,
+        **kwargs: Unpack[Dict]
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if self.training and inputs_embeds is not None:
+            inputs_embeds = self.dropout(inputs_embeds)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            **kwargs
+        )
+
+        hidden_states = outputs[0]
+        fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training
+
+        loss, logits = None, None
+        if not fuse_linear_and_cross_entropy or labels is None:
+            logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:])
+        if labels is not None:
+            if getattr(self, 'criterion', None) is None:
+                if fuse_linear_and_cross_entropy:
+                    criterion = FusedLinearCrossEntropyLoss()
+                elif self.config.fuse_cross_entropy:
+                    criterion = FusedCrossEntropyLoss(inplace_backward=True)
+                else:
+                    criterion = nn.CrossEntropyLoss()
+            else:
+                criterion = self.criterion
+            # Enable model parallelism
+            labels = labels.to(hidden_states.device)
+            labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1)
+            if fuse_linear_and_cross_entropy:
+                loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias)
+            else:
+                loss = criterion(logits.view(labels.numel(), -1), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+    
+    def copy_state_dict(self, state_dict: dict):
+        """从源 state dict 复制参数到当前模型，排除 embeddings 和 lm_head
+        The state dict is from original RWKV7 language model
+        Args:
+            state_dict: 源 state dict
+        """
+        # 获取当前模型的 state dict
+        target_dict = self.state_dict()
+        
+        # 创建新的 state dict 用于存储要复制的参数
+        new_state_dict = {}
+        
+        # 遍历源 state dict 的键
+        for key in state_dict.keys():
+            # 跳过 embeddings 和 lm_head 相关的参数
+            if key == 'model.embeddings.weight':
+                new_state_dict['text_embedder.weight'] = state_dict[key]
+                continue
+            if 'embeddings' in key or 'lm_head' in key:
+                continue
+            # 如果键在当前模型中存在，则复制参数
+            if key in target_dict:
+                new_state_dict[key] = state_dict[key]
+        
+        # 加载新的 state dict 到当前模型
+        info = self.load_state_dict(new_state_dict, strict=False)
+        print(info)
+        return self
+        
\ No newline at end of file
diff --git a/trained_50_percents/sparktts/models/__pycache__/audio_tokenizer.cpython-311.pyc b/trained_50_percents/sparktts/models/__pycache__/audio_tokenizer.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bdf69659d2fb3a69a209f09188701ead63de74a3
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diff --git a/trained_50_percents/sparktts/models/__pycache__/bicodec.cpython-311.pyc b/trained_50_percents/sparktts/models/__pycache__/bicodec.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ff89bfcaf69bd88d79f1ca6a70f7373d108bd0b6
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diff --git a/trained_50_percents/sparktts/models/audio_tokenizer.py b/trained_50_percents/sparktts/models/audio_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..568503c1aaf006e046bceef220f046ad5ba07c57
--- /dev/null
+++ b/trained_50_percents/sparktts/models/audio_tokenizer.py
@@ -0,0 +1,167 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import numpy as np
+from pathlib import Path
+from typing import Any, Dict, Tuple, Optional, Union
+from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model
+
+from sparktts.utils.file import load_config
+from sparktts.utils.audio import load_audio
+from sparktts.models.bicodec import BiCodec
+
+
+class BiCodecTokenizer:
+    """BiCodec tokenizer for handling audio input and tokenization."""
+
+    def __init__(self, model_dir: Path, device: torch.device = None, **kwargs):
+        super().__init__()
+        """
+        Args:
+            model_dir: Path to the model directory.
+            device: Device to run the model on (default is GPU if available).
+        """
+        self.device = device
+        self.model_dir = model_dir
+        self.config = load_config(f"{model_dir}/config.yaml")
+        self._initialize_model()
+
+    def _initialize_model(self):
+        """Load and initialize the BiCodec model and Wav2Vec2 feature extractor."""
+        self.model = BiCodec.load_from_checkpoint(f"{self.model_dir}/BiCodec").to(
+            self.device
+        )
+        self.processor = Wav2Vec2FeatureExtractor.from_pretrained(
+            f"{self.model_dir}/wav2vec2-large-xlsr-53"
+        )
+        self.feature_extractor = Wav2Vec2Model.from_pretrained(
+            f"{self.model_dir}/wav2vec2-large-xlsr-53"
+        ).to(self.device)
+        self.feature_extractor.config.output_hidden_states = True
+
+    def get_ref_clip(self, wav: np.ndarray) -> np.ndarray:
+        """Get reference audio clip for speaker embedding."""
+        ref_segment_length = (
+            int(self.config["sample_rate"] * self.config["ref_segment_duration"])
+            // self.config["latent_hop_length"]
+            * self.config["latent_hop_length"]
+        )
+        wav_length = len(wav)
+
+        if ref_segment_length > wav_length:
+            # Repeat and truncate to handle insufficient length
+            wav = np.tile(wav, ref_segment_length // wav_length + 1)
+
+        return wav[:ref_segment_length]
+
+    def process_audio(self, wav_path: Optional[Union[Path, np.ndarray]]) -> Tuple[np.ndarray, torch.Tensor]:
+        """load auido and get reference audio from wav path"""
+        if isinstance(wav_path, Path):
+            wav = load_audio(
+                wav_path,
+                sampling_rate=self.config["sample_rate"],
+                volume_normalize=self.config["volume_normalize"],
+            )
+        elif isinstance(wav_path, np.ndarray):
+            wav = wav_path
+        else:
+            raise ValueError(f"Unsupported audio type: {type(wav_path)}")
+
+        wav_ref = self.get_ref_clip(wav)
+
+        wav_ref = torch.from_numpy(wav_ref).unsqueeze(0).float()
+        return wav, wav_ref
+    
+    def extract_wav2vec2_features(self, wavs: torch.Tensor) -> torch.Tensor:
+        """extract wav2vec2 features"""
+        inputs = self.processor(
+            wavs,
+            sampling_rate=16000,
+            return_tensors="pt",
+            padding=True,
+            output_hidden_states=True,
+        ).input_values.to(self.feature_extractor.dtype)
+        feat = self.feature_extractor(inputs.to(self.feature_extractor.device))
+        feats_mix = (
+            feat.hidden_states[11] + feat.hidden_states[14] + feat.hidden_states[16]
+        ) / 3
+
+        return feats_mix
+
+    def tokenize_batch(self, batch: Dict[str, Any]) -> torch.Tensor:
+        """tokenize the batch of audio
+
+        Args:
+            batch:
+                wavs (List[np.ndarray]): batch of audio
+                ref_wavs (torch.Tensor): reference audio. shape: (batch_size, seq_len)
+
+        Returns:
+            semantic_tokens: semantic tokens. shape: (batch_size, seq_len, latent_dim)
+            global_tokens: global tokens. shape: (batch_size, seq_len, global_dim)
+        """
+        feats = self.extract_wav2vec2_features(batch["wav"])
+        batch["feat"] = feats
+        semantic_tokens, global_tokens = self.model.tokenize(batch)
+
+        return global_tokens, semantic_tokens
+
+    def tokenize(self, audio_path: str) -> Tuple[torch.Tensor, torch.Tensor]:
+        """tokenize the audio"""
+        wav, ref_wav = self.process_audio(audio_path)
+        feat = self.extract_wav2vec2_features(wav)
+        batch = {
+            "wav": torch.from_numpy(wav).unsqueeze(0).float().to(self.device),
+            "ref_wav": ref_wav.to(self.device),
+            "feat": feat.to(self.device),
+        }
+        semantic_tokens, global_tokens = self.model.tokenize(batch)
+
+        return global_tokens, semantic_tokens
+
+    def detokenize(
+        self, global_tokens: torch.Tensor, semantic_tokens: torch.Tensor
+    ) -> np.array:
+        """detokenize the tokens to waveform
+
+        Args:
+            global_tokens: global tokens. shape: (batch_size, global_dim)
+            semantic_tokens: semantic tokens. shape: (batch_size, latent_dim)
+
+        Returns:
+            wav_rec: waveform. shape: (batch_size, seq_len) for batch or (seq_len,) for single
+        """
+        global_tokens = global_tokens.unsqueeze(1)
+        wav_rec = self.model.detokenize(semantic_tokens, global_tokens)
+        return wav_rec.detach().squeeze().cpu().numpy()
+
+
+# test
+if __name__ == "__main__":
+    import soundfile as sf
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    tokenizer = BiCodecTokenizer(
+        model_dir="pretrained_models/Spark-TTS-0.5B",
+        device=device,
+    )
+    wav_path = "example/prompt_audio.wav"
+
+    global_tokens, semantic_tokens = tokenizer.tokenize(wav_path)
+
+    wav_rec = tokenizer.detokenize(global_tokens.squeeze(0), semantic_tokens)
+    sf.write("example/prompt_recon.wav", wav_rec, 16000)
diff --git a/trained_50_percents/sparktts/models/bicodec.py b/trained_50_percents/sparktts/models/bicodec.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cab2f0247221411f7504e1582df850eb7043409
--- /dev/null
+++ b/trained_50_percents/sparktts/models/bicodec.py
@@ -0,0 +1,247 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+from pathlib import Path
+from typing import Dict, Any
+from omegaconf import DictConfig
+from safetensors.torch import load_file
+
+from sparktts.utils.file import load_config
+from sparktts.modules.speaker.speaker_encoder import SpeakerEncoder
+from sparktts.modules.encoder_decoder.feat_encoder import Encoder
+from sparktts.modules.encoder_decoder.feat_decoder import Decoder
+from sparktts.modules.encoder_decoder.wave_generator import WaveGenerator
+from sparktts.modules.vq.factorized_vector_quantize import FactorizedVectorQuantize
+
+
+class BiCodec(nn.Module):
+    """
+    BiCodec model for speech synthesis, incorporating a speaker encoder, feature encoder/decoder,
+    quantizer, and wave generator.
+    """
+
+    def __init__(
+        self,
+        mel_params: Dict[str, Any],
+        encoder: nn.Module,
+        decoder: nn.Module,
+        quantizer: nn.Module,
+        speaker_encoder: nn.Module,
+        prenet: nn.Module,
+        postnet: nn.Module,
+        **kwargs
+    ) -> None:
+        """
+        Initializes the BiCodec model with the required components.
+
+        Args:
+            mel_params (dict): Parameters for the mel-spectrogram transformer.
+            encoder (nn.Module): Encoder module.
+            decoder (nn.Module): Decoder module.
+            quantizer (nn.Module): Quantizer module.
+            speaker_encoder (nn.Module): Speaker encoder module.
+            prenet (nn.Module): Prenet network.
+            postnet (nn.Module): Postnet network.
+        """
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.quantizer = quantizer
+        self.speaker_encoder = speaker_encoder
+        self.prenet = prenet
+        self.postnet = postnet
+        self.init_mel_transformer(mel_params)
+
+    @classmethod
+    def load_from_checkpoint(cls, model_dir: Path, **kwargs) -> "BiCodec":
+        """
+        Loads the model from a checkpoint.
+
+        Args:
+            model_dir (Path): Path to the model directory containing checkpoint and config.
+        
+        Returns:
+            BiCodec: The initialized BiCodec model.
+        """
+        ckpt_path = f'{model_dir}/model.safetensors'
+        config = load_config(f'{model_dir}/config.yaml')['audio_tokenizer']
+        mel_params = config["mel_params"]
+        encoder = Encoder(**config["encoder"])
+        quantizer = FactorizedVectorQuantize(**config["quantizer"])
+        prenet = Decoder(**config["prenet"])
+        postnet = Decoder(**config["postnet"])
+        decoder = WaveGenerator(**config["decoder"])
+        speaker_encoder = SpeakerEncoder(**config["speaker_encoder"])
+
+        model = cls(
+            mel_params=mel_params,
+            encoder=encoder,
+            decoder=decoder,
+            quantizer=quantizer,
+            speaker_encoder=speaker_encoder,
+            prenet=prenet,
+            postnet=postnet,
+        )
+
+        state_dict = load_file(ckpt_path)
+        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+
+        for key in missing_keys:
+            print(f"Missing tensor: {key}")
+        for key in unexpected_keys:
+            print(f"Unexpected tensor: {key}")
+
+        model.eval()
+        model.remove_weight_norm()
+
+        return model
+
+    def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Performs a forward pass through the model.
+
+        Args:
+            batch (dict): A dictionary containing features, reference waveform, and target waveform.
+        
+        Returns:
+            dict: A dictionary containing the reconstruction, features, and other metrics.
+        """
+        feat = batch["feat"]
+        mel = self.mel_transformer(batch["ref_wav"]).squeeze(1)
+
+        z = self.encoder(feat.transpose(1, 2))
+        vq_outputs = self.quantizer(z)
+
+        x_vector, d_vector = self.speaker_encoder(mel.transpose(1, 2))
+
+        conditions = d_vector
+        with_speaker_loss = False
+
+        x = self.prenet(vq_outputs["z_q"], conditions)
+        pred_feat = self.postnet(x)
+        x = x + conditions.unsqueeze(-1)
+        wav_recon = self.decoder(x)
+
+        return {
+            "vq_loss": vq_outputs["vq_loss"],
+            "perplexity": vq_outputs["perplexity"],
+            "cluster_size": vq_outputs["active_num"],
+            "recons": wav_recon,
+            "pred_feat": pred_feat,
+            "x_vector": x_vector,
+            "d_vector": d_vector,
+            "audios": batch["wav"].unsqueeze(1),
+            "with_speaker_loss": with_speaker_loss,
+        }
+
+    @torch.no_grad()
+    def tokenize(self, batch: Dict[str, Any]):
+        """
+        Tokenizes the input audio into semantic and global tokens.
+
+        Args:
+            batch (dict): The input audio features and reference waveform.
+
+        Returns:
+            tuple: Semantic tokens and global tokens.
+        """
+        feat = batch["feat"]
+        mel = self.mel_transformer(batch["ref_wav"]).squeeze(1)
+
+        z = self.encoder(feat.transpose(1, 2))
+        semantic_tokens = self.quantizer.tokenize(z)
+        global_tokens = self.speaker_encoder.tokenize(mel.transpose(1, 2))
+
+        return semantic_tokens, global_tokens
+
+    @torch.no_grad()
+    def detokenize(self, semantic_tokens, global_tokens):
+        """
+        Detokenizes the semantic and global tokens into a waveform.
+
+        Args:
+            semantic_tokens (tensor): Semantic tokens.
+            global_tokens (tensor): Global tokens.
+
+        Returns:
+            tensor: Reconstructed waveform.
+        """
+        z_q = self.quantizer.detokenize(semantic_tokens)
+        d_vector = self.speaker_encoder.detokenize(global_tokens)
+        x = self.prenet(z_q, d_vector)
+        x = x + d_vector.unsqueeze(-1)
+        wav_recon = self.decoder(x)
+
+        return wav_recon
+
+    def init_mel_transformer(self, config: Dict[str, Any]):
+        """
+        Initializes the MelSpectrogram transformer based on the provided configuration.
+
+        Args:
+            config (dict): Configuration parameters for MelSpectrogram.
+        """
+        import torchaudio.transforms as TT
+
+        self.mel_transformer = TT.MelSpectrogram(
+            config["sample_rate"],
+            config["n_fft"],
+            config["win_length"],
+            config["hop_length"],
+            config["mel_fmin"],
+            config["mel_fmax"],
+            n_mels=config["num_mels"],
+            power=1,
+            norm="slaney",
+            mel_scale="slaney",
+        )
+
+    def remove_weight_norm(self):
+        """Removes weight normalization from all layers."""
+        def _remove_weight_norm(m):
+            try:
+                torch.nn.utils.remove_weight_norm(m)
+            except ValueError:
+                pass  # The module didn't have weight norm
+
+        self.apply(_remove_weight_norm)
+
+
+# Test the model
+if __name__ == "__main__":
+
+    config = load_config("pretrained_models/SparkTTS-0.5B/BiCodec/config.yaml")
+    model = BiCodec.load_from_checkpoint(
+        model_dir="pretrained_models/SparkTTS-0.5B/BiCodec",
+    )
+
+    # Generate random inputs for testing
+    duration = 0.96
+    x = torch.randn(20, 1, int(duration * 16000))
+    feat = torch.randn(20, int(duration * 50), 1024)
+    inputs = {"feat": feat, "wav": x, "ref_wav": x}
+
+    # Forward pass
+    outputs = model(inputs)
+    semantic_tokens, global_tokens = model.tokenize(inputs)
+    wav_recon = model.detokenize(semantic_tokens, global_tokens)
+
+    # Verify if the reconstruction matches
+    if torch.allclose(outputs["recons"].detach(), wav_recon):
+        print("Test successful")
+    else:
+        print("Test failed")
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diff --git a/trained_50_percents/sparktts/modules/blocks/layers.py b/trained_50_percents/sparktts/modules/blocks/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..9de506e9c30bf48efcc00d09da1e210d4058e9a0
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/blocks/layers.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+
+# Adapted from https://github.com/descriptinc/descript-audio-codec under the Apache License 2.0
+
+
+import torch
+import torch.nn as nn
+from torch.nn.utils import weight_norm
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+# Scripting this brings model speed up 1.4x
+@torch.jit.script
+def snake(x, alpha):
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x):
+        return snake(x, self.alpha)
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.block = nn.Sequential(
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+
+    def forward(self, x):
+        y = self.block(x)
+        pad = (x.shape[-1] - y.shape[-1]) // 2
+        if pad > 0:
+            x = x[..., pad:-pad]
+        return x + y
+
+
+def init_weights(m):
+    if isinstance(m, nn.Conv1d):
+        nn.init.trunc_normal_(m.weight, std=0.02)
+        nn.init.constant_(m.bias, 0)
diff --git a/trained_50_percents/sparktts/modules/blocks/samper.py b/trained_50_percents/sparktts/modules/blocks/samper.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6673bf63e51577fe983d6d52598537eea7c1a95
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/blocks/samper.py
@@ -0,0 +1,115 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SamplingBlock(nn.Module):
+    """Sampling block for upsampling or downsampling"""
+
+    def __init__(
+        self,
+        dim: int,
+        groups: int = 1,
+        upsample_scale: int = 1,
+        downsample_scale: int = 1,
+    ) -> None:
+        """
+        Args:
+            dim: input dimension
+            groups: number of groups
+            upsample_scale: upsampling scale
+            downsample_scale: downsampling scale
+        """
+        super(SamplingBlock, self).__init__()
+
+        self.upsample_scale = upsample_scale
+        self.downsample_scale = downsample_scale
+
+        if self.upsample_scale > 1:
+            self.de_conv_upsampler = nn.Sequential(
+                nn.LeakyReLU(0.2),
+                nn.ConvTranspose1d(
+                    dim,
+                    dim,
+                    kernel_size=upsample_scale * 2,
+                    stride=upsample_scale,
+                    padding=upsample_scale // 2 + upsample_scale % 2,
+                    output_padding=upsample_scale % 2,
+                    groups=groups,
+                ),
+            )
+
+        if self.downsample_scale > 1:
+            self.conv_downsampler = nn.Sequential(
+                nn.LeakyReLU(0.2),
+                nn.Conv1d(
+                    dim,
+                    dim,
+                    kernel_size=2 * downsample_scale,
+                    stride=downsample_scale,
+                    padding=downsample_scale // 2 + downsample_scale % 2,
+                    groups=groups,
+                ),
+            )
+
+    @staticmethod
+    def repeat_upsampler(x, upsample_scale):
+        return x.repeat_interleave(upsample_scale, dim=2)
+
+    @staticmethod
+    def skip_downsampler(x, downsample_scale):
+        return F.avg_pool1d(x, kernel_size=downsample_scale, stride=downsample_scale)
+
+    def forward(self, x):
+        x = x.transpose(1, 2)
+        if self.upsample_scale > 1:
+            repeat_res = self.repeat_upsampler(x, self.upsample_scale)
+            deconv_res = self.de_conv_upsampler(x)
+            upmerge_res = repeat_res + deconv_res
+        else:
+            upmerge_res = x
+            repeat_res = x
+
+        if self.downsample_scale > 1:
+            conv_res = self.conv_downsampler(upmerge_res)
+            skip2_res = self.skip_downsampler(upmerge_res, self.downsample_scale)
+            skip1_res = self.skip_downsampler(repeat_res, self.downsample_scale)
+        else:
+            conv_res = upmerge_res
+            skip2_res = upmerge_res
+            skip1_res = repeat_res
+
+        final_res = conv_res + skip1_res + skip2_res
+
+        return final_res
+
+
+# test
+if __name__ == "__main__":
+    test_input = torch.randn(8, 1024, 50)  # Batch size = 8, 1024 channels, length = 50
+    model = SamplingBlock(1024, 1024, upsample_scale=2)
+    model_down = SamplingBlock(1024, 1024, downsample_scale=2)
+    output = model(test_input)
+    output_down = model_down(test_input)
+    print("shape after upsample * 2", output.shape)  # torch.Size([8, 1024, 100])
+    print("shape after downsample * 2", output_down.shape)  # torch.Size([8, 1024, 25])
+    if output.shape == torch.Size([8, 1024, 100]) and output_down.shape == torch.Size(
+        [8, 1024, 25]
+    ):
+        print("test successful")
diff --git a/trained_50_percents/sparktts/modules/blocks/vocos.py b/trained_50_percents/sparktts/modules/blocks/vocos.py
new file mode 100644
index 0000000000000000000000000000000000000000..31ff7900a8474d48ba794540553b4b7e3e61150c
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/blocks/vocos.py
@@ -0,0 +1,373 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+
+from typing import Tuple
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from typing import Optional
+
+
+class ConvNeXtBlock(nn.Module):
+    """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
+
+    Args:
+        dim (int): Number of input channels.
+        intermediate_dim (int): Dimensionality of the intermediate layer.
+        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
+            Defaults to None.
+        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
+            None means non-conditional LayerNorm. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        layer_scale_init_value: float,
+        condition_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=3, groups=dim
+        )  # depthwise conv
+        self.adanorm = condition_dim is not None
+        if condition_dim:
+            self.norm = AdaLayerNorm(condition_dim, dim, eps=1e-6)
+        else:
+            self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(
+            dim, intermediate_dim
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+
+    def forward(
+        self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        residual = x
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # (B, C, T) -> (B, T, C)
+        if self.adanorm:
+            assert cond_embedding_id is not None
+            x = self.norm(x, cond_embedding_id)
+        else:
+            x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.transpose(1, 2)  # (B, T, C) -> (B, C, T)
+
+        x = residual + x
+        return x
+
+
+class AdaLayerNorm(nn.Module):
+    """
+    Adaptive Layer Normalization module with learnable embeddings per `num_embeddings` classes
+
+    Args:
+        condition_dim (int): Dimension of the condition.
+        embedding_dim (int): Dimension of the embeddings.
+    """
+
+    def __init__(self, condition_dim: int, embedding_dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.dim = embedding_dim
+        self.scale = nn.Linear(condition_dim, embedding_dim)
+        self.shift = nn.Linear(condition_dim, embedding_dim)
+        torch.nn.init.ones_(self.scale.weight)
+        torch.nn.init.zeros_(self.shift.weight)
+
+    def forward(self, x: torch.Tensor, cond_embedding: torch.Tensor) -> torch.Tensor:
+        scale = self.scale(cond_embedding)
+        shift = self.shift(cond_embedding)
+        x = nn.functional.layer_norm(x, (self.dim,), eps=self.eps)
+        x = x * scale.unsqueeze(1) + shift.unsqueeze(1)
+        return x
+
+
+class ResBlock1(nn.Module):
+    """
+    ResBlock adapted from HiFi-GAN V1 (https://github.com/jik876/hifi-gan) with dilated 1D convolutions,
+    but without upsampling layers.
+
+    Args:
+        dim (int): Number of input channels.
+        kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3.
+        dilation (tuple[int], optional): Dilation factors for the dilated convolutions.
+            Defaults to (1, 3, 5).
+        lrelu_slope (float, optional): Negative slope of the LeakyReLU activation function.
+            Defaults to 0.1.
+        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
+            Defaults to None.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 3,
+        dilation: Tuple[int, int, int] = (1, 3, 5),
+        lrelu_slope: float = 0.1,
+        layer_scale_init_value: Optional[float] = None,
+    ):
+        super().__init__()
+        self.lrelu_slope = lrelu_slope
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=self.get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=self.get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=self.get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=self.get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=self.get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    nn.Conv1d(
+                        dim,
+                        dim,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=self.get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+
+        self.gamma = nn.ParameterList(
+            [
+                (
+                    nn.Parameter(
+                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
+                    )
+                    if layer_scale_init_value is not None
+                    else None
+                ),
+                (
+                    nn.Parameter(
+                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
+                    )
+                    if layer_scale_init_value is not None
+                    else None
+                ),
+                (
+                    nn.Parameter(
+                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
+                    )
+                    if layer_scale_init_value is not None
+                    else None
+                ),
+            ]
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for c1, c2, gamma in zip(self.convs1, self.convs2, self.gamma):
+            xt = torch.nn.functional.leaky_relu(x, negative_slope=self.lrelu_slope)
+            xt = c1(xt)
+            xt = torch.nn.functional.leaky_relu(xt, negative_slope=self.lrelu_slope)
+            xt = c2(xt)
+            if gamma is not None:
+                xt = gamma * xt
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+    @staticmethod
+    def get_padding(kernel_size: int, dilation: int = 1) -> int:
+        return int((kernel_size * dilation - dilation) / 2)
+
+
+class Backbone(nn.Module):
+    """Base class for the generator's backbone. It preserves the same temporal resolution across all layers."""
+
+    def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Args:
+            x (Tensor): Input tensor of shape (B, C, L), where B is the batch size,
+                        C denotes output features, and L is the sequence length.
+
+        Returns:
+            Tensor: Output of shape (B, L, H), where B is the batch size, L is the sequence length,
+                    and H denotes the model dimension.
+        """
+        raise NotImplementedError("Subclasses must implement the forward method.")
+
+
+class VocosBackbone(Backbone):
+    """
+    Vocos backbone module built with ConvNeXt blocks. Supports additional conditioning with Adaptive Layer Normalization
+
+    Args:
+        input_channels (int): Number of input features channels.
+        dim (int): Hidden dimension of the model.
+        intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock.
+        num_layers (int): Number of ConvNeXtBlock layers.
+        layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to `1 / num_layers`.
+        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
+                                                None means non-conditional model. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        input_channels: int,
+        dim: int,
+        intermediate_dim: int,
+        num_layers: int,
+        layer_scale_init_value: Optional[float] = None,
+        condition_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.input_channels = input_channels
+        self.embed = nn.Conv1d(input_channels, dim, kernel_size=7, padding=3)
+        self.adanorm = condition_dim is not None
+        if condition_dim:
+            self.norm = AdaLayerNorm(condition_dim, dim, eps=1e-6)
+        else:
+            self.norm = nn.LayerNorm(dim, eps=1e-6)
+        layer_scale_init_value = layer_scale_init_value or 1 / num_layers
+        self.convnext = nn.ModuleList(
+            [
+                ConvNeXtBlock(
+                    dim=dim,
+                    intermediate_dim=intermediate_dim,
+                    layer_scale_init_value=layer_scale_init_value,
+                    condition_dim=condition_dim,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv1d, nn.Linear)):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor, condition: torch.Tensor = None) -> torch.Tensor:
+        x = self.embed(x)
+        if self.adanorm:
+            assert condition is not None
+            x = self.norm(x.transpose(1, 2), condition)
+        else:
+            x = self.norm(x.transpose(1, 2))
+        x = x.transpose(1, 2)
+        for conv_block in self.convnext:
+            x = conv_block(x, condition)
+        x = self.final_layer_norm(x.transpose(1, 2))
+        return x
+
+
+class VocosResNetBackbone(Backbone):
+    """
+    Vocos backbone module built with ResBlocks.
+
+    Args:
+        input_channels (int): Number of input features channels.
+        dim (int): Hidden dimension of the model.
+        num_blocks (int): Number of ResBlock1 blocks.
+        layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        input_channels,
+        dim,
+        num_blocks,
+        layer_scale_init_value=None,
+    ):
+        super().__init__()
+        self.input_channels = input_channels
+        self.embed = weight_norm(
+            nn.Conv1d(input_channels, dim, kernel_size=3, padding=1)
+        )
+        layer_scale_init_value = layer_scale_init_value or 1 / num_blocks / 3
+        self.resnet = nn.Sequential(
+            *[
+                ResBlock1(dim=dim, layer_scale_init_value=layer_scale_init_value)
+                for _ in range(num_blocks)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
+        x = self.embed(x)
+        x = self.resnet(x)
+        x = x.transpose(1, 2)
+        return x
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diff --git a/trained_50_percents/sparktts/modules/encoder_decoder/feat_decoder.py b/trained_50_percents/sparktts/modules/encoder_decoder/feat_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..de409789f21691c6a648ce4d0e4cfb2dba1bef33
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/encoder_decoder/feat_decoder.py
@@ -0,0 +1,115 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+
+from typing import List
+
+from sparktts.modules.blocks.vocos import VocosBackbone
+from sparktts.modules.blocks.samper import SamplingBlock
+
+
+class Decoder(nn.Module):
+    """Decoder module with convnext and upsampling blocks
+
+    Args:
+        sample_ratios (List[int]): sample ratios
+            example: [2, 2] means downsample by 2x and then upsample by 2x
+    """
+
+    def __init__(
+        self,
+        input_channels: int,
+        vocos_dim: int,
+        vocos_intermediate_dim: int,
+        vocos_num_layers: int,
+        out_channels: int,
+        condition_dim: int = None,
+        sample_ratios: List[int] = [1, 1],
+        use_tanh_at_final: bool = False,
+    ):
+        super().__init__()
+
+        self.linear_pre = nn.Linear(input_channels, vocos_dim)
+        modules = [
+            nn.Sequential(
+                SamplingBlock(
+                    dim=vocos_dim,
+                    groups=vocos_dim,
+                    upsample_scale=ratio,
+                ),
+                VocosBackbone(
+                    input_channels=vocos_dim,
+                    dim=vocos_dim,
+                    intermediate_dim=vocos_intermediate_dim,
+                    num_layers=2,
+                    condition_dim=None,
+                ),
+            )
+            for ratio in sample_ratios
+        ]
+
+        self.downsample = nn.Sequential(*modules)
+
+        self.vocos_backbone = VocosBackbone(
+            input_channels=vocos_dim,
+            dim=vocos_dim,
+            intermediate_dim=vocos_intermediate_dim,
+            num_layers=vocos_num_layers,
+            condition_dim=condition_dim,
+        )
+        self.linear = nn.Linear(vocos_dim, out_channels)
+        self.use_tanh_at_final = use_tanh_at_final
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor = None):
+        """encoder forward.
+
+        Args:
+            x (torch.Tensor): (batch_size, input_channels, length)
+
+        Returns:
+            x (torch.Tensor): (batch_size, encode_channels, length)
+        """
+        x = self.linear_pre(x.transpose(1, 2))
+        x = self.downsample(x).transpose(1, 2)
+        x = self.vocos_backbone(x, condition=c)
+        x = self.linear(x).transpose(1, 2)
+        if self.use_tanh_at_final:
+            x = torch.tanh(x)
+
+        return x
+
+
+# test
+if __name__ == "__main__":
+    test_input = torch.randn(8, 1024, 50)  # Batch size = 8, 1024 channels, length = 50
+    condition = torch.randn(8, 256)
+    decoder = Decoder(
+        input_channels=1024,
+        vocos_dim=384,
+        vocos_intermediate_dim=2048,
+        vocos_num_layers=12,
+        out_channels=256,
+        condition_dim=256,
+        sample_ratios=[2, 2],
+    )
+    output = decoder(test_input, condition)
+    print(output.shape)  # torch.Size([8, 256, 200])
+    if output.shape == torch.Size([8, 256, 200]):
+        print("Decoder test passed")
+    else:
+        print("Decoder test failed")
diff --git a/trained_50_percents/sparktts/modules/encoder_decoder/feat_encoder.py b/trained_50_percents/sparktts/modules/encoder_decoder/feat_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1f861b10dd26bf6317acc6f60496be4a7c1abc3
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/encoder_decoder/feat_encoder.py
@@ -0,0 +1,105 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+
+from typing import List
+
+from sparktts.modules.blocks.vocos import VocosBackbone
+from sparktts.modules.blocks.samper import SamplingBlock
+
+
+class Encoder(nn.Module):
+    """Encoder module with convnext and downsampling blocks"""
+
+    def __init__(
+        self,
+        input_channels: int,
+        vocos_dim: int,
+        vocos_intermediate_dim: int,
+        vocos_num_layers: int,
+        out_channels: int,
+        sample_ratios: List[int] = [1, 1],
+    ):
+        super().__init__()
+        """
+        Encoder module with VocosBackbone and sampling blocks.
+
+        Args:
+            sample_ratios (List[int]): sample ratios
+                example: [2, 2] means downsample by 2x and then upsample by 2x
+        """
+        self.encoder = VocosBackbone(
+            input_channels=input_channels,
+            dim=vocos_dim,
+            intermediate_dim=vocos_intermediate_dim,
+            num_layers=vocos_num_layers,
+            condition_dim=None,
+        )
+
+        modules = [
+            nn.Sequential(
+                SamplingBlock(
+                    dim=vocos_dim,
+                    groups=vocos_dim,
+                    downsample_scale=ratio,
+                ),
+                VocosBackbone(
+                    input_channels=vocos_dim,
+                    dim=vocos_dim,
+                    intermediate_dim=vocos_intermediate_dim,
+                    num_layers=2,
+                    condition_dim=None,
+                ),
+            )
+            for ratio in sample_ratios
+        ]
+
+        self.downsample = nn.Sequential(*modules)
+
+        self.project = nn.Linear(vocos_dim, out_channels)
+
+    def forward(self, x: torch.Tensor, *args):
+        """
+        Args:
+            x (torch.Tensor): (batch_size, input_channels, length)
+
+        Returns:
+            x (torch.Tensor): (batch_size, encode_channels, length)
+        """
+        x = self.encoder(x)
+        x = self.downsample(x)
+        x = self.project(x)
+        return x.transpose(1, 2)
+
+
+# test
+if __name__ == "__main__":
+    test_input = torch.randn(8, 1024, 50)  # Batch size = 8, 1024 channels, length = 50
+    encoder = Encoder(
+        input_channels=1024,
+        vocos_dim=384,
+        vocos_intermediate_dim=2048,
+        vocos_num_layers=12,
+        out_channels=256,
+        sample_ratios=[2, 2],
+    )
+
+    output = encoder(test_input)
+    print(output.shape)  # torch.Size([8, 256, 12])
+    if output.shape == torch.Size([8, 256, 12]):
+        print("test successful")
diff --git a/trained_50_percents/sparktts/modules/encoder_decoder/wave_generator.py b/trained_50_percents/sparktts/modules/encoder_decoder/wave_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..13ca769aa26c2fa8fc7f62c0c0bb27063d34076b
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/encoder_decoder/wave_generator.py
@@ -0,0 +1,88 @@
+# Copyright (c) 2024 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+
+# Adapted from https://github.com/descriptinc/descript-audio-codec under the Apache License 2.0
+
+
+import torch.nn as nn
+
+from sparktts.modules.blocks.layers import (
+    Snake1d,
+    WNConv1d,
+    ResidualUnit,
+    WNConvTranspose1d,
+    init_weights,
+)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+        self,
+        input_dim: int = 16,
+        output_dim: int = 8,
+        kernel_size: int = 2,
+        stride: int = 1,
+    ):
+        super().__init__()
+        self.block = nn.Sequential(
+            Snake1d(input_dim),
+            WNConvTranspose1d(
+                input_dim,
+                output_dim,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=(kernel_size - stride) // 2,
+            ),
+            ResidualUnit(output_dim, dilation=1),
+            ResidualUnit(output_dim, dilation=3),
+            ResidualUnit(output_dim, dilation=9),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class WaveGenerator(nn.Module):
+    def __init__(
+        self,
+        input_channel,
+        channels,
+        rates,
+        kernel_sizes,
+        d_out: int = 1,
+    ):
+        super().__init__()
+
+        # Add first conv layer
+        layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]
+
+        # Add upsampling + MRF blocks
+        for i, (kernel_size, stride) in enumerate(zip(kernel_sizes, rates)):
+            input_dim = channels // 2**i
+            output_dim = channels // 2 ** (i + 1)
+            layers += [DecoderBlock(input_dim, output_dim, kernel_size, stride)]
+
+        # Add final conv layer
+        layers += [
+            Snake1d(output_dim),
+            WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
+            nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*layers)
+
+        self.apply(init_weights)
+
+    def forward(self, x):
+        return self.model(x)
diff --git a/trained_50_percents/sparktts/modules/fsq/__pycache__/finite_scalar_quantization.cpython-311.pyc b/trained_50_percents/sparktts/modules/fsq/__pycache__/finite_scalar_quantization.cpython-311.pyc
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diff --git a/trained_50_percents/sparktts/modules/fsq/finite_scalar_quantization.py b/trained_50_percents/sparktts/modules/fsq/finite_scalar_quantization.py
new file mode 100644
index 0000000000000000000000000000000000000000..da36155078290b5262f9a7b1b7b15b621cc88131
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/fsq/finite_scalar_quantization.py
@@ -0,0 +1,251 @@
+"""
+Finite Scalar Quantization: VQ-VAE Made Simple - https://arxiv.org/abs/2309.15505
+Code adapted from Jax version in Appendix A.1
+"""
+
+from __future__ import annotations
+from functools import wraps, partial
+from contextlib import nullcontext
+from typing import List, Tuple
+
+import torch
+import torch.nn as nn
+from torch.nn import Module
+from torch import Tensor, int32
+from torch.amp import autocast
+
+from einops import rearrange, pack, unpack
+
+# helper functions
+
+
+def exists(v):
+    return v is not None
+
+
+def default(*args):
+    for arg in args:
+        if exists(arg):
+            return arg
+    return None
+
+
+def maybe(fn):
+    @wraps(fn)
+    def inner(x, *args, **kwargs):
+        if not exists(x):
+            return x
+        return fn(x, *args, **kwargs)
+
+    return inner
+
+
+def pack_one(t, pattern):
+    return pack([t], pattern)
+
+
+def unpack_one(t, ps, pattern):
+    return unpack(t, ps, pattern)[0]
+
+
+# tensor helpers
+
+
+def round_ste(z: Tensor) -> Tensor:
+    """Round with straight through gradients."""
+    zhat = z.round()
+    return z + (zhat - z).detach()
+
+
+# main class
+
+
+class FSQ(Module):
+    def __init__(
+        self,
+        levels: List[int],
+        dim: int | None = None,
+        num_codebooks=1,
+        keep_num_codebooks_dim: bool | None = None,
+        scale: float | None = None,
+        allowed_dtypes: Tuple[torch.dtype, ...] = (torch.float32, torch.float64),
+        channel_first: bool = False,
+        projection_has_bias: bool = True,
+        return_indices=True,
+        force_quantization_f32=True,
+    ):
+        super().__init__()
+        _levels = torch.tensor(levels, dtype=int32)
+        self.register_buffer("_levels", _levels, persistent=False)
+
+        _basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=int32)
+        self.register_buffer("_basis", _basis, persistent=False)
+
+        self.scale = scale
+
+        codebook_dim = len(levels)
+        self.codebook_dim = codebook_dim
+
+        effective_codebook_dim = codebook_dim * num_codebooks
+        self.num_codebooks = num_codebooks
+        self.effective_codebook_dim = effective_codebook_dim
+
+        keep_num_codebooks_dim = default(keep_num_codebooks_dim, num_codebooks > 1)
+        assert not (num_codebooks > 1 and not keep_num_codebooks_dim)
+        self.keep_num_codebooks_dim = keep_num_codebooks_dim
+
+        self.dim = default(dim, len(_levels) * num_codebooks)
+
+        self.channel_first = channel_first
+
+        has_projections = self.dim != effective_codebook_dim
+        self.project_in = (
+            nn.Linear(self.dim, effective_codebook_dim, bias=projection_has_bias)
+            if has_projections
+            else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(effective_codebook_dim, self.dim, bias=projection_has_bias)
+            if has_projections
+            else nn.Identity()
+        )
+
+        self.has_projections = has_projections
+
+        self.return_indices = return_indices
+        if return_indices:
+            self.codebook_size = self._levels.prod().item()
+            implicit_codebook = self._indices_to_codes(torch.arange(self.codebook_size))
+            self.register_buffer(
+                "implicit_codebook", implicit_codebook, persistent=False
+            )
+
+        self.allowed_dtypes = allowed_dtypes
+        self.force_quantization_f32 = force_quantization_f32
+
+    def bound(self, z, eps: float = 1e-3):
+        """Bound `z`, an array of shape (..., d)."""
+        half_l = (self._levels - 1) * (1 + eps) / 2
+        offset = torch.where(self._levels % 2 == 0, 0.5, 0.0)
+        shift = (offset / half_l).atanh()
+        return (z + shift).tanh() * half_l - offset
+
+    def quantize(self, z):
+        """Quantizes z, returns quantized zhat, same shape as z."""
+        quantized = round_ste(self.bound(z))
+        half_width = self._levels // 2  # Renormalize to [-1, 1].
+        return quantized / half_width
+
+    def _scale_and_shift(self, zhat_normalized):
+        half_width = self._levels // 2
+        return (zhat_normalized * half_width) + half_width
+
+    def _scale_and_shift_inverse(self, zhat):
+        half_width = self._levels // 2
+        return (zhat - half_width) / half_width
+
+    def _indices_to_codes(self, indices):
+        level_indices = self.indices_to_level_indices(indices)
+        codes = self._scale_and_shift_inverse(level_indices)
+        return codes
+
+    def codes_to_indices(self, zhat):
+        """Converts a `code` to an index in the codebook."""
+        assert zhat.shape[-1] == self.codebook_dim
+        zhat = self._scale_and_shift(zhat)
+        return (zhat * self._basis).sum(dim=-1).to(int32)
+
+    def indices_to_level_indices(self, indices):
+        """Converts indices to indices at each level, perhaps needed for a transformer with factorized embeddings"""
+        indices = rearrange(indices, "... -> ... 1")
+        codes_non_centered = (indices // self._basis) % self._levels
+        return codes_non_centered
+
+    def indices_to_codes(self, indices):
+        """Inverse of `codes_to_indices`."""
+        assert exists(indices)
+
+        is_img_or_video = indices.ndim >= (3 + int(self.keep_num_codebooks_dim))
+
+        codes = self._indices_to_codes(indices)
+
+        if self.keep_num_codebooks_dim:
+            codes = rearrange(codes, "... c d -> ... (c d)")
+
+        codes = self.project_out(codes)
+
+        if is_img_or_video or self.channel_first:
+            codes = rearrange(codes, "b ... d -> b d ...")
+
+        return codes
+
+    def forward(self, z):
+        """
+        einstein notation
+        b - batch
+        n - sequence (or flattened spatial dimensions)
+        d - feature dimension
+        c - number of codebook dim
+        """
+
+        is_img_or_video = z.ndim >= 4
+        need_move_channel_last = is_img_or_video or self.channel_first
+
+        # standardize image or video into (batch, seq, dimension)
+
+        if need_move_channel_last:
+            z = rearrange(z, "b d ... -> b ... d")
+            z, ps = pack_one(z, "b * d")
+
+        assert (
+            z.shape[-1] == self.dim
+        ), f"expected dimension of {self.dim} but found dimension of {z.shape[-1]}"
+
+        z = self.project_in(z)
+
+        z = rearrange(z, "b n (c d) -> b n c d", c=self.num_codebooks)
+
+        # whether to force quantization step to be full precision or not
+
+        force_f32 = self.force_quantization_f32
+        quantization_context = (
+            partial(autocast, "cuda", enabled=False) if force_f32 else nullcontext
+        )
+
+        with quantization_context():
+            orig_dtype = z.dtype
+
+            if force_f32 and orig_dtype not in self.allowed_dtypes:
+                z = z.float()
+
+            codes = self.quantize(z)
+
+            # returning indices could be optional
+
+            indices = None
+
+            if self.return_indices:
+                indices = self.codes_to_indices(codes)
+
+            codes = rearrange(codes, "b n c d -> b n (c d)")
+
+            codes = codes.type(orig_dtype)
+
+        # project out
+
+        out = self.project_out(codes)
+
+        # reconstitute image or video dimensions
+
+        if need_move_channel_last:
+            out = unpack_one(out, ps, "b * d")
+            out = rearrange(out, "b ... d -> b d ...")
+
+            indices = maybe(unpack_one)(indices, ps, "b * c")
+
+        if not self.keep_num_codebooks_dim and self.return_indices:
+            indices = maybe(rearrange)(indices, "... 1 -> ...")
+
+        # return quantized output and indices
+
+        return out, indices
diff --git a/trained_50_percents/sparktts/modules/fsq/residual_fsq.py b/trained_50_percents/sparktts/modules/fsq/residual_fsq.py
new file mode 100644
index 0000000000000000000000000000000000000000..38a391d8c8b0427e4b21f20d545c7d4f2e367e7c
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/fsq/residual_fsq.py
@@ -0,0 +1,355 @@
+import random
+import torch
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from typing import List
+from torch import nn
+from torch.nn import Module
+from torch.amp import autocast
+from einx import get_at
+from einops import rearrange, reduce, pack, unpack
+
+from sparktts.modules.fsq.finite_scalar_quantization import FSQ
+
+
+def exists(val):
+    return val is not None
+
+
+def first(l):
+    return l[0]
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+def round_up_multiple(num, mult):
+    return ceil(num / mult) * mult
+
+
+# distributed helpers
+
+
+def is_distributed():
+    return dist.is_initialized() and dist.get_world_size() > 1
+
+
+def get_maybe_sync_seed(device, max_size=10_000):
+    rand_int = torch.randint(0, max_size, (), device=device)
+
+    if is_distributed():
+        dist.all_reduce(rand_int)
+
+    return rand_int.item()
+
+
+class ResidualFSQ(Module):
+    """Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf"""
+
+    def __init__(
+        self,
+        *,
+        levels: List[int],
+        num_quantizers,
+        dim=None,
+        is_channel_first=False,
+        quantize_dropout=False,
+        quantize_dropout_cutoff_index=0,
+        quantize_dropout_multiple_of=1,
+        **kwargs,
+    ):
+        super().__init__()
+        codebook_dim = len(levels)
+        dim = default(dim, codebook_dim)
+
+        requires_projection = codebook_dim != dim
+        self.project_in = (
+            nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
+        )
+        self.has_projections = requires_projection
+
+        self.is_channel_first = is_channel_first
+        self.num_quantizers = num_quantizers
+
+        self.levels = levels
+        self.layers = nn.ModuleList([])
+
+        levels_tensor = torch.Tensor(levels)
+
+        scales = []
+
+        for ind in range(num_quantizers):
+            scales.append((levels_tensor - 1) ** -ind)
+
+            fsq = FSQ(levels=levels, dim=codebook_dim, **kwargs)
+
+            self.layers.append(fsq)
+
+        assert all([not fsq.has_projections for fsq in self.layers])
+
+        self.codebook_size = self.layers[0].codebook_size
+
+        self.register_buffer("scales", torch.stack(scales), persistent=False)
+
+        self.quantize_dropout = quantize_dropout and num_quantizers > 1
+
+        assert quantize_dropout_cutoff_index >= 0
+
+        self.quantize_dropout_cutoff_index = quantize_dropout_cutoff_index
+        self.quantize_dropout_multiple_of = quantize_dropout_multiple_of  # encodec paper proposes structured dropout, believe this was set to 4
+
+    @property
+    def codebooks(self):
+        codebooks = [layer.implicit_codebook for layer in self.layers]
+        codebooks = torch.stack(codebooks, dim=0)
+        return codebooks
+
+    def get_codes_from_indices(self, indices):
+
+        batch, quantize_dim = indices.shape[0], indices.shape[-1]
+
+        # may also receive indices in the shape of 'b h w q' (accept_image_fmap)
+
+        indices, ps = pack([indices], "b * q")
+
+        # because of quantize dropout, one can pass in indices that are coarse
+        # and the network should be able to reconstruct
+
+        if quantize_dim < self.num_quantizers:
+            assert (
+                self.quantize_dropout > 0.0
+            ), "quantize dropout must be greater than 0 if you wish to reconstruct from a signal with less fine quantizations"
+            indices = F.pad(indices, (0, self.num_quantizers - quantize_dim), value=-1)
+
+        # take care of quantizer dropout
+
+        mask = indices == -1
+        indices = indices.masked_fill(
+            mask, 0
+        )  # have it fetch a dummy code to be masked out later
+
+        all_codes = get_at("q [c] d, b n q -> q b n d", self.codebooks, indices)
+
+        # mask out any codes that were dropout-ed
+
+        all_codes = all_codes.masked_fill(rearrange(mask, "b n q -> q b n 1"), 0.0)
+
+        # scale the codes
+
+        scales = rearrange(self.scales, "q d -> q 1 1 d")
+        all_codes = all_codes * scales
+
+        # if (accept_image_fmap = True) then return shape (quantize, batch, height, width, dimension)
+
+        (all_codes,) = unpack(all_codes, ps, "q b * d")
+
+        return all_codes
+
+    def get_output_from_indices(self, indices):
+        codes = self.get_codes_from_indices(indices)
+        codes_summed = reduce(codes, "q ... -> ...", "sum")
+        return self.project_out(codes_summed)
+
+    def forward(self, x, return_all_codes=False, rand_quantize_dropout_fixed_seed=None):
+        num_quant, quant_dropout_multiple_of, device = (
+            self.num_quantizers,
+            self.quantize_dropout_multiple_of,
+            x.device,
+        )
+
+        # handle channel first
+
+        if self.is_channel_first:
+            x = rearrange(x, "b d ... -> b ... d")
+            x, ps = pack([x], "b * d")
+
+        # maybe project in
+
+        x = self.project_in(x)
+
+        quantized_out = 0.0
+        residual = x
+
+        all_indices = []
+
+        should_quantize_dropout = self.training and self.quantize_dropout
+
+        # sample a layer index at which to dropout further residual quantization
+        # also prepare null indices
+
+        if should_quantize_dropout:
+
+            # check if seed is manually passed in
+
+            if not exists(rand_quantize_dropout_fixed_seed):
+                rand_quantize_dropout_fixed_seed = get_maybe_sync_seed(device)
+
+            rand = random.Random(rand_quantize_dropout_fixed_seed)
+
+            rand_quantize_dropout_index = rand.randrange(
+                self.quantize_dropout_cutoff_index, num_quant
+            )
+
+            if quant_dropout_multiple_of != 1:
+                rand_quantize_dropout_index = (
+                    round_up_multiple(
+                        rand_quantize_dropout_index + 1, quant_dropout_multiple_of
+                    )
+                    - 1
+                )
+
+            null_indices = torch.full(
+                x.shape[:2], -1.0, device=device, dtype=torch.long
+            )
+
+        # go through the layers
+
+        with autocast("cuda", enabled=False):
+            for quantizer_index, (layer, scale) in enumerate(
+                zip(self.layers, self.scales)
+            ):
+
+                if (
+                    should_quantize_dropout
+                    and quantizer_index > rand_quantize_dropout_index
+                ):
+                    all_indices.append(null_indices)
+                    continue
+
+                quantized, indices = layer(residual / scale)
+
+                quantized = quantized * scale
+
+                residual = residual - quantized.detach()
+                quantized_out = quantized_out + quantized
+
+                all_indices.append(indices)
+
+        # project out, if needed
+
+        quantized_out = self.project_out(quantized_out)
+
+        # stack all indices
+
+        all_indices = torch.stack(all_indices, dim=-1)
+
+        # channel first out
+
+        if self.is_channel_first:
+            (quantized_out,) = unpack(quantized_out, ps, "b * d")
+            (all_indices,) = unpack(all_indices, ps, "b * d")
+
+            quantized_out = rearrange(quantized_out, "b ... d -> b d ...")
+            all_indices = rearrange(all_indices, "b ... d -> b d ...")
+
+        # return
+
+        ret = (quantized_out, all_indices)
+
+        if not return_all_codes:
+            return ret
+
+        # whether to return all codes from all codebooks across layers
+
+        all_codes = self.get_codes_from_indices(all_indices)
+
+        # will return all codes in shape (quantizer, batch, sequence length, codebook dimension)
+
+        return (*ret, all_codes)
+
+
+# grouped residual fsq
+
+
+class GroupedResidualFSQ(Module):
+    def __init__(self, *, dim, groups=1, accept_image_fmap=False, **kwargs):
+        super().__init__()
+        self.dim = dim
+        self.groups = groups
+        assert (dim % groups) == 0
+        dim_per_group = dim // groups
+
+        self.accept_image_fmap = accept_image_fmap
+
+        self.rvqs = nn.ModuleList([])
+
+        for _ in range(groups):
+            self.rvqs.append(ResidualFSQ(dim=dim_per_group, **kwargs))
+
+        self.codebook_size = self.rvqs[0].codebook_size
+
+    @property
+    def codebooks(self):
+        return torch.stack(tuple(rvq.codebooks for rvq in self.rvqs))
+
+    @property
+    def split_dim(self):
+        return 1 if self.accept_image_fmap else -1
+
+    def get_codes_from_indices(self, indices):
+        codes = tuple(
+            rvq.get_codes_from_indices(chunk_indices)
+            for rvq, chunk_indices in zip(self.rvqs, indices)
+        )
+        return torch.stack(codes)
+
+    def get_output_from_indices(self, indices):
+        outputs = tuple(
+            rvq.get_output_from_indices(chunk_indices)
+            for rvq, chunk_indices in zip(self.rvqs, indices)
+        )
+        return torch.cat(outputs, dim=self.split_dim)
+
+    def forward(self, x, return_all_codes=False):
+        shape, split_dim, device = x.shape, self.split_dim, x.device
+        assert shape[split_dim] == self.dim
+
+        # split the feature dimension into groups
+
+        x = x.chunk(self.groups, dim=split_dim)
+
+        forward_kwargs = dict(
+            return_all_codes=return_all_codes,
+            rand_quantize_dropout_fixed_seed=(
+                get_maybe_sync_seed(device) if self.training else None
+            ),
+        )
+
+        # invoke residual vq on each group
+
+        out = tuple(rvq(chunk, **forward_kwargs) for rvq, chunk in zip(self.rvqs, x))
+        out = tuple(zip(*out))
+
+        # otherwise, get all the zipped outputs and combine them
+
+        quantized, all_indices, *maybe_all_codes = out
+
+        quantized = torch.cat(quantized, dim=split_dim)
+        all_indices = torch.stack(all_indices)
+
+        ret = (quantized, all_indices, *maybe_all_codes)
+        return ret
+
+
+if __name__ == "__main__":
+    model = ResidualFSQ(
+        levels=[4, 4, 4, 4, 4, 4],
+        num_quantizers=1,
+        dim=30,
+        is_channel_first=True,
+        quantize_dropout=False,
+    )
+    x = torch.randn(2, 30, 10)
+    quantize, embed_ind = model(x)
+
+    emb_from_ind = model.get_output_from_indices(embed_ind.transpose(1, 2))
+
+    print(quantize == emb_from_ind.transpose(1, 2))
+
+    print("quantize shape", quantize.shape)
+    print("embed_ind", embed_ind)
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diff --git a/trained_50_percents/sparktts/modules/speaker/ecapa_tdnn.py b/trained_50_percents/sparktts/modules/speaker/ecapa_tdnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..18f57ef00bc07cda6e24029f223f2bc11da8adc3
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/speaker/ecapa_tdnn.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2021 Zhengyang Chen (chenzhengyang117@gmail.com)
+#               2022 Hongji Wang (jijijiang77@gmail.com)
+#               2023 Bing Han (hanbing97@sjtu.edu.cn)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" This implementation is adapted from github repo:
+    https://github.com/lawlict/ECAPA-TDNN.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import sparktts.modules.speaker.pooling_layers as pooling_layers
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(
+        self,
+        channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+        scale=4,
+    ):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(
+                nn.Conv1d(
+                    self.width,
+                    self.width,
+                    kernel_size,
+                    stride,
+                    padding,
+                    dilation,
+                    bias=bias,
+                )
+            )
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        sp = spx[0]
+        for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
+            # Order: conv -> relu -> bn
+            if i >= 1:
+                sp = sp + spx[i]
+            sp = conv(sp)
+            sp = bn(F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+    ):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias
+        )
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+
+class SE_Res2Block(nn.Module):
+
+    def __init__(self, channels, kernel_size, stride, padding, dilation, scale):
+        super().__init__()
+        self.se_res2block = nn.Sequential(
+            Conv1dReluBn(channels, channels, kernel_size=1, stride=1, padding=0),
+            Res2Conv1dReluBn(
+                channels, kernel_size, stride, padding, dilation, scale=scale
+            ),
+            Conv1dReluBn(channels, channels, kernel_size=1, stride=1, padding=0),
+            SE_Connect(channels),
+        )
+
+    def forward(self, x):
+        return x + self.se_res2block(x)
+
+
+class ECAPA_TDNN(nn.Module):
+
+    def __init__(
+        self,
+        channels=512,
+        feat_dim=80,
+        embed_dim=192,
+        pooling_func="ASTP",
+        global_context_att=False,
+        emb_bn=False,
+    ):
+        super().__init__()
+
+        self.layer1 = Conv1dReluBn(feat_dim, channels, kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            channels, kernel_size=3, stride=1, padding=2, dilation=2, scale=8
+        )
+        self.layer3 = SE_Res2Block(
+            channels, kernel_size=3, stride=1, padding=3, dilation=3, scale=8
+        )
+        self.layer4 = SE_Res2Block(
+            channels, kernel_size=3, stride=1, padding=4, dilation=4, scale=8
+        )
+
+        cat_channels = channels * 3
+        out_channels = 512 * 3
+        self.conv = nn.Conv1d(cat_channels, out_channels, kernel_size=1)
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=out_channels, global_context_att=global_context_att
+        )
+        self.pool_out_dim = self.pool.get_out_dim()
+        self.bn = nn.BatchNorm1d(self.pool_out_dim)
+        self.linear = nn.Linear(self.pool_out_dim, embed_dim)
+        self.emb_bn = emb_bn
+        if emb_bn:  # better in SSL for SV
+            self.bn2 = nn.BatchNorm1d(embed_dim)
+        else:
+            self.bn2 = nn.Identity()
+
+    def forward(self, x, return_latent=False):
+        x = x.permute(0, 2, 1)  # (B,T,F) -> (B,F,T)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        latent = F.relu(self.conv(out))
+        out = self.bn(self.pool(latent))
+        out = self.linear(out)
+        if self.emb_bn:
+            out = self.bn2(out)
+
+        if return_latent:
+            return out, latent
+        return out
+
+
+def ECAPA_TDNN_c1024(feat_dim, embed_dim, pooling_func="ASTP", emb_bn=False):
+    return ECAPA_TDNN(
+        channels=1024,
+        feat_dim=feat_dim,
+        embed_dim=embed_dim,
+        pooling_func=pooling_func,
+        emb_bn=emb_bn,
+    )
+
+
+def ECAPA_TDNN_GLOB_c1024(feat_dim, embed_dim, pooling_func="ASTP", emb_bn=False):
+    return ECAPA_TDNN(
+        channels=1024,
+        feat_dim=feat_dim,
+        embed_dim=embed_dim,
+        pooling_func=pooling_func,
+        global_context_att=True,
+        emb_bn=emb_bn,
+    )
+
+
+def ECAPA_TDNN_c512(feat_dim, embed_dim, pooling_func="ASTP", emb_bn=False):
+    return ECAPA_TDNN(
+        channels=512,
+        feat_dim=feat_dim,
+        embed_dim=embed_dim,
+        pooling_func=pooling_func,
+        emb_bn=emb_bn,
+    )
+
+
+def ECAPA_TDNN_GLOB_c512(feat_dim, embed_dim, pooling_func="ASTP", emb_bn=False):
+    return ECAPA_TDNN(
+        channels=512,
+        feat_dim=feat_dim,
+        embed_dim=embed_dim,
+        pooling_func=pooling_func,
+        global_context_att=True,
+        emb_bn=emb_bn,
+    )
+
+
+if __name__ == "__main__":
+    x = torch.zeros(1, 200, 100)
+    model = ECAPA_TDNN_GLOB_c512(feat_dim=100, embed_dim=256, pooling_func="ASTP")
+    model.eval()
+    out, latent = model(x, True)
+    print(out.shape)
+    print(latent.shape)
+
+    num_params = sum(param.numel() for param in model.parameters())
+    print("{} M".format(num_params / 1e6))
+
+    # from thop import profile
+    # x_np = torch.randn(1, 200, 80)
+    # flops, params = profile(model, inputs=(x_np, ))
+    # print("FLOPs: {} G, Params: {} M".format(flops / 1e9, params / 1e6))
diff --git a/trained_50_percents/sparktts/modules/speaker/perceiver_encoder.py b/trained_50_percents/sparktts/modules/speaker/perceiver_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..bed1bb128f0bb1591c453480397b6d40d1e19cf3
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/speaker/perceiver_encoder.py
@@ -0,0 +1,360 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+
+# Adapted from https://github.com/lucidrains/naturalspeech2-pytorch/blob/659bec7f7543e7747e809e950cc2f84242fbeec7/naturalspeech2_pytorch/naturalspeech2_pytorch.py#L532
+
+from collections import namedtuple
+from functools import wraps
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+from packaging import version
+from torch import einsum, nn
+
+
+def exists(val):
+    return val is not None
+
+
+def once(fn):
+    called = False
+
+    @wraps(fn)
+    def inner(x):
+        nonlocal called
+        if called:
+            return
+        called = True
+        return fn(x)
+
+    return inner
+
+
+print_once = once(print)
+
+# main class
+
+
+class Attend(nn.Module):
+    def __init__(self, dropout=0.0, causal=False, use_flash=False):
+        super().__init__()
+        self.dropout = dropout
+        self.attn_dropout = nn.Dropout(dropout)
+
+        self.causal = causal
+        self.register_buffer("mask", None, persistent=False)
+
+        self.use_flash = use_flash
+        assert not (
+            use_flash and version.parse(torch.__version__) < version.parse("2.0.0")
+        ), "in order to use flash attention, you must be using pytorch 2.0 or above"
+
+        # determine efficient attention configs for cuda and cpu
+        self.config = namedtuple(
+            "EfficientAttentionConfig",
+            ["enable_flash", "enable_math", "enable_mem_efficient"],
+        )
+        self.cpu_config = self.config(True, True, True)
+        self.cuda_config = None
+
+        if not torch.cuda.is_available() or not use_flash:
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device("cuda"))
+
+        if device_properties.major == 8 and device_properties.minor == 0:
+            print_once(
+                "A100 GPU detected, using flash attention if input tensor is on cuda"
+            )
+            self.cuda_config = self.config(True, False, False)
+        else:
+            print_once(
+                "Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda"
+            )
+            self.cuda_config = self.config(False, True, True)
+
+    def get_mask(self, n, device):
+        if exists(self.mask) and self.mask.shape[-1] >= n:
+            return self.mask[:n, :n]
+
+        mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1)
+        self.register_buffer("mask", mask, persistent=False)
+        return mask
+
+    def flash_attn(self, q, k, v, mask=None):
+        _, heads, q_len, _, k_len, is_cuda = *q.shape, k.shape[-2], q.is_cuda
+
+        # Recommended for multi-query single-key-value attention by Tri Dao
+        # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
+
+        if k.ndim == 3:
+            k = rearrange(k, "b ... -> b 1 ...").expand_as(q)
+
+        if v.ndim == 3:
+            v = rearrange(v, "b ... -> b 1 ...").expand_as(q)
+
+        # Check if mask exists and expand to compatible shape
+        # The mask is B L, so it would have to be expanded to B H N L
+
+        if exists(mask):
+            mask = rearrange(mask, "b j -> b 1 1 j")
+            mask = mask.expand(-1, heads, q_len, -1)
+
+        # Check if there is a compatible device for flash attention
+
+        config = self.cuda_config if is_cuda else self.cpu_config
+
+        # pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
+
+        with torch.backends.cuda.sdp_kernel(**config._asdict()):
+            out = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=mask,
+                dropout_p=self.dropout if self.training else 0.0,
+                is_causal=self.causal,
+            )
+
+        return out
+
+    def forward(self, q, k, v, mask=None):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        n, device = q.shape[-2], q.device
+
+        scale = q.shape[-1] ** -0.5
+
+        if self.use_flash:
+            return self.flash_attn(q, k, v, mask=mask)
+
+        kv_einsum_eq = "b j d" if k.ndim == 3 else "b h j d"
+
+        # similarity
+
+        sim = einsum(f"b h i d, {kv_einsum_eq} -> b h i j", q, k) * scale
+
+        # key padding mask
+
+        if exists(mask):
+            mask = rearrange(mask, "b j -> b 1 1 j")
+            sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)
+
+        # causal mask
+
+        if self.causal:
+            causal_mask = self.get_mask(n, device)
+            sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)
+
+        # attention
+
+        attn = sim.softmax(dim=-1)
+        attn = self.attn_dropout(attn)
+
+        # aggregate values
+
+        out = einsum(f"b h i j, {kv_einsum_eq} -> b h i d", attn, v)
+
+        return out
+
+
+def Sequential(*mods):
+    return nn.Sequential(*filter(exists, mods))
+
+
+def exists(x):
+    return x is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if callable(d) else d
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, scale=True, dim_cond=None):
+        super().__init__()
+        self.cond = exists(dim_cond)
+        self.to_gamma_beta = nn.Linear(dim_cond, dim * 2) if self.cond else None
+
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(dim)) if scale else None
+
+    def forward(self, x, cond=None):
+        gamma = default(self.gamma, 1)
+        out = F.normalize(x, dim=-1) * self.scale * gamma
+
+        if not self.cond:
+            return out
+
+        assert exists(cond)
+        gamma, beta = self.to_gamma_beta(cond).chunk(2, dim=-1)
+        gamma, beta = map(lambda t: rearrange(t, "b d -> b 1 d"), (gamma, beta))
+        return out * gamma + beta
+
+
+class CausalConv1d(nn.Conv1d):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        (kernel_size,) = self.kernel_size
+        (dilation,) = self.dilation
+        (stride,) = self.stride
+
+        assert stride == 1
+        self.causal_padding = dilation * (kernel_size - 1)
+
+    def forward(self, x):
+        causal_padded_x = F.pad(x, (self.causal_padding, 0), value=0.0)
+        return super().forward(causal_padded_x)
+
+
+class GEGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return F.gelu(gate) * x
+
+
+def FeedForward(dim, mult=4, causal_conv=False):
+    dim_inner = int(dim * mult * 2 / 3)
+
+    conv = None
+    if causal_conv:
+        conv = nn.Sequential(
+            Rearrange("b n d -> b d n"),
+            CausalConv1d(dim_inner, dim_inner, 3),
+            Rearrange("b d n -> b n d"),
+        )
+
+    return Sequential(
+        nn.Linear(dim, dim_inner * 2), GEGLU(), conv, nn.Linear(dim_inner, dim)
+    )
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        dim_context=None,
+        causal=False,
+        dim_head=64,
+        heads=8,
+        dropout=0.0,
+        use_flash=False,
+        cross_attn_include_queries=False,
+    ):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.heads = heads
+        self.cross_attn_include_queries = cross_attn_include_queries
+
+        dim_inner = dim_head * heads
+        dim_context = default(dim_context, dim)
+
+        self.attend = Attend(causal=causal, dropout=dropout, use_flash=use_flash)
+        self.to_q = nn.Linear(dim, dim_inner, bias=False)
+        self.to_kv = nn.Linear(dim_context, dim_inner * 2, bias=False)
+        self.to_out = nn.Linear(dim_inner, dim, bias=False)
+
+    def forward(self, x, context=None, mask=None):
+        h, has_context = self.heads, exists(context)
+
+        context = default(context, x)
+
+        if has_context and self.cross_attn_include_queries:
+            context = torch.cat((x, context), dim=-2)
+
+        q, k, v = (self.to_q(x), *self.to_kv(context).chunk(2, dim=-1))
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+
+        out = self.attend(q, k, v, mask=mask)
+
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.to_out(out)
+
+
+class PerceiverResampler(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=2,
+        dim_context=None,
+        num_latents=32,
+        dim_head=64,
+        heads=8,
+        ff_mult=4,
+        use_flash_attn=False,
+    ):
+        super().__init__()
+        dim_context = default(dim_context, dim)
+
+        self.proj_context = (
+            nn.Linear(dim_context, dim) if dim_context != dim else nn.Identity()
+        )
+
+        self.latents = nn.Parameter(torch.randn(num_latents, dim))
+        nn.init.normal_(self.latents, std=0.02)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        Attention(
+                            dim=dim,
+                            dim_head=dim_head,
+                            heads=heads,
+                            use_flash=use_flash_attn,
+                            cross_attn_include_queries=True,
+                        ),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+        self.norm = RMSNorm(dim)
+
+    def forward(self, x, mask=None):
+        batch = x.shape[0]
+
+        x = self.proj_context(x)
+
+        latents = repeat(self.latents, "n d -> b n d", b=batch)
+
+        for attn, ff in self.layers:
+            latents = attn(latents, x, mask=mask) + latents
+            latents = ff(latents) + latents
+
+        return self.norm(latents)
+
+
+if __name__ == "__main__":
+    model = PerceiverResampler(dim=256, dim_context=80)
+    x = torch.randn(8, 200, 80)
+    out = model(x)
+    print(out.shape)  # [8, 32, 80]
+
+    num_params = sum(param.numel() for param in model.parameters())
+    print("{} M".format(num_params / 1e6))
diff --git a/trained_50_percents/sparktts/modules/speaker/pooling_layers.py b/trained_50_percents/sparktts/modules/speaker/pooling_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe8289885085c2377069dc57375953a6b87288bd
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/speaker/pooling_layers.py
@@ -0,0 +1,298 @@
+# Copyright (c) 2021 Shuai Wang (wsstriving@gmail.com)
+#
+# 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.
+"""
+Pooling functions to aggregate frame-level deep features
+into segment-level speaker embeddings
+
+High-order statistics are surprisingly effective, TSDP acts similarly as TSTP,
+even though we remove the mean statistic, on Voxceleb.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class TAP(nn.Module):
+    """
+    Temporal average pooling, only first-order mean is considered
+    """
+
+    def __init__(self, in_dim=0, **kwargs):
+        super(TAP, self).__init__()
+        self.in_dim = in_dim
+
+    def forward(self, x):
+        pooling_mean = x.mean(dim=-1)
+        # To be compatable with 2D input
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        return pooling_mean
+
+    def get_out_dim(self):
+        self.out_dim = self.in_dim
+        return self.out_dim
+
+
+class TSDP(nn.Module):
+    """
+    Temporal standard deviation pooling, only second-order std is considered
+    """
+
+    def __init__(self, in_dim=0, **kwargs):
+        super(TSDP, self).__init__()
+        self.in_dim = in_dim
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-7)
+        pooling_std = pooling_std.flatten(start_dim=1)
+        return pooling_std
+
+    def get_out_dim(self):
+        self.out_dim = self.in_dim
+        return self.out_dim
+
+
+class TSTP(nn.Module):
+    """
+    Temporal statistics pooling, concatenate mean and std, which is used in
+    x-vector
+    Comment: simple concatenation can not make full use of both statistics
+    """
+
+    def __init__(self, in_dim=0, **kwargs):
+        super(TSTP, self).__init__()
+        self.in_dim = in_dim
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_mean = x.mean(dim=-1)
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-7)
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        pooling_std = pooling_std.flatten(start_dim=1)
+        stats = torch.cat((pooling_mean, pooling_std), 1)
+        return stats
+
+    def get_out_dim(self):
+        self.out_dim = self.in_dim * 2
+        return self.out_dim
+
+
+class ASTP(nn.Module):
+    """ Attentive statistics pooling: Channel- and context-dependent
+        statistics pooling, first used in ECAPA_TDNN.
+    """
+
+    def __init__(self,
+                 in_dim,
+                 bottleneck_dim=128,
+                 global_context_att=False,
+                 **kwargs):
+        super(ASTP, self).__init__()
+        self.in_dim = in_dim
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't
+        # need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(
+                in_dim * 3, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(
+                in_dim, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(bottleneck_dim, in_dim,
+                                 kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        """
+        x: a 3-dimensional tensor in tdnn-based architecture (B,F,T)
+            or a 4-dimensional tensor in resnet architecture (B,C,F,T)
+            0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
+        """
+        if len(x.shape) == 4:
+            x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3])
+        assert len(x.shape) == 3
+
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(
+                torch.var(x, dim=-1, keepdim=True) + 1e-7).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! ReLU may be hard to converge.
+        alpha = torch.tanh(
+            self.linear1(x_in))  # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        var = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(var.clamp(min=1e-7))
+        return torch.cat([mean, std], dim=1)
+
+    def get_out_dim(self):
+        self.out_dim = 2 * self.in_dim
+        return self.out_dim
+
+
+class MHASTP(torch.nn.Module):
+    """ Multi head attentive statistics pooling
+    Reference:
+        Self Multi-Head Attention for Speaker Recognition
+        https://arxiv.org/pdf/1906.09890.pdf
+    """
+
+    def __init__(self,
+                 in_dim,
+                 layer_num=2,
+                 head_num=2,
+                 d_s=1,
+                 bottleneck_dim=64,
+                 **kwargs):
+        super(MHASTP, self).__init__()
+        assert (in_dim % head_num
+                ) == 0  # make sure that head num can be divided by input_dim
+        self.in_dim = in_dim
+        self.head_num = head_num
+        d_model = int(in_dim / head_num)
+        channel_dims = [bottleneck_dim for i in range(layer_num + 1)]
+        if d_s > 1:
+            d_s = d_model
+        else:
+            d_s = 1
+        self.d_s = d_s
+        channel_dims[0], channel_dims[-1] = d_model, d_s
+        heads_att_trans = []
+        for i in range(self.head_num):
+            att_trans = nn.Sequential()
+            for i in range(layer_num - 1):
+                att_trans.add_module(
+                    'att_' + str(i),
+                    nn.Conv1d(channel_dims[i], channel_dims[i + 1], 1, 1))
+                att_trans.add_module('tanh' + str(i), nn.Tanh())
+            att_trans.add_module(
+                'att_' + str(layer_num - 1),
+                nn.Conv1d(channel_dims[layer_num - 1], channel_dims[layer_num],
+                          1, 1))
+            heads_att_trans.append(att_trans)
+        self.heads_att_trans = nn.ModuleList(heads_att_trans)
+
+    def forward(self, input):
+        """
+        input: a 3-dimensional tensor in xvector architecture
+            or a 4-dimensional tensor in resnet architecture
+            0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
+        """
+        if len(input.shape) == 4:  # B x F x T
+            input = input.reshape(input.shape[0],
+                                  input.shape[1] * input.shape[2],
+                                  input.shape[3])
+        assert len(input.shape) == 3
+        bs, f_dim, t_dim = input.shape
+        chunks = torch.chunk(input, self.head_num, 1)
+        # split
+        chunks_out = []
+        # for i in range(self.head_num):
+        #     att_score = self.heads_att_trans[i](chunks[i])
+        for i, layer in enumerate(self.heads_att_trans):
+            att_score = layer(chunks[i])
+            alpha = F.softmax(att_score, dim=-1)
+            mean = torch.sum(alpha * chunks[i], dim=2)
+            var = torch.sum(alpha * chunks[i]**2, dim=2) - mean**2
+            std = torch.sqrt(var.clamp(min=1e-7))
+            chunks_out.append(torch.cat((mean, std), dim=1))
+        out = torch.cat(chunks_out, dim=1)
+        return out
+
+    def get_out_dim(self):
+        self.out_dim = 2 * self.in_dim
+        return self.out_dim
+
+
+class MQMHASTP(torch.nn.Module):
+    """ An attentive pooling
+    Reference:
+        multi query multi head attentive statistics pooling
+        https://arxiv.org/pdf/2110.05042.pdf
+    Args:
+        in_dim: the feature dimension of input
+        layer_num: the number of layer in the pooling layer
+        query_num: the number of querys
+        head_num: the number of heads
+        bottleneck_dim: the bottleneck dimension
+
+    SA (H = 1, Q = 1, n = 2, d_s = 1) ref:
+        https://www.danielpovey.com/files/2018_interspeech_xvector_attention.pdf
+    MHA (H > 1, Q = 1, n = 1, d_s = 1) ref:
+        https://arxiv.org/pdf/1906.09890.pdf
+    AS (H = 1, Q > 1, n = 2, d_s = 1) ref:
+        https://arxiv.org/pdf/1803.10963.pdf
+    VSA (H = 1, Q > 1, n = 2, d_s = d_h) ref:
+        http://www.interspeech2020.org/uploadfile/pdf/Mon-2-10-5.pdf
+    """
+
+    def __init__(self,
+                 in_dim,
+                 layer_num=2,
+                 query_num=2,
+                 head_num=8,
+                 d_s=2,
+                 bottleneck_dim=64,
+                 **kwargs):
+        super(MQMHASTP, self).__init__()
+        self.n_query = nn.ModuleList([
+            MHASTP(in_dim,
+                   layer_num=layer_num,
+                   head_num=head_num,
+                   d_s=d_s,
+                   bottleneck_dim=bottleneck_dim) for i in range(query_num)
+        ])
+        self.query_num = query_num
+        self.in_dim = in_dim
+
+    def forward(self, input):
+        """
+        input: a 3-dimensional tensor in xvector architecture
+            or a 4-dimensional tensor in resnet architecture
+            0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
+        """
+        if len(input.shape) == 4:  # B x F x T
+            input = input.reshape(input.shape[0],
+                                  input.shape[1] * input.shape[2],
+                                  input.shape[3])
+        assert len(input.shape) == 3
+        res = []
+        for i, layer in enumerate(self.n_query):
+            res.append(layer(input))
+        out = torch.cat(res, dim=-1)
+        return out
+
+    def get_out_dim(self):
+        self.out_dim = self.in_dim * 2 * self.query_num
+        return self.out_dim
+
+
+if __name__ == '__main__':
+    data = torch.randn(16, 512, 10, 35)
+    # model = StatisticsPooling()
+    model = MQMHASTP(512 * 10)
+    model = MHASTP(512 * 10)
+    model = MQMHASTP(512 * 10, context=False)
+    print(model)
+
+    out = model(data)
+    print(out.shape)
+    print(model.get_out_dim())
\ No newline at end of file
diff --git a/trained_50_percents/sparktts/modules/speaker/speaker_encoder.py b/trained_50_percents/sparktts/modules/speaker/speaker_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb5050ad1aa1c13c621cc0501992e3b74f00726b
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/speaker/speaker_encoder.py
@@ -0,0 +1,136 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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 torch
+import torch.nn as nn
+
+from typing import List, Tuple
+from sparktts.modules.fsq.residual_fsq import ResidualFSQ
+from sparktts.modules.speaker.ecapa_tdnn import ECAPA_TDNN_GLOB_c512
+from sparktts.modules.speaker.perceiver_encoder import PerceiverResampler
+
+"""
+x-vector + d-vector
+"""
+
+
+class SpeakerEncoder(nn.Module):
+    """
+
+    Args:
+        input_dim (int): acoustic feature dimension
+        out_dim (int): output dimension of x-vector and d-vector
+        latent_dim (int): latent dimension before quantization
+        token_num (int): sequence length of speaker tokens
+        fsq_levels (List[int]): number of levels for each quantizer
+        fsq_num_quantizers (int): number of quantizers
+
+    Return:
+        speaker_embs: (B, T2, out_dim)
+    """
+
+    def __init__(
+        self,
+        input_dim: int = 100,
+        out_dim: int = 512,
+        latent_dim: int = 128,
+        token_num: int = 32,
+        fsq_levels: List[int] = [4, 4, 4, 4, 4, 4],
+        fsq_num_quantizers: int = 1,
+    ):
+        super(SpeakerEncoder, self).__init__()
+
+        self.speaker_encoder = ECAPA_TDNN_GLOB_c512(
+            feat_dim=input_dim, embed_dim=out_dim
+        )
+        self.perceiver_sampler = PerceiverResampler(
+            dim=latent_dim, dim_context=512 * 3, num_latents=token_num
+        )
+        self.quantizer = ResidualFSQ(
+            levels=fsq_levels,
+            num_quantizers=fsq_num_quantizers,
+            dim=latent_dim,
+            is_channel_first=True,
+            quantize_dropout=False,
+        )
+
+        self.project = nn.Linear(latent_dim * token_num, out_dim)
+
+    def get_codes_from_indices(self, indices: torch.Tensor) -> torch.Tensor:
+        zq = self.quantizer.get_codes_from_indices(indices.transpose(1, 2))
+        return zq.transpose(1, 2)
+
+    def get_indices(self, mels: torch.Tensor) -> torch.Tensor:
+        mels = mels.transpose(1, 2)
+        x = self.perceiver_sampler(mels).transpose(1, 2)
+        zq, indices = self.quantizer(x)
+        return indices
+
+    def forward(self, mels: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            mels: (B, D_mel, T1)
+
+        Return:
+            x_vector: (B, out_dim)
+            d_vector: (B, out_dim)
+        """
+        # mels = mels.transpose(1,2)
+
+        x_vector, features = self.speaker_encoder(mels, True)
+        x = self.perceiver_sampler(features.transpose(1, 2)).transpose(1, 2)
+        zq, indices = self.quantizer(x)  # zq: (B, latent_dim, T2, latent_dim)
+        x = zq.reshape(zq.shape[0], -1)
+        d_vector = self.project(x)
+
+        return x_vector, d_vector
+    
+    def tokenize(self, mels: torch.Tensor) -> torch.Tensor:
+        """tokenize the input mel spectrogram"""
+        _, features = self.speaker_encoder(mels, True)
+        x = self.perceiver_sampler(features.transpose(1, 2)).transpose(1, 2)
+        zq, indices = self.quantizer(x)
+        return indices
+    
+    def detokenize(self, indices: torch.Tensor) -> torch.Tensor:
+        """detokenize the input indices to d-vector"""
+        zq = self.quantizer.get_output_from_indices(indices.transpose(1, 2)).transpose(1, 2)
+        x = zq.reshape(zq.shape[0], -1)
+        d_vector = self.project(x)
+        return d_vector
+
+if __name__ == "__main__":
+    model = SpeakerEncoder(
+        input_dim=100,
+        latent_dim=128,
+        token_num=32,
+        fsq_levels=[4, 4, 4, 4, 4, 4],
+        fsq_num_quantizers=1,
+    )
+    mel = torch.randn(8, 200, 100)
+    x_vector, d_vector = model(mel)
+    print("x-vector shape", x_vector.shape)
+    print("d-vector shape", d_vector.shape)
+
+    indices = model.tokenize(mel)
+    print("indices shape", indices.shape)
+    d_vector_post = model.detokenize(indices)
+    print("d-vector shape", d_vector_post.shape)
+    if d_vector_post.all() == d_vector.all():
+        print("d-vector post and d-vector are the same")
+    else:
+        print("d-vector post and d-vector are different")
+    num_params = sum(param.numel() for param in model.parameters())
+    print("{} M".format(num_params / 1e6))
\ No newline at end of file
diff --git a/trained_50_percents/sparktts/modules/vq/__pycache__/factorized_vector_quantize.cpython-311.pyc b/trained_50_percents/sparktts/modules/vq/__pycache__/factorized_vector_quantize.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fff50d7ab6d94b324b2d3cee0bb30b9339d8e468
Binary files /dev/null and b/trained_50_percents/sparktts/modules/vq/__pycache__/factorized_vector_quantize.cpython-311.pyc differ
diff --git a/trained_50_percents/sparktts/modules/vq/factorized_vector_quantize.py b/trained_50_percents/sparktts/modules/vq/factorized_vector_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..f820bc73b559c08e36cbc38de1db0fddd442a727
--- /dev/null
+++ b/trained_50_percents/sparktts/modules/vq/factorized_vector_quantize.py
@@ -0,0 +1,187 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+
+# Heavily based on https://github.com/lucidrains/vector-quantize-pytorch
+
+
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def ema_inplace(moving_avg, new, decay):
+    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
+
+
+class FactorizedVectorQuantize(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        codebook_size: int,
+        codebook_dim: int,
+        commitment: float,
+        codebook_loss_weight: float = 1.0,
+        decay: float = 0.99,
+        threshold_ema_dead_code: float = 2,
+        momentum: float = 0.99,
+        **kwargs,
+    ):
+        super().__init__()
+        self.input_dim = input_dim
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+        self.commitment = commitment
+        self.codebook_loss_weight = codebook_loss_weight
+        self.decay = decay
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.momentum = momentum
+
+        if input_dim != self.codebook_dim:
+            self.in_project = WNConv1d(input_dim, self.codebook_dim, kernel_size=1)
+            self.out_project = WNConv1d(self.codebook_dim, input_dim, kernel_size=1)
+
+        else:
+            self.in_project = nn.Identity()
+            self.out_project = nn.Identity()
+
+        self.codebook = nn.Embedding(self.codebook_size, self.codebook_dim)
+        self.register_buffer("cluster_size", torch.zeros(self.codebook_size))
+
+    def forward(self, z: torch.Tensor) -> Dict[str, Any]:
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+        # transpose since we use linear
+
+        # Factorized codes project input into low-dimensional space if self.input_dim != self.codebook_dim
+        z_e = self.in_project(z)
+        z_q, indices, dists = self.decode_latents(z_e)
+
+        # statistic the usage of codes
+        embed_onehot = F.one_hot(indices, self.codebook_size).type(z_e.dtype)
+        avg_probs = torch.mean(embed_onehot.reshape(-1, self.codebook_size), dim=0)
+        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
+
+        active_num = (embed_onehot.sum(0).sum(0) > 0).sum()
+        if self.training:
+            # We do the expiry of code at that point as buffers are in sync
+            # and all the workers will take the same decision.
+            ema_inplace(self.cluster_size, embed_onehot.sum(0).sum(0), self.decay)
+            active_num = sum(self.cluster_size > self.threshold_ema_dead_code)
+
+        if self.training:
+            commit_loss = (
+                F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
+                * self.commitment
+            )
+
+            codebook_loss = (
+                F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
+                * self.codebook_loss_weight
+            )
+
+        else:
+            commit_loss = torch.zeros(0, device=z.device)
+            codebook_loss = torch.zeros(0, device=z.device)
+
+        z_q = (
+            z_e + (z_q - z_e).detach()
+        )  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        z_q = self.out_project(z_q)
+
+        vq_loss = (commit_loss + codebook_loss).mean()
+
+        return {
+            "z_q": z_q,
+            "indices": indices,
+            "dists": dists,
+            "vq_loss": vq_loss,
+            "perplexity": perplexity,
+            "active_num": active_num.float(),
+        }
+
+    def vq2emb(self, vq, out_proj=True):
+        emb = self.embed_code(vq)
+        if out_proj:
+            emb = self.out_project(emb)
+        return emb
+
+    def tokenize(self, z: torch.Tensor) -> torch.Tensor:
+        """tokenize the input tensor"""
+        z_e = self.in_project(z)
+        _, indices, _ = self.decode_latents(z_e)
+        return indices
+
+    def detokenize(self, indices):
+        """detokenize the input indices"""
+        z_q = self.decode_code(indices)
+        z_q = self.out_project(z_q)
+        return z_q
+
+    def get_emb(self):
+        return self.codebook.weight
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight
+
+        # L2 normalize encodings and codebook
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance between encodings and codebook,
+        # with L2 normalization, the distance is equal to cosine distance
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+
+        return z_q, indices, dist
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diff --git a/trained_50_percents/sparktts/utils/audio.py b/trained_50_percents/sparktts/utils/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..105cd9cd676f3f204bb492b53205eda710d805d0
--- /dev/null
+++ b/trained_50_percents/sparktts/utils/audio.py
@@ -0,0 +1,271 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+"""
+Description:
+    This script contains a collection of functions designed to handle various
+    audio processing.
+"""
+
+import random
+import soxr
+import soundfile
+import torch
+import torchaudio
+import numpy as np
+
+from pathlib import Path
+from typing import Tuple
+from numpy.lib.stride_tricks import sliding_window_view
+
+
+def audio_volume_normalize(audio: np.ndarray, coeff: float = 0.2) -> np.ndarray:
+    """
+    Normalize the volume of an audio signal.
+
+    Parameters:
+        audio (numpy array): Input audio signal array.
+        coeff (float): Target coefficient for normalization, default is 0.2.
+
+    Returns:
+        numpy array: The volume-normalized audio signal.
+    """
+    # Sort the absolute values of the audio signal
+    temp = np.sort(np.abs(audio))
+
+    # If the maximum value is less than 0.1, scale the array to have a maximum of 0.1
+    if temp[-1] < 0.1:
+        scaling_factor = max(
+            temp[-1], 1e-3
+        )  # Prevent division by zero with a small constant
+        audio = audio / scaling_factor * 0.1
+
+    # Filter out values less than 0.01 from temp
+    temp = temp[temp > 0.01]
+    L = temp.shape[0]  # Length of the filtered array
+
+    # If there are fewer than or equal to 10 significant values, return the audio without further processing
+    if L <= 10:
+        return audio
+
+    # Compute the average of the top 10% to 1% of values in temp
+    volume = np.mean(temp[int(0.9 * L) : int(0.99 * L)])
+
+    # Normalize the audio to the target coefficient level, clamping the scale factor between 0.1 and 10
+    audio = audio * np.clip(coeff / volume, a_min=0.1, a_max=10)
+
+    # Ensure the maximum absolute value in the audio does not exceed 1
+    max_value = np.max(np.abs(audio))
+    if max_value > 1:
+        audio = audio / max_value
+
+    return audio
+
+
+def load_audio(
+    adfile: Path,
+    sampling_rate: int = None,
+    length: int = None,
+    volume_normalize: bool = False,
+    segment_duration: int = None,
+) -> np.ndarray:
+    r"""Load audio file with target sampling rate and lsength
+
+    Args:
+        adfile (Path): path to audio file.
+        sampling_rate (int, optional): target sampling rate. Defaults to None.
+        length (int, optional): target audio length. Defaults to None.
+        volume_normalize (bool, optional): whether perform volume normalization. Defaults to False.
+        segment_duration (int): random select a segment with duration of {segment_duration}s.
+                                Defualt to None which means the whole audio will be used.
+
+    Returns:
+        audio (np.ndarray): audio
+    """
+
+    audio, sr = soundfile.read(adfile)
+    if len(audio.shape) > 1:
+        audio = audio[:, 0]
+
+    if sampling_rate is not None and sr != sampling_rate:
+        audio = soxr.resample(audio, sr, sampling_rate, quality="VHQ")
+        sr = sampling_rate
+
+    if segment_duration is not None:
+        seg_length = int(sr * segment_duration)
+        audio = random_select_audio_segment(audio, seg_length)
+
+    # Audio volume normalize
+    if volume_normalize:
+        audio = audio_volume_normalize(audio)
+    # check the audio length
+    if length is not None:
+        assert abs(audio.shape[0] - length) < 1000
+        if audio.shape[0] > length:
+            audio = audio[:length]
+        else:
+            audio = np.pad(audio, (0, int(length - audio.shape[0])))
+    return audio
+
+
+def random_select_audio_segment(audio: np.ndarray, length: int) -> np.ndarray:
+    """get an audio segment given the length
+
+    Args:
+        audio (np.ndarray):
+        length (int): audio length = sampling_rate * duration
+    """
+    if audio.shape[0] < length:
+        audio = np.pad(audio, (0, int(length - audio.shape[0])))
+    start_index = random.randint(0, audio.shape[0] - length)
+    end_index = int(start_index + length)
+
+    return audio[start_index:end_index]
+
+
+def audio_highpass_filter(audio, sample_rate, highpass_cutoff_freq):
+    """apply highpass fileter to audio
+
+    Args:
+        audio (np.ndarray):
+        sample_rate (ind):
+        highpass_cutoff_freq (int):
+    """
+
+    audio = torchaudio.functional.highpass_biquad(
+        torch.from_numpy(audio), sample_rate, cutoff_freq=highpass_cutoff_freq
+    )
+    return audio.numpy()
+
+
+def stft(
+    x: torch.Tensor,
+    fft_size: int,
+    hop_size: int,
+    win_length: int,
+    window: str,
+    use_complex: bool = False,
+) -> torch.Tensor:
+    """Perform STFT and convert to magnitude spectrogram.
+    Args:
+        x (Tensor): Input signal tensor (B, T).
+        fft_size (int): FFT size.
+        hop_size (int): Hop size.
+        win_length (int): Window length.
+        window (str): Window function type.
+    Returns:
+        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
+    """
+
+    x_stft = torch.stft(
+        x, fft_size, hop_size, win_length, window.to(x.device), return_complex=True
+    )
+
+    # clamp is needed to avoid nan or inf
+    if not use_complex:
+        return torch.sqrt(
+            torch.clamp(x_stft.real**2 + x_stft.imag**2, min=1e-7, max=1e3)
+        ).transpose(2, 1)
+    else:
+        res = torch.cat([x_stft.real.unsqueeze(1), x_stft.imag.unsqueeze(1)], dim=1)
+        res = res.transpose(2, 3)  # [B, 2, T, F]
+        return res
+
+
+def detect_speech_boundaries(
+    wav: np.ndarray,
+    sample_rate: int,
+    window_duration: float = 0.1,
+    energy_threshold: float = 0.01,
+    margin_factor: int = 2
+) -> Tuple[int, int]:
+    """Detect the start and end points of speech in an audio signal using RMS energy.
+    
+    Args:
+        wav: Input audio signal array with values in [-1, 1]
+        sample_rate: Audio sample rate in Hz
+        window_duration: Duration of detection window in seconds
+        energy_threshold: RMS energy threshold for speech detection
+        margin_factor: Factor to determine extra margin around detected boundaries
+        
+    Returns:
+        tuple: (start_index, end_index) of speech segment
+        
+    Raises:
+        ValueError: If the audio contains only silence
+    """
+    window_size = int(window_duration * sample_rate)
+    margin = margin_factor * window_size
+    step_size = window_size // 10
+    
+    # Create sliding windows using stride tricks to avoid loops
+    windows = sliding_window_view(wav, window_size)[::step_size]
+    
+    # Calculate RMS energy for each window
+    energy = np.sqrt(np.mean(windows ** 2, axis=1))
+    speech_mask = energy >= energy_threshold
+    
+    if not np.any(speech_mask):
+        raise ValueError("No speech detected in audio (only silence)")
+    
+    start = max(0, np.argmax(speech_mask) * step_size - margin)
+    end = min(len(wav), (len(speech_mask) - 1 - np.argmax(speech_mask[::-1])) * step_size + margin)
+    
+    return start, end
+
+
+def remove_silence_on_both_ends(
+    wav: np.ndarray,
+    sample_rate: int,
+    window_duration: float = 0.1,
+    volume_threshold: float = 0.01
+) -> np.ndarray:
+    """Remove silence from both ends of an audio signal.
+    
+    Args:
+        wav: Input audio signal array
+        sample_rate: Audio sample rate in Hz
+        window_duration: Duration of detection window in seconds
+        volume_threshold: Amplitude threshold for silence detection
+        
+    Returns:
+        np.ndarray: Audio signal with silence removed from both ends
+        
+    Raises:
+        ValueError: If the audio contains only silence
+    """
+    start, end = detect_speech_boundaries(
+        wav,
+        sample_rate,
+        window_duration,
+        volume_threshold
+    )
+    return wav[start:end]
+
+
+
+def hertz_to_mel(pitch: float) -> float:
+    """
+    Converts a frequency from the Hertz scale to the Mel scale.
+
+    Parameters:
+    - pitch: float or ndarray
+        Frequency in Hertz.
+
+    Returns:
+    - mel: float or ndarray
+        Frequency in Mel scale.
+    """
+    mel = 2595 * np.log10(1 + pitch / 700)
+    return mel
\ No newline at end of file
diff --git a/trained_50_percents/sparktts/utils/file.py b/trained_50_percents/sparktts/utils/file.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcc7c7ca0713f0e67c6327398bc73990d10413ab
--- /dev/null
+++ b/trained_50_percents/sparktts/utils/file.py
@@ -0,0 +1,221 @@
+# Copyright (c) 2025 SparkAudio
+#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
+#
+# 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.
+"""
+Description:
+    This script contains a collection of functions designed to handle various
+    file reading and writing operations. It provides utilities to read from files,
+    write data to files, and perform file manipulation tasks.
+"""
+
+
+import os
+import json
+import json
+import csv
+
+from tqdm import tqdm
+from typing import List, Dict, Any, Set, Union
+from pathlib import Path
+from omegaconf import OmegaConf, DictConfig
+
+
+def resolve_symbolic_link(symbolic_link_path: Path) -> Path:
+    """
+    Resolves the absolute path of a symbolic link.
+
+    Args:
+        symbolic_link_path (Path): The path to the symbolic link.
+
+    Returns:
+        Path: The absolute path that the symbolic link points to.
+    """
+
+    link_directory = os.path.dirname(symbolic_link_path)
+    target_path_relative = os.readlink(symbolic_link_path)
+    return os.path.join(link_directory, target_path_relative)
+
+
+def write_jsonl(metadata: List[dict], file_path: Path) -> None:
+    """Writes a list of dictionaries to a JSONL file.
+
+    Args:
+    metadata : List[dict]
+        A list of dictionaries, each representing a piece of meta.
+    file_path : Path
+        The file path to save the JSONL file
+
+    This function writes each dictionary in the list to a new line in the specified file.
+    """
+    with open(file_path, "w", encoding="utf-8") as f:
+        for meta in tqdm(metadata, desc="writing jsonl"):
+            # Convert dictionary to JSON string and write it to the file with a newline
+            json_str = json.dumps(meta, ensure_ascii=False) + "\n"
+            f.write(json_str)
+    print(f"jsonl saved to {file_path}")
+
+
+def read_jsonl(file_path: Path) -> List[dict]:
+    """
+    Reads a JSONL file and returns a list of dictionaries.
+
+    Args:
+    file_path : Path
+        The path to the JSONL file to be read.
+
+    Returns:
+    List[dict]
+        A list of dictionaries parsed from each line of the JSONL file.
+    """
+    metadata = []
+    # Open the file for reading
+    with open(file_path, "r", encoding="utf-8") as f:
+        # Split the file into lines
+        lines = f.read().splitlines()
+    # Process each line
+    for line in lines:
+        # Convert JSON string back to dictionary and append to list
+        meta = json.loads(line)
+        metadata.append(meta)
+    # Return the list of metadata
+    return metadata
+
+def read_json_as_jsonl(file_path: Path) -> List[dict]:
+    metadata = []
+    with open(file_path, 'r', encoding='utf-8') as infile:
+        data = json.load(infile) 
+    for k in sorted(data.keys()):
+        meta = {'index': k}
+        meta.update(data[k])
+        metadata.append(meta)
+    return metadata
+
+
+
+def decode_unicode_strings(meta: Dict[str, Any]) -> Dict[str, Any]:
+    processed_meta = {}
+    for k, v in meta.items():
+        if isinstance(v, str):
+            processed_meta[k] = v.encode("utf-8").decode("unicode_escape")
+        else:
+            processed_meta[k] = v
+    return processed_meta
+
+
+def load_config(config_path: Path) -> DictConfig:
+    """Loads a configuration file and optionally merges it with a base configuration.
+
+    Args:
+    config_path (Path): Path to the configuration file.
+    """
+    # Load the initial configuration from the given path
+    config = OmegaConf.load(config_path)
+
+    # Check if there is a base configuration specified and merge if necessary
+    if config.get("base_config", None) is not None:
+        base_config = OmegaConf.load(config["base_config"])
+        config = OmegaConf.merge(base_config, config)
+
+    return config
+
+
+
+def jsonl_to_csv(jsonl_file_path: str, csv_file_path: str) -> None:
+    """
+    Converts a JSONL file to a CSV file.
+    
+    This function reads a JSONL file, determines all unique keys present in the file,
+    and writes the data to a CSV file with columns for all these keys.
+    """
+    
+    all_keys = set()
+    data_rows = []
+    
+    # Read the JSONL file once to extract keys and collect data
+    with open(jsonl_file_path, 'r') as file:
+        for line in file:
+            data = json.loads(line.strip())
+            data_rows.append(data)
+            all_keys.update(data.keys())
+    
+    # Convert the set of keys to a sorted list for consistent column order
+    sorted_keys = sorted(all_keys)
+    
+    # Write the data to a CSV file
+    with open(csv_file_path, 'w', newline='') as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=sorted_keys)
+        
+        # Write the header row
+        writer.writeheader()
+        
+        # Write each row of data
+        for data in data_rows:
+            writer.writerow(data)
+    
+    print(f"CSV file has been created at {csv_file_path}")
+
+
+def save_metadata(data, filename, headers=None):
+    """
+    Save metadata to a file.
+    
+    Args:
+        data (list of dict): Metadata to be saved.
+        filename (str): Name of the file to save the metadata.
+        headers (list of str): The order of column names to be saved; defaults to the keys from the first dictionary in data if not provided.
+    """
+    # Set headers to keys from the first dictionary in data if not explicitly provided
+    if headers is None:
+        headers = list(data[0].keys())
+    
+    with open(filename, "w", encoding="utf-8") as file:
+        # Write the headers to the file
+        file.write("|".join(headers) + "\n")
+        for entry in data:
+            # Retrieve values in the order of headers, replacing any '|' characters with a space to prevent formatting errors
+            formatted_values = [str(entry.get(key, "")).replace("|", " ") for key in headers]
+            # Write the formatted values to the file
+            file.write("|".join(formatted_values) + "\n")
+
+
+def read_metadata(filename, headers=None):
+    """
+    Read metadata from a file.
+    
+    Args:
+        filename (str): The file from which to read the metadata.
+    
+    Returns:
+        list of dict: The metadata read from the file.
+        list of str: The headers used in the file.
+    """
+    with open(filename, "r", encoding="utf-8") as file:
+        lines = file.readlines()
+
+    data = []
+    # Set headers from the first line of the file if not provided
+    if headers is None:
+        headers = lines[0].strip().split("|")
+        lines = lines[1:]
+
+    for line in lines:
+        line = line.strip()
+        # Skip empty lines
+        if not line:
+            continue
+        # Split the line by '|' and pair with headers to form a dictionary
+        entry_data = dict(zip(headers, line.split("|")))
+        data.append(entry_data)
+    
+    return data, headers
diff --git a/trained_50_percents/sparktts/utils/token_parser.py b/trained_50_percents/sparktts/utils/token_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc43782b762d6bb51e3c35f3585706750e8d5a22
--- /dev/null
+++ b/trained_50_percents/sparktts/utils/token_parser.py
@@ -0,0 +1,187 @@
+TASK_TOKEN_MAP = {
+    "vc": "<|task_vc|>",
+    "tts": "<|task_tts|>",
+    "asr": "<|task_asr|>",
+    "s2s": "<|task_s2s|>",
+    "t2s": "<|task_t2s|>",
+    "understand": "<|task_understand|>",
+    "caption": "<|task_cap|>",
+    "controllable_tts": "<|task_controllable_tts|>",
+    "prompt_tts": "<|task_prompt_tts|>",
+    "speech_edit": "<|task_edit|>",
+}
+
+LEVELS_MAP = {
+    "very_low": 0,
+    "low": 1,
+    "moderate": 2,
+    "high": 3,
+    "very_high": 4,
+}
+
+LEVELS_MAP_UI = {
+    1: 'very_low',
+    2: 'low',
+    3: 'moderate',
+    4: 'high',
+    5: 'very_high'
+}
+
+GENDER_MAP = {
+    "female": 0,
+    "male": 1,
+}
+
+AGE_MAP = {"Child": 0, "Teenager": 1, "Youth-Adult": 2, "Middle-aged": 3, "Elderly": 4}
+
+EMO_MAP = {
+    "UNKNOWN": 0,
+    "NEUTRAL": 1,
+    "ANGRY": 2,
+    "HAPPY": 3,
+    "SAD": 4,
+    "FEARFUL": 5,
+    "DISGUSTED": 6,
+    "SURPRISED": 7,
+    "SARCASTIC": 8,
+    "EXCITED": 9,
+    "SLEEPY": 10,
+    "CONFUSED": 11,
+    "EMPHASIS": 12,
+    "LAUGHING": 13,
+    "SINGING": 14,
+    "WORRIED": 15,
+    "WHISPER": 16,
+    "ANXIOUS": 17,
+    "NO-AGREEMENT": 18,
+    "APOLOGETIC": 19,
+    "CONCERNED": 20,
+    "ENUNCIATED": 21,
+    "ASSERTIVE": 22,
+    "ENCOURAGING": 23,
+    "CONTEMPT": 24,
+}
+
+
+class TokenParser:
+    """Turn label to special token"""
+
+    def __init__(self):
+        pass
+
+    """Parse the attributes of a person."""
+
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def age(age: str) -> str:
+        """Turn age token."""
+        age_id = AGE_MAP[age]
+        return f"<|age_{age_id}|>"
+
+    @staticmethod
+    def gender(gender: str) -> str:
+        """Turn gender token."""
+        gender_id = GENDER_MAP[gender]
+        return f"<|gender_{gender_id}|>"
+
+    @staticmethod
+    def mel_value(mel: int):
+        """Turn special token of mel scale pitch."""
+        mel = max(0, int(mel))
+        mel = min(1000, int(mel))
+        return f"<|pitch_value_{mel}|>"
+
+    @staticmethod
+    def mel_level(level: str):
+        """Turn special token of mel level."""
+        level_tag = LEVELS_MAP[level]
+        return f"<|pitch_label_{level_tag}|>"
+
+    @staticmethod
+    def pitch_var_value(pitch_std: int):
+        """Turn special token of pitch_std value."""
+        assert isinstance(pitch_std, int)
+        pitch_std = max(0, int(pitch_std))
+        pitch_std = min(10, int(pitch_std))
+        return f"<|pitch_var_value_{pitch_std}|>"
+
+    @staticmethod
+    def pitch_var_level(level: str):
+        """Turn special token of pitch std level."""
+        level_tag = LEVELS_MAP[level]
+        return f"<|pitch_var_label_{level_tag}|>"
+
+    @staticmethod
+    def loudness_value(loudness: int):
+        """Turn special toak of loudness value [0, 30]"""
+        assert loudness >= 0
+        loudness = max(0, int(loudness))
+        loudness = min(30, int(loudness))
+        return f"<|loudness_value_{loudness}|>"
+
+    @staticmethod
+    def loudness_level(level: str):
+        """Turn special token of loudness level."""
+        level_tag = LEVELS_MAP[level]
+        return f"<|loudness_label_{level_tag}|>"
+
+    @staticmethod
+    def speed_value(speed: int):
+        """Turn special token of speed value."""
+        speed = max(0, int(speed))
+        speed = min(10, int(speed))
+        return f"<|speed_value_{speed}|>"
+
+    @staticmethod
+    def speed_level(level: str):
+        """Turn special token of speed level."""
+        level_tag = LEVELS_MAP[level]
+        return f"<|speed_label_{level_tag}|>"
+
+    @staticmethod
+    def task(task: str) -> str:
+        """Turn special token of task."""
+        assert task in TASK_TOKEN_MAP.keys()
+
+        return TASK_TOKEN_MAP[task]
+
+    @staticmethod
+    def emotion(emotion: str):
+        emo_id = EMO_MAP[emotion]
+
+        return f"<|emotion_{emo_id}|>"
+
+
+# test
+if __name__ == "__main__":
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        "/aifs4su/xinshengwang/code/StyleCraft/tokenizer/stylecraft-bicodec-pitch-loudness-speed-emotion-tokenizer"
+    )
+
+    tasks = ["tts", "tts", "understand", "controllable_tts", "prompt_tts"]
+    ages = ["Child", "Teenager", "Youth-Adult", "Middle-aged", "Elderly"]
+    genders = ["female", "female", "female", "male", "male"]
+    mels = [100, 200, 300, 400, 500]
+    mel_levels = ["very_low", "low", "moderate", "high", "very_high"]
+    loudnesses = [1, 10, 23, 19, 30]
+    loudness_levels = ["very_low", "low", "moderate", "high", "very_high"]
+    emotions = ["UNKNOWN", "NEUTRAL", "ANGRY", "HAPPY", "SAD"]
+
+    for i in range(5):
+        task = TokenParser.task(tasks[i])
+        age = TokenParser.age(ages[i])
+        gender = TokenParser.gender(genders[i])
+        mel = TokenParser.mel_value(mels[i])
+        mel_level = TokenParser.mel_level(mel_levels[i])
+        loudness = TokenParser.loudness_value(loudnesses[i])
+        loudness_level = TokenParser.loudness_level(loudness_levels[i])
+        emotion = TokenParser.emotion(emotions[i])
+        inputs = [task, age, gender, mel, mel_level, loudness, loudness_level, emotion]
+        inputs = "".join(inputs)
+        ids = tokenizer.encode(inputs, add_special_tokens=False)
+        print(ids)
+        print("decode", tokenizer.decode(ids))
diff --git a/trained_50_percents/special_tokens_map.json b/trained_50_percents/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9f8551abfcede7c8c7282dbd1a927c70c4e8705
--- /dev/null
+++ b/trained_50_percents/special_tokens_map.json
@@ -0,0 +1,6 @@
+{
+  "bos_token": "<|rwkv_tokenizer_end_of_text|>",
+  "eos_token": "\n\n",
+  "unk_token": "<|rwkv_tokenizer_end_of_text|>",
+  "pad_token": "<|rwkv_tokenizer_end_of_text|>"
+}
diff --git a/trained_50_percents/test_rtf.py b/trained_50_percents/test_rtf.py
new file mode 100644
index 0000000000000000000000000000000000000000..0425b20aa9800dc7966648d16034057de13bf29a
--- /dev/null
+++ b/trained_50_percents/test_rtf.py
@@ -0,0 +1,156 @@
+import os
+import sys
+import time
+import torch
+import soundfile as sf
+import numpy as np
+from librosa import resample
+
+# Add current directory to sys.path to find local modules
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+from sparktts.models.audio_tokenizer import BiCodecTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from utilities import generate_embeddings
+
+def generate_speech(model, tokenizer, text, bicodec, prompt_text=None, prompt_audio=None, 
+                   max_new_tokens=3000, do_sample=True, top_k=50, top_p=0.95, 
+                   temperature=1.0, device="cuda:0"):
+    """
+    Generates speech from text and returns timing for each major step.
+    """
+    timings = {}
+
+    # --- 1. Generate Embeddings ---
+    t0 = time.perf_counter()
+    eos_token_id = model.config.vocab_size - 1
+    embeddings = generate_embeddings(
+        model=model,
+        tokenizer=tokenizer,
+        text=text,
+        bicodec=bicodec,
+        prompt_text=prompt_text,
+        prompt_audio=prompt_audio
+    )
+    torch.cuda.synchronize()
+    t1 = time.perf_counter()
+    timings['embedding_generation'] = t1 - t0
+
+    # --- 2. LLM Inference ---
+    global_tokens = embeddings['global_tokens'].unsqueeze(0)
+    model.eval()
+    with torch.no_grad():
+        generated_outputs = model.generate(
+            inputs_embeds=embeddings['input_embs'],
+            attention_mask=torch.ones((1, embeddings['input_embs'].shape[1]), dtype=torch.long, device=device),
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=eos_token_id,
+            pad_token_id=tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else tokenizer.eos_token_id,
+            use_cache=True
+        )
+    torch.cuda.synchronize()
+    t2 = time.perf_counter()
+    timings['llm_inference'] = t2 - t1
+
+    # --- 3. Detokenization ---
+    semantic_tokens_tensor = generated_outputs[:,:-1]
+    token_size = semantic_tokens_tensor.shape[1]
+    print(f"Token size: {token_size} tokens per second = {token_size / (t2 - t1)}")
+    with torch.no_grad():
+        wav = bicodec.detokenize(global_tokens, semantic_tokens_tensor)
+    torch.cuda.synchronize()
+    t3 = time.perf_counter()
+    timings['detokenization'] = t3 - t2
+    
+    return wav, timings
+
+def main():
+    device = 'cuda:2' if torch.cuda.is_available() else 'cpu'
+    print(f"Using device: {device}")
+
+    # --- Model Loading ---
+    print("Loading models and tokenizers...")
+    audio_tokenizer = BiCodecTokenizer(model_dir=current_dir, device=device)
+    tokenizer = AutoTokenizer.from_pretrained(current_dir, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(current_dir, trust_remote_code=True)
+    
+    model = model.bfloat16().to(device)
+    model.eval()
+    model = torch.compile(model)
+    print("Models and tokenizers loaded.")
+
+    # --- Prompt Loading ---
+    prompt_audio_file = os.path.join(current_dir, 'kafka.wav')
+    prompt_audio, sampling_rate = sf.read(prompt_audio_file)
+    
+    target_sample_rate = audio_tokenizer.config['sample_rate']
+    if sampling_rate != target_sample_rate:
+        prompt_audio = resample(prompt_audio, orig_sr=sampling_rate, target_sr=target_sample_rate)
+        prompt_audio = np.array(prompt_audio, dtype=np.float32)
+    
+    text_to_synthesize = "科学技术是第一生产力，最近AI的迅猛发展让我们看到了迈向星辰大海的希望。"
+
+    # --- Warm-up Run ---
+    print("\n--- Starting warm-up run (not timed) ---")
+    _, _ = generate_speech(model, tokenizer, text_to_synthesize, audio_tokenizer, 
+                           prompt_audio=prompt_audio, device=device)
+    print("--- Warm-up finished ---\n")
+
+    # --- Benchmarking ---
+    num_iterations = 100
+    total_generation_time = 0
+    total_audio_duration = 0
+    total_timings = {'embedding_generation': 0, 'llm_inference': 0, 'detokenization': 0}
+
+    print(f"--- Starting benchmark: {num_iterations} iterations ---")
+    for i in range(num_iterations):
+        start_time = time.perf_counter()
+        
+        wav, timings = generate_speech(model, tokenizer, text_to_synthesize, audio_tokenizer, 
+                                       prompt_audio=prompt_audio, device=device)
+        
+        end_time = time.perf_counter()
+
+        generation_time = end_time - start_time
+        audio_duration = len(wav) / target_sample_rate
+        
+        total_generation_time += generation_time
+        total_audio_duration += audio_duration
+        for key in total_timings:
+            total_timings[key] += timings[key]
+        
+        timing_details = f"Embed: {timings['embedding_generation']:.4f}s, LLM: {timings['llm_inference']:.4f}s, Decode: {timings['detokenization']:.4f}s"
+        print(f"Iteration {i+1}/{num_iterations}: Total: {generation_time:.4f}s, Audio: {audio_duration:.4f}s | {timing_details}")
+
+    # --- Results ---
+    if total_audio_duration > 0:
+        rtf = total_generation_time / total_audio_duration
+    else:
+        rtf = float('inf')
+
+    print("\n--- Benchmark Results ---")
+    print(f"Total iterations: {num_iterations}")
+    print(f"Total generation time: {total_generation_time:.4f} seconds")
+    print(f"Total audio duration: {total_audio_duration:.4f} seconds")
+    print(f"Average generation time: {total_generation_time / num_iterations:.4f} seconds")
+    print(f"Real-Time Factor (RTF): {rtf:.4f}")
+    print("-------------------------")
+
+    # --- Detailed Timings ---
+    print("\n--- Detailed Timing Breakdown ---")
+    avg_total_gen_time = total_generation_time / num_iterations
+    for name, total_time in total_timings.items():
+        avg_time = total_time / num_iterations
+        percentage = (avg_time / avg_total_gen_time) * 100 if avg_total_gen_time > 0 else 0
+        print(f"Average {name}: {avg_time:.4f}s ({percentage:.2f}%)")
+    print("---------------------------------")
+
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/trained_50_percents/tokenizer_config.json b/trained_50_percents/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..84e115663c32885db92fb6ee2a7f06518f27668b
--- /dev/null
+++ b/trained_50_percents/tokenizer_config.json
@@ -0,0 +1,28 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|rwkv_tokenizer_end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "hf_rwkv_tokenizer.RwkvTokenizer",
+      null
+    ]
+  },
+  "bos_token": "<|rwkv_tokenizer_end_of_text|>",
+  "pad_token": "<|rwkv_tokenizer_end_of_text|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "\n\n",
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "RwkvTokenizer",
+  "unk_token": "<|rwkv_tokenizer_end_of_text|>",
+  "use_fast": false,
+  "chat_template": "{{ '<|rwkv_tokenizer_end_of_text|>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{'User: ' + message['content'] + '\n\n'}}{% elif message['role'] == 'system' %}{{'System: ' + message['content'] + '\n\n'}}{% elif message['role'] == 'assistant' %}{{'Assistant: ' + message['content'] + '\n\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{% if enable_thinking is defined and enable_thinking == False %}{{ 'Assistant: <think></think>' }}{% else %}{{ 'Assistant: <think' }}{% endif %}{% endif %}"
+}
diff --git a/trained_50_percents/tts.py b/trained_50_percents/tts.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2dfcc6e1892a6e350997a82ea80bea8a3a06617
--- /dev/null
+++ b/trained_50_percents/tts.py
@@ -0,0 +1,129 @@
+import os
+import sys
+current_dir = os.path.dirname(os.path.abspath(__file__))
+print('add current dir to sys.path', current_dir)
+sys.path.append(current_dir)
+from sparktts.models.audio_tokenizer import BiCodecTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import soundfile as sf
+import numpy as np
+import torch
+from utilities import generate_embeddings
+def generate_speech(model, tokenizer, text, bicodec, prompt_text=None, prompt_audio=None, 
+                   max_new_tokens=3000, do_sample=True, top_k=50, top_p=0.95, 
+                   temperature=1.0, device="cuda:0"):
+    """
+    生成语音的函数
+    
+    Args:
+        model: 语言模型
+        tokenizer: 文本分词器
+        text: 要生成语音的文本
+        bicodec: BiCodecTokenizer 实例
+        prompt_text: 提示文本（可选）
+        prompt_audio: 提示音频数组（可选）
+        max_new_tokens: 最大生成token数
+        do_sample: 是否使用采样
+        top_k: top-k采样参数
+        top_p: top-p采样参数
+        temperature: 温度参数
+        device: 设备
+    
+    Returns:
+        wav: 生成的音频波形
+    """
+    # 设置eos_token_id - 根据训练代码，eos_token_id = model.config.vocab_size - 1
+    eos_token_id = model.config.vocab_size - 1
+    print(f"EOS token ID: {eos_token_id}")
+    
+    # 生成输入嵌入
+    embeddings = generate_embeddings(
+        model=model,
+        tokenizer=tokenizer,
+        text=text,
+        bicodec=bicodec,
+        prompt_text=prompt_text,
+        prompt_audio=prompt_audio
+    )
+    
+    print("开始生成语音...")
+    print(f"输入嵌入形状: {embeddings['input_embs'].shape}")
+    global_tokens = embeddings['global_tokens'].unsqueeze(0)
+    # 设置模型为评估模式
+    print(f'embeddings dtype: {embeddings["input_embs"].dtype}')
+    model.eval()
+    
+    with torch.no_grad():
+        # 使用模型的generate方法
+        generated_outputs = model.generate(
+            inputs_embeds=embeddings['input_embs'],
+            attention_mask=torch.ones((1, embeddings['input_embs'].shape[1]),dtype=torch.long,device=device),
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=eos_token_id,
+            pad_token_id=tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else tokenizer.eos_token_id,
+            use_cache=True
+        )
+    print(f"generated_outputs: {generated_outputs}")
+    
+    print(f"生成的token数量: {generated_outputs.shape}")
+    print(f"生成的token IDs: {generated_outputs.tolist()}")
+    
+    # 直接使用生成的token ID作为semantic tokens
+    # 注意：这里生成的token ID是模型词表中的ID，不是原始tokenizer的词表
+    semantic_tokens_tensor = generated_outputs[:,:-1]
+    
+    print(f"Semantic tokens shape: {semantic_tokens_tensor.shape}")
+    
+    
+    print(f"Global tokens shape: {global_tokens.shape}")
+    
+    # 使用BiCodec解码生成音频
+    with torch.no_grad():
+        wav = bicodec.detokenize(global_tokens, semantic_tokens_tensor)
+    
+    print(f"生成的音频形状: {wav.shape}")
+    return wav
+
+device = 'cuda:0'
+
+audio_tokenizer = BiCodecTokenizer(model_dir=current_dir, device=device)
+
+print(audio_tokenizer)
+
+tokenizer = AutoTokenizer.from_pretrained(current_dir, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(current_dir, trust_remote_code=True)
+print(tokenizer)
+print(model)
+
+model = model.bfloat16().to(device)
+model.eval()
+
+prompt_text = "我们并不是通过物理移动手段找到星河的。"
+prompt_audio_file = os.path.join(current_dir, 'kafka.wav')
+prompt_audio, sampling_rate = sf.read(prompt_audio_file)
+
+print(f"Loaded prompt audio from {prompt_audio_file}")
+print(f"Original sampling rate: {sampling_rate}Hz")
+print(f"Audio shape: {prompt_audio.shape}")
+
+target_sample_rate = audio_tokenizer.config['sample_rate']
+if sampling_rate != target_sample_rate:
+    print(f"Resampling from {sampling_rate}Hz to {target_sample_rate}Hz...")
+    from librosa import resample
+    prompt_audio = resample(prompt_audio, orig_sr=sampling_rate, target_sr=target_sample_rate)
+    prompt_audio = np.array(prompt_audio, dtype=np.float32)
+    print(f"Resampled audio shape: {prompt_audio.shape}")
+else:
+    print(f"Audio sampling rate already matches target ({target_sample_rate}Hz)")
+
+text = "为了点燃青少年对科技的热情，培养他们的创新思维与动手能力，杏花岭区巨轮街道社区教育学校携手中车社区教育分校，与太原市科学技术协会联手，于暑期精心策划了一场别开生面的青少年数智技术服务港探索之旅，吸引了众多社区青少年的积极参与。"
+wav = generate_speech(model, tokenizer, text, audio_tokenizer, prompt_audio=prompt_audio, device=device)
+sf.write('output.wav', wav, target_sample_rate)
+
+
+
+
diff --git a/trained_50_percents/tts_batch_infer.py b/trained_50_percents/tts_batch_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..beccd494afa4ed55034630d54f2ef3ead48fe880
--- /dev/null
+++ b/trained_50_percents/tts_batch_infer.py
@@ -0,0 +1,157 @@
+import os
+import sys
+current_dir = os.path.dirname(os.path.abspath(__file__))
+print('add current dir to sys.path', current_dir)
+sys.path.append(current_dir)
+from sparktts.models.audio_tokenizer import BiCodecTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import soundfile as sf
+import numpy as np
+import torch
+from utilities import generate_embeddings_batch
+
+def generate_speech_batch(model, tokenizer, texts, bicodec, prompt_text=None, prompt_audio=None, 
+                         max_new_tokens=3000, do_sample=True, top_k=50, top_p=0.95, 
+                         temperature=1.0, device="cuda:0"):
+    """
+    批量生成语音的函数
+    
+    Args:
+        model: 语言模型
+        tokenizer: 文本分词器
+        texts: 要生成语音的文本列表
+        bicodec: BiCodecTokenizer 实例
+        prompt_text: 提示文本（可选）
+        prompt_audio: 提示音频数组（可选）
+        max_new_tokens: 最大生成token数
+        do_sample: 是否使用采样
+        top_k: top-k采样参数
+        top_p: top-p采样参数
+        temperature: 温度参数
+        device: 设备
+    
+    Returns:
+        list: 生成的音频波形列表
+    """
+    # 设置eos_token_id - 根据训练代码，eos_token_id = model.config.vocab_size - 1
+    eos_token_id = model.config.vocab_size - 1
+    print(f"EOS token ID: {eos_token_id}")
+    
+    # 生成输入嵌入
+    embeddings, attention_mask = generate_embeddings_batch(
+        model=model,
+        tokenizer=tokenizer,
+        texts=texts,
+        bicodec=bicodec,
+        prompt_text=prompt_text,
+        prompt_audio=prompt_audio
+    )
+    
+    print("开始批量生成语音...")
+    print(f"输入嵌入形状: {embeddings['input_embs'].shape}")
+    print(f"注意力掩码形状: {attention_mask.shape}")
+    print(f'embeddings dtype: {embeddings["input_embs"].dtype}')
+    batch_size = len(texts)
+    global_tokens = embeddings['global_tokens']  # [batch_size, global_token_len]
+    
+    # 设置模型为评估模式
+    model.eval()
+    
+    with torch.no_grad():
+        # 使用模型的generate方法进行批量生成
+        generated_outputs = model.generate(
+            inputs_embeds=embeddings['input_embs'],
+            attention_mask=attention_mask,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            top_k=top_k,
+            top_p=top_p,
+            temperature=temperature,
+            eos_token_id=eos_token_id,
+            pad_token_id=tokenizer.pad_token_id if hasattr(tokenizer, 'pad_token_id') else tokenizer.eos_token_id,
+            use_cache=True
+        )
+    
+    print(f"生成的token形状: {generated_outputs.shape}")
+    print(f'generated_tokens :{generated_outputs.tolist()}')
+    # 处理每个样本的生成结果
+    wavs = []
+    eos_index = torch.where(generated_outputs[:,] == eos_token_id)[1]
+    print(f'eos_index :{eos_index}')
+    for i in range(batch_size):
+        # 获取当前样本的生成结果
+        sample_outputs = generated_outputs[i]
+        print(f'sample_outputs :{sample_outputs.tolist()}')
+        #find the first eos_token_id
+        eos_token_id_index = eos_index[i]
+        print(f'eos_token_id_index :{eos_token_id_index}')
+        sample_outputs = sample_outputs[:eos_token_id_index]
+        print(f'sample_outputs :{sample_outputs.tolist()}')
+        
+        print(f"样本 {i} - Semantic tokens shape: {sample_outputs.shape}")
+        print(f"样本 {i} - Global tokens shape: {global_tokens[i:i+1].shape}")
+        
+        # 使用BiCodec解码生成音频
+        with torch.no_grad():
+            wav = bicodec.detokenize(global_tokens[i:i+1], sample_outputs.unsqueeze(0))
+        
+        print(f"样本 {i} - 生成的音频形状: {wav.shape}")
+        wavs.append(wav)  # 移除批次维度
+    
+    return wavs
+
+device = 'cuda:2'
+
+audio_tokenizer = BiCodecTokenizer(model_dir=current_dir, device=device)
+
+print(audio_tokenizer)
+
+tokenizer = AutoTokenizer.from_pretrained(current_dir, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(current_dir, trust_remote_code=True)
+print(tokenizer)
+print(model)
+
+model = model.bfloat16().to(device)
+model.eval()
+
+prompt_text = "我们并不是通过物理移动手段找到星河的。"
+prompt_audio_file = os.path.join(current_dir, 'kafka.wav')
+prompt_audio, sampling_rate = sf.read(prompt_audio_file)
+
+print(f"Loaded prompt audio from {prompt_audio_file}")
+print(f"Original sampling rate: {sampling_rate}Hz")
+print(f"Audio shape: {prompt_audio.shape}")
+
+target_sample_rate = audio_tokenizer.config['sample_rate']
+if sampling_rate != target_sample_rate:
+    print(f"Resampling from {sampling_rate}Hz to {target_sample_rate}Hz...")
+    from librosa import resample
+    prompt_audio = resample(prompt_audio, orig_sr=sampling_rate, target_sr=target_sample_rate)
+    prompt_audio = np.array(prompt_audio, dtype=np.float32)
+    print(f"Resampled audio shape: {prompt_audio.shape}")
+else:
+    print(f"Audio sampling rate already matches target ({target_sample_rate}Hz)")
+
+texts = [
+    "一九五二年二月十日，志愿军大英雄张积慧击落美军双料王牌飞行员戴维斯，在自己飞机坠毁处距离戴维斯坠机处不足五百米的情况下，取得了世界空战史不可能复制的奇迹。伟大的张积慧。",
+    "在数字浪潮汹涌的今天，数智技术正以前所未有的力量重塑着社会的每一个角落。",
+    "为了点燃青少年对科技的热情，培养他们的创新思维与动手能力",
+    "杏花岭区巨轮街道社区教育学校携手中车社区教育分校，与太原市科学技术协会联手，于暑期精心策划了一场别开生面的青少年数智技术服务港探索之旅，吸引了众多社区青少年的积极参与。"
+    "一踏入数智技术服务港的大门，一股浓厚的科技气息便扑面而来。",
+    "科普课堂上，“简易红绿灯”科学实验更是将抽象的电路原理与日常生活紧密相连。",
+    "实验开始前，老师生动地介绍了实验物品，并引导青少年思考红绿灯的工作原理，激发了他们浓厚的探索兴趣。",
+    "在老师的指导下，青少年们开始动手组装电路，将红绿灯的各个部件连接起来。",
+    "他们小心翼翼地调整电路，确保每个部件都正确连接，红灯、绿灯、黄灯依次亮起，仿佛在讲述一个关于交通规则的故事。",
+    "实验过程中，青少年们不仅学到了电路知识，还体验到了动手实践的乐趣。",
+    "他们纷纷表示，这次实验不仅让他们对科技有了更深的理解，还培养了他们的创新思维和动手能力。",
+    "数智技术服务港，让科技触手可及，让创新无处不在。"
+
+]
+
+wavs = generate_speech_batch(model, tokenizer, texts, audio_tokenizer, prompt_audio=prompt_audio, device=device)
+for i, wav in enumerate(wavs):
+    sf.write(f'output_{i}.wav', wav, target_sample_rate)
+
+
+
+
diff --git a/trained_50_percents/utilities.py b/trained_50_percents/utilities.py
new file mode 100644
index 0000000000000000000000000000000000000000..23cc3e1d64f34df17ff00d8abd85e3229a866892
--- /dev/null
+++ b/trained_50_percents/utilities.py
@@ -0,0 +1,188 @@
+import numpy as np
+import torch
+
+def generate_embeddings(model, tokenizer, text, bicodec, prompt_text=None, prompt_audio=None):
+    """
+    为 Spark LLM 生成预测所需的输入嵌入
+    
+    Args:
+        model: Spark LLM 模型
+        tokenizer: 文本分词器
+        text: 要生成语音的文本
+        bicodec: BiCodecTokenizer 实例
+        prompt_text: 提示文本（可选）
+        prompt_audio: 提示音频数组（可选）
+    
+    Returns:
+        dict: 包含 input_embs 的字典，用于模型预测
+    """
+    device = next(model.parameters()).device
+    
+    # 1. 处理提示音频，提取 global_tokens 和 semantic_tokens
+    if prompt_audio is not None:
+        # 确保音频数据是 float32 类型
+        audio_data = np.array(prompt_audio, dtype=np.float32)
+        target_sample_rate = bicodec.config['sample_rate']
+        
+        # 检查是否需要重采样
+        # 注意：这里假设 prompt_audio 已经是从 soundfile 加载的，采样率信息在外部处理
+        # BiCodecTokenizer 期望 16kHz 采样率的音频
+        print(f"BiCodecTokenizer 期望的采样率: {target_sample_rate}Hz")
+        print(f"音频数据形状: {audio_data.shape}")
+        
+        # 使用 BiCodec 提取 tokens (返回顺序: global_tokens, semantic_tokens)
+        global_tokens, semantic_tokens = bicodec.tokenize(audio_data)
+        global_tokens = global_tokens.squeeze(0).squeeze(0).detach().cpu().tolist()
+        semantic_tokens = semantic_tokens.squeeze(0).squeeze(0).detach().cpu().tolist()
+    else:
+        global_tokens = []
+        semantic_tokens = []
+    
+    # 2. 处理文本
+    if prompt_text is not None:
+        # 连接提示文本和目标文本
+        full_text = prompt_text + text
+        # 初始的 semantic tokens 等于 prompt_audio 提取的 semantic tokens
+        initial_semantic_tokens = semantic_tokens.copy()
+    else:
+        full_text = text
+        initial_semantic_tokens = []
+    
+    # 3. 获取文本 tokens
+    text_tokens = tokenizer.encode(full_text, add_special_tokens=False)
+    
+    # 4. 转换为张量
+    text_tokens_tensor = torch.tensor(text_tokens, dtype=torch.long, device=device)
+    global_tokens_tensor = torch.tensor(global_tokens, dtype=torch.long, device=device)
+    semantic_tokens_tensor = torch.tensor(initial_semantic_tokens, dtype=torch.long, device=device)
+    
+    # 5. 获取嵌入
+    text_embs = model.text_embedder(text_tokens_tensor)
+    global_embs = model.global_embedder(global_tokens_tensor)
+    semantic_embs = model.model.embeddings(semantic_tokens_tensor)
+    
+    # 6. 获取特殊标记嵌入
+    tag_0_emb = model.tts_tag_embedder(torch.tensor([0], dtype=torch.long, device=device))
+    tag_1_emb = model.tts_tag_embedder(torch.tensor([1], dtype=torch.long, device=device))
+    tag_2_emb = model.tts_tag_embedder(torch.tensor([2], dtype=torch.long, device=device))
+    
+    # 7. 连接嵌入
+    input_embs = torch.cat([
+        tag_2_emb, 
+        text_embs, 
+        tag_0_emb, 
+        global_embs, 
+        tag_1_emb, 
+        semantic_embs
+    ], dim=0)
+    
+    # 8. 添加批次维度
+    input_embs = input_embs.unsqueeze(0)  # [1, seq_len, hidden_size]
+    
+    return {
+        "input_embs": input_embs,
+        "global_tokens": global_tokens_tensor,
+    }
+
+def generate_embeddings_batch(model, tokenizer, texts, bicodec, prompt_text=None, prompt_audio=None):
+    """
+    为 Spark LLM 批量生成预测所需的输入嵌入，支持多个文本的并行处理
+    
+    Args:
+        model: Spark LLM 模型
+        tokenizer: 文本分词器
+        texts: 要生成语音的文本列表
+        bicodec: BiCodecTokenizer 实例
+        prompt_text: 提示文本（可选）
+        prompt_audio: 提示音频数组（可选）
+    
+    Returns:
+        tuple: (embeddings_dict, attention_mask) 包含批量 input_embs 的字典和注意力掩码
+    """
+    device = next(model.parameters()).device
+    dtype = next(model.parameters()).dtype
+    batch_size = len(texts)
+    
+    # 1. 处理提示音频，提取 global_tokens 和 semantic_tokens
+    if prompt_audio is not None:
+        # 确保音频数据是 float32 类型
+        audio_data = np.array(prompt_audio, dtype=np.float32)
+        target_sample_rate = bicodec.config['sample_rate']
+        
+        print(f"BiCodecTokenizer 期望的采样率: {target_sample_rate}Hz")
+        print(f"音频数据形状: {audio_data.shape}")
+        
+        # 使用 BiCodec 提取 tokens (返回顺序: global_tokens, semantic_tokens)
+        global_tokens, semantic_tokens = bicodec.tokenize(audio_data)
+        global_tokens = global_tokens.squeeze(0).squeeze(0).detach().cpu().tolist()
+        semantic_tokens = semantic_tokens.squeeze(0).squeeze(0).detach().cpu().tolist()
+    else:
+        global_tokens = []
+        semantic_tokens = []
+    
+    # 2. 处理所有文本，获取每个样本的嵌入组件
+    all_input_embs = []
+    all_attention_masks = []
+    
+    for text in texts:
+        # 处理单个文本
+        if prompt_text is not None:
+            full_text = prompt_text + text
+            initial_semantic_tokens = semantic_tokens.copy()
+        else:
+            full_text = text
+            initial_semantic_tokens = []
+        
+        # 获取文本 tokens
+        text_tokens = tokenizer.encode(full_text, add_special_tokens=False)
+        
+        # 转换为张量
+        text_tokens_tensor = torch.tensor(text_tokens, dtype=torch.long, device=device)
+        global_tokens_tensor = torch.tensor(global_tokens, dtype=torch.long, device=device)
+        semantic_tokens_tensor = torch.tensor(initial_semantic_tokens, dtype=torch.long, device=device)
+        
+        # 获取嵌入
+        text_embs = model.text_embedder(text_tokens_tensor)
+        global_embs = model.global_embedder(global_tokens_tensor)
+        semantic_embs = model.model.embeddings(semantic_tokens_tensor)
+        
+        # 获取特殊标记嵌入
+        tag_0_emb = model.tts_tag_embedder(torch.tensor([0], dtype=torch.long, device=device))
+        tag_1_emb = model.tts_tag_embedder(torch.tensor([1], dtype=torch.long, device=device))
+        tag_2_emb = model.tts_tag_embedder(torch.tensor([2], dtype=torch.long, device=device))
+        
+        # 连接嵌入
+        input_embs = torch.cat([
+            tag_2_emb, 
+            text_embs, 
+            tag_0_emb, 
+            global_embs, 
+            tag_1_emb, 
+            semantic_embs
+        ], dim=0)  # [seq_len, hidden_size]
+        
+        all_input_embs.append(input_embs)
+        all_attention_masks.append(torch.ones(input_embs.shape[0], dtype=torch.long, device=device))
+    
+    # 3. 找到最大序列长度
+    max_seq_len = max(emb.shape[0] for emb in all_input_embs)
+    hidden_size = all_input_embs[0].shape[1]
+    
+    # 4. 创建批量张量，使用 left padding 和零填充
+    batch_input_embs = torch.zeros(batch_size, max_seq_len, hidden_size, device=device,dtype=dtype)
+    batch_attention_mask = torch.zeros(batch_size, max_seq_len, dtype=torch.long, device=device)
+    
+    for i, (input_embs, attention_mask) in enumerate(zip(all_input_embs, all_attention_masks)):
+        seq_len = input_embs.shape[0]
+        # Left padding: 将序列放在右侧，左侧填充零
+        batch_input_embs[i, -seq_len:, :] = input_embs
+        batch_attention_mask[i, -seq_len:] = attention_mask
+    
+    # 5. 创建 global_tokens 的批量版本
+    global_tokens_tensor = torch.tensor(global_tokens, dtype=torch.long, device=device)
+    batch_global_tokens = global_tokens_tensor.unsqueeze(0).expand(batch_size, -1)
+    
+    return {
+        "input_embs": batch_input_embs,
+        "global_tokens": batch_global_tokens,
+    }, batch_attention_mask
\ No newline at end of file
diff --git a/trained_50_percents/vocab.txt b/trained_50_percents/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..022664f903e199dcd06df011f274d6c2be2ca0f1
--- /dev/null
+++ b/trained_50_percents/vocab.txt
@@ -0,0 +1,77828 @@
+<|rwkv_tokenizer_end_of_text|>
+b'\x00'
+b'\x01'
+b'\x02'
+b'\x03'
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+b'\xec\xbc\x80'
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+b'\xed\x83\x80'
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+b'\xed\x83\x84'
+b'\xed\x83\x88'
+b'\xed\x83\x90'
+b'\xed\x83\x91'
+b'\xed\x83\x9c'
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+b'\xed\x85\x90'
+b'\xed\x85\x94'
+b'\xed\x85\x9c'
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+b' (\xe2\x80\x9c'
+b' (\xe2\x80\x9e'
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+b' turb'
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+b' tv\xc3\xa5'
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+b' t\xc3\xa3o'
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+b' t\xc3\xa9c'
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+b' t\xc3\xadt'
+b' t\xc3\xb4i'
+b' t\xc3\xb6r'
+b' t\xe1\xbb\xab'
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+b' v\xc3\xa4l'
+b' v\xc3\xa4r'
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+b' v\xc3\xa9g'
+b' v\xc3\xa9r'
+b' v\xe1\xbb\x81'
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+b' wake'
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+b' w\xc3\xbcr'
+b' w\xc5\x82a'
+b' yaml'
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+b' yard'
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+b' yoga'
+b' your'
+b' yuan'
+b' za\xc4\x8d'
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+b' zich'
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+b' zinc'
+b' zona'
+b' zone'
+b' zoom'
+b' zwei'
+b' z\xc3\xa1k'
+b' \xc2\xa7\xc2\xa7'
+b' \xc3\x81ng'
+b' \xc3\x85rs'
+b' \xc3\xa1ll'
+b' \xc3\xa5rs'
+b' \xc3\xa7ok'
+b' \xc3\xa9ch'
+b' \xc3\xa9qu'
+b' \xc3\xa9tr'
+b' \xc3\xaent'
+b' \xc3\xb6ff'
+b' \xc3\xb6ss'
+b' \xc3\xb6st'
+b' \xc3\xbalt'
+b' \xc4\x91\xc3\xa3'
+b' \xc4\x91\xc3\xb3'
+b' \xc5\x99\xc3\xad'
+b' \xc5\x9awi'
+b' \xc5\x9bwi'
+b' \xc5\xa1to'
+b' \xce\xb1\xce\xbd'
+b' \xce\xba\xce\xb1'
+b' \xce\xbc\xce\xb5'
+b' \xce\xbd\xce\xb1'
+b' \xcf\x80\xce\xb1'
+b' \xcf\x83\xcf\x85'
+b' \xcf\x84\xce\xb7'
+b' \xcf\x84\xce\xbf'
+b' \xd0\x86\xd0\xbd'
+b' \xd0\x90\xd0\xb1'
+b' \xd0\x90\xd0\xb2'
+b' \xd0\x90\xd0\xb4'
+b' \xd0\x90\xd0\xbb'
+b' \xd0\x90\xd0\xbd'
+b' \xd0\x90\xd1\x80'
+b' \xd0\x91\xd0\xb0'
+b' \xd0\x91\xd0\xb5'
+b' \xd0\x91\xd0\xb8'
+b' \xd0\x91\xd0\xbe'
+b' \xd0\x91\xd1\x83'
+b' \xd0\x91\xd1\x8b'
+b' \xd0\x91\xd1\x96'
+b' \xd0\x92\xd0\xb0'
+b' \xd0\x92\xd0\xb5'
+b' \xd0\x92\xd0\xb8'
+b' \xd0\x92\xd0\xbe'
+b' \xd0\x92\xd1\x8b'
+b' \xd0\x92\xd1\x96'
+b' \xd0\x93\xd0\xb0'
+b' \xd0\x93\xd0\xb5'
+b' \xd0\x93\xd0\xb8'
+b' \xd0\x93\xd0\xbe'
+b' \xd0\x93\xd1\x83'
+b' \xd0\x94\xd0\xb0'
+b' \xd0\x94\xd0\xb5'
+b' \xd0\x94\xd0\xb6'
+b' \xd0\x94\xd0\xb8'
+b' \xd0\x94\xd0\xbe'
+b' \xd0\x94\xd1\x83'
+b' \xd0\x95\xd0\xb2'
+b' \xd0\x96\xd0\xb8'
+b' \xd0\x97\xd0\xb0'
+b' \xd0\x98\xd0\xb7'
+b' \xd0\x98\xd0\xbd'
+b' \xd0\x98\xd1\x81'
+b' \xd0\x99\xd0\xbe'
+b' \xd0\x9a\xd0\xb0'
+b' \xd0\x9a\xd0\xb5'
+b' \xd0\x9a\xd0\xb8'
+b' \xd0\x9a\xd0\xbe'
+b' \xd0\x9a\xd1\x83'
+b' \xd0\x9b\xd0\xb0'
+b' \xd0\x9b\xd0\xb5'
+b' \xd0\x9b\xd0\xb8'
+b' \xd0\x9b\xd0\xbe'
+b' \xd0\x9b\xd1\x83'
+b' \xd0\x9b\xd1\x8c'
+b' \xd0\x9b\xd1\x8e'
+b' \xd0\x9b\xd1\x96'
+b' \xd0\x9c\xd0\xb0'
+b' \xd0\x9c\xd0\xb5'
+b' \xd0\x9c\xd0\xb8'
+b' \xd0\x9c\xd0\xbe'
+b' \xd0\x9c\xd1\x83'
+b' \xd0\x9c\xd1\x96'
+b' \xd0\x9d\xd0\xb0'
+b' \xd0\x9d\xd0\xb5'
+b' \xd0\x9d\xd0\xb8'
+b' \xd0\x9d\xd0\xbe'
+b' \xd0\x9d\xd1\x96'
+b' \xd0\x9e\xd0\xb1'
+b' \xd0\x9e\xd0\xb4'
+b' \xd0\x9e\xd0\xbd'
+b' \xd0\x9e\xd1\x80'
+b' \xd0\x9e\xd1\x81'
+b' \xd0\x9e\xd1\x82'
+b' \xd0\x9f\xd0\xb0'
+b' \xd0\x9f\xd0\xb5'
+b' \xd0\x9f\xd0\xb8'
+b' \xd0\x9f\xd0\xbe'
+b' \xd0\x9f\xd1\x83'
+b' \xd0\xa0\xd0\xb0'
+b' \xd0\xa0\xd0\xb5'
+b' \xd0\xa0\xd0\xb8'
+b' \xd0\xa0\xd0\xbe'
+b' \xd0\xa0\xd1\x83'
+b' \xd0\xa1\xd0\x9f'
+b' \xd0\xa1\xd0\xb0'
+b' \xd0\xa1\xd0\xb5'
+b' \xd0\xa1\xd0\xb8'
+b' \xd0\xa1\xd0\xbc'
+b' \xd0\xa1\xd0\xbe'
+b' \xd0\xa1\xd1\x80'
+b' \xd0\xa1\xd1\x82'
+b' \xd0\xa1\xd1\x83'
+b' \xd0\xa2\xd0\xb0'
+b' \xd0\xa2\xd0\xb5'
+b' \xd0\xa2\xd0\xb8'
+b' \xd0\xa2\xd0\xbe'
+b' \xd0\xa2\xd1\x83'
+b' \xd0\xa3\xd0\xb8'
+b' \xd0\xa4\xd0\xb0'
+b' \xd0\xa4\xd0\xb5'
+b' \xd0\xa4\xd0\xb8'
+b' \xd0\xa4\xd0\xbe'
+b' \xd0\xa5\xd0\xb0'
+b' \xd0\xa5\xd0\xbe'
+b' \xd0\xa5\xd1\x83'
+b' \xd0\xa6\xd0\xb5'
+b' \xd0\xa7\xd0\xb0'
+b' \xd0\xa7\xd0\xb5'
+b' \xd0\xa7\xd0\xb8'
+b' \xd0\xa8\xd0\xb0'
+b' \xd0\xa8\xd0\xb8'
+b' \xd0\xb0\xd0\xb2'
+b' \xd0\xb0\xd0\xb4'
+b' \xd0\xb0\xd0\xba'
+b' \xd0\xb0\xd0\xbb'
+b' \xd0\xb0\xd0\xbd'
+b' \xd0\xb0\xd1\x80'
+b' \xd0\xb0\xd1\x82'
+b' \xd0\xb1\xd0\xb0'
+b' \xd0\xb1\xd0\xb5'
+b' \xd0\xb1\xd0\xb8'
+b' \xd0\xb1\xd0\xbe'
+b' \xd0\xb1\xd1\x83'
+b' \xd0\xb1\xd1\x8b'
+b' \xd0\xb1\xd1\x96'
+b' \xd0\xb2\xd0\xb0'
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+b'\xed\x95\x98\xeb\x8a\x94'
+b'\xed\x95\x98\xeb\x8b\xa4'
+b'\xed\x95\x98\xeb\xa9\xb0'
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+b'\xef\xbc\x9f\xe3\x80\x8d'
+b'\xef\xbf\xbd\xef\xbf\xbd'
+b'\t\t\t\t\t\t\t'
+b'\n\t\t\t\t\t\t'
+b'\n\t\t\t   '
+b'\n\t     '
+b'\n\n\n\n\n\n\n'
+b'\n\n     '
+b'\n      '
+b'\r\n\t\t\t\t\t'
+b'\r\n\r\n   '
+b'\r\n     '
+b' \n     '
+b'       '
+b' ------'
+b' ACCESS'
+b' ACTION'
+b' ASSERT'
+b' AUTHOR'
+b' Abbott'
+b' Abrams'
+b' Academ'
+b' Accept'
+b' Access'
+b' Across'
+b' Acting'
+b' Action'
+b' Active'
+b' Actual'
+b' Adding'
+b' Adjust'
+b' Adrian'
+b' Advent'
+b' Affero'
+b' Afghan'
+b' Africa'
+b' Agency'
+b' Agenda'
+b' Agents'
+b' Alaska'
+b' Albany'
+b' Albert'
+b' Alexis'
+b' Alfred'
+b' Allied'
+b' Allies'
+b' Almost'
+b' Alonso'
+b' Altern'
+b' Alumni'
+b' Always'
+b' Amanda'
+b' Amazon'
+b' Americ'
+b' Amount'
+b' Analog'
+b' Anders'
+b' Andrea'
+b' Andrew'
+b' Andr\xc3\xa9'
+b' Angela'
+b' Angels'
+b' Animal'
+b' Annual'
+b' Answer'
+b' Anyone'
+b' Anyway'
+b' Apache'
+b' Apollo'
+b' Appeal'
+b' Append'
+b' Apr\xc3\xa8s'
+b' Arabia'
+b' Arabic'
+b' Archae'
+b' Archer'
+b' Archie'
+b' Arctic'
+b' Argent'
+b' Arnold'
+b' Around'
+b' Arrays'
+b' Arthur'
+b' Artist'
+b' Ashley'
+b' Assert'
+b' Assign'
+b' Assist'
+b' Associ'
+b' Assume'
+b' Athens'
+b' Athlet'
+b' Atomic'
+b' Attack'
+b' Auburn'
+b' August'
+b' Aurora'
+b' Austin'
+b' Author'
+b' Au\xc3\x9fer'
+b' Avatar'
+b' Avenue'
+b' Awards'
+b' Backup'
+b' Bailey'
+b' Baltic'
+b' Barack'
+b' Barber'
+b' Barker'
+b' Barnes'
+b' Barton'
+b' Basket'
+b' Batman'
+b' Battle'
+b' Baxter'
+b' Bayern'
+b' Beauty'
+b' Becker'
+b' Before'
+b' Behind'
+b' Bengal'
+b' Benson'
+b' Bergen'
+b' Berger'
+b' Berlin'
+b' Bernie'
+b' Better'
+b' Beyond'
+b' Bieber'
+b' Binary'
+b' Bishop'
+b' Bitmap'
+b' Blacks'
+b' Blocks'
+b' Boeing'
+b' Bolton'
+b' Bonnie'
+b' Booker'
+b' Border'
+b' Bosnia'
+b' Boston'
+b' Bottom'
+b' Branch'
+b' Brasil'
+b' Brazil'
+b' Breast'
+b' Brexit'
+b' Bridge'
+b' Bright'
+b' Broker'
+b' Bronze'
+b' Brooks'
+b' Browns'
+b' Bryant'
+b' Bubble'
+b' Buddha'
+b' Budget'
+b' Buenos'
+b' Buffer'
+b' Bulgar'
+b' Bullet'
+b' Bundes'
+b' Bundle'
+b' Bureau'
+b' Burger'
+b' Burton'
+b' Butler'
+b' Butter'
+b' Button'
+b' CGRect'
+b' COLUMN'
+b' CONFIG'
+b' CREATE'
+b' Caesar'
+b' Calcul'
+b' Calder'
+b' Called'
+b' Calvin'
+b' Camera'
+b' Campus'
+b' Canada'
+b' Cancel'
+b' Cancer'
+b' Candid'
+b' Cannon'
+b' Cannot'
+b' Canvas'
+b' Canyon'
+b' Carbon'
+b' Career'
+b' Carlos'
+b' Carmen'
+b' Carrie'
+b' Carson'
+b' Carter'
+b' Casino'
+b' Castle'
+b' Castro'
+b' Casual'
+b' Celebr'
+b' Celtic'
+b' Census'
+b' Center'
+b' Centre'
+b' Centro'
+b' Chance'
+b' Change'
+b' Chapel'
+b' Charge'
+b' Checks'
+b' Cheese'
+b' Cherry'
+b' Chiefs'
+b' Choice'
+b' Choose'
+b' Christ'
+b' Chrome'
+b' Church'
+b' Cinema'
+b' Circle'
+b' Cities'
+b' Ciudad'
+b' Claims'
+b' Claire'
+b' Clarke'
+b' Claude'
+b' Clause'
+b' Client'
+b' Clinic'
+b' Closed'
+b' Coffee'
+b' Colleg'
+b' Colomb'
+b' Colors'
+b' Colour'
+b' Columb'
+b' Column'
+b' Combat'
+b' Combin'
+b' Comedy'
+b' Comics'
+b' Coming'
+b' Commit'
+b' Common'
+b' Commun'
+b' Compan'
+b' Compar'
+b' Compet'
+b' Comput'
+b' Config'
+b' Connor'
+b' Conrad'
+b' Contin'
+b' Conway'
+b' Cookie'
+b' Cooper'
+b' Copper'
+b' Corbyn'
+b' Corner'
+b' Corona'
+b' Corpor'
+b' Corpus'
+b' Cotton'
+b' County'
+b' Course'
+b' Courts'
+b' Create'
+b' Credit'
+b' Crimea'
+b' Crisis'
+b' Cruise'
+b' Crypto'
+b' Cuando'
+b' Cursor'
+b' Curtis'
+b' Custom'
+b' Cyprus'
+b' C\xc3\xa9sar'
+b' DAMAGE'
+b' DELETE'
+b' DIRECT'
+b' Dakota'
+b' Dallas'
+b' Dalton'
+b' Damage'
+b' Danger'
+b' Daniel'
+b' Danish'
+b' Darwin'
+b' Davies'
+b' Dawson'
+b' Dayton'
+b' Debate'
+b' Debian'
+b' Define'
+b' Degree'
+b' Delete'
+b' Democr'
+b' Denise'
+b' Dennis'
+b' Dental'
+b' Denver'
+b' Depart'
+b' Depend'
+b' Deputy'
+b' Desert'
+b' Design'
+b' Detail'
+b' Detect'
+b' Device'
+b' Dexter'
+b' Dialog'
+b' Diesel'
+b' Digest'
+b' Dillon'
+b' Direct'
+b' Disney'
+b' Divine'
+b' Django'
+b' Docker'
+b' Doctor'
+b' Dollar'
+b' Domain'
+b' Donald'
+b' Double'
+b' Dragon'
+b' Dreams'
+b' Driver'
+b' Drupal'
+b' Dublin'
+b' Dudley'
+b' Duncan'
+b' Durant'
+b' Durham'
+b' During'
+b' EXISTS'
+b' EXPECT'
+b' Eagles'
+b' Easter'
+b' Eating'
+b' Econom'
+b' Edison'
+b' Edited'
+b' Editor'
+b' Edmund'
+b' Edward'
+b' Effect'
+b' Eighth'
+b' Either'
+b' Elaine'
+b' Eleven'
+b' Elliot'
+b' Empire'
+b' Employ'
+b' Enable'
+b' Energy'
+b' Engine'
+b' Ensure'
+b' Entity'
+b' Ernest'
+b' Errors'
+b' Escape'
+b' Espa\xc3\xb1'
+b' Essays'
+b' Establ'
+b' Estado'
+b' Estate'
+b' Esther'
+b' Ethics'
+b' Eugene'
+b' Europa'
+b' Europe'
+b' Events'
+b' Excell'
+b' Except'
+b' Expand'
+b' Expect'
+b' Expert'
+b' Export'
+b' Fabric'
+b' Factor'
+b' Failed'
+b' Falcon'
+b' Family'
+b' Farmer'
+b' Father'
+b' Fellow'
+b' Female'
+b' Fields'
+b' Figure'
+b' Filter'
+b' Finals'
+b' Finish'
+b' Finite'
+b' Fiscal'
+b' Fisher'
+b' Flickr'
+b' Flight'
+b' Flores'
+b' Flower'
+b' Flying'
+b' Folder'
+b' Follow'
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+b' Forces'
+b' Forest'
+b' Format'
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+b' Foster'
+b' Fourth'
+b' Fowler'
+b' France'
+b' Franco'
+b' Fran\xc3\xa7'
+b' Fraser'
+b' Freder'
+b' French'
+b' Freund'
+b' Friday'
+b' Friend'
+b' Frozen'
+b' Fuller'
+b' Fusion'
+b' Future'
+b' GLOBAL'
+b' Galaxy'
+b' Galile'
+b' Gaming'
+b' Gandhi'
+b' Garcia'
+b' Garden'
+b' Gareth'
+b' Gemini'
+b' Gender'
+b' Geneva'
+b' Genome'
+b' George'
+b' Gerald'
+b' Gerard'
+b' German'
+b' Giants'
+b' Gibson'
+b' GitHub'
+b' Github'
+b' Global'
+b' Gloria'
+b' Golden'
+b' Google'
+b' Gordon'
+b' Gospel'
+b' Gothic'
+b' Govern'
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+b' Grammy'
+b' Grande'
+b' Graves'
+b' Greece'
+b' Greeks'
+b' Greene'
+b' Greens'
+b' Ground'
+b' Groups'
+b' Growth'
+b' Gr\xc3\xbcnd'
+b' Guards'
+b' Guinea'
+b' Gustav'
+b' G\xc3\xb3mez'
+b' HEADER'
+b' Hammer'
+b' Handle'
+b' Hannah'
+b' Hansen'
+b' Harbor'
+b' Harlem'
+b' Harley'
+b' Harmon'
+b' Harold'
+b' Harper'
+b' Harris'
+b' Harvey'
+b' Hassan'
+b' Having'
+b' Hawaii'
+b' Hazard'
+b' Header'
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+b' Heaven'
+b' Hebrew'
+b' Height'
+b' Helena'
+b' Helper'
+b' Herald'
+b' Herman'
+b' Heroes'
+b' Hidden'
+b' Higher'
+b' Hilton'
+b' Hindus'
+b' Histor'
+b' Hitler'
+b' Hockey'
+b' Holder'
+b' Hollow'
+b' Holmes'
+b' Hoover'
+b' Howard'
+b' Huawei'
+b' Hubble'
+b' Hudson'
+b' Hughes'
+b' Humans'
+b' Hunter'
+b' Hybrid'
+b' INSERT'
+b' Ignore'
+b' Images'
+b' Impact'
+b' Import'
+b' Income'
+b' Indeed'
+b' Indian'
+b' Indies'
+b' Indust'
+b' Infant'
+b' Infect'
+b' Inform'
+b' Injury'
+b' Inputs'
+b' Insert'
+b' Inside'
+b' Instit'
+b' Integr'
+b' Intent'
+b' Intern'
+b' Invent'
+b' Invest'
+b' Irving'
+b' Isabel'
+b' Isaiah'
+b' Island'
+b' Israel'
+b' Issues'
+b' Italia'
+b' JQuery'
+b' Jackie'
+b' Jacobs'
+b' Jahren'
+b' Jensen'
+b' Jeremy'
+b' Jerome'
+b' Jersey'
+b' Jewish'
+b' Johann'
+b' Johnny'
+b' Jordan'
+b' Joseph'
+b' Joshua'
+b' Judges'
+b' Judith'
+b' Julian'
+b' Juliet'
+b' Julius'
+b' Junior'
+b' Justin'
+b' Kaiser'
+b' Kansas'
+b' Kaplan'
+b' Kazakh'
+b' Keller'
+b' Kelley'
+b' Kelvin'
+b' Kepler'
+b' Kerala'
+b' Kernel'
+b' Kimber'
+b' Kinder'
+b' Kindle'
+b' Knight'
+b' Korean'
+b' Kosovo'
+b' Kultur'
+b' Kuwait'
+b' K\xc3\xb6nig'
+b' LETTER'
+b' LIABLE'
+b' Labels'
+b' Labour'
+b' Ladies'
+b' Lakers'
+b' Lambda'
+b' Lastly'
+b' Latino'
+b' Latvia'
+b' Launch'
+b' Lauren'
+b' Lawson'
+b' Layout'
+b' Leader'
+b' League'
+b' Lebens'
+b' Legacy'
+b' Legend'
+b' Legion'
+b' Legisl'
+b' Length'
+b' Lennon'
+b' Lenovo'
+b' Leslie'
+b' Lesser'
+b' Letter'
+b' Levels'
+b' Levine'
+b' Libert'
+b' Libyan'
+b' Lights'
+b' Linear'
+b' Linked'
+b' Lionel'
+b' Liquid'
+b' Lisbon'
+b' Listen'
+b' Little'
+b' Living'
+b' Locale'
+b' Logger'
+b' London'
+b' Louise'
+b' Ludwig'
+b' Luther'
+b' L\xc3\xb3pez'
+b' MATLAB'
+b' METHOD'
+b' MODULE'
+b' Macron'
+b' Madame'
+b' Madrid'
+b' Maduro'
+b' Maggie'
+b' Magnet'
+b' Making'
+b' Malays'
+b' Manage'
+b' Manila'
+b' Manual'
+b' Manuel'
+b' Marcel'
+b' Marcos'
+b' Marcus'
+b' Marian'
+b' Marina'
+b' Marine'
+b' Marino'
+b' Marion'
+b' Marker'
+b' Market'
+b' Markov'
+b' Markus'
+b' Martha'
+b' Martin'
+b' Mart\xc3\xad'
+b' Marvel'
+b' Marvin'
+b' Mar\xc3\xada'
+b' Master'
+b' Matrix'
+b' Matter'
+b' McCain'
+b' Mechan'
+b' Median'
+b' Medium'
+b' Meghan'
+b' Member'
+b' Memory'
+b' Mental'
+b' Merkel'
+b' Meteor'
+b' Method'
+b' Metric'
+b' Mexico'
+b' Michel'
+b' Mickey'
+b' Middle'
+b' Miguel'
+b' Milano'
+b' Miller'
+b' Milton'
+b' Mining'
+b' Minist'
+b' Mirror'
+b' Mobile'
+b' Models'
+b' Modern'
+b' Module'
+b' Moment'
+b' Monaco'
+b' Monday'
+b' Monica'
+b' Monkey'
+b' Monroe'
+b' Moreno'
+b' Morgan'
+b' Mormon'
+b' Morris'
+b' Morton'
+b' Moscow'
+b' Mother'
+b' Motion'
+b' Motors'
+b' Moving'
+b' Mumbai'
+b' Munich'
+b' Murder'
+b' Murphy'
+b' Murray'
+b' Museum'
+b' Muslim'
+b' Mus\xc3\xa9e'
+b' Mutual'
+b' NOTICE'
+b' NUMBER'
+b' Naples'
+b' Napole'
+b' Napoli'
+b' Nathan'
+b' Nation'
+b' Native'
+b' Nature'
+b' Nearly'
+b' Nelson'
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+b' Nether'
+b' Neural'
+b' Nevada'
+b' Newark'
+b' Newman'
+b' Newton'
+b' Nguyen'
+b' Nickel'
+b' Nicola'
+b' Nicole'
+b' Nissan'
+b' Nobody'
+b' Normal'
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+b' Norris'
+b' Norton'
+b' Norway'
+b' Notice'
+b' Notify'
+b' Number'
+b' OBJECT'
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+b' Office'
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+b' Oliver'
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+b' PARTIC'
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+b' Palmer'
+b' Panama'
+b' Papers'
+b' Parade'
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+b' Parish'
+b' Parker'
+b' Parser'
+b' Par\xc3\xads'
+b' Pascal'
+b' Pastor'
+b' Patent'
+b' Patrol'
+b' PayPal'
+b' Pelosi'
+b' People'
+b' Pepper'
+b' Period'
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+b' Philip'
+b' Philos'
+b' Photos'
+b' Pierce'
+b' Pierre'
+b' Places'
+b' Plains'
+b' Planet'
+b' Plants'
+b' Plasma'
+b' Player'
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+b' Plugin'
+b' Pocket'
+b' Poetry'
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+b' Poland'
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+b' Policy'
+b' Polish'
+b' Portal'
+b' Porter'
+b' Portug'
+b' Postal'
+b' Posted'
+b' Potter'
+b' Powell'
+b' Powers'
+b' Prague'
+b' Prayer'
+b' Pretty'
+b' Priest'
+b' Primer'
+b' Prince'
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+b' Proced'
+b' PubMed'
+b' Public'
+b' Puerto'
+b' Punjab'
+b' Purple'
+b' Python'
+b' P\xc3\xa9rez'
+b' Quando'
+b' Quebec'
+b' Queens'
+b' RETURN'
+b' Rabbit'
+b' Rachel'
+b' Racing'
+b' Rafael'
+b' Rahman'
+b' Ramsey'
+b' Random'
+b' Ranger'
+b' Rapids'
+b' Rather'
+b' Rating'
+b' Ravens'
+b' Reader'
+b' Reagan'
+b' Really'
+b' Reason'
+b' Recall'
+b' Recent'
+b' Recipe'
+b' Recogn'
+b' Recomm'
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+b' Reddit'
+b' Reduce'
+b' Reform'
+b' Refuge'
+b' Regina'
+b' Region'
+b' Relief'
+b' Remark'
+b' Remote'
+b' Remove'
+b' Render'
+b' Repeat'
+b' Report'
+b' Rescue'
+b' Resort'
+b' Result'
+b' Retail'
+b' Retrie'
+b' Return'
+b' Review'
+b' Reward'
+b' Rhodes'
+b' Rights'
+b' Rising'
+b' Rivera'
+b' Rivers'
+b' Robert'
+b' Rocket'
+b' Rodrig'
+b' Rogers'
+b' Roland'
+b' Romans'
+b' Romero'
+b' Romney'
+b' Ronald'
+b' Rousse'
+b' Router'
+b' Runner'
+b' Russia'
+b' Rwanda'
+b' SELECT'
+b' SQLite'
+b' STATES'
+b' STATUS'
+b' STRICT'
+b' STRING'
+b' SYSTEM'
+b' Sacred'
+b' Saddam'
+b' Safari'
+b' Safety'
+b' Sahara'
+b' Saints'
+b' Salmon'
+b' Sample'
+b' Samuel'
+b' Sandra'
+b' Santos'
+b' Saturn'
+b' Savage'
+b' Saving'
+b' Sa\xc3\xbade'
+b' Scalar'
+b' Schema'
+b' Scheme'
+b' Schiff'
+b' School'
+b' Schwar'
+b' Sch\xc3\xb6n'
+b' Scient'
+b' Scotia'
+b' Screen'
+b' Script'
+b' Scroll'
+b' Search'
+b' Season'
+b' Sebast'
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+b' \xd0\xa2\xd0\xb0\xd0\xba'
+b' \xd0\xa2\xd0\xb5\xd1\x80'
+b' \xd0\xa2\xd1\x80\xd0\xb5'
+b' \xd0\xa2\xd1\x80\xd0\xb8'
+b' \xd0\xa2\xd1\x83\xd1\x80'
+b' \xd0\xa5\xd0\xb0\xd1\x80'
+b' \xd0\xa5\xd1\x80\xd0\xbe'
+b' \xd0\xa7\xd0\xb5\xd0\xbc'
+b' \xd0\xa7\xd0\xb5\xd1\x80'
+b' \xd0\xad\xd1\x82\xd0\xbe'
+b' \xd0\xb0\xd0\xb1\xd0\xbe'
+b' \xd0\xb0\xd0\xba\xd1\x82'
+b' \xd0\xb0\xd0\xbb\xd0\xb5'
+b' \xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\xb0\xd0\xbd\xd0\xb0'
+b' \xd0\xb0\xd0\xbd\xd0\xb3'
+b' \xd0\xb1\xd0\xb0\xd0\xbd'
+b' \xd0\xb1\xd0\xb5\xd0\xb7'
+b' \xd0\xb1\xd0\xb8\xd0\xbe'
+b' \xd0\xb1\xd0\xbb\xd0\xb8'
+b' \xd0\xb1\xd0\xbe\xd1\x80'
+b' \xd0\xb1\xd1\x80\xd0\xb0'
+b' \xd0\xb1\xd1\x80\xd0\xb8'
+b' \xd0\xb1\xd1\x80\xd0\xbe'
+b' \xd0\xb1\xd1\x83\xd0\xb2'
+b' \xd0\xb1\xd1\x83\xd0\xb4'
+b' \xd0\xb1\xd1\x8b\xd0\xbb'
+b' \xd0\xb2\xd0\xb0\xd0\xb6'
+b' \xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd0\xb2\xd0\xb7\xd1\x8f'
+b' \xd0\xb2\xd0\xb8\xd0\xb4'
+b' \xd0\xb2\xd0\xbb\xd0\xb0'
+b' \xd0\xb2\xd0\xbc\xd0\xb5'
+b' \xd0\xb2\xd0\xbd\xd0\xb5'
+b' \xd0\xb2\xd0\xbe\xd0\xb7'
+b' \xd0\xb2\xd0\xbe\xd0\xb9'
+b' \xd0\xb2\xd0\xbe\xd1\x81'
+b' \xd0\xb2\xd1\x80\xd0\xb0'
+b' \xd0\xb2\xd1\x80\xd0\xb5'
+b' \xd0\xb2\xd1\x81\xd0\xb5'
+b' \xd0\xb2\xd1\x81\xd1\x96'
+b' \xd0\xb2\xd1\x82\xd0\xbe'
+b' \xd0\xb2\xd1\x96\xd0\xb4'
+b' \xd0\xb2\xd1\x96\xd0\xb9'
+b' \xd0\xb2\xd1\x96\xd0\xbd'
+b' \xd0\xb3\xd0\xb4\xd0\xb5'
+b' \xd0\xb3\xd0\xb5\xd1\x80'
+b' \xd0\xb3\xd0\xbb\xd0\xb0'
+b' \xd0\xb3\xd0\xbe\xd0\xb4'
+b' \xd0\xb3\xd0\xbe\xd1\x80'
+b' \xd0\xb3\xd1\x80\xd0\xb0'
+b' \xd0\xb3\xd1\x80\xd0\xb5'
+b' \xd0\xb3\xd1\x80\xd1\x83'
+b' \xd0\xb4\xd0\xb0\xd0\xbd'
+b' \xd0\xb4\xd0\xb2\xd0\xb0'
+b' \xd0\xb4\xd0\xb2\xd0\xb5'
+b' \xd0\xb4\xd0\xb2\xd0\xb8'
+b' \xd0\xb4\xd0\xb2\xd0\xbe'
+b' \xd0\xb4\xd0\xb2\xd1\x83'
+b' \xd0\xb4\xd0\xb5\xd0\xb9'
+b' \xd0\xb4\xd0\xb5\xd1\x82'
+b' \xd0\xb4\xd0\xb5\xd1\x8f'
+b' \xd0\xb4\xd0\xb8\xd0\xb0'
+b' \xd0\xb4\xd0\xbb\xd1\x8f'
+b' \xd0\xb4\xd0\xbe\xd0\xb2'
+b' \xd0\xb4\xd0\xbe\xd0\xbb'
+b' \xd0\xb4\xd1\x80\xd0\xb0'
+b' \xd0\xb4\xd1\x80\xd1\x83'
+b' \xd0\xb4\xd1\x96\xd1\x8f'
+b' \xd0\xb5\xd0\xb3\xd0\xbe'
+b' \xd0\xb5\xd0\xb4\xd0\xb8'
+b' \xd0\xb5\xd0\xbc\xd1\x83'
+b' \xd0\xb5\xd1\x89\xd1\x91'
+b' \xd0\xb6\xd0\xb5\xd0\xbd'
+b' \xd0\xb6\xd0\xbe\xd0\xb2'
+b' \xd0\xb7\xd0\xb0\xd0\xb2'
+b' \xd0\xb7\xd0\xb0\xd0\xb4'
+b' \xd0\xb7\xd0\xb0\xd0\xb9'
+b' \xd0\xb7\xd0\xb0\xd0\xba'
+b' \xd0\xb7\xd0\xb0\xd0\xbd'
+b' \xd0\xb7\xd0\xb0\xd0\xbf'
+b' \xd0\xb7\xd0\xb2\xd1\x83'
+b' \xd0\xb7\xd0\xb4\xd0\xb0'
+b' \xd0\xb7\xd0\xb5\xd0\xbc'
+b' \xd0\xb7\xd0\xbc\xd1\x96'
+b' \xd0\xb7\xd0\xbd\xd0\xb0'
+b' \xd0\xb8\xd0\xb4\xd0\xb5'
+b' \xd0\xb8\xd0\xb7\xd1\x83'
+b' \xd0\xb8\xd0\xbb\xd0\xb8'
+b' \xd0\xb8\xd0\xbc\xd0\xb0'
+b' \xd0\xb8\xd0\xbc\xd0\xb5'
+b' \xd0\xb8\xd1\x81\xd0\xbf'
+b' \xd0\xba\xd0\xb0\xd0\xba'
+b' \xd0\xba\xd0\xb0\xd0\xbc'
+b' \xd0\xba\xd0\xb0\xd0\xbd'
+b' \xd0\xba\xd0\xb0\xd0\xbe'
+b' \xd0\xba\xd0\xb0\xd1\x80'
+b' \xd0\xba\xd0\xbb\xd0\xb0'
+b' \xd0\xba\xd0\xbb\xd0\xb8'
+b' \xd0\xba\xd0\xbb\xd1\x83'
+b' \xd0\xba\xd0\xbd\xd0\xb8'
+b' \xd0\xba\xd0\xbd\xd1\x8f'
+b' \xd0\xba\xd0\xbe\xd0\xbb'
+b' \xd0\xba\xd0\xbe\xd0\xbc'
+b' \xd0\xba\xd0\xbe\xd0\xbd'
+b' \xd0\xba\xd0\xbe\xd1\x80'
+b' \xd0\xba\xd1\x80\xd0\xb0'
+b' \xd0\xba\xd1\x80\xd0\xb5'
+b' \xd0\xba\xd1\x80\xd0\xb8'
+b' \xd0\xba\xd1\x83\xd1\x80'
+b' \xd0\xbb\xd0\xb5\xd1\x82'
+b' \xd0\xbb\xd1\x83\xd1\x87'
+b' \xd0\xbc\xd0\xb0\xd0\xb9'
+b' \xd0\xbc\xd0\xb0\xd1\x80'
+b' \xd0\xbc\xd0\xb0\xd1\x82'
+b' \xd0\xbc\xd0\xb0\xd1\x8f'
+b' \xd0\xbc\xd0\xb0\xd1\x94'
+b' \xd0\xbc\xd0\xb5\xd0\xbd'
+b' \xd0\xbc\xd0\xb5\xd1\x82'
+b' \xd0\xbc\xd0\xbd\xd0\xbe'
+b' \xd0\xbc\xd0\xbe\xd0\xb6'
+b' \xd0\xbc\xd0\xbe\xd1\x80'
+b' \xd0\xbc\xd1\x83\xd0\xb6'
+b' \xd0\xbc\xd1\x96\xd0\xb6'
+b' \xd0\xbc\xd1\x96\xd1\x81'
+b' \xd0\xbd\xd0\xb0\xd0\xb2'
+b' \xd0\xbd\xd0\xb0\xd0\xb4'
+b' \xd0\xbd\xd0\xb0\xd0\xb8'
+b' \xd0\xbd\xd0\xb0\xd0\xb9'
+b' \xd0\xbd\xd0\xb0\xd0\xbf'
+b' \xd0\xbd\xd0\xb0\xd1\x83'
+b' \xd0\xbd\xd0\xb0\xd1\x98'
+b' \xd0\xbd\xd0\xb5\xd0\xb9'
+b' \xd0\xbd\xd0\xb8\xd1\x85'
+b' \xd0\xbd\xd0\xbe\xd0\xb2'
+b' \xd0\xbd\xd0\xbe\xd1\x80'
+b' \xd0\xbd\xd1\x83\xd0\xb6'
+b' \xd0\xbe\xd0\xb1\xd0\xb8'
+b' \xd0\xbe\xd0\xb1\xd0\xbe'
+b' \xd0\xbe\xd0\xb1\xd1\x83'
+b' \xd0\xbe\xd0\xb1\xd1\x8a'
+b' \xd0\xbe\xd0\xb1\xd1\x8b'
+b' \xd0\xbe\xd0\xba\xd1\x82'
+b' \xd0\xbe\xd0\xbd\xd0\xb0'
+b' \xd0\xbe\xd0\xbd\xd0\xb8'
+b' \xd0\xbe\xd1\x80\xd0\xb8'
+b' \xd0\xbe\xd1\x80\xd1\x83'
+b' \xd0\xbe\xd1\x81\xd0\xbe'
+b' \xd0\xbe\xd1\x81\xd1\x82'
+b' \xd0\xbe\xd1\x81\xd1\x83'
+b' \xd0\xbe\xd1\x89\xd0\xb5'
+b' \xd0\xbf\xd0\xb0\xd1\x80'
+b' \xd0\xbf\xd0\xb5\xd1\x80'
+b' \xd0\xbf\xd0\xb5\xd1\x81'
+b' \xd0\xbf\xd0\xbb\xd0\xb0'
+b' \xd0\xbf\xd0\xbb\xd0\xbe'
+b' \xd0\xbf\xd0\xbe\xd0\xb2'
+b' \xd0\xbf\xd0\xbe\xd0\xb4'
+b' \xd0\xbf\xd0\xbe\xd0\xbb'
+b' \xd0\xbf\xd0\xbe\xd0\xbc'
+b' \xd0\xbf\xd0\xbe\xd1\x80'
+b' \xd0\xbf\xd0\xbe\xd1\x81'
+b' \xd0\xbf\xd0\xbe\xd1\x82'
+b' \xd0\xbf\xd0\xbe\xd1\x87'
+b' \xd0\xbf\xd0\xbe\xd1\x8d'
+b' \xd0\xbf\xd0\xbe\xd1\x8f'
+b' \xd0\xbf\xd1\x80\xd0\xb0'
+b' \xd0\xbf\xd1\x80\xd0\xb5'
+b' \xd0\xbf\xd1\x80\xd0\xb8'
+b' \xd0\xbf\xd1\x80\xd0\xbe'
+b' \xd0\xbf\xd1\x80\xd1\x8f'
+b' \xd0\xbf\xd1\x81\xd0\xb8'
+b' \xd0\xbf\xd1\x83\xd0\xbd'
+b' \xd0\xbf\xd1\x96\xd0\xb2'
+b' \xd0\xbf\xd1\x96\xd0\xb4'
+b' \xd1\x80\xd0\xb0\xd0\xb7'
+b' \xd1\x80\xd0\xb0\xd0\xb9'
+b' \xd1\x80\xd0\xb0\xd0\xbd'
+b' \xd1\x80\xd0\xb0\xd1\x81'
+b' \xd1\x80\xd0\xb5\xd0\xb0'
+b' \xd1\x80\xd0\xb5\xd0\xb4'
+b' \xd1\x80\xd0\xbe\xd0\xb4'
+b' \xd1\x80\xd0\xbe\xd0\xb7'
+b' \xd1\x80\xd0\xbe\xd1\x81'
+b' \xd1\x80\xd1\x83\xd1\x81'
+b' \xd1\x80\xd1\x96\xd0\xb2'
+b' \xd1\x80\xd1\x96\xd0\xb7'
+b' \xd1\x80\xd1\x96\xd0\xba'
+b' \xd1\x81\xd0\xb0\xd0\xb9'
+b' \xd1\x81\xd0\xb0\xd0\xbc'
+b' \xd1\x81\xd0\xb2\xd0\xb5'
+b' \xd1\x81\xd0\xb2\xd0\xb8'
+b' \xd1\x81\xd0\xb2\xd0\xbe'
+b' \xd1\x81\xd0\xb2\xd1\x8f'
+b' \xd1\x81\xd0\xb2\xd1\x96'
+b' \xd1\x81\xd0\xb5\xd0\xb7'
+b' \xd1\x81\xd0\xb5\xd0\xbc'
+b' \xd1\x81\xd0\xb5\xd1\x80'
+b' \xd1\x81\xd0\xb8\xd0\xbc'
+b' \xd1\x81\xd0\xb8\xd0\xbd'
+b' \xd1\x81\xd0\xba\xd0\xbe'
+b' \xd1\x81\xd0\xbb\xd0\xb0'
+b' \xd1\x81\xd0\xbb\xd0\xb5'
+b' \xd1\x81\xd0\xbb\xd0\xbe'
+b' \xd1\x81\xd0\xbb\xd1\x83'
+b' \xd1\x81\xd0\xbe\xd0\xb1'
+b' \xd1\x81\xd0\xbe\xd0\xb2'
+b' \xd1\x81\xd0\xbe\xd1\x82'
+b' \xd1\x81\xd0\xbf\xd0\xb5'
+b' \xd1\x81\xd0\xbf\xd0\xb8'
+b' \xd1\x81\xd0\xbf\xd0\xbe'
+b' \xd1\x81\xd1\x80\xd0\xb0'
+b' \xd1\x81\xd1\x80\xd0\xb5'
+b' \xd1\x81\xd1\x82\xd0\xb0'
+b' \xd1\x81\xd1\x82\xd0\xb5'
+b' \xd1\x81\xd1\x82\xd0\xb8'
+b' \xd1\x81\xd1\x82\xd0\xbe'
+b' \xd1\x81\xd1\x82\xd1\x83'
+b' \xd1\x82\xd0\xb0\xd0\xba'
+b' \xd1\x82\xd0\xb0\xd0\xbc'
+b' \xd1\x82\xd0\xb0\xd0\xbd'
+b' \xd1\x82\xd0\xb5\xd0\xb0'
+b' \xd1\x82\xd0\xb5\xd0\xba'
+b' \xd1\x82\xd0\xb5\xd0\xbc'
+b' \xd1\x82\xd0\xb5\xd0\xbe'
+b' \xd1\x82\xd0\xb5\xd1\x80'
+b' \xd1\x82\xd0\xb5\xd1\x85'
+b' \xd1\x82\xd0\xbe\xd0\xb9'
+b' \xd1\x82\xd0\xbe\xd0\xbc'
+b' \xd1\x82\xd0\xbe\xd1\x80'
+b' \xd1\x82\xd1\x80\xd0\xb0'
+b' \xd1\x82\xd1\x80\xd0\xb5'
+b' \xd1\x82\xd1\x80\xd0\xb8'
+b' \xd1\x82\xd1\x80\xd0\xbe'
+b' \xd1\x82\xd1\x80\xd1\x83'
+b' \xd1\x82\xd1\x83\xd1\x80'
+b' \xd1\x83\xd0\xb1\xd0\xb8'
+b' \xd1\x83\xd0\xb4\xd0\xb0'
+b' \xd1\x83\xd0\xb5\xd0\xb7'
+b' \xd1\x83\xd0\xb6\xd0\xb5'
+b' \xd1\x83\xd0\xbb\xd0\xb8'
+b' \xd1\x83\xd0\xbd\xd0\xb8'
+b' \xd1\x83\xd0\xbd\xd1\x96'
+b' \xd1\x83\xd0\xbf\xd0\xbe'
+b' \xd1\x83\xd1\x87\xd0\xb0'
+b' \xd1\x83\xd1\x87\xd0\xb5'
+b' \xd1\x83\xd1\x87\xd0\xb8'
+b' \xd1\x83\xd1\x87\xd1\x91'
+b' \xd1\x84\xd0\xb0\xd0\xb9'
+b' \xd1\x84\xd0\xb5\xd0\xb2'
+b' \xd1\x84\xd0\xbb\xd0\xbe'
+b' \xd1\x84\xd0\xbe\xd0\xbd'
+b' \xd1\x84\xd0\xbe\xd1\x80'
+b' \xd1\x85\xd1\x80\xd0\xb0'
+b' \xd1\x86\xd0\xb0\xd1\x80'
+b' \xd1\x86\xd0\xb5\xd1\x80'
+b' \xd1\x87\xd0\xb0\xd1\x81'
+b' \xd1\x87\xd0\xb5\xd0\xbc'
+b' \xd1\x87\xd0\xb5\xd1\x80'
+b' \xd1\x87\xd0\xb8\xd1\x81'
+b' \xd1\x87\xd1\x82\xd0\xbe'
+b' \xd1\x88\xd0\xba\xd0\xbe'
+b' \xd1\x88\xd1\x82\xd0\xb0'
+b' \xd1\x88\xd1\x82\xd0\xbe'
+b' \xd1\x8d\xd0\xba\xd1\x81'
+b' \xd1\x8d\xd1\x82\xd0\xb0'
+b' \xd1\x8d\xd1\x82\xd0\xb8'
+b' \xd1\x8d\xd1\x82\xd0\xbe'
+b' \xd1\x8e\xd0\xb3\xd0\xbe'
+b' \xd1\x8f\xd0\xb7\xd1\x8b'
+b' \xd1\x8f\xd0\xba\xd0\xb0'
+b' \xd1\x8f\xd0\xba\xd1\x96'
+b' \xd1\x98\xd0\xbe\xd1\x88'
+b' \xd8\xa7\xd8\xb3\xd8\xaa'
+b' \xd8\xa7\xd9\x84\xd8\xa3'
+b' \xd8\xa7\xd9\x84\xd8\xaa'
+b' \xd8\xa7\xd9\x84\xd8\xb9'
+b' \xd8\xa7\xd9\x84\xd9\x85'
+b' \xd8\xa8\xd8\xa7\xd9\x84'
+b' \xd9\x88\xd8\xa7\xd9\x84'
+b' \xe2\x80\x98\xe2\x80\x98'
+b' \xe2\x80\x9c\xe2\x80\xa6'
+b' \xe2\x80\xb2\xe2\x80\xb2'
+b' \xe2\x88\x92\xe2\x86\x92'
+b' \xea\xb0\x80\xeb\x8a\xa5'
+b' \xea\xb0\x80\xec\x9e\xa5'
+b' \xea\xb0\x80\xec\xa7\x80'
+b' \xea\xb0\x90\xeb\x8f\x85'
+b' \xea\xb0\x95\xec\x9b\x90'
+b' \xea\xb0\x95\xec\xb6\x94'
+b' \xea\xb0\x99\xec\x9d\x80'
+b' \xea\xb0\x9c\xec\x9d\xb8'
+b' \xea\xb2\x83\xec\x9c\xbc'
+b' \xea\xb2\x83\xec\x9d\x80'
+b' \xea\xb2\x83\xec\x9d\x84'
+b' \xea\xb2\x83\xec\x9d\xb4'
+b' \xea\xb2\x8c\xec\x9e\x84'
+b' \xea\xb2\xbd\xea\xb8\xb0'
+b' \xea\xb2\xbd\xeb\x82\xa8'
+b' \xea\xb2\xbd\xeb\xb6\x81'
+b' \xea\xb2\xbd\xec\x9a\xb0'
+b' \xea\xb4\x80\xeb\xa0\xa8'
+b' \xea\xb5\xad\xea\xb0\x80'
+b' \xea\xb7\xb8\xeb\x8a\x94'
+b' \xea\xb7\xb8\xeb\x9f\xac'
+b' \xea\xb7\xb8\xeb\xa6\xac'
+b' \xea\xb7\xb8\xec\x9d\x98'
+b' \xea\xb8\xb0\xeb\xa1\x9d'
+b' \xeb\x82\xb4\xec\x9a\xa9'
+b' \xeb\x8b\xa4\xeb\xa5\xb8'
+b' \xeb\x8b\xa4\xec\x8b\x9c'
+b' \xeb\x8b\xa4\xec\x9d\x8c'
+b' \xeb\x8b\xb9\xec\x8b\x9c'
+b' \xeb\x8c\x80\xed\x95\x99'
+b' \xeb\x8c\x80\xed\x95\x9c'
+b' \xeb\x8c\x80\xed\x95\xb4'
+b' \xeb\x8d\xa4\xed\x94\x84'
+b' \xeb\x90\x98\xec\x97\x88'
+b' \xeb\x90\x9c\xeb\x8b\xa4'
+b' \xeb\x93\xa4\xec\x96\xb4'
+b' \xeb\x94\xb0\xeb\x9d\xbc'
+b' \xeb\x95\x8c\xeb\xac\xb8'
+b' \xeb\x98\x90\xeb\x8a\x94'
+b' \xeb\x98\x90\xed\x95\x9c'
+b' \xeb\xa6\xac\xeb\xb7\xb0'
+b' \xeb\xa7\x81\xed\x81\xac'
+b' \xeb\xa7\x8c\xeb\x93\xa4'
+b' \xeb\xa7\x8e\xec\x9d\x80'
+b' \xeb\xaa\xa8\xeb\x91\x90'
+b' \xeb\xaa\xa8\xeb\x93\xa0'
+b' \xeb\xac\xb8\xec\xa0\x9c'
+b' \xeb\xaf\xb8\xea\xb5\xad'
+b' \xeb\xb0\xa9\xec\x86\xa1'
+b' \xeb\xb6\x80\xec\x82\xb0'
+b' \xec\x82\xac\xeb\x9e\x8c'
+b' \xec\x82\xac\xec\x9a\xa9'
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+b'\t\t\t\t\t\t\t\t'
+b'\n\t\t\t\t\t\t\t'
+b'\n\n\n\n\n\n\n\n'
+b'\n\n      '
+b'\n       '
+b'\r\n\r\n\r\n\r\n'
+b'\r\n      '
+b' \n      '
+b'        '
+b' -------'
+b' ARISING'
+b' Ability'
+b' Abraham'
+b' Academy'
+b' Account'
+b' Actions'
+b' Adapter'
+b' Address'
+b' Adem\xc3\xa1s'
+b' Admiral'
+b' Advance'
+b' Affairs'
+b' African'
+b' Against'
+b' Airport'
+b' Airways'
+b' Alabama'
+b' Alberta'
+b' Alberto'
+b' Alcohol'
+b' Alexand'
+b' Algebra'
+b' Algeria'
+b' Allison'
+b' Already'
+b' Alvarez'
+b' Amazing'
+b' America'
+b' Ancient'
+b' Andreas'
+b' Andrews'
+b' Android'
+b' Angeles'
+b' Angular'
+b' Animals'
+b' Another'
+b' Anthony'
+b' Anthrop'
+b' Antoine'
+b' Antonio'
+b' Anxiety'
+b' Appeals'
+b' Applied'
+b' Arabian'
+b' Archive'
+b' Arduino'
+b' Arizona'
+b' Armenia'
+b' Arsenal'
+b' Article'
+b' Artikel'
+b' Artists'
+b' Assange'
+b' Atlanta'
+b' Attempt'
+b' Auditor'
+b' Austral'
+b' Austria'
+b' Authors'
+b' Average'
+b' Awesome'
+b' BETWEEN'
+b' Babylon'
+b' Baghdad'
+b' Bahamas'
+b' Bahrain'
+b' Balance'
+b' Baldwin'
+b' Bangkok'
+b' Baptist'
+b' Barbara'
+b' Barrett'
+b' Battery'
+b' Beatles'
+b' Because'
+b' Bedford'
+b' Beijing'
+b' Belarus'
+b' Belfast'
+b' Belgian'
+b' Belgium'
+b' Believe'
+b' Bennett'
+b' Bernard'
+b' Besides'
+b' Between'
+b' Beverly'
+b' Binding'
+b' Biology'
+b' Bitcoin'
+b' Bolivia'
+b' Boolean'
+b' Borough'
+b' Bradley'
+b' Brandon'
+b' Brennan'
+b' Brewing'
+b' Brigade'
+b' Bristol'
+b' Britain'
+b' Britann'
+b' British'
+b' Broncos'
+b' Brother'
+b' Browser'
+b' Buffalo'
+b' Builder'
+b' B\xc3\xbcrger'
+b' CHAPTER'
+b' COMMAND'
+b' COPYING'
+b' Cabinet'
+b' Calgary'
+b' Calling'
+b' Cameron'
+b' Capital'
+b' Capitol'
+b' Captain'
+b' Caption'
+b' Capture'
+b' Cardiff'
+b' Carroll'
+b' Catalan'
+b' Catalog'
+b' Celsius'
+b' Centers'
+b' Central'
+b' Century'
+b' Certain'
+b' Chamber'
+b' Changed'
+b' Changes'
+b' Channel'
+b' Chapman'
+b' Chapter'
+b' Charles'
+b' Charlie'
+b' Charter'
+b' Chelsea'
+b' Chennai'
+b' Chester'
+b' Chicago'
+b' Chicken'
+b' Chinese'
+b' Chronic'
+b' Circuit'
+b' Citizen'
+b' Classes'
+b' Classic'
+b' Claudia'
+b' Clayton'
+b' Clearly'
+b' Clement'
+b' Climate'
+b' Clinton'
+b' Cluster'
+b' Coleman'
+b' Collect'
+b' College'
+b' Collins'
+b' Cologne'
+b' Colonel'
+b' Columns'
+b' Combine'
+b' Comit\xc3\xa9'
+b' Command'
+b' Comment'
+b' Commons'
+b' Compact'
+b' Company'
+b' Compare'
+b' Complex'
+b' Compute'
+b' Concent'
+b' Concept'
+b' Concert'
+b' Concord'
+b' Conduct'
+b' Confirm'
+b' Connect'
+b' Conserv'
+b' Console'
+b' Constit'
+b' Consult'
+b' Contact'
+b' Content'
+b' Contest'
+b' Context'
+b' Contrib'
+b' Control'
+b' Convers'
+b' Convert'
+b' Cookies'
+b' Corinth'
+b' Cornell'
+b' Correct'
+b' Council'
+b' Counsel'
+b' Counter'
+b' Country'
+b' Cowboys'
+b' Created'
+b' Creates'
+b' Creator'
+b' Cricket'
+b' Critics'
+b' Croatia'
+b' Crystal'
+b' Culture'
+b' Current'
+b' Customs'
+b' Cynthia'
+b' DAMAGES'
+b' DEFAULT'
+b' Daniels'
+b' Dataset'
+b' Deborah'
+b' Decimal'
+b' Default'
+b' Defence'
+b' Defense'
+b' Denmark'
+b' Derived'
+b' Desktop'
+b' Despite'
+b' Destroy'
+b' Details'
+b' Detroit'
+b' Deutsch'
+b' Develop'
+b' Devices'
+b' Diagram'
+b' Diamond'
+b' Digital'
+b' Disable'
+b' Disease'
+b' Display'
+b' Doctors'
+b' Dodgers'
+b' Dominic'
+b' Donovan'
+b' Dorothy'
+b' Douglas'
+b' Dragons'
+b' Drawing'
+b' Dropbox'
+b' Duchess'
+b' Duterte'
+b' Dynamic'
+b' Dynasty'
+b' EXPRESS'
+b' Earlier'
+b' Eastern'
+b' Eclipse'
+b' Economy'
+b' Ecuador'
+b' Edition'
+b' Edwards'
+b' Effects'
+b' Elastic'
+b' Eleanor'
+b' Electro'
+b' Element'
+b' Elliott'
+b' Emanuel'
+b' Embassy'
+b' Emerson'
+b' Emperor'
+b' Encoder'
+b' England'
+b' English'
+b' Enrique'
+b' Episode'
+b' Epstein'
+b' Erdogan'
+b' Espa\xc3\xb1a'
+b' Estados'
+b' Estonia'
+b' Evangel'
+b' Evening'
+b' Everton'
+b' Example'
+b' Execute'
+b' Experts'
+b' Express'
+b' Extract'
+b' Extreme'
+b' FITNESS'
+b' Factors'
+b' Factory'
+b' Faculty'
+b' Failure'
+b' Falcons'
+b' Fantasy'
+b' Farmers'
+b' Fashion'
+b' Feature'
+b' Federal'
+b' Ferrari'
+b' Fiction'
+b' Fighter'
+b' Figures'
+b' Filters'
+b' Finally'
+b' Finance'
+b' Finding'
+b' Finland'
+b' Finnish'
+b' Firefox'
+b' Firstly'
+b' Fischer'
+b' Fleming'
+b' Florida'
+b' Flowers'
+b' Foreign'
+b' Forever'
+b' Formula'
+b' Forrest'
+b' Fortune'
+b' Forward'
+b' Fourier'
+b' Frances'
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+b'\xd1\x86\xd0\xb8\xd0\xb5\xd0\xb9'
+b'\xd1\x86\xd0\xb8\xd0\xbc\xd0\xb0'
+b'\xd1\x86\xd0\xb8\xd0\xbe\xd0\xbd'
+b'\xd1\x86\xd0\xb8\xd0\xbf\xd0\xb0'
+b'\xd1\x86\xd0\xb8\xd1\x98\xd0\xb0'
+b'\xd1\x86\xd0\xb8\xd1\x98\xd0\xb5'
+b'\xd1\x87\xd0\xb5\xd0\xbd\xd1\x8c'
+b'\xd1\x87\xd0\xb8\xd0\xbd\xd0\xb0'
+b'\xd1\x87\xd0\xb8\xd1\x82\xd0\xb0'
+b'\xd1\x87\xd0\xbd\xd0\xb0\xd1\x8f'
+b'\xd1\x87\xd0\xbd\xd0\xb8\xd0\xb9'
+b'\xd1\x87\xd0\xbd\xd0\xb8\xd0\xba'
+b'\xd1\x87\xd0\xbd\xd0\xb8\xd1\x85'
+b'\xd1\x87\xd0\xbd\xd0\xbe\xd0\xb9'
+b'\xd1\x87\xd0\xbd\xd0\xbe\xd1\x97'
+b'\xd1\x87\xd0\xbd\xd1\x8b\xd0\xb5'
+b'\xd1\x87\xd0\xbd\xd1\x8b\xd0\xb9'
+b'\xd1\x87\xd0\xbd\xd1\x8b\xd1\x85'
+b'\xd1\x88\xd0\xb5\xd0\xb3\xd0\xbe'
+b'\xd1\x88\xd0\xbb\xd0\xb5\xd0\xbd'
+b'\xd1\x88\xd0\xbe\xd0\xb3\xd0\xbe'
+b'\xd1\x8e\xd1\x82\xd1\x81\xd1\x8f'
+b'\xd1\x8e\xd1\x89\xd0\xb5\xd0\xb9'
+b'\xd1\x8e\xd1\x89\xd0\xb8\xd0\xb5'
+b'\xd1\x8e\xd1\x89\xd0\xb8\xd0\xb9'
+b'\xd1\x8e\xd1\x89\xd0\xb8\xd0\xbc'
+b'\xd1\x8e\xd1\x89\xd0\xb8\xd1\x85'
+b'\xd1\x8f\xd0\xb1\xd1\x80\xd0\xb5'
+b'\xd1\x8f\xd0\xb1\xd1\x80\xd1\x8f'
+b'\xd1\x94\xd0\xb4\xd0\xbd\xd0\xb0'
+b'\xd1\x96\xd1\x81\xd0\xbb\xd1\x8f'
+b'\n\t\t\t\t\t\t\t\t'
+b'\n\n       '
+b'\n    \n   '
+b'\n        '
+b'\r\n       '
+b' \n       '
+b'         '
+b' --------'
+b' Abdullah'
+b' Absolute'
+b' Abstract'
+b' Academic'
+b' Accounts'
+b' Accuracy'
+b' Activity'
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+b' \xd8\xb4\xd8\xb1\xd9\x83\xd8\xa9'
+b'---------'
+b'.........'
+b'../../../'
+b'========='
+b'ASHINGTON'
+b'AVAILABLE'
+b'According'
+b'Activated'
+b'Alexander'
+b'Algorithm'
+b'Alignment'
+b'Americans'
+b'Animation'
+b'Anonymous'
+b'Arguments'
+b'ArrayList'
+b'Assembler'
+b'Attention'
+b'Attribute'
+b'Australia'
+b'Automatic'
+b'Available'
+b'Basically'
+b'BatchNorm'
+b'Beautiful'
+b'Benchmark'
+b'Bootstrap'
+b'BoxLayout'
+b'Broadcast'
+b'ByteArray'
+b'CONDITION'
+b'Calculate'
+b'Callbacks'
+b'Cap\xc3\xadtulo'
+b'Certainly'
+b'CharField'
+b'Character'
+b'Christian'
+b'ClassName'
+b'Clipboard'
+b'Collector'
+b'Collision'
+b'Community'
+b'Completed'
+b'Component'
+b'Composite'
+b'Condition'
+b'Configure'
+b'Connected'
+b'Connector'
+b'Constants'
+b'Construct'
+b'Container'
+b'Converter'
+b'Copyright'
+b'Currently'
+b'DIRECTORY'
+b'Dashboard'
+b'DataFrame'
+b'DataTable'
+b'Decorator'
+b'Detection'
+b'Determine'
+b'Developer'
+b'Different'
+b'Dimension'
+b'Direction'
+b'Directive'
+b'Directory'
+b'Discovery'
+b'Documents'
+b'Duplicate'
+b'Education'
+b'Effective'
+b'Elizabeth'
+b'Estimated'
+b'Evaluator'
+b'EventArgs'
+b'Everybody'
+b'Excellent'
+b'Exception'
+b'Execution'
+b'Extension'
+b'Extractor'
+b'FORMATION'
+b'Financial'
+b'FirstName'
+b'Following'
+b'Forbidden'
+b'Formatter'
+b'Framework'
+b'Frequency'
+b'Functions'
+b'Generally'
+b'Generated'
+b'Generator'
+b'GroupName'
+b'Helvetica'
+b'Hierarchy'
+b'Highlight'
+b'Histogram'
+b'IFICATION'
+b'INCLUDING'
+b'ImageView'
+b'Implement'
+b'Important'
+b'Increment'
+b'IndexPath'
+b'Indicator'
+b'Instagram'
+b'Installer'
+b'Instances'
+b'Interface'
+b'Inventory'
+b'Iteration'
+b'Locations'
+b'Longitude'
+b'LowerCase'
+b'Materials'
+b'Meanwhile'
+b'Microsoft'
+b'Migration'
+b'Namespace'
+b'Navigator'
+b'Normalize'
+b'ORIZONTAL'
+b'Objective'
+b'Obviously'
+b'Operation'
+b'Operators'
+b'Optimizer'
+b'Otherwise'
+b'PLICATION'
+b'Paragraph'
+b'Parameter'
+b'Partition'
+b'Placement'
+b'Political'
+b'Positions'
+b'Precision'
+b'Predicate'
+b'Preferred'
+b'President'
+b'Primitive'
+b'Principal'
+b'Procedure'
+b'Processor'
+b'Professor'
+b'Prototype'
+b'Providers'
+b'Published'
+b'Publisher'
+b'Questions'
+b'REFERENCE'
+b'Recommend'
+b'Recording'
+b'Rectangle'
+b'Recursive'
+b'Reference'
+b'Regarding'
+b'Regressor'
+b'Relations'
+b'Reporting'
+b'Represent'
+b'Requested'
+b'Resources'
+b'Responder'
+b'ResultSet'
+b'Scheduled'
+b'Scheduler'
+b'ScrollBar'
+b'Secondary'
+b'Selection'
+b'Separator'
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+b'----------'
+b'=========='
+b'Activation'
+b'Additional'
+b'Annotation'
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+b'Assignment'
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+b'\n\n         '
+b'\n          '
+b'\r\n\r\n       '
+b'\r\n         '
+b' \n         '
+b'           '
+b' ----------'
+b' Aboriginal'
+b' Activation'
+b' Activities'
+b' Additional'
+b' Adventures'
+b' Affordable'
+b' Afterwards'
+b' Alexandria'
+b' Ambassador'
+b' Annotation'
+b' Antarctica'
+b' Apparently'
+b' Archbishop'
+b' Arithmetic'
+b' Artificial'
+b' Assessment'
+b' Assignment'
+b' Assistance'
+b' Associated'
+b' Associates'
+b' Attachment'
+b' Attributes'
+b' Australian'
+b' Azerbaijan'
+b' BACKGROUND'
+b' Background'
+b' Bangladesh'
+b' Basketball'
+b' Biological'
+b' Birmingham'
+b' BlackBerry'
+b' Blockchain'
+b' Buckingham'
+b' CONDITIONS'
+b' CONNECTION'
+b' Calculator'
+b' California'
+b' Categories'
+b' Chancellor'
+b' Charleston'
+b' Christians'
+b' Cincinnati'
+b' Classifier'
+b' Collection'
+b' Commercial'
+b' Commission'
+b' Comparison'
+b' Components'
+b' Conclusion'
+b' Concurrent'
+b' Conditions'
+b' Conference'
+b' Connection'
+b' Consortium'
+b' Constantin'
+b' Constraint'
+b' Continuous'
+b' Controller'
+b' Convention'
+b' Conversely'
+b' Conversion'
+b' Coordinate'
+b' Copenhagen'
+b' Correction'
+b' Correspond'
+b' Cunningham'
+b' Defendants'
+b' Definition'
+b' Democratic'
+b' Department'
+b' Dependency'
+b' Depression'
+b' Developers'
+b' Developing'
+b' Diagnostic'
+b' Dictionary'
+b' Difference'
+b' Dimensions'
+b' Directions'
+b' Discussion'
+b' Disneyland'
+b' Electrical'
+b' Electronic'
+b' Elementary'
+b' Employment'
+b' Enterprise'
+b' Especially'
+b' Evaluation'
+b' Eventually'
+b' Everything'
+b' Excellence'
+b' Exhibition'
+b' Expedition'
+b' Experience'
+b' Experiment'
+b' Expression'
+b' Extensions'
+b' Federation'
+b' Fellowship'
+b' Fern\xc3\xa1ndez'
+b' Fitzgerald'
+b' Foundation'
+b' Functional'
+b' Generation'
+b' Geographic'
+b' Geological'
+b' Government'
+b' Guidelines'
+b' Healthcare'
+b' Hern\xc3\xa1ndez'
+b' Historical'
+b' Horizontal'
+b' Huntington'
+b' Identifier'
+b' Increasing'
+b' Indigenous'
+b' Individual'
+b' Indonesian'
+b' Industrial'
+b' Industries'
+b' Initialize'
+b' Initiative'
+b' Innovation'
+b' Institutes'
+b' Instrument'
+b' Integrated'
+b' Interstate'
+b' Investment'
+b' JavaScript'
+b' Javascript'
+b' Kazakhstan'
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+b' scattering'
+b' scheduling'
+b' scientific'
+b' scientists'
+b' screenshot'
+b' sculptures'
+b' securities'
+b' segregated'
+b' seguran\xc3\xa7a'
+b' selections'
+b' sensations'
+b' sentencing'
+b' sentiments'
+b' separately'
+b' separating'
+b' separation'
+b' sequencing'
+b' sequential'
+b' serialized'
+b' serializer'
+b' setTimeout'
+b' settlement'
+b' signalling'
+b' signatures'
+b' siguientes'
+b' similarity'
+b' simplicity'
+b' simplified'
+b' simulation'
+b' situaci\xc3\xb3n'
+b' situations'
+b' situa\xc3\xa7\xc3\xa3o'
+b' skepticism'
+b' smartphone'
+b' solidarity'
+b' soundtrack'
+b' spacecraft'
+b' specialist'
+b' specifying'
+b' spectators'
+b' speculated'
+b' stabilized'
+b' staggering'
+b' standalone'
+b' standpoint'
+b' starvation'
+b' statements'
+b' stationary'
+b' statistics'
+b' stimulated'
+b' stimulates'
+b' stochastic'
+b' strategies'
+b' strengthen'
+b' stretching'
+b' structural'
+b' structured'
+b' structures'
+b' struggling'
+b' stylesheet'
+b' subclasses'
+b' subjective'
+b' submission'
+b' submitting'
+b' subprocess'
+b' subscribed'
+b' subscriber'
+b' subsection'
+b' subsequent'
+b' subsidiary'
+b' substances'
+b' substitute'
+b' substrates'
+b' succeeding'
+b' successful'
+b' succession'
+b' successive'
+b' sufficient'
+b' suggesting'
+b' suggestion'
+b' suggestive'
+b' summarized'
+b' summarizes'
+b' supervised'
+b' supervisor'
+b' supplement'
+b' supporters'
+b' supporting'
+b' supportive'
+b' supposedly'
+b' suppressed'
+b' surprising'
+b' surrounded'
+b' suspension'
+b' suspicions'
+b' suspicious'
+b' sustaining'
+b' swallowing'
+b' synonymous'
+b' systematic'
+b' s\xc3\xa9curit\xc3\xa9'
+b' s\xc3\xa9lection'
+b' tablespoon'
+b' technician'
+b' techniques'
+b' technology'
+b' television'
+b' temptation'
+b' tendencies'
+b' tensorflow'
+b' terminated'
+b' terrifying'
+b' terrorists'
+b' theatrical'
+b' themselves'
+b' therapists'
+b' thereafter'
+b' thoroughly'
+b' thoughtful'
+b' threatened'
+b' thresholds'
+b' throughout'
+b' throughput'
+b' touchdowns'
+b' tournament'
+b' trademarks'
+b' traditions'
+b' trajectory'
+b' transcript'
+b' transforms'
+b' transistor'
+b' transition'
+b' translated'
+b' translates'
+b' translator'
+b' transplant'
+b' transports'
+b' transverse'
+b' travellers'
+b' travelling'
+b' treatments'
+b' tremendous'
+b' triangular'
+b' triggering'
+b' troisi\xc3\xa8me'
+b' turbulence'
+b' ubiquitous'
+b' ultimately'
+b' ultrasound'
+b' unaffected'
+b' undergoing'
+b' underlying'
+b' underneath'
+b' underscore'
+b' understand'
+b' understood'
+b' undertaken'
+b' underwater'
+b' unemployed'
+b' unexpected'
+b' unfamiliar'
+b' unfinished'
+b' unilateral'
+b' unintended'
+b' uniqueness'
+b' university'
+b' unofficial'
+b' unpleasant'
+b' unreliable'
+b' unresolved'
+b' unsuitable'
+b' unterst\xc3\xbct'
+b' usefulness'
+b' vaccinated'
+b' validating'
+b' validation'
+b' variations'
+b' vegetables'
+b' vegetarian'
+b' vegetation'
+b' velocities'
+b' vertically'
+b' veterinary'
+b' vibrations'
+b' vigorously'
+b' violations'
+b' visibility'
+b' vocabulary'
+b' vocational'
+b' volatility'
+b' volunteers'
+b' vulnerable'
+b' v\xc3\xa9ritable'
+b' wastewater'
+b' wavelength'
+b' weaknesses'
+b' whatsoever'
+b' wheelchair'
+b' whitespace'
+b' widespread'
+b' wilderness'
+b' withdrawal'
+b' witnessing'
+b' wojew\xc3\xb3dzt'
+b' workaround'
+b' worthwhile'
+b' wrongdoing'
+b' yourselves'
+b' \xc3\xa9galement'
+b' \xc3\xa9lections'
+b' \xc3\xa9l\xc3\xa9ments'
+b' \xd0\x90\xd0\xbc\xd0\xb5\xd1\x80\xd0\xb8'
+b' \xd0\x90\xd1\x80\xd1\x85\xd0\xb8\xd0\xb2'
+b' \xd0\x92\xd0\xbb\xd0\xb0\xd0\xb4\xd0\xb8'
+b' \xd0\x93\xd0\xb5\xd1\x80\xd0\xbc\xd0\xb0'
+b' \xd0\x98\xd1\x82\xd0\xb0\xd0\xbb\xd0\xb8'
+b' \xd0\x9c\xd0\xb5\xd0\xb6\xd0\xb4\xd1\x83'
+b' \xd0\x9c\xd0\xb5\xd0\xba\xd1\x81\xd0\xb8'
+b' \xd0\x9c\xd0\xb8\xd1\x85\xd0\xb0\xd0\xb9'
+b' \xd0\x9e\xd1\x81\xd0\xbd\xd0\xbe\xd0\xb2'
+b' \xd0\x9e\xd1\x82\xd0\xb5\xd1\x87\xd0\xb5'
+b' \xd0\x9f\xd0\xbe\xd0\xbf\xd0\xb8\xd1\x81'
+b' \xd0\x9f\xd0\xbe\xd1\x81\xd0\xbb\xd0\xb5'
+b' \xd0\x9f\xd1\x80\xd0\xb5\xd0\xbc\xd0\xb0'
+b' \xd0\x9f\xd1\x96\xd1\x81\xd0\xbb\xd1\x8f'
+b' \xd0\xa0\xd0\xb0\xd1\x81\xd0\xbf\xd0\xbe'
+b' \xd0\xa0\xd0\xbe\xd1\x81\xd1\x81\xd0\xb8'
+b' \xd0\xa1\xd0\xb0\xd0\xb2\xd0\xb5\xd0\xb7'
+b' \xd0\xa1\xd0\xb0\xd0\xbd\xd0\xba\xd1\x82'
+b' \xd0\xa1\xd0\xb5\xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd0\xa1\xd0\xbe\xd0\xb2\xd0\xb5\xd1\x82'
+b' \xd0\xa2\xd0\xb0\xd0\xba\xd0\xb6\xd0\xb5'
+b' \xd0\xa3\xd0\xba\xd1\x80\xd0\xb0\xd0\xb8'
+b' \xd0\xa3\xd0\xba\xd1\x80\xd0\xb0\xd1\x97'
+b' \xd0\xb0\xd0\xb2\xd1\x82\xd0\xbe\xd1\x80'
+b' \xd0\xb0\xd0\xba\xd0\xb0\xd0\xb4\xd0\xb5'
+b' \xd0\xb0\xd0\xba\xd1\x82\xd0\xb8\xd0\xb2'
+b' \xd0\xb0\xd0\xbb\xd1\x8c\xd0\xb1\xd0\xbe'
+b' \xd0\xb0\xd1\x80\xd0\xbc\xd0\xb8\xd0\xb8'
+b' \xd0\xb1\xd0\xbb\xd0\xb0\xd0\xb3\xd0\xbe'
+b' \xd0\xb1\xd0\xbe\xd0\xbb\xd0\xb5\xd0\xb5'
+b' \xd0\xb1\xd1\x80\xd0\xb8\xd0\xb3\xd0\xb0'
+b' \xd0\xb1\xd1\x80\xd0\xbe\xd1\x98\xd0\xb0'
+b' \xd0\xb2\xd0\xb5\xd1\x80\xd0\xbd\xd1\x83'
+b' \xd0\xb2\xd0\xbe\xd0\xb7\xd1\x80\xd0\xb0'
+b' \xd0\xb2\xd0\xbe\xd0\xb9\xd0\xbd\xd1\x8b'
+b' \xd0\xb2\xd0\xbe\xd0\xbf\xd1\x80\xd0\xbe'
+b' \xd0\xb2\xd0\xbe\xd1\x81\xd1\x82\xd0\xbe'
+b' \xd0\xb2\xd1\x80\xd0\xb5\xd0\xbc\xd0\xb5'
+b' \xd0\xb2\xd1\x80\xd0\xb5\xd0\xbc\xd1\x8f'
+b' \xd0\xb2\xd1\x81\xd0\xb5\xd0\xb3\xd0\xbe'
+b' \xd0\xb2\xd1\x81\xd1\x82\xd1\x80\xd0\xb5'
+b' \xd0\xb2\xd1\x8b\xd0\xbf\xd0\xbe\xd0\xbb'
+b' \xd0\xb2\xd1\x96\xd0\xb4\xd0\xb1\xd1\x83'
+b' \xd0\xb2\xd1\x96\xd0\xb9\xd0\xbd\xd0\xb8'
+b' \xd0\xb2\xd1\x96\xd0\xb9\xd1\x81\xd1\x8c'
+b' \xd0\xb3\xd0\xbe\xd0\xb4\xd0\xb0\xd1\x85'
+b' \xd0\xb3\xd0\xbe\xd0\xb4\xd0\xbe\xd0\xb2'
+b' \xd0\xb3\xd0\xbe\xd0\xbb\xd0\xbe\xd0\xb2'
+b' \xd0\xb3\xd0\xbe\xd1\x80\xd0\xbe\xd0\xb4'
+b' \xd0\xb3\xd1\x80\xd0\xbe\xd0\xbc\xd0\xb0'
+b' \xd0\xb3\xd1\x83\xd0\xb1\xd0\xb5\xd1\x80'
+b' \xd0\xb4\xd0\xb5\xd1\x80\xd0\xb5\xd0\xb2'
+b' \xd0\xb4\xd0\xb5\xd1\x80\xd0\xb6\xd0\xb0'
+b' \xd0\xb4\xd0\xbe\xd1\x81\xd1\x82\xd0\xb8'
+b' \xd0\xb4\xd1\x80\xd1\x83\xd0\xb3\xd0\xb8'
+b' \xd0\xb4\xd1\x80\xd1\x83\xd0\xb3\xd0\xbe'
+b' \xd0\xb6\xd0\xb5\xd0\xbb\xd0\xb5\xd0\xb7'
+b' \xd0\xb6\xd0\xb5\xd0\xbd\xd1\x89\xd0\xb8'
+b' \xd0\xb6\xd0\xb8\xd0\xb7\xd0\xbd\xd0\xb8'
+b' \xd0\xb6\xd1\x83\xd1\x80\xd0\xbd\xd0\xb0'
+b' \xd0\xb7\xd0\xb0\xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd0\xb7\xd0\xb0\xd0\xbf\xd0\xb0\xd0\xb4'
+b' \xd0\xb7\xd0\xb0\xd1\x81\xd0\xbb\xd1\x83'
+b' \xd0\xb7\xd0\xb0\xd1\x82\xd0\xb5\xd0\xbc'
+b' \xd0\xb7\xd0\xbd\xd0\xb0\xd1\x87\xd0\xb8'
+b' \xd0\xb8\xd0\xbc\xd0\xb5\xd0\xb5\xd1\x82'
+b' \xd0\xb8\xd0\xbc\xd0\xb5\xd0\xbd\xd0\xb8'
+b' \xd0\xb8\xd0\xbd\xd1\x82\xd0\xb5\xd1\x80'
+b' \xd0\xb8\xd1\x81\xd0\xba\xd1\x83\xd1\x81'
+b' \xd0\xb8\xd1\x81\xd0\xbf\xd0\xbe\xd0\xbb'
+b' \xd0\xba\xd0\xbb\xd0\xb0\xd1\x81\xd1\x81'
+b' \xd0\xba\xd0\xbe\xd0\xb3\xd0\xb4\xd0\xb0'
+b' \xd0\xba\xd0\xbe\xd0\xbb\xd0\xbb\xd0\xb5'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xb0\xd0\xbd'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xbc\xd1\x83'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xbf\xd0\xb0'
+b' \xd0\xba\xd0\xbe\xd0\xbd\xd1\x86\xd0\xb5'
+b' \xd0\xba\xd0\xbe\xd0\xbf\xd0\xb8\xd1\x8f'
+b' \xd0\xba\xd0\xbe\xd1\x80\xd0\xb0\xd0\xb1'
+b' \xd0\xba\xd0\xbe\xd1\x80\xd0\xbf\xd1\x83'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80'
+b' \xd0\xbb\xd0\xb8\xd0\xbf\xd0\xbd\xd1\x8f'
+b' \xd0\xbb\xd0\xb8\xd1\x81\xd1\x82\xd0\xbe'
+b' \xd0\xbb\xd1\x8e\xd0\xb4\xd0\xb5\xd0\xb9'
+b' \xd0\xbb\xd1\x8e\xd0\xb4\xd1\x8f\xd1\x85'
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+b' \xd0\xbc\xd0\xb5\xd0\xb6\xd0\xb4\xd1\x83'
+b' \xd0\xbc\xd0\xb5\xd1\x81\xd1\x82\xd0\xb0'
+b' \xd0\xbc\xd0\xb5\xd1\x81\xd1\x82\xd0\xbe'
+b' \xd0\xbc\xd0\xbd\xd0\xbe\xd0\xb3\xd0\xb8'
+b' \xd0\xbc\xd0\xbd\xd0\xbe\xd0\xb3\xd0\xbe'
+b' \xd0\xbc\xd0\xbe\xd0\xb6\xd0\xb5\xd1\x82'
+b' \xd0\xbc\xd0\xbe\xd0\xb6\xd0\xbd\xd0\xbe'
+b' \xd0\xbc\xd1\x96\xd1\x81\xd1\x82\xd0\xb0'
+b' \xd0\xbc\xd1\x96\xd1\x81\xd1\x86\xd0\xb5'
+b' \xd0\xbd\xd0\xb0\xd0\xb3\xd1\x80\xd0\xb0'
+b' \xd0\xbd\xd0\xb0\xd0\xb7\xd0\xb2\xd0\xb0'
+b' \xd0\xbd\xd0\xb0\xd0\xb7\xd0\xbd\xd0\xb0'
+b' \xd0\xbd\xd0\xb0\xd1\x80\xd0\xbe\xd0\xb4'
+b' \xd0\xbd\xd0\xb5\xd0\xbc\xd0\xb5\xd1\x86'
+b' \xd0\xbd\xd0\xb5\xd1\x81\xd0\xba\xd0\xbe'
+b' \xd0\xbe\xd0\xb1\xd0\xbe\xd1\x80\xd0\xbe'
+b' \xd0\xbe\xd0\xb1\xd1\x80\xd0\xb0\xd0\xb7'
+b' \xd0\xbe\xd0\xb1\xd1\x8a\xd0\xb5\xd0\xba'
+b' \xd0\xbe\xd0\xb4\xd0\xbd\xd0\xb8\xd0\xbc'
+b' \xd0\xbe\xd0\xb4\xd0\xbd\xd0\xbe\xd0\xb9'
+b' \xd0\xbe\xd0\xba\xd0\xbe\xd0\xbb\xd0\xbe'
+b' \xd0\xbe\xd0\xbf\xd0\xb5\xd1\x80\xd0\xb0'
+b' \xd0\xbe\xd0\xbf\xd1\x88\xd1\x82\xd0\xb8'
+b' \xd0\xbe\xd1\x80\xd0\xb3\xd0\xb0\xd0\xbd'
+b' \xd0\xbe\xd1\x81\xd0\xbd\xd0\xbe\xd0\xb2'
+b' \xd0\xbe\xd1\x81\xd0\xbe\xd0\xb1\xd0\xb8'
+b' \xd0\xbe\xd1\x81\xd1\x82\xd0\xb0\xd0\xbd'
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+b' \xd0\xbf\xd0\xbb\xd0\xbe\xd1\x89\xd0\xb0'
+b' \xd0\xbf\xd0\xbe\xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd0\xbf\xd0\xbe\xd0\xb7\xd0\xb2\xd0\xbe'
+b' \xd0\xbf\xd0\xbe\xd0\xbb\xd1\x83\xd1\x87'
+b' \xd0\xbf\xd0\xbe\xd0\xbc\xd0\xbe\xd1\x89'
+b' \xd0\xbf\xd0\xbe\xd1\x81\xd0\xbb\xd0\xb5'
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+b' \xd0\xbf\xd1\x80\xd0\xb0\xd0\xb2\xd0\xb8'
+b' \xd0\xbf\xd1\x80\xd0\xb0\xd0\xb2\xd0\xbe'
+b' \xd0\xbf\xd1\x80\xd0\xb0\xd1\x86\xd1\x8e'
+b' \xd0\xbf\xd1\x80\xd0\xb5\xd0\xbc\xd0\xb0'
+b' \xd0\xbf\xd1\x80\xd0\xb8\xd0\xbd\xd1\x8f'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb2\xd0\xb5'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb2\xd0\xbe'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb4\xd0\xb0'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb4\xd1\x83'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb5\xd0\xba'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb8\xd0\xb7'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb8\xd1\x81'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd1\x82\xd0\xb5'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd1\x82\xd0\xb8'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd1\x86\xd0\xb5'
+b' \xd0\xbf\xd1\x83\xd0\xb1\xd0\xbb\xd0\xb8'
+b' \xd0\xbf\xd1\x83\xd0\xbd\xd0\xba\xd1\x82'
+b' \xd0\xbf\xd1\x96\xd0\xb2\xd0\xbd\xd1\x96'
+b' \xd0\xbf\xd1\x96\xd1\x81\xd0\xbb\xd1\x8f'
+b' \xd1\x80\xd0\xb0\xd0\xb1\xd0\xbe\xd1\x82'
+b' \xd1\x80\xd0\xb0\xd0\xb7\xd0\xb2\xd0\xb8'
+b' \xd1\x80\xd0\xb0\xd0\xb7\xd0\xbb\xd0\xb8'
+b' \xd1\x80\xd0\xb0\xd0\xb9\xd0\xbe\xd0\xbd'
+b' \xd1\x80\xd0\xb0\xd1\x81\xd0\xbf\xd0\xbe'
+b' \xd1\x80\xd0\xbe\xd0\xb7\xd1\x82\xd0\xb0'
+b' \xd1\x80\xd0\xbe\xd0\xba\xd0\xb0\xd1\x85'
+b' \xd1\x80\xd0\xbe\xd0\xba\xd1\x96\xd0\xb2'
+b' \xd1\x80\xd0\xbe\xd0\xbc\xd0\xb0\xd0\xbd'
+b' \xd1\x81\xd0\xb0\xd0\xb2\xd0\xb5\xd0\xb7'
+b' \xd1\x81\xd0\xb0\xd0\xb9\xd1\x82\xd0\xb5'
+b' \xd1\x81\xd0\xb0\xd0\xb9\xd1\x82\xd1\x96'
+b' \xd1\x81\xd0\xb2\xd0\xbe\xd0\xb1\xd0\xbe'
+b' \xd1\x81\xd0\xb2\xd0\xbe\xd0\xb5\xd0\xb9'
+b' \xd1\x81\xd0\xb2\xd0\xbe\xd0\xb8\xd1\x85'
+b' \xd1\x81\xd0\xb2\xd1\x8f\xd0\xb7\xd0\xb8'
+b' \xd1\x81\xd0\xb5\xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd1\x81\xd0\xb5\xd0\xba\xd1\x80\xd0\xb5'
+b' \xd1\x81\xd0\xb5\xd0\xbc\xd0\xb5\xd0\xb9'
+b' \xd1\x81\xd0\xb5\xd1\x80\xd0\xb5\xd0\xb4'
+b' \xd1\x81\xd0\xb8\xd1\x81\xd1\x82\xd0\xb5'
+b' \xd1\x81\xd0\xbb\xd0\xb5\xd0\xb4\xd1\x83'
+b' \xd1\x81\xd0\xbb\xd0\xbe\xd0\xb2\xd0\xb0'
+b' \xd1\x81\xd0\xbb\xd1\x83\xd1\x87\xd0\xb0'
+b' \xd1\x81\xd0\xbe\xd0\xb2\xd0\xb5\xd1\x80'
+b' \xd1\x81\xd0\xbe\xd0\xb2\xd0\xb5\xd1\x82'
+b' \xd1\x81\xd0\xbe\xd0\xb7\xd0\xb4\xd0\xb0'
+b' \xd1\x81\xd0\xbe\xd1\x81\xd1\x82\xd0\xb0'
+b' \xd1\x81\xd0\xbe\xd1\x85\xd1\x80\xd0\xb0'
+b' \xd1\x81\xd1\x80\xd0\xb5\xd0\xb4\xd0\xb8'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd0\xbb\xd0\xb0'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd0\xbb\xd0\xb8'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd1\x80\xd0\xbe'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd1\x82\xd1\x83'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd1\x82\xd1\x8c'
+b' \xd1\x81\xd1\x82\xd0\xbe\xd0\xbb\xd1\x96'
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+b' \xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd'
+b'-------------'
+b'============='
+b'ADVERTISEMENT'
+b'Advertisement'
+b'Alternatively'
+b'Authorization'
+b'ClickListener'
+b'Configuration'
+b'EventListener'
+b'Hozz\xc3\xa1f\xc3\xa9r\xc3\xa9s'
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+b'stackoverflow'
+b'\n\n            '
+b'\n             '
+b'              '
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+b' Alternatively'
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+b' Bibliograf\xc3\xada'
+b' Biblioth\xc3\xa8que'
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+b' Supplementary'
+b' Understanding'
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+b' semiconductor'
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+b' substitutions'
+b' supplementary'
+b' technological'
+b' theoretically'
+b' traditionally'
+b' transcription'
+b' transmissions'
+b' uncertainties'
+b' uncomfortable'
+b' unconditional'
+b' underestimate'
+b' undergraduate'
+b' understanding'
+b' unfortunately'
+b' unprecedented'
+b' unpredictable'
+b' visualization'
+b' vulnerability'
+b'--------------'
+b'=============='
+b'ActivityThread'
+b'AndroidRuntime'
+b'Authentication'
+b'Classification'
+b'Implementation'
+b'LayoutInflater'
+b'RelativeLayout'
+b'Representation'
+b'ViewController'
+b'abstractmethod'
+b'act\xc3\xa9ristiques'
+b'authentication'
+b'classification'
+b'communications'
+b'disambiguation'
+b'getElementById'
+b'identification'
+b'implementation'
+b'longrightarrow'
+b'preventDefault'
+b'representation'
+b'transformation'
+b'\xd0\x92\xd0\xb2\xd0\xb5\xd0\xb4\xd0\xb8\xd1\x82\xd0\xb5'
+b'\xd0\xb2\xd0\xb5\xd0\xb4\xd0\xb5\xd0\xbd\xd0\xb8\xd1\x8f'
+b'\xd0\xb2\xd0\xb5\xd1\x80\xd1\x81\xd0\xb8\xd1\x82\xd0\xb5'
+b'\xd0\xb8\xd1\x87\xd0\xb5\xd1\x81\xd1\x82\xd0\xb2\xd0\xbe'
+b'\xd0\xbb\xd0\xb5\xd0\xba\xd1\x81\xd0\xb0\xd0\xbd\xd0\xb4'
+b'\xd0\xbc\xd0\xb5\xd0\xbd\xd1\x82\xd0\xb0\xd0\xbb\xd1\x8c'
+b'\xd0\xbc\xd0\xb5\xd1\x80\xd0\xb8\xd0\xba\xd0\xb0\xd0\xbd'
+b'\xd0\xbe\xd0\xb3\xd1\x80\xd0\xb0\xd1\x84\xd0\xb8\xd1\x8f'
+b'\xd0\xbf\xd0\xb5\xd1\x80\xd0\xb0\xd1\x82\xd0\xbe\xd1\x80'
+b'\xd0\xbf\xd1\x80\xd0\xbe\xd1\x81\xd1\x82\xd1\x80\xd0\xb0'
+b'\xd1\x81\xd0\xb8\xd0\xbb\xd0\xb0\xd0\xbd\xd0\xbd\xd1\x8f'
+b'\xd1\x81\xd0\xbb\xd0\xb5\xd0\xb4\xd0\xbe\xd0\xb2\xd0\xb0'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd0\xbe'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xbe\xd0\xb2\xd0\xb0\xd0\xbb'
+b'\xd1\x82\xd0\xb5\xd0\xbb\xd1\x8c\xd0\xbd\xd0\xbe\xd0\xb9'
+b'\xd1\x84\xd0\xb8\xd1\x86\xd0\xb8\xd0\xb0\xd0\xbb\xd1\x8c'
+b'\xd1\x86\xd0\xb8\xd0\xbe\xd0\xbd\xd0\xb0\xd0\xbb\xd1\x8c'
+b'\xd1\x86\xd1\x96\xd0\xbe\xd0\xbd\xd0\xb0\xd0\xbb\xd1\x8c'
+b'\xd1\x87\xd0\xb5\xd1\x81\xd0\xba\xd0\xbe\xd0\xb3\xd0\xbe'
+b'\n\n             '
+b'\n              '
+b'\r\n\r\n           '
+b'\r\n             '
+b'               '
+b' Administration'
+b' Administrative'
+b' Authentication'
+b' Classification'
+b' Communications'
+b' Constitutional'
+b' Identification'
+b' Implementation'
+b' Infrastructure'
+b' Initialization'
+b' NotImplemented'
+b' Rehabilitation'
+b' Representative'
+b' Superintendent'
+b' Transformation'
+b' Transportation'
+b' accommodations'
+b' accomplishment'
+b' accountability'
+b' administration'
+b' administrative'
+b' administrators'
+b' advertisements'
+b' aforementioned'
+b' archaeological'
+b' authentication'
+b' cardiovascular'
+b' characteristic'
+b' classification'
+b' communications'
+b' concentrations'
+b' configurations'
+b' considerations'
+b' constitutional'
+b' correspondence'
+b' cryptocurrency'
+b' demonstrations'
+b' differentiated'
+b' disappointment'
+b' discrimination'
+b' discriminatory'
+b' distinguishing'
+b' d\xc3\xa9veloppement'
+b' electronically'
+b' establishments'
+b' experimentally'
+b' franz\xc3\xb6sischen'
+b' generalization'
+b' identification'
+b' implementation'
+b' infrastructure'
+b' initialization'
+b' interconnected'
+b' interpretation'
+b' investigaci\xc3\xb3n'
+b' investigations'
+b' manifestations'
+b' multiplication'
+b' municipalities'
+b' organizational'
+b' overwhelmingly'
+b' pharmaceutical'
+b' principalmente'
+b' professionally'
+b' qualifications'
+b' questionnaires'
+b' recommendation'
+b' reconciliation'
+b' reconnaissance'
+b' reconstruction'
+b' redistribution'
+b' regularization'
+b' rehabilitation'
+b' representation'
+b' representative'
+b' responsibility'
+b' responsiveness'
+b' scientifically'
+b' setContentView'
+b' simultaneously'
+b' specialization'
+b' specifications'
+b' susceptibility'
+b' sustainability'
+b' systematically'
+b' transformation'
+b' transportation'
+b' unconventional'
+b' underestimated'
+b' understandable'
+b' \xc3\xa5rsnederb\xc3\xb6rd'
+b' \xd0\x86\xd1\x81\xd1\x82\xd0\xbe\xd1\x80\xd1\x96\xd1\x8f'
+b' \xd0\x93\xd0\xbe\xd1\x81\xd1\x83\xd0\xb4\xd0\xb0\xd1\x80'
+b' \xd0\x94\xd0\xb6\xd0\xb5\xd1\x80\xd0\xb5\xd0\xbb\xd0\xb0'
+b' \xd0\x98\xd1\x81\xd1\x82\xd0\xbe\xd1\x80\xd0\xb8\xd1\x8f'
+b' \xd0\x9a\xd0\xbe\xd0\xbd\xd1\x81\xd1\x82\xd0\xb0\xd0\xbd'
+b' \xd0\x9c\xd0\xb5\xd0\xba\xd1\x81\xd0\xb8\xd0\xba\xd0\xb0'
+b' \xd0\x9c\xd0\xb5\xd0\xba\xd1\x81\xd0\xb8\xd0\xba\xd1\x83'
+b' \xd0\x9d\xd0\xb8\xd0\xba\xd0\xbe\xd0\xbb\xd0\xb0\xd0\xb9'
+b' \xd0\xa1\xd0\xb0\xd0\xb2\xd0\xb5\xd0\xb7\xd0\xbd\xd0\xb5'
+b' \xd0\xa3\xd0\xba\xd1\x80\xd0\xb0\xd0\xb8\xd0\xbd\xd1\x8b'
+b' \xd0\xa3\xd0\xba\xd1\x80\xd0\xb0\xd1\x97\xd0\xbd\xd0\xb8'
+b' \xd0\xb0\xd0\xb2\xd0\xb3\xd1\x83\xd1\x81\xd1\x82\xd0\xb0'
+b' \xd0\xb1\xd0\xb5\xd1\x80\xd0\xb5\xd0\xb7\xd0\xbd\xd1\x8f'
+b' \xd0\xb2\xd0\xb5\xd1\x80\xd0\xb5\xd1\x81\xd0\xbd\xd1\x8f'
+b' \xd0\xb2\xd1\x80\xd0\xb5\xd0\xbc\xd0\xb5\xd0\xbd\xd0\xb8'
+b' \xd0\xb2\xd1\x8b\xd1\x81\xd1\x82\xd1\x83\xd0\xbf\xd0\xb0'
+b' \xd0\xb3\xd0\xb5\xd0\xbd\xd0\xb5\xd1\x80\xd0\xb0\xd0\xbb'
+b' \xd0\xb3\xd0\xbe\xd1\x81\xd1\x83\xd0\xb4\xd0\xb0\xd1\x80'
+b' \xd0\xb4\xd0\xb5\xd0\xb9\xd1\x81\xd1\x82\xd0\xb2\xd0\xb8'
+b' \xd0\xb4\xd0\xb5\xd0\xba\xd0\xb0\xd0\xb1\xd1\x80\xd1\x8f'
+b' \xd0\xb4\xd0\xb5\xd1\x80\xd0\xb5\xd0\xb2\xd0\xbd\xd1\x8f'
+b' \xd0\xb4\xd0\xb5\xd1\x8f\xd1\x82\xd0\xb5\xd0\xbb\xd1\x8c'
+b' \xd0\xb8\xd0\xbd\xd1\x81\xd1\x82\xd0\xb8\xd1\x82\xd1\x83'
+b' \xd0\xb8\xd0\xbd\xd1\x84\xd0\xbe\xd1\x80\xd0\xbc\xd0\xb0'
+b' \xd0\xb8\xd1\x81\xd1\x82\xd0\xbe\xd1\x80\xd0\xb8\xd0\xb8'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xb0\xd0\xbd\xd0\xb4\xd0\xb8'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xb0\xd0\xbd\xd0\xb4\xd1\x83'
+b' \xd0\xba\xd0\xbe\xd0\xbc\xd0\xbf\xd0\xbe\xd0\xb7\xd0\xb8'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd0\xb0\xd1\x8f'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd0\xbe\xd0\xb9'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd1\x8b\xd0\xb5'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd1\x8b\xd0\xb9'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd1\x8b\xd1\x85'
+b' \xd0\xbe\xd0\xb1\xd0\xbb\xd0\xb0\xd1\x81\xd1\x82\xd0\xb8'
+b' \xd0\xbe\xd0\xb1\xd0\xbb\xd0\xb0\xd1\x81\xd1\x82\xd1\x8c'
+b' \xd0\xbe\xd0\xb1\xd0\xbb\xd0\xb0\xd1\x81\xd1\x82\xd1\x96'
+b' \xd0\xbe\xd0\xba\xd1\x82\xd1\x8f\xd0\xb1\xd1\x80\xd1\x8f'
+b' \xd0\xbe\xd0\xbf\xd1\x88\xd1\x82\xd0\xb8\xd0\xbd\xd0\xb8'
+b' \xd0\xbf\xd0\xb5\xd1\x80\xd1\x81\xd0\xbe\xd0\xbd\xd0\xb0'
+b' \xd0\xbf\xd0\xbe\xd0\xbb\xd1\x83\xd1\x87\xd0\xb8\xd0\xbb'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb2\xd0\xbe\xd0\xb4\xd0\xb8'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb3\xd1\x80\xd0\xb0\xd0\xbc'
+b' \xd1\x80\xd0\xb0\xd0\xb1\xd0\xbe\xd1\x82\xd0\xb0\xd0\xbb'
+b' \xd1\x80\xd0\xb0\xd0\xb7\xd1\x80\xd0\xb0\xd0\xb1\xd0\xbe'
+b' \xd1\x80\xd0\xb0\xd1\x81\xd0\xbf\xd0\xbe\xd0\xbb\xd0\xbe'
+b' \xd1\x81\xd0\xb5\xd0\xbb\xd1\x8c\xd1\x81\xd0\xbe\xd0\xb2'
+b' \xd1\x81\xd0\xb8\xd1\x81\xd1\x82\xd0\xb5\xd0\xbc\xd0\xb0'
+b' \xd1\x81\xd0\xbe\xd0\xbe\xd1\x82\xd0\xb2\xd0\xb5\xd1\x82'
+b' \xd1\x81\xd0\xbe\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2\xd0\xb5'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2\xd0\xb8'
+b' \xd1\x81\xd1\x82\xd0\xb5\xd0\xbf\xd0\xb5\xd0\xbd\xd0\xb8'
+b' \xd1\x82\xd0\xb5\xd0\xbc\xd0\xbf\xd0\xb5\xd1\x80\xd0\xb0'
+b' \xd1\x82\xd0\xb5\xd1\x80\xd0\xb8\xd1\x82\xd0\xbe\xd1\x80'
+b' \xd1\x82\xd0\xb5\xd1\x80\xd1\x80\xd0\xb8\xd1\x82\xd0\xbe'
+b' \xd1\x82\xd0\xb5\xd1\x87\xd0\xb5\xd0\xbd\xd0\xb8\xd0\xb5'
+b' \xd1\x83\xd1\x81\xd1\x82\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2'
+b' \xd1\x83\xd1\x87\xd0\xb0\xd1\x81\xd1\x82\xd0\xb8\xd0\xb5'
+b' \xd1\x84\xd0\xb5\xd0\xb2\xd1\x80\xd0\xb0\xd0\xbb\xd1\x8f'
+b' \xd1\x84\xd1\x80\xd0\xb0\xd0\xbd\xd1\x86\xd1\x83\xd0\xb7'
+b' \xd1\x87\xd0\xb5\xd0\xbb\xd0\xbe\xd0\xb2\xd0\xb5\xd0\xba'
+b' \xd1\x8d\xd0\xbb\xd0\xb5\xd0\xbc\xd0\xb5\xd0\xbd\xd1\x82'
+b'---------------'
+b'==============='
+b'OnClickListener'
+b'PropertyChanged'
+b'ValidationError'
+b'backgroundColor'
+b'printStackTrace'
+b'springframework'
+b'\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n'
+b'\n               '
+b'                '
+b' Characteristics'
+b' Fu\xc3\x9fballspieler'
+b' MERCHANTABILITY'
+b' Redistributions'
+b' Representatives'
+b' accomplishments'
+b' administrations'
+b' characteristics'
+b' classifications'
+b' competitiveness'
+b' confidentiality'
+b' differentiation'
+b' electromagnetic'
+b' environmentally'
+b' experimentation'
+b' extraordinarily'
+b' implementations'
+b' inconsistencies'
+b' instrumentation'
+b' internationally'
+b' interpretations'
+b' notwithstanding'
+b' phosphorylation'
+b' recommendations'
+b' representations'
+b' representatives'
+b' responsabilit\xc3\xa9'
+b' straightforward'
+b' supplementation'
+b' synchronization'
+b' transcriptional'
+b' transformations'
+b' transplantation'
+b' ver\xc3\xb6ffentlicht'
+b' vulnerabilities'
+b'################'
+b'%%%%%%%%%%%%%%%%'
+b'****************'
+b'++++++++++++++++'
+b'----------------'
+b'................'
+b'////////////////'
+b'================'
+b'________________'
+b'addEventListener'
+b'~~~~~~~~~~~~~~~~'
+b'\xc2\xa0\xc2\xa0\xc2\xa0\xc2\xa0\xc2\xa0\xc2\xa0\xc2\xa0\xc2\xa0'
+b'\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82'
+b'\xd0\xbc\xd0\xb8\xd0\xbd\xd0\xb8\xd1\x81\xd1\x82\xd1\x80\xd0\xb0'
+b'\xd0\xbd\xd1\x96\xd1\x86\xd0\xb8\xd0\xbf\xd0\xb0\xd0\xbb\xd1\x96'
+b'\xd0\xbe\xd0\xbb\xd1\x8c\xd0\xb7\xd0\xbe\xd0\xb2\xd0\xb0\xd1\x82'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd0\xbe\xd0\xb9'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd1\x8b\xd0\xb9'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd1\x8b\xd1\x85'
+b'\xd1\x82\xd0\xb5\xd0\xbb\xd1\x8c\xd1\x81\xd1\x82\xd0\xb2\xd0\xb0'
+b'\xd1\x82\xd0\xb5\xd0\xbb\xd1\x8c\xd1\x81\xd1\x82\xd0\xb2\xd0\xbe'
+b'\xd1\x82\xd0\xb5\xd1\x80\xd0\xb0\xd1\x82\xd1\x83\xd1\x80\xd0\xb0'
+b'\xd1\x85\xd1\x96\xd0\xb2\xd0\xbe\xd0\xb2\xd0\xb0\xd0\xbd\xd0\xbe'
+b'\n\n               '
+b'\n        \n       '
+b'\n                '
+b'\r\n               '
+b' \n               '
+b'                 '
+b' ----------------'
+b' autom\xc3\xa1ticamente'
+b' caracter\xc3\xadsticas'
+b' characterization'
+b' cryptocurrencies'
+b' disproportionate'
+b' gastrointestinal'
+b' misunderstanding'
+b' responsibilities'
+b' unconstitutional'
+b' \xd0\x90\xd0\xbb\xd0\xb5\xd0\xba\xd1\x81\xd0\xb0\xd0\xbd\xd0\xb4'
+b' \xd0\x90\xd1\x80\xd1\x85\xd0\xb8\xd0\xb2\xd0\xbd\xd0\xb0\xd1\x8f'
+b' \xd0\x92\xd0\xb8\xd0\xba\xd0\xb8\xd0\xbf\xd0\xb5\xd0\xb4\xd0\xb8'
+b' \xd0\x92\xd0\xbb\xd0\xb0\xd0\xb4\xd0\xb8\xd0\xbc\xd0\xb8\xd1\x80'
+b' \xd0\x9c\xd0\xb5\xd0\xba\xd1\x81\xd0\xb8\xd1\x87\xd0\xba\xd0\xb0'
+b' \xd0\x9e\xd1\x84\xd0\xb8\xd1\x86\xd0\xb8\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\xa0\xd0\xb5\xd1\x81\xd0\xbf\xd1\x83\xd0\xb1\xd0\xbb\xd0\xb8'
+b' \xd0\xa1\xd0\xbf\xd0\xbe\xd1\x99\xd0\xb0\xd1\x88\xd1\x9a\xd0\xb5'
+b' \xd0\xb0\xd0\xbc\xd0\xb5\xd1\x80\xd0\xb8\xd0\xba\xd0\xb0\xd0\xbd'
+b' \xd0\xb8\xd1\x81\xd0\xbf\xd0\xbe\xd0\xbb\xd1\x8c\xd0\xb7\xd1\x83'
+b' \xd0\xba\xd0\xb0\xd1\x87\xd0\xb5\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5'
+b' \xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd0\xbe\xd0\xb3\xd0\xbe'
+b' \xd0\xbc\xd1\x83\xd0\xbd\xd0\xb8\xd1\x86\xd0\xb8\xd0\xbf\xd0\xb0'
+b' \xd0\xbd\xd0\xb0\xd0\xb7\xd0\xb2\xd0\xb0\xd0\xbd\xd0\xb8\xd0\xb5'
+b' \xd0\xbd\xd0\xb5\xd0\xba\xd0\xbe\xd1\x82\xd0\xbe\xd1\x80\xd1\x8b'
+b' \xd0\xbe\xd0\xb1\xd1\x80\xd0\xb0\xd0\xb7\xd0\xbe\xd0\xb2\xd0\xb0'
+b' \xd0\xbe\xd1\x80\xd0\xb3\xd0\xb0\xd0\xbd\xd0\xb8\xd0\xb7\xd0\xb0'
+b' \xd0\xbe\xd1\x84\xd0\xb8\xd1\x86\xd0\xb8\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\xbf\xd0\xbe\xd0\xb4\xd0\xb0\xd1\x86\xd0\xb8\xd0\xbc\xd0\xb0'
+b' \xd0\xbf\xd0\xbe\xd0\xbb\xd0\xb8\xd1\x82\xd0\xb8\xd1\x87\xd0\xb5'
+b' \xd0\xbf\xd1\x80\xd0\xb5\xd0\xb4\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2'
+b' \xd0\xbf\xd1\x80\xd0\xbe\xd0\xb8\xd0\xb7\xd0\xb2\xd0\xbe\xd0\xb4'
+b' \xd1\x80\xd0\xb5\xd0\xb7\xd1\x83\xd0\xbb\xd1\x8c\xd1\x82\xd0\xb0'
+b' \xd1\x81\xd0\xb0\xd0\xb2\xd0\xb5\xd0\xb7\xd0\xbd\xd0\xbe\xd1\x98'
+b' \xd1\x81\xd0\xb5\xd0\xbd\xd1\x82\xd1\x8f\xd0\xb1\xd1\x80\xd1\x8f'
+b' \xd1\x81\xd0\xbe\xd0\xb2\xd1\x80\xd0\xb5\xd0\xbc\xd0\xb5\xd0\xbd'
+b' \xd1\x81\xd0\xbe\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2\xd0\xbb\xd1\x8f'
+b' \xd1\x81\xd0\xbf\xd0\xb5\xd1\x86\xd0\xb8\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd1\x84\xd0\xb0\xd0\xbc\xd0\xb8\xd0\xbb\xd0\xb8\xd0\xb5\xd0\xb9'
+b' \xd1\x87\xd0\xb5\xd0\xbb\xd0\xbe\xd0\xb2\xd0\xb5\xd0\xba\xd0\xb0'
+b' \xd1\x87\xd0\xb5\xd0\xbc\xd0\xbf\xd0\xb8\xd0\xbe\xd0\xbd\xd0\xb0'
+b' \xd1\x8f\xd0\xb2\xd0\xbb\xd1\x8f\xd0\xb5\xd1\x82\xd1\x81\xd1\x8f'
+b'ArgumentException'
+b'\n                 '
+b'                  '
+b' particuli\xc3\xa8rement'
+b'\xd1\x81\xd1\x82\xd0\xb2\xd0\xb5\xd0\xbd\xd0\xbd\xd0\xbe\xd0\xb3\xd0\xbe'
+b'\n                  '
+b'                   '
+b' telecommunications'
+b' \xd0\x90\xd0\xbb\xd0\xb5\xd0\xba\xd1\x81\xd0\xb0\xd0\xbd\xd0\xb4\xd1\x80'
+b' \xd0\x91\xd0\xb8\xd0\xbe\xd0\xb3\xd1\x80\xd0\xb0\xd1\x84\xd0\xb8\xd1\x8f'
+b' \xd0\x92\xd0\xb8\xd0\xba\xd0\xb8\xd0\xbf\xd0\xb5\xd0\xb4\xd0\xb8\xd0\xb8'
+b' \xd0\x9d\xd0\xb0\xd1\x81\xd0\xb5\xd0\xbb\xd0\xb5\xd0\xbd\xd0\xb8\xd0\xb5'
+b' \xd0\x9d\xd0\xb0\xd1\x81\xd0\xb5\xd0\xbb\xd0\xb5\xd0\xbd\xd0\xbd\xd1\x8f'
+b' \xd0\x9d\xd0\xb0\xd1\x86\xd0\xb8\xd0\xbe\xd0\xbd\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\x9f\xd0\xbe\xd1\x81\xd0\xb8\xd0\xbb\xd0\xb0\xd0\xbd\xd0\xbd\xd1\x8f'
+b' \xd0\xa0\xd0\xb0\xd1\x81\xd0\xbf\xd0\xbe\xd0\xb4\xd0\xb5\xd0\xbb\xd0\xb0'
+b' \xd0\xa0\xd0\xb5\xd0\xb7\xd1\x83\xd0\xbb\xd1\x82\xd0\xb0\xd1\x82\xd0\xb8'
+b' \xd0\xa4\xd0\xb5\xd0\xb4\xd0\xb5\xd1\x80\xd0\xb0\xd1\x86\xd0\xb8\xd0\xb8'
+b' \xd0\xa5\xd1\x80\xd0\xbe\xd0\xbd\xd0\xbe\xd0\xbb\xd0\xbe\xd0\xb3\xd0\xb8'
+b' \xd0\xb8\xd1\x81\xd1\x81\xd0\xbb\xd0\xb5\xd0\xb4\xd0\xbe\xd0\xb2\xd0\xb0'
+b' \xd0\xbb\xd0\xb8\xd1\x81\xd1\x82\xd0\xbe\xd0\xbf\xd0\xb0\xd0\xb4\xd0\xb0'
+b' \xd0\xbd\xd0\xb0\xd1\x81\xd0\xb5\xd0\xbb\xd0\xb5\xd0\xbd\xd0\xb8\xd1\x8f'
+b' \xd0\xbd\xd0\xb0\xd1\x81\xd0\xb5\xd0\xbb\xd0\xb5\xd0\xbd\xd0\xbd\xd1\x8f'
+b' \xd0\xbd\xd0\xb0\xd1\x85\xd0\xbe\xd0\xb4\xd0\xb8\xd1\x82\xd1\x81\xd1\x8f'
+b' \xd0\xbd\xd0\xb0\xd1\x86\xd0\xb8\xd0\xbe\xd0\xbd\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\xbd\xd0\xb0\xd1\x86\xd1\x96\xd0\xbe\xd0\xbd\xd0\xb0\xd0\xbb\xd1\x8c'
+b' \xd0\xbd\xd0\xb5\xd1\x81\xd0\xba\xd0\xbe\xd0\xbb\xd1\x8c\xd0\xba\xd0\xbe'
+b' \xd0\xbe\xd0\xb1\xd1\x80\xd0\xb0\xd1\x89\xd0\xb5\xd0\xbd\xd0\xb8\xd1\x8f'
+b' \xd0\xbf\xd0\xbe\xd0\xbb\xd1\x8c\xd0\xb7\xd0\xbe\xd0\xb2\xd0\xb0\xd1\x82'
+b' \xd0\xbf\xd1\x80\xd0\xb5\xd0\xb4\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2\xd0\xb8'
+b' \xd1\x81\xd0\xbe\xd0\xb2\xd0\xb5\xd1\x82\xd1\x81\xd0\xba\xd0\xb8\xd0\xb9'
+b'\n                   '
+b'                    '
+b' NotImplementedError'
+b'--------------------'
+b'getElementsByTagName'
+b'\xd0\x90\xd1\x80\xd1\x85\xd1\x96\xd0\xb2\xd0\xbe\xd0\xb2\xd0\xb0\xd0\xbd\xd0\xbe'
+b'\xd0\xbd\xd1\x86\xd0\xb8\xd0\xba\xd0\xbb\xd0\xbe\xd0\xbf\xd0\xb5\xd0\xb4\xd0\xb8'
+b'\n\n                   '
+b'\n                    '
+b'\r\n                   '
+b'                     '
+b' --------------------'
+b' \xd0\x9b\xd0\xb8\xd1\x82\xd0\xb5\xd1\x80\xd0\xb0\xd1\x82\xd1\x83\xd1\x80\xd0\xb0'
+b' \xd0\xa0\xd0\xb5\xd1\x81\xd0\xbf\xd1\x83\xd0\xb1\xd0\xbb\xd0\xb8\xd0\xba\xd0\xb8'
+b' \xd0\xa0\xd0\xbe\xd1\x81\xd1\x81\xd0\xb8\xd0\xb9\xd1\x81\xd0\xba\xd0\xbe\xd0\xb9'
+b' \xd0\xb8\xd1\x81\xd0\xbf\xd0\xbe\xd0\xbb\xd1\x8c\xd0\xb7\xd0\xbe\xd0\xb2\xd0\xb0'
+b' \xd0\xbc\xd0\xb5\xd0\xb6\xd0\xb4\xd1\x83\xd0\xbd\xd0\xb0\xd1\x80\xd0\xbe\xd0\xb4'
+b' \xd0\xbc\xd1\x83\xd0\xbd\xd1\x96\xd1\x86\xd0\xb8\xd0\xbf\xd0\xb0\xd0\xbb\xd1\x96'
+b' \xd0\xbd\xd0\xb0\xd0\xb4\xd0\xbc\xd0\xbe\xd1\x80\xd1\x81\xd0\xba\xd0\xbe\xd1\x98'
+b' \xd1\x80\xd0\xb0\xd1\x81\xd0\xbf\xd0\xbe\xd0\xbb\xd0\xbe\xd0\xb6\xd0\xb5\xd0\xbd'
+b' \xd1\x80\xd0\xb5\xd0\xb7\xd1\x83\xd0\xbb\xd1\x8c\xd1\x82\xd0\xb0\xd1\x82\xd0\xb5'
+b' \xd1\x81\xd0\xbe\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2\xd0\xbb\xd1\x8f\xd0\xb5\xd1\x82'
+b' \xd1\x81\xd1\x82\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2\xd0\xbd\xd0\xb8\xd0\xba\xd0\xb0'
+b' \xd1\x82\xd0\xb5\xd1\x80\xd1\x80\xd0\xb8\xd1\x82\xd0\xbe\xd1\x80\xd0\xb8\xd0\xb8'
+b' \xd1\x83\xd0\xbd\xd0\xb8\xd0\xb2\xd0\xb5\xd1\x80\xd1\x81\xd0\xb8\xd1\x82\xd0\xb5'
+b' \xd1\x83\xd0\xbd\xd1\x96\xd0\xb2\xd0\xb5\xd1\x80\xd1\x81\xd0\xb8\xd1\x82\xd0\xb5'
+b'\n                     '
+b'                      '
+b'\xd0\xbc\xd0\xb8\xd0\xbd\xd0\xb8\xd1\x81\xd1\x82\xd1\x80\xd0\xb0\xd1\x82\xd0\xb8\xd0\xb2'
+b'\n                      '
+b'                       '
+b' \xd0\xa5\xd1\x80\xd0\xbe\xd0\xbd\xd0\xbe\xd0\xbb\xd0\xbe\xd0\xb3\xd0\xb8\xd1\x98\xd0\xb0'
+b' \xd0\xb2\xd0\xb8\xd0\xba\xd0\xbe\xd1\x80\xd0\xb8\xd1\x81\xd1\x82\xd0\xbe\xd0\xb2\xd1\x83'
+b' \xd1\x8d\xd0\xbd\xd1\x86\xd0\xb8\xd0\xba\xd0\xbb\xd0\xbe\xd0\xbf\xd0\xb5\xd0\xb4\xd0\xb8'
+b'\n                       '
+b'                        '
+b'************************'
+b'------------------------'
+b'\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93\xe2\x80\x93'
+b'\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94'
+b'\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6\xe2\x80\xa6'
+b'\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80'
+b'\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90\xe2\x95\x90'
+b'\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88\xe2\x96\x88'
+b'\n\n                       '
+b'\n            \n           '
+b'\n                        '
+b'\r\n                       '
+b'                         '
+b' \xd0\xa1\xd1\x82\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2\xd0\xbd\xd0\xb8\xd1\x88\xd1\x82\xd0\xb2\xd0\xbe'
+b' \xd1\x83\xd0\xbd\xd0\xb8\xd0\xb2\xd0\xb5\xd1\x80\xd1\x81\xd0\xb8\xd1\x82\xd0\xb5\xd1\x82\xd0\xb0'
+b'\n                         '
+b'                          '
+b'\n                          '
+b'                           '
+b' \xd0\xb0\xd0\xb4\xd0\xbc\xd0\xb8\xd0\xbd\xd0\xb8\xd1\x81\xd1\x82\xd1\x80\xd0\xb0\xd1\x82\xd0\xb8\xd0\xb2'
+b'\n                           '
+b'                            '
+b'\n\n                           '
+b'\n                            '
+b'                             '
+b'\n                             '
+b'                              '
+b'------------------------------'
+b'\n                              '
+b'                               '
+b'\n                               '
+b'                                '
+b'################################'
+b'********************************'
+b'--------------------------------'
+b'................................'
+b'////////////////////////////////'
+b'================================'
+b'________________________________'
+b'\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82\xc3\x83\xc3\x82'
+b'\n\n                               '
+b'\n                                '
+b'                                 '
+b' --------------------------------'
+b'\n                                 '
+b'                                  '
+b'\n                                  '
+b'                                   '
+b'\n                                   '
+b'                                    '
+b'\n                                    '
+b'                                     '
+b'\n                                     '
+b'                                      '
+b'\n                                      '
+b'                                       '
+b'\n                                       '
+b'                                        '
+b'\n                                        '
+b'                                         '
+b'                                          '
+b'                                           '
+b'\n                                           '
+b'                                            '
+b'                                             '
+b'                                              '
+b'                                               '
+b'\n                                               '
+b'                                                '
+b'################################################'
+b'************************************************'
+b'------------------------------------------------'
+b'================================================'
+b'\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94\xe2\x80\x94'
+b'\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80'
+b'                                                 '
+b' ------------------------------------------------'
+b'                                                  '
+b'                                                   '
+b'\n                                                   '
+b'                                                    '
+b'                                                     '
+b'                                                      '
+b'                                                       '
+b'                                                        '
+b'                                                         '
+b'                                                          '
+b'                                                           '
+b'                                                              '
+b'                                                                '
+b'################################################################'
+b'****************************************************************'
+b'----------------------------------------------------------------'
+b'////////////////////////////////////////////////////////////////'
+b'================================================================'
+b' ----------------------------------------------------------------'
+b' ================================================================='
+b'//----------------------------------------------------------------'
+b'                                                                        '
+b'************************************************************************'
+b'                                                                          '
+b'############################################################################'
+b'****************************************************************************'
+b'################################################################################'
+b'--------------------------------------------------------------------------------'
+b'                                                                                                                                '
+
+
+
+<tts>
+<text_start>
+<text_end>
+<global_start>
+<global_end>
+<sementic_start>
+<sementic_end>
+<|bicodec_global_0|>
+<|bicodec_global_1000|>
+<|bicodec_global_1001|>
+<|bicodec_global_1002|>
+<|bicodec_global_1003|>
+<|bicodec_global_1004|>
+<|bicodec_global_1005|>
+<|bicodec_global_1006|>
+<|bicodec_global_1007|>
+<|bicodec_global_1008|>
+<|bicodec_global_1009|>
+<|bicodec_global_100|>
+<|bicodec_global_1010|>
+<|bicodec_global_1011|>
+<|bicodec_global_1012|>
+<|bicodec_global_1013|>
+<|bicodec_global_1014|>
+<|bicodec_global_1015|>
+<|bicodec_global_1016|>
+<|bicodec_global_1017|>
+<|bicodec_global_1018|>
+<|bicodec_global_1019|>
+<|bicodec_global_101|>
+<|bicodec_global_1020|>
+<|bicodec_global_1021|>
+<|bicodec_global_1022|>
+<|bicodec_global_1023|>
+<|bicodec_global_1024|>
+<|bicodec_global_1025|>
+<|bicodec_global_1026|>
+<|bicodec_global_1027|>
+<|bicodec_global_1028|>
+<|bicodec_global_1029|>
+<|bicodec_global_102|>
+<|bicodec_global_1030|>
+<|bicodec_global_1031|>
+<|bicodec_global_1032|>
+<|bicodec_global_1033|>
+<|bicodec_global_1034|>
+<|bicodec_global_1035|>
+<|bicodec_global_1036|>
+<|bicodec_global_1037|>
+<|bicodec_global_1038|>
+<|bicodec_global_1039|>
+<|bicodec_global_103|>
+<|bicodec_global_1040|>
+<|bicodec_global_1041|>
+<|bicodec_global_1042|>
+<|bicodec_global_1043|>
+<|bicodec_global_1044|>
+<|bicodec_global_1045|>
+<|bicodec_global_1046|>
+<|bicodec_global_1047|>
+<|bicodec_global_1048|>
+<|bicodec_global_1049|>
+<|bicodec_global_104|>
+<|bicodec_global_1050|>
+<|bicodec_global_1051|>
+<|bicodec_global_1052|>
+<|bicodec_global_1053|>
+<|bicodec_global_1054|>
+<|bicodec_global_1055|>
+<|bicodec_global_1056|>
+<|bicodec_global_1057|>
+<|bicodec_global_1058|>
+<|bicodec_global_1059|>
+<|bicodec_global_105|>
+<|bicodec_global_1060|>
+<|bicodec_global_1061|>
+<|bicodec_global_1062|>
+<|bicodec_global_1063|>
+<|bicodec_global_1064|>
+<|bicodec_global_1065|>
+<|bicodec_global_1066|>
+<|bicodec_global_1067|>
+<|bicodec_global_1068|>
+<|bicodec_global_1069|>
+<|bicodec_global_106|>
+<|bicodec_global_1070|>
+<|bicodec_global_1071|>
+<|bicodec_global_1072|>
+<|bicodec_global_1073|>
+<|bicodec_global_1074|>
+<|bicodec_global_1075|>
+<|bicodec_global_1076|>
+<|bicodec_global_1077|>
+<|bicodec_global_1078|>
+<|bicodec_global_1079|>
+<|bicodec_global_107|>
+<|bicodec_global_1080|>
+<|bicodec_global_1081|>
+<|bicodec_global_1082|>
+<|bicodec_global_1083|>
+<|bicodec_global_1084|>
+<|bicodec_global_1085|>
+<|bicodec_global_1086|>
+<|bicodec_global_1087|>
+<|bicodec_global_1088|>
+<|bicodec_global_1089|>
+<|bicodec_global_108|>
+<|bicodec_global_1090|>
+<|bicodec_global_1091|>
+<|bicodec_global_1092|>
+<|bicodec_global_1093|>
+<|bicodec_global_1094|>
+<|bicodec_global_1095|>
+<|bicodec_global_1096|>
+<|bicodec_global_1097|>
+<|bicodec_global_1098|>
+<|bicodec_global_1099|>
+<|bicodec_global_109|>
+<|bicodec_global_10|>
+<|bicodec_global_1100|>
+<|bicodec_global_1101|>
+<|bicodec_global_1102|>
+<|bicodec_global_1103|>
+<|bicodec_global_1104|>
+<|bicodec_global_1105|>
+<|bicodec_global_1106|>
+<|bicodec_global_1107|>
+<|bicodec_global_1108|>
+<|bicodec_global_1109|>
+<|bicodec_global_110|>
+<|bicodec_global_1110|>
+<|bicodec_global_1111|>
+<|bicodec_global_1112|>
+<|bicodec_global_1113|>
+<|bicodec_global_1114|>
+<|bicodec_global_1115|>
+<|bicodec_global_1116|>
+<|bicodec_global_1117|>
+<|bicodec_global_1118|>
+<|bicodec_global_1119|>
+<|bicodec_global_111|>
+<|bicodec_global_1120|>
+<|bicodec_global_1121|>
+<|bicodec_global_1122|>
+<|bicodec_global_1123|>
+<|bicodec_global_1124|>
+<|bicodec_global_1125|>
+<|bicodec_global_1126|>
+<|bicodec_global_1127|>
+<|bicodec_global_1128|>
+<|bicodec_global_1129|>
+<|bicodec_global_112|>
+<|bicodec_global_1130|>
+<|bicodec_global_1131|>
+<|bicodec_global_1132|>
+<|bicodec_global_1133|>
+<|bicodec_global_1134|>
+<|bicodec_global_1135|>
+<|bicodec_global_1136|>
+<|bicodec_global_1137|>
+<|bicodec_global_1138|>
+<|bicodec_global_1139|>
+<|bicodec_global_113|>
+<|bicodec_global_1140|>
+<|bicodec_global_1141|>
+<|bicodec_global_1142|>
+<|bicodec_global_1143|>
+<|bicodec_global_1144|>
+<|bicodec_global_1145|>
+<|bicodec_global_1146|>
+<|bicodec_global_1147|>
+<|bicodec_global_1148|>
+<|bicodec_global_1149|>
+<|bicodec_global_114|>
+<|bicodec_global_1150|>
+<|bicodec_global_1151|>
+<|bicodec_global_1152|>
+<|bicodec_global_1153|>
+<|bicodec_global_1154|>
+<|bicodec_global_1155|>
+<|bicodec_global_1156|>
+<|bicodec_global_1157|>
+<|bicodec_global_1158|>
+<|bicodec_global_1159|>
+<|bicodec_global_115|>
+<|bicodec_global_1160|>
+<|bicodec_global_1161|>
+<|bicodec_global_1162|>
+<|bicodec_global_1163|>
+<|bicodec_global_1164|>
+<|bicodec_global_1165|>
+<|bicodec_global_1166|>
+<|bicodec_global_1167|>
+<|bicodec_global_1168|>
+<|bicodec_global_1169|>
+<|bicodec_global_116|>
+<|bicodec_global_1170|>
+<|bicodec_global_1171|>
+<|bicodec_global_1172|>
+<|bicodec_global_1173|>
+<|bicodec_global_1174|>
+<|bicodec_global_1175|>
+<|bicodec_global_1176|>
+<|bicodec_global_1177|>
+<|bicodec_global_1178|>
+<|bicodec_global_1179|>
+<|bicodec_global_117|>
+<|bicodec_global_1180|>
+<|bicodec_global_1181|>
+<|bicodec_global_1182|>
+<|bicodec_global_1183|>
+<|bicodec_global_1184|>
+<|bicodec_global_1185|>
+<|bicodec_global_1186|>
+<|bicodec_global_1187|>
+<|bicodec_global_1188|>
+<|bicodec_global_1189|>
+<|bicodec_global_118|>
+<|bicodec_global_1190|>
+<|bicodec_global_1191|>
+<|bicodec_global_1192|>
+<|bicodec_global_1193|>
+<|bicodec_global_1194|>
+<|bicodec_global_1195|>
+<|bicodec_global_1196|>
+<|bicodec_global_1197|>
+<|bicodec_global_1198|>
+<|bicodec_global_1199|>
+<|bicodec_global_119|>
+<|bicodec_global_11|>
+<|bicodec_global_1200|>
+<|bicodec_global_1201|>
+<|bicodec_global_1202|>
+<|bicodec_global_1203|>
+<|bicodec_global_1204|>
+<|bicodec_global_1205|>
+<|bicodec_global_1206|>
+<|bicodec_global_1207|>
+<|bicodec_global_1208|>
+<|bicodec_global_1209|>
+<|bicodec_global_120|>
+<|bicodec_global_1210|>
+<|bicodec_global_1211|>
+<|bicodec_global_1212|>
+<|bicodec_global_1213|>
+<|bicodec_global_1214|>
+<|bicodec_global_1215|>
+<|bicodec_global_1216|>
+<|bicodec_global_1217|>
+<|bicodec_global_1218|>
+<|bicodec_global_1219|>
+<|bicodec_global_121|>
+<|bicodec_global_1220|>
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diff --git a/trained_50_percents/wav2vec2-large-xlsr-53/README.md b/trained_50_percents/wav2vec2-large-xlsr-53/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..24378c9a959534e0e16d76d11951f165584084e0
--- /dev/null
+++ b/trained_50_percents/wav2vec2-large-xlsr-53/README.md
@@ -0,0 +1,29 @@
+---
+language: multilingual
+datasets:
+- common_voice
+tags:
+- speech
+license: apache-2.0
+---
+
+# Wav2Vec2-XLSR-53 
+
+[Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
+
+The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information.
+
+[Paper](https://arxiv.org/abs/2006.13979)
+
+Authors: Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli
+
+**Abstract**
+This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.
+
+The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
+
+# Usage
+
+See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) for more information on how to fine-tune the model.
+
+![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xlsr_wav2vec2.png)
diff --git a/trained_50_percents/wav2vec2-large-xlsr-53/config.json b/trained_50_percents/wav2vec2-large-xlsr-53/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ee4a4cc9f9d3cc2502849543db423c45e3343c4
--- /dev/null
+++ b/trained_50_percents/wav2vec2-large-xlsr-53/config.json
@@ -0,0 +1,83 @@
+{
+  "activation_dropout": 0.0,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForPreTraining"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "codevector_dim": 768,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.075,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 768,
+  "transformers_version": "4.7.0.dev0",
+  "vocab_size": 32
+}
diff --git a/trained_50_percents/wav2vec2-large-xlsr-53/preprocessor_config.json b/trained_50_percents/wav2vec2-large-xlsr-53/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..36ebe8b7c1cc967b3059f0494ae8a1069dd67655
--- /dev/null
+++ b/trained_50_percents/wav2vec2-large-xlsr-53/preprocessor_config.json
@@ -0,0 +1,9 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
diff --git a/trained_50_percents/wav2vec2-large-xlsr-53/pytorch_model.bin b/trained_50_percents/wav2vec2-large-xlsr-53/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..018f63e67f709452f601e6ef4fc0bd046e956adf
--- /dev/null
+++ b/trained_50_percents/wav2vec2-large-xlsr-53/pytorch_model.bin
@@ -0,0 +1,3 @@
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