commit 0a1c6fdda00c65ce40e7454ffb1da2aa535d38c5 Author: ModelHub XC Date: Tue Jun 16 19:08:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..440f6f4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,51 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00001-of-00003.safetensors filter=lfs diff=lfs merge=lfs -text +model-00002-of-00003.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00003.safetensors filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..1ec5679 --- /dev/null +++ b/LICENSE @@ -0,0 +1,62 @@ +HyperCLOVA X SEED Model License Agreement + +Model Release Date: April 24, 2025 + +This HyperCLOVA X SEED Model License Agreement (the “Agreement”) is a legal agreement between you and NAVER Corporation and NAVER Cloud Corporation (“NAVER”) and governs your use of the Models that NAVER provides to You under this Agreement. + +NAVER Corp., as the holder of the intellectual property of the Model, and its affiliate, NAVER Cloud Corp., as the exclusive business operator of HyperCLOVA X, enter into this Agreement with you. 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Any modifications will be effective upon posting the updated Agreement on our website or through other means of communication. You are responsible for reviewing the Agreement periodically for changes. + +13. No Waiver. NAVER will not be treated as having waived any rights by not exercising (or delaying the exercise of) any rights under this Agreement. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..6741d64 --- /dev/null +++ b/README.md @@ -0,0 +1,348 @@ +--- +license: other +license_name: hyperclovax-seed +license_link: LICENSE +library_name: transformers +--- + + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/6512d9827fccffe1e9e28fa7/Lra7yfdthGdKcNk7vP5RS.png) + + +## **Overview** + +HyperCLOVAX-SEED-Vision-Instruct-3B is a model developed by NAVER, built upon its proprietary backbone model and fine-tuned through post-training. It is capable of understanding both text and images, as well as generating text. + +The model is primarily designed with a focus on lightweight architecture, optimizing computational efficiency. In terms of visual understanding, it can handle visual question answering (VQA), chart and diagram interpretation, and even comprehend content. HyperCLOVAX-SEED-Vision-Instruct-3B aims for a Pareto-optimal balance specifically tuned for the Korean language, and it demonstrates competitive performance using fewer visual tokens compared to other models of similar size in inference scenarios. + +Particularly, the model shows relative strengths in handling Korean-language inputs and outperforms similarly sized open-source models in related benchmarks. As the first open-source vision-language model in Korea capable of visual understanding, it is expected to significantly contribute to strengthening Korea's sovereign AI capabilities. + + +## **Updates** +- **(2025.07.25)**: vLLM engine is available with [our repository](https://github.com/NAVER-Cloud-HyperCLOVA-X/vllm/tree/v0.9.2rc2_hyperclovax_vision_seed) +- **(2025.07.08)**: Major code update for supporting vLLM engine ([link - related_discussion](https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B/discussions/27)) +- **(2025.04.22)**: Initial release of the repository. + + +## **Basic Information** + +- **Model Architecture**: LLaVA-based Vision-Language Model + - **LLM Module**: Transformer-based architecture (Dense Model) + - **Vision Encoder** : SigLIP-based architecture with 378x378px input resolution per grid. + - **Vision-Language Connector** : C-Abstractor based architecture with AnyRes mechanism, supporting up to 1.29M total pixels across 9 grids. +- **Parameter Count**: 3.2B (LLM Module) + 0.43B (Vision Module) +- **Input/Output Format**: Text + Image + Video / Text +- **Context Length**: 16k +- **Knowledge Cutoff Date**: The model was trained on data collected before August 2024. + + +## **Training** + +#### **Text** + +Securing high-quality data is essential even during post-training, but having humans manually create or revise large-scale datasets posed significant limitations in terms of both cost and resources. Additionally, tasks requiring domain expertise were difficult to handle, and the risk of human error was high. To overcome these challenges, we utilized an automated validation system powered by HyperCLOVA X, which improved data quality and streamlined the training process — ultimately leading to enhanced overall model performance. As a result, the model showed significant improvements in areas with definitive answers, such as mathematics and coding. + +While reducing the cost of data collection is important, finding efficient training strategies is equally critical. HyperCLOVAX-SEED-Vision-Instruct-3B was developed starting from the HyperCLOVAX-SEED-Text-Base-3B and applied both Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) based on an online reinforcement algorithm called GRPO. + +#### **Vision** + +The Vision Understanding feature — where the model receives images and questions as input and generates text-based answers — was not part of the initial design of HyperCLOVA X. Therefore, the model architecture was carefully designed to add capabilities for handling vision-related tasks, such as image-based question answering (VQA) and chart/diagram interpretation, without compromising the existing performance of the HCX LLM. Special attention was given to handling auxiliary information within the input, especially considering the context length. + +Although HyperCLOVAX-SEED-Vision-Instruct-3B is a lightweight model, it is capable of performing basic image VQA tasks and even supports OCR-free processing. One of the key focus areas for this 3B model was optimizing the efficiency of video input tokens. Since input token length directly affects computational cost, the number of tokens extracted per frame was carefully adjusted to enable efficient video understanding with as few tokens as possible. Additionally, during the RLHF training phase, vision-specific V-RLHF data was used to enhance the model’s learning, just like in the text domain. + +## Benchmark +#### Text + +| **Model** | **KMMLU (5-shot, acc)** | **HAE-RAE (5-shot, acc)** | **CLiCK (5-shot, acc)** | **KoBEST (5-shot, acc)** | +|----------------------------|--------|---------|---------|-------| +| HyperCLOVAX-SEED-Text-Base-3B | 0.4847 | 0.7635 | 0.6386 | 0.7792 | +| HyperCLOVAX-SEED-Vision-Instruct-3B| 0.4422 | 0.6499 | 0.5599 | 0.7180 | +| Qwen2.5-3B-instruct | 0.4451 | 0.6031 | 0.5649 | 0.7053 | +| gemma-3-4b-it | 0.3895 | 0.6059 | 0.5303 | 0.7262 | + +#### Vision + +| Model Name | Max Token Count per Video | VideoMME (Ko) | NAVER-TV-CLIP (Ko) | VideoChatGPT (Ko) | PerceptionTest (En) | ActivityNet-QA (En) | KoNet (Ko) | MMBench-Val (En) | TextVQA-Val (En) | Korean VisIT-Bench (Ko) | Image (4 benchmarks) | Video (5 benchmarks) | All (9 benchmarks) | +|-----------------------------------|--------------------------------|----------------|---------------------|--------------------|-----------------------|----------------------|------------|-------------------|-------------------|--------------------------|------------------------|------------------------|----------------------| +| HyperCLOVAX-SEED-Vision-Instruct-3B | 1856 tokens, 108 frames | 48.2 | 61.0 | 53.6 | 55.2 | 50.6 | 69.2 | 81.8 | 79.2 | 37.0 | 46.68 | 53.70 | 59.54 | +| HyperCLOVAX-SEED-Vision-Instruct-3B (without OCR)| 1856 tokens, 108 frames | 48.2 | 61.0 | 53.6 | 55.2 | 50.6 | 36.6 | 80.7 | 76.0 | 43.5 | 56.74 | 53.70 | 55.05 | +| Qwen-2.5-VL-3B | 24576 tokens, 768 frames | 55.1 | 48.3 | 45.6 | 66.9 | 55.7 | 58.3 | 84.3 | 79.6 | 81.5 | 59.35 | 54.31 | 56.55 | +| Qwen-2.5-VL-3B (w/ 2000 tokens) | 2000 tokens, 128 frames | 50.3 | 43.9 | 44.3 | 58.3 | 54.2 | 58.5 | 84.3 | 79.3 | 15.7 | 59.50 | 50.18 | 54.33 | +| Qwen-2.5-VL-7B | 24576 tokens, 768 frames | 60.6 | 66.7 | 51.8 | 70.5 | 56.6 | 68.4 | 88.3 | 84.9 | 85.6 | 69.34 | 61.23 | 64.84 | +| Gemma-3-4B | 4096 tokens, 16 frames | 45.4 | 36.8 | 57.1 | 50.6 | 46.3 | 25.0 | 79.2 | 58.9 | 32.3 | 48.91 | 47.24 | 47.98 | +| GPT4V (gpt-4-turbo-2024-04-09) | Unknown, Original Image , 8 frames | 49.1 | 75.0 | 55.5 | 57.4 | 45.7 | 38.7 | 84.2 | 60.4 | 52.0 | 58.88 | 51.59 | 54.83 | +| GPT4o (gpt-4o-2024-08-06) | Unknown, 512 resize, 128 frames| 61.6 | 66.6 | 61.8 | 50.2 | 41.7 | 60.6 | 84.2 | 73.2 | 50.5 | 67.15 | 56.42 | 61.19 | +| InternV-2-2B | 4096 tokens, 16 frames | 28.9 | 21.1 | 40.2 | 50.5 | 50.3 | 3.3 | 79.3 | 75.1 | 51.1 | 39.74 | 38.19 | 38.88 | +| InternV-2-4B | 4096 tokens, 16 frames | 33.8 | 36.0 | 22.8 | 54.2 | 52.0 | 22.7 | 83.0 | 76.9 | 51.6 | 46.11 | 39.75 | 42.58 | +| InternV-2-8B | 4096 tokens, 16 frames | 43.7 | 41.2 | 32.4 | 58.5 | 53.2 | 28.5 | 86.6 | 79.0 | 97.0 | 50.32 | 45.79 | 47.81 | + +## Dependencies +- [einops](https://einops.rocks/) +- [timm](https://github.com/huggingface/pytorch-image-models) +- [av](https://github.com/PyAV-Org/PyAV) +- [decord](https://github.com/dmlc/decord) + +## Example +**(code & benchmark score) checked with transformers 4.52.4** + +```python + +from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer + +model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B" +model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device="cuda") +processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# LLM Example +# It is recommended to use the chat template with HyperCLOVAX models. +# Using the chat template allows you to easily format your input in ChatML style. +llm_chat = [ + {"role": "system", "content": [{"type": "text", "text": "you are helpful assistant!"}]}, + { + "role": "user", + "content": [ + {"type": "text", "text": "Hello, how are you?"}, + {"type": "text", "text": "I said. Hello, how are you today?"}, + ] + }, + {"role": "assistant", "content": [{"type": "text", "text": "I'm doing great. How can I help you today?"}]}, + {"role": "user", "content": [{"type": "text", "text": "I'd like to show off how chat templating works!"}]}, +] +model_inputs = processor.apply_chat_template( + llm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True +) +model_inputs = model_inputs.to(device="cuda") + +# Please adjust parameters like top_p appropriately for your use case. +output_ids = model.generate( + **model_inputs, + max_new_tokens=64, + do_sample=True, + top_p=0.6, + temperature=0.5, + repetition_penalty=1.0, +) +print("=" * 80) +print("LLM EXAMPLE") +print(processor.batch_decode(output_ids)[0]) +print("=" * 80) + +# VLM Example +# For images and videos, you can use url, local_path, base64, or bytes as input sources. +vlm_chat = [ + {"role": "system", "content": [{"text": "System Prompt", "type": "text"}]}, + {"role": "user", "content": [{"text": "User Text Prompt 1", "type": "text"}]}, + { + "role": "user", + "content": [{ + "filename": "tradeoff_sota.png", + "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff_sota.png?raw=true", + "lens_keywords": "Gucci Ophidia, cross bag, Ophidia small, GG, Supreme shoulder bag", + "lens_local_keywords": "[0.07, 0.21, 0.92, 0.90] Gucci Ophidia", + "ocr": "List the words in the image in raster order. Even if the word order feels unnatural for reading, the model will handle it as long as it follows raster order.", "type": "image", + }], + }, + { + "role": "user", + "content": [{ + "filename": "tradeoff.png", + "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff.png?raw=true", + "type": "image", + }], + }, + {"role": "assistant", "content": [{"text": "Assistant Text Prompt 1", "type": "text"}]}, + {"role": "user", "content": [{"text": "User Text Prompt 2", "type": "text"}]}, + { + "role": "user", + "content": [ + { + "type": "video", + "video": "freenaturestock-rolling-mist-clouds.mp4", + "lens_keywords": "Prada re-edition, nylon bag, mini cross bag, logo strap, essential shoulder bag", + "lens_local_keywords": "[0.12, 0.34, 0.85, 0.76] Prada re-edition", + "speech_to_text": "Please enter the dialogue, voice, sound, lines, and words in the video in text format.", + }, + {"text": "User Text Prompt 3", "type": "text"}, + ] + }, +] + +model_inputs = processor.apply_chat_template( + vlm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True, +) +model_inputs = model_inputs.to(device="cuda") +output_ids = model.generate( + **model_inputs, + max_new_tokens=64, + do_sample=True, + top_p=0.6, + temperature=0.5, + repetition_penalty=1.0, +) +print("=" * 80) +print("VLM EXAMPLE") +print(processor.batch_decode(output_ids)[0]) +print("=" * 80) + +``` + +## Example for v0.1.0 +**(code & benchmark score) checked with transformers 4.45.0** + +```python + +from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer + +model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B" +revision="v0.1.0" +model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, revision=revision).to(device="cuda") +preprocessor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True, revision=revision) +tokenizer = AutoTokenizer.from_pretrained(model_name, revision=revision) + +# LLM Example +# It is recommended to use the chat template with HyperCLOVAX models. +# Using the chat template allows you to easily format your input in ChatML style. +chat = [ + {"role": "system", "content": "you are helpful assistant!"}, + {"role": "user", "content": "Hello, how are you?"}, + {"role": "assistant", "content": "I'm doing great. How can I help you today?"}, + {"role": "user", "content": "I'd like to show off how chat templating works!"}, +] +input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt", tokenize=True) +input_ids = input_ids.to(device="cuda") + +# Please adjust parameters like top_p appropriately for your use case. +output_ids = model.generate( + input_ids, + max_new_tokens=64, + do_sample=True, + top_p=0.6, + temperature=0.5, + repetition_penalty=1.0, +) +print("=" * 80) +print("LLM EXAMPLE") +print(tokenizer.batch_decode(output_ids)[0]) +print("=" * 80) + +# VLM Example +# For image and video inputs, you can use url, local_path, base64, or bytes. +vlm_chat = [ + {"role": "system", "content": {"type": "text", "text": "System Prompt"}}, + {"role": "user", "content": {"type": "text", "text": "User Text 1"}}, + { + "role": "user", + "content": { + "type": "image", + "filename": "tradeoff_sota.png", + "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff_sota.png?raw=true", + "ocr": "List the words in the image in raster order. Even if the word order feels unnatural for reading, the model will handle it as long as it follows raster order.", + "lens_keywords": "Gucci Ophidia, cross bag, Ophidia small, GG, Supreme shoulder bag", + "lens_local_keywords": "[0.07, 0.21, 0.92, 0.90] Gucci Ophidia", + } + }, + { + "role": "user", + "content": { + "type": "image", + "filename": "tradeoff.png", + "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff.png?raw=true", + } + }, + {"role": "assistant", "content": {"type": "text", "text": "Assistant Text 1"}}, + {"role": "user", "content": {"type": "text", "text": "User Text 2"}}, + { + "role": "user", + "content": { + "type": "video", + "filename": "rolling-mist-clouds.mp4", + "video": "freenaturestock-rolling-mist-clouds.mp4", + } + }, + {"role": "user", "content": {"type": "text", "text": "User Text 3"}}, +] + +new_vlm_chat, all_images, is_video_list = preprocessor.load_images_videos(vlm_chat) +preprocessed = preprocessor(all_images, is_video_list=is_video_list) +input_ids = tokenizer.apply_chat_template( + new_vlm_chat, return_tensors="pt", tokenize=True, add_generation_prompt=True, +) + +output_ids = model.generate( + input_ids=input_ids.to(device="cuda"), + max_new_tokens=8192, + do_sample=True, + top_p=0.6, + temperature=0.5, + repetition_penalty=1.0, + **preprocessed, +) +print("=" * 80) +print("VLM EXAMPLE") +print(tokenizer.batch_decode(output_ids)[0]) +print("=" * 80) +``` + +- To ensure the highest level of image understanding performance, it is recommended to include additional information such as Optical Character Recognition (OCR) results and entity recognition (Lens). The provided usage examples are written under the assumption that OCR and Lens results are available. If you input data in this format, you can expect significantly improved output quality. + +## vLLM +To speed up your inference, you can use the vLLM engine from [our repository](https://github.com/NAVER-Cloud-HyperCLOVA-X/vllm/tree/v0.9.2rc2_hyperclovax_vision_seed). + +Make sure to switch to the `v0.9.2rc2_hyperclovax_vision_seed` branch. + +**Launch API server**: + +```bash +pyenv virtualenv 3.10.2 .vllm +pyenv activate .vllm +sudo apt-get install -y kmod +pip install --upgrade setuptools wheel pip +pip install setuptools_scm + +# install latest commit (e.g. v0.9.0) +VLLM_USE_PRECOMPILED=1 pip install -e .[serve] --cache-dir=/mnt/tmp +pip install -U pynvml +pip install timm av decord + +# or install previous commit (e.g. v0.8.4) +pip install -r ./requirements/build.txt +pip install -r ./requirements/common.txt +pip install -r ./requirements/cuda.txt +pip install flash_attn==2.7.4.post1 +pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ +export VLLM_COMMIT=dc1b4a6f1300003ae27f033afbdff5e2683721ce +export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl +VLLM_USE_PRECOMPILED=1 pip install -e .[serve] --cache-dir=/mnt/tmp +pip install -U pynvml +pip install timm av decord + +# Then launch api +MODEL=your/mode/path +export ATTENTION_BACKEND=FLASH_ATTN_VLLM_V1 +VLLM_USE_V1=1 VLLM_ATTENTION_BACKEND=${ATTENTION_BACKEND} CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \ + --seed 20250525 \ + --port ${PORT} \ + --allowed-local-media-path $ALLOWED_LOCAL_MEDIA_PATH \ + --max-model-len 8192 \ + --max-num-batched-tokens 8192 \ + --max-num-seqs 128 \ + --max-parallel-loading-workers 128 \ + --limit-mm-per-prompt.image="32" \ + --limit-mm-per-prompt.viedo="32" \ + --max-num-frames 256 \ + --tensor-parallel-size 1 \ + --data-parallel-size 1 \ + --model ${MODEL} \ + --dtype float16 \ + --trust-remote-code \ + --chat-template-content-format "openai" \ + --download-dir $DONWLOAD_DIR +``` + +**Request Example**: +- https://github.com/vllm-project/vllm/pull/20931#issue-3229161410 + +**Offline Inference Examples**: +- https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/vision_language.py +- https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/vision_language_multi_image.py \ No newline at end of file diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..b73b324 --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,35 @@ +{ + "": 110521, + "": 110522, + "": 110520, + "": 110523, + "": 110502, + "": 110501, + "": 110492, + "": 110503, + "": 110495, + "": 110494, + "": 110493, + "": 110498, + "": 110499, + "": 110500, + "": 110496, + "": 110497, + "": 110504, + "": 110507, + "": 110509, + "": 110512, + "": 110510, + "": 110511, + "": 110519, + "": 110513, + "": 110508, + "": 110518, + "": 110517, + "": 110506, + "": 110514, + "": 110516, + "": 110515, + "": 110505, + "": 110491 +} diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..6c1aced --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,65 @@ +<|im_start|>tool_list +<|im_end|> +{% for message in messages %} +{% set content = message['content'] %} +{% set role = message['role'] %} +{% if loop.first and role != 'system' %} +<|im_start|>system +You are a helpful assistant.<|im_end|> +{% endif %} +{% if message['content'] is string %} +<|im_start|>{{ role }} +{{ message['content'] }}<|im_end|> +{% elif message['content'] is mapping %} +{% if content['type'] == 'image' %} +<|im_start|>{{ role }} (mime) +{"type": "image/jpeg", "filename": "{{ content['filename'] }}"}<|im_end|> +<|im_start|>{{ role }} (vector) +<|dummy3|><|im_end|> +<|im_start|>image/aux +다음 중 ocr은 사진에서 검출된 글자이고, lens_keyword는 사진에서 추출된 keyword와 bbox 위치입니다. bbox는 0~1 사이로 정규화된 [x1, y1, x2, y2]의 형태입니다. 참고하여 답변하세요. {"ocr": "{{ content['ocr'] or '' }}", "lens_keywords": "{{ content['lens_keywords'] or '' }}", "lens_local_keywords": "{{ content['lens_local_keywords'] or '' }}"}<|im_end|> +{% elif content['type'] == 'video' %} +<|im_start|>{{ role }} (mime) +{"type": "video/mp4", "filename": "{{ content['filename'] }}"}<|im_end|> +<|im_start|>{{ role }} (vector) +<|_unuse_missing_100270|><|im_end|> +<|im_start|>image/aux +{% if content.get('is_final_grid') %} +다음 중 lens_keyword는 사진에서 추출된 keyword와 bbox 위치입니다. bbox는 0~1 사이로 정규화된 [x1, y1, x2, y2]의 형태입니다. video_time_stamp는 비디오에서 해당 구간의 시간 정보입니다. speech_to_text는 비디오 속에서의 대화, 음성, 소리, 대사, 그리고 말을 전부 글로 받아 적은 것 입니다. 참고하여 답변하세요. {"video_time_stamp": "{{ content['video_time_stamp'] }}", "lens_keywords": "{{ content.get('lens_keywords', '') }}", "lens_local_keywords": "{{ content.get('lens_local_keywords', '') }}", "speech_to_text": "{{ content.get('speech_to_text', '') }}"} +{% else %} +다음 중 video_time_stamp는 비디오에서 해당 구간의 시간 정보입니다. 참고하여 답변하세요. {"video_time_stamp": "{{ content['video_time_stamp'] }}"} +{% endif %}<|im_end|> +{% elif content['type'] == 'text' %} +<|im_start|>{{ role }} +{{ content['text'] }}<|im_end|> +{% endif %} +{% elif message['content'] is sequence %} +{% for content in message['content'] %} +{% if content['type'] == 'image' %} +<|im_start|>{{ role }} (mime) +{"type": "image/jpeg", "filename": "{{ content['filename'] }}"}<|im_end|> +<|im_start|>{{ role }} (vector) +<|dummy3|><|im_end|> +<|im_start|>image/aux +다음 중 ocr은 사진에서 검출된 글자이고, lens_keyword는 사진에서 추출된 keyword와 bbox 위치입니다. bbox는 0~1 사이로 정규화된 [x1, y1, x2, y2]의 형태입니다. 참고하여 답변하세요. {"ocr": "{{ content['ocr'] or '' }}", "lens_keywords": "{{ content['lens_keywords'] or '' }}", "lens_local_keywords": "{{ content['lens_local_keywords'] or '' }}"}<|im_end|> +{% elif content['type'] == 'video' %} +<|im_start|>{{ role }} (mime) +{"type": "video/mp4", "filename": "{{ content['filename'] }}"}<|im_end|> +<|im_start|>{{ role }} (vector) +<|_unuse_missing_100270|><|im_end|> +<|im_start|>image/aux +{% if content.get('is_final_grid') %} +다음 중 lens_keyword는 사진에서 추출된 keyword와 bbox 위치입니다. bbox는 0~1 사이로 정규화된 [x1, y1, x2, y2]의 형태입니다. video_time_stamp는 비디오에서 해당 구간의 시간 정보입니다. speech_to_text는 비디오 속에서의 대화, 음성, 소리, 대사, 그리고 말을 전부 글로 받아 적은 것 입니다. 참고하여 답변하세요. {"video_time_stamp": "{{ content['video_time_stamp'] }}", "lens_keywords": "{{ content.get('lens_keywords', '') }}", "lens_local_keywords": "{{ content.get('lens_local_keywords', '') }}", "speech_to_text": "{{ content.get('speech_to_text', '') }}"} +{% else %} +다음 중 video_time_stamp는 비디오에서 해당 구간의 시간 정보입니다. 참고하여 답변하세요. {"video_time_stamp": "{{ content['video_time_stamp'] }}"} +{% endif %}<|im_end|> +{% elif content['type'] == 'text' %} +<|im_start|>{{ role }} +{{ content['text'] }}<|im_end|> +{% endif %} +{% endfor %} +{% endif %} +{% endfor %} +{% if add_generation_prompt %} +<|im_start|>assistant +{% endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..20158c0 --- /dev/null +++ b/config.json @@ -0,0 +1,202 @@ +{ + "anyres": true, + "architectures": [ + "HCXVisionForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_hyperclovax.HCXVisionConfig", + "AutoModelForCausalLM": "modeling_hyperclovax.HCXVisionForCausalLM" + }, + "decoder_max_length": 16384, + "freeze_decoder": false, + "freeze_encoder": true, + "freeze_mm_projector": false, + "hidden_size": 3072, + "ignore_index": -100, + "video_token_id": 100270, + "image_token_id": 100271, + "mm_projector_type": "cabstractor", + "text_config": { + "_attn_implementation_autoset": true, + "_name_or_path": "", + "add_cross_attention": false, + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bad_words_ids": null, + "begin_suppress_tokens": null, + "bos_token_id": 100257, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": null, + "decoder_start_token_id": null, + "diversity_penalty": 0.0, + "do_sample": false, + "early_stopping": false, + "encoder_no_repeat_ngram_size": 0, + "end_token_id": 100257, + "eos_token_id": 100257, + "exponential_decay_length_penalty": null, + "finetuning_task": null, + "forced_bos_token_id": null, + "forced_eos_token_id": null, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 3072, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "initializer_range": 0.02, + "intermediate_size": 7168, + "is_decoder": false, + "is_encoder_decoder": false, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "length_penalty": 1.0, + "logits_scaling": 1.0, + "max_length": 20, + "max_position_embeddings": 131072, + "min_length": 0, + "mlp_bias": false, + "model_type": "llama", + "no_repeat_ngram_size": 0, + "num_attention_heads": 24, + "num_beam_groups": 1, + "num_beams": 1, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "num_return_sequences": 1, + "output_attentions": false, + "output_hidden_states": false, + "output_scores": false, + "pad_token_id": 100257, + "prefix": null, + "pretraining_tp": 1, + "problem_type": null, + "pruned_heads": {}, + "remove_invalid_values": false, + "repetition_penalty": 1.0, + "resid_pdrop": 0.2, + "return_dict": true, + "return_dict_in_generate": false, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 100000000, + "sep_token_id": null, + "suppress_tokens": null, + "task_specific_params": null, + "temperature": 1.0, + "tf_legacy_loss": false, + "tie_encoder_decoder": false, + "tie_word_embeddings": true, + "tokenizer_class": null, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": false, + "transformers_version": "4.52.4", + "typical_p": 1.0, + "use_bfloat16": false, + "use_cache": true, + "vocab_size": 110592 + }, + "max_image_cnt": 12, + "max_num_grids": 9, + "model_type": "hyperclovax_vlm", + "num_queries_vis_abstractor_image": 81, + "num_queries_vis_abstractor_video_slow": 81, + "num_queries_vis_abstractor_video_fast": 9, + "first_last_frames_slow": false, + "proj_pos_emb": true, + "proj_prenorm": false, + "q_former_model_name_or_path": null, + "torch_dtype": "bfloat16", + "transformers_version": "4.52.4", + "unpad": true, + "use_1x1_grid": true, + "use_nth_layer": -2, + "vision_config": { + "_attn_implementation_autoset": true, + "_name_or_path": "", + "add_cross_attention": false, + "architectures": [ + "SiglipVisionModel" + ], + "attention_dropout": 0.0, + "auto_map": {}, + "bad_words_ids": null, + "begin_suppress_tokens": null, + "bos_token_id": null, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": null, + "decoder_start_token_id": null, + "diversity_penalty": 0.0, + "do_sample": false, + "early_stopping": false, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": null, + "exponential_decay_length_penalty": null, + "finetuning_task": null, + "forced_bos_token_id": null, + "forced_eos_token_id": null, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "id2label": { + "0": "LABEL_0", + "1": "LABEL_1" + }, + "image_size": 378, + "initializer_factor": 1.0, + "intermediate_size": 4304, + "is_decoder": false, + "is_encoder_decoder": false, + "label2id": { + "LABEL_0": 0, + "LABEL_1": 1 + }, + "layer_norm_eps": 1e-06, + "length_penalty": 1.0, + "max_length": 20, + "max_num_grids": 9, + "min_length": 0, + "model_type": "siglip_vision_model", + "no_repeat_ngram_size": 0, + "num_attention_heads": 16, + "num_beam_groups": 1, + "num_beams": 1, + "num_channels": 3, + "num_hidden_layers": 27, + "num_return_sequences": 1, + "output_attentions": false, + "output_hidden_states": false, + "output_scores": false, + "pad_token_id": null, + "patch_size": 14, + "prefix": null, + "problem_type": null, + "pruned_heads": {}, + "remove_invalid_values": false, + "repetition_penalty": 1.0, + "return_dict": true, + "return_dict_in_generate": false, + "sep_token_id": null, + "suppress_tokens": null, + "task_specific_params": null, + "temperature": 1.0, + "tf_legacy_loss": false, + "tie_encoder_decoder": false, + "tie_word_embeddings": true, + "tokenizer_class": null, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": "bfloat16", + "torchscript": false, + "transformers_version": "4.52.4", + "typical_p": 1.0, + "use_bfloat16": true + } +} \ No newline at end of file diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..159097f --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "others", "allow_remote": true} \ No newline at end of file diff --git a/configuration_hyperclovax.py b/configuration_hyperclovax.py new file mode 100644 index 0000000..d371985 --- /dev/null +++ b/configuration_hyperclovax.py @@ -0,0 +1,66 @@ +from transformers import AutoConfig +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class HCXVisionConfig(PretrainedConfig): + model_type = "hyperclovax_vlm" + keys_to_ignore_at_inference = ["past_key_values"] + + # The `gpt2` class has a different name, so it needs to be updated accordingly. + text_config_attribute_map = { + "n_embd": "hidden_size", + "n_positions": "max_position_embeddings", + "n_head": "num_attention_heads", + "n_layer": "num_hidden_layers", + } + + def __init__( + self, + text_config=None, + vision_config=None, + use_nth_layer=-2, + img_start_id=100009, # <|dummy3|> + decoder_max_length=4096, + anyres=False, + unpad=False, + max_num_grids=-1, + num_queries_vis_abstractor=-1, + ignore_index=-100, + proj_pos_emb=True, + proj_prenorm=False, + use_1x1_grid=False, + **kwargs, + ): + for key, val in self.text_config_attribute_map.items(): + if text_config is not None and key in text_config: + text_config[val] = text_config.pop(key) + + if text_config is not None: + _text_config = AutoConfig.for_model(text_config["model_type"]) + self.text_config = _text_config.from_dict(text_config) + + # In DeepSpeed ZeRO-3, the memory size is automatically determined based on the `hidden_size` specified in the config. + self.hidden_size = text_config["hidden_size"] if "hidden_size" in text_config else text_config["n_embd"] + if vision_config is not None: + _vision_config = AutoConfig.for_model(vision_config["model_type"]) + self.vision_config = _vision_config.from_dict(vision_config) + + # add VLM configs + self.use_nth_layer = use_nth_layer + self.decoder_max_length = decoder_max_length + self.anyres = anyres + self.unpad = unpad + self.max_num_grids = max_num_grids + self.num_queries_vis_abstractor = num_queries_vis_abstractor + self.img_start_id = img_start_id + self.ignore_index = ignore_index + self.proj_pos_emb = proj_pos_emb + self.proj_prenorm = proj_prenorm + self.use_1x1_grid = use_1x1_grid + super().__init__(**kwargs) + + def get_text_config(self, decoder=False): + return self.text_config diff --git a/image_processing_hyperclovax.py b/image_processing_hyperclovax.py new file mode 100644 index 0000000..f719f14 --- /dev/null +++ b/image_processing_hyperclovax.py @@ -0,0 +1,789 @@ +import copy +import math +import os +from typing import Dict, List, Optional, Union + +import numpy as np +import torch +from PIL import Image +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_processing_utils import ( + BaseImageProcessor, + get_size_dict, +) +from transformers.image_transforms import ( + convert_to_rgb, + get_resize_output_image_size, + resize, + to_channel_dimension_format, +) +from transformers.image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, +) +from transformers.utils import TensorType, logging + +logger = logging.get_logger(__name__) + + +class HCXImageProcessor(BaseImageProcessor): + r""" + Constructs a VLM image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques for processing high resolution images. + Args: + anyres: (bool) anyres 기능을 사용할지 안할지 + unpad: (bool) anyres 사용시, unpad 기능 (순수 pad 영역에 해당하는 visual tokens 은 LLM input 에서 제거) 을 사용할지 안할지 + num_queries_vis_abstractor: (int) 각 grid 에 대해서 resampler 를 사용하는 경우, visual query 수 + possible_resolutions: (List) anyres 기능 사용시, 가능한 resolution 조합, 예: [[336, 336], [336, 672], [672, 336]] + patch_size: (int) ViT patch size + pad_to_square: (bool) 정사각형으로 padding 을 수행할지, 안할지를 결정. False 이면 정사각형이 아니기 때문에 center crop 을 거쳐 ViT 의 입력으로 들어감 + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + anyres: bool = False, + unpad: bool = False, + num_queries_vis_abstractor_image: int = 81, + num_queries_vis_abstractor_video_slow: int = 81, + num_queries_vis_abstractor_video_fast: int = 9, + first_last_frames_slow_video: bool = False, + possible_resolutions: List = [], + patch_size: int = 14, + pad_to_square: bool = True, + resample: PILImageResampling = PILImageResampling.BICUBIC, + do_center_crop: bool = True, + crop_size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = True, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"shortest_edge": 336} + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else {"height": 336, "width": 336} + crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size") + + self.do_resize = do_resize + self.size = size + self.anyres = anyres + self.unpad = unpad + self.num_queries_vis_abstractor_image = num_queries_vis_abstractor_image + self.num_queries_vis_abstractor_video_slow = num_queries_vis_abstractor_video_slow + self.num_queries_vis_abstractor_video_fast = num_queries_vis_abstractor_video_fast + self.first_last_frames_slow_video = first_last_frames_slow_video + self.possible_resolutions = [_resolution for _resolution in possible_resolutions] + self.patch_size = patch_size + self.pad_to_square = pad_to_square + self.resample = resample + self.do_center_crop = do_center_crop + self.crop_size = crop_size + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN + self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD + self.do_convert_rgb = do_convert_rgb + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BICUBIC, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + default_to_square = True + if "shortest_edge" in size: + size = size["shortest_edge"] + default_to_square = False + elif "height" in size and "width" in size: + size = (size["height"], size["width"]) + else: + raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") + + output_size = get_resize_output_image_size( + image, + size=size, + default_to_square=default_to_square, + input_data_format=input_data_format, + ) + + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def _preprocess( + self, + images: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: int = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> Image.Image: + images = make_list_of_images(images) + + if do_resize: + images = [ + self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + for image in images + ] + + if do_center_crop: + images = [ + self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images + ] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + return images + + def _resize_for_local_grids( + self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension + ) -> np.array: + new_height, new_width = _get_local_grids_output_size(image, target_resolution, input_data_format) + + # Resize the image + resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format) + + return resized_image + + def _pad_for_patching( + self, image: np.array, target_resolution: tuple, input_data_format: ChannelDimension + ) -> np.array: + """ + Pad an image to a target resolution while maintaining aspect ratio. + """ + target_height, target_width = target_resolution + + background_color = tuple(int(x * 255) for x in self.image_mean) + padded_image = pad( + image, + target_size=(target_height, target_width), + background_color=background_color, + input_data_format=input_data_format, + ) + + return padded_image + + def get_image_grids( + self, + image: np.array, + possible_resolutions, + grid_size: int, + resample: PILImageResampling, + data_format: ChannelDimension, + input_data_format: ChannelDimension, + ) -> List[np.array]: + if not isinstance(possible_resolutions, list): + raise ValueError("possible_resolutions must be a list of possible resolutions.") + + image_size = get_image_size(image, channel_dim=input_data_format) + best_resolution = select_best_resolution(image_size, possible_resolutions) + resized_image = self._resize_for_local_grids( + image, best_resolution, resample=resample, input_data_format=input_data_format + ) + padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format) + local_grids = divide_to_grids(padded_image, grid_size=grid_size, input_data_format=input_data_format) + + # make sure that all patches are in the input data format + local_grids = [ + to_channel_dimension_format(grid, channel_dim=data_format, input_channel_dim=input_data_format) + for grid in local_grids + ] + + return local_grids + + def preprocess( + self, + images: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + anyres: bool = None, + unpad: bool = None, + is_video: bool = False, + num_queries_vis_abstractor_image: int = None, + num_queries_vis_abstractor_video_slow: int = None, + num_queries_vis_abstractor_video_fast: int = None, + first_last_frames_slow_video: bool = None, + possible_resolutions: List = None, + patch_size: int = None, + pad_to_square: bool = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: int = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + return_dummy_image: bool = False, + first_last_frames_slow: bool = False, + is_first_or_last_frames: bool = False, + **kwargs, + ): + """ + HCXVisionImageProcessor 로 image tensor, original image size (width, height), visual tokens + :return pixel_values: List of 4D tensor 로 image tensor + :return image_sizes: List of Dict 로 image width, height [{"width": image 1 의 width, "height": image 1 의 height}, {"width": image 2 의 width, "height": image 2 의 height}, ...] + :return vision_query_lengths: List of int 로 각 image 가 LLM 입력으로 전달될때 변환되는 visual token 수 + """ + + do_resize = do_resize if do_resize is not None else self.do_resize + size = size if size is not None else self.size + size = get_size_dict(size, param_name="size", default_to_square=False) + anyres = anyres if anyres is not None else self.anyres + unpad = unpad if unpad is not None else self.unpad + num_queries_vis_abstractor_image = ( + num_queries_vis_abstractor_image + if num_queries_vis_abstractor_image is not None + else self.num_queries_vis_abstractor_image + ) + num_queries_vis_abstractor_video_slow = ( + num_queries_vis_abstractor_video_slow + if num_queries_vis_abstractor_video_slow is not None + else self.num_queries_vis_abstractor_video_slow + ) + num_queries_vis_abstractor_video_fast = ( + num_queries_vis_abstractor_video_fast + if num_queries_vis_abstractor_video_fast is not None + else self.num_queries_vis_abstractor_video_fast + ) + first_last_frames_slow_video = ( + first_last_frames_slow_video + if first_last_frames_slow_video is not None + else self.first_last_frames_slow_video + ) + possible_resolutions = possible_resolutions if possible_resolutions is not None else self.possible_resolutions + patch_size = patch_size if patch_size is not None else self.patch_size + pad_to_square = pad_to_square if pad_to_square is not None else self.pad_to_square + resample = resample if resample is not None else self.resample + do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop + crop_size = crop_size if crop_size is not None else self.crop_size + crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True) + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb + + if is_video: + num_queries_vis_abstractor = num_queries_vis_abstractor_video_fast + num_queries_vis_abstractor_slow = num_queries_vis_abstractor_video_slow + unpad = False + else: + num_queries_vis_abstractor = num_queries_vis_abstractor_image + num_queries_vis_abstractor_slow = 0 + + if return_dummy_image: + images = Image.new("RGB", (224, 224), (0, 0, 0)) + + images = make_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + if do_convert_rgb: + images = [convert_to_rgb(image) for image in images] + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + new_images = [] + image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images] + vision_query_lengths = [] + + assert crop_size["height"] == crop_size["width"] + + # global image 의 padding 연산은, image original width, height 가 클 때 bottleneck 이 될 수 있음 + # 장축의 길이를 size["shortest_edge"] 로 resize 를 먼저 한 뒤에, padding + if anyres: + anyres_global_images = copy.deepcopy(images) + if pad_to_square: + background_color = tuple(int(x * 255) for x in self.image_mean) + anyres_global_images = [ + resize_longside(copy.deepcopy(image), size["shortest_edge"], resample, input_data_format) + for image in anyres_global_images + ] + anyres_global_images = [ + expand2square(image, background_color=background_color, input_data_format=input_data_format)[0] + for image in anyres_global_images + ] + else: + anyres_global_images = [ + self.resize( + image=image, + size={"height": size["shortest_edge"], "width": size["shortest_edge"]}, + resample=resample, + input_data_format=input_data_format, + ) + for image in anyres_global_images + ] + else: + anyres_global_images = [None for _ in range(len(images))] + if pad_to_square: + background_color = tuple(int(x * 255) for x in self.image_mean) + images = [ + resize_longside(image, size["shortest_edge"], resample, input_data_format) for image in images + ] + images = [ + expand2square(image, background_color=background_color, input_data_format=input_data_format)[0] + for image in images + ] + + for image, anyres_global_image, image_size in zip(images, anyres_global_images, image_sizes): + if anyres: + # convert image into a list of grids + # we intentially use the same data format as the input data format + image_grids = self.get_image_grids( + image, + possible_resolutions, + grid_size=crop_size["height"], + resample=resample, + data_format=input_data_format, + input_data_format=input_data_format, + ) + # video 에 대해서는 global image (thumbnail) 를 사용하지 않음 + if not is_video: + image_grids = [anyres_global_image] + image_grids + else: + image_grids = [image] + + pixel_values = self._preprocess( + image_grids, + do_resize=do_resize, + size=size, + resample=resample, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + input_data_format=input_data_format, + ) + + pixel_values = np.array(pixel_values) + new_images.append(pixel_values) + + vision_query_length = determine_anyres_num_vision_patches( + image_size=image_size, + grid_size=crop_size["height"], + patch_size=patch_size, + possible_resolutions=possible_resolutions, + anyres=anyres, + unpad=unpad, + num_queries_vis_abstractor=num_queries_vis_abstractor, + num_queries_vis_abstractor_slow=num_queries_vis_abstractor_slow, + is_video=is_video, + first_last_frames_slow=first_last_frames_slow, + is_first_or_last_frames=is_first_or_last_frames, + ) + + vision_query_lengths.append(vision_query_length) + + if return_dummy_image: + vision_query_lengths = [] + + data = { + "pixel_values": [torch.tensor(new_image) for new_image in new_images], + "image_sizes": [{"width": image_size[1], "height": image_size[0]} for image_size in image_sizes], + "vision_query_lengths": vision_query_lengths, + } + + return BatchFeature(data=data, tensor_type=return_tensors) + + def save_pretrained( + self, + save_directory: Union[str, os.PathLike], + *args, + **kwargs, + ): + self.register_for_auto_class() + super().save_pretrained(save_directory, *args, **kwargs) + + +def determine_anyres_num_vision_patches( + image_size, + grid_size, + patch_size, + possible_resolutions, + anyres=False, + unpad=True, + num_queries_vis_abstractor=0, + num_queries_vis_abstractor_slow=0, + is_video=False, + first_last_frames_slow=False, # sample-wise option + is_first_or_last_frames=False, # grid-wise option +): + """ + Computes the number of visual tokens (patches) based on image resolution, grid configuration, and patch size. + + This function supports both fixed-size and any-resolution settings, as well as video-specific configurations + such as handling slow frames and frame position flags. + + Args: + num_grids (int): Number of grids per image (e.g., 1 for 1x1, 4 for 2x2, etc.). + image_size (tuple): The original image size as (height, width). + grid_size (int): Size of each grid in pixels (e.g., 336). + patch_size (int): Size of each vision patch (e.g., 14 for ViT models). + possible_resolutions (list): List of possible resolution tuples [(h1, w1), (h2, w2), ...]. + anyres (bool, optional): Whether to use any-resolution mode. Defaults to False. + unpad (bool, optional): Whether to unpad the image before computing patches. Defaults to True. + num_queries_vis_abstractor (int, optional): Number of query tokens for vision abstractor (fast path). + num_queries_vis_abstractor_slow (int, optional): Number of query tokens for vision abstractor (slow path). + is_video (bool, optional): Whether the input is a video. Defaults to False. + first_last_frames_slow (bool, optional): Whether to treat first/last video frames as "slow". Defaults to False. + is_first_or_last_frames (bool, optional): Whether current grid corresponds to first/last frame. Defaults to False. + + Returns: + int: Total number of visual tokens (patches) after processing. + """ + + if not anyres: + return num_queries_vis_abstractor if num_queries_vis_abstractor > 0 else (grid_size // patch_size) ** 2 + + if num_queries_vis_abstractor > 0: + num_patch_per_grid = int(num_queries_vis_abstractor**0.5) + else: + num_patch_per_grid = grid_size // patch_size + + num_global_per_grid = num_patch_per_grid + + # In anyres mode, a global image is included, so there are always at least 2 grids. + # However, for video inputs, there is no global image, so it's possible to have only 1 grid. + # Therefore, the assertion below is commented out: + # assert num_grids > 1 + + # Compute the number of vision patches. + height, width = select_best_resolution(image_size, possible_resolutions) + + num_patch_height = (height // grid_size) * num_patch_per_grid + num_patch_width = (width // grid_size) * num_patch_per_grid + + # local images + if unpad: + original_height, original_width = image_size + + original_aspect_ratio = original_width / original_height + current_aspect_ratio = num_patch_width / num_patch_height + + if original_aspect_ratio > current_aspect_ratio: + scale_factor = num_patch_width / original_width + new_height = int(original_height * scale_factor) + padding = (num_patch_height - new_height) // 2 + num_patch_height = num_patch_height - padding * 2 + else: + scale_factor = num_patch_height / original_height + new_width = int(original_width * scale_factor) + padding = (num_patch_width - new_width) // 2 + num_patch_width = num_patch_width - padding * 2 + + num_patches = num_patch_width * num_patch_height + num_patch_height + else: + num_patches = num_patch_width * num_patch_height + + # In the "slow" strategy, when applying to first and last frames only, it is applied exclusively to those two frames. + if num_queries_vis_abstractor_slow > 0: + if first_last_frames_slow: + if is_first_or_last_frames: + num_patches += num_queries_vis_abstractor_slow - num_queries_vis_abstractor + else: + num_patches += num_queries_vis_abstractor_slow - num_queries_vis_abstractor + # The slowfast feature is only applicable when unpad is set to False. + assert unpad is False + + # Global image is not included for video inputs. + if not is_video: + num_patches += num_global_per_grid**2 + + return num_patches + + +def divide_to_grids(image: np.array, grid_size: int, input_data_format=None) -> List[np.array]: + """ + Divides a local image into grids of size (grid_size x grid_size). + + Args: + image (np.array): Input image as a NumPy array. + grid_size (int): The size (in pixels) of each square grid. + input_data_format (optional): Optional format specifier (e.g., "channels_first" or "channels_last"). + + Returns: + List[np.array]: A list of image patches, each of size (grid_size x grid_size). + """ + grids = [] + height, width = get_image_size(image, channel_dim=input_data_format) + for i in range(0, height, grid_size): + for j in range(0, width, grid_size): + if input_data_format == ChannelDimension.LAST: + grid = image[i : i + grid_size, j : j + grid_size] + else: + grid = image[:, i : i + grid_size, j : j + grid_size] + grids.append(grid) + + return grids + + +def pad( + image: np.array, + target_size: tuple, + background_color=(127, 127, 127), + input_data_format=None, +) -> np.array: + """ + Pads the input image on the sides (top/bottom and left/right) to match the target height and width. + + Args: + image (np.array): Input image as a NumPy array. + target_size (tuple): Target size as (target_height, target_width). + background_color (tuple, optional): RGB color value used for padding. Defaults to (127, 127, 127). + input_data_format (optional): Optional format specifier (e.g., "channels_first" or "channels_last"). + + Returns: + np.array: The padded image with the specified target size. + """ + target_height, target_width = target_size + height, width = get_image_size(image, channel_dim=input_data_format) + + # result = np.ones((target_height, target_width, image.shape[2]), dtype=image.dtype) * background_color + result = np.empty((target_height, target_width, image.shape[2]), dtype=image.dtype) + for i in range(image.shape[2]): + result[..., i].fill(background_color[i]) + + paste_x = (target_width - width) // 2 + paste_y = (target_height - height) // 2 + + result[paste_y : paste_y + height, paste_x : paste_x + width, :] = image + + return result + + +def expand2square( + image: np.array, + bboxes_dict=None, + background_color=(127, 127, 127), + input_data_format=None, +) -> np.array: + """ + Expands the input image to a square shape by placing it at the center of a new square canvas, + with padding added to the shorter side (either top/bottom or left/right). + + The image is always centered on the new canvas, and padding is applied symmetrically. + + Args: + image (np.array): Input image as a NumPy array. + bboxes_dict (dict, optional): A dictionary of bounding boxes, where each value is an NDArray of shape (N, 4, 2) + with box coordinates in the format [[xtl, ytl], [xtr, ytr], [xbr, ybr], [xbl, ybl]]. + Supports multiple categories (e.g., "ocr", "html") simultaneously. + background_color (tuple, optional): RGB color to fill the padding area. Defaults to (127, 127, 127). + input_data_format (optional): Optional format specifier for image data (e.g., "channels_first" or "channels_last"). + + Returns: + np.array: A square-shaped image with the original image centered and padded as needed. + + Example: + >>> _img = np.ones((80, 100), dtype=np.uint8) * 100 + >>> _bboxes_dict = {"words": np.array([[[10, 10], [20, 10], [20, 20], [10, 20]], + ... [[30, 30], [40, 30], [40, 40], [30, 40]]])} + >>> _img, _bboxes_dict = expand2square(_img, _bboxes_dict, (255, 255, 255)) + >>> _img.shape + (100, 100) + >>> guessed_ocr_bboxes = np.array([[[20, 10], [30, 10], [30, 20], [20, 20]], + ... [[40, 30], [50, 30], [50, 40], [40, 40]]]) + >>> np.testing.assert_array_almost_equal(_bboxes_dict["words"], guessed_ocr_bboxes) is None + True + """ + height, width = get_image_size(image, channel_dim=input_data_format) + if width == height: + return image, bboxes_dict + elif width > height: + # result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color + result = np.empty((width, width, image.shape[2]), dtype=image.dtype) + for i in range(image.shape[2]): + result[..., i].fill(background_color[i]) + + result[(width - height) // 2 : (width - height) // 2 + height, :] = image + if bboxes_dict is not None: + for key in bboxes_dict: + bboxes_dict[key][:, :, 1] += (width - height) // 2 + return result, bboxes_dict + else: + # result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color + result = np.empty((height, height, image.shape[2]), dtype=image.dtype) + for i in range(image.shape[2]): + result[..., i].fill(background_color[i]) + + result[:, (height - width) // 2 : (height - width) // 2 + width] = image + if bboxes_dict is not None: + for key in bboxes_dict: + bboxes_dict[key][:, :, 0] += (height - width) // 2 + return result, bboxes_dict + + +def resize_longside( + image: np.array, + size: int, + resample: PILImageResampling = PILImageResampling.BICUBIC, # type: ignore + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +): + """ + Resizes the image so that its longer side matches the specified size, maintaining the original aspect ratio. + + Args: + image (np.array): Input image as a NumPy array. + size (int): Target size for the longer side of the image. + resample (PILImageResampling, optional): Resampling method to use during resizing. Defaults to BICUBIC. + data_format (str or ChannelDimension, optional): Output data format (e.g., "channels_first" or "channels_last"). + input_data_format (str or ChannelDimension, optional): Input data format of the image. + + Returns: + np.array: The resized image with its aspect ratio preserved. + """ + height, width = get_image_size(image, channel_dim=input_data_format) + + if width == height: + target_height, target_width = size, size + elif width > height: + target_width = size + target_height = math.ceil(height / width * size) + else: + target_width = math.ceil(width / height * size) + target_height = size + + return resize( + image, + size=(target_height, target_width), + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + ) + + +def _get_local_grids_output_size(image: np.array, target_resolution: tuple, input_data_format=None): + """ + Computes the number of local grids (patches) along the height and width when resizing an image + to the target resolution. + + Args: + image (np.array): Input image as a NumPy array. + target_resolution (tuple): Target resolution in the format (target_height, target_width). + input_data_format (optional): Optional format specifier (e.g., "channels_first" or "channels_last"). + + Returns: + tuple: A tuple (grid_h, grid_w) representing the number of grids along the height and width. + """ + original_height, original_width = get_image_size(image, channel_dim=input_data_format) + target_height, target_width = target_resolution + + scale_w = target_width / original_width + scale_h = target_height / original_height + + if scale_w < scale_h: + new_width = target_width + new_height = min(math.ceil(original_height * scale_w), target_height) + else: + new_height = target_height + new_width = min(math.ceil(original_width * scale_h), target_width) + + return new_height, new_width + + +def select_best_resolution(original_size: tuple, possible_resolutions: list) -> tuple: + """ + Selects the best-fit resolution from a list of possible resolutions based on the original image size. + + This function, adapted from LLaVA-Next + (https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/llava_next/image_processing_llava_next.py), + evaluates each resolution by computing its effective and wasted area compared to the original size. + The optimal resolution is the one that maximizes the effective area while minimizing unused (wasted) space. + + Args: + original_size (tuple): The original image size in the format (height, width). + possible_resolutions (list): A list of candidate resolutions in the format [(height1, width1), (height2, width2), ...]. + + Returns: + tuple: The best-fit resolution in the format (height, width). + """ + original_height, original_width = original_size + best_fit = None + max_effective_resolution = 0 + min_wasted_resolution = float("inf") + + for height, width in possible_resolutions: + scale = min(width / original_width, height / original_height) + downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale) + effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height) + wasted_resolution = (width * height) - effective_resolution + + if effective_resolution > max_effective_resolution or ( + effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution + ): + max_effective_resolution = effective_resolution + min_wasted_resolution = wasted_resolution + best_fit = (height, width) + + return best_fit diff --git a/merges.txt b/merges.txt new file mode 100644 index 0000000..02d800d --- /dev/null +++ b/merges.txt @@ -0,0 +1,110306 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. 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+Ġê· Ģ +Ġë³´ ìŬ +Ġë ij +ìłľ ë¡ľ +Ġìī ½ +ë¦ Ń +ì¼ Ģ +Ġì¸ ¡ +Ġí Ĥ +ê·¸ëŀĺ íĶĦ +í Ĥ¬ +ëį ¸ +룬 íķľ +주 ëĬĶ +ê´Ģ 리 +ëĿ¼ ëıĦ +íĸĪ ëįĺ +ìľĦìĽIJ íļĮ +Ġ기 ëĬ¥ +ĠëIJĺ ëĬĶ +ĠëĮĢ íķĻ +ĠêµŃ ìłľ +ĠìķĪ ìłĦ +ĠìĹĨ ìĿ´ +ëĭ µ +Ġëĸ ł +Ġ매 ìļ° +ĠìķĦëĭ Į +ĠìŀĪëĭ¤ ëĬĶ +ĠìŀIJìĭł ìĿĺ +ìħľ ê·¸ëŀĺíĶĦ +ì ·¨ +ìĹ° 구 +íĺ ¼ +ĠìŀIJ ëıĻ +ĠìĤ° ìĹħ +ìĿ¸ ìĿ´ +ê²ł ëĭ¤ +Ġë°© 문 +Ġìĭľ ìŀ¥ +ĠìĪĺ ëıĦ +Ġ1 4 +문 íĻĶ +Ġ2 1 +Ġìĭľ ìĬ¤íħľ +Ġë© ´ +íĸĪ ìĬµëĭĪëĭ¤ +ìĹ ĩ +ë¡ ¯ +ë³´ íĹĺ +Ġìĺ¬ íķ´ +ìļ´ ëį° +Ġìĸ´ëĸ »ê²Į +ìĤ¬ ì§Ģ +Ġíķ µ +Ġíĺ¸ íħĶ +íĺ ģ +íĺ ľ +Ġìĸ´ 볤 +ë ij +Ġë¨ ¸ +ĠìĤ ¶ +ìĭľ íĤ¤ +Ġê³Ħ íļį +Ġë³Ģ íĻĶ +Ġíıī ê·ł +ĠìĺĪ ìłķ +ìĽIJ ìĿ´ +Ġë² Į +ì´ Ŀ +Ġí Ħ +ĠíķĺëĤĺ ëĭĺ +ĠìĿ¼ ë°ĺ +Ġ기 ëĮĢ +íļĮ 를 +Ġ1 3 +Ġìłģ ìļ© +ìĨĮ ìħľê·¸ëŀĺíĶĦ +리 ìĬ¤ +Ġê²½ íĹĺ +í ĮĢ +ĠíĴ į +Ġ물 ë¡ł +ĠíĻľ ìļ© +Ġë Ł +ĠìĿ´ íķ´ +ĠëĤ ľ +ì¶Ķ ì²ľ +Ġë ī´ +Ġâĸ ² +Ġë¶Ħ ìĦĿ +ĠëĤĺ ëĬĶ +ĠìŬ ìĦ± +ëı ¼ +리 ìĹIJ +ì¼ ľ +ìħ Ķ +ìĹ°íķ© ëī´ìĬ¤ +Ġí ĭ +Ġê³Ħ ìĨį +Ġ1 00 +ĠìĹ Ħ +ĠêµIJ ìĪĺ +ĠëĶ° 르면 +ìĿ¸ ìĿĺ +ĠìłĦ ì²´ +ì ĩ +Ġëª ĩ +Ġ3 1 +ëıĦ ìĿĺ +ìĦľ ìļ¸ +Ġê°Ļ ìĿ´ +íķĺ ê±°ëĤĺ +Ġìµľ ëĮĢ +Ġì£ ½ +ìĪ Ļ +íĽĦ 기 +ĠíķĦìļĶ íķľ +Ġì¦Ŀ ê°Ģ +Ġì¤ij ìĭ¬ +Ġìĸ » +Ġê·¸ëŀĺ ìĦľ +Ġë°Ľ ìķĦ +Ġë§ Ľ +k Wh +ĠìĿ´ 룬íķľ +íħ IJ +Ġê´Ģ íķľ +Ġì¢ Ģ +Ġê· ¹ +ìĽIJ ìĿĢ +기 ë¡ľ +ìĬ¤ íĥĢ +Ġë¶Ħ ìķ¼ +Ġ기 ì¤Ģ +Ġìłķ ì¹ĺ +ĠëIJĺ ìĸ´ +Ġê³µ ëıĻ +Ġ and +ĠíĤ ¤ +ìĹ ¼ +ìĽIJ ìĿĺ +ìľ¼ ëĤĺ +Ġê°Ģ 격 +íģ ´ +íĥ Ī +Ġë°ľ ìłĦ +ëł¥ ìĿ´ +ìĨ IJ +ĠìĽ Ķ +ë§Ī íĬ¸ +Ġê°Ģ ìļ´ëį° +ìĤ¬ íļĮ +Ġìļ°ë¦¬ ëĬĶ +Ġìłķ ì±ħ +ĠëĨĴ ìĿĢ +Ġìĺ¤ íĽĦ +Ġíıī ê°Ģ +기 ê´Ģ +ë · +Ġë ¿IJ +ë³ ¼ +Ġê¸ ī +¶ ģ +ì º +Ġìº IJ +ĠìļĶ 구 +ĠëĬIJ ëĤ +ëģ ¼ +ìľĦ 를 +í ĽĪ +ë² ¤ +ìĭľ ìĹIJ +Ġí ľ´ +íķ ij +íļ ¨ +ĠíĻ © +íķĺ ìĺĢ +íĥ ģ +ê° Ļ +ëĭ¤ ë©´ +Ġê³¼ ìłķ +ĠëįĶ ìļ± +Ġìĺģ íĸ¥ +ĠëıĮ ìķĦ +Ġê°Ģ ì§Ģ +ĠìĦ¤ ì¹ĺ +Ġ무 ìĹĩ +ĠìĿ´ 루 +Ġì¦ ī +Ġë§Ī 볨 +ìĿĺ 를 +ëĤĺ ëĿ¼ +ë ľ +ëĶĶ ìĸ´ +Ġì§Ģ ìĨį +ìķ Ķ +Ġë² Ķ +ëıĦ ê°Ģ +Ġìŀ ł +Ġëį° ìĿ´íĦ° +ì§Ģ ìĹŃ +ìĬ¤ 를 +ìĤ° ìĹħ +Ġì Ķ +ëĵľ ëĬĶ +Ġìĸ´ ëĬIJ +Ġê°Ģ 족 +ìĹĨ ìĿ´ +ì ģ +ì ī +Ġìŀij íĴĪ +ĠìĦ± ìŀ¥ +Ġì½ ĺ +ìŀ ¡ +ë¡ Ń +Ġëĭ¹ ìĭł +Ġëª ¸ +Ġê±´ ê°ķ +ìĥ µ +ĠìĿ ij +ĠìĿ¸ ê°Ħ +Ġìĥģ íĥľ +Ġê³µ ê°ľ +ĩ ´ +ĠíĮIJ 매 +ëıĦ ë¡ľ +ĠìĿ¸íĦ° ëĦ· +Ġìłķ ë§IJ +ë£ ¹ +ìĿ¸ ëį° +Ġëª ° +Ġì¶Ķ ì§Ħ +ĠíķĻ êµIJ +Ġ1 7 +ĠìĿ¼ ë¶Ģ +Ġëħ¸ ëıĻ +Ġëĭ¹ ìĭľ +Ġë°© ìĨ¡ +ì½ľ 걸 +ëĪ Ħ +êµ ´ +ìĥ Ī +ëªħ ìĿ´ +ëIJ ©ëĭĪëĭ¤ +Ġê³ ¨ +ĠëĮĢ ë¶Ģë¶Ħ +ìĸ´ ìĦľ +ëĤĺ ëĬĶ +르 ëĬĶ +Ġíķĺ 루 +ĠëĤ¨ ë¶ģ +ĠëķĮ문 ìĿ´ëĭ¤ +íĺ Ģ +ĠìĬ¤ íĥĢ +ëĦ¤ ìļĶ +ĠëĤ ® +íķ Ħ +ĠìĿ ½ +Ġ기 본 +Ġìĺ¤ ëĬĺ +ĠëĤ´ ê°Ģ +Ġìĥ ī +ĠìŀĪëĭ¤ ê³ł +ĠìĿ´ ìłľ +Ġíļ¨ ê³¼ +Ġí Ľ +Ġì±ħ ìŀĦ +ĠìĥĿ íĻľ +ĠëıĦ ìĭľ +ĠíĬ¹ ë³Ħ +Ġê°Ģ ì§Ģê³ł +기 ìĪł +Ġë³´ ë©´ +ìħ ¨ +ë¯ Ģë¡ľ +íķĺ ì§Ģë§Į +Ġì ¼ +ĠìĨĮ ê°ľ +íķĺ 볤 +ëIJĺ ì§Ģ +ìĭľ ìŀ¥ +ĠìĪĺ ì¤Ģ +ëªħ ìĿĺ +Ġê°ľ ìµľ +ĠìĤ¬ ê±´ +íı Ń +Ġëĵ± ìĿ´ +ĠìĽ ¹ +ĠìŀIJ ìľł +Ġìłķ ìĥģ +Ġê²½ìļ° ìĹIJëĬĶ +ĠëĨ ĵ +Ġê²° ìłķ +ãħ ĭ +Ġì¹ ¨ +ĠíĺĦ ëĮĢ +ĠìĤ¬ëŀĮ ìĿ´ +ìłľ 를 +ì¹ĺ ëĬĶ +ìĹĪ ëįĺ +ĠìķĦ 무 +Ġë Ń +ë¡ľ ìĦľ +ëį ķ +ĠìĹŃ íķł +Ġ기 ê°Ħ +ĠìĿ´ ëĤł +ĠìĹŃ ìĭľ +ĠëĤĺ ìĺ¤ +ĠìĦ± ê³µ +Ġì¢ĭ ìķĦ +ë§Į ìĽIJ +ìĤ ¼ +íĴ į +ìłIJ ìĿĦ +ĠìĦł ìĪĺ +ĠìŀIJ ìĹ° +ë² ½ +ĠìĥĿ ìĤ° +ìĿĮ ìĿĦ +ìĬ ¬ +Ġë¨ ¼ +Ġìĺ¤ ëĬĶ +ëĮĢ 를 +ëĵ ¯ +íķĺ ìĺĢëĭ¤ +ë§ Īëĭ¤ +Ġì¡´ ìŀ¬ +ìĿ¸ ì§Ģ +ë ĺIJ +ĠìķĬ ìķĺ +ëı Į +ĠìĻ ľ +ëıĻ ìķĪ +ê°ľ ìĽĶ +Ġì ħ +ĠíĿ ¬ +íĬ¸ 를 +ĠíĻ Ī +ëĨ į +Ġíķ´ ê²° +Ġìĥģ íĴĪ +본 ëĭ¤ +ĠíĶ¼ íķ´ +ì¶ © +Ġìŀij ìĹħ +ê³ ¨ +ì° ¸ +Ġíı IJ +ê²ł ìĬµëĭĪëĭ¤ +ëĤ´ ëĬĶ +Ġìĸ¼ ë§Ī +Ġìľł ì§Ģ +Ġê·¸ ëłĩ +ëł Į +ë¨ ¼ +Ġì¤ijìļĶ íķľ +Ġìķ ħ +ĠíĽĦ ë³´ +ĠìĹ° ê²° +Ġìĭľ 민 +íĸĪ ê³ł +Ġìķ Ķ +Ġì¡° ê¸Ī +íĿ ¥ +į ëĭĪëĭ¤ +ë³´ ê³ł +Ġ주 ìļĶ +ëĶ © +ĠìĹħ 무 +ĠíĦ ° +Ġë¯ ¿ +Ġ미 ëŀĺ +ëªħ ìĿĦ +ì§Ģ ëıĦ +ëĨ ĵ +ìĬ¤ ë¡ľ +ĠìĿ´ìķ¼ 기 +ëĭ¤ ê°Ģ +ĠìłĦ êµŃ +ĠëĶ° 른 +Ġê ¿ +Ġ모 ëį¸ +ìĿ¸ ìĿĦ +Ġê³µ ê°Ħ +Ġ기 ì¡´ +ìĻ Ķëĭ¤ +Ġë¹Ħ 롯 +ìłķ íķľ +ë£Į 를 +ĠìĿ´ ëłĩê²Į +ĠìķĮ ìķĦ +ëĵ¤ ëıĦ +ëıĻ ìĿĦ +Ġìłľ ìŀij +ĠìĨĮ ë¹Ħ +물 ìĿĦ +Ġëı Ī +ë³´ 기 +ìłĦ ìĹIJ +Ġëľ » +ëĦĪ ì§Ģ +Ġê ½ +ìĪĺ 를 +Ġë £ +Ġëħ¸ ëł¥ +ìĤ¬ ìĹIJ +ĠìŀĪëĬĶ ëį° +ë¡ľ ë¶ĢíĦ° +ìŀ¥ ìĹIJìĦľ +Ġíijľ íĺĦ +ĠìĹ´ 린 +Ġë¸Į ëŀľëĵľ +ì§ ľ +ĵ ¨ +Ġê²ĥ ëıĦ +Ġíĺ ¼ +Ġê²½ 기 +ij ¸ +Ġê±° ìĿĺ +Ġë³´ íĺ¸ +ìĥģ ìĿĺ +Ġíķĺ ì§Ģ +Ġì» ¨ +íĦ ¸ +ëĬĶ ëĭ¤ +ĠëIJ ©ëĭĪëĭ¤ +Ġìĺ¨ ëĿ¼ìĿ¸ +Ġìĭ¤ ìĭľ +ëIJ IJ +ì¦ ĺ +ĠìĻ Ģ +Ġ2 5 +ìĥģ ìĿ´ +ĠìŀIJ ì²´ +íĸĪëĭ¤ ê³ł +ĸ ìĹIJ +íĨ ¡ +Ġìłľ 주 +íĶ ½ +ìĨĮ ëħĦ +Ġì» ´ +Ġíķ´ ìĻ¸ +íĥ ķ +ì¹ Ļ +ìłķ ë¶Ģ +Ġëij ĺ +Ĭ ìĿĢ +ĠìĿ´ ìĹIJ +Ġâĸ ³ +ĠìŀIJ 기 +Ġë°© ìĭĿ +Ġìļ°ë¦¬ ê°Ģ +ëĥ ¥ +ĠìŀĪ ì§Ģë§Į +ĠìķĦ ì§ģ +íı¬ ì¸ł +Ġ무 ë£Į +ĠìĤ´ ìķĦ +Ġëĸ ¨ìĸ´ +ìĸ ¼ +ê²½ ìłľ +ìĿ¼ ê¹Įì§Ģ 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+ìľ¼ë¡ľ ìĦľ +Ġìĸ´ ëĶĶ +ë¡Ń ê²Į +Ġ구 ì¶ķ +ìĪĺ ëĬĶ +Ġìłľ ìĭľ +Ġíķµ ìĭ¬ +ëĭĪ ìĬ¤ +Ġë¶Ģ 족 +Ġê³µ ìĹ° +ĠìĿ´ 벤 +ì¢ Į +Ġíĭ ° +ëĿ¼ ìļ° +Ġìłľ ëĮĢë¡ľ +ë³´ íĺ¸ +Ġì§ģ ìĽIJ +íķ´ 주 +ìĦ¸ ê³Ħ +ì¼ ĵ +Ġìłķ ìĭł +Ġê¼ Ń +Ġê¸ Ģë¡ľë²Į +ĠìłĦ ëŀµ +¤ ëĭ¤ +Ġíıī íĻĶ +ìĸ´ 를 +Ġê°Ģ ê¹Į +Ġë³´ 기 +ê±° 리 +ĠëĿ¼ ìĿ´ +Ġ룬 ìĭľìķĦ +ë² ¨ +ì§Ģ íĦ¸ +Ġë¹Ħ ìļ© +ĠìĹŃíķł ìĿĦ +ĠìĤ¬ íķŃ +Ġë³´ íĹĺ +ìĤ¬ íķŃ +Ġìłľ ì¡° +ĠìŀIJ ìĦ¸ +Ġê°ģ ì¢ħ +ìĥģ ìĹIJ +Ġê´ij ê³ł +ìĺ¤ ìłĦ +ìĸ´ ëĤľ +ĠëĮĢíķ´ ìĦľ +ĠìľĦ íĹĺ +Ġìļ°ë¦¬ ìĿĺ +Ġë¶Ģ 모 +Ġê³µ 격 +ëħĦ ê°Ħ +ĠìłĢ ëĬĶ +ĠìĬ¤ ìĬ¤ë¡ľ +Ġìĭ ¸ +Ġìĩ ¼ +ìĬ¤ ëŁ½ +ì² Ļ +Ġìŀ¥ ìķł +ì ¿ +Ġê³¼ ê±° +Ġìĭľ ìĦ¤ +Ġ2 7 +ìŬ íĸī +ëĭ Ŀ +ìĿ´ 기 +Ġí į¼ +í ĩ´ +ĠëĤ´ìļ© ìĿĦ +ìĺ¤ ëĬĶ +ìĽ ¨ +Ġìĭľ íĹĺ +ĠìĺĪ ìĪł +ê°Ģ 를 +ìĹħ ìĿĦ +ëįĶ ëĿ¼ëıĦ +Ġê° ij +Ġë¶Ħ ìľĦ +ĠìĽ Ģ +ĠëĤĺ ìĺ¨ +ìĦ Ń +Ġíĸī ìĤ¬ +ê·¸ 룬 +ĠìŬ룬 ë¶Ħ +Ġê² © +ĠëĦĺ ìĸ´ +Ġìļ´ ëıĻ +Ġíķĺ ë©° +Ġëħ ¹ +Ġìĥģ 기 +ìĽĶ ìĿĺ +Ġê² ¬ +Ġíķĺ ìŬ +Ġë° ¤ +íĮĮ íĬ¸ +ìĽIJ ìĹIJ +íİĺ ìĿ´ì§Ģ +¬ ìĺģ +Ġê²½ ìĺģ +Ġìĭ¤ ìłľ +ìĿ´ ì§Ģë§Į +Ġë³´ ëĤ´ +êµŃ ìĿĺ +Ġë§ ¥ +ëĭĺ ìĿĺ +ĩ Į +ìĦ± ìĹIJ +Ġëĭ Ī +Ġìĥģ ëĮĢ +Ġëĵ± ìľ¼ë¡ľ +íŀ Į +ëIJĺ ë©° +ĠëIJ IJëĭ¤ +ìĿ´ ìŀIJ +ĠìĺĪìłķ ìĿ´ëĭ¤ +Ġì»´ íĵ¨íĦ° +Ġìļ°ë¦¬ ëĤĺëĿ¼ +ëŀ įëĭĪëĭ¤ +Ġìľł ëŁ½ +ìķĦ ìĿ´ +ìĭľìĺ¤ ìłĦ +Ġ2 6 +머 ëĭĪ +ë¹Ħ 를 +Ġíĺ¹ ìĿĢ +Ġìłľ íķľ +ĠìŬ ìŀIJ +ħ ëĭĪëĭ¤ +ìĿ¸ íĬ¸ +ĠìŀĪ 기 +Ġëģ Į +ë°© ìĨ¡ +Ġíķľ ë²Ī +Ġìĸ¼ë§Ī ëĤĺ +ĠìĿ µ +ìĸ´ ìķ¼ +Ġê°IJ ìĨĮ +Ġë³´ ëıĦ +Ġê·¸ ëħĢ +íķ¨ ìĿĦ +ë´ ĩ +Ġìĸ ĺ +ë¦ ¿ +Ġ모ìĬµ ìĿĦ +ĠëĮĢíķľ 민êµŃ +Ġë³´ ìĿ¸ëĭ¤ +ĠìĤ¬ìļ© íķł +ìŀĦ ìĿĦ +Ġì°¨ ì§Ģ +ĠìĿ¸ ì²ľ +Ġë© Ģ +Ġê°ĸ ê³ł +ĠìķĦ ë²Ħ +ê»ĺ ìĦľ +ìĽIJ ìĹIJìĦľ +ĠìłĦ 기 +ìħĺ ìĿĦ +ìĹĪ ëĬĶëį° +íĬ¸ ìĽĮíģ¬ +Ġëľ ¨ +ëł ´ +ì¶ ° +Ġê±° ëŀĺ +ìĬ¤ íĨł +ĠìĦł ë³´ +Ġë¬¸ìłľ 를 +ê² ¼ +ĠìłĦ íĨµ +ëŁ¼ íĶĦ +Ġìŀ Ķ +or e +ĠëıĻ ìĭľìĹIJ +ìķĦ ìļĶ +ì§Ģ ìĿĺ +ë° ĸìĹIJ +ì» ¬ +Ġìĭľê°Ħ ìĿĦ +Ġ19 9 +Ġê° Ŀ +° ëĭ¤ +ĠìĦ ¬ +ĠëĤ´ 볤 +ĠíĻķ ë³´ +ĠìłĦ íĸĪëĭ¤ +ë¶Ģ ìĿĺ +Ġì ı +Ġìµľ ì´Ī +ĠëĮĢ ìĭł +ë¹ Ī +Ġê·¸ ëĮĢë¡ľ +Ĩ į +ëĶ ° +Ġì½ĺ íħIJì¸ł +ĠìĿ´ ìłĦ +ìķĪ ìłĦ +Ġê·¸ ëĥ¥ +ëĿ¼ ìļ´ +ìĿ´ëĿ¼ ë©° +ëĮĢ íļĮ +ãħ ł +ã Ĩį +Ġê·¸ 림 +ê±° ëŀĺ +ìļ° 리 +Ġë²ķ ë¥ł +ê¹Į ìļĶ +ìŀ Īëĭ¤ +ëľ » +ĠíĮ ¬ +ëıĦ ìĭľ +ë§Į ìĿĺ +ë¶Ī ìĸ´ +Ġìłľê³µ íķĺëĬĶ +Ġìĭľê°Ħ ìĿ´ +ë°© ë²ķ +Ġ목 ìłģ +ìĿ¼ ë³´ +Ġì²Ń ìĨĮëħĦ +ĠìłĦ íĻĶ +Ġíķ´ ìĦľ +Ġë°Ķ ìĿ´ +ìŀ¥ ê³¼ +ĠìŀIJ ë£Į +ĠìĬ¤ íĬ¸ +ê° ij +ĠíĿ ¥ +ë§ ¨ +ìĭľ ì¼ľ +Ġì§Ģ ëıĦ +201 7 +ĠìĽĢ ì§ģ +ĠìĪĺ ì§ij +ê²ł ëĭ¤ëĬĶ +ĠìĿ´ë ¤Ħ +Ġ주 ëĬĶ +ëłĪ ìĬ¤ +ĠìĪ ¨ +ë¹ ł +Ġìĺģ ìĸ´ +ë§ İ +ê·¸ 룹 +ìľĦ ìĹIJ +ê ¿ +ĠìłķëıĦ ë¡ľ +Ġë°© íĸ¥ +íİ ´ +ë³´ ë©´ +ì © +ìĩ ¼ +ëĬĶ ì§Ģ +ëĨ ĢìĿ´ +Ġì° į +ĠìĤ¬ëŀĮ ìĿĢ +ìľ¼ë¡ľ ëĬĶ +Ġíĺij ëł¥ +ìŀIJ ëĵ¤ìĿĢ +Ġì²´ íĹĺ +íķĺ ëįĺ +Ġ2 9 +Ġê°Ģ ì¹ĺ +Ġê³µ ìĭĿ +ìĭł 문 +ĠìĿ´ ëģ +ãħ İ +ìĤ¬ ì§Ħ +ĠìĿ¸ ìĭĿ +ĠìĪľ ê°Ħ +리 ì¹´ +Ġë°Ľ ìĿĦ +Ġíķ© ìĿĺ +Ġìļ° ìĦł +Ġ모 ë°ĶìĿ¼ +ĠíĨµ íķ© +Ġìĺ¤ ëŀĺ +Ġëĭ¨ ìĪľ +ĠìĿ´ìĥģ ìĿĺ +물 ìĿ´ +Ġëķ ħ +ìĦł ê±° +ĠëĮĢ ìĿij +ì¹´ ì§Ģëħ¸ +Ġ201 4 +Ġìĸ¼ êµ´ +ë³´ ëĭ¤ëĬĶ +ìŀ¬ ìĿ¸ +Ġìĥģ ìĬ¹ +Ġíķľ ëĭ¤ëĬĶ +Ġê³µ ê³µ +ê³¼ íķĻ +ĠíĨµ ìĭł +Ġë³´ ëĬĶ +Ġë§ Ŀ +ë¶Ģ ìĹIJ +íķĺ ëĭ¤ê³ł +ĠìķĦ 침 +íı ¼ +ĠíĨµ íķľ +Ġë£ ¨ +ĠëıĦ ìŀħ +ìĥģ ìĿĢ +Ġëĵ± ìŀ¥ +ëħĦ ë¶ĢíĦ° +ìĹŃ ì¶ľìŀ¥ +Ġëĭ¨ ê³Ħ +구 를 +ĠìķĪ ìłķ +Ġì ± +ìłģ ìĿ´ê³ł +ëıĻ ìĤ° +ê°Ļ ìĿĢ +Ġë°Ķ ëĿ¼ +ĠìĤ´ íİ´ +Ġëĭ¬ 리 +Ġìļ ķ +ëħ ķ +Ġìķ ¡ +리 ì¦Ī +ĠìĬ¤ íı¬ì¸ł +ìĸ´ ëıĦ +Ġ주 목 +ì¹ĺ ê³ł +ì§Ģ ì§Ģ +Ġíĺ Ī +ìŀIJ 리 +Ġë¹ Ī +Ġë² Ĺ +ì» ¨ +민 주 +Ġìµľê³ł ìĿĺ +ĠìĹĨ ìĹĪëĭ¤ +Ġë°Ķ ëĭ¤ +ìĬ · +ëŁ ´ +ëĵ± íķĻêµIJ +ĠìĿ¸ ëıĦ +Ġë§IJ ìĿĦ +Ġìĭľ ì¦Į +문 ìĿĦ +Ġ8 0 +ìĿ¼ ìĿĺ +ìĿ¼ ìĿ´ +Ġìłľ ìĻ¸ +Ġìī ¬ +ê´Ģ ìĿ´ +Ġíķ´ ëıĦ +ìķ½ ìĿĦ +ĠìĤ¬íļĮ ìłģ +º ¼ +ĠëijIJ ê³ł +Ġ기 ëħIJ +ĠìĿ´ìķ¼ 기를 +ëĭ ¥ +ĠìķĦ ìĭľìķĦ +Ġìłij ê·¼ +Ġìŀħ ëĭĪëĭ¤ +Ġê°Ģ ëĬĶ +Ġìŀ¬ 미 +Ġë³´ ìľł +Ġìĭ¶ ìĿĢ +ë ®¤ +íķĺ ëĤĺ +Ġìļ° ìĪĺ +ĠìĿ´ ê³³ +Ġìŀij ìĦ± +ĠíķĺëĬĶ ëį° +Ġê² ª +Ġì´ ī +Ġëĭ´ ëĭ¹ +ĠìľĦíķ´ ìĦľ +Ġ기 íļį +ê´Ģ ìĿĦ +ĠëĬIJ ëģ¼ +ê±° ìļ´ +ĠìĦ± ê³¼ +ìłķ ìĿĺ +ĠíĶĦë¡ľìłĿ íĬ¸ +ĠëĶĶ ì§ĢíĦ¸ +Ġíŀĺ ëĵ¤ +Ġ못 íķľ +Ġê²Ģ ìĥī +Ġì§Ģ ê¸ī +ìĵ ° +Ġì¼ ĢìĿ´ +구 ìļĶ +ìłIJ ìĿ´ +Ġíĥ ĢìĿ´ +ĠìĿ´ë²¤ íĬ¸ +ĠìķĦ ë§Ī +ĠìĹIJ ëĦĪì§Ģ +ĠìľĦíķ´ ìĦľëĬĶ +ìĦ¸ 를 +ê²½ ë§Ī +íĸĪ ì§Ģë§Į +Ġë¹ Į +ĠíĿ¬ ë§Ŀ +ìĬ¤ ì½Ķ +Ġìŀij ê°Ģ +Ġë°Ķ íĥķ +린 ëĭ¤ +Ġìĺģ êµŃ +ëĵľ ìĭľ +Ġìĸ´ëł¤ ìļ´ +Ġë°Ķ ëŀįëĭĪëĭ¤ +ìķĺ ìĬµëĭĪëĭ¤ +ëĥ Īëĭ¤ +íķĺ ìĭł +ĠëıĦ ìĽĢìĿ´ +Ġì¡ ¸ +íķĺ ìŀIJ +Ġ모 르 +ë© ĺ +ĠíĬ¹ ì§ķ +Ġíĺģ ìĭł +ĠíķŃ ìĥģ +ìŀIJ ëıĻ +Ġìº ł +ì´ ī +ĠìĨĮ ìŀ¬ +Ġì§Ģ ì§Ģ +ëħ¸ ëıĻ +ĠìĿĺ íķľ +Ġì» ¬ +ìĶ¨ ëĬĶ +Ġë°Ľ ëĬĶ +Ġì¤ijìĭ¬ ìľ¼ë¡ľ +ë ĸ +Ġíķĺ 기 +íĻľ ëıĻ +Ġì¡° ìĦ± +Ġëį ķ +Ġë¹Ħ ìĬ· +Ġì¡° ê±´ +ĠìŬ ë¦Ħ +íĭ ± +ìĭŃ ìĭľìĺ¤ +ĠìĤ¬ 무 +Ġë¶Ħ ëªħ +Ġëı ¼ +ĠìĤ¬ ë¡Ģ +íĸĪ ìĿĦ +Ġìķŀ ìĦľ +ĠíĶĦ ëŀij +ĠëĮĢ 구 +ìľ¼ë¡ľ ë¶ĢíĦ° +ĠëĮĢ íķĺìŬ +Ġê·¸ 룹 +ëŁ Ń +Ġì´ ¬ìĺģ +ĠëıĻ ìĿĺ +Ġ http +ĠëĤĺ ëĪ +ìĩ Ħ +ê°Ģ ê³ł +ìĥµ ì¶Ķì²ľ +ìłķ ìĥģ +Ġìĭ¤ ìłľë¡ľ +ìĻĢ ìĿĺ +ìķĦ ìĦľ +ëĬIJ ëĥIJ +ĠìĤ¬ìļ© íķĺëĬĶ +íļĮ ìĹIJìĦľ +ĠíķĺëĤĺ ìĿĺ +ĠëĦ ĵ +ëĭĺ ìĿ´ +íķĺê³ł ìŀIJ +ìĿ´ ìķ¼ +ĠìĽ ĥ +ìłľ ëĬĶ +Ġê°ľ ìłķ +ĠëķĮ ëĬĶ +ê°Ħ ìĿĺ +íķĺ기 ëıĦ +ĠãĢ Į +Ġë¹Ħ íĮIJ +ĠìłĦ íĺĢ +ĠìķĪ ëĤ´ +Ġìŀĺ 못 +Ġí ĽĪ +ìĹĨ ëĬĶ +Ġ구 ì²´ +ĠíĹ Į +ĠìĦ¤ 립 +íķ¨ ìľ¼ë¡ľìį¨ +Ġëĭ¤ìĿĮ ê³¼ +Ġë°ĺ ìĿij +Ġê´Ģìĭ¬ ìĿĦ +ĠìłĢ íĿ¬ +ìĭľ ìĬ¤íħľ +ìļĶ ìĿ¼ +Ġê²½ìļ° ê°Ģ +Ġíķĺ ëĬĺ +Ġì²Ń ëħĦ +ìĪĺ ë¡Ŀ +ì°¨ 를 +íŀĪ 볤 +Ġíı ´ +Ġíıī ìĸij +Ġê½ ĥ +íķĺìŬ ìķ¼ +íĻ Ģ +Ġíijľ ìĭľ +Ġëħ¸ ëŀĺ +íķĺ ëĭ¤ëĬĶ +ĠìłĦ ìŁģ +ĠìĺĪ ìĪĺ +ĠíĬ¸ ëŁ¼íĶĦ +Ġë§Į ëĵľëĬĶ +Ġê°Ļ ìĬµëĭĪëĭ¤ +ìĭ ¸ +ĠìĺĪ ìĤ° +ĠìķĬ ëĭ¤ +ëĤ Ļ +Ġëĭ¨ ì²´ +Ġë Į +기 ìĻĢ +ĠëŃ IJ +Ġë¹Ħ íķ´ +âĺ Ľ +ì¡° ìĤ¬ +ìĺģ ìĥģ +Ġ기 ëĭ¤ +Ġê¹ Į +Ġëĥ ī +Ġìĸ´ë¦° ìĿ´ +ĠìĹ ¼ +ìĦ± íĻĶ +Ġìµľ ì¢ħ +Ġíĸī ëıĻ +ëŁī ìĿĦ +ê²ł ì§Ģë§Į +ĠìĬ Ī +âĺĽ ãĢ +Ġê³ł ìļ© +Ġìłľ ì¶ľ +ĠìĦ¤ ê³Ħ +Ġìļ° 볤 +ìĺ¤ íĽĦ +Ġíĥ IJ +ì§Ģ ë¡ľ +Ġí ij¸ +Ġíĺ IJ +ĠìŀIJìĭł ìĿ´ +ëĭĪ ìķĦ +ĠìĪĺ ìĥģ +ëħĦ ëĮĢ +Ġë¹ Ľ +ĠëįĶ ë¶Īìĸ´ +ê³Ħ íļį +Ġëħ¼ ìĿĺ +Ġíķľ íݸ +Ġìłľ 기 +Ġëĭ¬ 룬 +ê ½ +ĠëĦ¤ íĬ¸ìĽĮíģ¬ +ĠìĬ µ +ëĭ¨ ìĿĦ +ìłĦ 문 +Ġìĸ¸ ê¸ī +림 íĶ½ +Ġíĸī ìłķ +Ġë§Į 족 +주 ê³ł +ë§İ ìĿĢ +Ġê´Ģê³Ħ ìŀIJëĬĶ +Ġê³ ¡ +Ġ6 0 +ìĭľ ê³ł +Ġìłķ ìĿĺ +ìĬ¤ëŁ¬ ìļ´ +ì» ´ +ë¹ Ļ +ë©Ķ ëĿ¼ +ë§Į ìĿĦ +ìĬ ´ +ì ¾Į +ĠìĤ¬ ë§Ŀ +ì§Ģ ìļĶ +ï¼ĺï¼ĺ ï¼ĺï¼ĺ +Ġê·¸ê²ĥ ìĿĢ +íĨ ¤ +Ġ커 íĶ¼ +ĠêµIJ íĨµ +ë²Ī 째 +íķĺ기 ë¡ľ +ĠëĤ Ļ +ìĦ± ìĿĢ +ìº IJ +Ġì§Ģ ìłķ +Ġë§Ī ìĿĦ +Ġê³ § +ĠìĿ¸ ìĤ¬ +ëĵľ ë¡ľ +Ġìĺ¤ íŀĪ볤 +Ġì¤ij ìĹIJ +ĠìłĢ ìŀ¥ +ë°ĺ ëıĦ +ìĥģ ëĭ´ +ĠìĻ Ķ +ì¼Ģ íĮħ +Ġí ĩ´ +ë§Į ìĿ´ +Ġì¢ħ íķ© +ëĿ¼ ë©° +íļĮ ìĹIJ +Ġë°ĺ ëĵľìĭľ +ĠëIJĺ ë©´ +ĠëĮĢ ì¤ij +Ġ201 3 +ë³´ ëĭĪ +Ġìĭł ê³ł +ĠìĨį ìĹIJìĦľ +구 íķĺê³ł +Ġê³µ ë¶Ģ +ë³ij ìĽIJ +ê ¶ģ +Ġê·¸ ê°Ģ +Ġë³´ ìŀ¥ +íĻĶ ëIJľ +ë¶ Ļ +í ľĺ +ĠíĺĦ ì§Ģ +ĠìĽIJ íķĺëĬĶ +ëĿ¼ ë©´ +ì§ ĵ +ë§ ¥ +ĠìĿ´ íķĺ +Ġ기 ê´Ģ +ĠìĦ¤ëªħ íĸĪëĭ¤ +Ġíķĺ ëĭ¤ +íĸĪ ëĬĶëį° +ĠìŀIJ 주 +Ġìŀ ĥ +ì¤ij ìĹIJ +Ġìĺģ ìĹŃ +ĠíĻĪ íİĺìĿ´ì§Ģ +ĠìķĦ íĮĮíĬ¸ +ìĭĿ ìĿ´ +Ġê¹ Ĭ +ĠìĿĺ ìĽIJ +ëŀ« íı¼ +Ġ주 íĥĿ +ĠíķĻ ìĬµ +ĠíĻį ë³´ +ê´ ´ +Ġëĭ¬ ëĿ¼ +ĠìŬ ìłĦíŀĪ +Ġíļ¨ ìľ¨ +ĠìĤ¬ëŀĮ ëĵ¤ìĿĢ +ë§ ¹ +Ġìĺģ ìĥģ +Ġëģ Ĭ +ĠëĤĺ 무 +íĭ ´ +ĠìĿ¼ ìĸ´ëĤĺ +Ġê²° íĺ¼ +ìĿ į +ìģ ľ +ĠìĹĨ ìĸ´ +ìŀIJ ìĹIJ +Ġë© ĭ +ĠìĤ¬ìĭ¤ ìĿĦ +ĠíķŃ ê³µ +íĸĪ ìľ¼ë©° +Ġíŀ Ī +ëĭ¬ 룬 +Ġë°ĺ ìĺģ +ĠìĨĮ ìĨį +íĴĪ ìĿĦ +ì§ Ŀ +ĠìĪĺ ìĪł +Ġë§Į ìĹIJ +ĠíĶĦ 리 +ìĿ´ ìħĺ +Ġì§ § +ĠíĮĮ ìķħ +Ġë³´ ìķĪ +Ġë¶Ī ë²ķ +ìĭľíĤ¤ ëĬĶ +ê» ´ +Ġì° ¬ +ĠìłĦ íĻĺ +ëIJĺ ìĹĪ +ëĮĢ ëĬĶ +Ġê·¸ 리 +Ġ미 ëĶĶìĸ´ +íĬ Ģ +Ġìĭł 뢰 +ìĤ¬ ë¡ľ +ëħĦ ìĿĦ +ë Ģ +ĠìĪĺ ìĹħ +ĠëĮĢíĨµëł¹ ìĿ´ +Ġìĭľ ìłĪ +ĠìĿ¸ íķľ +ìĦ± ìĿĺ +Ġë¹Ħ íķµ +Ġëĵľ ëĿ¼ë§Ī +ĠëĤĺ ëĪĦ +ĠìĬ¤ë§ĪíĬ¸ íı° +ĠíķĦìļĶ ê°Ģ +ê¸Ī ìľµ +íĺij íļĮ +ĠíķĺëĤĺëĭĺ ìĿĺ +ĠëĤĺ ìĦľ +Ġë³´ ëĭĪ +Ġìĭľ ì²Ń +ëħĦ ëıĦ +ĠìĪĺ ì¶ľ +ë¡ ¤ +Ġë§ĪìĿĮ ìĿĦ +ê³¼ ëĬĶ +Ġ못 íķĺê³ł +íĻĶ ê°Ģ +êµŃ ìłľ +Ġê± ± +Ġê²ĥ ìĿ´ëĿ¼ê³ł +Ġ201 2 +ĠëĮĢíĨµëł¹ ìĿĢ +ĠíĬ¹ ìłķ +ìķ ŀ +Ġë§IJ ìĿ´ +ìĭ¬ ìĿĦ +ĠìĿ¼ ìłķ +ëIJĺ ë©´ +ĠìĹĨ ìĿĦ +ĠìĺĪ ë°© +Ġê°ľ ëħIJ +ìĶ ¬ +Ġìłķ 리 +ëģ Ķ +Ġìĺģ ìĹħ +격 ìĿĦ +ĠìŀĪ ëįĺ +ìĸ´ ê°Ģ +íķ Ģ +Ġìĭ ± +ê°Ģ ê°Ģ +ìĤ¬ ìĿ´ +íĸĪëĭ¤ ëĬĶ +Ġëĵ¤ìĸ´ ê°Ģ +Ġë°° ê²½ +í ij¸ +ë¶Ħ ìĿ´ +ê²½ 기 +ì° ® +Ġê°ķ ëł¥ +ìĿ¼ íķľ +ĠìŀĪ ìľ¼ +ì²´ 를 +ĠìĹ° ëĿ½ +Ļ ëĭĪëĭ¤ +Ġ7 0 +ĠìĪĺ ìŀħ +ê²ł ëĭ¤ê³ł +ìĨ Ķ +ë´ IJ +Ġì°½ ìĹħ +ëħ ģ +ëĺIJ íķľ +Ġ기ëĬ¥ ìĿĦ +Ġìĭ¤ íĸī +ìĺ µ +Ġ주 ë¡ľ +ĠíĨµ íķĺìŬ +ëł ¬ +Ġë°Ķ ëĢ +ë§ ŀ +ìŀIJëıĻ ì°¨ +ë¦Ń íĦ° +ìŀ¬ ëĭ¨ +ìĺĪ ìĪł +íı ´ +Ġ구 ìŀħ +ĠëIJĺ ì§Ģ +Ġì¹ ¼ +Ġì¶ľ ìŀ¥ +ĠìĨĮ íĶĦíĬ¸ +ĠìľĦì¹ĺ íķľ +ë©´ ìĿĦ +ĠìĤ¬ ëĬĶ +íķ´ ëıĦ +Ġì ª½ +ìļ° ëĬĶ +Ġ주 ìĿ¸ +ìģ ĺ +ĠëĤ © +ĠìĤ¬ ìłĦ +Ġì¶ľ ìĭł +Ġê°Ħ ëĭ¨ +ĠëĤĺ ìĻĢ +ĠìĥĿê°ģ ìĿ´ +ì¦Ŀ ìĿĦ +ĠëĤĺ ê°Ģ +ĠìĨĮ íĨµ +ĠíĸĪ ìĬµëĭĪëĭ¤ +ĠìĹĦ ë§Ī +Ġê°Ģ ìłķ +Ġìŀ¥ ìĨĮ +Ġê³¼ìłķ ìĹIJìĦľ +Ġì į¨ +ëıĦ ìĻĢ +ì§ IJ +ëĵľ 립ëĭĪëĭ¤ +íĮ Ķ +Ġ겨 ìļ¸ +Ġë¶Ģ ëıĻìĤ° +Ġíĺ ľ +ĠìķĦë¦Ħ ëĭ¤ìļ´ +ê° Ķëĭ¤ +ëijIJ ê³ł +Ġê·¸ ëıĻìķĪ +Ġë°Ķíĥķ ìľ¼ë¡ľ +ĠìĪĺ ê°Ģ +ĠìķĪ ìĹIJ +Ġë°ĺ ë©´ +Ġë¶Ģ ìłķ +ĠëĶ ¸ +Ġëıħ 립 +ê·ľ 모 +ì° Į +Ġíĺķ ìĦ± +ìĿ¼ ìĺ¤íĽĦ +ìł ł +ìļ° ìĬ¤ +Ġ기 ì´Ī +ĠìļĶ ì¦ĺ +Ġì²´ ê²° +ĠëIJ IJ +ĠíĶĮ ëłĪìĿ´ +ĠìĺĪ ìķ½ +Ġ목 ìĨĮ +ê°IJ ìĿ´ +Ġì¶ľ ìĹ° +Ġê²ĥ ìĹIJ +ĠìĮ ĵ +Ġê±° ì³IJ +ĠëĮĢíķ´ ìĦľëĬĶ +물 ìĿĺ +ĠìĪ ł +주 íĥĿ +Ġìĺ ® +íļĮ ìŀ¥ +맼 ìĤ¬ì§Ģ +ĠìĤ¬ ëĿ¼ +Ġë°ĺ ëĮĢ +¨ ë²Ķ +Ġìķ ± +Ġê°ĢëĬ¥ ìĦ±ìĿ´ +Ġì·¨ ìĹħ +íĬ ľ +ĠëĬIJ ê»´ +Ġê·ľ ìłķ +ìŀIJ ì¹ĺ +ĠìĹĨ ê³ł +ìħ Ģ +리 ë¡ľ +ìŀ¥ ìķł +Ġê±´ ì¶ķ +Ġì£ Ħ +ìĦ¸ ëĮĢ +ê´Ģ ê´ij +ĠìĿĺ ìĤ¬ +ìķ ¤ +íĬ¹ íŀĪ +ĠíĶĮ ëŀ«íı¼ +ĠìĻĦ ìĦ± +Ġíݸ ìķĪ +ĠíĺĦ ìŀ¥ +Ġê´Ģ볨 ëIJľ +ĠìĿ´ëģ Į +¿ Į +리 ê·¸ +Ġìķł íĶĮ +ìĭł ì²Ń +ëŁī ìĿ´ +ëĤĺ 무 +Ġ문 ìŀ¬ìĿ¸ +ĠìĿ¸ ìĥĿ +ìĤ¬ ê³ł +Ġë°Ľ ìķĺëĭ¤ +ĠëĨĴ ìķĦ +Ġëıħ ìĿ¼ +ĠíĻľëıĻ ìĿĦ +Ġíķĺ ë©´ìĦľ +ë¹ Ľ +ë°Ľ ìĿĢ +Ġìł ĬìĿĢ +Ġìĭľ ê°ģ +ì¶ ¤ +ìĤ¬ ëŀĮ +ë§Į íķľ +구 ê°Ģ +ìĽ ħ +ĠëĮĢ íĺķ +ĠëĤ¨ ìŀIJ +ë°Ķ ìĿ´ +ëª ¬ +íķľëĭ¤ ë©´ +Ġê·¼ 무 +Ġê·¼ ê±° +Ġ본 격 +ĠëıĦ ì°© +Ġê¶ģ ê¸Ī +Ġë³´ ìĿ´ëĬĶ +íģ IJ +ë¡ ± +ĠìĤ¬ëŀĮ ìĿĺ +Ġíĥ ij +Ġë¶Ī 구íķĺê³ł +ĠìĤ Ń +Ġë§İ ëĭ¤ +Ġ미êµŃ ìĿĺ +Ġì² ¨ +ì» µ +Ġë¹ ¼ +âĪ ¼ +ĠìĬ¤ íİĺ +ìŀ Ķ +ĠìĿ´ ìĻĢ +ĠëĤ´ ë¶Ģ +ģ ëĭĪëĭ¤ +리 ìĻĢ +Ġ기 ìŬ +Ġìłľ ìķĪ +Ġì¢ħ êµIJ +ë¹ Į +Ġíķľëĭ¤ ê³ł +Ġê°ķì¡° íĸĪëĭ¤ +ĠìºIJ ë¦ŃíĦ° +Ġëĭ¤ 르 +ĠìĿ´ìľł ëĬĶ +룬 ìĬ¤ +Ġ매 ì¶ľ +ĠëĮĢ ì²´ +ì¹ĺ ê°Ģ +ëĤ´ ìĹIJ +ìĸ´ ë¡ľ +ìĺĢ ìĬµëĭĪëĭ¤ +ìĸ´ ì§Ħ +ĠíĶ¼ ë¶Ģ +Ġíĸī ìľĦ +ìĭ¤ ìĿĦ +른 ëĭ¤ +Ġë°©ë²ķ ìĿĦ +Ġë´ IJ +Ġ주 문 +Ġ졸 ìĹħ +ê½ ĥ +íķ´ ë³´ +ìĽIJ ìľ¼ë¡ľ +Ġê°Ģ 르 +리 기 +ĠìĦ ¹ +Ġë¸Ķ ë¡ľê·¸ +Ġë¹Ħ ì¦Ī +ìŀIJ 기 +Ġìĺ¤ íĶĪ +Ġì¢ħ ë¥ĺ +ĠëĮĢ ì¶ľ +íĺĦ ì§Ģ +Ġì¶ľ ë°ľ +Ġë¶Ī íݸ +뮤 ëĭĪ +Ġê²ĥ ì²ĺëŁ¼ +ĠíĿ ĺ +Ġì½ ľ +Ġìĸ µ +í Ģ +ĠíĬ¹ë³Ħ íķľ +ìĿ¸ ê³¼ +Ġë¬ » +Ġëĭ¹ ìĹ° +ìĤ° ìĿĦ +êµIJ íĨµ +ĠìĥĿê°ģ ìĿĦ +ëıĻ ì½ľê±¸ +ë° į +ĠëĤĺíĥĢ ëĤ¬ëĭ¤ +Ġì¤ij ìĨĮ +ìĿ´ëĿ¼ ë©´ +ê²° ê³¼ +Ġë§Ī ì°¬ +ĺ ij +Ġë©Ķ ìĭľ +ĠíĨł ë¡ł +ëĵ¤ ë¡ľ +Î Ł +ĠìĦł ìĥĿ +ĠìĿ¸íĦ° ë·° +ê²½ ìĿĦ +Ġë ĩĮ +ĠíĶĦëŀij ìĬ¤ +ĠëĤ¨ ìķĦ +ĠìłĢ ëħģ +ëĭ¨ ìĿĺ +Ġìĺ ¥ +볤 ëĬĶ +Ġê¸Ģ ìĿĦ +미 êµŃ +ê³µ ìĤ¬ +Ġìķ½ ìĨį +ëĤ ¼ +Ġê²°ê³¼ 를 +ĠíļĮ ìĤ¬ëĬĶ +ìłľ ìĿĺ +ĠìŀIJ ëħĢ +íĬ¸ ë¡ľ +Ġ기ìĪł ìĿĦ +Ġìłķë¶Ģ ê°Ģ +ĠìĦ± ëĬ¥ +Ġ주 ìĿĺ +Ġë°Ľ ìķĺ +ĠëIJĺ ìĹĪ +ĠíĻĶ ìŀ¥ +ëĭ ī +Ġë§Īì°¬ ê°Ģì§Ģ +ë¿IJ ë§Į +ë¶Ħ ìĹIJ +Ġì¤ij ìĿ¸ +Ġë§ŀ ëĬĶ +ë³Ħ ë¡ľ +Ġì§Ģ ì¼ľ +íķĺ ê²łëĭ¤ +ëı Ī +Ġê°Ģ ìĿĦ +ê´Ģ ìĿĺ +ĠëıĦ ìłĦ +본 ë¶Ģ +Ġì¹´ ë©ĶëĿ¼ +ĠìĤ¶ ìĿĦ +Ġì§Ħ ì¶ľ +ëĭ¤ 른 +ëįĶ ëĭĪ +ĠìŀĪëĭ¤ ë©´ +리 ìĸ´ +Ġìļ° ìĬ¹ +ëĵľ ê°Ģ +ëıĦ ìĹIJ +ìłģ ìĿĦ +¯ ¤ +Ġíķĺ ìĿ´ +Ġë ½ +Ġíķ´ ìĦĿ +ĠíĽ ¨ +ë° ¤ +Ġì§ ķ +ëŀ IJëĭ¤ +Ġë§Ī ì¼ĢíĮħ +Ġì§Ģ ìĭĿ +íĹ ¤ +Ġê·¼ ë¡ľ +ìĿĺ íļĮ +âĸ ² +ìħĶ ìĦľ +ëĵľ ìĿĺ +Ġê·¸ ëĵ¤ìĿĺ +êµ° ìļĶ +ĠìĨį ìĹIJ +Ġíĥ Ŀ +ë²ķ ìĿĦ +ë°ľ ìłĦ +ĠêµŃ íļĮ +Ġëĭ¤ ê°Ģ +Ġíĥľ ìĸij +ĠìĿ´ ìĤ¬ +ìĽĶ ìĹIJ +ĠìĻ Ķëĭ¤ +ë°Ģ 리 +ìŀ¥ ìľ¼ë¡ľ +Ġìĺ Ĩ +ĠíĨµíķ´ ìĦľ +ìĿ¸ íĦ° +Ġì¤ij ëĭ¨ +Ġëijĺ 룬 +Ġ문 ìĿĺ +Ġë¹Ħ롯 íķľ +ê°ľ 를 +ìĭľ ë©´ +Ġê²Ģ íĨł +Ġê²½ 기ëıĦ +ê³µ ëıĻ +ìĿ¼ 리 +ëĵł ì§Ģ +Ġìķ ī +Ġ201 0 +Ġêµ° ìĤ¬ +ĠíĶĦë¡ľê·¸ëŀ¨ ìĿĦ +ìĭ¬ íŀĪ +ĠìĪĺ ìĿµ +ĠëĮĢ íĻĶ +ë§Ī ìĿĦ +ì¤Ģ ëĭ¤ +Ġê·¸ê²ĥ ìĿĦ +ìĿ¸ ëĵ¤ìĿ´ +Ġìĥģ íĺ¸ +íķĺ ìĭ¤ +Ġì¤ij ê°Ħ +ëĤ´ ê³ł +ëĭ¹ íķľ +Ġì§Ģê¸Ī ê¹Įì§Ģ +Ġìłķë¶Ģ ìĿĺ +ìłľ íĴĪ +ì§Ģ ìĻĢ +Ġ201 1 +ìłĪ íķľ +ê°Ļ ìĿ´ +Ġë¿IJ ë§Į +Ġ못 íķĺëĬĶ +íħ į +ì£ ½ +Ġëĭ¤ìļ´ ë¡ľëĵľ +볤 ë©´ +Ġë¸Ķ ëŀĻ +ĠìĿĢ íĸī +ĠìĿĺ 무 +Ġì°¸ ê³ł +ĠíĽ¨ ìĶ¬ +ĠìłĦ ë°ĺ +ĠìĦľ ë²Ħ +Ġëĭ¤ ëĭĪ +ë£ ¡ +Ġë¹Ħ ë°Ģ +ìĿ´ ëĦĪ +Ġìĭľ ëĮĢ +ĠíĽ Į +ìĿ´ ìļ© +Ģ ëĭ¤ +ĠìĿ Ħ +Ġê´Ģìĭ¬ ìĿ´ +Ġê¸ į +기 ê³ł +무 ìĽIJ +ìĿ´ ë©´ +ì§Ģ ë°© +íķľ íݸ +ĠìĪĺ ëĬĶ +Ġ민 족 +Ġ미 리 +Ġë¹Ħíķµ íĻĶ +ĠìĻĦ ë²½ +ëĨĢìĿ´ íĦ° +ê³Ħ ìĿĺ +ëĵ± ë¡Ŀ +Ġê·¸ ëĵ¤ìĿĢ +ĠìķĦ ëıĻ +ĠìĤ¬ëŀĮ ìĿĦ +Ġë°ĺ ë³µ +ĠìĨĮíĶĦíĬ¸ ìĽ¨ìĸ´ +ìĿ´ ìĹĪ +Ġìĭ¤ ì²ľ +Ġê°ķ ìĿĺ +ĠëĮĢ ìłĦ +ĠíķľêµŃ ìĿĺ +ĠìĹĦ ì²Ń +ĠìŬ 기ìĹIJ +ìµľ ê·¼ +íĶĮ ëłĪìĿ´ +ì¼ĢìĿ´ ìħĺ +ĠëĤĺ ìĿĺ +기 ì¤Ģ +Ġê·¸ 리ìĬ¤ +ìľ¼ ë©´ìĦľ +Ġíķĺ ê²Į +ì²´ ìĿĺ +ĠìŀĪ ì§Ģ +ëĭ Į +Ġê²ĥ ê³¼ +ĠíĥĢ ê³ł +ì¡° ìĦł +ëIJĺ ë©´ìĦľ +Ġìĩ¼ íķij +ĠìĿ¼ ìľ¼ +ĠìĦł 물 +구 ìĿĺ +Ġë§Ī ì¹ĺ +Ġìĺ¨ ëıĦ +ĠëĬ ¦ +Ġë° ĸ +ìĺ¤ ëĬĺ +Ġíŀĺ ìĿĦ +ĠìĻ¸ ë¶Ģ +íĴ Ģ +Ġê³¼ ìłķìĿĦ +ëħ ¼ +ì¢ ĭ +Ġ충ë¶Ħ íŀĪ +Ġìľł íĨµ +Ġ매 ìĿ¼ +ë©Ķ ìĿ¼ +Ġíĭ Ģ +Ġìĭł ê·ľ +ìłIJ ìĹIJìĦľ +ìĿ´ 기ëıĦ +ìĬ¤ëŁ½ ê²Į +Ġë§IJ íķľëĭ¤ +ĠìŀIJ ëŀij +ĠíķĦìļĶ íķĺëĭ¤ +Ġìĭ¬ ê°ģ +ê±´ ìĦ¤ +ì§Ī ìĿĦ +Ġëªħ ìĿĺ +ĠìļĶ ìĨĮ +ĠìĥĪ ë¡ľ +ĠìłĢ ëł´ +ê°Ģ 족 +Ġìĭľ ëıĦ +ĠìĿ´ ëĵ¤ +Ġìŀ¥ 기 +Ġìłģ ìĿ´ +ĠìĽ ¨ +Ġê´ ľ +ëĤ © +Ġ주 ê³ł +ëıħ êµIJ +Ġì±Ħ ìļ© +ìķĦ ìķ¼ +ĠìľĦ ë°ĺ +Ġíĸ¥ íĽĦ +Ġê·¸ ëķĮ +ĠíļĮ ìĿĺ +ĠìĿ¼ ìĥģ +ĠíĽĪ 볨 +Ġìĺ¤ ë¥¸ +ĠíķĦ ìĪĺ +ìĿ´ëĿ¼ ëıĦ +ĠíĻķ ìĭ¤ +ì½ © +Ġë¹Ħ롯 íķ´ +ê±´ ê°ķ +Ġìķŀ ìĹIJ +ĠìĬ¤ íĨł +ì¡° ê±´ +ĠìŀĪ ìĹĪìĬµëĭĪëĭ¤ +Ġê°Ģ ìĬ´ +Ġê³ł íĨµ +íĬ¹ ë³Ħ +Ġì§ĢëĤľ ëĭ¬ +ĠìŀIJ 본 +Ġë©Ķ ëī´ +ĠìĿ¸ ê³µ +ĠëłĪ ìĿ´ +íķĺ ìĭľëĬĶ +Ġìĵ ´ +ĠìĤ¬ ìľł +ìľ¼ë¡ľ ëıĦ +ĠìĪĺ ë°ĸìĹIJ +ëĤĺ ìļĶ +ĠìŀĪìĹĪ ëįĺ +ëĭ¨ ìĿĢ +Ġê°ľìĿ¸ìłķë³´ 를 +문 ìĹIJ +ìĶ¨ ê°Ģ +ìĿĺ ë£Į +ëł¥ ê³¼ +ë¶Ħ ìĿĦ +ĠìĹ° ìĨį +Ġë ļ +Ġëĭ¤ ìĿ´ +Ġíݼ ì³IJ +ĠìĿ¸ ìĥģ +Ġë¶Ī ìķĪ +Ġìĸ´ëĸ ł +íıī ê°Ģ +Ġíĸ¥ ìĥģ +Ġë³ĢíĻĶ ê°Ģ +Ġë³´ íĨµ +Ġì¦IJ 길 +¨ ¸ +ĠìĪĺ ë§İìĿĢ +Ġê³Ħ ìłĪ +Ġìłľ ê±° +Ġë³Ģ íĺ¸ +Ġìĵ ¸ +ìĥģ íĴĪ +ĠìłĪ ëĮĢ +ê³Ħ ìķ½ +Ġê±´ 물 +Ġìĸ´ë ¨¸ +ìłģ íķľ +Ġíľ´ ëĮĢ +기 ìŀIJ +Ġ미 ìĪł +ĠëıĦ ë¡ľ +ĠíĸĪ ëįĺ +ĠìłľíĴĪ ìĿĦ +íĤ¬ë¡ľë¯¸íĦ° ìĿ´ +ĠìķĦ íĶĦ +ìĺĢ ëįĺ +ĠëĮĢ ì±ħ +Ġê°Ģ ëĵĿ +Ġëį § +ī ìŀ¥ +ìľĦ ê°Ģ +ĠíĻľ ìĦ±íĻĶ +ĠíļĮ ë³µ +ë¹Ħ ê°Ģ +Ġê²Ģ ì°° +ĠìĦł ìĸ¸ +ì¡° ìłľ +Ġê±° ë¶Ģ +ĠëĤĺìĺ¤ ëĬĶ +Ġìķ ¨ë²Ķ +ļ ìĭľ +ĠìĹ ĺ +ìĬ¤ íħĮ +Ġìĭľ 리ì¦Ī +ìĭľ 민 +Ġë´ Ħ +ê²½ ì°° +ëħĦ ê¹Įì§Ģ +ëıĻì¶ľìŀ¥ ë§ĮëĤ¨ +ĠìĿ´ìľł ë¡ľ +ëħĦ ìĿ´ +ê·¸ëŁ¬ ëĤĺ +ĠëĶ° ëľ» +Ġê² ¸ +Ġ기 íļĮ를 +ëŀ IJ +Ġìĸĺ 기 +ĠìĤ¬ìĹħ ìĿĦ +ĠìĺĪ 를 +ìłķ ëıĦ +Ġíĺľ íĥĿ +Ġë¬¸ìłľ ëĬĶ +ĠìĨĶ 루 +ìĤ¬ ëŀij +ë©´ ìĹIJ +ìĦ ¯ +íĶĦ ëĿ¼ +Ġë¶ģ 미 +ì¹´ ëĵľ +ì³ ¤ëĭ¤ +Ġëĭ¬ ìĦ± +ĠëĮĢ íļĮ +ê´Ģ ìĹIJìĦľ +Ġë¹ ħ +ĠìĤ¶ ìĿĺ +Ġê³Ħíļį ìĿ´ëĭ¤ +ëĵľ 리 +ì²´ ìľ¡ +Ġ매 ëħĦ +ìĬ¤ íı¬ì¸ł +ìĹ°êµ¬ ìĨĮ +Ġ커 뮤ëĭĪ +ìĦł ìĸ¸ +ë° ¥ +ĠíĻķ ìŀ¥ +ĠìŀIJ 격 +ĠëĤ® ìĿĢ +ëįĶ ëĿ¼ +ìĤ¬ ìĹIJìĦľ +Ġì§Ħ ì§ľ +ĠëĬ Ķ +íĿ ¡ +ìĭľ íĤ¨ +Ġëģ ¼ +ĠíĽĦ ìĹIJ +ĠìĹ´ ìĭ¬íŀĪ +ëģ Ĺ +ëľ ¨ +ĠêµŃ 무 +ĠìĨĮ ìľł +Ġì§Ħ ëĭ¨ +Ġì¡° íķ© +ĠìĤ¬ìĿ´ ìĹIJ +ĠíĬ¹ íĮĮ +ĠìķĬ ìľ¼ë©´ +Ġëĭ¨ ì§Ģ +Ġì²Ń ìĻĢ +Ġë² ½ +ìłķìĥģ íļĮëĭ´ +ê³¼ ìłķ +ĠëIJĺ ìĹĪìĬµëĭĪëĭ¤ +Ġë°©ìĭĿ ìľ¼ë¡ľ +Ġìĭ¶ ëĭ¤ +ìĹIJê²Į ëĬĶ +Ġê´Ģ ê°Ŀ +ëħķ íķĺìĦ¸ìļĶ +Ġë°ľìĥĿ íķľ +ìłľ ëıĦ +ë°Ľ ëĬĶ +ìĭľ íĹĺ +ĠìĥĿê°ģ íķ´ +Ġê°ĸ ì¶Ķ +Ġë¶Ģ ìŬ +Ġíģ´ ëŀĺ +Ġë²Ķ ì£Ħ +ĠìŀĪìĸ´ ìļĶ +ìŀ¬ 를 +Ġìŀ¬ íĻĶ +Ġ목 ìłģìľ¼ë¡ľ +ì ¯¤ +Ġê°ķ íķľ +ê·¸ 룰 +ë¥ Ń +Ġìĸ´ ì© +ĠìķĦëĭ IJ +ì²´ ê°Ģ +ĠêµŃ íļĮìĿĺ +ëł¥ ìĿĢ +ì² ¨ +ìľĦ ëĬĶ +ëŀĢ ëĭ¤ +Ġ모 ìĸij +Ġìĺ¤ ëŀľ +Ġ민 ê°Ħ +ĠãĢ İ +ìľĦ ë¡ľ +íĺĦ ìŀ¬ +íķ´ 를 +Ġë¶ģíķľ ìĿĺ +Ġ꾸 ì¤Ģ +íķĺ볤 ëĬĶ +Ġê°ķ ëĤ¨ +Ġíݸ ì§ij +Ġë¥ ¼ +íĸī ìĿĦ +Ġë ¿Į +Ġ구 íĺĦ +ĠìĤ¬ìļ© ìŀIJ +ĠìĻĦ ìłĦíŀĪ +ĠëĶ ± +Ġíķľ ë°ĺëıĦ +ĠìŀĪëĬĶ ì§Ģ +Ġìĺ · +ê² IJ +âĢĻ ( +ĠìĿ´ë¦Ħ ìĿĦ +ë¶Ħ ìĦĿ +ëĿ¼ ìĬ¤ +Ġ기 ê³Ħ +íĺ¸ íħĶ +ĠìĿ¼ ëĭ¨ +ĠìĽIJ ì¹Ļ +ìľĦ ìĿĺ +ĠìĪĺ ìļĶ +Ġë²ł ìĿ´ +ìĬ¤ íĭ° +ĠëĨ ĢìĿ´ +ĠìŀĪìĸ´ ìķ¼ +íķ´ ì§Ħ +Ġíģ´ ë¦Ń +Ġíݸ ìĿĺ +Ġì½Ķ ìĬ¤ +ê°Ģ ìĿĺ +ĠíĬ¹íĮĮ ìĽIJ +Ġì§Ģ íĤ¤ +Ġìĭł ìļ© +ê´ Ħ +ìłľ 주 +Ġë¶Ī ê³¼ +ĠëıĪ ìĿĦ +Ġë§ º +ëĭ¹ ìĿ´ +ĠìľĦ ìĹIJ +Ġì±ħ ìĿĦ +Ġëĸł ëĤĺ +ì¤ij êµŃ +ĠìĤ¬ì§Ħ ìĿĦ +ĠíĨµ ìĿ¼ +Ġìłľ ëıĦ +Ġë¹ĦìĬ· íķľ +ĠìĿ¸ ì¦Ŀ +ì¤ij ìķĻ +Ġìĭľìŀij íĸĪëĭ¤ +Ġë° ĸìĹIJ +ĠìĿ´ê²ĥ ìĿĢ +ĠëĴ · +ëIJĺ ìĹĪìĬµëĭĪëĭ¤ +Ġíķĺ ëĿ½ +ë§Į ìĹIJ +Ġì§Ģ íķĺ +ìĨĮ ëĵĿ +íĬ¸ ëĬĶ +ĠíĺIJ ìĿĺ +Ġ주 ìłľë¡ľ +리 íķľ +ĠìĹĨ ëĭ¤ëĬĶ +Ġíĺ¼ ìŀIJ +Ġìĭ¤ íĮ¨ +ĠìĿ¸ 물 +ëĵ¯ ìĿ´ +ĠëĤĺ 를 +ìĽIJ ê³¼ +Ġê´ ´ +ëł¥ ìĿĺ +ìĹIJ ëĦĪì§Ģ +ë¥ĺ 를 +ì¶ľìŀ¥ ìķĪë§Ī +êµIJ íļĮ +Ġê¸į ìłķ +Ġë° ´ +Ġìĺ µ +ĠëĪĪ ìĹIJ +ĠíĪ¬ íijľ +ĠìĽĶ ëĵľ +Ġ빨 리 +ĠìĦ¸ ìĥģ +Ġ길 ìĿĦ +ĠìĥĿ ëªħ +Ġíķľ ìĿ¸ +ëĭ¨ ìĿ´ +ìĸ´ ëĵ¤ +Ġìľł ì¹ĺ +Ġìµľ ìĭł +Ġê´ij 주 +구 ëĤĺ +ìĭľ 기 +ĠìĤ¬ëŀĮ ëĵ¤ +ĠìĦ¼ íĦ° +Ġìĸ´ë¨¸ ëĭĪ +ìĭł ìĿĦ +Ġ an +ĠíħĮ ìĬ¤íĬ¸ +ê² ģ +Ġê°ľ ìĿĺ +Ġìµľ ìłĢ +Ġìŀħ ìŀ¥ +ĠíĻĺ ìŀIJ +Ġí Ĩ +Ġ측 ë©´ +íķĺìĺĢ ìĬµëĭĪëĭ¤ +Ġë³´ ê´Ģ +ĠìŀIJìĭł ìĿĦ +ij ìĿĢ +ìĿ¸ ìľ¼ë¡ľ +ë²Ħ ìĬ¤ +ê³ł 를 +Ġì¢ĭ ëĭ¤ +ìłľ ìĹIJ +Ġë¶Ģ íĥģ +Ġê± · +ìĦł íķľ +ĠìĿ´ 르 +Ġ측 ìłķ +ìłĦ ìĿ´ +구 ì¡° +ë§Ī íĨł +ĠíĿ ¡ +íĪ¬ ìŀIJ +ì§Ħ íĿ¥ +ĠëĮĢíijľ ëĬĶ +ìĭľ ê°Ģ +Ġë§Į ìķ½ +ë°ĺ 기 +Ġíı¬íķ¨ íķľ +ĠìĹ° ì¶ľ +ìĹŃ ìĿĦ +ìĿ¼ 본 +주 민 +Ġ무 ìĬ¨ +Ġe x +Ġë¶Ī 룬 +ĠêµIJ íļĮ +Ġë°Ķ ê¿ +Ġìŀ Ĭ +ĠìĥĿê°ģ íķľëĭ¤ +Ġë² ¤ +Ġíķ´ëĭ¹ íķĺëĬĶ +Ġ200 8 +ëĭ · +Ġë¹Ħì¦Ī ëĭĪìĬ¤ +ĠìĿ¸ ê¶Į +ìķĪ ìĹIJ +ĠëģĿ ëĤĺ +Ġë°Ķ 꾸 +Ġìĭ¤ íĺĦ +ĠíĽĮ ë¥Ń +Ġë§ŀ ìķĦ +Ġì¹´ íİĺ +ĠìĿ´ìļ© ìŀIJìĿĺ +ĠìłIJ ìĿĦ +ìĦ¸ 기 +ìĭ¤ ìĹIJìĦľ +Ġë ®¤ +Ġìĭ¤ íĹĺ +Ġë§ ģ +ĠìĥģíĻ© ìĹIJìĦľ +ì¡° íķ© +ĠìĹIJ ìĦľ +Ġê²½íĹĺ ìĿĦ +Ġ깨 ëģĹ +목 ìĿĦ +ĠëıĦ ìĽĢìĿĦ +ê·¼ íĺľ +ĠíĹĪ ìļ© +Ġë§İ ìķĦ +ĠëĤ¨ ìĦ± +Ġê°Ģê¹Į ìļ´ +Ġë¹ł 른 +ìĭľ íĤ¬ +Ġìĭł ê²½ +ĠëĤĺíĥĢ ëĤĺ +ìĭĿ ìľ¼ë¡ľ +íķŃ ê³µ +ĠíĨµ ê³Ħ +ĠìĤ¬ëŀĮ ëĵ¤ìĿĺ +ĠìĨĮ ëĵĿ +Ġê· ł +ĠêµŃ ìłķ +ìķĺ ëįĺ +ë´ ¤ +Ġë°ĶëĢ Į +ëŀ Ħ +Ġë©Ķ ìĿ´ +Ġì±ħìŀĦ ìĿĦ +íĺĦì§Ģ ìĭľê°Ħ +ìĹIJ ë§Į +ì²ĺ 리 +ë ķ +ëªħ ìľ¼ë¡ľ +Ġë¶Ī ê°ĢëĬ¥ +Ġìłķìĥģ íļĮëĭ´ +ĠíĴĪ ì§Ī +íĭ ¸ +Ġíķł ìĿ¸ +Ġê³µ ìłķ +ë¶ģ íķľ +Ġëī´ ìĬ¤ +Ġë³´ ìĪĺ +ëij Ķ +ëij ij +Ġë¦ ´ +Ġë³´ ê±´ +ìº ł +Ġ주ìĿ¸ ê³µ +ê±° ëĵł +ì¹´ ìĺ¤ +Ġ걸 ì³IJ +Ġë ĺij +Ġì¼ Ģ +êµŃ 민 +ĠêµŃ ë°© +Ġíļ į +ĠìĿ¸ 기 +Ġë§ ¨ +Ġ5 00 +Ġê¹Ģ ìłķìĿĢ +íĮIJ 매 +ĠëĮĢë¶Ģë¶Ħ ìĿĺ +Ġê´ľ ì°® +무 ë£Į +ĠìĤŃ ìłľ +ë°Ģ리 미íĦ° +ĠìĹ´ 리ëĬĶ +ac e +Ġìĸ´ëĸł íķľ +ë£ © +íĭ° ë¸Į +Ġìļ´ ìłĦ +Ġìľłëªħ íķľ +Ġìĭł íĺ¸ +ĠìĿ´ ëĵ¤ìĿĢ +ìĤ¬ ìļ© +Ĩ Ĵ +ĠìĿ¼ ìŀIJ리 +Ġì°¾ ëĬĶ +ìĿ´ ìĥģ +Ġê°ķ ìĽIJ +ĠìĿ´ ë¡ł +Ġì§ĢìĨį ìłģìľ¼ë¡ľ +ìĦľ ìĹIJ +ìľ¨ ìĿ´ +ĠìĻĦ ë£Į +ê³µ ìĽIJ +Ġëī´ ìļķ +ĠëĤ´ ëħĦ +Ġëį°ìĿ´íĦ° 를 +ĠëĪĦ구 ëĤĺ +ĠìĿ´ìļ© íķĺìŬ +ìī ½ +ëªħ ìĿĢ +Ġìłľ ìĸ´ +ĠìĥĪ ë¡Ńê²Į +ĠíĮ¨ ìħĺ +Ġëį ĺ +Ġì§ ĵ +ìļ° ê³ł +ac k +ìĿ¼ ê¹Į +주 를 +ĠìĻĦ ìłĦ +ë¡ľ 를 +ëIJĺ 기 +ìĹIJ ìĦł +Ġì¹´ ëĵľ +Ġ9 0 +ëĵ Ń +리 ìłģ +Ġê°ĢëĬ¥ íķĺëĭ¤ +í Ī +ì° į +īìŀ¥ íŀĪ +Ġì²´ íģ¬ +무 리 +Ġìłķë¶Ģ ëĬĶ +ëĮĢ ìĥģ +ĠìĨĮë¹Ħ ìŀIJ +ì¡° ì°¨ +Ġ못 íĸĪëĭ¤ +Ġë§ŀ 춰 +ê° Ķ +ê°ľ ìĿ¸ +Ġê·¸ëŀĺ ëıĦ +ĠìłĢ ìŀij +ì§Ģ ê²Į +ĠìłĦ íķ´ +íķĻ ìĿĦ +Ġì§ģ ìĹħ +Ġì¡° ìłķ +íķĺ ëĿ¼ +ì§Ģ ìĸ´ +ĠíļĮ ìŀ¥ +ìĦ °ëĭ¤ +ĠìłĦ ë¶ģ +íĬ¸ ëĤ¨ +Ġê±± ìłķ +ê ¾ +êµŃ ê°Ģ +ê²½ ìĺģ +ìĿ´ ë¡ľ +Ġëį§ ë¶Ļ +Ġê¸Ī ìķ¡ +ìĬ¤ íĮħ +리 ìĺ¤ +ê¸Ī ìĿ´ +ĠìķĮ ê²Į +ìĽł ëĭ¤ +Ġê¼ ½ +Ġ200 0 +ĠìŀĪ ìľ¼ë©´ +Ġ짧 ìĿĢ +ìĬµ ëĭĪê¹Į +ìłIJ ìĿĢ +ĠëĮĢíĨµëł¹ ìĿĺ +ë¶Ģ ìŀ¥ +ë¶Ģ 모 +Ġìŀ ĩ +Ġìı Ł +s os +Ġ · +ìľł ìĿĺ +ĠìľĦ íĺij +Ġì°¨ ë³Ħ +Ġ주 ëıĦ +ë¹Ħ ìļ© +Ġëĵ ł +íķĻ ìłģ +Ġêµ ³ +Ġ먹 ê³ł +ìĿ´ ë²Ħ +ìĽĶ ë¶ĢíĦ° +Ġì±Ħ ëĦIJ +Ġ컬 룬 +ĠìĿ¸ ê·¼ +Ġê²½ ëĤ¨ +ëį° ìĿ´íĬ¸ +Ġìĥģëĭ´ íĨ¡ +ĠíĨµ ê³¼ +Ġì¶ķ 구 +íĺķ ìĿĦ +íķ¨ ìĿ´ +ê²¼ ëĭ¤ +ìĿĮ ìĿ´ +âĺĽãĢ İ +ëĿ¼ ìĹĺ +Ġì§Ģ 구 +Ġìłķ ê¶Į +ë Į +Ġê¹ Ķ +ĠëıĻ 물 +Ġì§ģ ìŀ¥ +ìĦ± ìŀ¥ +ãħł ãħł +ĠíĿIJ ë¦Ħ +ê³Ħ ê°Ģ +ëĪ Ī +Ġë³µ ì§Ģ +ĠëĨĵ ê³ł +ìĭľ ëĮĢ +ĠìłĦ ìļ© +ìĿ´ ìĤ¬ +ĠìĬ¤ ìľĦ +기 기 +Ġê³ł ê¸ī +Ġìĺ Ľ +Ġíijľ ì¤Ģ +¤ ë²Ħ +ac t +Ġ기 ëıħêµIJ +ëĬĶëį° ìļĶ +Ġì§Ģê¸Ī ìĿĢ +ĠìĹĶ ì§Ħ +ĠìłĦ ìĨ¡ +ĠìĿ´ ìĬ¤ +ë¶Ħ ìķ¼ +ĠìĹ° 기 +ê±´ ìĿĦ +Ġìłľê³µ íķ©ëĭĪëĭ¤ +ì½ ¤ +ìĭľíĤ¤ ê³ł +ëĵ± ìĿĦ +íĺij ëł¥ +Ġì°½ ì¶ľ +Ġ구 ë¶Ħ +Ġë§Įëĵ¤ 기 +Ġë²Ĺ ìĸ´ëĤĺ +ì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ +Ġë§Įëĵ¤ ê³ł +Ġìŀ¥ ì¹ĺ +ĠìĹIJ ìĸ´ +ĠìĹ¬ê¸° ìĦľ +ë©´ìĦľ ëıĦ +ĠëıĦ ìĻĢ +ĠìĤ´ ê³ł +Ġ주 ì°¨ +ì¹ ¸ +Ġìŀ¬ íĮIJ +êµ° ìĿĺ +ì§Ģ ê¸Ī +Ġ3 00 +Ġìŀ¥ ë©´ +ĠìķĦëĭ ĻëĭĪëĭ¤ +ĠìĿ´ë¯¸ ì§Ģ +Ġê°ĢëĬ¥ íķ©ëĭĪëĭ¤ +Ġ리 ëįĶ +Ġ200 9 +ìĦ ¬ +ëĭ¹ ìĿĢ +ĠìķĦëĭĪ ë©´ +Ġìĸ¸ ìĸ´ +ìĭ ¼ +Ġê·¸ 린 +Ġì¡° ì¹ĺ +Ġíļ¨ê³¼ 를 +Ġ주 ê´Ģ +ëĭĺ ìĿĢ +ĠëĤ´ìļ© ìĿ´ +ĠëĦĺ ëĬĶ +ì¹ĺ ì§Ģ +íķŃ ìĹIJ +ĠìĿĺ íĺ¹ +íĿ IJ +기 ëĬ¥ +길 ìĿĦ +Ġë¹ł ìł¸ +Ġìħ Ī +Ģ â +ì¹ĺ ë£Į +ì½Ķ 리ìķĦ +Ġìłij ìĨį +ìł ¤ +ë¥ Ļ +íĺĦ ëĮĢ +ĠìĿ´ ìĿµ +Ġëı ķ +Ġìĭ Ń +ë°Ľ ê³ł +ê°Ģ ìŀħ +Ġê°ľ ë³Ħ +ĠìĿ´ìļ© íķ´ +ĠìłĦ íĺķ +ĠìłIJ ìĹIJìĦľ +¬ ëĭ¤ +Ġì²ĺìĿĮ ìľ¼ë¡ľ +ê¾ ¼ +ì¡° ìĿĺ +ĠìĿĺ íķĺìŬ +Ġê²Ģ ìĤ¬ +Ġìľł ìĹĶ +íļĮ ìĽIJ +Ġë³µ ìŀ¡ +Ġì ¥ +ĠìĿ´ íĥĪ +ìĦ¸ ê°Ģ +Ġëħ¸ ì¶ľ +ìĭľ 를 +ëĤĺ ëĭ¤ +ë¹Ħ ìķĦ +Ġëĭ¹ìĭł ìĿ´ +ë ļ +Ġê³³ ìĹIJ +ĠìĿ¼ ìĿĢ +Ġìĥģëĭ¹ íŀĪ +ìĽĶ ëĵľ +Ġê·¸ê²ĥ ìĿ´ +ëij ¥ +Ġê¹ ĬìĿĢ +ĠêµIJìĪĺ ëĬĶ +미 ëĶĶìĸ´ +Ġìĺ¬ ëł¤ +ĠìĺĪ 측 +ĠìĨĮ ìĦ¤ +íĬ¸ ê°Ģ +Ġê³³ ìĿ´ +Ġëĭµ ë³Ģ +ĠìĬ¤ íĶ¼ +Ġê±° 리 +ĠìĿĺ견 ìĿĦ +Ġìķŀ ëijIJê³ł +ëıĻì¶ľìŀ¥ ëĮĢíĸī +Ġí Ģ +ìĭľ ìĿĺ +ìŬ ìĦ± +Ġìłķ 기 +ĠìŀIJ 리를 +¨ ìĿĦ +ê¹ ¨ +Ġë¡ ¯ +ĠìĻ¸ êµIJ +ìĤ¬ 무 +Ġ주 ìµľ +Ġë§ĪìĿĮ ìĿ´ +ĠëŃ Ķ +Ġ모 ì§ij +Ġì§Ī ë³ij +Ġê·¸ ëĵ¤ìĿ´ +ĠìĿ´ìļ© íķł +Ġê²ĥ ìĿ´ëĿ¼ëĬĶ +íķĻ ëħĦ +ĠëĤ´ ëĨĵ +ĠìĬ¤ íģ¬ +ĠíĿ¥ 미 +ìĿ´ ìĻĢ +ê´Ģ ìĹIJ +Ġìľł ìĤ¬ +r om +ìĹĪ ì§Ģë§Į +Ġìĭľìŀij íķľ +Ġëij Ķ +ëĵ ¬ +Ġë³ij ìĽIJ +Ġëĵ£ ê³ł +Ġê±° 주 +ĠíĶĦë¡ľ ìĦ¸ +ĠëķĮ ìĹIJëĬĶ +Ġìµľ ìłģ +Ġë¹ł 르ê²Į +ĠíĻĶ ë©´ +구 ëĬĶ +ê´Ģ 볨 +Ġë° ¥ +ĠíħĮ ìĿ´ +Ġë§IJ íķĺëĬĶ +ëij ĺ +Ġë°Ŀíĺ Ģ +íŀĪ ëĬĶ +ĠëĤ´ìļ© ìĿĢ +ëį° ìĿ´íĦ° +Ġë¶Ģ 문 +Ġìĥī ìĥģ +ĠìķĦ ëĬĶ +Ġë¹Ħ íĸī +Ġê´Ģ볨 íķ´ +Ġ구 ê¸Ģ +Ġíİĺ ìĿ´ìĬ¤ +íĬ¸ 리 +Ġ3 5 +ĠíĻķ ìłķ +ì¤ij íķľ +ìłĦ ê±° +Ġìĭ¤ ìĭľê°Ħ +ĠìĥĿê°ģ íķ©ëĭĪëĭ¤ +íķĺ ìĿĺ +ëį ´ +ĠìłIJ ìłIJ +íķĺ볤 ê³ł +Ġë³´ ìĺĢëĭ¤ +ëĤ´ ìļ© +ĠìĪĺ ìļ© +ĠìĿ¸ê°Ħ ìĿĺ +Ġìķŀ ìĹIJìĦľ +ĠíĬ¹ ìĪĺ +ĠìłĪ ì°¨ +Ġê²½ìļ° ëĬĶ +Ġë³Ģ íĻĶ를 +ëħĦ ìĿĺ +ĠêµŃ 립 +Ġëĭ¹ìĭł ìĿĢ +Ġíıī ìĨĮ +ĠìĿ´ìļ© íķľ +Ġíİĺ ìĿ´ì§Ģ +íĮ © +ëĤ ® +ìŀ ī +Ġë° ij +Ġê²° íķ© +ĠìķĦëĭĪ ê³ł +ìĪĺ ë¡ľ +ìĨĮ ê°Ģ +ĠíĶĦ ëłĪ +ê º¼ +Ġìŀ¥ ë¹Ħ +Ġìĵ° ëĬĶ +ĠìĿĺ ìĽIJìĿĢ +Ġì°½ ì¡° +Ġë²Į ìĸ´ +Ġìľ µ +ĠìĥģíĻ© ìĿĦ +Ġë§IJ ê³ł +ì³ ¤ +ê°Ħ ëĭ¤ +ëĭ¨ ê³Ħ +Ġ무 ìĹŃ +ìłĦ ìĿĺ +Ġ먹 íĬĢ +ëĭĪ ìĸ´ +Ġê°ķëł¥ íķľ +ĠìĦ Ń +ĠìĦł ë°ľ +ãħİ ãħİ +ëĤ´ ê°Ģ +ĠK BS +íķ´ ìļĶ +ì¶ľ 물 +Ġì²ł ìłĢ +Ġê°IJìĤ¬ íķ©ëĭĪëĭ¤ +Ġê°Ģ ìĦľ +Ġê°Ħ ìĿĺ +Ġë°ľìĥĿ íķĺëĬĶ +ĠìĿ´ ìĻ¸ +리 íĭ° +Ġë°©ë²ķ ìľ¼ë¡ľ +ĠìĤ¬ ìĦ¤ +ìĿĦ ê¹Į +Ġì¤ij ìĿ´ëĭ¤ +ê·¸ ëĬĶ +ìĿ¸ ëĵ¤ìĿĺ +ê·¸ ë¦¬ê³ł +ìŀIJ ìľł +Ġì°¨ ìĿ´ê°Ģ +Ġë¶Ī ê°Ģ +Ġë¬ ĺ +ëĭĺ ìĿĦ +Ġì´Ī ê³¼ +Ġê°Ģ ìĥģ +ì¢ħ ìĿĺ +ĠëĤ¨ íݸ +ì§Ģ ë©´ìĦľ +Ġë°° ìĨ¡ +ìĤ¬ ìĭ¤ +ìłĦ ìŁģ +ëį° ìĿ´ +ìĿ´ ëĤł +Ġì§ĢìĹŃ ìĿĺ +Ġëªħ ìĭľ +Ġ롯 ëį° +ĠëŁ ° +Ġì§Ī íĻĺ +Ġê½ ¤ +ìĪĺ ëıĦ +ëĭĪ íĦ° +Ġë¡ľ ë´ĩ +Ġë¸Ķ ë¡Ŀ +Ġíķĺ ìĺĢëĭ¤ +Ġì¦ī ìĭľ +Ġìłľ 목 +ĠëķĮ문 ìŀħëĭĪëĭ¤ +ìĺĪ ìļĶ +íĻĶ ìĿĺ +Ġìłģ íķ© +ìłIJ ìĹIJ +ĠìĽIJ ëŀĺ +íĻ Ī +ê³µ ëĭ¨ +ìĬ¤ ìĻĢ +Ġìĥģ ì§ķ +Ġë°ľ ìĸ¸ +ĠíĮĮ íĬ¸ +ëĭ¹ ìĿĺ +ëĭ IJ +ìĭľ ì¼ +ĠìĪĺ ìĤ¬ +ĠíĻķìĿ¸ íķł +ĠìĬ¤íĥĢ ìĿ¼ +Ġê²Ģ ì¦Ŀ +Ġëĭ¹ìĭł ìĿĺ +리 íĬ¸ +Ġë°° ì¹ĺ +Ġê³ł ìĸij +ĠìĹħ ëį°ìĿ´íĬ¸ +ìľĦìĽIJ ìŀ¥ +ĠìĹ° ì¤ij +ĠìĹħ ê³Ħ +Ġê¹Ģ ìĺģ +ìĬ¤ ìĹIJ +Ġ모 ìŬ +ĠìķĦìĿ´ íħľ +ìłIJ ìľ¼ë¡ľ +ëŁī ìĿĢ +ĠìĤ¬ìļ© íķĺìŬ +ĠìĦ¸ ìĥģìĿĦ +íķ¨ ìĹIJ +Ġë§IJ ìĿ´ëĭ¤ +ĠìĹ° íķ© +Ġê°Ģ ì¹ĺ를 +ĠíĻ Ģ +ĠëĬIJëĤ Ħ +ĠìĿĺ미 를 +ìļ° 리ëĬĶ +ĠíĿ ij +Ġë°Ķ ëŀĢëĭ¤ +ìľ¨ ìĿĦ +Ġë§Ī ìĿ´íģ¬ +ãİ ¡ +Ġíı¬ ìĿ¸íĬ¸ +Ġ기ì¤Ģ ìľ¼ë¡ľ +Ġëªħ ìĿ´ +ë¦ ī +íĦ° 리 +Ġâij ł +Ġëĵ¤ ê³ł +무 를 +íĶĦë¡ľ ê·¸ëŀ¨ +ìĥĿ ìĿĦ +Ġ모ìĬµ ìĿ´ +êµŃ ìĿ´ +Ġë§Į ëĤľ +ì¹ ¼ +ĠíĹ ¬ +ìĬ¤ íı¬ +Ġìļ´ìĺģ íķĺëĬĶ +Ġê º¼ +ë£ ¸ +Ġê°Ļ ìķĦìļĶ +Ġì§Ħ ë³´ +모 ëį¸ +Ġë© ¤ë²Ħ +ìŀ ĸ +ì§Ģ 기 +Ġ무ìĹĩ ë³´ëĭ¤ +Ġë§ŀ 춤 +ĠëĤĺ ìķĦê°Ģ +ìŀIJ ìĽIJ +ĠëĪĦ ê°Ģ +ĠìĿ´ ìĬĪ +Ġêµ ´ +Ġì§Ģ ë°° +ë¥ł ìĿ´ +Ġì§ IJ +Ġ기 ìļ¸ +Ġì¤ijêµŃ ìĿĺ +ì¤ ĺ +ìłĦ ëŀµ +Ġíı¬ 기 +ĠìłĦ íĪ¬ +íĥľ ìĸij +íĻĶ ìĹIJ +Ġìµľì´Ī ë¡ľ +ĠìĭľìĬ¤íħľ ìĿĦ +ĠìĦ± ìĿ¸ +Ġê¸Ī ì§Ģ +Ġë°ľíijľ íķľ +ac h +Ġê¼ ¬ +ĠíĻķ ìĤ° +ìļ´ ìĺģ +ĠìłĦ ëł¥ +Ġ기 ëıĦ +Ġìĭ¤ ì§Ī +ĠíĸĪ ëĬĶëį° +ìĿ´ìķ¼ 기 +ê³µ ê°Ħ +ìĹ°êµ¬ ìĽIJ +Ġ뾨 ê±°ìļ´ +Ġìľ Ī +ĠëĨį ìĹħ +Ġìĥ ¤ +ĠëķĮ ë§Īëĭ¤ +ëģ Ħ +ìķĦ ìĭľìķĦ +ĠíĸĪ ì§Ģë§Į +Ġì¡° ì¹ĺ를 +Ġì²´ ê³Ħ +Ġì¤ijìĨĮ 기ìĹħ +Ġìĭł 문 +ê¹Įì§Ģ ëĬĶ +Ġí ľĺ +ì§Ģ ë©´ +ĠìĬĪ íį¼ +íĥ ij +ì¹´ ëĿ¼ +Ġì·¨ ìĨĮ +Ġëĭ¤ 루 +ìĿ¸íĦ° ëĦ· +ìłģ ìĿ´ëĭ¤ +Ġê´Ģ 측 +ê³µ íķŃ +Ġë°© ìķĪ +íİ ľ +ĠìĿ´ ëªħ +Ġëĭ¤ ìĨĮ +Ġë³´ ìĿ´ +Ġê´Ģ ëŀĮ +ë¶Ģ ìĻĢ +Ġì§ ľ +Ġê·¸ ëŁ¼ +ìłķ ì¹ĺ +ëĿ¼ ìĦľ +Ġ미 ìĦ¸ +Ġë¸Ķ 루 +ë¶Ħ ìĿĺ +Ġì¿ł íĤ¤ +ĠìłĦ ê³µ +ì±ħ ìŀĦ +Ġíŀĺ ëĵł +ìĦł ìĿ´ +ì°¸ ê³ł +ĠìĹĨ ëĭ¤ê³ł +ëĿ¼ìļ° ëĵľ +ê±°ëĵł ìļĶ +ë¡ł ìĿĦ +Ġìµľ ìĦłìĿĦ +ĠìŀijìĹħ ìĿĦ +주 ë¡ľ +ĠëĤĺ ìĻĶëĭ¤ +Ġë¶Ħ ëĵ¤ìĿ´ +Ġë´ ¤ +ëĮĢ ìĹIJìĦľ +ë¶Ģ ìĤ° +ë ĩ +íķĺ ìĿ´ +Ġì¢ĭìķĦ íķĺëĬĶ +ĠìĤ¬ëŀij ìĿĦ +ìĿ¸ ìĹIJ +Ġê°ĸ ì¶ĺ +ĠìŀĪ ìľ¼ëĭĪ +Ġë©ĭ ì§Ħ +ìĦľ ê°Ģ +주 ê°Ģ +ĠëĪĦ êµ° +ĠìķĪ ìĹIJìĦľ +ì ¥ +Ġì´Ī ì²Ń +ìĭł ìĿ´ +ëĥ Ī +ëªħ íķľ +Ġ기 íĽĦ +ëŀĢ ëĵľ +Ġë²ł íĬ¸ëĤ¨ +ĠìĨIJ íķ´ +ìĹ° ìĿĦ +Ġc ont +ĠìŀIJì²´ ê°Ģ +Ġì°¨ ë¡Ģ +Ġë§Ī 무리 +ìĥ ¤ +Ġê´Ģê³Ħ 를 +Ġë¹Ħ ì¤ij +ìŀIJ ëĵ¤ìĹIJê²Į +êµ° ìĿĢ +ìŀĦ ê¸Ī +íĶ¼ ìĬ¤ +íķĺ ëĵľ +Ġíķ´ 주 +Ġ주 ë§IJ +ĠìķĦìĿ´ ëĶĶìĸ´ +Ġìŀij ëıĻ +ĠìĿ´ íĸī +ìĬ¤íĨł ëŀij +ĠìĿ¸ ì§Ģ +Ġ먹 ëĬĶ +Ġì¡° ìłĪ +ĠêµŃ íĨł +ìĿ´ ìĿµ +Ġìłľ ìĿ¼ +ìĭĿ íĴĪ +íļ¨ ê³¼ +ê°IJ ëıħ +íŀ IJ +ĠìĭĿ íĴĪ +ìĤ¬ ìĿ¼ +Ġê°ľ íĺģ +Ġ본 ëĭ¤ +ĠìĻ¸êµŃ ìĿ¸ +Ġê³ł ìĨį +ĠìĹ° ê³Ħ +Ġêµ īìŀ¥íŀĪ +ĠìķĦìĿ´ íı° +Ġì§ĢìĽIJ ìĿĦ +ĠìŀĪ ìĿĮìĿĦ +ĠìĨĮ ìĨ¡ +Ġë§ģ íģ¬ +먼 íĬ¸ +ĠìķĬ ëıĦë¡Ŀ +ĠìĦľìļ¸ ìĭľ +Ġìĸ» ìĿĦ +ĠëĤĺ 머 +Ġë¶Ģë¶Ħ ìĿ´ +Ġ기존 ìĿĺ +ìĦł ìĿĺ +ìŀIJ ë£Į +ëĵ¤ ìĸ´ +ĠìĬ¤ íħĮ +ĠìĿ´ìĬ¤ ëĿ¼ìĹĺ +ĠëĶ° ë¡ľ +ĠãĢ Ĭ +ĠìłIJ ê²Ģ +íķľ íħĮ +Ġìŀij ëħĦ +Ġë¬ µ +ìŀ ĺ +ĠìŀIJ ê·¹ +미 ëĤĺ +ì¢ħ íķ© +ê¹ Ŀ +Ġì·¨ ëĵĿ +ë· Ķ +ëŀ ĢìĿ´ +ì§ij ëĭĪëĭ¤ +Ġì¤ij ìĹIJìĦľ +ĠìłķíĻķ íķľ +Ġëĵľ ëĿ¼ìĿ´ +Ġ200 7 +Ġê·¸ 를 +ĠêµIJìľ¡ ìĿĦ +âĢ ¤ +ì§Ħ ìĿĦ +ĠêµIJ ë¥ĺ +Ġë°ľ íĸī +ë§IJ ë¡ľ +기 구 +ĠìĿ´ 른 +ĠìŀIJ ë£Į를 +Ġ기ë°ĺ ìľ¼ë¡ľ +Ġë°ľ êµ´ +ìĿ´ ìĿĺ +íĤ¬ë¡ľë¯¸íĦ°ìĿ´ ëĤ´ëĬĶ +Ġê³µ íķŃ +ìĭĿ ìĹIJ +Ġ꾸ì¤Ģ íŀĪ +Ġëħ¸ ëł¥ìĿĦ +Ġìŀij ìļ© +ĠìĪĺ ìĽIJ +Ġ매 ëł¥ +ìŀIJ ê¸Ī +ëĭ¨ ìĹIJ +ê·¸ ëłĪìĿ´ +Ġìħ Ģ +Ġê³µ 모 +ëĦ ĺ +Ġ무 ìĦł +ĠíĮIJ ê²° +Ġìŀ¬ ìĥĿ +ĠìĿ´ìłľ ëĬĶ +ĠìŀĪ ìĿĮ +ĠíĪ¬ ìŀħ +Ġ기ìĹħ ìĿĺ +ê·ľ ì§ģ +Ġë½ ij +ìļ© íĴĪ +ĠìķĮ볤 ì¡Įëĭ¤ +Ġìłģê·¹ ìłģìľ¼ë¡ľ +문 ìłľ +Ġë°© ì§Ģ +ì¶Ķ ì¶ľë¬¼ +ì į +ĠìĿ´ ëķĮ +Ġìĭ « +Ġëªħ ëł¹ +ĠëıĻ ìĺģìĥģ +ĠìĿ´ ìĽĥ +ëIJ ¨ +ĠìĥģíĻ© ìĿ´ +ĠëĮĢ 기 +êµŃ ìĿĢ +Ġëį ľ +ĠìĦ± 격 +ĠëĤľ 민 +주 ìĨĮ +Ġê°IJ ìĹ¼ +Ġëľ» ìĿĦ +Ġì± Ļ +Ġê² ģëĭĪëĭ¤ +문 íķĻ +ì§Ī íĻĺ +Ġê³¼ ìĹ° +Ġ본 ìĿ¸ +íķĺ ëĭĪ +Ġëĵ± ìĿĢ +ĠëĤ´ ìĹIJ +íħĮ íģ¬ +ĠìĤ¬ëŀĮ ëĵ¤ìĹIJê²Į +Ġìĸ´ ìļ¸ +ĠìĿ´ìļ© ìŀIJê°Ģ +íĮĮ íģ¬ +Ġìĥģ ìĥģ +ĠìĥĿê°ģ íķĺëĬĶ +Ġë°ľíijľ íĸĪëĭ¤ +Ġì¹ľ ìłĪ +ìŀIJ ëĵ¤ìĿĦ +ĠìĤ¬ëŀĮ ëĵ¤ìĿĦ +ł Ģâ +Ġ모 ëĭĪíĦ° +ì¾Į ìłģíķľ +문 ìĿ´ +ĠëĮĢ ë³Ģ +ĠëĮĢíijľ ìłģìĿ¸ +ĠíĿIJ 린 +Ġ모 르ëĬĶ +ĠìĨĮ 리 +Ġìŀ¬ ìłķ +Ġë§ŀ ìĿ´ +201 6 +ë Ĵ +ĠìĿ´ë²Ī ìĹIJ +ĠìŀIJ ìľ¨ +ëĩ ¨ +ëĨĵ ê³ł +ëĵ± ìĿĺ +Ġìĭł ìĨį +ë¥ ľ +리 ë©° +ëł Ľ +ĠëłĪ ìĬ¤íĨłëŀij +Ġë§ĪìĿĮ ìĹIJ +Ġë°ķ ê·¼íĺľ +미 ìĪł +Ġìĭ¬ ì§Ģìĸ´ +Ġë´ī ìĤ¬ +Ġíķĺ ëĵľ +Ġê³µ ê°IJ +Ġëį§ë¶Ļ ìĺĢëĭ¤ +Ġê²° ìłľ +Ġ골 íĶĦ +Ġë¹Ħ ëĶĶìĺ¤ +Ġë°ľ ëĭ¬ +ĠêµŃ민 ìĿĺ +ĠìĨĶ루 ìħĺ +문 ìĿĺ +íĻľ ëıĻìĿĦ +ĠíļĮ ìĤ¬ìĿĺ +Ġê´Ģê´ij ê°Ŀ +ì Į +Ġë°Ľ ì§Ģ +ìĦľ ìĿĺ +Ġíķ ¸ +ìĿ¼ ê°Ħ +ĠìĿĺ ìĭ¬ +ĠíĮĮ ìĽĮ +ĠëĬ¥ ëł¥ +ĠëĮĢ ë¶ģ +ìĭĿ ìĿĺ +ìĺ¬ íķ´ +ĠìĿij ëĭµ +ĠìıŁ ìķĦ +íı¬ 르 +ĠìĹ ij +ĠìĪĺ 립 +Ġ3 2 +ìĸ´ ëĬĶ +ëª ½ +ĠìĨIJ ìĿĦ +ìĺ¬ ë¦¼íĶ½ +Ġê²½ ë¶ģ +민주 ëĭ¹ +ë¶Ħ 기 +ĠìŀIJ ìĽIJ +ã Ī +Ġìī½ ì§Ģ +Ġë » +Ġëį ® +ìħ ī +ì° ¾ +ìĭľ íĭ° +Ġë§Į íķľ +Ġ주ìŀ¥ íĸĪëĭ¤ +Ġ커뮤ëĭĪ íĭ° +íļĮ 견 +ìķĺ ê³ł +íķĺ ìļ° +ìĭĿ ìĿĢ +ëįĶ êµ°ìļĶ +Ġê° ¤ +ĠìĹ° ìĬµ +ìłģìľ¼ë¡ľ ëĬĶ +ĠìŀIJ ìłĦê±° +ìĹĪ ìĿĦ +Ġëĭ¤ ëħĢ +ĠíĽĦ ìĽIJ +Ġíĺij ìĿĺ +Ġíħ į +íķľ ìĿĺ +ĠëĤĺ ìĦł +Ġë³´ìŬ 주 +Ġìŀł ìĭľ +ëĭ¤ ìĿ´ +ëĬĶ ê°Ģ +íķĺ ìĭŃìĭľìĺ¤ +ëĮĢ ì±ħ +Ġì² Ļ +Ġê·¸ ìĹIJ +ĠìĿ¸ ë¥ĺ +ìĥĿ ëªħ +ëĵ± ê¸ī +Ġë¸Į ëĿ¼ +ĠìķĦëĭĮ ê°Ģ +ĠìĹ° ìŀ¥ +ĠìĽ¹ ìĤ¬ìĿ´íĬ¸ +ìļ¸ 룬 +ĠìķĦ ìī¬ +ìŀij ìļ© +ëĭ¤ ëĭĪ +íĻĶ ìĻĢ +ĠìĥĿ íĥľ +ĠëįĶ 보기 +Ġì¶Ķ ìļ´ +ëĤĺ ê°Ģ +Ġìĭľ 기 +Ġëĭ¹ ìĤ¬ +ëĭĺ ê»ĺìĦľ +Ġ기 ìĤ¬ +Ġì°¸ ì¡° +Ġë²Ī ìĹŃ +ĠìĿ´ ëĵ¤ìĿ´ +Ġ기 íļĮ +íı¬ 커 +ë Ń +Ġìłģ ìĿĢ +ëıĻ ìĿĺ +Ġìĥģ ê´Ģ +Ġëĵ± ê³¼ +ĠìĺĪ ë¹Ħ +ëŁī ìĿĺ +Ġëªħ íĻķ +Ġíķ´ ìĨĮ +Ġì§Ģìłģ íĸĪëĭ¤ +ĠìĹ´ ê³ł +Ġìķ½ ê°Ħ +ĠìĨĮ ë°© +íĸ¥ ìĿĦ +Ġìĵ° ê³ł +ĠëĬĺ ìĸ´ +Ġê°ij ìŀIJ기 +ëIJĺìĸ´ ìķ¼ +íİĺ ìĿ´ +ĠìķĬ ê²Į +ĠS NS +oc k +ĠíĻĶ 볤 +ë¬ ĺ +ĠëĤĺ ìĿ´ +íĶ¼ ìĸ¸ +¼ ìĨIJ +ĠìĤ¬ ìĥģ +ëį ¤ +Ġë¶Ħ ë¥ĺ +ĠìĪ « +Ġ먹 ìĿĦ +기 ì§Ģ +ĠìķĮ볤 ì§Ħ +ĠìłĦ ê°ľ +Ġê³µ ì§Ģ +íĬ¸ ìĿĺ +ĠìŀIJìĦ¸ íķľ +ĠëģĮ ìĸ´ +Ġìłľê³µ íķł +ëŀ © +ĠëķĮ ê¹Įì§Ģ +Ġ모ëijIJ ê°Ģ +Ġ뼰 ìĸ´ëĤľ +ìĸ´ ë²Ħ +ãĥ»ãĥ» ãĥ»ãĥ» +ì§Ģ ëĬ¥ +ëłĪ ìĿ´íĬ¸ +íĸī ìłķ +ìĭľ ëĭ¤ +Ġê²ĥ ìĿ¸ê°Ģ +Ġë°° ì¶ľ +Ġê¶Į íķľ +ìķ Ĭ +ĠìĪĺ ìĺģ +ãĪ ľ +ìŀIJ ëĵ¤ +Ġë² ¨ +ĠìŬ ìľł +기 ê¹Įì§Ģ +ê» ı +ì¡° íķľ +ĠìķĦë²Ħ ì§Ģ +ìļ© íķľ +ĠíħĮ ë§Ī +ëªħ ìĹIJ +ĠìķĬ ìľ¼ë©° +Ġìĭ¬ ìĤ¬ +Ġëĭ´ ìķĦ +Ġìľł íĺķ +Ġìŀ¥ìķł ìĿ¸ +ĠëĤĺìĺ¨ ëĭ¤ +ëĬ Ħ +ìĦĿ ìĿĦ +íķĺ ìħ¨ +Ġ머 리 +ì¶Ķ ìļ´ +ĠìĭĿ ìĤ¬ +ìłĦ ìĹIJìĦľ +ĠíļĮ ìĤ¬ê°Ģ +ê¶Į ìĿ´ +Ġê°Ŀ ìĭ¤ +ë£ ° +Ġ19 8 +Ġ문 ìĿĦ +Ġê¹Ģ ìłķ +ĠìĤ¬ íĥľ +ĠíķĺëĤĺ ë¡ľ +ë³´ ìŀIJ +ìħ ĭ +Ġìłľê³µ íķĺê³ł +Ġì¦Ŀ ê±° +ĠìĿ´ ë©ĶìĿ¼ +Ġë · +물 ì§Ī +ĠíĴĢ ìĸ´ +ê²ĥ ëıĦ +Ġ모 ìķĦ +ë³´ ëĵľ +Ġì¹ ł +미 ëŀĺ +Ġë°© 침 +Ġì½Ķ ëĵľ +ĠíĴį ë¶Ģ +ì²´ ìĿ¸ +êµ° ìĿ´ +ìĭł ìĿĺ +Ġê·¹ ë³µ +ĠìĦ¹ ìĬ¤ +ë© ¸ +Ġìŀħ ìŀ¥ìĿĦ +리 ì¼ĢìĿ´ìħĺ +구 ìĹŃ +ê¶Į ìĿĺ +ìĭŃ ëĭĪëĭ¤ +ìĦł ìĪĺ +ëª ĩ +ë³´ 를 +ĠìĿ¼ ìĿ´ëĭ¤ +ê°Ģ 격 +ĠìŀĪìĿĦ ê¹Į +Ġí ľ +ë°© ìĿĦ +ì¶Ķ ìĸ´ +ìłķ ìľ¼ë¡ľ +Ġë¶Ģ ëĵľ +ìĥģ ìľ¼ë¡ľ +Ġê³Ħ ìĤ° +ì§Ģ ìĪĺ +ìĥģ íĥľ +ĠíĸĪ ê³ł +ĠìĮ į +ĠìĻ ĢìĿ´ +Ġìĺ¬ ë¦¼íĶ½ +리 ê²Į +Ġëıħ íĬ¹ +íķĺë©´ìĦľ ëıĦ +ĠìĿ´ ëĵ¤ìĿĺ +ĠìĿ¼ ìĸ´ +Ġìłķì¹ĺ ìłģ +Ġì¶Ķ 구 +ĠíĴį ìĨį +Ġëł Į +ĠìĿ´ê²ĥ ìĿ´ +ëĿ¼ìļ´ ëĵľ +Ķ íĶ¼ìĸ¸ +ë²ķ ìĿ¸ +ĠëĤĺ ìĻĶ +ĠíĬ¸ ëłĮ +Ġì¦IJ ê±° +Ġê·ľëª¨ ìĿĺ +Ġ뼰 ìĸ´ +Ġìłķ ëĭ¹ +ìľĦ íķľ +Ġëĵ¤ ìŬ +Ġë©Ķ 모 +ĠëĤ´ ìĹIJìĦľ +Ġì¤ijìļĶ íķĺëĭ¤ +ĠëĮĢ ê·ľëª¨ +Ġ목 ìĤ¬ +Ġíĺķíĥľ ë¡ľ +ë²ķ ìĹIJ +Ġíļ¨ê³¼ ê°Ģ +ĠíĥĦ ìĥĿ +łĢâ łĢâ +ë´ī ìĤ¬ +CO M +Ġì²Ń 구 +ĠìĿĺ미 ê°Ģ +ìĿ´ 룰 +Ġë¶Ħ 리 +ìľ¨ ìĿĢ +íıŃ ëł¥ +ë¹Ħ ëĬĶ +Ġì¢ĭ ìĬµëĭĪëĭ¤ +ìŀĪ ê²Į +ĠëĦĪ íĿ¬ +ĠìĹ´ ìĸ´ +ê¸Ī ìĿĢ +Ġê²½ìļ° ìĹIJ +Ġíĸ¥ íķ´ +íĮĮ ìĿ´ +ì¦Ŀ ìĿ´ +ĠëĿ¼ìĿ´ ë¸Į +Ġê·ľ ìłķìĹIJ +ìľ Ī +ĠìłĢëł´ íķľ +ĠìĿ´ ìłķ +ĠìłĦ ë©´ +ê°Ģ ìĬ¤ +ĠíĻĺ ìĺģ +Ġìļ° 주 +Ġê²ĥìĿ´ ê³ł +ë » +Ġ3 6 +Ġì ¿ +Ġ길 ìĿ´ +ĠëijIJ 볤 +ìĽIJ íķľ +ĠìĤ¬ìļ© íķĺê³ł +Ġëĸł ìĺ¬ +ĠëıĻ ìŀij +ëĶĶ ìŀIJìĿ¸ +ì¤ įëĭĪëĭ¤ +ĠìĿ¼ë³¸ ìĿĺ +ìĨĮ ëĬĶ +리 íķĢ +Ġìľł 리 +ĠìłķëıĦ ìĿĺ +쪽 ìľ¼ë¡ľ +ìĽĶ íĺ¸ +ĠíħĮìĿ´ ë¸Ķ +íķĺìĭľ ë©´ +ĠíĮ¨ íĦ´ +ĠìĦ± ê²½ +Ġìłij ìĪĺ +ì²ĺ 를 +Ġ4 5 +Ġëªħ ìĺĪ +ë³´ ê±´ +ì°¨ ê°Ģ +기 íļį +Ġìľ ¼ +Ġíĥ ģ +ĠëĬ¥ ëł¥ìĿĦ +Ġ구 ìĨį +íı¬ ìĿ¸íĬ¸ +Ġìĭľìŀij ìľ¼ë¡ľ +Ġê³³ ê³³ +ìķĪ ìĿ´ +ĠìĤ¬ ê³¼ +룬 ìļ´ +ĠìĥĿ ì¡´ +ë°Ķ ë¡ľ +Ġì²ŃìĻĢ ëĮĢ +Ġë¹Ħ ëĤľ +ĠìĦł íĺ¸ +Ġê²½ìļ° ëıĦ +íĶ Ķ +Ġìĭ¶ ìĸ´ +ëıĻì¶ľìŀ¥ ìĥµì¶Ķì²ľ +Ġë³Ħ ë¡ľ +ìĿ´ ì¦Ī +ìĦľ ê´Ģ +Ġëį° ë·Ķ +Ġì°¾ ìĿĦ +ĠëĬĺ ìĸ´ëĤĺ +Ġíĥ ĵ +Ġëĭ¨ìĪľ íŀĪ +Ġìļ¸ ìĤ° +Ġëĭ¬ 볤 +ĠìĿ´ ìŀ¬ +Ġê·¸ëŁ¬ íķľ +ĠìĪĺì¤Ģ ìĿĺ +ĠíļĮ ëĭ´ +ĠëĮĢíĨµëł¹ ê³¼ +Ġëħ¼ ëŀĢ +Ġ기 íļĮê°Ģ +ìļ´ ëĵľ +ĠëıĻ ìĿ¼ +Ġê° Ķ +ëıĦ ìļ° +Ġë²Ħ ìĬ¤ +ë²ł ìĿ´ +ĠëĿ¼ ëĬĶ +Ġìĥģëĭ¹ íķľ +Ġì ³ +ĠìłĦ ë¶Ģ +Ġë³´ ìĿ´ì§Ģ +Ġê·ľ ìłľ +ĠìĤ¬ìĿ´ ë²Ħ +Ġìĺģ ìĸij +ĠêµIJ íĻĺ +Ġëĭ´ ìĿĢ +íķĺ볤 ë©´ +ê²½ë§Ī ìŀ¥ +ì§Ģ ìĤ¬ +ì¿ Ħ +ran s +Ġìŀ¬ ë£Į +ë ±ħ +Ġê·¼ 본 +ĠìĿ´íĥĪ 리ìķĦ +íĥ IJ +íĥĢ ìŀĦ +íĮĮ ìĿ¼ +ĠìĤ¬ìĭ¤ ìĥģ +ĠìķĮ볤 ìł¸ +ĠíĮĮíĬ¸ ëĦĪ +ĠíĻľ ë°ľ +ĠëĮĢ ëĭ¨ +ìŀĪ ìĬµëĭĪëĭ¤ +Ġì¶ľìŀ¥ 맼ìĤ¬ì§Ģ +Ġë³´ ìĿ´ê³ł +ĠìĪĻ ë°ķ +Ġê±° ì§ĵ +Ġ머 물 +ëĭĺ ê»ĺ +ĠìŀĪ ìľ¼ëĤĺ +Ġì°¨ ëĭ¨ +íı¬ íĬ¸ +ĠëĮ ĵ +Ġì¶ľ íĮIJ +ëħĦ ìĹIJëĬĶ +ĠíķĻìĥĿ ëĵ¤ìĿ´ +ĠìķĦ무 리 +ëĿ¼ìļ° ìłĢ +Ġëĵ¤ ìĹĪëĭ¤ +ìĻĢ ëĬĶ +ë°Ľ ìĿĦ +ĠëıĻ ìĿ¼íķľ +ĠìłIJ ìĿ´ +ĠëĤł ìĿ¸ +Ġë¶Ħìķ¼ ìĹIJìĦľ +Ġëĭ « +Ġê°Ģ ê³ł +Ġê±° ëĵŃ +ì¶ķ 구 +ĠìŬ ë¡ł +Ġìļ°ë¦¬ ìĹIJê²Į +ĠíĤ¤ ìĽĮ +Ġê³ ¤ +ê·¸ëŁ° ëį° +목 ìłģ +Ġìłķ ì²´ +ìĬ¤ íĶĮëłĪìĿ´ +ĠìĹŃ ìĤ¬ë¥¼ +ĠëĦ IJ +Ġ측 ìĿĢ +ìĿ´ ëĭĪ +ĠìļĶ 리 +ì§Ģ 구 +Ġìĺ¤ëĬĺ ëĤł +Ġê·¸ ê²Į +Ġì§ij ëĭ¨ +ì§Ģ ìĹIJìĦľ +ĠíİĺìĿ´ìĬ¤ ë¶ģ +Ġì¼ĢìĿ´ ë¸Ķ +Ġì « +Ġ구 ë¦Ħ +ĠëĤ ¸ +ĠíĻĶ ìŀ¬ +ĠëĿ Ħ +íĻĶ ëĬĶ +íİĺ ìĿ´ìĬ¤ +ĠìĥģíĻ© ìĹIJ +ĠìŬíĸī ìĿĦ +ì§ģ ìĽIJ +Ġì´Ī ëĮĢ +ĠëģĿ ìĹIJ +Ġë¥ ĺ +ì¿ ¨ +íķ´ 주ëĬĶ +ĠëĭĪ ê°Ģ +Ġê·¸ 볤 +íĦ° 를 +ëį° ìĿ¼ë¦¬ +물 ìĹIJ +Ġë³Ħ ëıĦìĿĺ +Ġì°¸ìĦĿ íķľ +ìĤ Ń +Ġê³³ ìĹIJìĦľ +ë°Ľ ìķĦ +Ġìĭł ì²´ +매 ìļ° +Ġìĸ¸ìłľ ëĤĺ +ìĹŃ ìĭľ +Ġ ãħ +Ġ구ìĦ± ëIJľ +Ġ미 ëĭĪ +Ġë¶Ģë¶Ħ 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+ìķĪë§Ī íĽĦ기 +Ġì¶ľ ëł¥ +ì²´ íĹĺ +ĠíķŃ ëª© +ĠìĿ´ìľł ê°Ģ +ìĿ¸ ì²ľ +Ġìłģ ìłĪíķľ +Ġíı¬ 르 +Ġì¶ķ ìłľ +Ġë³´ ìĥģ +ìĦ¸ íı¬ +ĠíĹĮ ë²ķ +ãħĭãħĭ ãħĭãħĭ +ĠëĮĢ ë²ķ +ìĨĮ ìĿĺ +ë§Ī ìłĢ +Ġ리 ë·° +ìĭ¬ ìĿĺ +Ġë°ĺ ëıĦ +Ġë²ķ ëł¹ +ê°Ģ ìĹIJ +ë¦ ħëĭĪëĭ¤ +Ġëħ¸ ìĿ¸ +201 5 +ê²ĥ ìĿ´ +ĠìĿ´ ë¡ľ +Ġì§Ħ ìłķíķľ +Ġë© Ī +ĠëĦĪ무 ëĤĺ +ĠëĤ Ń +ìķĦ ë²Ħ +Ġë³´ ì¡´ +Ġíķ´ ìĸij +Ġì§Ħ ìŀħ +íĥĢ ê²½ë§Īìŀ¥ +íĸ¥ ìĿ´ +ëł Ģ +ĠìĿ¸ ìĪĺ +ìĬ¤ ìĹIJìĦľ +Ġì¶Ķ ìĦĿ +âĺĽãĢ IJ +ë² ¼ +Ġíķľ ëĭ¬ +ìĥģëĭ´ íĨ¡ +# ãĢ +s ÎŁ +t ÎŁ +ĠëĤĺ ë¦Ħ +ĠìĿ´ìĥģ ìĿ´ +Ġê°ľìĿ¸ìłķë³´ ìĿĺ +#ãĢ ĸ +sÎŁ s +tÎŁ p +ìĿĮ ìķħ +ĠëķĮ ëıĦ +Ġëĸł ìĺ¤ +Ġê³Ħíļį ìĿĦ +Ġìłķì±ħ ìĿĦ +ëį ľ +ìłľ ê³µ +Ġì§ĢìĽIJ íķĺëĬĶ +Ġë¶ģíķľ ìĿ´ +ìĸ´ ìĿĺ +Ġì į +Ġë¶Ģ íĴĪ +ĠëĬIJëĤĮ ìĿĦ +ìłľ íķľ +êµŃ ìĿĦ +Ġ무 ìĭľ +ĠëĿ¼ ê³ł +Ġ충 ëĤ¨ +Ġê³µ ìĤ¬ +ìĺ¤ ê³ł +ĠìĦ± ìłģ +ĠìłĢ ëıĦ +ĠìķĦì§ģ ëıĦ +ĠìĹĦì²Ń ëĤľ +ì¹´ ìĿ´ +ĠëĮĢíķĻ ìĥĿ +Ġ몸 ìĿĦ +Ġëĸ¨ìĸ´ ì§Ħ +ĠìĿµ ìĪĻ +ê°ľ íĺģ +Ġë³µ íķ© +ĠìŀijíĴĪ ìĿĦ +Ġ기 ë¶Ģ +íķ´ ìĺ¨ +Ġê²ĥ ìĿ¸ì§Ģ +Ġìĭ¬ 리 +Ġ200 5 +ĠM BC +ĠìķĪ ë³´ +Ġìľł ëĭĪ +ĠìĤ¬ìĦ¤ ëĨĢìĿ´íĦ° +Ġë§ ijìĿĢ +Ġì¶Ķ ìĸµ +ë°Ķ 른 +ìĺ¨ ìĿĢ +ë¡ ¬ +ĠìĦ¸ ë¶Ģ +ĠëĺIJ ë´ĩ +ìĭ¤ ë¹Ħ +Ġìĥ Į +ĠìĦł êµIJ +ĠìĿ½ ìĸ´ +Ġë¹Ħ ê°Ģ +ĠíıIJ 기 +Ġ1 000 +Ġ3 8 +ĠëĮĢ ë¡ľ +ìĨĮ ìĹIJ +Ġë© ľ +Ġì± ĶíĶ¼ìĸ¸ +Ġíļį ëĵĿ +ĠìĿ´ ëĤ´ìĹIJ +Ġë§Ī ìĿ´ +ëĦ¤ ìĭľìķĦ +ĠëĪĪ ìĿĦ +Ġë¬¸ìłľ ìĹIJ +ĠëĭĪê°Ģ íĥĢê²½ë§Īìŀ¥ +Ġ무 ê²Į +Ġê°ľ ìĦ± +Ġê°ľ ë§ī +Ġê²Į ìŀ¬ +Ġ문 ìĦľ +Ġ ^^ +Ġì§ĢìĹŃ ìĹIJ +Ġê°ľ ë°© +Ġíĸīë³µ íķľ +ĠíĿĶ ëĵ¤ +ĠëĶ°ëľ» íķľ +ĠìķĦ 르 +Ġ물 ìĸ´ +ê³ł 리 +ĠìĿ´ ëĭ¬ +ĠëĤĺ ìĦ°ëĭ¤ +Ġë°Ľ ìľ¼ +ìłĢ ëĬĶ +ĠíĮĮ ê´´ +Ġê³µ íĨµ +ê°Ģ íı¬ë¥´ +íķ¨ ê³¼ +ĠìĻĢ ìĦľ +ìĤ° ìĿĺ +íŀĪ ê³ł +ê¸Ī ìķ¡ +Ġê³³ ìĿĢ +Ġê²½ìłľ ìłģ +Ġê°Ģ 구 +ìĨĮ íķľ +ĠìĦ¸ ìĽĮ +Ġâ ħ +Ġì§ij íĸī +ì½ľê±¸ íĽĦ기 +Ġ물 리 +Ġë§IJ ìĿĢ +ëĵ Ģ +ìĿ¼ ìŀIJ +Ġë¶Ģ íĦ° +무 íĺĦ +ĠíĬ¸ ìľĦ +Ġë¶ĦìľĦ 기 +ì°¨ ë¡Ģ +Ġ15 0 +Ġíĥij ìŀ¬ +ìĽ ¹ +Ġê¶Į 리 +ëĮĢ ìłĦ +ĠëķĮ ê°Ģ +ĠíĨµ ìłľ +ì²ľ ë§Į +ī ìĿĢ +기 ë¡Ŀ +Ġ주 ê±° +ëĶ ĺ +Ġì§ij ìĹIJ +Ġíŀĺ ìĿ´ +ìķĪ ëħķíķĺìĦ¸ìļĶ +Ġë§Į ëĤ¨ +ĠíĿ¬ ìĥĿ +Ġ주 ìĭľ +ãĢ Ĺ +ã Ħ +ëĤĺ ë§Ī +Ġíķ© 리 +íĨ ± +¸ IJ +ĠìĿ¸ 기를 +ĠëIJľ ëĭ¤ëĬĶ +ĠíĻį 콩 +Ġë°ĶëŀĮ ìĿ´ +ĠëĤ¨ ìĿĢ +ĠãĢ ij +ĠìĥĪ ë²½ +ì¡Į ìĬµëĭĪëĭ¤ +ìĿ¸ ìĹIJê²Į +ìĭľ ìķĪ +ĠëıĻ ë£Į +ë¸Į 리 +ëªħ ê³¼ +Ġê°ĸ ëĬĶ +ĠìĿij ìļ© +ëĤ¸ ëĭ¤ +Ġê°Ģê¹Į ìĿ´ +ë¡ľ ìĬ¤ +ëĤľ ëĭ¤ +ĠëĤ¨ë¶ģ ìłķìĥģíļĮëĭ´ +Ġìķ ¤ +ìĭľ ì¦Į +Ġì²ł ëıĦ +Ġìŀ¥ ì°© +Ġë³´ ê¸ī +ëĭ¤ ìĿĮ +Ġ걸 ìĸ´ +Ġìļ°ìĪĺ íķľ +ë§Ŀ ìĿĦ +ĠíķĦ 리íķĢ +ìĭľ ë¡ľ +ĠëĤĺ ê°Ī +ëĭ¨ ì§Ģ +íķĺ ê²łëĭ¤ëĬĶ +ìĸ´ 린 +ë§Ī íģ¬ +Ġëĵľ 립ëĭĪëĭ¤ +Ġê·¸ ë§Įíģ¼ +ĠìĹ° ëł¹ +ĠíĿĺ 룬 +ĠíĺĦ íĻ© +ìĿ¼ ìĿĢ +Ġê³µ 무ìĽIJ +ìĦ¤ íĮħ +Ġê²ĮìŀĦ ìĿĦ +íĸĪ ìĸ´ìļĶ +Ġ죽 ìĿĮ +ìķ Ĺ +ìŬ ìŀIJ +ê°ľ ìłķ +ĠìĹ° ê°Ħ +Ġëĭ¹ìĹ° íŀĪ +Ġìļ°ë¦¬ 를 +Ġë°ĽìķĦ ëĵ¤ +ĠíıIJ ìĩĦ +ëł ģ +Ġ3 3 +ì¡° 를 +ëĦ ¥ +Ġìĺģ íĻĶ를 +ìĨį ìĹIJ +ìĥģ ìĹIJìĦľ +ìķĦ 리 +Ġë°Ķ ëĭ¥ +ëĨ Ĵ +Ġë°©ë²ķ ìĿ´ +Ġë°´ ëĵľ +íķĻ íļĮ +Ġìľł ëıĦ +ĠìłIJ ìĿĢ +ĠíıŃ ë°ľ +ĠìĬ¹ 리 +Ġì¤ij ê³ł +Ġë¹ ļ +Ġë°ľ íľĺ +ì§Ħ ìĿĢ +Ġ al +Ġíİ ľ +Ġê°ľ ë´ī +íķľêµŃ ëĭ¹ +Ġë°Ķê¿ Ķ +ĠëıĮìķĦ ê°Ģ +ĠìĽĮ íģ¬ +ĠìĤ¬ ìŀ¥ +민 ìĿĺ +Ġê³Ħìķ½ ìĿĦ +ĠìłĦë§Ŀ ìĿ´ëĭ¤ +ĠìķĪìłĦ íķľ +ë© į +ĠìĦ± ëł¹ +Ġì į¼ +Ġìŀħ íķĻ +Ġê°ľìĿ¸ ìĿĺ +리 ë©´ +ëį° ìļĶ +ë¡ľìĦľ ìĿĺ +ìĭĿ ê³¼ +ë©´ì¶ľìŀ¥ ë§ĮëĤ¨ +ĠëĤĺëĪ ł +ìĩ¼ íķij +ĠìĿ´ ìłĦìĹIJ +ìķĦ íĬ¸ +ĠìķĬ ìľ¼ +Ġìºł íİĺ +ĠëĨĢìĿ´ íĦ° +ĠìķĦìĿ´ ëĵ¤ìĿ´ +íģ´ ëŁ½ +ìĿ¼ ë¡ľ +ìĭł ê³ł +Ġì±ħ ìĿĢ +íķ´ ê°Ģ +ĠìĬ¹ ìĿ¸ +ĠíķĺëĤĺ ëĬĶ +ìĭľ ë¶ĢíĦ° +ìĹ ł +ĠìĻ¸ ìĹIJëıĦ +ĠëłĪ 벨 +ĠêµIJ ìĤ¬ +Ġíıī ìĥĿ +Ġì·¨ ìķ½ +Ġì§Ģ ëĭĮ +Ġê°IJ ìłķ +ëĤ´ 기 +ãİ ŀ +ĠìĶ¨ ëĬĶ +ìĿ¼ ë°ĺ +Ġ200 6 +ìĨĮ ë¹Ħ +ì¡ ¸ +Ġë¶Ģ족 íķľ +ĠìĨĮ íĻĶ +Ġê±° 기 +ê°ķ ìĿĺ +ê²ĥ ìĿĢ +ëłĪìĿ´ ìħĺ +ë ¸IJ +ëł¥ ìĹIJ +Ġíħ IJ +ĠìĤ¬ìĭ¤ ìĿ´ +ë¡ľ ìĿĺ +ìĬ¤íĥĢ ê·¸ëŀ¨ +Ġê±´ê°ķ íķľ +ëĭ¤ ìĭľ +Ġê°IJ ê°ģ +Ġê´Ģ볨 íķĺìŬ +Ġë¸Į ëĿ¼ìļ°ìłĢ +Ġ미ëŀĺ 를 +Ġì´ī 구 +ìĭ¤ ìĹIJ +본 ìĬµëĭĪëĭ¤ +Ġìľ¡ ìĦ± +Ġìłķë³´ íĨµìĭł +Ġ커 ë²Ħ +ĠëĦ¤ ìŀĦ +Ġìĸ´ íĶĮ +ĠíķĻ ìĪł +ĠíĻĺê²½ ìĿĦ +ĠíĦ° íĤ¤ +ê³µ ìĹ° +Ġë¶Ģë¶Ħ ìĿĢ +Ġê°Ģ ìķ¼ +ĠëĦ£ ìĸ´ +ìĥģ ê³¼ +Ġê·¸ëħĢ ëĬĶ +주 ëħĦ +íİ Ļ +ìĤ¼ ìĦ± +ĠìķĦíĶĦ 리카 +( ?) +íĸĪ ìľ¼ëĤĺ +Ġíĺ¸ íĿ¡ +ìĿĺ ìĽIJ +íĺ¸ ë¥¼ +ìĹIJ ìļĶ +Ġíķ© 격 +ì¹ĺ ê²Į +ĠãĢ IJ +ëķ ħ +ĠìłĦ ìĦ¸ê³Ħ +Ġê´Ģ ì°° +Ġìľł ìĿ¼íķľ +ĠìĥĿê°ģ íķĺê³ł +ìĤ° íĻĶ +ìĪĺ ìĻĢ +ì²Ń ìĿĢ +ĠëıĻ 기 +Ġìľł ìĿĺ +Ġìĭľ ìĤ¬ +Ġê³µ ìŀ¥ +Ġìĥģ ìĹħ +르 íģ¬ +ĠìĤ¬ìĿ´ ìĿĺ +Ġì¢ ģ +Ġìłľ ìŀ¬ +Ġê¹Ĭ ìĿ´ +ìĿ´ ìĹĪìĬµëĭĪëĭ¤ +ĠìĽĶëĵľ 컵 +Ġ3 4 +ĠìĤ¬íļĮ ìĿĺ +Ġë§IJìĶĢ ìĿĦ +ĠëıħíĬ¹ íķľ +ĠìĦł ëıĦ +Ġ매 ì²´ +ì§Ī ìĿĺ +Ġë°° íı¬ +Ġ기 ìģ +구 ìĹIJ +Ġì½Ķ 리ìķĦ +Ġe m +ê±´ ì¶ķ +Ġë¶Ħ ìŁģ +Ġëĵ± ê¸ī +Ġëĭ´ 긴 +Ġë°ķ ìĤ¬ +Ġë°ľìłĦ ìĿĦ +ĠëĤ¨ ëħĢ +Ġ기 ìŀ¬ +ê¹ IJ +ĠìĹĨ ì§Ģë§Į +Ġì¡° ìŀij +ĠíĬ¹ ìĦ± +Ġìłģ ìĿij +Ġìĺ® ê²¨ +ëª ¸ +ìĦ¸ íķľ +ê ¶ +ìľĦìĽIJíļĮ ëĬĶ +Ġìĸ´ëłµ ëĭ¤ +ĠëĤ´ ê³ł +ĠëĤ´ ëĤ´ +¼ 쪽 +ìĦł ìĹIJ +Ġë¹Ħë°Ģ ë²Īíĺ¸ +Ġëĭ¤ íĸī +ìĦľ íĬ¸ +Ġë¿IJ ìĿ´ëĭ¤ +ĠëĦĵ ìĿĢ +íĬ¸ 롤 +Ġì¹´ ì§Ģëħ¸ +ë´IJ ìķ¼ +ĠíĽĦ ë°ĺ +ĠìĦľë¹ĦìĬ¤ ìĿĺ +ĠíĿĶ íŀĪ +Ġìŀ¡ ê³ł +Ġ긴 ìŀ¥ +Ġê°Ħëĭ¨ íķľ +ĠíĺIJìĿĺ ë¡ľ +Ġë³´ ì¦Ŀ +Ġëĵ¤ìĸ´ ìĦľ +ĠìĦł ìĤ¬ +Ġëħ¸ íĬ¸ +ë°° ìļ° +Ġë§Įëĵ¤ ìĹĪëĭ¤ +Ġìŀĺ못 ëIJľ +ĠìĿ´ ì¤ij +Ġì¹´ ì¹´ìĺ¤ +ëł¥ ìľ¼ë¡ľ +ĠìĹ° ìĺĪ +ĠëIJĺ 기 +ĠíķĺëĤĺ ê°Ģ +ĠìĿ¸ê³µ ì§ĢëĬ¥ +ĠëĦ¤ ìĿ´ë²Ħ +Ġ충 격 +ĠíļĮìĽIJ ìĿ´ +Ġëħ¹ ìĥī +ĠíĻĶìŀ¥ íĴĪ +Ġë§Īì°¬ê°Ģì§Ģ ë¡ľ +ê°Ģ ëĿ½ +íĻĶ ë¡ľ +물 ë¡ł +ĠëĤ ļìĭľ +ĠìłĢìŀij ê¶Į +Ġíķľ ë§Ī +Ġë§Ī ê°IJ +íĸĪ 기 +ĠìŀIJìĹ° ìĬ¤ëŁ½ê²Į +íķ´ ìł¸ +ë³´ ìŀ¥ +ëĶĶ ìķĦ +ĠìĦł ê³ł +Ġíĺķíĥľ ìĿĺ +ëĭ· ì»´ +ìĭ¤ íķľ +íķĺëĭ¤ ê°Ģ +Ġë©Ķ ìĿ¸ +Ġì°½ ìĽIJ +ìŀ¬ ìĥĿ +ì¢ Ģ +ĠìĹIJ ìĿ´ +ìŀĦ ìĿ´ +Ġê³ł ëıĦ +ĠíĺĦìŀ¬ ìĿĺ +ë¥ł ìĿĢ +ĠìĤ¬ëŀij íķĺëĬĶ +ĠíĴį ê²½ +ĠëĤĺ ì¤ijìĹIJ +Ġì¹ľ íĻĺê²½ +Ġë°ľìĥĿ íķł +ĠêµŃíļĮìĿĺ ìĽIJ +íĥ ł +Ġìłķ íķľ +ìŬ ìĦľ +Ġìĭ¶ ìĬµëĭĪëĭ¤ +ĠëĴ¤ ìĹIJ +ĠìĨĮ ìļĶ +ìĿ¸ëį° ìļĶ +íķĺìĺĢ ìľ¼ë©° +Ġê·¸ëħĢ ìĿĺ +ëijij ìĿ´ +Ġë¶Ħ ëħ¸ +Ġì ĮĢ +Ġìłķ ì§Ģ +Ġì·¨ ìŀ¬ +ëıĻ ìĿ´ +ìŀ¬ ìĤ° +ìĹ° íŀĪ +Ġë¹Ħ ìĥģ +Ġëĭ¹ íķ´ +ìĹ ij +ìŀIJ 격 +Ġìķī ìķĦ +ĠìĤ¬ëŀĮ ìĹIJê²Į +Ġìĭľìŀij ëIJľ +Ġìĭľê°Ħ ìĹIJ +ìĪĺ íķľ +ëĤ Ń +Ġë§Į ëĤł +Ġëĭ¨ ëıħ +ĠíĮĮ ìĿ¼ìĿĦ +ĠìĿ´ìļ© íķĺëĬĶ +êµIJ ìĪĺ +ëĭĺ ê³¼ +ëıĻì½ľê±¸ íĽĦ기 +Ġ뮤 ì§Ģ +ĠìķĬìķĦ ëıĦ +Ġíı¬ ìŀ¥ +Ġì¹ĺ ë£Į를 +Ġ맼 ì§ij +ë°° 를 +ĠëĶĶ ìĬ¤íĶĮëłĪìĿ´ +Ġì ¾Į +Ġê°Ģ ìĪĺ +ĠìķĬ ì§Ģë§Į +Ġ201 9 +íķ´ ì§Ģê³ł +ĠìľĦ íĥģ +Ġì§Ģ ë¶Ī +Ġì§ģ 무 +Ġì§Ħíĸī ëIJľ +ĠìłĦ 주 +Ġë© ° +ï¼ £ +Ġë¶ĦìľĦ 기를 +ĠìĪĺ ìĦĿ +ëıĻ ìĹIJ +Ġ물 ë¥ĺ +ĠìŀĦ ëĮĢ +Ġì§ĢìĹŃ ìĹIJìĦľ +ĠìķĦ ìļ° +ĠëĨĴ ìĿ´ +Ġì´Ī ë°ĺ +Ġíijľ ë©´ +Ġ미ìĦ¸ 먼 +ìŀIJ ìĹ° +íĶĦ ëłĪ +ĠíĪ¬ ìĸ´ +íĬľ ë¸Į +ìļ° ìĦł +ìĹĪ ìĸ´ìļĶ +ĠìħĪ ìĿ´ëĭ¤ +Ġìłķ ì°© +Ġê·Ģ íķĺìĿĺ +Ġë°ľ ê¸ī +ë¡ľ ìļ° +Ġë§Įëĵ¤ìĸ´ ì§Ħ +íĺģ ìĭł +Ġìłģ ìĸ´ëıĦ +Ġì¹ĺ ìĹ´ +Ġê³ł ëıĦìĿĺ +ĠìłĪ ë°ĺ +íķĻ ìĽIJ +Ġìĭ¤ ëĤ´ +ĠìĿ´ ìĬ¹ +Ġì±Ħ íĥĿ +Ġì¦IJ ê±°ìļ´ +ì½Ķ ìĬ¤ +Ġê·¸ ê³³ +ĠëĮĢ ëĭµ +ì²ľ ì§Ģ +ê¹ Ķ +ë° Ń +Ġì·¨ ìŀĦ +Ġì¶ķ íķĺ +ĠìĿĮ ìĭĿìĿĦ +모 ëĵł +ĠìĹ ī +ëķĮ ëĬĶ +Ġë© ĺ +Ġ충 족 +ëĭ¨ ìľ¼ë¡ľ +ĠìĥĿ 겨 +Ġíİ Ģ +ĠíĶĦ 린 +Ġ3 7 +ĠìĺĪ ìĻ¸ +ĠëĦ · +ĠëĮ Ħ +Ġíŀ IJ +ĠìŀĪìĹĪ ëĬĶëį° +Ġê°Ģ격 ìĿ´ +ĠìķĦ ìļ¸ëŁ¬ +ĠëĮĢíķ´ìĦľ ëıĦ +ìĨĮ íĶĦíĬ¸ +ìĺ¤ íĶĦ +íĻĶ íıIJ +Ġì²ľ ìķĪ +ìĿ¸ ëĵ¤ìĿĢ +ĠëĤ´ ìĦ¸ +ĠìŬ ë¶Ģ를 +ĠìłĦ ìĽIJ +Ġìĥģ ìľĦ +ĠëıĻ ìĿĺ를 +ĠìĦ¸ê³Ħ ìłģìĿ¸ +Ġíıīê°Ģ 를 +Ġìĭľ ìĨį +ĠìĿĺ ì¡´ +ë¡ľ ëıĦ +ĠíĨµ ì§Ģ +ĠìŀĪ ê²ł +Ġë°Ľ ê²Į +ĠíļĮìĽIJ ìĿĺ +ê´Ģ ê³¼ +ĠìŀĦ ìĭľ +ĠêµŃëĤ´ ìĻ¸ +íķĺ기 ìĹIJ +ìĿ´ìĹIJ ìļĶ +ê´ij ê³ł +ĠìłIJ ì°¨ +ìĦł ìĦłíķľ +Ġê²Į ëĭ¤ê°Ģ +Ġì²ł íķĻ +ĠìŀIJ 리ìĹIJ +Ġìľł ë°ľ +ë¶ģ ëıĦ +Ġ미êµŃ ê³¼ +Ġëħ¼ ëŀĢìĿ´ +Ġë²ł ìĬ¤íĬ¸ +ìĤ¬ìĹħ ìĿĦ +ĠíĦ ¸ +ì² © +Ġ4 8 +Ġìļ´ìĺģ íķĺê³ł +Ġë³´ê³ł ìĦľ +ìĪĺ ìĪł +Ġëŀ ľ +Ġë¶Ħëªħ íŀĪ +íĻĶ ìŀ¬ +ì¹ĺ ìĿĺ +Ġì¦Ŀ ëªħ +Ġ믿 ìĿĮ +ĠìĿ¼ë°ĺ ìłģìĿ¸ +Ġíķ Ģ +Ġ4 00 +ëł Ģëĭ¤ +Ġ기ìĹħ ìĿ´ +ĠìĿ´ ê±´ +ê³µ ê°ľ +ĠíĻ© ê¸Ī +ëįĶëĿ¼ 구ìļĶ +ì§Ī ìĿ´ +ĠìĻĦ íĻĶ +Ġë©Ģ íĭ° +ĠìĻĦë²½ íķľ +ĠìķĦëĭIJ ê¹Į +Ġìŀħ ê³ł +Ġê²°ê³¼ ê°Ģ +Ġì¦IJ 겨 +ĠëĮĢ ì²ĺ +Ġê³µ 기 +ĠìĦ¸ê³Ħ 를 +Ġê³µê°Ħ ìĿĦ +íĨ ¨ +ì½ľê±¸ ì¶Ķì²ľ +Ħ ìĥĪ +ĠìłĦ ëĤ¨ +Ġìĭľìŀ¥ ìĿĺ +Ġ민주 ëĭ¹ +Ġìłij ì´ī +ëĤ ļìĭľ +ìłķ ê³¼ +ìĹħ 무 +Ġê°Ģ ëģĶ +íĺ¸ ìĿĺ +Ġì¶ľ ë²Ķ +Ġì¹´ íħĮ +ë¥ł ìĿĦ +ëıĻì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ +Ġìĺ¬ ëŀIJëĭ¤ +íĺģ ëªħ +ëħĦ ìĥĿ +ĠíĬ¹ íĹĪ +Ġë· ° +ĠìĪĺ ìŀĪëĬĶ +ë¹Ħ ìĿĺ +Ġì§ĢìĨį ìłģìĿ¸ +ĠìĪĺ ìĭŃ +Ġíķĺ ê²łìĬµëĭĪëĭ¤ +¼ Ī +ìĸ´ ìł¸ +íķĺìĭľ ê³ł +ëĤ¨ ë¶ģ +ĠìłĦ ë¶ĢíĦ° +ĠëĤĺ ëıĦ +ĠìŀĪìľ¼ ë¯Ģë¡ľ +Ġíģ¬ ëĭ¤ +Ġ0 2 +Ġëĵ¯ íķľ +ĠìĿ¼ ìŀIJ +ĠìĿ´ëªħ ë°ķ +Ġìĺ¤ ë¥ĺ +íĨłíĨł ìĤ¬ìĿ´íĬ¸ +Ġ모 ëĵĪ +Ġë©Ķ 르 +ĠìĦŃ ì·¨ +Ġì²´ ìľ¡ +Ġíĺ¹ ìĭľ +ëĭ Ľ +íĮĢ ìĿĢ +ĠìĬ¤íİĺ ìĿ¸ +Ġì°¨ ìĿ´ +Ġìķł ëĭĪ +ëĭ « +ĠìĿ´ ê²Į +ĠìĥģëĮĢ ìłģìľ¼ë¡ľ +íķ´ ìĸij +Ġìłķë³´ ëĬĶ +Ġ19 7 +Ġ충ë¶Ħ íķľ +ì°¸ê³ł ë¡ľ +Ġëĸ¨ìĸ´ ìł¸ +ĠìĿ´ 주 +Ġê°Ģ ë©´ +ê³µ íķĻ +물 ë¡ľ +íĥĢ ìļ´ +ì¶ķ ìłľ +ĠìłľíĴĪ ìĿĢ +ĠìĻ¸ ìĹIJ +Ġì°¾ ê³ł +ëĤĺ ìĿ´ +Ġëĵ Ŀ +ĠìłĦ ëħĦ +Ġíķľ ê¸Ģ +ìĤ¬ ìĿ¸ +Ġíķĺ ìŀIJ +êµŃ ìĹIJ +ĠëıĦ 구 +Ġ주 ìłľ +ĠìĿ¸ íķĺìŬ +Ġê¹Ģ ìĦ± +Ġê°ĢëĬ¥ ìĦ±ìĿĦ +ê³ł 기 +Ġë°ĺëıĦ ì²´ +ĠìĿ¼ ëħĦ +Ġê°IJ ìĥģ +ì¦Ī 를 +Ġì¤Ģ ëĭ¤ +Ġ무 기 +Ġì¸ µ +Ġíķĺ ëįĶëĿ¼ëıĦ +Ġëĭ¤ 르ê²Į +ìľł ìĤ° +Ġê°ģê°ģ ìĿĺ +침 ìĿĦ +ĠìĦľ ìĦľ +ì§Ī ëŀľëĵľ +Ġíķ¸ ëĵľ +Ġê²ĥ ìĿ´ëĿ¼ +Ġê°ķ ìłľ +ìĬ¤íĭ° ë²Į +Ġìĭľ ìŀ¥ìĿĦ +Ġê²½ ê³ł +ĠìĨĮ ìħľ +Ġíķĺ ëĭĪ +íģ ° +Ġ첨 ëĭ¨ +Ġd ev +Ġìµľì´Ī ìĿĺ +ĠëĮĵ ê¸Ģ +ìłľ ìŀij +Ġìŀ¥ 르 +ë²ł ìĿ´ìĬ¤ +ìĭľ ì½Ķ +ĠìŀIJ ìĤ° +ìķ½ íĴĪ +ìĸ´ íĶĮ +ë³´ ìĦ¸ìļĶ +Ġê³łê°Ŀ ìĿĺ +Ġìķŀìľ¼ë¡ľ ëıĦ +Ġìĸ´ 른 +ĠìĹ° ë°© +Ġëĵ¤ 볤 +Ġíı´ 리 +ìĦ± ëıĦ +ĠìĿ¸ ìŀ¬ +ĠíķľêµŃ ìĹIJìĦľ +ĠìĿ´ìķ¼ 기ê°Ģ +íķĺ ê²łëĭ¤ê³ł +Ġìĸ´ëĶĶ ìĦľ +Ġë°©íĸ¥ ìľ¼ë¡ľ +ìĿ¸ ëıĦ +Ġê°ĸì¶Ķ ê³ł +Ġìĭľ ëĤ´ +ĠëĤĺ ìķĦ +ĠêµIJ ê³¼ +ĠíĺĦ ìŀ¥ìĹIJìĦľ +Ġì¶Ķê°Ģ ë¡ľ +ĠìĿ´ ëŀĺ +Ġìľł ìłĢ +민 족 +ë©Ķ ìĿ´ìħĺ +ĠìķĦ무 룰 +ì²´ ê³Ħ +Ġë§IJ íķ´ +쪽 ìĹIJ +ĠìĿ¼ ìĭľ +Ġìĸ´ëł¤ ìĽĢìĿĦ +Ġìłľì£¼ ëıĦ +Ġìĺģ ê´ij +ë°ľ ìĿĦ +ĠìĽIJ íĻľ +Ġìĭ± ê°Ģíı¬ë¥´ +ĠìĤ ½ +Ġìķ¼ 구 +Ġìĺµ ìħĺ +ê³µ ìĹħ +ĠíĮĮ ìĿ´ +Ġì§Ģê¸Ī ëıĦ +ĠìĬ¤íĥĢ íĬ¸ +íĻĶ íķĻ +ĠìłIJ ìĿ´ëĭ¤ +íĪ¬ ìĸ´ +ĠìĪĺ ê°ķ +Ġíķĺ ìĭľ +Ġë¥ ´ +ìĹħ ê³Ħ +ĠëĤł ìĶ¨ +Ġ미 ë§Į +íħĮ ìĿ¸ +ĠìĦ¸ íı¬ +ê¶Į ìĹIJ +Ġì²ľ ìĹ° +ìĬ¤ íĥĦ +Ġìĸ´ ì°Į +ĠìĤ¬ìĹħ ìŀIJ +ĠìłľìĻ¸ íķľ +기 ëħIJ +ĠìĦ¸ ìĥģìĹIJ +ëħ¸ ì¡° +ãħ ľ +ĠìĹ°êµ¬ 를 +ë²ķ ìĿ´ +Ġê±´ ëĦĪ +Ġìĸ» ìĿĢ +ì§Ģ ë©° +ê¸Ī ìľ¼ë¡ľ +Ġë°ľ 매 +ĠíĮĮ íĭ° +ì±ħ ìĿĦ +Ġëĸ ¨ +ĠíĶĮ ëŀĺ +ĠíĽ ¼ìĨIJ +ĠìĪĺ íķĻ +룬 리 +Ġìĺ¤ íĶĦ +ìĪľ ìľĦ +ê¼ ¬ +ĠìĬ¤íĬ¸ ëłĪ +ì²ľ ìĭľ +Ġ미êµŃ ìĿ´ +ê·¸ëŀĺ ìĦľ +Ġ긴 ê¸ī +Ġì§Ģ ìĥģ +ĠìĦľ ë¥ĺ +ĠëıĻ ëĦ¤ +ê¸Ī ìĿĺ +ë²Ķ ì£Ħ +Ġì§Ģ ì²´ +ĠìŀIJ ìķĦ +Ġê²½íĹĺ ìĿ´ +Ġê·¸ 걸 +ë´ ¤ëĭ¤ +ĠíķĻ ë¶Ģ모 +Ġ기본 ìłģìĿ¸ +ĠíĮ © +ëŁŃ ìĭľ +ë¶Ģ ë¡ľ +Ġíĸ¥ íķľ +Ġê° Ķëĭ¤ +Ġìľł ìķĦ +âij ¡ +ĠìķĦëĭĪ ì§Ģë§Į +Ġëĵľ 리 +Ġë°Ŀ íŀĮ +Ġì°¸ìŬ íķľ +Ġë³´ ë©´ìĦľ +ĠìĦ¸ 미ëĤĺ +Ġì¢ħ ì¢ħ +Ġë§ŀ ì¶Ķ +ĠëĶĶ ìŀIJìĿ´ëĦĪ +Ġì§Ģ ìĹ° +머 ìĭł +ĠíĺľíĥĿ ìĿĦ +ìĹIJìĦľ ë¶ĢíĦ° +Ġìĥģ ìĨį +Ġì¢ĭ ì§Ģ +ëĪĦ 리 +Ġëı ĭ +Ġ모 íĦ° +ìķĺ ëĬĶëį° +ĠìĿ¼ ê´ij +Ġ문 íķĻ +Ġ주 ìĸ´ +Ġê³ł ìľĦ +ìĭ¬ ìľ¼ë¡ľ +Ġë¹ĦêµIJ ìłģ +ĠE TN +Ġ물 ìĿĦ +ĠíĻĶ ìĿ´íĬ¸ +ìĸµ ìĽIJìĿĦ +ĠëıĮ 볤 +ĠëĤ´ ì§Ģ +ê°ľ ê°Ģ +Ġì§ij ìĹIJìĦľ +íķ´ ì§ĢëĬĶ +Ġê³¼ ìłľ +Ġìļ´ ìļ© +íĺij ìĿĺíļĮ +Ġ무ìĹĩ ìĿ´ +ĠëģĬìŀĦ ìĹĨìĿ´ +Ġì¡° ìĸ¸ +ĠíĻľìļ© íķľ +ì¡Į ê³ł +ìĿ¸ ì¦Ŀ +ĠìĦł ì§Ħ +ëįĶ ëĿ¼ê³ł +Ġë§Ī 주 +ĠìĿij ìĽIJ +ĠíĶĦ리미 ìĹĦ +ĠìĿ½ ê³ł +ĠìĿ´ ëŀĢ +주 ìĿ¼ +Ġ모 른ëĭ¤ +Ġê³µ íı¬ +ìŀ¬ íĮIJ +Ġë°Ķ 른 +Ġì¡°ê¸Ī ìĶ© +ĠìŀIJ리 ìĹIJìĦľ +ĠëĤ ¼ +ìĸ´ ëĸ +ĠìľĦìĽIJ ìŀ¥ìĿĢ +Ġë°Ŀ ìĿĢ +Ġìĭľìŀ¥ ìĹIJ +Ġìķķ ë°ķ +ëĵ¤ ìĿ´ëĭ¤ +Ġ3 9 +무 ìĹŃ +201 4 +Ġê·¸ ëŀ¬ +ĠìķĦë¦Ħ ëĭ¤ +ëĬ¥ ëł¥ +Ġëĭ¨ ìĿ¼ +Ġìŀ ī +Ġt rans +ĠìĤ¬ê±´ ìĿĦ +Ġê³Ħ ìĹ´ +ĠëĨĴ ëĭ¤ +ì°½ ìĹħ +Ġëħ¸ëıĻ ìŀIJ +ĠìĥĿíĻľ ìĿĦ +ìĽĶ ê¹Įì§Ģ +Ġâ ĨĴ +Ġì½ © +ëĦ¤ ìŀĦ +ìĭľì¼ °ëĭ¤ +Ġë§ĪìĿ´íģ¬ ë¡ľ +ĠìŀIJ ê°Ģ +Ġë§İ ê³ł +ìĿ´ ìłľ +ĠìĥģíĴĪ ìĿĦ +물 ìĿĢ +ì¼Ģ ìĸ´ +Ġì° Į +íŀ ĺ +Ġ기 ë¶Ħ +ëĭ¹ ìĿĦ +ê¶Į ëıĦ +Ġìłģ ìłĪ +Ġíıī ì°½ +Ġìłģê·¹ ìłģìĿ¸ +Ġê°Ģì¹ĺ ê°Ģ +ĠëŃĶ ê°Ģ +ëĤĺ ê²Į +ĠëĦIJ 리 +기 본 +ĠëĤ´ ëĬĶ +ĠìĦľìļ¸ ëĮĢ +Ġë§ĪìĿĮ ìľ¼ë¡ľ +ĠìĿ´ ì²ĺëŁ¼ +ĠìĥĿ 기 +íı¬ ëŁ¼ +ĠìĤ¬íļĮ ë³µì§Ģ +Ġê¹Ķ ëģĶ +ĠìľĦ 기 +íĥĢ 민 +Ġì§ij ìĿĦ +Ġìĭľìŀ¥ ìĹIJìĦľ +ĠìĤ¬ ë²ķ +ĠìĤ¬ìļ© íķ´ +ìķĦ ìĽĥ +ĠíĶ ½ +Ġì²Ń ìķ½ +Ġì¡° ìļ© +Ġë§ŀ ê²Į +Ġê·¸ëŁ¬ ë©´ +Ġì¤Ģ ìĪĺ +ìĭ± íĦ´ +ê³ł ëĬĶ +ë¹Ħ êµIJ +Ġëijĺ 째 +Ġëĭ ¿ +ìĸ¸ ë¡ł +Ġìļ° ìļ¸ +Ġìŀ¬ ìĤ° +ìĦľ ë¡ľ +ĠìŀĪìĹĪ ì§Ģë§Į +ĠëĤĺ ìģľ +Ġì¢ĭ ê³ł +Ġ기ìĪł ìĿ´ +ìħ¨ ìĬµëĭĪëĭ¤ +Ġëį° ìĿ´ +Ġëĭ¤ìĿĮ ìĿĺ +Ġíķĺ ìĺĢ +Ġê³ł ì§Ģ +Ġ충 ëıĮ +íĸī ìĤ¬ +Ġ200 4 +ìĿ¼ ìĺ¤ìłĦ +ìĨĮ 리 +Ġ기ëĬ¥ ìĿ´ +C ìĹIJìĦľ +Ġë¶Ħ ìĿ´ +ì¢ħ ìĿĦ +íħĮ 리ìĸ´ +Ġì§Ī문 ìĿĦ +ìĦ¸ ìĿĺ +Ġë°ķ ìłķ +ê´Ģê³Ħ 를 +ìłķ ê·ľì§ģ +ĠëĮĢ 리 +íĺ¸ ê°Ģ +Ġì§ģ íĽĦ +ĠìĹħ ê·¸ëłĪìĿ´ +ìĨĮìħľê·¸ëŀĺíĶĦìĨĮìħľê·¸ëŀĺíĶĦ ìĨĮìħľê·¸ëŀĺíĶĦìĨĮìħľê·¸ëŀĺíĶĦ +Ġë©Ķìĭľ ì§Ģ를 +Ġë¬¸ìłľ ë¡ľ +ĠíķĺëĤĺëĭĺ ìĿ´ +ĠìļĶ구 íķĺëĬĶ +íĺij ìĿĺ +ì¹ľ 구 +Ġì§Ģ íĸ¥ +Ġìĭľ ìŀ¥ìĿĢ +ĠìĿ´ íĭĢ +Ġìłķ íķĺëĬĶ +Ġì°¾ 기 +Ġì§Ħíĸī íķĺê³ł +Ġìĭł ìĦ¤ +ĠíĬ Ģ +ìķĪ ìĿĺ +Ġíĸī ìĤ¬ëĬĶ +Ġê·¸ëłĩ ëĭ¤ë©´ +íķĻ ìĬµ +ĠìĨĮ ìĭĿ +ĠìĹŃ ëĮĢ +ĠëĨĢ ëĿ¼ìļ´ +ê°IJ ìĤ¬ +Î ķ +ìĨĮ ìĻĢ +ìłķë³´ 를 +ìļĶ íķľ +ëIJĺ ìĹĪê³ł +ĠëıĦ ìĭ¬ +Ġê°IJëıħ ìĿĢ +구 매 +Ġ모 ìĥī +Ġìŀħ ì°° +Ġê³¼íķĻ 기ìĪł +미 ëĭĪ +Ġê³³ ìĿĦ +Ġë©´ ìłij +ìº Ķ +ê³ł ê°Ŀ +ĠìķĦ ë¹ł +ëĵ± íķĻìĥĿ +ĠíŤ ìĸ´ +Ġë³Ģíĺ¸ ìĤ¬ +ìŀ İ +ĠëĤĺìĺ¤ ê³ł +ĠìĤ¬ ë¡ľ +ìĻ ľ +ìłĦ 기 +ĠìĿĮìķħ ìĿĦ +ëIJĺ ê±°ëĤĺ +ê·¸ ëłĩ +ë©Ķ ëĭ¬ +í Ľ +ĠëĤĺ ìĿĢ +ĠëĤĺíĥĢ ëĤĺëĬĶ +Ġê³Ħ ê¸°ë¡ľ +Ġìŀł ìŀ¬ +ìĽĮ ìĦľ +ìŀ¥ìķł ìĿ¸ +ĠìĪĺ ìĪĺ +Ġê°Ģ ì§Ī +ìĬ¹ ìĿĦ +Ġíģ´ ëŁ½ +ķ ìĭľì½Ķ +âĢ § +ëĮĢ ì¤ij +ĠìķĦ ìĺĪ +Ġë¶Ģ ìĿ¸ +Ġ목 ë¡Ŀ +ĠëĪĪ ìĿ´ +ĠëĤł ìĿĢ +Ġê·ľ ì¹Ļ +ëł ĺ +ĠìĤ¬ ìłĦìĹIJ +Ġë¬ ¶ +ĠìĿ¼ ìĹIJ +Ġë¸ĮëĿ¼ ì§Ī +Ġëħ¼ 문 +Ġê¹Į ì§Ģ +Ġê°ľ ìĦ¤ +Ġì§Ħ íĻĶ +ìĭ¬ íķľ +ĠìĽIJ ìĿ¸ +Ġëħ¸ 무íĺĦ +Ġëıħ ìŀIJ +ê³ł ìĿĺ +Ġê²½ 기를 +íĺĦ ìĭ¤ +ĠìĤ¬ìļ© íķľ +Ġìĺ¤ ëŀ« +ĠìĹĨ ìľ¼ë©° +ĠìĺĪ ìłĦ +ĠíļĮ ê³Ħ +Ġíķł 머ëĭĪ +Ġíķ´ ìĥģ +ìĦ¤ ëªħ +Ġë²ķ ìĿ¸ +Ġ방문 íķ´ +ëĮĢ ë¹Ħ +ĠìĿ¼ ì°į +ĠëĮĢ ë§Į +ëĨ Ģ +ëħ ¹ +ĠëĤ¨ 겨 +ĠìĤ¬ìļ© ìŀIJê°Ģ +Ġ믿 ê³ł +ê¹Įì§Ģ ëıĦ +ĠìĿ´ë¥¸ ë°Ķ +Ġíĥľ íĴį +Ġê¼ ¼ +Ġë§İ ìĬµëĭĪëĭ¤ +ĠìłķíĻķ íŀĪ +íķ´ì£¼ ìĦ¸ìļĶ +Ġì§Ģíķĺ ì²ł +ë´ Ħ +íĬ¸ ìĹIJ +ĠíĨµ íĻĶ +ìłĦ ìĿĢ +ìĦĿ ìĿ´ +미 를 +ìĹ° 맹 +Ġëĭ¨ìĪľ íķľ +Ġê°ķ ìĪĺ +Ġë³´ ìĻĦ +ìķĪ ìĿĢ +기 ë§Į +Ġê³ł ëĵ±íķĻêµIJ +íĬľ ëĶĶìĺ¤ +ĠìĹĨ ìĸ´ìĦľ +ìĤ¬ íļĮìĿĺ +Ġê³³ ìľ¼ë¡ľ +ëĵľ ìĹIJ +Ġí Ī +Ġìłķ ë¹Ħ +Ġ무 볤 +Ġë°ĽìķĦ ìķ¼ +ĠìŀIJìĦ¸ íŀĪ +ìŀIJ ì²´ +ê±´ ìĿ´ +ĠìķĮìķĦ ë³´ +ë ® +Ġëĭ¤ìĭľ 보기 +ëĿ¼ìĿ´ ìĸ¸ +íķł ìĪĺ +Ġìľł ì¶ľ +âij ł +ëį © +ìŬ ëªħìĿ´ +Ġê±° ëĮĢíķľ +ĠìĪĻ ìĨĮ +Ġë°ĺìĿij ìĿĦ +ìĿ´ íķľ +ìĤ° ìĭľ +Ġë°© ìĭĿìĿĦ +ìĺ¨ ëĿ¼ìĿ¸ +Ġìłľ ìķ½ +Ġ무 ê´Ģ +Ġìº ĺ +ĠìŀIJ ëĬĶ +ë°Ķ ì¹´ëĿ¼ +Ġë°ĽìķĦ ëĵ¤ìĿ´ +ìĪĺ ìĽIJ +Ġìĭł íķľ +Ġìŀħ ì¦Ŀ +Ġíİĺ ìĿ´ +ìĿ¼ ê³¼ +ĠìķĬ 기 +ìĹĪëĭ¤ ê³ł +ĠëıĮ íĮĮ +Ġì¡´ ì¤ij +ĠëIJĺ ëĬĶëį° +ìŀIJì¹ĺ ëĭ¨ì²´ +ë° ´ +미 ëĦIJ +Ġë§IJ íķ©ëĭĪëĭ¤ +ĠíĿ ī +Ġíķľ 미 +ìĿĮ ìĹIJëıĦ +ĠëĦ ¥ +Ġìĺ¤ ë¸Į +ĠìŀĦ ê¸Ī +주 íĸī +Ġíķ´ ë³Ģ +Ġìľł íļ¨ +ëĶ ľ +Ġê·¸ëŀĺ íĶ½ +ìĭľíĤ¤ 기 +ë¶ ķ +ĠìĿ¸ëıĦ ëĦ¤ìĭľìķĦ +ĠìĤ¬ëŀĮ ëıĦ +ĠìłĢ íķŃ +âĸ ¶ +Ġíķĺ ê±°ëĤĺ +ĠìĨĮ ì¤ijíķľ +ë§IJ ìĿĦ +ìŀĸ ìķĦìļĶ +ĠìĦ ŀ +주 ëħĦìĿĦ +Ġìħ ĭ +ìľł ëŁ½ +ìĺĪ ìĤ° +ĠìµľìĨĮ íĻĶ +± ìĭł +Ġë§IJ ë¡ľ +ìĵ ¸ +Ġê·¸ ë§Į +et w +Ġ문 íĻĶ를 +ĠìĿ´ìļ© ìŀIJìĹIJê²Į +리 íı¬ +Ġìĭľê°Ħ ìĿĢ +Ġìłľ ìłķ +ìŀij ìĹħ +Ġë³Ģ íĻĺ +íĶ¼ íķ´ +Ġëļ ľ +Ġìĭľ ìĥģ +ĠìłĪ 차를 +ĠìĦł ë°° +ì² « +ìĥģ ê³µ +Ġ목 ìĪ +ĠëıĦ ìĽĢ +ĠìĽIJ ëĤ´ +ĠíĻķìĭ¤ íŀĪ +ĠìĬ¤íģ¬ 린 +ì¤ij ìĿ¸ +íİ Ģ +Ġë¶Ħ ëĵ¤ +Ġê±° 리ìĹIJ +Ġìķ¼ ìĻ¸ +Ġë¶Ģë¶Ħ ìĹIJ +ìĹ° íķľ +Ġê¹Ģ ëıĻ +ìĿ´ 미 +Ġì¢ħë¥ĺ ìĿĺ +ĠìľłìĤ¬ íķľ +Ġëħ¸ íķĺìļ° +ĠìĤ¬ìĿ´ ì¦Ī +Ġì¦IJ 기ëĬĶ +Ľ ë¶Ī +Ġë§ ¹ +Ġ무 ëĦĪ +íĤ · +Ġìĥģíĥľ ìĹIJìĦľ +ĠìŀĪëĬĶëį° ìļĶ +ĠëıĻ ìĽIJ +Ġê°ľìĿ¸ìłķë³´ ë³´íĺ¸ +ĠìĤ¬ìļ© ëIJĺëĬĶ +ëķĮ 문ìĹIJ +Ġë¹Ħ íĬ¸ +ĠíĻ© íĺ¼ +ĠìĨį ëıĦ +Ġë²Į ìį¨ +Ġê²½ ê³Ħ +ĠëĨį ì´Į +기ê´Ģ ìĿĺ +ìī½ ê²Į +ê°IJ ê³¼ +Ġê°ĸ ê²Į +Ġìĥ ĺ +ëĵľ ëĿ¼ë§Ī +ĠëķĮ ë¶ĢíĦ° +ĠìĿ´ë¦Ħ ìľ¼ë¡ľ +ĠëĬIJëģ¼ ëĬĶ +ëĮĢ 리 +ê²½ ìŁģ +Ġê·¸ëŁ¬ ë¯Ģë¡ľ +ê²ģ ê²Į +Ġë³´ ì§Ģ +ĠíĨµ ë³´ +ĠíĻĶ볤 íķľ +ëĿ¼ ëĭ¤ +Ġê·¼ ëį° +ì¡° íĬ¸ +ê´ij ìĹŃìĭľ +ĠíķĻìĥĿ ëĵ¤ìĿĺ +Ġì¡° ëªħ +Ġ못 íķ´ +ìĺµ ëĭĪëĭ¤ +ĠìĭĿ ìľ¼ë¡ľ +머ëĭĪ ìĭľ +Ġìłľ íľ´ +Ġíĸī ìľĦ를 +Ġ본격 ìłģìĿ¸ +ìĿ´ 룬íķľ +르 기 +Ġíı¬íķ¨ íķ´ +ĠíĮ¨ ëĦIJ +ĠìĤ¬ëŀij ìĿĺ +â ĨĴ +ĠìĹ´ ëŀĮ +ë°Ģ íķľ +ĠìŀIJëıĻ ìľ¼ë¡ľ +ĠëĮĢ ëŀµ +Ġì¶ ¤ +Ġë°° 볤 +ìĦł 물 +Ġê²ĥìĿ´ ìĹĪëĭ¤ +ĠíĶĦëłĪ ìŀĦ +리 ì¦ĺ +Ġìĺ¤ ëĶĶìĺ¤ +Ġì¡° ìĭ¬ +ĠíĽĦ 기 +Ġì½ĺíħIJì¸ł 를 +Ġëªĩ ëªĩ +ĠìľĪ ëıĦìļ° +Ġì¡° ê°ģ +ìĹŃ ìĹIJ +ìŀĪ ê³ł +ĠíĪ¬ ëªħ +ëĮĢ íĻĶ +Ġê°Ģ ìĬ¤ +Ġê°Ģ ê³µ +ĠìŀIJ ìĤ´ +ĠíĻľ ìķ½ +Ġ매 ìŀ¥ +ìĦ± íķľ +Ġì¶ ĺ +Ġíķľëĭ¤ ë©´ +ìĽIJ ìŀ¥ +Ġìĭľ ëĮĢìĿĺ +ĠëĤĺ ìĹIJê²Į +ro up +ĠëĤĺëĪ Ķ +Ġìľµ íķ© +ĠìĦ± ëĤ¨ +Ġêµ³ ìĿ´ +íĽĦ ë³´ +Ġìĺ¬ ë¦¬ +ĠëĨĢìĿ´íĦ° íĨłíĨł +ëĭ Ń +ë¶Ģ 문 +ì¹´ ëį° +ìĤ¬íķŃ ìĿĦ +Ġíħį ìĬ¤íĬ¸ +Ġë¶Ģ íĻľ +르 ìĭł +ĠêµŃ무 ìľĦìĽIJ +Ġíķ ij +ĠìķĪ ëħķíķĺìĦ¸ìļĶ +ìĨĮ íĨµ +ìĹ° ëĮĢ +Ġíģ¬ 기 +ĠìĹ´ 본ëĭ¤ +ìłĪ ìĿĦ +ê²ĮìŀĦ íı¬ì»¤ +Ġ목íijľ 를 +ìĬ¤ íĤ¤ +ëĤ¨ ëıĦ +Ġì¢ħ ìĤ¬ +ĠíĨł íĨł +ĠìķĦ ìĽĥ +ìĸij ìĿĺ +íį¼ 룰 +ĠìķłíĶĮ 리ì¼ĢìĿ´ìħĺ +Ġì§Ħ ë£Į +Ġìĥģíĥľ ë¡ľ +ìŀIJ ìłķ +ë³µ ìĿĦ +Ġì¤Ħ ìĸ´ëĵ¤ +Ġì´ī ì§Ħ +ĠëĮĢ íĻĶ를 +íĺķ ìĿĢ +Ġì§Ī문 ìĹIJ +Ġìĸ´ ëijIJ +Ġ미 ìĤ¬ìĿ¼ +ìĦĿ ìĿĢ +ë°Ķ ëŀĮ +ĠìĦ¸ê³Ħ ìĹIJìĦľ +ĠìķĮìķĦ ìķ¼ +Ġê²½ ë§Ī +Ġê²° ë¡ł +ĠëĬIJ ê¼ +Ġ맼 ìŀĪëĬĶ +íĪ ° +ĠíĬ¸ìľĦ íĦ° +ĠìĺĪ ëĬ¥ +ì¹´ íİĺ +Ġì´Ī ëĵ±íķĻêµIJ +Ġâij ¢ +ìĻĶ ìĬµëĭĪëĭ¤ +ëľ» íķľ +주 ìĹIJ +미 ê°Ģ +림 ìĿĦ +Ġë°©ë²ķ ìĹIJ +Ġë Ĥ¬ +Ġëĵ¤ ìĿĦ +Ġìĺ¤ëŀ« ëıĻìķĪ +Ġê°ľ íݸ +Ġíı¬íķ¨ ëIJľ +ĠëıĦ ì¿Ħ +âĹ ĭ +êµŃ ìĹIJìĦľ +ìłĦ íķľ +ìĥĿ ìĤ° +Ġìŀħ ìĿĦ +Ġëĭ´ ê³ł +ë³´ ìķĪ +ë´ ħëĭĪëĭ¤ +ĠìĿĮ ìĦ± +Ġëĭ´ 겨 +Ġíĺij ìĥģ +ìĦł ìĿĢ +Ġëĭ¹ ë¶Ģ +ĠìĿ´ìĥģ ìĿĦ +ë¸Ķ ëŀĻ +Ġê²½ ìĥģ +ĠìĦ¸ ëĮĢ +Ġê¸ī ìĨį +Ġë¹Ħìļ© ìĿĦ +ĠëĮĢ íķĻêµIJ +Ġ모 ìŀĦ +Ġìĸ´ ìłľ +Ġíı¬ íķŃ +íķ´ ìĻĶëĭ¤ +íĻĶ ìłIJ +ëłĪ ëĵľ +Ġë³´ ìĿ¼ +Ġì§Ħ ìłķ +Ġì§Ħíĸī ëIJĺëĬĶ +ĠìŬìĦ± ìĿĺ +ĠìĤ¬ë¡ľ ìŀ¡ +ë§Ī ì¼ĵ +ìĹŃ ìĿĺ +ĠíĨł ëĮĢë¡ľ +ëĺIJ ëĬĶ +ĠìĿ´ê²ĥ ìĿĦ +ĠìĤ¼ìĦ± ìłĦìŀIJ +ĠëħĦ ì¤ij +Ġë¶ ķ +ìĹŃ ìĤ¬ +ĠìĦ± ëªħ +íĨł ë¡ł +Ġ주 ìĦ¸ìļĶ +ĠìŀĦ ìĥģ +ĠëĪĪ 길ìĿĦ +ëĦIJ ëĵľ +íĪ¬ íijľ +ëĭĪê¹Į ìļĶ +Ġë³ĢíĻĶ ìĹIJ +Ġê°ķ ìĹ° +Ġê°Ħ íĺ¸ +ĠìĤ¬ê±´ ìĿ´ +íĦ° íħĮìĿ¸ +구 ì²Ń +Ġê±° ìĺĪìļĶ +Ġ5 5 +Ġì¦IJ 기 +Ġìŀħ 주 +기 ê³Ħ +Ġê¹Ģ ì¢ħ +Ġê±´ ì¡° +볤 ìķ¼ +ìĹ° ê¸Ī +Ġíİ ´ +Ġê°IJ ìķĪ +Ġë¹ µ +Ġë¡ľ ëĵľ +ìķ¡ ìĿĦ +ĠíĶĦë¡ľê·¸ëŀ¨ ìĿĢ +Ġìĸ» ìĸ´ +ê²ĮìŀĦíı¬ì»¤ 머ëĭĪìĭľ +Ġë§ ĺ +Ġìŀħ ìĿĢ +ê²ł ì£ł +Ġíľ´ëĮĢ íı° +ìĭľ ê¹Įì§Ģ +Ġíķĺ ëįĺ +Ġë²ķ ìłģ +ĠíļĮìĽIJ ìĿĢ +Ġ무ìĹĩ ìĿ¸ê°Ģ +Ġíķĺ ëĬIJ +Ġëĭ¤ ìĦ¯ +ìķĪ ë³´ +ĠìĹĨ ìĹĪ +Ġê²° ìĬ¹ +ĠìķĦë¦Ħ ëĭµ +Ġëļ « +Ġê°Ģ ì¡Įëĭ¤ +Ġê±° 기ìĹIJ +Ġë§ŀ춤 íĺķ +Ġë³´ ëĭ¤ëĬĶ +Ġ충 ë¶ģ +ì · +ĠìłĦ ìĦ¸ +ĠìĿ´ë£¨ìĸ´ ìł¸ +Ġ주 기 +ĠìĹĨ ìĹĪëįĺ +ĠíĸĪëĭ¤ ê³ł +ëįĶëĿ¼ê³ł ìļĶ +Ġ본 ìĿ¸ìĿĺ +ëģ Ŀ +ëģ¼ 리 +ìĭ¤ë¹Ħ ë³´íĹĺ +Ġë³µ ìĽIJ +Ġ첫 째 +Ġê°Ģ격 ìĿĢ +Ġë¶Ģ ì²ĺ +Ġìĺ¤ íĨł +Ġê³łìĨį ëıĦë¡ľ +Ġê²ĥ ë§Į +Ġ무 íķľ +Ġë¶Ħ ìĸij +볤 ìĦľ +Ġê¸Ī ê°ķ +Ġì¤ijìĭ¬ ìĿĺ +ê°ľ ë¡ľ +ê±´ ì¡°íķľ +ëĵ¤ìĹIJê²Į ëĬĶ +ĠëĮĢíijľ íķĺëĬĶ +Ġë¡ ¤ +ĠìłľíĴĪ ìĿĺ +íķĺìĭľ 기 +ë³ į +ì§Ħ ìĿĺ +Ġë³´ìľł íķĺê³ł +ĠíĴįìĨį ìĿĢ +ê³ł ìĨį +ìķ¼ ë§IJë¡ľ +ĠìĪĺ ì¹ĺ +Ġëĭ¹ ìŀ¥ +íķĺì§Ģ ëĬĶ +íĥľìĸij ìŀIJìłķ +Ġì°¨ ìĽIJìĹIJìĦľ +Ġìłķë³´ ê°Ģ +ĠëĤ ¯ +êµIJ ë¥ĺ +ì¥ ¬ +ëĤĺ ìĿĺ +ê·¼ íķľ +ìĥĿíĻľ ìĿĦ +Ġê¸įìłķ ìłģìĿ¸ +Ġë©ĶìĿ´ ìłĢ +ìĺ · +ìłĦ ìĭľ +목 ìĿ´ +Ġê°ľ ìŀħ +ìĺģ ìĸ´ +ĠëıĦ ëįķ +Ġ6 5 +Ġê³ĦìĨį íķ´ìĦľ +Ġë³´ ìĿ¸ +ĠíķľêµŃ ìĿ¸ +íĮIJ ìĹIJ +Ġ리 ê·¸ +Ġì°½ ìŀij +Ġë¶Ħìķ¼ ìĹIJ +ĠìĿ¸ ìļ© +Ġìļ´ íĸī +ĠëįĶë¶Īìĸ´ 민주ëĭ¹ +Ġëĭ ĺ +ìŀ¥ ì¹ĺ +Ġ구 ê°Ħ +Ġì§Ģ ìĭľ +ĠëıĦ ìĦľ +Ġ200 2 +Ġìľł ë£Į +매 매 +ê²° ìĿĦ +ĠìĤ´ìķĦ ê°ĢëĬĶ +Ġê°Ŀ ê´Ģ +Ġê°Ģ ëĤľ +Ġìĭľ ëĮĢìĹIJ +Ġê³ł íĸ¥ +Ġì²´ ìłľ +ĠíĻľìļ© íķ´ +ĠìłķíĻķ íķĺê²Į +Ġê·ł íĺķ +ëıĻ 물 +ĠìļĶ ê¸Ī +ĠíķĺëĤĺëĭĺ ìĿĦ +ĠìĿij ê¸ī +ĠìķĦë§Ī ëıĦ +Ġë¹ħ ëį°ìĿ´íĦ° +Ġê²ĥ ëĵ¤ìĿ´ +ìŀIJ ëıĦ +Ġê°ķ ìļ° +Ġê¸Ī ìĨį +ĠìŀIJìľł ë¡Ńê²Į +ê¼ ¼ +ëŀĢ íŀĪ +ëĤ ©ëĭĪëĭ¤ +Ġëĭ¤ ìĪĺìĿĺ +ĠíĬ¹ ìľłìĿĺ +Ġê³¼ ëıĦ +Ġìłľê³µ íķĺ기 +Ġëĵľ ëĬĶ +ĠëĤ´ìļ© ìĹIJ +ìĪ į +ĠíĹĪ ëĿ½ +ëŀ Ń +Ġìłķ ê·ľ +Ġíķ¨ê»ĺ íķĺëĬĶ +ĠìŬ 겨 +Ġì¡° 기 +Ġì§Ħ ìĭ¤ +he n +Ġìĺ¬ ë°Ķ른 +Ġíĺķ ìĤ¬ +Ġì²Ń ìĨĮ +ĠìŀĦ ìĭł +Ġê¸ī 격 +ĠìłĦíĻĶ ë²Īíĺ¸ +Ġ1 20 +ë§Ī ìĿ´ +Ġëį ¸ +ĠìĦł ìŀĦ +Ġìĸ» ê³ł +주 기 +Ġ주 ì§Ģ +ìĨĮ ìĦľ +Ġìŀ¬ 무 +íĭ° ìĬ¤íĬ¸ +ê³ł íķľ +ê°Ģ ìĻĢ +ĠìĨĮ 모 +íķĺ ìļ°ìĬ¤ +íĶĦ 리 +Ġì¶ľ ìŀħ +ì½ĺ íħIJì¸ł +Ġíķĺ ê²łëĭ¤ +Ġë¶ īìĿĢ +ĠíĪ¬ ìŁģ +ë©Ķ ìĿ´ +ĠìŀIJìľł íķľêµŃëĭ¹ +ĠìĺĪìĥģ ëIJľëĭ¤ +ĠìµľìĨĮ íķľ +ê¿ Ī +Ġìķ¡ ìĦ¸ +Ġì¹´ íĨ¡ +Ġ200 3 +ĠìĤ¬ìĭ¤ ìĿĢ +ìĦ ° +Ġëĥ ĦìĥĪ +Ġìĭľ ëĮĢ를 +Ġë© ¸ +ĠíĮĮ 리 +ĠíĻľ ìĦ± +ĠìŴ린 ëĭ¤ +ĠëIJľ ëĭ¤ê³ł +주 ì°¨ +Ġëħ¸ ì¡° +ĠíĴįë¶Ģ íķľ +ìŬ ìķ¼ +ĠìĿ¸ 민 +Ġë§İ ìķĺëĭ¤ +Ġíĺ¼ ëŀĢ +ë°© ìĿ´ +ĠíĮĮ 견 +Ġ민 ê°IJ +Ġìĺ¤ëĬĺ ìĿĢ +ëĮĢ íĨµëł¹ +ëĦ £ +ëģ Ī +ìĨĮ ê°ľ +ë§Ī ìŀIJ +Ġìĸ´ 깨 +) · +문 ìłIJ +Ġê±° ìłľ +ì¹ĺ 기 +Ġë°© íķ´ +ĠìķĬìķĺ ìĬµëĭĪëĭ¤ +ê°Ģ 기 +ìĦł íĥĿ +ìĥģ ê±°ëŀĺ +ĠìķĦëĭĪ ìĹĪëĭ¤ +ĠíķĦìļĶ ë¡ľ +Ġ측면 ìĹIJìĦľ +Ġìĺ¤ ì§ģ +ëIJľ ëĭ¤ê³ł +Ġê°Ħ íݸ +ĠìĨĮ ìĭĿìĿĦ +ì°¨ ë¡ľ +Ġì§Ħíĸī íķĺëĬĶ +ĠìĬ¤ ìĽ¨ +Ġì§Ģê¸Ī ìĿĺ +Ġíķ´ ìļĶ +ëıĻì¶ľìŀ¥ ìĥµ +ĠìľĦìĽIJ ìŀ¥ +ĠìłľìĻ¸ íķĺê³ł +ìĩ ł +Ġ he +Ġíģ¬ ê³ł +Ġëĵľ ë¡ł +Ġê·¸ëŁ¬ ë©´ìĦľ +Ġê·¸ ëĵ¤ìĿĦ +Ġ컨 íĬ¸ë¡¤ +Ġ목ìĨĮ 리를 +Ġìĭľ ë²Ķ +Ġì¶Ķ ìĦ¸ +ĠìĿ¼ íĻĺ +Ġìŀ¬ íĻľ +ìĹŃì¶ľìŀ¥ ë§ĮëĤ¨ +ëĭ¤ ëŀĢ +ĠíĥĢ ìŀĦ +ĠìĬ¤íĥĢíĬ¸ ìĹħ +Ġ ãĪľ +ìĬ¤ ë§ĪíĬ¸ +Ġëĭ¤ë¥´ ëĭ¤ +ĠìĦł ì¶ľ +ĠíħĮ 룬 +ĠìĤ¬ìĿ´ ìĹIJìĦľ +ìłķ ìĭł +ĠìŀIJ ì¹ĺ +íĺķ ìĿĺ +o T +Ġì°¨ 를 +Ġê³µëıĻ ìľ¼ë¡ľ +ĠìĹ° ê´Ģ +Ġ무 ë¦ +Ġ기본 ìłģìľ¼ë¡ľ +Ġ4 2 +ê³ł ê°Ģ +ĠìĿ´ ìĬ¬ +ì§ij ìĿĦ +ëĵľë¡ľ ìĿ´ëĵľ +ìĿ´ ìĹĪëįĺ +ëĵľ 릴 +ë¸Į ëĿ¼ +Ġë°ĶìĿ´ ìĺ¤ +Ġë³µ ê·Ģ +ëĭ¨ ê³¼ +ĠìĤ¬ìļ© íķĺì§Ģ +ĠìķĦìĿ´ ëĶĶ +Ġë¡ľ ë§Ī +ĠìĿ´ 르ëĬĶ +ìķ¼ 구 +Ġëĭ¨ ìĸ´ +ë°ĶìĿ´ ìĺ¤ +ê¸Ī ì§Ģ +ĠìĺĨ ìĹIJ +ëĦ¤ìŀĦ ëĵľ +Ġë§Ī 침 +Ġë°Ľ ëĬĶëĭ¤ +ĠìĿ´ë¦Ħ ìĿ´ +ĠëIJĺ ë©° +ë² ³ +ĠëĤ´ ê²Į +ëŀĺ ëıĦ +Ġìľł ìĿ¼ +Ġë¶Ħ ëĵ¤ìĿĢ +ĠìĿ¼ ì¹ĺ +ê¶Į ìĿĢ +ĠëĤ´ìļ© ìĿĺ +ìĹIJ ìĿ´ +Ġìĭľ ìŀ¥ìĿ´ +ìļ° 리ê°Ģ +íĸī ìľĦ +ëĥIJ ê³ł +ĠíıŃ ëł¥ +Ġíĸī ëıĻìĿĦ +ĠìĦłíĥĿ íķł +ì¹ľ ëĭ¤ +ìĦ¼íĦ° ìĹIJìĦľ +íķĺ ë¯Ģë¡ľ +ĠìłĦ íĮĮ +Ġ주 íĸī +ì²´ ëĬĶ +ìĦ¤ ê³Ħ +Ġê³łê°Ŀ ìĿ´ +Ġì¥ IJ +ìľ¼ ë¯Ģë¡ľ +ĠìĭĿ ìĤ¬ë¥¼ +ĠìķĦëŀĺ ìĻĢ +ì§Ģ ëĭĪìĸ´ +Ġë° Ł +íĨł 리 +Ġì°¸ìŬ íķĺëĬĶ +Ġëĵ£ ëĬĶ +ĠìĨĮ 리를 +Ġê°Ļ ìķĦ +ìĺ¨ ëĭ¤ +ĠíĻľìļ© íķł +Ġ주 ëł¥ +Ġê´Ģ ìĦ¸ +Ġë°ľ ìĨ¡ +Ġ무 ì¡°ê±´ +본 ëĭ¤ +ĠìķĦìĿ´ ê°Ģ +ĠìĨĶ ì§ģ +Ħ ëĬĶ +ê°ľ ìĦł +ĠêµŃíĨł êµIJíĨµ +ĠíݸìķĪ íķľ +Ġ룰 ëįĺ +Ġì§Ħíĸī ëIJľëĭ¤ +íĶĮ 룬ìĬ¤ +Ġ20 20 +Ġìĸ´ëł¤ ìĽĮ +ī ĺ +ìĿĺ ëıĦ +ĠìĿ¼ íķĺëĬĶ +ĠìĿĺ ìĭĿ +íĶĦ 를 +Ġìŀ¬ ë°° +ì¤ ¬ +ëłī ìħĺ +Ġíķĺ ë°ĺ기 +ìĤ° ìĿĢ +ĠìĥĿ 물 +Ġìŀ¥ ê´ĢìĿĢ +Ġíĺ¸ ìĨĮ +ë³Ģ ìĭł +ĠëłĪ ëĵľ +ĠìĿĮ ë£Į +ëĮĢíķľ 민êµŃ +ì© į +Ġìĺ¤ëŀĺ ëIJľ +ĠìķĦìĿ´ 를 +Ġë¹Ħ ë¡Ŀ +ë°© íĸ¥ +ì§ģ ìĿĦ +Ġíı ° +Ġê°IJ ëıĻ +ìĤ¬ ìŀ¥ +Ġê°Ģ ëıĻ +Ġê°Ģ ë³į +Ġ5 2 +Ġë³µìŀ¡ íķľ +ĠìĪĺ ëĬ¥ +Ġíıī ë²Ķ +ëĿ¼ìĿ´ ë¸Į +ĠìĿ´íķ´ íķł +ëĶ° ëľ»íķľ +ì°¸ ìŬ +ë¡ľ ê°Ģ +ĠìķĦ íĬ¸ +ìļ° ìĪĺ +ì²´ ìĹIJ +íĿ ĺ +ìĭł ìļ© +êµ° ìĿĦ +길 ìĿ´ +ind ows +Ġê°Ģ ê¹Ŀ +ĠëIJĺ ë©´ìĦľ +ĠíķľêµŃ ìĿĢ +ìĮ į +Ġì¡° ìĤ¬ë¥¼ +ìŀ Ń +ê¸ ĭ +ë¹ ¨ +ëĿ¼ìĬ¤ íĭ± +ĠìĹŃìĤ¬ ìłģ +ĠíŤ ëĵľ +Ġê°ķìĽIJ ëıĦ +Ġê³µ íĻĶ +ë¹ µ +Ġíļ ¡ +Ġì§Ģ ëĭĪ +Ġì§Ģ íĺľ +Ġê±´ 립 +Ġëĸ ¼ +ê³ ± +ìĭľ ìĻĢ +Ġìĥģ ì²ĺ +ĠìłĢ ì§Ģ +ĠìĦ¼ ìĦľ +Ġëĭ Ń +Ġê°Ģ ë²¼ +ĠìķĦ 기 +ĠìµľìłĢ ìŀĦê¸Ī +Ġìłģíķ© íķľ +Ġ모ëĭĪíĦ° ë§ģ +Ġìŀ¬ ëĬ¥ +ë©´ì¶ľìŀ¥ ëĮĢíĸī +ĠíĻĺ ìŀIJìĿĺ +Ġëī´ ì§Īëŀľëĵľ +ëIJĺìĹĪ ìľ¼ë©° +ê² ¸ +ĠìŀIJ ê¸Ī +Ġì¢ĭ ê²łëĭ¤ +ëĴ ¤ +ĠíĮ Ŀ +ĠìĦ¤ ëĵĿ +Ġìļ´ ëıĻìĿĦ +Ġê·¼ ìľ¡ +ì§ĢëĤľ íķ´ +ìĨĮ ìĨ¡ +Ġ모 ìľ¼ +Ġê¿Ī 꾸 +ĠìĹ° ê·¹ +Ġìº ¡ +ëĨĵ ìĿĢ +ìĸij ìĿĦ +ĠìĦ± ìŀ¥ìĿĦ +ĠíĿ ° +Ġìĺ ³ +Ġìĵ° ëłĪ +Ġê²½ ìŀij +ìĸ´ë¦° ìĿ´ +Ġê´Ģ íķ´ +ĠëĮĢ ìĤ¬ +ìķĦ ìĿĺ +Ġë³´ ëĤ¸ +êµIJìľ¡ ìĿĦ +Ġì¹´íħĮ ê³łë¦¬ +Ġê¹Ģ ìĪĺ +ĠìĭĿ ë³Ħ +ëĵ¤ê³¼ ìĿĺ +ìĤ¬ ë¡Ģ +ĠìĪĺ 리 +Ġëª ¬ +Ġ4 7 +ë° ı +ë²ķ ìĿĢ +모 를 +ĠíıIJ ì§Ģ +Ġì« ĵ +Ġê´Ģê³Ħ ê°Ģ +Ġìļ°ëł¤ ê°Ģ +ìĿ´ ê²Į +Ġ무 ëĮĢ +ë²ķ ëł¹ +ê°IJ ìĿĢ +Ġëĥī ìŀ¥ +ë¶Ģ ìĹIJìĦľ +Ġì§ĢìĹŃ ìĤ¬íļĮ +Ġíĭ Ī +ĠëĨĢ ëĿ¼ +ĠìĹ° íľ´ +Ġë§IJ íķĺê³ł +Ġíķľ íĻĶ +Ġìĥģ ë°ĺ기 +ĠìŬ룬 ê°Ģì§Ģ +ê·¼ ë¡ľ +ĠëģĿ ê¹Įì§Ģ +Ġì¤Ģë¹Ħ 를 +ìĭ¤ íĹĺ +Ġëĭ¬ íķĺëĬĶ +ëĥIJ ëĬĶ +Ġ몰 ìķĦ +Ġ구체 ìłģìľ¼ë¡ľ +ĠìĿ¸ ëł¥ +ĠëıĦ ëĭ¬ +ê¸ Ģë¡ľë²Į +ĠíķľêµŃ ìĹIJ +ìĿ¼ ëŀľëĵľ +ĠëIJĺ ëıĮ +Ġì§Ħ ë¡ľ +ec ond +ĠíĥĢìĿ´ íĭĢ +ìĪĺ ìĤ¬ +Ġê¹ ľ +Ġë¶Ī êµIJ +Ġì¶ľ ê°Ħ +ĠìĦ¸ê³Ħ ìĿĺ +Ġë³Ħ ëıĦë¡ľ +ĠìĪĺëıĦ ê¶Į +Ġê³ ģ +ìĻĢ ìĦľ +íĭ° ì¦Į +ĠíĬ¸ ëłĪìĿ´ +íĮĢ ìĿ´ +Ġ몸 ìĹIJ +ìķĦ ëĭĪ +ë§Į ìĽIJìĿĦ +ë¹Ħ ìĹIJ +Ġ매 ëĭ¬ +ìĹ°íķ© íļĮ +ëĨį ìĹħ +ë® ¬ +Ġë§Ī ì¹ĺê³ł +ĠìĻ ¼ìª½ +Ġê²°ê³¼ ìĹIJ +본 ëįĺ +ĠêµŃê°Ģ ìĿĺ +먹 íĬĢ +ëIJľ ëĭ¤ëĬĶ +ĠìĿ´ìĸ´ ì§ĢëĬĶ +ĠìĪĺ ë¡Ŀ +Ġë³´ ìŀħëĭĪëĭ¤ +Ġìŀ¬ ê°ľ +ìļ© ìľ¼ë¡ľ +Ġíħ Ķ +ìģĺ ê²Į +Ġ모 ìŀIJ +Ġìĺģ ìĥģìĿĦ +Ġìĸ¼êµ´ ìĿĦ +Ġì¹ ¸ +ì¶ľìŀ¥ ë§ĮëĤ¨ +Ġëĵľ 릴 +ëĮĢ ì²´ +ëĤ´ ìĿĺ +Ġê¹Ģ íĥľ +Ġëĭ¹ ìĿ¼ +Ġ못 íķľëĭ¤ +201 3 +Ġê²°ê³¼ ëĬĶ +ov e +ëıĻì¶ľìŀ¥ë§ĮëĤ¨ íĽĦ기 +íķĺ기 를 +Ġ커 ëĭ¤ëŀĢ +á Ĩ +Ġê´Ģ íķł +ìĹ° ìĿĺ +ĠìĬ¤ íĥł +Ġìŀ¥ íķĻ +ĠìĿ´ìľł 를 +ì ± +ĠìĿ´ ëħIJ +Ġìĺ¬ ë¦° +Ġ19 80 +리 ëĭ¤ +ĠëıĻ ë¶ģ +Ġìĺģ íĺ¼ +Ġë²ķ 무 +Ġëĭ¤ 를 +Ġë¶Ģ ìĥģ +ĠíĶĦë¡ľìłĿ íĬ¸ë¥¼ +ìĪĺ ì¤Ģ +ê²½ ë¶ģ +ĠìŀIJ본 주ìĿĺ +Ġë³´ ë©° +ĠìĤ¬ëŀĮ ê³¼ +ìłĦ êµŃ +Ġì²Ń 주 +ĠìĥģíĻ© ìĿ´ëĭ¤ +ì¹´ëį° 미 +ìĭľ ìĬ¤ +Ġì¶Ķ ìłģ +ĠëĿ¼ ëĶĶìĺ¤ +Ġ구ìĦ± ëIJĺìĸ´ +ĠìĹ ł +ìłĦ ê³µ +ĠìĽIJ ìŀIJ +Ġì¶ľ ìĤ° +ê°ĢëĬ¥ íķľ +Ġë§Īì§Ģë§ī ìľ¼ë¡ľ +Ġ맼 ìĿ´ +ĠìĿ´ ìĺģ +ëĮĢ 문 +íĶĦ 린 +Ġê°ľë°ľ íķľ +Ġê·¸ ê±´ +Ġìłķ ìĭĿ +Ġìł ĸ +Ġë¶Ģ íļĮ +ĠëķĮ ìĹIJ +Ġì°¨ ê°Ģ +ê·ľ ì¹Ļ +ĠëĤĺëĿ¼ ìĿĺ +Ġì¡ ± +Ġë©´ ìĹŃ +ìħ¨ ëĭ¤ +Ġë§Ī ì¹ľ +ĠìĤ¬íļĮ ìĹIJ +ìĽł ëįĺ +íķĺ ìĹIJ +ë°© ìĹIJ +ĠìĿĺ미 íķľëĭ¤ +ĠìľĦìĽIJ ìŀ¥ìĿ´ +ëĥ ī +ĠìĽ¹ íķĺëĵľ +Ġì Łģ +Ġ기 ìĥģ +ĠìĹĨ 기 +Ġíı¬ íĬ¸ +Ġê°Ħ ìĹIJ +Ġì²´ íı¬ +Ġì°¾ ìĿĢ +ëıĻì¶ľìŀ¥ ìķĪë§Ī +ìĭł ìĿĢ +Ġíı¬ ëŁ¼ +ì§Ģ ìĿ¸ +ìłIJ ê³¼ +Ġì§Ħíĸī íķľëĭ¤ +ë¡Ń ê³ł +Ġë§IJ ìĹIJ +ĠëģĿ ëĤľ +ĠìĿ´ 기 +ìłģ ìĿĢ +ë§Ī 를 +Ġê°Ħ ëĭ´ +Ġìĸ´ 르ìĭł +ëĭ¹ ìĭľ +ĠíĮĮ 기 +ìĪ ² +Ġì¤Ħ ìĸ´ +ëıĻì½ľê±¸ ì¶Ķì²ľ +Ġ 01 +ê³ł ìļ© +ĠìĿ´ ëĵ¤ìĿĦ +Ġíķľ 층 +Ġíı¬ íĨł +Ġê°Ģ 리 +ìĦ¸ ìĿ´ +ì¹ĺ ìĹIJ +ìķ¡ ìĿĢ +Ġ구 ë©į +ĠìĨIJ ëĭĺ +ê±´ ìĿĺ +íĹ ¬ +ĠëIJĺ ìĹĪê³ł +ĠëĦĺ ì¹ĺëĬĶ +ĠëıĪ ìĿ´ +ìŬ 룬 +Ġì°¨ ìĦ¸ëĮĢ +íħĶ ëłĪ +Ġìľł íĬľë¸Į +Ġ4 4 +Ġë°Ŀíĺ Ķ +Ġë³´ìŬ ì£¼ê³ł +ĠìĤ¬ 기 +ë³´ ì¦Ŀ +Ġ맡 ìĿĢ +Ġê³µ ìĿµ +ĠëIJĺìĸ´ ìķ¼ +ĠìĻľ ëĥIJ +ĠìĤ´ ì§Ŀ +층 ìĿĺ +ê°ľìĿ¸ ìłķë³´ +ë©´ ìľ¼ë¡ľ +ĠìĿĺ ëıĦ +르 ì§Ģ +ì²Ń ìĿ´ +Ġëıħ ìĦľ +ĠíĮĢ ìĿĦ +ĠëĤ© ë¶Ģ +Ġë³´ ìŀIJ +ĠìĹŃ ëŁī +ëĦĪ ì§Ģ를 +Ġê°IJëıħ ìĿĺ +ê°Ģ 구 +ìļĶ ì¦ĺ +Ġìĥģíĥľ 를 +ĠìĹħê·¸ëłĪìĿ´ ëĵľ +Ġë°© ë¶ģ +Ġëĭ¨ ë§IJ +ĠìĨIJ ìĹIJ +롯 머ìĭł +Ġë°° íĦ°ë¦¬ +ëł · +ë² ħ +íĺ¸ ìĻĢ +ëĶĶ ìħĺ +ë¹ Ĺ +ì§ľ 리 +Ġê³µìĹ° ìĿĦ +Ġ본격 ìłģìľ¼ë¡ľ +Ġíģ´ëŀĺ ìĭĿ +ëĭ¹ ìĹIJìĦľ +ĠëıĻ ëĤ¨ +Ġ무 ì²Ļ +íĨł ë¡Ŀ +Ġëijĺ룬 ìĭ¼ +Ġê³ł ìľł +Ġëĭ¨ ìĨį +ìĭľê°Ħ ìĿĦ +ìľĦìĽIJ íļĮìĿĺ +ĠëįĶ íķ´ +Ġì¢ħ 목 +Ġê¶Į 리를 +ĠìķĦ무 ê²ĥëıĦ +ê³ ½ +Ġë¹Ħ ìłķê·ľì§ģ +ĠíĺĦ ê¸Ī +ìĿ´ ëĵł +ë¥ ¨ +ìŀ¥ ê´Ģ +Ġê²Įìĭľ 물 +리 ìĦ¸ +Ġê³ł ìĦ± +ìľł 를 +Ġìĭ¬ íķľ +ìĻĶ ëįĺ +Ġ맼 ìĿĦ +ĠìĦ±ëĬ¥ ìĿĦ +ë¡ Ģë¡ľ +주 ìĸ¼ +Ġìĺ¬íķ´ ëĬĶ +ĠìľĦ ìĹIJìĦľ +âĢĿ ( +Ġê°ĢëĬ¥ íķĺë©° +ĠíĥĢ ìŀħ +Ġëĭ¤ìĿĮ ìĹIJ +Ġëĭ¨ ì¶ķ +Ġì°¨ì§Ģ íĸĪëĭ¤ +ĠìĹ° ë§IJ +ĠìĤ¬ìļ© íķĺë©´ +ĠëĮĢìĥģ ìĿ´ +Ġëĭ¤ 문íĻĶ +ìĿ¼ ìĹIJëĬĶ +Ġ깨 ëĭ¬ +Ġì¥ ¬ +Ġë§IJ íķľ +Ġìŀ¥ ìĭĿ +ĠíĬ¹ ìĦ±ìĿĦ +Ġíı¬íķ¨ ëIJĺìĸ´ +ëIJIJ ëĭ¤ê³ł +Ġìĺ¬ëĿ¼ ê°Ģ +ëĮĢ ìĤ¬ +ĠìĨĮ íĺķ +Ġ매 ëĭĪ +ĠíķľêµŃ ìĸ´ +íıī ê·ł +Ġ무ìĹĩ ìĿ¸ì§Ģ +Ġë³´ ê²Į +Ġìľł ìĸ´ +Ġê°ľë°ľ ìĿĦ +Ġìµľê·¼ ìĹIJëĬĶ +Ġ겪 ê³ł +ĠíħĮ íģ¬ +íĥĢ ìĿ´ +ĠíĺĦ ìĥģ +ĠìĦ¤ 문 +ĠìĹŃ ëŁīìĿĦ +íį¼ ìĬ¤ +ĠìĿ¼ ìłľ +ìĪł ìĿĦ +Ġë§ī ìĿĦ +ëĭ ĻëĭĪëĭ¤ +ĠìĪĺ ë°± +Ġì§Ģ íľĺ +ìŬ ë¦Ħ +Ġìŀ¬ 구 +Ġì§Ħíĸī ëIJIJëĭ¤ +ĠìĿĢ íĺľ +Ġ주ë³Ģ ìĹIJ +Ġìĸ¸ìłľ ëĵłì§Ģ +Ġ격 볤 +ĠìĿ´ë²¤ íĬ¸ë¥¼ +ëłĪ ìĿ¸ +ĠìĪĺì¤Ģ ìľ¼ë¡ľ +ĠìĿ¼ ìĸ´ëĤľ +ĠìŀĪëĬĶ ê°Ģ +Ġê³ł ëł¹ +Ġê¹Ģ ìŀ¬ +Ġíķ© ë³ij +Ġë§ ij +ìĹŃ ìĹIJìĦľ +ìłIJ ìĿĺ +ĠìĨIJ ìĥģ +ĠíĶĮ 룬 +ëĿ¼ëĭ¤ ìĿ´ìĬ¤ +~ ! +íķĻ ìĿĺ +ĠëıĻ ìĸij +Ġê·¸ ëĵ¤ +ë°© ìĿĺ +Ġê·¸ëłĩ ëĭ¤ê³ł +Ġìħ Ķ +Ġ컨 ìĦ¤íĮħ +Ġê°Ģ ìĨį +Ġ4 3 +Ġì´ Ľë¶Ī +루 미 +Ġ기 ìĤ¬ë¥¼ +ĠìĹĨ ê²Į +ĠìĦłíĥĿ íķľ +Ġë º +Ġíķ « +ê¸Ī 리 +Ġë° ¸ +Ġ미 ìħĺ +Ġë°© ìĸ´ +ĠìĹ´ ìĹĪëĭ¤ +íķ´ ì§Ī +ìĭľ íķľ +ìĬ¤ ì¹´ +ê³µ ìŀ¥ +ĠíķĦìļĶ íķ©ëĭĪëĭ¤ +Ġì§ĢìĹŃ ìĿĦ +ĠìĿ¸ê°Ħ ìĿ´ +ë° ¸ +ìļ° íݸ +Ġëĭ¨ ìľĦ +ĠëįĶ ìļ´ +Ġë°ĺ ë°ľ +ìľĦìĽIJíļĮ ê°Ģ +Ġë°©íĸ¥ ìĿĦ +ìĪ Ń +ê·¸ 림 +Ġìŀ¬ ëĤľ +Ġìłģ ì§Ģ +Ġì´Ŀ ê´Ħ +Ġë³´ ëĤ´ëĬĶ +Ġìŀħ ìŀ¥ìĹIJìĦľ +ìĭ¤ ë¡ľ +ëĿ¼ ìĿĺ +Ġë¶Ģ ë¶Ģ +ìĥĿ ìĿ´ +ìĪľ íĻĺ +Ġë£ ¸ +Ġê´Ģ ìłIJìĹIJìĦľ +Ġìŀ¥ ëĤľ +Ġ ãħİãħİ +ĠëĮĢ íıŃ +ì¶Ķ ì§Ħ +Ġ미êµŃ ìĹIJìĦľ +Ġ목ìłģ ìĿĦ +볬 íķľ +ìĦ¤ ì¹ĺ +Ġ매 주 +ìĥī ìĿĦ +Ġ깨 ëĭ« +ìĨĮ ë¡ľ +무 ëĬĶ +Ġê²Į ìĿ´ +Ġì±Ħ ìĽĮ +Ġì¡´ìŀ¬ íķĺëĬĶ +ìłľ ìĻĢ +Ġìĭľ 리ìķĦ +Ġìĭ¤ ë§Ŀ +Ġê·¸ëŁ¼ ìĹIJëıĦ +ìĦ¸ ë¡ľ +íĺ¸ ìĹIJ +âĢ¦ ] +Ġìķħ íĻĶ +ĠìĬ¤íĨł 리 +ëĶ ± +íķĺìŀIJ ë©´ +ë§ Ļ +ëĵ±ë¡Ŀ ë²Īíĺ¸ +ë²ķ ë¥ł +Ġíģ¬ ì§Ģ +ĠìĿ´ìĿµ ìĿĦ +ì§Ģ ìŀIJ +ëĵľ ìĻĢ +ĠìĹĨ ìķł +Ġì§ij ê³Ħ +ëŀĮ íļĮ +Ġê°IJ ìĭľ +ìŀ¥ ë¹Ħ +Ġë³´ íݸ +Ġìĥģ ìĦ¸ +Ġìľł íķ´ +Ġê³µê°ľ íĸĪëĭ¤ +Ġ모ëį¸ ìĿĦ +ê³ł ëĵ±íķĻêµIJ +ï¼ Ń +Ġê³³ ìĿ´ëĭ¤ +Ġ기ëĮĢ 를 +Ġ기 ìĽIJ +Ġê¹Ģ ê²½ +ìŀIJ 본 +ĠëĮĢ ìķĪ +ìĦ¸ íĬ¸ +ĠíķĻìĥĿ ëĵ¤ìĿĢ +ĠìĻľ 곡 +âĻ ¡ +ìĦľ ëıĦ +ìĺĪ 를 +Ġìĭ¬ ìŀ¥ +ĠíĻķìĿ¸ íķĺê³ł +Ġëŀ ĺ +Ġê³µ ìĿ¸ +Ġìĭ¤ ìļ© +ì¤ ¬ëĭ¤ +ĠìĬ¤íĶ¼ 커 +Ġê » +ë§Į ìĿĢ +룬 ì§Ħ +Ġ4 1 +Ġë°Ŀ íŀĪ +ë° Į +ë§Ī ìĬ¤ +ìĥĿ ìĿĺ +ĠíķĦìļĶ íķĺëĭ¤ê³ł +Ġìĸ´ì©Į ë©´ +ĠìĦ¸ íĬ¸ +Ġì¦Ŀ ìĥģìĿ´ +ĠëģĮ ê³ł +ìĿ´ ì£ł +ë³ ĢìĿ´ +ì°¨ ëŁī +Ġìĺģ 구 +ĠìĤ¬ìļ© ìĿĦ +ë°Ķ ìĿ´ìĬ¤ +ìŀ¬ ê°Ģ +ë²Ī ìĹIJ +ĠíļĮ ìĤ¬ìĹIJ +Ġê³Ħ ìĭł +ìĥģëĭ´íĨ¡ B +Ġë¬´ë¦ İ +ĠìĿ´ ëĭ¤ +Ġ기 ìĺ¨ìĿĢ +Ġìĭľ ê³µ +Ġíķł ì§Ģ +ĠìĿ¼ë¶Ģ 를 +all y +ìĿ¸ ê°Ħ +ìķĺ ìĿĦ +Ġê°ij ëĭĪëĭ¤ +ĠìĿ´ ìĦ± +ëĤĺ ê³ł +Ġê°Ģ ìĭľ +Ġë¹Ħ íĥĢ민 +ĠìĦł ëijIJ +Ġíĥľ êµŃ +ìľ¼ë¡ľìĦľ ìĿĺ +ĠíĺĪ ìķ¡ +ê° ĸ +ìĿ¼ ê²½ +ìĹIJê²Į ëıĦ +ë£Į ê°Ģ +Ġëģ Ī +ĠìĿ½ ëĬĶ +íĺ Ķëĭ¤ +ĠìľĦ ìĿĺ +ìľĦ 기 +ì²ĺ ìĿĮ +ĠíĬ¸ ëŀĻ +Ġ기ìĪł ìłģ +골 íĶĦ +ĠìĿĺ ìĽIJìĿ´ +ë°ķ íķľ +Ġê°IJëıħ ìĿ´ +ëĶ° ëĿ¼ +ĠìķĬëĬĶ ëĭ¤ëĬĶ +ìĺģíĻĶ 무ë£Į +Ġë³´ìľł íķľ +Ġëĭ¤ìĸij íķĺê²Į +Ġíķĺ루 ìĿĺ +ĠìĹ°ëĿ½ ì²ĺ +ĠìŬ ë¶Ģ +Ġë³´ìŬ ì¤Ģëĭ¤ +Ġ미ëŀĺ ìĹIJ +ìĿ´ ëįĺ +ĠS BS +ë» IJ +Ġëĭ¤ ìĪĺ +Ġëĭ¤ ìĿ´ìĸ´ +Ġìĺ¤ ì§Ģ +ë²ķ ìĿĺ +Ġë¹ Ĺ +Ġì§Ģ ì¶ľ +Ġíķľ ë°ĺ +ĠëĤ´ 린 +ĠìĿĺ 뢰 +ĠëıĻ ë¶Ģ +ĠëıĻ ìĺģ +Ġê³¼ 목 +ìłķë¶Ģ ê°Ģ +Ġëĵ± ëĵ± +Ġë¶Ģ ìŀijìļ© +ĠìĬ¬ ëĿ¼ìĿ´ +ĠìĪĺ ìĹħìĿĦ +ìŀ¬ ë¡ľ +Ġë§IJ ëĿ¼ +Ġ몸 ìĿ´ +íķł ì§Ģ +ĠìĬ¤ ì¼Ģ +ĠíĺĦ íĸī +Ġëĵ¤ìĸ´ ê°ĢëĬĶ +Ġëĭ¤ ë¦Ħ +ìĭ¬ ìĤ¬ +Ġê³Ħ 기ê°Ģ +íݸ ìĿĺ +Ġê³µ ì§ģ +Ġëĭ¨ 기 +ë°ķ ìĬ¤ +ìĶ¨ 를 +ĠëĦ¤ íĭ°ì¦Į +Ġì§Ģ ìĭĿìĿĦ +ìĽĶ íķľ +ĠìĽIJ ìĿĦ +Ġëĸ ¡ +ĠìĤ¬ìĭ¤ ìĿ´ëĭ¤ +ĠìĦł ë°ķ +ìłķë³´ íĨµìĭł +Ġëĵ¤ìĸ´ ê°Ħ +ëIJIJ ê³ł +ĠíĥĪ ë¶ģ +ĠìĽIJëĤ´ ëĮĢíijľ +Ġê²ĥ ë³´ëĭ¤ +Ġìĺ¤ ìĹ¼ +Ġìŀij 곡 +íı´ 리 +Ġê²ĥ ëĵ¤ìĿĦ +Ġìľł íĸī +Ġì§Ģë°© ìĦłê±° +ĠìĿ´íķ´ íķĺê³ł +Ġíĭ° ì¼ĵ +Ġíķ´ ë³´ +Ġ본 ìĿ¸ìĿ´ +ĠëĤ® ìķĦ +ĠìĨĮ ëħĦ +ĠìĤ´ ìĿ¸ +Ġì¢ħ ìłĦ +ĠìĪľ ìľĦ +Ġ모ìĬµ ìĿĢ +ìĦľë¹ĦìĬ¤ 를 +ĠìĤ¬ ìļ° +ìĦ¤ ìĿĦ +ĠìŀIJìĭł ëĵ¤ìĿĺ +Ġ기ì¤Ģ ìĿĦ +ĠìķĦ íĭ°ìĬ¤íĬ¸ +Ġìĭł ìŀħ +ĠëĤ¨ íķľ +Ġ기ë¡Ŀ ìĿĦ +Ġ컨 íħIJì¸ł +Ġìķķ ëıĦ +ĠìķłëĭĪ ë©ĶìĿ´ìħĺ +ĠìĿ´ ê°ĻìĿ´ +Ġ주 ìĭł +Ġìĥģ ìļ© +íĥĢ 를 +ĠíĻķ ë¥ł +Ġì½Ķ ìĸ´ +Ġ맥 주 +ëĸ ¼ +Ġë´IJ ìķ¼ +ĠìľĦ ìķĪ +ĠíĬ¸ ëŀľ +ĠíĥĪ ì¶ľ +Ġ뮤ì§Ģ 컬 +Ġë ¼Ī +ìĦľ ìĻĢ +ĠìľĦ ëĮĢíķľ +ê°ľ êµŃ +Ġë§Ī 리 +ĠìĿ¸íĦ°ë·° ìĹIJìĦľ +Ġëĵ Ģ +Ġ6 00 +Ġëĭ¬ ìĿ´ +Ġë§ŀ ìĿĢ +Ġ믿 ëĬĶ +íĺĪ ìķķ +ĠìłĦ íĻĶ를 +Ġìĭľ ìĦłìĿĦ +íĻĶ íķĺëĬĶ +ĠìĿĺ ì§Ģ를 +ê¸ī ìŬ +Ġì¶Ķ ì¶ľ +ĠìķĦëĭĪ ëĿ¼ëĬĶ +ĠìĽĮ ìĭ±íĦ´ +리íı¬ ëĭĪìķĦ +ĠíķĺëĤĺ 를 +Ġëªħ ì¹Ń +ĠëĦ¤ ëįľ +ìĪĺ ìĹħ +ì¹ĺ ë¡ľ +ĠìĤ¬ìĹħ ìĹIJ +ĠìłĦëŀµ ìĿĦ +Ġìĺ· ìĿĦ +ëĬ ¦ +íļĮ ê³Ħ +Ġìľł ìłĦ +ĠìĨį ëıĦ를 +Ġë³µ 구 +ĠìķĮ ì§Ģ +ìĤ¬ ëĵ¤ìĿ´ +ë§Į ëªħ +Ġ본 ì§Ī +ì½ľê±¸ ì¶ľìŀ¥ë§ĪìĤ¬ì§Ģ +ìķĺ ì§Ģë§Į +ĠëĴ¤ ë¡ľ +ĠëŃ ĺ +ĠìĥĿ ìĥĿ +Ġë°Ķ ìĹIJ +ë¹ ħ +Ġê°Ī ìĪĺë¡Ŀ +ìĭľ 리ì¦Ī +ĠìĿ¸ íħĮ리ìĸ´ +Ġìĭľíĸī ëł¹ +Ġìĭ± ê¸Ģ +ĠëĦĺ ê²Į +Ġê¼¼ ê¼¼ +Ġê²½ìŀij ì§Ģ +ìŀ¥ 기 +Ġ모 ë¥´ê³ł +Ġìķŀ ìŀ¥ +ĠìĿ´íĽĦ ìĹIJ +ë°Ķ ëijijìĿ´ +ë©´ì¶ľìŀ¥ ìĥµì¶Ķì²ľ +ìĪĺ ìŀħ +Ġê²½ ê³¼ +ìķĪ ëĤ´ +Ġì´Ī ëŀĺ +ĠìłIJ ìĭ¬ +Ġìķ Ļ +ĠëĤĺ ë +Ġìĥģ ìĺģ +ĠìĦ¸ ìĽĶ +ĠíĬ¹ë³Ħ íŀĪ +ĠìĤ¬ìĿ´íĬ¸ 를 +Ġì¢Į ìļ° +Ġë°ĸ ìľ¼ë¡ľ +ìĭľ íĶ¼ +Ġë¶ģ 측 +Ġì¶Ķì§Ħ íķĺê³ł +ìĨĮ ë°© +ĠëĤĺ ê°ĢëĬĶ +ĠìĿĺ íķĺë©´ +íĨµ ìĿ¼ +Ġê°ĸ 춰 +ëĤĺ ìĻĢ +Ġ무 ìĥģ +ĠìĦľë¹ĦìĬ¤ ê°Ģ +Ġê·¸ëłĩ ëĭ¤ +Ġíķ©ë¦¬ ìłģìĿ¸ +ìĹIJìĦľ ë§Į +Ġì¤ij ëıħ +Ġíķ´ ì§Ģ +ì» · +Ġìĭ¤ì§Ī ìłģìĿ¸ +ì ¨ +Ġê´Ģ íķĺìŬ +Ġë§Ī ëĿ¼ +ì¤Ģ ë¹Ħ +ĠìĦ¸ê³Ħ ìłģìľ¼ë¡ľ +Ġëħ¸ëł¥ íķĺê³ł +기 ë°ĺ +Ġëĵ± ëıĦ +Ġìĥģ ìŀĦ +ë§Ī ìĬ¤íĦ° +ë¶Ħ ìĿĢ +Ġë§Ī íģ¬ +물 ê³¼ +Ġê°ľìĿ¸ ìłģìľ¼ë¡ľ +ìŀij ê°Ģ +ĠìĬ¤íĬ¸ ëłĪìĬ¤ +ìķ ĬìĿĢ +ĠìķĦ ìĭľìķĪ +ĠìķĪ ëĵľë¡ľìĿ´ëĵľ +ĠíĽĦ ìĹIJëĬĶ +Ġìłģ 립 +ìĻ¸ êµŃ +Ġê·¸ ì¤ij +ì¢ħ ìĿ´ +ìĭľê°Ħ ìĿ´ +ĠìĹĺ 리 +Ġìłķ ìŀij +ĠìĿ¼ ì¢ħìĿĺ +Ġê±° ë¡ł +Ġíı¬ ìĬ¤íĮħ +Ġê¶Į ëł¥ +íĬ¸ ëĿ¼ +Ġë°ľ ìĤ¬ +Ġ4 9 +Ġ물 ê±´ +a ìĹIJìĦľ +주 ê°Ħ +ĠìĿ¸ ìĩĦ +Ġ매 매 +ĠíĸĪ ìĿĦ +ì¡° ìĤ¬ë¥¼ +ìĹ° ìĿ´ +Ġ물 ìĿ´ +Ġíĺij ì¡° +ĠëĬ¥ ëł¥ìĿ´ +Ġê³ł ëĮĢ +ì°¨ ë³Ħ +ĠìĦ± ì·¨ +ĠìľĦì¹ĺ ìĹIJ +ĠìĹĦ 격 +ìŀIJ ìĤ° +ĠìĿ¼ ê´Ģ +ĠëĪ Į +ĠìłķëıĦ ê°Ģ +Ġì¢ħ ìĿ´ +ĠìĿ´ë²Ī ìĹIJëĬĶ +ĠìĺĽ ëĤł +Ġ기 ìĨĮ +문 ìŀ¬ìĿ¸ +íĹ ¥ +Ġê°ľìµľ íĸĪëĭ¤ +ĠìĿ´ë£¨ìĸ´ ì§Ħ +Ġ공공 기ê´Ģ +ĠìķĦ ê°Ģ +Ġê²½ìļ° ìĹIJëıĦ +ĠíĺĦìŀ¬ ëĬĶ +ìłIJ ê²Ģ +ìĭľìĦ¤ ìĿĦ +Ġìĸ´ì© Ķ +ĠíĮ¨íĤ¤ ì§Ģ +ìļ´ ìłĦ +Ġìŀ¬ ë°Į +ĠìłľíĴĪ ìĿ´ +ĠíĥĪ íĩ´ +Ġìľ Į +Ġìłķ ë°Ģ +Ġ민 ìĽIJ +Ġ길 ìĿ´ëĬĶ +Ġìĥģ ìĭ¤ +ëĦ Į +Ġì´Ī ìłIJìĿĦ +Ġìľ¼ ë¡ľ +ì ı +ê³Ħ ìĺ¬ë¦¼íĶ½ +ìĭ¤ íŀĪ +ĠìĦ¸ 볨 +Ġcl ass +íķ´ ìĻ¸ +íĸī ë³µ +Ġìĸ´ ìļ° +ĠìĬ¤ íĭ° +ĠìķĦëĭĪ ëĿ¼ê³ł +ê´ij 주 +Ġìı ĺ +ak e +Ġìľ ¨ +íĺķ ìĿ´ +Ġìłķë³´ ìĿĺ +ĠëĤł ìĿ´ +ë©´ ìĿĺ +ëħ¸ ì´Ŀ +Ġíķ© ëıĻ +ëĮĢíķĻ ìĽIJ +ë© Ģ +ê°ģ ìĿĦ +ĠìłĢ ìĿĺ +Ġ19 90 +ĠëĤł ì§ľ +Ġíĺij ëł¥ìĿĦ +Ġë°ĺ ëĮĢë¡ľ +Ġ미êµŃ ìĿĢ +íķľ ëıĦ +Ġ매 ëł¥ìĿĦ +Ġë¸Ķë¡Ŀ ì²´ìĿ¸ +íķĻ ìŀIJ +ìĦ¸ ì¢ħ +Ġê³ł ìłĦ +Ġìļ´ ìĨ¡ +ĠìĤ¬íļĮ 를 +ĠìĨĮ ìĻ¸ +Ġëĭ¹ ëĩ¨ +ĠìĦłìĪĺ ê°Ģ +ĠìĥĿ 기ëĬĶ +ì¦ ī +ĠíķĺëĤĺ ìĿ¸ +ìĽĮ ëĵľ +ĠìĿĢ íĩ´ +ë±ħ íģ¬ +íĨ¨ ë¦Ń +ì· Ħ +Ġë ± +ëĿ¼ ìĿ´íĬ¸ +íķĻ 기 +íķĻ ë¶Ģ +ĠíļĮìĽIJ ìĹIJê²Į +ìŀ¡ ê³ł +보기 ìĸ´íĶĮ +Ġìĸĺ 기를 +ìĬ¤íĨł 리 +Ġë¹ Ļ +Ġì¤ijêµŃ ìĿ´ +ĠìķĪìłķ ìłģìĿ¸ +ê³¼ ìłķìĿĦ +ìł¸ ìķ¼ +Ġíĥľ ìĸ´ëĤĺ +Ġì¤ij ìĿĺ +Ġê´Ģ 리를 +Ġê°ķ íķĺê²Į +ì²´ ìłľ +Ġìĺ¤ ìħĺ +Ġìľł ìļ© +ĠìĦľ 민 +íĶĦ 리카 +Ġê±° ì¹ĺ +Ġì°¨ ìĿ´ë¥¼ +ì±Ħ ëĦIJ +Ġê°¤ ëŁŃìĭľ +íĦ°íħĮìĿ¸ 먼íĬ¸ +ìĬ¤ 룰 +ë³´ëĭ¤ ëıĦ +Ġë¹ł ì§Ħ +Ġê³µê°Ħ ìĿ´ +ĠíĺĦìĭ¤ ìĿĦ +ĠìĿ¼ ëıĦ +ëı¼ ìķ¼ +ĠìķĬìķĺ ëįĺ +Ġìĺ¤ë¥¸ 쪽 +ĠìĿ´ë¥´ 기ê¹Įì§Ģ +ĠìłĦíĺķ ìłģìľ¼ë¡ľ +ĠìĿ´ ìĪĺ +íķ© ìĿĺ +Ġìľł ìŀħ +Ġ커 íĶĮ +Ġëĭ ® +ìŀ¥ íķľ +ĠíĨµ ìĥģ +ĠìŀĦ ëªħ +Ġì§Ģ íijľ +Ġìĭľ ëĤĺ +ĠìĬ¤ íĬľëĶĶìĺ¤ +ëĶĶ ì§ĢíĦ¸ +ĠìķĪìłĦ íķĺê²Į +Ġìĺµ ìħĺìĿĦ +Ġ리ëįĶ ìĭŃ +Ġì§Ģ ëĭĻëĭĪëĭ¤ +Ġê¹Ģ ì¹ĺ +Ġs econd +ãĥ»ãĥ»ãĥ»ãĥ» ãĥ»ãĥ»ãĥ»ãĥ» +Ġëĭ¤ ëĵ¤ +Ġë°° ëĭ¬ +ĠìĨ¡ ìĭł +ĠìĦ¤ëªħ ìĿĦ +Ġê·¸ ëĤł +Ġ모ìĬµ ìľ¼ë¡ľ +기 íķľ +íĬ¸ ìĻĢ +Ġê³µ ìķ½ +íĭ° ëĵľ +ĠìĦľë¹ĦìĬ¤ ëĬĶ +íĮ¨ ìħĺ +ëĦ ĵ +íĶĦ ëĬĶ +Ġë¸Į 리 +ìķħ íķľ +Ġê°ľë°ľ ìĹIJ +ĠìĽĮ ëĤĻ +ìŀ¥ ìĿ¸ +ìĦ¸ ìĹIJ +Ġ6 4 +Ġì¦Ŀ ìĥģ +Ġíĥľìĸij ê´ij +ëĵ± ìĿ´ +ĠìŀijíĴĪ ìĿ´ +íķĺ ëĿ¼ê³ł +ìķĦ ëŀĺ +Ġë¹Ħ ìľ¨ +Ġëħ Ģ +Ġ머 리를 +ĠìŀĦ ìĽIJ +Ġ기ëĮĢ íķľëĭ¤ +Ġëį°ìĿ´íĦ° ë²łìĿ´ìĬ¤ +íķ¨ ìĿĢ +Ġê ¶ +Ġíĥij ìĬ¹ +ê³ł ëıĦ +Ġê³ł 구 +Ġ충 ì²Ń +Ġë³Ħ ëıĦ +Ġì» ¸ +ĠìĪľ ìĪĺ +ĠìĿ¸íĦ° íİĺìĿ´ìĬ¤ +ĠìĪĺì¤Ģ ìĿĦ +ë¹Ħ ìĻĢ +ĠìĥĿ ê²¼ +Ġì¶ķ ìĨĮ +ë¶Ģ ëĭ´ +ê»ĺ ìĦľëĬĶ +Ġì±Ļ 겨 +ĠìĿ¸ ìĤ¬ë¥¼ +Ġì¼ ľ +ĠíĽĦë³´ ëĬĶ +ëIJIJ ìľ¼ë©° +Ġë°ĶëĿ¼ ë³´ëĬĶ +Ġë©° ì¹ł +ìľ¼ ìĭł +Ġíĺij ìķ½ +Ġë§Į ëĤĺëĬĶ +ì¹´ 르 +ì§ĢìĽIJ ìĦ¼íĦ° +Ġì²łìłĢ íŀĪ +ìĺģíĻĶ무ë£Į 보기ìĸ´íĶĮ +ĠìĿ¼ ë°© +ĠìĤ¬ìļ© ìŀIJìĿĺ +Ġíĺķ ìĭĿ +먼 ìłĢ +ĠìĪĺ ëł¹ +ĠíĶĦë¡ľ ê·¸ëŀĺ +Ġì¹´ ìļ´ +ë°© ì§Ģ +íıī ìĿĦ +Ġê·¸ ìĹIJê²Į +ìłĦ ìļ© +Ġ무 ìĸ¸ +ĠëIJĺ ëıĦë¡Ŀ +2 000 +Ġì¤ij ë³µ +Ġì°¨ ë¡ľ +Ġë°° ìłľ +Ġë¶ĦìľĦ 기ê°Ģ +ë§Ī ê°Ģ +Ġë°© ìĤ¬ +Ġ기 기 +Ġì¤ij ì§Ģ +ê²ĥ ìĿĦ +Ġì§ij ëĭĪëĭ¤ +ì´Ī ëĵ±íķĻêµIJ +Ġì¹ĺ ìľł +ĠëĮĢíijľ ê°Ģ +ĠëĤĺíĥĢ ëĤ¬ +ĠìĤ´íİ´ ë³´ +ĠìŬ ìķ¼ +Ġë§Į íĻĶ +ĠìŀĬ ì§Ģ +ìļ° 기 +íħ Ģ +ì°¨ ìĿĺ +ĠìĿ¼ìĸ´ëĤĺ ì§Ģ +Ġëĭ¤ëĭĪ ëĬĶ +ĠìĿ¼íĻĺ ìľ¼ë¡ľ +Ġê°Ħ 주 +Ġìī½ ê³ł +Ġê³łíĨµ ìĿĦ +ìĿ¼ ìłĦ +Ġê°Ģ íķ´ +ĠëĤ´ìļ© ê³¼ +Ġìķ¡ ìħĺ +Ġ기ìģ ¨ +Ġ기 ë¦Ħ +ìķĦ ëĬĶ +Ġ7 5 +ĠíķĺìŬ ê¸Ī +ĠíķĺëĬĶ ì§Ģ +ĠìĤ¬ë¡Ģ 를 +ĠìĦľ íķ´ +ë°© ìķĪ +Ġì§ĢìĨį ëIJĺë©° +ĠìķĦ무 ëıĦ +Ġíķľë§Ī ëĶĶ +민 ìĿ´ +Ġê´Ģê³Ħ ìŀIJ +Ġ미ìĦ¸ë¨¼ ì§Ģ +Ġê²½ 주 +ë¸Į ëŀľëĵľ +Ġëĭ¤ íģIJ +Ġë© ķìĭľì½Ķ +Ġíı¬ íĦ¸ +Ġíķĺ ìĭľëĬĶ +êµŃ ìŀ¥ +ĠìĿ¼ ëĮĢ +ĠíĽĦë³´ ê°Ģ +Ġë¶Ļ ìŬ +ĠìĹIJìĸ´ 컨 +ìĭľì¼ ° +ëĿ¼ 리 +Ġì¤ij ëıĦ +ê²½ ìĿ´ +ĠëĤ´ ìĿĺ +민 ìĿĢ +íĹ ¨ +기 ìĤ¬ +ĠìĽIJ ìĿ¸ìĿ´ +ëĦ¤ íĬ¸ìĽĮíģ¬ +Ġìĭľìŀij íķĺëĬĶ +Ġë¶Ģëĭ´ ìĿĦ +Ġì§ķ ìĹŃ +Ġë©Ķ모 리 +ì²´ ë¡ľ +Ġê¸ ĢìĿ´ +Ġê²Ģ ì¶ľ +ì²ł ëıĦ +ë¡ľ ìĹIJ +Ġê¹Ģ íĺĦ +Ġìĸ´ëĸ ¨ +Ġ맡 ê³ł +ê°Ģ ìŀ¥ +ìĬ¤ íĶ¼ +ê³µ ê³µ +ĠìłľíĴĪ ìĹIJ +Ġíĺģ ëªħ +ĠìĿ¼ìĸ´ëĤĺ ëĬĶ +Ġ그리ìĬ¤ ëıĦ +ĠìĹĨ ìĹĪìĬµëĭĪëĭ¤ +ëĵľ 림 +Ġíķ´ 주ëĬĶ +ĠíļĮ ìŀ¥ìĿ´ +Ġìĭ¬ ìĿĺ +ĠíĺĦìŀ¬ ê¹Įì§Ģ +ìħĶ ëĦIJ +ĠìĨĮê°ľ íĸĪëĭ¤ +Ġëĵł ëĭ¤ +Ġì§Ģ ëĬ¥ +ëıĻ 기 +Ġê³µ ë¶Ģ를 +Ġê³ł ë§Ļ +Ġë¡ ± +Ġ기ë°ĺ ìĿĺ +Ġì³ IJ +ĠëıĦ 모 +ìł¸ ìĦľ +ĠíļĮìĽIJ ê°Ģìŀħ +Ġë°°ìļ° ê³ł +Ġëĭ ¦ +ë ½ +ìĿ´ ëłĩê²Į +ĠëıĻ íķ´ +íķĺ기 ëĬĶ +ĠëĨĴ ìĿ´ê³ł +Ġê³ł ë°± +ĠìĨĮ 리ê°Ģ +Ġë§İ ì§Ģ +Ġë¬¸ìłľ ìĿĺ +Ġê¸Ī 리 +Ġ죽 ìĿĢ +ĠëĨĵ ìĿĢ +Ġëıĭ ë³´ +ìĿ´ íĽĦ +íļĮ ê´Ģ +Ġìĥģ ìŀ¥ +Ġê²° ìłķìĿĦ +ëĭ´ ë³´ +Ġíı¬íķ¨ íķĺëĬĶ +ĠìĦłìĥĿ ëĭĺ +Ġì°¸ìŬ íķ´ +Ġìħ ° +Ġíķ´ ìłľ +Ġê°ķ ìĤ¬ +Ġê²Ģ ìĿĢ +Ġì½ĺ ìĦľíĬ¸ +íİ ł +ëłĪ ìĿ¼ +Ġìĸ´ëł¤ ìļ¸ +ĠìĶ » +Ġëħ¸ëŀĺ 를 +ìĭ¤ ìĿĢ +Ġìĺģ ìĽħ +ĠíĶĦë¡ľê·¸ëŀ¨ ìĿ´ +ĠìŬ룬ë¶Ħ ìĿĺ +ìŀ ĩ +ìľĦ ì¹ĺ +ĠëıĻ ì°¸ +ĠíĬ ľ +ĠìĤ¬íķŃ ìĿĢ +Ġìĸµ ìłľ +Ġdev ice +Ġí ĵ¨ +Ġ4 6 +ìłĢ íŀĪ +ĠìłĪ ìķ½ +ì·¨ ìĹħ +Ġëķ Ģ +Ġë¶Ī 리 +ĠìĿ´ìļ© ìŀIJëĬĶ +êµ° ìĹIJ +Ġìļ°ë¦¬ëĤĺëĿ¼ ìĿĺ +ĠìŀĪ ê±°ëĤĺ +íĴ ĢìĿ´ +ĠìĦ¸ ê¸Ī +ëĵ± ìĹIJ +p pl +ìľł íĨµ +Ġì¶ľ ìĦĿ +ëįĶ ìļ´ +Ġ물 룬 +Ġì¦Ŀê°Ģ íķľ +íķ´ ì¡Įëĭ¤ +ìĿ¸ ëł¥ +ĠíĻ Ģë¡ľ +Ġìľł ìĹ° +ìĺģ ìĿĢ +ìĸij ìĿ´ +ê²° ìłľ +Ġ목 ìłģìĿ´ +ĠíĪ¬ ìŀIJ를 +Ġ그림 ìĿĦ +ĠìłĦ íķĺëĬĶ +ë²Ħ 린 +Ġìĸij 측 +ëĨ Ī +ë°Ľ ì§Ģ +ĠíĹĪ ìľĦ +ĠìĬ¬ ë¡ľ +ëĿ ł +ĠëĮĢ ëĭ¤ +ë¶Ģ íĴĪ +ĠìĦľ ëªħ +Ġë¶Ī ìĸ´ +ì¦Ī ëĬĶ +Ġì¦Ŀ ê¶Į +Ġo ut +ĠëĪĦ ìłģ +Ġê³µ ëŀµ +Ġìļ© ìĿ¸ +ëĿ¼ ìĿ´ëĤĺ +ìĭĿ ìĹIJìĦľ +ëĦĪ 무 +Ġì±Ħ 무 +ĠìĨĮ ìŀ¥ +Ġìĭł ë¶Ħ +Ġìŀ¥ ìłIJ +íĤ ´ +íĺķ íĥľ +ë°ķ 물ê´Ģ +ìŀĪ ìĸ´ +ì§Ħ ê³¼ +íijľ ì¤Ģ +ï¼ ĭ +Ġ[ âĢ¦] +층 ìĹIJ +Ġ구ìĦ± ìĽIJ +Ġì§ģìĽIJ ëĵ¤ìĿĢ +Ġìĸ´ëijIJ ìļ´ +Ġê³µ ìĽIJ +ëĨ ¨ +Ġëij ¥ +Ġ ä¸ +Ġê·¸ ëĤĺë§Ī +Ġëħ¸ íĽĦ +Ġìŀħ ìĽIJ +Ġ물 ìĹIJ +ĠìĦľë¹ĦìĬ¤ ìĹIJ +ĠìĺģíĻĶ ìĿĺ +Ġê³µ ìĭľ +ĠìĥĿ 길 +ĠìĦ±ê³µ ìłģìľ¼ë¡ľ +ĠìĽĢì§ģ ìŀĦ +ect ion +Ġìŀ¬ë¯¸ ìŀĪëĬĶ +Ġê°Ģ 볤 +ĠëıĦ ëĦIJëĵľ +Ġíķ¨ ìľł +Ġ기ë¡Ŀ íķľ +un d +ê³¼ ìŀ¥ +ĠëĮĢ ìŀ¥ +ĠâĢ Ļ +ì¦ IJ +Ġíĺij ìĹħ +Ġë¶ģíķľ ìĿĢ +ĠìĹ° ìķł +ĠìĦ¸ íĥģ +ì´ ¬ìĺģ +Ġëªħ íĴĪ +Ġë³µ ìļ© +Ġíľ ł +ë§ģ ìĿĦ +Ġ목ìĪ ¨ìĿĦ +ìĤ° ìĹIJ +ĠìĹĨ ìľ¼ë©´ +ì²ľ ìĽIJ +ìķĺ ìĸ´ìļĶ +Ġê¹Ģ ë³ij +Ġë¶Ī ë§Į +Ġì¢Ģ ëįĶ +Ġì§Ģë°© ìŀIJì¹ĺëĭ¨ì²´ +ĠìĬ¤ìĽ¨ ëį´ +ĠìĨĮ ìĪĺ +Ġë§IJ ëıĦ +ĠìĥĿê°ģ íķł +ëĿ½ ìĿĦ +ĠíĶ¼íķ´ ìŀIJ +ĠëĤĺëĪĦ ìĸ´ +ìĭľ ì²Ń +ëĵľ 볤 +Ġíķ´ ìķĪ +Ġê¹Ģ ìļ© +ë³µ íķ© +Ġ걸 린 +주ìĭľ ë©´ +ê°Ģ ë©° +주 ëıĦ +ĠíĽĦ ìĨį +Ġê°ĻìĿĢ ëį° +Ġìĭ« ìĸ´ +íĻĶ ìŀ¥ +íĻĶ íķĺê³ł +Ġê³µ ìĤ° +Ġê²° ìĿĺ +ìłķë¶Ģ ìĿĺ +ìķĦ ëıĻ +ĠìĽ ħ +ĠíĮIJ 문ìłIJ +Ġ200 1 +ëłĪìĿ´ íģ¬ +ĠíĻĺê²½ ìĹIJìĦľ +ìĿ¸ ìĤ¬ +ĠëĮĢ ìĻ¸ +Ġê°Ģ ì§ĢëĬĶ +ĠíĶĮ ëĿ¼ìĬ¤íĭ± +ê³µ ê¸ī +Ġê±° ì£ł +ĠíĻĺ ë¶Ī +ĠíĻĺê²½ ìĹIJ +ëĭ¤ ë§Į +ĠëĤĺ ë©´ +ìĹĪëĭ¤ ëĬĶ +Ġê²½ìļ° 를 +ëĭ¬ ëĿ¼ê³ł +ë¹Ħ ì¦Ī +ìĥĿ 물 +Ġê°IJ ì§Ģ +Ġì¦ī ê°ģ +íķĺ 루 +Ġíķľ ìļ° +루미 ëĬĦ +ĺ ¥ +ì¹´ ê³ł +Ġíİĺ ìĬ¤íĭ°ë²Į +ĠìĤ½ ìŀħ +ì ĮĢ +ìĥ · +ĠëĶ ľ +ëłī ìĬ¤ +ì¤ĺ ìķ¼ +Ġìľł ê¶Į +ĠìĥĿê°ģ íĸĪëĭ¤ +Ġ리 ìĸ¼ +ĠìĿ´íķ´ 를 +ê°Ģ ê²Į +ĠìĨĮ ìĬ¤ +Ġ방문 íķľ +ĠëĬ¦ ê²Į +리 ìĸ¼ +íļĮ ìĻĢ +ĠëĤ´ 리 +Ġ물 íĴĪ +ëIJIJ ëįĺ +íĮ ¡ +Ġë¹Ħ ìĭ¼ +ĠìŀIJìĭł ìĹIJê²Į +ĠíĿ¡ ìĪĺ +ê°Ģ ì¹ĺ +Ġì°½ 립 +ë°Ľ ìķĺëĭ¤ +Ġíĺ¸íħĶ ìĹIJìĦľ +Ġê²½ ëł¥ +Ġìĭł ìĦłíķľ +ë°Ķ ì§Ģ +ĠíĹĪ ê°Ģ +ĠìĻĦìłĦ íķľ +ë°Ķ ìĿ´íĬ¸ +ĠìĬ¹ ëĤĻ +ĠìĤ´íİ´ ë³´ë©´ +ë¹Ħ ë¡ľ +매 를 +ê²° ìłķ +Ġìĭľìŀij íķ´ +ĠìĤ¬íļĮ 주ìĿĺ +ĠìĦłìĪĺ ëĵ¤ìĿ´ +Ġ쪽 ìľ¼ë¡ľ +ĠìľĦ ì¹ĺ를 +ìŀ¬ ëĬĶ +ì¤ij ìĭ¬ +ĠìĿ´íķ´ ê°Ģ +ê³Ħ ìķ½ìĿĦ +커 íĶ¼ +ĠëĤĺíĥĢ ëĤľ +Ġìŀijê°Ģ ìĿĺ +ìĭľíĤ¨ ëĭ¤ +ë³´ ê°Ģ +Ġë§IJ ê¹Įì§Ģ +ë¡Ģ 를 +Ġìĸ´ëł¤ ìĽĢ +ĠíĦ° ì¹ĺ +Ġìĩ ł +ĠëĦ£ ê³ł +ìľł ì¹ĺ +201 2 +ĠíĶĦë¡ľê·¸ëŀ¨ ìĹIJ +ëĦ¤ìŀĦëĵľ ìĤ¬ìĿ´íĬ¸ +Ġê°ľ ì²Ļ +Ġíķ´ ìĦ¤ +Ġê°IJ ìłķìĿĦ +Ġìĺ¬ ëł¸ëĭ¤ +íĮIJ ìĿ´ +Ġìķŀ ëijĶ +ìĿĢíĸī ìĿĢ +íĥ ± +ì¶Ķ ê°Ģ +Ġ구조 를 +Ġëħ¸íĬ¸ ë¶ģ +기 íĥĢ +íĦ° ê°Ģ +ëIJĺ ëĬĶëį° +êµIJ 를 +Ġ구 ê²½ +Ġë§Įëĵł ëĭ¤ +Ġtrans m +íĺ¸ ëĬĶ +ĠìĬ¤ íİĻ +Ġëĵ¤ìĸ´ ìĺ¤ +Ġê°Ģ격 ìĹIJ +ë©´ì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ +Ġê² ī +ĠìĪĺ ë¶Ħ +ë³´ ìĪĺ +ĠìľĦ ìĦ± +Ġë¶Ģ ìĦľ +Ġ구 ìĽIJ +Ġë¹ĦêµIJ íķ´ +ê±° 리ëĬĶ +Ġìĭł ë°ľ +ĠëĶĶ ë°ĶìĿ´ìĬ¤ +Ġìłķë¶Ģ ìĻĢ +ë§Ī ë¸Ķ +ì°¨ ìĹIJ +ĠìĤ¬ìĹħ ìĿĺ +ĠìĪĺ ê¸ī +Ġì§Ģ ìŀIJì²´ +íĶĦ ëŀij +Ġê¶Į ê³ł +ëį° ìĬ¤ +Ġë¶Ģ 르ëĬĶ +Ġê°Ģ르 ì³IJ +ĠíĬ¸ 리 +od im +Ġì¢ĭìķĦ ìļĶ +Ġëŀ © +ĠíĿ¬ë§Ŀ ìĿĦ +ê³µ íĻĶ +ĠëıĻ ìĭľ +ĠìķĪ ìĭ¬ +ĠëĮĢíķĻ ìĽIJ +Ġë§Ī ìĭľ +Ġìŀħ ìĤ¬ +Ġì¢ħ ë¡ľ +Ġìĭľìŀij íķľëĭ¤ +Ġíݸ íķĺê²Į +ĠíĬ¸ ëŀĺ +ê¹ ĥ +ĠíĹĪ 리 +ĠìĺģíĻĶ ëĬĶ +Ġê²½ìŁģ ëł¥ +ĠíĿIJë¦Ħ ìĿĦ +Ġê º +Ġíı ¼ +ìĦł ìĹIJìĦľ +Ġë¡ ł +Ġíĥľ ìĸ´ëĤľ +Ġíı´ ëįĶ +무 기 +ĠìĤ° íķĺ +ìĺģ ìĿĺ +ìĺĢ ëĬĶëį° +Ġ기ëĮĢ ëIJľëĭ¤ +ìĿ¸ ëĵ¤ +ìłĦ ê³¼ +ëħĦ 째 +ĠìĿ¼ ëĭ¹ +Ġíģ IJ +Ġì§Ħíĸī íĸĪëĭ¤ +íģ´ ëŀĺ +ê³¼íķĻ 기ìĪł +ëıĦ ìĦľê´Ģ +ë§ µ +ĠìĪĺ ìŬ +ĠìłĦ ëĿ¼ +ë°ľ íijľ +Ġëĭ¹ 첨 +Ġë©Ķ ìĿ¼ +ĠìĦĿ ìĤ¬ +Ġíķľ ê³Ħ +ìľĦ ìĻĢ +ëĦ ¬ +Ġì¦Ŀê°Ģ íķĺê³ł +ĠêµIJíĨµ ìĤ¬ê³ł +Ġìłķ ìłķ +ëIJĺ 기ëıĦ +ĠíķĦìļĶ íķł +ìĿ¸ ì¹ĺ +ìłĦ ìľ¼ë¡ľ +ì¶ľìŀ¥ ëĮĢíĸī +ĠíĪ ´ +ĠìĻľëĥIJ íķĺë©´ +ĠëĤ « +ìĥģ 기 +Ġìĭľ 기ìĹIJ +Ġëĸ¨ìĸ´ ì§ĢëĬĶ +Ġê¹Ģ ì§Ħ +ĠíĽĦ ë°° +Ġëħ¼ ìŁģ +ì¤ij ìĿĺ +íģ¬ ë¡ľ +Ġë°Ľ 기 +ìĭľê°Ħ ìĹIJ +ĠíķĻìĥĿ ëĵ¤ìĹIJê²Į +Ġíľ´ ê°Ģ +Ġê´ľì°® ìĿĢ +Ġíķĺëĵľ ìĽ¨ìĸ´ +ê³ł ìŀIJ +Ġ주 ìĸ´ì§Ħ +ìĦł ìľ¼ë¡ľ +an ag +Ġëĵ¤ ë©´ +ĠìķĦ무 ëŀĺëıĦ +Ġë°ĶëĿ¼ ë³´ +Ġ기 ë¶ĦìĿ´ +Ġì¤ij ìĹIJìĦľëıĦ +ĠíķľêµŃ ê³¼ +ĠìĥĿê°ģ ë³´ëĭ¤ +ì´Ŀ 리 +ĠëĿ¼ìĿ´ íĶĦ +ĠëĤĺëĪĦ ëĬĶ +Ġ목ìĨĮ 리ê°Ģ +Ġìĺ¤ ë²Ħ +Ġë°° ìĥģ +ĠëĨĴ ìŬ +ĠíĸĪëĭ¤ ëĬĶ +ĠìĭĿ 물 +ĠìłĪ ê°IJ +ĠêµŃëĤ´ ìĹIJìĦľ +íŀĮ ëĭ¤ +ĠìĻĶ ìĬµëĭĪëĭ¤ +ĠëĵľëĿ¼ìĿ´ ë¸Į +ìĺ ¹ +êµIJ ìĤ¬ +매 ìĿ¼ +ĠìĦ¸ê³Ħ ìĹIJ +ìķĦ 주 +Ġì§Ģ ëĤ´ +ĠìŀIJ 꾸 +겨 ì§Ħ +Ġíļ¨ê³¼ ìłģìĿ¸ +Ġìĸ ĩ +Ġìĭł ìŀĦ +ĠìķĦìĿ´ ì½ĺ +ë¡ľ ì§Ģ +ê°Ģ 루 +íĻĶ íķľ +Ġëį © +ĠìĹ°êµ¬ ê°ľë°ľ +ĠìĬ¬ íĶĶ +Ġemb odim +ìķĦ ëħ¸ +ìĽĶ ìĹIJëĬĶ +ë§Ī ìĿĺ +ì¡Į ëĭ¤ê³ł +Ġíķ´ê²° íķł +Ġìĺ¤íĶĦ ëĿ¼ìĿ¸ +ĠìĿ´ íĺ¼ +Ġëĭ¤ ìĬ¤ +ëIJĺ ê²Į +Ġê³µëıĻ ì²´ +Ġíļ¨ê³¼ ìłģìľ¼ë¡ľ +Ġìĭ¸ ìĽĢ +Ġë´IJ ëıĦ +ĠëıĦ ëĤ´ +íķ¨ ê»ĺ +Ġê´Ģìĭ¬ ê³¼ +ĠìłĦì²´ 를 +ìłķë¶Ģ ëĬĶ +ì§Ģ ìķĬ +ë§Ī ëĬĶ +Ġíķ´ ê°Ģ +ĠìĹ°êµ¬ ëĬĶ +Ġê²½ìŁģ ëł¥ìĿĦ +ìĿ´íģ¬ ìĹħ +ì¶Ķìļ´ ìĦłìĦłíķľ +ĠìĿ´ ìĿĢ +ì¡Į ëįĺ +먹 ëĬĶ +ìľ Ĺ +ìķħ ìĿĺ +ëĬIJ ëĿ¼ +Ġìĥī ê¹Ķ +ĠìķĬìķĺ ì§Ģë§Į +ĠìĪĺ ëĭ¨ +Ġê±° ëijIJ +ĠìĤ¶ ìĹIJ +ê°Ģ ìłķ +ë§ ĺ +Ġìĭľ ìĽIJ +Ġëĭ¹ ì´Ī +Ġ못 íĸĪëįĺ +머 리 +ĠêµIJìĪĺ ê°Ģ +ê·Ģ íı¬ +łĢâłĢâ łĢâłĢâ +Ġíı¬ë¥´ ëħ¸ +íķľ ëį° +Ġê´ij ë²Ķ +Ġ문íĻĶ ìłģ +Ġìľł ëĮĢ +Ġë§Ī ìĬ¤íĦ° +Ġê·¸ê²ĥ ëıĦ +ì¤ Į +ìĽĥ ìĿĮ +Ġìı ł +Ġë©Ķìĭľ ì§Ģ +ĠìĨĶ루 ìħĺìĿĦ +Ġëļľ ëł· +ĠìłĦ íĽĦ +Ġìĭľ ìłIJ +ìĦł ìĥĿ +ĠìķĬìķĦ ìĦľ +íķ´ ì§Ģ +ëIJĺ ìĹĪëįĺ +ìĦ¸ ëĬĶ +ĠíķľêµŃ ìĿĦ +Ġ문íĻĶ ìĺĪìĪł +Ġì¹ľêµ¬ ê°Ģ +Ġë¹Ħìļ© ìĿ´ +ĠìĿ´ê³³ ìĹIJìĦľ +미 ëĬĶ +ìĺĢ ì§Ģë§Į +ĠìļĶ ìķ½ +ĠíĻĺ ìŀIJê°Ģ +ĠëĨĴ ìĿ´ëĬĶ +ĠìĿ´ ëłĩ +ê³¼ ëıĦ +ë²ķ ìľ¼ë¡ľ +ê¹Įì§Ģ ìĿĺ +Ġìŀ¬ ë²Į +ĠíĻĶ ê°Ģ +êµIJìľ¡ ì²Ń +ĠìĤ°ìĹħ ìĿĺ +Ġê±´ê°ķ ìĿĦ +âij ¢ +Ġì¹Ń ì°¬ +ìĤ¬ ì¹´ +Ġíķĺ ìĭ¤ +Ġëĭ¤ ì±Ħ +ĠìĺĪìłķ ìŀħëĭĪëĭ¤ +ìĪĺ ìĤ° +ĠìĤ¬ëŀĮ ëĵ¤ê³¼ +Ġëħ¸ ëł¥ìĿ´ +ëĦ¤ ìĿ´ +Ġ첨 ë¶Ģ +ĠìĦ¸ ìĽĶíĺ¸ +Ġìĺ¨ ê°ĸ +ĠëĮĢíĨµëł¹ ìĿĦ +Ġíĺ¸íħĶ ìĿĢ +Ġ모르 ê²ł +íķŃ ìĿĦ +Ġìĺ¤ ê³ł +Ġì²ĺìĿĮ ë¶ĢíĦ° +ĠìĺģìĹħ ìĿ´ìĿµ +ìħĢ 룬 +Ġì§Ħíĸī íķł +Ġì°¸ìĦĿ íķ´ +íķĺëĬĶ ì§Ģ +ĠìĿ¼ ê°Ħ +Ġìłģ ìĸ´ +Ġë§ĪìĿĮ ìĿĺ +ĠìĶ¨ ê°Ģ +Ġ기ìĸµ ìĿ´ +Ġë¿Į 리 +Ġë¡ľê·¸ ìĿ¸ +Ġìĺ ¹ +ìłģ ìĿ´ë©° +Ġë°ľ íļ¨ +ĠìĤ¬ìļ© íķ©ëĭĪëĭ¤ +ĠìĭľìĦ¤ ìĿĦ +Ġì°¨ì§Ģ íķĺëĬĶ +ĠìķĦë§Ī ì¡´ +ì¾Į íķľ +ëļ Ŀ +ëĭ¤ ë³´ëĭĪ +ì§Ģ ëĮĢ +Ġëĵ± ìĿ´ëĭ¤ +ĠìĿ¸ ìĥģìĿĦ +ĠìĨĮ ìľĦ +모 íĦ° +Ġìŀ¬ ë°ľ +Ġê³µê°ľ ëIJľ +Ġ모르 ê²łëĭ¤ +ĠìĿ´íķĺ ìĿĺ +ë§Ī 리 +ìļ´ ëıĻìĿĦ +Ġê±° ëĭ¤ +Ġë³µ ìĿĮ +기ìĹħ ìĿĺ +Ġê³¼ìłķ ìĿ´ +Ġ깨ëģĹ íķĺê³ł +Ġëĭ¤ ì§IJ +Ġìĭ¤ íĥľ +Ġê·¸ëłĩ ì§Ģë§Į +ìĿĺ ê°Ģ +ĠìĿ´ ì¢ħ +Ġë°ľ ê°Ħ +Ġìĺģ ìĽIJ +ë¶ĢíĦ° ëĬĶ +Ġë°ĺ 볤 +ĠëĤĺëĿ¼ 를 +ìĦľ 리 +Ġë³´ ìķĦ +ìŬ ëªħìĿĺ +ĠìĦľ ìĸij +Ġë¶Ħ ìĤ° +ĠíĺĦ ìĥģìĿ´ +Ġê¸ī ìŬ +Ġê¸ī ì¦Ŀ +Ġìºĺ 리íı¬ëĭĪìķĦ +Ġ미 ì¹ľ +Ġë°Ķ ëĿ¼ëĬĶ +Ġíķ© ë¥ĺ +ĠìŀĪìĿĦ ê¹ĮìļĶ +Ġì²ĺìĿĮ ìĹIJëĬĶ +Ġê·¸ ìĻĢ +ĠìĿ¸ 기ê°Ģ +ĠìĻĦë²½ íķĺê²Į +Ġìµľìłģ ìĿĺ +ĠëĮĢ ìļ° +모 ë°ĶìĿ¼ +ìĥī ìĿ´ +Ġ방침 ìĿ´ëĭ¤ +ĠìĿ¸ íĦ´ +ê° ¤ +ìĺ¤ ëĭĪ +ĠìĿĺ ì§Ģ +ĠëķĮ ìĿĺ +íĮħ ìĿĦ +Ġê·¹ ëĮĢíĻĶ +íħĶëłĪ 콤 +ìĤ¬ ìľł +Ġê³ ½ +ë©´ ìłģ +Ġë¶Ģ ê°Ģ +Ġê°ķ ìĿĺ를 +Ġëĭ¨ ë°± +Ġìķ½ ê´ĢìĿĺ +ĠëĶĶìŀIJìĿ¸ ìĿĦ +Ń ī +ĠìĤ¬ 물 +Ġê·¸ëŁ¬ ëĭĪê¹Į +ĠìŀĦ ìĿĺ +Ġëŀ Ń +Ġë©Ī ì¶Ķ +Ġë³´ëĤ´ ê³ł +Ġê·¸ ê°Ħ +ĠìĿ¼ ìłķíķľ +Ġëĥīìŀ¥ ê³ł +ĠìĿ´ ê±° +Ġ기 íļĮëĬĶ +ìŀ¬ ë£Į +Ġë°ľ 주 +Ġ무 ìĦľ +íı¬ 먼 +ìĥī ìĿĺ +Ġì§Ħíĸī ëIJĺê³ł +Ġê¶Į ìŀ¥ +ĠìĿ´ìĸ´ ì§Ī +íĹĪ ê°Ģ +ĠëķĮ ë¡ľëĬĶ +ĠëĨ Ī +ì²ł íļĮ +Ġì°¸ìŬ íķł +Ġì°¸ìĦĿ íĸĪëĭ¤ +Ġ겨 ìļ° +ĠíĶĮ룬 ê·¸ +ĠìĦł íı¬ +ĠìķĦìĿ´ ëĵ¤ +Ġìļ°ìĬ¹ ìĿĦ +Ġ뮤 ì§ģ +ĠìķĪíĥĢ ê¹Į +ê²Į ìļĶ +Ġ주 ìĭľê¸° +Ġê³ł ìĥĿ +ë²ķ ê³¼ +íģ¬ 릿 +ĠìĽIJ ìŀ¥ +ĠìłĢ ìŀIJ +ĠìłIJ ìľł +ĠíĨł ì§Ģ +Ġê³¼ìłķ ìĹIJ +Ġíķĺ ìľĦ +Ġë¹Ħ ìĭ¸ +Ġì§Ħ ì§Ģ +ĠìķĦìĿ´ ëĵ¤ìĿĦ +ìĪĻ íķľ +Ġë³´ ìĦ¸ìļĶ +íĨµ ìĥģ +ĠêµIJ ìłķ +ìĤ°ìĹħ ìĿĺ +ìĭľìŀ¥ ìĿĺ +âĸ ³ +Ġ2 50 +Ġìĭ¤ ìĹħ +Ġê³łëł¤ íķ´ +ij ¼ +íĬ¸ ë¦Ń +Ġë§ŀ ì§Ģ +ĠìĬ¤íİĺ ìĿ´ìĬ¤ +ìĤ¬ ëł¹ +íĻĶ ë©´ +Ġëĭ¤ 리 +íķĺ기 ê°Ģ +ìħĶ ì¸ł +Ġë» Ķ +ĠìķĦ ë©Ķ +ê²½ ëĤ¨ +ìŀ¬ ìłķ +íĨµ ìĿĦ +ĠìĨį ìĿĺ +íķĺ ëĿ¼ëĬĶ +ê³µ ìłķ +ĠìĹIJ ìĬ¤ +201 1 +ĠìĬ¹ 리를 +Ġì¤ įëĭĪëĭ¤ +ìŬ 기 +Ġìĸ´ë ij +ĠëĶĶ ìĬ¤íģ¬ +íĥĦ ìĿĦ +íĸī ëıĻ +Ġ무 리 +ĠëŁ Ń +Ġíĸĩ ìĤ´ +ëĭ¹ ìĹIJ +ĠìĿĺ견 ìĿ´ +ĠìķĦë²Ħ ì§ĢìĿĺ +Ġíıīë²Ķ íķľ +ëĤĺ ëĿ¼ëĬĶ +ìĥĿ ê°ģ +ĠëĤ¨ ë¶Ģ +Ġìĭľê°Ħ ê³¼ +ĠëŃ ī +Ġëĵ±ìŀ¥ íķĺëĬĶ +ĠìĤ¬ê³ł ê°Ģ +ìŀij ìĿĢ +íĪ¬ ìĬ¤ +ĠíħĶ ëłĪ +íķĺ ê°Ģ +Ġìĭľ ëĭ¬ +Ġë¶Ģ ì°© +Ġ걸 볤 +ìĤ¬ìĹħ ìŀIJ +Ġë¶ģíķľ ìĹIJ +c ont +íļĮ ë¡ľ +Ġê·¸ëŁ¬ ëĭĪ +Ġìµľê·¼ ìĹIJ +Ġ몰 볤 +ì»´ íĵ¨íĦ° +ê² ¹ +ĠìłĦ ê¹Įì§Ģ +Ġë² ¼ +Ġëį ´ +ì¡° ì¹ĺ +ĠìŬ ìĦ±ìĿ´ +Ġë§Į ìĦ± +ìĹŃ ìĿ´ +Ġíĺ¸ ìĿij +ìħĶ ëıĦ +Ġëĭµ ëĭµ +ĠìĿ´ê³³ ìĹIJ +Ġìľłì¹ĺ ìĽIJ +Ġë©ĺ íĨł +주 ìĭł +Ġ5 4 +Ġë¶Ģëĵľ 룬ìļ´ +Ġë²ĦíĬ¼ ìĿĦ +ĠìĦ± ë³Ħ +Ġíı¬ ìĬ¤íĬ¸ +ìķŀ ìľ¼ë¡ľ +ë° ĸ +룬 ìĭľìķĦ +ëIJĺ ëıĦë¡Ŀ +ĠëijIJ ë²Ī째 +ë°° íĮħ +Ġë°ķ 물ê´Ģ +Ġë°ľìłĦ ìĹIJ +ëĵ¤ ê³ł +ëĮĢ ìĿij +ëį ± +ĠìŀIJ ë°ľ +Ġëĵ¤ ìĿ´ +ë§Ŀ ìĿ´ +ĠìĿĮ ë°ĺ +Ġì¦Ŀê°Ģ íĸĪëĭ¤ +Ġê³łë¯¼ ìĿĦ +ëĨĴ ìĿĢ +ĠìķĦ íĮĮ +ìľĦ ìĹIJìĦľ +ìĺ¤ ìĬ¤ +Ġìĭ¤ ìłģ +Ġê¹Ģ íķ´ +ë°ķ ìĿĦ +ëłĮ ì§Ģ +Ġë°Ģ ìĸ´ +Ġë¶Ģìłķ ìłģìĿ¸ +ĠëĵľëĿ¼ìĿ´ ë²Ħ +ĠìŀIJìĽIJ ë´īìĤ¬ +ĠìĪĺ ìĭľë¡ľ +Ġê·¸ ëłĪìĿ´ +ĠëĮĢ ë¥Ļ +Ġíı¬ ê´Ħ +ĠìĤ¬ìĹħ ìĿĢ +ëĪ Ķ +Ġë¶Ģíĥģ ëĵľë¦½ëĭĪëĭ¤ +Ġëĺij ê°ĻìĿĢ +ìĺĪ ë°© +Ġê³łê°Ŀ ìĹIJê²Į +Ġ18 0 +Ġ미ëŀĺ ìĿĺ +ê´Ģê³Ħ ê°Ģ +Ġê·ľëª¨ ê°Ģ +Ġìłľì¡° ìĹħ +ì´ī ì§Ħ +Ġë¹Ľ ìĿĦ +êµIJ ìĿĺ +ì¡° ìĽIJ +ì²Ń ëħĦ +ìłij íķľ +ĠìķĬìķĺ ê³ł +Ġëĵ¤ìŬ ëĭ¤ +Ġíķĺ ëł¤ê³ł +ìĨĮ ìĦ¤ +Ġíĺ¸íħĶ ìĿĦ +ĠìĿ´ë¦Ħ ìĿĢ +Ġê°Ģ족 ìĿ´ +Ġë³´ê±´ ë³µì§Ģ +ìŀIJ ë¡ľìĦľ +Ġìĺ¤ ë¦¬ +ĠëĤĺíĥĢ ëĤ´ëĬĶ +Ġíľ © +ìĨĮ ìŀ¬ +ĠìĿ¼ 몰 +르 íĬ¸ +기 ê¸Ī +Ġê²½ íĸ¥ìĿ´ +Ġë¶Ģ ê°ģ +íķĻ ëħĦëıĦ +ê·¸ ëĿ¼ +Ġíķł ê¹Į +Ġì±Ħ ê¶Į +Ġì¤Ģë¹Ħ íķĺê³ł +ĠìĿ´ìĬ¬ ëŀĮ +ĠëĬIJëĤ Ģ +ĠëĮĢíķľë¯¼êµŃ ìĿĺ +amp le +ĠìłĦ ìĦ¤ +ëıĻ ìĺģìĥģ +구 ë¡ľ +Ġ주 ê°Ħ +Ġê³µ ê³ł +ë°° ìĨ¡ +ĠìŀĪìĿĦ ì§Ģ +êµŃ ê³¼ +Ġíķ´ ë°ľ +ĠìķĦìĿ´ ëĵ¤ìĿĺ +Ġê°ľìµľ íķľëĭ¤ +ìĺģíĻĶ ìłľ +ìĪĺ ìłķ +Ġë¶Ģ ëģĦ +Ġìĥģ ìĿĺ +Ġê³ł ë°ľ +Ġë§Į ìĿ¼ +Ġ5 1 +Ġë¡ Ŀ +ìķ¡ ìĿ´ +ĠíķĺëĤĺëĭĺ ìĿĢ +ìĬ¤íħĮ ìķĦ +Ġíķĺ ëĭ¤ê°Ģ +Ġ리 ì¡°íĬ¸ +íĺķ ìľ¼ë¡ľ +ĠíķĻ 문 +íĿ Ķ +Ġë§ī 기 +Ġëıķ ëĬĶ +ĠìĪĺ ì²ľ +Ġíķĺ ìĭł +Ġíķĺ ìĺĢìĬµëĭĪëĭ¤ +Ġì¤ij ë°ĺ +ì§Ħ ëĭ¨ +Ġë§Į ëĤ¬ +ĠìłĢ ìŀIJëĬĶ +Ġëĭ¬ ìķĦ +ìĻ¸ ê³¼ +Ġì°¸ìŬ 를 +Ġad d +ë°Ķ ëĭ¥ +Ġë³´ìŬ ì¤Ģ +ĠìĭŃ ìŀIJê°Ģ +ĠìĦľìĦľ íŀĪ +ë İ +Ġë°© ìĿĢ +ĠìķĮ 리 +ĠëĤ¨ ìŀIJìĿĺ +ìĺ¨ ìĿ´ +Ġíݸ ìĦ± +ë© ľ +ë¶Ģ 르íģ¬ +Ġíķľ ì°¸ +Ġì¤ij ìĹIJëĬĶ +ĠíıŃ ìĹ¼ +Ġ주목 ìĿĦ +ìĭł íķľ +ĠíĶĦë¡ľ 모 +Ġ기ìĪł ìĿĺ +ìłĦ문 ê°Ģ +Ġì¶Ķ구 íķĺëĬĶ +ëı Ķ +Ġìłķ ìĽIJ +ë°Ľ 침 +ëŁ½ ê²Į +ìĭľìŀ¥ ìĹIJìĦľ +Ġìķķ ì¶ķ +Ġëĭ¹ë¶Ģ íĸĪëĭ¤ +Ġëĭ ¥ +ĠìĿ´ìĥģ íķľ +Ġ공격 ìĿĦ +ĠìĽĢì§ģ ìĿ´ëĬĶ +Ġíļ¨ìľ¨ ìłģìĿ¸ +jp g +¤ ĺ +íķł ëķĮ +ĠìŬ ìĿĺëıĦ +Ġë§IJ íķĺë©´ +íıī ëĵ± +견 ìĿĦ +ĠëĬĺ ìĸ´ëĤľ +ĠìĹĶ ì§ĢëĭĪìĸ´ +Ġê°ĢëĵĿ íķľ +IG ER +Ġì¹ĺìĹ´ íķľ +Ġ매 ìŀħ +ĠìŀIJì²´ 를 +íį¼ëŁ° ìĬ¤ +íĸī íķľ +ĠìĨį ìľ¼ë¡ľ +ĠìĬ¹ ê°Ŀ +Ġìŀł ê¹IJ +ĠëĮĢ ê²° +ĠìķĦ íĶĶ +Ġë°° ìļ¸ +Ġíģ¬ 루 +ĠìĪľ ìľ¼ë¡ľ +Ġíķ´ê²° íķĺ기 +Ġ과거 ìĿĺ +주 ìĭĿ +íķ¨ ìľ¼ë¡ľ +ë°° ëĭ¹ +ìŀĪ ìĿĦ +ĠíĨł ìļĶìĿ¼ +ë¹Ħ íķľ +ĠìĬ¤ íĤ¨ +Ġìŀ¬ íĺĦ +Ġê³Ħ ìĭľ +êµ° ê³¼ +Ġìłľê³µ íķ´ +Ġìĺ¤ ìĬ¤íĬ¸ +ĠíĺĦ ìŀ¥ìĿĦ +Ġëªħ ë°± +Ġëĵ¤ìĸ´ ê°Ķëĭ¤ +본 ê³ł +Ġë²ł ìĿ´ìĬ¤ +긴 ëĭ¤ +Ġì°¾ìķĦ ê°Ģ +ê²ĮìŀĦíı¬ì»¤ë¨¸ëĭĪìĭľ ìĦ¸ +ìĦ± íĺĦ +Ġìĭł ìĿ¸ +Ġìĸij êµŃ +Ġë¡ľ 맨 +ĠëĴ¤ ì§ij +ĠìĿ´ìķ¼ 기ëĬĶ +Ġê·¹ ëĭ¨ +ĠìĿ´ëģ ĦëĬĶ +tern ational +íķĺ ëĬĺ +ëĵ¤ ë¡ľë¶ĢíĦ° +ĠìĬ¤ íĮĮ +íĤ¤ ìĬ¤íĥĦ +ĠìĤ¬íļĮ ìĹIJìĦľ +Ġ골 ëĵľ +ëĿ¼ ê°Ģ +ëĭ´ ìĿĦ +ĠëĨį íĺij +ëł IJ +ĠëĤĺ ìĿ´ê°Ģ +ĠìĦľ ë¶Ģ +Ġë¶Ħ íķł +ê·¼ 무 +ĠëĨĴ ê²Į +커 ìĬ¤ +Ġì¤Ģë¹Ħ íķĺëĬĶ +ì¨ Į +T BC +ĠëĮĢ 립 +Ġì²Ń ì¶ĺ +Ġëĵľ ëĶĶìĸ´ +ĠíĦ ± +ĠìĿ½ ìĿĦ +ĠìĻĢ ìĿ¸ +ìĿ´ë²Ī ìĹIJ +ĠëĬIJëģ¼ ê³ł +ĠëĦ¤ëįľ ëŀĢëĵľ +Ġìĭľ ì¼ľ +Ġëį° ìĬ¤íģ¬ +ĠëĤĺìĺ¤ ì§Ģ +ê³µëıĻ ìĦłìĸ¸ +ĠT IGER +ĠëĿ¼ ìĿ´íĬ¸ +ìŀIJ ê³ł +Ġ주 ë¥ĺ +ĠëıĻ ìķĦ +ìºł íĶĦ +Ġíķ´ íĤ¹ +Ġíݸ ìĿĺ를 +Ġì°į ìĸ´ +ëĤĻ íĥľ +ĠìŀĪ ì£ł +ĠìĪĺ ë©´ +Ġë¶Ģ ì²ľ +벤 ì²ĺ +Ġê°Ģë²¼ ìļ´ +Ġë³´ ëĥĪëĭ¤ +ê±° 리를 +ĠìĹħ ì¢ħ +ĠíĻľëıĻ ìĹIJ +ê²½ ìĿĢ +íĮĮ ëĿ¼ëĭ¤ìĿ´ìĬ¤ +Ġì§ģ ë©´ +Ġìķ± ìĿĦ +ëĤĺ ê°Ī +ëį ¥ +ĠìŀIJ ëĿ¼ +Ġì§ij ìľ¼ë¡ľ +Ġíı¬íķ¨ íķľëĭ¤ +Ġìķ¼ ê°Ħ +ĠíĻĺê²½ ìĿ´ +ĠìĹ°íķ© ëī´ìĬ¤ +ìĭľ íķĺëĬĶ +Ġê¸Ī ë©Ķëĭ¬ +ĠìķĬ ëĭ¤ëĬĶ +ê°ķ ìĿĦ +ĠìĤ° ì±ħ +Ġë³µ ìĤ¬ +Ġì§ĢìĽIJ íķĺê³ł +ĠìĿ¼ ìłķìĿĦ +ë§Ī ì¼ĢíĮħ +볨 ìĿĺ +Ġë°ĶìĿ´ 룬ìĬ¤ +Ġìĥģê´Ģ ìĹĨìĿ´ +ìĿ¸ ê³µ +Ġê°Ģ ë°© +Ġìĭľ íĤ¤ +ê²½ ìļ° +ĠìĨĮ ì¤ij +ìĦĿ ìĿĺ +íĶĮ ë¡ľ +ĠìłķëıĦ ëĬĶ +ĠíĮ¨ 룬 +Ġ경찰 ìĿĢ +ĠìĬµ ëıĦ +ĠìŀIJëŀij íķĺëĬĶ +Ġê² ¹ +ìłľ ìķ½ +Ġìŀħ ë²ķ +íĬ¸ ë¡ł +Ġìľł ë¡ľ +ĠìĺĪ ìģľ +Ġìĭłì²Ń ìĿĦ +Ġíį¼ íı¬ë¨¼ +ĠìĬ¤íİĺ ìħľ +ìĿ¸ ëĵ¤ìĹIJê²Į +ìķĦ íĮĮíĬ¸ +íĽĦ ìĹIJëĬĶ +Ġ9 5 +ĠìĤ° 림 +ë¸Ķ 릿 +íĮ¨ 를 +ì§ĢìĹŃ ìĿĺ +Ġ기ê°Ħ ìĿĦ +ĠìµľìĨĮ íķľìĿĺ +ë£Į ëĬĶ +Ġ5 6 +Ġë§ŀ ê³ł +Ġë²ķ ìĿĦ +ëī´ ìĸ¼ +Ġìŀ¥ ìĿĦ +ĠìĿ´ìĥģ ìľ¼ë¡ľ +ĠìĿ¼ìľ¼ íĤ¤ +ĠìŀIJìľ¨ 주íĸī +Ġ기ìŀIJíļĮ견 ìĿĦ +Ġìłķ ìĦľ +Ġ주 íĮĮ +ìĭ¤ ìŀ¥ +ìĹĪ ìľ¼ë©° +Ġ물 ê±´ìĿĦ +ĠìķĦìĿ´ ëĵ¤ìĿĢ +íļ¨ ìľ¨ +Ġì¢ĭ ìķĺìĬµëĭĪëĭ¤ +ĠìĤ¬ì§Ħ ìĿĢ +주 ì§Ģ +ìĭ¤ ìĿĺ +Ġë¹¼ ìķĹ +ì¡° ê°Ģ +ĠìĨĮ ìķĦ +ë¦Ħ ìĿ´ +Ġì¤ijêµŃ ìĿĢ +Ġëĥ Īëĭ¤ +Ġíķĺ ìħ¨ +íĦ ± +ëĵľ ë¦¬ê³ł +ĠíĶĦë¡ľ íĨł +ĠìĦ±ê³µ ìłģìĿ¸ +ì§Ħ íķľ +ĠìĥĿê°ģ ìĿĢ +Ġì§ij ê¶Į +íģ´ ë¡ľ +ëĤĺ ëĪĶ +Ġìĭľ ìłķ +문 ìĿĢ +ĠìĹ° ëĮĢ +Ġì¦Ŀ ìłķ +Ġ기ìĹħ ëĵ¤ìĿ´ +Ġíıīê°Ģ íĸĪëĭ¤ +ê°ij ëĭĪëĭ¤ +Ġìŀĥ ìĸ´ë²Ħ +Ġì§Ģ ìĸ´ +Ġ주 ì²´ +ì½ ķ +ĠíķĻ ìĽIJ +ìľ¼ ëĭĪê¹Į +íĸī ìĿ´ +ĠìķĮ 리ëĬĶ +Ġìłģ ëĭ¹ +ĠìĥĿê°ģ ìĹIJ +Ġ기ìĪł ê³¼ +ĠìĦĿ ìľł +Ġíĸī ìĤ¬ìĹIJ +Ġëªħ ìłĪ +Ġìº Ķ +ĠìłĦì²´ ìĿĺ +Ġ주ìŀ¥ íķĺëĬĶ +Ġëĭ¹ìĹ° íķľ +íĺ¸ ìĦł +Ġê²Ģ ìĤ¬ë¥¼ +Ġë¸Ķ ë¡ľ +íķĺ ëįĶëĿ¼ëıĦ +ë³´ ë¡ľ +ĠìĿ¼ ì¶ľ +Ġê³ł ìĬ¤ +ĠìĥĿ ëªħìĿĦ +ĠëĤ¨ 쪽 +Ġìŀ¡ ìĿĢ +Ġ몰 ìŀħ +ĠìĪĺ íĻķ +ìķĦ ì§ģ +Ġë³´ ëĤ¼ +Ġë°° íĦ° +ĠìµľëĮĢ ìĿĺ +ech n +ĠëĮĢ ëŁī +Ġë°° ìļ´ +íĺij ìķ½ +ĠìĨIJ ê°ĢëĿ½ +Ġê°ľëħIJ ìĿĦ +5 000 +Ġê±° 리를 +ĠìĤ´ íķ´ +Ġìķ½ ê°ĦìĿĺ +Ġ커 ì§Ģê³ł +ìĵ° 기 +ĠíĻľìĦ± íĻĶ를 +ê°Ģ ìĿĦ +ĠìķĦ ëģ¼ +Ġë°© ì¹ĺ +201 0 +ĠíĥĢ ìĿ¸ìĿĺ +Ġìłij 목 +ìĸ´ëĸ ¤ +Ġ기 ìķĦ +ê³µ 무ìĽIJ +Ġê°ķ ìļĶ +ĠìĤ¬ìļ© íķĺ기 +ĠìĥĪ ëĪĦ리 +Ġíĺķ ìłľ +ëłĪìĿ´ íĦ° +ì ij +ĠìŀIJ ê¶ģ +Ġì° ° +Ġ모 ëĵľ +Ġìľł ëĭĽ +Ġê¹Ģ ìĥģ +ĠíĻĺ ìĥģ +ìŀIJ ë£Į를 +Ġì§Ģ ì§Ħ +ĠìĦľ ë©´ +ĠìĿ¼ë³¸ ìĸ´ +본 ìĿĦ +ëĸ ¡ +Ġê³ł ê°ľë¥¼ +ĠëĤ´ ìŀ¥ +Ġìĭ¬ íĮIJ +ĠíĻķìĭ¤ íķľ +Ġê°ĢëĬ¥ ìĦ± +ì¿ ¼ +Ġ주ìĭľ ìĺµ +Ġê° ±ìĭł +Ġê±° ì¹ľ +ĠìĤ¬ìļ© ìĿ´ +íĶĮ ëĿ¼ +Ġ몰 ëŀIJ +Ġíĸīë³µ ìĿĦ +ĠëĮĢì²´ ë¡ľ +ìŀIJ ëĵ¤ê³¼ +ì¤ij ê³Ħ +Ġíĺ¸ ìĿĺ +ĠìķĦìĿ´ ìĿĺ +ë©Ķ ìĿ¸ +ĠìĤ¶ ìĿ´ +Ġëķħ ìĹIJ +íķľ ìĿ¸ +Ġê·¸ ìķ¼ë§IJë¡ľ +ì¦Ī ìĿĺ +Ġíģ¬ ë¡ľ +ĠìŀĦ ì§ģìĽIJ +Ġë°ĶëŀĮ ì§ģ +Ġì¡°ê±´ ìĿĦ +ëıĻì½ľê±¸ ì¶ľìŀ¥ë§ĪìĤ¬ì§Ģ +ĠëĮĢì±ħ ìĿĦ +ĠìĪĺ íĹĺ +Ġ기 ìļ´ +ĠëįĶ ë¸Ķ +Ġìľłì§Ģ íķĺê³ł +Ġìĭľë¯¼ ëĵ¤ìĿĺ +Ġ맡 ìķĦ +ĠëĦĺ ìĸ´ìĦľ +ĠìĤ¬ì§Ħ ìĿ´ +ĠëĬĺ ê³ł +ĠìľĦíĹĺ ìĿ´ +ìĤ¬ ëĭ¤ë¦¬ +ìľĦ íķ´ +Ġ5 9 +ĠìĿ´ìļ© ìĿĦ +ĠíĻĶ ìĦ± +ìĨIJ íķ´ +Ġì¢Į íĮĮ +Ġê°Ħ ëĭ¤ +ìĿ¸ ëĵ¤ìĿĦ +ĠëIJĺ ê²Į +Ġê°ľìĿ¸ ìłģìĿ¸ +ê²Ģ ì¦Ŀ +ĠìķĦìĿ´ëĶĶìĸ´ 를 +Ġì¡° 리 +Ġëªħ ìĿĦ +ĠìĤ¬íļĮ ê°Ģ +Ġp resent +Ġë²Ī íĺ¸ +Ġë¡ľ ìĬ¤ +ĠëĴ¤ 를 +ëļ ± +. > +ë¶Ħ ëĵ¤ +Ġë§Ī ìĬ¤íģ¬ +ĠêµŃ ê²½ +Ġìĺģ êµŃìĿĺ +Ġì°¸ ìľ¼ë¡ľ +ĠìĹŃ ìĤ¬ìĿĺ +Ġì¤ijêµŃ ê³¼ +Ġíķ© ë²ķ +ìĿ¸ 민 +ĠìĤ¬ ìŀ¥ìĿĢ +Ġìĭľ 뮬 +ê´Ģ íķľ +Ġ모ëijIJ ìĹIJê²Į +Ġ골 목 +Ġíķĺ루 ìĹIJ +Ġ믿 ìĿĦ +ĠìĦłìłķ ëIJľ +ĠìŀĶ ìŀĶ +Ġíĩ´ ì§ģ +ĠìĮĵ ìķĦ +Ġ주 ìŀ¥ìĿĦ +ê³Ħ 층 +Ġìłľê³µ ëIJĺëĬĶ +ĠëĤł ìķĦ +ë¸Ķ 리 +Ġ주ë³Ģ ìĿĺ +íĻĢ ìĹIJìĦľ +Ġêº ¾ +Ġë¶Ģ ìłģ +Ġê³ł ë§Ī +ĠìĽ ° +ĠìłĦìŀIJ ìĥģê±°ëŀĺ +ìĦľ ë²Ħ +ìĬ¤ íĭ± +ìĬµ ìĿĦ +ê·¸ ëıĻìķĪ +ìŀij íĴĪ +Ġê°IJ ìĦ± +Ġì¶ľ ê·¼ +Ġì¤ijìļĶ íķ©ëĭĪëĭ¤ +ĠìłľíĴĪ ìľ¼ë¡ľ +Ġìķ¼ ë§ĪíĨł +Ġë¶ĦìĦĿ ìĿĦ +ëIJ¨ ìĹIJ +ìķ ± +ĠìĹ ¿ +ê°IJ ìĹIJ +Ġì¦Ŀ ìĸ¸ +Ġìķ¼ ìĥĿ +ëĨį íĺij +ĠìĤ¬ìĿ´íĬ¸ ìĹIJ +ë ¿ +Ġì§Ģ 침 +ëıĻ ì¡°íķ© +Ġëĭ¨ íĮĮ +Ġ못 íķĺê²Į +ĠìĨIJ ìľ¼ë¡ľ +ĠìĦ¸ ìĥģìĿĺ +ë¥ĺ ìĿĺ +Ġë°ľìĥĿ íĸĪëĭ¤ +ĠíĻĪ íİĺìĿ´ +ëĭ¤ìļ´ ë¡ľëĵľ +Ġì§ Ŀ +ĠëĤ´ 본ëĭ¤ +Ġ구 ìĦĿ +ë©´ íĹĪ +Ġë¹Ħ ë¡ľ +Ġëĵ¤ ìĿĢ +Ġë©´ ìĹIJìĦľ +Ġëķ IJ +ìĸµìĽIJ ìľ¼ë¡ľ +ì¹ « +ìłĦ íĨµ +ĠìĹĨ ëıĦë¡Ŀ +Ġìŀ¬ ëĭ¨ +ĠìĥĿê°ģ íķĺë©´ +Ġìĺ¤ëĬĺ ìĿĺ +ìĤ¬ ìĿ´ëĵľ +ê²Į ëıĦ +Ġê±´ ìĿĺ +ĠíĻķìĿ¸ íķ´ +ìľ Ļ +íĬ¸ ëŁ¼íĶĦ +르 몬 +ĠìĿĺ íķ´ìĦľ +ìĭ¬ 리 +Ġ- > +Ġëĭ¹ìĭł ìĿĦ +ìĥĪ ë¡ľìļ´ +ĠìĤ¬ê³ł 를 +Ġê°Ģ ì§ľ +ĠìĦ¸ ìļ´ +ê²ł ìĬµëĭĪê¹Į +Ġêµ° ìĤ° +Ġìķł ì´Ī +ëıĻì¶ľìŀ¥ 맼ìĤ¬ì§ĢíĽĦ기 +ĠìłĦíĨµ ìłģìĿ¸ +ĠìĤ¬ë¡Ģ ê°Ģ +ëĭ¤ ìĸij +ĠìŀIJ ë¶Ģ +ĠìľĦ ì£¼ë¡ľ +Ġì°¾ ìľ¼ +Ġê³¼ìłķ ìĿĢ +Ġ침 ëĮĢ +Ġ경기 ìĹIJìĦľ +Ġë·° íĭ° +ìĪĺ ìļĶ +Ġìĭł ì¤ij +Ġê³ ± +ìķĺ ëĭ¤ê³ł +ĠB MW +Ġëķħ ìĿĦ +ìĿ´ ë¯Ģë¡ľ +Ġìķ ĵ +ìĤ¬ ìłĦ +ĠíĮ ½ +ëĶ ¸ +Ġ무 ëłµ +Ġ첫 ë²Ī째 +Ġìĸ» ëĬĶ +Ġë°°ìļ° ëĬĶ +ĠëłĪìĿ´ ìłĢ +Ġëĭ¤ë£¨ ëĬĶ +Ġë¶ķ ê´´ +리 ì¼ĢìĿ´ +Ġë³´ ìķĺëĭ¤ +Ġì¤ij ëıĻ +ê´Ģ ìľ¼ë¡ľ +ĠíĻķ ìĭł +ìĺĪ ìĥģ +ĠìķĪìłĦ ìĿĦ +ĠìłĦìŀIJ ìļ°íݸ +Ġë²Ī째 ë¡ľ +ĠëĬIJëģ¼ ê²Į +Ġ기ëĭ¤ ë¦¬ê³ł +기 ì¡´ +ĠìĿ´ ëĿ¼ëĬĶ +ìĺ¤ íĶĪ +ĠìĦł íĸī +ĠìĦ± 립 +ì²ľ ìŬ +Ġê°Ħ ìłĪ +Ġê·¼ ëĮĢ +Ġìĥĺ íĶĮ +ìłķ ìĦ± +Ġ미 ì¹ł +íĭ° ë¹Ħ +ĠíĶ¼ ê³ł +ĠëĤĺëĿ¼ ê°Ģ +ĠíĬ¹ì§ķ ìĿ´ëĭ¤ +Ġì¹ľìłĪ íķĺê³ł +ìĥ ĺ +구 ìĦ± +Ġê´Ģ ëĤ´ +ê·¸ 린 +Ġê°ķ ì¢Į +ĠìĽIJ ìĿ¸ìĿĦ +ĠìłIJ ëıĦ +Ġìķ½ ê´ĢìĹIJ +ìĽĢ ê³¼ +ĠëĪĦêµ° ê°Ģ +Ġê³ ° +ĠëıĻ ìĦ± +ĠìĦ¸ ìĥģìĿ´ +ĠìĦ±ê³µ ìĿĦ +Ġë°Ģ 볤 +Ġê³µ ì¤ij +ĠìĿĺ ê²° +ëĶĶ ìĸ¸ +ëħ¸ ìĿ¸ +ĠëıĦìĭľ ìĿĺ +ĠìĤ´ìķĦ ëĤ¨ +Ġë¶Ģëĭ´ ìĿ´ +ê³Ħíļį ìĿĦ +ìľĦ ìĿ¸ +ìĥĿ íĥľ +íİĺ ëĿ¼ +Ġ미êµŃ ìĹIJ +Ġë²Ķ ìľĦ를 +ĠëĮĢë³Ģ ìĿ¸ìĿĢ +ëĭ´ë³´ ëĮĢì¶ľ +Ġì±ħ ìĿ´ +ĠëIJľëĭ¤ ë©´ +Ġìºł íķij +Ġ커뮤ëĭĪ ì¼ĢìĿ´ìħĺ +Ġì·¨ íĸ¥ +ëĿ¼ìĿ´ íĶĦ +íķ´ì£¼ ê³ł +Ġì¹¼ ëŁ¼ +Ġë²łìĿ´ ì§ķ +Ġìĭł íĺķ +íĢ ĺ +Ġìĭľ ìĿĺ +Ġìĭľ ëĮĢê°Ģ +구 ìĻĢ +Ġë§ŀ ìĦľ +겨 ìļ¸ +Ġíĺ¸íħĶ ë¡ľ +ĠíıīíĻĶ ìĿĺ +ĠíĿ¥ë¯¸ ë¡ľìļ´ +Ġê´ĢëŀĮ ê°Ŀ +ìĿ´ ë¡ł +Ġê²ĥ ìĿ¼ê¹Į +Ġìłķ ê·ľì§ģ +ëĭĪ ê°Ģ +ìĨĮ ìĬ¤ +Ġìŀ¬ ì§ģ +ë³Ģ ê²½ +Ġì½Ķ ëĦĪ +ëłĪìĿ´ ìĭľìķĦ +ĠìĨĶ ë¡ľ +Ġë¬ĺ ìĤ¬ +ĠìĤ¬ íĽĦ +Ġíķľ ìĿĺ +íĻĶ ëIJĺê³ł +ĠìŀIJ 기ê°Ģ +ìĿĮ ìĭĿ +Ġì¡´ ê²½ +ĠëĮĢë¶Ģë¶Ħ ìĿ´ +ìĹŃì¶ľìŀ¥ ëĮĢíĸī +Ġë§¤ëł¥ ìłģìĿ¸ +ĠìĭľëĤĺ 리ìĺ¤ +ìĻ¸ ë¡ľ +Ġìļ´ìĺģ ìĿĦ +ìĭŃ ëĭĪê¹Į +Ġì¦IJ ê²ģê²Į +Ġ컬 ëłīìħĺ +Ġ곧 ë°Ķë¡ľ +Ġìĭľ ê¸ī +Ġê³ł ìĿĺ +Ġê±° ìłĪ +ĠëĤ¨ 측 +ìłĢ ì¶ķ +Ġë³Ģ ëıĻ +Ġê°ĢëĬ¥ íķĺê²Į +Ġìī IJ +ĠêµŃ민 ìĿ´ +ì´Ŀ ìŀ¥ +기 ìĹIJëĬĶ +ĠìĦ± íĸ¥ +Ġìĭ¤ ëł¥ìĿĦ +ĠíĮĮ ëĬĶ +ĠìĤ¬ìĹħ ìĿ´ +Ġ꺼 ëĤ´ +ìĦ ¹ +Ġë§IJ íķĺë©° +ĠìĦ± íıŃ +ĠëijIJ ëĵľ +Ġíĺ¸ ëĤ¨ +ĠíĻĶ 물 +Ġíķ© ìĦ± +Ġ ãħĭãħĭ +Ġ9 9 +ĠêµŃê°Ģ ëĮĢíijľ +ĠìĪł ìĿĦ +Ġ몽 골 +ĠìĪľ íĻĺ +ĠìĦłë³´ ìĺĢëĭ¤ +ĠìĿ¸ìĥĿ ìĿĦ +Ġ깨ëģĹ íķľ +ë¹Ħ íĬ¸ +Ġê´Ģ ìłĪ +Ġíķ´ ì²´ +ìŀ¬ ìĿĺ +ê²ł ëĬĶê°Ģ +ëIJľëĭ¤ ë©´ +Ġì²łìłĢ íķľ +ë©´ ìĹIJìĦľ +ĠëĤ´ ìĿ¼ +ê°IJ ìľ¼ë¡ľ +Ġìłķìĭł ìĿĦ +ĠìĦ¬ ìľł +ĠìķĦ ìłĢ +ìĭĿ ëĭ¹ +Ġê±° 리ê°Ģ +Ġëĭ¤ìĿĮ ëĤł +ĠìĿ´ìĸ´ ì§Ģê³ł +ë§ī ìĿĦ +ìļ°ë¦¬ ëĤĺëĿ¼ +ë°¤ ë°¤ +ê³µ ìľł +무 ìŀ¥ +Ġë°Ķ 르 +Ġì§Ħ ìĭ¬ìľ¼ë¡ľ +Ġìłľê³µ ìĿĦ +íļĮìĿĺ 를 +ëıĻì¶ľìŀ¥ ìķĦê°ĢìĶ¨ +Ġê°Ģ족 ê³¼ +ĠìłĦ기 ì°¨ +Ġìºł íĶĦ +nb sp +ìĿ´ ë¸Į +íħĮ ìĿ´ëĦĪ +ĠëĤł ìĿĦ +ì°½ ì¡° +ĠíĮĢ ìĿ´ +ê°Ģ ìķ¼ +ìĿ´ëĿ¼ ìĦľ +ë¦Ħ ìĿĦ +Ġìĥī ìĥģìĿĦ +Ġ기ê°Ħ ìĿ´ +ĠíĥĿ ìĭľ +Ġë¹Ħì¤ij ìĿ´ +Ġê°ľ ìĦłìĿĦ +ĠìķĪ ëIJĺëĬĶ +Ġë§İ ìķĦìĦľ +Ġ5 8 +Ġìŀħ êµŃ +Ġë°©ìĭĿ ìĿĺ +Ġê¶ģ ê·¹ +ìķŀ ìĦľ +Ġë°°ê²½ ìľ¼ë¡ľ +ì¾Įìłģíķľ ëĶ°ëľ»íķľ +ìĬ¤ ëłĪ +ë°© 침 +ĠíļĮ ìłĦ +ĠíļĮ ìĤ¬ë¥¼ +Ġê¹Ģ ì§Ģ +ĠíĶĦ ëĿ¼ìĿ´ +ìĹ´ ìĿĦ +ĠíķĻìĥĿ ìĿ´ +Ġ기ë°ĺ ìĿĦ +ë°Ģ리미íĦ° 를 +Ġìķ Īëĭ¤ +ĠíĿ Ļ +Ġìĸij ëıĦ +Ġê´Ģ볨 ìĿ´ +Ġëĵ¤ìĸ´ ê°Ī +Ġë¶Ļ ìĸ´ +Ġë¶Īê°Ģ íĶ¼ +ìķ ī +ìłĦ ë¶ģ +ĠìĿĺ ìĤ¬ë¥¼ +Ġìķ½ ê´Ģ +ĠíķĻìĥĿ ëĵ¤ìĿĦ +Ġìĸ´ëł¤ ìĽĢìĿ´ +Ġë¶Īê°ĢëĬ¥ íķľ +ìķĦ ë§Ī +ĠìķĦ íĶĪ +Ġ무 ìŀ¥ +ĠìĦ¤ ë¹Ħ +Ġì¶ľ ë§Ī +ë³Ģ íĻĶ +Ġì½Ķ ëĵľë¥¼ +Ġë³´ìŬ ì¤įëĭĪëĭ¤ +Ġë£ ° +ĠìĺģìĹŃ ìĿĦ +ì±ħìŀĦ ìŀIJ +ĠìĿ´ 룬 +Ġ본 ëŀĺ +Ġìĭľìŀij íķł +Ġëħ¼ 리 +ĠìĦłíĥĿ ìĿĦ +Ġì¶ķ ìłģ +Ġìľł ìłĦìŀIJ +Ġ구 미 +ĠìĬ¤ íĤ¤ +ê¶Į ê³¼ +ëįĶ ë¶Īìĸ´ +Ġìłľê³µ íķľ +ĠìĨIJ ìī½ê²Į +겨 ëłĪ +ĠìłĦì²´ ìłģìľ¼ë¡ľ +Ġìĥģíĥľ ê°Ģ +etw ork +ĠëĮĢ ê±° +Ġê²½ ë¹Ħ +ìŀĦ ìĹIJëıĦ +Ġ묻 ëĬĶ +ĠíĨ ± +pr is +1 01 +ìĬ¤ íİĺ +ĠìŀĪ ê²łì§Ģë§Į +Ġ모 를 +ĠìĦ± íıŃëł¥ +Ġê³Ħ ì¢Į +Ġ목 격 +ë¸Ķ 루 +Ġ벤 ì²ĺ +c he +ëıĦ ìĹIJìĦľ +ì¹ĺ ë©° +Ġê°Ħ ìłij +Ġìĸ¸ ìłł +ĠêµIJìľ¡ ê³¼ +ĠìĿ´ìĸ´ ì¡Įëĭ¤ +Ġíĺ¼ íķ© +ĠìĺĪìĤ° ìĿĦ +ĠìŀIJ ìĿĺ +ë§Ī ì§Ģë§ī +Ġê¹Ģ 기 +ĠìŀijíĴĪ ìĿĢ +Ġë²łìĿ´ ë¹Ħ +íķ´ ì§Ħëĭ¤ +Ġíķľ ìĺ¥ +ĠìĹ° ìĥģ +ëł¹ ìĿĦ +ĠìĻ¸ ë©´ +Ġëģ ĵ +ĠìĹħ ë¡ľëĵľ +Ġì¤ijêµŃ ìĹIJìĦľ +ª ¼ +Ġíķľ íķ´ +구 ìŀħ +ĠìŀIJ ìĭĿ +ĠìķĮ 기 +Ġë¶Ī íķĦ +ĠìĽIJ 격 +Ġíĸī ìĤ¬ê°Ģ +ĠíĻĺ ê¸ī +Ġ기ê°Ħ ìĹIJ +ĠëĪĦ구 ëıĦ +Ġê·¸ëħĢ ê°Ģ +리 ì½ĺ +ê²½ 기ëıĦ +Ġê³¼ ìĿ¼ +ĠìĹIJ ëĦĪì§Ģ를 +ìī IJ +Ġëĭµ íĸĪëĭ¤ +Ġë°ĶëŀĮ ìĹIJ +íĬ¹ë³Ħ ìĭľ +Ġë°Ķê¿ Ģ +ìļ° 주 +ê±´ ìľ¼ë¡ľ +ĠìĹ´ ìłķ +Ġì§ĢìĹŃ ìĿĢ +Ġìłķìĥģ ìłģìĿ¸ +Ġìĭ¤ìĭľ íķľ +Ġë°Ķëĭ¤ ìĿ´ìķ¼ê¸° +Ġë°Ķ ìģľ +ëĵ± íı¬ +íħĮ ìĿ´ +ĠëĪĪ ìľ¼ë¡ľ +ĠìłĦë°ĺ ìłģìĿ¸ +Ġê¶Įíķľ ìĿĦ +Ġíķ¸ëĵľ íı° +ìĹ° ìĿĢ +Ġ목 íļĮ +ĠíĮIJ매 íķĺëĬĶ +Ġ겪 ëĬĶ +ì¥ IJ +Ġíķľ ê°Ģ +ëĤ´ ë©° +림 ìĿ´ +Ġ ãħłãħł +ëĭ¤ ì§Ģ +ë¡ľ ìį¨ +íĮ ½ +íİ Į +Ġê°Ļ ìķĦìĦľ +ĠìĽIJ ì²ľ +ìĦ¼ íĬ¸ +Ġíĥĵ ìĹIJ +ìĬ¤ íħĶ +Ġê¹Ģ 민 +íĤ¤ ë¡ľ +Ġë¶ĦìĦĿ íķľ +Ġê°Ģ격 ìĿĦ +Ġì§ĢëĤĺ ì¹ĺê²Į +Ġ목 ìĤ¬ëĬĶ +ì§ĦíĿ¥ ìĽIJ +ìĿĮ ìĹIJ +Ġìłģ ê²Į +Ġíݸ ìĿ´ëĭ¤ +ĠìĪĺíĸī íķĺëĬĶ +Ġë©ĶìĿ´ 커 +ìĭľ ìłĪ +ìŀħ ëĭĪê¹Į +Ġìľł ìļ©íķľ +ĠìķĮ 루미ëĬĦ +ĠìłĢ 를 +Ġâij £ +Ġíij¸ 른 +ĠIC T +Ġë°ķìłķ íĿ¬ +Ġ ãģ +리 ìķĪ +ĠìĿ¸ íķĺ +ëĶĶ ìĬ¤íģ¬ +íĺĦ ìŀ¥ +ĠíķĺëĤĺ íķĺëĤĺ +Ġì¤Ģ ê³µ +ĠâĹ ĩ +ĠëĬIJê¼ Ī +ê¸ į +Ġë¹Ħ ì¹ĺ +Ġëĭ¨ ëĭ¨ +Ġê³¼ ê°IJ +ëijIJ ìĹIJ +ĠìĤ´ 볤 +Ġíĥľ ìĸ´ +기ê°Ħ ìĿ´ +ĠëĤĺ ìģĺ +ĠíĮ ģ +ĠëıĻ ìĥĿ +íı¬ 를 +Ġê°ģ ìŀIJìĿĺ +Ġì±ħ ìĿĺ +걸 ìĿ´ +Ġê°ľìĿ¸ìłķë³´ ëĬĶ +ĠêµIJìĪĺ ëĭĺ +Ġìĸ¸ê¸ī íķľ +ìĿ¼ ìĹIJìĦľ +ĠìĿ¸ 문 +ĠìĿ¸ ìĬ¤íĥĢê·¸ëŀ¨ +모 ìŀĦ +Ġíİ Į +ĠíĮĮ ëŀij +Ġëĭµë³Ģ ìĿĦ +ĠëĤĺìĦł ëĭ¤ +Ġê°ľìĦ± ê³µëĭ¨ +ëıĦ ìĦľ +ĠìĿ¼ ìĿ¼ +ĠìĦľ 구 +Ġìļ° ìĹ°íŀĪ +Ġìĭľ 골 +ë¹Ħ ìłĦ +Ġ모 ëŀĺ +ë¶Ħ ê»ĺ +Ġê¹Ģ ëĮĢì¤ij +목 íijľ +ĠìĺģíĻĶ ê°Ģ +기ìĪł ìĿĦ +Ġ맥 ëĿ½ +Ġê´´ ë¡Ń +ì§Ģ ê·¼íķľ +ĠìĪĺ 를 +Ġê·¸ ëĵ¤ìĹIJê²Į +Ġì¤ij íķĻêµIJ +ìĤ° ê³¼ +ìŀĦ ëĮĢ +ëħIJ ìĿĦ +ĠìĭľìĬ¤íħľ ìĿ´ +ĠíĦ¸ ìĸ´ +Ġê°Ģ 죽 +ìĦł ê³¼ +ì²ľ íŀĪ +Ġê³Ħ ê¸ī +ĠíķĻ ìĹħ +Ġê°ģ ìŀIJ +Ġê³łê°Ŀ ìĿĦ +ĠìķĦë§Ī ì¶Ķìĸ´ +ê³¼ìłķ ìĹIJìĦľ +ĠìĿ¼ìŀIJ 리를 +ìĤ¬ íĥľ +ĠìĭľìĬ¤íħľ ìĿĺ +ĠìĿ¸ íħĶ +ĠìĿ¼ 주ìĿ¼ +Ġìĭł 기 +Ġê²Į ìĿ´íĬ¸ +주ìĭľ ê³ł +Ġì°į ìĿĢ +hic h +Ġê°Ģ 기 +ë§Į ìŬ +Ġ모 ìĿ¸ +ĠëĬIJê»´ ì§ĢëĬĶ +Ġìĸ´ìļ¸ 리ëĬĶ +Ġëª « +룬 ê°Ģ +Ġì¤ij 구 +ëĦ ¨ +ë°© ìĭĿ +ëĭ¬ ëĿ¼ +Ġìĵ° 기 +Ġë¶ĦìĦĿ íĸĪëĭ¤ +Ġ199 8 +ìĹIJ ìĸ´ +ìĦ± ê¸ī +ĠìĿĺ ìŀ¥ +Ġì²ĺ ëŁ¼ +Ġìļ© ìĿ´ +ĠìĦ±ê³µ íķľ +ìļ° ë©° +ĠìĹ° ìĪĺ +Ġìĭł ì¶ķ +Ġìĭ¤ ìłķ +ĠíĴ ĢìĿ´ +íģ´ 리 +ĠìĤ¬ê±´ ìĹIJ +리 ë©´ìĦľ +Ġê°IJ ëĭ¹ +ë°± ìĿĦ +ĠìĹĨëĭ¤ ë©´ +ìĹŃì¶ľìŀ¥ ìĥµì¶Ķì²ľ +ĠâĹ ĭ +Ġ 00 +ĠìĤ¬ ìļ´ëĵľ +ĠìĥĿ ìķł +ì¤ij ìĨĮ +Ġì¿ł íı° +ĠìĿ´ 걸 +Ġìĥ IJ +Ġì§Ģ 리 +íĦ° ëĬĶ +ìļ° ëĵľ +Ġê³ł ìĺ¨ +Ġ미 ìļ© +ĠíķľêµŃ ìĿ´ +Ġì§ģ ìłĦ +Ġ첫 ëĤł +Ġìľłì§Ģ íķĺëĬĶ +ìĿ´ ìĸ´ìĦľ +Ġê²½ ë¡ľ +Ġìĭł ìĦł +Ġê¹ ĥ +ĠìĪ Ń +ĠíĶ¼ ìķĦëħ¸ +Ġëıħ ìłIJ +ĠìĪĺìĪł ìĿĦ +Ġìĭłë¢° 를 +ëĮĢ ë§Į +Ġë¹Ħ ê·¹ +íĮĮ ìĽĮ +ï¼ ¯ +Ġíݸ 리íķľ +ì²ł ìĪĺ +ê¼ Ń +Ġë¸Į리 íķij +Ġìĭľ ìĽIJíķľ +ìĪĺ ìĺģ +ëIJĺ ìŀIJ +Ġíķ´ ë°© +ìĹŃ ìľ¼ë¡ľ +ĠìĦ± ë¶Ħ +Ġë¶Ħ ìĿĢ +ĠìĽ¹ íĪ° +ĠìŀIJ격 ì¦Ŀ +ìĿ´ ë©´ìĦľ +Ġë¶Ģ ìŀ¥ +Ġê³ł ëĵ± +Ġë§Į ëĤ¬ëĭ¤ +ìŀij ìĿĦ +ĠíĶ¼ ë¶ĢìĹIJ +Ġê·ľ ìłķìĿĦ +Ġw hich +ĠëĤ ļ +ĠìĹĨ ìĸ´ìļĶ +Ġìĥģëĭ´ ìĿĦ +íĬ¹ë³Ħ ìŀIJì¹ĺ +ĠìĻ ł +íĴĪ ì§Ī +Ġìĵ° ì§Ģ +Ġëijĺ룬 ìĭ¸ +3 31 +ĠìĹ° ìĦ¤ +Ġë§IJ ìŀħëĭĪëĭ¤ +Ġìĺ¬ ëŀIJ +ìĤ¬ìĹħ ìĿĺ +곡 ìĿĦ +Ġ주 ë°© +ìķ¼ ë§Į +Ġíı¬ ìĬ¤íĦ° +íĺij ìłķ +Ġ기ìĪł ìĿĢ +ĠíķĻêµIJ ìĹIJìĦľ +Ġìĸ´ëłµ ê²Į +ĠìĤ¬ë¬´ ìĭ¤ +ĠìłĦë°ĺ ìĹIJ +ë ± +Ġì¤Ħ ìĿ´ê³ł +ìĤ´ ìĿĦ +ĠìĦ¤ì¹ĺ ëIJľ +us e +ìłķì±ħ ìĿĦ +ê² Ł +ìĺ¤ ìĹ¼ +ĠìĹŃ ìĤ¬ê°Ģ +Ġìłķë³´ ë³´íĺ¸ +Ġ길 ê²Į +ë²ł 르 +ìĦ¼íĦ° 를 +ĠìĤ¬ê±´ ìĿĺ +Ġ커íĶ¼ 를 +ĠìĪĺìĿµ ìĿĦ +íķ Į +ìĭľ ë§Ī +ĠíĪ¬ ìŬ +Ġíĥľ ê¶ĮëıĦ +ĠìĿ´ë£¨ ê³ł +ĠíĺĦëĮĢ ìŀIJëıĻì°¨ +Ġìķħ ìĦ± +êµŃ ìľ¼ë¡ľ +ê²½ íĹĺ +ìĹĨ ëĭ¤ +Ġìłķì±ħ ìĹIJ +ìĭľìĬ¤íħľ ìĿĦ +Ġ8 5 +Ġìĺ¬ ë¦¬ëĬĶ +ë¥ĺ ê°Ģ +ë³Ģ íĺ¸ +Ġì¤Ģë¹Ħ ëIJĺìĸ´ +ĠìľĦíĹĺ íķľ +ë°Ģ리미íĦ° ë¡ľ +n ews +ìĬ IJ +ĠëĮĢ ê°ľ +Ġíı¬ ì°© +Ġê°Ħëĭ¨ íķĺê²Į +ì²ľ ë§ĮìĽIJ +íģ¬ 를 +Ġê²Į ìĬ¤íĬ¸ +ĠíĶĮ ë¡ľ +ĠìĽĥ ìĿĮ +Ġ곳곳 ìĹIJ +ë¶Ģ ëıĻìĤ° +íķł ê¹Į +Ġë¶Ģ íķ© +ì¼ Ī +Ġë³ij íĸī +ĠíĥĦ íĥĦ +Ġìĵ°ëłĪ 기 +ĠìĿ´ ëĵ¤ìĹIJê²Į +ëĤĺ ê°ĢëĬĶ +Ġë°Ķ ëĶĶ +Ġìµľ ìĥģìĿĺ +ĠìĽIJ ìŀ¥ìĿĢ +Ġì§Ħíĸī íķľ +Ġ죽 ëĬĶ +Ġ기존 ìĹIJ +Ġê³¼ëıĦ íķľ +ë ĺij +ìĿ¸ íĬ¸ë¥¼ +ĠìĹĨ ìĿĮ +ìĺĢ ëĭ¤ê³ł +ë§ ijìĿĢ +ìĭľ íĬ¸ +Ġìĸ ½ +ĠëĤ´ ëĭ¤ +Ġ5 000 +ĠìłĢ íķĺ +Ġì¹ĺ 매 +ĠíĶĮëłĪìĿ´ ìĸ´ +Ġê¸Īê°ķ ìĤ° +ìĺ¤ ë¥¼ +ì°¨ ìĿ´ì¦Ī +Ġì» µ +ĠìĿ´ìĻ¸ ìĿĺ +ĠëijIJ볤 ìĽĮ +íķ ı +ìĭľ ìŀ¥ìĿĢ +Ġê³µ ìĿĦ +Ġê³µ 무 +ĠìĤ¬ëŀĮ ëĵ¤ëıĦ +ĠìķĦìĿ´ ëıĮ +ë¹Į ëĶ© +ĠìķĦ ìī½ +ĠìĦ± ëıĦ +ĠëıĦ ì¶ľ +Ġì§ij ê°Ĵ +ìĹŃì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ +ĠìľłìĿ¼ íķĺê²Į +Ġì§ ļ +ìłľ ìŀ¬ +ëĭĪ ëĿ¼ +Ġëĵľ 물 +Ġëĭ¤íģIJ ë©ĺ +ìĦľ 쪽 +Ġêµ µ +Ġê³ł íĺĪìķķ +Ġ5 3 +Ġê³łìĸij ìĿ´ +ìłĦ ëł¥ +ĠìĿ¼ íķĺê³ł +ë¶Ħ ê°Ħ +Ġë¹Ħ ìľ¨ìĿ´ +ĠíĺĦ ìŀ¥ìĹIJ +Ġë°° ê°Ģ +ĠìĹŃ ìŀĦ +ìĬ¤íĬ¸ ëĿ¼ +Ġì§Ħíĸī ëIJł +Ġíĸ¥ ìĥģìĿĦ +ëħ¼ ìĪł +Ġì´Īê³¼ íķĺê±°ëĤĺ +ĠìłĦ ëıĦ +ĠìĬ¤ íı° +ĠìłĢ ìĨĮëĵĿ +Ġë°Ľ ìľ¼ë©° +ĠìŀĪìĸ´ ëıĦ +ĠíıŃ ëĦĵ +ĠìĬ¤ìľĦ ìĬ¤ +Ġíıī íĻĶ를 +ĠìĨĮê°ľ íķ´ +Ġì§ģìĽIJ ëĵ¤ìĿ´ +Ġê ¾ +íķ´ ìĦľëĬĶ +ê¹ ¥ +Ġìĥģ ìĥĿ +Ġë¹Ħ ë¡Ģ +Ġìµľ ìļ°ìĪĺ +Ġì¹ĺ ê³ł +ĠêµŃ íļĮìĹIJìĦľ +Ġ7 00 +ëħ¸ íĬ¸ +ĠëĦ¤ ìĿ´ +Ġì±ħìŀĦ ìĿ´ +ĠíķĺìĿ´ ë¸Į리 +ìĿ¸ ëĭ¹ +Ġíķľ ëį° +미 ìĤ¬ìĿ¼ +ëŀĢ íķľ +íݸ ìĿĦ +ĠíĶĮ ëĿ¼ +Ġë°©ìĭĿ ìĿ´ +ĠëĵľëŁ¬ ëĤ¬ëĭ¤ +ĠìŀIJë£Į ìĹIJ +ĠíĽĪ볨 ìĿĦ +ĠìĪĺìļĶ ê°Ģ +Ġ모 íĹĺ +ìĭ¬ ê³¼ +Ġíı¬ ìĬ¤ì½Ķ +Ġ먹 ìĸ´ +ĠìĨĮìŀ¬ ë¡ľ +ìĭľ ê°ģ +ìŀIJ ëĤĺ +ìĺģ ìĪĺ +Ġ미 ìķĪ +ìĹĪ 기 +ì°¨ ìĽIJ +Ġë°Ľ ìķĺìĬµëĭĪëĭ¤ +ê²ł ì§ĢìļĶ +Ġíĺ¸ ê¸° +Ġëģ Ħ +ĠíĸĪ ìĸ´ìļĶ +ĠëģĿ ëĤ´ +Ġê²Įìĭľ íĮIJ +ê³ł ìĤ¬ +ë§Ī ìĿ¼ +Ġë¹Ħ ë§Į +ĠìĹ° ë£Į +Ġ매 ë²Ī +Ġë°± ìĦ± +Ġê¼ ´ +Ġìĥģíĥľ ëĭ¤ +Ġ경찰 ìĹIJ +ĠìłĦíķ´ ì¡Įëĭ¤ +ĠíļĮ ìĿĺ를 +Ġì¤ijìļĶ ìĦ±ìĿĦ +Ġê´Ģ리 íķĺëĬĶ +ĠíĴĪ 목 +ĠëĦ¤íĬ¸ìĽĮíģ¬ 를 +Ġíļ¨ìľ¨ ìłģìľ¼ë¡ľ +r ip +íķĺ ëIJĺ +Ġ주 ìĤ¬ +ì¡° ìĦ¸ +Ġëį° 볤 +ĠêµIJìľ¡ ìĿĺ +Ġìĥģíĥľ ìĹIJ +Ġìłķë§IJ ë¡ľ +Ġì¶Ķì§Ħ íķĺëĬĶ +Ġ기ê°Ħ ìĿĢ +ĠìĦłë³´ ìĿ¸ +ĠìĿ¸íĦ°ë·° 를 +ë ¤Ħ +ê¸ Ģë¡ľ +Ġë§Ī 르 +Ġìµľ ìĦłìĿĺ +ĠíĺĦ ìĥģìĿĦ +Ġ민 ì¤ij +Ġ기ìĹħ ìĿĢ +ìľµ íķ© +200 9 +ĠìĤ¬ìľł ë¡ľ +ìĤ¬ë¬´ ìĨĮ +ĠìĿ¼ìĸ´ ëĤł +ĠëĮĢë²ķ ìĽIJ +문 ìłľë¥¼ +Ġ3 000 +ë³ij ìĿĦ +ĠìŀijìĹħ ìĿ´ +Ġìĭłìļ© ì¹´ëĵľ +ëĵ IJ +ìĤ¬ ìĦ¤ +ê´Ģ ìłĪ +ëĶ ª +ë²Ħ ê·¸ +ĠìļĶ ìĨĮ를 +Ġìłľìĭľ íķľ +Ġ뼰ìĸ´ ëĦĺ +Ġìĭł ë¶Ģ +ìłĢ ëĦIJ +Ġì§ģ ìĽIJìĿ´ +ĠìłIJ ìĪĺ +ê°ľìĽĶ ê°Ħ +200 8 +Ġë°ĸ ìĿĺ +ìķĦ 무 +Ġíķľ ê³Ħê°Ģ +Ġì¡° ë¡Ģ +Ġíģ¬ 림 +ëĪĪ ìĹIJ +ê³¼ ê±° +Ġíķľ ëĤĺëĿ¼ +ìļ° ê°Ģ +ĠìłķëıĦ ëĭ¤ +Ġì²ľ ì²ľíŀĪ +Ġê°Ģë³į ê²Į +ĠìŀIJ 문 +íĴĪ ìĿ´ +ìłĢ íĿ¬ +Ġìĸij ìĤ° +Ġë²ķ ìĽIJ +íĿ¬ ë§Ŀ +ĠìĨIJíķ´ ë°° +Ġê°ľ íĹĮ +Ġê°ķ ë¦ī +ĠíļĮ ìŀ¥ìĿĺ +Ġ8 00 +ìĸµ ëĭ¬ëŁ¬ +ĠíĬ¸ ëŁŃ +ĠìĤ¼ ìķĦ +Ġì²ł íļĮ +ĠëĪĮ 룬 +´ ëĭ¤ +Ġíĺķ ìĭĿìľ¼ë¡ľ +Ġê·¸ëŁ° ì§Ģ +íĮĢ ìĿĺ +Ġìĸ´ëĶĶ ìĹIJ +Ġë¹Ī 곤 +ëŃ ĩ +ì¹´ ë©ĶëĿ¼ +ĠíıŃ íĸī +ìĬ¤íĥĢ ìĿ¼ +ĠìŀijìĦ± íķľ +Ġìĭłíĺ¸ ë¥¼ +ĠíŬ ìĬ¤ +Ġë¹Ħë¡ľ ìĨĮ +ĠìĪĺ 볨 +Ġìľł ìĿµ +ì¶ľìŀ¥ ìĥµì¶Ķì²ľ +íħĮ ìĿ¼ +Ġíı¬íķ¨ íķĺìŬ +ë§Į ëĭ¬ëŁ¬ +ĠìĨĮ ë§Ŀ +ĠìĬ¤ íķĢ +Ġìµľ ëĭ¤ +ĠíĶ¼ ë¡ľ +ĠëĨĴ ìĿ¼ +Ġì¤Ħ ìŬ +ê²Ģ ì°° +ìĽł ìĬµëĭĪëĭ¤ +Ġ빨 ê°Ħ +Ġíĥģ ìĽĶíķľ +ĠìĨĶì§ģ íŀĪ +Ġìłķ ìĦ± +ĠìľĦ 기ìĹIJ +ĠìĦľ 쪽 +Ġë°Ķ ìĻĢ +Ġìĭ¤ ì¢ħ +Ġì§Ħ ìĥģ +ì¦Ŀ ëªħ +Ġìĭ¶ ëĭ¤ë©´ +ëIJIJ ìĬµëĭĪëĭ¤ +íĶĦë¡ľ ìłĿ +Ġíķľ ì°½ +ĠìĨĮ ê·ľëª¨ +Ġëĵľ 림 +ìĦ¼íĦ° ëĬĶ +ĠíĥĦ íķµ +ĠìķĦ침 ìĹIJ +Ġì½ľ ëĿ¼ +ëĮ Ħ +ê¸ ĢìĿ´ +Ġìłľ ê³ł +Ġë³´ ì¢Į +ìĽĶ ë§IJ +ĠìŀIJ ì¡´ +íĮ Ŀ +ìľł ìķĦ +Ġê³Ħ 층 +íĭ° ëĤĺ +ĠëĪĪ 물ìĿĦ +Ġë°± ìķħ +ĠíĨ ¤ +Ġìºłíİĺ ìĿ¸ +Ġì§Ģ 루 +Ġìĭľ ìĪł +ìķ¼ 기 +Ġìļ© ìĤ° +Ġê¸Ģ ìĿĢ +Ġìĥ¤ ìĽĮ +ĠìĿ´ ëĿ¼ +Ġíķľ 쪽 +íĻĶ ìĿ´íĬ¸ +Ġì´Ī ëĵ± +ìĤ¬íļĮ ë³µì§Ģ +ìĿ¸ ì§Ģ를 +Ġ모 ìĿĢ +Ġ5 7 +ì¡Į ì§Ģë§Į +ì¡Į ëĭ¤ëĬĶ +ĠìĦľìļ¸ ìĹIJìĦľ +ĠëĬĺ ìĹĪëĭ¤ +Ġë°©ìĨ¡ ìĿĦ +ĠíĶ¼íķ´ ê°Ģ +Ġìĺ¤ íķ´ +ĠëĨĴ ê³ł +ĠíĻķìĿ¸ íķĺëĬĶ +ĠìĬ¤íĥĢ ìĿ¼ìĿĺ +ĠëĵľëŁ¬ ëĤĺ +ìĭľ íĸī +Ġëĸł ëĤľ +ĠìĤ°ìĹħ íĺģëªħ +Ġëĭ¹ìĭľ ìĿĺ +Ġ컨 ìħī +Ġ199 9 +ĠíĥIJ ìĥī +ì§Ģ ë¶Ģ +ìłķ ë¹Ħ +Ġê·¸ ë¡ľ +Ġê²ĥ ìĿ¸ëį° +주 ìĻĢ +ĠìĿ¸ ìłģ +ĠìĿĺ ê±° +ĠëıĦ ë§Ŀ +ìĤ¬ìĹħ ìĹIJ +ĠíļĮìĤ¬ ìĹIJìĦľ +Ġì§ĢëĭĪ ê³ł +íķĺ 길 +ĠìĿ´ 겨 +ìĬµ 기ê°Ģ +ê³Ħ ëĬĶ +ìĹ° ìĪĺ +ë°© 문 +ì¼ ° +Ġê·Ģ ìŬ +Ġë©´ ìłľ +ĠìĭľíĹĺ ìĿĦ +ë¡ľ ë´ĩ +Ġë¹Ħ ìĦľ +ĠìĹĨ ëĬĶëį° +Ġì§Ħ íķĻ +ĠìĿ´ìļ© íķ´ìĦľ +ëıħ 립 +ĠI oT +Ġì¤ijêµŃ ìĹIJ +Ġë°± ëijIJ +ĠìĿ½ 기 +Ġì§Ģ ìłIJ +ĠìķĦ ëĵ¤ìĿ´ +Ġê²½ íĸ¥ +ìĦ¤ ë¹Ħ +ĠíķĻ ê³¼ +Ġìļ© ëıĦë¡ľ +Ġê·¸ ëŀľëĵľ +Ġìĥģ ì²ĺ를 +Ġìłģ ë°ľ +Ġë¹ł ë¥´ê³ł +os e +ĠìĿ´ë£¨ ëĬĶ +먹 ê³ł +l ob +ê³¼ ìłľ +ìłķ ë§IJ +íĮĮ ëĬĶ +ëijIJ ëĬĶ +ĠìĥĿê°ģ íķĺì§Ģ +Ġê´ij ìĹŃ +ĠêµŃëĤ´ ìĹIJ +Ġë¹Į ëĶ© +Ġì¡°ê±´ ìĿĢ +ĠíĺIJ ìĿĺ를 +ĠìķĦ ìĿ¼ëŀľëĵľ +íķĺê²Į ëıĦ +Ġëĭ¹ íķľ +Ġíķł ìķĦë²Ħ +ê²ł ìĸ´ìļĶ +Ġ7 2 +Ġì°¾ ìķĺëĭ¤ +Ġíģ´ ëĿ¼ìĿ´ìĸ¸ +ĠìłĦ문ê°Ģ ëĵ¤ìĿĢ +ìĽ į +Ġìĭ ¼ +ëĭ¹ ê³¼ +Ġê¹Ģ ìĿ¼ +Ġ0 9 +ĠëĤ® ì¶Ķ +ĠìĦłë³´ ìĿ¸ëĭ¤ +ĠìłĬìĿĢ ìĿ´ +ik e +Ġê¶ ¤ +Ġê°Ģ ì¤ij +ìļ© ëŁī +íݸ ì§ij +Ġë³´íĺ¸ ë¥¼ +Ġ목ìłģ ìĿĢ +Ġëĭ¤ì±Ħ ë¡ľìļ´ +Ġê¹Ģ ìĿĢ +Ġ6 6 +ìľĦìĽIJíļĮ 를 +Ġíľ´ ìĭĿ +Ġë¶Īíݸ íķľ +Ġìŀ ¦ +ìĪĺ ìĿµ +ëłĪ ìĬ¤íĥĢ +ĠíķĦìļĶ íķĺëĭ¤ëĬĶ +Ġë²ķ ì¹Ļ +ĠìĹ°êµ¬ ìĿĺ +ë°± íĻĶìłIJ +ĠìłĦìĭľ íļĮ +Ġ겨 ëĥ¥ +Ġëĭ¤íĸī íŀĪ diff --git a/model-00001-of-00003.safetensors b/model-00001-of-00003.safetensors new file mode 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+import torch.nn as nn +from einops import rearrange +from timm.layers import LayerNorm, LayerNorm2d +from timm.models.regnet import RegStage +from torch.nn import CrossEntropyLoss +from transformers import ( + AutoConfig, + AutoModel, + AutoModelForCausalLM, + AutoTokenizer, + PreTrainedModel, +) +from transformers.generation.utils import GenerationMixin +from transformers.modeling_utils import ( + is_fsdp_enabled, + is_local_dist_rank_0, + no_init_weights, +) +from transformers.models.auto import CONFIG_MAPPING +from transformers.utils import ModelOutput + +from .configuration_hyperclovax import HCXVisionConfig +from .image_processing_hyperclovax import select_best_resolution + +EOT = "<|endofturn|>" +IMAGE_LOC = "<|dummy3|>" +VIDEO_LOC = "<|_unuse_missing_100270|>" + + +def get_rank(): + if dist.is_initialized(): + return dist.get_rank() + return 0 + + +def get_world_size(): + if torch.distributed.is_initialized(): + world_size = torch.distributed.get_world_size() + else: + world_size = 1 + return world_size + + +def unpad_image(tensor: torch.Tensor, original_size: Tuple[int, int]) -> torch.Tensor: + """Unpads a PyTorch tensor of a padded and resized image. + + This function removes padding from a tensor image that was previously padded and resized. + The padding is removed based on the aspect ratio difference between the original and current image dimensions. + + Args: + tensor: The image tensor, assumed to be in CxHxW format. + original_size: The original size of the image as (width, height). + + Returns: + The unpadded image tensor. + + Examples: + >>> import torch + >>> # Example 1: Unpadding with height padding + >>> padded_tensor = torch.randn(1, 64, 48) # Padded tensor (C=1, H=64, W=48) + >>> original_size = (32, 32) # Original size (width=32, height=32) + >>> unpadded_tensor = unpad_image(padded_tensor, original_size) + >>> unpadded_tensor.shape + torch.Size([1, 48, 48]) + >>> # Example 2: Unpadding with width padding + >>> padded_tensor = torch.randn(1, 48, 64) # Padded tensor (C=1, H=48, W=64) + >>> original_size = (32, 32) # Original size (width=32, height=32) + >>> unpadded_tensor = unpad_image(padded_tensor, original_size) + >>> unpadded_tensor.shape + torch.Size([1, 48, 48]) + """ + original_width, original_height = original_size + current_height, current_width = tensor.shape[1:] + + original_aspect_ratio = original_width / original_height + current_aspect_ratio = current_width / current_height + + if original_aspect_ratio > current_aspect_ratio: + scale_factor = current_width / original_width + new_height = int(original_height * scale_factor) + padding = (current_height - new_height) // 2 + unpadded_tensor = tensor[:, padding : current_height - padding, :] + else: + scale_factor = current_height / original_height + new_width = int(original_width * scale_factor) + padding = (current_width - new_width) // 2 + unpadded_tensor = tensor[:, :, padding : current_width - padding] + + return unpadded_tensor + + +def get_anyres_image_grid_shape( + image_size: Tuple[int, int], + grid_pinpoints: Union[str, List[Tuple[int, int]]], + patch_size: int, +) -> Tuple[int, int]: + """Calculates the image patch grid shape after any-resolution preprocessing. + + Selects the optimal resolution from predefined grid pinpoints based on input image + dimensions using `select_best_resolution`, then computes the grid layout by + dividing the selected resolution by the patch size using integer division. + + Args: + image_size (Tuple[int, int]): Original image dimensions in (width, height) format. + grid_pinpoints (Union[str, List[Tuple[int, int]]]): Accepts either: + - List of (height, width) resolution tuples + - String representation of list (e.g., "[(224, 224), (336, 336)]") + patch_size (int): Spatial dimension of square patches for grid division. + + Returns: + Tuple[int, int]: Grid dimensions as (num_patches_width, num_patches_height). + + Examples: + >>> # Basic case with list input + >>> get_anyres_image_grid_shape((1000, 800), [(224, 224), (448, 448)], 112) + (4, 4) + + >>> # Basic case with string input + >>> get_anyres_image_grid_shape((600, 400), "[(336, 336), (672, 672)]", 112) + (6, 6) + + >>> # Case where resolution is not perfectly divisible by patch_size + >>> # select_best_resolution picks (224, 224). 224 // 100 = 2 + >>> get_anyres_image_grid_shape((500, 500), [(224, 224)], 100) + (2, 2) + + >>> # Different patch size + >>> # select_best_resolution picks (448, 448). 448 // 224 = 2 + >>> get_anyres_image_grid_shape((1200, 900), [(448, 448), (224, 224)], 224) + (2, 2) + + Note: + String-formatted grid_pinpoints are converted via ast.literal_eval. Invalid formats + may raise syntax exceptions. The actual resolution selection depends on the + implementation of `select_best_resolution`. The doctests assume + `select_best_resolution` picks the *first* resolution provided in `grid_pinpoints`. + """ + possible_resolutions = grid_pinpoints if isinstance(grid_pinpoints, list) else ast.literal_eval(grid_pinpoints) + + original_width, original_height = image_size + height, width = select_best_resolution((original_height, original_width), possible_resolutions) + return width // patch_size, height // patch_size + + +def reshape_and_unpad_image_features( + image_feature: torch.Tensor, + height: int, + width: int, + image_size: Tuple[int, int], + possible_resolutions: List[Tuple[int, int]], + grid_size: int, + unpad: bool, + image_newline: torch.Tensor, +) -> torch.Tensor: + """Reshapes and processes image features with optional unpadding operation. + + Processes input image features by: + 1. Separating base features from spatial features + 2. Reshaping spatial features into a 5D tensor (num_patch_height, num_patch_width, height, width, channels) + 3. Performing either unpadding operation or simple reshaping based on 'unpad' flag + 4. Concatenating processed features with base features + + Args: + image_feature: Input tensor containing image features with shape + [1 + num_patches, feature_dim] where the first element is the base feature + height: Original image height in pixels + width: Original image width in pixels + image_size: Target image size as (width, height) tuple + possible_resolutions: List of possible [height, width] resolutions for multi-scale processing + grid_size: Grid dimension for patch arrangement + unpad: Flag to enable unpadding operation + image_newline: Special token tensor used as separator when unpadding + + Returns: + torch.Tensor: Processed image features tensor with shape [1 + num_processed_patches, feature_dim] + + Raises: + AssertionError: If base feature dimension doesn't match height*width + """ + base_image_feature = image_feature[0] + image_feature = image_feature[1:] + + assert ( + height * width == base_image_feature.shape[0] + ), f"height: {height}, width: {width}, base_image_feature.shape[0]: {base_image_feature.shape[0]}" + + num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_size, possible_resolutions, grid_size) + image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) + + if unpad: + image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() + image_feature = image_feature.flatten(1, 2).flatten(2, 3) + image_feature = unpad_image(image_feature, image_size) + image_feature = torch.cat( + ( + image_feature, + image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device), + ), + dim=-1, + ) + image_feature = image_feature.flatten(1, 2).transpose(0, 1) + else: + image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous() + image_feature = image_feature.flatten(0, 3) + image_feature = torch.cat((base_image_feature, image_feature), dim=0) + + return image_feature + + +def anyres_postprocessing( + image_forward_outs: List[torch.FloatTensor], + image_sizes: List[List[int]], + possible_resolutions: List[Tuple[int, int]], + patch_size: int, + grid_size: int, + image_newline: torch.FloatTensor, + num_queries_vis_abstractor: int = -1, + unpad: bool = False, +) -> List[torch.FloatTensor]: + """Processes 2D visual features into 1D sequences with post-processing steps. + + Performs AnyRes postprocessing by flattening 2D visual features from grid partitions into 1D sequences, adding + newline embeddings at row boundaries for images, and optionally removing padding regions based on original image + sizes. For video data, processes each frame's features separately into a single sequence per video and disables + unpadding and newline insertion. + + Args: + image_forward_outs (List[torch.FloatTensor]): List of input tensors with shape + (number_of_images_in_grid, total_patches, feature_dim) containing visual features. + split_sizes (List[int]): A list containing the number of patches for each sample in the batch. The sum of + `split_sizes` should equal `image_forward_outs.shape[0]`. + image_sizes (List[List[int]]): A list where each element is a list `[width, height]` representing the original + dimensions of the corresponding image sample. Used for unpadding. + possible_resolutions (List[Tuple[int, int]]): A list of supported resolution tuples `(height, width)` used by + `reshape_and_unpad_image_features` for spatial reconstruction, especially during unpadding. + patch_size (int): The spatial dimension (height and width) of the square patches the image was divided into. + grid_size (int): The spatial dimension (height and width) of the square grid onto which patches are mapped. + `grid_size` should be divisible by `patch_size`. + image_newline (torch.FloatTensor): A learnable tensor representing the newline embedding, typically with shape + (1, feature_dim). Added after each row of image patches when not unpadding. + num_queries_vis_abstractor (int, optional): If a visual abstractor with a fixed number of output queries is used + instead of grid patching, this specifies the number of queries. Must be a perfect square if > 0. + Defaults to -1 (indicating standard grid patching is used). + unpad (bool, optional): If `True`, removes padding tokens from image features based on `image_sizes` and + `possible_resolutions`. Does not apply to video features. Defaults to False. + + Returns: + List[torch.FloatTensor]: A list of tensors, where each tensor represents the processed 1D sequence of visual + features for a single sample from the input batch. The length of the sequence varies depending on processing + (unpadding, newlines, video flattening). + + Raises: + AssertionError: If `num_queries_vis_abstractor` is greater than 0 but not a perfect square. + """ + height = width = grid_size // patch_size + + if num_queries_vis_abstractor > 0: + assert (num_queries_vis_abstractor**0.5).is_integer(), "n_queries must be square number" + height = width = int(num_queries_vis_abstractor**0.5) + + # post-processing (unpad, add newline) + new_image_features = [] + for image_idx, image_feature in enumerate(image_forward_outs): + if image_feature.shape[0] > 1: + image_feature = reshape_and_unpad_image_features( + image_feature=image_feature, + height=height, + width=width, + image_size=image_sizes[image_idx], + possible_resolutions=possible_resolutions, + grid_size=grid_size, # Pass grid info if needed by helper + unpad=unpad, + image_newline=image_newline, + ) + else: + image_feature = image_feature[0] + image_feature = torch.cat((image_feature, image_newline[None].to(image_feature.device)), dim=0) + new_image_features.append(image_feature) + image_features = new_image_features + return image_features + + +@dataclass +class HCXVisionOutput(ModelOutput): + """Output class for vision models, containing various computation results. + + Args: + loss (Optional[torch.FloatTensor], optional): Total cross-entropy loss calculated from logits and labels. + loss_per_sample (Optional[torch.FloatTensor], optional): Per-sample loss values for advanced loss processing. + logits (torch.FloatTensor): Classification scores (before SoftMax) of shape (batch_size, num_classes). + past_key_values (Optional[Tuple[Tuple[torch.FloatTensor]]], optional): Contains precomputed hidden-states + that can be used (see `past_key_values` input) to speed up sequential decoding. + hidden_states (Optional[Tuple[torch.FloatTensor]], optional): + Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of + shape (batch_size, sequence_length, hidden_size). + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (Optional[Tuple[torch.FloatTensor]], optional): Tuple of torch.FloatTensor (one for each layer) + of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention + softmax, used to compute the weighted average in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + loss_per_sample: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class HCXVisionForCausalLM(PreTrainedModel, GenerationMixin): + """HCX Vision model for causal language modeling with vision-language capabilities. + + This class combines a vision model with a language model to create a multimodal model + capable of processing images or videos and generating text based on the visual inputs. + + Attributes: + config_class: Configuration class for the model. + vision_model_name: Name of the vision model component. + _no_split_modules: List of modules that should not be split during parallel processing. + supports_gradient_checkpointing: Whether the model supports gradient checkpointing. + _skip_keys_device_placement: Keys to skip during device placement. + """ + + config_class = HCXVisionConfig + vision_model_name = "vision_model" + _no_split_modules = ["SiglipEncoderLayer", "LlamaDecoderLayer", "HyperCLOVAXDecoderLayer"] + supports_gradient_checkpointing = True + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + + def __init__( + self, + config: HCXVisionConfig, + **kwargs: Optional[Any], + ) -> None: + """Initialize the HCXVisionForCausalLM model. + + Args: + config: Configuration object for the model containing parameters for both + vision and language components. + **kwargs: Additional keyword arguments: + - use_liger: Whether to use liger kernel for hyperclovax models. + - use_fused_ce: Whether to use fused cross-entropy loss. + - use_sum_loss: Whether to use sum reduction for loss instead of mean. + - is_safetensor_save: Whether to save model using safetensors format. + + Raises: + ValueError: If vision_config is not defined or if text_config is not defined. + """ + super().__init__(config) # self.config = config + + # init configs + text_config = self._init_text_config(config) + vision_config = self._init_vision_config(config) + + ## possible_resolution should be matched with preprocessor_config.json + config.possible_resolutions = self._init_possible_resolutions(config, vision_config) + + # init models & parameters + with no_init_weights(): # weight will be loaded in from_pretrained + self.vision_model = AutoModel.from_config(vision_config, trust_remote_code=True) + + self.mm_projector = self._init_mm_projector(config, text_config, vision_config) + + self.language_model = AutoModelForCausalLM.from_config(text_config) + self.lm_head_vocab_size = getattr(text_config, "padded_vocab_size", text_config.vocab_size) + self.language_model.lm_head = nn.Linear(text_config.hidden_size, self.lm_head_vocab_size, bias=False) + + if config.anyres: + self.image_newline = nn.Parameter(torch.empty(text_config.hidden_size, dtype=self.dtype)) + + # modify configs or model settings + if text_config.model_type in ["llama", "hyperclovax", "gpt2"]: + self.language_model.gradient_checkpointing_enable() + if text_config.model_type == "hyperclovax" and self.use_liger: + self.language_model._get_apply_liger_kernel_converter()(model=self.language_model) + + # update configs + self.vision_config = vision_config = self.vision_model.config + self.text_config = text_config = self.language_model.config + config.update({"vision_config": vision_config}) + config.update({"text_config": text_config}) + + # etc + self.use_liger = kwargs.pop("use_liger", False) + self.use_fused_ce = kwargs.pop("use_fused_ce", False) + self.use_meansum_loss = kwargs.pop("use_meansum_loss", False) + self.freeze_before_sampler = kwargs.pop("freeze_before_sampler", False) + self.use_turnmeansum_loss = kwargs.pop("use_turnmeansum_loss", False) + self.vision_input_chunk_size = kwargs.pop("vision_input_chunk_size", None) + self.is_safetensor_save = kwargs.get("is_safetensor_save", True) + + use_sum_loss = True if kwargs.pop("use_sum_loss", False) else False + self.reduction = self._init_reduction_type(use_sum_loss) + + self.vision_model_use_no_grad = None # forward 시 체크 및 할당 + + self._backward_compatibility_gradient_checkpointing() # self.post_init() 에 포함되어 있는 gc 가능한지 확인하고 켜주는 함수 + + def _init_weights(self, module): + # copies from https://github.com/kakaobrain/honeybee/blob/main/honeybee/common_layers.py#L55 + if ( + isinstance(module, nn.Conv2d) # noqa: SIM101 + or isinstance(module, nn.Embedding) + or isinstance(module, nn.Linear) + ): + module.weight.data.normal_(mean=0.0, std=0.02) + if hasattr(module, "bias") and module.bias is not None: + module.bias.data.zero_() + + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Parameter): + embed_std = 1 / torch.sqrt(torch.tensor(module.size(0), dtype=torch.float)).to(module.dtype) + module.data.normal_(mean=0.0, std=embed_std) + + def _init_reduction_type(self, use_sum_loss): + assert not ( + self.use_meansum_loss and self.use_turnmeansum_loss + ), "use_meansum_loss and use_turnmeansum_loss cannot both be True; only one or neither may be True." + if self.use_meansum_loss or self.use_turnmeansum_loss: + reduction = "none" + elif use_sum_loss: + reduction = "sum" + else: + reduction = "mean" + return reduction + + def _init_vision_config(self, config): + vision_model_type = config.vision_config.model_type + if vision_model_type in CONFIG_MAPPING: + vision_config = CONFIG_MAPPING[vision_model_type](**config.vision_config.to_dict()) + vision_config.auto_map = {} + else: + if config.vision_model_name_or_path is not None: + vision_config = AutoConfig.from_pretrained(config.vision_model_name_or_path, trust_remote_code=True) + elif config.vision_config._name_or_path is not None: + vision_config = AutoConfig.from_pretrained(config.vision_config._name_or_path, trust_remote_code=True) + else: + raise ValueError("vision_config is not defined") + + vision_config.anyres = config.anyres + vision_config.max_num_grids = config.max_num_grids + return vision_config + + def _init_text_config(self, config): + if hasattr(config, "text_config") and config.text_config is not None: + model_type = config.text_config.model_type + text_config = CONFIG_MAPPING[model_type](**config.text_config.to_dict()) + else: + raise ValueError("text_config is not defined") + text_config._attn_implementation = config._attn_implementation + if text_config.model_type != "hyperclovax": + text_config.logits_scaling = 1.0 + return text_config + + def _init_possible_resolutions(self, config, vision_config): + """possible_resolution should be matched with preprocessor_config.json""" + if not getattr(config, "possible_resolutions", []): + possible_resolutions = [] + if config.anyres: + assert config.max_num_grids > 0 + for i in range(1, config.max_num_grids + 1): + for j in range(1, config.max_num_grids + 1): + if i == 1 and j == 1 and not config.use_1x1_grid: + continue + if i * j <= config.max_num_grids: + possible_resolutions.append([i, j]) + + possible_resolutions = [ + [ys * vision_config.image_size, xs * vision_config.image_size] for ys, xs in possible_resolutions + ] + return possible_resolutions + else: + return config.possible_resolutions + + def _init_mm_projector(self, config, text_config, vision_config): + input_hidden_size = vision_config.hidden_size + if config.mm_projector_type == "linear": + mm_projector = nn.Linear(input_hidden_size, text_config.hidden_size) + mm_projector.dtype = next(mm_projector.parameters()).dtype + elif config.mm_projector_type == "cabstractor": + mm_projector = HCXVisionCAbstractor( + num_queries=config.num_queries_vis_abstractor_image, + num_input_tokens=(vision_config.image_size // vision_config.patch_size) ** 2, + encoder_hidden_size=input_hidden_size, + hidden_size=input_hidden_size, + output_hidden_size=text_config.hidden_size, + pos_emb=config.proj_pos_emb, + prenorm=config.proj_prenorm, + ) + else: + mm_projector = HCXVisionMlp( + config.mm_projector_type, + input_hidden_size, + hidden_features=input_hidden_size, # TODO: llava 처럼 hidden_size 를 input_hidden_size 가 아니라 LLM embedding size 로 바꿔주기 + out_features=self.text_config.hidden_size, + ) + return mm_projector + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values_images: Optional[List[List[torch.FloatTensor]]] = None, + image_sizes_images: Optional[List[List[Tuple[int, int]]]] = None, + pixel_values_videos: Optional[List[List[torch.FloatTensor]]] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = 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, + **kwargs, + ) -> Union[Tuple, HCXVisionOutput]: + """Forward pass of the model. + + This method processes the input tokens and images, combines them into a unified + representation, and generates text output based on the inputs. + + Args: + input_ids: Input token IDs. In positions where images are inputted, the value is replaced by "<|dummy3|>" + pixel_values: List of lists of 4D tensors for images. Each outer list corresponds to a batch and contains + inner lists of image tensors. + past_key_values: Pre-computed key and value states of the attention layers for faster inference. + attention_mask: Mask to avoid performing attention on padding token indices. + inputs_embeds: Input embeddings. If provided, input_ids will not be used. + labels: Labels for computing the language modeling loss. + use_cache: Whether to use past key/values for faster inference. + output_attentions: Whether to return attention weights of each layer. + output_hidden_states: Whether to return hidden states of each layer. + return_dict: Whether to return a ModelOutput instead of a tuple. + image_sizes: List of lists representing image dimensions (width, height). + vision_query_lengths: List of lists containing lengths when each image is converted into visual tokens. + non_vision_query_lengths: List of lengths of text tokens (excluding visual tokens) for each sample. + img_start_ids_list: List of lists containing indices of img_start_id tokens for each sample. + num_queries_vis_abstractors: List of lists containing number of visual tokens for each image grid.\ + For video frames, this is the number of visual tokens for the fast part. + num_queries_vis_abstractors_slow: List of lists containing number of visual tokens for + the slow part when applying the slowfast algorithm to video frames. + first_last_frames_slows: List of booleans indicating whether the slowfast algorithm is + applied to the first or last frames of the video. + is_video_list: List of booleans indicating which inputs are videos. + **kwargs: Additional keyword arguments. + + Returns: + If return_dict=True, returns an HCXVisionOutput object containing: + - loss: Language modeling loss if labels are provided, otherwise None. + - loss_per_sample: Per-sample loss if labels are provided, otherwise None. + - logits: Prediction scores of the language modeling head. + - past_key_values: Past key/values for faster inference if use_cache=True. + - hidden_states: Hidden states of all layers if output_hidden_states=True. + - attentions: Attention weights of all layers if output_attentions=True. + If return_dict=False, returns a tuple containing the above items except loss_per_sample. + """ + output_attentions = ( + output_attentions if output_attentions is not None else self.config.vision_config.output_attentions + ) + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.vision_config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if inputs_embeds is None and past_key_values is None: + if pixel_values_images is not None or pixel_values_videos is not None: + inputs_embeds = self.extract_inputs_embeds( + input_ids=input_ids, + pixel_values_images=pixel_values_images, + image_sizes_images=image_sizes_images, + pixel_values_videos=pixel_values_videos, + ) + else: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if inputs_embeds is not None: + input_ids = None + + ################################ + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.language_model.base_model( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = hidden_states * self.text_config.logits_scaling + + loss = None + loss_per_sample = None + logits = self.language_model.lm_head(hidden_states) + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="none") # ignore IGNORE_INDEX(-100) + shift_logits = shift_logits.view(-1, self.lm_head_vocab_size) + shift_labels = shift_labels.view(-1) + + # Enable model/pipeline parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + if get_rank() == 0: + loss_per_sample = loss.view(logits.shape[0], -1).sum(axis=1) / ( + shift_labels.view(logits.shape[0], -1) != self.config.ignore_index + ).sum(axis=1) + loss = loss[shift_labels != self.config.ignore_index].mean() + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return HCXVisionOutput( + loss=loss, + loss_per_sample=loss_per_sample, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings + def get_output_embeddings(self): + return self.language_model.get_output_embeddings() + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder + def set_decoder(self, decoder): + self.language_model.set_decoder(decoder) + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder + def get_decoder(self): + return self.language_model.get_decoder() + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.tie_weights + def tie_weights(self): + return self.language_model.tie_weights() + + # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.resize_token_embeddings + def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: + model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + self.config.text_config.vocab_size = model_embeds.num_embeddings + self.vocab_size = model_embeds.num_embeddings + return model_embeds + + def extract_inputs_embeds( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values_images: Optional[List[List[torch.FloatTensor]]] = None, + image_sizes_images: Optional[List[List[Tuple[int, int]]]] = None, + pixel_values_videos: Optional[List[List[torch.FloatTensor]]] = None, + ): + """Extract input embeddings by processing text tokens and visual features. + + This method processes the input tokens and image features, extracts the visual features + using the vision model, and combines them with the text token embeddings to create + a unified input representation for the language model. + + Args: + input_ids: Input token IDs with img_start_id markers for image positions. + pixel_values: List of lists of image tensors. + past_key_values: Pre-computed key and value states for faster inference. + image_sizes: List of lists of image dimensions (width, height). + vision_query_lengths: List of lists of lengths when each image is converted to visual tokens. + non_vision_query_lengths: List of lengths of text tokens (excluding visual tokens) for each sample. + img_start_ids_list: List of lists containing indices of img_start_id tokens for each sample. + first_last_frames_slows: List of booleans indicating whether the slowfast algorithm is + applied to the first or last frames of the video. + is_videos: List of booleans indicating which inputs are videos. + + Returns: + Combined embeddings of text tokens and visual features. + """ + # for convert back to List of List format + len_pixel_values_images = [len(pixel_value) for pixel_value in pixel_values_images] if pixel_values_images else [] + len_pixel_values_videos = [len(pixel_value) for pixel_value in pixel_values_videos] if pixel_values_videos else [] + + if sum(len_pixel_values_images) + sum(len_pixel_values_videos) == 0: + return None + + inputs_embeds = self.get_input_embeddings()(input_ids) + + if sum(len_pixel_values_images) > 0: + image_features_batch = self.forward_images( + pixel_values_images, image_sizes_images, len_pixel_values_images + ) + for i, image_features in enumerate(image_features_batch): + if len(image_features) > 0: + image_token_indices = (input_ids[i] == self.config.image_token_id).nonzero().squeeze() + inputs_embeds[i][image_token_indices] = torch.cat(image_features).to(inputs_embeds.dtype) + + if sum(len_pixel_values_videos) > 0: + video_features_batch = self.forward_videos(pixel_values_videos, len_pixel_values_videos) + for i, video_features in enumerate(video_features_batch): + if len(video_features) > 0: + video_token_indices = (input_ids[i] == self.config.video_token_id).nonzero().squeeze() + inputs_embeds[i][video_token_indices] = torch.cat(video_features).to(inputs_embeds.dtype) + + return inputs_embeds + + def forward_images( + self, + pixel_values_images: List[List[torch.FloatTensor]], + image_sizes_images: List[List[Tuple[int, int]]], + len_pixel_values_images: List[int], + ) -> List[List[torch.Tensor]]: + if sum(len_pixel_values_images) == 0: + return None + + concat_pixel_values_images = torch.cat(list(chain(*pixel_values_images)), dim=0) + + visual_token_idx = 0 if "siglip" in self.vision_config.model_type else 1 + context_vision_model = torch.no_grad() if self.vision_model_use_no_grad else contextlib.nullcontext() + with context_vision_model: + if self.config.use_nth_layer == -1: + # Replace post_layernorm of the last layer with Identity + self.vision_model.vision_model.post_layernorm = nn.Identity() + image_forward_outs = self.vision_model(concat_pixel_values_images) + image_forward_outs = image_forward_outs.last_hidden_state[:, visual_token_idx:] + else: + image_forward_outs = self.vision_model(concat_pixel_values_images, output_hidden_states=True) + image_forward_outs = image_forward_outs.hidden_states[self.config.use_nth_layer][:, visual_token_idx:] + + image_forward_outs = image_forward_outs.to(dtype=self.mm_projector.dtype) + image_forward_outs = self.mm_projector(image_forward_outs) # b (h w) d + + # feature 를 분할. e.g. torch.Size([18, 81, 3072]) -> [torch.Size([9, 81, 3072]), torch.Size([9, 81, 3072])] + split_sizes = [pixel_value.shape[0] for pixel_value in chain(*pixel_values_images)] + image_forward_outs = torch.split(image_forward_outs, split_sizes, dim=0) + + # newline 붙여주기 (anyres postprocessing) + image_features = anyres_postprocessing( + image_forward_outs=image_forward_outs, + image_sizes=[image_size for image_sizes in image_sizes_images for image_size in image_sizes], + num_queries_vis_abstractor=self.config.num_queries_vis_abstractor_image, + unpad=self.config.unpad, + patch_size=self.vision_config.patch_size, + grid_size=self.vision_config.image_size, + image_newline=self.image_newline, + possible_resolutions=self.config.possible_resolutions, + ) + + # 원래 pixel_values_images 형태로 복원 + image_features = [ + image_features[sum(len_pixel_values_images[:i]) : sum(len_pixel_values_images[: i + 1])] + for i in range(len(len_pixel_values_images)) + ] + + return image_features + + def forward_videos( + self, + pixel_values_videos: List[List[torch.FloatTensor]], + len_pixel_values_videos: List[int], + ) -> List[torch.Tensor]: + + len_video_grids = sum(len_pixel_values_videos) + if len_video_grids == 0: + return None + + # Run Vision Model + concat_pixel_values_videos = torch.cat(list(chain(*pixel_values_videos)), dim=0) + + visual_token_idx = 0 if "siglip" in self.vision_config.model_type else 1 + context_vision_model = torch.no_grad() if self.vision_model_use_no_grad else contextlib.nullcontext() + with context_vision_model: + if self.config.use_nth_layer == -1: + # Replace post_layernorm of the last layer with Identity + self.vision_model.vision_model.post_layernorm = nn.Identity() + video_forward_outs = self.vision_model(concat_pixel_values_videos) + video_forward_outs = video_forward_outs.last_hidden_state[:, visual_token_idx:] + else: + video_forward_outs = self.vision_model(concat_pixel_values_videos, output_hidden_states=True) + video_forward_outs = video_forward_outs.hidden_states[self.config.use_nth_layer][:, visual_token_idx:] + + video_forward_outs = video_forward_outs.to(dtype=self.mm_projector.dtype) + + # Run MM-Projector + # len(num_grids) == len(num_queries_vis_abstractors) + 1 + grid_idx = 0 + num_grids = [grid_idx] # e.g. [0, 9, 18, 19, 27, 28, 36, 37, 45, 46, 54, 55, 56] + num_queries_vis_abstractors = [] # e.g. [81, 81, 81, 9, 81, 9, 81, 9, 81, 9, 81, 9] + len_total_frames = video_forward_outs.shape[0] + + if self.config.first_last_frames_slow: + # TODO: 동작 확인 안 했음. 해야 함. + # slowfast (first_last_frames_slow) + assert len_total_frames != 0 + if len_total_frames <= 2: + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_slow) + grid_idx += len_total_frames + num_grids.append(grid_idx) + else: + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_slow) + grid_idx += 1 + num_grids.append(grid_idx) + + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_fast) + grid_idx += len_total_frames - 2 + num_grids.append(grid_idx) + + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_slow) + grid_idx += 1 + num_grids.append(grid_idx) + else: + # slowfast + for pixel_values_frames in pixel_values_videos: + for pixel_values_frame in pixel_values_frames: + if len(pixel_values_frame) > 0: + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_slow) + grid_idx += 1 + num_grids.append(grid_idx) + num_queries_vis_abstractors.append(self.config.num_queries_vis_abstractor_video_fast) + grid_idx = grid_idx + len(pixel_values_frame) - 1 + num_grids.append(grid_idx) + + video_forward_outs = self.mm_projector(video_forward_outs, num_queries_vis_abstractors, num_grids) + + # video_group 별로 concat 처리. + # 예를 들어, 3x3 grid 를 사용했을 경우, 총 9개의 feature 가 모일 때까지, grouped_features 에 리스트를 모아주고, concat 처리. + video_features = [] # what we want to return + target_features = [] + target_group_size = 0 + group_counter = 0 + video_groups = [ + len(frame) for frames in pixel_values_videos for frame in frames + ] # for concat video features after projector + + for forward_out in video_forward_outs: + target_group_size += len(forward_out) + target_features.append(forward_out.flatten(0, 1)) + + video_group_size = video_groups[group_counter] + if video_group_size == target_group_size: + video_features.append(torch.cat(target_features, dim=0)) + target_features = [] + group_counter += 1 + target_group_size = 0 + + elif video_group_size < target_group_size: + raise RuntimeError(f"video_group_size < target_group_size!! [{video_group_size} < {target_group_size}]") + + assert len(target_features) == 0, f"target_features is not empty!! {target_features}" + assert len(video_groups) == len(video_features) + + # 원래 pixel_values_videos 형태로 복원 + video_features = [ + video_features[sum(len_pixel_values_videos[:i]) : sum(len_pixel_values_videos[: i + 1])] + for i in range(len(len_pixel_values_videos)) + ] + + return video_features + + @torch.no_grad() + def generate( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values_images: Optional[List[List[torch.FloatTensor]]] = None, + image_sizes_images: Optional[List[List[Tuple[int, int]]]] = None, + pixel_values_videos: Optional[List[List[torch.FloatTensor]]] = None, + pad_token_id: Optional[int] = None, + eos_token_id: Optional[int] = None, + bad_words_ids: Optional[List[List[int]]] = None, + max_length: int = 196, + min_length: int = 2, + do_sample: bool = True, + num_beams: int = 1, + top_p: float = 0.6, + top_k: int = 0, + temperature: float = 0.5, + repetition_penalty: float = 1.0, + length_penalty: int = 1, + use_cache: bool = True, + verbose: bool = False, + **kwargs, + ) -> torch.LongTensor: + """Generate text based on input tokens and images. + + This method generates text based on the provided input tokens and images using + beam search and/or sampling strategies. + + Args: + input_ids: Input token IDs with img_start_id markers for image positions. + pixel_values: List of lists of image tensors. + image_sizes: List of lists of image dimensions (width, height). + vision_query_lengths: List of lists of lengths when each image is converted to visual tokens. + non_vision_query_lengths: List of lengths of text tokens (excluding visual tokens) for each sample. + num_queries_vis_abstractors: List of lists containing number of visual tokens for each image grid. + num_queries_vis_abstractors_slow: List of lists containing number of visual tokens for the slow part when + applying the slowfast algorithm to video frames. + first_last_frames_slows: List of booleans indicating whether the slowfast algorithm is applied to the first + or last frames of the video. + is_videos: List of booleans indicating which inputs are videos. + img_start_ids_list: List of lists containing indices of img_start_id tokens for each sample. + pad_token_id: Token ID used for padding. + eos_token_id: Token ID used to signal the end of a sequence. + bad_words_ids: List of token ID sequences that should not be generated. + max_length: Maximum length of the sequence to be generated (input length + max_new_tokens). + min_length: Minimum length of the sequence to be generated (input length + min_new_tokens). + do_sample: Whether to use sampling for generation (otherwise uses greedy decoding). + num_beams: Number of beams for beam search. 1 means no beam search. + top_p: Nucleus sampling parameter. Tokens with cumulative probability > top_p are kept. + top_k: Number of highest probability tokens to keep for top-k-filtering. + temperature: Value used to modulate the next token probabilities. + repetition_penalty: Penalty applied to tokens that have already appeared in the sequence. + length_penalty: Exponential penalty applied to sequence length. + use_cache: Whether to use past key/values for faster inference. + **kwargs: Additional keyword arguments. + + Returns: + Generated token IDs. + """ + # inputs_embeds: torch.bfloat16 : [batchsize, variable(visual token, text token, system prompt 모두 포함)] + if pad_token_id is None: + pad_token_id = self.tokenizer.pad_token_id + if eos_token_id is None: + eos_token_id = self.tokenizer.encode("<|endofturn|>")[0] + if bad_words_ids is None: + bad_words_ids = [ + [ + self.config.text_config.bos_token_id, + ], + [ + self.config.text_config.eos_token_id, + ], + ] + + if (pixel_values_images is None or all(len(pixel_values) == 0 for pixel_values in pixel_values_images)) and ( + pixel_values_videos is None or all(len(pixel_values) == 0 for pixel_values in pixel_values_videos) + ): + return self.language_model.generate( + input_ids, pad_token_id=pad_token_id, eos_token_id=eos_token_id, bad_words_ids=bad_words_ids, **kwargs + ) + + inputs_embeds = self.extract_inputs_embeds( + input_ids=input_ids, + pixel_values_images=pixel_values_images, + image_sizes_images=image_sizes_images, + pixel_values_videos=pixel_values_videos, + ) + + inputs_embeds = inputs_embeds.to(device=self.language_model.device, dtype=self.language_model.dtype) + + # pred : torch.int64 : [batchsize, generated token_length] + pred = self.language_model.generate( + inputs_embeds=inputs_embeds, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + bad_words_ids=bad_words_ids, + max_new_tokens=max_length, + min_length=min_length, + num_beams=num_beams, + do_sample=(False if temperature == 0.0 else do_sample), # set do_sample=False if invalid temperature + top_k=top_k, + top_p=top_p, + temperature=temperature, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + early_stopping=(False if num_beams <= 1 else True), # set early_stopping=False when not beam_search + use_cache=use_cache, + ) + + if verbose: + llm_query = self.tokenizer.batch_decode( + [ + [token_id for token_id in input_ids_row if token_id != self.tokenizer.pad_token_id] + for input_ids_row in input_ids.detach().cpu().tolist() + ], + skip_special_tokens=False, + )[0] + llm_pred = self.tokenizer.batch_decode( + [ + [token_id for token_id in pred_row if token_id != self.tokenizer.pad_token_id] + for pred_row in pred.detach().cpu().tolist() + ], + skip_special_tokens=False, + )[0] + print(f"# [info] llm_query: {llm_query}") + print(f"# [info] llm_pred: {llm_pred}") + + return pred + + def to_vision_model_device(self, input_tensor: Union[torch.Tensor, List]) -> Union[torch.Tensor, List]: + """Move input tensors to the vision model's device. + This method recursively moves input tensors or lists of tensors to the vision model's device. + + Args: + input_tensor: Input tensor or list of tensors to be moved to the vision model's device. + + Returns: + The input tensor or list of tensors moved to the vision model's device. + + Raises: + TypeError: If the input is neither a tensor nor a list. + """ + if isinstance(input_tensor, list): + return [self.to_vision_model_device(item) for item in input_tensor] + elif isinstance(input_tensor, torch.Tensor): + return input_tensor.to(self.vision_model.device) + else: + raise TypeError("Unsupported data type. Only tensors and lists are allowed.") + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + **kwargs, + ) -> Dict[str, Any]: + """Prepare inputs for the generation algorithm. + + This method prepares the input for each generation step based on the model's needs. + + Args: + input_ids: Input token IDs. + past_key_values: Pre-computed key and value states for faster inference. + attention_mask: Mask to avoid performing attention on padding token indices. + inputs_embeds: Input embeddings. If provided, input_ids will not be used. + **kwargs: Additional keyword arguments. + + Returns: + Dictionary containing the prepared inputs for the model. + """ + input_ids = kwargs.get("decoder_input_ids", input_ids) + + if past_key_values: + 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 past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "pixel_values": kwargs.get("pixel_values", None), + } + ) + return model_inputs + + @classmethod + def from_config(cls, config, vision_model_name_or_path): + return cls(config, vision_model_name_or_path) + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, + *model_args, + **kwargs, + ) -> "HCXVisionForCausalLM": + assert pretrained_model_name_or_path is not None + + save_only_vision = kwargs.pop("save_only_vision") if "save_only_vision" in kwargs else False + save_only_qformer = kwargs.pop("save_only_qformer") if "save_only_qformer" in kwargs else False + save_shard_size = kwargs.pop("save_shard_size") if "save_shard_size" in kwargs else "5GB" + + if pretrained_model_name_or_path is not None: # when evaluate or load instruction tunned model + model: HCXVisionForCausalLM = super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + model.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path) + + image_token_id = model.tokenizer.encode(IMAGE_LOC, add_special_tokens=False) + assert ( + len(image_token_id) == 1 + ), f'"<|dummy3|>" was not encoded into a single special token. Encoding result: {image_token_id}' + model.config.image_token_id = image_token_id[0] + + video_token_id = model.tokenizer.encode(VIDEO_LOC, add_special_tokens=False) + assert ( + len(video_token_id) == 1 + ), f'"<|_unuse_missing_100270|>" was not encoded into a single special token. Encoding result: {video_token_id}' + model.config.video_token_id = video_token_id[0] + + model.save_only_vision = save_only_vision + model.save_only_qformer = save_only_qformer + model.save_shard_size = save_shard_size + + return model + + def get_language_model(self): + return self.language_model.base_model + + def get_vision_model(self): + return self.vision_model + + def save_pretrained( + self, + save_directory: Union[str, os.PathLike], + *args, + **kwargs, + ): + state_dict = kwargs["state_dict"] if "state_dict" in kwargs else self.state_dict() + partial_state_dict = self.get_pretrained_state_dict( + state_dict, + save_directory, + ) + kwargs["state_dict"] = partial_state_dict + kwargs["safe_serialization"] = self.is_safetensor_save + kwargs.setdefault("max_shard_size", self.save_shard_size) + super().save_pretrained(save_directory, *args, **kwargs) + + def get_pretrained_state_dict(self, state_dict, save_dir): + vision_key = "vision_model." + llm_keys = ["language_model."] + head_key = "lm_head." + + for key in list(state_dict.keys()): + if self.save_only_vision: + for llm_key in llm_keys: + if llm_key in key: + state_dict.pop(key) + if key.startswith(head_key): + state_dict.pop(key) + + elif self.save_only_qformer: + if f"{vision_key}" in key: + state_dict.pop(key) + + return state_dict + + + +class HCXVisionMlp(nn.Module): + def __init__( + self, + mm_projector_type, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.mm_projector_type = mm_projector_type + if self.mm_projector_type == "mlp": + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + elif self.mm_projector_type == "inverted_mlp": + self.fc1 = nn.Linear(in_features, 2 * hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(2 * hidden_features, out_features) + else: + raise NotImplementedError("{} is not implemented".format(self.mm_projector_type)) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + return x + + +class HCXVisionCAbstractor(nn.Module): + """ + This module is based on C-Abstractor, whose license is under apache-2.0. + You can check the original code at https://github.com/khanrc/honeybee/blob/main/honeybee/projectors/projectors.py + and we made necessary modifications. + """ + + def __init__( + self, + num_queries: int, + num_input_tokens: int, + encoder_hidden_size: int, + hidden_size: int, + output_hidden_size: int, + pos_emb: bool = True, + prenorm: bool = False, + ): + super().__init__() + self.num_input_tokens = num_input_tokens + self.output_hidden_size = output_hidden_size + + # Positional embedding + if pos_emb: + self.pos_emb = torch.nn.Parameter(torch.zeros(1, num_input_tokens, encoder_hidden_size)) + self.pos_emb.data.normal_(mean=0.0, std=0.02) + else: + self.pos_emb = None + + # (Optional) Pre-normalization layer + if prenorm: + self.prenorm = LayerNorm(encoder_hidden_size) + else: + self.prenorm = None + + self.build_net(num_queries, encoder_hidden_size, hidden_size, output_hidden_size) + self.dtype = next(self.parameters()).dtype + + def forward( + self, + x: torch.Tensor, + num_queries_vis_abstractors: Optional[List[List[int]]] = None, + num_grids: Optional[List[int]] = None, + ) -> torch.Tensor: + """ + Args: + x: (B, L, encoder_hidden_size) tensor from the visual backbone (e.g. CLIP visual encoder), including cls token. + """ + if self.prenorm is not None: + x = self.prenorm(x) + + if self.pos_emb is not None: + x = x + self.pos_emb + + x = self._forward( + x, + num_queries_vis_abstractors=num_queries_vis_abstractors, + num_grids=num_grids, + ) # (B, L, output_hidden_size) + + return x + + def _forward( + self, + x: torch.Tensor, + num_queries_vis_abstractors: Optional[List[List[int]]] = None, + num_grids: Optional[List[int]] = None, + ) -> torch.Tensor: + # x: [B, L, dim] + B, L, dim = x.shape + hw = int(L**0.5) + x = rearrange(x, "b (h w) d -> b d h w", h=hw, w=hw) + + if num_queries_vis_abstractors is not None: + assert num_grids is not None + return self._forward_adaptive_num_query(x, num_queries_vis_abstractors, num_grids) + + x = self.net(x) + x = rearrange(x, "b d h w -> b (h w) d") + x = self.readout(x) + return x + + def _forward_adaptive_num_query( + self, + x: torch.Tensor, + num_queries_vis_abstractors: Optional[List[List[int]]] = None, + num_grids: Optional[List[int]] = None, + ) -> List[torch.Tensor]: + # self.net is consisted by 3 layers (s1, sampler, s2) + assert len(self.net) == 3 + + x = self.net[0](x) # s1 + new_x = [] + for i, num_queries in enumerate(num_queries_vis_abstractors): + hw = int(num_queries**0.5) + sampler = nn.AdaptiveAvgPool2d((hw, hw)) + out = sampler(x[num_grids[i] : num_grids[i + 1], :]) + out = self.net[2](out) # s2 + + out = rearrange(out, "b d h w -> b (h w) d") + out = self.readout(out) + + new_x.append(out) + return new_x + + def build_net( + self, + n_queries: int, + encoder_hidden_size: int, + hidden_size: int, + output_hidden_size: int, + depth: int = 3, + mlp_depth: int = 2, + ): + assert (n_queries**0.5).is_integer(), f"n_queries must be square number. n_queries: {n_queries}" + hw = int(n_queries**0.5) + + # RegBlock = ResBlock + SE + RegBlock = partial( + RegStage, + stride=1, + dilation=1, + act_layer=nn.SiLU, + norm_layer=LayerNorm2d, + ) + + s1 = RegBlock( + depth, + encoder_hidden_size, + hidden_size, + ) + sampler = nn.AdaptiveAvgPool2d((hw, hw)) + s2 = RegBlock( + depth, + hidden_size, + hidden_size, + ) + + self.net = nn.Sequential(s1, sampler, s2) + self.readout = self.build_mlp(mlp_depth, hidden_size, output_hidden_size) + + def build_mlp( + self, + depth: int, + hidden_size: int, + output_hidden_size: int, + ): + layers = [nn.Linear(hidden_size, output_hidden_size)] + for _ in range(1, depth): + layers.append(nn.SiLU()) + layers.append(nn.Linear(output_hidden_size, output_hidden_size)) + return nn.Sequential(*layers) + diff --git a/preprocessor_config.json b/preprocessor_config.json new file mode 100644 index 0000000..752bb12 --- /dev/null +++ b/preprocessor_config.json @@ -0,0 +1,135 @@ +{ + "anyres": true, + "auto_map": { + "AutoImageProcessor": "image_processing_hyperclovax.HCXImageProcessor", + "AutoProcessor": "processing_hyperclovax.HCXProcessor" + }, + "crop_size": { + "height": 378, + "width": 378 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.5, + 0.5, + 0.5 + ], + "image_processor_class": "AutoImageProcessor", + "image_processor_type": "HCXImageProcessor", + "image_std": [ + 0.5, + 0.5, + 0.5 + ], + "num_queries_vis_abstractor_image": 81, + "num_queries_vis_abstractor_video_slow": 81, + "num_queries_vis_abstractor_video_fast": 9, + "first_last_frames_slow_video": false, + "pad_to_square": true, + "patch_size": 14, + "possible_resolutions": [ + [ + 378, + 378 + ], + [ + 378, + 756 + ], + [ + 378, + 1134 + ], + [ + 378, + 1512 + ], + [ + 378, + 1890 + ], + [ + 378, + 2268 + ], + [ + 378, + 2646 + ], + [ + 378, + 3024 + ], + [ + 378, + 3402 + ], + [ + 756, + 378 + ], + [ + 756, + 756 + ], + [ + 756, + 1134 + ], + [ + 756, + 1512 + ], + [ + 1134, + 378 + ], + [ + 1134, + 756 + ], + [ + 1134, + 1134 + ], + [ + 1512, + 378 + ], + [ + 1512, + 756 + ], + [ + 1890, + 378 + ], + [ + 2268, + 378 + ], + [ + 2646, + 378 + ], + [ + 3024, + 378 + ], + [ + 3402, + 378 + ] + ], + "processor_class": "HCXProcessor", + "resample": 2, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 378 + }, + "unpad": true +} diff --git a/processing_hyperclovax.py b/processing_hyperclovax.py new file mode 100644 index 0000000..13a975c --- /dev/null +++ b/processing_hyperclovax.py @@ -0,0 +1,912 @@ +import copy +import os +import re +import uuid +from typing import Dict, List, Optional, Union + +import numpy as np +import PIL +from PIL import Image +import torch +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_utils import ImageInput, load_image +from transformers.processing_utils import ( + AllKwargsForChatTemplate, + ChatTemplateLoadKwargs, + ProcessingKwargs, + ProcessorMixin, + Unpack, +) +from transformers.tokenization_utils_base import AudioInput, TextInput +from transformers.utils import ( + is_torch_device, + is_torch_dtype, + logging, + requires_backends, +) +from transformers.utils.chat_template_utils import render_jinja_template +from transformers.video_utils import VideoInput, VideoMetadata, load_video + +logger = logging.get_logger(__name__) + + +class HCXBatchFeature(BatchFeature): + def to(self, *args, **kwargs) -> "BatchFeature": + """ + Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in + different `dtypes` and sending the `BatchFeature` to a different `device`. + + Args: + args (`Tuple`): + Will be passed to the `to(...)` function of the tensors. + kwargs (`Dict`, *optional*): + Will be passed to the `to(...)` function of the tensors. + To enable asynchronous data transfer, set the `non_blocking` flag in `kwargs` (defaults to `False`). + + Returns: + [`BatchFeature`]: The same instance after modification. + """ + requires_backends(self, ["torch"]) + import torch # noqa + + new_data = {} + device = kwargs.get("device") + non_blocking = kwargs.get("non_blocking", False) + # Check if the args are a device or a dtype + if device is None and len(args) > 0: + # device should be always the first argument + arg = args[0] + if is_torch_dtype(arg): + # The first argument is a dtype + pass + elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int): + device = arg + else: + # it's something else + raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.") + # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor` + for k, v in self.items(): + # check if v is a floating point + if isinstance(v, torch.Tensor) and torch.is_floating_point(v): + # cast and send to device + new_data[k] = v.to(*args, **kwargs) + elif isinstance(v, torch.Tensor) and device is not None: + new_data[k] = v.to(device=device, non_blocking=non_blocking) + elif "pixel_values" in k: + new_pixel_values_batch = [] + for _v in v: + pixel_values = [pixel_value.to(device=device, non_blocking=non_blocking) for pixel_value in _v] + new_pixel_values_batch.append(pixel_values) + new_data[k] = new_pixel_values_batch + else: + new_data[k] = v + self.data = new_data + return self + + +class HCXProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "return_tensors": "pt", + "calc_non_vision_query_lengths": False, + }, + "images_kwargs": {}, + "audio_kwargs": {}, + "videos_kwargs": { + "max_image_cnt": 12, + "max_num_grids": 9, + }, + } + + +class HCXProcessor(ProcessorMixin): + attributes = ["image_processor", "tokenizer"] + valid_kwargs = ["chat_template"] + + image_processor_class = "AutoImageProcessor" + tokenizer_class = ("GPT2Tokenizer", "GPT2TokenizerFast") + + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + self.image_token = "<|dummy3|>" + self.video_token = "<|_unuse_missing_100270|>" + self.image_token_pattern = re.compile(r"<\|dummy3\|>") + self.video_token_pattern = re.compile(r"<\|_unuse_missing_100270\|>") + self.image_video_token_pattern = re.compile(r"<\|dummy3\|>|<\|_unuse_missing_100270\|>") + self.image_token_id = ( + tokenizer.image_token_id + if getattr(tokenizer, "image_token_id", None) + else tokenizer.convert_tokens_to_ids(self.image_token) + ) + self.video_token_id = ( + tokenizer.video_token_id + if getattr(tokenizer, "video_token_id", None) + else tokenizer.convert_tokens_to_ids(self.video_token) + ) + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def apply_chat_template( + self, + conversation: Union[list[dict[str, str]], list[list[dict[str, str]]]], + chat_template: Optional[str] = None, + **kwargs: Unpack[AllKwargsForChatTemplate], + ) -> str: + """ + Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input + conversations to turn them into a single tokenizable string. + + The input is expected to be in the following format, where each message content is a list consisting of text and + optionally image or video inputs. One can also provide an image, video, URL or local path which will be used to form + `pixel_values` when `return_dict=True`. If not provided, one will get only the formatted text, optionally tokenized text. + + conversation = [ + { + "role": "user", + "content": [ + {"type": "image", "image": "https://www.ilankelman.org/stopsigns/australia.jpg"}, + {"type": "text", "text": "Please describe this image in detail."}, + ], + }, + ] + + Args: + conversation (`Union[List[Dict, [str, str]], List[List[Dict[str, str]]]]`): + The conversation to format. + chat_template (`Optional[str]`, *optional*): + The Jinja template to use for formatting the conversation. If not provided, the tokenizer's + chat template is used. + """ + + if chat_template is None: + if isinstance(self.chat_template, dict) and "default" in self.chat_template: + chat_template = self.chat_template["default"] + elif isinstance(self.chat_template, dict): + raise ValueError( + 'The processor has multiple chat templates but none of them are named "default". You need to specify' + " which one to use by passing the `chat_template` argument. Available templates are: " + f"{', '.join(self.chat_template.keys())}" + ) + elif self.chat_template is not None: + chat_template = self.chat_template + else: + raise ValueError( + "Cannot use apply_chat_template because this processor does not have a chat template." + ) + else: + if isinstance(self.chat_template, dict) and chat_template in self.chat_template: + # It's the name of a template, not a full template string + chat_template = self.chat_template[chat_template] + else: + # It's a template string, render it directly + chat_template = chat_template + + if kwargs.get("continue_final_message", False): + if kwargs.get("add_generation_prompt", False): + raise ValueError( + "continue_final_message and add_generation_prompt are not compatible. Use continue_final_message when you want the model to continue the final message, and add_generation_prompt when you want to add a header that will prompt it to start a new assistant message instead." + ) + if kwargs.get("return_assistant_tokens_mask", False): + raise ValueError("continue_final_message is not compatible with return_assistant_tokens_mask.") + + # Fill sets of kwargs that should be used by different parts of template + processed_kwargs = { + "mm_load_kwargs": {}, + "template_kwargs": {}, + } + + for kwarg_type in processed_kwargs: + for key in AllKwargsForChatTemplate.__annotations__[kwarg_type].__annotations__.keys(): + kwarg_type_defaults = AllKwargsForChatTemplate.__annotations__[kwarg_type] + default_value = getattr(kwarg_type_defaults, key, None) + value = kwargs.pop(key, default_value) + if value is not None and not isinstance(value, dict): + processed_kwargs[kwarg_type][key] = value + + # Pass unprocessed custom kwargs + processed_kwargs["template_kwargs"].update(kwargs) + + if isinstance(conversation, (list, tuple)) and ( + isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "content") + ): + is_batched = True + conversations = conversation + else: + is_batched = False + conversations = [conversation] + + tokenize = processed_kwargs["template_kwargs"].pop("tokenize", False) + return_dict = processed_kwargs["template_kwargs"].pop("return_dict", False) + mm_load_kwargs = processed_kwargs["mm_load_kwargs"] + + if tokenize: + batch_images, batch_videos = [], [] + batch_audios = [] + batch_video_metadata = [] + for conversation in conversations: + images, videos = [], [] + video_metadata = [] + for message in conversation: + visuals = [content for content in message["content"] if content["type"] in ["image", "video"]] + audio_fnames = [ + content[key] + for content in message["content"] + for key in ["audio", "url", "path"] + if key in content and content["type"] == "audio" + ] + image_fnames = [ + vision_info[key] + for vision_info in visuals + for key in ["image", "url", "path", "base64"] + if key in vision_info and vision_info["type"] == "image" + ] + video_fnames = [ + vision_info[key] + for vision_info in visuals + for key in ["video", "url", "path"] + if key in vision_info and vision_info["type"] == "video" + ] + + for fname in image_fnames: + images.append(load_image(fname)) + + # Audio models do not accept nested list of audios (yet!) so we construct a flat input audio list + if not mm_load_kwargs["load_audio_from_video"]: + for fname in audio_fnames: + batch_audios.append(load_audio(fname, sampling_rate=mm_load_kwargs["sampling_rate"])) + else: + for fname in video_fnames: + batch_audios.append(load_audio(fname, sampling_rate=mm_load_kwargs["sampling_rate"])) + + for fname in video_fnames: + if isinstance(fname, (list, tuple)) and isinstance(fname[0], str): + video = [np.array(load_image(image_fname)) for image_fname in fname] + # create a 4D video because `load_video` always returns a 4D array + video = np.stack(video) + metadata = None + logger.warning( + "When loading the video from list of images, we cannot infer metadata such as `fps` or `duration`. " + "If your model uses this metadata during processing, please load the whole video and let the model sample frames instead." + ) + else: + # TODO: raushan, should be `self.video_processor.load_video_for_model` when API is added + video, metadata = self._load_video_for_model( + fname, + num_frames=mm_load_kwargs.get("num_frames", None), + fps=mm_load_kwargs.get("video_fps", None), + backend=mm_load_kwargs["video_load_backend"], + **kwargs, + ) + videos.append(video) + video_metadata.append(metadata) + + # Currently all processors can accept nested list of batches, but not flat list of visuals + # So we'll make a batched list of images and let the processor handle it + if images: + batch_images.append(images) + if videos: + batch_videos.append(videos) + batch_video_metadata.append(video_metadata) + + # Process conversation with video/image information if needed. Then convert into a prompt using Jinja template + conversations = self._process_messages_for_chat_template( + conversations, + batch_images=batch_images, + batch_videos=batch_videos, + batch_video_metadata=batch_video_metadata, + **processed_kwargs["mm_load_kwargs"], + ) + + prompt, generation_indices = render_jinja_template( + conversations=conversations, + chat_template=chat_template, + **processed_kwargs["template_kwargs"], # different flags such as `return_assistant_mask` + **self.tokenizer.special_tokens_map, # tokenizer special tokens are used by some templates + ) + + if not is_batched: + prompt = prompt[0] + + if tokenize: + # Tokenizer's `apply_chat_template` never adds special tokens when tokenizing + # But processor's `apply_chat_template` didn't have an option to tokenize, so users had to format the prompt + # and pass it to the processor. Users thus never worried about special tokens relying on processor handling + # everything internally. The below line is to keep BC for that and be able to work with model that have + # special tokens in the template (consistent with tokenizers). We dont want to raise warning, it will flood command line + # without actionable solution for users + single_prompt = prompt[0] if is_batched else prompt + if self.tokenizer.bos_token is not None and single_prompt.startswith(self.tokenizer.bos_token): + kwargs["add_special_tokens"] = False + + out = self( + text=prompt, + images=batch_images if batch_images else None, + videos=batch_videos if batch_videos else None, + audio=batch_audios if batch_audios else None, + **kwargs, + ) + if return_dict: + if processed_kwargs["template_kwargs"].get("return_assistant_tokens_mask", False): + assistant_masks = [] + input_ids = out["input_ids"] + for i in range(len(input_ids)): + current_mask = [0] * len(input_ids[i]) + for assistant_start_char, assistant_end_char in generation_indices[i]: + start_token = out.char_to_token(i, assistant_start_char) + end_token = out.char_to_token(i, assistant_end_char - 1) + if start_token is None: + # start_token is out of bounds maybe due to truncation. + break + for token_id in range(start_token, end_token + 1 if end_token else len(input_ids[i])): + current_mask[token_id] = 1 + assistant_masks.append(current_mask) + out["assistant_masks"] = assistant_masks + out.convert_to_tensors(tensor_type=kwargs.get("return_tensors", None)) + + # vllm needs vision_query_lengths, but hf model doesn't need it + del out["vision_query_lengths_images"] + del out["vision_query_lengths_videos"] + return out + else: + return out["input_ids"] + + def repeat_dummy_tokens(self, input_ids, target_token_id, vision_query_lengths): + input_ids = input_ids.clone().detach() + batch_indices, target_indices = torch.where(input_ids == target_token_id) + batch_size = input_ids.shape[0] + + new_input_ids = [[] for _ in range(batch_size)] + start_indices = [0 for _ in range(batch_size)] + counter = [0 for _ in range(batch_size)] + for batch_idx, target_idx in zip(batch_indices, target_indices): + start_idx = start_indices[batch_idx] + new_input_ids[batch_idx].append(input_ids[batch_idx][start_idx:target_idx]) + query_length = vision_query_lengths[batch_idx][counter[batch_idx]] + new_input_ids[batch_idx].append(input_ids[batch_idx][target_idx].repeat(query_length)) + start_indices[batch_idx] = target_idx + 1 + counter[batch_idx] += 1 + + for batch_idx in range(batch_size): + start_idx = start_indices[batch_idx] + new_input_ids[batch_idx].append(input_ids[batch_idx][start_idx:]) # append remaining tokens + new_input_ids[batch_idx] = torch.cat(new_input_ids[batch_idx], dim=0) + + new_input_ids = torch.stack(new_input_ids) + return new_input_ids + + def _load_video_for_model( + self, + video: str, + num_frames: Optional[int] = None, + fps: Optional[int] = None, + backend: str = "opencv", + **kwargs: Unpack[HCXProcessorKwargs], + ) -> List[ImageInput]: + """ + Overrided function. + + Loads `video` to a List[PIL.Image] (llava style) + + Args: + video (`str`): + The video to convert to the numpy array format. Can be a link to video or local path. + num_frames (`int`, *optional*): + Number of frames to sample uniformly. If not passed, the whole video is loaded. + fps (`int`, *optional*): + Number of frames to sample per second. Should be passed only when `num_frames=None`. + If not specified and `num_frames==None`, all frames are sampled. + backend (`str`, *optional*, defaults to `"opencv"`): + The backend to use when loading the video. Can be any of ["decord", "pyav", "opencv", "torchvision"]. Defaults to "opencv". + + Returns: + Tuple[`np.array`, Dict]: A tuple containing: + - List[PIL.Image] of frames in RGB. + - Metadata dictionary. + """ + output_kwargs = self._merge_kwargs( + HCXProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + logger.warning_once(f"num_frames control via argument is not supported yet. Ignored num_frames: {num_frames}.") + logger.warning_once(f"fps control via argument is not supported yet. Ignored fps: {fps}.") + logger.warning_once(f"backend control via argument is not supported yet. Ignored backend: {backend}.") + + # video_loaded, video_metadata = load_video( + # video, backend="decord", num_frames=32 + # ) + # frame_interval = int(video_metadata.total_num_frames / 32) + # time_interval = frame_interval / video_metadata.fps + # video_metadata.time_interval = time_interval + + def _hcx_sample_indices_fn(metadata: VideoMetadata, num_frames=None, fps=None, **kwargs): + max_num_grids = output_kwargs["videos_kwargs"]["max_num_grids"] + max_image_cnt = output_kwargs["videos_kwargs"]["max_image_cnt"] + frame_indices, time_interval = extract_frame_indices( + metadata.duration, + metadata.total_num_frames, + metadata.fps, + max_num_grids, + max_image_cnt, + default_interval=0.4, + ) + metadata.time_interval = time_interval + return np.array(frame_indices) + + video_loaded, video_metadata = None, None + for backend in ["decord", "pyav", "opencv", "torchvision"]: + try: + video_loaded, video_metadata = load_video( + video, sample_indices_fn=_hcx_sample_indices_fn, backend=backend + ) + break + except Exception as e: + logger.error(f"Error loading video with {backend} backend: {e}") + continue + + assert video_loaded is not None, "Failed to load video with any backend" + + return video_loaded, video_metadata + + def _process_messages_for_chat_template( + self, + conversation: List[List[Dict[str, str]]], + batch_images: List[List[ImageInput]], + batch_videos: List[List[VideoInput]], + batch_video_metadata: List[List[Dict[str, any]]], + **mm_load_kwargs: Unpack[ChatTemplateLoadKwargs], + ): + """ + Overrided function. + Used within `apply_chat_template` when a model has a special way to process conversation history. For example, + video models might want to specify in the prompt the duration of video or which frame indices at which timestamps + were sampled. This information cannot be accessed before the video is loaded. + + For most models it is a no-op, and must be overridden by model processors which require special processing. + + Args: + conversation (`List[Dict, str, str]`): + The conversation to process. Always comes in batched format. + batch_images (`List[List[ImageInput]]`): + Batch of images that were loaded from url/path defined in the conversation. The images + are ordered in the same way as in the conversation. Comes in nested list format, one list of `PIL` images + per batch. + batch_videos (`List[List[ImageInput]]`): + Batch of videos that were loaded from url/path defined in the conversation. The videos + are ordered in the same way as in the conversation. Comes in nested list format, one list of `PIL.Image` + per batch. + batch_video_metadata (`List[List[Dict[[str, any]]]]`): + Batch of metadata returned from loading videos. That includes video fps, duration and total number of framer in original video. + Metadata are ordered in the same way as `batch_videos`. Comes in nested list format, one list of `Dict` + per batch. + """ + + is_video_in_conversation = False + for batch_idx, messages in enumerate(conversation): + is_video_in_messages = False + is_image_in_messages = False + for message in messages: + for content in message["content"]: + if content["type"] == "video": + is_video_in_messages = True + elif content["type"] == "image": + is_image_in_messages = True + if not is_video_in_messages: + batch_videos.insert(batch_idx, []) + batch_video_metadata.insert(batch_idx, []) + if not is_image_in_messages: + batch_images.insert(batch_idx, []) + + is_video_in_conversation = is_video_in_conversation or is_video_in_messages + + if not is_video_in_conversation: + return conversation + + # conversation processing + new_conversation = [] + for batch_idx, messages in enumerate(conversation): + video_counter = 0 + new_messages = [] + + for message in messages: + new_message = { + "role": message["role"], + "content": [], + } + for content in message["content"]: + if content["type"] == "video": + video = batch_videos[batch_idx][video_counter] + video_meta = batch_video_metadata[batch_idx][video_counter] + + time_stamps = calc_timestamp_video_grids(video, video_meta.time_interval, max_grid_shape=(3, 3)) + video_counter += 1 + + if "filename" in content: + filename = content["filename"] + else: + filename = content["video"].split("/")[-1] + if len(filename) > 50: + filename = f"{uuid.uuid4().hex}.mp4" + basename, ext = os.path.splitext(filename) + if ext == "": + ext = ".mp4" + + for frame_idx, time_stamp in enumerate(time_stamps): + if frame_idx == len(video) - 1: + # final_grid + new_content = { + "filename": f"{basename}-{frame_idx}{ext}", + "video": content["video"], + "type": "video", + "video_time_stamp": time_stamp, + "lens_keywords": content["lens_keywords"], + "lens_local_keywords": content["lens_local_keywords"], + "speech_to_text": content["speech_to_text"], + "is_final_grid": True, + } + new_message["content"].append(new_content) + else: + new_content = { + "filename": f"{basename}-{frame_idx}{ext}", + "video": content["video"], + "type": "video", + "video_time_stamp": time_stamp, + } + new_message["content"].append(new_content) + else: + new_message["content"].append(copy.deepcopy(content)) + new_messages.append(new_message) + new_conversation.append(new_messages) + + return new_conversation + + def __call__( + self, + text: TextInput = None, + images: List[List[ImageInput]] = None, + videos: List[List[VideoInput]] = None, + audio: AudioInput = None, + **kwargs: Unpack[HCXProcessorKwargs], + ): + output_kwargs = self._merge_kwargs( + HCXProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + # prepare model inputs + mm_inputs = { + "pixel_values_images": [], + "image_sizes_images": [], + "vision_query_lengths_images": [], + "pixel_values_videos": [], + # "image_sizes_videos": [], + "vision_query_lengths_videos": [], + } + calc_non_vision_query_lengths = output_kwargs["text_kwargs"].pop("calc_non_vision_query_lengths") + if calc_non_vision_query_lengths: + mm_inputs["non_vision_query_lengths"] = [] + + # video processing + if videos is not None: + vit_input_size = self.image_processor.crop_size["width"] + + video_kwargs = copy.deepcopy(output_kwargs["videos_kwargs"]) + + for videos_in_single_conversation in videos: + pixel_values_videos = [] + vision_query_lengths_videos = [] + + for video_frames in videos_in_single_conversation: + if len(video_frames) == 0: + mm_inputs["pixel_values_videos"].append([]) + mm_inputs["vision_query_lengths_videos"].append([]) + continue + video_frames_combined = combine_frames_into_images( + video_frames, max_grid_shape=(3, 3), vit_input_size=vit_input_size + ) + video_kwargs["is_video"] = True + video_kwargs["return_tensors"] = None + + frames_processed = self.image_processor(images=video_frames_combined, **video_kwargs) + sizes = [(size["width"], size["height"]) for size in frames_processed["image_sizes"]] + + pixel_values_videos.extend(frames_processed["pixel_values"]) + vision_query_lengths_videos.extend(frames_processed["vision_query_lengths"]) + + mm_inputs["pixel_values_videos"].append(pixel_values_videos) + mm_inputs["vision_query_lengths_videos"].append(vision_query_lengths_videos) + + # image processing + if images is not None: + image_kwargs = copy.deepcopy(output_kwargs["images_kwargs"]) + image_kwargs["is_video"] = False + image_kwargs["return_tensors"] = None + + for images_in_single_conversation in images: + if isinstance(images_in_single_conversation, PIL.Image.Image): # single item to batch + images_in_single_conversation = [images_in_single_conversation, ] + if len(images_in_single_conversation) == 0: + mm_inputs["pixel_values_images"].append([]) + mm_inputs["image_sizes_images"].append([]) + mm_inputs["vision_query_lengths_images"].append([]) + continue + images_processed = self.image_processor(images=images_in_single_conversation, **image_kwargs) + sizes = [(size["width"], size["height"]) for size in images_processed["image_sizes"]] + + mm_inputs["pixel_values_images"].append(images_processed["pixel_values"]) + mm_inputs["image_sizes_images"].append(sizes) + mm_inputs["vision_query_lengths_images"].append(images_processed["vision_query_lengths"]) + + # text processing + def _create_replacer(_target_token, _replacements): + _iterator = iter(_replacements) + + def _replacer(match_obj): + # return self.image_token + num_query_tokens = next(_iterator) + return "".join([_target_token for _ in range(num_query_tokens)]) + return _replacer + + text_inputs = {} + if text is not None: + if not isinstance(text, list): + text = [text] + + if images is not None: + new_texts = [] + for batch_idx, text_in_single_conversation in enumerate(text): + new_text = self.image_token_pattern.sub( + _create_replacer(self.image_token, mm_inputs["vision_query_lengths_images"][batch_idx]), + text_in_single_conversation, + ) + new_texts.append(new_text) + text = new_texts + + if videos is not None: + new_texts = [] + for batch_idx, text_in_single_conversation in enumerate(text): + new_text = self.video_token_pattern.sub( + _create_replacer(self.video_token, mm_inputs["vision_query_lengths_videos"][batch_idx]), + text_in_single_conversation, + ) + new_texts.append(new_text) + text = new_texts + + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + + # audio processing + if audio is not None: + raise NotImplementedError("Audio processing is not supported yet.") + + return HCXBatchFeature(data={**text_inputs, **mm_inputs}) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Siglip2Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Siglip2Tokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def post_process_image_text_to_text( + self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs + ): + """ + Post-process the output of the model to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): + Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `List[str]`: The decoded text. + """ + return self.tokenizer.batch_decode( + generated_outputs, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + return names_from_processor + [] + + +def extract_frame_indices(play_time, total_frames, fps, max_num_grids, max_image_cnt, default_interval=0.4): + """ + Extracts specific frame indices from a video based on duration, frame count, and sampling strategy. + + The function determines which frames to extract given the video duration (`play_time`), + total frame count, and frame rate. It samples frames at regular intervals (default: 0.4s), + but if the number of frames exceeds the limit defined by `max_num_grids * max_image_cnt`, + it performs uniform sampling to stay within that limit. + + Args: + play_time (float): Total play time of the video in seconds. + total_frames (int): Total number of frames in the video. + fps (float): Frames per second of the video. + max_num_grids (int): Maximum number of grids to display. + max_image_cnt (int): Maximum number of images per grid. + default_interval (float, optional): Interval in seconds between frame samples. Defaults to 0.4. + + Returns: + Tuple: + frame_indices (List[int]): A list of selected frame indices. + time_interval (float): Time interval between selected frames (in seconds). + """ + + # Calculate how many frames to extract with the default interval + default_frame_count = int(play_time / default_interval) + + # Maximum frames allowed based on max_num_grids and max_image_cnt + max_frames_allowed = max_num_grids * max_image_cnt + + # Determine whether we can use the default interval or need uniform sampling + if default_frame_count <= max_frames_allowed: + # Default interval is sufficient, extract frames every 0.4 seconds + frame_interval = int(total_frames / default_frame_count) + else: + # Use uniform sampling to fit within max_frames_allowed + frame_interval = int(total_frames / max_frames_allowed) + + # Extract frame indices at the calculated interval + selected_indices = list(range(0, total_frames, frame_interval)) + + time_interval = frame_interval / fps + + # Ensure the number of selected indices does not exceed max_frames_allowed + return selected_indices[:max_frames_allowed], time_interval + + +def calc_timestamp_video_grids(frames, time_interval, max_grid_shape=(3, 3)): + """ + Calculates the time range labels for each grid in a video. + + Args: + frames (List[PIL.Image.Image]): A list of frames extracted from a video. + time_interval (float): Time interval (in seconds) between consecutive frames. + max_grid_shape (Tuple[int, int], optional): The maximum grid shape as (rows, cols). Defaults to (3, 3). + vit_input_size (int, optional): The target size (height and width) for the Vision Transformer input. Defaults to 378. + + Returns: + Tuple: + image_time_stamps (List[str]): A list of time span labels for each combined image, + e.g., ["0.00s~1.50s", "1.50s~3.00s", ...]. + """ + max_num_grids = max_grid_shape[0] * max_grid_shape[1] + # assert ( + # max_grid_shape[1] == 1 + # ), f"For video processing, decided to concatenate frames horizontally into a wide image." + + # Calculate the number of canvases needed. + num_frames = len(frames) + num_canvases = num_frames // max_num_grids + leftover_frames = num_frames % max_num_grids + + time_stamp = 0 # second + image_time_stamps = [] + + for canvas_idx in range(num_canvases): + # Determine the frames to fill in the current canvas. + start_idx = canvas_idx * max_num_grids + end_idx = min(start_idx + max_num_grids, num_frames) + + # Append the current canvas to the result list. + frame_cnt = end_idx - start_idx + image_time_stamps.append(f"{time_stamp:.2f}s~{time_stamp + frame_cnt * time_interval:.2f}s") + time_stamp += frame_cnt * time_interval + + if leftover_frames > 0: + # Add the current canvas to the list of combined images. + frame_cnt = leftover_frames + image_time_stamps.append(f"{time_stamp:.2f}s~{time_stamp + frame_cnt * time_interval:.2f}s") + time_stamp += frame_cnt * time_interval + + return image_time_stamps + + +def combine_frames_into_images(frames, max_grid_shape=(3, 3), vit_input_size=378): + """ + Combines a sequence of video frames into grid-based images and generates corresponding time range labels. + + Frames are grouped and arranged into a grid (e.g., 3x3) such that each combined image contains up to + `max_grid_shape[0] * max_grid_shape[1]` frames. Each combined image is resized to the given ViT input size. + + Args: + frames (NDArray): (num_frames, H, W, C) shape. A list of frames extracted from a video. + time_interval (float): Time interval (in seconds) between consecutive frames. + max_grid_shape (Tuple[int, int], optional): The maximum grid shape as (rows, cols). Defaults to (3, 3). + vit_input_size (int, optional): The target size (height and width) for the Vision Transformer input. Defaults to 378. + + Returns: + Tuple: + image_list (List[PIL.Image.Image]): A list of grid-combined images. + """ + max_num_grids = max_grid_shape[0] * max_grid_shape[1] + # assert ( + # max_grid_shape[1] == 1 + # ), f"For video processing, decided to concatenate frames horizontally into a wide image." + + # List to store the resulting combined images. + image_list = [] + + # Calculate the number of canvases needed. + num_frames = len(frames) + num_canvases = num_frames // max_num_grids + leftover_frames = num_frames % max_num_grids + + # change frames (4d numpy tensor) to List[PIL.Image.Image] + frames = [Image.fromarray(frame) for frame in frames] + + for canvas_idx in range(num_canvases): + # Initialize the current canvas. + combined_image = Image.new( + "RGB", (vit_input_size * max_grid_shape[0], vit_input_size * max_grid_shape[1]), color=(0, 0, 0) + ) + + # Determine the frames to fill in the current canvas. + start_idx = canvas_idx * max_num_grids + end_idx = min(start_idx + max_num_grids, num_frames) + + for idx in range(start_idx, end_idx): + img = frames[idx] + + # Resize each frame to a square shape. + img_resized = img.resize((vit_input_size, vit_input_size)) + + # Calculate the (row, column) position to place the frame within the grid layout. + local_idx = idx - start_idx + x_offset = (local_idx % max_grid_shape[0]) * vit_input_size + y_offset = (local_idx // max_grid_shape[0]) * vit_input_size + + # Calculate the position to place the frame in the grid. + combined_image.paste(img_resized, (x_offset, y_offset)) + + # Append the current canvas to the result list. + image_list.append(combined_image) + + if leftover_frames > 0: + # canvas_idx might be undefined; default to 0 if not previously assigned to avoid "referenced before assignment" error. + canvas_idx = num_canvases + # Add the remaining frames to the final canvas. + # combined_image = Image.new("RGB", (vit_input_size * leftover_frames, vit_input_size * 1), color=(0, 0, 0)) # hsk + combined_image = Image.new( + "RGB", (vit_input_size * max_grid_shape[0], vit_input_size * max_grid_shape[1]), color=(0, 0, 0) + ) + + for idx in range(leftover_frames): + img = frames[num_canvases * max_num_grids + idx] + + # Resize the frame to a square (equal width and height). + img_resized = img.resize((vit_input_size, vit_input_size)) + + # Calculate the (row, column) position to place the frame within the grid layout. + # x_offset = (idx % leftover_frames) * vit_input_size # hsk + # y_offset = (idx // leftover_frames) * vit_input_size # hsk + x_offset = (idx % max_grid_shape[0]) * vit_input_size + y_offset = (idx // max_grid_shape[0]) * vit_input_size + + # Calculate the position to place the frame within the grid layout. + combined_image.paste(img_resized, (x_offset, y_offset)) + + # Add the current canvas to the list of combined images. + image_list.append(combined_image) + + return image_list diff --git a/processor_config.json b/processor_config.json new file mode 100644 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