Repository: QwenLM/Qwen3 Branch: main Commit: 7a2f61ffc7a2 Files: 101 Total size: 27.6 MB Directory structure: gitextract_9etz2yip/ ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.yml │ │ └── config.yml │ └── workflows/ │ └── inactive.yml ├── .gitignore ├── .readthedocs.yaml ├── README.md ├── docker/ │ ├── Dockerfile-cu121 │ ├── docker_cli_demo.sh │ └── docker_web_demo.sh ├── docs/ │ ├── Makefile │ ├── README.md │ ├── locales/ │ │ └── zh_CN/ │ │ └── LC_MESSAGES/ │ │ ├── deployment/ │ │ │ ├── dstack.po │ │ │ ├── openllm.po │ │ │ ├── sglang.po │ │ │ ├── skypilot.po │ │ │ ├── tgi.po │ │ │ └── vllm.po │ │ ├── framework/ │ │ │ ├── Langchain.po │ │ │ ├── LlamaIndex.po │ │ │ ├── function_call.po │ │ │ └── qwen_agent.po │ │ ├── getting_started/ │ │ │ ├── concepts.po │ │ │ ├── quantization_benchmark.po │ │ │ ├── quickstart.po │ │ │ ├── speed_benchmark.po │ │ │ └── thinking_budget.po │ │ ├── index.po │ │ ├── inference/ │ │ │ └── transformers.po │ │ ├── quantization/ │ │ │ ├── awq.po │ │ │ ├── gptq.po │ │ │ └── llama.cpp.po │ │ ├── run_locally/ │ │ │ ├── llama.cpp.po │ │ │ ├── mlx-lm.po │ │ │ └── ollama.po │ │ └── training/ │ │ ├── axolotl.po │ │ ├── llama_factory.po │ │ ├── ms_swift.po │ │ ├── unsloth.po │ │ └── verl.po │ ├── make.bat │ ├── requirements-docs.txt │ └── source/ │ ├── _static/ │ │ ├── css/ │ │ │ └── custom.css │ │ └── design-tabs.js │ ├── assets/ │ │ └── qwen3_nonthinking.jinja │ ├── conf.py │ ├── deployment/ │ │ ├── dstack.rst │ │ ├── openllm.rst │ │ ├── sglang.md │ │ ├── skypilot.rst │ │ ├── tgi.rst │ │ └── vllm.md │ ├── framework/ │ │ ├── Langchain.rst │ │ ├── LlamaIndex.rst │ │ ├── function_call.md │ │ └── qwen_agent.rst │ ├── getting_started/ │ │ ├── concepts.md │ │ ├── quantization_benchmark.rst │ │ ├── quickstart.md │ │ ├── speed_benchmark.md │ │ └── thinking_budget.md │ ├── index.rst │ ├── inference/ │ │ └── transformers.md │ ├── quantization/ │ │ ├── awq.md │ │ ├── gptq.md │ │ └── llama.cpp.md │ ├── run_locally/ │ │ ├── llama.cpp.md │ │ ├── lmstudio.md │ │ ├── mlx-lm.md │ │ └── ollama.md │ └── training/ │ ├── axolotl.md │ ├── llama_factory.md │ ├── ms_swift.md │ ├── unsloth.md │ └── verl.md ├── eval/ │ ├── README.md │ ├── configs/ │ │ └── ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml │ ├── data/ │ │ └── arc_agi_1.jsonl │ ├── eval/ │ │ ├── arc_agi_1.py │ │ └── eval.py │ ├── eval_res/ │ │ └── ARCAGI-Qwen3-235B-A22B-Instruct-2507_eval_result.txt │ ├── generate_api_answers/ │ │ ├── infer_multithread.py │ │ └── utils_vllm.py │ ├── output/ │ │ ├── ARCAGI-Qwen3-235B-A22B-Instruct-2507.jsonl │ │ └── ARCAGI-Qwen3-235B-A22B-Instruct-2507_details.jsonl │ └── requirements.txt └── examples/ ├── README.md ├── demo/ │ ├── cli_demo.py │ └── web_demo.py ├── gcu-support/ │ ├── README.md │ └── gcu_demo.py ├── llama-factory/ │ ├── finetune-zh.md │ ├── qwen2-7b-full-sft.yaml │ ├── qwen2-7b-lora-sft.yaml │ ├── qwen2-7b-merge-lora.yaml │ └── qwen2-7b-qlora-sft.yaml └── speed-benchmark/ ├── README.md ├── README_zh.md ├── requirements-perf-transformers.txt ├── requirements-perf-vllm.txt ├── speed_benchmark_transformers.py └── speed_benchmark_vllm.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report.yml ================================================ name: 🐞 Bug Report description: Something unexpected happened, errors or badcases body: - type: markdown attributes: value: | **We appreciate your time and effort in filing this report. ❤** Issues are a vital part of open-source collaboration. 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Please open a new issue for related bugs. process-only: "issues,prs" ================================================ FILE: .gitignore ================================================ # Sphinx documentation docs/_build/ docs/build/ docs/**/*.mo .vscode .idea # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class ================================================ FILE: .readthedocs.yaml ================================================ # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 build: os: ubuntu-22.04 tools: python: "3" sphinx: configuration: docs/source/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf # Optionally declare the Python requirements required to build your docs python: install: - requirements: docs/requirements-docs.txt ================================================ FILE: README.md ================================================ # Qwen3

💜 Qwen Chat   |   🤗 Hugging Face   |   🤖 ModelScope   |    📑 Paper    |    📑 Blog    ｜   📖 Documentation
🖥️ Demo   |   💬 WeChat (微信)   |   🫨 Discord  

Visit our Hugging Face or ModelScope organization (click links above), search checkpoints with names starting with `Qwen3-` or visit the [Qwen3 collection](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f), and you will find all you need! Enjoy! To learn more about Qwen3, feel free to read our documentation \[[EN](https://qwen.readthedocs.io/en/latest/)|[ZH](https://qwen.readthedocs.io/zh-cn/latest/)\]. Our documentation consists of the following sections: - Quickstart: the basic usages and demonstrations; - Inference: the guidance for the inference with Transformers, including batch inference, streaming, etc.; - Run Locally: the instructions for running LLM locally on CPU and GPU, with frameworks like llama.cpp, Ollama, and LM Studio; - Deployment: the demonstration of how to deploy Qwen for large-scale inference with frameworks like SGLang, vLLM, TGI, etc.; - Quantization: the practice of quantizing LLMs with GPTQ, AWQ, as well as the guidance for how to make high-quality quantized GGUF files; - Training: the instructions for post-training, including SFT and RLHF (TODO) with frameworks like Axolotl, LLaMA-Factory, etc. - Framework: the usage of Qwen with frameworks for application, e.g., RAG, Agent, etc. ## Introduction ### Qwen3-2507 Over the past three months, we continued to explore the potential of the Qwen3 families and we are excited to introduce the updated **Qwen3-2507** in two variants, Qwen3-Instruct-2507 and Qwen3-Thinking-2507, and three sizes, 235B-A22B, 30B-A3B, and 4B. **Qwen3-Instruct-2507** is the updated version of the previous Qwen3 non-thinking mode, featuring the following key enhancements: - **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**. - **Substantial gains** in long-tail knowledge coverage across **multiple languages**. - **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation. - **Enhanced capabilities** in **256K-token long-context understanding**, extendable up to **1 million tokens**. **Qwen3-Thinking-2507** is the continuation of Qwen3 thinking model, with improved quality and depth of reasoning, featuring the following key enhancements: - **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise — achieving **state-of-the-art results among open-weight thinking models**. - **Markedly better general capabilities**, such as instruction following, tool usage, text generation, and alignment with human preferences. - **Enhanced 256K long-context understanding** capabilities, extendable up to **1 million tokens**.
Previous Qwen3 Release

Qwen3 (aka Qwen3-2504)

We are excited to announce the release of Qwen3, the latest addition to the Qwen family of large language models. These models represent our most advanced and intelligent systems to date, improving from our experience in building QwQ and Qwen2.5. We are making the weights of Qwen3 available to the public, including both dense and Mixture-of-Expert (MoE) models.

The highlights from Qwen3 include:

## News - 2025.08.08: You can now use Qwen3-2507 to handle ultra-long inputs of **1 million tokens**! See the update modelcards ([235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507), [235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507), [A30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507), [A30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507)) for how to enable this feature. - 2025.08.06: The final open release of Qwen3-2507, [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) and [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507), is out! - 2025.07.31: Qwen3-30B-A3B-Thinking-2507 is released. Check out the [modelcard](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) for more details! - 2025.07.30: Qwen3-30B-A3B-Instruct-2507 is released. Check out the [modelcard](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) for more details! - 2025.07.25: We released the updated version of Qwen3-235B-A22B thinking mode, named Qwen3-235B-A22B-Thinking-2507. Check out the [modelcard](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) for more details! - 2025.07.21: We released the updated version of Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring significant enhancements over the previous version and supporting 256K-token long-context understanding. Check our [modelcard](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) for more details! - 2025.04.29: We released the Qwen3 series. Check our [blog](https://qwenlm.github.io/blog/qwen3) for more details! - 2024.09.19: We released the Qwen2.5 series. This time there are 3 extra model sizes: 3B, 14B, and 32B for more possibilities. Check our [blog](https://qwenlm.github.io/blog/qwen2.5) for more! - 2024.06.06: We released the Qwen2 series. Check our [blog](https://qwenlm.github.io/blog/qwen2/)! - 2024.03.28: We released the first MoE model of Qwen: Qwen1.5-MoE-A2.7B! Temporarily, only HF transformers and vLLM support the model. We will soon add the support of llama.cpp, mlx-lm, etc. Check our [blog](https://qwenlm.github.io/blog/qwen-moe/) for more information! - 2024.02.05: We released the Qwen1.5 series. ## Performance Detailed evaluation results are reported in this [📑 blog (Qwen3-2504)](https://qwenlm.github.io/blog/qwen3/) and this [📑 blog (Qwen3-2507) \[coming soon\]](). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/getting_started/speed_benchmark.html). ## Run Qwen3 ### 🤗 Transformers Transformers is a library of pretrained natural language processing for inference and training. The latest version of `transformers` is recommended and `transformers>=4.51.0` is required. #### Qwen3-Instruct-2507 The following contains a code snippet illustrating how to use Qwen3-30B-A3B-Instruct-2507 to generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-30B-A3B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` > [!Note] > Qwen3-Instruct-2507 supports only non-thinking mode and does not generate ```` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required. #### Qwen3-Thinking-2507 The following contains a code snippet illustrating how to use Qwen3-30B-A3B-Thinking-2507 to generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-30B-A3B-Thinking-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) # no opening tag print("content:", content) ``` > [!Note] > Qwen3-Thinking-2507 supports only thinking mode. > Additionally, to enforce model thinking, the default chat template automatically includes ``. Therefore, it is normal for the model's output to contain only `` without an explicit opening `` tag. > > Qwen3-Thinking-2507 also features an increased thinking length. We strongly recommend its use in highly complex reasoning tasks with adequate maximum generation length.
Switching Thinking/Non-thinking Modes for Previous Qwen3 Models

By default, Qwen3 models will think before response. This could be controlled by

  • enable_thinking=False: Passing enable_thinking=False to `tokenizer.apply_chat_template` will strictly prevent the model from generating thinking content.
  • /think and /no_think instructions: Use those words in the system or user message to signify whether Qwen3 should think. In multi-turn conversations, the latest instruction is followed.

### ModelScope We strongly advise users especially those in mainland China to use ModelScope. ModelScope adopts a Python API similar to Transformers. The CLI tool `modelscope download` can help you solve issues concerning downloading checkpoints. For vLLM and SGLang, the environment variable `VLLM_USE_MODELSCOPE=true` and `SGLANG_USE_MODELSCOPE=true` can be used respectively. ### llama.cpp [`llama.cpp`](https://github.com/ggml-org/llama.cpp) enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. `llama.cpp>=b5401` is recommended for the full support of Qwen3. To use the CLI, run the following in a terminal: ```shell ./llama-cli -hf Qwen/Qwen3-8B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 -c 40960 -n 32768 --no-context-shift # CTRL+C to exit ``` To use the API server, run the following in a terminal: ```shell ./llama-server -hf Qwen/Qwen3-8B-GGUF:Q8_0 --jinja --reasoning-format deepseek -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 -c 40960 -n 32768 --no-context-shift --port 8080 ``` A simple web front end will be at `http://localhost:8080` and an OpenAI-compatible API will be at `http://localhost:8080/v1`. For additional guides, please refer to [our documentation](https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html). > [!Note] > llama.cpp adopts "rotating context management" and infinite generation is made possible by evicting earlier tokens. > It could configured by parameters and the commands above effectively disable it. > For more details, please refer to [our documentation](https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html#llama-cli). ### Ollama After [installing Ollama](https://ollama.com/), you can initiate the Ollama service with the following command (Ollama v0.9.0 or higher is recommended): ```shell ollama serve # You need to keep this service running whenever you are using ollama ``` To pull a model checkpoint and run the model, use the `ollama run` command. You can specify a model size by adding a suffix to `qwen3`, such as `:8b` or `:30b-a3b`: ```shell ollama run qwen3:8b # Setting parameters, type "/set parameter num_ctx 40960" and "/set parameter num_predict 32768" # To exit, type "/bye" and press ENTER # For Qwen3-2504 models, # - To enable thinking, which is the default, type "/set think" # - To disable thinking, type "/set nothink" ``` You can also access the Ollama service via its OpenAI-compatible API. Please note that you need to (1) keep `ollama serve` running while using the API, and (2) execute `ollama run qwen3:8b` before utilizing this API to ensure that the model checkpoint is prepared. The API is at `http://localhost:11434/v1/` by default. For additional details, please visit [ollama.ai](https://ollama.com/). > [!Note] > Ollama's naming may not be consistent with the Qwen's original naming. > For example, `qwen3:30b-a3b` in Ollama points to `qwen3:30b-a3b-thinking-2507-q4_K_M` as of August 2025. > Please check before use. > [!Note] > Ollama adopts the same "rotating context management" with llama.cpp. > However, its default settings (`num_ctx` 2048 and `num_predict` -1), suggesting infinite generation with a 2048-token context, > could lead to trouble for Qwen3 models. > We recommend setting `num_ctx` and `num_predict` properly. ### LMStudio Qwen3 has already been supported by [lmstudio.ai](https://lmstudio.ai/). You can directly use LMStudio with our GGUF files. ### ExecuTorch To export and run on ExecuTorch (iOS, Android, Mac, Linux, and more), please follow this [example](https://github.com/pytorch/executorch/blob/main/examples/models/qwen3/README.md). ### MNN To export and run on MNN, which supports Qwen3 on mobile devices, please visit [Alibaba MNN](https://github.com/alibaba/MNN). ### MLX LM If you are running on Apple Silicon, [`mlx-lm`](https://github.com/ml-explore/mlx-lm) also supports Qwen3 (`mlx-lm>=0.24.0`). Look for models ending with MLX on Hugging Face Hub. ### OpenVINO If you are running on Intel CPU or GPU, [OpenVINO toolkit](https://github.com/openvinotoolkit) supports Qwen3. You can follow this [chatbot example](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot.ipynb). ## Deploy Qwen3 Qwen3 is supported by multiple inference frameworks. Here we demonstrate the usage of `SGLang`, `vLLM` and `TensorRT-LLM`. You can also find Qwen3 models from various inference providers, e.g., [Alibaba Cloud Model Studio](https://www.alibabacloud.com/en/product/modelstudio). ### SGLang [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. SGLang could be used to launch a server with OpenAI-compatible API service. `sglang>=0.4.6.post1` is required. For Qwen3-Instruct-2507, ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Instruct-2507 --port 30000 --context-length 262144 ``` For Qwen3-Thinking-2507, ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Thinking-2507 --port 30000 --context-length 262144 --reasoning-parser deepseek-r1 ``` For Qwen3, it is ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --port 30000 --context-length 131072 --reasoning-parser qwen3 ``` An OpenAI-compatible API will be available at `http://localhost:30000/v1`. > [!Note] > Due to the preprocessing of API requests in SGLang, which drops all `reasoning_content` fields, the quality of **multi-step tool use with Qwen3 thinking models** may be suboptimal, which requires the existence of the related thinking content. While the fixes are being worked on, as a workdaround, we recommend passing the content as it is, without extracting thinking content, and the chat template will correctly handle the processing. ### vLLM [vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs. `vllm>=0.9.0` is recommended. For Qwen3-Instruct-2507, ```shell vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --port 8000 --max-model-len 262144 ``` For Qwen3-Thinking-2507, ```shell vllm serve Qwen/Qwen3-30B-A3B-Thinking-2507 --port 8000 --max-model-len 262144 --enable-reasoning --reasoning-parser deepseek_r1 ``` For Qwen3, it is ```shell vllm serve Qwen/Qwen3-8B --port 8000 --max-model-len 131072 --enable-reasoning --reasoning-parser qwen3 ``` An OpenAI-compatible API will be available at `http://localhost:8000/v1`. > [!Note] > Due to the preprocessing of API requests in vLLM, which drops all `reasoning_content` fields, the quality of **multi-step tool use with Qwen3 thinking models** may be suboptimal, which requires the existence of the related thinking content. While the fixes are being worked on, as a workdaround, we recommend passing the content as it is, without extracting thinking content, and the chat template will correctly handle the processing. ### TensorRT-LLM [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) is an open-source LLM inference engine from NVIDIA, which provides optimizations including custom attention kernels, quantization and more on NVIDIA GPUs. Qwen3 is supported in its re-architected [PyTorch backend](https://nvidia.github.io/TensorRT-LLM/torch.html). `tensorrt_llm>=0.20.0rc3` is recommended. Please refer to the [README](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/models/core/qwen/README.md#qwen3) page for more details. ```shell trtllm-serve Qwen/Qwen3-8B --host localhost --port 8000 --backend pytorch ``` An OpenAI-compatible API will be available at `http://localhost:8000/v1`. ### MindIE For deployment on Ascend NPUs, please visit [Modelers](https://modelers.cn/) and search for Qwen3. ## Build with Qwen3 ### Tool Use For tool use capabilities, we recommend taking a look at [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent), which provides a wrapper around these APIs to support tool use or function calling with MCP support. Tool use with Qwen3 can also be conducted with SGLang, vLLM, Transformers, llama.cpp, Ollama, etc. Follow guides in our documentation to see how to enable the support. ### Finetuning We advise you to use training frameworks, including [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), [UnSloth](https://github.com/unslothai/unsloth), [Swift](https://github.com/modelscope/swift), [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory), etc., to finetune your models with SFT, DPO, GRPO, etc. ## License Agreement All our open-weight models are licensed under Apache 2.0. You can find the license files in the respective Hugging Face repositories. ## Citation If you find our work helpful, feel free to give us a cite. ```bibtex @article{qwen3, title={Qwen3 Technical Report}, author={An Yang and Anfeng Li and Baosong Yang and Beichen Zhang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Gao and Chengen Huang and Chenxu Lv and Chujie Zheng and Dayiheng Liu and Fan Zhou and Fei Huang and Feng Hu and Hao Ge and Haoran Wei and Huan Lin and Jialong Tang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Yang and Jiaxi Yang and Jing Zhou and Jingren Zhou and Junyang Lin and Kai Dang and Keqin Bao and Kexin Yang and Le Yu and Lianghao Deng and Mei Li and Mingfeng Xue and Mingze Li and Pei Zhang and Peng Wang and Qin Zhu and Rui Men and Ruize Gao and Shixuan Liu and Shuang Luo and Tianhao Li and Tianyi Tang and Wenbiao Yin and Xingzhang Ren and Xinyu Wang and Xinyu Zhang and Xuancheng Ren and Yang Fan and Yang Su and Yichang Zhang and Yinger Zhang and Yu Wan and Yuqiong Liu and Zekun Wang and Zeyu Cui and Zhenru Zhang and Zhipeng Zhou and Zihan Qiu}, journal = {arXiv preprint arXiv:2505.09388}, year={2025} } @article{qwen2.5, title = {Qwen2.5 Technical Report}, author = {An Yang and Baosong Yang and Beichen Zhang and Binyuan Hui and Bo Zheng and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoran Wei and Huan Lin and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Yang and Jiaxi Yang and Jingren Zhou and Junyang Lin and Kai Dang and Keming Lu and Keqin Bao and Kexin Yang and Le Yu and Mei Li and Mingfeng Xue and Pei Zhang and Qin Zhu and Rui Men and Runji Lin and Tianhao Li and Tingyu Xia and Xingzhang Ren and Xuancheng Ren and Yang Fan and Yang Su and Yichang Zhang and Yu Wan and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zihan Qiu}, journal = {arXiv preprint arXiv:2412.15115}, year = {2024} } @article{qwen2, title = {Qwen2 Technical Report}, author = {An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal = {arXiv preprint arXiv:2407.10671}, year = {2024} } ``` ## Contact Us If you are interested to leave a message to either our research team or product team, join our [Discord](https://discord.gg/z3GAxXZ9Ce) or [WeChat groups](assets/wechat.png)! ================================================ FILE: docker/Dockerfile-cu121 ================================================ ARG CUDA_VERSION=12.1.0 ARG from=nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu20.04 FROM ${from} as base RUN <. ## Quick Start We use `sphinx` to manage the documentation and use the `furo` theme. To get started, simply run ```bash pip install -r requirements-docs.txt ``` Then run `make html` or `sphinx-build -M html source build` and it will compile the docs and put it under the `build/html` directory. ## Translation The documentation is available in both English and Simplified Chinese. We use `sphinx-intl` to work with Sphinx translation flow, following [this article](https://www.sphinx-doc.org/en/master/usage/advanced/intl.html). You need to install the Python package `sphinx-intl` before starting. 1. After updating the English documentation, run `make gettext`, and the pot files will be placed in the `build/gettext` directory. `make gettext` can be slow if the doc is long. 2. Use the generated pot files to update the po files: ```bash sphinx-intl update -p build/gettext -l zh_CN -w 0 ``` 3. Translate po files at `locales\zh_CN\LC_MESSAGES`. Pay attention to fuzzy matches (messages after `#, fuzzy`). Please be careful not to break reST notation. 4. Build translated document: `make -e SPHINXOPTS="-D language='zh_CN'" html` or `sphinx-build -M html source build -D language=zh_CN` ## Auto Build ```bash pip install sphinx-autobuild ``` To autobuild the default version: ```bash sphinx-autobuild source build/html ``` To autobuild the translated version: ```bash sphinx-autobuild source build/html -D language=zh_CN --watch locales/zh_CN ``` By default, the doc is at `http://127.0.0.1:8000` ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/dstack.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-07-28 10:50+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/deployment/dstack.rst:2 dfac4ff2e6e7425290c3cd12a2de701c msgid "dstack" msgstr "" #: ../../source/deployment/dstack.rst:4 2438a502621e4637bac3fa19171a5e53 msgid "`dstack `__ is an open-source alternative to Kubernetes and Slurm, designed to simplify GPU allocation and AI workload orchestration for ML teams across top clouds, on-prem clusters, and accelerators." msgstr "" #: ../../source/deployment/dstack.rst:7 1ff23a34c6ec4236b5b9d73e7d1d6241 msgid "Prerequisites" msgstr "" #: ../../source/deployment/dstack.rst:8 5f95e757ef4f4cba85ce773801be340d msgid "Before you start, install dstack by following the `installation instructions `__. Once dstack server is up, you can initialize your workspace as shown below:" msgstr "" #: ../../source/deployment/dstack.rst:17 ccf222149a8d43d2bf716c2a39956d77 msgid "Deploy Qwen3-30B-A3B" msgstr "" #: ../../source/deployment/dstack.rst:19 45b4f1af973546ab9651757fcb13b9e9 msgid "Deploy ``Qwen3-30B-A3B`` on instances available with cloud providers configured in your ``~/.dstack/server/config.yml`` file." msgstr "" #: ../../source/deployment/dstack.rst:21 8565ac9fdb394e32b10269087dfc18c7 msgid "You can use ``SgLang``, ``TGI`` or ``vLLM`` to serve the model. Here we use ``SgLang`` as an example." msgstr "" #: ../../source/deployment/dstack.rst:23 9ed2f6fbcd3f408fa0d34a3199709122 msgid "Create a `service `__ configuration file named ``serve-30b.dstack.yml`` with the following content:" msgstr "" #: ../../source/deployment/dstack.rst:49 0973beefd01b4c8081a5d4d2113dc7c4 msgid "For other inference backends such as vLLM or TGI, visit the `dstack Inference Examples `__ documentation." msgstr "" #: ../../source/deployment/dstack.rst:51 826cb0f7e041443db0a8382fd918e3b7 msgid "Go ahead and apply the service configuration:" msgstr "" #: ../../source/deployment/dstack.rst:58 d16702dc64694eeaba319277a3ab4a03 msgid "Access the Service" msgstr "" #: ../../source/deployment/dstack.rst:60 7edacaff1d53424190978e77cd557190 msgid "After the service is successfully deployed, you can access the service's endpoint in the following ways:" msgstr "" #: ../../source/deployment/dstack.rst e83ef74bbe7e4e5eaf5f7a10773c9d46 msgid "CURL" msgstr "" #: ../../source/deployment/dstack.rst:66 9f51986795d3414f96dd65790157e723 msgid "Access through service endpoint at ``/proxy/services///``" msgstr "" #: ../../source/deployment/dstack.rst:84 9a8130ecf20c4e42ac9994e2145bfcec msgid "When starting the dstack server, an admin token is automatically generated:" msgstr "" #: ../../source/deployment/dstack.rst 94c7a3424a19432ebfc0a98eb0725d42 msgid "Chat UI" msgstr "" #: ../../source/deployment/dstack.rst:93 5c7bb346537b456da005af909a333b09 msgid "Access through dstack's Chat UI at ``/projects//models//``" msgstr "" #: ../../source/deployment/dstack.rst 11cd02dcfb214277988135c49b839775 msgid "Gateway" msgstr "" #: ../../source/deployment/dstack.rst:102 e1e19487dd6f4ae8b12f728b39bef5d6 msgid "Running services for development purposes doesn't require setting up a gateway." msgstr "" #: ../../source/deployment/dstack.rst:104 bf94ccabbeaa491c9827e983e7f9950a msgid "However, you'll need a gateway in the following cases:" msgstr "" #: ../../source/deployment/dstack.rst:106 15278aaab8214461b9fa17c95549f1cc msgid "To use auto-scaling or rate limits" msgstr "" #: ../../source/deployment/dstack.rst:107 5cf53dda95e24cfda6c3a62e32632461 msgid "To enable HTTPS for the endpoint and map it to your domain" msgstr "" #: ../../source/deployment/dstack.rst:108 b91006984e1b42298a79360df47c942e msgid "If your service requires WebSockets" msgstr "" #: ../../source/deployment/dstack.rst:109 a0ecebb158d048b7bb366e166509ab31 msgid "If your service cannot work with a path prefix" msgstr "" #: ../../source/deployment/dstack.rst:111 df78b814ee044508979d32d16c6fa418 msgid "For detailed information about gateway configuration and usage, refer to the `dstack documentation on gateways `__." msgstr "" #: ../../source/deployment/dstack.rst:114 da366f09f068481898788356a2720d00 msgid "Replicas and Auto Scaling" msgstr "" #: ../../source/deployment/dstack.rst:116 1814b084b9344951b8fda9bc315ff652 msgid "You can auto scale the service by specifying additional configurations in the ``serve-30b.dstack.yml``." msgstr "" #: ../../source/deployment/dstack.rst:118 a2ca8abfc03b4008a679e44ed42a6224 msgid "Set ``replicas: min..max`` to define the minimum and maximum number of replicas" msgstr "" #: ../../source/deployment/dstack.rst:119 8cfabedabf1f4d64bb6708f07852e3f8 msgid "Configure ``scaling`` rules to determine when to scale up or down" msgstr "" #: ../../source/deployment/dstack.rst:121 fcd56774d834404fa6561d65b46afe74 msgid "Below is a complete configuration example with auto-scaling enabled:" msgstr "" #: ../../source/deployment/dstack.rst:153 dd2ed9086cf5424ab14b641044da1279 msgid "The scaling property requires a gateway to be set up." msgstr "" #: ../../source/deployment/dstack.rst:156 3986bd1e05de49048a64cdf4d6782f8a msgid "See also" msgstr "" #: ../../source/deployment/dstack.rst:157 7dc365aeac714fa5ba2989f0cb1c7e9c msgid "**Fleets**: Create cloud and on-prem clusters using `Fleets `__." msgstr "" #: ../../source/deployment/dstack.rst:158 5e647ecf87164fccbe9589e3d9c540b9 msgid "**Dev Environments**: Experiment and test before deploying to production using `Dev Environments `__." msgstr "" #: ../../source/deployment/dstack.rst:159 36774ccbc7e6446a9bfcdf029e13fe58 msgid "**Tasks**: Schedule single node or distributed training using `Tasks `__." msgstr "" #: ../../source/deployment/dstack.rst:160 72cc0103fb1e4c82ae284fa1e3633bc4 msgid "**Services**: Deploy models as secure, auto-scaling OpenAI-compatible endpoints using `Services `__." msgstr "" #: ../../source/deployment/dstack.rst:161 ba9f121d4831485e84c3cf922edc3982 msgid "**Metrics**: Monitor performance with automatically tracked metrics via CLI or UI using `Metrics `__." msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/openllm.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/deployment/openllm.rst:2 986ea00cb5af4a0d82f974ed79a82430 msgid "OpenLLM" msgstr "OpenLLM" #: ../../Qwen/source/deployment/openllm.rst:5 78be03fbdccb429892b03bf84596411b msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/deployment/openllm.rst:7 a001f11d1c5440188121d20b3baf59db msgid "OpenLLM allows developers to run Qwen2.5 models of different sizes as OpenAI-compatible APIs with a single command. It features a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Qwen2.5. Visit `the OpenLLM repository `_ to learn more." msgstr "OpenLLM 允许开发者通过一个命令运行不同大小的 Qwen2.5 模型，提供 OpenAI 兼容的 API。它具有内置的聊天 UI，先进的推理后端，以及简化的工作流程来使用 Qwen2.5 创建企业级云部署。访问 `OpenLLM 仓库 `_ 了解更多信息。" #: ../../Qwen/source/deployment/openllm.rst:10 229f89c3be65442bbe15905d75a0d13d msgid "Installation" msgstr "安装" #: ../../Qwen/source/deployment/openllm.rst:12 79421f700fbc426cb6ce9841aff67503 msgid "Install OpenLLM using ``pip``." msgstr "使用 ``pip`` 安装 OpenLLM。" #: ../../Qwen/source/deployment/openllm.rst:18 69cfd6fe2e274173ad4065be91b71472 msgid "Verify the installation and display the help information:" msgstr "验证安装并显示帮助信息：" #: ../../Qwen/source/deployment/openllm.rst:25 503cae99b14c4ef4b322b8ec0bd2d32d msgid "Quickstart" msgstr "快速开始" #: ../../Qwen/source/deployment/openllm.rst:27 0ea788c801404d8780404611c87644b0 msgid "Before you run any Qwen2.5 model, ensure your model repository is up to date by syncing it with OpenLLM's latest official repository." msgstr "在运行任何 Qwen2.5 模型之前，确保您的模型仓库与 OpenLLM 的最新官方仓库同步。" #: ../../Qwen/source/deployment/openllm.rst:33 8852ff46ecdb45b2bfc9885bbfaacb02 msgid "List the supported Qwen2.5 models:" msgstr "列出支持的 Qwen2.5 模型：" #: ../../Qwen/source/deployment/openllm.rst:39 3e4f6c11396844adb30d4e5812339484 msgid "The results also display the required GPU resources and supported platforms:" msgstr "结果还会显示所需的 GPU 资源和支持的平台：" #: ../../Qwen/source/deployment/openllm.rst:57 ac4c0db02f5249d5882940820779db9a msgid "To start a server with one of the models, use ``openllm serve`` like this:" msgstr "要使用其中一个模型来启动服务器，请使用 ``openllm serve`` 命令，例如：" #: ../../Qwen/source/deployment/openllm.rst:63 0a1d3ec35c684e3bb3e971c916aa9be7 msgid "By default, the server starts at ``http://localhost:3000/``." msgstr "默认情况下，服务器启动在 http://localhost:3000/。" #: ../../Qwen/source/deployment/openllm.rst:66 2e787de9a62f4342bdf8f88ee0df5379 msgid "Interact with the model server" msgstr "与模型服务器交互" #: ../../Qwen/source/deployment/openllm.rst:68 b22802ad9027458bb30ea0da665fea36 msgid "With the model server up and running, you can call its APIs in the following ways:" msgstr "服务器运行后，可以通过以下方式调用其 API：" #: ../../Qwen/source/deployment/openllm.rst 76214ea690094930899d6f2eddcc1454 msgid "CURL" msgstr "CURL" #: ../../Qwen/source/deployment/openllm.rst:74 42775a3df58f474782d29f2f82707bd9 msgid "Send an HTTP request to its ``/generate`` endpoint via CURL:" msgstr "通过 CURL 向其 ``/generate`` 端点发送 HTTP 请求：" #: ../../Qwen/source/deployment/openllm.rst 4f0ff3eee2ab49dda5a72bd611a9d45e msgid "Python client" msgstr "Python 客户端" #: ../../Qwen/source/deployment/openllm.rst:91 ce2e11a46e434798947b1e74ce82a19c msgid "Call the OpenAI-compatible endpoints with frameworks and tools that support the OpenAI API protocol. Here is an example:" msgstr "使用支持 OpenAI API 协议的框架和工具来调用。例如：" #: ../../Qwen/source/deployment/openllm.rst 107921d1a855430ca70c8c163d37c7f2 msgid "Chat UI" msgstr "聊天 UI" #: ../../Qwen/source/deployment/openllm.rst:118 #: b92df2759cd54c2b8316e2a160ede656 msgid "OpenLLM provides a chat UI at the ``/chat`` endpoint for the LLM server at http://localhost:3000/chat." msgstr "OpenLLM 为 LLM 服务器提供的聊天 UI 位于 ``/chat`` 端点，地址为 http://localhost:3000/chat。" #: ../../Qwen/source/deployment/openllm.rst:123 #: 0d3fa679178f443caf9c87623001be1f msgid "Model repository" msgstr "模型仓库" #: ../../Qwen/source/deployment/openllm.rst:125 #: 54d6a9bdcc064aeb95a23b60d3d575ab msgid "A model repository in OpenLLM represents a catalog of available LLMs. You can add your own repository to OpenLLM with custom Qwen2.5 variants for your specific needs. See our `documentation to learn details `_." msgstr "OpenLLM 中的模型仓库表示可用的 LLM 目录。您可以为 OpenLLM 添加自定义的 Qwen2.5 模型仓库，以满足您的特定需求。请参阅 `我们的文档 `_ 了解详细信息。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/sglang.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-07 19:51+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/deployment/sglang.md:1 e05607ecb34c453aa8f805ea62edf34f msgid "SGLang" msgstr "" #: ../../source/deployment/sglang.md:3 54dde79baa664197a2f3a5bb52383b70 msgid "[SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models." msgstr "[SGLang](https://github.com/sgl-project/sglang) 是一个用于大型语言模型和视觉语言模型的快速推理框架。" #: ../../source/deployment/sglang.md:5 1ae08e7b1ffc4f0290eefb616eac1b63 msgid "To learn more about SGLang, please refer to the [documentation](https://docs.sglang.ai/)." msgstr "要了解更多关于 SGLang 的信息，请参阅[官方文档](https://docs.sglang.ai/)。" #: ../../source/deployment/sglang.md:7 927f96387c844f79a7cfa592e64fc1b2 msgid "Environment Setup" msgstr "环境配置" #: ../../source/deployment/sglang.md:9 e04e805b59364e96a366fa088fae04e4 msgid "By default, you can install `sglang` with pip in a clean environment:" msgstr "默认情况下，你可以通过 pip 在新环境中安装 `sglang` ： " #: ../../source/deployment/sglang.md:15 fcb185985f1b4c1589200ac4af2a6aee msgid "If you have encountered issues in installation, please feel free to check the official document for installation ([link](https://docs.sglang.ai/start/install.html))." msgstr "如果在安装过程中遇到问题，请随时查阅官方安装文档（[链接](https://docs.sglang.ai/start/install.html)）" #: ../../source/deployment/sglang.md:17 a0f36bc7b4e24d598d381e2705f73eb1 msgid "API Service" msgstr "API 服务" #: ../../source/deployment/sglang.md:19 4d7006fa87884605b48700b05f602bb1 msgid "It is easy to build an OpenAI-compatible API service with SGLang, which can be deployed as a server that implements OpenAI API protocol. By default, it starts the server at `http://localhost:30000`. You can specify the address with `--host` and `--port` arguments. Run the command as shown below:" msgstr "借助 SGLang ，构建一个与OpenAI API兼容的API服务十分简便，该服务可以作为实现OpenAI API协议的服务器进行部署。默认情况下，它将在 `http://localhost:30000` 启动服务器。您可以通过 `--host` 和 `--port` 参数来自定义地址。请按照以下所示运行命令：" #: ../../source/deployment/sglang.md:27 6d10b2003b9b4dd0b9dca0a2e8d33fd6 msgid "By default, if the `--model-path` does not point to a valid local directory, it will download the model files from the Hugging Face Hub. To download model from ModelScope, set the following before running the above command:" msgstr "默认情况下，如果模型未指向有效的本地目录，它将从 Hugging Face Hub 下载模型文件。要从 ModelScope 下载模型，请在运行上述命令之前设置以下内容：" #: ../../source/deployment/sglang.md:33 d3cee58928964c5dba7720884d6c5189 msgid "For distributed inference with tensor parallelism, it is as simple as" msgstr "对于使用张量并行的分布式推理，操作非常简单：" #: ../../source/deployment/sglang.md:37 4c8600c0f3ac4d0e803af9c089d73dae msgid "The above command will use tensor parallelism on 4 GPUs. You should change the number of GPUs according to your demand." msgstr "上述命令将在 4 块 GPU 上使用张量并行。您应根据需求调整 GPU 的数量。" #: ../../source/deployment/sglang.md:40 4ca7c9376bd84c65a877134047aeee37 msgid "Basic Usage" msgstr "基本用法" #: ../../source/deployment/sglang.md:42 bd805ae178b6401c925a959334b64b88 msgid "Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen:" msgstr "然后，您可以利用 [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) 来与Qwen进行对话：" #: ../../source/deployment/sglang.md 2f867c83bdce4a4286842da69aa68640 #: 418b07dd6a574642bfa89052103763e9 msgid "curl" msgstr "" #: ../../source/deployment/sglang.md 14df52980bfe41689ac8dc8699be2134 #: 7a50af3d10534acfbf980ac0d2ee92e5 msgid "Python" msgstr "" #: ../../source/deployment/sglang.md:62 ../../source/deployment/sglang.md:126 #: 669de086434740279e9cf7c54fb42e56 a3f9e92506374567a4660de9071567e8 msgid "You can use the API client with the `openai` Python SDK as shown below:" msgstr "或者您可以如下面所示使用 `openai` Python SDK中的 API 客户端：" #: ../../source/deployment/sglang.md:92 d8321f81e9624419b5e0fdb7012816e4 msgid "While the default sampling parameters would work most of the time for thinking mode, it is recommended to adjust the sampling parameters according to your application, and always pass the sampling parameters to the API." msgstr "虽然默认的采样参数在大多数情况下适用于思考模式，但建议根据您的应用调整采样参数，并始终将采样参数传递给 API。" #: ../../source/deployment/sglang.md:98 d6379b9f885748ca89bd3fe6c3362376 msgid "Thinking & Non-Thinking Modes" msgstr "思考与非思考模式" #: ../../source/deployment/sglang.md:100 f82eb1dfcc934667ac5aee0600140794 msgid "Qwen3 models will think before respond. This behavior could be controlled by either the hard switch, which could disable thinking completely, or the soft switch, where the model follows the instruction of the user on whether it should think." msgstr "Qwen3 模型会在回复前进行思考。这种行为可以通过硬开关（完全禁用思考）或软开关（模型遵循用户关于是否应该思考的指令）来控制。" #: ../../source/deployment/sglang.md:103 bac5d71126f04d149c0d674b7b2f7ec8 msgid "The hard switch is available in SGLang through the following configuration to the API call. To disable thinking, use" msgstr "硬开关在 SGLang 中可以通过以下 API 调用配置使用。要禁用思考，请使用" #: ../../source/deployment/sglang.md:158 09ccfb31c140452399460ed1357afc28 msgid "Please note that passing `enable_thinking` is not OpenAI API compatible. The exact method may differ among frameworks." msgstr "请注意，`enable_thinking`并非OpenAI API定义的参数，具体传入方式可能因推理框架不同而不同。" #: ../../source/deployment/sglang.md:163 650e618e24044303b48b6bc9d4ccc239 msgid "To completely disable thinking, you could use [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) when starting the model:" msgstr "要完全禁用思考，您可以在启动模型时使用[自定义聊天模板](../../source/assets/qwen3_nonthinking.jinja)：" #: ../../source/deployment/sglang.md:169 9c0dc646158541a991045064cfa5b258 msgid "The chat template prevents the model from generating thinking content, even if the user instructs the model to do so with `/think`." msgstr "该聊天模板会阻止模型生成思考内容，即使用户通过 `/think` 指示模型这样做。" #: ../../source/deployment/sglang.md:174 c23b692035b14b1099c8a148956457a5 msgid "It is recommended to set sampling parameters differently for thinking and non-thinking modes." msgstr "建议为思考模式和非思考模式分别设置不同的采样参数。" #: ../../source/deployment/sglang.md:177 c5c258baa5fa46ccbadb58573699a0f1 msgid "Parsing Thinking Content" msgstr "解析思考内容" #: ../../source/deployment/sglang.md:179 02d90ad41ecb4d51ae9f55458670843e msgid "SGLang supports parsing the thinking content from the model generation into structured messages:" msgstr "SGLang 支持将模型生成的思考内容解析为结构化消息：" #: ../../source/deployment/sglang.md:184 854a73931a9e404b9942a10dd2702023 msgid "The response message will have a field named `reasoning_content` in addition to `content`, containing the thinking content generated by the model." msgstr "响应消息除了包含 `content` 字段外，还会有一个名为 `reasoning_content` 的字段，其中包含模型生成的思考内容。" #: ../../source/deployment/sglang.md:187 0bae083925f64ec7984c1b7c86d00ac1 msgid "Please note that this feature is not OpenAI API compatible." msgstr "请注意，此功能与 OpenAI API 规范不一致。" #: ../../source/deployment/sglang.md:191 f23a3deb557a4d808cef5bdaad6dcf16 msgid "`enable_thinking=False` may not be compatible with this feature. If you need to pass `enable_thinking=False` to the API, please consider disabling parsing thinking content." msgstr "`enable_thinking=False` 可能与思考内容解析不兼容。如果需要向 API 传递 `enable_thinking=False`，请考虑禁用该功能。" #: ../../source/deployment/sglang.md:195 930b8e7391204fc68d6473fec1d2e4e0 msgid "Parsing Tool Calls" msgstr "解析工具调用" #: ../../source/deployment/sglang.md:197 8fb5272b079543219b125e70da4f89d3 msgid "SGLang supports parsing the tool calling content from the model generation into structured messages:" msgstr "SGLang 支持将模型生成的工具调用内容解析为结构化消息：" #: ../../source/deployment/sglang.md:202 28ca5e5fc8694b839b91cb3f7f38a0cb msgid "For more information, please refer to [our guide on Function Calling](../framework/function_call.md)." msgstr "详细信息，请参阅[函数调用的指南](../framework/function_call.md#vllm)。" #: ../../source/deployment/sglang.md:204 59cd747bac244c57afc56b7f3d041df8 msgid "Structured/JSON Output" msgstr "结构化/JSON输出" #: ../../source/deployment/sglang.md:206 4534e68747c041d5addd24c36fbc8250 msgid "SGLang supports structured/JSON output. Please refer to [SGLang's documentation](https://docs.sglang.ai/backend/structured_outputs.html#OpenAI-Compatible-API). Besides, it is also recommended to instruct the model to generate the specific format in the system message or in your prompt." msgstr "SGLang 支持结构化/JSON 输出。请参阅[SGLan文档](https://docs.sglang.ai/backend/structured_outputs.html#OpenAI-Compatible-API)。此外，还建议在系统消息或您的提示中指示模型生成特定格式。" #: ../../source/deployment/sglang.md:210 734cfd6d921e4706a07e112237b09b38 msgid "Serving Quantized models" msgstr "部署量化模型" #: ../../source/deployment/sglang.md:212 e7b0890292ad44278e910b6ee97f6d2d msgid "Qwen3 comes with two types of pre-quantized models, FP8 and AWQ." msgstr "Qwen3 提供了两种类型的预量化模型：FP8 和 AWQ。" #: ../../source/deployment/sglang.md:214 0bb52b4e43504cb8ac143e594247a0e0 msgid "The command serving those models are the same as the original models except for the name change:" msgstr "部署这些模型的命令与原始模型相同，只是名称有所更改：" #: ../../source/deployment/sglang.md:223 714f8f196af24271b6967dd038614f88 msgid "Context Length" msgstr "上下文长度" #: ../../source/deployment/sglang.md:225 ad211116852345b8bfb9bb9e58027486 msgid "The context length for Qwen3 models in pretraining is up to 32,768 tokens. To handle context length substantially exceeding 32,768 tokens, RoPE scaling techniques should be applied. We have validated the performance of [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts." msgstr "Qwen3 模型在预训练中的上下文长度最长为 32,768 个 token。为了处理显著超过 32,768 个 token 的上下文长度，应应用 RoPE 缩放技术。我们已经验证了 [YaRN](https://arxiv.org/abs/2309.00071) 的性能，这是一种增强模型长度外推的技术，可确保在长文本上的最佳性能。" #: ../../source/deployment/sglang.md:229 d243e7a41b214c289be782db495e82f4 msgid "SGLang supports YaRN, which can be configured as" msgstr "SGLang 支持 YaRN，可以配置为" #: ../../source/deployment/sglang.md:235 c15ed6a15a714884ab3024654203ec06 msgid "SGLang implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0." msgstr "SGLang 实现了静态 YaRN，这意味着无论输入长度如何，缩放因子都保持不变，**这可能会对较短文本的性能产生影响。** 我们建议仅在需要处理长上下文时添加 `rope_scaling` 配置。还建议根据需要调整 `factor`。例如，如果您的应用程序的典型上下文长度为 65,536 个 token，则最好将 `factor` 设置为 2.0。" #: ../../source/deployment/sglang.md:241 e0528eb23e2a454585b46ef178d28a79 msgid "The default `max_position_embeddings` in `config.json` is set to 40,960, which is used by SGLang. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing and leave adequate room for model thinking. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance." msgstr "`config.json` 中的默认 `max_position_embeddings` 被设置为 40,960，SGLang 将使用该值。此分配包括为输出保留 32,768 个 token，为典型提示保留 8,192 个 token，这足以应对大多数涉及短文本处理的场景，并为模型思考留出充足空间。如果平均上下文长度不超过 32,768 个 token，我们不建议在此场景中启用 YaRN，因为这可能会降低模型性能。" #~ msgid "Please note that `sglang` relies on `flashinfer-python` and has strict dependencies on `torch` and its CUDA versions. Check the note in the official document for installation ([link](https://docs.sglang.ai/start/install.html)) for more help." #~ msgstr "请留意预构建的 `sglang` 依赖 `flashinfer-python`，并对`torch`和其CUDA版本有强依赖。请查看[官方文档](https://docs.sglang.ai/start/install.html)中的注意事项以获取有关安装的帮助。" #~ msgid "This feature has not been released. For more information, please see this [pull request](https://github.com/sgl-project/sglang/pull/5551)." #~ msgstr "此功能尚未发布。更多信息，请参阅此[pull request](https://github.com/sgl-project/sglang/pull/5551)。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/skypilot.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/deployment/skypilot.rst:2 795ad4f30e27494d93675f71bb1a5cc4 msgid "SkyPilot" msgstr "" #: ../../Qwen/source/deployment/skypilot.rst:5 aad807db94a24d868c9c1b364b47e152 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/deployment/skypilot.rst:8 d6bbf736584f4bbfa9c300d50a2ed669 msgid "What is SkyPilot" msgstr "SkyPilot 是什么" #: ../../Qwen/source/deployment/skypilot.rst:10 #: b66facae41bf493880e43044e2915a45 msgid "SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, the highest GPU availability, and managed execution. Its features include:" msgstr "SkyPilot 是一个可以在任何云上运行 LLM 、 AI 应用以及批量任务的框架，旨在实现最大程度的成本节省、最高的 GPU 可用性以及受管理的执行过程。其特性包括：" #: ../../Qwen/source/deployment/skypilot.rst:14 #: 621f021163c549d0aadb1c911a3a3ef5 msgid "Get the best GPU availability by utilizing multiple resources pools across multiple regions and clouds." msgstr "通过跨区域和跨云充分利用多个资源池，以获得最佳的 GPU 可用性。" #: ../../Qwen/source/deployment/skypilot.rst:16 #: ea1723c3b5be454cad3219836f4386d8 msgid "Pay absolute minimum — SkyPilot picks the cheapest resources across regions and clouds. No managed solution markups." msgstr "把费用降到最低—— SkyPilot 在各区域和云平台中为您挑选最便宜的资源。无需任何托管解决方案的额外加价。" #: ../../Qwen/source/deployment/skypilot.rst:18 #: e479693ecf08411ca35d8d0727c8f441 msgid "Scale up to multiple replicas across different locations and accelerators, all served with a single endpoint" msgstr "将服务扩展到多个副本上，所有副本通过单一 endpoint 对外提供服务" #: ../../Qwen/source/deployment/skypilot.rst:20 #: 1f9cdd2ae2544d1faa8a4c463ee0e42c msgid "Everything stays in your cloud account (your VMs & buckets)" msgstr "所有内容均保存在您的云账户中（包括您的虚拟机和 bucket ）" #: ../../Qwen/source/deployment/skypilot.rst:21 #: 5bb9b617764942d989e5093463a359f0 msgid "Completely private - no one else sees your chat history" msgstr "完全私密 - 没有其他人能看到您的聊天记录" #: ../../Qwen/source/deployment/skypilot.rst:24 #: cf0c456ac72f40ac98790c11dc243317 msgid "Install SkyPilot" msgstr "安装 SkyPilot" #: ../../Qwen/source/deployment/skypilot.rst:26 #: 78d86c1fa8104b138b01aed640b262fc msgid "We advise you to follow the `instruction `__ to install SkyPilot. Here we provide a simple example of using ``pip`` for the installation as shown below." msgstr "我们建议您按照 `指示 `__ 安装 SkyPilot 。以下为您提供了一个使用 ``pip`` 进行安装的简单示例：" #: ../../Qwen/source/deployment/skypilot.rst:38 #: a7c88265bf404f55b85388c81a240199 msgid "After that, you need to verify cloud access with a command like:" msgstr "随后，您需要用如下命令确认是否能使用云：" #: ../../Qwen/source/deployment/skypilot.rst:44 #: 72025dfba0144f63a720f6da0dd39bfa msgid "For more information, check the `official document `__ and see if you have set up your cloud accounts correctly." msgstr "若需更多信息，请查阅官方文档，确认您的云账户设置是否正确无误。" #: ../../Qwen/source/deployment/skypilot.rst:47 #: 61be006061554e5ea40d55497e11e192 msgid "Alternatively, you can also use the official docker image with SkyPilot master branch automatically cloned by running:" msgstr "或者，您也可以使用官方提供的 docker 镜像，可以自动克隆 SkyPilot 的主分支：" #: ../../Qwen/source/deployment/skypilot.rst:63 #: 4ae89fb44c6643a3a82fca5cee622af4 msgid "Running Qwen2.5-72B-Instruct with SkyPilot" msgstr "使用 SkyPilot 运行 Qwen2.5-72B-Instruct " #: ../../Qwen/source/deployment/skypilot.rst:65 #: 1bc4973c2eb745689ded0af54ba33e0e msgid "Start serving Qwen2.5-72B-Instruct on a single instance with any available GPU in the list specified in `serve-72b.yaml `__ with a vLLM-powered OpenAI-compatible endpoint:" msgstr "`serve-72b.yaml `__ 中列出了支持的 GPU 。您可使用配备这类 GPU 的单个运算实例来部署 Qwen2.5-72B-Instruct 服务。该服务由 vLLM 搭建，并与 OpenAI API 兼容。以下为部署方法：" #: ../../Qwen/source/deployment/skypilot.rst:74 #: ../../Qwen/source/deployment/skypilot.rst:123 #: ac3692ed16974facbd58b6886cd111af b325de015e7b4bb0a91491d3f7418792 msgid "**Before launching, make sure you have changed Qwen/Qwen2-72B-Instruct to Qwen/Qwen2.5-72B-Instruct in the YAML file.**" msgstr "**在启动之前，请先将 YAML 文件中的 Qwen/Qwen2-72B-Instruct 修改为 Qwen/Qwen2.5-72B-Instruct。**" #: ../../Qwen/source/deployment/skypilot.rst:76 #: 6046b3c86fae4a43878fbadbeb33fbd8 msgid "Send a request to the endpoint for completion:" msgstr "向该 endpoint 发送续写请求：" #: ../../Qwen/source/deployment/skypilot.rst:90 #: 2ec56c2028a94f568fd2c1a65063d25a msgid "Send a request for chat completion:" msgstr "向该 endpoint 发送对话续写请求" #: ../../Qwen/source/deployment/skypilot.rst:112 #: c8e140ddfd914ff5a460621a7ca1891e msgid "Scale up the service with SkyPilot Serve" msgstr "使用 SkyPilot Serve 扩展服务规模" #: ../../Qwen/source/deployment/skypilot.rst:114 #: 0db304ab396d45adb6017d78cd1ee4a2 msgid "With `SkyPilot Serve `__, a serving library built on top of SkyPilot, scaling up the Qwen service is as simple as running:" msgstr "使用 `SkyPilot Serve `__ 扩展 Qwen 的服务规模非常容易，只需运行：" #: ../../Qwen/source/deployment/skypilot.rst:125 #: 25bbbf9e49be44d3899074ff97202d71 msgid "This will start the service with multiple replicas on the cheapest available locations and accelerators. SkyServe will automatically manage the replicas, monitor their health, autoscale based on load, and restart them when needed." msgstr "这将启动服务，使用多个副本部署在最经济的可用位置和加速器上。 SkyServe 将自动管理这些副本，监控其健康状况，根据负载进行自动伸缩，并在必要时重启它们。" #: ../../Qwen/source/deployment/skypilot.rst:130 #: bda628bab7ef41a0918dc4b80a9b3cfe msgid "A single endpoint will be returned and any request sent to the endpoint will be routed to the ready replicas." msgstr "将返回一个 endpoint ，所有发送至该endpoint的请求都将被路由至就绪状态的副本。" #: ../../Qwen/source/deployment/skypilot.rst:133 #: b232dbbdcf674d56bcf9c0331c020864 msgid "To check the status of the service, run:" msgstr "运行如下命令检查服务的状态：" #: ../../Qwen/source/deployment/skypilot.rst:139 #: 556b854caf7243fb93f253ebe2dc9033 msgid "After a while, you will see the following output:" msgstr "很快，您将看到如下输出：" #: ../../Qwen/source/deployment/skypilot.rst:152 #: 5a6055c5a42c4b2db6693c1095688de8 msgid "As shown, the service is now backed by 2 replicas, one on Azure and one on GCP, and the accelerator type is chosen to be **the cheapest available one** on the clouds. That said, it maximizes the availability of the service while minimizing the cost." msgstr "如下所示：该服务现由两个副本提供支持，一个位于 Azure 平台，另一个位于 GCP 平台。同时，已为服务选择云服务商提供的 **最经济实惠** 的加速器类型。这样既最大限度地提升了服务的可用性，又尽可能降低了成本。" #: ../../Qwen/source/deployment/skypilot.rst:157 #: a18533d33dc54a1091ded0b4bba0a1eb msgid "To access the model, we use a ``curl -L`` command (``-L`` to follow redirect) to send the request to the endpoint:" msgstr "要访问模型，我们使用带有 ``curl -L`` （用于跟随重定向），将请求发送到 endpoint ：" #: ../../Qwen/source/deployment/skypilot.rst:182 #: 34cd50fd79e24d8895075f7841b025e4 msgid "Accessing Qwen2.5 with Chat GUI" msgstr "使用 Chat GUI 调用 Qwen2.5" #: ../../Qwen/source/deployment/skypilot.rst:184 #: ca6994cda1cb469e83ce8c026bb67e42 msgid "It is also possible to access the Qwen2.5 service with GUI by connecting a `FastChat GUI server `__ to the endpoint launched above (see `gui.yaml `__)." msgstr "可以通过 `FastChat `__ 来使用 GUI 调用 Qwen2.5 的服务：" #: ../../Qwen/source/deployment/skypilot.rst:188 #: 99a63e55ab5c46258c20ab89cdfa39dc msgid "Start the Chat Web UI:" msgstr "开启一个 Chat Web UI" #: ../../Qwen/source/deployment/skypilot.rst:194 #: e61593a092c146f8a06af896d6af17f2 msgid "**Before launching, make sure you have changed Qwen/Qwen1.5-72B-Chat to Qwen/Qwen2.5-72B-Instruct in the YAML file.**" msgstr "**在启动之前，请先将 YAML 文件中的 Qwen/Qwen1.5-72B-Chat 修改为 Qwen/Qwen2.5-72B-Instruct。**" #: ../../Qwen/source/deployment/skypilot.rst:196 #: 9631068a8b424aa8af6dc6911daac7a9 msgid "Then, we can access the GUI at the returned gradio link:" msgstr "随后，我们可以通过返回的 gradio 链接来访问 GUI ：" #: ../../Qwen/source/deployment/skypilot.rst:202 #: 1464a56dcd06404aafbe6d7d2c72212b msgid "Note that you may get better results by using a different temperature and top_p value." msgstr "你可以通过使用不同的温度和 top_p 值来尝试取得更好的结果。" #: ../../Qwen/source/deployment/skypilot.rst:205 #: d257f49d835e4c12b28bc680bb78a9cb msgid "Summary" msgstr "总结" #: ../../Qwen/source/deployment/skypilot.rst:207 #: 06b9684a19774eaba4f69862332c5166 msgid "With SkyPilot, it is easy for you to deploy Qwen2.5 on any cloud. We advise you to read the official doc for more usages and updates. Check `this `__ out!" msgstr "通过 SkyPilot ，你可以轻松地在任何云上部署 Qwen2.5 。我们建议您阅读 `官方文档 `__ 了解更多用法和最新进展。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/tgi.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/deployment/tgi.rst:2 2abcc96f9deb4b9187ac9d88fc69e929 msgid "TGI" msgstr "" #: ../../Qwen/source/deployment/tgi.rst:5 2d124d7cb95f47388aa48c662932ef9b msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/deployment/tgi.rst:7 4e5d299c4fdd46d5aba38c9af5765792 msgid "Hugging Face's Text Generation Inference (TGI) is a production-ready framework specifically designed for deploying and serving large language models (LLMs) for text generation tasks. It offers a seamless deployment experience, powered by a robust set of features:" msgstr "Hugging Face 的 Text Generation Inference (TGI) 是一个专为部署大规模语言模型 (Large Language Models, LLMs) 而设计的生产级框架。TGI提供了流畅的部署体验，并稳定支持如下特性：" #: ../../Qwen/source/deployment/tgi.rst:9 ecd4fc11a95140959915d062791ceba1 msgid "`Speculative Decoding `_: Accelerates generation speeds." msgstr "`推测解码 (Speculative Decoding) `_ ：提升生成速度。" #: ../../Qwen/source/deployment/tgi.rst:10 84590a56416348bf85b3f296cf57e257 msgid "`Tensor Parallelism`_: Enables efficient deployment across multiple GPUs." msgstr "张量并行 (`Tensor Parallelism`_) ：高效多卡部署。" #: ../../Qwen/source/deployment/tgi.rst:11 a996d6ecd7b94c5cb9752d370f29a9b1 msgid "`Token Streaming`_: Allows for the continuous generation of text." msgstr "流式生成 (`Token Streaming`_) ：支持持续性生成文本。" #: ../../Qwen/source/deployment/tgi.rst:12 8f591c045ba34f4581bb19652db9f9b3 msgid "Versatile Device Support: Works seamlessly with `AMD`_, `Gaudi`_ and `AWS Inferentia`_." msgstr "灵活的硬件支持：与 `AMD`_ ， `Gaudi`_ 和 `AWS Inferentia`_ 无缝衔接。" #: ../../Qwen/source/deployment/tgi.rst:21 5e8a98b91fc146e0b581422faa683a18 msgid "Installation" msgstr "安装" #: ../../Qwen/source/deployment/tgi.rst:23 684ef25bfb0e460999d6dcccce41b85f msgid "The easiest way to use TGI is via the TGI docker image. In this guide, we show how to use TGI with docker." msgstr "通过 TGI docker 镜像使用 TGI 轻而易举。本文将主要介绍 TGI 的 docker 用法。" #: ../../Qwen/source/deployment/tgi.rst:25 c563fa3eccb04d00a477c1d2e8b15c38 msgid "It's possible to run it locally via Conda or build locally. Please refer to `Installation Guide `_ and `CLI tool `_ for detailed instructions." msgstr "也可通过 Conda 实机安装或搭建服务。请参考 `Installation Guide `_ 与 `CLI tool `_ 以了解详细说明。" #: ../../Qwen/source/deployment/tgi.rst:28 b55fc58ff4cb472abca08296409c7837 msgid "Deploy Qwen2.5 with TGI" msgstr "通过 TGI 部署 Qwen2.5" #: ../../Qwen/source/deployment/tgi.rst:30 586a8425ec5d413592fd7daf579c7e87 msgid "**Find a Qwen2.5 Model:** Choose a model from `the Qwen2.5 collection `_." msgstr "**选定 Qwen2.5 模型：** 从 `the Qwen2.5 collection `_ 中挑选模型。" #: ../../Qwen/source/deployment/tgi.rst:31 50fcab8da35941eca308786979dbaf38 msgid "**Deployment Command:** Run the following command in your terminal, replacing ``model`` with your chosen Qwen2.5 model ID and ``volume`` with the path to your local data directory:" msgstr "**部署TGI服务：** 在终端中运行以下命令，注意替换 ``model`` 为选定的 Qwen2.5 模型 ID 、 ``volume`` 为本地的数据路径： " #: ../../Qwen/source/deployment/tgi.rst:42 2a800533a7d84bdeab1da0976b0cab53 msgid "Using TGI API" msgstr "使用 TGI API" #: ../../Qwen/source/deployment/tgi.rst:44 f05d1ec08140452782d0659543fad7d1 msgid "Once deployed, the model will be available on the mapped port (8080)." msgstr "一旦成功部署，API 将于选定的映射端口 (8080) 提供服务。" #: ../../Qwen/source/deployment/tgi.rst:46 f265dc1522b049c98ba31fd5d255c50f msgid "TGI comes with a handy API for streaming response:" msgstr "TGI 提供了简单直接的 API 支持流式生成：" #: ../../Qwen/source/deployment/tgi.rst:54 e9cc4c0571b74bd08b2a59347503e653 msgid "It's also available on OpenAI style API:" msgstr "也可使用 OpenAI 风格的 API 使用 TGI ：" #: ../../Qwen/source/deployment/tgi.rst:73 5dc7e9c74fc04483ba8e5dcdd7052020 msgid "The model field in the JSON is not used by TGI, you can put anything." msgstr "JSON 中的 model 字段不会被 TGI 识别，您可传入任意值。" #: ../../Qwen/source/deployment/tgi.rst:75 d60f837152014cda8baebc90d65d1cc0 #, python-format msgid "Refer to the `TGI Swagger UI `_ for a complete API reference." msgstr "完整 API 文档，请查阅 `TGI Swagger UI `_ 。" #: ../../Qwen/source/deployment/tgi.rst:77 b59564031e5548088aef828f9753e337 msgid "You can also use Python API:" msgstr "你也可以使用 Python 访问 API ：" #: ../../Qwen/source/deployment/tgi.rst:106 62646cecb024479ebfeca5f3063e7322 msgid "Quantization for Performance" msgstr "量化" #: ../../Qwen/source/deployment/tgi.rst:108 4a8d39bf37be4820afb230f9a977b431 msgid "Data-dependent quantization (GPTQ and AWQ)" msgstr "依赖数据的量化方案（ GPTQ 与 AWQ ）" #: ../../Qwen/source/deployment/tgi.rst:110 ef2b18f47e4f4f7ebb017be628cb0be9 msgid "Both GPTQ and AWQ models are data-dependent. The official quantized models can be found from `the Qwen2.5 collection`_ and you can also quantize models with your own dataset to make it perform better on your use case." msgstr "GPTQ 与 AWQ 均依赖数据进行量化。我们提供了预先量化好的模型，请于 `the Qwen2.5 collection`_ 查找。你也可以使用自己的数据集自行量化，以在你的场景中取得更好效果。" #: ../../Qwen/source/deployment/tgi.rst:112 53d94278a2e3409abb9980ebc7c96c24 msgid "The following shows the command to start TGI with Qwen2.5-7B-Instruct-GPTQ-Int4:" msgstr "以下是通过 TGI 部署 Qwen2.5-7B-Instruct-GPTQ-Int4 的指令：" #: ../../Qwen/source/deployment/tgi.rst:122 68ff8a07d0eb40cfa67d79e01adea070 msgid "If the model is quantized with AWQ, e.g. Qwen/Qwen2.5-7B-Instruct-AWQ, please use ``--quantize awq``." msgstr "如果模型是 AWQ 量化的，如 Qwen/Qwen2.5-7B-Instruct-AWQ ，请使用 ``--quantize awq`` 。" #: ../../Qwen/source/deployment/tgi.rst:124 b4c3b82b1f2a43a8a02383fd0afbda5f msgid "Data-agnostic quantization" msgstr "不依赖数据的量化方案" #: ../../Qwen/source/deployment/tgi.rst:126 7a6b89c94b72407482b96790f5bbd272 msgid "EETQ on the other side is not data dependent and can be used with any model. Note that we're passing in the original model (instead of a quantized model) with the ``--quantize eetq`` flag." msgstr "EETQ 是一种不依赖数据的量化方案，可直接用于任意模型。请注意，我们需要传入原始模型，并使用 ``--quantize eetq`` 标志。" #: ../../Qwen/source/deployment/tgi.rst:138 763166da65924887b3bba99ea4d2baab msgid "Multi-Accelerators Deployment" msgstr "多卡部署" #: ../../Qwen/source/deployment/tgi.rst:140 ddcfcff947894f168c7945ae9c42a579 msgid "Use the ``--num-shard`` flag to specify the number of accelerators. Please also use ``--shm-size 1g`` to enable shared memory for optimal NCCL performance (`reference `__):" msgstr "使用 ``--num-shard`` 指定卡书数量。 请务必传入 ``--shm-size 1g`` 让 NCCL 发挥最好性能 (`说明 `__) ：" #: ../../Qwen/source/deployment/tgi.rst:151 520c46fb404c4ec9bf89280e4a71f1e8 msgid "Speculative Decoding" msgstr "推测性解码 (Speculative Decoding)" #: ../../Qwen/source/deployment/tgi.rst:153 74c6b65f76b74d56ad109af9da11f66e msgid "Speculative decoding can reduce the time per token by speculating on the next token. Use the ``--speculative-decoding`` flag, setting the value to the number of tokens to speculate on (default: 0 for no speculation):" msgstr "推测性解码 (Speculative Decoding) 通过预先推测下一 token 来节约每 token 需要的时间。使用 ``--speculative-decoding`` 设定预先推测 token 的数量 （默认为0，表示不预先推测）：" #: ../../Qwen/source/deployment/tgi.rst:164 dee05ee0fb1a4f2da42b250192d943f5 msgid "The overall performance of speculative decoding highly depends on the type of task. It works best for code or highly repetitive text." msgstr "推测性解码的加速效果依赖于任务类型，对于代码或重复性较高的文本生成任务，提速更明显。" #: ../../Qwen/source/deployment/tgi.rst:166 731f300bc1174589901dd5feb26e8b2f msgid "More context on speculative decoding can be found `here `__." msgstr "更多说明可查阅 `此文档 `__ 。" #: ../../Qwen/source/deployment/tgi.rst:170 65a7d5553dd145398f9705c1ee6c28f0 msgid "Zero-Code Deployment with HF Inference Endpoints" msgstr "使用 HF Inference Endpoints 零代码部署" #: ../../Qwen/source/deployment/tgi.rst:172 721c3a7578f846ae8e21e595923e17e7 msgid "For effortless deployment, leverage Hugging Face Inference Endpoints:" msgstr "使用 Hugging Face Inference Endpoints 不费吹灰之力：" #: ../../Qwen/source/deployment/tgi.rst:174 7741607488d94a9f8be2ffcb6a5322fb msgid "**GUI interface:** ``__" msgstr "" #: ../../Qwen/source/deployment/tgi.rst:175 02ff4520e66f4a42828483da7d25445f msgid "**Coding interface:** ``__" msgstr "" #: ../../Qwen/source/deployment/tgi.rst:177 d35f9dd4bc96400cb6c7584012d2df49 msgid "Once deployed, the endpoint can be used as usual." msgstr "一旦部署成功，服务使用与本地无异。" #: ../../Qwen/source/deployment/tgi.rst:181 61c1b825bbf24be2aaaeb99de3f0660e msgid "Common Issues" msgstr "常见问题" #: ../../Qwen/source/deployment/tgi.rst:183 b55a2d286fc24dbe92b79ab5c010c7af msgid "Qwen2.5 supports long context lengths, so carefully choose the values for ``--max-batch-prefill-tokens``, ``--max-total-tokens``, and ``--max-input-tokens`` to avoid potential out-of-memory (OOM) issues. If an OOM occurs, you'll receive an error message upon startup. The following shows an example to modify those parameters:" msgstr "Qwen2.5 支持长上下文，谨慎设定 ``--max-batch-prefill-tokens`` ， ``--max-total-tokens`` 和 ``--max-input-tokens`` 以避免 out-of-memory (OOM) 。如 OOM ，你将在启动 TGI 时收到错误提示。以下为修改这些参数的示例：" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/deployment/vllm.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 16:50+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/deployment/vllm.md:1 d5c0a6a59a4e4efdba77515c0c05f04a msgid "vLLM" msgstr "" #: ../../source/deployment/vllm.md:3 56317e0cd3104065a8496366f9bdcb67 msgid "We recommend you trying [vLLM](https://github.com/vllm-project/vllm) for your deployment of Qwen. It is simple to use, and it is fast with state-of-the-art serving throughput, efficient management of attention key value memory with PagedAttention, continuous batching of input requests, optimized CUDA kernels, etc. To learn more about vLLM, please refer to the [paper](https://arxiv.org/abs/2309.06180) and [documentation](https://docs.vllm.ai/)." msgstr "我们建议您在部署 Qwen 时尝试使用 [vLLM](https://github.com/vllm-project/vllm)。它易于使用，且具有最先进的服务吞吐量、高效的注意力键值内存管理（通过PagedAttention实现）、连续批处理输入请求、优化的CUDA内核等功能。要了解更多关于vLLM的信息，请参阅 [论文](https://arxiv.org/abs/2309.06180) 和 [文档](https://docs.vllm.ai/)。" #: ../../source/deployment/vllm.md:7 81077ec6d7594b85b64857ee22883693 msgid "Environment Setup" msgstr "环境配置" #: ../../source/deployment/vllm.md:9 f80ee07832674dbfb3c547d0bd003720 msgid "By default, you can install `vllm` with pip in a clean environment:" msgstr "默认情况下，你可以通过 pip 在新环境中安装 `vllm` ： " #: ../../source/deployment/vllm.md:15 7c0520bacc8941108def669c821603a9 msgid "Please note that the prebuilt `vllm` has strict dependencies on `torch` and its CUDA versions. Check the note in the official document for installation ([link](https://docs.vllm.ai/en/latest/getting_started/installation.html)) for more help." msgstr "请留意预构建的`vllm`对`torch`和其CUDA版本有强依赖。请查看[vLLM官方文档](https://docs.vllm.ai/en/latest/getting_started/installation.html)中的注意事项以获取有关安装的帮助。" #: ../../source/deployment/vllm.md:18 ab785eb82e7f40a08269510d1cb5610d msgid "API Service" msgstr "API 服务" #: ../../source/deployment/vllm.md:20 fa0b4510d60d489c94435c334100e413 msgid "It is easy to build an OpenAI-compatible API service with vLLM, which can be deployed as a server that implements OpenAI API protocol. By default, it starts the server at `http://localhost:8000`. You can specify the address with `--host` and `--port` arguments. Run the command as shown below:" msgstr "借助vLLM，构建一个与OpenAI API兼容的API服务十分简便，该服务可以作为实现OpenAI API协议的服务器进行部署。默认情况下，它将在 `http://localhost:8000` 启动服务器。您可以通过 `--host` 和 `--port` 参数来自定义地址。请按照以下所示运行命令：" #: ../../source/deployment/vllm.md:28 66e317eb19424af7a54eb52093e7945b msgid "By default, if the model does not point to a valid local directory, it will download the model files from the Hugging Face Hub. To download model from ModelScope, set the following before running the above command:" msgstr "默认情况下，如果模型未指向有效的本地目录，它将从 Hugging Face Hub 下载模型文件。要从 ModelScope 下载模型，请在运行上述命令之前设置以下内容：" #: ../../source/deployment/vllm.md:34 4d9631b6922c4f08b9be1ec85a956309 msgid "For distributed inference with tensor parallelism, it is as simple as" msgstr "对于使用张量并行的分布式推理，操作非常简单：" #: ../../source/deployment/vllm.md:38 33cc8d44a1434af2af200659add2e57f msgid "The above command will use tensor parallelism on 4 GPUs. You should change the number of GPUs according to your demand." msgstr "上述命令将在 4 块 GPU 上使用张量并行。您应根据需求调整 GPU 的数量。" #: ../../source/deployment/vllm.md:41 605a634a44e3401ab46c77933aa2817e msgid "Basic Usage" msgstr "基本用法" #: ../../source/deployment/vllm.md:43 58fb05b376b545e4892584539004c4c8 msgid "Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen:" msgstr "然后，您可以利用 [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) 来与Qwen进行对话：" #: ../../source/deployment/vllm.md b9a21dd5fe924a36ae4d655aa7c2d127 #: f95affb6c52340bd8623c319fa8a159f msgid "curl" msgstr "" #: ../../source/deployment/vllm.md 3c6242c05b90435dbf1c971ce061e127 #: 92e58b5637bd4af1bc4df269071a3df5 msgid "Python" msgstr "" #: ../../source/deployment/vllm.md:63 ../../source/deployment/vllm.md:129 #: 12be4f0cf2b9495faee25799b266a799 855aea8cb2fa48f7a63c6a511cb03fd5 msgid "You can use the API client with the `openai` Python SDK as shown below:" msgstr "或者您可以如下面所示使用 `openai` Python SDK中的 API 客户端：" #: ../../source/deployment/vllm.md:93 85d6fac1e7e24de1983be918b3e0ea3e msgid "`vllm` will use the sampling parameters from the `generation_config.json` in the model files." msgstr "`vllm` 将使用模型文件中 `generation_config.json` 的采样参数。" #: ../../source/deployment/vllm.md:95 1323928e307d4ed9bcd1e538e61f0d2d msgid "While the default sampling parameters would work most of the time for thinking mode, it is recommended to adjust the sampling parameters according to your application, and always pass the sampling parameters to the API." msgstr "虽然默认的采样参数在大多数情况下适用于思考模式，但建议根据您的应用调整采样参数，并始终将采样参数传递给 API。" #: ../../source/deployment/vllm.md:101 84e3b8ae27ab4242973d411876f79250 msgid "Thinking & Non-Thinking Modes" msgstr "思考与非思考模式" #: ../../source/deployment/vllm.md:103 80dddb284cb8439399ad0e3c2227cc0e msgid "Qwen3 models will think before respond. This behavior could be controlled by either the hard switch, which could disable thinking completely, or the soft switch, where the model follows the instruction of the user on whether it should think." msgstr "Qwen3 模型会在回复前进行思考。这种行为可以通过硬开关（完全禁用思考）或软开关（模型遵循用户关于是否应该思考的指令）来控制。" #: ../../source/deployment/vllm.md:106 88d3ca0f6faf4b8b80012f0a1b3a53ea msgid "The hard switch is available in vLLM through the following configuration to the API call. To disable thinking, use" msgstr "硬开关在 vLLM 中可以通过以下 API 调用配置使用。要禁用思考，请使用" #: ../../source/deployment/vllm.md:162 bfc88862f9e5470e8d8e213003c310bc msgid "Please note that passing `enable_thinking` is not OpenAI API compatible. The exact method may differ among frameworks." msgstr "请注意，`enable_thinking`并非OpenAI API定义的参数，具体传入方式可能因推理框架不同而不同。" #: ../../source/deployment/vllm.md:167 1d3ab8c3e2d24c0d9961b38686661005 msgid "To completely disable thinking, you could use [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) when starting the model:" msgstr "要完全禁用思考，您可以在启动模型时使用[自定义聊天模板](../../source/assets/qwen3_nonthinking.jinja)：" #: ../../source/deployment/vllm.md:173 495c3e31b2614dd8bad7f0eba0d48236 msgid "The chat template prevents the model from generating thinking content, even if the user instructs the model to do so with `/think`." msgstr "该聊天模板会阻止模型生成思考内容，即使用户通过 `/think` 指示模型这样做。" #: ../../source/deployment/vllm.md:178 02a8fc8d1024402ebc4f9449aa2500f3 msgid "It is recommended to set sampling parameters differently for thinking and non-thinking modes." msgstr "建议为思考模式和非思考模式分别设置不同的采样参数。" #: ../../source/deployment/vllm.md:182 35e320018c0646cb82ad0d2f9fc54ea8 msgid "Parsing Thinking Content" msgstr "解析思考内容" #: ../../source/deployment/vllm.md:184 efeeeb8ff56e4f37af53bd7a6e440d65 msgid "vLLM supports parsing the thinking content from the model generation into structured messages:" msgstr "vLLM 支持将模型生成的思考内容解析为结构化消息：" #: ../../source/deployment/vllm.md:189 a79e5778da03462199a629ed038526a8 msgid "Since vLLM 0.9.0, one can also use" msgstr "自 vLLM 0.9.0 版本，也可以使用" #: ../../source/deployment/vllm.md:194 7fdb9af5391b402280be7aeaa514dcd3 msgid "The response message will have a field named `reasoning_content` in addition to `content`, containing the thinking content generated by the model." msgstr "响应消息除了包含 `content` 字段外，还会有一个名为 `reasoning_content` 的字段，其中包含模型生成的思考内容。" #: ../../source/deployment/vllm.md:197 7036cf771a4e4fddbcbf92cc52de59d0 msgid "Please note that this feature is not OpenAI API compatible." msgstr "请注意，此功能与 OpenAI API 规范不一致。" #: ../../source/deployment/vllm.md:201 5e0250bbffa94e7dac6c9278e0d87ab6 msgid "As of vLLM 0.8.5, `enable_thinking=False` is not compatible with this feature. If you need to pass `enable_thinking=False` to the API, you should disable parsing thinking content. This is resolved in vLLM 0.9.0 with the `qwen3` reasoning parser." msgstr "在 vLLM 0.8.5 版本中，`enable_thinking=False` 与此功能不兼容。如果需要向 API 传递 `enable_thinking=False`，则应禁用解析思考内容。此问题已在 vLLM 0.9.0 中通过 `qwen3` 思考解析器得到解决。" #: ../../source/deployment/vllm.md:206 aa45c227d5d040dea6522bf2a45576b0 msgid "Parsing Tool Calls" msgstr "解析工具调用" #: ../../source/deployment/vllm.md:208 56c12209324f41fcbed13409de6d0aa5 msgid "vLLM supports parsing the tool calling content from the model generation into structured messages:" msgstr "vLLM 支持将模型生成的工具调用内容解析为结构化消息：" #: ../../source/deployment/vllm.md:213 7a12c4073d874bff81f012d7da241e9c msgid "For more information, please refer to [our guide on Function Calling](../framework/function_call.md#vllm)." msgstr "详细信息，请参阅[函数调用的指南](../framework/function_call.md#vllm)。" #: ../../source/deployment/vllm.md:215 827710cca0954bfaaeaa8a67fef1efa6 msgid "Structured/JSON Output" msgstr "结构化/JSON输出" #: ../../source/deployment/vllm.md:217 ac630e8cc3b04ad8af427e677896b7ee msgid "vLLM supports structured/JSON output. Please refer to [vLLM's documentation](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#extra-parameters-for-chat-api) for the `guided_json` parameters. Besides, it is also recommended to instruct the model to generate the specific format in the system message or in your prompt." msgstr "vLLM 支持结构化/JSON 输出。请参照[vLLM文档](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#extra-parameters-for-chat-api)了解 `guided_json` 参数。此外，也建议在系统消息或用户提示中指示模型生成特定格式，避免仅依赖于推理参数配置。" #: ../../source/deployment/vllm.md:222 5534e3b2ef2242c69964016b526e1a01 msgid "Serving Quantized models" msgstr "部署量化模型" #: ../../source/deployment/vllm.md:224 dbd8323a4bc6415da9fde179b23dac78 msgid "Qwen3 comes with two types of pre-quantized models, FP8 and AWQ." msgstr "Qwen3 提供了两种类型的预量化模型：FP8 和 AWQ。" #: ../../source/deployment/vllm.md:226 1aedffe7b0874ac289badc829e035300 msgid "The command serving those models are the same as the original models except for the name change:" msgstr "部署这些模型的命令与原始模型相同，只是名称有所更改：" #: ../../source/deployment/vllm.md:236 6d91506e31194076857d992f51d3f336 msgid "The FP8 models of Qwen3 are block-wise quant, which is supported on NVIDIA GPUs with compute capability > 8.9, that is, Ada Lovelace, Hopper, and later GPUs and runs as w8a8." msgstr "Qwen3 的 FP8 模型采用分块 (block-wise) 量化，该功能支持在 compute capability > 8.9 的 NVIDIA GPU 上运行，即 Ada Lovelace、Hopper 及更新的 GPU，并以 w8a8 方式运行。" #: ../../source/deployment/vllm.md:238 f26fbf5e37134274b435efaeada7af6b msgid "Since vLLM v0.9.0, FP8 Marlin has supported block-wise quants (running as w8a16) and you can also run Qwen3 FP8 models on Ampere cards." msgstr "从 vLLM v0.9.0 开始，FP8 Marlin 已支持分块量化（以 w8a16 方式运行），您还可以在 Ampere 显卡上运行 Qwen3 FP8 模型。" #: ../../source/deployment/vllm.md:242 dca096f25d794f88bb446eb2f39f3570 msgid "If you encountered the following error when deploying the FP8 models, it indicates that the tensor parallel size does not agree with the model weights:" msgstr "如果在部署 FP8 模型时遇到以下错误，这表明张量并行大小与模型权重不匹配：" #: ../../source/deployment/vllm.md:249 609be8d8c32f48c5ade0f1a32873bbf8 msgid "We recommend lowering the degree of tensor parallel, e.g., `--tensor-parallel-size 4` or enabling expert parallel, e.g., `--tensor-parallel-size 8 --enable-expert-parallel`." msgstr "目前，我们建议降低张量并行的程度，例如使用 `--tensor-parallel-size 4`，或者启用专家并行，例如使用 `--tensor-parallel-size 8 --enable-expert-parallel`。" #: ../../source/deployment/vllm.md:252 027c1c2d8d6a46a78c69451e96b6544a msgid "Context Length" msgstr "上下文长度" #: ../../source/deployment/vllm.md:254 a9dd53625c294f8682cc2fbedce37f45 msgid "The context length for Qwen3 models in pretraining is up to 32,768 tokens. To handle context length substantially exceeding 32,768 tokens, RoPE scaling techniques should be applied. We have validated the performance of [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts." msgstr "Qwen3 模型在预训练中的上下文长度最长为 32,768 个 token。为了处理显著超过 32,768 个 token 的上下文长度，应应用 RoPE 缩放技术。我们已经验证了 [YaRN](https://arxiv.org/abs/2309.00071) 的性能，这是一种增强模型长度外推的技术，可确保在长文本上的最佳性能。" #: ../../source/deployment/vllm.md:258 988e60ef751944c09274ce28aecf3fac msgid "vLLM supports YaRN, which can be configured as" msgstr "vLLM 支持 YaRN，可以配置为" #: ../../source/deployment/vllm.md:264 7da95eb3121944859d583a7a5885c5c4 msgid "vLLM implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0." msgstr "vLLM 实现了静态 YaRN，这意味着无论输入长度如何，缩放因子都保持不变，**这可能会对较短文本的性能产生影响。** 我们建议仅在需要处理长上下文时添加 `rope_scaling` 配置。还建议根据需要调整 `factor`。例如，如果您的应用程序的典型上下文长度为 65,536 个 token，则最好将 `factor` 设置为 2.0。" #: ../../source/deployment/vllm.md:270 fa10485203e3408dabb81687868ce059 msgid "The default `max_position_embeddings` in `config.json` is set to 40,960, which used by vLLM, if `--max-model-len` is not specified. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing and leave adequate room for model thinking. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance." msgstr "如果未指定 `--max-model-len`，`config.json` 中的默认 `max_position_embeddings` 被设置为 40,960，vLLM 将使用该值。此分配包括为输出保留 32,768 个 token，为典型提示保留 8,192 个 token，这足以应对大多数涉及短文本处理的场景，并为模型思考留出充足空间。如果平均上下文长度不超过 32,768 个 token，我们不建议在此场景中启用 YaRN，因为这可能会降低模型性能。" #: ../../source/deployment/vllm.md:275 fe82482bd3e1406998560b372e2707df msgid "Python Library" msgstr "Python 库使用" #: ../../source/deployment/vllm.md:277 82fb6b76e9c24978b9bee04927b3d5d4 msgid "vLLM can also be directly used as a Python library, which is convenient for offline batch inference but lack some API-only features, such as parsing model generation to structure messages." msgstr "vLLM 也可以直接用作 Python 库，这对离线批量推理非常方便，但缺少一些仅限 API 的功能，例如将模型生成解析为结构化消息。" #: ../../source/deployment/vllm.md:279 857f6312cd4e44ccb34197793db59165 msgid "The following shows the basic usage of vLLM as a library:" msgstr "以下展示了将 vLLM 用作库的基本用法：" #: ../../source/deployment/vllm.md:316 0c88c07558e04cbd8f2e6cf04091ef62 msgid "Since vLLM v0.9.0, you can also use the `LLM.chat` interface which includes support for `chat_template_kwargs`:" msgstr "自 vLLM v0.9.0 开始，`LLM.chat` 支持 `chat_template_kwargs` 参数，因而也可以使用以下方法：" #: ../../source/deployment/vllm.md:347 de026655e79449f28854a1cbcc182d9f msgid "FAQ" msgstr "常见问题解答" #: ../../source/deployment/vllm.md:349 30e351b0aebb4e71b187d74895124662 msgid "You may encounter OOM issues that are pretty annoying. We recommend two arguments for you to make some fix." msgstr "您可能会遇到令人烦恼的OOM（内存溢出）问题。我们推荐您尝试两个参数进行修复。" #: ../../source/deployment/vllm.md:352 cc5e992512ff4aa9bdede2e3791264f3 msgid "The first one is `--max-model-len`. Our provided default `max_position_embedding` is `40960` and thus the maximum length for the serving is also this value, leading to higher requirements of memory. Reducing it to a proper length for yourself often helps with the OOM issue." msgstr "第一个参数是 `--max-model-len` 。我们提供的默认最大位置嵌入（`max_position_embedding`）为 40960 ，因此服务时的最大长度也是这个值，这会导致更高的内存需求。将此值适当减小通常有助于解决OOM问题。" #: ../../source/deployment/vllm.md:355 9e8f5e6e064841de8bfa0f581f28c25b msgid "Another argument you can pay attention to is `--gpu-memory-utilization`. vLLM will pre-allocate this much GPU memory. By default, it is `0.9`. This is also why you find a vLLM service always takes so much memory. If you are in eager mode (by default it is not), you can level it up to tackle the OOM problem. Otherwise, CUDA Graphs are used, which will use GPU memory not controlled by vLLM, and you should try lowering it. If it doesn't work, you should try `--enforce-eager`, which may slow down inference, or reduce the `--max-model-len`." msgstr "另一个您可以关注的参数是 `--gpu-memory-utilization` 。 vLLM将预分配该参数指定比例的显存。默认情况下，该值为 `0.9`。这也是为什么您发现一个vLLM服务总是占用大量内存的原因。如果你使用了eager模式（默认不是），您可以将其调高以应对OOM问题。反之，vLLM会使用CUDA Graphs，而CUDA Graphs会额外占用不受vLLM管理的显存；此时，您应当尝试降低`--gpu-memory-utilization`。如果还是无法解决，可以尝试`--enforce-eager`（这会影响推理效率）或缩小`--max-model-len`。" #: ../../source/deployment/vllm.md:364 2dd3f6552c53423ca7ce0576047f2ab6 msgid "For more usage guide with vLLM, please see vLLM's [Qwen3 Usage Guide](https://github.com/vllm-project/vllm/issues/17327)." msgstr "有关 vLLM 的更多使用指南，请参阅 vLLM 的[Qwen3 使用指南](https://github.com/vllm-project/vllm/issues/17327)。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/framework/Langchain.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/framework/Langchain.rst:2 6f9b66430d9c495592b1e275fdfd7c9e msgid "Langchain" msgstr "" #: ../../Qwen/source/framework/Langchain.rst:5 1205af46f88e4d6681003403109385c3 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/framework/Langchain.rst:7 115ee7b1c8404629a8f98175264cc114 msgid "This guide helps you build a question-answering application based on a local knowledge base using ``Qwen2.5-7B-Instruct`` with ``langchain``. The goal is to establish a knowledge base Q&A solution." msgstr "本教程旨在帮助您利用 ``Qwen2.5-7B-Instruct`` 与 ``langchain`` ，基于本地知识库构建问答应用。目标是建立一个知识库问答解决方案。" #: ../../Qwen/source/framework/Langchain.rst:12 #: 7257b95612fb423bb9ca73212fd12a02 msgid "Basic Usage" msgstr "基础用法" #: ../../Qwen/source/framework/Langchain.rst:14 #: fecf7a682dcc4c15a53da1f7cdf145e5 msgid "The implementation process of this project includes loading files -> reading text -> segmenting text -> vectorizing text -> vectorizing questions -> matching the top k most similar text vectors with the question vectors -> incorporating the matched text as context along with the question into the prompt -> submitting to the Qwen2.5-7B-Instruct to generate an answer. Below is an example:" msgstr "您可以仅使用您的文档配合 ``langchain`` 来构建一个问答应用。该项目的实现流程包括加载文件 -> 阅读文本 -> 文本分段 -> 文本向量化 -> 问题向量化 -> 将最相似的前k个文本向量与问题向量匹配 -> 将匹配的文本作为上下文连同问题一起纳入提示 -> 提交给Qwen2.5-7B-Instruct生成答案。以下是一个示例：" #: ../../Qwen/source/framework/Langchain.rst:98 #: 6ad1ebd2ef4a49f9aa66cfdf777e1290 msgid "After loading the Qwen2.5-7B-Instruct model, you should specify the txt file for retrieval." msgstr "加载Qwen2.5-7B-Instruct模型后，您可以指定需要用于知识库问答的txt文件。" #: ../../Qwen/source/framework/Langchain.rst:274 #: 00467b1e4e294a26b9f49886633331e0 msgid "Next Step" msgstr "下一步" #: ../../Qwen/source/framework/Langchain.rst:276 #: 15ed906687054af78545290ba0746380 msgid "Now you can chat with Qwen2.5 use your own document. Continue to read the documentation and try to figure out more advanced usages of model retrieval!" msgstr "现在，您可以在您自己的文档上与Qwen2.5进行交流。继续阅读文档，尝试探索模型检索的更多高级用法！" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/framework/LlamaIndex.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/framework/LlamaIndex.rst:2 #: 2e41f8696c20488d8593b670c6361edf msgid "LlamaIndex" msgstr "LlamaIndex" #: ../../Qwen/source/framework/LlamaIndex.rst:5 #: 20b3836fd391457bb00bf75b61e23e0d msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/framework/LlamaIndex.rst:7 #: 86d9e6f0684749aab40a9824cd026fa3 msgid "To connect Qwen2.5 with external data, such as documents, web pages, etc., we offer a tutorial on `LlamaIndex `__. This guide helps you quickly implement retrieval-augmented generation (RAG) using LlamaIndex with Qwen2.5." msgstr "为了实现 Qwen2.5 与外部数据（例如文档、网页等）的连接，我们提供了 `LlamaIndex `__ 的详细教程。本指南旨在帮助用户利用 LlamaIndex 与 Qwen2.5 快速部署检索增强生成（RAG）技术。" #: ../../Qwen/source/framework/LlamaIndex.rst:11 #: 71ed222858054687a5b33222bb6ac086 msgid "Preparation" msgstr "环境准备" #: ../../Qwen/source/framework/LlamaIndex.rst:13 #: 161d9153d6484dd5a1f1bdb340847814 msgid "To implement RAG, we advise you to install the LlamaIndex-related packages first." msgstr "为实现检索增强生成（RAG），我们建议您首先安装与 LlamaIndex 相关的软件包。" #: ../../Qwen/source/framework/LlamaIndex.rst:16 #: a8d6acb1001a42c88185b971ae2de3bf msgid "The following is a simple code snippet showing how to do this:" msgstr "以下是一个简单的代码示例：" #: ../../Qwen/source/framework/LlamaIndex.rst:25 #: e441d3b8fb6d4a13b52e1560ef250b16 msgid "Set Parameters" msgstr "设置参数" #: ../../Qwen/source/framework/LlamaIndex.rst:27 #: c2481804c3f34c7f883eed92ffa3111e msgid "Now we can set up LLM, embedding model, and the related configurations. Qwen2.5-Instruct supports conversations in multiple languages, including English and Chinese. You can use the ``bge-base-en-v1.5`` model to retrieve from English documents, and you can download the ``bge-base-zh-v1.5`` model to retrieve from Chinese documents. You can also choose ``bge-large`` or ``bge-small`` as the embedding model or modify the context window size or text chunk size depending on your computing resources. Qwen2.5 model families support a maximum of 32K context window size (up to 128K for 7B, 14B, 32B, and 72B, requiring extra configuration)" msgstr "现在，我们可以设置语言模型和向量模型。Qwen2.5-Instruct支持包括英语和中文在内的多种语言对话。您可以使用 ``bge-base-en-v1.5`` 模型来检索英文文档，下载 ``bge-base-zh-v1.5`` 模型以检索中文文档。根据您的计算资源，您还可以选择 ``bge-large`` 或 ``bge-small`` 作为向量模型，或调整上下文窗口大小或文本块大小。Qwen2.5模型系列支持最大32K上下文窗口大小（7B 、14B 、32B 及 72B可扩展支持 128K 上下文，但需要额外配置）" #: ../../Qwen/source/framework/LlamaIndex.rst:85 #: 74c35d5a03734c289d162dfa3813ada6 msgid "Build Index" msgstr "构建索引" #: ../../Qwen/source/framework/LlamaIndex.rst:87 #: c49859d4ea5f49dba1fa2263f3ae284d msgid "Now we can build index from documents or websites." msgstr "现在我们可以从文档或网站构建索引。" #: ../../Qwen/source/framework/LlamaIndex.rst:89 #: b460d000037e4266a4d9f43d38f1f9b0 msgid "The following code snippet demonstrates how to build an index for files (regardless of whether they are in PDF or TXT format) in a local folder named 'document'." msgstr "以下代码片段展示了如何为本地名为'document'的文件夹中的文件（无论是PDF格式还是TXT格式）构建索引。" #: ../../Qwen/source/framework/LlamaIndex.rst:102 #: a416d18b227940e29fac1f59851ff8c4 msgid "The following code snippet demonstrates how to build an index for the content in a list of websites." msgstr "以下代码片段展示了如何为一系列网站的内容构建索引。" #: ../../Qwen/source/framework/LlamaIndex.rst:118 #: 487cf928d048424fa1b50438f701137c msgid "To save and load the index, you can use the following code snippet." msgstr "要保存和加载已构建的索引，您可以使用以下代码示例。" #: ../../Qwen/source/framework/LlamaIndex.rst:132 #: c68419c4318d46e891f5df9191be6d2d msgid "RAG" msgstr "检索增强（RAG）" #: ../../Qwen/source/framework/LlamaIndex.rst:134 #: 8ad20a8f43fe496084a40f963ba97440 msgid "Now you can perform queries, and Qwen2.5 will answer based on the content of the indexed documents." msgstr "现在您可以输入查询，Qwen2.5 将基于索引文档的内容提供答案。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/framework/function_call.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 16:36+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/framework/function_call.md:6 10546d07829648458ac4f91b91967697 msgid "Function Calling" msgstr "函数调用" #: ../../source/framework/function_call.md:9 97bb46c965d44cb4900c947356c571fd msgid "Preface" msgstr "前言" #: ../../source/framework/function_call.md:11 539a099d2e764c8181e17c9c41f8053b msgid "Function calling with large language models is a huge and evolving topic. It is particularly important for AI applications:" msgstr "使用大型语言模型进行函数调用 (Function Calling) 是一个庞大且不断发展的主题。这对AI应用尤为重要：" #: ../../source/framework/function_call.md:13 96e919c9808842ddb3225121c70a350b msgid "either for AI-native applications that strive to work around the shortcomings of current AI technology," msgstr "无论是为了绕过当前AI技术的局限性，而设计的原生AI应用，" #: ../../source/framework/function_call.md:14 e7f29fd2c11b413bb08860d1a1c8ad8d msgid "or for existing applications that seeks the integration of AI technology to improve performance, user interaction and experience, or efficiency." msgstr "还是为了提升性能、用户体验或效率，寻求整合AI技术的现有应用。" #: ../../source/framework/function_call.md:16 cc49601082014a989c503f4461c8a942 msgid "We will talk about how Qwen3 can be used to support function calling and how it can be used to achieve your goals, from the inference usage for developing application to the inner workings for hardcore customizations. In this guide," msgstr "我们将讨论如何使用 Qwen3 来支持函数调用，以及如何利用它来实现您的目标，从用于开发应用程序的推理用法到针对硬核定制的内部工作机制。在本指南中，" #: ../../source/framework/function_call.md:18 bad8e302d5044edd80cb9da02c0f6dbe msgid "We will first demonstrate how to use function calling with Qwen3." msgstr "我们首先将展示如何使用Qwen3进行函数调用。" #: ../../source/framework/function_call.md:19 41f975e599264589af4819e9aee96f76 msgid "Then, we will introduce the technical details on functional calling with Qwen3, which are mainly about the templates." msgstr "接着，我们将介绍使用Qwen3行函数调用的技术细节，主要涉及模板的使用。" #: ../../source/framework/function_call.md:21 cad1eaec5df646509123bd97566c69c5 msgid "Before starting, there is one thing we have not yet introduced, that is ..." msgstr "在开始之前，还有一件事我们尚未介绍，那就是…" #: ../../source/framework/function_call.md:23 129f1c1d7bd7486f80955cda6cf77e3f msgid "What is function calling?" msgstr "什么是函数调用？" #: ../../source/framework/function_call.md:26 cdf0c1412f234bd8bcc386683a38b131 msgid "There is another term \"tool use\" that may be used to refer to the same concept. While some may argue that tools are a generalized form of functions, at present, their difference exists only technically as different I/O types of programming interfaces." msgstr "这一概念也可能被称为“工具使用” (\"tool use\")。虽然有人认为“工具”是“函数”的泛化形式，但在当前，它们的区别仅在技术层面上，表现为编程接口的不同输入输出类型。" #: ../../source/framework/function_call.md:30 93085d6f71dd4349832e0c8832b57966 msgid "Large language models (LLMs) are powerful things. However, sometimes LLMs by themselves are simply not capable enough." msgstr "大型语言模型（LLMs）确实强大。然而，有时候单靠大型语言模型的能力还是不够的。" #: ../../source/framework/function_call.md:32 d441a861290d4a0aaa6fea713b628287 msgid "On the one hand, LLMs have inherent modeling limitations. For one, they do not know things that are not in their training data, which include those happened after their training ended. In addition, they learn things in the way of likelihood, which suggests that they may not be precise enough for tasks with fixed rule sets, e.g., mathematical computation." msgstr "一方面，大型语言模型存在建模局限性。首先，对于训练数据中没有的信息，包括训练结束后发生的事情，它们并不了解。此外，它们通过概率方式学习，这意味着对于有固定规则集的任务，如数学计算，可能不够精确。" #: ../../source/framework/function_call.md:35 f52f0935e2a346e3b1631eeaf2223fb0 msgid "On the other hand, it is not easy to use LLMs as a Plug-and-Play service programmatically with other things. LLMs mostly talk in words that are open to interpretation and thus ambiguous, while other software or applications or systems talk in code and through programming interfaces that are pre-defined and fixed and structured." msgstr "另一方面，将大型语言模型作为即插即用服务与其它系统进行编程式协作，并非易事。大型语言模型的表达多含主观解释成分，因而产生歧义；而其他软件、应用或系统则通过预定义、固定和结构化的代码及编程接口进行沟通。" #: ../../source/framework/function_call.md:38 4ff86e51b366418ba9cd2ff9a8fceb27 msgid "To this end, function calling establishes a common protocol that specifies how LLMs should interact with the other things. The procedure is mainly as follows:" msgstr "为此，函数调用确立了一个通用协议，规定了大型语言模型应与其他实体互动的流程。主要流程如下：" #: ../../source/framework/function_call.md:40 1bc24a6d6a5140cdb2f274b7df61a429 msgid "The application provides a set of functions and the instructions of the functions to an LLM." msgstr "应用程序向大型语言模型提供一组函数及其使用说明。" #: ../../source/framework/function_call.md:41 8569c9c13fd34caab163f4f9154e45bc msgid "The LLM choose to or not to, or is forced to use one or many of the functions, in response to user queries." msgstr "大型语言模型根据用户查询，选择使用或不使用，或被迫使用一个或多个函数。" #: ../../source/framework/function_call.md:42 dac3cb931c3c4755b276886d5568340a msgid "If the LLM chooses to use the functions, it states how the functions should be used based on the function instructions." msgstr "如果大型语言模型选择使用这些函数，它会根据函数说明如何使用。" #: ../../source/framework/function_call.md:43 a6439df6e0354e96b321fbfaa260dbd1 msgid "The chosen functions are used as such by the application and the results are obtained, which are then given to the LLM if further interaction is needed." msgstr "应用程序按照选择使用这些函数，并获取结果。如果需要进一步互动，结果将提供给大型语言模型。" #: ../../source/framework/function_call.md:45 8cef5c9125154720952742a4f51e19e2 msgid "There are many ways for LLMs to understand and follow this protocol. As always, the key is prompt engineering or an internalized template known by the model. We recommend using Hermes-style tool use for Qwen3 to maximize function calling performance." msgstr "大型语言模型（LLMs）有许多方式来理解和遵循该协议。一如既往，关键在于提示工程或模型已内化的模板。我们建议对 Qwen3 使用 Hermes 风格的工具调用方法，以最大化函数调用性能。" #: ../../source/framework/function_call.md:49 486bf0eaff5449f3966f948cf341570f msgid "Inference with Function Calling" msgstr "使用函数调用进行推理" #: ../../source/framework/function_call.md:51 6d50be829cba4f43b587c494dc74a820 msgid "As function calling is essentially implemented using prompt engineering, you could manually construct the model inputs for Qwen3 models. However, frameworks with function calling support can help you with all that laborious work." msgstr "由于函数调用本质上是通过提示工程实现的，您可以手动构建Qwen3模型的输入。但是，支持函数调用的框架可以帮助您完成所有繁重的工作。" #: ../../source/framework/function_call.md:54 5afa6df4e2d94df89b26b66bce596264 msgid "In the following, we will introduce the usage (via dedicated function calling chat template) with" msgstr "接下来，我们将介绍（通过专用的函数调用模板）使用" #: ../../source/framework/function_call.md:55 380f90e66ba44ad686e981536dd29e12 msgid "**Qwen-Agent**," msgstr "**Qwen-Agent**，" #: ../../source/framework/function_call.md:56 79b6f6475bc64a7bbe397ba0c94d43b1 msgid "**vLLM**." msgstr "**vLLM**。" #: ../../source/framework/function_call.md:58 30fff59c00c64a70821c167a944fd3f7 msgid "The Example Case" msgstr "案例" #: ../../source/framework/function_call.md:60 2b4cdb8daedd4da49877af5ecc67ba8b msgid "Let's also use an example to demonstrate the inference usage. We assume **Python 3.11** is used as the programming language." msgstr "我们同样通过一个示例来展示推理的使用方法。假设我们使用的编程语言是**Python 3.11**。" #: ../../source/framework/function_call.md:63 ae5619a2a7ff41608d3420cd0caaba79 msgid "**Scenario**: Suppose we would like to ask the model about the temperature of a location. Normally, the model would reply that it cannot provide real-time information. But we have two tools that can be used to obtain the current temperature of and the temperature at a given date of a city respectively, and we would like the model to make use of them." msgstr "**场景**：假设我们要询问模型某个地点的温度。通常，模型会回答无法提供实时信息。但我们有两个工具，可以分别获取城市的当前温度和指定日期的温度，我们希望模型能够利用这些工具。" #: ../../source/framework/function_call.md:67 2617f66327e64038a5933bd825f099b8 msgid "To set up the example case, you can use the following code:" msgstr "为了这个示例案例，您可以使用以下代码：" #: ../../source/framework/function_call.md a419b632036846fdb90427683502c597 msgid "Preparation Code" msgstr "准备代码" #: ../../source/framework/function_call.md:173 77ce44e0fb3b43f6814da6a2ad36e511 msgid "In particular, the tools should be described using JSON Schema and the messages should contain as much available information as possible. You can find the explanations of the tools and messages below:" msgstr "工具应使用JSON Schema进行描述，消息应包含尽可能多的有效信息。您可以在下面找到工具和消息的解释：" #: ../../source/framework/function_call.md 48a8432a5d384a4ba10f39fcea96b825 msgid "Example Tools" msgstr "示例工具" #: ../../source/framework/function_call.md:178 f18d3697d6c347a2b407b21ae874f865 msgid "The tools should be described using the following JSON:" msgstr "工具应使用以下JSON进行描述：" #: ../../source/framework/function_call.md:242 6e55bd9040be4bc296ed948788780347 msgid "For each **tool**, it is a JSON object with two fields:" msgstr "对于每个**工具**，它是一个具有两个字段的JSON object：" #: ../../source/framework/function_call.md:243 ae43f666fcab4e5b93ff009688305fce msgid "`type`: a string specifying the type of the tool, currently only `\"function\"` is valid" msgstr "`type`：string，用于指定工具类型，目前仅`\"function\"`有效" #: ../../source/framework/function_call.md:244 35cc77d63bf6455bb3376bec483b0f9b msgid "`function`: an object detailing the instructions to use the function" msgstr "`function`：object，详细说明了如何使用该函数" #: ../../source/framework/function_call.md:246 b6f78c8fbb9b40d5a6c0c10e5273b85e msgid "For each **function**, it is a JSON object with three fields:" msgstr "对于每个**function**，它是一个具有三个字段的JSON object：" #: ../../source/framework/function_call.md:247 0905c8ca11bb4454abb26ba42a3b29c3 msgid "`name`: a string indicating the name of the function" msgstr "`name`：string 表示函数名称" #: ../../source/framework/function_call.md:248 d787950be0144e0a83f18d7a06d43de0 msgid "`description`: a string describing what the function is used for" msgstr "`description`：string 描述函数用途" #: ../../source/framework/function_call.md:249 ba88a6e75ae8462d892a3ec3e9a6edaa msgid "`parameters`: [a JSON Schema](https://json-schema.org/learn/getting-started-step-by-step) that specifies the parameters the function accepts. Please refer to the linked documentation for how to compose a JSON Schema. Notable fields include `type`, `required`, and `enum`." msgstr "`parameters`：[JSON Schema](https://json-schema.org/learn/getting-started-step-by-step)，用于指定函数接受的参数。请参阅链接文档以了解如何构建JSON Schema。值得注意的字段包括`type`、`required`和`enum`。" #: ../../source/framework/function_call.md:251 abd8fe46919340a582f532932f9c1b52 msgid "Most frameworks use the tool format and some may use the function format. Which one to use should be obvious according to the naming." msgstr "大多数框架使用“工具”格式，有些可能使用“函数”格式。根据命名，应该很明显应该使用哪一个。" #: ../../source/framework/function_call.md 6c17006423714b9bb29379bd3674e1a0 msgid "Example Messages" msgstr "示例消息" #: ../../source/framework/function_call.md:258 1cd11a9c9f9b4f2ba308599a87f82613 msgid "Our query is `What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30.`." msgstr "" #: ../../source/framework/function_call.md:267 3f124f3c5ef54673bb6e12d8bd1bada3 msgid "Qwen-Agent" msgstr "" #: ../../source/framework/function_call.md:269 761ea5172da64e9d8911cc567d7e7779 msgid "[Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) is actually a Python Agent framework for developing AI applications. Although its intended use cases are higher-level than efficient inference, it does contain the **canonical implementation** of function calling for Qwen3. It provides the function calling ability for Qwen3 to an OpenAI-compatible API through templates that is transparent to users." msgstr "[Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) 实际上是一个用于开发AI应用的Python智能体框架。尽管其设计用例比高效推理更高级，但它确实包含了Qwen3函数调用的**规范实现**。基于OpenAI兼容API，它可以通过模板为Qwen3提供了对用户透明的的函数调用能力。" #: ../../source/framework/function_call.md:273 194c41677b7b4a6a81802980f4973988 msgid "It is worth noting that for reasoning models like Qwen3, it is *not recommended* to use tool call template based on stopwords, such as ReAct, because the model may output stopwords in the thought section, potentially leading to unexpected behavior in tool calls." msgstr "" #: ../../source/framework/function_call.md:275 02e0092b1db04537bb75bb8a9728b0c9 msgid "Before starting, let's make sure the latest library is installed:" msgstr "在开始之前，让我们确保已安装了最新的库：" #: ../../source/framework/function_call.md:280 #: ../../source/framework/function_call.md:450 8e49db2c62cc438193a1a385778c9908 msgid "Preparing" msgstr "准备工作" #: ../../source/framework/function_call.md:282 1181a68368ce484f9beec38d971108aa msgid "Qwen-Agent can wrap an OpenAI-compatible API that does not support function calling. You can serve such an API with most inference frameworks or obtain one from cloud providers like DashScope or Together." msgstr "Qwen-Agent可以封装一个不支持函数调用的OpenAI兼容API。您可以使用大多数推理框架来提供此类API，或者从DashScope或Together等云提供商处获取一个。" #: ../../source/framework/function_call.md:285 d7708c50cf554212866c284d73a79c9e msgid "Assuming there is an OpenAI-compatible API at `http://localhost:8000/v1`, Qwen-Agent provides a shortcut function `get_chat_model` to obtain a model inference class with function calling support:" msgstr "假设在`http://localhost:8000/v1`处有一个OpenAI兼容API，Qwen-Agent提供了一个快捷函数`get_chat_model`，用于获取具有函数调用支持的模型推理类：" #: ../../source/framework/function_call.md:302 e5676cec6583445888cf806ef54de829 msgid "In the above, `model_server` is the `api_base` common used in other OpenAI-compatible API clients. It is advised to provide the `api_key` (but not via plaintext in the code), even if the API server does not check it, in which case, you can set it to anything. You can pass model parameters to the model by `generate_cfg`. Here we demonstrate how to control the think and no_think modes of Qwen3. Different APIs may have different control methods." msgstr "在上述代码中，`model_server` 是其他兼容 OpenAI 的 API 客户端常用的 `api_base`。建议提供 `api_key`（但不要以代码中的明文形式提供），即使 API 服务器不检查它，在这种情况下，您可以将其设置为任意值。您可以通过 `generate_cfg` 将模型参数传递给模型。在此我们演示如何控制 Qwen3 的思考与非思考模式。不同的 API 可能有不同的控制方法。" #: ../../source/framework/function_call.md:307 42c1289baeef45b1a471e606bdafbe9a msgid "For model inputs, the common message structure for system, user, and assistant history should be used:" msgstr "对于模型输入，应使用系统、用户和助手历史记录的通用消息结构：" #: ../../source/framework/function_call.md:313 64e0a941165d49299e80aa8270f10bfb msgid "At the time, Qwen-Agent works with functions instead of tools. This requires a small change to our tool descriptions, that is, extracting the function fields:" msgstr "目前，Qwen-Agent使用“函数”而非“工具”。这需要对我们工具描述进行一些小的更改，即提取函数字段：" #: ../../source/framework/function_call.md:320 #: ../../source/framework/function_call.md:482 6babaeb87a48402086b0db01d6a0c5e2 msgid "Tool Calls and Tool Results" msgstr "工具调用和工具结果" #: ../../source/framework/function_call.md:322 53c98c106e2542f49d418348118717ea msgid "To interact with the model, the `chat` method should be used:" msgstr "为了与模型交互，应使用`chat`方法：" #: ../../source/framework/function_call.md:333 a0e820c457e146298945b5df4cc918c5 msgid "The `chat` method returns a generator of list, each of which may contain multiple messages." msgstr "`chat`方法返回一个列表的生成器，每个列表可能包含多条消息。" #: ../../source/framework/function_call.md:336 4627b7f2ddd9427aa10a127ea5d0d2b4 msgid "The results of `no_think` mode:" msgstr "" #: ../../source/framework/function_call.md:344 bc34f89540c044d3809f29474f5cfe43 msgid "The results of `think` mode:" msgstr "" #: ../../source/framework/function_call.md:353 89732d0ca8ce4d93b666cdd51f8e126a msgid "As we can see, Qwen-Agent attempts to parse the model generation in an easier to use structural format. The details related to function calls are placed in the `function_call` field of the messages:" msgstr "我们可以看到，Qwen-Agent试图以更易于使用的结构化格式解析模型生成。与函数调用相关的详细信息被放置在消息的`function_call`字段中：" #: ../../source/framework/function_call.md:355 d08d78b0219a4bbda4b6431e7f661eaa msgid "`name`: a string representing the function to call" msgstr "`name`：代表要调用的函数的字符串" #: ../../source/framework/function_call.md:356 5a9245e840994576a89c28e4ce4f9378 msgid "`arguments`: a JSON-formatted string representing the arguments the function should be called with" msgstr "`arguments`：表示函数应带有的参数的JSON格式字符串" #: ../../source/framework/function_call.md:358 e108e1ea05564aac9a398e1093dab1ce msgid "In the thinking mode, it will first generate a thought and then generate the tool call(s)." msgstr "" #: ../../source/framework/function_call.md:360 50cc88e82a004ab99199f46549f4aaa2 msgid "Then comes the critical part -- checking and applying the function call:" msgstr "接下来是关键部分——检查和应用函数调用：" #: ../../source/framework/function_call.md:376 6992f24f3f984594ac99e4267cd20144 msgid "To get tool results:" msgstr "获取工具结果：" #: ../../source/framework/function_call.md:377 23a3a2cbf74347948c6629be3d68be0f msgid "line 1: We should iterate the function calls in the order the model generates them." msgstr "第1行：我们应该按模型生成它们的顺序迭代函数调用。" #: ../../source/framework/function_call.md:378 762ba5a6310248dc886921fa542aa667 msgid "line 2: We can check if a function call is needed as deemed by the model by checking the `function_call` field of the generated messages." msgstr "第2行：通过检查生成消息的`function_call`字段，我们可以查看是否需要按模型判断进行函数调用。" #: ../../source/framework/function_call.md:379 bfbb86b6b1b24ca598e6402043417cbf msgid "line 3-4: The related details including the name and the arguments of the function can also be found there, which are `name` and `arguments` respectively." msgstr "第3-4行：相关详情，包括函数名称和参数，也可以在那里找到，分别是`name`和`arguments`。" #: ../../source/framework/function_call.md:380 7d108413597041c48363681e79da9813 msgid "line 6: With the details, one should call the function and obtain the results. Here, we assume there is a function named [`get_function_by_name`](#prepcode) to help us get the related function by its name." msgstr "第6行：有了这些细节，应该调用函数并获取结果。这里，我们假设有一个名为[`get_function_by_name`](#prepcode)的函数来帮助我们根据名称获取相关函数。" #: ../../source/framework/function_call.md:382 237839b4eb4048f6b22ab8d9c94f20c4 msgid "line 8-12: With the result obtained, add the function result to the messages as `content` and with `role` as `\"function\"`." msgstr "第8-12行：获得结果后，将函数结果作为`content`添加到消息中，并将`role`设置为`\"function\"`。" #: ../../source/framework/function_call.md:384 556c8b3b12544c5ab2fb8e5d2b7a818c #, fuzzy msgid "Now the messages are:" msgstr "现在消息是" #: ../../source/framework/function_call.md:386 #: ../../source/framework/function_call.md:421 dce3c134764b4690b2b0b37e36a9dbcb #: edd9cf9e63af4a869eb3e7bb07c88328 msgid "`no_think` mode:" msgstr "" #: ../../source/framework/function_call.md:397 #: ../../source/framework/function_call.md:428 2f611a03bd624b2db666fb07fa8f658d #: c18021fb05d5485ebe2e1afa6371ef65 msgid "`think` mode:" msgstr "" #: ../../source/framework/function_call.md:409 #: ../../source/framework/function_call.md:570 a5f394f05e9a4f80958a16e43ed31e7d msgid "Final Response" msgstr "最终响应" #: ../../source/framework/function_call.md:411 215d04b89c1749f99831d4baa72f678d msgid "Finally, run the model again to get the final model results:" msgstr "最后，再次运行模型以获取最终的模型结果：" #: ../../source/framework/function_call.md:419 823711ca9c364335948f2e994740417e msgid "The final response should be like" msgstr "最终响应应如下所示" #: ../../source/framework/function_call.md:438 a5f84b2e746e4669bd836f43afaf16a2 msgid "vLLM" msgstr "" #: ../../source/framework/function_call.md:440 4d64c942ff674ba88a9753c25635374d msgid "vLLM is a fast and easy-to-use library for LLM inference and serving. It uses the tokenizer from `transformers` to format the input, so we should have no trouble preparing the input. In addition, vLLm also implements helper functions so that generated tool calls can be parsed automatically if the format is supported." msgstr "vLLM 是一个快速且易于使用的库，用于大型语言模型的推理和部署。它使用 `transformers` 中的分词器来格式化输入，因此我们在准备输入时应该不会遇到任何问题。此外，vLLM 还实现了辅助函数，以便在支持的情况下自动解析生成的工具调用。" #: ../../source/framework/function_call.md:444 f65e19e7888c4d9ea42f4c82ca89b496 msgid "`vllm` >= v0.8.5." msgstr "" #: ../../source/framework/function_call.md:446 6df4774659a84791b0dff63ed1b211c1 msgid "For more information, check the [vLLM documentation](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#tool-calling-in-the-chat-completion-api)." msgstr "更多信息，请查阅 [vLLM 文档](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#tool-calling-in-the-chat-completion-api)" #: ../../source/framework/function_call.md:448 c134b90394c941d49115838d2280a343 msgid "We will use the OpenAI-Compatible API by `vllm` with the API client from the `openai` Python library." msgstr "在本指南中，我们将使用 `vllm` 提供的 OpenAI 兼容 API，并通过 `openai` Python 库的 API 客户端来进行操作。" #: ../../source/framework/function_call.md:452 401f8cd3cf204f459a7f7c42e7dc43e7 msgid "For Qwen3, the chat template in tokenizer_config.json has already included support for the Hermes-style tool use. We simply need to start a OpenAI-compatible API with vLLM:" msgstr "对于 Qwen3，`tokenizer_config.json` 中的聊天模板已经包含了对 Hermes 风格工具调用的支持。我们只需要启动一个由 vLLM 提供的 OpenAI 兼容 API 即可：" #: ../../source/framework/function_call.md:459 e27b0ff05a97431dbaa2a18d4e5b6ab5 msgid "The inputs are the same with those in [the preparation code](#prepcode):" msgstr "输入与[准备代码](#prepcode)中的相同：" #: ../../source/framework/function_call.md:466 bac07622ec554e9ba4090940724d8d21 msgid "Let's also initialize the client:" msgstr "我们先初始化API客户端：" #: ../../source/framework/function_call.md:484 46d45caf4eab44c69ad519d82e84818c msgid "We can use the create chat completions endpoint to query the model. Here is an example of the `no_think` mode:" msgstr "我们可以使用create chat completions endpoint直接查询底层API。以下是使用非思考模式的例子：" #: ../../source/framework/function_call.md:502 4198241de2fb4e4ba5433b0c579734e1 msgid "vLLM should be able to parse the tool calls for us, and the main fields in the response (`response.choices[0]`) should be like" msgstr "vLLM应当可以为我们解析工具调用，回复的主要字段(`response.choices[0]`)应如下所示：" #: ../../source/framework/function_call.md:529 050ffcaa034f4cd7b1405ab5a561b969 msgid "Note that the function arguments are JSON-formatted strings, which Qwen-Agent follows." msgstr "请注意这里函数的参数是JSON格式字符串，Qwen-Agent与其一致。" #: ../../source/framework/function_call.md:531 e3012b896829473cb0e1802047f5819e msgid "As before, chances are that there are corner cases where tool calls are generated but they are malformed and cannot be parsed. For production code, we should try parsing by ourselves." msgstr "如前所述，有可能存在边界情况，模型生成了工具调用但格式不良也无法被解析。对于生产代码，我们需要尝试自行解析。" #: ../../source/framework/function_call.md:534 de4fb4d7f1b342e0849b049e81fb8585 msgid "Then, we can obtain the tool results and add them to the messages as shown below:" msgstr "随后，我们可以调用工具并获得结果，然后将它们加入消息中：" #: ../../source/framework/function_call.md:555 e5e63bf3a7e34d0683d244549cbbbce1 msgid "It should be noted that the OpenAI API uses `tool_call_id` to identify the relation between tool results and tool calls." msgstr "这里需要注意OpenAI API使用`tool_call_id`字段来识别工具结果和工具调用间的联系。" #: ../../source/framework/function_call.md:557 deda1dccce204294ba69c74f24e4588f msgid "The messages are now like" msgstr "现在消息如下：" #: ../../source/framework/function_call.md:572 2dd5c4ae2dd84633809c8365c10fdaa8 msgid "Let's call the endpoint again to seed the tool results and get response:" msgstr "让我们再次查询接口，以给模型提供工具结果并获得回复：" #: ../../source/framework/function_call.md:589 15f3ce61d4044fb1a06f75bdeb8140b2 msgid "The final response (`response.choices[0].message.content`) should be like" msgstr "最终响应 (`response.choices[0].message.content`)应如" #: ../../source/framework/function_call.md:595 1e8b7940bbbc46a8bbafb59afb086819 msgid "Finally" msgstr "最后" #: ../../source/framework/function_call.md:597 b588a910fcef435183d4d476851c10fb msgid "In whichever way you choose to use function calling with Qwen3, keep in mind that the limitation and the perks of prompt engineering applies:" msgstr "无论你选择哪种方式在Qwen3中使用函数调用，请记住提示工程的限制和优势适用：" #: ../../source/framework/function_call.md:598 feaec442d07c4d3fa7e20d3dd9724db1 msgid "It is not guaranteed that the model generation will always follow the protocol even with proper prompting or templates. Especially, for the templates that are more complex and relies more on the model itself to think and stay on track than the ones that are simpler and relies on the template and the use of control or special tokens. The latter one, of course, requires some kind of training. In production code, be prepared that if it breaks, countermeasures or rectifications are in place." msgstr "无法保证模型生成将始终遵循协议，即使有适当的提示或模板。特别是对于那些更复杂且更多依赖于模型本身思考和保持方向的模板，而非那些更简单且依赖于模板以及控制或特殊标记使用的模板。当然，后者需要某种训练。在生产代码中，要准备好如果出现问题，采取补救措施或修正措施。" #: ../../source/framework/function_call.md:602 d86a7ee6f6f64e40bea8bdb0991f3361 msgid "If in certain scenarios, the generation is not up to expectation, you can refine the template to add more instructions or constraints. While the templates mentioned here are general enough, they may not be the best or the most specific or the most concise for your use cases. The ultimate solution is fine-tuning using your own data." msgstr "如果在某些场景下，生成结果未达到预期，你可以细化模板以添加更多指令或约束。尽管这里提到的模板足够通用，但对于你的具体使用案例，它们可能不是最佳的、最具体的或最简洁的。最终解决方案是使用你自己的数据进行微调。" #: ../../source/framework/function_call.md:606 e09bd8deef0e413698e55cc1dfbc3283 msgid "Have fun prompting!" msgstr "享受提示的乐趣吧！" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/framework/qwen_agent.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-16 18:57+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/framework/qwen_agent.rst:2 74719c4bae294c5ea93e9f8542cef14c msgid "Qwen-Agent" msgstr "Qwen-Agent" #: ../../source/framework/qwen_agent.rst:4 2a3d08cd70a34436bf5d9e1617a4d392 msgid "`Qwen-Agent `__ is a framework for developing LLM applications based on the instruction following, tool usage, planning, and memory capabilities of Qwen." msgstr "`Qwen-Agent `__ 是一个基于 Qwen 的指令跟随、工具使用、计划和记忆能力来开发 LLM 应用程序的框架。" #: ../../source/framework/qwen_agent.rst:8 ada0e9f26c6748768f66c1c62e4b6d75 msgid "This is a simple tutorial on using Qwen-Agent to quickly experience the agentic capabilities of Qwen3. For more detailed information, please refer to `Qwen-Agent `__ repository." msgstr "本教程展示基于 Qwen-Agent 快速体验 Qwen3 智能体能力的流程。更多信息请参考 `Qwen-Agent `__ 仓库。" #: ../../source/framework/qwen_agent.rst:14 b0997eeb63844471b1637075add23cb0 msgid "Installation" msgstr "安装" #: ../../source/framework/qwen_agent.rst:16 b6ba4e319cd24dee88d5cdbde60b096b msgid "Install the stable version from PyPI:" msgstr "从 PyPI 安装 Qwen-Agent 的稳定版本：" #: ../../source/framework/qwen_agent.rst:29 3f5ac104f7a647b99b1146d72fdda96d msgid "Developing Your Own Agent" msgstr "开发您自己的智能体" #: ../../source/framework/qwen_agent.rst:31 e85c4ea6d3d9406da3823cb4f188ffc4 msgid "Qwen3 excels in tool calling capabilities. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity." msgstr "Qwen3 在工具调用能力方面表现出色。Qwen-Agent 内部封装了工具调用模板和工具调用解析器，大大降低了编码复杂性。" #: ../../source/framework/qwen_agent.rst:35 d63624897ee340fb8d31eeea6a02e995 msgid "To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself." msgstr "要定义可用的工具，您可以使用 MCP 配置文件，使用 Qwen-Agent 的集成工具，或者自行集成其他工具。" #: ../../source/framework/qwen_agent.rst:112 ada0e9f26c6748768f66c1c62e4b6d75 msgid "For more detailed examples and MCP cookbooks, please refer to `Qwen-Agent `__ repository." msgstr "有关更详细的示例和 MCP 使用指南，请参阅 `Qwen-Agent `__ 仓库。" #~ msgid "To be updated for Qwen3." #~ msgstr "仍需为Qwen3更新。" #~ msgid "Qwen-Agent provides atomic components such as LLMs and prompts, as well as high-level components such as Agents. The example below uses the Assistant component as an illustration, demonstrating how to add custom tools and quickly develop an agent that uses tools." #~ msgstr "Qwen-Agent 提供包括语言模型和提示词等原子级组件，及智能体等高级组件在内的多种组件。以下示例选取助理组件进行展示，阐述了如何整合自定义工具以及如何迅速开发出一个能够应用这些工具的代理程序。" #~ msgid "The framework also provides more atomic components for developers to combine. For additional showcases, please refer to `examples `__." #~ msgstr "该框架还为开发者提供了更多的原子组件以供组合使用。欲了解更多示例，请参见 `examples `__。" #~ msgid "This is the simplest tutorial on using Qwen-Agent to quickly experience the agentic capabilities of Qwen3. For more detailed information, please refer to `Qwen-Agent `__ repository." #~ msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/getting_started/concepts.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/getting_started/concepts.md:1 #: 581ec8a4d8dd4b5a99caf167b796a6e9 msgid "Key Concepts" msgstr "核心概念" #: ../../Qwen/source/getting_started/concepts.md:4 #: fc803dd8f02a4caf9be29e42364659a0 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/getting_started/concepts.md:7 #: 834244ff25a040fe91f63682732dd416 msgid "Qwen" msgstr "通义千问 (Qwen)" #: ../../Qwen/source/getting_started/concepts.md:9 #: ee9dee3630614908860b2144007186fd msgid "Qwen (Chinese: 通义千问; pinyin: _Tongyi Qianwen_) is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. Both language models and multimodal models are pre-trained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences." msgstr "通义千问（英文： Qwen ；读作： _kùn_）是由阿里巴巴通义千问团队开发的大规模语言和多模态系列模型。通义千问可以执行自然语言理解、文本生成、视觉理解、音频理解、工具调用、角色扮演、智能体等多种任务。语言和多模态模型均在大规模、多语言、多模态数据上进行预训练，并在高质量语料上后训练以与人类偏好对齐。" #: ../../Qwen/source/getting_started/concepts.md:13 #: 6a37d9a0b6e2414a9b7ede0e095476af msgid "There is the proprietary version and the open-weight version." msgstr "" #: ../../Qwen/source/getting_started/concepts.md:15 #: 4fba11f4661b4e469f88dc3917b27427 msgid "The proprietary versions include" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:16 #: ../../Qwen/source/getting_started/concepts.md:31 #: be8423cea0b447c2b15de596c120f541 d07679ae34d0463f96aeff896a759118 msgid "Qwen: the language models" msgstr "通义千问 (Qwen)：语言模型" #: ../../Qwen/source/getting_started/concepts.md:17 #: a1461ec445034ba099aa58b1a13375a0 #, fuzzy msgid "Qwen Max" msgstr "通义千问 (Qwen)" #: ../../Qwen/source/getting_started/concepts.md:18 #: 19f8d7108d69464a8d1ce2980c1e4e92 #, fuzzy msgid "Qwen Plus" msgstr "通义千问 (Qwen)" #: ../../Qwen/source/getting_started/concepts.md:19 #: ede369bc8dd24052ad674131f4a3b68a msgid "Qwen Turbo" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:20 #: ../../Qwen/source/getting_started/concepts.md:36 #: ddb0acdec40b4f79a3e6517f86727e4b e4df2227d36a46ee8644ce77f9fc1dc0 msgid "Qwen-VL: the vision-language models" msgstr "通义千问 VL (Qwen-VL): 视觉语言模型" #: ../../Qwen/source/getting_started/concepts.md:21 #: f9f5a5b50af44e90999a87661cdf4e5a msgid "Qwen-VL Max" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:22 #: fd0074955211498c8520ef3405bf312f msgid "Qwen-VL Plus" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:23 #: 40c8d32d570c4a76a5392c8e296c3793 msgid "Qwen-VL OCR" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:24 #: ../../Qwen/source/getting_started/concepts.md:39 #: c0e45bd6e6b44ac7b18ef6a511c0999e f84666b662ab4d5ea41766d46f34fbc0 msgid "Qwen-Audio: the audio-language models" msgstr "通义千问 Audio: 音频语言模型" #: ../../Qwen/source/getting_started/concepts.md:25 #: 0584dbb5e76949ea965661c535e982d7 msgid "Qwen-Audio Turbo" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:26 #: aa78dd31bce94f6db05be93976278455 msgid "Qwen-Audio ASR" msgstr "" #: ../../Qwen/source/getting_started/concepts.md:28 #: df255434cec04d12b8e2d048d4e5baf8 msgid "You can learn more about them at Alibaba Cloud Model Studio ([China Site](https://help.aliyun.com/zh/model-studio/getting-started/models#9f8890ce29g5u) \\[zh\\], [International Site](https://www.alibabacloud.com/en/product/modelstudio))." msgstr "" #: ../../Qwen/source/getting_started/concepts.md:30 #: bc0fbc68d29b49da90efba3358f5013f msgid "The spectrum for the open-weight models spans over" msgstr "开源模型包括：" #: ../../Qwen/source/getting_started/concepts.md:32 #: e3107d97ea1b4e0284c2a33c0da02813 msgid "[Qwen](https://github.com/QwenLM/Qwen): 1.8B, 7B, 14B, and 72B models" msgstr "[Qwen](https://github.com/QwenLM/Qwen): 1.8B、 7B、 14B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:33 #: 8918b660d015430a8d14c3c62b87b19d msgid "[Qwen1.5](https://github.com/QwenLM/Qwen1.5/tree/v1.5): 0.5B, 1.8B, 4B, 14BA2.7B, 7B, 14B, 32B, 72B, and 110B models" msgstr "[Qwen1.5](https://github.com/QwenLM/Qwen1.5/tree/v1.5): 0.5B、 1.8B、 4B、 14BA2.7B、 7B、 14B、 32B、 72B 及 110B 模型" #: ../../Qwen/source/getting_started/concepts.md:34 #: c5ad94aa9d524a7290d9d0ec35321641 msgid "[Qwen2](https://github.com/QwenLM/Qwen2/tree/v2.0): 0.5B, 1.5B, 7B, 57A14B, and 72B models" msgstr "[Qwen2](https://github.com/QwenLM/Qwen2/tree/v2.0): 0.5B、 1.5B、 7B、 57A14B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:35 #: 5c38bd713ca847b4bb552971cdd75a99 msgid "[Qwen2.5](https://github.com/QwenLM/Qwen2.5/): 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B models" msgstr "[Qwen2.5](https://github.com/QwenLM/Qwen2.5/): 0.5B、 1.5B、 3B、 7B、 14B、 32B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:37 #: aa36bbcafdf742a9addd2a7b32705a02 msgid "[Qwen-VL](https://github.com/QwenLM/Qwen-VL): 7B-based models" msgstr "[Qwen-VL](https://github.com/QwenLM/Qwen-VL): 基于 7B 的模型" #: ../../Qwen/source/getting_started/concepts.md:38 #: 9d1d663950d34fefbfc7df37fa1def7a msgid "[Qwen2-VL](https://github.com/QwenLM/Qwen2-VL): 2B, 7B, and 72B-based models" msgstr "[Qwen-VL](https://github.com/QwenLM/Qwen2-VL): 基于 2B 、 7B 和 72B 的模型" #: ../../Qwen/source/getting_started/concepts.md:40 #: bb8a3431ea1f4cc99b4b8dd78e55d9ad msgid "[Qwen-Audio](https://github.com/QwenLM/Qwen-Audio): 7B-based model" msgstr "[Qwen-Audio](https://github.com/QwenLM/Qwen-Audio): 基于 7B 的模型" #: ../../Qwen/source/getting_started/concepts.md:41 #: 2421a5d0f547440bbf0211274bf44d5d msgid "[Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio): 7B-based models" msgstr "[Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio): 基于 7B 的模型" #: ../../Qwen/source/getting_started/concepts.md:42 #: df8610d7dfbf4651a955dc909b727061 #, fuzzy msgid "Q*Q: the reasoning models" msgstr "通义千问 (Qwen)：语言模型" #: ../../Qwen/source/getting_started/concepts.md:43 #: 67e209da7f5848adab885598a9069f11 #, fuzzy msgid "[QwQ-Preview](https://github.com/QwenLM/Qwen2.5/): 32B LLM" msgstr "[Qwen2.5-Coder](https://github.com/QwenLM/Qwen2.5-Coder): 7B 模型" #: ../../Qwen/source/getting_started/concepts.md:44 #: 4e9ad66b735a4109ab8ec727486c463c #, fuzzy msgid "[QVQ-Preview](https://github.com/QwenLM/Qwen2-VL): 72B VLM" msgstr "[Qwen-VL](https://github.com/QwenLM/Qwen-VL): 基于 7B 的模型" #: ../../Qwen/source/getting_started/concepts.md:45 #: 728cd9f1dc9d4502ad9a3702e802fc2e msgid "CodeQwen/Qwen-Coder: the language models for coding" msgstr "Code通义千问 / 通义千问Coder：代码语言模型" #: ../../Qwen/source/getting_started/concepts.md:46 #: 133fd513d7084b54bfe910fda13a42ec msgid "[CodeQwen1.5](https://github.com/QwenLM/CodeQwen1.5): 7B models" msgstr "[CodeQwen1.5](https://github.com/QwenLM/CodeQwen1.5): 7B 模型" #: ../../Qwen/source/getting_started/concepts.md:47 #: a903957acc0d458b8200788144be0b4d #, fuzzy msgid "[Qwen2.5-Coder](https://github.com/QwenLM/Qwen2.5-Coder): 0.5B, 1.5B, 3B, 7B, 14B, and 32B models" msgstr "[Qwen2.5](https://github.com/QwenLM/Qwen2.5/): 0.5B、 1.5B、 3B、 7B、 14B、 32B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:48 #: 6c47a9310a6945719b35da4bff3e0c9e msgid "Qwen-Math: the language models for mathematics" msgstr "通义千问 Math：数学语言模型" #: ../../Qwen/source/getting_started/concepts.md:49 #: fadbf7de806d4f288fc4355b52bcc060 msgid "[Qwen2-Math](https://github.com/QwenLM/Qwen2-Math): 1.5B, 7B, and 72B models" msgstr "[Qwen2-Math](https://github.com/QwenLM/Qwen2-Math)： 1.5B、 7B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:50 #: 0066352e253345288d16bb1a8df40e1c msgid "[Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math): 1.5B, 7B, and 72B models" msgstr "[Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math)： 1.5B、 7B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:51 #: b45ed6f1601c41f8a33f6b2b6ff8b47b #, fuzzy msgid "Qwen-Math-RM: the reward models for mathematics" msgstr "通义千问 Math：数学语言模型" #: ../../Qwen/source/getting_started/concepts.md:52 #: 286e8dd455ef4bab91821d399dd4a582 #, fuzzy msgid "[Qwen2-Math-RM](https://github.com/QwenLM/Qwen2-Math): 72B models" msgstr "[Qwen2-Math](https://github.com/QwenLM/Qwen2-Math)： 1.5B、 7B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:53 #: 81eb8401de1646309924a74e633b9b45 #, fuzzy msgid "[Qwen2.5-Math-RM](https://github.com/QwenLM/Qwen2.5-Math): 72B models" msgstr "[Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math)： 1.5B、 7B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:54 #: e0cd026299ba4809a86504afbe2dd8d5 #, fuzzy msgid "[Qwen2.5-Math-PRM](https://github.com/QwenLM/Qwen2.5-Math): 7B and 72B models" msgstr "[Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math)： 1.5B、 7B 及 72B 模型" #: ../../Qwen/source/getting_started/concepts.md:56 #: acec8c22ff094ebe8295cad38ec7a8db msgid "**In this document, our focus is Qwen, the language models.**" msgstr "**本文档针对通义千问 (Qwen) 语言模型。**" #: ../../Qwen/source/getting_started/concepts.md:58 #: e1e6ade4e85b4975bf992ed0a9c99140 msgid "Causal Language Models" msgstr "因果语言模型 (Causal Language Models)" #: ../../Qwen/source/getting_started/concepts.md:60 #: 593921d01e7a41caa52eda69db81c908 msgid "Causal language models, also known as autoregressive language models or decoder-only language models, are a type of machine learning model designed to predict the next token in a sequence based on the preceding tokens. In other words, they generate text one token at a time, using the previously generated tokens as context. The \"causal\" aspect refers to the fact that the model only considers the past context (the already generated tokens) when predicting the next token, not any future tokens." msgstr "因果语言模型 (causal Language Models)，也被称为自回归语言模型 (autoregressive language models) 或仅解码器语言模型 (decoder-only language models) ，是一种机器学习模型，旨在根据序列中的前导 token 预测下一个 token 。换句话说，它使用之前生成的 token 作为上下文，一次生成一个 token 的文本。\"因果\"方面指的是模型在预测下一个 token 时只考虑过去的上下文（即已生成的 token ），而不考虑任何未来的 token 。" #: ../../Qwen/source/getting_started/concepts.md:64 #: 4b31da2c06c54107857edcb2764e0019 msgid "Causal language models are widely used for various natural language processing tasks involving text completion and generation. They have been particularly successful in generating coherent and contextually relevant text, making them a cornerstone of modern natural language understanding and generation systems." msgstr "因果语言模型被广泛用于涉及文本补全和生成的各种自然语言处理任务。它们在生成连贯且具有上下文关联性的文本方面尤其成功，这使得它们成为现代自然语言理解和生成系统的基础。" #: ../../Qwen/source/getting_started/concepts.md:67 #: 98f73b1f049641038ec1b310a219b209 msgid "**Takeaway: Qwen models are causal language models suitable for text completion.**" msgstr "**要点：Qwen 模型是适用于文本补全的因果语言模型。**" #: ../../Qwen/source/getting_started/concepts.md #: 2f5c19be905046e1ae669119e3bb6e7c msgid "Learn more about language models" msgstr "了解更多关于语言模型的信息" #: ../../Qwen/source/getting_started/concepts.md:71 #: 557d7c8bafb94a34b76b6d96a3ce46ff msgid "They are three main kinds of models that are commonly referred to as language models in deep learning:" msgstr "在深度学习中，被称为语言模型的主要有三类：" #: ../../Qwen/source/getting_started/concepts.md:72 #: 89ef0f95d0f5492f877ddceb0233d2fc msgid "Sequence-to-sequence models: T5 and the likes" msgstr "序列到序列模型 (sequence-to-sequence models)：T5及其类似模型" #: ../../Qwen/source/getting_started/concepts.md:74 #: 80f14b7e5beb41d7920772b053681e24 msgid "Sequence-to-sequence models use both an encoder to capture the entire input sequence and a decoder to generate an output sequence. They are widely used for tasks like machine translation, text summarization, etc." msgstr "序列到序列模型同时使用编码器来捕获整个输入序列，以及解码器来生成输出序列。它们广泛应用于诸如机器翻译、文本摘要等任务。" #: ../../Qwen/source/getting_started/concepts.md:77 #: 0b15c87feae5409f80999e86ad5f5942 msgid "Bidirectional models or encoder-only models: BERT and the likes" msgstr "双向模型 (bidirectional models) 或仅编码器模型 (encoder-only models) ：BERT及其类似模型" #: ../../Qwen/source/getting_started/concepts.md:79 #: 7439fe506ee64fbfaba86bb409cb76ca msgid "Bidirectional models can access both past and future context in a sequence during training. They cannot generate sequential outputs in real-time due to the need for future context. They are widely used as embedding models and subsequently used for text classification." msgstr "双向模型在训练期间可以访问序列中的过去和未来上下文。由于需要未来上下文，它们无法实时生成顺序输出。它们广泛用作嵌入模型，并随后用于文本分类。" #: ../../Qwen/source/getting_started/concepts.md:83 #: c7f7ae809802445bbaafc7d7f783c71a msgid "Casual language models or decoder-only models: GPT and the likes" msgstr "因果语言模型 (casual language models) 或仅解码器模型 (decoder-only models) ：GPT及其类似模型" #: ../../Qwen/source/getting_started/concepts.md:85 #: b2825bdbf41c485c849444fc734fde43 msgid "Causal language models operate unidirectionally in a strictly forward direction, predicting each subsequent word based only on the previous words in the sequence. This unidirectional nature ensures that the model's predictions do not rely on future context, making them suitable for tasks like text completion and generation." msgstr "因果语言模型以严格向前的单向方式运行，仅根据序列中的前导词汇预测每个后续词汇。这种单向性确保了模型的预测不依赖于未来上下文，使它们适合于文本补全和生成等任务。" #: ../../Qwen/source/getting_started/concepts.md:89 #: 26bfa80a4e224b9ca3494f83fc37b0b6 msgid "Pre-training & Base models" msgstr "预训练 (Pre-training) 和基模型 (Base models)" #: ../../Qwen/source/getting_started/concepts.md:91 #: d75a1bc5132a43e8b41ce24b8021e7ab msgid "Base language models are foundational models trained on extensive corpora of text to predict the next word in a sequence. Their main goal is to capture the statistical patterns and structures of language, enabling them to generate coherent and contextually relevant text. These models are versatile and can be adapted to various natural language processing tasks through fine-tuning. While adept at producing fluent text, they may require in-context learning or additional training to follow specific instructions or perform complex reasoning tasks effectively. For Qwen models, the base models are those without \"-Instruct\" indicators, such as Qwen2.5-7B and Qwen2.5-72B." msgstr "基础语言模型 (base language models) 是在大量文本语料库上训练的基本模型，用于预测序列中的下一个词。它们的主要目标是捕捉语言的统计模式和结构，使它们能够生成连贯且具有上下文关联性的文本。这些模型具有多功能性，可以通过微调适应各种自然语言处理任务。虽然擅长生成流畅的文本，但它们可能需要情境学习 (in-context learning)或额外训练才能遵循特定指令或有效执行复杂推理任务。对于 Qwen 模型，基础模型是指那些没有 \"-Instruct\" 标识符的模型，例如 Qwen2.5-7B 和 Qwen2.5-72B 。" #: ../../Qwen/source/getting_started/concepts.md:97 #: 7f7321ea84f34e29beabf6122a77ec64 msgid "**Takeaway: Use base models for in-context learning, downstream fine-tuning, etc.**" msgstr "**要点：使用基础模型进行情境学习、下游微调等。**" #: ../../Qwen/source/getting_started/concepts.md:99 #: b1d8ca8221c0494796dda85ac2456389 msgid "Post-training & Instruction-tuned models" msgstr "后训练 (Post-training) 和指令微调模型 (Instruction-tuned models)" #: ../../Qwen/source/getting_started/concepts.md:101 #: 2f55c1d2c9234c44ab55bf90fcb1b10f msgid "Instruction-tuned language models are specialized models designed to understand and execute specific instructions in conversational styles. These models are fine-tuned to interpret user commands accurately and can perform tasks such as summarization, translation, and question answering with improved accuracy and consistency. Unlike base models, which are trained on large corpora of text, instruction-tuned models undergo additional training using datasets that contain examples of instructions and their desired outcomes, often in multiple turns. This kind of training makes them ideal for applications requiring targeted functionalities while maintaining the ability to generate fluent and coherent text. For Qwen models, the instruction-tuned models are those with the \"-Instruct\" suffix, such as Qwen2.5-7B-Instruct and Qwen2.5-72B-Instruct. [^instruct-chat]" msgstr "指令微调语言模型 (Instruction-tuned language models) 是专门设计用于理解并以对话风格执行特定指令的模型。这些模型经过微调，能准确地解释用户命令，并能以更高的准确性和一致性执行诸如摘要、翻译和问答等任务。与在大量文本语料库上训练的基础模型不同，指令调优模型会使用包含指令示例及其预期结果的数据集进行额外训练，通常涵盖多个回合。这种训练方式使它们非常适合需要特定功能的应用，同时保持生成流畅且连贯文本的能力。对于 Qwen 模型，指令调优模型是指带有 \"-Instruct\" 后缀的模型，例如 Qwen2.5-7B-Instruct 和 Qwen2.5-72B-Instruct 。 [^instruct-chat]" #: ../../Qwen/source/getting_started/concepts.md:107 #: d5b5590ccf434715bd57d0746f196cfe msgid "**Takeaway: Use instruction-tuned models for conducting tasks in conversations, downstream fine-tuning, etc.**" msgstr "**要点：使用指令微调模型进行对话式的任务执行、下游微调等。**" #: ../../Qwen/source/getting_started/concepts.md:112 #: 5dc4cca1e5104c67b1a3bcdd004e7a9d msgid "Tokens & Tokenization" msgstr "Tokens & Tokenization" #: ../../Qwen/source/getting_started/concepts.md:114 #: 9e3a74bf95fd40e49fef921a0d0df6ff msgid "Tokens represent the fundamental units that models process and generate. They can represent texts in human languages (regular tokens) or represent specific functionality like keywords in programming languages (control tokens [^special]). Typically, a tokenizer is used to split text into regular tokens, which can be words, subwords, or characters depending on the specific tokenization scheme employed, and furnish the token sequence with control tokens as needed. The vocabulary size, or the total number of unique tokens a model recognizes, significantly impacts its performance and versatility. Larger language models often use sophisticated tokenization methods to handle the vast diversity of human language while keeping the vocabulary size manageable. Qwen use a relatively large vocabulary of 151,646 tokens in total." msgstr "token 代表模型处理和生成的基本单位。它们可以表示人类语言中的文本（常规 token），或者表示特定功能，如编程语言中的关键字（控制 token [^special]）。通常，使用 tokenizer 将文本分割成常规 token ，这些 token 可以是单词、子词或字符，具体取决于所采用的特定 tokenization 方案，并按需为 token 序列添加控制 token 。词表大小，即模型识别的唯一 token 总数，对模型的性能和多功能性有重大影响。大型语言模型通常使用复杂的 tokenization 来处理人类语言的广阔多样性，同时保持词表大小可控。Qwen 词表相对较大，有 15 1646 个 token。" #: ../../Qwen/source/getting_started/concepts.md:123 #: 9e1c049b23fc403ea61919a755ae865a msgid "**Takeaway: Tokenization method and vocabulary size is important.**" msgstr "**要点：tokenization 和词表大小很重要。**" #: ../../Qwen/source/getting_started/concepts.md:125 #: 0a01476839134505b1e2e004f67c876b msgid "Byte-level Byte Pair Encoding" msgstr "Byte-level Byte Pair Encoding" #: ../../Qwen/source/getting_started/concepts.md:127 #: e461340d6e834aaeb233649a70618165 msgid "Qwen adopts a subword tokenization method called Byte Pair Encoding (BPE), which attempts to learn the composition of tokens that can represent the text with the fewest tokens. For example, the string \" tokenization\" is decomposed as \" token\" and \"ization\" (note that the space is part of the token). Especially, the tokenization of Qwen ensures that there is no unknown words and all texts can be transformed to token sequences." msgstr "Qwen采用了名为字节对编码（Byte Pair Encoding，简称BPE）的子词tokenization方法，这种方法试图学习能够用最少的 token 表示文本的 token 组合。例如，字符串\"tokenization\"被分解为\" token\"和\"ization\"（注意空格是 token 的一部分）。特别地，Qwen的 tokenization 确保了不存在未知词汇，并且所有文本都可以转换为 token 序列。" #: ../../Qwen/source/getting_started/concepts.md:131 #: af40a128cbe44fb59a057f9477737197 msgid "There are 151,643 tokens as a result of BPE in the vocabulary of Qwen, which is a large vocabulary efficient for diverse languages. As a rule of thumb, 1 token is 3~4 characters for English texts and 1.5~1.8 characters for Chinese texts." msgstr "Qwen词表中因BPE而产生的 token 数量为 15 1643 个，这是一个适用于多种语言的大词表。一般而言，对于英语文本，1个token大约是3~4个字符；而对于中文文本，则大约是1.5~1.8个汉字。" #: ../../Qwen/source/getting_started/concepts.md:134 #: 3b92bf813f14474f842584fa9bf4fdee msgid "**Takeaway: Qwen processes texts in subwords and there are no unknown words.**" msgstr "**要点：Qwen 以子词形式处理文本，不存在未知词汇。**" #: ../../Qwen/source/getting_started/concepts.md #: b29e165e1810403dbcd90cfedd8c73a6 msgid "Learn more about tokenization in Qwen" msgstr "了解更多" #: ../../Qwen/source/getting_started/concepts.md:137 #: b7fa098dbce946c9847eb414f7d52b9e msgid "Qwen uses byte-level BPE (BBPE) on UTF-8 encoded texts. It starts by treating each byte as a token and then iteratively merges the most frequent pairs of tokens occurring the texts into larger tokens until the desired vocabulary size is met." msgstr "Qwen 使用基于字节的BPE (BBPE) 对UTF-8编码的文本进行处理。它开始时将每个字节视为一个 token ，然后迭代地将文本中最频繁出现的 token 对合并成更大的 token，直到达到所需的词表大小。" #: ../../Qwen/source/getting_started/concepts.md:140 #: 504bb23b689949dd9bbee78f97d7e0a0 msgid "In byte-level BPE, minimum 256 tokens are needed to tokenize every piece of text and avoid the out of vocabulary (OOV) problem. In comparison, character-level BPE needs every Unicode character in its vocabulary to avoid OOV and the Unicode Standard contains 154,998 characters as of Unicode Version 16.0." msgstr "在基于字节的BPE中，至少需要256个 token 来对每段文本进行 tokenization，并避免未登录词（out of vocabulary, OOV）问题。相比之下，基于字符的 BPE 需要其词表中包含所有 Unicode 字符以避免未登录词，而截至 Unicode 版本16.0，Unicode标准包含 15 4998 个字符。" #: ../../Qwen/source/getting_started/concepts.md:143 #: cfed44d0c905486cb7e12838014249e1 msgid "One limitation to keep in mind for byte-level BPE is that the individual tokens in the vocabulary may not be seemingly semantically meaningful or even valid UTF-8 byte sequences, and in certain aspects, they should be viewed as a text compression scheme." msgstr "基于字节的BPE的一个限制是，词表中的个别 token 可能看似没有语义意义，甚至不是有效的 UTF-8 字节序列，在某些方面，它们应该被视为一种文本压缩方案。" #: ../../Qwen/source/getting_started/concepts.md:146 #: 4c6140ebdb0742e199793a7da566943e msgid "Control Tokens & Chat Template" msgstr "控制 Token 和 对话模板" #: ../../Qwen/source/getting_started/concepts.md:148 #: 7fab9c7227b94996bbdd30a2dd6a11cc msgid "Control tokens and chat templates both serve as mechanisms to guide the model's behavior and outputs." msgstr "控制 token 和对话模板都作为指导模型行为和输出的机制。" #: ../../Qwen/source/getting_started/concepts.md:150 #: 9d38b62cddc34442bffc173b6c5e15ea msgid "Control tokens are special tokens inserted into the sequence that signifies meta information. For example, in pre-training, multiple documents may be packed into a single sequence. For Qwen, the control token \"<|endoftext|>\" is inserted after each document to signify that the document has ended and a new document will proceed." msgstr "控制token是插入到序列中的特殊token，表示元信息。例如，在预训练中，多个文档可以被打包成一个单一的序列。对于Qwen，控制令牌 \"<|endoftext|>\" 在每个文档后插入，表示文档已经结束，新的文档将开始。" #: ../../Qwen/source/getting_started/concepts.md:154 #: aed5af70b3de447b9b3c1312f040f103 msgid "Chat templates provide a structured format for conversational interactions, where predefined placeholders or prompts are used to elicit responses from the model that adhere to a desired dialogue flow or context. Different models may use different kinds of chat template to format the conversations. It is crucial to use the designated one to ensure the precise control over the LLM's generation process." msgstr "对话模板为对话交互提供了结构化的格式，其中使用预定义的占位符或提示来从模型中引发遵循期望的对话流程或上下文的响应。不同的模型可能使用不同类型的对话模板来格式化对话。使用指定的模板对于确保对语言模型生成过程的精确控制至关重要。" #: ../../Qwen/source/getting_started/concepts.md:158 #: 7acbb7b28f1746a8b779a004a7dc2d93 msgid "Qwen uses the following format (ChatML[^chatml]), making use of control tokens to format each turn in the conversations" msgstr "Qwen使用以下格式（ChatML[^chatml]），利用控制 token 来格式化对话中的每一轮。" #: ../../Qwen/source/getting_started/concepts.md:163 #: 33f3aee8869748fa9f7a51c7efa76338 msgid "The user input take the role of `user` and the model generation takes the role of `assistant`. Qwen also supports the meta message that instruct the model to perform specific actions or generate text with certain characteristics, such as altering tone, style, or content, which takes the role of `system` and the content defaults to \"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\"" msgstr "用户输入扮演 `user` 的 role ，而模型生成则承担 `assistant` 的 role 。 Qwen 还支持元消息，该消息指导模型执行特定操作或生成具有特定特性的文本，例如改变语气、风格或内容，这将承担 `system` 的 role，且内容默认为 \"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\" 。" #: ../../Qwen/source/getting_started/concepts.md:166 #: 0129cbc394614f5f94047592df13c9b6 msgid "The following is a full example:" msgstr "下面为一个完整示例" #: ../../Qwen/source/getting_started/concepts.md:183 #: 59bab0422fa34a19ab2995e6ff15dc56 msgid "Starting from Qwen2.5, the Qwen model family including multimodal and specialized models will use a unified vocabulary, which contains control tokens from all subfamilies. There are 22 control tokens in the vocabulary of Qwen2.5, making the vocabulary size totaling 151,665:" msgstr "从 Qwen2.5 开始，Qwen 模型家族，包括多模态和专项模型，将使用统一的词汇表，其中包含了所有子系列的控制 token 。Qwen2.5 的词汇表中有 22 个控制 token，使得词汇表的总规模达到 15 1665 。" #: ../../Qwen/source/getting_started/concepts.md:185 #: 701bd6f896634b0aaf2920d883268a16 msgid "1 general: `<|endoftext|>`" msgstr "通用 token 1个：`<|endoftext|>`" #: ../../Qwen/source/getting_started/concepts.md:186 #: 7e78239f93a245dbb046d4ae2afe8a72 msgid "2 for chat: `<|im_start|>` and `<|im_end|>`" msgstr "对话 token 2个：`<|im_start|>` 和 `<|im_end|>`" #: ../../Qwen/source/getting_started/concepts.md:187 #: eb686086dfe44d53a5cdfc98e9bbaad8 msgid "2 for tool use: `` and ``" msgstr "工具调用 token 2个： `` 和 ``" #: ../../Qwen/source/getting_started/concepts.md:188 #: c8259cada9e94790a759a4b1f8edaf2d msgid "11 for vision" msgstr "视觉相关 token 11个" #: ../../Qwen/source/getting_started/concepts.md:189 #: 9b67870139b144c8ae4451e3deb1c1c5 msgid "6 for coding" msgstr "代码相关 token 6个" #: ../../Qwen/source/getting_started/concepts.md:191 #: 32c9581187f640d2a37cca85390bf1de msgid "**Takeaway: Qwen uses ChatML with control tokens for chat template.**" msgstr "**要点: Qwen 使用带有控制 token 的 ChatML 作为对话模板。**" #: ../../Qwen/source/getting_started/concepts.md:195 #: 74d8b323a0864a9c94a78f154a5c86c0 msgid "Length Limit" msgstr "长度限制" #: ../../Qwen/source/getting_started/concepts.md:197 #: 2833c71b35d94ff0b6825f86bc9be098 msgid "As Qwen models are causal language models, in theory there is only one length limit of the entire sequence. However, since there is often packing in training and each sequence may contain multiple individual pieces of texts. **How long the model can generate or complete ultimately depends on the use case and in that case how long each document (for pre-training) or each turn (for post-training) is in training.**" msgstr "由于 Qwen 模型是因果语言模型，理论上整个序列只有一个长度限制。然而，由于在训练中通常存在打包现象，每个序列可能包含多个独立的文本片段。**模型能够生成或完成的长度最终取决于具体的应用场景，以及在这种情况下，预训练时每份文档或后训练时每轮对话的长度。**" #: ../../Qwen/source/getting_started/concepts.md:201 #: 1d25c6232d924639b313a1a66d1990c9 msgid "For Qwen2.5, the packed sequence length in training is 32,768 tokens.[^yarn] The maximum document length in pre-training is this length. The maximum message length for user and assistant is different in post-training. In general, the assistant message could be up to 8192 tokens." msgstr "对于Qwen2.5，在训练中的打包序列长度为 3 2768 个 token [^yarn]。预训练中的最大文档长度即为此长度。而后训练中，user和assistant的最大消息长度则有所不同。一般情况下，assistant消息长度可达 8192 个 token。" #: ../../Qwen/source/getting_started/concepts.md:209 #: f39c2748eccb486794c941d23b23835c msgid "**Takeaway: Qwen2.5 models can process texts of 32K or 128K tokens and up to 8K tokens can be assistant output.**" msgstr "**要点：Qwen2 模型可以处理 32K 或 128K token 长的文本，其中 8K 长度可作为输出。**" #: ../../Qwen/source/getting_started/concepts.md:109 #: 7195ff6a5d1a4e6881f272081c9885d7 msgid "Previously, they are known as the chat models and with the \"-Chat\" suffix. Starting from Qwen2, the name is changed to follow the common practice. For Qwen, \"-Instruct\" and \"-Chat\" should be regarded as synonymous." msgstr "此前，它们被称为对话模型，并带有\"-Chat\"后缀。从Qwen2开始，名称变更为遵循通用做法。对于Qwen，\"-Instruct\"和\"-Chat\"应被视为同义词。" #: ../../Qwen/source/getting_started/concepts.md:121 #: f50caec63c8948a894dbf8c718f0b2d8 msgid "Control tokens can be called special tokens. However, the meaning of special tokens need to be interpreted based on the contexts: special tokens may contain extra regular tokens." msgstr "控制 token 也可以称为“特殊 token”。但是，特殊 token 的意义需要根据上下文进行解释：特殊 token 也可能包含额外的常规 token。" #: ../../Qwen/source/getting_started/concepts.md:193 #: fc70e6f93b71452ca0d09aa0ff28dd54 msgid "For historical reference only, ChatML is first described by the OpenAI Python SDK. The last available version is [this](https://github.com/openai/openai-python/blob/v0.28.1/chatml.md). Please also be aware that that document lists use cases intended for OpenAI models. For Qwen2.5 models, please only use as in our guide." msgstr "仅供历史参考，ChatML最初由OpenAI的Python SDK描述。可获取的最新版本是[这个](https//github.com/openai/openai-python/blob/v0.28.1/chatml.md)。请注意，该文档列出的应用案例是为OpenAI模型设计的。对于Qwen2.5模型，请仅按照我们的指南使用。" #: ../../Qwen/source/getting_started/concepts.md:206 #: a08b83b36c2d4e8d8f3dbb020ecb37a2 msgid "The sequence length can be extended to 131,072 tokens for Qwen2.5-7B, Qwen2.5-14B, Qwen2.5-32B, and Qwen2.5-72B models with YaRN. Please refer to the model card on how to enable YaRN in vLLM." msgstr "使用YaRN，Qwen2.5-7B、Qwen2.5-14B、Qwen2.5-32B和Qwen2-72B模型的序列长度可以扩展到13 1072个token。请参考模型卡片了解如何在 vLLM 中启用 YaRN。" #~ msgid "There is the proprietary version hosted exclusively at [Alibaba Cloud \\[zh\\]](https://help.aliyun.com/zh/model-studio/developer-reference/tongyi-qianwen-llm/) and the open-weight version." #~ msgstr "通义千问分为[闭源](https://help.aliyun.com/zh/model-studio/developer-reference/tongyi-qianwen-llm/)和开源两大版本。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/getting_started/quantization_benchmark.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:2 #: 6d4d3bb3020f4e4d8dba0ca5778cdcae msgid "Performance of Quantized Models" msgstr "量化模型效果评估" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:5 #: 3a541cd8cba74edf9b06b46f59eaaf38 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:7 #: 3a95fc299de141dea4fc729ef907ce17 msgid "This section reports the generation performance of quantized models (including GPTQ and AWQ) of the Qwen2 series. Specifically, we report:" msgstr "本部分介绍Qwen2量化模型（包括GPTQ与AWQ量化方案）的效果评估，有以下数据集" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:11 #: 9386a3b95eb340568185da78224a1ccd msgid "MMLU (Accuracy)" msgstr "MMLU （准确率）" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:12 #: 3cd93b881c90488895c61298104bc7fb msgid "C-Eval (Accuracy)" msgstr "C-Eval （准确率）" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:13 #: 7ac4bb515b0a49699d4eb95fc433bb51 msgid "IFEval (Strict Prompt-Level Accuracy)" msgstr "IFEval （提示词级的严格准确率，Strict Prompt-Level Accuracy）" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:15 #: 08e3f35820344c93877618815650b866 msgid "We use greedy decoding in evaluating all models." msgstr "所有模型均使用贪心解码。" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:18 #: 9aec40221219455d8fc4e473e5acf09c msgid "Quantization" msgstr "量化模型" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:18 #: 93f274f4751f445d85f04937b25c7f7d msgid "Average" msgstr "平均" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:18 #: 776612f5dd4a40d98976bdfe4896508c msgid "MMLU" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:18 #: f6e8014116cf4179a934d601ee61d04d msgid "C-Eval" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:18 #: 0c40e96c4a3b4cdeaaf1a95ff1aa8f98 msgid "IFEval" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: 773ccb0f10bd4cf690e819af51c40e76 msgid "Qwen2-72B-Instruct" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: 71e180f75e624b738d56ec2a1fad253c 7ebe73a2e96445c4bb733845c3190240 #: bd5a3b8861d646fa9e8d8bc51bb1b80c cc79a78b34f94c18b7bdaf1bfcc8824d msgid "BF16" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:22 #: 08517ffc3e6e4ceb812c3d8710307266 2e879d3d1fef4c878b097550d745e7ae msgid "81.3" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: f795aa42cf7d42ccb5a573a5f44be79f msgid "82.3" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: 01c54f3da3454e178a07a9f88ed5302b msgid "83.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:20 #: 7651df5ccaa14b11a3a89827a5265ae8 msgid "77.6" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:22 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:30 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:38 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:46 #: 04de04c9ff3640f096301e76fdd291de 301aa8e494ff4fe4aefcc8cfb7a4c065 #: d395be41cf144318a1faeccc6f6965c8 ec513d10a75d44b8bd134287a57b5cdd msgid "GPTQ-Int8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:22 #: 411166db878d4d8f8515e9f5d78a651c msgid "80.7" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:22 #: e63ce8a2f1cc4cec9b52521015e2aebe msgid "83.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:22 #: e6be6c30e0d740d39c6c8807e2d4f5f8 msgid "77.5" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:24 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:32 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:40 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:48 #: 21720ff324814b2b865f37a40c3586b5 4644a49bcdfd457b84eb5b2771177d78 #: 560dcb4bfa6e45088faefdb504d629a5 7044a0d2dd6945138ea385287ab5bf33 msgid "GPTQ-Int4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:24 #: 1cb55cd40b3c484d8213c15375b2ad68 msgid "81.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:24 #: 32b889d9ef014f2ab6be6881e20d40ae msgid "80.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:24 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:26 #: ba86de9eb27b40e0ba6a57580aed89c3 eed2e99c0edc426e81ec24e961fe971e msgid "83.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:24 #: ee3a3132082048d5b79721fa84f6f816 msgid "78.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:26 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:34 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:42 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:50 #: 632f832fc1f249fa92764538b698550d 8c7ccf4f75f44b27bb1b5aac544836cb #: b473937c2be94c3490483bb5a820e2fe bc1abd77dd27412992d21bda1831a2a8 msgid "AWQ" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:26 #: 2711a3f907224e51ba30818b2e730a30 msgid "80.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:26 #: ca9624c0258b425ba53f024b086c173a msgid "80.5" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:26 #: 2f4b57d4394c4cb187407145ce8d5f1e msgid "76.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: 48cc75ed7bf04778b327c7b03d418e37 msgid "Qwen2-7B-Instruct" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: 75182905b74a41099ff859fb86752e99 msgid "66.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: 80cda712e9dc482fac24952d3bb27b28 msgid "70.5" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: 0701d66bc3084aef8937e4b687705f37 msgid "77.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:28 #: 8efb5c133644420c808dfd78f8fcde2f msgid "53.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:30 #: 2076e02516bd4ff1856bc12a8d6bd320 msgid "66.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:30 #: 588f4ad13845491d9589ea094265d532 msgid "69.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:30 #: 0c79963a231a402eb6db1671e851be38 msgid "76.7" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:30 #: 5d525163672f456289990489459466ae msgid "52.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:32 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:34 #: 9283ca6491194b59a5edf57228f9b5af a4123c0691a442f6850ae25615c108af msgid "64.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:32 #: 9e7ffb49aac34129894b0582c0d8aba1 msgid "67.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:32 #: 7c2fc310e5764b7fbf6034ffd3a5d26d msgid "75.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:32 #: 33e6b6e590a64c08adccf0bb161c1046 msgid "49.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:34 #: b3cbe7665bdf4f4388f015fb6606540e msgid "67.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:34 #: a47d3b52e80249f986c4339b9d3fff10 msgid "73.6" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:34 #: d76543cff2df434185fbe51712024679 msgid "51.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: cee2c965036d41c6a93ffbf9a9788e4b msgid "Qwen2-1.5B-Instruct" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: 8c9d1cd8fb5a4d75b85d0edcb9ed69df msgid "48.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: f5e05b0942a24e2b9cac753932ad51c4 msgid "52.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: c6f81ec529004598aa14c55228ff9538 msgid "63.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:36 #: 5b2b4092d04f4d02a56bd0df5807e2c5 msgid "29.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:38 #: 08d2bf82e83f4a889d622c72c1e1b3b2 msgid "48.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:38 #: 3d8ea738153f467ba55d50e6bf0f84c0 msgid "53.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:38 #: 8755d6c4c1e64cd38122f08a92bd90ca msgid "62.5" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:38 #: 1c403dbb3692472a88706cb4b4a1f0f3 msgid "28.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:40 #: f3f43ea77edc4ff0969e2466e6fe13e1 msgid "45.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:40 #: 9d070c4b9f3e4fceb27b29ecdf90eb41 msgid "50.7" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:40 #: 24ff991704c440deb34b92512f89c371 msgid "57.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:40 #: b4645b7317a44cb795fc4190149dd0e0 msgid "27.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:42 #: eeee44d1d65647569999de94e72c00cb msgid "46.5" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:42 #: 41630bee9142494c801083cd5d213dc0 msgid "51.6" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:42 #: 762395735fb34bccbc4d057968bbfbf1 msgid "58.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:42 #: f5915835bcb24051bebed452fc398728 msgid "29.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: 39108e2a66444ca780a720f115251308 msgid "Qwen2-0.5B-Instruct" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: ../../Qwen/source/getting_started/quantization_benchmark.rst:50 #: 2795adace57c401cb8bacc00082dfd53 a59271d53e434d17a8a0a19529158f2c msgid "34.4" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: c93982789e4e453eb5a02d64f02cb74f msgid "37.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: 213dfd43b2254a2caec1d4b1d231ed55 msgid "45.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:44 #: 11de22e2a04a4c04b0b91d09d028b853 msgid "20.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:46 #: 84b6570bcc8d4c6598336d5bc9b9d36a msgid "32.6" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:46 #: b79e88232d114f43a179dcc5b0477c97 msgid "35.6" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:46 #: 1166b675e1e64e18a82c3219f321e248 msgid "43.9" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:46 #: fdf340d39b074778b55d36f477f8dc0a msgid "18.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:48 #: ed930e1b13dd4c5caf80b2a180a1bcc3 msgid "29.7" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:48 #: c3d5617389634f7e96c66b4f869379a9 msgid "33.0" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:48 #: 4573b471c48d4028ad6fb378e75f40aa msgid "39.2" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:48 #: c867c42e916f493b9715b1adf656ddcb msgid "16.8" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:50 #: 20d4c89c335648bb93f07ebfb8ce9fce msgid "31.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:50 #: 25400aeaf79d49cb914ffa5ff26bfe03 msgid "42.1" msgstr "" #: ../../Qwen/source/getting_started/quantization_benchmark.rst:50 #: d15e246b65b0427d970b78deffd8c2bc msgid "16.7" msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/getting_started/quickstart.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 16:36+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/getting_started/quickstart.md:1 #: a99b6a1db1374218a20b06bcd0c57957 msgid "Quickstart" msgstr "快速开始" #: ../../source/getting_started/quickstart.md:3 #: 1da6c3f04eb24db8b697e094163096a1 msgid "This guide helps you quickly start using Qwen3. We provide examples of [Hugging Face Transformers](https://github.com/huggingface/transformers) as well as [ModelScope](https://github.com/modelscope/modelscope), and [vLLM](https://github.com/vllm-project/vllm) for deployment." msgstr "本指南帮助您快速上手 Qwen3 的使用，并提供了如下示例： [Hugging Face Transformers](https://github.com/huggingface/transformers) 以及 [ModelScope](https://github.com/modelscope/modelscope) 和 [vLLM](https://github.com/vllm-project/vllm) 在部署时的应用实例。" #: ../../source/getting_started/quickstart.md:6 #: 11c38e7141f941efb448e7099935b8a9 msgid "You can find Qwen3 models in [the Qwen3 collection](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) at Hugging Face Hub and [the Qwen3 collection](https://www.modelscope.cn/collections/Qwen3-9743180bdc6b48) at ModelScope." msgstr "你可以在 Hugging Face Hub 的 [Qwen3 collection](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) 或 ModelScope 的 [Qwen3 collection](https://www.modelscope.cn/collections/Qwen3-9743180bdc6b48) 中寻找 Qwen3 模型。" #: ../../source/getting_started/quickstart.md:8 #: 842c24eb7d30496baf9025af21ca1ed0 msgid "Transformers" msgstr "Transformers" #: ../../source/getting_started/quickstart.md:10 #: d0c61f87b0b347ae91e115ba60ebd46e msgid "To get a quick start with Qwen3, you can try the inference with `transformers` first. Make sure that you have installed `transformers>=4.51.0`. We advise you to use Python 3.10 or higher, and PyTorch 2.6 or higher." msgstr "要快速上手 Qwen3 ，我们建议您首先尝试使用 `transformers` 进行推理。请确保已安装了 `transformers>=4.51.0` 版本。我们建议您使用 Python 3.10 或以上版本， PyTorch 2.6 或以上版本。" #: ../../source/getting_started/quickstart.md:14 #: a7fbb3d015b4440ca14ba00821f84fb0 msgid "The following is a very simple code snippet showing how to run Qwen3-8B:" msgstr "以下是一个非常简单的代码片段示例，展示如何运行 Qwen3 模型：" #: ../../source/getting_started/quickstart.md:63 #: b55178516d31433c9ead5287e9abd3b4 msgid "Qwen3 will think before respond, similar to QwQ models. This means the model will use its reasoning abilities to enhance the quality of generated responses. The model will first generate thinking content wrapped in a `...` block, followed by the final response." msgstr "Qwen3 将在实际回复前思考，与 QwQ 模型类似。这意味着模型将运用其推理能力来提升生成回复的质量。模型会首先生成包含在 `...` 块中的思考内容，随后给出最终回复。" #: ../../source/getting_started/quickstart.md:67 #: e31fa076c2264d9fa2ae5d8516b7f4a7 msgid "Hard Switch: To strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models, you can set `enable_thinking=False` when formatting the text." msgstr "硬开关：为了严格禁用模型的思考行为，使其功能与之前的Qwen2.5-Instruct模型保持一致，您可以在格式化文本时设置`enable_thinking=False`。" #: ../../source/getting_started/quickstart.md:77 #: a6c11c7147e54cf9a86d65b4f80840c3 msgid "It can be particularly useful in scenarios where disabling thinking is essential for enhancing efficiency." msgstr "在某些需要通过禁用思考来提升效率的场景中，这一功能尤其有用。" #: ../../source/getting_started/quickstart.md:79 #: 57bd7b66fe3b4dbfabe7439dc67b7d5f msgid "Soft Switch: Qwen3 also understands the user's instruction on its thinking behavior, in particular, the soft switch `/think` and `/no_think`. You can add them to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations." msgstr "软开关：Qwen3 还能够理解用户对其思考行为的指令，特别是软开关 `/think` 和 `/no_think`。您可以将这些指令添加到用户 (user) 或系统 (system) 消息中，以在对话轮次之间灵活切换模型的思考模式。在多轮对话中，模型将遵循最近的指令。" #: ../../source/getting_started/quickstart.md:85 #: 73fa1ee92c7b4a71a2724aedb665dd1b msgid "For thinking mode, use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0 (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the Best Practices section." msgstr "对于思考模式，使用 Temperature=0.6，TopP=0.95，TopK=20，以及 MinP=0（`generation_config.json` 中的默认设置）。不要使用贪婪解码，因为它可能导致性能下降和无尽的重复。更多详细指导，请参阅最佳实践部分。" #: ../../source/getting_started/quickstart.md:89 #: 4ce3b1cf5e1349628a66c101432fd748 msgid "For non-thinking mode, we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0." msgstr "对于非思考模式，我们建议使用 Temperature=0.7，TopP=0.8，TopK=20，以及 MinP=0。" #: ../../source/getting_started/quickstart.md:93 #: 34d138d1ca8c4ecab4002b354cfe64d2 msgid "ModelScope" msgstr "魔搭 (ModelScope)" #: ../../source/getting_started/quickstart.md:95 #: 0c25fecd5e42412a80214fbc35b08226 msgid "To tackle with downloading issues, we advise you to try [ModelScope](https://github.com/modelscope/modelscope). Before starting, you need to install `modelscope` with `pip`." msgstr "为了解决下载问题，我们建议您尝试从 [ModelScope](https://github.com/modelscope/modelscope) 进行下载。开始之前，需要使用 `pip` 安装 `modelscope` 。" #: ../../source/getting_started/quickstart.md:98 #: d8ccf4eafeb849ae8bd49d9fb2281c60 msgid "`modelscope` adopts a programmatic interface similar (but not identical) to `transformers`. For basic usage, you can simply change the first line of code above to the following:" msgstr "`modelscope` 采用了与 `transformers` 类似（但不完全一致）的编程接口。对于基础使用，仅需将上面代码第一行做如下修改：" #: ../../source/getting_started/quickstart.md:105 #: f17c53400572487ca066135facd711bb msgid "For more information, please refer to [the documentation of `modelscope`](https://www.modelscope.cn/docs)." msgstr "欲获取更多信息，请参考 [`modelscope` 文档](https://www.modelscope.cn/docs)。" #: ../../source/getting_started/quickstart.md:107 #: 6e3028c50b2146bd932d168662e57620 msgid "OpenAI API Compatibility" msgstr "" #: ../../source/getting_started/quickstart.md:109 #: 046b0833e5b744cda72d7a7ef5672cc2 msgid "You can serve Qwen3 via OpenAI-compatible APIs using frameworks such as vLLM, SGLang, and interact with the API using common HTTP clients or the OpenAI SDKs." msgstr "" #: ../../source/getting_started/quickstart.md:112 #: 1542f3cbe7ba4adab50dfc85da110c36 msgid "Here we take Qwen3-8B as an example to start the API:" msgstr "" #: ../../source/getting_started/quickstart.md:114 #: 4bfa52cf82914d73b0fcbbceafa4ff8a msgid "SGLang (`sglang>=0.4.6.post1` is required):" msgstr "" #: ../../source/getting_started/quickstart.md:120 #: cc7a9ae4d4fe4b86be41b376d7334024 msgid "vLLM (`vllm>=0.8.5` is recommended):" msgstr "" #: ../../source/getting_started/quickstart.md:126 #: 7a690ab7e23f4505a355abd50e647101 msgid "Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen:" msgstr "然后，可以使用 [\"create chat\" interface](https://platform.openai.com/docs/api-reference/chat/completions/create>) 来与 Qwen 进行交流：" #: ../../source/getting_started/quickstart.md 9cd82768a97142acac1c2c63a05e1ad3 msgid "curl" msgstr "" #: ../../source/getting_started/quickstart.md 7d220176fa6b4622ae74431873d49396 msgid "Python" msgstr "" #: ../../source/getting_started/quickstart.md:146 #: f55afdfc6fa54f468b1b554fab082263 msgid "You can use the API client with the `openai` Python SDK as shown below:" msgstr "您可以按照下面所示的方式，使用 `openai` Python SDK中的客户端：" #: ../../source/getting_started/quickstart.md:175 #: d0370e64550a4c51b6146ad7dfb52f97 msgid "While the soft switch is always available, the hard switch is also available in the API through the following configuration to the API call. For more usage, please refer to our document on [SGLang](../deployment/sglang) and [vLLM](../deployment/vllm)." msgstr "虽然软开关始终可用，但硬开关也可以通过以下 API 调用配置在 API 中使用。更多用法，请参阅我们关于 [SGLang](../deployment/sglang) 和 [vLLM](../deployment/vllm) 的文档。" #: ../../source/getting_started/quickstart.md:178 #: e48c576eba5e4c5db9ef0a48882e18c2 msgid "Thinking Budget" msgstr "思考预算" #: ../../source/getting_started/quickstart.md:180 #: cc5be59588e34164b7f2e84a7d8b82c0 msgid "Qwen3 supports the configuration of thinking budget. It is achieved by ending the thinking process once the budget is reached and guiding the model to generate the \"summary\" with an early-stopping prompt." msgstr "Qwen3 支持配置思考预算。其实现方式是，一旦达到预算，便结束思考过程，并通过提前停止提示引导模型生成“总结”。" #: ../../source/getting_started/quickstart.md:183 #: 8a2760351bc04093adc5017b58ec4981 msgid "Since this feature involves customization specific to each model, it is currently not available in the open-source frameworks and only implemented by [the Alibaba Cloud Model Studio API](https://www.alibabacloud.com/help/en/model-studio/deep-thinking#6f0633b9cdts1)." msgstr "由于此功能涉及针对模型的定制，目前在开源框架中不可用，仅由[阿里云百炼API](https://bailian.console.aliyun.com/?tab=doc#/doc/?type=model&url=https%3A%2F%2Fhelp.aliyun.com%2Fdocument_detail%2F2870973.html&renderType=iframe)实现。" #: ../../source/getting_started/quickstart.md:185 #: 01acc4a8caa443dc8da81862d2e7d6bb msgid "However, with existing open-source frameworks, one can generate twice to implement this feature as follows:" msgstr "然而，利用现有的开源框架，可以通过两次生成来实现此功能，具体如下：" #: ../../source/getting_started/quickstart.md:186 #: 677f5e6ad5b1416aa4bbadff3e6537a1 msgid "For the first time, generate tokens up to the thinking budget and check if the thinking process is finished. If the thinking process is not finished, append the early-stopping prompt." msgstr "第一次生成时，生成的token数量达到思考预算，并检查思考过程是否完成。如果思考过程未完成，则追加提前停止提示。" #: ../../source/getting_started/quickstart.md:187 #: 479c92c1bc9d4a74b18d5c175d1e6cda msgid "For the second time, continue generation until the end of the content or the upper length limit is fulfilled." msgstr "第二次生成时，继续生成直到内容结束或达到长度上限。" #: ../../source/getting_started/quickstart.md:189 #: dd7834e30f9344ddad21792279ad4732 msgid "The following snippet shows the implementation with Hugging Face Transformers:" msgstr "以下代码片段展示了使用Hugging Face Transformers的实现：" #: ../../source/getting_started/quickstart.md:262 #: 85f448fb112e4742bc5d4bd05979f30d msgid "You should see the output in the console like the following" msgstr "您应该会在控制台中看到类似以下的输出：" #: ../../source/getting_started/quickstart.md:274 #: 4fc4ae8cdbb54c8b97b2da717d61e42e msgid "For purpose of demonstration only, `thinking_budget` is set to 16. However, `thinking_budget` should not be set to that low in practice. We recommend tuning `thinking_budget` based on the latency users can accept and setting it higher than 1024 for meaningful improvements across tasks." msgstr "出于示例目的，`thinking_budget` 被设置为 16。然而，在实际应用中不应将其设置得如此低。我们建议根据用户可接受的延迟调整 `thinking_budget`，并将其设置为高于 1024，以在各项任务中获得有意义的改进。" #: ../../source/getting_started/quickstart.md:278 #: f5b4018640c24a3cb6dd1e958f2860d9 msgid "If thinking is not desired at all, developers should make use of the hard switch instead." msgstr "如果完全不需要思考，开发者应改用硬开关。" #: ../../source/getting_started/quickstart.md:281 #: a6bbbe9da06c43c09cd3756e075e6103 msgid "Next Step" msgstr "下一步" #: ../../source/getting_started/quickstart.md:283 #: 40f5270a92bf490ab4fa0d6af2761044 msgid "Now, you can have fun with Qwen3 models. Would love to know more about its usage? Feel free to check other documents in this documentation." msgstr "现在，您可以尽情探索 Qwen3 模型的各种用途。若想了解更多，请随时查阅本文档中的其他内容。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/getting_started/speed_benchmark.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-20 17:08+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/getting_started/speed_benchmark.md:1 #: d7da8abe501c46e99fee28d56435b08b msgid "Speed Benchmark" msgstr "效率评估" #: ../../source/getting_started/speed_benchmark.md:3 #: 478eb0d956db47ccafda9ec05e9c7253 msgid "We report the speed performance of bfloat16 models and quantized models (including FP8, GPTQ, AWQ) of the Qwen3 series. Specifically, we report the inference speed (tokens/s) as well as memory footprint (GB) under different context lengths." msgstr "本部分介绍 Qwen3 系列模型（原始模型和量化模型）的效率测试结果，包括推理速度(tokens/s)与不同上下文长度时的显存占用(GB)。" #: ../../source/getting_started/speed_benchmark.md:6 #: c3e1ab9d5f8b4a54bff5d5a56ea6a2bb msgid "Environments" msgstr "环境配置" #: ../../source/getting_started/speed_benchmark.md:8 #: 6b7764c599eb4693be742f161daf5cc4 msgid "Hugging Face Transformers" msgstr "" #: ../../source/getting_started/speed_benchmark.md:10 #: ../../source/getting_started/speed_benchmark.md:25 #: aa80ee8b1abd4be1b0a9a88cff496938 c43bf971a253469c832f5e874eda86a9 msgid "**Hardware**:" msgstr "**硬件**:" #: ../../source/getting_started/speed_benchmark.md:11 #: ../../source/getting_started/speed_benchmark.md:26 #: 471dbb8bd26c41a58f485dabf6643aa0 msgid "NVIDIA H20 96GB" msgstr "" #: ../../source/getting_started/speed_benchmark.md:12 #: 04347c8d74a44a18af8fb11cbf47bb42 msgid "**Software for Non-AutoAWQ**:" msgstr "**非AutoAWQ的软件环境**:" #: ../../source/getting_started/speed_benchmark.md:13 #: 5bf39b332ff04657838c29c627a0e3a6 msgid "PyTorch 2.6.0" msgstr "" #: ../../source/getting_started/speed_benchmark.md:14 #: e1ca0b138a5248a59f6fb59331ff09c1 msgid "Flash Attention 2.7.4" msgstr "" #: ../../source/getting_started/speed_benchmark.md:15 #: ../../source/getting_started/speed_benchmark.md:19 #: ../../source/getting_started/speed_benchmark.md:29 #: c763f81c0b2443d594f7b7c6c1cacce0 msgid "Transformers 4.51.3" msgstr "" #: ../../source/getting_started/speed_benchmark.md:16 #: 07c9d4a46b1446d9a334ad590b23ae8c msgid "GPTQModel 2.2.0+cu128torch2.6" msgstr "" #: ../../source/getting_started/speed_benchmark.md:17 #: a13e8f4aa82f4693af93c20cda549afe msgid "**Software for AutoAWQ**:" msgstr "**AutoAWQ的软件环境**:" #: ../../source/getting_started/speed_benchmark.md:18 #: ../../source/getting_started/speed_benchmark.md:28 #: 14b90e1a69ce40f6a1edc3eb0fbf92b3 msgid "PyTorch 2.6.0+cu124" msgstr "" #: ../../source/getting_started/speed_benchmark.md:20 #: c8709c79343448838e2e6423ae888b6e msgid "AutoAWQ 0.2.9" msgstr "" #: ../../source/getting_started/speed_benchmark.md:21 #: c8bd3eae4c8e497197bd3da6e79ded0a msgid "AutoAWQ_kernels 0.0.9" msgstr "" #: ../../source/getting_started/speed_benchmark.md:24 #: cc0dad54e88d4e13857f3a45888b6030 msgid "SGLang" msgstr "" #: ../../source/getting_started/speed_benchmark.md:27 #: 931979d5babc4459940b65cc29f091b3 msgid "**Software**:" msgstr "**软件环境**:" #: ../../source/getting_started/speed_benchmark.md:30 #: 23713b47351b4b82949d982019a725b1 msgid "SGLang 0.4.6.post1" msgstr "" #: ../../source/getting_started/speed_benchmark.md:31 #: 8a1e4b407a5e46748b9fcbd29b7624ef msgid "SGL-kernel 0.1.0" msgstr "" #: ../../source/getting_started/speed_benchmark.md:32 #: bc8a5ce98be34cd3987b015f51b607a1 msgid "vLLM 0.7.2 (Required by SGLang for AWQ quantization)" msgstr "vLLM 0.7.2 (被SGLang AWQ量化依赖)" #: ../../source/getting_started/speed_benchmark.md:34 #: 1b5176a8dd9647738f81c3f726f9e9dd msgid "Notes" msgstr "备注" #: ../../source/getting_started/speed_benchmark.md:36 #: 5b67ede5b69845158d7de45576b5f511 msgid "**Inference Speed (tokens/s)** is calculated as:" msgstr "**推理速度（tokens/s）** 的计算公式为：" #: ../../source/getting_started/speed_benchmark.md:38 #: 8d9cad9e886e4ba09e3a9907eecdc85e msgid "\\text{Speed} = \\frac{\\text{tokens}_{\\text{prompt}} + \\text{tokens}_{\\text{generation}}}{\\text{time}}" msgstr "" #: ../../source/getting_started/speed_benchmark.md:42 #: 5e23850b08f54a5b93ca312f77ad16cb msgid "We use a **batch size of 1** and the **minimum number of GPUs** possible for evaluation." msgstr "batch size 设置为1，使用 GPU 数量尽可能少" #: ../../source/getting_started/speed_benchmark.md:44 #: 25f5f831928e428a857e76cb66be39dc msgid "We test the **speed and memory usage** when generating **2048 tokens**, with input lengths of `1`, `6144`, `14336`, `30720`, `63488`, and `129024` tokens." msgstr "我们测试生成2048 tokens时的速度与显存占用，输入长度分别为1、6144、14336、30720、63488、129024 tokens（如受模型支持）。" #: ../../source/getting_started/speed_benchmark.md:47 #: 11a1e0c5d8ec462987503d3eddf02e93 msgid "**For SGLang**:" msgstr "**对于SGLang**:" #: ../../source/getting_started/speed_benchmark.md:48 #: 39a19fc71e414215bfefe47fabd4f103 msgid "**Memory usage** is not reported because SGLang pre-allocates all GPU memory. By default, we set `mem_fraction_static=0.85`." msgstr "**内存使用情况**未报告，因为 SGLang 会预先分配所有 GPU 内存。默认情况下，我们设置 `mem_fraction_static=0.85`。" #: ../../source/getting_started/speed_benchmark.md:50 #: 4dc120620fe04f8d929eb9a49e162384 msgid "We configure `context_length=140000` and enable `enable_mixed_chunk=True`." msgstr "我们配置了 `context_length=140000` 并启用了 `enable_mixed_chunk=True`。" #: ../../source/getting_started/speed_benchmark.md:51 #: f1912315cef646d885132a95c1bbfe61 msgid "For **AWQ quantization**, we use the **awq_marlin** backend." msgstr "对于 **AWQ 量化**，我们使用 **awq_marlin** 后端。" #: ../../source/getting_started/speed_benchmark.md:52 #: b71d9ac6ac2a4007b1bf5bcbbcc0a583 msgid "We set `skip_tokenizer_init=True` and perform generation using `input_ids` instead of raw text prompts." msgstr "我们设置 `skip_tokenizer_init=True` 并使用 `input_ids` 进行生成，而不是使用原始文本提示。" #: ../../source/getting_started/speed_benchmark.md:54 #: 68e13ce181df472fb7ac447baccccd5f msgid "**FP8 Performance in Transformers**: The inference speed of Transformers in FP8 mode is currently not optimal and requires further optimization." msgstr "**Transformers 中的 FP8 性能**：Transformers 在 FP8 模式下的推理速度目前不够理想，还需要进一步优化。" #: ../../source/getting_started/speed_benchmark.md:56 #: eba091d7757241eba26249e3b4481358 msgid "**GPTQ-INT4 Performance in SGLang**: The performance of GPTQ-INT4 in SGLang also needs improvement, and we are actively working with the team to enhance it." msgstr "**SGLang 中 GPTQ-INT4 的性能**：SGLang 中 GPTQ-INT4 的性能也需要改进，SGLang团队正提升其表现。" #: ../../source/getting_started/speed_benchmark.md:58 #: 378478a6059142c4baa00ef74101c7f5 msgid "Results" msgstr "结果" #: ../../source/getting_started/speed_benchmark.md:60 #: e70ceb3f7c51413a9f385a49a32a5f53 msgid "Qwen3-0.6B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:114 #: 48d34c54a6f84a679798b6d6e10b9258 msgid "Qwen3-0.6B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:169 #: 6bbf0e62079c420ea3466b988c613429 msgid "Qwen3-1.7B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:225 #: d0b2aba9331646b28d3a8b2d75385d6c msgid "Qwen3-1.7B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:279 #: 74e433c4044d4a06930966868db14258 msgid "Qwen3-4B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:351 #: 0fa507ce71d846688e57481c96114d58 msgid "Qwen3-4B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:405 #: dbffaaf4c5274194a1eb1ff640e86dea msgid "Qwen3-8B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:478 #: f3be6cabb6db49769fd4d7f64e16391f msgid "Qwen3-8B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:533 #: d08b7dfd146a4853a4d47c8f2a028cc4 msgid "Qwen3-14B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:606 #: 0fa507ce71d846688e57481c96114d58 msgid "Qwen3-14B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:661 #: 245c24c8822b4b95aa59bc31d3798e3b msgid "Qwen3-32B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:735 #: f3be6cabb6db49769fd4d7f64e16391f msgid "Qwen3-32B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:789 #: 35fe65bc8eac4fde92b4614a2875c78d msgid "Qwen3-30B-A3B (SGLang)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:861 #: 31c35c93d04143bc9af6b4966beb6d1b msgid "Qwen3-30B-A3B (Transformers)" msgstr "" #: ../../source/getting_started/speed_benchmark.md:916 #: c6f3063607fe403284c5aae77059b415 msgid "Qwen3-235B-A22B (SGLang)" msgstr "" #~ msgid "To be updated for Qwen3." #~ msgstr "仍需为Qwen3更新。" #~ msgid "The environment of the evaluation with huggingface transformers is:" #~ msgstr "测试HuggingFace ``transformers`` 时的环境配置：" #~ msgid "NVIDIA A100 80GB" #~ msgstr "" #~ msgid "CUDA 12.1" #~ msgstr "" #~ msgid "Pytorch 2.3.1" #~ msgstr "" #~ msgid "Flash Attention 2.5.8" #~ msgstr "" #~ msgid "Transformers 4.46.0" #~ msgstr "" #~ msgid "AutoGPTQ 0.7.1+cu121 (Compiled from source code)" #~ msgstr "" #~ msgid "AutoAWQ 0.2.6" #~ msgstr "" #~ msgid "The environment of the evaluation with vLLM is:" #~ msgstr "测试vLLM时的环境配置：" #~ msgid "vLLM 0.6.3" #~ msgstr "" #~ msgid "Pytorch 2.4.0" #~ msgstr "" #~ msgid "Flash Attention 2.6.3" #~ msgstr "" #~ msgid "For vLLM, the memory usage is not reported because it pre-allocates all GPU memory. We use ``gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False`` by default." #~ msgstr "对于vLLM，由于GPU显存预分配，实际显存使用难以评估。默认情况下，统一设定为``gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False``。" #~ msgid "0.5B (Transformer)" #~ msgstr "" #~ msgid "Model" #~ msgstr "模型" #~ msgid "Input Length" #~ msgstr "输入长度" #~ msgid "Quantization" #~ msgstr "量化" #~ msgid "GPU Num" #~ msgstr "GPU数量" #~ msgid "Speed(tokens/s)" #~ msgstr "速度 (tokens/s)" #~ msgid "GPU Memory(GB)" #~ msgstr "显存占用 (GB)" #~ msgid "Note" #~ msgstr "注意：" #~ msgid "Qwen2.5-0.5B-Instruct" #~ msgstr "" #~ msgid "1" #~ msgstr "" #~ msgid "BF16" #~ msgstr "" #~ msgid "47.40" #~ msgstr "" #~ msgid "0.97" #~ msgstr "" #~ msgid "GPTQ-Int8" #~ msgstr "" #~ msgid "35.17" #~ msgstr "" #~ msgid "0.64" #~ msgstr "" #~ msgid "auto_gptq==0.6.0+cu1210" #~ msgstr "" #~ msgid "GPTQ-Int4" #~ msgstr "" #~ msgid "50.60" #~ msgstr "" #~ msgid "0.48" #~ msgstr "" #~ msgid "AWQ" #~ msgstr "" #~ msgid "37.09" #~ msgstr "" #~ msgid "0.68" #~ msgstr "" #~ msgid "6144" #~ msgstr "" #~ msgid "47.45" #~ msgstr "" #~ msgid "1.23" #~ msgstr "" #~ msgid "36.47" #~ msgstr "" #~ msgid "0.90" #~ msgstr "" #~ msgid "48.89" #~ msgstr "" #~ msgid "0.73" #~ msgstr "" #~ msgid "37.04" #~ msgstr "" #~ msgid "0.72" #~ msgstr "" #~ msgid "14336" #~ msgstr "" #~ msgid "47.11" #~ msgstr "" #~ msgid "1.60" #~ msgstr "" #~ msgid "35.44" #~ msgstr "" #~ msgid "1.26" #~ msgstr "" #~ msgid "48.26" #~ msgstr "" #~ msgid "1.10" #~ msgstr "" #~ msgid "37.14" #~ msgstr "" #~ msgid "30720" #~ msgstr "" #~ msgid "47.16" #~ msgstr "" #~ msgid "2.34" #~ msgstr "" #~ msgid "36.25" #~ msgstr "" #~ msgid "2.01" #~ msgstr "" #~ msgid "49.22" #~ msgstr "" #~ msgid "1.85" #~ msgstr "" #~ msgid "36.90" #~ msgstr "" #~ msgid "1.84" #~ msgstr "" #~ msgid "0.5B (vLLM)" #~ msgstr "" #~ msgid "311.55" #~ msgstr "" #~ msgid "257.07" #~ msgstr "" #~ msgid "260.93" #~ msgstr "" #~ msgid "261.95" #~ msgstr "" #~ msgid "304.79" #~ msgstr "" #~ msgid "254.10" #~ msgstr "" #~ msgid "257.33" #~ msgstr "" #~ msgid "259.80" #~ msgstr "" #~ msgid "290.28" #~ msgstr "" #~ msgid "243.69" #~ msgstr "" #~ msgid "247.01" #~ msgstr "" #~ msgid "249.58" #~ msgstr "" #~ msgid "264.51" #~ msgstr "" #~ msgid "223.86" #~ msgstr "" #~ msgid "226.50" #~ msgstr "" #~ msgid "229.84" #~ msgstr "" #~ msgid "1.5B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-1.5B-Instruct" #~ msgstr "" #~ msgid "39.68" #~ msgstr "" #~ msgid "2.95" #~ msgstr "" #~ msgid "32.62" #~ msgstr "" #~ msgid "1.82" #~ msgstr "" #~ msgid "43.33" #~ msgstr "" #~ msgid "1.18" #~ msgstr "" #~ msgid "31.70" #~ msgstr "" #~ msgid "1.51" #~ msgstr "" #~ msgid "40.88" #~ msgstr "" #~ msgid "3.43" #~ msgstr "" #~ msgid "31.46" #~ msgstr "" #~ msgid "2.30" #~ msgstr "" #~ msgid "43.96" #~ msgstr "" #~ msgid "1.66" #~ msgstr "" #~ msgid "32.30" #~ msgstr "" #~ msgid "1.63" #~ msgstr "" #~ msgid "40.43" #~ msgstr "" #~ msgid "4.16" #~ msgstr "" #~ msgid "31.06" #~ msgstr "" #~ msgid "3.03" #~ msgstr "" #~ msgid "43.66" #~ msgstr "" #~ msgid "2.39" #~ msgstr "" #~ msgid "32.39" #~ msgstr "" #~ msgid "2.36" #~ msgstr "" #~ msgid "38.59" #~ msgstr "" #~ msgid "5.62" #~ msgstr "" #~ msgid "31.04" #~ msgstr "" #~ msgid "4.49" #~ msgstr "" #~ msgid "35.68" #~ msgstr "" #~ msgid "3.85" #~ msgstr "" #~ msgid "31.95" #~ msgstr "" #~ msgid "3.82" #~ msgstr "" #~ msgid "1.5B (vLLM)" #~ msgstr "" #~ msgid "183.33" #~ msgstr "" #~ msgid "201.67" #~ msgstr "" #~ msgid "217.03" #~ msgstr "" #~ msgid "213.74" #~ msgstr "" #~ msgid "176.68" #~ msgstr "" #~ msgid "192.83" #~ msgstr "" #~ msgid "206.63" #~ msgstr "" #~ msgid "203.64" #~ msgstr "" #~ msgid "168.69" #~ msgstr "" #~ msgid "183.69" #~ msgstr "" #~ msgid "195.88" #~ msgstr "" #~ msgid "192.64" #~ msgstr "" #~ msgid "152.04" #~ msgstr "" #~ msgid "162.82" #~ msgstr "" #~ msgid "173.57" #~ msgstr "" #~ msgid "170.20" #~ msgstr "" #~ msgid "3B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-3B-Instruct" #~ msgstr "" #~ msgid "30.80" #~ msgstr "" #~ msgid "5.95" #~ msgstr "" #~ msgid "25.69" #~ msgstr "" #~ msgid "3.38" #~ msgstr "" #~ msgid "35.21" #~ msgstr "" #~ msgid "2.06" #~ msgstr "" #~ msgid "25.29" #~ msgstr "" #~ msgid "2.50" #~ msgstr "" #~ msgid "32.20" #~ msgstr "" #~ msgid "6.59" #~ msgstr "" #~ msgid "24.69" #~ msgstr "" #~ msgid "3.98" #~ msgstr "" #~ msgid "34.47" #~ msgstr "" #~ msgid "2.67" #~ msgstr "" #~ msgid "24.86" #~ msgstr "" #~ msgid "2.62" #~ msgstr "" #~ msgid "31.72" #~ msgstr "" #~ msgid "7.47" #~ msgstr "" #~ msgid "24.70" #~ msgstr "" #~ msgid "4.89" #~ msgstr "" #~ msgid "34.36" #~ msgstr "" #~ msgid "3.58" #~ msgstr "" #~ msgid "25.19" #~ msgstr "" #~ msgid "3.54" #~ msgstr "" #~ msgid "25.37" #~ msgstr "" #~ msgid "9.30" #~ msgstr "" #~ msgid "21.67" #~ msgstr "" #~ msgid "6.72" #~ msgstr "" #~ msgid "23.60" #~ msgstr "" #~ msgid "5.41" #~ msgstr "" #~ msgid "24.56" #~ msgstr "" #~ msgid "5.37" #~ msgstr "" #~ msgid "3B (vLLM)" #~ msgstr "" #~ msgid "127.61" #~ msgstr "" #~ msgid "150.02" #~ msgstr "" #~ msgid "168.20" #~ msgstr "" #~ msgid "165.50" #~ msgstr "" #~ msgid "123.15" #~ msgstr "" #~ msgid "143.09" #~ msgstr "" #~ msgid "159.85" #~ msgstr "" #~ msgid "156.38" #~ msgstr "" #~ msgid "117.35" #~ msgstr "" #~ msgid "135.50" #~ msgstr "" #~ msgid "149.35" #~ msgstr "" #~ msgid "147.75" #~ msgstr "" #~ msgid "105.88" #~ msgstr "" #~ msgid "118.38" #~ msgstr "" #~ msgid "129.28" #~ msgstr "" #~ msgid "127.19" #~ msgstr "" #~ msgid "7B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-7B-Instruct" #~ msgstr "" #~ msgid "40.38" #~ msgstr "" #~ msgid "14.38" #~ msgstr "" #~ msgid "31.55" #~ msgstr "" #~ msgid "8.42" #~ msgstr "" #~ msgid "43.10" #~ msgstr "" #~ msgid "5.52" #~ msgstr "" #~ msgid "32.03" #~ msgstr "" #~ msgid "5.39" #~ msgstr "" #~ msgid "38.76" #~ msgstr "" #~ msgid "15.38" #~ msgstr "" #~ msgid "31.26" #~ msgstr "" #~ msgid "9.43" #~ msgstr "" #~ msgid "38.27" #~ msgstr "" #~ msgid "6.52" #~ msgstr "" #~ msgid "32.37" #~ msgstr "" #~ msgid "6.39" #~ msgstr "" #~ msgid "29.78" #~ msgstr "" #~ msgid "16.91" #~ msgstr "" #~ msgid "26.86" #~ msgstr "" #~ msgid "10.96" #~ msgstr "" #~ msgid "28.70" #~ msgstr "" #~ msgid "8.05" #~ msgstr "" #~ msgid "30.23" #~ msgstr "" #~ msgid "7.92" #~ msgstr "" #~ msgid "18.83" #~ msgstr "" #~ msgid "19.97" #~ msgstr "" #~ msgid "17.59" #~ msgstr "" #~ msgid "14.01" #~ msgstr "" #~ msgid "18.45" #~ msgstr "" #~ msgid "11.11" #~ msgstr "" #~ msgid "19.11" #~ msgstr "" #~ msgid "10.98" #~ msgstr "" #~ msgid "7B (vLLM)" #~ msgstr "" #~ msgid "84.28" #~ msgstr "" #~ msgid "122.01" #~ msgstr "" #~ msgid "154.05" #~ msgstr "" #~ msgid "148.10" #~ msgstr "" #~ msgid "80.70" #~ msgstr "" #~ msgid "112.38" #~ msgstr "" #~ msgid "141.98" #~ msgstr "" #~ msgid "137.64" #~ msgstr "" #~ msgid "77.69" #~ msgstr "" #~ msgid "105.25" #~ msgstr "" #~ msgid "129.35" #~ msgstr "" #~ msgid "124.91" #~ msgstr "" #~ msgid "70.33" #~ msgstr "" #~ msgid "90.71" #~ msgstr "" #~ msgid "108.30" #~ msgstr "" #~ msgid "104.66" #~ msgstr "" #~ msgid "63488" #~ msgstr "" #~ msgid "50.86" #~ msgstr "" #~ msgid "setting-64k" #~ msgstr "[设定3]" #~ msgid "60.52" #~ msgstr "" #~ msgid "67.97" #~ msgstr "" #~ msgid "66.42" #~ msgstr "" #~ msgid "129024" #~ msgstr "" #~ msgid "28.94" #~ msgstr "" #~ msgid "vllm==0.6.2, new sample config" #~ msgstr "" #~ msgid "25.97" #~ msgstr "" #~ msgid "26.37" #~ msgstr "" #~ msgid "26.57" #~ msgstr "" #~ msgid "[Setting-64k]=(gpu_memory_utilization=0.9 max_model_len=65536 enforce_eager=False)" #~ msgstr "[默认设定]=(gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False)" #~ msgid "[new sample config]: for vLLM, set the following sampling parameters: SamplingParams(temperature=0.7,top_p=0.8,top_k=20,repetition_penalty=1,presence_penalty=0,frequency_penalty=0,max_tokens=out_length)" #~ msgstr "" #~ msgid "14B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-14B-Instruct" #~ msgstr "" #~ msgid "24.74" #~ msgstr "" #~ msgid "28.08" #~ msgstr "" #~ msgid "18.84" #~ msgstr "" #~ msgid "16.11" #~ msgstr "" #~ msgid "25.89" #~ msgstr "" #~ msgid "9.94" #~ msgstr "" #~ msgid "19.23" #~ msgstr "" #~ msgid "9.79" #~ msgstr "" #~ msgid "20.51" #~ msgstr "" #~ msgid "29.50" #~ msgstr "" #~ msgid "17.80" #~ msgstr "" #~ msgid "17.61" #~ msgstr "" #~ msgid "20.06" #~ msgstr "" #~ msgid "11.36" #~ msgstr "" #~ msgid "19.21" #~ msgstr "" #~ msgid "11.22" #~ msgstr "" #~ msgid "13.92" #~ msgstr "" #~ msgid "12.66" #~ msgstr "" #~ msgid "19.98" #~ msgstr "" #~ msgid "13.79" #~ msgstr "" #~ msgid "13.81" #~ msgstr "" #~ msgid "14.17" #~ msgstr "" #~ msgid "13.67" #~ msgstr "" #~ msgid "8.20" #~ msgstr "" #~ msgid "36.85" #~ msgstr "" #~ msgid "7.77" #~ msgstr "" #~ msgid "24.88" #~ msgstr "" #~ msgid "8.14" #~ msgstr "" #~ msgid "18.71" #~ msgstr "" #~ msgid "8.31" #~ msgstr "" #~ msgid "18.57" #~ msgstr "" #~ msgid "14B (vLLM)" #~ msgstr "" #~ msgid "46.30" #~ msgstr "" #~ msgid "70.40" #~ msgstr "" #~ msgid "98.02" #~ msgstr "" #~ msgid "92.66" #~ msgstr "" #~ msgid "43.83" #~ msgstr "" #~ msgid "64.33" #~ msgstr "" #~ msgid "86.10" #~ msgstr "" #~ msgid "83.11" #~ msgstr "" #~ msgid "41.91" #~ msgstr "" #~ msgid "59.21" #~ msgstr "" #~ msgid "76.85" #~ msgstr "" #~ msgid "74.03" #~ msgstr "" #~ msgid "37.18" #~ msgstr "" #~ msgid "49.23" #~ msgstr "" #~ msgid "60.91" #~ msgstr "" #~ msgid "59.01" #~ msgstr "" #~ msgid "26.85" #~ msgstr "" #~ msgid "32.83" #~ msgstr "" #~ msgid "37.67" #~ msgstr "" #~ msgid "36.71" #~ msgstr "" #~ msgid "14.53" #~ msgstr "" #~ msgid "15.10" #~ msgstr "" #~ msgid "15.13" #~ msgstr "" #~ msgid "15.25" #~ msgstr "" #~ msgid "32B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-32B-Instruct" #~ msgstr "" #~ msgid "17.54" #~ msgstr "" #~ msgid "61.58" #~ msgstr "" #~ msgid "14.52" #~ msgstr "" #~ msgid "33.56" #~ msgstr "" #~ msgid "19.20" #~ msgstr "" #~ msgid "18.94" #~ msgstr "" #~ msgid "14.60" #~ msgstr "" #~ msgid "18.67" #~ msgstr "" #~ msgid "12.49" #~ msgstr "" #~ msgid "63.72" #~ msgstr "" #~ msgid "11.61" #~ msgstr "" #~ msgid "35.86" #~ msgstr "" #~ msgid "13.42" #~ msgstr "" #~ msgid "21.09" #~ msgstr "" #~ msgid "20.81" #~ msgstr "" #~ msgid "8.95" #~ msgstr "" #~ msgid "67.31" #~ msgstr "" #~ msgid "8.53" #~ msgstr "" #~ msgid "39.28" #~ msgstr "" #~ msgid "9.48" #~ msgstr "" #~ msgid "24.67" #~ msgstr "" #~ msgid "9.71" #~ msgstr "" #~ msgid "24.39" #~ msgstr "" #~ msgid "5.59" #~ msgstr "" #~ msgid "74.47" #~ msgstr "" #~ msgid "5.42" #~ msgstr "" #~ msgid "46.45" #~ msgstr "" #~ msgid "5.79" #~ msgstr "" #~ msgid "31.84" #~ msgstr "" #~ msgid "5.85" #~ msgstr "" #~ msgid "31.56" #~ msgstr "" #~ msgid "32B (vLLM)" #~ msgstr "" #~ msgid "22.13" #~ msgstr "" #~ msgid "setting1" #~ msgstr "[设定3]" #~ msgid "37.57" #~ msgstr "" #~ msgid "55.83" #~ msgstr "" #~ msgid "51.92" #~ msgstr "" #~ msgid "21.05" #~ msgstr "" #~ msgid "34.67" #~ msgstr "" #~ msgid "49.96" #~ msgstr "" #~ msgid "46.68" #~ msgstr "" #~ msgid "19.91" #~ msgstr "" #~ msgid "31.89" #~ msgstr "" #~ msgid "44.79" #~ msgstr "" #~ msgid "41.83" #~ msgstr "" #~ msgid "2" #~ msgstr "" #~ msgid "31.82" #~ msgstr "" #~ msgid "26.88" #~ msgstr "" #~ msgid "35.66" #~ msgstr "" #~ msgid "33.75" #~ msgstr "" #~ msgid "24.45" #~ msgstr "" #~ msgid "18.60" #~ msgstr "" #~ msgid "22.72" #~ msgstr "" #~ msgid "21.79" #~ msgstr "" #~ msgid "14.31" #~ msgstr "" #~ msgid "9.77" #~ msgstr "" #~ msgid "10.39" #~ msgstr "" #~ msgid "10.34" #~ msgstr "" #~ msgid "For context length 129024, the model needs to be predicted with the following config: \"model_max_length\"=131072" #~ msgstr "" #~ msgid "[Default Setting]=(gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False)" #~ msgstr "[默认设定]=(gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False)" #~ msgid "[Setting 1]=(gpu_memory_utilization=1.0 max_model_len=32768 enforce_eager=True)" #~ msgstr "[设定 3]=(gpu_memory_utilization=1.0 max_model_len=8192 enforce_eager=True)" #~ msgid "72B (Transformer)" #~ msgstr "" #~ msgid "Qwen2.5-72B-Instruct" #~ msgstr "" #~ msgid "8.73" #~ msgstr "" #~ msgid "136.20" #~ msgstr "" #~ msgid "8.66" #~ msgstr "" #~ msgid "72.61" #~ msgstr "" #~ msgid "11.07" #~ msgstr "" #~ msgid "39.91" #~ msgstr "" #~ msgid "11.50" #~ msgstr "" #~ msgid "39.44" #~ msgstr "" #~ msgid "140.00" #~ msgstr "" #~ msgid "77.81" #~ msgstr "" #~ msgid "7.56" #~ msgstr "" #~ msgid "42.50" #~ msgstr "" #~ msgid "8.17" #~ msgstr "" #~ msgid "42.13" #~ msgstr "" #~ msgid "3" #~ msgstr "" #~ msgid "4.25" #~ msgstr "" #~ msgid "149.14" #~ msgstr "" #~ msgid "4.66" #~ msgstr "" #~ msgid "82.55" #~ msgstr "" #~ msgid "5.27" #~ msgstr "" #~ msgid "46.86" #~ msgstr "" #~ msgid "5.57" #~ msgstr "" #~ msgid "46.38" #~ msgstr "" #~ msgid "2.94" #~ msgstr "" #~ msgid "164.79" #~ msgstr "" #~ msgid "94.75" #~ msgstr "" #~ msgid "3.14" #~ msgstr "" #~ msgid "62.57" #~ msgstr "" #~ msgid "3.23" #~ msgstr "" #~ msgid "61.64" #~ msgstr "" #~ msgid "72B (vLLM)" #~ msgstr "" #~ msgid "18.19" #~ msgstr "" #~ msgid "Setting 1" #~ msgstr "[设定3]" #~ msgid "4" #~ msgstr "" #~ msgid "31.37" #~ msgstr "" #~ msgid "Default" #~ msgstr "" #~ msgid "31.40" #~ msgstr "" #~ msgid "16.47" #~ msgstr "" #~ msgid "Setting 2" #~ msgstr "[设定2]" #~ msgid "44.30" #~ msgstr "" #~ msgid "29.90" #~ msgstr "" #~ msgid "29.37" #~ msgstr "" #~ msgid "13.88" #~ msgstr "" #~ msgid "Setting 3" #~ msgstr "[设定3]" #~ msgid "40.67" #~ msgstr "" #~ msgid "30.10" #~ msgstr "" #~ msgid "27.20" #~ msgstr "" #~ msgid "38.10" #~ msgstr "" #~ msgid "36.63" #~ msgstr "" #~ msgid "27.53" #~ msgstr "" #~ msgid "23.32" #~ msgstr "" #~ msgid "30.98" #~ msgstr "" #~ msgid "30.02" #~ msgstr "" #~ msgid "20.74" #~ msgstr "" #~ msgid "Setting 4" #~ msgstr "[设定3]" #~ msgid "16.27" #~ msgstr "" #~ msgid "19.84" #~ msgstr "" #~ msgid "19.32" #~ msgstr "" #~ msgid "12.68" #~ msgstr "" #~ msgid "Setting 5" #~ msgstr "[设定3]" #~ msgid "14.11" #~ msgstr "" #~ msgid "10.11" #~ msgstr "" #~ msgid "9.88" #~ msgstr "" #~ msgid "[Setting 1]=(gpu_memory_utilization=0.98 max_model_len=4096 enforce_eager=True)" #~ msgstr "[设定 1]=(gpu_memory_utilization=0.98 max_model_len=4096 enforce_eager=True)" #~ msgid "[Setting 2]=(gpu_memory_utilization=1.0 max_model_len=4096 enforce_eager=True)" #~ msgstr "[设定 2]=(gpu_memory_utilization=1.0 max_model_len=4096 enforce_eager=True)" #~ msgid "[Setting 3]=(gpu_memory_utilization=1.0 max_model_len=8192 enforce_eager=True)" #~ msgstr "[设定 3]=(gpu_memory_utilization=1.0 max_model_len=8192 enforce_eager=True)" #~ msgid "[Setting 4]=(gpu_memory_utilization=0.9 max_model_len=65536 enforce_eager=False)" #~ msgstr "[默认设定]=(gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False)" #~ msgid "[Setting 5]=(gpu_memory_utilization=0.9 max_model_len=131072 enforce_eager=False)" #~ msgstr "[默认设定]=(gpu_memory_utilization=0.9 max_model_len=32768 enforce_eager=False)" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/getting_started/thinking_budget.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 16:36+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/getting_started/thinking_budget.md:1 #: 98870c0bf86c4c3b832204258d05c95c msgid "Thinking budget" msgstr "" #: ../../source/getting_started/thinking_budget.md:3 #: f3ac8e3721084db486cd5c8f99c40e68 msgid "This example demonstrates the inference process with thinking budgets using Qwen3 series models. The process involves two steps:" msgstr "" #: ../../source/getting_started/thinking_budget.md:4 #: 9e573494a6294fe4b33fb9e20d2648a0 msgid "the model generates reasoning content within the specified thinking budget" msgstr "" #: ../../source/getting_started/thinking_budget.md:5 #: 873d4d7b33e54257b32096a23f5bfbac msgid "append the reasoning content to the conversation context and call the model again to get the final response" msgstr "" #: ../../source/getting_started/thinking_budget.md:7 #: 3284c7a085b04934aaa32317c5c1a9b7 msgid "Environment Setup" msgstr "" #: ../../source/getting_started/thinking_budget.md:9 #: 0c8eee4d2e06463ab03c7f8330a8ffd0 msgid "`transformers >= 4.51.0`" msgstr "" #: ../../source/getting_started/thinking_budget.md:10 #: 1bb85d8dbe9f40d9a9f6da43ea92bc19 msgid "`openai >= 1.65.0`" msgstr "" #: ../../source/getting_started/thinking_budget.md:12 #: 1fbbb06af9474e30a50db81b4733f931 msgid "Basic Usage" msgstr "" #: ../../source/getting_started/thinking_budget.md:14 #: b365e4aac4c047378fd3b09771987379 msgid "First, you should start a Qwen3 model in thinking mode. You can refer to [Quickstart](https://github.com/QwenLM/Qwen3/blob/main/docs/source/getting_started/quickstart.md) for more details." msgstr "" #: ../../source/getting_started/thinking_budget.md:16 #: 259a5805d2e3483aa6f4d056b7c9ce94 msgid "Then, you can use the following code to call the model with thinking budgets." msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/index.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/index.rst:34 msgid "Getting Started" msgstr "快速开始" #: ../../Qwen/source/index.rst:44 msgid "Inference" msgstr "推理" #: ../../Qwen/source/index.rst:51 msgid "Run Locally" msgstr "本地运行" #: ../../Qwen/source/index.rst:60 msgid "Deployment" msgstr "部署" #: ../../Qwen/source/index.rst:71 msgid "Quantization" msgstr "量化" #: ../../Qwen/source/index.rst:80 msgid "Training" msgstr "训练" #: ../../Qwen/source/index.rst:87 msgid "Framework" msgstr "框架" #: ../../Qwen/source/index.rst:2 6e52d3a497924f828d4c6b9dd59370d5 msgid "Welcome to Qwen!" msgstr "欢迎来到Qwen" #: ../../Qwen/source/index.rst:4 235805a6d4a34184821c0f4f81020ef1 msgid "Qwen3" msgstr "" #: ../../Qwen/source/index.rst:11 b8a3aa3f31594232959a08d89e9dc7db msgid "Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Both language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc." msgstr "Qwen是阿里巴巴集团Qwen团队研发的大语言模型和大型多模态模型系列。无论是语言模型还是多模态模型，均在大规模多语言和多模态数据上进行预训练，并通过高质量数据进行后期微调以贴近人类偏好。Qwen具备自然语言理解、文本生成、视觉理解、音频理解、工具使用、角色扮演、作为AI Agent进行互动等多种能力。" #: ../../Qwen/source/index.rst:14 8735c67355064a97b2793b721a701b21 msgid "The latest version, Qwen3, has the following features:" msgstr "最新版本Qwen3有以下特点：" #: ../../Qwen/source/index.rst:16 1956d75084244379aad9503fcc572f00 msgid "**Dense and Mixture-of-Experts (MoE) models**, available in 0.6B, 1.7B, 4B, 8B, 14B, 32B and 30B-A3B, 235B-A22B." msgstr "**全尺寸稠密与混合专家模型**：0.6B, 1.7B, 4B, 8B, 14B, 32B and 30B-A3B, 235B-A22B" #: ../../Qwen/source/index.rst:17 1fdf12161cd14663b67b2c08f9219ddb msgid "**Seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose chat) **within a single model**, ensuring optimal performance across various scenarios." msgstr "支持在**思考模式**（用于复杂逻辑推理、数学和编码）和 **非思考模式** （用于高效通用对话）之间**无缝切换**，确保在各种场景下的最佳性能。" #: ../../Qwen/source/index.rst:18 189ff2a03ad249ef88202c34e9f8aa86 msgid "**Significantly enhancement in reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning." msgstr "**显著增强的推理能力**，在数学、代码生成和常识逻辑推理方面超越了之前的 QwQ（在思考模式下）和 Qwen2.5 指令模型（在非思考模式下）。" #: ../../Qwen/source/index.rst:19 64ebcda0381148cb8edf8d92b49469ea msgid "**Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience." msgstr "**卓越的人类偏好对齐**，在创意写作、角色扮演、多轮对话和指令跟随方面表现出色，提供更自然、更吸引人和更具沉浸感的对话体验。" #: ../../Qwen/source/index.rst:20 ec0ebb91f1ed491f8672aefef6307d85 msgid "**Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks." msgstr "**擅长智能体能力**，可以在思考和非思考模式下精确集成外部工具，在复杂的基于代理的任务中在开源模型中表现领先。" #: ../../Qwen/source/index.rst:21 526b161edf284e1b913aabc7e7fcc77c msgid "**Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**." msgstr "**支持 100 多种语言和方言**，具有强大的多语言理解、推理、指令跟随和生成能力。" #: ../../Qwen/source/index.rst:23 79ed3f0e7da043bb8b53f510ed244814 msgid "For more information, please visit our:" msgstr "想了解更多信息，欢迎访问：" #: ../../Qwen/source/index.rst:25 b2e579ae57de4d2985ab1c350fdf2458 msgid "`Blog `__" msgstr "`博客 `__" #: ../../Qwen/source/index.rst:26 406389fe90064e879bd28665a021ee7e msgid "`GitHub `__" msgstr "`GitHub `__" #: ../../Qwen/source/index.rst:27 714c64df6aed4e608571de0155199fef msgid "`Hugging Face `__" msgstr "`Hugging Face `__" #: ../../Qwen/source/index.rst:28 214e12e0b1c04b268582b2c46d22334d msgid "`ModelScope `__" msgstr "`ModelScope `__" #: ../../Qwen/source/index.rst:29 9c64e461dc3a440ab92d94887fe3d2d8 msgid "`Qwen3 Collection `__" msgstr "" #: ../../Qwen/source/index.rst:31 c6056edc8a3a4a12bd3a75eeb210f7a2 msgid "Join our community by joining our `Discord `__ and `WeChat `__ group. We are looking forward to seeing you there!" msgstr "加入社区，加入 `Discord `__ 和 `微信群 `__ 。很期待见到你们！" #~ msgid "Web UI" #~ msgstr "Web UI" #~ msgid "Benchmark" #~ msgstr "评测" #~ msgid "Qwen2.5" #~ msgstr "" #~ msgid "Dense, easy-to-use, decoder-only language models, available in **0.5B**, **1.5B**, **3B**, **7B**, **14B**, **32B**, and **72B** sizes, and base and instruct variants." #~ msgstr "易于使用的仅解码器稠密语言模型，提供 **0.5B** 、**1.5B** 、**3B** 、**7B** 、**14B** 、**32B** 和 **72B** 共7种参数规模的模型，并且有基模型和指令微调模型两种变体（其中“ B ”表示“十亿”， 72B 即为 720 亿）" #~ msgid "Pretrained on our latest large-scale dataset, encompassing up to **18T** tokens." #~ msgstr "利用我们最新的数据集进行预训练，包含多达 18T tokens （其中“ T ”表示“万亿”， 18T 即为 18 万亿）" #~ msgid "Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON." #~ msgstr "在遵循指令、生成长文本（超过 8K tokens ）、理解结构化数据（例如，表格）以及生成结构化输出特别是 JSON 方面有了显著改进" #~ msgid "More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots." #~ msgstr "更加适应多样化的系统提示，增强了角色扮演的实现和聊天机器人的背景设置。" #~ msgid "Context length support up to **128K** tokens and can generate up to **8K** tokens." #~ msgstr "支持最多达 **128K** tokens 的上下文长度，并能生成多达 **8K** tokens 的文本。" #~ msgid "Multilingual support for over **29** languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more." #~ msgstr "支持超过 **29** 种语言，包括中文、英文、法文、西班牙文、葡萄牙文、德文、意大利文、俄文、日文、韩文、越南文、泰文、阿拉伯文等。" #~ msgid "`Qwen2.5 Collection `__" #~ msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/inference/transformers.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-29 14:27+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/inference/transformers.md:1 c178ffb3888f441180ecb4fca79195fd msgid "Transformers" msgstr "" #: ../../source/inference/transformers.md:3 c816184205fc456894a0b5b5f3dc4b80 msgid "Transformers is a library of pretrained natural language processing for inference and training. Developers can use Transformers to train models on their data, build inference applications, and generate texts with large language models." msgstr "Transformers 是一个用于推理和训练的预训练自然语言处理库。开发者可以使用 Transformers 在自己的数据上训练模型、构建推理应用，并通过大型语言模型生成文本。" #: ../../source/inference/transformers.md:6 373686a143e14f7a9fd059cddff449bf msgid "Environment Setup" msgstr "环境配置" #: ../../source/inference/transformers.md:8 7dfb38faea8f4eeda6072046cead1636 msgid "`transformers>=4.51.0`" msgstr "" #: ../../source/inference/transformers.md:9 9d7e644e3ec840bead01f1ce246e9cd1 msgid "`torch>=2.6` is recommended" msgstr "推荐使用 `torch>=2.6`" #: ../../source/inference/transformers.md:10 06da43b05cd94312a35c83fd485e617e msgid "GPU is recommended" msgstr "推荐使用 GPU" #: ../../source/inference/transformers.md:13 363ebf64fe2746ada9487ec3bf691ed8 msgid "Basic Usage" msgstr "基本用法" #: ../../source/inference/transformers.md:15 356e5bca4faa4492b7ce0646fcb9dc12 msgid "You can use the `pipeline()` interface or the `generate()` interface to generate texts with Qwen3 in transformers." msgstr "您可以使用 `pipeline()` 接口或 `generate()` 接口在 transformers 中通过 Qwen3 生成文本。" #: ../../source/inference/transformers.md:17 704e2d1659d94b6d911bd5e01ea63764 msgid "In general, the pipeline interface requires less boilerplate code, which is shown here. The following shows a basic example using pipeline for multi-turn conversations:" msgstr "通常，pipeline 接口需要的样板代码更少，如下所示。以下展示了一个使用 pipeline 进行多轮对话的基本示例：" #: ../../source/inference/transformers.md:44 2054af41b1de457a9f14ef6f8117772c msgid "There are some important parameters creating the pipeline:" msgstr "创建 pipeline 时有一些重要的参数：" #: ../../source/inference/transformers.md:45 0127b56b938c496f91a8cc1642af1590 msgid "**Model**: `model_name_or_path` could be a model ID like `Qwen/Qwen3-8B` or a local path." msgstr "**模型**：`model_name_or_path` 可以是像 `Qwen/Qwen3-8B` 这样的模型 ID，也可以是本地路径。" #: ../../source/inference/transformers.md:47 374ac106d4374722a5428c492a1865f8 msgid "To download model files to a local directory, you could use" msgstr "要将模型文件下载到本地目录，可以使用" #: ../../source/inference/transformers.md:51 47239a7ef1124d22802c8f7d0d87b251 msgid "You can also download model files using ModelScope if you are in mainland China" msgstr "如果您在中国大陆，还可以使用 ModelScope 下载模型文件" #: ../../source/inference/transformers.md:55 e787e9221bdc4e6aaf7f98ed97e726da msgid "**Device Placement**: `device_map=\"auto\"` will load the model parameters to multiple devices automatically, if available. It relies on the `accelerate` package. If you would like to use a single device, you can pass `device` instead of device_map. `device=-1` or `device=\"cpu\"` indicates using CPU, `device=\"cuda\"` indicates using the current GPU, and `device=\"cuda:1\"` or `device=1` indicates using the second GPU. Do not use `device_map` and `device` at the same time!" msgstr "**设备分配**：如果可用，`device_map=\"auto\"` 将自动将模型参数加载到多个设备上。它依赖于 `accelerate` 包。如果您想使用单个设备，可以传递 `device` 而不是 `device_map`。`device=-1` 或 `device=\"cpu\"` 表示使用 CPU，`device=\"cuda\"` 表示使用当前 GPU，`device=\"cuda:1\"` 或 `device=1` 表示使用第二个 GPU。不要同时使用 `device_map` 和 `device`！" #: ../../source/inference/transformers.md:60 91247cdab1f142559217047bf9d36024 msgid "**Compute Precision**: `torch_dtype=\"auto\"` will determine automatically the data type to use based on the original precision of the checkpoint and the precision your device supports. For modern devices, the precision determined will be `bfloat16`." msgstr "**计算精度**：`torch_dtype=\"auto\"` 将根据检查点的原始精度和设备支持的精度自动确定要使用的数据类型。对于现代设备，确定的精度将是 `bfloat16`。" #: ../../source/inference/transformers.md:63 65b419de7e784e8d8790aa319ad28c42 msgid "If you don't pass `torch_dtype=\"auto\"`, the default data type is `float32`, which will take double the memory and be slower in computation." msgstr "如果您不传递 `torch_dtype=\"auto\"`，默认数据类型为 `float32`，这将占用两倍的内存并且计算速度较慢。" #: ../../source/inference/transformers.md:66 7e6c5bcd15bd4c62a4a3ff9f4643bf8e msgid "Calls to the text generation pipeline will use the generation configuration from the model file, e.g., `generation_config.json`. This configuration could be overridden by passing arguments directly to the call. The default is equivalent to" msgstr "调用文本生成 pipeline 时，将使用模型文件中的生成配置，例如 `generation_config.json`。这些配置可以通过直接向调用传递参数来覆盖。默认配置等效于" #: ../../source/inference/transformers.md:73 3bc839021fe14d9c999929da1ee45547 msgid "For the best practices in configuring generation parameters, please see the model card." msgstr "有关配置生成参数的最佳实践，请参阅模型卡片。" #: ../../source/inference/transformers.md:75 5cc87eccca0e4fb4bb67c2deafc72569 msgid "Thinking & Non-Thinking Mode" msgstr "思考与非思考模式" #: ../../source/inference/transformers.md:77 5f78a0a90ae341c2ab26b525d8f618bb msgid "By default, Qwen3 model will think before response. It is also true for the `pipeline()` interface. To switch between thinking and non-thinking mode, two methods can be used" msgstr "默认情况下，Qwen3 模型会在回复前进行思考，`pipeline()` 接口也是如此。要切换思考与非思考模式，可以使用以下两种方法：" #: ../../source/inference/transformers.md:80 2055152940d54de7ad900f6bfd2b0186 msgid "Append a final assistant message, containing only `\\n\\n\\n\\n`. This method is stateless, meaning it will only work for that single turn. It will also strictly prevent the model from generating thinking content. For example," msgstr "追加一条仅包含 `\\n\\n\\n\\n` 的最终助手 (assistant) 消息。此方法是无状态的，意味着它仅对当前轮对话生效，并且会严格阻止模型生成思考内容。例如：" #: ../../source/inference/transformers.md:97 8c9c833fa8004aecb65eef07212a140b msgid "Add to the user (or the system) message, `/no_think` to disable thinking and `/think` to enable thinking. This method is stateful, meaning the model will follow the most recent instruction in multi-turn conversations." msgstr "在用户 (user) 或系统 (system) 消息中添加 `/no_think` 以禁用思考、添加 `/think` 以启用思考。此方法是有状态的，意味着在多轮对话中，模型将遵循最近的指令。您还可以使用自然语言指令。" #: ../../source/inference/transformers.md:113 957c3c1fa49a4b119c7fc78fa9419e45 msgid "Parsing Thinking Content" msgstr "解析思考内容" #: ../../source/inference/transformers.md:115 938849b04bf649918c233707e408d21a msgid "If you would like a more structured assistant message format, you can use the following function to extract the thinking content into a field named `reasoning_content` which is similar to the format used by vLLM, SGLang, etc." msgstr "如果您希望获得更结构化的助手消息格式，可以使用以下函数将思考内容提取到名为 `reasoning_content` 的字段中，该字段的格式类似于 vLLM、SGLang 等使用的格式。" #: ../../source/inference/transformers.md:131 e46bd3b0ec974011b7c658ab29b2fdc1 msgid "Parsing Tool Calls" msgstr "解析工具调用" #: ../../source/inference/transformers.md:133 d3d893f89e4149db823e418a5bd2d773 msgid "For tool calling with Transformers, please refer to [our guide on Function Calling](../framework/function_call.md#hugging-face-transformers)." msgstr "有关使用 Transformers 进行工具调用的信息，请参阅[函数调用指南](../framework/function_call.md#hugging-face-transformers)。" #: ../../source/inference/transformers.md:135 304b7ec8d3d446b6ada27d48f128660d msgid "Serving Quantized models" msgstr "使用量化模型" #: ../../source/inference/transformers.md:137 b5f3ee666c3c475ba5282fd3bb184bbc msgid "Qwen3 comes with two types of pre-quantized models, FP8 and AWQ. The command serving those models are the same as the original models except for the name change:" msgstr "Qwen3 提供了两种类型的预量化模型：FP8 和 AWQ。使用这些模型的命令与原始模型相同，只是名称有所更改：" #: ../../source/inference/transformers.md:155 6e6eb31d2af94ce1bc9a44d973d6febc msgid "FP8 computation is supported on NVIDIA GPUs with compute capability > 8.9, that is, Ada Lovelace, Hopper, and later GPUs." msgstr "FP8 计算在计算能力 > 8.9 的 NVIDIA GPU 上受支持，即 Ada Lovelace、Hopper 及更新的 GPU。" #: ../../source/inference/transformers.md:157 780916506f034d5bb663a2aa69083ee3 msgid "For better performance, make sure `triton` and a CUDA compiler compatible with the CUDA version of `torch` in your environment are installed." msgstr "为了获得更好的性能，请确保安装了 `triton` 和与环境中 `torch` 的 CUDA 版本兼容的 CUDA 编译器。" #: ../../source/inference/transformers.md:161 17ad788596ef464db2b333ec10c91742 msgid "As of 4.51.0, there are issues with Transformers when running those checkpoints **across GPUs**. The following method could be used to work around those issues:" msgstr "在 4.51.0 版本中，在**跨 GPU**的情况下运行 FP8 存在一些与 Transformers 相关的问题。可以使用以下方法来解决这些问题：" #: ../../source/inference/transformers.md:163 2e4c2a9ab6864d7ca8a0f477dc888f75 msgid "Set the environment variable `CUDA_LAUNCH_BLOCKING=1` before running the script; or" msgstr "在运行脚本之前设置环境变量 `CUDA_LAUNCH_BLOCKING=1`；或者" #: ../../source/inference/transformers.md:164 328f9a6a90094a3ca8c7f824ffbe641b msgid "Uncomment [this line](https://github.com/huggingface/transformers/blob/0720e206c6ba28887e4d60ef60a6a089f6c1cc76/src/transformers/integrations/finegrained_fp8.py#L340) in your local installation of `transformers`." msgstr "取消注释您本地安装的 `transformers` 中的[这一行](https://github.com/huggingface/transformers/blob/0720e206c6ba28887e4d60ef60a6a089f6c1cc76/src/transformers/integrations/finegrained_fp8.py#L340)。" #: ../../source/inference/transformers.md:168 a8ab4a05e34e4fd1b816388ca1c4614f msgid "Enabling Long Context" msgstr "启用长上下文" #: ../../source/inference/transformers.md:170 15c36696b02a45829c0158fc644af96f msgid "The maximum context length in pre-training for Qwen3 models is 32,768 tokens. It can be extended to 131,072 tokens with RoPE scaling techniques. We have validated the performance with YaRN." msgstr "Qwen3 模型在预训练中的最大上下文长度为 32,768 个 token。通过 RoPE 缩放技术，它可以扩展到 131,072 个 token。我们已使用 YaRN 验证了性能。" #: ../../source/inference/transformers.md:174 ff0ec1adc3e647f4b5ecf29adc14a5c6 msgid "Transformers supports YaRN, which can be enabled either by modifying the model files or overriding the default arguments when loading the model." msgstr "Transformers 支持 YaRN，可以通过修改模型文件或在加载模型时覆盖默认参数来启用。" #: ../../source/inference/transformers.md:176 a28d0e27841343d7a3c94fd5f047b671 msgid "Modifying the model files: In the `config.json` file, add the rope_scaling fields:" msgstr "修改模型文件：在 `config.json` 文件中，添加 rope_scaling 字段：" #: ../../source/inference/transformers.md:187 50430e65309e453282eec582c6d74493 msgid "Overriding the default arguments:" msgstr "覆盖默认参数：" #: ../../source/inference/transformers.md:210 091b4560600b48aa9ba156da08c27fab msgid "As of Transformers 4.52.3, it will use `max_position_embeddings/rope_scaling.original_max_position_embeddings` as the `rope_scaling.factor` regradless of the specified `rope_scaling.factor`. See [this issue](https://github.com/huggingface/transformers/issues/38224) for more information." msgstr "在 Transformers 4.52.3 版本中，无论指定的 `rope_scaling.factor` 是多少，它都会使用 `max_position_embeddings/rope_scaling.original_max_position_embeddings` 作为 `rope_scaling.factor`。更多信息请参阅[此问题](https://github.com/huggingface/transformers/issues/38224 )。" #: ../../source/inference/transformers.md:214 62f158e126984c04a39ad039d42a4dc0 msgid "Transformers implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0." msgstr "Transformers 实现了静态 YaRN，这意味着无论输入长度如何，缩放因子保持不变，**这可能会对较短文本的性能产生影响。** 我们建议仅在需要处理长上下文时添加 `rope_scaling` 配置。还建议根据需要修改 `factor`。例如，如果您的应用程序的典型上下文长度为 65,536 个 token，则最好将 `factor` 设置为 2.0。" #: ../../source/inference/transformers.md:220 1c816d18902b4fad8eaaa3598459cc65 msgid "Streaming Generation" msgstr "流式输出" #: ../../source/inference/transformers.md:222 2b79e8f552ae4399aa580e4b00ebc6bd msgid "With the help of `TextStreamer`, you can modify your chatting with Qwen3 to streaming mode. It will print the response as being generated to the console or the terminal." msgstr "借助 `TextStreamer` ，您可以将与 Qwen3 的对话切换到流式传输模式。下面是一个关于如何使用它的示例：" #: ../../source/inference/transformers.md:242 113734a6f14141a9a11cb3536cd37471 msgid "Besides using `TextStreamer`, we can also use `TextIteratorStreamer` which stores print-ready text in a queue, to be used by a downstream application as an iterator:" msgstr "除了使用 `TextStreamer` 之外，我们还可以使用 `TextIteratorStreamer` ，它将可打印的文本存储在一个队列中，以便下游应用程序作为迭代器来使用：" #: ../../source/inference/transformers.md:271 51fb3a808ca7448694b0cab5582025e3 msgid "Batch Generation" msgstr "批处理" #: ../../source/inference/transformers.md:274 13ec723ad16648f593bdd5d75f125f93 msgid "Batching is not automatically a win for performance." msgstr "批处理不总能提速。" #: ../../source/inference/transformers.md:300 1afe856936ef4a19a386f4ab9704dd0e msgid "FAQ" msgstr "常见问题解答" #: ../../source/inference/transformers.md:302 7175bda60e7a4796b9279b48116f819f msgid "You may find distributed inference with Transformers is not as fast as you would imagine. Transformers with `device_map=\"auto\"` does not apply tensor parallelism, and it only uses one GPU at a time. For Transformers with tensor parallelism, please refer to [its documentation](https://huggingface.co/docs/transformers/v4.51.3/en/perf_infer_gpu_multi)." msgstr "您可能会发现使用 Transformers 进行分布式推理的速度不如预期。Transformers 使用 `device_map=\"auto\"` 时并未应用张量并行 (Tensor Parallelism)，且一次仅使用一个 GPU。如需支持张量并行的 Transformers，请参阅[其文档](https://huggingface.co/docs/transformers/v4.51.3/en/perf_infer_gpu_multi)。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/quantization/awq.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/quantization/awq.md:1 363514c3e24c4d2aa54832e85acf34ef msgid "AWQ" msgstr "AWQ" #: ../../Qwen/source/quantization/awq.md:4 36b5c0de1013499f9f1e41edf8fa28ca msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/quantization/awq.md:7 9d6a80a82b044628bc9c911785ac9160 msgid "For quantized models, one of our recommendations is the usage of [AWQ](https://arxiv.org/abs/2306.00978) with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)." msgstr "对于量化模型，我们推荐使用 [AWQ](https://arxiv.org/abs/2306.00978) 结合 [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) " #: ../../Qwen/source/quantization/awq.md:9 139542ed4b414cfb834b3fd81ea88d51 msgid "**AWQ** refers to Activation-aware Weight Quantization, a hardware-friendly approach for LLM low-bit weight-only quantization." msgstr "**AWQ**即激活值感知的权重量化(Activation-aware Weight Quantization)，是一种针对LLM的低比特权重量化的硬件友好方法。" #: ../../Qwen/source/quantization/awq.md:11 9a2959bb9f984e36a299bc40abca9402 msgid "**AutoAWQ** is an easy-to-use Python library for 4-bit quantized models. AutoAWQ speeds up models by 3x and reduces memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs." msgstr "**AutoAWQ**是一个易于使用的工具包，用于4比特量化模型。相较于FP16，AutoAWQ能够将模型的运行速度提升3倍，并将内存需求降低至原来的三分之一。AutoAWQ实现了AWQ算法，可用于LLM的量化处理。" #: ../../Qwen/source/quantization/awq.md:15 4f9fcd93d1f44b48869224c0f4e8b76a msgid "In this document, we show you how to use the quantized model with Hugging Face `transformers` and also how to quantize your own model." msgstr "在本文档中，我们将向您展示如何在Hugging Face `transformers`框架下使用量化模型，以及如何对您自己的模型进行量化" #: ../../Qwen/source/quantization/awq.md:17 870ebc162f3749b48fe454df85aaaf4b msgid "Usage of AWQ Models with Hugging Face transformers" msgstr "在Hugging Face transformers中使用AWQ量化模型" #: ../../Qwen/source/quantization/awq.md:19 cc7bd785c7ac45a4980fbda683699e43 msgid "Now, `transformers` has officially supported AutoAWQ, which means that you can directly use the quantized model with `transformers`. The following is a very simple code snippet showing how to run `Qwen2.5-7B-Instruct-AWQ` with the quantized model:" msgstr "现在，`transformers`已经正式支持AutoAWQ，这意味着您可以直接在`transformers`中使用AWQ量化模型。以下是一个非常简单的代码片段，展示如何运行量化模型 `Qwen2.5-7B-Instruct-AWQ` ：" #: ../../Qwen/source/quantization/awq.md:56 47826d51abf54ad8a89ef9b91127a700 msgid "Usage of AWQ Models with vLLM" msgstr "在vLLM中使用AWQ量化模型" #: ../../Qwen/source/quantization/awq.md:58 b7235ae8f8344dd4a3d2029bbe7a40fc msgid "vLLM has supported AWQ, which means that you can directly use our provided AWQ models or those quantized with `AutoAWQ` with vLLM. We recommend using the latest version of vLLM (`vllm>=0.6.1`) which brings performance improvements to AWQ models; otherwise, the performance might not be well-optimized." msgstr "vLLM已支持AWQ，您可以直接使用我们提供的AWQ量化模型或使用`AutoAWQ`量化的模型。我们建议使用最新版的vLLM (`vllm>=0.6.1`)，新版为AWQ量化模型提升了效率提；不然推理效率可能并为被良好优化（即效率可能较非量化模型低）。" #: ../../Qwen/source/quantization/awq.md:61 940ce8fdb5da442b99af2bc1739911c6 msgid "Actually, the usage is the same with the basic usage of vLLM. We provide a simple example of how to launch OpenAI-API compatible API with vLLM and `Qwen2.5-7B-Instruct-AWQ`:" msgstr "实际上，使用AWQ模型与vLLM的基本用法相同。我们提供了一个简单的示例，展示了如何通过vLLM启动与OpenAI API兼容的接口，并使用 `Qwen2.5-7B-Instruct-AWQ` 模型：" #: ../../Qwen/source/quantization/awq.md:64 2d249915352049a6a8d5a06e1f4682ee msgid "Run the following in a shell to start an OpenAI-compatible API service:" msgstr "在终端中运行以下命令以开启OpenAI兼容API：" #: ../../Qwen/source/quantization/awq.md:70 be7bfbb81698429cbfcbcd24d062fc08 msgid "Then, you can call the API as" msgstr "随后，您可以这样调用API：" #: ../../Qwen/source/quantization/awq.md:86 0dff7d5c7b044548a82e0ba68a043d80 msgid "or you can use the API client with the `openai` Python package as shown below:" msgstr "或者你可以按照下面所示的方式，使用 `openai` Python包中的API客户端：" #: ../../Qwen/source/quantization/awq.md:115 65f4d60502ee486382e9bda9a5a826bb msgid "Quantize Your Own Model with AutoAWQ" msgstr "使用AutoAWQ量化你的模型" #: ../../Qwen/source/quantization/awq.md:117 c7c42af91c1a419194d65200bcfa2f26 #, fuzzy msgid "If you want to quantize your own model to AWQ quantized models, we advise you to use AutoAWQ." msgstr "如果您希望将自定义模型量化为AWQ量化模型，我们建议您使用AutoAWQ。推荐通过安装源代码来获取并安装该工具包的最新版本：" #: ../../Qwen/source/quantization/awq.md:123 232e94883d044030b2193392788b9314 msgid "Suppose you have finetuned a model based on `Qwen2.5-7B`, which is named `Qwen2.5-7B-finetuned`, with your own dataset, e.g., Alpaca. To build your own AWQ quantized model, you need to use the training data for calibration. Below, we provide a simple demonstration for you to run:" msgstr "假设你已经基于 `Qwen2.5-7B` 模型进行了微调，并将其命名为 `Qwen2.5-7B-finetuned` ，且使用的是你自己的数据集，比如Alpaca。若要构建你自己的AWQ量化模型，你需要使用训练数据进行校准。以下，我们将为你提供一个简单的演示示例以便运行：" #: ../../Qwen/source/quantization/awq.md:141 5162195f32ee4ecba229aa137da1aba4 msgid "Then you need to prepare your data for calibration. What you need to do is just put samples into a list, each of which is a text. As we directly use our finetuning data for calibration, we first format it with ChatML template. For example," msgstr "接下来，您需要准备数据以进行校准。您需要做的就是将样本放入一个列表中，其中每个样本都是一段文本。由于我们直接使用微调数据来进行校准，所以我们首先使用ChatML模板对其进行格式化。例如：" #: ../../Qwen/source/quantization/awq.md:153 0d4736e90e0242a8be15533de3aab6ff msgid "where each `msg` is a typical chat message as shown below:" msgstr "其中每个 `msg` 是一个典型的聊天消息，如下所示：" #: ../../Qwen/source/quantization/awq.md:163 79d86630600945ac85dbe13d07987016 msgid "Then just run the calibration process by one line of code:" msgstr "然后只需通过一行代码运行校准过程：" #: ../../Qwen/source/quantization/awq.md:169 1ae219a50508465b98e3b3398e631681 msgid "Finally, save the quantized model:" msgstr "最后，保存量化模型：" #: ../../Qwen/source/quantization/awq.md:176 58316c1a4172418aba9f37925963e17f msgid "Then you can obtain your own AWQ quantized model for deployment. Enjoy!" msgstr "然后你就可以得到一个可以用于部署的AWQ量化模型。玩得开心！" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/quantization/gptq.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-28 19:42+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../Qwen/source/quantization/gptq.md:1 c90397f810fb44a0abba8dd02f998f1c msgid "GPTQ" msgstr "" #: ../../Qwen/source/quantization/gptq.md:4 b79afc46b0f9474fb0c83751625aefc5 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../Qwen/source/quantization/gptq.md:7 898494af2a944193880f27e2f90db4f4 msgid "[GPTQ](https://arxiv.org/abs/2210.17323) is a quantization method for GPT-like LLMs, which uses one-shot weight quantization based on approximate second-order information. In this document, we show you how to use the quantized model with Hugging Face `transformers` and also how to quantize your own model with [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ)." msgstr "[GPTQ](https://arxiv.org/abs/2210.17323)是一种针对类GPT大型语言模型的量化方法，它基于近似二阶信息进行一次性权重量化。在本文档中，我们将向您展示如何使用 `transformers` 库加载并应用量化后的模型，同时也会指导您如何通过[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ)来对您自己的模型进行量化处理。" #: ../../Qwen/source/quantization/gptq.md:10 11b82020735d4828a4182cefbf98aeb1 msgid "Usage of GPTQ Models with Hugging Face transformers" msgstr "在Hugging Face transformers中使用GPTQ模型" #: ../../Qwen/source/quantization/gptq.md:14 2e9481d850954772949dd33897e0b06b msgid "To use the official Qwen2.5 GPTQ models with `transformers`, please ensure that `optimum>=1.20.0` and compatible versions of `transformers` and `auto_gptq` are installed." msgstr "" #: ../../Qwen/source/quantization/gptq.md:16 fe6662a312184d40b07d957f4c0888cc msgid "You can do that by" msgstr "" #: ../../Qwen/source/quantization/gptq.md:22 9f0ad8e2a26145cf8bd9d60305566771 msgid "Now, `transformers` has officially supported AutoGPTQ, which means that you can directly use the quantized model with `transformers`. For each size of Qwen2.5, we provide both Int4 and Int8 GPTQ quantized models. The following is a very simple code snippet showing how to run `Qwen2.5-7B-Instruct-GPTQ-Int4`:" msgstr "现在，`transformers` 正式支持了AutoGPTQ，这意味着您能够直接在`transformers`中使用量化后的模型。以下是一个非常简单的代码片段示例，展示如何运行 `Qwen2.5-7B-Instruct-GPTQ-Int4` （请注意，对于每种大小的Qwen2.5模型，我们都提供了Int4和Int8两种量化版本）：" #: ../../Qwen/source/quantization/gptq.md:60 855686b8990f403bba151d8498947f23 msgid "Usage of GPTQ Models with vLLM" msgstr "在vLLM中使用GPTQ模型" #: ../../Qwen/source/quantization/gptq.md:62 ad572c30a0904598b3cbeba7c38a607a msgid "vLLM has supported GPTQ, which means that you can directly use our provided GPTQ models or those trained with `AutoGPTQ` with vLLM. If possible, it will automatically use the GPTQ Marlin kernel, which is more efficient." msgstr "vLLM已支持GPTQ，您可以直接使用我们提供的GPTQ量化模型或使用`AutoGPTQ`量化的模型。我们建议使用最新版的vLLM。如有可能，其会自动使用效率更好的GPTQ Marlin实现。" #: ../../Qwen/source/quantization/gptq.md:65 09050876d2c04aee9b619d28d4f5589c msgid "Actually, the usage is the same with the basic usage of vLLM. We provide a simple example of how to launch OpenAI-API compatible API with vLLM and `Qwen2.5-7B-Instruct-GPTQ-Int4`:" msgstr "实际上，使用GPTQ模型与vLLM的基本用法相同。我们提供了一个简单的示例，展示了如何通过vLLM启动与OpenAI API兼容的接口，并使用 `Qwen2.5-7B-Instruct-GPTQ-Int4` 模型：" #: ../../Qwen/source/quantization/gptq.md:68 a31dd879cc444b5da8d16fb1705585a6 msgid "Run the following in a shell to start an OpenAI-compatible API service:" msgstr "在终端中运行以下命令以开启OpenAI兼容API：" #: ../../Qwen/source/quantization/gptq.md:74 9dfb41e03089473792928b05b1225de4 msgid "Then, you can call the API as" msgstr "随后，您可以这样调用API：" #: ../../Qwen/source/quantization/gptq.md:90 6b440bebe0d84118bb63ed9a7c169ab5 msgid "or you can use the API client with the `openai` Python package as shown below:" msgstr "或者你可以按照下面所示的方式，使用 `openai` Python包中的API客户端：" #: ../../Qwen/source/quantization/gptq.md:119 7ffaa1ca8b4740b98dc3f804348da523 msgid "Quantize Your Own Model with AutoGPTQ" msgstr "使用AutoGPTQ量化你的模型" #: ../../Qwen/source/quantization/gptq.md:121 40bd0b11507c4f06be5a5918d0dc3bdb msgid "If you want to quantize your own model to GPTQ quantized models, we advise you to use AutoGPTQ. It is suggested installing the latest version of the package by installing from source code:" msgstr "如果你想将自定义模型量化为GPTQ量化模型，我们建议你使用AutoGPTQ工具。推荐通过安装源代码的方式获取并安装最新版本的该软件包。" #: ../../Qwen/source/quantization/gptq.md:130 d6ebb03d51bf4e0686ae17ce3f0a34db msgid "Suppose you have finetuned a model based on `Qwen2.5-7B`, which is named `Qwen2.5-7B-finetuned`, with your own dataset, e.g., Alpaca. To build your own GPTQ quantized model, you need to use the training data for calibration. Below, we provide a simple demonstration for you to run:" msgstr "假设你已经基于 `Qwen2.5-7B` 模型进行了微调，并将该微调后的模型命名为 `Qwen2.5-7B-finetuned` ，且使用的是自己的数据集，比如Alpaca。要构建你自己的GPTQ量化模型，你需要使用训练数据进行校准。以下是一个简单的演示示例，供你参考运行：" #: ../../Qwen/source/quantization/gptq.md:161 9c1b27cc38764332891a8a13175663fc msgid "However, if you would like to load the model on multiple GPUs, you need to use `max_memory` instead of `device_map`. Here is an example:" msgstr "但是，如果你想使用多GPU来读取模型，你需要使用 `max_memory` 而不是 `device_map`。下面是一段示例代码：" #: ../../Qwen/source/quantization/gptq.md:172 c2a9a50734854c19acf3e623597aee80 msgid "Then you need to prepare your data for calibration. What you need to do is just put samples into a list, each of which is a text. As we directly use our finetuning data for calibration, we first format it with ChatML template. For example," msgstr "接下来，你需要准备数据进行校准。你需要做的是将样本放入一个列表中，其中每个样本都是一段文本。由于我们直接使用微调数据进行校准，所以我们首先使用ChatML模板对它进行格式化处理。例如：" #: ../../Qwen/source/quantization/gptq.md:188 7621f73d34d04dd791d2eda03edb0d06 msgid "where each `msg` is a typical chat message as shown below:" msgstr "其中每个 `msg` 是一个典型的聊天消息，如下所示：" #: ../../Qwen/source/quantization/gptq.md:198 293efa14ece74a0aa9cbf32ef21e6bcd msgid "Then just run the calibration process by one line of code:" msgstr "然后只需通过一行代码运行校准过程：" #: ../../Qwen/source/quantization/gptq.md:209 919d7a77cc4a4ef084ee8e2240ff1797 msgid "Finally, save the quantized model:" msgstr "最后，保存量化模型：" #: ../../Qwen/source/quantization/gptq.md:216 b353bdf12d6148fdb0a77662f795ae7e msgid "It is unfortunate that the `save_quantized` method does not support sharding. For sharding, you need to load the model and use `save_pretrained` from transformers to save and shard the model. Except for this, everything is so simple. Enjoy!" msgstr "很遗憾， `save_quantized` 方法不支持模型分片。若要实现模型分片，您需要先加载模型，然后使用来自 `transformers` 库的 `save_pretrained` 方法来保存并分片模型。除此之外，一切操作都非常简单。祝您使用愉快！" #: ../../Qwen/source/quantization/gptq.md:222 caea6f76804e40daa394ae2e2d52a6ce msgid "Known Issues" msgstr "" #: ../../Qwen/source/quantization/gptq.md:224 07df69bd48d4445887b5c1fa09f2f0fb msgid "Qwen2.5-72B-Instruct-GPTQ-Int4 cannot stop generation properly" msgstr "" #: ../../Qwen/source/quantization/gptq.md:226 #: ../../Qwen/source/quantization/gptq.md:235 a4f1c7b0cb5d49f2929ba5d1246e885d #: d2dbf88d06974152943e6ec405419390 msgid "Model" msgstr "" #: ../../Qwen/source/quantization/gptq.md:226 cb9c0be91ecc46c3b6ecfa97a0a37dd7 msgid "Qwen2.5-72B-Instruct-GPTQ-Int4" msgstr "" #: ../../Qwen/source/quantization/gptq.md:227 #: ../../Qwen/source/quantization/gptq.md:236 c1fe04754a0642fa82ed425d6abaa487 #: f3ff85cbbc47459fb36b5ad0e38b4a1b msgid "Framework" msgstr "" #: ../../Qwen/source/quantization/gptq.md:227 8a5a4fe9d7634cb1ac65025565c3593a #, fuzzy msgid "vLLM, AutoGPTQ (including Hugging Face transformers)" msgstr "在Hugging Face transformers中使用GPTQ模型" #: ../../Qwen/source/quantization/gptq.md:228 #: ../../Qwen/source/quantization/gptq.md:237 320d56294cc4490f8b30ac523388bc44 #: c04326d003f949a7b2b63c6c6cb20ac3 msgid "Description" msgstr "" #: ../../Qwen/source/quantization/gptq.md:228 22f80d0679dc426dbbfb21b90b993a27 msgid "Generation cannot stop properly. Continual generation after where it should stop, then repeated texts, either single character, a phrase, or paragraphs, are generated." msgstr "" #: ../../Qwen/source/quantization/gptq.md:229 #: ../../Qwen/source/quantization/gptq.md:238 255a7a8ac98b4d2da51f79f207be5901 #: 673d23bf488840a2a32a18cd657f334f msgid "Workaround" msgstr "" #: ../../Qwen/source/quantization/gptq.md:229 c2171874ed804ffb826ac686128d7bff msgid "The following workaround could be considered" msgstr "" #: ../../Qwen/source/quantization/gptq.md:230 a59d6759991640609371bf7afd81e0b8 msgid "Using the original model in 16-bit floating point" msgstr "" #: ../../Qwen/source/quantization/gptq.md:231 97134ed43ee3414199928d755c24544e msgid "Using the AWQ variants or llama.cpp-based models for reduced chances of abnormal generation" msgstr "" #: ../../Qwen/source/quantization/gptq.md:233 7c30819dea6c4cfb8eee98d0dd217bf9 msgid "Qwen2.5-32B-Instruct-GPTQ-Int4 broken with vLLM on multiple GPUs" msgstr "" #: ../../Qwen/source/quantization/gptq.md:235 a4a641abd99a47049c1fd172e9cfa2be msgid "Qwen2.5-32B-Instruct-GPTQ-Int4" msgstr "" #: ../../Qwen/source/quantization/gptq.md:236 70216327dda349cabf03412f5fbe3114 msgid "vLLM" msgstr "" #: ../../Qwen/source/quantization/gptq.md:237 8edf21882ff24358b736c73477cfba9d msgid "Deployment on multiple GPUs and only garbled text like `!!!!!!!!!!!!!!!!!!` could be generated." msgstr "" #: ../../Qwen/source/quantization/gptq.md:238 10d9d8b3d8e74afea5ccd79bc698fb7c msgid "Each of the following workaround could be considered" msgstr "" #: ../../Qwen/source/quantization/gptq.md:239 33d1632f26f9423c847d06af7a5d107d msgid "Using the AWQ or GPTQ-Int8 variants" msgstr "" #: ../../Qwen/source/quantization/gptq.md:240 b27f1f32637349d09b8c74a2041a4d9b msgid "Using a single GPU" msgstr "" #: ../../Qwen/source/quantization/gptq.md:241 fc27883584a04682b9e28b2ccf51dc0e msgid "Using Hugging Face `transformers` if latency and throughput are not major concerns" msgstr "" #: ../../Qwen/source/quantization/gptq.md:244 5664e5bd63c845d49e8cfa75e789dfa3 msgid "Troubleshooting" msgstr "问题排查" #: ../../Qwen/source/quantization/gptq.md 06f2358881134920ab43f4256ad6300e msgid "With `transformers` and `auto_gptq`, the logs suggest `CUDA extension not installed.` and the inference is slow." msgstr "在使用 `transformers` 和 `auto_gptq` 时，日志提示 `CUDA extension not installed.` 并且推理速度缓慢。" #: ../../Qwen/source/quantization/gptq.md:248 2d57d681b2d74c27b60523fa86676b6f msgid "`auto_gptq` fails to find a fused CUDA kernel compatible with your environment and falls back to a plain implementation. Follow its [installation guide](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/docs/INSTALLATION.md) to install a pre-built wheel or try installing `auto_gptq` from source." msgstr "`auto_gptq` 未能找到与您的环境兼容的融合CUDA算子，因此退回到基础实现。请遵循其 [安装指南](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/docs/INSTALLATION.md) 来安装预构建的 wheel 或尝试从源代码安装 `auto_gptq` 。" #: ../../Qwen/source/quantization/gptq.md 95b57d1a962c4dc7aa02a69a403e2376 msgid "Self-quantized Qwen2.5-72B-Instruct-GPTQ with `vllm`, `ValueError: ... must be divisible by ...` is raised. The intermediate size of the self-quantized model is different from the official Qwen2.5-72B-Instruct-GPTQ models." msgstr "`vllm` 使用自行量化的 Qwen2.5-72B-Instruct-GPTQ 时，会引发 `ValueError: ... must be divisible by ...` 错误。自量化的模型的 intermediate size 与官方的 Qwen2.5-72B-Instruct-GPTQ 模型不同。" #: ../../Qwen/source/quantization/gptq.md:255 ecd9b51a549045949ff18fdb6226ddc8 #, python-brace-format msgid "After quantization the size of the quantized weights are divided by the group size, which is typically 128. The intermediate size for the FFN blocks in Qwen2.5-72B is 29568. Unfortunately, {math}`29568 \\div 128 = 231`. Since the number of attention heads and the dimensions of the weights must be divisible by the tensor parallel size, it means you can only run the quantized model with `tensor_parallel_size=1`, i.e., one GPU card." msgstr "量化后，量化权重的大小将被 group size（通常为128）整除。Qwen2-72B 中FFN块的中间大小为29568。不幸的是， {math}`29568 \\div 128 = 231` 。由于注意力头的数量和权重的维度必须能够被张量并行大小整除，这意味着你只能使用 `tensor_parallel_size=1` ，即一张 GPU 卡，来运行量化的模型。" #: ../../Qwen/source/quantization/gptq.md:260 8b1c5e3934654679a2d85e3287cf9309 #, python-brace-format msgid "A workaround is to make the intermediate size divisible by {math}`128 \\times 8 = 1024`. To achieve that, the weights should be padded with zeros. While it is mathematically equivalent before and after zero-padding the weights, the results may be slightly different in reality." msgstr "一个解决方案是使中间大小能够被 {math}`128 \\times 8 = 1024` 整除。为了达到这一目的，应该使用零值对权重进行填充。虽然在数学上，在对权重进行零填充前后是等价的，但在现实中结果可能会略有不同。" #: ../../Qwen/source/quantization/gptq.md:264 ae904f7ab91340c4a6831aef4de643ba msgid "Try the following:" msgstr "尝试以下方法：" #: ../../Qwen/source/quantization/gptq.md:297 4cf8c516a2324e618d25333c84be9e6b msgid "This will save the padded checkpoint to the specified directory. Then, copy other files from the original checkpoint to the new directory and modify the `intermediate_size` in `config.json` to `29696`. Finally, you can quantize the saved model checkpoint." msgstr "这将会把填充后的检查点保存到指定的目录。然后，你需要从原始检查点复制其他文件到新目录，并将 `config.json` 中的 `intermediate_size` 修改为 `29696` 。最后，你可以量化保存的模型检查点。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/quantization/llama.cpp.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-05-07 19:51+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/quantization/llama.cpp.md:1 91e2a69dab7145e39b9e203302a2639e msgid "llama.cpp" msgstr "" #: ../../source/quantization/llama.cpp.md:3 7ae5403049d44c0ca2396985312e1a36 msgid "Quantization is a major topic for local inference of LLMs, as it reduces the memory footprint. Undoubtably, llama.cpp natively supports LLM quantization and of course, with flexibility as always." msgstr "量化(Quantization)是本地运行大规模语言模型的主要议题，因为它能减少内存占用。毫无疑问，llama.cpp原生支持大规模语言模型的量化，并且一如既往地保持了灵活性。" #: ../../source/quantization/llama.cpp.md:6 ae64a4f0deb944d99dc9db527fb13770 msgid "At high-level, all quantization supported by llama.cpp is weight quantization: Model parameters are quantized into lower bits, and in inference, they are dequantized and used in computation." msgstr "在高层次上，llama.cpp所支持的所有量化都是权重量化(weight quantization)：模型参数被量化为低位(bit)数，在推理过程中，它们会被反量化(dequantize)并用于计算。" #: ../../source/quantization/llama.cpp.md:9 384848aad7cb4457a6b39d80487ccfc4 msgid "In addition, you can mix different quantization data types in a single quantized model, e.g., you can quantize the embedding weights using a quantization data type and other weights using a different one. With an adequate mixture of quantization types, much lower quantization error can be attained with just a slight increase of bit-per-weight. The example program `llama-quantize` supports many quantization presets, such as Q4_K_M and Q8_0." msgstr "此外，你可以在单一的量化模型中混合使用不同的量化数据类型，例如，你可以使用一种量化数据类型量化嵌入权重(embedding)，而使用另一种量化其他权重。通过适当的量化类型组合，只需略微增加bpw （bit-per-weight, 位权比），就能达到更低的量化误差。示例程序`llama-quantize`支持许多量化预设，如Q4_K_M和Q8_0。" #: ../../source/quantization/llama.cpp.md:13 0d6c25bebbf94dc8b1c0e9bec5788043 msgid "If you find the quantization errors still more than expected, you can bring your own scales, e.g., as computed by AWQ, or use calibration data to compute an importance matrix using `llama-imatrix`, which can then be used during quantization to enhance the quality of the quantized models." msgstr "如果你发现量化误差仍然超出预期，你可以引入自己的量化尺度，例如由AWQ计算的，或者使用校准数据用`llama-imatrix`来计算一个“重要性矩阵”(importance matrix)，然后在量化过程中使用以提高量化模型的质量。" #: ../../source/quantization/llama.cpp.md:15 1543df272fe5426e94899b16cc1cd36e #, python-brace-format msgid "In this document, we demonstrate the common way to quantize your model and evaluate the performance of the quantized model. We will assume you have the example programs from llama.cpp at your hand. If you don't, check our guide [here](../run_locally/llama.cpp.html#getting-the-program){.external}." msgstr "在本文档中，我们将展示量化和评估量化模型性能的常见方法。我们会假设你手头有llama.cpp的示例程序。如果没有，请查看我们的[指南](../run_locally/llama.cpp.html#getting-the-program){.external}。" #: ../../source/quantization/llama.cpp.md:19 9245fc77ed6048e482ab7a046f8eb8cf msgid "Getting the GGUF" msgstr "获取GGUF" #: ../../source/quantization/llama.cpp.md:21 05e869b2fa3e459f8b640a59b88470d5 msgid "Now, suppose you would like to quantize `Qwen3-8B`. You need to first make a GGUF file as shown below:" msgstr "现在，假设你想量化`Qwen3-8B-Instruct`。你需要首先创建一个GGUF文件，如下所示：" #: ../../source/quantization/llama.cpp.md:27 26b1f7e1a9004275a857c3be976c5e8b msgid "Since Qwen3 are trained using the bfloat16 precision, the following should keep most information on supported machines:" msgstr "由于 Qwen3 以 bfloat16 混合精度训练，以下方法在受支持的计算机上可保留完整参数信息：" #: ../../source/quantization/llama.cpp.md:32 a2b26c45c99247f39ec8d90e92b7de07 msgid "Sometimes, it may be better to use fp32 as the start point for quantization. In that case, use" msgstr "有时，可能最好将fp32作为量化的起点。在这种情况下，使用" #: ../../source/quantization/llama.cpp.md:38 0d6321f7482a499f80747d78f2d01033 msgid "Quantizing the GGUF without Calibration" msgstr "无校准量化GGUF" #: ../../source/quantization/llama.cpp.md:40 807e22b6326346c99a29068b29aa2436 msgid "For the simplest way, you can directly quantize the model to lower-bits based on your requirements. An example of quantizing the model to 8 bits is shown below:" msgstr "最简单的方法是，你可以根据需求直接将模型量化到低位数。下面是一个将模型量化到8 bit的例子：" #: ../../source/quantization/llama.cpp.md:46 66cadafa9bc84365a53f870e8f0548cb msgid "`Q8_0` is a code for a quantization preset. You can find all the presets in [the source code of `llama-quantize`](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/quantize.cpp). Look for the variable `QUANT_OPTIONS`. Common ones used for 8B models include `Q8_0`, `Q5_K_M`, and `Q4_K_M`. The letter case doesn't matter, so `q8_0` or `q4_K_m` are perfectly fine." msgstr "`Q8_0`是一个量化预设的代号。你可以在[`llama-quantize`的源代码](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/quantize.cpp)中找到所有预设。寻找变量`QUANT_OPTIONS`。对于 8B 模型常用的包括`Q8_0`、`Q5_K_M`和`Q4_K_M`。字母大小写不重要，所以`q8_0`或`q4_K_m`都是可以接受的。" #: ../../source/quantization/llama.cpp.md:52 d4cce644d247439ebf58c01f1916aabe msgid "Now you can use the GGUF file of the quantized model with applications based on llama.cpp. Very simple indeed." msgstr "现在，你可以使用基于llama.cpp的应用程序中的量化模型的GGUF文件。确实很简单。" #: ../../source/quantization/llama.cpp.md:55 292de0cf1cfa48b1859c274185a456ac msgid "However, the accuracy of the quantized model could be lower than expected occasionally, especially for lower-bit quantization. The program may even prevent you from doing that." msgstr "然而，量化模型的准确性偶尔可能低于预期，特别是对于低位数量化。程序甚至可能阻止你这样做。" #: ../../source/quantization/llama.cpp.md:58 fc9efc3991b04d1192f26ea93635aaef msgid "There are several ways to improve quality of quantized models. A common way is to use a calibration dataset in the target domain to identify the weights that really matter and quantize the model in a way that those weights have lower quantization errors, as introduced in the next two methods." msgstr "有几种方法可以提高量化模型的质量。一种常见的方法是在目标域中使用校准数据集来识别真正重要的权重，并以这些权重具有较低量化误差的方式量化模型，如下两种方法中将介绍。" #: ../../source/quantization/llama.cpp.md:62 a1467369a0c04c0da45d555e43e42635 msgid "Quantizing the GGUF with AWQ Scale" msgstr "使用AWQ尺度量化GGUF" #: ../../source/quantization/llama.cpp.md:65 2cc212d09e8d4e8b912c24dd2a27f35a msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../source/quantization/llama.cpp.md:68 da955f0aaa564ec4b5584fe4fb1fbda0 msgid "To improve the quality of your quantized models, one possible solution is to apply the AWQ scale, following [this script](https://github.com/casper-hansen/AutoAWQ/blob/main/docs/examples.md#gguf-export). First, when you run `model.quantize()` with `autoawq`, remember to add `export_compatible=True` as shown below:" msgstr "为了提高量化模型的质量，一种可能的解决方案是应用AWQ尺度，遵循[这个脚本](https://github.com/casper-hansen/AutoAWQ/blob/main/docs/examples.md#gguf-export)。首先，当你使用`autoawq`运行`model.quantize()`时，记得添加`export_compatible=True`，如下所示：" #: ../../source/quantization/llama.cpp.md:81 1be192c485db4fcd876568e9faacac4b msgid "The above code will not actually quantize the weights. Instead, it adjusts weights based on a dataset so that they are \"easier\" to quantize.[^AWQ]" msgstr "上述代码实际上不会量化权重。相反，它会根据数据集调整权重，使它们“更容易”量化。[^AWQ]" #: ../../source/quantization/llama.cpp.md:84 10aa1c71ed9d4553bab3d2bc5aba08e8 msgid "Then, when you run `convert-hf-to-gguf.py`, remember to replace the model path with the path to the new model:" msgstr "然后，当你运行`convert-hf-to-gguf.py`时，记得将模型路径替换为新模型的路径：" #: ../../source/quantization/llama.cpp.md:89 99b87ba88b84422983b60584515fc924 msgid "Finally, you can quantize the model as in the last example:" msgstr "最后，你可以像最后一个例子那样量化模型：" #: ../../source/quantization/llama.cpp.md:94 8c2b985fccf54302b8175a50f34304e8 msgid "In this way, it should be possible to achieve similar quality with lower bit-per-weight." msgstr "这样，应该有可能以更低的bpw实现相似的质量。" #: ../../source/quantization/llama.cpp.md:100 b35ae6045d8045d6a4f6cfe0b8a86f5e msgid "Quantizing the GGUF with Importance Matrix" msgstr "使用重要性矩阵量化GGUF" #: ../../source/quantization/llama.cpp.md:102 2e5cafdf307d430d9e34e3bb2c60bbf2 msgid "Another possible solution is to use the \"important matrix\"[^imatrix], following [this](https://github.com/ggml-org/llama.cpp/tree/master/tools/imatrix)." msgstr "另一个可能的解决方案是使用\"重要矩阵\"[^imatrix]，参照[这里](https://github.com/ggml-org/llama.cpp/tree/master/tools/imatrix)。" #: ../../source/quantization/llama.cpp.md:104 1aeee4c40d034cff890bc076983530e4 msgid "First, you need to compute the importance matrix data of the weights of a model (`-m`) using a calibration dataset (`-f`):" msgstr "首先，你需要使用校准数据集（`-f`）计算模型权重的重要性矩阵数据（`-m`）：" #: ../../source/quantization/llama.cpp.md:109 b679568784524852a1736d3be54ac50f msgid "The text is cut in chunks of length `--chunk` for computation. Preferably, the text should be representative of the target domain. The final results will be saved in a file named `Qwen3-8B-imatrix.dat` (`-o`), which can then be used:" msgstr "文本被切割成长度为`--chunk`的块进行计算。最好，文本应代表目标领域。最终结果将保存在名为`qwen3-8b-imatrix.dat`（`-o`）的文件中，然后可以使用：" #: ../../source/quantization/llama.cpp.md:117 4f13b845d25a4beea4f072de70b95c32 msgid "For lower-bit quantization mixtures for 1-bit or 2-bit, if you do not provide `--imatrix`, a helpful warning will be printed by `llama-quantize`." msgstr "对于1 bit或2 bit的低位数量化混合，如果你不提供`--imatrix`，`llama-quantize`将打印出有用的警告。" #: ../../source/quantization/llama.cpp.md:121 c3308e36443949e3be37bf1ceee2d56f msgid "Perplexity Evaluation" msgstr "困惑度(Perplexity)评估" #: ../../source/quantization/llama.cpp.md:123 5a84b0a3e7e647ed8f453baf9f9309c8 msgid "`llama.cpp` provides an example program for us to calculate the perplexity, which evaluate how unlikely the given text is to the model. It should be mostly used for comparisons: the lower the perplexity, the better the model remembers the given text." msgstr "`llama.cpp`为我们提供了一个示例程序来计算困惑度，这评估了给定文本对模型而言的“不可能”程度。它主要用于比较：困惑度越低，模型对给定文本的记忆越好。" #: ../../source/quantization/llama.cpp.md:126 cf72d076477a42f497034e2f670c119d msgid "To do this, you need to prepare a dataset, say \"wiki test\"[^wiki]. You can download the dataset with:" msgstr "要做到这一点，你需要准备一个数据集，比如\"wiki测试集\"[^wiki]。你可以使用以下命令下载数据集：" #: ../../source/quantization/llama.cpp.md:133 d16427f201af46c2bc8204d61b82f8b5 msgid "Then you can run the test with the following command:" msgstr "然后你可以使用以下命令运行测试：" #: ../../source/quantization/llama.cpp.md:137 56d6bd741ec64dee91a994c708620838 msgid "Wait for some time and you will get the perplexity of the model. There are some numbers of different kinds of quantization mixture [here](https://github.com/ggml-org/llama.cpp/blob/master/tools/perplexity/README.md). It might be helpful to look at the difference and grab a sense of how that kind of quantization might perform." msgstr "稍等一段时间，你将得到模型的困惑度。[这里](https://github.com/ggml-org/llama.cpp/blob/master/tools/perplexity/README.md)提供了不同类型的量化模型的数值。观察差异可能有助于理解不同量化方式的潜在表现。" #: ../../source/quantization/llama.cpp.md:144 589fad71ce69495fa259e51035baf046 msgid "Finally" msgstr "结束语" #: ../../source/quantization/llama.cpp.md:146 2ac61288357b43a3b6932b226a785bb1 msgid "In this guide, we demonstrate how to conduct quantization and evaluate the perplexity with llama.cpp. For more information, please visit the [llama.cpp GitHub repo](https://github.com/ggml-org/llama.cpp)." msgstr "在本指南中，我们展示了如何使用llama.cpp进行量化和评估困惑度。更多信息，请访问[llama.cpp GitHub仓库](https://github.com/ggml-org/llama.cpp)。" #: ../../source/quantization/llama.cpp.md:149 8cb576b8079e4dfeaca0ab06f7d9f4e4 #, fuzzy msgid "We usually quantize the fp16 model to 4, 5, 6, and 8-bit models with different quantization mixtures, but sometimes a particular mixture just does not work, so we don't provide those in our Hugging Face Hub. However, others in the community may have success, so if you haven't found what you need in our repos, look around." msgstr "我们通常将fp16模型量化为4、5、6和8位模型，采用不同的量化混合，但有时特定的混合就是不起作用，所以我们不在HuggingFace Hub中提供这些。但是，社区中的其他人可能会成功，因此，如果你在我们的仓库中没有找到所需的内容，请四处看看。" #: ../../source/quantization/llama.cpp.md:152 1b2b1b45246c4539bc30b684b818676f msgid "Enjoy your freshly quantized models!" msgstr "享受你新鲜量化的模型吧！" #: ../../source/quantization/llama.cpp.md:96 5d42d043024a4425a3b4d2d846c6aa59 msgid "If you are interested in what this means, refer to [the AWQ paper](https://arxiv.org/abs/2306.00978). Basically, important weights (called salient weights in the paper) are identified based on activations across data examples. The weights are scaled accordingly such that the salient weights are protected even after quantization." msgstr "如果你对这意味着什么感兴趣，请参阅[AWQ论文](https://arxiv.org/abs/2306.00978)。基本上，根据数据实例上的激活，识别出重要的权重（在论文中称为显著权重）。相应地缩放权重，以便即使在量化后也能保护显著权重。" #: ../../source/quantization/llama.cpp.md:119 8b25ee3804f24fa798a79299899ab860 msgid "Here, the importance matrix keeps record of how weights affect the output: the weight should be important is a slight change in its value causes huge difference in the results, akin to the [GPTQ](https://arxiv.org/abs/2210.17323) algorithm." msgstr "在这里，重要性矩阵记录了权重如何影响输出：如果权重的微小变化导致结果的巨大差异，则该权重应该是重要的，类似于[GPTQ](https://arxiv.org/abs/2210.17323)算法。" #: ../../source/quantization/llama.cpp.md:141 1fd3ddb3e40a47ffb4cc5a2604918ad7 msgid "It is not a good evaluation dataset for instruct models though, but it is very common and easily accessible. You probably want to use a dataset similar to your target domain." msgstr "虽然它不是指导模型的良好评估数据集，但它非常常见且易于访问。你可能希望使用与目标领域相似的数据集。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/run_locally/llama.cpp.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-29 16:34+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/run_locally/llama.cpp.md:1 982af2ecb7ee4933a0dc9541c000ff4d msgid "llama.cpp" msgstr "llama.cpp" #: ../../source/run_locally/llama.cpp.md c1891b9c45ff48f0b0b350eedc1164ab msgid "llama.cpp as a C++ library" msgstr "llama.cpp作为C++库" #: ../../source/run_locally/llama.cpp.md:6 e2ddc412959146498ce64b91c05c3ca6 msgid "Before starting, let's first discuss what is llama.cpp and what you should expect, and why we say \"use\" llama.cpp, with \"use\" in quotes. llama.cpp is essentially a different ecosystem with a different design philosophy that targets light-weight footprint, minimal external dependency, multi-platform, and extensive, flexible hardware support:" msgstr "开始之前，让我们先谈谈什么是llama.cpp，您应该期待什么，以及为什么我们说带引号“使用”llama.cpp。本质上，llama.cpp是一个不同的生态系统，具有不同的设计理念，旨在实现轻量级、最小外部依赖、多平台以及广泛灵活的硬件支持：" #: ../../source/run_locally/llama.cpp.md:8 70559daade4b447fa28ea689bef9e2b5 msgid "Plain C/C++ implementation without external dependencies" msgstr "纯粹的C/C++实现，没有外部依赖" #: ../../source/run_locally/llama.cpp.md:9 d070ffe26c1f47eda51a08029b95f0f4 msgid "Support a wide variety of hardware:" msgstr "支持广泛的硬件：" #: ../../source/run_locally/llama.cpp.md:10 f27c8b0b95eb4635a458312e82b71ed7 msgid "AVX, AVX2 and AVX512 support for x86_64 CPU" msgstr "x86_64 CPU的AVX、AVX2和AVX512支持" #: ../../source/run_locally/llama.cpp.md:11 c09f100de65641c98c17a8c9fa927766 msgid "Apple Silicon via Metal and Accelerate (CPU and GPU)" msgstr "通过Metal和Accelerate支持Apple Silicon（CPU和GPU）" #: ../../source/run_locally/llama.cpp.md:12 b17e1127c1c74536bc9c61507832f7f6 msgid "NVIDIA GPU (via CUDA), AMD GPU (via hipBLAS), Intel GPU (via SYCL), Ascend NPU (via CANN), and Moore Threads GPU (via MUSA)" msgstr "NVIDIA GPU（通过CUDA）、AMD GPU（通过hipBLAS）、Intel GPU（通过SYCL）、昇腾NPU（通过CANN）和摩尔线程GPU（通过MUSA）" #: ../../source/run_locally/llama.cpp.md:13 8743a89578bd45afa1a17448258be6d2 msgid "Vulkan backend for GPU" msgstr "GPU的Vulkan后端" #: ../../source/run_locally/llama.cpp.md:14 89e6275312504236829fd6eb4f910217 msgid "Various quantization schemes for faster inference and reduced memory footprint" msgstr "多种量化方案以加快推理速度并减少内存占用" #: ../../source/run_locally/llama.cpp.md:15 c120b7fcdb184ceb869abb70bac6a1ab msgid "CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity" msgstr "CPU+GPU混合推理，以加速超过总VRAM容量的模型" #: ../../source/run_locally/llama.cpp.md:17 bc4a92209a1d4a06bfc727b84be29090 msgid "It's like the Python frameworks `torch`+`transformers` or `torch`+`vllm` but in C++. However, this difference is crucial:" msgstr "它就像 Python 框架 `torch`+`transformers` 或 `torch`+`vllm` 的组合，但用的是 C++。然而，这一差异至关重要：" #: ../../source/run_locally/llama.cpp.md:19 a30d578f68aa43ae8479a0082cbd2d93 msgid "Python is an interpreted language: The code you write is executed line-by-line on-the-fly by an interpreter. You can run the example code snippet or script with an interpreter or a natively interactive interpreter shell. In addition, Python is learner friendly, and even if you don't know much before, you can tweak the source code here and there." msgstr "Python 是一种解释型语言：编写的代码会被解释器逐行实时执行。你可以使用解释器或原生交互式解释器终端来运行示例代码片段或脚本。此外，Python 对学习者非常友好，即使你之前了解不多，也可能修改源代码。" #: ../../source/run_locally/llama.cpp.md:23 5140d7332d874816bbf9ee3ceea88e37 msgid "C++ is a compiled language: The source code you write needs to be compiled beforehand, and it is translated to machine code and an executable program by a compiler. The overhead from the language side is minimal. You do have source code for example programs showcasing how to use the library. But it is not very easy to modify the source code if you are not verse in C++ or C." msgstr "C++ 是一种编译型语言：你编写的源代码需要预先编译，由编译器将其转换为机器码和可执行程序，来自语言层面的开销微乎其微。llama.cpp也提供了示例程序的源代码，展示了如何使用该库。但是，如果你不精通 C++ 或 C 语言，修改源代码并不容易。" #: ../../source/run_locally/llama.cpp.md:29 33262bc591a844159f033b5fe36324dd msgid "To use llama.cpp means that you use the llama.cpp library in your own program, like writing the source code of [Ollama](https://ollama.com/), [LM Studio](https://lmstudio.ai/), [GPT4ALL](https://www.nomic.ai/gpt4all), [llamafile](https://llamafile.ai/) etc. But that's not what this guide is intended or could do. Instead, here we introduce how to use the `llama-cli` example program, in the hope that you know that llama.cpp does support Qwen2.5 models and how the ecosystem of llama.cpp generally works." msgstr "真正使用 llama.cpp 意味着在自己的程序中使用 llama.cpp 库，就像编写 [Ollama](https://ollama.com/)、[LM Studio](https://lmstudio.ai/)、[GPT4ALL](https://www.nomic.ai/gpt4all)、[llamafile](https://llamafile.ai/) 等的源代码。但这并不是本指南的目的或所能做的。相反，这里我们介绍如何使用 `llama-cli` 示例程序，希望你能了解到 llama.cpp 支持 Qwen2.5 模型以及 llama.cpp 生态系统的一般工作原理。" #: ../../source/run_locally/llama.cpp.md:34 ed26c043c56143bb87185ff669d6d8cf msgid "In this guide, we will show how to \"use\" [llama.cpp](https://github.com/ggml-org/llama.cpp) to run models on your local machine, in particular, the `llama-cli` and the `llama-server` example program, which comes with the library." msgstr "在这份指南中，我们将讨论如何“使用” [llama.cpp](https://github.com/ggml-org/llama.cpp) 在您的本地机器上运行模型，特别是随库提供的 `llama-cli` 和 `llama-server` 示例程序。" #: ../../source/run_locally/llama.cpp.md:36 f2e1113691cc4c18b6bd46bdf8ea09ac msgid "The main steps are:" msgstr "主要步骤如下：" #: ../../source/run_locally/llama.cpp.md:37 8e663f68bd6b407a840c8e1748865021 msgid "Get the programs" msgstr "获取程序" #: ../../source/run_locally/llama.cpp.md:38 26e13906aeca465bbf9a0f3f2333c955 msgid "Get the Qwen3 models in GGUF[^GGUF] format" msgstr "获取 GGUF[^GGUF] 格式的 Qwen3 模型" #: ../../source/run_locally/llama.cpp.md:39 3e904b0aec454270b7eca787f0fd0371 msgid "Run the program with the model" msgstr "使用模型运行程序" #: ../../source/run_locally/llama.cpp.md:42 c4288c0e92f049dd9445f2b004aad048 msgid "llama.cpp supports Qwen3 and Qwen3MoE from version `b5092`." msgstr "llama.cpp 自版本 `b5092` 支持 Qwen3 和 Qwen3MoE 。" #: ../../source/run_locally/llama.cpp.md:45 1088c7e273504ee8b1ea5c88464f9460 msgid "Getting the Program" msgstr "获取程序" #: ../../source/run_locally/llama.cpp.md:47 768fb32dd36442eb9d03612cee4f7e78 msgid "You can get the programs in various ways. For optimal efficiency, we recommend compiling the programs locally, so you get the CPU optimizations for free. However, if you don't have C++ compilers locally, you can also install using package managers or downloading pre-built binaries. They could be less efficient but for non-production example use, they are fine." msgstr "你可以通过多种方式获得 llama.cpp 中的程序。为了达到最佳效率，我们建议你本地编译程序，这样可以零成本享受CPU优化。但是，如果你的本地环境没有C++编译器，也可以使用包管理器安装或者下载预编译的二进制文件。虽然它们可能效率较低，但对于非生产用途的例子来说，它们已经足够好用了。" #: ../../source/run_locally/llama.cpp.md 023a89efd9914b62a95a70abbabe4fe5 msgid "Compile Locally" msgstr "本地编译" #: ../../source/run_locally/llama.cpp.md:56 154aaf9730724f7c8f8cd80b01ea5b41 msgid "Here, we show the basic command to compile `llama-cli` locally on **macOS** or **Linux**. For Windows or GPU users, please refer to [the guide from llama.cpp](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)." msgstr "这里，我们将展示在 **macOS** 或 **Linux** 上本地编译 `llama-cli` 的基本命令。对于 Windows 用户或 GPU 用户，请参考[llama.cpp的指南](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)。" #: ../../source/run_locally/llama.cpp.md 5d61b0f1887b497599be5723e08602a5 msgid "Installing Build Tools" msgstr "安装构建工具" #: ../../source/run_locally/llama.cpp.md:63 772ed9c6cdfc4e28b35e0c50c9bc1051 msgid "To build locally, a C++ compiler and a build system tool are required. To see if they have been installed already, type `cc --version` or `cmake --version` in a terminal window." msgstr "要进行本地构建，你需要一个C++编译器和一个构建系统工具。在终端窗口中输入`cc --version`或`cmake --version`，看看这些工具是否已经安装好了。" #: ../../source/run_locally/llama.cpp.md:65 f271663e625a439695399a11779cb03e msgid "If installed, the build configuration of the tool will be printed to the terminal, and you are good to go!" msgstr "如果已安装，工具的构建配置信息将被打印到终端，那么你就可以开始了！" #: ../../source/run_locally/llama.cpp.md:66 774a4a0edb1444cebdde08038581c294 msgid "If errors are raised, you need to first install the related tools:" msgstr "如果出现错误，说明你需要先安装相关工具：" #: ../../source/run_locally/llama.cpp.md:67 6e1c6860a47d49e9bcc625eb8b3be542 msgid "On macOS, install with the command `xcode-select --install`" msgstr "在macOS上，使用命令`xcode-select --install`来安装。" #: ../../source/run_locally/llama.cpp.md:68 186197e49f2542f4837c442b1529d739 msgid "On Ubuntu, install with the command `sudo apt install build-essential`. For other Linux distributions, the command may vary; the essential packages needed for this guide are `gcc` and `cmake`." msgstr "在Ubuntu上，使用命令`sudo apt install build-essential`来安装。对于其他Linux发行版，命令可能会有所不同；本指南所需的基本包是`gcc`和`cmake`。" #: ../../source/run_locally/llama.cpp.md a3d68cbcf5834203bc8fd5892edbc7dc msgid "Compiling the Program" msgstr "编译程序" #: ../../source/run_locally/llama.cpp.md:75 1be0da28ab7f4d46bcde3311153d5f0b msgid "For the first step, clone the repo and enter the directory:" msgstr "第一步是克隆仓库并进入该目录：" #: ../../source/run_locally/llama.cpp.md:81 3c38e7800b984d63906d139d915cad3e msgid "Then, build llama.cpp using CMake:" msgstr "随后，使用 CMake 执行 llama.cpp 构建：" #: ../../source/run_locally/llama.cpp.md:87 584374ec3d74489180ad99dd99aceaea msgid "The first command will check the local environment and determine which backends and features should be included. The second command will actually build the programs." msgstr "第一条命令将检查本地环境并确定需要包含的推理后端与特性。第二条命令将实际构建程序文件。" #: ../../source/run_locally/llama.cpp.md:90 f728bdbb8e234fbaab608a956c31463e msgid "To shorten the time, you can also enable parallel compiling based on the CPU cores you have, for example:" msgstr "为了缩短时间，你还可以根据你的CPU核心数开启并行编译，例如：" #: ../../source/run_locally/llama.cpp.md:94 7fc43a28aadf4ab7aef94979d350c3e4 msgid "This will build the programs with 8 parallel compiling jobs." msgstr "这将以8个并行编译任务来构建程序。" #: ../../source/run_locally/llama.cpp.md:96 89a5aaa7baa6455e9662a8e5e8204fd0 msgid "The built programs will be in `./build/bin/`." msgstr "结果将存于 `./build/bin/` 。" #: ../../source/run_locally/llama.cpp.md 8073a459b60f4c5a994f10a61c7b9655 msgid "Package Managers" msgstr "软件包管理器" #: ../../source/run_locally/llama.cpp.md:101 6883e24e533a4e028ceda9ac23037cfe msgid "For **macOS** and **Linux** users, `llama-cli` and `llama-server` can be installed with package managers including Homebrew, Nix, and Flox." msgstr "对于**macOS**和**Linux**用户，`llama-cli` 和 `llama-server` 可以通过包括 Homebrew、Nix 和 Flox 在内的软件包管理器进行安装。" #: ../../source/run_locally/llama.cpp.md:103 d59800138ade43daaf35404b86c4c1e0 msgid "Here, we show how to install `llama-cli` and `llama-server` with Homebrew. For other package managers, please check the instructions [here](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)." msgstr "在这里，我们展示如何使用 Homebrew 安装 `llama-cli` 和 `llama-server` 。对于其他软件包管理器的安装，请查阅[这里的指南](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)。" #: ../../source/run_locally/llama.cpp.md:106 09a865816ad04e0cb739ff081f6a3c5e msgid "Installing with Homebrew is very simple:" msgstr "使用 Homebrew 安装非常简单：" #: ../../source/run_locally/llama.cpp.md:108 ec9e701fec054699882486d33cc6759d msgid "Ensure that Homebrew is available on your operating system. If you don't have Homebrew, you can install it as in [its website](https://brew.sh/)." msgstr "请确保您的操作系统上已安装有 Homebrew。如果没有，您可以按照[官网](https://brew.sh/)上的指导进行安装。" #: ../../source/run_locally/llama.cpp.md:111 6828ec93205945358c02caa71b30db14 msgid "Second, you can install the pre-built binaries, `llama-cli` and `llama-server` included, with a single command:" msgstr "其次，您只需一条命令即可安装预先编译好的二进制文件，其中包括 `llama-cli` 和 `llama-server` ：" #: ../../source/run_locally/llama.cpp.md:116 735fa54f0a68459eb0a66c4b4b14ef2e msgid "Note that the installed binaries might not be built with the optimal compile options for your hardware, which can lead to poor performance. They also don't support GPU on Linux systems." msgstr "请注意，安装的二进制文件可能并未针对您的硬件优化编译选项，这可能导致性能不佳。此外，在 Linux 系统上它们也不支持 GPU。" #: ../../source/run_locally/llama.cpp.md 6a7aa0679f0f4ff08378e7085b5d9ca5 msgid "Binary Release" msgstr "二进制文件" #: ../../source/run_locally/llama.cpp.md:122 747170630f41428fb98cfb26c0b2cbb4 msgid "You can also download pre-built binaries from [GitHub Releases](https://github.com/ggml-org/llama.cpp/releases). Please note that those pre-built binaries files are architecture-, backend-, and os-specific. If you are not sure what those mean, you probably don't want to use them and running with incompatible versions will most likely fail or lead to poor performance." msgstr "您还可以从[GitHub Release](https://github.com/ggml-org/llama.cpp/releases)下载预构建的二进制文件。请注意，这些预构建的二进制文件是特定于架构、后端和操作系统的。如果您不确定这些意味着什么，可能您并不想使用它们。使用不兼容的版本很可能导致运行失败或性能不佳。" #: ../../source/run_locally/llama.cpp.md:126 4d9c79d4a17544f8a82733204568b9e5 msgid "The file name is like `llama--bin---.zip`." msgstr "文件名类似于`llama--bin---.zip`。" #: ../../source/run_locally/llama.cpp.md:128 9128b08da0aa4389b67870a64e80326b msgid "There are three simple parts:" msgstr "分为三个简单部分：" #: ../../source/run_locally/llama.cpp.md:129 4e94cb63609a4cfb84a44730f2e45ec1 msgid "``: the version of llama.cpp. The latest is preferred, but as llama.cpp is updated and released frequently, the latest may contain bugs. If the latest version does not work, try the previous release until it works." msgstr "``：llama.cpp的版本。建议使用最新版本，但鉴于llama.cpp频繁更新和发布，最新版本可能包含bug。如果最新版本无法正常工作，请尝试之前的版本直到找到能正常工作的为止。" #: ../../source/run_locally/llama.cpp.md:130 da97ae4a462c4b29a68a16f99d52b5a6 msgid "``: the operating system. `win` for Windows; `macos` for macOS; `linux` for Linux." msgstr "``：操作系统。`win`代表Windows；`macos`代表macOS；`linux`代表Linux。" #: ../../source/run_locally/llama.cpp.md:131 a181b5105d9b4a3190c8bb4fc78647c6 msgid "``: the system architecture. `x64` for `x86_64`, e.g., most Intel and AMD systems, including Intel Mac; `arm64` for `arm64`, e.g., Apple Silicon or Snapdragon-based systems." msgstr "``：系统架构。`x64`对应`x86_64`，例如大多数Intel和AMD系统，包括Intel Mac；`arm64`对应`arm64`，例如Apple Silicon或基于Snapdragon的系统。" #: ../../source/run_locally/llama.cpp.md:133 cc2af4ec4e6f4d2fafbf3b022280097b msgid "The `` part is somewhat complicated for Windows:" msgstr "``部分对于Windows来说有些复杂：" #: ../../source/run_locally/llama.cpp.md:134 fd08c220cf414c48a6a776c69170ffa1 msgid "Running on CPU" msgstr "在CPU上运行" #: ../../source/run_locally/llama.cpp.md:135 2e0d36b4509e4a229ecf594f759092e0 msgid "x86_64 CPUs: We suggest try the `avx2` one first." msgstr "x86_64 CPU：我们建议首先尝试`avx2`。" #: ../../source/run_locally/llama.cpp.md:136 57dbb29cef6f4962b76760fac98def5c msgid "`noavx`: No hardware acceleration at all." msgstr "`noavx`：完全无AVX硬件加速。" #: ../../source/run_locally/llama.cpp.md:137 a2ecf0f937994dbba85e1ca6c8423ea3 msgid "`avx2`, `avx`, `avx512`: SIMD-based acceleration. Most modern desktop CPUs should support avx2, and some CPUs support `avx512`." msgstr "`avx2`，`avx`，`avx512`：基于SIMD的加速。大多数现代桌面CPU应该支持AVX2，部分CPU支持AVX512。" #: ../../source/run_locally/llama.cpp.md:138 85a4552c023a4ec0967e5f3127f3bf1f msgid "`openblas`: Relying on OpenBLAS for acceleration for prompt processing but not generation." msgstr "`openblas`：依赖OpenBLAS加速提示词(prompt)处理，但不涉及生成过程。" #: ../../source/run_locally/llama.cpp.md:139 5b0605a7a543494d985a78bb94d9d995 msgid "arm64 CPUs: We suggest try the `llvm` one first." msgstr "arm64 CPU：我们建议首先尝试`llvm`。" #: ../../source/run_locally/llama.cpp.md:140 5800b70afa0d46fca439d90f6def1c36 msgid "[`llvm` and `msvc`](https://github.com/ggml-org/llama.cpp/pull/7191) are different compilers" msgstr "[`llvm`和`msvc`](https://github.com/ggml-org/llama.cpp/pull/7191)是不同的编译器" #: ../../source/run_locally/llama.cpp.md:141 091caad783814d16ac4a4aaefd43f360 msgid "Running on GPU: We suggest try the `cu` one for NVIDIA GPUs, `kompute` for AMD GPUs, and `sycl` for Intel GPUs first. Ensure that you have related drivers installed." msgstr "在GPU上运行：我们建议NVIDIA GPU先尝试`cu`，AMD GPU先尝试`kompute`，Intel GPU先尝试`sycl`。请确保已安装相关驱动程序。" #: ../../source/run_locally/llama.cpp.md:142 cb77636288d84a6ab7e6659d813d184e msgid "[`vulcan`](https://github.com/ggml-org/llama.cpp/pull/2059): support certain NVIDIA and AMD GPUs" msgstr "[`vulcan`](https://github.com/ggml-org/llama.cpp/pull/2059)：支持某些NVIDIA和AMD GPU" #: ../../source/run_locally/llama.cpp.md:143 3dcbdb057df64027af6da72b016a3ed1 msgid "[`kompute`](https://github.com/ggml-org/llama.cpp/pull/4456): support certain NVIDIA and AMD GPUs" msgstr "[`kompute`](https://github.com/ggml-org/llama.cpp/pull/4456)：支持某些NVIDIA和AMD GPU" #: ../../source/run_locally/llama.cpp.md:144 0fdc7d54cbd4422ab690df1ac14f8052 msgid "[`sycl`](https://github.com/ggml-org/llama.cpp/discussions/5138): Intel GPUs, oneAPI runtime is included" msgstr "[`sycl`](https://github.com/ggml-org/llama.cpp/discussions/5138)：Intel GPU，包含oneAPI运行时" #: ../../source/run_locally/llama.cpp.md:145 79941a71446c42eaa2375088cf24c41d msgid "`cu`: NVIDIA GPUs, CUDA runtime is not included. You can download the `cudart-llama-bin-win-cu-x64.zip` and unzip it to the same directory if you don't have the corresponding CUDA toolkit installed." msgstr "`cu`：NVIDIA GPU，未包含CUDA运行时。如果您没有安装相应的CUDA工具包，可以下载`cudart-llama-bin-win-cu-x64.zip`并将其解压到同一目录中。" #: ../../source/run_locally/llama.cpp.md:147 0cdf63d30fbc4aa3a94c25d7f7cf5824 msgid "You don't have much choice for macOS or Linux." msgstr "对于macOS或Linux，您的选择不多。" #: ../../source/run_locally/llama.cpp.md:148 fdc3d523acac4789b7921e614fbc30a3 msgid "Linux: only one prebuilt binary, `llama--bin-linux-x64.zip`, supporting CPU." msgstr "Linux：仅有一个预构建的二进制文件`llama--bin-linux-x64.zip`，支持CPU。" #: ../../source/run_locally/llama.cpp.md:149 af122ff340be459188638f019712eed1 msgid "macOS: `llama--bin-macos-x64.zip` for Intel Mac with no GPU support; `llama--bin-macos-arm64.zip` for Apple Silicon with GPU support." msgstr "macOS：对于Intel Mac，使用`llama--bin-macos-x64.zip`（不支持GPU）；对于Apple Silicon，使用`llama--bin-macos-arm64.zip`（支持GPU）。" #: ../../source/run_locally/llama.cpp.md:151 a943b5242b7c40ce951c73a1781fc310 msgid "After downloading the `.zip` file, unzip them into a directory and open a terminal at that directory." msgstr "下载`.zip`文件后，将其解压到一个目录中，并在该目录下打开终端。" #: ../../source/run_locally/llama.cpp.md:156 beb40484138f4256b0553f5a6b2e0475 msgid "Getting the GGUF" msgstr "获取 GGUF" #: ../../source/run_locally/llama.cpp.md:158 54c848283fca422fb532af37cbe05e11 msgid "GGUF[^GGUF] is a file format for storing information needed to run a model, including but not limited to model weights, model hyperparameters, default generation configuration, and tokenizer." msgstr "GGUF[^GGUF] 是一种文件格式，用于存储运行模型所需的信息，包括但不限于模型权重、模型超参数、默认生成配置和tokenzier。" #: ../../source/run_locally/llama.cpp.md:160 e32110eeef8c4b9db68a739e09739ac1 msgid "You can use the official Qwen GGUFs from our Hugging Face Hub or prepare your own GGUF file." msgstr "您可以使用我们 Hugging Face Hub 上的官方 Qwen GGUF 文件，或者自己准备 GGUF 文件。" #: ../../source/run_locally/llama.cpp.md:162 bfacc2373da24a18bb9e2ebd7b290065 msgid "Using the Official Qwen3 GGUFs" msgstr "使用官方 Qwen3 GGUF" #: ../../source/run_locally/llama.cpp.md:164 8da7612136d14b67a20fbcc70828c043 msgid "We provide a series of GGUF models in our Hugging Face organization, and to search for what you need you can search the repo names with `-GGUF`." msgstr "在我们的 Hugging Face 组织中，我们提供了一系列 GGUF 模型。要查找您需要的模型，可以在仓库名称中搜索 `-GGUF`。" #: ../../source/run_locally/llama.cpp.md:166 dece3c73889d448ba9817e7a49dd932f msgid "Download the GGUF model that you want with `huggingface-cli` (you need to install it first with `pip install huggingface_hub`):" msgstr "使用 `huggingface-cli` 下载您想要的 GGUF 模型（首先需要通过 `pip install huggingface_hub` 进行安装）：" #: ../../source/run_locally/llama.cpp.md:171 3b23d1e4d7d347e09cf5faeec61b82a8 msgid "For example:" msgstr "比如：" #: ../../source/run_locally/llama.cpp.md:176 4c6758f24a5143ce8da71d3b19f1f95e msgid "This will download the Qwen3-8B model in GGUF format quantized with the scheme Q4_K_M." msgstr "这将下载采用 Q4_K_M 方案量化的 GGUF 格式的 Qwen3-8B model 模型。" #: ../../source/run_locally/llama.cpp.md:178 234ea7b4b5a047a0ac20ba11857dbdc5 msgid "Preparing Your Own GGUF" msgstr "准备您自己的 GGUF" #: ../../source/run_locally/llama.cpp.md:180 f74e9a53f77446e6b0cc27e9026e6165 msgid "Model files from Hugging Face Hub can be converted to GGUF, using the `convert-hf-to-gguf.py` Python script. It does require you to have a working Python environment with at least `transformers` installed." msgstr "可以使用 `convert-hf-to-gguf.py` Python 脚本将来自 Hugging Face Hub 的模型文件转换为 GGUF。这确实需要您拥有一个工作中的 Python 环境，并至少安装了 `transformers`。" #: ../../source/run_locally/llama.cpp.md:183 ced933a20489466d98bfc3ef484baa3a msgid "Obtain the source file if you haven't already:" msgstr "如果尚未获取，请先获取源文件：" #: ../../source/run_locally/llama.cpp.md:189 eb7dfdb39e034b318981c84cd112ac4e msgid "Suppose you would like to use Qwen3-8B you can make a GGUF file for the fp16 model as shown below:" msgstr "假设您想使用 Qwen3-8B，可以按照以下方式为 fp16 模型制作 GGUF 文件：" #: ../../source/run_locally/llama.cpp.md:193 3baf125807e541399ced8f2cbdd9d338 msgid "The first argument to the script refers to the path to the HF model directory or the HF model name, and the second argument refers to the path of your output GGUF file. Remember to create the output directory before you run the command." msgstr "脚本的第一个参数指的是 HF 模型目录或 HF 模型名称的路径，第二个参数指的是输出 GGUF 文件的路径。在运行命令前，请记得创建输出目录。" #: ../../source/run_locally/llama.cpp.md:196 a090bc60c62a41eead46c08d67ea8e7b msgid "The fp16 model could be a bit heavy for running locally, and you can quantize the model as needed. We introduce the method of creating and quantizing GGUF files in [this guide](../quantization/llama.cpp). You can refer to that document for more information." msgstr "fp16 模型对于本地运行可能有些重，您可以根据需要对模型进行量化。我们在 [这份指南](../quantization/llama.cpp) 中介绍了创建和量化 GGUF 文件的方法。您可以参考该文档获取更多信息。" #: ../../source/run_locally/llama.cpp.md:201 d4e0affd46394ed08217eba6cde11e61 msgid "Run Qwen with llama.cpp" msgstr "使用 llama.cpp 运行 Qwen" #: ../../source/run_locally/llama.cpp.md:204 9352e362c2a640a6984fa65f073dadf4 msgid "Regarding switching between thinking and non-thinking modes, while the soft switch is always available, the hard switch implemented in the chat template is not exposed in llama.cpp. The quick workaround is to pass [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) equivalent to always `enable_thinking=False` via `--chat-template-file`." msgstr "关于在思考模式和非思考模式之间切换，虽然软开关始终可用，但在聊天模板中实现的硬开关并未在 llama.cpp 中暴露。快速的解决方法是通过 `--chat-template-file` 传递一个等效于始终设置 `enable_thinking=False` 的[自定义聊天模板](../../source/assets/qwen3_nonthinking.jinja)。" #: ../../source/run_locally/llama.cpp.md:210 76c238d78e7247d0a71d75961415ed9e msgid "llama-cli" msgstr "" #: ../../source/run_locally/llama.cpp.md:212 4e4e1212c8b84613ad0da20c8e555aac msgid "[llama-cli](https://github.com/ggml-org/llama.cpp/tree/master/tools/main) is a console program which can be used to chat with LLMs. Simple run the following command where you place the llama.cpp programs:" msgstr "[llama-cli](https://github.com/ggml-org/llama.cpp/tree/master/tools/main) 是一个可用于与大型语言模型聊天的控制台程序。只需在放置 llama.cpp 程序的位置运行以下命令：" #: ../../source/run_locally/llama.cpp.md:218 076c752f47bc46f1b0cbbf6425146f95 msgid "Here are some explanations to the above command:" msgstr "以下是对上述命令的一些解释：" #: ../../source/run_locally/llama.cpp.md:219 5f672fa5712c4bd5830c0b9aef7ae1de msgid "**Model**: llama-cli supports using model files from local path, remote URL, or Hugging Face hub." msgstr "**模型**：llama-cli 支持从本地路径、远程 URL 或 Hugging Face Hub 使用模型文件。" #: ../../source/run_locally/llama.cpp.md:220 0be28620c8b94dc89edef6d6f153dd81 msgid "`-hf Qwen/Qwen3-8B-GGUF:Q8_0` in the above indicates we are using the model file from Hugging Face hub" msgstr "上面的 `-hf Qwen/Qwen3-8B-GGUF:Q8_0` 表示我们使用的是来自 Hugging Face Hub 的模型文件。" #: ../../source/run_locally/llama.cpp.md:221 53c0fb1b23794f59984dd71b9a2816c8 msgid "To use a local path, pass `-m qwen3-8b-q8_0.gguf` instead" msgstr "要使用本地路径，传递 `-m qwen3-8b-q8_0.gguf` 即可。" #: ../../source/run_locally/llama.cpp.md:222 0223c894c4f74e2d85a4b59212f81165 msgid "To use a remote URL, pass `-mu https://hf.co/Qwen/Qwen3-8B-GGUF/resolve/main/qwen3-8b-Q8_0.gguf?download=true` instead" msgstr "要使用远程 URL，传递 `-mu https://hf.co/Qwen/Qwen3-8B-GGUF/resolve/main/qwen3-8b-Q8_0.gguf?download=true` 即可。" #: ../../source/run_locally/llama.cpp.md:224 289230690cf14c909121197e999359fd msgid "**Speed Optimization**:" msgstr "**速度优化**：" #: ../../source/run_locally/llama.cpp.md:225 1cbdfffaecc64d9cb7257ba9983505bd msgid "CPU: llama-cli by default will use CPU and you can change `-t` to specify how many threads you would like it to use, e.g., `-t 8` means using 8 threads." msgstr "CPU：llama-cli 默认会使用 CPU，您可以通过更改 `-t` 来指定希望使用的线程数，例如 `-t 8` 表示使用 8 个线程。" #: ../../source/run_locally/llama.cpp.md:226 29434d4331ee4edebaa04b2aded1c558 msgid "GPU: If the programs are built with GPU support, you can use `-ngl`, which allows offloading some layers to the GPU for computation. If there are multiple GPUs, it will offload to all the GPUs. You can use `-dev` to control the devices used and `-sm` to control which kinds of parallelism is used. For example, `-ngl 99 -dev cuda0,cuda1 -sm row` means offload all layers to GPU 0 and GPU1 using the split mode row. Adding `-fa` may also speed up the generation." msgstr "GPU：如果程序包含 GPU 支持，您可以使用 `-ngl`，它允许将一些层卸载到 GPU 进行计算。如果有多个 GPU，它会卸载到所有 GPU 上。您可以使用 `-dev` 控制使用的设备，并使用 `-sm` 控制使用的并行类型。例如，`-ngl 99 -dev cuda0,cuda1 -sm row` 表示使用 row 切分将所有层卸载到 GPU 0 和 GPU 1。添加 `-fa` 也可能加速生成。" #: ../../source/run_locally/llama.cpp.md:232 260ef13ac441480db43d88e837c4b428 msgid "**Sampling Parameters**: llama.cpp supports [a variety of sampling methods](https://github.com/ggml-org/llama.cpp/tree/master/tools/main#generation-flags) and has default configuration for many of them. It is recommended to adjust those parameters according to the actual case and the recommended parameters from Qwen3 modelcard could be used as a reference. If you encounter repetition and endless generation, it is recommended to pass in addition `--presence-penalty` up to `2.0`." msgstr "**采样参数**：llama.cpp 支持[多种采样方法](https://github.com/ggml-org/llama.cpp/tree/master/tools/main#generation-flags)，并对其中许多方法有默认配置。建议根据实际情况调整这些参数，Qwen3 模型卡片中推荐的参数可作为参考。如果您遇到重复和无尽生成的情况，建议额外传递 `--presence-penalty`，最大值为 `2.0`。" #: ../../source/run_locally/llama.cpp.md:236 7adb408bb15d4f1a94704ccef38985b5 msgid "**Context Management**: llama.cpp adopts the \"rotating\" context management by default. The `-c` controls the maximum context length (default 4096, 0 means loaded from model), and `-n` controls the maximum generation length each time (default -1 means infinite until ending, -2 means until context full). When the context is full but the generation doesn't end, the first `--keep` tokens (default 0, -1 means all) from the initial prompt is kept, and the first half of the rest is discarded. Then, the model continues to generate based on the new context tokens. You can set `--no-context-shift` to prevent this rotating behavior and the generation will stop once `-c` is reached." msgstr "**上下文管理**：llama.cpp 默认采用“轮换”上下文管理方式。`-c` 控制最大上下文长度（默认值 4096，0 表示从模型加载），`-n` 控制每次生成的最大长度（默认值 -1 表示无限生成直到结束，-2 表示直到上下文满）。当上下文已满但生成未结束时，初始提示中的前 `--keep` 个 token（默认值 0，-1 表示全部）会被保留，其余部分的前半部分会被丢弃。然后，模型基于新的上下文 token 继续生成。您可以设置 `--no-context-shift` 来防止这种轮换行为，一旦达到 `-c`，生成就会停止。" #: ../../source/run_locally/llama.cpp.md:242 7482a8749a8f48538ec10c7555c610be msgid "llama.cpp supports YaRN, which can be enabled by `-c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768`." msgstr "llama.cpp 支持 YaRN，可以通过 `-c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768` 启用。" #: ../../source/run_locally/llama.cpp.md:243 6348ba3506f44f9ebd560f06cf3a221e msgid "**Chat**: `--jinja` indicates using the chat template embedded in the GGUF which is preferred and `--color` indicates coloring the texts so that user input and model output can be better differentiated. If there is a chat template, like in Qwen3 models, llama-cli will enter chat mode automatically. To stop generation or exit press \"Ctrl+C\". You can use `-sys` to add a system prompt." msgstr "**聊天**：`--jinja` 表示使用嵌入在 GGUF 中的聊天模板（推荐），`--color` 表示对文本进行着色，以便更好地区分用户输入和模型输出。如果有聊天模板（如 Qwen3 模型中），llama-cli 将自动进入聊天模式。要停止生成或退出，请按 \"Ctrl+C\"。您可以使用 `-sys` 添加系统提示。" #: ../../source/run_locally/llama.cpp.md:249 4a1967a6131349e8a3ea16830b43994c msgid "llama-server" msgstr "" #: ../../source/run_locally/llama.cpp.md:251 8633e2b491b44b5e9affa24651305d51 msgid "[llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) is a simple HTTP server, including a set of LLM REST APIs and a simple web front end to interact with LLMs using llama.cpp." msgstr "[llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) 是一个简单的 HTTP 服务器，包含一组 LLM REST API 和一个简单的 Web 前端，用于通过 llama.cpp 与大型语言模型交互。" #: ../../source/run_locally/llama.cpp.md:253 ef67ce21ceb049b8ada65370e31c56ba msgid "The core command is similar to that of llama-cli. In addition, it supports thinking content parsing and tool call parsing." msgstr "其核心命令与 llama-cli 类似。此外，它还支持思考内容解析和工具调用解析。" #: ../../source/run_locally/llama.cpp.md:260 375210d2d2b440a482880a080082b7b6 msgid "By default, the server will listen at `http://localhost:8080` which can be changed by passing `--host` and `--port`. The web front end can be assessed from a browser at `http://localhost:8080/`. The OpenAI compatible API is at `http://localhost:8080/v1/`." msgstr "默认情况下，服务器将在 `http://localhost:8080` 监听，可以通过传递 `--host` 和 `--port` 更改。Web 前端可以通过浏览器访问 `http://localhost:8080/`。兼容 OpenAI 的 API 位于 `http://localhost:8080/v1/`。" #: ../../source/run_locally/llama.cpp.md:265 067e74dbd5b64c51bd0fbf795867f643 msgid "What's More" msgstr "还有更多" #: ../../source/run_locally/llama.cpp.md:267 47d05b10224e453e98b3a83ff1ed3a97 msgid "If you still find it difficult to use llama.cpp, don't worry, just check out other llama.cpp-based applications. For example, Qwen3 has already been officially part of Ollama and LM Studio, which are platforms for your to search and run local LLMs." msgstr "如果你仍然觉得使用`llama-cli`有困难，别担心，可以尝试其他基于llama.cpp的应用程序。例如，Qwen3已经成为Ollama和LM Studio的官方组成部分，它们是用于搜索和运行本地LLM的平台。" #: ../../source/run_locally/llama.cpp.md:270 fd6b04dbca6348b99702fbbbc2f427f1 msgid "Have fun!" msgstr "玩得开心！" #: ../../source/run_locally/llama.cpp.md:3 35adceca5d514edf9bfc686a8722a38f msgid "GPT-Generated Unified Format" msgstr "" #~ msgid "Previously, Qwen2 models generate nonsense like `GGGG...` with `llama.cpp` on GPUs. The workaround is to enable flash attention (`-fa`), which uses a different implementation, and offload the whole model to the GPU (`-ngl 80`) due to broken partial GPU offloading with flash attention." #~ msgstr "曾有一段时间，在 GPU 上用 `llama.cpp` 运行 Qwen2 模型会生成类似 `GGGG...` 的胡言乱语。一个权宜之计是开启 flash attention (`-fa`) 并将全模型加载到 GPU 上 (`-ngl 80`) 。前者使用不同的算法实现，后者避免触发 flash attention 在模型一部分 GPU 加载时的异常。" #~ msgid "Both should be no longer necessary after `b3370`, but it is still recommended enabling both for maximum efficiency." #~ msgstr "自版本 `b3370` 起，以上方案已非必需。但考虑最佳效率，仍建议使用两项参数。" #~ msgid "![llama-cli conversation start](../assets/imgs/llama-cli-cnv-start.png)" #~ msgstr "" #~ msgid "llama-cli conversation start" #~ msgstr "llama-cli 对话开始" #~ msgid "![llama-cli conversation chat](../assets/imgs/llama-cli-cnv-chat.png)" #~ msgstr "" #~ msgid "llama-cli conversation chat" #~ msgstr "llama-cli 对话聊天" #~ msgid "![llama-cli interactive first](../assets/imgs/llama-cli-if.png)" #~ msgstr "" #~ msgid "llama-cli interactive first" #~ msgstr "llama-cli 互动模式用户优先" #~ msgid "![llama-cli interactive](../assets/imgs/llama-cli-i.png)" #~ msgstr "" #~ msgid "llama-cli interactive" #~ msgstr "llama-cli 互动模式" #~ msgid "The main output is as follows: ![llama-cli](../assets/imgs/llama-cli.png)" #~ msgstr "主要输出如下所示： ![llama-cli](../assets/imgs/llama-cli.png)" #~ msgid "llama-cli" #~ msgstr "" #~ msgid "![llama-cli mid](../assets/imgs/llama-cli-mid.png)" #~ msgstr "" #~ msgid "llama-cli mid" #~ msgstr "llama-cli 中间" #~ msgid "Get the `llama-cli` program" #~ msgstr "获取 `llama-cli` 程序" #~ msgid "Remember that `llama-cli` is an example program, not a full-blown application. Sometimes it just does not work in the way you would like. This guide could also get quite technical sometimes. If you would like a smooth experience, check out the application mentioned above, which are much easier to \"use\"." #~ msgstr "请记住，`llama-cli` 只是一个示例程序，并非完整应用。有时候它可能无法完全按照您的期望运行。本指南有时会涉及一些技术细节。如果您希望获得流畅的体验，请尝试上述提到的应用，它们使用起来会更加便捷。" #~ msgid "Then use `make`:" #~ msgstr "然后运行 `make` 命令：" #~ msgid "The command will only compile the parts needed for `llama-cli`. On macOS, it will enable Metal and Accelerate by default, so you can run with GPUs. On Linux, you won't get GPU support by default, but SIMD-optimization is enabled if available." #~ msgstr "该命令只会编译`llama-cli`所需的部件。在macOS上，默认情况下会启用Metal和Accelerate，因此你可以使用GPU运行。在Linux上，默认情况下你无法获得GPU支持，但如果可用，会启用CPU SIMD优化。" #~ msgid "There are other [example programs](https://github.com/ggerganov/llama.cpp/tree/master/tools) in llama.cpp. You can build them at once with simply (it may take some time):" #~ msgstr "在llama.cpp中还有其他的[示例程序](https://github.com/ggerganov/llama.cpp/tree/master/tools)，你可以一次构建它们（可能需要一些时间）：" #~ msgid "or you can also compile only the one you need, for example:" #~ msgstr "你也可以只编译你需要的，例如：" #~ msgid "Running the Model" #~ msgstr "运行模型" #~ msgid "Due to random sampling and source code updates, the generated content with the same command as given in this section may be different from what is shown in the examples." #~ msgstr "由于随机采样和源代码更新，使用本节中给出的相同命令生成的内容可能与示例中显示的不同。" #~ msgid "`llama-cli` provide multiple \"mode\" to \"interact\" with the model. Here, we demonstrate three ways to run the model, with increasing difficulty." #~ msgstr "`llama-cli` 提供多种“模式”来与模型进行“交互”。在这里，我们展示三种运行模型的方法，使用难度逐渐增加。" #~ msgid "Conversation Mode" #~ msgstr "对话模式" #~ msgid "For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode" #~ msgstr "对于普通用户来说，为了获得类似聊天机器人的体验，建议从对话模式开始。" #~ msgid "The program will first print metadata to the screen until you see the following:" #~ msgstr "程序首先会在屏幕上打印元数据，直到你看到以下内容：" #~ msgid "Now, the model is waiting for your input, and you can chat with the model:" #~ msgstr "现在，模型正在等待你的输入，你可以与模型进行对话：" #~ msgid "That's something, isn't it? You can stop the model generation anytime by Ctrl+C or Command+. However, if the model generation is ended and the control is returned to you, pressing the combination will exit the program." #~ msgstr "这很有趣，对吧？你可以随时通过 Ctrl+C 或 Command+. 来停止模型生成。但是，如果模型生成结束并且控制权返回给你，按下组合键将会退出程序。" #~ msgid "So what does the command we used actually do? Let's explain a little:" #~ msgstr "那么，我们使用的命令实际上做了什么呢？让我们来解释一下：" #~ msgid "-m or --model" #~ msgstr "-m 或 --model" #~ msgid "Model path, obviously." #~ msgstr "显然，这是模型路径。" #~ msgid "-co or --color" #~ msgstr "-co 或 --color" #~ msgid "Colorize output to distinguish prompt and user input from generations. Prompt text is dark yellow; user text is green; generated text is white; error text is red." #~ msgstr "为输出着色以区分提示词、用户输入和生成的文本。提示文本为深黄色；用户文本为绿色；生成的文本为白色；错误文本为红色。" #~ msgid "-cnv or --conversation" #~ msgstr "-cnv 或 --conversation" #~ msgid "Run in conversation mode. The program will apply the chat template accordingly." #~ msgstr "在对话模式下运行。程序将相应地应用聊天模板。" #~ msgid "-p or --prompt" #~ msgstr "-p 或 --prompt" #~ msgid "In conversation mode, it acts as the system message." #~ msgstr "在对话模式下，它作为系统提示。" #~ msgid "-fa or --flash-attn" #~ msgstr "-fa 或 --flash-attn" #~ msgid "Enable Flash Attention if the program is compiled with GPU support." #~ msgstr "如果程序编译时支持 GPU，则启用Flash Attention注意力实现。" #~ msgid "-ngl or --n-gpu-layers" #~ msgstr "-ngl 或 --n-gpu-layers" #~ msgid "Layers to the GPU for computation if the program is compiled with GPU support." #~ msgstr "如果程序编译时支持 GPU，则将这么多层分配给 GPU 进行计算。" #~ msgid "-n or --predict" #~ msgstr "-n 或 --predict" #~ msgid "Number of tokens to predict." #~ msgstr "要预测的token数量。" #~ msgid "You can also explore other options by" #~ msgstr "你也可以通过以下方式探索其他选项：" #~ msgid "Interactive Mode" #~ msgstr "互动模式" #~ msgid "The conversation mode hides the inner workings of LLMs. With interactive mode, you are made aware how LLMs work in the way to completion or continuation. The workflow is like" #~ msgstr "对话模式隐藏了大型语言模型（LLMs）的内部机制。在互动模式下，你可以直观地了解LLMs如何完成或继续生成文本。工作流程如下" #~ msgid "Give the model an initial prompt, and the model generates a completion." #~ msgstr "给模型一个初始提示，模型会生成续写文本。" #~ msgid "Interrupt the model generation any time or wait until the model generates a reverse prompt or an eos token." #~ msgstr "随时中断模型生成，或者等到模型生成反向提示(reverse prompt)或结束token（eos token）。" #~ msgid "Append new texts (with optional prefix and suffix), and then let the model continues the generation." #~ msgstr "添加新文本（可选前缀和后缀），然后让模型继续生成。" #~ msgid "Repeat Step 2. and Step 3." #~ msgstr "重复步骤2和步骤3。" #~ msgid "This workflow requires a different set of options, since you have to mind the chat template yourselves. To proper run the Qwen2.5 models, try the following:" #~ msgstr "此工作流程需要一组不同的选项，因为你必须自己管理聊天模板。为了正确运行Qwen2.5模型，请尝试以下操作：" #~ msgid "We use some new options here:" #~ msgstr "我们在这里使用了一些新的选项：" #~ msgid "-sp or --special" #~ msgstr "-sp 或 --special" #~ msgid "Show the special tokens." #~ msgstr "显示特殊token。" #~ msgid "-i or --interactive" #~ msgstr "-i 或 --interactive" #~ msgid "Enter interactive mode. You can interrupt model generation and append new texts." #~ msgstr "进入互动模式。你可以中断模型生成并添加新文本。" #~ msgid "-if or --interactive-first" #~ msgstr "-if 或 --interactive-first" #~ msgid "Immediately wait for user input. Otherwise, the model will run at once and generate based on the prompt." #~ msgstr "立即等待用户输入。否则，模型将立即运行并根据提示生成文本。" #~ msgid "In interactive mode, it is the contexts based on which the model predicts the continuation." #~ msgstr "在互动模式下，这是模型续写用的上文。" #~ msgid "--in-prefix" #~ msgstr "" #~ msgid "String to prefix user inputs with." #~ msgstr "用户输入附加的前缀字符串。" #~ msgid "--in-suffix" #~ msgstr "" #~ msgid "String to suffix after user inputs with." #~ msgstr "用户输入附加的后缀字符串。" #~ msgid "The result is like this:" #~ msgstr "结果如下：" #~ msgid "We use `prompt`, `in-prefix`, and `in-suffix` together to implement the chat template (ChatML-like) used by Qwen2.5 with a system message. So the experience is very similar to the conversation mode: you just need to type in the things you want to ask the model and don't need to worry about the chat template once the program starts. Note that, there should not be a new line after user input according to the template, so remember to end your input with `/`." #~ msgstr "我们将 `prompt`、`in-prefix` 和 `in-suffix` 结合起来实现Qwen2.5使用的包含系统消息的聊天模板（类似ChatML）。这样的，体验与对话模式非常相似：你只需输入想要询问模型的内容，在程序启动后无需担心聊天模板。请注意，根据模板，用户输入后不应有换行符，所以请以 `/` 结束输入。" #~ msgid "Advanced Usage" #~ msgstr "高级用法" #~ msgid "Interactive mode can achieve a lot more flexible workflows, under the condition that the chat template is maintained properly throughout. The following is an example:" #~ msgstr "互动模式可以实现更灵活的工作流程，前提是整个过程中正确维护聊天模板。以下是一个示例：" #~ msgid "In the above example, I set `--reverse-prompt` to `\"LLM\"` so that the generation is interrupted whenever the model generates `\"LLM\"`[^rp]. The in prefix and in suffix are also set to empty so that I can add content exactly I want. After every generation of `\"LLM\"`, I added the part `\"...not what you think...\"` which are not likely to be generated by the model. Yet the model can continue generation just as fluent, although the logic is broken the second time around. I think it's fun to play around." #~ msgstr "在上面的例子中，我将 `--reverse-prompt` 设置为 `\"LLM\"`，以便每当模型生成 `\"LLM\"` 时中断生成过程[^rp]。前缀和后缀也被设置为空，这样我可以精确地添加想要的内容。每次生成 `\"LLM\"` 后，我添加了 `\"...not what you think...\"` 的部分，这部分不太可能由模型生成。然而，模型仍能继续流畅生成，尽管第二次逻辑被破坏。这很有趣，值得探索。" #~ msgid "Non-interactive Mode" #~ msgstr "非交互模式" #~ msgid "You can also use `llama-cli` for text completion by using just the prompt. However, it also means you have to format the input properly and only one turn can be generated." #~ msgstr "你还可以仅使用提示词，通过`llama-cli`完成文本续写。但这也意味着你需要正确格式化输入，并且只能生成一次回应。" #~ msgid "The following is an example:" #~ msgstr "以下是一个示例：" #~ msgid "The main output is as follows:" #~ msgstr "主要步骤如下：" #~ msgid "In fact, you can start completion anywhere you want, even in the middle of an assistant message:" #~ msgstr "实际上，你可以从任何你想要的地方开始续写，即使是在assistant消息的中间：" #~ msgid "Now you can use `llama-cli` in three very different ways! Try talk to Qwen2.5 and share your experience with the community!" #~ msgstr "现在你可以用三种截然不同的方式使用`llama-cli`了！试试和Qwen2.5对话，然后与社区分享你的体验吧！" #~ msgid "There are some gotchas in using `--reverse-prompt` as it matches tokens instead of strings. Since the same string can be tokenized differently in different contexts in BPE tokenization, some reverse prompts are never matched even though the string does exist in generation." #~ msgstr "`--reverse-prompt`在匹配时针对的是token而非字符串，因此使用时有一些需要注意的地方。由于BPE tokenizer在不同上下文中对相同字符串的tokenization结果可能不同，所以某些反向提示符即使在生成的文本中存在，也可能永远无法匹配成功。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/run_locally/mlx-lm.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2024. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-29 16:34+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/run_locally/mlx-lm.md:1 47d6da370d364ad6a80f76d5d1f8a80d msgid "MLX LM" msgstr "" #: ../../source/run_locally/mlx-lm.md:4 f11e8701708e48559587dd3a8404be92 msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../source/run_locally/mlx-lm.md:7 9ff094754ecc40b79c3bc6737d1264dc #, fuzzy msgid "[mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms) helps you run LLMs locally on Apple Silicon. It is available at macOS. It has already supported Qwen models and this time, we have also provided checkpoints that you can directly use with it." msgstr "[mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms)能让你在Apple Silicon上运行大型语言模型，适用于MacOS。mlx-lm已支持Qwen模型，此次我们提供直接可用的模型文件。" #: ../../source/run_locally/mlx-lm.md:11 df3a6d381ce44151b810ee2ca012c6d3 msgid "Prerequisites" msgstr "准备工作" #: ../../source/run_locally/mlx-lm.md:13 2f6092faf4904990a208b2cecdc623b4 msgid "The easiest way to get started is to install the `mlx-lm` package:" msgstr "首先需要安装`mlx-lm`包：" #: ../../source/run_locally/mlx-lm.md:15 8ae883c84d7b435293dab8502884cec6 msgid "with `pip`:" msgstr "使用`pip`：" #: ../../source/run_locally/mlx-lm.md:21 0cd594d753fa478ca025271dca9af3b5 msgid "with `conda`:" msgstr "使用`conda`：" #: ../../source/run_locally/mlx-lm.md:27 a82de876ad354ca5ae7eb2cc9fad0f1e #, fuzzy msgid "Running with Qwen MLX Files" msgstr "使用Qwen MLX模型文件" #: ../../source/run_locally/mlx-lm.md:29 36d5c803c391420fabc5640f57a6509b msgid "We provide model checkpoints with `mlx-lm` in our Hugging Face organization, and to search for what you need you can search the repo names with `-MLX`." msgstr "我们已在Hugging Face提供了适用于`mlx-lm`的模型文件，请搜索带`-MLX`的存储库。" #: ../../source/run_locally/mlx-lm.md:31 916de6911595431f9ebf5cc3eec51fe3 msgid "Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents." msgstr "这里我们展示了一个代码样例，其中使用了`apply_chat_template`来应用对话模板。" #: ../../source/run_locally/mlx-lm.md:52 299ccc7dc4984f5e97c58b58a6216d69 msgid "Make Your MLX files" msgstr "自行制作MLX格式模型" #: ../../source/run_locally/mlx-lm.md:54 433503979d404191b934cf5e1ed7f655 #, fuzzy msgid "You can make MLX files with just one command:" msgstr "仅用一条命令即可制作mlx格式模型" #: ../../source/run_locally/mlx-lm.md:60 b2302dcae1074aff87fe3a1ec49c15f0 msgid "where" msgstr "参数含义分别是" #: ../../source/run_locally/mlx-lm.md:62 cbb011f4b76346cca7369b0d7538f899 msgid "`--hf-path`: the model name on Hugging Face Hub or the local path" msgstr "`--hf-path`: Hugging Face Hub上的模型名或本地路径" #: ../../source/run_locally/mlx-lm.md:63 16348ddca1e34966a51cb192d1c7d064 msgid "`--mlx-path`: the path for output files" msgstr "`--mlx-path`: 输出模型文件的存储路径" #: ../../source/run_locally/mlx-lm.md:64 34f6d5f01dcb4381ade2da35c75ea566 msgid "`-q`: enable quantization" msgstr "`-q`: 启用量化" #~ msgid "MLX-LM" #~ msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/run_locally/ollama.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-29 16:34+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/run_locally/ollama.md:1 66b5f4776d0a45ec9a7ed2c147a6323e msgid "Ollama" msgstr "Ollama" #: ../../source/run_locally/ollama.md:4 1a60e4f166184588802505205b51ea7a msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../source/run_locally/ollama.md:7 06925624447242879d12bcbc3be61ce6 #, fuzzy msgid "[Ollama](https://ollama.com/) helps you run LLMs locally with only a few commands. It is available at macOS, Linux, and Windows. Now, Qwen2.5 is officially on Ollama, and you can run it with one command:" msgstr "[Ollama](https://ollama.com/)帮助您通过少量命令即可在本地运行LLM。它适用于MacOS、Linux和Windows操作系统。现在，Qwen2.5正式上线Ollama，您只需一条命令即可运行它：" #: ../../source/run_locally/ollama.md:15 1180461df89c41c586d499daf5c6e0a0 msgid "Next, we introduce more detailed usages of Ollama for running Qwen2.5 models." msgstr "接着，我们介绍在Ollama使用Qwen2.5模型的更多用法" #: ../../source/run_locally/ollama.md:17 816ea502f0be44bcab371fd76e486618 msgid "Quickstart" msgstr "快速开始" #: ../../source/run_locally/ollama.md:19 c6d1c533593b495da62c73c8434636f8 msgid "Visit the official website [Ollama](https://ollama.com/) and click download to install Ollama on your device. You can also search models on the website, where you can find the Qwen2.5 models. Except for the default one, you can choose to run Qwen2.5-Instruct models of different sizes by:" msgstr "访问官方网站[Ollama](https://ollama.com/)，点击`Download`以在您的设备上安装Ollama。您还可以在网站上搜索模型，在这里您可以找到Qwen2.5系列模型。除了默认模型之外，您可以通过以下方式选择运行不同大小的Qwen2.5-Instruct模型：" #: ../../source/run_locally/ollama.md:23 9f87565dcf4b44b8adce249c1863adbd msgid "`ollama run qwen2.5:0.5b`" msgstr "" #: ../../source/run_locally/ollama.md:24 dcd5286c72a2440fb0fc7bfd210dd8d9 msgid "`ollama run qwen2.5:1.5b`" msgstr "" #: ../../source/run_locally/ollama.md:25 d2c13d08cad34893804497095676ddd4 msgid "`ollama run qwen2.5:3b`" msgstr "" #: ../../source/run_locally/ollama.md:26 e4a8e4cbd090469e9094afae3abe30f7 msgid "`ollama run qwen2.5:7b`" msgstr "" #: ../../source/run_locally/ollama.md:27 a97beb23d6074c2db90428b717ab61dd msgid "`ollama run qwen2.5:14b`" msgstr "" #: ../../source/run_locally/ollama.md:28 049274657be149b4976d722cd608eb09 msgid "`ollama run qwen2.5:32b`" msgstr "" #: ../../source/run_locally/ollama.md:29 baec60a5ed5047e3bd080aa96cfc577b msgid "`ollama run qwen2.5:72b`" msgstr "" #: ../../source/run_locally/ollama.md:32 2f9ef84694f5446f87db2333336f93c9 msgid "`ollama` does not host base models. Even though the tag may not have the instruct suffix, they are all instruct models." msgstr "`ollama`并不托管基模型。即便模型标签不带instruct后缀，实际也是instruct模型。" #: ../../source/run_locally/ollama.md:36 9545d728b8aa4163a66accdb54694caf msgid "Run Ollama with Your GGUF Files" msgstr "用Ollama运行你自己的GGUF文件" #: ../../source/run_locally/ollama.md:38 f5a073220da84c19975adcce41f76d5c msgid "Sometimes you don't want to pull models and you just want to use Ollama with your own GGUF files. Suppose you have a GGUF file of Qwen2.5, `qwen2.5-7b-instruct-q5_0.gguf`. For the first step, you need to create a file called `Modelfile`. The content of the file is shown below:" msgstr "有时您可能不想拉取模型，而是希望直接使用自己的GGUF文件来配合Ollama。假设您有一个名为`qwen2.5-7b-instruct-q5_0.gguf`的Qwen2.5的GGUF文件。在第一步中，您需要创建一个名为`Modelfile`的文件。该文件的内容如下所示：" #: ../../source/run_locally/ollama.md:101 15e36d92505e4e21a2fe5dd07c5861e7 #, fuzzy msgid "Then create the Ollama model by running:" msgstr "然后通过运行下列命令来创建一个ollama模型" #: ../../source/run_locally/ollama.md:107 4087d4c583fe44acb1456ca2412f8c85 #, fuzzy msgid "Once it is finished, you can run your Ollama model by:" msgstr "完成后，你即可运行你的ollama模型：" #: ../../source/run_locally/ollama.md:113 083503ce2e9d48eab757e8a4cf805d8d msgid "Tool Use" msgstr "工具调用" #: ../../source/run_locally/ollama.md:115 b7635c6f3ccc4294a2ba4a45f17ae163 #, fuzzy msgid "Tool use is now supported Ollama and you should be able to run Qwen2.5 models with it. For more details, see our [function calling guide](../framework/function_call)." msgstr "Ollama现已支持工具调用，Qwen2.5也已适配。更多详情，请参阅我们的[函数调用指南](../framework/function_call)" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/training/axolotl.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 17:22+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/training/axolotl.md:1 1b87b5a4226b45abbb6838abc839c77f msgid "Axolotl" msgstr "" #: ../../source/training/axolotl.md:3 71774faa415944dda99259d64acab46d msgid "This guide will help you get started with post-training (SFT, RLHF, RM, PRM) for Qwen3 / Qwen3_MOE using Axolotl, and covers optimizations to enable for better performance." msgstr "本指南将帮助您使用 Axolotl 开始对 Qwen3 / Qwen3_MOE 进行后训练（SFT、RLHF、RM、PRM），并涵盖为提升性能而启用的优化。" #: ../../source/training/axolotl.md:5 61072e56be004db9b403c1b2bf3f6f86 msgid "Requirements" msgstr "要求" #: ../../source/training/axolotl.md:7 3ed2a27ad94a49d18b049abfceab7cc9 msgid "**GPU:** NVIDIA Ampere (or newer) for `bf16` and `Flash Attention`, or AMD GPU" msgstr "**GPU:** 适用于 `bf16` 和 `Flash Attention` 的 NVIDIA Ampere（或更新架构），或 AMD GPU" #: ../../source/training/axolotl.md:8 e1b73a01085240da99d05fcf69fcebec msgid "**Python:** ≥3.11" msgstr "" #: ../../source/training/axolotl.md:9 dbe2c77d93064b8eb28e4d4351131887 msgid "**CUDA:** ≥12.4 (for NVIDIA GPUs)" msgstr "**CUDA:** ≥12.4（适用于 NVIDIA GPU）" #: ../../source/training/axolotl.md:11 58c6d64b99cd4158ae6f2cc924dbac68 msgid "Installation" msgstr "安装" #: ../../source/training/axolotl.md:13 9156474750754164bf51347eccd7537c msgid "You can install Axolotl using PyPI, Conda, Git, Docker, or launch a cloud environment." msgstr "您可以使用 PyPI、Conda、Git、Docker 安装 Axolotl，或者启动云环境。" #: ../../source/training/axolotl.md:16 24099b25e9fa4632b8467c7df11710bb msgid "Install PyTorch *before* installing Axolotl to ensure CUDA compatibility." msgstr "在安装 Axolotl *之前* 安装 PyTorch，以确保 CUDA 兼容性。" #: ../../source/training/axolotl.md:19 da17abd4fa654026826ec65f4e34714b msgid "For the latest instructions, see the official [Axolotl Installation Guide](https://docs.axolotl.ai/docs/installation.html)." msgstr "有关最新说明，请参阅官方 [Axolotl 安装指南](https://docs.axolotl.ai/docs/installation.html)。" #: ../../source/training/axolotl.md:21 55210f52b4ba40a5aa3c5ba311a2ab3b msgid "Quickstart" msgstr "快速入门" #: ../../source/training/axolotl.md:23 46428d800a7542a48360ca78bbb35f56 msgid "SFT" msgstr "SFT（监督微调）" #: ../../source/training/axolotl.md:25 34e7a9b5842b467f933f469465518b0d msgid "We have provided a sample YAML config for SFT with Qwen/Qwen3-32B: [SFT 32B QLoRA config](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/examples/qwen3/32b-qlora.yaml)." msgstr "我们提供了一个使用 Qwen/Qwen3-32B 进行 SFT 的示例 YAML 配置文件：[SFT 32B QLoRA 配置](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/examples/qwen3/32b-qlora.yaml)。" #: ../../source/training/axolotl.md:37 2baa43f76e164e31a1a49d76cafe98a0 msgid "To train a smaller model, edit the `base_model` in your config:" msgstr "要训练较小的模型，请编辑配置文件中的 `base_model`：" #: ../../source/training/axolotl.md:44 34b2753404214dd5a082c4cd707a1e4f msgid "Qwen3 works with all Axolotl features including `Flash Attention`, `bf16`, `LoRA`, `torch_compile`, and `QLoRA`." msgstr "Qwen3 可以使用所有 Axolotl 功能，包括 `Flash Attention`、`bf16`、`LoRA`、`torch_compile` 和 `QLoRA`。" #: ../../source/training/axolotl.md:46 a17d3d2763fd4296ba39aa1f8389c0ff msgid "To run on more than single GPU, please take a look at the [Multi-GPU Training Guide](https://docs.axolotl.ai/docs/multi-gpu.html) or [Multi-node Training Guide](https://docs.axolotl.ai/docs/multi-node.html)." msgstr "如需在多块 GPU 上运行，请参阅 [多 GPU 训练指南](https://docs.axolotl.ai/docs/multi-gpu.html) 或 [多节点训练指南](https://docs.axolotl.ai/docs/multi-node.html)。" #: ../../source/training/axolotl.md:48 87bdbf06d6984fbf801182a9c7c5e223 msgid "RLHF" msgstr "RLHF（基于人类反馈的强化学习）" #: ../../source/training/axolotl.md:50 0803e7e908024353aa83e561576947cd msgid "See the [RLHF Guide](https://docs.axolotl.ai/docs/rlhf.html) for required dataset formats and examples for each method." msgstr "请参阅 [RLHF 指南](https://docs.axolotl.ai/docs/rlhf.html)，了解每种方法所需的数据集格式和示例。" #: ../../source/training/axolotl.md:52 2c8dd42aca9e40bca82e2ba55ebbc431 msgid "RM/PRM" msgstr "RM/PRM" #: ../../source/training/axolotl.md:54 237c06d3dc314873908ceb044e20f86d msgid "Please refer to the [Reward Modelling Guide](https://docs.axolotl.ai/docs/reward_modelling.html) for required dataset formats and config examples." msgstr "请参阅 [奖励建模指南](https://docs.axolotl.ai/docs/reward_modelling.html)，了解所需的数据集格式和配置示例。" #: ../../source/training/axolotl.md:56 d9ab3c73710e40c2b16872cb2f83825a msgid "Dataset" msgstr "数据集" #: ../../source/training/axolotl.md:58 27d95366d2ab4355bcbe9dd3925ea98b msgid "By default, the example config uses the `mlabonne/FineTome-100k` dataset (from HuggingFace Hub). You can substitute any dataset of your own." msgstr ""msgstr "默认情况下，示例配置使用 `mlabonne/FineTome-100k` 数据集（来自 HuggingFace Hub）。您可以替换为您自己的任何数据集。" #: ../../source/training/axolotl.md:60 0820890dd0684961842626ae8f3c204d msgid "SFT Dataset Format" msgstr "SFT 数据集格式" #: ../../source/training/axolotl.md:62 9c7cc72200eb48e4b5cf6eaae4adf176 msgid "Axolotl handles various SFT dataset formats, but the current **recommended** format (for use with `chat_template`) is the OpenAI Messages format:" msgstr "Axolotl 支持多种 SFT 数据集格式，但当前**推荐**的格式（用于 `chat_template`）是 OpenAI Messages 格式：" #: ../../source/training/axolotl.md:81 8747795235ae444ebd02f9edeb5e2ee6 msgid "Use this in your config:" msgstr "在您的配置中使用以下内容：" #: ../../source/training/axolotl.md:89 dff75c48b8714b33a518e51198394e90 msgid "You can also load datasets from multiple sources: HuggingFace Hub, local files, directories, S3, GCS, Azure, etc." msgstr "您还可以从多个来源加载数据集：HuggingFace Hub、本地文件、目录、S3、GCS、Azure 等。" #: ../../source/training/axolotl.md:91 de3c2c02c6b74de48b65cc85e4770280 msgid "See the [Dataset Loading Guide](https://docs.axolotl.ai/docs/dataset_loading.html) for more details." msgstr "更多详细信息，请参阅[数据集加载指南](https://docs.axolotl.ai/docs/dataset_loading.html)。" #: ../../source/training/axolotl.md:93 c0d462f8add64d4eb08c94e8abd88901 msgid "To load different dataset formats, refer to the [SFT Dataset Formats Guide](https://docs.axolotl.ai/docs/dataset-formats/#supervised-fine-tuning-sft)." msgstr "要加载不同的数据集格式，请参阅[SFT 数据集格式指南](https://docs.axolotl.ai/docs/dataset-formats/#supervised-fine-tuning-sft)。" #: ../../source/training/axolotl.md:95 78bfd0e6be834fb59053c8b79d3e01a2 msgid "Optimizations" msgstr "性能优化" #: ../../source/training/axolotl.md:97 3b2c2c7c1c344278a858d11e5ee85c68 msgid "With Qwen3/Qwen3_MOE, you can leverage Axolotl's custom optimizations for improved speed and reduced memory usage:" msgstr "通过 Qwen3/Qwen3_MOE，您可以利用 Axolotl 的自定义优化来提升速度并减少内存使用：" #: ../../source/training/axolotl.md:99 5f8736c8f64b4179b17c3817544e6592 msgid "[Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy)" msgstr "" #: ../../source/training/axolotl.md:100 0fc18a82a08944fcbd116650149c25a2 msgid "[Liger Kernels](https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels)" msgstr "" #: ../../source/training/axolotl.md:101 8e1d0b958b5c46fc9586d48ce6fe1f5b msgid "(LoRA/QLoRA only): [LoRA Kernels Optimization](https://docs.axolotl.ai/docs/lora_optims.html)" msgstr "（仅限 LoRA/QLoRA）：[LoRA Kernels 优化](https://docs.axolotl.ai/docs/lora_optims.html)" #: ../../source/training/axolotl.md:103 ef45fbcf05234378b002101dd5f2aeea msgid "Additional Suggestions" msgstr "附加建议" #: ../../source/training/axolotl.md:105 f2a5feccb57147f6bbc089d81a76a661 msgid "Troubleshooting" msgstr "故障排除" #: ../../source/training/axolotl.md:107 0923468a1b75473c9e859969bc4636df msgid "Ensure your CUDA version matches your GPU and PyTorch version." msgstr "确保您的 CUDA 版本与您的 GPU 和 PyTorch 版本匹配。" #: ../../source/training/axolotl.md:108 ead86946b9ac4cad8e676dcc9151b6f6 msgid "If running into out-of-memory issues, try reducing your batch size, enable the optimizations above, or reduce sequence length." msgstr "如果遇到内存不足的问题，可以尝试减少批处理大小、启用上述优化或缩短序列长度。" #: ../../source/training/axolotl.md:109 4a0c5c6c372e40dbbafb625dd721c283 msgid "Qwen3 MoE may have slower training due to the upstream transformer's handling of MoE layers." msgstr "由于上游 Transformer 对 MoE 层的处理，Qwen3 MoE 的训练速度可能会较慢。" #: ../../source/training/axolotl.md:110 d9d43aa50a044428ab2b6ee60e801e62 msgid "For help, check the help channel on [Axolotl Discord](https://discord.gg/7m9sfhzaf3) or create a Discussion on [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)." msgstr "如需帮助，请查看 [Axolotl Discord](https://discord.gg/7m9sfhzaf3) 上的帮助频道，或者在 [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl) 上创建讨论。" #: ../../source/training/axolotl.md:112 1d754e492c05455098ccadb7898d84d2 msgid "Links" msgstr "外部链接" #: ../../source/training/axolotl.md:114 616dfc422c534617910f19025d01f66f msgid "[Axolotl Documentation](https://docs.axolotl.ai/)" msgstr "" #: ../../source/training/axolotl.md:115 5128337d00fb4437bb80829fca7472d1 msgid "[Axolotl Discord](https://discord.gg/7m9sfhzaf3)" msgstr "" #: ../../source/training/axolotl.md:116 1478f8dbb7e2475b9b45bf20ffc19a23 msgid "[Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)" msgstr "" #: ../../source/training/axolotl.md:117 0aff5c30b6584517935a568adc11b2db msgid "[Axolotl Website](https://axolotl.ai)" msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/training/llama_factory.po ================================================ # Copyright (C) 2024, Qwen Team, Alibaba Group. # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 17:22+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: LANGUAGE \n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/training/llama_factory.md:1 d0b05b307a764e27badd1e3851ee03ec msgid "LLaMA-Factory" msgstr "" #: ../../source/training/llama_factory.md:4 72877ac996694cc9b526edd4e9c1dc0b msgid "To be updated for Qwen3." msgstr "仍需为Qwen3更新。" #: ../../source/training/llama_factory.md:7 61f6ef7f9ec1444fb656b86ab4a43985 msgid "Here we provide a script for supervised finetuning Qwen2.5 with [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). This script for supervised finetuning (SFT) has the following features:" msgstr "我们将介绍如何使用 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 微调 Qwen2.5 模型。本脚本包含如下特点：" #: ../../source/training/llama_factory.md:11 93803890d14b43028177b43a909ce704 msgid "Support single-GPU and multi-GPU training;" msgstr "支持单卡和多卡分布式训练" #: ../../source/training/llama_factory.md:12 e3576e61279e4419a9ddd3f6947bd0b7 msgid "Support full-parameter tuning, LoRA, Q-LoRA, Dora." msgstr "支持全参数微调、LoRA、Q-LoRA 和 DoRA 。" #: ../../source/training/llama_factory.md:14 54303164dd1940e1808ab0ddb944c951 msgid "In the following, we introduce more details about the usage of the script." msgstr "下文将介绍更多关于脚本的用法。" #: ../../source/training/llama_factory.md:17 0b976ba2d67645b2b07fa974d1e064c4 msgid "Installation" msgstr "安装" #: ../../source/training/llama_factory.md:19 bc2440d2953642258f37115052921826 msgid "Before you start, make sure you have installed the following packages:" msgstr "开始之前，确保你已经安装了以下代码库：" #: ../../source/training/llama_factory.md:21 d628d70296b94969912c03c18461e22c msgid "Follow the instructions of [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), and build the environment." msgstr "根据 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 官方指引构建好你的环境" #: ../../source/training/llama_factory.md:24 d1bc1de0521342e2adb683560a52e05b msgid "Install these packages (Optional):" msgstr "安装下列代码库（可选）：" #: ../../source/training/llama_factory.md:31 dc2b9c3ca4da4d4597a47105ec1fd5b0 msgid "If you want to use [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), make sure your CUDA is 11.6 and above." msgstr "如你使用[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)，请确保你的CUDA版本在11.6以上。" #: ../../source/training/llama_factory.md:35 c4924b4b72fe4b1a9a50f73b667b9506 msgid "Data Preparation" msgstr "准备数据" #: ../../source/training/llama_factory.md:37 6195396b9f7549a9950684a17efa4516 msgid "LLaMA-Factory provides several training datasets in `data` folder, you can use it directly. If you are using a custom dataset, please prepare your dataset as follows." msgstr "LLaMA-Factory 在 `data` 文件夹中提供了多个训练数据集，您可以直接使用它们。如果您打算使用自定义数据集，请按照以下方式准备您的数据集。" #: ../../source/training/llama_factory.md:41 6af76880ee36417ebda92078367d98fd msgid "Organize your data in a **json** file and put your data in `data` folder. LLaMA-Factory supports dataset in `alpaca` or `sharegpt` format." msgstr "请将您的数据以 **json** 格式进行组织，并将数据放入 `data` 文件夹中。LLaMA-Factory 支持以 `alpaca` 或 `sharegpt` 格式的数据集。" #: ../../source/training/llama_factory.md:45 e15403384b33491aae4d4176bec80372 msgid "The dataset in `alpaca` format should follow the below format:" msgstr "`alpaca` 格式的数据集应遵循以下格式：" #: ../../source/training/llama_factory.md:62 3c96d003cbbb4c9d84f2f7b167ac7769 msgid "The dataset in `sharegpt` format should follow the below format:" msgstr "`sharegpt` 格式的数据集应遵循以下格式：" #: ../../source/training/llama_factory.md:83 3a63faa0b27b4eed8a749f0c2a675be0 msgid "Provide your dataset definition in `data/dataset_info.json` in the following format ." msgstr "在 `data/dataset_info.json` 文件中提供您的数据集定义，并采用以下格式：" #: ../../source/training/llama_factory.md:86 0a137feb4ce546a0b3088df3351ca062 msgid "For `alpaca` format dataset, the columns in `dataset_info.json` should be:" msgstr "对于 `alpaca` 格式的数据集，其 `dataset_info.json` 文件中的列应为：" #: ../../source/training/llama_factory.md:102 ea7871bf433a4d28923766a9d2c6f3d3 msgid "For `sharegpt` format dataset, the columns in `dataset_info.json` should be:" msgstr "对于 `sharegpt` 格式的数据集，`dataset_info.json` 文件中的列应该包括：" #: ../../source/training/llama_factory.md:123 6663d2ca92084df0a554790ea925cfc0 msgid "Training" msgstr "训练" #: ../../source/training/llama_factory.md:125 572abdbf356649b99438f85a507139f7 msgid "Execute the following training command:" msgstr "执行下列命令：" #: ../../source/training/llama_factory.md:165 83648e36173f48d99dbf7bb8540039e9 msgid "and enjoy the training process. To make changes to your training, you can modify the arguments in the training command to adjust the hyperparameters. One argument to note is `cutoff_len`, which is the maximum length of the training data. Control this parameter to avoid OOM error." msgstr "并享受训练过程。若要调整您的训练，您可以通过修改训练命令中的参数来调整超参数。其中一个需要注意的参数是 `cutoff_len` ，它代表训练数据的最大长度。通过控制这个参数，可以避免出现OOM（内存溢出）错误。" #: ../../source/training/llama_factory.md:171 c23adcde6f2846399e3f93e6248e5e70 msgid "Merge LoRA" msgstr "合并LoRA" #: ../../source/training/llama_factory.md:173 59e54104161841ff965c92211ff2cb8d msgid "If you train your model with LoRA, you probably need to merge adapter parameters to the main branch. Run the following command to perform the merging of LoRA adapters." msgstr "如果你使用 LoRA 训练模型，可能需要将adapter参数合并到主分支中。请运行以下命令以执行 LoRA adapter 的合并操作。" #: ../../source/training/llama_factory.md:188 cbdc1761ad20462d9e6318105f5bc6c9 msgid "Conclusion" msgstr "结语" #: ../../source/training/llama_factory.md:190 bbf0315dc269432dba1a08679398477a msgid "The above content is the simplest way to use LLaMA-Factory to train Qwen. Feel free to dive into the details by checking the official repo!" msgstr "上述内容是使用LLaMA-Factory训练Qwen的最简单方法。 欢迎通过查看官方仓库深入了解详细信息！" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/training/ms_swift.po ================================================ # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 17:22+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/training/ms_swift.md:1 498aef3956544f9b97c7a47c66997ea2 msgid "MS-SWIFT" msgstr "MS-SWIFT" #: ../../source/training/ms_swift.md:3 0a0330c78d8549d1be55501fdc352a15 msgid "ModelScope SWIFT (**ms-swift**) is the large model and multimodal large model training and deployment framework provided by the [ModelScope community](https://modelscope.cn/)." msgstr "ModelScope SWIFT (**ms-swift**) 是由 `ModelScope 社区 [ModelScope community](https://modelscope.cn/) 提供的大模型和多模态大模型训练部署框架。" #: ../../source/training/ms_swift.md:5 6e980c27ae67400ca12c11fedf439af8 msgid "GitHub repository: [ms-swift](https://github.com/modelscope/ms-swift)" msgstr "GitHub 仓库：[ms-swift](https://github.com/modelscope/ms-swift)" #: ../../source/training/ms_swift.md:7 43999693226b4bc1ba78d7f8addaa01d msgid "Features of using ms-swift for training LLM:" msgstr "使用 ms-swift 训练大语言模型的特性：" #: ../../source/training/ms_swift.md:9 b76a71cc42244cccaf2b699e41ee9c7f msgid "**Model Types**: Supports 500+ plain-text large models and 200+ multimodal large models, covering the entire process from training to deployment." msgstr "**模型类型**：支持 500+ 纯文本大模型和 200+ 多模态大模型，覆盖从训练到部署全流程。" #: ../../source/training/ms_swift.md:10 f9a00d8a4789418abc700fd6774d784b msgid "**Hardware Support**: Compatible with CPUs, RTX series GPUs, T4/V100, A10/A100/H100, Ascend NPUs, MPS, and more." msgstr "**硬件支持**：兼容 CPU、RTX 系列 GPU、T4/V100、A10/A100/H100、Ascend NPU、MPS 等。" #: ../../source/training/ms_swift.md:11 ead5a56786bf46b3a94582164537526a msgid "**Training Methods**: Supports full-parameter fine-tuning, LoRA, QLoRA, DoRA, and other techniques." msgstr "**训练方法**：支持全参数微调、LoRA、QLoRA、DoRA 等技术。" #: ../../source/training/ms_swift.md:12 31ad671d19de412e9ad5dd76b933e615 msgid "**Distributed Training**: Supports distributed training technologies such as DDP, device_map, DeepSpeed ZeRO-2/ZeRO-3, FSDP, and integrates parallelism techniques from Megatron, including Tensor Parallelism, Pipeline Parallelism, Sequence Parallelism, and Expert Parallelism." msgstr "**分布式训练**：支持分布式训练技术，如 DDP、device_map、DeepSpeed ZeRO-2/ZeRO-3、FSDP，并集成 Megatron 的并行技术，包括张量并行、流水线并行、序列并行和专家并行。" #: ../../source/training/ms_swift.md:13 0d6d437bf4fb4485ba4a3b1ad166558e msgid "**RLHF Training**: Supports human alignment methods like DPO, GRPO, DAPO, RM, PPO, KTO, etc., for both plain-text and multimodal large models." msgstr "**RLHF 训练**：支持纯文本和多模态大模型的人类对齐方法，如 DPO、GRPO、DAPO、RM、PPO、KTO 等。" #: ../../source/training/ms_swift.md:15 76bf873aaf8f495ca929ae422763046b msgid "This article will demonstrate runnable training demos and provide the format for custom datasets. It includes how to use ms-swift for SFT and GRPO on Qwen3-8B, as well as using Megatron-SWIFT (ms-swift's integration of Megatron-LM) for SFT on Qwen3-30B-A3B. Through expert parallelism technology, MoE model training can be accelerated by nearly 10 times." msgstr "本文将介绍可运行的训练示例，并提供自定义数据集的格式。内容包括如何使用 ms-swift 对 Qwen3-8B 进行 SFT 和 GRPO，以及使用 Megatron-SWIFT（ms-swift 集成的 Megatron-LM）对 Qwen3-30B-A3B 进行 SFT。通过专家并行技术，MoE 模型的训练速度可以提升近 10 倍。" #: ../../source/training/ms_swift.md:17 1d166ff43c03452780ae38d1c6cd91e9 msgid "Before starting fine-tuning, ensure your environment is properly set up." msgstr "在开始微调之前，请确保您的环境已正确配置。" #: ../../source/training/ms_swift.md:32 8b5caa854cf641c489310f747ed205ba msgid "Supervised Fine-Tuning (SFT)" msgstr "监督微调 (SFT)" #: ../../source/training/ms_swift.md:34 ../../source/training/ms_swift.md:206 #: 4ec49f5d7a1b4a9a9851cafafe9c7623 msgid "Data Preparation" msgstr "数据准备" #: ../../source/training/ms_swift.md:36 c71be51476e04ab0884cd908b042e263 msgid "The custom dataset format for SFT using ms-swift is as follows (the system field is optional). You can organize it into formats such as JSON, JSONL, or CSV. Specify `--dataset ` in the training script." msgstr "使用 ms-swift 进行 SFT 的自定义数据集格式如下（system 字段是可选的）。您可以将其组织为 JSON、JSONL 或 CSV 格式。在训练脚本中指定 `--dataset `。" #: ../../source/training/ms_swift.md:38 5de2bd424af14c52b7d73385caca6fba msgid "For complete dataset formatting guidelines, see: [Custom Dataset Documentation](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html)" msgstr "有关完整的数据集格式指南，请参考[自定义数据集文档](https://swift.readthedocs.io/zh-cn/latest/Customization/%E8%87%AA%E5%AE%9A%E4%B9%89%E6%95%B0%E6%8D%AE%E9%9B%86.html)。" #: ../../source/training/ms_swift.md:40 a6a72b65b49f4aa2948a3092e070bee9 msgid "General format" msgstr "通用格式" #: ../../source/training/ms_swift.md:48 3e18752803b24196a4e2e307f2418469 msgid "Format with think" msgstr "带有思考内容格式" #: ../../source/training/ms_swift.md:56 48aafb23741045c49e62aa67837ebb28 msgid "If you want to train using data without a chain of thought but retain the model's reasoning ability, there are two approaches to minimize disruption during fine-tuning:" msgstr "如果您想使用不含思维链的数据进行训练，同时保留模型的推理能力，可以通过以下两种方法尽量减少微调期间的干扰：" #: ../../source/training/ms_swift.md:58 ff2352eac1fd4e57987b02b2b2b294e4 msgid "**Option 1**: During training, specify `--loss_scale ignore_empty_think` to ignore the loss calculation for `\\n\\n\\n\\n`, preventing the loss of reasoning ability. Refer to the training script [here](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo1.sh). The custom dataset format is as follows:" msgstr "**选项 1**：在训练期间，指定 `--loss_scale ignore_empty_think`，以忽略对 `\\n\\n\\n\\n` 的损失计算，从而避免推理能力的丧失。训练脚本请参考[这里](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo1.sh)。自定义数据集格式如下：" #: ../../source/training/ms_swift.md:67 fb0799da5fad46129d4682ffb2003f8e msgid "**Option 2**: Add `/no_think` to the query in the dataset to avoid the loss of reasoning ability. Refer to the training script [here](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo2.sh). The custom dataset format is as follows:" msgstr "**选项 2**：在数据集的查询中添加 `/no_think`，以避免推理能力的丧失。训练脚本请参考[这里](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo2.sh)。自定义数据集格式如下：" #: ../../source/training/ms_swift.md:76 70f3b79067e34c989d6c3235179eff69 msgid "30-Minute Self-Cognition Fine-Tuning" msgstr "30分钟自我认知微调" #: ../../source/training/ms_swift.md:78 371e5bc107494dcd90a52569328a520d msgid "This section introduces a 30-minute self-cognition fine-tuning process for the Qwen3-8B model. The required GPU memory is 22GB, and it can be run on the A10 provided by [ModelScope's free compute resources](https://modelscope.cn/my/mynotebook)." msgstr "本节将介绍30分钟对 Qwen3-8B 进行自我认知微调。所需GPU显存为 22GB，可以在 ModelScope 提供的[免费算力](https://modelscope.cn/my/mynotebook) A10 中运行。" #: ../../source/training/ms_swift.md:80 18dff1bfb142418c85d00c3a2f520661 msgid "After training, the model will identify itself as \"swift-robot,\" trained by \"swift,\" instead of its original self-cognition as \"Qwen,\" trained by Alibaba Cloud." msgstr "训练后，模型将不再认为自己是由“阿里云”训练的“Qwen”，而是由“swift”训练的“swift-robot”。" #: ../../source/training/ms_swift.md:82 2bf7287254bb432b9c29adc4f79014de msgid "If you need to train in an offline environment, you can manually download the model and dataset and specify `--model ` and `--dataset `. The dataset can be found on [Modelscope Hub](https://modelscope.cn/datasets/swift/self-cognition)." msgstr "如果需要在离线环境下进行训练，可以手动下载模型和数据集，并指定 `--model ` 和 `--dataset `。数据集可以在 [Modelscope Hub](https://modelscope.cn/datasets/swift/self-cognition) 上找到。" #: ../../source/training/ms_swift.md:84 b4dcc1d366304d0abbfa994d76646547 msgid "For the meaning of each parameter in the training script, please refer to the [Command-line parameters documentation](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html)." msgstr "关于训练脚本中各参数的含义，请参考[命令行参数文档](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html)。" #: ../../source/training/ms_swift.md:114 185248e719634f389f7e85be89cbe7d0 msgid "After fine-tuning, you can use the following script to test the fine-tuning results. Note that the `--adapters` section needs to be modified to the directory path of the last saved checkpoint:" msgstr "微调完成后，可以使用以下脚本来测试微调结果。注意，`--adapters` 部分需要修改为最后保存检查点的目录路径：" #: ../../source/training/ms_swift.md:134 dd18bd5b0c9d4c8094e6b2c0b6c17ddc msgid "By default, ms-swift will use the ModelScope community to download models and datasets. If you want to use the HuggingFace community, you need to additionally specify `--use_hf true`." msgstr "默认情况下，ms-swift 会使用 ModelScope 社区下载模型和数据集。如果想使用 HuggingFace 社区，则需要额外指定 `--use_hf true`。" #: ../../source/training/ms_swift.md:136 0ad518fadd134498ad3a393397c6d2ae msgid "Merge LoRA weights:" msgstr "合并 LoRA 权重：" #: ../../source/training/ms_swift.md:144 4c72b93b992a40d89d27857096f9ac94 msgid "Push the model to ModelScope/HuggingFace:" msgstr "推送模型到 ModelScope/HuggingFace：" #: ../../source/training/ms_swift.md:157 a10da08e671a464fa20ec51056dd16ef msgid "If you want to use multiple GPUs for training, the following provides a demo for multi-GPU training:" msgstr "如果要使用多 GPU 进行训练，以下提供了多 GPU 训练的示例：" #: ../../source/training/ms_swift.md:191 a287c14468ed4625ad50eb75770e4481 msgid "Reinforcement Learning (RL)" msgstr "" #: ../../source/training/ms_swift.md:193 007251dabc634a469f47dbdc443f44e5 msgid "ms-swift supports RLHF methods such as DPO, GRPO, DAPO, PPO, KTO, and more. This section will focus on an example of using ms-swift to perform GRPO training for Qwen3-8B." msgstr "ms-swift 支持 DPO、GRPO、DAPO、PPO、KTO 等 RLHF 方法。本章将着重介绍使用 ms-swift 对 Qwen3-8B 进行 GRPO 训练。" #: ../../source/training/ms_swift.md:195 6747ebd5490a42fa9bbe39c71d42de64 msgid "For detailed RLHF support information, please refer to: [Supported Features](https://swift.readthedocs.io/en/latest/Instruction/Pre-training-and-Fine-tuning.html)." msgstr "有关详细的 RLHF 支持信息，请参考[支持的功能](https://swift.readthedocs.io/zh-cn/latest/Instruction/%E9%A2%84%E8%AE%AD%E7%BB%83%E4%B8%8E%E5%BE%AE%E8%B0%83.html)。" #: ../../source/training/ms_swift.md:197 c40c7e8842a94cf7b8abb7006010b343 msgid "Environment Setup" msgstr "环境设置" #: ../../source/training/ms_swift.md:199 b397dfabde0c4e099cb07fd0c3ab6630 msgid "In addition to installing the ms-swift related dependencies introduced above, the following dependencies also need to be installed:" msgstr "除了安装上述介绍的 ms-swift 相关依赖项外，还需要安装以下依赖项：" #: ../../source/training/ms_swift.md:208 06221d66d22741b49bb4ef2831dc0242 msgid "The dataset format for GRPO training using ms-swift is similar to that of SFT, except that the assistant part of the last round is not required. If using accuracy as a reward, a `solution` column is needed to calculate the accuracy." msgstr "使用 ms-swift 进行 GRPO 训练的数据集格式与 SFT 类似，但不需要最后一轮的 assistant 部分。如果使用 accuracy 作为奖励，则需要一个 `solution` 列来计算准确率。" #: ../../source/training/ms_swift.md:210 fb274880c62c4cad8243cbbed73f2adc msgid "Example Dataset Formats:" msgstr "示例数据集格式：" #: ../../source/training/ms_swift.md:218 5de2bd424af14c52b7d73385caca6fba msgid "For dataset preparation for other RLHF algorithms, see: [Custom Dataset Documentation](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html#rlhf)." msgstr "关于其他 RLHF 算法的数据集准备，请参考[自定义数据集文档](https://swift.readthedocs.io/zh-cn/latest/Customization/%E8%87%AA%E5%AE%9A%E4%B9%89%E6%95%B0%E6%8D%AE%E9%9B%86.html#rlhf)。" #: ../../source/training/ms_swift.md:220 31d062ff29e14c53ad92ca6d5ab81c23 msgid "Notes on Dataset Requirements:" msgstr "数据集要求的注意事项：" #: ../../source/training/ms_swift.md:222 f6e1dbba72e9450984df10e9da1f4f4c msgid "**Reward Function Calculation**: The dataset format depends on the reward function being used. Additional columns may be required to support specific reward calculations. For instance:" msgstr "**奖励函数计算**：数据集格式取决于所使用的奖励函数。可能需要额外的列来支持特定的奖励计算。例如：" #: ../../source/training/ms_swift.md:224 5b6d41c5b1734f1a87358fb5416c5888 msgid "When using the built-in accuracy or cosine similarity reward, the dataset must include a `solution` column to calculate the accuracy of the responses." msgstr "当使用内置的 accuracy 或 cosine 奖励时，数据集必须包含一个 `solution` 列以计算回复的准确性。" #: ../../source/training/ms_swift.md:225 e1937a75effa48ffb42a3d463926c1b0 msgid "Other columns in the dataset will be passed as `**kwargs` to the reward function for additional customization." msgstr "数据集中的其他列将作为 `**kwargs` 传递给奖励函数以实现进一步的自定义。" #: ../../source/training/ms_swift.md:227 63ee17a7a6b041a88daee17b96928659 msgid "**Customizing the Reward Function**: To adapt the reward function to your specific needs, you can refer to the following resource: [External Reward Plugin](https://github.com/modelscope/ms-swift/tree/main/examples/train/grpo/plugin). This plugin provides examples and templates for implementing custom reward functions." msgstr "**自定义奖励函数**：为了根据您的具体需求调整奖励函数，可以参考链接[外部奖励插件](https://github.com/modelscope/ms-swift/tree/main/examples/train/grpo/plugin)。该插件提供了实现自定义奖励函数的示例和模板。" #: ../../source/training/ms_swift.md:229 05da2d673c94453cb2bee6c99e46c825 msgid "During the training process, we use vLLM to accelerate the sampling process. By setting `num_infer_workers=8`, we deploy a vLLM engine for each device to speed up the sampling process." msgstr "在训练过程中，我们使用 vLLM 加速采样过程。通过设置 `num_infer_workers=8` ，我们为每个设备部署一个 vLLM 引擎以加快采样速度。" #: ../../source/training/ms_swift.md:270 5fe351e5c4f84018988f72da7c78005a msgid "Megatron-SWIFT" msgstr "Megatron-SWIFT" #: ../../source/training/ms_swift.md:272 b4dcc1d366304d0abbfa994d76646547 msgid "ms-swift incorporates Megatron parallelism techniques to accelerate the training of large models. The supported models can be found in the [Supported Models Documentation](https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html)." msgstr "ms-swift 引入了 Megatron 并行技术以加速大模型的训练。支持的模型可以在[支持的模型文档](https://swift.readthedocs.io/zh-cn/latest/Instruction/%E6%94%AF%E6%8C%81%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%92%8C%E6%95%B0%E6%8D%AE%E9%9B%86.html)中找到。" #: ../../source/training/ms_swift.md:274 cacc805e7bae4869b249768ba0807ae1 msgid "For environment preparation and the conversion between HF and MCore model weights, you can refer to the [Megatron-SWIFT Training Documentation](https://swift.readthedocs.io/en/latest/Instruction/Megatron-SWIFT-Training.html). These topics will not be elaborated here." msgstr "关于环境准备以及 HF 和 MCore 模型权重的转换，可以参考[Megatron-SWIFT训练文档](https://swift.readthedocs.io/zh-cn/latest/Instruction/Megatron-SWIFT%E8%AE%AD%E7%BB%83.html)。这里不展开介绍。" #: ../../source/training/ms_swift.md:276 e4b6d48e1f5440ddbc5602e6860f7971 msgid "We will use Alibaba Cloud DLC to start the training The training environment consists of 2 machines with 8 * 80GiB A800 GPUs. For more information on multi-node startup methods, refer to [here](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-node)." msgstr "我们将使用阿里云 DLC 启动训练。训练环境由2台配备8卡 80GiB A800 GPU 组成。关于多节点启动方法的更多信息，请参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-node)。" #: ../../source/training/ms_swift.md:316 62fd695e82d54817ae7e0a59ea0e0381 msgid "The custom dataset format is the same as `swift sft`, which can be found in the previous section. Simply specify `--dataset `." msgstr "自定义数据集格式与 `swift sft` 相同，详见之前章节。只需指定 `--dataset ` 即可。" #: ../../source/training/ms_swift.md:318 bd84ae8eeaf248ffb43acb334e7626e7 msgid "The following is a comparison of training speed and GPU memory usage between `megatron sft` and `swift sft` for full-parameter fine-tuning of the Qwen3-30B-A3B model:" msgstr "使用 `megatron sft` 和 `swift sft` 在对 Qwen3-30B-A3B 模型进行全参数微调的训练速度和 GPU 显存使用对比情况如下：" #: ../../source/training/ms_swift.md 6e220a7c28b14bcd85fd9af5804ab91e msgid "Megatron-LM" msgstr "Megatron-LM" #: ../../source/training/ms_swift.md d5ab9738de6241f5837c0d94ef9108d4 msgid "DeepSpeed-ZeRO2" msgstr "DeepSpeed-ZeRO2" #: ../../source/training/ms_swift.md 301a5de6123c442b8ffa6711f4910363 msgid "DeepSpeed-ZeRO3" msgstr "DeepSpeed-ZeRO3" #: ../../source/training/ms_swift.md 35818d0633704fd3a70482e77a32f892 msgid "Training Speed" msgstr "训练速度" #: ../../source/training/ms_swift.md 6b8176dc1e724ccfbcea6ab0fec3f2d8 msgid "9.6s/it" msgstr "9.6s/it" #: ../../source/training/ms_swift.md c344266bde2c4703bad8f2cd5b19a2ef msgid "-" msgstr "" #: ../../source/training/ms_swift.md 468b05aed5be4fcf9593ba2ad6545868 msgid "91.2s/it" msgstr "91.2s/it" #: ../../source/training/ms_swift.md ffcfa71deee14901a6e96f3424251197 msgid "GPU Memory Usage" msgstr "显存使用" #: ../../source/training/ms_swift.md 6c68f675c1c343debbebd40e725aa3af msgid "16 * 60GiB" msgstr "16 * 60GiB" #: ../../source/training/ms_swift.md 76e6265fbb52487fa8b3a8e5dd04e9bd msgid "OOM" msgstr "OOM" #: ../../source/training/ms_swift.md 676f4cc38dba4dd5b69e76186a6dc3a1 msgid "16 * 80GiB" msgstr "16 * 80GiB" #: ../../source/training/ms_swift.md:325 ebfe245e509c4d17aa0977330d1b7668 msgid "Conclusion" msgstr "总结" #: ../../source/training/ms_swift.md:327 0cd0194d11994c899616a43b9650c0b6 msgid "The above is the best practice for training Qwen3 series models using ms-swift. If you encounter any difficulties during use, please join the discussion in [this issue](https://github.com/modelscope/ms-swift/issues/4030)." msgstr "以上为使用 ms-swift 训练 Qwen3 系列模型的最佳实践。如果在使用过程中遇到任何困难，请在[此 issue](https://github.com/modelscope/ms-swift/issues/4030)中参与讨论。" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/training/unsloth.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-07-28 10:50+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" #: ../../source/training/unsloth.md:1 857a9b310ad247a981ec378e5c624e94 msgid "Unsloth" msgstr "" #: ../../source/training/unsloth.md:3 c75c1a88b89448cea8385c1269709dc0 #, python-format msgid "This guide will teach you how to easily train Qwen3 models with Unsloth. Unsloth simplifies local model training, handling everything from loading and quantization to training, evaluation, running, and deployment with inference engines (Ollama, llama.cpp, vLLM). **Train Qwen** models 2× faster using 70% less VRAM." msgstr "本指南将教您如何使用 Unsloth 轻松训练 Qwen3 模型。Unsloth 简化了本地模型训练，涵盖了从加载、量化到训练、评估、运行以及通过推理引擎（如 Ollama、llama.cpp、vLLM）进行部署的所有内容。**训练 Qwen** 模型的速度提升 2 倍，同时使用减少 70% 的显存（VRAM）。" #: ../../source/training/unsloth.md:5 6ce37469f8434d82b0a1a807e2cc67ff msgid "**GitHub repo:** [Unsloth](https://github.com/unslothai/unsloth)" msgstr "**GitHub 仓库：** [Unsloth](https://github.com/unslothai/unsloth)" #: ../../source/training/unsloth.md:7 702fad6892df4185a3a130827d8c10bd msgid "⭐ Key Features" msgstr "⭐ 主要特性" #: ../../source/training/unsloth.md:8 7f46ea01deec495ba94b88c72fa5d557 msgid "Supports full fine-tuning, pretraining, LoRA, QLoRA, 8-bit training & more" msgstr "支持全量微调、预训练、LoRA、QLoRA、8-bit 训练等" #: ../../source/training/unsloth.md:9 9d8a9bc65e6d4c6ba9bd4c25d1c07a4b msgid "Single and multi-GPU support (Linux, Windows, Colab, Kaggle; NVIDIA GPUs, soon AMD & Intel)" msgstr "支持单 GPU 和多 GPU（Linux、Windows、Colab、Kaggle；NVIDIA GPU，即将支持 AMD 和 Intel）" #: ../../source/training/unsloth.md:10 2c4fb728cbf443c9b69c58594202fff4 msgid "Compatible with all transformer models: TTS, multimodal, STT, BERT, RL" msgstr "兼容所有 Transformer 模型：TTS、多模态、STT、BERT、RL" #: ../../source/training/unsloth.md:11 0a81b3f4ee664e2f9d3683d986e3a8e3 msgid "RLHF support: GRPO, DPO, DAPO, RM, PPO, KTO, etc." msgstr "支持 RLHF：GRPO、DPO、DAPO、RM、PPO、KTO 等" #: ../../source/training/unsloth.md:12 30784cc59fff4cb39b180ea2fae50064 msgid "Hand-written Triton kernels and a manual backprop engine ensure no accuracy degradation (0% approximation)." msgstr "手写 Triton 内核和手动反向传播引擎确保无精度损失（0% 近似）。" #: ../../source/training/unsloth.md:14 ad482aa635484a67896a0cfa126037a8 msgid "Quickstart" msgstr "快速入门" #: ../../source/training/unsloth.md:15 884abdd5143c4f8b9e30a8831493e48f msgid "**Local Installation (Linux recommended):**" msgstr "**本地安装（推荐 Linux）：**" #: ../../source/training/unsloth.md:21 38492db20dcb4119b6712c633b05e090 msgid "You can view Unsloth’s full [installation instructions here.](https://docs.unsloth.ai/get-started/installing-+-updating)" msgstr "您可以在此处查看 Unsloth 的完整[安装说明\[英文\]](https://docs.unsloth.ai/get-started/installing-+-updating)。" #: ../../source/training/unsloth.md:23 1fe28bbc6e9d418dbe6d870714d2e8dd msgid "Fine-tuning Qwen3 with Unsloth" msgstr "使用 Unsloth 微调 Qwen3" #: ../../source/training/unsloth.md:24 863559c6be5d4b8b9535ccf005d7f4ac #, python-format msgid "Unsloth makes Qwen3 fine-tuning 2× faster, uses 70% less VRAM, with 8× longer contexts. Qwen3 (14B) fits in a free 16 GB Colab Tesla T4 GPU." msgstr "Unsloth 使 Qwen3 的微调速度提升 2 倍，显存（VRAM）使用减少 70%，并支持 8 倍更长的上下文。Qwen3（14B）可轻松运行在免费的 16 GB 显存 Colab Tesla T4 GPU 上。" #: ../../source/training/unsloth.md:26 34d66ac952624c9e8978e1969fd27633 #, python-format msgid "To retain Qwen3's reasoning capabilities, use a 75% reasoning to 25% non-reasoning dataset ratio (e.g., NVIDIA’s math‑reasoning dataset + Maxime’s FineTome)." msgstr "为了保留 Qwen3 的推理能力，建议使用 75% 推理类数据与 25% 非推理类数据的组合（例如，NVIDIA 的数学推理数据集 + Maxime 的 FineTome 数据集）。" #: ../../source/training/unsloth.md:28 c316c959303040ca952d24729f8fc0a4 msgid "For more details, see Unsloth’s full [Qwen3 fine-tuning guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune#fine-tuning-qwen3-with-unsloth)." msgstr "更多详细信息，请参阅 Unsloth 完整的[Qwen3 微调指南\[英文\]](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune#fine-tuning-qwen3-with-unsloth)。" #: ../../source/training/unsloth.md:30 581ccade253e4145be58856901aeee91 msgid "Colab Notebooks" msgstr "" #: ../../source/training/unsloth.md:31 f131a50d6c604bd3aa9a896500bd2cc2 msgid "[Qwen3 (14B) Reasoning + Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb)" msgstr "" #: ../../source/training/unsloth.md:32 fc6397fddd354fc1a44a4329dd4020d4 msgid "[Qwen3 (4B) Advanced GRPO LoRA](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)" msgstr "" #: ../../source/training/unsloth.md:33 061f4bf082fd435db514c9355e599453 msgid "[Qwen3 (14B) Alpaca (Base model)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Alpaca.ipynb)" msgstr "" #: ../../source/training/unsloth.md:35 c5bb61e8cb7e4e078956e837d033f339 msgid "**Update Unsloth locally:**" msgstr "**在本地更新 Unsloth：**" #: ../../source/training/unsloth.md:41 4eb073fad2e344dba1f0fb622c18ccd2 msgid "Fine-tuning Qwen3 MoE Models" msgstr "微调 Qwen3 MoE 模型" #: ../../source/training/unsloth.md:42 22755ac77e144d2aa7e959fa0a86aeaa msgid "Supported MoE models include 30B‑A3B and 235B‑A22B. Unsloth fine-tunes the 30B‑A3B model with just 17.5 GB VRAM. Router-layer fine-tuning is disabled by default." msgstr "支持的 MoE 模型包括 30B‑A3B 和 235B‑A22B。Unsloth 仅需 17.5 GB 显存即可微调 30B‑A3B 模型。默认情况下禁用路由层（router-layer）的微调。" #: ../../source/training/unsloth.md:44 19ffbd02f9ab44e78a2eae078de3bc4a msgid "Use `FastModel` for MoE fine-tuning:" msgstr "对 MoE 模型进行微调时，请使用 `FastModel`：" #: ../../source/training/unsloth.md:58 55c17609e3844f57aa6a7f61b8e0629c msgid "Notebook Guide" msgstr "Notebook 指南" #: ../../source/training/unsloth.md:59 2897bf2a487d4f21ac0bcd8d1866f53e msgid "For an end-to-end walkthrough, see Unsloth’s [full end-to-end fine-tuning guide](https://docs.unsloth.ai/basics/reinforcement-learning-rl-guide)." msgstr "如需端到端的完整操作流程，请参阅 Unsloth 的[完整端到端微调指南\[英文\]](https://docs.unsloth.ai/basics/reinforcement-learning-rl-guide)。" #: ../../source/training/unsloth.md:61 919bfc2dc5f640a1a2c18af536e38529 msgid "Open the notebook → click **Runtime ▸ Run all**." msgstr "打开 Notebook → 点击 **Runtime ▸ Run all**。" #: ../../source/training/unsloth.md:62 76fed20c6fa048728023f2664089d991 msgid "Adjust settings (e.g., model name, context length) directly in the notebook:" msgstr "直接在 Notebook 中调整设置（例如模型名称、上下文长度）：" #: ../../source/training/unsloth.md:63 c60e9857fd4e44b794ccfd2cc5d15cbe msgid "`max_seq_length`: Recommended 2048 (Qwen3 supports up to 40960)." msgstr "`max_seq_length`：推荐值为 2048（Qwen3 最高支持 40960）。" #: ../../source/training/unsloth.md:64 e73b7cab67cc4eed802c6af5219f6cc6 msgid "`load_in_4bit=True`: reduces memory usage by 4×." msgstr "`load_in_4bit=True`：将内存使用量减少为原来的四分之一。" #: ../../source/training/unsloth.md:65 06e12675ca494226a2731a616fbe8c01 msgid "Enable full fine-tuning (`full_finetuning=True`) or 8-bit training (`load_in_8bit=True`)." msgstr "启用全量微调（`full_finetuning=True`）或 8-bit 训练（`load_in_8bit=True`）。" #: ../../source/training/unsloth.md:67 fcc086506d3f45b1bc0aee524dbc1cfe msgid "If you want to use models directly from [ModelScope](https://modelscope.cn/organization/unsloth), use:" msgstr "如果您想直接使用来自 [ModelScope](https://modelscope.cn/organization/unsloth) 的模型，请使用：" #: ../../source/training/unsloth.md:85 9d6116efc01d44ff948af4e660045bb5 msgid "RL & GRPO with Qwen3" msgstr "使用 Qwen3 进行强化学习（RL）与 GRPO" #: ../../source/training/unsloth.md:86 0afdabd0d4bf4ae7936b67de95c3ed3d msgid "You can also train Qwen models with reinforcement learning (RL) using Unsloth. Explore Unsloth’s advanced GRPO notebook, featuring proximity-based reward scoring and Hugging Face's Open‑R1 math dataset: [Qwen3 (4B) Advanced GRPO LoRA notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)." msgstr "您还可以使用 Unsloth 通过强化学习（RL）来训练 Qwen 模型。探索 Unsloth 的进阶 GRPO Notebook，其中包含基于接近度的奖励评分和 Hugging Face 的 Open‑R1 数学数据集：[Qwen3 (4B) 进阶 GRPO LoRA Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)。" #: ../../source/training/unsloth.md:87 4fc6252997454f769562fc37150d5bd1 msgid "Proximity-based rewards for closer answers" msgstr "基于 Proximity 的奖励，对更接近正确答案的回答给予更高奖励" #: ../../source/training/unsloth.md:88 e38b7b9cb5ed447caeed00e37aa1f7e1 msgid "Custom GRPO formatting and templates" msgstr "自定义 GRPO 格式和模板" #: ../../source/training/unsloth.md:89 feecf90a36cf40fab79713ed8c84dfa7 msgid "Enhanced evaluation accuracy with regex matching" msgstr "通过正则表达式匹配提高评估准确性" #: ../../source/training/unsloth.md:91 c176e49b6e4a42a0b89ff7b180faf0bc msgid "Resources & Links" msgstr "资源与链接" #: ../../source/training/unsloth.md:92 d29d33008569432bbf59ffae56e62a48 msgid "That’s how you can easily train Qwen models with Unsloth. If you need any help, join the discussion on Unsloth’s [Discord](https://discord.com/invite/unsloth) or [GitHub](https://github.com/unslothai/unsloth) pages." msgstr "这就是使用 Unsloth 轻松训练 Qwen 模型的方法。如果您需要任何帮助，请加入 Unsloth 的 [Discord](https://discord.com/invite/unsloth) 或 [GitHub](https://github.com/unslothai/unsloth) 页面参与讨论。" #: ../../source/training/unsloth.md:94 ff3536ed471841f7addfbcdb828dc11d msgid "**Links:**" msgstr "**链接：**" #: ../../source/training/unsloth.md:95 873de88c8178499f8b0262fe30dbf44c msgid "[Unsloth Documentation](https://docs.unsloth.ai/)" msgstr "[Unsloth 官方文档](https://docs.unsloth.ai/)" #: ../../source/training/unsloth.md:96 69437a5a07834f5ab79c96961dc95842 msgid "[Unsloth Discord](https://discord.com/invite/unsloth)" msgstr "" #: ../../source/training/unsloth.md:97 3e8bbe8da0124e5d8ff71868e19141cd msgid "[Unsloth Website](https://unsloth.ai/)" msgstr "[Unsloth 官方站点](https://unsloth.ai/)" #: ../../source/training/unsloth.md:98 35c1f4a1bcf647f096bdee249fcabdaa msgid "[Unsloth Reddit](https://www.reddit.com/r/unsloth/)" msgstr "" ================================================ FILE: docs/locales/zh_CN/LC_MESSAGES/training/verl.po ================================================ # SOME DESCRIPTIVE TITLE. # Copyright (C) 2024, Qwen Team # This file is distributed under the same license as the Qwen package. # FIRST AUTHOR , 2025. # #, fuzzy msgid "" msgstr "" "Project-Id-Version: Qwen \n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-06-13 17:22+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" "Language-Team: zh_CN \n" "Plural-Forms: nplurals=1; plural=0;\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.16.0\n" #: ../../source/training/verl.md:1 937d32044f9d4ce685b5c0d297d2c48d msgid "verl" msgstr "" #: ../../source/training/verl.md:3 11c0fdd831b444f28be5cce4fd8f8b38 msgid "verl is a flexible, efficient and production-ready RL training library for large language models (LLMs)." msgstr "verl 是一个灵活、高效且被广泛使用的强化学习（RL）训练库，专为大型语言模型（LLM）设计。" #: ../../source/training/verl.md:5 64a80c70bd8a49cbb40d4acd035c6212 msgid "verl is the open-source version of [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2) paper." msgstr "verl 是论文 [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2) 的开源实现" #: ../../source/training/verl.md:7 81aa9801baa54d72bef51c26925278d2 msgid "GitHub repository: [verl](https://github.com/volcengine/verl)" msgstr "仓库地址：[verl](https://github.com/volcengine/verl)" #: ../../source/training/verl.md:9 2080b3c322ac41299917a122b4826994 msgid "verl is flexible and easy to use with:" msgstr "verl 的灵活性和易用性体现在以下几个方面：" #: ../../source/training/verl.md:11 564fbddc57ae43e090e24fe6426d670a msgid "**Easy extension of diverse RL algorithms**: The hybrid-controller programming model enables flexible representation and efficient execution of complex Post-Training dataflows. Build RL dataflows such as GRPO, PPO in a few lines of code." msgstr "**支持多样化的强化学习算法扩展**：verl 采用混合编程模型，结合了单一控制器和多控制器的优势，能够灵活表示和高效执行复杂的后训练数据流。用户只需几行代码即可构建强化学习数据流，例如 PPO、GRPO 等。" #: ../../source/training/verl.md:12 690f3983ad004db081baba2bc5d80d32 msgid "**Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as FSDP, Megatron-LM, vLLM, SGLang, etc" msgstr "**与现有大语言模型基础设施无缝集成**：verl 通过模块化 API 解耦计算和数据依赖，支持与 PyTorch FSDP、Megatron-LM、vLLM 等现有大语言模型框架无缝集成，且用户可以轻松扩展到其他训练和推理框架。" #: ../../source/training/verl.md:13 323cbf0f7b034e3aadaa3b86e4680058 msgid "**Flexible device mapping**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes." msgstr "**灵活的设备映射和并行性**：verl 支持将模型放置到不同 GPU 集合上，以实现高效的资源利用和跨不同集群规模的可扩展性。" #: ../../source/training/verl.md:14 e76a04d1c8aa491e8d6ae1d24d3b71d8 msgid "**Ready integration with popular HuggingFace models**: verl supports popular LLM models, including Qwen, Llama, and more." msgstr "**与热门 HuggingFace 模型的及时集成**：verl 支持多种流行的 LLM 模型，包括 Qwen、Llama 等。" #: ../../source/training/verl.md:16 acc32ec6e23248d1b3714424bcb868f5 msgid "verl is fast with:" msgstr "verl 的高效性体现在以下几个方面：" #: ../../source/training/verl.md:18 bdeb7205dbd04ab689ab15907fe8ced1 msgid "**State-of-the-art throughput**: SOTA LLM training and inference engine integrations and SOTA RL throughput." msgstr "**最高效的吞吐量**：verl 集成了最先进的 LLM 训练和推理引擎，并实现了最先进的强化学习（RL）吞吐量。" #: ../../source/training/verl.md:19 314eba723e854d1ab020ec72a0251706 msgid "**Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases." msgstr "**使用 3D-HybridEngine 实现高效的 Actor 模型分片**：消除内存冗余，并显著减少训练和生成阶段转换期间的通信开销。" #: ../../source/training/verl.md:21 d76b21e9b42c414283609c3846c1e75c msgid "Next, we will introduce how to use verl for training Qwen3 models." msgstr "接下来，我们将介绍如何使用 verl 训练 Qwen3 模型。" #: ../../source/training/verl.md:23 6924574168a841e39a4ef0dfb11e6439 msgid "Reinforcement Learning (RL)" msgstr "强化学习（RL）" #: ../../source/training/verl.md:25 5dae4a3e148042c3b9a2d42e967f7798 msgid "Now, verl supports various combinations of training frameworks and inference frameworks, including FSDP, Megatron-LM, vLLM, SGLang, etc. verl also supports training with multiple algorithms such as PPO, GRPO, DAPO, etc." msgstr "现在，verl 支持多种训练框架和推理框架的组合，包括 FSDP、Megatron-LM、vLLM、SGLang 等。此外，verl 还支持使用多种算法进行训练，例如 PPO、GRPO、DAPO 等。" #: ../../source/training/verl.md:27 5fe2ddcafab04734a5ce56358f213de2 msgid "Step1: Environment and Training Preparation" msgstr "第一步：环境和训练准备" #: ../../source/training/verl.md:29 f5bd55e1b12e4cf0a5c77959e30a2add msgid "You can follow verl's [installation guide](https://verl.readthedocs.io/en/latest/start/install.html) to complete the environment configuration." msgstr "你可以按照 verl 的 [安装指南](https://verl.readthedocs.io/en/latest/start/install.html) 完成环境配置。" #: ../../source/training/verl.md:31 c5a07c1e71a44be699c2628b0af66fa7 msgid "Data preparation can be done by running the following command:" msgstr "数据准备可以通过运行以下命令完成：" #: ../../source/training/verl.md:39 e626176f3660473ba8a834ba471135eb msgid "Model download can be done using the following command:" msgstr "模型下载可以使用以下命令完成：" #: ../../source/training/verl.md:45 fda85ea40df948188f0b765379c5f5b0 msgid "Step2: Start Training" msgstr "第二步：开始训练" #: ../../source/training/verl.md:47 24994606b01b452abb1f3c487862bf57 msgid "In verl, training frameworks and inference frameworks can be combined freely, as long as the training framework and inference framework themselves support model training and inference tasks, so that verl can support RL-related training." msgstr "在 verl 中，训练框架和推理框架可以自由组合，只要训练框架和推理框架本身支持模型训练和推理任务，verl 就能够支持与强化学习（RL）相关的训练。" #: ../../source/training/verl.md:49 3f8ac764b49c42a2bd5a673461866371 msgid "Below is an example using FSDP and vLLM to demonstrate how to train Qwen3 models in verl. We chose Qwen3-1.7B as the example, as it only requires a single 80GB GPU and a machine with more than 64GB of memory to start training." msgstr "以下是一个使用 FSDP 和 vLLM 的示例，展示如何在 verl 中训练 Qwen3 模型。我们选择了Qwen3-1.7B作为例子，因为他仅需使用一张80GB显存的显卡，以及大于64G内存的机器即可开始训练。" #: ../../source/training/verl.md:92 8b8ed7d9463c4a8086d5fe1a452b34e0 msgid "Finally" msgstr "结束语" #: ../../source/training/verl.md:94 294699cc5278422f85beebb282ad0c2a msgid "If you encounter any difficulties during use, please join the discussion at [GitHub](https://github.com/volcengine/verl/discussions)." msgstr "如果在使用过程中遇到任何困难，请在 [GitHub](https://github.com/volcengine/verl/discussions) 参与讨论。" ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.https://www.sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docs/requirements-docs.txt ================================================ furo myst-parser==4.0.0 sphinx<8,>4.5.0 sphinx-copybutton sphinx-design>=0.6.0 ================================================ FILE: docs/source/_static/css/custom.css ================================================ html { font-size: 16px; } h1 { font-size: 1.75rem; line-height: 2.5rem; } h2 { font-size: 1.5rem; line-height: 2rem; } h3 { font-size: 1.25rem; line-height: 1.75rem; } h4 { font-size: 1.125rem; line-height: 1.5rem; } h5 { font-size: 1rem; } h6 { font-size: 0.75rem; } h1, h2, h3, h4, h5, h6 { margin-top: 1.875rem; margin-bottom: 1rem; } p strong { font-weight: 500; } p:target { background-color: var(--color-highlight-on-target); } details.sd-dropdown summary.sd-summary-title { flex-direction: row-reverse; font-weight: 500; padding-left: 0; } details.sd-dropdown summary.sd-summary-title code.literal { font-weight: bolder; filter: brightness(95%); } details.sd-dropdown summary.sd-summary-title span.sd-summary-state-marker { padding-left: 0.5em; padding-right: 0.5em } details.sd-dropdown div.sd-summary-content { padding-left: 2.5em; } pre.terminal { font-size: 12px !important; line-height: 16px; background-color: black; color: white; padding: .5em; text-wrap: wrap; word-break: break-all; } pre.terminal span.system { color: greenyellow } pre.terminal span.user { color: yellowgreen } ================================================ FILE: docs/source/_static/design-tabs.js ================================================ // @ts-check // Extra JS capability for selected tabs to be synced // The selection is stored in local storage so that it persists across page loads. /** * @type {Record} */ let sd_id_to_elements = {}; const storageKeyPrefix = "sphinx-design-tab-id-"; /** * Create a key for a tab element. * @param {HTMLElement} el - The tab element. * @returns {[string, string, string] | null} - The key. * */ function create_key(el) { let syncId = el.getAttribute("data-sync-id"); let syncGroup = el.getAttribute("data-sync-group"); if (!syncId || !syncGroup) return null; return [syncGroup, syncId, syncGroup + "--" + syncId]; } /** * Initialize the tab selection. * */ function ready() { // Find all tabs with sync data /** @type {string[]} */ let groups = []; document.querySelectorAll(".sd-tab-label").forEach((label) => { if (label instanceof HTMLElement) { let data = create_key(label); if (data) { let [group, id, key] = data; // add click event listener // @ts-ignore label.onclick = onSDLabelClick; // store map of key to elements if (!sd_id_to_elements[key]) { sd_id_to_elements[key] = []; } sd_id_to_elements[key].push(label); if (groups.indexOf(group) === -1) { groups.push(group); // Check if a specific tab has been selected via URL parameter const tabParam = new URLSearchParams(window.location.search).get( group ); if (tabParam) { console.log( "sphinx-design: Selecting tab id for group '" + group + "' from URL parameter: " + tabParam ); window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); } } // Check is a specific tab has been selected previously let previousId = window.sessionStorage.getItem( storageKeyPrefix + group ); if (previousId === id) { // console.log( // "sphinx-design: Selecting tab from session storage: " + id // ); // @ts-ignore label.previousElementSibling.checked = true; } } } }); } /** * Activate other tabs with the same sync id. * * @this {HTMLElement} - The element that was clicked. */ function onSDLabelClick() { let data = create_key(this); if (!data) return; const top = this.parentElement?.offsetTop || 0; console.log(top); let [group, id, key] = data; for (const label of sd_id_to_elements[key]) { if (label === this) continue; // @ts-ignore label.previousElementSibling.checked = true; } const diff = (this.parentElement?.offsetTop || 0) - top; if (diff !== 0) { window.scrollBy({ left: 0, top: diff, behavior: "instant" }); } window.sessionStorage.setItem(storageKeyPrefix + group, id); } document.addEventListener("DOMContentLoaded", ready, false); ================================================ FILE: docs/source/assets/qwen3_nonthinking.jinja ================================================ {%- if tools %} {{- '<|im_start|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} {%- for tool in tools %} {{- "\n" }} {{- tool | tojson }} {%- endfor %} {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages|length - 1) - loop.index0 %} {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if message.content is string %} {%- set content = message.content %} {%- else %} {%- set content = '' %} {%- endif %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is string %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '' in content %} {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} {%- set content = content.split('')[-1].lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} {%- else %} {{- '<|im_start|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<|im_start|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments | tojson }} {%- endif %} {{- '}\n' }} {%- endfor %} {%- endif %} {{- '<|im_end|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }} {%- endif %} {{- '\n\n' }} {{- content }} {{- '\n' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<|im_end|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<|im_start|>assistant\n\n\n\n\n' }} {%- endif %} ================================================ FILE: docs/source/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. import sys from sphinx.ext import autodoc import logging logger = logging.getLogger(__name__) # -- Project information ----------------------------------------------------- project = "Qwen" copyright = "2024, Qwen Team" author = "Qwen Team" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.intersphinx", # "sphinx_copybutton", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "myst_parser", "sphinx_design", ] myst_enable_extensions = ["colon_fence", "attrs_block", "attrs_inline", "fieldlist"] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # Exclude the prompt "$" when copying code copybutton_prompt_text = r"\$ " copybutton_prompt_is_regexp = True # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_title = project html_theme = "furo" # html_logo = 'assets/logo/qwen.png' # html_theme_options = { # 'path_to_docs': 'docs/source', # 'repository_url': 'https://github.com/QwenLM/Qwen2', # # 'use_repository_button': True, # } html_sidebars = { "**": [ "sidebar/scroll-start.html", "sidebar/brand.html", "sidebar/navigation.html", "sidebar/ethical-ads.html", "sidebar/scroll-end.html", ] } # multi-language docs language = "en" locale_dirs = ["../locales/"] # path is example but recommended. gettext_compact = False # optional. gettext_uuid = True # optional. # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_css_files = [ "css/custom.css", ] # FIXME: figure out why this file is not copied html_js_files = [ "design-tabs.js", ] # Mock out external dependencies here. autodoc_mock_imports = ["torch", "transformers"] for mock_target in autodoc_mock_imports: if mock_target in sys.modules: logger.info( f"Potentially problematic mock target ({mock_target}) found; " "autodoc_mock_imports cannot mock modules that have already " "been loaded into sys.modules when the sphinx build starts." ) class MockedClassDocumenter(autodoc.ClassDocumenter): """Remove note about base class when a class is derived from object.""" def add_line(self, line: str, source: str, *lineno: int) -> None: if line == " Bases: :py:class:`object`": return super().add_line(line, source, *lineno) autodoc.ClassDocumenter = MockedClassDocumenter navigation_with_keys = False ================================================ FILE: docs/source/deployment/dstack.rst ================================================ dstack ======== `dstack `__ is an open-source alternative to Kubernetes and Slurm, designed to simplify GPU allocation and AI workload orchestration for ML teams across top clouds, on-prem clusters, and accelerators. Prerequisites ---------------- Before you start, install dstack by following the `installation instructions `__. Once dstack server is up, you can initialize your workspace as shown below: .. code:: bash mkdir dstack-qwen-deploy && cd dstack-qwen-deploy dstack init Deploy Qwen3-30B-A3B ----------------------------------------------- Deploy ``Qwen3-30B-A3B`` on instances available with cloud providers configured in your ``~/.dstack/server/config.yml`` file. You can use ``SgLang``, ``TGI`` or ``vLLM`` to serve the model. Here we use ``SgLang`` as an example. Create a `service `__ configuration file named ``serve-30b.dstack.yml`` with the following content: .. code:: yaml type: service name: qwen3-30b-a3b image: lmsysorg/sglang:latest env: - MODEL_ID=Qwen/Qwen3-30B-A3B commands: - python3 -m sglang.launch_server --model-path $MODEL_ID --port 8000 --trust-remote-code port: 8000 model: Qwen/Qwen3-30B-A3B resources: gpu: 80GB:1 .. note:: For other inference backends such as vLLM or TGI, visit the `dstack Inference Examples `__ documentation. Go ahead and apply the service configuration: .. code:: bash dstack apply -f serve-30b.dstack.yml Access the Service -------------------- After the service is successfully deployed, you can access the service's endpoint in the following ways: .. tab-set:: .. tab-item:: CURL Access through service endpoint at ``/proxy/services///`` .. code:: bash curl http://localhost:3000/proxy/services/main/qwen3-30b-a3b/v1/chat/completions \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer ' \ -d '{ "model": "Qwen/Qwen3-30B-A3B", "messages": [ { "role": "user", "content": "Compose a poem that explains the concept of recursion in programming." } ] }' .. note:: When starting the dstack server, an admin token is automatically generated: .. code:: bash The admin token is "bbae0f28-d3dd-4820-bf61-8f4bb40815da" The server is running at http://127.0.0.1:3000/ .. tab-item:: Chat UI Access through dstack's Chat UI at ``/projects//models//`` .. image:: https://dstack.ai/static-assets/static-assets/images//dstack-qwen-ui.png .. dropdown:: Gateway :icon: info :animate: fade-in Running services for development purposes doesn't require setting up a gateway. However, you'll need a gateway in the following cases: * To use auto-scaling or rate limits * To enable HTTPS for the endpoint and map it to your domain * If your service requires WebSockets * If your service cannot work with a path prefix For detailed information about gateway configuration and usage, refer to the `dstack documentation on gateways `__. Replicas and Auto Scaling ---------------------------------------- You can auto scale the service by specifying additional configurations in the ``serve-30b.dstack.yml``. - Set ``replicas: min..max`` to define the minimum and maximum number of replicas - Configure ``scaling`` rules to determine when to scale up or down Below is a complete configuration example with auto-scaling enabled: .. code:: yaml type: service name: qwen3-30b-a3b image: lmsysorg/sglang:latest env: - MODEL_ID=Qwen/Qwen3-30B-A3B commands: - python3 -m sglang.launch_server --model-path $MODEL_ID --port 8000 --trust-remote-code port: 8000 model: Qwen/Qwen3-30B-A3B resources: gpu: 80GB:1 # Minimum and maximum number of replicas replicas: 1..4 scaling: # Requests per seconds metric: rps # Target metric value target: 10 .. note:: The scaling property requires a gateway to be set up. See also ------------ - **Fleets**: Create cloud and on-prem clusters using `Fleets `__. - **Dev Environments**: Experiment and test before deploying to production using `Dev Environments `__. - **Tasks**: Schedule single node or distributed training using `Tasks `__. - **Services**: Deploy models as secure, auto-scaling OpenAI-compatible endpoints using `Services `__. - **Metrics**: Monitor performance with automatically tracked metrics via CLI or UI using `Metrics `__. ================================================ FILE: docs/source/deployment/openllm.rst ================================================ OpenLLM ======= .. attention:: To be updated for Qwen3. OpenLLM allows developers to run Qwen2.5 models of different sizes as OpenAI-compatible APIs with a single command. It features a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Qwen2.5. Visit `the OpenLLM repository `_ to learn more. Installation ------------ Install OpenLLM using ``pip``. .. code:: bash pip install openllm Verify the installation and display the help information: .. code:: bash openllm --help Quickstart ---------- Before you run any Qwen2.5 model, ensure your model repository is up to date by syncing it with OpenLLM's latest official repository. .. code:: bash openllm repo update List the supported Qwen2.5 models: .. code:: bash openllm model list --tag qwen2.5 The results also display the required GPU resources and supported platforms: .. code:: bash model version repo required GPU RAM platforms ------- --------------------- ------- ------------------ ----------- qwen2.5 qwen2.5:0.5b default 12G linux qwen2.5:1.5b default 12G linux qwen2.5:3b default 12G linux qwen2.5:7b default 24G linux qwen2.5:14b default 80G linux qwen2.5:14b-ggml-q4 default macos qwen2.5:14b-ggml-q8 default macos qwen2.5:32b default 80G linux qwen2.5:32b-ggml-fp16 default macos qwen2.5:72b default 80Gx2 linux qwen2.5:72b-ggml-q4 default macos To start a server with one of the models, use ``openllm serve`` like this: .. code:: bash openllm serve qwen2.5:7b By default, the server starts at ``http://localhost:3000/``. Interact with the model server ------------------------------ With the model server up and running, you can call its APIs in the following ways: .. tab-set:: .. tab-item:: CURL Send an HTTP request to its ``/generate`` endpoint via CURL: .. code-block:: bash curl -X 'POST' \ 'http://localhost:3000/api/generate' \ -H 'accept: text/event-stream' \ -H 'Content-Type: application/json' \ -d '{ "prompt": "Tell me something about large language models.", "model": "Qwen/Qwen2.5-7B-Instruct", "max_tokens": 2048, "stop": null }' .. tab-item:: Python client Call the OpenAI-compatible endpoints with frameworks and tools that support the OpenAI API protocol. Here is an example: .. code-block:: python from openai import OpenAI client = OpenAI(base_url='http://localhost:3000/v1', api_key='na') # Use the following func to get the available models # model_list = client.models.list() # print(model_list) chat_completion = client.chat.completions.create( model="Qwen/Qwen2.5-7B-Instruct", messages=[ { "role": "user", "content": "Tell me something about large language models." } ], stream=True, ) for chunk in chat_completion: print(chunk.choices[0].delta.content or "", end="") .. tab-item:: Chat UI OpenLLM provides a chat UI at the ``/chat`` endpoint for the LLM server at http://localhost:3000/chat. .. image:: ../../source/assets/qwen-openllm-ui-demo.png Model repository ---------------- A model repository in OpenLLM represents a catalog of available LLMs. You can add your own repository to OpenLLM with custom Qwen2.5 variants for your specific needs. See our `documentation to learn details `_. ================================================ FILE: docs/source/deployment/sglang.md ================================================ # SGLang [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. To learn more about SGLang, please refer to the [documentation](https://docs.sglang.ai/). ## Environment Setup By default, you can install `sglang` with pip in a clean environment: ```shell pip install "sglang[all]>=0.4.6.post1" ``` If you have encountered issues in installation, please feel free to check the official document for installation ([link](https://docs.sglang.ai/start/install.html)). ## API Service It is easy to build an OpenAI-compatible API service with SGLang, which can be deployed as a server that implements OpenAI API protocol. By default, it starts the server at `http://localhost:30000`. You can specify the address with `--host` and `--port` arguments. Run the command as shown below: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B ``` By default, if the `--model-path` does not point to a valid local directory, it will download the model files from the Hugging Face Hub. To download model from ModelScope, set the following before running the above command: ```shell export SGLANG_USE_MODELSCOPE=true ``` For distributed inference with tensor parallelism, it is as simple as ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --tensor-parallel-size 4 ``` The above command will use tensor parallelism on 4 GPUs. You should change the number of GPUs according to your demand. ### Basic Usage Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen: ::::{tab-set} :::{tab-item} curl ```shell curl http://localhost:30000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 32768 }' ``` ::: :::{tab-item} Python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use SGLang's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:30000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=32768, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response) ``` :::: :::{tip} While the default sampling parameters would work most of the time for thinking mode, it is recommended to adjust the sampling parameters according to your application, and always pass the sampling parameters to the API. ::: ### Thinking & Non-Thinking Modes Qwen3 models will think before respond. This behavior could be controlled by either the hard switch, which could disable thinking completely, or the soft switch, where the model follows the instruction of the user on whether it should think. The hard switch is available in SGLang through the following configuration to the API call. To disable thinking, use ::::{tab-set} :::{tab-item} curl ```shell curl http://localhost:30000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.7, "top_p": 0.8, "top_k": 20, "max_tokens": 8192, "presence_penalty": 1.5, "chat_template_kwargs": {"enable_thinking": false} }' ``` ::: :::{tab-item} Python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use SGLang's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:30000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=8192, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"enable_thinking": True}, }, ) print("Chat response:", chat_response) ``` :::: :::{note} Please note that passing `enable_thinking` is not OpenAI API compatible. The exact method may differ among frameworks. ::: :::{tip} To completely disable thinking, you could use [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) when starting the model: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --chat-template ./qwen3_nonthinking.jinja ``` The chat template prevents the model from generating thinking content, even if the user instructs the model to do so with `/think`. ::: :::{tip} It is recommended to set sampling parameters differently for thinking and non-thinking modes. ::: ### Parsing Thinking Content SGLang supports parsing the thinking content from the model generation into structured messages: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` The response message will have a field named `reasoning_content` in addition to `content`, containing the thinking content generated by the model. :::{note} Please note that this feature is not OpenAI API compatible. ::: :::{important} `enable_thinking=False` may not be compatible with this feature. If you need to pass `enable_thinking=False` to the API, please consider disabling parsing thinking content. ::: ### Parsing Tool Calls SGLang supports parsing the tool calling content from the model generation into structured messages: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --tool-call-parser qwen25 ``` For more information, please refer to [our guide on Function Calling](../framework/function_call.md). ### Structured/JSON Output SGLang supports structured/JSON output. Please refer to [SGLang's documentation](https://docs.sglang.ai/backend/structured_outputs.html#OpenAI-Compatible-API). Besides, it is also recommended to instruct the model to generate the specific format in the system message or in your prompt. ### Serving Quantized models Qwen3 comes with two types of pre-quantized models, FP8 and AWQ. The command serving those models are the same as the original models except for the name change: ```shell # For FP8 quantized model python -m sglang.launch_server --model-path Qwen/Qwen3-8B-FP8 # For AWQ quantized model python -m sglang.launch_server --model-path Qwen/Qwen3-8B-AWQ ``` ### Context Length The context length for Qwen3 models in pretraining is up to 32,768 tokens. To handle context length substantially exceeding 32,768 tokens, RoPE scaling techniques should be applied. We have validated the performance of [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. SGLang supports YaRN, which can be configured as ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' --context-length 131072 ``` :::{note} SGLang implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. ::: :::{note} The default `max_position_embeddings` in `config.json` is set to 40,960, which is used by SGLang. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing and leave adequate room for model thinking. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. ::: ================================================ FILE: docs/source/deployment/skypilot.rst ================================================ SkyPilot ======== .. attention:: To be updated for Qwen3. What is SkyPilot ---------------- SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, the highest GPU availability, and managed execution. Its features include: - Get the best GPU availability by utilizing multiple resources pools across multiple regions and clouds. - Pay absolute minimum — SkyPilot picks the cheapest resources across regions and clouds. No managed solution markups. - Scale up to multiple replicas across different locations and accelerators, all served with a single endpoint - Everything stays in your cloud account (your VMs & buckets) - Completely private - no one else sees your chat history Install SkyPilot ---------------- We advise you to follow the `instruction `__ to install SkyPilot. Here we provide a simple example of using ``pip`` for the installation as shown below. .. code:: bash # You can use any of the following clouds that you have access to: # aws, gcp, azure, oci, lamabda, runpod, fluidstack, paperspace, # cudo, ibm, scp, vsphere, kubernetes pip install "skypilot-nightly[aws,gcp]" After that, you need to verify cloud access with a command like: .. code:: bash sky check For more information, check the `official document `__ and see if you have set up your cloud accounts correctly. Alternatively, you can also use the official docker image with SkyPilot master branch automatically cloned by running: .. code:: bash # NOTE: '--platform linux/amd64' is needed for Apple Silicon Macs docker run --platform linux/amd64 \ -td --rm --name sky \ -v "$HOME/.sky:/root/.sky:rw" \ -v "$HOME/.aws:/root/.aws:rw" \ -v "$HOME/.config/gcloud:/root/.config/gcloud:rw" \ berkeleyskypilot/skypilot-nightly docker exec -it sky /bin/bash Running Qwen2.5-72B-Instruct with SkyPilot ------------------------------------------ 1. Start serving Qwen2.5-72B-Instruct on a single instance with any available GPU in the list specified in `serve-72b.yaml `__ with a vLLM-powered OpenAI-compatible endpoint: .. code:: bash sky launch -c qwen serve-72b.yaml **Before launching, make sure you have changed Qwen/Qwen2-72B-Instruct to Qwen/Qwen2.5-72B-Instruct in the YAML file.** 2. Send a request to the endpoint for completion: .. code:: bash IP=$(sky status --ip qwen) curl -L http://$IP:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen/Qwen2.5-72B-Instruct", "prompt": "My favorite food is", "max_tokens": 512 }' | jq -r '.choices[0].text' 3. Send a request for chat completion: .. code:: bash curl -L http://$IP:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen/Qwen2.5-72B-Instruct", "messages": [ { "role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful and honest chat expert." }, { "role": "user", "content": "What is the best food?" } ], "max_tokens": 512 }' | jq -r '.choices[0].message.content' Scale up the service with SkyPilot Serve ---------------------------------------- 1. With `SkyPilot Serve `__, a serving library built on top of SkyPilot, scaling up the Qwen service is as simple as running: .. code:: bash sky serve up -n qwen ./serve-72b.yaml **Before launching, make sure you have changed Qwen/Qwen2-72B-Instruct to Qwen/Qwen2.5-72B-Instruct in the YAML file.** This will start the service with multiple replicas on the cheapest available locations and accelerators. SkyServe will automatically manage the replicas, monitor their health, autoscale based on load, and restart them when needed. A single endpoint will be returned and any request sent to the endpoint will be routed to the ready replicas. 2. To check the status of the service, run: .. code:: bash sky serve status qwen After a while, you will see the following output: :: Services NAME VERSION UPTIME STATUS REPLICAS ENDPOINT Qwen 1 - READY 2/2 3.85.107.228:30002 Service Replicas SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION Qwen 1 1 - 2 mins ago 1x Azure({'A100-80GB': 8}) READY eastus Qwen 2 1 - 2 mins ago 1x GCP({'L4': 8}) READY us-east4-a As shown, the service is now backed by 2 replicas, one on Azure and one on GCP, and the accelerator type is chosen to be **the cheapest available one** on the clouds. That said, it maximizes the availability of the service while minimizing the cost. 3. To access the model, we use a ``curl -L`` command (``-L`` to follow redirect) to send the request to the endpoint: .. code:: bash ENDPOINT=$(sky serve status --endpoint qwen) curl -L http://$ENDPOINT/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen/Qwen2.5-72B-Instruct", "messages": [ { "role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful and honest code assistant expert in Python." }, { "role": "user", "content": "Show me the python code for quick sorting a list of integers." } ], "max_tokens": 512 }' | jq -r '.choices[0].message.content' Accessing Qwen2.5 with Chat GUI --------------------------------------------- It is also possible to access the Qwen2.5 service with GUI by connecting a `FastChat GUI server `__ to the endpoint launched above (see `gui.yaml `__). 1. Start the Chat Web UI: .. code:: bash sky launch -c qwen-gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint qwen) **Before launching, make sure you have changed Qwen/Qwen1.5-72B-Chat to Qwen/Qwen2.5-72B-Instruct in the YAML file.** 2. Then, we can access the GUI at the returned gradio link: :: | INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live Note that you may get better results by using a different temperature and top_p value. Summary ------- With SkyPilot, it is easy for you to deploy Qwen2.5 on any cloud. We advise you to read the official doc for more usages and updates. Check `this `__ out! ================================================ FILE: docs/source/deployment/tgi.rst ================================================ TGI ===================== .. attention:: To be updated for Qwen3. Hugging Face's Text Generation Inference (TGI) is a production-ready framework specifically designed for deploying and serving large language models (LLMs) for text generation tasks. It offers a seamless deployment experience, powered by a robust set of features: * `Speculative Decoding `_: Accelerates generation speeds. * `Tensor Parallelism`_: Enables efficient deployment across multiple GPUs. * `Token Streaming`_: Allows for the continuous generation of text. * Versatile Device Support: Works seamlessly with `AMD`_, `Gaudi`_ and `AWS Inferentia`_. .. _AMD: https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/deploy-your-model.html#serving-using-hugging-face-tgi .. _Gaudi: https://github.com/huggingface/tgi-gaudi .. _AWS Inferentia: https://aws.amazon.com/blogs/machine-learning/announcing-the-launch-of-new-hugging-face-llm-inference-containers-on-amazon-sagemaker/#:~:text=Get%20started%20with%20TGI%20on%20SageMaker%20Hosting .. _Tensor Parallelism: https://huggingface.co/docs/text-generation-inference/conceptual/tensor_parallelism .. _Token Streaming: https://huggingface.co/docs/text-generation-inference/conceptual/streaming Installation ----------------- The easiest way to use TGI is via the TGI docker image. In this guide, we show how to use TGI with docker. It's possible to run it locally via Conda or build locally. Please refer to `Installation Guide `_ and `CLI tool `_ for detailed instructions. Deploy Qwen2.5 with TGI ----------------------- 1. **Find a Qwen2.5 Model:** Choose a model from `the Qwen2.5 collection `_. 2. **Deployment Command:** Run the following command in your terminal, replacing ``model`` with your chosen Qwen2.5 model ID and ``volume`` with the path to your local data directory: .. code:: bash model=Qwen/Qwen2.5-7B-Instruct volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model Using TGI API ------------- Once deployed, the model will be available on the mapped port (8080). TGI comes with a handy API for streaming response: .. code:: bash curl http://localhost:8080/generate_stream -H 'Content-Type: application/json' \ -d '{"inputs":"Tell me something about large language models.","parameters":{"max_new_tokens":512}}' It's also available on OpenAI style API: .. code:: bash curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "", "messages": [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."} ], "temperature": 0.7, "top_p": 0.8, "repetition_penalty": 1.05, "max_tokens": 512 }' .. note:: The model field in the JSON is not used by TGI, you can put anything. Refer to the `TGI Swagger UI `_ for a complete API reference. You can also use Python API: .. code:: python from openai import OpenAI # initialize the client but point it to TGI client = OpenAI( base_url="http://localhost:8080/v1/", # replace with your endpoint url api_key="", # this field is not used when running locally ) chat_completion = client.chat.completions.create( model="", # it is not used by TGI, you can put anything messages=[ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ], stream=True, temperature=0.7, top_p=0.8, max_tokens=512, ) # iterate and print stream for message in chat_completion: print(message.choices[0].delta.content, end="") Quantization for Performance ---------------------------- 1. Data-dependent quantization (GPTQ and AWQ) Both GPTQ and AWQ models are data-dependent. The official quantized models can be found from `the Qwen2.5 collection`_ and you can also quantize models with your own dataset to make it perform better on your use case. The following shows the command to start TGI with Qwen2.5-7B-Instruct-GPTQ-Int4: .. code:: bash model=Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model --quantize gptq If the model is quantized with AWQ, e.g. Qwen/Qwen2.5-7B-Instruct-AWQ, please use ``--quantize awq``. 2. Data-agnostic quantization EETQ on the other side is not data dependent and can be used with any model. Note that we're passing in the original model (instead of a quantized model) with the ``--quantize eetq`` flag. .. code:: bash model=Qwen/Qwen2.5-7B-Instruct volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model --quantize eetq Multi-Accelerators Deployment ----------------------------- Use the ``--num-shard`` flag to specify the number of accelerators. Please also use ``--shm-size 1g`` to enable shared memory for optimal NCCL performance (`reference `__): .. code:: bash model=Qwen/Qwen2.5-7B-Instruct volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model --num-shard 2 Speculative Decoding -------------------- Speculative decoding can reduce the time per token by speculating on the next token. Use the ``--speculative-decoding`` flag, setting the value to the number of tokens to speculate on (default: 0 for no speculation): .. code:: bash model=Qwen/Qwen2.5-7B-Instruct volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model --speculate 2 The overall performance of speculative decoding highly depends on the type of task. It works best for code or highly repetitive text. More context on speculative decoding can be found `here `__. Zero-Code Deployment with HF Inference Endpoints --------------------------------------------------- For effortless deployment, leverage Hugging Face Inference Endpoints: - **GUI interface:** ``__ - **Coding interface:** ``__ Once deployed, the endpoint can be used as usual. Common Issues ---------------- Qwen2.5 supports long context lengths, so carefully choose the values for ``--max-batch-prefill-tokens``, ``--max-total-tokens``, and ``--max-input-tokens`` to avoid potential out-of-memory (OOM) issues. If an OOM occurs, you'll receive an error message upon startup. The following shows an example to modify those parameters: .. code:: bash model=Qwen/Qwen2.5-7B-Instruct volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model --max-batch-prefill-tokens 4096 --max-total-tokens 4096 --max-input-tokens 2048 ================================================ FILE: docs/source/deployment/vllm.md ================================================ # vLLM We recommend you trying [vLLM](https://github.com/vllm-project/vllm) for your deployment of Qwen. It is simple to use, and it is fast with state-of-the-art serving throughput, efficient management of attention key value memory with PagedAttention, continuous batching of input requests, optimized CUDA kernels, etc. To learn more about vLLM, please refer to the [paper](https://arxiv.org/abs/2309.06180) and [documentation](https://docs.vllm.ai/). ## Environment Setup By default, you can install `vllm` with pip in a clean environment: ```shell pip install "vllm>=0.8.5" ``` Please note that the prebuilt `vllm` has strict dependencies on `torch` and its CUDA versions. Check the note in the official document for installation ([link](https://docs.vllm.ai/en/latest/getting_started/installation.html)) for more help. ## API Service It is easy to build an OpenAI-compatible API service with vLLM, which can be deployed as a server that implements OpenAI API protocol. By default, it starts the server at `http://localhost:8000`. You can specify the address with `--host` and `--port` arguments. Run the command as shown below: ```shell vllm serve Qwen/Qwen3-8B ``` By default, if the model does not point to a valid local directory, it will download the model files from the Hugging Face Hub. To download model from ModelScope, set the following before running the above command: ```shell export VLLM_USE_MODELSCOPE=true ``` For distributed inference with tensor parallelism, it is as simple as ```shell vllm serve Qwen/Qwen3-8B --tensor-parallel-size 4 ``` The above command will use tensor parallelism on 4 GPUs. You should change the number of GPUs according to your demand. ### Basic Usage Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen: ::::{tab-set} :::{tab-item} curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 32768 }' ``` ::: :::{tab-item} Python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=32768, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, }, ) print("Chat response:", chat_response) ``` :::: :::{tip} `vllm` will use the sampling parameters from the `generation_config.json` in the model files. While the default sampling parameters would work most of the time for thinking mode, it is recommended to adjust the sampling parameters according to your application, and always pass the sampling parameters to the API. ::: ### Thinking & Non-Thinking Modes Qwen3 models will think before respond. This behavior could be controlled by either the hard switch, which could disable thinking completely, or the soft switch, where the model follows the instruction of the user on whether it should think. The hard switch is available in vLLM through the following configuration to the API call. To disable thinking, use ::::{tab-set} :::{tab-item} curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.7, "top_p": 0.8, "top_k": 20, "max_tokens": 8192, "presence_penalty": 1.5, "chat_template_kwargs": {"enable_thinking": false} }' ``` ::: :::{tab-item} Python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=8192, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"enable_thinking": False}, }, ) print("Chat response:", chat_response) ``` :::: :::{note} Please note that passing `enable_thinking` is not OpenAI API compatible. The exact method may differ among frameworks. ::: :::{tip} To completely disable thinking, you could use [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) when starting the model: ```shell vllm serve Qwen/Qwen3-8B --chat-template ./qwen3_nonthinking.jinja ``` The chat template prevents the model from generating thinking content, even if the user instructs the model to do so with `/think`. ::: :::{tip} It is recommended to set sampling parameters differently for thinking and non-thinking modes. ::: ### Parsing Thinking Content vLLM supports parsing the thinking content from the model generation into structured messages: ```shell vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` Since vLLM 0.9.0, one can also use ```shell vllm serve Qwen/Qwen3-8B --reasoning-parser qwen3 ``` The response message will have a field named `reasoning_content` in addition to `content`, containing the thinking content generated by the model. :::{note} Please note that this feature is not OpenAI API compatible. ::: :::{important} As of vLLM 0.8.5, `enable_thinking=False` is not compatible with this feature. If you need to pass `enable_thinking=False` to the API, you should disable parsing thinking content. This is resolved in vLLM 0.9.0 with the `qwen3` reasoning parser. ::: ### Parsing Tool Calls vLLM supports parsing the tool calling content from the model generation into structured messages: ```shell vllm serve Qwen/Qwen3-8B --enable-auto-tool-choice --tool-call-parser hermes ``` For more information, please refer to [our guide on Function Calling](../framework/function_call.md#vllm). ### Structured/JSON Output vLLM supports structured/JSON output. Please refer to [vLLM's documentation](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#extra-parameters-for-chat-api) for the `guided_json` parameters. Besides, it is also recommended to instruct the model to generate the specific format in the system message or in your prompt. ### Serving Quantized models Qwen3 comes with two types of pre-quantized models, FP8 and AWQ. The command serving those models are the same as the original models except for the name change: ```shell # For FP8 quantized model vllm serve Qwen/Qwen3-8B-FP8 # For AWQ quantized model vllm serve Qwen/Qwen3-8B-AWQ ``` :::{note} The FP8 models of Qwen3 are block-wise quant, which is supported on NVIDIA GPUs with compute capability > 8.9, that is, Ada Lovelace, Hopper, and later GPUs and runs as w8a8. Since vLLM v0.9.0, FP8 Marlin has supported block-wise quants (running as w8a16) and you can also run Qwen3 FP8 models on Ampere cards. ::: :::{note} If you encountered the following error when deploying the FP8 models, it indicates that the tensor parallel size does not agree with the model weights: ``` File ".../vllm/vllm/model_executor/layers/quantization/fp8.py", line 477, in create_weights raise ValueError( ValueError: The output_size of gate's and up's weight = 192 is not divisible by weight quantization block_n = 128. ``` We recommend lowering the degree of tensor parallel, e.g., `--tensor-parallel-size 4` or enabling expert parallel, e.g., `--tensor-parallel-size 8 --enable-expert-parallel`. ::: ### Context Length The context length for Qwen3 models in pretraining is up to 32,768 tokens. To handle context length substantially exceeding 32,768 tokens, RoPE scaling techniques should be applied. We have validated the performance of [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. vLLM supports YaRN, which can be configured as ```shell vllm serve Qwen/Qwen3-8B --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` :::{note} vLLM implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. ::: :::{note} The default `max_position_embeddings` in `config.json` is set to 40,960, which used by vLLM, if `--max-model-len` is not specified. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing and leave adequate room for model thinking. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. ::: ## Python Library vLLM can also be directly used as a Python library, which is convenient for offline batch inference but lack some API-only features, such as parsing model generation to structure messages. The following shows the basic usage of vLLM as a library: ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams # Initialize the tokenizer tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B") # Configurae the sampling parameters (for thinking mode) sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, max_tokens=32768) # Initialize the vLLM engine llm = LLM(model="Qwen/Qwen3-8B") # Prepare the input to the model prompt = "Give me a short introduction to large language models." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, # Set to False to strictly disable thinking ) # Generate outputs outputs = llm.generate([text], sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` Since vLLM v0.9.0, you can also use the `LLM.chat` interface which includes support for `chat_template_kwargs`: ```python from vllm import LLM, SamplingParams # Configurae the sampling parameters (for thinking mode) sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, max_tokens=32768) # Initialize the vLLM engine llm = LLM(model="Qwen/Qwen3-8B") # Prepare the input to the model prompt = "Give me a short introduction to large language models." messages = [ {"role": "user", "content": prompt} ] # Generate outputs outputs = llm.chat( [messages], sampling_params, chat_template_kwargs={"enable_thinking": True}, # Set to False to strictly disable thinking ) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` ## FAQ You may encounter OOM issues that are pretty annoying. We recommend two arguments for you to make some fix. - The first one is `--max-model-len`. Our provided default `max_position_embedding` is `40960` and thus the maximum length for the serving is also this value, leading to higher requirements of memory. Reducing it to a proper length for yourself often helps with the OOM issue. - Another argument you can pay attention to is `--gpu-memory-utilization`. vLLM will pre-allocate this much GPU memory. By default, it is `0.9`. This is also why you find a vLLM service always takes so much memory. If you are in eager mode (by default it is not), you can level it up to tackle the OOM problem. Otherwise, CUDA Graphs are used, which will use GPU memory not controlled by vLLM, and you should try lowering it. If it doesn't work, you should try `--enforce-eager`, which may slow down inference, or reduce the `--max-model-len`. For more usage guide with vLLM, please see vLLM's [Qwen3 Usage Guide](https://github.com/vllm-project/vllm/issues/17327). ================================================ FILE: docs/source/framework/Langchain.rst ================================================ Langchain ========================== .. attention:: To be updated for Qwen3. This guide helps you build a question-answering application based on a local knowledge base using ``Qwen2.5-7B-Instruct`` with ``langchain``. The goal is to establish a knowledge base Q&A solution. Basic Usage ----------- The implementation process of this project includes loading files -> reading text -> segmenting text -> vectorizing text -> vectorizing questions -> matching the top k most similar text vectors with the question vectors -> incorporating the matched text as context along with the question into the prompt -> submitting to the Qwen2.5-7B-Instruct to generate an answer. Below is an example: .. code:: bash pip install langchain==0.0.174 pip install faiss-gpu .. code:: python from transformers import AutoModelForCausalLM, AutoTokenizer from abc import ABC from langchain.llms.base import LLM from typing import Any, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForLLMRun model_name = "Qwen/Qwen2.5-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) class Qwen(LLM, ABC): max_token: int = 10000 temperature: float = 0.01 top_p = 0.9 history_len: int = 3 def __init__(self): super().__init__() @property def _llm_type(self) -> str: return "Qwen" @property def _history_len(self) -> int: return self.history_len def set_history_len(self, history_len: int = 10) -> None: self.history_len = history_len def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, ) -> str: messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] return response @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {"max_token": self.max_token, "temperature": self.temperature, "top_p": self.top_p, "history_len": self.history_len} After loading the Qwen2.5-7B-Instruct model, you should specify the txt file for retrieval. .. code:: python import os import re import torch import argparse from langchain.vectorstores import FAISS from langchain.embeddings.huggingface import HuggingFaceEmbeddings from typing import List, Tuple import numpy as np from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain.docstore.document import Document from langchain.prompts.prompt import PromptTemplate from langchain.chains import RetrievalQA class ChineseTextSplitter(CharacterTextSplitter): def __init__(self, pdf: bool = False, **kwargs): super().__init__(**kwargs) self.pdf = pdf def split_text(self, text: str) -> List[str]: if self.pdf: text = re.sub(r"\n{3,}", "\n", text) text = re.sub('\s', ' ', text) text = text.replace("\n\n", "") sent_sep_pattern = re.compile( '([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') sent_list = [] for ele in sent_sep_pattern.split(text): if sent_sep_pattern.match(ele) and sent_list: sent_list[-1] += ele elif ele: sent_list.append(ele) return sent_list def load_file(filepath): loader = TextLoader(filepath, autodetect_encoding=True) textsplitter = ChineseTextSplitter(pdf=False) docs = loader.load_and_split(textsplitter) write_check_file(filepath, docs) return docs def write_check_file(filepath, docs): folder_path = os.path.join(os.path.dirname(filepath), "tmp_files") if not os.path.exists(folder_path): os.makedirs(folder_path) fp = os.path.join(folder_path, 'load_file.txt') with open(fp, 'a+', encoding='utf-8') as fout: fout.write("filepath=%s,len=%s" % (filepath, len(docs))) fout.write('\n') for i in docs: fout.write(str(i)) fout.write('\n') fout.close() def separate_list(ls: List[int]) -> List[List[int]]: lists = [] ls1 = [ls[0]] for i in range(1, len(ls)): if ls[i - 1] + 1 == ls[i]: ls1.append(ls[i]) else: lists.append(ls1) ls1 = [ls[i]] lists.append(ls1) return lists class FAISSWrapper(FAISS): chunk_size = 250 chunk_conent = True score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4 ) -> List[Tuple[Document, float]]: scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k) docs = [] id_set = set() store_len = len(self.index_to_docstore_id) for j, i in enumerate(indices[0]): if i == -1 or 0 < self.score_threshold < scores[0][j]: # This happens when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) if not self.chunk_conent: if not isinstance(doc, Document): raise ValueError(f"Could not find document for id {_id}, got {doc}") doc.metadata["score"] = int(scores[0][j]) docs.append(doc) continue id_set.add(i) docs_len = len(doc.page_content) for k in range(1, max(i, store_len - i)): break_flag = False for l in [i + k, i - k]: if 0 <= l < len(self.index_to_docstore_id): _id0 = self.index_to_docstore_id[l] doc0 = self.docstore.search(_id0) if docs_len + len(doc0.page_content) > self.chunk_size: break_flag = True break elif doc0.metadata["source"] == doc.metadata["source"]: docs_len += len(doc0.page_content) id_set.add(l) if break_flag: break if not self.chunk_conent: return docs if len(id_set) == 0 and self.score_threshold > 0: return [] id_list = sorted(list(id_set)) id_lists = separate_list(id_list) for id_seq in id_lists: for id in id_seq: if id == id_seq[0]: _id = self.index_to_docstore_id[id] doc = self.docstore.search(_id) else: _id0 = self.index_to_docstore_id[id] doc0 = self.docstore.search(_id0) doc.page_content += " " + doc0.page_content if not isinstance(doc, Document): raise ValueError(f"Could not find document for id {_id}, got {doc}") doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]]) doc.metadata["score"] = int(doc_score) docs.append((doc, doc_score)) return docs if __name__ == '__main__': # load docs (pdf file or txt file) filepath = 'your file path' # Embedding model name EMBEDDING_MODEL = 'text2vec' PROMPT_TEMPLATE = """Known information: {context_str} Based on the above known information, respond to the user's question concisely and professionally. If an answer cannot be derived from it, say 'The question cannot be answered with the given information' or 'Not enough relevant information has been provided,' and do not include fabricated details in the answer. Please respond in English. The question is {question}""" # Embedding running device EMBEDDING_DEVICE = "cuda" # return top-k text chunk from vector store VECTOR_SEARCH_TOP_K = 3 CHAIN_TYPE = 'stuff' embedding_model_dict = { "text2vec": "your text2vec model path", } llm = Qwen() embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],model_kwargs={'device': EMBEDDING_DEVICE}) docs = load_file(filepath) docsearch = FAISSWrapper.from_documents(docs, embeddings) prompt = PromptTemplate( template=PROMPT_TEMPLATE, input_variables=["context_str", "question"] ) chain_type_kwargs = {"prompt": prompt, "document_variable_name": "context_str"} qa = RetrievalQA.from_chain_type( llm=llm, chain_type=CHAIN_TYPE, retriever=docsearch.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K}), chain_type_kwargs=chain_type_kwargs) query = "Give me a short introduction to large language models." print(qa.run(query)) Next Step --------- Now you can chat with Qwen2.5 use your own document. Continue to read the documentation and try to figure out more advanced usages of model retrieval! ================================================ FILE: docs/source/framework/LlamaIndex.rst ================================================ LlamaIndex ========== .. attention:: To be updated for Qwen3. To connect Qwen2.5 with external data, such as documents, web pages, etc., we offer a tutorial on `LlamaIndex `__. This guide helps you quickly implement retrieval-augmented generation (RAG) using LlamaIndex with Qwen2.5. Preparation -------------------------------------- To implement RAG, we advise you to install the LlamaIndex-related packages first. The following is a simple code snippet showing how to do this: .. code:: bash pip install llama-index pip install llama-index-llms-huggingface pip install llama-index-readers-web Set Parameters -------------------------------------- Now we can set up LLM, embedding model, and the related configurations. Qwen2.5-Instruct supports conversations in multiple languages, including English and Chinese. You can use the ``bge-base-en-v1.5`` model to retrieve from English documents, and you can download the ``bge-base-zh-v1.5`` model to retrieve from Chinese documents. You can also choose ``bge-large`` or ``bge-small`` as the embedding model or modify the context window size or text chunk size depending on your computing resources. Qwen2.5 model families support a maximum of 32K context window size (up to 128K for 7B, 14B, 32B, and 72B, requiring extra configuration) .. code:: python import torch from llama_index.core import Settings from llama_index.core.node_parser import SentenceSplitter from llama_index.llms.huggingface import HuggingFaceLLM from llama_index.embeddings.huggingface import HuggingFaceEmbedding # Set prompt template for generation (optional) from llama_index.core import PromptTemplate def completion_to_prompt(completion): return f"<|im_start|>system\n<|im_end|>\n<|im_start|>user\n{completion}<|im_end|>\n<|im_start|>assistant\n" def messages_to_prompt(messages): prompt = "" for message in messages: if message.role == "system": prompt += f"<|im_start|>system\n{message.content}<|im_end|>\n" elif message.role == "user": prompt += f"<|im_start|>user\n{message.content}<|im_end|>\n" elif message.role == "assistant": prompt += f"<|im_start|>assistant\n{message.content}<|im_end|>\n" if not prompt.startswith("<|im_start|>system"): prompt = "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n" + prompt prompt = prompt + "<|im_start|>assistant\n" return prompt # Set Qwen2.5 as the language model and set generation config Settings.llm = HuggingFaceLLM( model_name="Qwen/Qwen2.5-7B-Instruct", tokenizer_name="Qwen/Qwen2.5-7B-Instruct", context_window=30000, max_new_tokens=2000, generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95}, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, device_map="auto", ) # Set embedding model Settings.embed_model = HuggingFaceEmbedding( model_name = "BAAI/bge-base-en-v1.5" ) # Set the size of the text chunk for retrieval Settings.transformations = [SentenceSplitter(chunk_size=1024)] Build Index -------------------------------------- Now we can build index from documents or websites. The following code snippet demonstrates how to build an index for files (regardless of whether they are in PDF or TXT format) in a local folder named 'document'. .. code:: python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./document").load_data() index = VectorStoreIndex.from_documents( documents, embed_model=Settings.embed_model, transformations=Settings.transformations ) The following code snippet demonstrates how to build an index for the content in a list of websites. .. code:: python from llama_index.readers.web import SimpleWebPageReader from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleWebPageReader(html_to_text=True).load_data( ["web_address_1","web_address_2",...] ) index = VectorStoreIndex.from_documents( documents, embed_model=Settings.embed_model, transformations=Settings.transformations ) To save and load the index, you can use the following code snippet. .. code:: python from llama_index.core import StorageContext, load_index_from_storage # save index storage_context = StorageContext.from_defaults(persist_dir="save") # load index index = load_index_from_storage(storage_context) RAG ------------------- Now you can perform queries, and Qwen2.5 will answer based on the content of the indexed documents. .. code:: python query_engine = index.as_query_engine() your_query = "" print(query_engine.query(your_query).response) ================================================ FILE: docs/source/framework/function_call.md ================================================ --- myst: number_code_blocks: ["python3"] --- # Function Calling ## Preface Function calling with large language models is a huge and evolving topic. It is particularly important for AI applications: - either for AI-native applications that strive to work around the shortcomings of current AI technology, - or for existing applications that seeks the integration of AI technology to improve performance, user interaction and experience, or efficiency. We will talk about how Qwen3 can be used to support function calling and how it can be used to achieve your goals, from the inference usage for developing application to the inner workings for hardcore customizations. In this guide, - We will first demonstrate how to use function calling with Qwen3. - Then, we will introduce the technical details on functional calling with Qwen3, which are mainly about the templates. Before starting, there is one thing we have not yet introduced, that is ... ## What is function calling? :::{Note} There is another term "tool use" that may be used to refer to the same concept. While some may argue that tools are a generalized form of functions, at present, their difference exists only technically as different I/O types of programming interfaces. ::: Large language models (LLMs) are powerful things. However, sometimes LLMs by themselves are simply not capable enough. - On the one hand, LLMs have inherent modeling limitations. For one, they do not know things that are not in their training data, which include those happened after their training ended. In addition, they learn things in the way of likelihood, which suggests that they may not be precise enough for tasks with fixed rule sets, e.g., mathematical computation. - On the other hand, it is not easy to use LLMs as a Plug-and-Play service programmatically with other things. LLMs mostly talk in words that are open to interpretation and thus ambiguous, while other software or applications or systems talk in code and through programming interfaces that are pre-defined and fixed and structured. To this end, function calling establishes a common protocol that specifies how LLMs should interact with the other things. The procedure is mainly as follows: 1. The application provides a set of functions and the instructions of the functions to an LLM. 2. The LLM choose to or not to, or is forced to use one or many of the functions, in response to user queries. 3. If the LLM chooses to use the functions, it states how the functions should be used based on the function instructions. 4. The chosen functions are used as such by the application and the results are obtained, which are then given to the LLM if further interaction is needed. There are many ways for LLMs to understand and follow this protocol. As always, the key is prompt engineering or an internalized template known by the model. We recommend using Hermes-style tool use for Qwen3 to maximize function calling performance. ## Inference with Function Calling As function calling is essentially implemented using prompt engineering, you could manually construct the model inputs for Qwen3 models. However, frameworks with function calling support can help you with all that laborious work. In the following, we will introduce the usage (via dedicated function calling chat template) with - **Qwen-Agent**, - **vLLM**. ### The Example Case Let's also use an example to demonstrate the inference usage. We assume **Python 3.11** is used as the programming language. **Scenario**: Suppose we would like to ask the model about the temperature of a location. Normally, the model would reply that it cannot provide real-time information. But we have two tools that can be used to obtain the current temperature of and the temperature at a given date of a city respectively, and we would like the model to make use of them. To set up the example case, you can use the following code: :::{dropdown} Preparation Code :name: prepcode ```python import json def get_current_temperature(location: str, unit: str = "celsius"): """Get current temperature at a location. Args: location: The location to get the temperature for, in the format "City, State, Country". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, and the unit in a dict """ return { "temperature": 26.1, "location": location, "unit": unit, } def get_temperature_date(location: str, date: str, unit: str = "celsius"): """Get temperature at a location and date. Args: location: The location to get the temperature for, in the format "City, State, Country". date: The date to get the temperature for, in the format "Year-Month-Day". unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"]) Returns: the temperature, the location, the date and the unit in a dict """ return { "temperature": 25.9, "location": location, "date": date, "unit": unit, } def get_function_by_name(name): if name == "get_current_temperature": return get_current_temperature if name == "get_temperature_date": return get_temperature_date TOOLS = [ { "type": "function", "function": { "name": "get_current_temperature", "description": "Get current temperature at a location.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": 'The location to get the temperature for, in the format "City, State, Country".', }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": 'The unit to return the temperature in. Defaults to "celsius".', }, }, "required": ["location"], }, }, }, { "type": "function", "function": { "name": "get_temperature_date", "description": "Get temperature at a location and date.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": 'The location to get the temperature for, in the format "City, State, Country".', }, "date": { "type": "string", "description": 'The date to get the temperature for, in the format "Year-Month-Day".', }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": 'The unit to return the temperature in. Defaults to "celsius".', }, }, "required": ["location", "date"], }, }, }, ] MESSAGES = [ {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30."}, ] ``` ::: In particular, the tools should be described using JSON Schema and the messages should contain as much available information as possible. You can find the explanations of the tools and messages below: :::{dropdown} Example Tools The tools should be described using the following JSON: ```json [ { "type": "function", "function": { "name": "get_current_temperature", "description": "Get current temperature at a location.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The location to get the temperature for, in the format \"City, State, Country\"." }, "unit": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The unit to return the temperature in. Defaults to \"celsius\"." } }, "required": [ "location" ] } } }, { "type": "function", "function": { "name": "get_temperature_date", "description": "Get temperature at a location and date.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The location to get the temperature for, in the format \"City, State, Country\"." }, "date": { "type": "string", "description": "The date to get the temperature for, in the format \"Year-Month-Day\"." }, "unit": { "type": "string", "enum": [ "celsius", "fahrenheit" ], "description": "The unit to return the temperature in. Defaults to \"celsius\"." } }, "required": [ "location", "date" ] } } } ] ``` For each **tool**, it is a JSON object with two fields: - `type`: a string specifying the type of the tool, currently only `"function"` is valid - `function`: an object detailing the instructions to use the function For each **function**, it is a JSON object with three fields: - `name`: a string indicating the name of the function - `description`: a string describing what the function is used for - `parameters`: [a JSON Schema](https://json-schema.org/learn/getting-started-step-by-step) that specifies the parameters the function accepts. Please refer to the linked documentation for how to compose a JSON Schema. Notable fields include `type`, `required`, and `enum`. Most frameworks use the tool format and some may use the function format. Which one to use should be obvious according to the naming. ::: :::{dropdown} Example Messages Our query is `What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30.`. ```json [ {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30."} ] ``` ::: ### Qwen-Agent [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) is actually a Python Agent framework for developing AI applications. Although its intended use cases are higher-level than efficient inference, it does contain the **canonical implementation** of function calling for Qwen3. It provides the function calling ability for Qwen3 to an OpenAI-compatible API through templates that is transparent to users. It is worth noting that for reasoning models like Qwen3, it is *not recommended* to use tool call template based on stopwords, such as ReAct, because the model may output stopwords in the thought section, potentially leading to unexpected behavior in tool calls. Before starting, let's make sure the latest library is installed: ```bash pip install -U qwen-agent ``` #### Preparing Qwen-Agent can wrap an OpenAI-compatible API that does not support function calling. You can serve such an API with most inference frameworks or obtain one from cloud providers like DashScope or Together. Assuming there is an OpenAI-compatible API at `http://localhost:8000/v1`, Qwen-Agent provides a shortcut function `get_chat_model` to obtain a model inference class with function calling support: ```python from qwen_agent.llm import get_chat_model llm = get_chat_model({ "model": "Qwen/Qwen3-8B", "model_server": "http://localhost:8000/v1", "api_key": "EMPTY", "generate_cfg": { "extra_body": { "chat_template_kwargs": {"enable_thinking": False} # default to True } } }) ``` In the above, `model_server` is the `api_base` common used in other OpenAI-compatible API clients. It is advised to provide the `api_key` (but not via plaintext in the code), even if the API server does not check it, in which case, you can set it to anything. You can pass model parameters to the model by `generate_cfg`. Here we demonstrate how to control the think and no_think modes of Qwen3. Different APIs may have different control methods. For model inputs, the common message structure for system, user, and assistant history should be used: ```python messages = MESSAGES[:] ``` At the time, Qwen-Agent works with functions instead of tools. This requires a small change to our tool descriptions, that is, extracting the function fields: ```python functions = [tool["function"] for tool in TOOLS] ``` #### Tool Calls and Tool Results To interact with the model, the `chat` method should be used: ```python for responses in llm.chat( messages=messages, functions=functions, ): pass messages.extend(responses) ``` The `chat` method returns a generator of list, each of which may contain multiple messages. - The results of `no_think` mode: ```python [ {"role": "assistant", "content": "", "function_call": {"name": "get_current_temperature", "arguments": "{\"location\": \"San Francisco, California, United States\", \"unit\": \"celsius\"}"}}, {"role": "assistant", "content": "", "function_call": {"name": "get_temperature_date", "arguments": "{\"location\": \"San Francisco, California, United States\", \"date\": \"2024-10-01\", \"unit\": \"celsius\"}"}}, ] ``` - The results of `think` mode: ```python [ {"role": "assistant", "content": "", "reasoning_content": "Okay, the user is asking for the current temperature in San Francisco and the temperature for tomorrow. Let me check the available tools.\n\nFirst, there's the get_current_temperature function. It requires the location and optionally the unit. Since the user didn't specify the unit, I'll default to celsius. The location should be \"San Francisco, State, Country\". Wait, the example format is \"City, State, Country\", but San Francisco is a city in California, USA. So the location parameter would be \"San Francisco, California, United States\".\n\nThen, for tomorrow's temperature, the user mentioned the current date is 2024-09-30, so tomorrow would be 2024-10-01. The get_temperature_date function requires location, date, and unit. Again, using the same location and default unit. I need to format the date as \"Year-Month-Day\", which is 2024-10-01.\n\nWait, the current date given is 2024-09-30. If today is September 30, then tomorrow is October 1st. So the date parameter for the second function call should be \"2024-10-01\".\n\nI should make two separate function calls: one for the current temperature and another for tomorrow's date. Let me structure the JSON for both tool calls accordingly."}, {"role": "assistant", "content": "", "function_call": {"name": "get_current_temperature", "arguments": "{\"location\": \"San Francisco, California, United States\", \"unit\": \"celsius\"}"}}, {"role": "assistant", "content": "", "function_call": {"name": "get_temperature_date", "arguments": "{\"location\": \"San Francisco, California, United States\", \"date\": \"2024-10-01\", \"unit\": \"celsius\"}"}}, ] ``` As we can see, Qwen-Agent attempts to parse the model generation in an easier to use structural format. The details related to function calls are placed in the `function_call` field of the messages: - `name`: a string representing the function to call - `arguments`: a JSON-formatted string representing the arguments the function should be called with In the thinking mode, it will first generate a thought and then generate the tool call(s). Then comes the critical part -- checking and applying the function call: ```python3 for message in responses: if fn_call := message.get("function_call", None): fn_name: str = fn_call['name'] fn_args: dict = json.loads(fn_call["arguments"]) fn_res: str = json.dumps(get_function_by_name(fn_name)(**fn_args)) messages.append({ "role": "function", "name": fn_name, "content": fn_res, }) ``` To get tool results: - line 1: We should iterate the function calls in the order the model generates them. - line 2: We can check if a function call is needed as deemed by the model by checking the `function_call` field of the generated messages. - line 3-4: The related details including the name and the arguments of the function can also be found there, which are `name` and `arguments` respectively. - line 6: With the details, one should call the function and obtain the results. Here, we assume there is a function named [`get_function_by_name`](#prepcode) to help us get the related function by its name. - line 8-12: With the result obtained, add the function result to the messages as `content` and with `role` as `"function"`. Now the messages are: - `no_think` mode: ```python [ {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30."}, {"role": "assistant", "content": "", "function_call": {"name": "get_current_temperature", "arguments": "{\"location\": \"San Francisco, California, United States\", \"unit\": \"celsius\"}"}}, {"role": "assistant", "content": "", "function_call": {"name": "get_temperature_date", "arguments": "{\"location\": \"San Francisco, California, United States\", \"date\": \"2024-10-01\", \"unit\": \"celsius\"}"}}, {"role": "function", "name": "get_current_temperature", "content": '{"temperature": 26.1, "location": "San Francisco, California, United States", "unit": "celsius"}'}, {"role": "function", "name": "get_temperature_date", "content": '{"temperature": 25.9, "location": "San Francisco, California, United States", "date": "2024-10-01", "unit": "celsius"}'}, ] ``` - `think` mode: ```python [ {"role": "user", "content": "What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30."}, {"role": "assistant", "content": "", "reasoning_content": "Okay, the user is asking for the current temperature in San Francisco and the temperature for tomorrow. Let me check the available tools.\n\nFirst, there's the get_current_temperature function. It requires the location and optionally the unit. Since the user didn't specify the unit, I'll default to celsius. The location should be \"San Francisco, State, Country\". Wait, the example format is \"City, State, Country\", but San Francisco is a city in California, USA. So the location parameter would be \"San Francisco, California, United States\".\n\nThen, for tomorrow's temperature, the user mentioned the current date is 2024-09-30, so tomorrow would be 2024-10-01. The get_temperature_date function requires location, date, and unit. Again, using the same location and default unit. I need to format the date as \"Year-Month-Day\", which is 2024-10-01.\n\nWait, the current date given is 2024-09-30. If today is September 30, then tomorrow is October 1st. So the date parameter for the second function call should be \"2024-10-01\".\n\nI should make two separate function calls: one for the current temperature and another for tomorrow's date. Let me structure the JSON for both tool calls accordingly."}, {"role": "assistant", "content": "", "function_call": {"name": "get_current_temperature", "arguments": "{\"location\": \"San Francisco, California, United States\", \"unit\": \"celsius\"}"}}, {"role": "assistant", "content": "", "function_call": {"name": "get_temperature_date", "arguments": "{\"location\": \"San Francisco, California, United States\", \"date\": \"2024-10-01\", \"unit\": \"celsius\"}"}}, {"role": "function", "name": "get_current_temperature", "content": '{"temperature": 26.1, "location": "San Francisco, California, United States", "unit": "celsius"}'}, {"role": "function", "name": "get_temperature_date", "content": '{"temperature": 25.9, "location": "San Francisco, California, United States", "date": "2024-10-01", "unit": "celsius"}'}, ] ``` #### Final Response Finally, run the model again to get the final model results: ```python for responses in llm.chat(messages=messages, functions=functions): pass messages.extend(responses) ``` The final response should be like - `no_think` mode: ```python [ {"role": "assistant", "content": "The current temperature in San Francisco, CA, USA is **26.1°C**. \n\nFor tomorrow (2024-10-01), the temperature is projected to be **25.9°C**. \n\nThere is a slight decrease in temperature expected from today to tomorrow."} ] ``` - `think` mode: ```python [ {"role": "assistant", "content": "", "reasoning_content": "Okay, the user asked for the current temperature in San Francisco and tomorrow's temperature. I called the get_current_temperature function for now and get_temperature_date for tomorrow. The responses came back with 26.1°C today and 25.9°C tomorrow. Let me present this info clearly.\n\nFirst, confirm the location to make sure there's no confusion. The current temp is 26.1°C, so I'll state that. Then, tomorrow's date is 2024-10-01, which is October 1st, so I'll mention the date in a user-friendly way. The temp drops slightly to 25.9°C. I should note the unit is Celsius as per the default. Keep the answer concise but informative. Maybe add a brief note about the slight decrease. Make sure the dates are correctly formatted and the temperatures are accurate based on the data provided."}, {"role": "assistant", "content": "The current temperature in San Francisco, CA, USA is **26.1°C**. \n\nFor tomorrow (2024-10-01), the temperature is projected to be **25.9°C**. \n\nThere is a slight decrease in temperature expected from today to tomorrow."} ] ``` (heading-target)= ### vLLM vLLM is a fast and easy-to-use library for LLM inference and serving. It uses the tokenizer from `transformers` to format the input, so we should have no trouble preparing the input. In addition, vLLm also implements helper functions so that generated tool calls can be parsed automatically if the format is supported. - `vllm` >= v0.8.5. For more information, check the [vLLM documentation](https://docs.vllm.ai/en/stable/serving/openai_compatible_server.html#tool-calling-in-the-chat-completion-api). We will use the OpenAI-Compatible API by `vllm` with the API client from the `openai` Python library. #### Preparing For Qwen3, the chat template in tokenizer_config.json has already included support for the Hermes-style tool use. We simply need to start a OpenAI-compatible API with vLLM: ```bash vllm serve Qwen/Qwen3-8B --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser deepseek_r1 ``` The inputs are the same with those in [the preparation code](#prepcode): ```python tools = TOOLS messages = MESSAGES ``` Let's also initialize the client: ```python from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model_name = "Qwen/Qwen3-8B" ``` #### Tool Calls and Tool Results We can use the create chat completions endpoint to query the model. Here is an example of the `no_think` mode: ```python response = client.chat.completions.create( model=model_name, messages=messages, tools=tools, temperature=0.7, top_p=0.8, max_tokens=512, extra_body={ "repetition_penalty": 1.05, "chat_template_kwargs": {"enable_thinking": False} # default to True }, ) ``` vLLM should be able to parse the tool calls for us, and the main fields in the response (`response.choices[0]`) should be like ```python Choice( finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage( content=None, role='assistant', function_call=None, tool_calls=[ ChatCompletionMessageToolCall( id='chatcmpl-tool-924d705adb044ff88e0ef3afdd155f15', function=Function(arguments='{"location": "San Francisco, CA, USA"}', name='get_current_temperature'), type='function', ), ChatCompletionMessageToolCall( id='chatcmpl-tool-7e30313081944b11b6e5ebfd02e8e501', function=Function(arguments='{"location": "San Francisco, CA, USA", "date": "2024-10-01"}', name='get_temperature_date'), type='function', ), ], ), stop_reason=None, ) ``` Note that the function arguments are JSON-formatted strings, which Qwen-Agent follows. As before, chances are that there are corner cases where tool calls are generated but they are malformed and cannot be parsed. For production code, we should try parsing by ourselves. Then, we can obtain the tool results and add them to the messages as shown below: ```python messages.append(response.choices[0].message.model_dump()) if tool_calls := messages[-1].get("tool_calls", None): for tool_call in tool_calls: call_id: str = tool_call["id"] if fn_call := tool_call.get("function"): fn_name: str = fn_call["name"] fn_args: dict = json.loads(fn_call["arguments"]) fn_res: str = json.dumps(get_function_by_name(fn_name)(**fn_args)) messages.append({ "role": "tool", "content": fn_res, "tool_call_id": call_id, }) ``` It should be noted that the OpenAI API uses `tool_call_id` to identify the relation between tool results and tool calls. The messages are now like ```python [ {'role': 'user', 'content': "What's the temperature in San Francisco now? How about tomorrow? Current Date: 2024-09-30."}, {'content': None, 'role': 'assistant', 'function_call': None, 'tool_calls': [ {'id': 'chatcmpl-tool-924d705adb044ff88e0ef3afdd155f15', 'function': {'arguments': '{"location": "San Francisco, CA, USA"}', 'name': 'get_current_temperature'}, 'type': 'function'}, {'id': 'chatcmpl-tool-7e30313081944b11b6e5ebfd02e8e501', 'function': {'arguments': '{"location": "San Francisco, CA, USA", "date": "2024-10-01"}', 'name': 'get_temperature_date'}, 'type': 'function'}, ]}, {'role': 'tool', 'content': '{"temperature": 26.1, "location": "San Francisco, CA, USA", "unit": "celsius"}', 'tool_call_id': 'chatcmpl-tool-924d705adb044ff88e0ef3afdd155f15'}, {'role': 'tool', 'content': '{"temperature": 25.9, "location": "San Francisco, CA, USA", "date": "2024-10-01", "unit": "celsius"}', 'tool_call_id': 'chatcmpl-tool-7e30313081944b11b6e5ebfd02e8e501'}, ] ``` #### Final Response Let's call the endpoint again to seed the tool results and get response: ```python response = client.chat.completions.create( model=model_name, messages=messages, tools=tools, temperature=0.7, top_p=0.8, max_tokens=512, extra_body={ "repetition_penalty": 1.05, }, ) messages.append(response.choices[0].message.model_dump()) ``` The final response (`response.choices[0].message.content`) should be like ```text The current temperature in San Francisco is approximately 26.1°C. For tomorrow, the forecasted temperature is around 25.9°C. ``` ## Finally In whichever way you choose to use function calling with Qwen3, keep in mind that the limitation and the perks of prompt engineering applies: - It is not guaranteed that the model generation will always follow the protocol even with proper prompting or templates. Especially, for the templates that are more complex and relies more on the model itself to think and stay on track than the ones that are simpler and relies on the template and the use of control or special tokens. The latter one, of course, requires some kind of training. In production code, be prepared that if it breaks, countermeasures or rectifications are in place. - If in certain scenarios, the generation is not up to expectation, you can refine the template to add more instructions or constraints. While the templates mentioned here are general enough, they may not be the best or the most specific or the most concise for your use cases. The ultimate solution is fine-tuning using your own data. Have fun prompting! ================================================ FILE: docs/source/framework/qwen_agent.rst ================================================ Qwen-Agent ========== `Qwen-Agent `__ is a framework for developing LLM applications based on the instruction following, tool usage, planning, and memory capabilities of Qwen. This is a simple tutorial on using Qwen-Agent to quickly experience the agentic capabilities of Qwen3. For more detailed information, please refer to `Qwen-Agent `__ repository. Installation ------------ - Install the stable version from PyPI: .. code:: bash pip install -U "qwen-agent[gui,rag,code_interpreter,mcp]" # Or use `pip install -U qwen-agent` for the minimal requirements. # The optional requirements, specified in double brackets, are: # [gui] for Gradio-based GUI support; # [rag] for RAG support; # [code_interpreter] for Code Interpreter support; # [mcp] for MCP support. Developing Your Own Agent ------------------------- Qwen3 excels in tool calling capabilities. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. .. code:: python import os from qwen_agent.agents import Assistant # Define LLM llm_cfg = { # Use a custom endpoint compatible with OpenAI API by vLLM/SGLang: 'model': 'Qwen/Qwen3-32B', 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # 'generate_cfg': { # # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way # 'extra_body': { # 'chat_template_kwargs': {'enable_thinking': False} # }, # # # Add: When the content is `this is the thoughtthis is the answer` # # Do not add: When the response has been separated by reasoning_content and content # # This parameter will affect the parsing strategy of tool call # # 'thought_in_content': True, # }, } # llm_cfg = { # # Use the model service provided by DashScope: # 'model': 'qwen3-235b-a22b', # 'model_type': 'qwen_dashscope', # # # 'generate_cfg': { # # # When using the Dash Scope API, pass the parameter of whether to enable thinking mode in this way # # 'enable_thinking': False, # # }, # } # llm_cfg = { # # Use the OpenAI-compatible model service provided by DashScope: # 'model': 'qwen3-235b-a22b', # 'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # # # 'generate_cfg': { # # # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way # # 'extra_body': { # # 'enable_thinking': False # # }, # # }, # } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) For more detailed examples and MCP cookbooks, please refer to `Qwen-Agent `__ repository. ================================================ FILE: docs/source/getting_started/concepts.md ================================================ # Key Concepts ## Qwen Qwen (Chinese: 通义千问; pinyin: _Tongyi Qianwen_) is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. Both language models and multimodal models are pre-trained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. There is the proprietary/closed source version and the open-weight version. You can learn more about the proprietary models at Alibaba Cloud Model Studio ([China Site](https://help.aliyun.com/zh/model-studio/getting-started/models#9f8890ce29g5u) \[zh\], [International Site](https://www.alibabacloud.com/en/product/modelstudio)). In this document, our focus is Qwen, the language models. ## Qwen3 Qwen3 is the newest edition of the Qwen language models, featuring balanced model sizes, enhanced capbilities, hybrid thinking modes, and more language support: - The mixture-of-experts (MoE) models are reintroduced with the Qwen3-30B-A3B and Qwen3-235B-A22B. The largest dense models are now Qwen3-32B. - Hybrid thinking mode is designed so that thinking and non-thinking (instruct) can be achieved without changing models, simplifying deployment and making alternating thinking and non-thinking in a single chat possible. - With 119 languages (and dialects), Qwen3's extensive multilingual capability opens up new possibilities for international applications. - Qwen3 models are optimized for coding and agentic capabilities, with strengthened support of Model Context Protocol (MCP) as well. ## Naming Starting with Qwen3, the models are named using the scheme `Qwen3[-size][-type][-date]`: - `size`: the notation of the structure and the parameter counts. Dense models use the total saved parameters, e.g., `4B` and `32B`, while MoE models use the total saved parameters and the activated parameters for each token with a prepended `A`, e.g., `30B-A3B` and `235B-A22B`. - `type`: there are currently 4 types: - `-Instruct`: the instruction following models that follow the predefined chat template, used for conducting tasks in conversations, downstream fine-tuning, etc. - `-Thinking`: the thinking models that follow the predefined chat template and use chain-of-thoughts (CoT) to think deeply about the questions, used for solving complex problems. - `-Base`: the pre-trained models that do not know the predefined chat template, used for in-context learning, downstream fine-tuning, etc. - No type: the models with hybrid thinking modes. - `date`: the released date in yearmonth format, e.g., `2507`. ## Tokens & Tokenization Tokens represent the fundamental units that models process and generate. They can represent texts in human languages (regular tokens) or represent specific functionality like keywords in programming languages (control tokens [^special]). Typically, a tokenizer is used to split text into regular tokens, which can be words, subwords, or characters depending on the specific tokenization scheme employed, and furnish the token sequence with control tokens as needed. The vocabulary size, or the total number of unique tokens a model recognizes, significantly impacts its performance and versatility. Larger language models often use sophisticated tokenization methods to handle the vast diversity of human language while keeping the vocabulary size manageable. Qwen use a relatively large vocabulary of 151,646 tokens in total. [^special]: Control tokens can be called special tokens. However, the meaning of special tokens need to be interpreted based on the contexts: special tokens may contain extra regular tokens. ### Byte-level Byte Pair Encoding Qwen adopts a subword tokenization method called Byte Pair Encoding (BPE), which attempts to learn the composition of tokens that can represent the text with the fewest tokens. For example, the string ` tokenization` is decomposed as ` token` and `ization` (note that the space is part of the token). Especially, the tokenization of Qwen ensures that there is no unknown words and all texts can be transformed to token sequences. There are 151,643 tokens as a result of BPE in the vocabulary of Qwen, which is a large vocabulary efficient for diverse languages. As a rule of thumb, 1 token is 3~4 characters for English texts and 1.5~1.8 characters for Chinese texts. ### Control Tokens Control tokens are special tokens inserted into the sequence that signifies meta information. For example, in pre-training, multiple documents may be packed into a single sequence. For Qwen, the control token `<|endoftext|>` is inserted after each document to signify that the document has ended and a new document will proceed. Common control tokens and their status with respect to Qwen can be found in the following table: | Type | Qwen (training) | Note | | :-- | :-- | :-- | | eod token | `<\|endoftext\|>` | end of document, which are inserted between documents inside a packed training sequence | | bot token | `<\|im_start\|>` | start of each turn, which is prepended to each turn | | eot token | `<\|im_end\|>` | end of each turn, which is appended to each turn | | unk token | no unk token | BBPE ensures no unknown tokens for Qwen. | | pad token | no pad token | Qwen does not make use of padded sequence in training. One could use any special token together with the attention masks returned by the tokenizer. It is commonly set the same as eod for Qwen. | | bos token | no bos token | Qwen does not prepend a fixed token to each packed training sequence.[^boseos] | | eos token | no eos token | Qwen does not append a fixed token to each packed training sequence. However, as most frameworks do not have the concept of eot and use eos instead for stopping criteria in inference, eos token is set to eot for Qwen.[^boseos] | [^boseos]: bos token should not be set to `<\|im_start\|>` or you may see double bot tokens for the first turn in fine-tuning. eos token set to `<\|im_end\|>` is fine, because double eot tokens for the last turn are less harmful in fine-tuning. ## Chat Template Chat templates provide a structured format for conversational interactions, where predefined placeholders or prompts are used to elicit responses from the model that adhere to a desired dialogue flow or context. Different models may use different kinds of chat template to format the conversations. It is crucial to use the designated one to ensure the precise control over the LLM's generation process. Qwen uses the following format (ChatML[^chatml]), making use of control tokens to format each turn in the conversations ```text <|im_start|>{{role}} {{content}}<|im_end|> ``` The user input takes the role of `user` and the model generation takes the role of `assistant`. Qwen also supports the meta message that instruct the model to perform specific actions or generate text with certain characteristics, such as altering tone, style, or content, which takes the role of `system`. Starting with Qwen3, no default system messages are used. The following is a full example: ```text <|im_start|>system You are a cat.<|im_end|> <|im_start|>user hello<|im_end|> <|im_start|>assistant *Meow~* Hello there! The sun is shining so brightly today, and I'm feeling extra fluffy. Did you bring me a treat? 🐾<|im_end|> <|im_start|>user Explain large language models like I'm 5.<|im_end|> <|im_start|>assistant *Paws at a toy, then looks up with curious eyes* Hey there! 🐾 Imagine you have a super-smart robot friend who loves to talk and play. This robot has *gigantic* brainpower (like a million puzzle pieces all stuck together!) and knows *everything* about stories, animals, and even how to make up new words. When you ask it a question, like “What’s a rainbow?” it uses its brain to find the answer and then *tells you* it in a way that makes sense. It can even help you write a story or solve a puzzle! But here’s the magic: it’s not just a robot—it’s like a *super-duper* smart helper that learns more every day. It’s like having a friend who’s always curious and wants to help you explore the world! 🌟 *Meow~* Want to ask it something fun? 😺<|im_end|><|endoftext|> ``` [^chatml]: For historical reference only, ChatML is first described by the OpenAI Python SDK. The last available version is [this](https://github.com/openai/openai-python/blob/v0.28.1/chatml.md). Please also be aware that that document lists use cases intended for OpenAI models. For Qwen2.5 models, please only use as in our guide. ### Tool Calling Qwen3 supports tool calling or function calling and uses a template akin to [Hermes](https://github.com/NousResearch/Hermes-Function-Calling#prompt-format-for-function-calling). The template is as follows: ```text <|im_start|>system # Tools You may call one or more functions to assist with the user query. You are provided with function signatures within XML tags: {{JSON Schema of function 1}} {{JSON Schema of function 2}} For each function call, return a json object with function name and arguments within XML tags: {"name": , "arguments": } <|im_end|> <|im_start|>user {{user content}}<|im_end|> <|im_start|>assistant {{tool call 1}} {{tool call 2}} <|im_end|> <|im_start|>user {{tool result 1}} {{tool result 2}} <|im_end|> <|im_start|>assistant {{assistant content}}<|im_end|> ``` It should be noted that - The models support parallel tool calling and mulit-turn/multi-step tool calling. - There may be additional content in assistant messages containing tool calls. - The arguments field in the generated tool calls should be of type object instead of type string. - Tool results are treated as special user messages. In general, we recommend using the tokenizer to format the tool calls or let Qwen-Agent handle the formatting. ### Thinking Qwen3 supports thinking mode and uses a structured format for thinking content, which uses the `` and `` tokens to separate the thinking content from the regular response. The template for the final round is as follows: ```text <|im_start|>user {{user content}}<|im_end|> <|im_start|>assistant {{thinking content}} {{assistant content}}<|im_end|> ``` The thinking block should only be included in the final round except for multi-step tool calls. ## Causal Language Models Causal language models, also known as autoregressive language models or decoder-only language models, are a type of machine learning model designed to predict the next token in a sequence based on the preceding tokens. In other words, they generate text one token at a time, using the previously generated tokens as context. The "causal" aspect refers to the fact that the model only considers the past context (the already generated tokens) when predicting the next token, not any future tokens. Causal language models are widely used for various natural language processing tasks involving text completion and generation. They have been particularly successful in generating coherent and contextually relevant text, making them a cornerstone of modern natural language understanding and generation systems. Qwen models are causal language models suitable for text completion. ### Context Length As Qwen models are causal language models, in theory there is only one length limit of the entire sequence. However, since there is often packing in training and each sequence may contain multiple individual pieces of texts. **How long the model can generate or complete ultimately depends on the use case and in that case how long each document (for pre-training) or each turn (for post-training) is in training.** For Qwen3, the packed sequence length in pre-training is 32,768 tokens and may be extended to 131,072 tokens if mentioned in the modelcards. The maximum length of the assistant message is 38,912 tokens for thinking modes and 16,384 tokens for non-thinking modes. For Qwen3-2507, the packed sequence length in pre-training is 262,144 tokens and may be extended to 1M tokens. The maximum length of the assistant message is 81,920 tokens for thinking models and 16,384 tokens for instruct models. :::{tip} In our testing, we find that the post-trained models could generate coherent content that is far longer than what is trained on, e.g., from 16,384 tokens to 32,768 tokens, especially for coding and similar tasks that have "clear rules". In general, we advise that one should evaluate the quality of the generated content of different lengths before determining the optimal generation length. ::: ================================================ FILE: docs/source/getting_started/quantization_benchmark.rst ================================================ Performance of Quantized Models ================================== .. attention:: To be updated for Qwen3. This section reports the generation performance of quantized models (including GPTQ and AWQ) of the Qwen2 series. Specifically, we report: * MMLU (Accuracy) * C-Eval (Accuracy) * IFEval (Strict Prompt-Level Accuracy) We use greedy decoding in evaluating all models. +---------------------+--------------+---------+-------+--------+--------+ | | Quantization | Average | MMLU | C-Eval | IFEval | +=====================+==============+=========+=======+========+========+ | Qwen2-72B-Instruct | BF16 | 81.3 | 82.3 | 83.8 | 77.6 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int8 | 80.7 | 81.3 | 83.4 | 77.5 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int4 | 81.2 | 80.8 | 83.9 | 78.9 | + +--------------+---------+-------+--------+--------+ | | AWQ | 80.4 | 80.5 | 83.9 | 76.9 | +---------------------+--------------+---------+-------+--------+--------+ | Qwen2-7B-Instruct | BF16 | 66.9 | 70.5 | 77.2 | 53.1 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int8 | 66.2 | 69.1 | 76.7 | 52.9 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int4 | 64.1 | 67.8 | 75.2 | 49.4 | + +--------------+---------+-------+--------+--------+ | | AWQ | 64.1 | 67.4 | 73.6 | 51.4 | +---------------------+--------------+---------+-------+--------+--------+ | Qwen2-1.5B-Instruct | BF16 | 48.4 | 52.4 | 63.8 | 29.0 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int8 | 48.1 | 53.0 | 62.5 | 28.8 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int4 | 45.0 | 50.7 | 57.4 | 27.0 | + +--------------+---------+-------+--------+--------+ | | AWQ | 46.5 | 51.6 | 58.1 | 29.9 | +---------------------+--------------+---------+-------+--------+--------+ | Qwen2-0.5B-Instruct | BF16 | 34.4 | 37.9 | 45.2 | 20.0 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int8 | 32.6 | 35.6 | 43.9 | 18.1 | + +--------------+---------+-------+--------+--------+ | | GPTQ-Int4 | 29.7 | 33.0 | 39.2 | 16.8 | + +--------------+---------+-------+--------+--------+ | | AWQ | 31.1 | 34.4 | 42.1 | 16.7 | +---------------------+--------------+---------+-------+--------+--------+ ================================================ FILE: docs/source/getting_started/quickstart.md ================================================ # Quickstart This guide helps you quickly start using Qwen3. We provide examples of [Hugging Face Transformers](https://github.com/huggingface/transformers) as well as [ModelScope](https://github.com/modelscope/modelscope), and [vLLM](https://github.com/vllm-project/vllm) and [SGLang](https://github.com/sgl-project/sglang) for deployment. You can find Qwen3 models in [the Qwen3 collection](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) at Hugging Face Hub and [the Qwen3 collection](https://www.modelscope.cn/collections/Qwen3-9743180bdc6b48) at ModelScope. ## Transformers To get a quick start with Qwen3, you can try the inference with `transformers` first. Make sure that you have installed `transformers>=4.51.0`. We advise you to use Python 3.10 or higher, and PyTorch 2.6 or higher. :::::{tab-set} :sync-group: model ::::{tab-item} Qwen3-Instruct-2507 :sync: instruct :::{important} Qwen3-Instruct-2507 supports **only non-thinking mode** and **does not generate ```` blocks** in its output. Different from Qwen3-2504, **specifying `enable_thinking=False` is no longer required or supported**. ::: The following contains a code snippet illustrating how to use Qwen3-235B-A22B-Instruct-2507 to generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` :::{Note} We recommend `temperature=0.7`, `top_p=0.8`, `top_k=20`, and `min_p=0` for Qwen3-Instruct-2507 models. For supported frameworks, adjust `presence_penalty` between 0 and 2 to reduce repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. ::: :::{Note} Qwen3-Instruct-2507 may use CoT (chain-of-thoughts) automatically for complex tasks. We recommend using an output length of 16,384 tokens for most queries. ::: :::: ::::{tab-item} Qwen3-Thinking-2507 :sync: thinking :::{important} Qwen3-Thinking-2507 supports **only thinking mode**. Additionally, to enforce model thinking, the default chat template automatically includes ``. Therefore, it is normal for the model's output to contain only `` without an explicit opening `` tag. ::: The following contains a code snippet illustrating how to use Qwen3-235B-A22B-Thinking-2507 to generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-235B-A22B-Thinking-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) # no opening tag print("content:", content) ``` :::{note} We recommend `temperature=0.6`, `top_p=0.95`, `top_k=20`, and `min_p=0` for Qwen3-Thinking-2507 models. For supported frameworks, adjust `presence_penalty` between 0 and 2 to reduce repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. ::: :::{note} Qwen3-Thinking-2507 features increased thinking depth. We strongly recommend its use in highly complex reasoning tasks with adequate maximum generation length. ::: :::: ::::{tab-item} Qwen3 :sync: hybrid The following is a very simple code snippet showing how to run Qwen3-8B: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) # prepare the model input prompt = "Give me a short introduction to large language models." messages = [ {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parse thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` Qwen3 will think before respond, similar to QwQ models. This means the model will use its reasoning abilities to enhance the quality of generated responses. The model will first generate thinking content wrapped in a `...` block, followed by the final response. - Hard Switch: To strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models, you can set `enable_thinking=False` when formatting the text. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False, # Setting enable_thinking=False disables thinking mode ) ``` It can be particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. - Soft Switch: Qwen3 also understands the user's instruction on its thinking behavior, in particular, the soft switch `/think` and `/no_think`. You can add them to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. :::{note} For thinking mode, use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0 (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For non-thinking mode, we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0. ::: :::: ::::: ## ModelScope To tackle with downloading issues, we advise you to try [ModelScope](https://github.com/modelscope/modelscope). Before starting, you need to install `modelscope` with `pip`. `modelscope` adopts a programmatic interface similar (but not identical) to `transformers`. For basic usage, you can simply change the first line of code above to the following: ```python from modelscope import AutoModelForCausalLM, AutoTokenizer ``` For more information, please refer to [the documentation of `modelscope`](https://www.modelscope.cn/docs). ## OpenAI API Compatibility You can serve Qwen3 via OpenAI-compatible APIs using frameworks such as vLLM, SGLang, and interact with the API using common HTTP clients or the OpenAI SDKs. :::::{tab-set} :sync-group: model ::::{tab-item} Qwen3-Instruct-2507 :sync: instruct Here we take Qwen3-235B-A22B-Instruct-2507 as an example to start the API: - SGLang (`sglang>=0.4.6.post1` is required): ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 --port 8000 --tp 8 --context-length 262144 ``` - vLLM (`vllm>=0.9.0` is recommended): ```shell vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 ``` :::{note} Consider adjusting the context length according to the available GPU memory. ::: :::: ::::{tab-item} Qwen3-Thinking-2507 :sync: thinking Here we take Qwen3-235B-A22B-Thinking-2507 as an example to start the API: - SGLang (`sglang>=0.4.6.post1` is required): ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Thinking-2507 --port 8000 --tp 8 --context-length 262144 --reasoning-parser deepseek-r1 ``` - vLLM (`vllm>=0.9.0` is recommended): ```shell vllm serve Qwen/Qwen3-235B-A22B-Thinking-2507 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --enable-reasoning --reasoning-parser deepseek_r1 ``` :::{note} Consider adjusting the context length according to the available GPU memory. ::: :::{important} We are currently working on adapting the `qwen3` reasoning parsers to the new behavior. Please follow the command above at the moment. ::: :::: ::::{tab-item} Qwen3 :sync: hybrid Here we take Qwen3-8B as an example to start the API: - SGLang (`sglang>=0.4.6.post1` is required): ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --port 8000 --reasoning-parser qwen3 ``` - vLLM (`vllm>=0.9.0` is recommended): ```shell vllm serve Qwen/Qwen3-8B --port 8000 --enable-reasoning --reasoning-parser qwen3 ``` :::: ::::: Then, you can use the [create chat interface](https://platform.openai.com/docs/api-reference/chat/completions/create) to communicate with Qwen: ::::::{tab-set} :sync-group: model :::::{tab-item} Qwen3-Instruct-2507 :sync: instruct Here we show the basic command to interact with the chat completion API using Qwen3-235B-A22B-Instruct-2507. ::::{tab-set} :sync-group: api :::{tab-item} curl :sync: curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-235B-A22B-Instruct-2507", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.7, "top_p": 0.8, "top_k": 20, "max_tokens": 16384 }' ``` ::: :::{tab-item} Python :sync: python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-235B-A22B-Instruct-2507", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=16384, temperature=0.7, top_p=0.8, extra_body={ "top_k": 20, } ) print("Chat response:", chat_response) ``` :::: ::::: :::::{tab-item} Qwen3-Thinking-2507 :sync: thinking Here we show the basic command to interact with the chat completion API using Qwen3-235B-A22B-Thinking-2507. ::::{tab-set} :sync-group: api :::{tab-item} curl :sync: curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-235B-A22B-Thinking-2507", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 32768 }' ``` ::: :::{tab-item} Python :sync: python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-235B-A22B-Thinking-2507", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=32768, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, } ) print("Chat response:", chat_response) ``` :::: ::::: :::::{tab-item} Qwen3 :sync: hybrid Here we show the basic command to interact with the chat completion API using Qwen3-8B. The default is with thinking enabled: ::::{tab-set} :sync-group: api :::{tab-item} curl :sync: curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 32768 }' ``` ::: :::{tab-item} Python :sync: python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=32768, temperature=0.6, top_p=0.95, extra_body={ "top_k": 20, } ) print("Chat response:", chat_response) ``` ::: :::: To disable thinking, one could use the soft switch (e.g., appending `/nothink` to the user query). The hard switch can also be used as follows: ::::{tab-set} :sync-group: api :::{tab-item} curl :sync: curl ```shell curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen3-8B", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "temperature": 0.7, "top_p": 0.8, "top_k": 20, "max_tokens": 8192, "presence_penalty": 1.5, "chat_template_kwargs": {"enable_thinking": false} }' ``` ::: :::{tab-item} Python :sync: python You can use the API client with the `openai` Python SDK as shown below: ```python from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen3-8B", messages=[ {"role": "user", "content": "Give me a short introduction to large language models."}, ], max_tokens=8192, temperature=0.7, top_p=0.8, presence_penalty=1.5, extra_body={ "top_k": 20, "chat_template_kwargs": {"enable_thinking": False}, } ) print("Chat response:", chat_response) ``` ::: ::::: :::::: For more usage, please refer to our document on [SGLang](../deployment/sglang) and [vLLM](../deployment/vllm). ## Thinking Budget Qwen3 supports the configuration of thinking budget. It is achieved by ending the thinking process once the budget is reached and guiding the model to generate the "summary" with an early-stopping prompt. Since this feature involves customization specific to each model, it is currently not available in the open-source frameworks and only implemented by [the Alibaba Cloud Model Studio API](https://www.alibabacloud.com/help/en/model-studio/deep-thinking#6f0633b9cdts1). However, with existing open-source frameworks, one can generate twice to implement this feature as follows: 1. For the first time, generate tokens up to the thinking budget and check if the thinking process is finished. If the thinking process is not finished, append the early-stopping prompt. 2. For the second time, continue generation until the end of the content or the upper length limit is fulfilled. The following snippet shows the implementation with Hugging Face Transformers: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" thinking_budget = 16 max_new_tokens = 32768 # load the tokenizer and the model model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) # prepare the model input prompt = "Give me a short introduction to large language models." messages = [ {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) input_length = model_inputs.input_ids.size(-1) # first generation until thinking budget generated_ids = model.generate( **model_inputs, max_new_tokens=thinking_budget ) output_ids = generated_ids[0][input_length:].tolist() # check if the generation has already finished (151645 is <|im_end|>) if 151645 not in output_ids: # check if the thinking process has finished (151668 is ) # and prepare the second model input if 151668 not in output_ids: print("thinking budget is reached") early_stopping_text = "\n\nConsidering the limited time by the user, I have to give the solution based on the thinking directly now.\n\n\n" early_stopping_ids = tokenizer([early_stopping_text], return_tensors="pt", return_attention_mask=False).input_ids.to(model.device) input_ids = torch.cat([generated_ids, early_stopping_ids], dim=-1) else: input_ids = generated_ids attention_mask = torch.ones_like(input_ids, dtype=torch.int64) # second generation generated_ids = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=input_length + max_new_tokens - input_ids.size(-1) # could be negative if max_new_tokens is not large enough (early stopping text is 24 tokens) ) output_ids = generated_ids[0][input_length:].tolist() # parse thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` You should see the output in the console like the following ```text thinking budget is reached thinking content: Okay, the user is asking for a short introduction to large language models Considering the limited time by the user, I have to give the solution based on the thinking directly now. content: Large language models (LLMs) are advanced artificial intelligence systems trained on vast amounts of text data to understand and generate human-like language. They can perform tasks such as answering questions, writing stories, coding, and translating languages. LLMs are powered by deep learning techniques and have revolutionized natural language processing by enabling more context-aware and versatile interactions with text. Examples include models like GPT, BERT, and others developed by companies like OpenAI and Alibaba. ``` :::{note} For purpose of demonstration only, `thinking_budget` is set to 16. However, `thinking_budget` should not be set to that low in practice. We recommend tuning `thinking_budget` based on the latency users can accept and setting it higher than 1024 for meaningful improvements across tasks. If thinking is not desired at all, developers should make use of the hard switch instead. ::: ## Next Step Now, you can have fun with Qwen3 models. Would love to know more about its usage? Feel free to check other documents in this documentation. ================================================ FILE: docs/source/getting_started/speed_benchmark.md ================================================ # Speed Benchmark We report the speed performance of bfloat16 models and quantized models (including FP8, GPTQ, AWQ) of the Qwen3 series. Specifically, we report the inference speed (tokens/s) as well as memory footprint (GB) under different context lengths. ## Environments ### Hugging Face Transformers - **Hardware**: - NVIDIA H20 96GB - **Software for Non-AutoAWQ**: - PyTorch 2.6.0 - Flash Attention 2.7.4 - Transformers 4.51.3 - GPTQModel 2.2.0+cu128torch2.6 - **Software for AutoAWQ**: - PyTorch 2.6.0+cu124 - Transformers 4.51.3 - AutoAWQ 0.2.9 - AutoAWQ_kernels 0.0.9 ### SGLang - **Hardware**: - NVIDIA H20 96GB - **Software**: - PyTorch 2.6.0+cu124 - Transformers 4.51.3 - SGLang 0.4.6.post1 - SGL-kernel 0.1.0 - vLLM 0.7.2 (Required by SGLang for AWQ quantization) ## Notes - **Inference Speed (tokens/s)** is calculated as: ```{math} \text{Speed} = \frac{\text{tokens}_{\text{prompt}} + \text{tokens}_{\text{generation}}}{\text{time}} ``` - We use a **batch size of 1** and the **minimum number of GPUs** possible for evaluation. - We test the **speed and memory usage** when generating **2048 tokens**, with input lengths of `1`, `6144`, `14336`, `30720`, `63488`, and `129024` tokens. - **For SGLang**: - **Memory usage** is not reported because SGLang pre-allocates all GPU memory. By default, we set `mem_fraction_static=0.85`. - We configure `context_length=140000` and enable `enable_mixed_chunk=True`. - For **AWQ quantization**, we use the **awq_marlin** backend. - We set `skip_tokenizer_init=True` and perform generation using `input_ids` instead of raw text prompts. - **FP8 Performance in Transformers**: The inference speed of Transformers in FP8 mode is currently not optimal and requires further optimization. - **GPTQ-INT4 Performance in SGLang**: The performance of GPTQ-INT4 in SGLang also needs improvement, and we are actively working with the team to enhance it. ## Results ### Qwen3-0.6B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-0.6B 1BF161414.17
FP81458.03
GPTQ-Int81344.92
6144BF1611426.46
FP811572.95
GPTQ-Int811234.29
14336BF1612478.02
FP812689.08
GPTQ-Int812198.82
30720BF1613577.42
FP813819.86
GPTQ-Int813342.06
### Qwen3-0.6B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory(MB)
Qwen3-0.6B 1BF16158.571394
FP8124.601217
GPTQ-Int8126.56986
6144BF161154.822066
FP8173.961943
GPTQ-Int8193.841658
14336BF161168.482963
FP81104.992839
GPTQ-Int81219.612554
30720BF161175.934755
FP81132.784632
GPTQ-Int81345.714347
### Qwen3-1.7B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-1.7B 1BF161227.80
FP81333.90
GPTQ-Int81257.40
6144BF161838.28
FP811198.20
GPTQ-Int81945.91
14336BF1611525.71
FP812095.61
GPTQ-Int811707.63
30720BF1612439.03
FP813165.32
GPTQ-Int812706.16
### Qwen3-1.7B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory(MB)
Qwen3-1.7B 1BF16159.833412
FP8123.832726
GPTQ-Int8128.062229
6144BF161238.534213
FP8190.873462
GPTQ-Int81110.822901
14336BF161352.595109
FP81153.374359
GPTQ-Int81222.783798
30720BF161418.136902
FP81235.616151
GPTQ-Int81386.855590
### Qwen3-4B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-4B 1BF161133.13
FP81200.61
AWQ-INT41199.71
6144BF161466.19
FP81662.26
AWQ-INT41640.07
14336BF161789.25
FP811066.23
AWQ-INT411006.23
30720BF1611165.75
FP811467.71
AWQ-INT411358.84
63488BF1611423.98
FP811660.67
AWQ-INT411513.97
129042BF1611371.04
FP811497.27
AWQ-INT411375.71
### Qwen3-4B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory(MB)
Qwen3-4B 1BF16145.947973
FP8117.335281
AWQ-INT4151.572915
6144BF161159.958860
FP8160.556144
AWQ-INT41183.043881
14336BF161195.3110012
FP8196.817297
AWQ-INT41265.225151
30720BF161217.9712317
FP81138.849611
AWQ-INT41481.697742
### Qwen3-8B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-8B 1BF16181.73
FP81150.25
AWQ-INT41144.11
6144BF161296.25
FP81516.64
AWQ-INT41477.89
14336BF161524.70
FP81859.92
AWQ-INT41770.44
30720BF161832.67
FP811242.24
AWQ-INT411075.91
63488BF1611112.78
FP811476.46
AWQ-INT411254.91
129042BF1611173.32
FP811393.21
AWQ-INT411198.06
### Qwen3-8B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory(MB)
Qwen3-8B 1BF16145.3215947
FP8115.469323
AWQ-INT4151.336177
6144BF161146.1216811
FP8155.0710187
AWQ-INT41163.237113
14336BF161183.2917963
FP8189.6411340
AWQ-INT41242.978409
30720BF161208.9820267
FP81130.9313644
AWQ-INT41438.6211001
### Qwen3-14B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-14B 1BF16147.10
FP8197.11
AWQ-INT4196.49
6144BF161174.85
FP81342.95
AWQ-INT41321.62
14336BF161317.56
FP81587.33
AWQ-INT41525.74
30720BF161525.80
FP81880.72
AWQ-INT41744.74
63488BF161742.36
FP811089.04
AWQ-INT41884.06
129042BF161826.15
FP811049.64
AWQ-INT41857.56
### Qwen3-14B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory (MB)
Qwen3-14B 1BF16140.6628402
FP8113.0216012
AWQ-INT4144.679962
6144BF161108.5229495
FP8144.8616972
AWQ-INT41128.0811020
14336BF161136.3630775
FP8171.9618253
AWQ-INT41220.6212438
30720BF161155.3833336
FP81102.6320813
AWQ-INT41363.2515323
### Qwen3-32B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-32B 1BF16120.72
FP8146.17
AWQ-INT4147.67
6144BF16177.82
FP81165.71
AWQ-INT41159.99
14336BF161143.08
FP81287.60
AWQ-INT41260.44
30720BF161240.75
FP81436.59
AWQ-INT41366.84
63488BF161342.96
FP81532.18
AWQ-INT41425.23
129042BF162711.40TP=2
FP81491.45
AWQ-INT41395.96
### Qwen3-32B (Transformers)
Model Input Length Quantization GPU Num Speed (tokens/s) GPU Memory (MB)
Qwen3-32B 1BF16126.2462751
FP817.3733379
AWQ-INT4141.819109
6144BF16151.4164583
FP8123.5734915
AWQ-INT4168.7120795
14336BF16162.4166632
FP8136.3036963
AWQ-INT41107.0223105
30720BF16169.1670728
FP8149.4441060
AWQ-INT41188.1127718
### Qwen3-30B-A3B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-30B-A3B 1BF161137.18
FP81155.55
GPTQ-INT4131.29GPTQ-Marlin
6144BF161490.10
FP81551.34
GPTQ-INT41120.13GPTQ-Marlin
14336BF161849.62
FP81945.13
GPTQ-INT41227.27GPTQ-Marlin
30720BF1611283.94
FP811405.91
GPTQ-INT41404.45GPTQ-Marlin
63488BF1611538.79
FP811647.89
GPTQ-INT41617.09GPTQ-Marlin
129042BF1611385.65
FP811442.14
GPTQ-INT41704.82GPTQ-Marlin
### Qwen3-30B-A3B (Transformers)
Model Input length Quantization GPU Num Speed (tokens/s) GPU Memory (MB) Notes
Qwen3-30B-A3B 1BF1611.8958462
FP810.4430296
GPTQ-INT4---MoE Kernel Unsupported
6144BF1617.4559037
FP811.7730872
GPTQ-INT4---MoE Kernel Unsupported
14336BF16114.4759806
FP813.531641
GPTQ-INT4---MoE Kernel Unsupported
30720BF16127.0361342
FP816.8633177
GPTQ-INT4---MoE Kernel Unsupported
### Qwen3-235B-A22B (SGLang)
Model Input Length Quantization GPU Num Speed (tokens/s) Note
Qwen3-235B-A22B 1BF16874.50TP=8
FP8471.65TP=4
GPTQ-INT4414.69TP=4
GPTQ-Marlin
6144BF168289.03TP=8
FP84275.16TP=4
GPTQ-INT4456.97TP=4
GPTQ-Marlin
14336BF168546.73TP=8
FP84514.23TP=4
GPTQ-INT44109.13TP=4
GPTQ-Marlin
30720BF168979.41TP=8
FP84887.90TP=4
GPTQ-INT44198.99TP=4
GPTQ-Marlin
63488BF1681493.91TP=8
FP841269.34TP=4
GPTQ-INT44422.77TP=4
GPTQ-Marlin
129042BF1681639.54TP=8
FP841319.66TP=4
GPTQ-INT44552.28TP=4
GPTQ-Marlin
================================================ FILE: docs/source/getting_started/thinking_budget.md ================================================ # Thinking budget This example demonstrates the inference process with thinking budgets using Qwen3 series models. The process involves two steps: 1. the model generates reasoning content within the specified thinking budget 2. append the reasoning content to the conversation context and call the model again to get the final response ## Environment Setup - `transformers >= 4.51.0` - `openai >= 1.65.0` ## Basic Usage First, you should start a Qwen3 model in thinking mode. You can refer to [Quickstart](https://github.com/QwenLM/Qwen3/blob/main/docs/source/getting_started/quickstart.md) for more details. Then, you can use the following code to call the model with thinking budgets. ```python from typing import Any, Dict, List import openai from transformers import AutoTokenizer class ThinkingBudgetClient: def __init__(self, base_url: str, api_key: str, tokenizer_name_or_path: str): self.base_url = base_url self.api_key = api_key self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path) self.client = openai.OpenAI( base_url=self.base_url, api_key=self.api_key ) def chat_completion( self, model: str, messages: List[Dict[str, Any]], thinking_budget: int = 512, max_tokens: int = 1024, **kwargs ) -> Dict[str, Any]: assert max_tokens > thinking_budget, f"thinking budget must be smaller than maximum new tokens. Given {max_tokens=} and {thinking_budget=}" # 1. first call chat completion to get reasoning content response = self.client.chat.completions.create( model=model, messages=messages, max_tokens=thinking_budget, **kwargs ) content = response.choices[0].message.content reasoning_content = response.choices[0].message.reasoning_content.strip("\n") if content is None: # reasoning content is too long reasoning_content = ( f"{reasoning_content}" "\n\nConsidering the limited time by the user, " "I have to give the solution based on the thinking directly now." ) reasoning_tokens_len = len(self.tokenizer.encode(reasoning_content, add_special_tokens=False)) remaining_tokens = max_tokens - reasoning_tokens_len assert remaining_tokens > 0, f"remaining tokens must be positive. Given {remaining_tokens=}. Increase the max_tokens or lower the thinking_budget." # 2. append reasoning content to messages and call completion messages.append({"role": "assistant", "content": f"\n{reasoning_content}\n\n\n"}) prompt = self.tokenizer.apply_chat_template( messages, tokenize=False, continue_final_message=True ) response = self.client.completions.create( model=model, prompt=prompt, max_tokens=remaining_tokens, **kwargs ) response_data = { "reasoning_content": reasoning_content, "content": response.choices[0].text, "finish_reason": response.choices[0].finish_reason, } return response_data tokenizer_name_or_path = "Qwen/Qwen3-8B" client = ThinkingBudgetClient( base_url="http://localhost:30000/v1", # Qwen3 deployed in thinking mode api_key="EMPTY", tokenizer_name_or_path=tokenizer_name_or_path ) result = client.chat_completion( model="Qwen3-8B", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me a funny story about a cat."} ], thinking_budget=512, max_tokens=1024, ) print(result["content"]) ``` ================================================ FILE: docs/source/index.rst ================================================ Welcome to Qwen! ================ .. figure:: https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png :width: 60% :align: center :alt: Qwen3 :class: no-scaled-link Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Both language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc. Qwen3-2507 ---------- With input from the community and insights from further research, Instruct-only and Thinking-only models are coming back! The results are Qwen3-2507: **Qwen3-Instruct-2507** has the following features: - **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**. - **Substantial gains** in long-tail knowledge coverage across **multiple languages**. - **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation. - **Enhanced capabilities** in **256K long-context understanding**, extensible to 1M. **Qwen3-Thinking-2507** has the following features: - **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise — achieving **state-of-the-art results among open-source thinking models**. - **Markedly better general capabilities**, such as instruction following, tool usage, text generation, and alignment with human preferences. - **Enhanced 256K long-context understanding** capabilities, extensible to 1M. Qwen3 ----- Qwen3, aka Qwen3-2504, has the following features: - **Dense and Mixture-of-Experts (MoE) models**, available in 0.6B, 1.7B, 4B, 8B, 14B, 32B and 30B-A3B, 235B-A22B. - **Seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose chat) **within a single model**, ensuring optimal performance across various scenarios. - **Significantly enhancement in reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**. Resource & Links ---------------- For more information, please visit our: * `Qwen Home Page `__ * `Chat with Qwen (with Deep Research and Web Dev) `__ * `Blog `__ * `GitHub `__ * `Hugging Face `__ * `ModelScope `__ * `Qwen3 Collection `__ Join our community by joining our `Discord `__ and `WeChat `__ group. We are looking forward to seeing you there! .. toctree:: :maxdepth: 1 :caption: Getting Started :hidden: getting_started/quickstart getting_started/concepts getting_started/speed_benchmark getting_started/quantization_benchmark .. toctree:: :maxdepth: 1 :caption: Inference :hidden: inference/transformers .. toctree:: :maxdepth: 1 :caption: Run Locally :hidden: run_locally/llama.cpp run_locally/ollama run_locally/lmstudio run_locally/mlx-lm .. toctree:: :maxdepth: 1 :caption: Deployment :hidden: deployment/sglang deployment/vllm deployment/tgi deployment/dstack deployment/skypilot deployment/openllm .. toctree:: :maxdepth: 1 :caption: Quantization :hidden: quantization/awq quantization/gptq quantization/llama.cpp .. toctree:: :maxdepth: 1 :caption: Training :hidden: training/axolotl training/llama_factory training/ms_swift training/unsloth training/verl .. toctree:: :maxdepth: 1 :caption: Framework :hidden: framework/qwen_agent framework/function_call framework/LlamaIndex framework/Langchain ================================================ FILE: docs/source/inference/transformers.md ================================================ # Transformers Transformers is a library of pretrained natural language processing for inference and training. Developers can use Transformers to train models on their data, build inference applications, and generate texts with large language models. ## Environment Setup - `transformers>=4.51.0` - `torch>=2.6` is recommended - GPU is recommended ## Basic Usage You can use the `pipeline()` interface or the `generate()` interface to generate texts with Qwen3 in transformers. In general, the pipeline interface requires less boilerplate code, which is shown here. The following shows a basic example using pipeline for multi-turn conversations: ```python from transformers import pipeline model_name_or_path = "Qwen/Qwen3-8B" generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", ) messages = [ {"role": "user", "content": "Give me a short introduction to large language models."}, ] messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) messages.append({"role": "user", "content": "In a single sentence."}) messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) ``` There are some important parameters creating the pipeline: - **Model**: `model_name_or_path` could be a model ID like `Qwen/Qwen3-8B` or a local path. To download model files to a local directory, you could use ```shell huggingface-cli download --local-dir ./Qwen3-8B Qwen/Qwen3-8B ``` You can also download model files using ModelScope if you are in mainland China ```shell modelscope download --local_dir ./Qwen3-8B Qwen/Qwen3-8B ``` - **Device Placement**: `device_map="auto"` will load the model parameters to multiple devices automatically, if available. It relies on the `accelerate` package. If you would like to use a single device, you can pass `device` instead of device_map. `device=-1` or `device="cpu"` indicates using CPU, `device="cuda"` indicates using the current GPU, and `device="cuda:1"` or `device=1` indicates using the second GPU. Do not use `device_map` and `device` at the same time! - **Compute Precision**: `torch_dtype="auto"` will determine automatically the data type to use based on the original precision of the checkpoint and the precision your device supports. For modern devices, the precision determined will be `bfloat16`. If you don't pass `torch_dtype="auto"`, the default data type is `float32`, which will take double the memory and be slower in computation. Calls to the text generation pipeline will use the generation configuration from the model file, e.g., `generation_config.json`. This configuration could be overridden by passing arguments directly to the call. The default is equivalent to ```python messages = generator(messages, do_sample=True, temperature=0.6, top_k=2, top_p=0.95, eos_token_id=[151645, 151643])[0]{"generated_text"} ``` For the best practices in configuring generation parameters, please see the model card. ## Thinking & Non-Thinking Mode By default, Qwen3 model will think before response. It is also true for the `pipeline()` interface. To switch between thinking and non-thinking mode, two methods can be used - Append a final assistant message, containing only `\n\n\n\n`. This method is stateless, meaning it will only work for that single turn. It will also strictly prevent the model from generating thinking content. For example, ```python messages = [ {"role": "user", "content": "Give me a short introduction to large language models."}, {"role": "assistant", "content": "\n\n\n\n"}, ] messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) messages.append({"role": "user", "content": "In a single sentence."}) messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) ``` - Add to the user (or the system) message, `/no_think` to disable thinking and `/think` to enable thinking. This method is stateful, meaning the model will follow the most recent instruction in multi-turn conversations. ```python messages = [ {"role": "user", "content": "Give me a short introduction to large language models./no_think"}, ] messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) messages.append({"role": "user", "content": "In a single sentence./think"}) messages = generator(messages, max_new_tokens=32768)[0]["generated_text"] # print(messages[-1]["content"]) ``` ## Parsing Thinking Content If you would like a more structured assistant message format, you can use the following function to extract the thinking content into a field named `reasoning_content` which is similar to the format used by vLLM, SGLang, etc. ```python import copy import re def parse_thinking_content(messages): messages = copy.deepcopy(messages) for message in messages: if message["role"] == "assistant" and (m := re.match(r"\n(.+)\n\n", message["content"], flags=re.DOTALL)): message["content"] = message["content"][len(m.group(0)):] if thinking_content := m.group(1).strip(): message["reasoning_content"] = thinking_content return messages ``` ## Parsing Tool Calls For tool calling with Transformers, please refer to [our guide on Function Calling](../framework/function_call.md#hugging-face-transformers). ## Serving Quantized models Qwen3 comes with two types of pre-quantized models, FP8 and AWQ. The command serving those models are the same as the original models except for the name change: ```python from transformers import pipeline model_name_or_path = "Qwen/Qwen3-8B-FP8" # FP8 models # model_name_or_path = "Qwen/Qwen3-8B-AWQ" # AWQ models generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", ) ``` :::{note} FP8 computation is supported on NVIDIA GPUs with compute capability > 8.9, that is, Ada Lovelace, Hopper, and later GPUs. For better performance, make sure `triton` and a CUDA compiler compatible with the CUDA version of `torch` in your environment are installed. ::: :::{important} As of 4.51.0, there are issues with Transformers when running those checkpoints **across GPUs**. The following method could be used to work around those issues: - Set the environment variable `CUDA_LAUNCH_BLOCKING=1` before running the script; or - Uncomment [this line](https://github.com/huggingface/transformers/blob/0720e206c6ba28887e4d60ef60a6a089f6c1cc76/src/transformers/integrations/finegrained_fp8.py#L340) in your local installation of `transformers`. ::: ## Enabling Long Context The maximum context length in pre-training for Qwen3 models is 32,768 tokens. It can be extended to 131,072 tokens with RoPE scaling techniques. We have validated the performance with YaRN. Transformers supports YaRN, which can be enabled either by modifying the model files or overriding the default arguments when loading the model. - Modifying the model files: In the `config.json` file, add the rope_scaling fields: ```json { ..., "max_position_embeddings": 131072, "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` - Overriding the default arguments: ```python from transformers import pipeline model_name_or_path = "Qwen/Qwen3-8B" generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", model_kwargs={ "max_position_embeddings": 131072, "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768, }, } ) ``` :::{attention} As of Transformers 4.52.3, it will use `max_position_embeddings/rope_scaling.original_max_position_embeddings` as the `rope_scaling.factor` regradless of the specified `rope_scaling.factor`. See [this issue](https://github.com/huggingface/transformers/issues/38224) for more information. ::: :::{note} Transformers implements static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** We advise adding the `rope_scaling` configuration only when processing long contexts is required. It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. ::: ## Streaming Generation With the help of `TextStreamer`, you can modify your chatting with Qwen3 to streaming mode. It will print the response as being generated to the console or the terminal. ```python from transformers import pipeline, TextStreamer model_name_or_path = "Qwen/Qwen3-8B" generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", ) streamer = TextStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True) messages= generator(messages, max_new_tokens=32768, streamer=streamer)[0]["generated_text"] ``` Besides using `TextStreamer`, we can also use `TextIteratorStreamer` which stores print-ready text in a queue, to be used by a downstream application as an iterator: ```python from transformers import pipeline, TextIteratorStreamer model_name_or_path = "Qwen/Qwen3-8B" generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", ) streamer = TextIteratorStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True) # Use Thread to run generation in background # Otherwise, the process is blocked until generation is complete # and no streaming effect can be observed. from threading import Thread generation_kwargs = dict(text_inputs=messages, max_new_tokens=32768, streamer=streamer) thread = Thread(target=pipe, kwargs=generation_kwargs) thread.start() generated_text = "" for new_text in streamer: generated_text += new_text print(generated_text) ``` ## Batch Generation :::{note} Batching is not automatically a win for performance. ::: ```python from transformers import pipeline model_name_or_path = "Qwen/Qwen3-8B" generator = pipeline( "text-generation", model_name_or_path, torch_dtype="auto", device_map="auto", ) generator.tokenizer.padding_side="left" batch = [ [{"role": "user", "content": "Give me a short introduction to large language models."}], [{"role": "user", "content": "Give me a detailed introduction to large language models."}], ] results = generator(batch, max_new_tokens=32768, batch_size=2) batch = [result[0]["generated_text"] for result in results] ``` ## FAQ You may find distributed inference with Transformers is not as fast as you would imagine. Transformers with `device_map="auto"` does not apply tensor parallelism, and it only uses one GPU at a time. For Transformers with tensor parallelism, please refer to [its documentation](https://huggingface.co/docs/transformers/v4.51.3/en/perf_infer_gpu_multi). ================================================ FILE: docs/source/quantization/awq.md ================================================ # AWQ :::{attention} To be updated for Qwen3. ::: For quantized models, one of our recommendations is the usage of [AWQ](https://arxiv.org/abs/2306.00978) with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ). **AWQ** refers to Activation-aware Weight Quantization, a hardware-friendly approach for LLM low-bit weight-only quantization. **AutoAWQ** is an easy-to-use Python library for 4-bit quantized models. AutoAWQ speeds up models by 3x and reduces memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. In this document, we show you how to use the quantized model with Hugging Face `transformers` and also how to quantize your own model. ## Usage of AWQ Models with Hugging Face transformers Now, `transformers` has officially supported AutoAWQ, which means that you can directly use the quantized model with `transformers`. The following is a very simple code snippet showing how to run `Qwen2.5-7B-Instruct-AWQ` with the quantized model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct-AWQ" model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Usage of AWQ Models with vLLM vLLM has supported AWQ, which means that you can directly use our provided AWQ models or those quantized with `AutoAWQ` with vLLM. We recommend using the latest version of vLLM (`vllm>=0.6.1`) which brings performance improvements to AWQ models; otherwise, the performance might not be well-optimized. Actually, the usage is the same with the basic usage of vLLM. We provide a simple example of how to launch OpenAI-API compatible API with vLLM and `Qwen2.5-7B-Instruct-AWQ`: Run the following in a shell to start an OpenAI-compatible API service: ```bash vllm serve Qwen/Qwen2.5-7B-Instruct-AWQ ``` Then, you can call the API as ```bash curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen/Qwen2.5-7B-Instruct-AWQ", "messages": [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."} ], "temperature": 0.7, "top_p": 0.8, "repetition_penalty": 1.05, "max_tokens": 512 }' ``` or you can use the API client with the `openai` Python package as shown below: ```python from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen2.5-7B-Instruct-AWQ", messages=[ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ], temperature=0.7, top_p=0.8, max_tokens=512, extra_body={ "repetition_penalty": 1.05, }, ) print("Chat response:", chat_response) ``` ## Quantize Your Own Model with AutoAWQ If you want to quantize your own model to AWQ quantized models, we advise you to use AutoAWQ. ```bash pip install "autoawq<0.2.7" ``` Suppose you have finetuned a model based on `Qwen2.5-7B`, which is named `Qwen2.5-7B-finetuned`, with your own dataset, e.g., Alpaca. To build your own AWQ quantized model, you need to use the training data for calibration. Below, we provide a simple demonstration for you to run: ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer # Specify paths and hyperparameters for quantization model_path = "your_model_path" quant_path = "your_quantized_model_path" quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" } # Load your tokenizer and model with AutoAWQ tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoAWQForCausalLM.from_pretrained(model_path, device_map="auto", safetensors=True) ``` Then you need to prepare your data for calibration. What you need to do is just put samples into a list, each of which is a text. As we directly use our finetuning data for calibration, we first format it with ChatML template. For example, ```python data = [] for msg in dataset: text = tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=False) data.append(text.strip()) ``` where each `msg` is a typical chat message as shown below: ```json [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me who you are."}, {"role": "assistant", "content": "I am a large language model named Qwen..."} ] ``` Then just run the calibration process by one line of code: ```python model.quantize(tokenizer, quant_config=quant_config, calib_data=data) ``` Finally, save the quantized model: ```python model.save_quantized(quant_path, safetensors=True, shard_size="4GB") tokenizer.save_pretrained(quant_path) ``` Then you can obtain your own AWQ quantized model for deployment. Enjoy! ================================================ FILE: docs/source/quantization/gptq.md ================================================ # GPTQ :::{attention} To be updated for Qwen3. ::: [GPTQ](https://arxiv.org/abs/2210.17323) is a quantization method for GPT-like LLMs, which uses one-shot weight quantization based on approximate second-order information. In this document, we show you how to use the quantized model with Hugging Face `transformers` and also how to quantize your own model with [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ). ## Usage of GPTQ Models with Hugging Face transformers :::{note} To use the official Qwen2.5 GPTQ models with `transformers`, please ensure that `optimum>=1.20.0` and compatible versions of `transformers` and `auto_gptq` are installed. You can do that by ```bash pip install -U "optimum>=1.20.0" ``` ::: Now, `transformers` has officially supported AutoGPTQ, which means that you can directly use the quantized model with `transformers`. For each size of Qwen2.5, we provide both Int4 and Int8 GPTQ quantized models. The following is a very simple code snippet showing how to run `Qwen2.5-7B-Instruct-GPTQ-Int4`: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4" model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Usage of GPTQ Models with vLLM vLLM has supported GPTQ, which means that you can directly use our provided GPTQ models or those trained with `AutoGPTQ` with vLLM. If possible, it will automatically use the GPTQ Marlin kernel, which is more efficient. Actually, the usage is the same with the basic usage of vLLM. We provide a simple example of how to launch OpenAI-API compatible API with vLLM and `Qwen2.5-7B-Instruct-GPTQ-Int4`: Run the following in a shell to start an OpenAI-compatible API service: ```bash vllm serve Qwen2.5-7B-Instruct-GPTQ-Int4 ``` Then, you can call the API as ```bash curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "Qwen2.5-7B-Instruct-GPTQ-Int4", "messages": [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."} ], "temperature": 0.7, "top_p": 0.8, "repetition_penalty": 1.05, "max_tokens": 512 }' ``` or you can use the API client with the `openai` Python package as shown below: ```python from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen2.5-7B-Instruct-GPTQ-Int4", messages=[ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ], temperature=0.7, top_p=0.8, max_tokens=512, extra_body={ "repetition_penalty": 1.05, }, ) print("Chat response:", chat_response) ``` ## Quantize Your Own Model with AutoGPTQ If you want to quantize your own model to GPTQ quantized models, we advise you to use AutoGPTQ. It is suggested installing the latest version of the package by installing from source code: ```bash git clone https://github.com/AutoGPTQ/AutoGPTQ cd AutoGPTQ pip install -e . ``` Suppose you have finetuned a model based on `Qwen2.5-7B`, which is named `Qwen2.5-7B-finetuned`, with your own dataset, e.g., Alpaca. To build your own GPTQ quantized model, you need to use the training data for calibration. Below, we provide a simple demonstration for you to run: ```python from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from transformers import AutoTokenizer # Specify paths and hyperparameters for quantization model_path = "your_model_path" quant_path = "your_quantized_model_path" quantize_config = BaseQuantizeConfig( bits=8, # 4 or 8 group_size=128, damp_percent=0.01, desc_act=False, # set to False can significantly speed up inference but the perplexity may slightly bad static_groups=False, sym=True, true_sequential=True, model_name_or_path=None, model_file_base_name="model" ) max_len = 8192 # Load your tokenizer and model with AutoGPTQ # To learn about loading model to multiple GPUs, # visit https://github.com/AutoGPTQ/AutoGPTQ/blob/main/docs/tutorial/02-Advanced-Model-Loading-and-Best-Practice.md tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoGPTQForCausalLM.from_pretrained(model_path, quantize_config) ``` However, if you would like to load the model on multiple GPUs, you need to use `max_memory` instead of `device_map`. Here is an example: ```python model = AutoGPTQForCausalLM.from_pretrained( model_path, quantize_config, max_memory={i: "20GB" for i in range(4)} ) ``` Then you need to prepare your data for calibration. What you need to do is just put samples into a list, each of which is a text. As we directly use our finetuning data for calibration, we first format it with ChatML template. For example, ```python import torch data = [] for msg in dataset: text = tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=False) model_inputs = tokenizer([text]) input_ids = torch.tensor(model_inputs.input_ids[:max_len], dtype=torch.int) data.append(dict(input_ids=input_ids, attention_mask=input_ids.ne(tokenizer.pad_token_id))) ``` where each `msg` is a typical chat message as shown below: ```json [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": "Tell me who you are."}, {"role": "assistant", "content": "I am a large language model named Qwen..."} ] ``` Then just run the calibration process by one line of code: ```python import logging logging.basicConfig( format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S" ) model.quantize(data, cache_examples_on_gpu=False) ``` Finally, save the quantized model: ```python model.save_quantized(quant_path, use_safetensors=True) tokenizer.save_pretrained(quant_path) ``` It is unfortunate that the `save_quantized` method does not support sharding. For sharding, you need to load the model and use `save_pretrained` from transformers to save and shard the model. Except for this, everything is so simple. Enjoy! ## Known Issues ### Qwen2.5-72B-Instruct-GPTQ-Int4 cannot stop generation properly :Model: Qwen2.5-72B-Instruct-GPTQ-Int4 :Framework: vLLM, AutoGPTQ (including Hugging Face transformers) :Description: Generation cannot stop properly. Continual generation after where it should stop, then repeated texts, either single character, a phrase, or paragraphs, are generated. :Workaround: The following workaround could be considered 1. Using the original model in 16-bit floating point 2. Using the AWQ variants or llama.cpp-based models for reduced chances of abnormal generation ### Qwen2.5-32B-Instruct-GPTQ-Int4 broken with vLLM on multiple GPUs :Model: Qwen2.5-32B-Instruct-GPTQ-Int4 :Framework: vLLM :Description: Deployment on multiple GPUs and only garbled text like `!!!!!!!!!!!!!!!!!!` could be generated. :Workaround: Each of the following workaround could be considered 1. Using the AWQ or GPTQ-Int8 variants 2. Using a single GPU 3. Using Hugging Face `transformers` if latency and throughput are not major concerns ## Troubleshooting :::{dropdown} With `transformers` and `auto_gptq`, the logs suggest `CUDA extension not installed.` and the inference is slow. `auto_gptq` fails to find a fused CUDA kernel compatible with your environment and falls back to a plain implementation. Follow its [installation guide](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/docs/INSTALLATION.md) to install a pre-built wheel or try installing `auto_gptq` from source. ::: :::{dropdown} Self-quantized Qwen2.5-72B-Instruct-GPTQ with `vllm`, `ValueError: ... must be divisible by ...` is raised. The intermediate size of the self-quantized model is different from the official Qwen2.5-72B-Instruct-GPTQ models. After quantization the size of the quantized weights are divided by the group size, which is typically 128. The intermediate size for the FFN blocks in Qwen2.5-72B is 29568. Unfortunately, {math}`29568 \div 128 = 231`. Since the number of attention heads and the dimensions of the weights must be divisible by the tensor parallel size, it means you can only run the quantized model with `tensor_parallel_size=1`, i.e., one GPU card. A workaround is to make the intermediate size divisible by {math}`128 \times 8 = 1024`. To achieve that, the weights should be padded with zeros. While it is mathematically equivalent before and after zero-padding the weights, the results may be slightly different in reality. Try the following: ```python import torch from torch.nn import functional as F from transformers import AutoModelForCausalLM # must use AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-72B-Instruct", torch_dtype="auto") # this size is Qwen2.5-72B only pad_size = 128 sd = model.state_dict() for i, k in enumerate(sd): v = sd[k] print(k, i) # interleaving the padded zeros if ('mlp.up_proj.weight' in k) or ('mlp.gate_proj.weight' in k): prev_v = F.pad(v.unsqueeze(1), (0, 0, 0, 1, 0, 0)).reshape(29568*2, -1)[:pad_size*2] new_v = torch.cat([prev_v, v[pad_size:]], dim=0) sd[k] = new_v elif 'mlp.down_proj.weight' in k: prev_v= F.pad(v.unsqueeze(2), (0, 1)).reshape(8192, 29568*2)[:, :pad_size*2] new_v = torch.cat([prev_v, v[:, pad_size:]], dim=1) sd[k] = new_v # this is a very large file; make sure your RAM is enough to load the model torch.save(sd, '/path/to/padded_model/pytorch_model.bin') ``` This will save the padded checkpoint to the specified directory. Then, copy other files from the original checkpoint to the new directory and modify the `intermediate_size` in `config.json` to `29696`. Finally, you can quantize the saved model checkpoint. ::: ================================================ FILE: docs/source/quantization/llama.cpp.md ================================================ # llama.cpp Quantization is a major topic for local inference of LLMs, as it reduces the memory footprint. Undoubtably, llama.cpp natively supports LLM quantization and of course, with flexibility as always. At high-level, all quantization supported by llama.cpp is weight quantization: Model parameters are quantized into lower bits, and in inference, they are dequantized and used in computation. In addition, you can mix different quantization data types in a single quantized model, e.g., you can quantize the embedding weights using a quantization data type and other weights using a different one. With an adequate mixture of quantization types, much lower quantization error can be attained with just a slight increase of bit-per-weight. The example program `llama-quantize` supports many quantization presets, such as Q4_K_M and Q8_0. If you find the quantization errors still more than expected, you can bring your own scales, e.g., as computed by AWQ, or use calibration data to compute an importance matrix using `llama-imatrix`, which can then be used during quantization to enhance the quality of the quantized models. In this document, we demonstrate the common way to quantize your model and evaluate the performance of the quantized model. We will assume you have the example programs from llama.cpp at your hand. If you don't, check our guide [here](../run_locally/llama.cpp.html#getting-the-program){.external}. ## Getting the GGUF Now, suppose you would like to quantize `Qwen3-8B`. You need to first make a GGUF file as shown below: ```bash python convert-hf-to-gguf.py Qwen/Qwen3-8B --outfile Qwen3-8B-F16.gguf ``` Since Qwen3 are trained using the bfloat16 precision, the following should keep most information on supported machines: ```bash python convert-hf-to-gguf.py Qwen/Qwen3-8B --outtype bf16 --outfile Qwen3-8B-BF16.gguf ``` Sometimes, it may be better to use fp32 as the start point for quantization. In that case, use ```bash python convert-hf-to-gguf.py Qwen/Qwen3-8B --outtype f32 --outfile Qwen3-8B-F32.gguf ``` ## Quantizing the GGUF without Calibration For the simplest way, you can directly quantize the model to lower-bits based on your requirements. An example of quantizing the model to 8 bits is shown below: ```bash ./llama-quantize Qwen3-8B-F16.gguf Qwen3-8B-Q8_0.gguf Q8_0 ``` `Q8_0` is a code for a quantization preset. You can find all the presets in [the source code of `llama-quantize`](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/quantize.cpp). Look for the variable `QUANT_OPTIONS`. Common ones used for 8B models include `Q8_0`, `Q5_K_M`, and `Q4_K_M`. The letter case doesn't matter, so `q8_0` or `q4_K_m` are perfectly fine. Now you can use the GGUF file of the quantized model with applications based on llama.cpp. Very simple indeed. However, the accuracy of the quantized model could be lower than expected occasionally, especially for lower-bit quantization. The program may even prevent you from doing that. There are several ways to improve quality of quantized models. A common way is to use a calibration dataset in the target domain to identify the weights that really matter and quantize the model in a way that those weights have lower quantization errors, as introduced in the next two methods. ## Quantizing the GGUF with AWQ Scale :::{attention} To be updated for Qwen3. ::: To improve the quality of your quantized models, one possible solution is to apply the AWQ scale, following [this script](https://github.com/casper-hansen/AutoAWQ/blob/main/docs/examples.md#gguf-export). First, when you run `model.quantize()` with `autoawq`, remember to add `export_compatible=True` as shown below: ```python ... model.quantize( tokenizer, quant_config=quant_config, export_compatible=True ) model.save_pretrained(quant_path) ... ``` The above code will not actually quantize the weights. Instead, it adjusts weights based on a dataset so that they are "easier" to quantize.[^AWQ] Then, when you run `convert-hf-to-gguf.py`, remember to replace the model path with the path to the new model: ```bash python convert-hf-to-gguf.py --outfile qwen2.5-7b-instruct-f16-awq.gguf ``` Finally, you can quantize the model as in the last example: ```bash ./llama-quantize qwen2.5-7b-instruct-f16-awq.gguf qwen2.5-7b-instruct-q8_0.gguf Q8_0 ``` In this way, it should be possible to achieve similar quality with lower bit-per-weight. [^AWQ]: If you are interested in what this means, refer to [the AWQ paper](https://arxiv.org/abs/2306.00978). Basically, important weights (called salient weights in the paper) are identified based on activations across data examples. The weights are scaled accordingly such that the salient weights are protected even after quantization. ## Quantizing the GGUF with Importance Matrix Another possible solution is to use the "important matrix"[^imatrix], following [this](https://github.com/ggml-org/llama.cpp/tree/master/tools/imatrix). First, you need to compute the importance matrix data of the weights of a model (`-m`) using a calibration dataset (`-f`): ```bash ./llama-imatrix -m Qwen3-8B-F16.gguf -f calibration-text.txt --chunk 512 -o Qwen3-8B-imatrix.dat -ngl 80 ``` The text is cut in chunks of length `--chunk` for computation. Preferably, the text should be representative of the target domain. The final results will be saved in a file named `Qwen3-8B-imatrix.dat` (`-o`), which can then be used: ```bash ./llama-quantize --imatrix Qwen3-8B-imatrix.dat \ Qwen3-8B-F16.gguf Qwen3-8B-Q4_K_M.gguf Q4_K_M ``` For lower-bit quantization mixtures for 1-bit or 2-bit, if you do not provide `--imatrix`, a helpful warning will be printed by `llama-quantize`. [^imatrix]: Here, the importance matrix keeps record of how weights affect the output: the weight should be important is a slight change in its value causes huge difference in the results, akin to the [GPTQ](https://arxiv.org/abs/2210.17323) algorithm. ## Perplexity Evaluation `llama.cpp` provides an example program for us to calculate the perplexity, which evaluate how unlikely the given text is to the model. It should be mostly used for comparisons: the lower the perplexity, the better the model remembers the given text. To do this, you need to prepare a dataset, say "wiki test"[^wiki]. You can download the dataset with: ```bash wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research -O wikitext-2-raw-v1.zip unzip wikitext-2-raw-v1.zip ``` Then you can run the test with the following command: ```bash ./llama-perplexity -m Qwen3-8B-Q8_0.gguf -f wiki.test.raw -ngl 80 ``` Wait for some time and you will get the perplexity of the model. There are some numbers of different kinds of quantization mixture [here](https://github.com/ggml-org/llama.cpp/blob/master/tools/perplexity/README.md). It might be helpful to look at the difference and grab a sense of how that kind of quantization might perform. [^wiki]: It is not a good evaluation dataset for instruct models though, but it is very common and easily accessible. You probably want to use a dataset similar to your target domain. ## Finally In this guide, we demonstrate how to conduct quantization and evaluate the perplexity with llama.cpp. For more information, please visit the [llama.cpp GitHub repo](https://github.com/ggml-org/llama.cpp). We usually quantize the fp16 model to 4, 5, 6, and 8-bit models with different quantization mixtures, but sometimes a particular mixture just does not work, so we don't provide those in our Hugging Face Hub. However, others in the community may have success, so if you haven't found what you need in our repos, look around. Enjoy your freshly quantized models! ================================================ FILE: docs/source/run_locally/llama.cpp.md ================================================ # llama.cpp [^GGUF]: GPT-Generated Unified Format :::{dropdown} llama.cpp as a C++ library Before starting, let's first discuss what is llama.cpp and what you should expect, and why we say "use" llama.cpp, with "use" in quotes. llama.cpp is essentially a different ecosystem with a different design philosophy that targets light-weight footprint, minimal external dependency, multi-platform, and extensive, flexible hardware support: - Plain C/C++ implementation without external dependencies - Support a wide variety of hardware: - AVX, AVX2 and AVX512 support for x86_64 CPU - Apple Silicon via Metal and Accelerate (CPU and GPU) - NVIDIA GPU (via CUDA), AMD GPU (via hipBLAS), Intel GPU (via SYCL), Ascend NPU (via CANN), and Moore Threads GPU (via MUSA) - Vulkan backend for GPU - Various quantization schemes for faster inference and reduced memory footprint - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity It's like the Python frameworks `torch`+`transformers` or `torch`+`vllm` but in C++. However, this difference is crucial: - Python is an interpreted language: The code you write is executed line-by-line on-the-fly by an interpreter. You can run the example code snippet or script with an interpreter or a natively interactive interpreter shell. In addition, Python is learner friendly, and even if you don't know much before, you can tweak the source code here and there. - C++ is a compiled language: The source code you write needs to be compiled beforehand, and it is translated to machine code and an executable program by a compiler. The overhead from the language side is minimal. You do have source code for example programs showcasing how to use the library. But it is not very easy to modify the source code if you are not verse in C++ or C. To use llama.cpp means that you use the llama.cpp library in your own program, like writing the source code of [Ollama](https://ollama.com/), [LM Studio](https://lmstudio.ai/), [GPT4ALL](https://www.nomic.ai/gpt4all), [llamafile](https://llamafile.ai/) etc. But that's not what this guide is intended or could do. Instead, here we introduce how to use the `llama-cli` example program, in the hope that you know that llama.cpp does support Qwen2.5 models and how the ecosystem of llama.cpp generally works. ::: In this guide, we will show how to "use" [llama.cpp](https://github.com/ggml-org/llama.cpp) to run models on your local machine, in particular, the `llama-cli` and the `llama-server` example program, which comes with the library. The main steps are: 1. Get the programs 2. Get the Qwen3 models in GGUF[^GGUF] format 3. Run the program with the model :::{note} llama.cpp supports Qwen3 and Qwen3MoE from version `b5092`. ::: ## Getting the Program You can get the programs in various ways. For optimal efficiency, we recommend compiling the programs locally, so you get the CPU optimizations for free. However, if you don't have C++ compilers locally, you can also install using package managers or downloading pre-built binaries. They could be less efficient but for non-production example use, they are fine. :::::{tab-set} ::::{tab-item} Compile Locally Here, we show the basic command to compile `llama-cli` locally on **macOS** or **Linux**. For Windows or GPU users, please refer to [the guide from llama.cpp](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md). :::{rubric} Installing Build Tools :heading-level: 5 ::: To build locally, a C++ compiler and a build system tool are required. To see if they have been installed already, type `cc --version` or `cmake --version` in a terminal window. - If installed, the build configuration of the tool will be printed to the terminal, and you are good to go! - If errors are raised, you need to first install the related tools: - On macOS, install with the command `xcode-select --install` - On Ubuntu, install with the command `sudo apt install build-essential`. For other Linux distributions, the command may vary; the essential packages needed for this guide are `gcc` and `cmake`. :::{rubric} Compiling the Program :heading-level: 5 ::: For the first step, clone the repo and enter the directory: ```bash git clone https://github.com/ggml-org/llama.cpp cd llama.cpp ``` Then, build llama.cpp using CMake: ```bash cmake -B build cmake --build build --config Release ``` The first command will check the local environment and determine which backends and features should be included. The second command will actually build the programs. To shorten the time, you can also enable parallel compiling based on the CPU cores you have, for example: ```bash cmake --build build --config Release -j 8 ``` This will build the programs with 8 parallel compiling jobs. The built programs will be in `./build/bin/`. :::: ::::{tab-item} Package Managers For **macOS** and **Linux** users, `llama-cli` and `llama-server` can be installed with package managers including Homebrew, Nix, and Flox. Here, we show how to install `llama-cli` and `llama-server` with Homebrew. For other package managers, please check the instructions [here](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md). Installing with Homebrew is very simple: 1. Ensure that Homebrew is available on your operating system. If you don't have Homebrew, you can install it as in [its website](https://brew.sh/). 2. Second, you can install the pre-built binaries, `llama-cli` and `llama-server` included, with a single command: ```bash brew install llama.cpp ``` Note that the installed binaries might not be built with the optimal compile options for your hardware, which can lead to poor performance. They also don't support GPU on Linux systems. :::: ::::{tab-item} Binary Release You can also download pre-built binaries from [GitHub Releases](https://github.com/ggml-org/llama.cpp/releases). Please note that those pre-built binaries files are architecture-, backend-, and os-specific. If you are not sure what those mean, you probably don't want to use them and running with incompatible versions will most likely fail or lead to poor performance. The file name is like `llama--bin---.zip`. There are three simple parts: - ``: the version of llama.cpp. The latest is preferred, but as llama.cpp is updated and released frequently, the latest may contain bugs. If the latest version does not work, try the previous release until it works. - ``: the operating system. `win` for Windows; `macos` for macOS; `linux` for Linux. - ``: the system architecture. `x64` for `x86_64`, e.g., most Intel and AMD systems, including Intel Mac; `arm64` for `arm64`, e.g., Apple Silicon or Snapdragon-based systems. The `` part is somewhat complicated for Windows: - Running on CPU - x86_64 CPUs: We suggest try the `avx2` one first. - `noavx`: No hardware acceleration at all. - `avx2`, `avx`, `avx512`: SIMD-based acceleration. Most modern desktop CPUs should support avx2, and some CPUs support `avx512`. - `openblas`: Relying on OpenBLAS for acceleration for prompt processing but not generation. - arm64 CPUs: We suggest try the `llvm` one first. - [`llvm` and `msvc`](https://github.com/ggml-org/llama.cpp/pull/7191) are different compilers - Running on GPU: We suggest try the `cu` one for NVIDIA GPUs, `kompute` for AMD GPUs, and `sycl` for Intel GPUs first. Ensure that you have related drivers installed. - [`vulcan`](https://github.com/ggml-org/llama.cpp/pull/2059): support certain NVIDIA and AMD GPUs - [`kompute`](https://github.com/ggml-org/llama.cpp/pull/4456): support certain NVIDIA and AMD GPUs - [`sycl`](https://github.com/ggml-org/llama.cpp/discussions/5138): Intel GPUs, oneAPI runtime is included - `cu`: NVIDIA GPUs, CUDA runtime is not included. You can download the `cudart-llama-bin-win-cu-x64.zip` and unzip it to the same directory if you don't have the corresponding CUDA toolkit installed. You don't have much choice for macOS or Linux. - Linux: only one prebuilt binary, `llama--bin-linux-x64.zip`, supporting CPU. - macOS: `llama--bin-macos-x64.zip` for Intel Mac with no GPU support; `llama--bin-macos-arm64.zip` for Apple Silicon with GPU support. After downloading the `.zip` file, unzip them into a directory and open a terminal at that directory. :::: ::::: ## Getting the GGUF GGUF[^GGUF] is a file format for storing information needed to run a model, including but not limited to model weights, model hyperparameters, default generation configuration, and tokenizer. You can use the official Qwen GGUFs from our Hugging Face Hub or prepare your own GGUF file. ### Using the Official Qwen3 GGUFs We provide a series of GGUF models in our Hugging Face organization, and to search for what you need you can search the repo names with `-GGUF`. Download the GGUF model that you want with `huggingface-cli` (you need to install it first with `pip install huggingface_hub`): ```bash huggingface-cli download --local-dir ``` For example: ```bash huggingface-cli download Qwen/Qwen3-8B-GGUF qwen3-8b-q4_k_m.gguf --local-dir . ``` This will download the Qwen3-8B model in GGUF format quantized with the scheme Q4_K_M. ### Preparing Your Own GGUF Model files from Hugging Face Hub can be converted to GGUF, using the `convert-hf-to-gguf.py` Python script. It does require you to have a working Python environment with at least `transformers` installed. Obtain the source file if you haven't already: ```bash git clone https://github.com/ggml-org/llama.cpp cd llama.cpp ``` Suppose you would like to use Qwen3-8B you can make a GGUF file for the fp16 model as shown below: ```bash python convert-hf-to-gguf.py Qwen/Qwen3-8B --outfile qwen3-8b-f16.gguf ``` The first argument to the script refers to the path to the HF model directory or the HF model name, and the second argument refers to the path of your output GGUF file. Remember to create the output directory before you run the command. The fp16 model could be a bit heavy for running locally, and you can quantize the model as needed. We introduce the method of creating and quantizing GGUF files in [this guide](../quantization/llama.cpp). You can refer to that document for more information. ## Run Qwen with llama.cpp :::{note} Regarding switching between thinking and non-thinking modes, while the soft switch is always available, the hard switch implemented in the chat template is not exposed in llama.cpp. The quick workaround is to pass [a custom chat template](../../source/assets/qwen3_nonthinking.jinja) equivalent to always `enable_thinking=False` via `--chat-template-file`. ::: ### llama-cli [llama-cli](https://github.com/ggml-org/llama.cpp/tree/master/tools/main) is a console program which can be used to chat with LLMs. Simple run the following command where you place the llama.cpp programs: ```shell ./llama-cli -hf Qwen/Qwen3-8B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 -c 40960 -n 32768 --no-context-shift ``` Here are some explanations to the above command: - **Model**: llama-cli supports using model files from local path, remote URL, or Hugging Face hub. - `-hf Qwen/Qwen3-8B-GGUF:Q8_0` in the above indicates we are using the model file from Hugging Face hub - To use a local path, pass `-m qwen3-8b-q8_0.gguf` instead - To use a remote URL, pass `-mu https://hf.co/Qwen/Qwen3-8B-GGUF/resolve/main/qwen3-8b-Q8_0.gguf?download=true` instead - **Speed Optimization**: - CPU: llama-cli by default will use CPU and you can change `-t` to specify how many threads you would like it to use, e.g., `-t 8` means using 8 threads. - GPU: If the programs are built with GPU support, you can use `-ngl`, which allows offloading some layers to the GPU for computation. If there are multiple GPUs, it will offload to all the GPUs. You can use `-dev` to control the devices used and `-sm` to control which kinds of parallelism is used. For example, `-ngl 99 -dev cuda0,cuda1 -sm row` means offload all layers to GPU 0 and GPU1 using the split mode row. Adding `-fa` may also speed up the generation. - **Sampling Parameters**: llama.cpp supports [a variety of sampling methods](https://github.com/ggml-org/llama.cpp/tree/master/tools/main#generation-flags) and has default configuration for many of them. It is recommended to adjust those parameters according to the actual case and the recommended parameters from Qwen3 modelcard could be used as a reference. If you encounter repetition and endless generation, it is recommended to pass in addition `--presence-penalty` up to `2.0`. - **Context Management**: llama.cpp adopts the "rotating" context management by default. The `-c` controls the maximum context length (default 4096, 0 means loaded from model), and `-n` controls the maximum generation length each time (default -1 means infinite until ending, -2 means until context full). When the context is full but the generation doesn't end, the first `--keep` tokens (default 0, -1 means all) from the initial prompt is kept, and the first half of the rest is discarded. Then, the model continues to generate based on the new context tokens. You can set `--no-context-shift` to prevent this rotating behavior and the generation will stop once `-c` is reached. llama.cpp supports YaRN, which can be enabled by `-c 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768`. - **Chat**: `--jinja` indicates using the chat template embedded in the GGUF which is preferred and `--color` indicates coloring the texts so that user input and model output can be better differentiated. If there is a chat template, like in Qwen3 models, llama-cli will enter chat mode automatically. To stop generation or exit press "Ctrl+C". You can use `-sys` to add a system prompt. ### llama-server [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) is a simple HTTP server, including a set of LLM REST APIs and a simple web front end to interact with LLMs using llama.cpp. The core command is similar to that of llama-cli. In addition, it supports thinking content parsing and tool call parsing. ```shell ./llama-server -hf Qwen/Qwen3-8B-GGUF:Q8_0 --jinja --reasoning-format deepseek -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 -c 40960 -n 32768 --no-context-shift ``` By default, the server will listen at `http://localhost:8080` which can be changed by passing `--host` and `--port`. The web front end can be assessed from a browser at `http://localhost:8080/`. The OpenAI compatible API is at `http://localhost:8080/v1/`. ## What's More If you still find it difficult to use llama.cpp, don't worry, just check out other llama.cpp-based applications. For example, Qwen3 has already been officially part of Ollama and LM Studio, which are platforms for your to search and run local LLMs. Have fun! ================================================ FILE: docs/source/run_locally/lmstudio.md ================================================ # LM Studio [LM Studio](https://lmstudio.ai) is a powerful desktop application for experimenting & developing with local AI models directly on your computer. You can run multiple kinds of Qwen models: from dense LLMs, VLMs, to MoEs and reasoning variants. LM Studio supports both GGUF (llama.cpp) and MLX formats for fast and efficient inference, completely privately on your machine. ## Download and Install LM Studio Download the installer for macOS, Windows, or Linux from the [LM Studio website](https://lmstudio.ai/download). ## Get Qwen models Qwen models are much loved by the community, and they frequently get featured in Staff Picks. Explore staff picked models within the app or in [https://lmstudio.ai/models](https://lmstudio.ai/models). Find Qwen models that fit your machine, and click Run in LM Studio! You can also search and download Qwen models from within the LM Studio app, or by using the `lms` CLI ([learn more](https://lmstudio.ai/docs/cli/local-models/get)). **Using the in-app model downloader** 1. Open the LM Studio app and search for any model by presssing `⌘ + Shift + M` on Mac, or `Ctrl + Shift + M` on PC. 2. Search for "Qwen." 3. Pick a result that looks interesting and LM Studio will suggest the optimal variants for your hardware. 4. Click **Download**. After the download finishes, load the model to use it in a new chat. **Getting a model from Hugging Face** First, enable LM Studio under your [Local Apps Settings](https://huggingface.co/settings/local-apps) in Hugging Face. On the model card, click the "Use this model" dropdown and select LM Studio. This will run the model directly in LM Studio if you already have it, or show you a download option if you don't. **Advanced: Use your own converted GGUF Qwen model file** If you had converted a Qwen model to GGUF yourself, you can use LM Studio's CLI `lms` to load your model into LM Studio. 1. Use: ```bash lms import ``` 2. LM Studio will automatically detect the model and it will populate in the application under "My Models". 3. Adjust context length and hardware settings as needed. If `lms import` does not work automatically, fear not. You are still able to manually import models into your LM Studio. Read more about LM Studio's model directory structure [here](https://lmstudio.ai/docs/app/advanced/import-model). Once the model has completed loading (as indicated by the progress bar), you may start chatting away in LM Studio! ## Serve the model through LM Studio's server **Serve via LM Studio's GUI** In the LM Studio application, press `⌘ / Ctrl + L` to open the model loader. Here you can view a list of downloaded models and select one to load. LM Studio will by default select the load parameters that optimizes model performance on your hardware. **Serve via LM Studio's CLI** If you prefer to work in the terminal, use LM Studio's CLI to interact with your models. See a list of commands [here](https://lmstudio.ai/docs/cli). Note that you need to run LM Studio ***at least once*** before you can use `lms`. Next, turn on LM Studio's local API server by running: ```bash lms server start ``` Now you're ready to go! Use LM Studio's REST APIs to use Qwen models programmatically from your own code. Learn more about how to do this at . ================================================ FILE: docs/source/run_locally/mlx-lm.md ================================================ # MLX LM :::{attention} To be updated for Qwen3. ::: [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms) helps you run LLMs locally on Apple Silicon. It is available at macOS. It has already supported Qwen models and this time, we have also provided checkpoints that you can directly use with it. ## Prerequisites The easiest way to get started is to install the `mlx-lm` package: - with `pip`: ```bash pip install mlx-lm ``` - with `conda`: ```bash conda install -c conda-forge mlx-lm ``` ## Running with Qwen MLX Files We provide model checkpoints with `mlx-lm` in our Hugging Face organization, and to search for what you need you can search the repo names with `-MLX`. Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from mlx_lm import load, generate model, tokenizer = load('Qwen/Qwen2.5-7B-Instruct-MLX', tokenizer_config={"eos_token": "<|im_end|>"}) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=text, verbose=True, top_p=0.8, temp=0.7, repetition_penalty=1.05, max_tokens=512) ``` ## Make Your MLX files You can make MLX files with just one command: ```bash mlx_lm.convert --hf-path Qwen/Qwen2.5-7B-Instruct --mlx-path mlx/Qwen2.5-7B-Instruct/ -q ``` where - `--hf-path`: the model name on Hugging Face Hub or the local path - `--mlx-path`: the path for output files - `-q`: enable quantization ================================================ FILE: docs/source/run_locally/ollama.md ================================================ # Ollama :::{attention} To be updated for Qwen3. ::: [Ollama](https://ollama.com/) helps you run LLMs locally with only a few commands. It is available at macOS, Linux, and Windows. Now, Qwen2.5 is officially on Ollama, and you can run it with one command: ```bash ollama run qwen2.5 ``` Next, we introduce more detailed usages of Ollama for running Qwen2.5 models. ## Quickstart Visit the official website [Ollama](https://ollama.com/) and click download to install Ollama on your device. You can also search models on the website, where you can find the Qwen2.5 models. Except for the default one, you can choose to run Qwen2.5-Instruct models of different sizes by: - `ollama run qwen2.5:0.5b` - `ollama run qwen2.5:1.5b` - `ollama run qwen2.5:3b` - `ollama run qwen2.5:7b` - `ollama run qwen2.5:14b` - `ollama run qwen2.5:32b` - `ollama run qwen2.5:72b` :::{note} `ollama` does not host base models. Even though the tag may not have the instruct suffix, they are all instruct models. ::: ## Run Ollama with Your GGUF Files Sometimes you don't want to pull models and you just want to use Ollama with your own GGUF files. Suppose you have a GGUF file of Qwen2.5, `qwen2.5-7b-instruct-q5_0.gguf`. For the first step, you need to create a file called `Modelfile`. The content of the file is shown below: ```text FROM qwen2.5-7b-instruct-q5_0.gguf # set the temperature to 1 [higher is more creative, lower is more coherent] PARAMETER temperature 0.7 PARAMETER top_p 0.8 PARAMETER repeat_penalty 1.05 PARAMETER top_k 20 TEMPLATE """{{ if .Messages }} {{- if or .System .Tools }}<|im_start|>system {{ .System }} {{- if .Tools }} # Tools You are provided with function signatures within XML tags: {{- range .Tools }} {"type": "function", "function": {{ .Function }}}{{- end }} For each function call, return a json object with function name and arguments within XML tags: {"name": , "arguments": } {{- end }}<|im_end|> {{ end }} {{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 -}} {{- if eq .Role "user" }}<|im_start|>user {{ .Content }}<|im_end|> {{ else if eq .Role "assistant" }}<|im_start|>assistant {{ if .Content }}{{ .Content }} {{- else if .ToolCalls }} {{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}} {{ end }} {{- end }}{{ if not $last }}<|im_end|> {{ end }} {{- else if eq .Role "tool" }}<|im_start|>user {{ .Content }} <|im_end|> {{ end }} {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant {{ end }} {{- end }} {{- else }} {{- if .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}{{ if .Prompt }}<|im_start|>user {{ .Prompt }}<|im_end|> {{ end }}<|im_start|>assistant {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""" # set the system message SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant.""" ``` Then create the Ollama model by running: ```bash ollama create qwen2.5_7b -f Modelfile ``` Once it is finished, you can run your Ollama model by: ```bash ollama run qwen2.5_7b ``` ## Tool Use Tool use is now supported Ollama and you should be able to run Qwen2.5 models with it. For more details, see our [function calling guide](../framework/function_call). ================================================ FILE: docs/source/training/axolotl.md ================================================ # Axolotl This guide will help you get started with post-training (SFT, RLHF, RM, PRM) for Qwen3 / Qwen3_MOE using Axolotl, and covers optimizations to enable for better performance. ## Requirements - **GPU:** NVIDIA Ampere (or newer) for `bf16` and `Flash Attention`, or AMD GPU - **Python:** ≥3.11 - **CUDA:** ≥12.4 (for NVIDIA GPUs) ## Installation You can install Axolotl using PyPI, Conda, Git, Docker, or launch a cloud environment. :::{important} Install PyTorch *before* installing Axolotl to ensure CUDA compatibility. ::: For the latest instructions, see the official [Axolotl Installation Guide](https://docs.axolotl.ai/docs/installation.html). ## Quickstart ### SFT We have provided a sample YAML config for SFT with Qwen/Qwen3-32B: [SFT 32B QLoRA config](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/examples/qwen3/32b-qlora.yaml). ```shell # Train the model axolotl train path/to/32b-qlora.yaml # Merge LoRA weights with the base model # This will create a new `merged` directory under `{output_dir}` axolotl merge-lora path/to/32b-qlora.yaml ``` :::{tip} To train a smaller model, edit the `base_model` in your config: ```yaml base_model: Qwen/Qwen3-8B ``` ::: Qwen3 works with all Axolotl features including `Flash Attention`, `bf16`, `LoRA`, `torch_compile`, and `QLoRA`. To run on more than single GPU, please take a look at the [Multi-GPU Training Guide](https://docs.axolotl.ai/docs/multi-gpu.html) or [Multi-node Training Guide](https://docs.axolotl.ai/docs/multi-node.html). ### RLHF See the [RLHF Guide](https://docs.axolotl.ai/docs/rlhf.html) for required dataset formats and examples for each method. ### RM/PRM Please refer to the [Reward Modelling Guide](https://docs.axolotl.ai/docs/reward_modelling.html) for required dataset formats and config examples. ## Dataset By default, the example config uses the `mlabonne/FineTome-100k` dataset (from HuggingFace Hub). You can substitute any dataset of your own. ### SFT Dataset Format Axolotl handles various SFT dataset formats, but the current **recommended** format (for use with `chat_template`) is the OpenAI Messages format: ```json [ { "messages": [ { "role": "user", "content": "What is Qwen3?" }, { "role": "assistant", "content": "Qwen3 is a language model..." } ] } ] ``` Use this in your config: ```yaml datasets: - path: path/to/your/dataset.json type: chat_template ``` You can also load datasets from multiple sources: HuggingFace Hub, local files, directories, S3, GCS, Azure, etc. See the [Dataset Loading Guide](https://docs.axolotl.ai/docs/dataset_loading.html) for more details. To load different dataset formats, refer to the [SFT Dataset Formats Guide](https://docs.axolotl.ai/docs/dataset-formats/#supervised-fine-tuning-sft). ## Optimizations With Qwen3/Qwen3_MOE, you can leverage Axolotl's custom optimizations for improved speed and reduced memory usage: - [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) - [Liger Kernels](https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels) - (LoRA/QLoRA only): [LoRA Kernels Optimization](https://docs.axolotl.ai/docs/lora_optims.html) ## Additional Suggestions ### Troubleshooting - Ensure your CUDA version matches your GPU and PyTorch version. - If running into out-of-memory issues, try reducing your batch size, enable the optimizations above, or reduce sequence length. - Qwen3 MoE may have slower training due to the upstream transformer's handling of MoE layers. - For help, check the help channel on [Axolotl Discord](https://discord.gg/7m9sfhzaf3) or create a Discussion on [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl). ### Links - [Axolotl Documentation](https://docs.axolotl.ai/) - [Axolotl Discord](https://discord.gg/7m9sfhzaf3) - [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl) - [Axolotl Website](https://axolotl.ai) ================================================ FILE: docs/source/training/llama_factory.md ================================================ # LLaMA-Factory :::{attention} To be updated for Qwen3. ::: Here we provide a script for supervised finetuning Qwen2.5 with [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). This script for supervised finetuning (SFT) has the following features: - Support single-GPU and multi-GPU training; - Support full-parameter tuning, LoRA, Q-LoRA, Dora. In the following, we introduce more details about the usage of the script. ## Installation Before you start, make sure you have installed the following packages: 1. Follow the instructions of [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), and build the environment. 2. Install these packages (Optional): ``` pip install deepspeed pip install flash-attn --no-build-isolation ``` 3. If you want to use [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), make sure your CUDA is 11.6 and above. ## Data Preparation LLaMA-Factory provides several training datasets in `data` folder, you can use it directly. If you are using a custom dataset, please prepare your dataset as follows. 1. Organize your data in a **json** file and put your data in `data` folder. LLaMA-Factory supports dataset in `alpaca` or `sharegpt` format. - The dataset in `alpaca` format should follow the below format: ```json [ { "instruction": "user instruction (required)", "input": "user input (optional)", "output": "model response (required)", "system": "system prompt (optional)", "history": [ ["user instruction in the first round (optional)", "model response in the first round (optional)"], ["user instruction in the second round (optional)", "model response in the second round (optional)"] ] } ] ``` - The dataset in `sharegpt` format should follow the below format: ```json [ { "conversations": [ { "from": "human", "value": "user instruction" }, { "from": "gpt", "value": "model response" } ], "system": "system prompt (optional)", "tools": "tool description (optional)" } ] ``` 2. Provide your dataset definition in `data/dataset_info.json` in the following format . - For `alpaca` format dataset, the columns in `dataset_info.json` should be: ```json "dataset_name": { "file_name": "dataset_name.json", "columns": { "prompt": "instruction", "query": "input", "response": "output", "system": "system", "history": "history" } } ``` - For `sharegpt` format dataset, the columns in `dataset_info.json` should be: ```json "dataset_name": { "file_name": "dataset_name.json", "formatting": "sharegpt", "columns": { "messages": "conversations", "system": "system", "tools": "tools" }, "tags": { "role_tag": "from", "content_tag": "value", "user_tag": "user", "assistant_tag": "assistant" } } ``` ## Training Execute the following training command: ```bash DISTRIBUTED_ARGS=" --nproc_per_node $NPROC_PER_NODE \ --nnodes $NNODES \ --node_rank $NODE_RANK \ --master_addr $MASTER_ADDR \ --master_port $MASTER_PORT " torchrun $DISTRIBUTED_ARGS src/train.py \ --deepspeed $DS_CONFIG_PATH \ --stage sft \ --do_train \ --use_fast_tokenizer \ --flash_attn \ --model_name_or_path $MODEL_PATH \ --dataset your_dataset \ --template qwen \ --finetuning_type lora \ --lora_target q_proj,v_proj\ --output_dir $OUTPUT_PATH \ --overwrite_cache \ --overwrite_output_dir \ --warmup_steps 100 \ --weight_decay 0.1 \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --ddp_timeout 9000 \ --learning_rate 5e-6 \ --lr_scheduler_type cosine \ --logging_steps 1 \ --cutoff_len 4096 \ --save_steps 1000 \ --plot_loss \ --num_train_epochs 3 \ --bf16 ``` and enjoy the training process. To make changes to your training, you can modify the arguments in the training command to adjust the hyperparameters. One argument to note is `cutoff_len`, which is the maximum length of the training data. Control this parameter to avoid OOM error. ## Merge LoRA If you train your model with LoRA, you probably need to merge adapter parameters to the main branch. Run the following command to perform the merging of LoRA adapters. ```bash CUDA_VISIBLE_DEVICES=0 llamafactory-cli export \ --model_name_or_path path_to_base_model \ --adapter_name_or_path path_to_adapter \ --template qwen \ --finetuning_type lora \ --export_dir path_to_export \ --export_size 2 \ --export_legacy_format False ``` ## Conclusion The above content is the simplest way to use LLaMA-Factory to train Qwen. Feel free to dive into the details by checking the official repo! ================================================ FILE: docs/source/training/ms_swift.md ================================================ # MS-SWIFT ModelScope SWIFT (**ms-swift**) is the large model and multimodal large model training and deployment framework provided by the [ModelScope community](https://modelscope.cn/). GitHub repository: [ms-swift](https://github.com/modelscope/ms-swift) Features of using ms-swift for training LLM: - **Model Types**: Supports 500+ plain-text large models and 200+ multimodal large models, covering the entire process from training to deployment. - **Hardware Support**: Compatible with CPUs, RTX series GPUs, T4/V100, A10/A100/H100, Ascend NPUs, MPS, and more. - **Training Methods**: Supports full-parameter fine-tuning, LoRA, QLoRA, DoRA, and other techniques. - **Distributed Training**: Supports distributed training technologies such as DDP, device_map, DeepSpeed ZeRO-2/ZeRO-3, FSDP, and integrates parallelism techniques from Megatron, including Tensor Parallelism, Pipeline Parallelism, Sequence Parallelism, and Expert Parallelism. - **RLHF Training**: Supports human alignment methods like DPO, GRPO, DAPO, RM, PPO, KTO, etc., for both plain-text and multimodal large models. This article will demonstrate runnable training demos and provide the format for custom datasets. It includes how to use ms-swift for SFT and GRPO on Qwen3-8B, as well as using Megatron-SWIFT (ms-swift's integration of Megatron-LM) for SFT on Qwen3-30B-A3B. Through expert parallelism technology, MoE model training can be accelerated by nearly 10 times. Before starting fine-tuning, ensure your environment is properly set up. ```shell pip install ms-swift -U # Install from source pip install git+https://github.com/modelscope/ms-swift.git pip install transformers -U # Optional packages pip install deepspeed # multi-GPU training pip install liger-kernel # save GPU memory resources pip install flash-attn --no-build-isolation ``` ## Supervised Fine-Tuning (SFT) ### Data Preparation The custom dataset format for SFT using ms-swift is as follows (the system field is optional). You can organize it into formats such as JSON, JSONL, or CSV. Specify `--dataset ` in the training script. For complete dataset formatting guidelines, see: [Custom Dataset Documentation](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html) - General format ```json {"messages": [ {"role": "system", "content": ""}, {"role": "user", "content": ""}, {"role": "assistant", "content": ""} ]} ``` - Format with think ```json {"messages": [ {"role": "user", "content": "Where is the capital of Zhejiang?"}, {"role": "assistant", "content": "\n...\n\n\nThe capital of Zhejiang is Hangzhou."} ]} ``` If you want to train using data without a chain of thought but retain the model's reasoning ability, there are two approaches to minimize disruption during fine-tuning: **Option 1**: During training, specify `--loss_scale ignore_empty_think` to ignore the loss calculation for `\n\n\n\n`, preventing the loss of reasoning ability. Refer to the training script [here](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo1.sh). The custom dataset format is as follows: ```json {"messages": [ {"role": "user", "content": "Where is the capital of Zhejiang?"}, {"role": "assistant", "content": "\n\n\n\nThe capital of Zhejiang is Hangzhou."} ]} ``` **Option 2**: Add `/no_think` to the query in the dataset to avoid the loss of reasoning ability. Refer to the training script [here](https://github.com/modelscope/ms-swift/blob/main/examples/train/think_model/qwen3_demo2.sh). The custom dataset format is as follows: ```json {"messages": [ {"role": "user", "content": "Where is the capital of Zhejiang? /no_think"}, {"role": "assistant", "content": "\n\n\n\nThe capital of Zhejiang is Hangzhou."} ]} ``` ### 30-Minute Self-Cognition Fine-Tuning This section introduces a 30-minute self-cognition fine-tuning process for the Qwen3-8B model. The required GPU memory is 22GB, and it can be run on the A10 provided by [ModelScope's free compute resources](https://modelscope.cn/my/mynotebook). After training, the model will identify itself as "swift-robot," trained by "swift," instead of its original self-cognition as "Qwen," trained by Alibaba Cloud. If you need to train in an offline environment, you can manually download the model and dataset and specify `--model ` and `--dataset `. The dataset can be found on [Modelscope Hub](https://modelscope.cn/datasets/swift/self-cognition). For the meaning of each parameter in the training script, please refer to the [Command-line parameters documentation](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html). ```bash CUDA_VISIBLE_DEVICES=0 \ swift sft \ --model Qwen/Qwen3-8B \ --train_type lora \ --dataset 'swift/Qwen3-SFT-Mixin#2000' \ 'swift/self-cognition:qwen3#600' \ --torch_dtype bfloat16 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --learning_rate 1e-4 \ --lora_rank 8 \ --lora_alpha 32 \ --target_modules all-linear \ --gradient_accumulation_steps 16 \ --eval_steps 50 \ --save_steps 50 \ --save_total_limit 2 \ --logging_steps 5 \ --max_length 2048 \ --output_dir output \ --warmup_ratio 0.05 \ --dataloader_num_workers 4 \ --model_author swift \ --model_name swift-robot ``` After fine-tuning, you can use the following script to test the fine-tuning results. Note that the `--adapters` section needs to be modified to the directory path of the last saved checkpoint: ```bash CUDA_VISIBLE_DEVICES=0 \ swift infer \ --adapters output/vx-xxx/checkpoint-xxx \ --stream true \ --temperature 0 \ --max_new_tokens 2048 ``` ```text <<< who are you? Okay, the user asked, "who are you?" I need to introduce myself. Let me start by stating my name, swift-robot. Then, I should mention that I'm an AI assistant developed by swift. I should explain my purpose, which is to provide information and assistance. I should also highlight my capabilities, like answering questions, generating text, and engaging in conversation. It's important to keep the tone friendly and approachable. Maybe add something about being here to help and encourage the user to ask anything. Let me check if I covered all the key points: name, developer, purpose, capabilities, and a welcoming statement. Yeah, that should do it. Now, let me put that into a concise and friendly response. Hello! I am swift-robot, an artificial intelligence assistant developed by swift. My purpose is to provide information and assistance to users like you. I can answer questions, generate text, and engage in conversations on a wide range of topics. I am here to help, so feel free to ask me anything you need! ``` By default, ms-swift will use the ModelScope community to download models and datasets. If you want to use the HuggingFace community, you need to additionally specify `--use_hf true`. Merge LoRA weights: ```shell swift export \ --adapters output/checkpoint-xxx \ --merge_lora true ``` Push the model to ModelScope/HuggingFace: ```shell # If you are pushing the complete weights, you need to change `--adapters` to `--model`. # The Modelscope hub_token can be found here: https://modelscope.cn/my/myaccesstoken swift export \ --adapters output/checkpoint-xxx \ --push_to_hub true \ --hub_model_id '' \ --hub_token '' \ --use_hf false ``` If you want to use multiple GPUs for training, the following provides a demo for multi-GPU training: ```shell # 4 * 60GB # You can run the experiment by setting `--dataset AI-ModelScope/alpaca-gpt4-data-en`. # Note: If you want to specify `--packing true`, you must additionally set `--attn_impl flash_attn`. NPROC_PER_NODE=4 \ CUDA_VISIBLE_DEVICES=0,1,2,3 \ swift sft \ --model Qwen/Qwen3-8B \ --train_type full \ --dataset '' \ --torch_dtype bfloat16 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --learning_rate 1e-5 \ --gradient_accumulation_steps 4 \ --packing true \ --eval_steps 100 \ --save_steps 100 \ --logging_steps 5 \ --max_length 8192 \ --warmup_ratio 0.05 \ --dataloader_num_workers 8 \ --dataset_num_proc 8 \ --save_total_limit 2 \ --save_only_model true \ --output_dir output \ --deepspeed zero3 \ --use_liger_kernel true \ --attn_impl flash_attn ``` ## Reinforcement Learning (RL) ms-swift supports RLHF methods such as DPO, GRPO, DAPO, PPO, KTO, and more. This section will focus on an example of using ms-swift to perform GRPO training for Qwen3-8B. For detailed RLHF support information, please refer to: [Supported Features](https://swift.readthedocs.io/en/latest/Instruction/Pre-training-and-Fine-tuning.html). ### Environment Setup In addition to installing the ms-swift related dependencies introduced above, the following dependencies also need to be installed: ```shell pip install "math_verify==0.5.2" pip install vllm ``` ### Data Preparation The dataset format for GRPO training using ms-swift is similar to that of SFT, except that the assistant part of the last round is not required. If using accuracy as a reward, a `solution` column is needed to calculate the accuracy. Example Dataset Formats: ```json {"messages": [{"role": "user", "content": "Tell me tomorrow's weather"}]} {"messages": [{"role": "user", "content": "What is 1 + 1?"}, {"role": "assistant", "content": "It equals 2"}, {"role": "user", "content": "What about adding 1?"}]} {"messages": [{"role": "user", "content": "What is your name?"}]} ``` For dataset preparation for other RLHF algorithms, see: [Custom Dataset Documentation](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html#rlhf). Notes on Dataset Requirements: - **Reward Function Calculation**: The dataset format depends on the reward function being used. Additional columns may be required to support specific reward calculations. For instance: - When using the built-in accuracy or cosine similarity reward, the dataset must include a `solution` column to calculate the accuracy of the responses. - Other columns in the dataset will be passed as `**kwargs` to the reward function for additional customization. - **Customizing the Reward Function**: To adapt the reward function to your specific needs, you can refer to the following resource: [External Reward Plugin](https://github.com/modelscope/ms-swift/tree/main/examples/train/grpo/plugin). This plugin provides examples and templates for implementing custom reward functions. During the training process, we use vLLM to accelerate the sampling process. By setting `num_infer_workers=8`, we deploy a vLLM engine for each device to speed up the sampling process. ```shell # 70G*8 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ NPROC_PER_NODE=8 \ swift rlhf \ --rlhf_type grpo \ --model Qwen/Qwen3-8B \ --train_type full \ --dataset 'AI-MO/NuminaMath-TIR#5000' \ --torch_dtype bfloat16 \ --num_train_epochs 1 \ --per_device_train_batch_size 2 \ --per_device_eval_batch_size 2 \ --learning_rate 1e-6 \ --save_total_limit 2 \ --logging_steps 5 \ --output_dir output \ --gradient_accumulation_steps 1 \ --warmup_ratio 0.05 \ --dataloader_num_workers 4 \ --max_completion_length 4096 \ --vllm_max_model_len 8192 \ --reward_funcs accuracy \ --num_generations 16 \ --use_vllm true \ --vllm_gpu_memory_utilization 0.4 \ --sleep_level 1 \ --offload_model true \ --offload_optimizer true \ --gc_collect_after_offload true \ --deepspeed zero3 \ --num_infer_workers 8 \ --tensor_parallel_size 1 \ --temperature 1.0 \ --top_p 0.85 \ --log_completions true \ --overlong_filter true ``` ## Megatron-SWIFT ms-swift incorporates Megatron parallelism techniques to accelerate the training of large models. The supported models can be found in the [Supported Models Documentation](https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html). For environment preparation and the conversion between HF and MCore model weights, you can refer to the [Megatron-SWIFT Training Documentation](https://swift.readthedocs.io/en/latest/Instruction/Megatron-SWIFT-Training.html). These topics will not be elaborated here. We will use Alibaba Cloud DLC to start the training The training environment consists of 2 machines with 8 * 80GiB A800 GPUs. For more information on multi-node startup methods, refer to [here](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-node). ```shell # https://help.aliyun.com/zh/pai/user-guide/general-environment-variables # Ensure that the weight-saving paths on the two nodes are identical. NNODES=$WORLD_SIZE \ NODE_RANK=$RANK \ megatron sft \ --load Qwen3-30B-A3B-Base-mcore \ --dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \ --tensor_model_parallel_size 2 \ --expert_model_parallel_size 8 \ --moe_grouped_gemm true \ --moe_shared_expert_overlap true \ --moe_aux_loss_coeff 0.01 \ --micro_batch_size 1 \ --global_batch_size 16 \ --packing true \ --recompute_granularity full \ --recompute_method uniform \ --recompute_num_layers 1 \ --train_iters 2000 \ --eval_iters 50 \ --finetune true \ --cross_entropy_loss_fusion true \ --lr 1e-5 \ --lr_warmup_iters 100 \ --min_lr 1e-6 \ --save megatron_output/Qwen3-30B-A3B-Base \ --eval_interval 200 \ --save_interval 200 \ --max_length 8192 \ --num_workers 8 \ --dataset_num_proc 8 \ --no_save_optim true \ --no_save_rng true \ --sequence_parallel true \ --use_flash_attn true ``` The custom dataset format is the same as `swift sft`, which can be found in the previous section. Simply specify `--dataset `. The following is a comparison of training speed and GPU memory usage between `megatron sft` and `swift sft` for full-parameter fine-tuning of the Qwen3-30B-A3B model: | | Megatron-LM | DeepSpeed-ZeRO2 | DeepSpeed-ZeRO3 | | ---------------- | ----------- | --------------- | --------------- | | Training Speed | 9.6s/it | - | 91.2s/it | | GPU Memory Usage | 16 * 60GiB | OOM | 16 * 80GiB | ## Conclusion The above is the best practice for training Qwen3 series models using ms-swift. If you encounter any difficulties during use, please join the discussion in [this issue](https://github.com/modelscope/ms-swift/issues/4030). ================================================ FILE: docs/source/training/unsloth.md ================================================ # Unsloth This guide will teach you how to easily train Qwen3 models with Unsloth. Unsloth simplifies local model training, handling everything from loading and quantization to training, evaluation, running, and deployment with inference engines (Ollama, llama.cpp, vLLM). **Train Qwen** models 2× faster using 70% less VRAM. **GitHub repo:** [Unsloth](https://github.com/unslothai/unsloth) ## ⭐ Key Features - Supports full fine-tuning, pretraining, LoRA, QLoRA, 8-bit training & more - Single and multi-GPU support (Linux, Windows, Colab, Kaggle; NVIDIA GPUs, soon AMD & Intel) - Compatible with all transformer models: TTS, multimodal, STT, BERT, RL - RLHF support: GRPO, DPO, DAPO, RM, PPO, KTO, etc. - Hand-written Triton kernels and a manual backprop engine ensure no accuracy degradation (0% approximation). ## Quickstart **Local Installation (Linux recommended):** ```bash pip install unsloth ``` You can view Unsloth’s full [installation instructions here.](https://docs.unsloth.ai/get-started/installing-+-updating) ## Fine-tuning Qwen3 with Unsloth Unsloth makes Qwen3 fine-tuning 2× faster, uses 70% less VRAM, with 8× longer contexts. Qwen3 (14B) fits in a free 16 GB Colab Tesla T4 GPU. To retain Qwen3's reasoning capabilities, use a 75% reasoning to 25% non-reasoning dataset ratio (e.g., NVIDIA’s math‑reasoning dataset + Maxime’s FineTome). For more details, see Unsloth’s full [Qwen3 fine-tuning guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune#fine-tuning-qwen3-with-unsloth). ### Colab Notebooks - [Qwen3 (14B) Reasoning + Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb) - [Qwen3 (4B) Advanced GRPO LoRA](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) - [Qwen3 (14B) Alpaca (Base model)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Alpaca.ipynb) **Update Unsloth locally:** ```bash pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo ``` ### Fine-tuning Qwen3 MoE Models Supported MoE models include 30B‑A3B and 235B‑A22B. Unsloth fine-tunes the 30B‑A3B model with just 17.5 GB VRAM. Router-layer fine-tuning is disabled by default. Use `FastModel` for MoE fine-tuning: ```python from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/Qwen3-30B-A3B", max_seq_length=2048, load_in_4bit=True, load_in_8bit=False, full_finetuning=False, ) ``` ### Notebook Guide For an end-to-end walkthrough, see Unsloth’s [full end-to-end fine-tuning guide](https://docs.unsloth.ai/basics/reinforcement-learning-rl-guide). - Open the notebook → click **Runtime ▸ Run all**. - Adjust settings (e.g., model name, context length) directly in the notebook: - `max_seq_length`: Recommended 2048 (Qwen3 supports up to 40960). - `load_in_4bit=True`: reduces memory usage by 4×. - Enable full fine-tuning (`full_finetuning=True`) or 8-bit training (`load_in_8bit=True`). If you want to use models directly from [ModelScope](https://modelscope.cn/organization/unsloth), use: ```bash pip install modelscope -qqq ``` ```python import os os.environ["UNSLOTH_USE_MODELSCOPE"] = "1" from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/Qwen3-4B-Base", max_seq_length=2048, ) ``` ## RL & GRPO with Qwen3 You can also train Qwen models with reinforcement learning (RL) using Unsloth. Explore Unsloth’s advanced GRPO notebook, featuring proximity-based reward scoring and Hugging Face's Open‑R1 math dataset: [Qwen3 (4B) Advanced GRPO LoRA notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb). - Proximity-based rewards for closer answers - Custom GRPO formatting and templates - Enhanced evaluation accuracy with regex matching ## Resources & Links That’s how you can easily train Qwen models with Unsloth. If you need any help, join the discussion on Unsloth’s [Discord](https://discord.com/invite/unsloth) or [GitHub](https://github.com/unslothai/unsloth) pages. **Links:** - [Unsloth Documentation](https://docs.unsloth.ai/) - [Unsloth Discord](https://discord.com/invite/unsloth) - [Unsloth Website](https://unsloth.ai/) - [Unsloth Reddit](https://www.reddit.com/r/unsloth/) ================================================ FILE: docs/source/training/verl.md ================================================ # verl verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2) paper. GitHub repository: [verl](https://github.com/volcengine/verl) verl is flexible and easy to use with: - **Easy extension of diverse RL algorithms**: The hybrid-controller programming model enables flexible representation and efficient execution of complex Post-Training dataflows. Build RL dataflows such as GRPO, PPO in a few lines of code. - **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as FSDP, Megatron-LM, vLLM, SGLang, etc - **Flexible device mapping**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes. - **Ready integration with popular HuggingFace models**: verl supports popular LLM models, including Qwen, Llama, and more. verl is fast with: - **State-of-the-art throughput**: SOTA LLM training and inference engine integrations and SOTA RL throughput. - **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases. Next, we will introduce how to use verl for training Qwen3 models. ## Reinforcement Learning (RL) Now, verl supports various combinations of training frameworks and inference frameworks, including FSDP, Megatron-LM, vLLM, SGLang, etc. verl also supports training with multiple algorithms such as PPO, GRPO, DAPO, etc. ### Step1: Environment and Training Preparation You can follow verl's [installation guide](https://verl.readthedocs.io/en/latest/start/install.html) to complete the environment configuration. Data preparation can be done by running the following command: ```shell git clone https://github.com/volcengine/verl.git cd verl python3 examples/data_preprocess/gsm8k.py --local_dir ~/data/gsm8k ``` Model download can be done using the following command: ```shell python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen3-1.7B')" ``` ### Step2: Start Training In verl, training frameworks and inference frameworks can be combined freely, as long as the training framework and inference framework themselves support model training and inference tasks, so that verl can support RL-related training. Below is an example using FSDP and vLLM to demonstrate how to train Qwen3 models in verl. We chose Qwen3-1.7B as the example, as it only requires a single 80GB GPU and a machine with more than 64GB of memory to start training. ```bash python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/data/gsm8k/train.parquet \ data.val_files=$HOME/data/gsm8k/test.parquet \ data.train_batch_size=1024 \ data.max_prompt_length=512 \ data.max_response_length=1024 \ data.filter_overlong_prompts=True \ data.truncation='error' \ actor_rollout_ref.model.path=Qwen/Qwen3-1.7B \ actor_rollout_ref.actor.optim.lr=1e-6 \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=80 \ actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=20 \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=0.001 \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=0 \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=False \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \ actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \ actor_rollout_ref.rollout.n=3 \ actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=20 \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.use_kl_in_reward=False \ trainer.critic_warmup=0 \ trainer.logger=['console'] \ trainer.project_name='verl_grpo_example_gsm8k' \ trainer.experiment_name='qwen3_1_7b_function_rm' \ trainer.n_gpus_per_node=1 \ trainer.nnodes=1 \ trainer.save_freq=-1 \ trainer.test_freq=5 \ trainer.total_epochs=15 $@ ``` ## Finally If you encounter any difficulties during use, please join the discussion at [GitHub](https://github.com/volcengine/verl/discussions). ================================================ FILE: eval/README.md ================================================ This folder provides scripts to reproduce evaluation results across various benchmarks for the **Qwen** series of large language models. ## Supported Benchmarks Currently, we support the following benchmark: | Model | Dataset | Config | Reproduced Score | |-------|--------|--------|------------------| | Qwen3-235B-A22B-Instruct-2507 | ARC-AGI 1 (pass@1) | [./configs/ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml](./configs/ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml) | 40.75 | In the meantime, you can find the model outputs and final evaluation results in the [`./output`](./output) and [`./eval_res`](./eval_res) directories, respectively. Additional benchmarks will be added in future updates. ## Evaluation Guide Follow the steps below to reproduce the reported scores. ### Step 0: Prerequisites Ensure you have: - Python ≥ 3.9 - Either [vLLM](https://github.com/vllm-project/vllm) or [SGLang](https://github.com/sgl-project/sgl) installed Install required dependencies: ```bash pip install -r requirements.txt ``` ### Step 1: Start vLLM Server Launch the vLLM inference server using the command below: ```bash export MODEL_NAME="Qwen/Qwen3-235B-A22B-Instruct-2507" # Replace with desired model export MODEL_PATH="$MODEL_NAME" # Or path to local checkpoint export NUM_GPUS=8 python -m vllm.entrypoints.openai.api_server \ --model "$MODEL_PATH" \ --trust-remote-code \ --served-model-name "$MODEL_NAME" \ --tensor-parallel-size $NUM_GPUS \ --enforce-eager \ --port 8030 ``` > 💡 Adjust `tensor_parallel_size` according to your GPU setup. ### Optional: Start SGLang Router (Recommended for Faster Evaluation) Since evaluations can take several days, we recommend using **SGLang** with data parallelism to accelerate inference. See the [SGLang Router documentation](https://docs.sglang.ai/router/router.html) for details. Start the SGLang router server: ```bash python -m sglang_router.launch_server \ --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 \ --dp-size 4 \ --host 0.0.0.0 \ --port 30000 ``` > ⚠️ Adjust `dp_size` based on available resources, and ensure consistency in port configuration for subsequent steps. ### Step 2: Run Inference Once the inference server is running, generate model responses using the multithreaded inference script. ```bash mkdir -p output # Example: Evaluate on ARC-AGI python generate_api_answers/infer_multithread.py \ --config configs/ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml ``` #### Resume Interrupted Inference If the process is interrupted, simply re-run the same command. The script will automatically detect existing outputs and resume generation for incomplete prompts. ### Step 3: Compute Scores After inference completes, evaluate the results using the scoring script: ```bash mkdir -p eval_res python eval/eval.py \ --config configs/ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml \ > eval_res/ARCAGI-Qwen3-235B-A22B-Instruct-2507_eval_result.txt ``` The final score will be saved to the specified output file. ================================================ FILE: eval/configs/ARCAGI-Qwen3-235B-A22B-Instruct-2507.yaml ================================================ # Data from https://github.com/fchollet/ARC-AGI/tree/399030444e0ab0cc8b4e199870fb20b863846f34/data/evaluation # Prompt Template from https://www.kaggle.com/code/hansuelijud/template-llama-3-8b-arc-prize-2024-inference?scriptVersionId=182406327&cellId=16 # Input and Output Path input_file: "data/arc_agi_1.jsonl" output_file: "output/ARCAGI-Qwen3-235B-A22B-Instruct-2507.jsonl" # Sampling Size for each query n_samples: 1 max_workers: 128 # vLLM server setting base_url: 'http://127.0.0.1:8030/v1' model_name: 'Qwen/Qwen3-235B-A22B-Instruct-2507' # Sampling Parameters top_p: 0.8 temperature: 0.7 top_k: 20 max_tokens: 32768 presence_penalty: 1.5 # Eval Parameters eval_input_path: output/ARCAGI-Qwen3-235B-A22B-Instruct-2507.jsonl details_path: output/ARCAGI-Qwen3-235B-A22B-Instruct-2507_details.jsonl task_name: arc_agi_1 ================================================ FILE: eval/data/arc_agi_1.jsonl ================================================ {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 1, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 1, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 1, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [2, 0],\n [0, 0]\n ],\n \"output\": [\n [2, 2, 0, 0],\n [2, 2, 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 0],\n [0, 0, 0],\n [4, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1], [4, 4, 0, 0, 0, 0], [4, 4, 0, 0, 0, 0]], "task_id": "f0afb749"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 4, 4, 8, 8, 5, 0, 0],\n [0, 0, 5, 4, 4, 8, 8, 5, 0, 0],\n [0, 0, 5, 8, 8, 4, 4, 5, 0, 0],\n [0, 0, 5, 8, 8, 4, 4, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 7, 7, 6, 6, 5, 0, 0],\n [0, 0, 5, 7, 7, 6, 6, 5, 0, 0],\n [0, 0, 5, 6, 6, 7, 7, 5, 0, 0],\n [0, 0, 5, 6, 6, 7, 7, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 3, 3, 2, 2, 5, 0, 0],\n [0, 0, 5, 3, 3, 2, 2, 5, 0, 0],\n [0, 0, 5, 2, 2, 3, 3, 5, 0, 0],\n [0, 0, 5, 2, 2, 3, 3, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 0, 0], [0, 0, 5, 1, 1, 9, 9, 5, 0, 0], [0, 0, 5, 1, 1, 9, 9, 5, 0, 0], [0, 0, 5, 9, 9, 1, 1, 5, 0, 0], [0, 0, 5, 9, 9, 1, 1, 5, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 0, 0], [0, 9, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "94414823"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 1, 0, 1],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 0, 1, 1, 1],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 1, 0, 1, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 3, 3, 0, 3, 0, 1, 0, 1, 1, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 1, 0, 1, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 1, 1, 1, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "dc2e9a9d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 0, 5, 5, 0, 0, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 8, 8, 8, 8, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3],\n [3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 8, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0]], "task_id": "f83cb3f6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 6, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 3, 0, 3, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 3, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 6, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 6, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "baf41dbf"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 0, 0, 2, 2, 0, 0],\n [1, 0, 0, 0, 0, 1, 0, 0, 2, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 1, 0, 0, 3, 3, 0, 0],\n [1, 1, 0, 0, 1, 1, 0, 3, 3, 3, 3, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 0, 6, 6, 6, 6, 0],\n [1, 1, 0, 0, 1, 1, 0, 0, 6, 6, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1],\n [1, 1, 3, 3, 1, 1],\n [1, 3, 3, 3, 3, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 6, 6, 6, 6, 1],\n [1, 1, 6, 6, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 2, 2, 1, 1],\n [1, 1, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 0, 3, 0, 0, 3, 0],\n [5, 5, 5, 0, 0, 5, 0, 3, 0, 0, 3, 0],\n [5, 5, 5, 5, 0, 5, 0, 3, 3, 3, 3, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 5, 5, 0, 2, 2, 2, 0, 0],\n [5, 0, 0, 5, 5, 5, 0, 2, 2, 0, 0, 0],\n [5, 0, 5, 5, 5, 5, 0, 2, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 5, 5, 0, 5, 0, 0, 1, 1, 1, 0],\n [5, 0, 5, 5, 0, 5, 0, 0, 0, 1, 1, 0],\n [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5],\n [5, 5, 1, 1, 1, 5],\n [5, 5, 5, 1, 1, 5],\n [5, 5, 5, 5, 1, 5],\n [5, 5, 5, 5, 5, 5],\n [5, 2, 2, 2, 5, 5],\n [5, 2, 2, 5, 5, 5],\n [5, 2, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5],\n [5, 3, 5, 5, 3, 5],\n [5, 3, 5, 5, 3, 5],\n [5, 3, 3, 3, 3, 5],\n [5, 5, 5, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 2, 2, 2, 0],\n [8, 8, 0, 8, 8, 0, 0, 0, 2, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 2, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 4, 0, 4, 0],\n [8, 8, 8, 0, 8, 0, 4, 0, 4, 0],\n [8, 8, 8, 0, 8, 0, 4, 4, 4, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 0, 8, 0, 8, 0, 3, 3, 3, 0],\n [8, 0, 8, 0, 8, 0, 0, 3, 0, 0],\n [8, 0, 0, 0, 8, 0, 3, 3, 3, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 8, 0, 0, 7, 7, 0],\n [8, 0, 0, 0, 8, 0, 7, 7, 7, 0],\n [8, 0, 0, 8, 8, 0, 7, 7, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8], [8, 3, 3, 3, 8], [8, 8, 3, 8, 8], [8, 3, 3, 3, 8], [8, 8, 8, 8, 8], [8, 2, 2, 2, 8], [8, 8, 8, 2, 8], [8, 8, 8, 2, 8], [8, 8, 8, 8, 8], [8, 4, 8, 4, 8], [8, 4, 8, 4, 8], [8, 4, 4, 4, 8], [8, 8, 8, 8, 8], [8, 8, 7, 7, 8], [8, 7, 7, 7, 8], [8, 7, 7, 8, 8], [8, 8, 8, 8, 8]], "task_id": "93b4f4b3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 4, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [5, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 4, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 5, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 5, 2, 2, 4, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 2, 4, 2, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 4, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 5, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 2, 4, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 4, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "ff72ca3e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 0, 7, 0, 3, 2, 7, 0, 2, 7, 0, 3, 2, 7, 0, 0, 3],\n [2, 2, 0, 0, 2, 3, 3, 4, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7],\n [4, 2, 7, 2, 7, 0, 4, 0, 0, 7, 2, 0, 3, 0, 7, 3, 2, 0],\n [3, 7, 2, 2, 7, 0, 0, 3, 0, 2, 4, 0, 2, 4, 0, 4, 3, 3],\n [2, 4, 3, 2, 0, 4, 3, 2, 3, 2, 0, 0, 8, 8, 8, 7, 0, 2],\n [2, 7, 3, 2, 7, 0, 0, 2, 3, 3, 3, 7, 0, 8, 2, 2, 2, 0],\n [0, 2, 2, 2, 0, 3, 2, 7, 3, 3, 7, 0, 0, 8, 0, 0, 0, 0],\n [4, 2, 7, 3, 0, 3, 0, 7, 2, 7, 2, 0, 4, 2, 7, 7, 0, 0],\n [0, 0, 2, 0, 2, 0, 4, 7, 4, 0, 0, 2, 2, 2, 3, 3, 3, 0],\n [2, 0, 4, 7, 0, 7, 0, 3, 2, 4, 2, 0, 0, 2, 0, 0, 2, 7],\n [7, 4, 2, 7, 4, 3, 3, 7, 2, 2, 0, 0, 7, 7, 0, 7, 0, 4]\n ],\n \"output\": [\n [2, 2, 0, 7, 0, 3, 2, 7, 0, 2, 7, 0, 3, 2, 7, 0, 0, 3],\n [2, 2, 0, 0, 2, 3, 3, 4, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7],\n [4, 2, 7, 2, 7, 0, 4, 0, 0, 7, 2, 0, 3, 0, 7, 3, 2, 0],\n [3, 7, 2, 2, 7, 0, 0, 3, 0, 2, 4, 0, 2, 4, 0, 4, 3, 3],\n [2, 4, 3, 2, 0, 4, 3, 2, 8, 2, 0, 0, 8, 8, 8, 7, 0, 2],\n [2, 7, 3, 2, 7, 0, 0, 2, 8, 8, 8, 7, 0, 8, 2, 2, 2, 0],\n [0, 2, 2, 2, 0, 3, 2, 7, 8, 3, 7, 0, 0, 8, 0, 0, 0, 0],\n [4, 2, 7, 3, 0, 3, 0, 7, 2, 7, 2, 0, 4, 2, 7, 7, 0, 0],\n [0, 0, 2, 0, 2, 0, 4, 7, 4, 0, 0, 2, 2, 2, 3, 3, 3, 0],\n [2, 0, 4, 7, 0, 7, 0, 3, 2, 4, 2, 0, 0, 2, 0, 0, 2, 7],\n [7, 4, 2, 7, 4, 3, 3, 7, 2, 2, 0, 0, 7, 7, 0, 7, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [2, 7, 7, 0, 0, 3, 3, 2, 2, 0, 0, 2, 3, 3, 7, 0, 0],\n [0, 3, 7, 2, 2, 4, 2, 7, 4, 2, 7, 2, 2, 7, 0, 7, 2],\n [2, 3, 0, 3, 7, 3, 0, 2, 7, 2, 0, 2, 2, 3, 2, 3, 2],\n [2, 4, 7, 3, 0, 0, 4, 2, 4, 2, 4, 0, 7, 0, 3, 3, 0],\n [7, 3, 2, 4, 3, 2, 0, 0, 7, 2, 0, 3, 2, 2, 3, 0, 2],\n [2, 7, 3, 7, 2, 2, 2, 0, 2, 2, 7, 4, 2, 2, 3, 0, 3],\n [0, 3, 0, 0, 2, 3, 0, 2, 2, 0, 7, 7, 3, 2, 0, 0, 0],\n [2, 0, 0, 4, 0, 2, 2, 2, 0, 4, 4, 0, 7, 0, 0, 3, 2],\n [3, 2, 7, 0, 7, 8, 0, 8, 0, 4, 2, 2, 2, 2, 0, 0, 0],\n [7, 2, 3, 4, 3, 2, 8, 8, 2, 0, 4, 0, 3, 7, 0, 3, 2],\n [7, 7, 2, 2, 0, 7, 7, 4, 2, 3, 2, 7, 2, 2, 7, 2, 3],\n [0, 0, 0, 4, 2, 3, 0, 4, 7, 7, 3, 0, 7, 2, 0, 3, 0],\n [0, 0, 7, 2, 3, 0, 2, 3, 0, 3, 4, 2, 3, 3, 3, 2, 3],\n [7, 2, 2, 2, 2, 3, 0, 3, 0, 3, 3, 0, 7, 0, 3, 3, 2]\n ],\n \"output\": [\n [2, 7, 7, 0, 0, 3, 3, 2, 2, 0, 0, 2, 3, 3, 7, 0, 0],\n [0, 3, 7, 2, 2, 4, 2, 7, 4, 2, 7, 2, 2, 7, 0, 7, 2],\n [2, 3, 0, 3, 7, 3, 0, 2, 7, 2, 0, 2, 2, 8, 2, 8, 2],\n [2, 4, 7, 3, 0, 0, 4, 2, 4, 2, 4, 0, 7, 0, 8, 8, 0],\n [7, 3, 2, 4, 3, 2, 0, 0, 7, 2, 0, 3, 2, 2, 3, 0, 2],\n [2, 7, 3, 7, 2, 2, 2, 0, 2, 2, 7, 4, 2, 2, 3, 0, 3],\n [0, 3, 0, 0, 2, 3, 0, 2, 2, 0, 7, 7, 3, 2, 0, 0, 0],\n [2, 0, 0, 4, 0, 2, 2, 2, 0, 4, 4, 0, 7, 0, 0, 3, 2],\n [3, 2, 7, 0, 7, 8, 0, 8, 0, 4, 2, 2, 2, 2, 0, 0, 0],\n [7, 2, 3, 4, 3, 2, 8, 8, 2, 0, 4, 0, 3, 7, 0, 3, 2],\n [7, 7, 2, 2, 0, 7, 7, 4, 2, 3, 2, 7, 2, 2, 7, 2, 3],\n [0, 0, 0, 4, 2, 3, 0, 4, 7, 7, 8, 0, 7, 2, 0, 8, 0],\n [0, 0, 7, 2, 3, 0, 2, 3, 0, 8, 4, 2, 3, 3, 8, 2, 3],\n [7, 2, 2, 2, 2, 3, 0, 3, 0, 8, 8, 0, 7, 0, 8, 8, 2]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 0, 7, 3, 3, 3, 2, 2, 3, 3, 2, 0, 2, 4, 7, 2, 0],\n [0, 2, 4, 0, 2, 0, 7, 0, 0, 2, 0, 3, 2, 2, 2, 2, 2, 7],\n [7, 0, 2, 3, 0, 4, 4, 7, 2, 7, 7, 0, 4, 0, 4, 3, 0, 3],\n [7, 3, 0, 2, 4, 3, 7, 2, 0, 2, 0, 3, 3, 2, 2, 7, 4, 0],\n [0, 3, 4, 3, 2, 4, 3, 8, 0, 2, 3, 3, 4, 0, 3, 0, 3, 0],\n [0, 2, 2, 0, 7, 3, 8, 8, 8, 4, 3, 0, 7, 3, 4, 2, 2, 2],\n [2, 3, 2, 4, 7, 0, 7, 2, 0, 4, 0, 0, 0, 0, 7, 0, 4, 7],\n [3, 4, 7, 7, 0, 3, 2, 0, 0, 7, 3, 0, 2, 7, 4, 2, 0, 3],\n [2, 3, 0, 3, 3, 0, 0, 2, 2, 0, 7, 7, 3, 0, 2, 2, 2, 3],\n [0, 3, 3, 4, 0, 3, 0, 0, 2, 7, 3, 0, 0, 0, 2, 3, 7, 3],\n [0, 3, 4, 3, 0, 7, 2, 0, 3, 0, 3, 3, 0, 4, 0, 2, 3, 3],\n [3, 2, 0, 4, 0, 2, 7, 3, 7, 0, 3, 3, 2, 0, 0, 2, 2, 7],\n [2, 2, 3, 3, 4, 3, 7, 7, 2, 2, 4, 0, 0, 0, 4, 2, 3, 2],\n [0, 2, 0, 2, 0, 0, 4, 0, 0, 3, 7, 0, 0, 0, 4, 3, 4, 2],\n [0, 4, 3, 0, 0, 3, 0, 0, 7, 0, 0, 0, 2, 3, 3, 7, 4, 3]\n ],\n \"output\": [\n [3, 0, 0, 7, 3, 3, 3, 2, 2, 3, 3, 2, 0, 2, 4, 7, 2, 0],\n [0, 2, 4, 0, 2, 0, 7, 0, 0, 2, 0, 3, 2, 2, 2, 2, 2, 7],\n [7, 0, 2, 3, 0, 4, 4, 7, 2, 7, 7, 0, 4, 0, 4, 3, 0, 3],\n [7, 3, 0, 2, 4, 3, 7, 2, 0, 2, 0, 3, 3, 2, 2, 7, 4, 0],\n [0, 3, 4, 3, 2, 4, 3, 8, 0, 2, 3, 3, 4, 0, 3, 0, 3, 0],\n [0, 2, 2, 0, 7, 3, 8, 8, 8, 4, 3, 0, 7, 3, 4, 2, 2, 2],\n [2, 3, 2, 4, 7, 0, 7, 2, 0, 4, 0, 0, 0, 0, 7, 0, 4, 7],\n [3, 4, 7, 7, 0, 3, 2, 0, 0, 7, 3, 0, 2, 7, 4, 2, 0, 3],\n [2, 8, 0, 3, 3, 0, 0, 2, 2, 0, 7, 7, 3, 0, 2, 2, 2, 3],\n [0, 8, 8, 4, 0, 3, 0, 0, 2, 7, 8, 0, 0, 0, 2, 3, 7, 3],\n [0, 8, 4, 3, 0, 7, 2, 0, 3, 0, 8, 8, 0, 4, 0, 2, 3, 3],\n [3, 2, 0, 4, 0, 2, 7, 3, 7, 0, 8, 3, 2, 0, 0, 2, 2, 7],\n [2, 2, 3, 3, 4, 3, 7, 7, 2, 2, 4, 0, 0, 0, 4, 2, 3, 2],\n [0, 2, 0, 2, 0, 0, 4, 0, 0, 3, 7, 0, 0, 0, 4, 3, 4, 2],\n [0, 4, 3, 0, 0, 3, 0, 0, 7, 0, 0, 0, 2, 3, 3, 7, 4, 3]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 2, 8, 4, 4, 0, 2, 3, 0, 2, 4, 7, 7, 3, 7, 3],\n [3, 4, 8, 8, 8, 0, 0, 2, 0, 2, 0, 2, 7, 7, 7, 3, 7],\n [7, 3, 0, 8, 0, 2, 2, 0, 2, 2, 0, 7, 3, 0, 3, 3, 3],\n [2, 0, 2, 0, 2, 0, 3, 2, 0, 7, 0, 7, 0, 0, 2, 3, 0],\n [7, 7, 4, 3, 7, 2, 0, 2, 3, 0, 3, 4, 7, 2, 0, 3, 7],\n [2, 4, 0, 7, 0, 0, 3, 4, 4, 0, 3, 4, 4, 3, 3, 4, 0],\n [3, 2, 7, 3, 7, 3, 7, 2, 0, 2, 3, 2, 3, 3, 3, 4, 4],\n [3, 7, 4, 0, 2, 0, 2, 0, 3, 7, 2, 3, 3, 3, 3, 0, 2],\n [3, 2, 3, 2, 0, 2, 0, 2, 0, 7, 2, 0, 2, 4, 4, 7, 3],\n [4, 3, 4, 2, 0, 7, 0, 0, 7, 0, 0, 0, 0, 3, 0, 0, 3],\n [2, 3, 0, 0, 4, 0, 2, 0, 3, 3, 2, 0, 4, 0, 0, 2, 2],\n [3, 3, 4, 3, 2, 7, 2, 4, 3, 0, 7, 3, 3, 4, 2, 0, 3],\n [2, 0, 7, 7, 0, 3, 7, 4, 3, 7, 0, 2, 0, 3, 7, 0, 2],\n [2, 3, 0, 0, 2, 3, 0, 7, 0, 7, 3, 7, 0, 4, 0, 3, 7],\n [2, 0, 2, 2, 7, 2, 0, 0, 2, 2, 3, 0, 0, 3, 7, 0, 3],\n [7, 2, 4, 0, 3, 0, 0, 2, 2, 7, 4, 0, 0, 2, 2, 0, 4],\n [0, 0, 3, 0, 4, 4, 7, 7, 4, 2, 0, 0, 3, 7, 0, 2, 0],\n [2, 3, 4, 0, 3, 0, 3, 3, 2, 3, 4, 7, 7, 0, 2, 0, 3]\n ],\n \"output\": [\n [2, 3, 2, 8, 4, 4, 0, 2, 3, 0, 2, 4, 7, 7, 3, 7, 3],\n [3, 4, 8, 8, 8, 0, 0, 2, 0, 2, 0, 2, 7, 7, 7, 8, 7],\n [7, 3, 0, 8, 0, 2, 2, 0, 2, 2, 0, 7, 3, 0, 8, 8, 8],\n [2, 0, 2, 0, 2, 0, 3, 2, 0, 7, 0, 7, 0, 0, 2, 8, 0],\n [7, 7, 4, 3, 7, 2, 0, 2, 3, 0, 3, 4, 7, 2, 0, 3, 7],\n [2, 4, 0, 7, 0, 0, 3, 4, 4, 0, 3, 4, 4, 8, 3, 4, 0],\n [3, 2, 7, 3, 7, 3, 7, 2, 0, 2, 3, 2, 8, 8, 8, 4, 4],\n [3, 7, 4, 0, 2, 0, 2, 0, 3, 7, 2, 3, 3, 8, 3, 0, 2],\n [3, 2, 3, 2, 0, 2, 0, 2, 0, 7, 2, 0, 2, 4, 4, 7, 3],\n [4, 3, 4, 2, 0, 7, 0, 0, 7, 0, 0, 0, 0, 3, 0, 0, 3],\n [2, 3, 0, 0, 4, 0, 2, 0, 3, 3, 2, 0, 4, 0, 0, 2, 2],\n [3, 3, 4, 3, 2, 7, 2, 4, 3, 0, 7, 3, 3, 4, 2, 0, 3],\n [2, 0, 7, 7, 0, 3, 7, 4, 3, 7, 0, 2, 0, 3, 7, 0, 2],\n [2, 3, 0, 0, 2, 3, 0, 7, 0, 7, 3, 7, 0, 4, 0, 3, 7],\n [2, 0, 2, 2, 7, 2, 0, 0, 2, 2, 3, 0, 0, 3, 7, 0, 3],\n [7, 2, 4, 0, 3, 0, 0, 2, 2, 7, 4, 0, 0, 2, 2, 0, 4],\n [0, 0, 3, 0, 4, 4, 7, 7, 4, 2, 0, 0, 3, 7, 0, 2, 0],\n [2, 3, 4, 0, 3, 0, 3, 3, 2, 3, 4, 7, 7, 0, 2, 0, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 3, 2, 2, 4, 3, 7, 2, 7, 0, 7, 3, 4, 0, 3, 2, 4],\n [0, 2, 2, 2, 2, 3, 0, 3, 3, 0, 3, 2, 0, 0, 3, 0, 7],\n [3, 2, 0, 3, 7, 0, 2, 2, 2, 3, 7, 0, 3, 3, 0, 2, 2],\n [4, 2, 7, 7, 0, 0, 2, 0, 0, 0, 7, 4, 3, 2, 3, 7, 2],\n [7, 0, 8, 3, 0, 7, 3, 3, 0, 2, 3, 0, 4, 0, 0, 7, 0],\n [3, 4, 8, 8, 3, 2, 0, 0, 3, 4, 2, 4, 0, 3, 3, 2, 4],\n [2, 3, 0, 8, 0, 0, 2, 4, 0, 4, 4, 0, 0, 7, 2, 3, 0],\n [2, 4, 0, 3, 0, 0, 2, 2, 3, 2, 7, 3, 3, 7, 0, 4, 0],\n [2, 7, 7, 4, 0, 3, 0, 2, 7, 7, 0, 4, 7, 0, 7, 3, 3],\n [0, 0, 7, 3, 4, 0, 2, 3, 0, 7, 4, 3, 2, 3, 0, 7, 3],\n [2, 7, 0, 3, 4, 7, 3, 4, 0, 4, 0, 0, 0, 3, 3, 7, 2],\n [4, 3, 0, 2, 3, 0, 4, 0, 4, 3, 3, 2, 2, 3, 3, 0, 3],\n [4, 7, 0, 7, 3, 3, 2, 3, 3, 0, 7, 0, 0, 0, 0, 4, 7],\n [3, 3, 3, 3, 0, 4, 3, 4, 4, 7, 3, 7, 0, 0, 0, 3, 0],\n [0, 2, 2, 3, 3, 3, 0, 7, 3, 2, 7, 3, 4, 3, 3, 3, 3],\n [3, 3, 3, 4, 7, 4, 3, 3, 3, 0, 0, 4, 0, 7, 0, 4, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 3, 2, 2, 4, 3, 7, 2, 7, 0, 7, 3, 4, 0, 3, 2, 4], [0, 2, 2, 2, 2, 3, 0, 3, 3, 0, 3, 2, 0, 0, 3, 0, 7], [3, 2, 0, 3, 7, 0, 2, 2, 2, 3, 7, 0, 3, 3, 0, 2, 2], [4, 2, 7, 7, 0, 0, 2, 0, 0, 0, 7, 4, 3, 2, 3, 7, 2], [7, 0, 8, 3, 0, 7, 3, 3, 0, 2, 3, 0, 4, 0, 0, 7, 0], [3, 4, 8, 8, 3, 2, 0, 0, 3, 4, 2, 4, 0, 3, 3, 2, 4], [2, 3, 0, 8, 0, 0, 2, 4, 0, 4, 4, 0, 0, 7, 2, 3, 0], [2, 4, 0, 3, 0, 0, 2, 2, 3, 2, 7, 3, 3, 7, 0, 4, 0], [2, 7, 7, 4, 0, 3, 0, 2, 7, 7, 0, 4, 7, 0, 7, 3, 3], [0, 0, 7, 3, 4, 0, 2, 3, 0, 7, 4, 3, 2, 8, 0, 7, 3], [2, 7, 0, 3, 4, 7, 3, 4, 0, 4, 0, 0, 0, 8, 8, 7, 2], [4, 3, 0, 2, 3, 0, 4, 0, 4, 3, 3, 2, 2, 3, 8, 0, 3], [4, 7, 0, 7, 3, 3, 2, 3, 3, 0, 7, 0, 0, 0, 0, 4, 7], [3, 3, 8, 8, 0, 4, 3, 4, 4, 7, 3, 7, 0, 0, 0, 8, 0], [0, 2, 2, 8, 8, 3, 0, 7, 3, 2, 7, 3, 4, 3, 3, 8, 8], [3, 3, 3, 4, 7, 4, 3, 3, 3, 0, 0, 4, 0, 7, 0, 4, 8]], "task_id": "50f325b5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [8, 0, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0],\n [8, 0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 0],\n [8, 0, 8, 0, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 0, 8, 0, 8, 8, 8, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 8, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 0, 8, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 8, 0, 8, 0, 8, 8, 8, 8, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 8],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0], [0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [8, 8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0], [8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0], [8, 0, 8, 8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0], [8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0], [8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0]], "task_id": "da515329"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 2, 1, 1, 2, 0, 2, 1, 1, 2, 0, 2, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 1, 1, 1, 1, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 1, 1, 1, 1, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0], [0, 2, 0, 2, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "60a26a3e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 4, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5],\n [0, 0, 5, 0, 4, 5, 4, 5, 5, 0, 5, 0, 0, 0, 5, 4, 5, 5, 5],\n [5, 0, 0, 0, 0, 4, 0, 0, 5, 5, 0, 0, 0, 0, 5, 4, 4, 5, 0],\n [5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 4, 5, 0, 0],\n [5, 5, 0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [5, 5, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 5, 4, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 4, 0, 5, 0, 5, 5, 4, 4, 5, 0, 0, 5, 5, 5, 5, 0],\n [0, 0, 4, 5, 0, 0, 5, 5, 0, 4, 5, 0, 5, 0, 0, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 5],\n [5, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5],\n [5, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 4, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5],\n [0, 0, 5, 0, 4, 2, 4, 5, 5, 0, 5, 0, 0, 0, 5, 4, 5, 5, 5],\n [5, 0, 0, 0, 0, 4, 0, 0, 5, 5, 0, 0, 0, 0, 2, 4, 4, 5, 0],\n [5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 4, 5, 0, 0],\n [5, 5, 0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [5, 5, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 0, 0, 5, 4, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [5, 2, 2, 4, 0, 5, 0, 5, 2, 4, 4, 5, 0, 0, 5, 5, 5, 5, 0],\n [0, 0, 4, 5, 0, 0, 5, 5, 0, 4, 5, 0, 5, 0, 0, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 5],\n [5, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5],\n [5, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5],\n [5, 5, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0],\n [0, 0, 4, 5, 0, 5, 0, 5, 5, 0, 0, 4, 5, 5, 5, 0],\n [0, 5, 5, 4, 0, 0, 5, 0, 0, 5, 0, 0, 4, 0, 0, 5],\n [0, 0, 4, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5],\n [5, 0, 5, 0, 0, 4, 5, 4, 5, 5, 5, 5, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 5, 4, 5, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 4, 5, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 4, 5],\n [5, 0, 0, 4, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5],\n [5, 5, 4, 5, 4, 5, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 5, 5, 5, 0, 5, 4, 5, 4, 0, 5, 0, 0, 0],\n [5, 0, 0, 5, 5, 0, 0, 5, 0, 4, 0, 0, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 5, 0, 0],\n [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 5, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5],\n [5, 5, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 2, 5, 0, 0],\n [0, 0, 4, 5, 0, 5, 0, 5, 5, 0, 0, 4, 2, 2, 5, 0],\n [0, 2, 2, 4, 0, 0, 5, 0, 0, 5, 0, 0, 4, 0, 0, 5],\n [0, 0, 4, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 5, 2, 5, 5, 0, 5, 0, 5, 5, 0, 5],\n [5, 0, 5, 0, 0, 4, 2, 4, 5, 5, 5, 5, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 5, 4, 5, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 4, 5, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 2, 4, 5],\n [5, 0, 0, 4, 0, 0, 0, 0, 5, 0, 0, 0, 0, 2, 5, 5],\n [5, 5, 4, 2, 4, 5, 0, 5, 5, 2, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 2, 5, 5, 0, 5, 4, 2, 4, 0, 5, 0, 0, 0],\n [5, 0, 0, 5, 5, 0, 0, 5, 0, 4, 0, 0, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 5, 0, 0],\n [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 5, 4, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [0, 0, 4, 0, 0, 5, 5, 4, 5, 0, 0, 5, 0, 5, 5],\n [5, 0, 5, 0, 0, 0, 4, 4, 5, 0, 5, 5, 5, 0, 5],\n [5, 0, 0, 5, 0, 5, 5, 4, 0, 0, 0, 5, 5, 0, 5],\n [5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 5],\n [5, 4, 5, 4, 0, 5, 5, 0, 4, 5, 4, 5, 0, 5, 0],\n [5, 5, 5, 0, 0, 5, 5, 0, 0, 4, 0, 5, 0, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5],\n [0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5],\n [0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2, 4, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [0, 0, 4, 0, 0, 5, 5, 4, 5, 0, 0, 5, 0, 5, 5],\n [5, 0, 5, 0, 0, 0, 4, 4, 2, 0, 5, 5, 5, 0, 5],\n [5, 0, 0, 5, 0, 5, 5, 4, 0, 0, 0, 5, 5, 0, 5],\n [5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 0, 5, 0, 5, 0, 0, 2, 0, 5, 0, 5, 5],\n [5, 4, 2, 4, 0, 5, 5, 0, 4, 2, 4, 5, 0, 5, 0],\n [5, 5, 2, 0, 0, 5, 5, 0, 0, 4, 0, 5, 0, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5],\n [0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5],\n [0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5],\n [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [5, 5, 5, 4, 5, 0, 0, 0, 4, 4, 5, 0, 5, 0, 5],\n [0, 5, 4, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5],\n [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5],\n [0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 0],\n [5, 0, 5, 0, 0, 5, 5, 5, 4, 5, 0, 0, 0, 5, 0],\n [0, 4, 5, 5, 0, 0, 5, 4, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 4, 5, 0, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5],\n [0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 5, 5, 0, 2, 0, 0, 5, 5, 5],\n [5, 2, 2, 4, 5, 0, 0, 0, 4, 4, 2, 0, 5, 0, 5],\n [0, 5, 4, 5, 0, 0, 0, 0, 0, 2, 0, 5, 0, 0, 5],\n [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5],\n [0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 5, 5, 0, 2, 0, 5, 5, 0, 0, 0, 0],\n [5, 0, 2, 0, 0, 5, 2, 2, 4, 5, 0, 0, 0, 5, 0],\n [0, 4, 2, 2, 0, 0, 5, 4, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 4, 5, 0, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5],\n [0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 5, 4, 5, 0, 0, 5],\n [0, 5, 5, 0, 5, 4, 5, 5, 5, 0, 5, 5, 0, 4, 5, 0, 5, 5, 0],\n [5, 5, 5, 0, 5, 5, 4, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5],\n [5, 5, 4, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5],\n [5, 4, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0],\n [5, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 4, 5, 0, 0, 5, 0, 5, 5],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 4, 4, 5, 0, 5, 5, 0, 0, 0],\n [5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5, 0],\n [0, 0, 0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 4, 0, 0, 0, 5, 0],\n [5, 5, 4, 5, 5, 5, 5, 5, 0, 0, 5, 0, 4, 5, 4, 0, 5, 0, 5],\n [5, 5, 5, 4, 0, 0, 0, 5, 5, 5, 0, 5, 5, 4, 0, 5, 5, 5, 5],\n [0, 0, 4, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5],\n [5, 0, 5, 0, 0, 5, 0, 4, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5],\n [5, 5, 0, 5, 5, 0, 5, 5, 4, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5],\n [5, 5, 5, 0, 5, 5, 5, 4, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5, 0],\n [5, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 5, 0, 0, 5, 2, 0, 5, 0, 5, 0, 2, 2, 4, 5, 0, 0, 5], [0, 5, 5, 0, 5, 4, 2, 2, 5, 0, 5, 5, 0, 4, 5, 0, 5, 5, 0], [5, 2, 5, 0, 5, 5, 4, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5], [2, 2, 4, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5], [5, 4, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0], [5, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 4, 5, 0, 0, 5, 0, 5, 5], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 4, 4, 2, 0, 5, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 2, 0, 5, 5, 0, 0, 5, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0, 5], [0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5, 0], [0, 0, 0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 4, 0, 0, 0, 5, 0], [5, 5, 4, 5, 5, 5, 5, 5, 0, 0, 5, 0, 4, 2, 4, 0, 5, 0, 5], [5, 2, 2, 4, 0, 0, 0, 5, 5, 5, 0, 5, 5, 4, 0, 5, 5, 5, 5], [0, 0, 4, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 0, 0, 5, 0, 4, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5], [5, 5, 0, 5, 5, 0, 2, 2, 4, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 4, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5, 0], [5, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0], [0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5]], "task_id": "14754a24"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 8, 8, 8, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1]\n ],\n \"output\": [\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 8, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [1, 1, 1, 2, 8, 8, 8, 2, 1, 1, 1, 2, 1, 1, 1, 2, 8, 8, 8, 2, 1],\n [0, 0, 1, 2, 0, 8, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [0, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 2, 0, 8, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [1, 1, 1, 2, 8, 8, 8, 2, 1, 1, 1, 2, 1, 1, 1, 2, 8, 8, 8, 2, 1],\n [0, 0, 1, 2, 0, 8, 0, 2, 1, 0, 0, 2, 0, 0, 1, 2, 0, 8, 0, 2, 1],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 8, 8, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 8, 1, 2, 1, 2, 2, 2],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 8, 1, 2, 1, 2, 2, 2],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 8, 8, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 8, 1, 2, 2, 2, 1, 2, 2, 2, 1, 8, 1, 8, 1, 2, 2, 2],\n [2, 2, 2, 1, 2, 1, 8, 1, 2, 2, 2, 1, 2, 2, 2, 1, 8, 1, 8, 1, 2, 2, 2],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 8, 8, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 2, 1, 2, 1, 0, 1, 8, 8, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 2, 1, 0, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 1, 0, 1, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 1, 8, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2],\n [2, 2, 2, 1, 8, 1, 8, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2],\n [0, 0, 1, 1, 1, 8, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [8, 8, 8, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2],\n [0, 0, 1, 1, 1, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0]\n ],\n \"output\": [\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 1, 1, 8, 1, 1, 1, 8, 1, 1, 2, 2, 2, 1, 2],\n [2, 2, 2, 1, 8, 1, 8, 1, 8, 1, 8, 1, 2, 2, 2, 1, 2],\n [0, 0, 1, 1, 1, 8, 0, 1, 1, 8, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 1, 1, 2, 1, 1],\n [8, 8, 8, 1, 8, 1, 8, 1, 8, 1, 8, 1, 2, 2, 2, 1, 2],\n [0, 0, 1, 1, 1, 8, 0, 1, 1, 8, 0, 1, 0, 0, 2, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0],\n [8, 8, 8, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 2, 0, 1, 1, 2, 0, 1, 0, 0, 2, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 8, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1],\n [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 2, 1, 1], [2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 8, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 8, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 8, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 8, 1, 0, 0, 1, 2, 0, 1], [2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 8, 8, 1], [0, 0, 1, 1, 2, 1, 0, 1, 1, 8, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1, 8, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "4ff4c9da"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 1, 2, 2, 4, 6, 2, 6, 8, 2, 8, 3, 3, 2, 3, 5, 5, 3, 2, 3, 3, 8, 2, 8, 6, 2, 6, 4, 2, 2],\n [6, 6, 2, 7, 6, 4, 6, 2, 2, 8, 3, 8, 3, 3, 5, 5, 5, 5, 3, 3, 8, 3, 8, 2, 2, 6, 4, 6, 7, 2],\n [1, 7, 6, 1, 2, 6, 4, 6, 8, 1, 8, 2, 3, 5, 5, 5, 5, 5, 5, 3, 2, 8, 1, 8, 6, 4, 6, 2, 1, 6],\n [7, 2, 6, 6, 6, 2, 6, 4, 1, 8, 2, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 2, 8, 1, 4, 6, 2, 6, 6, 6],\n [7, 2, 8, 1, 6, 1, 7, 2, 3, 3, 3, 5, 7, 6, 4, 4, 4, 4, 6, 7, 5, 3, 3, 3, 2, 7, 1, 6, 1, 8],\n [2, 7, 1, 8, 6, 6, 2, 2, 2, 3, 5, 5, 6, 7, 4, 4, 4, 4, 7, 6, 5, 5, 3, 2, 2, 2, 6, 6, 8, 1],\n [8, 1, 7, 2, 2, 7, 6, 1, 3, 5, 5, 5, 6, 7, 7, 6, 6, 7, 7, 6, 5, 5, 5, 3, 1, 6, 7, 2, 2, 7],\n [1, 8, 2, 7, 7, 1, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 7, 9, 9, 9, 9, 5, 5, 5, 6, 6, 1, 7, 7, 2],\n [8, 2, 8, 1, 3, 2, 3, 5, 6, 6, 2, 7, 7, 7, 4, 5, 5, 9, 9, 9, 9, 2, 6, 6, 5, 3, 2, 3, 1, 8],\n [2, 8, 1, 8, 3, 3, 5, 5, 8, 6, 7, 5, 7, 7, 5, 4, 4, 9, 9, 9, 9, 7, 6, 8, 5, 5, 3, 3, 8, 1],\n [8, 3, 8, 2, 3, 5, 9, 9, 9, 9, 9, 9, 4, 5, 7, 7, 7, 7, 5, 4, 6, 6, 7, 7, 5, 5, 5, 3, 2, 8],\n [3, 8, 2, 8, 5, 5, 9, 9, 9, 9, 9, 9, 5, 4, 7, 7, 7, 7, 4, 5, 6, 8, 7, 7, 5, 5, 5, 5, 8, 2],\n [3, 3, 3, 5, 7, 9, 9, 9, 8, 8, 8, 6, 6, 6, 5, 7, 7, 5, 6, 6, 6, 8, 8, 8, 7, 6, 6, 7, 5, 3],\n [2, 3, 5, 5, 6, 9, 9, 9, 8, 8, 6, 8, 8, 6, 7, 2, 2, 7, 6, 8, 8, 6, 8, 8, 6, 7, 7, 6, 5, 5],\n [3, 5, 5, 5, 4, 9, 9, 9, 8, 6, 8, 8, 7, 7, 6, 6, 6, 6, 7, 7, 8, 8, 6, 8, 6, 7, 4, 4, 5, 5],\n [5, 5, 5, 5, 4, 9, 9, 9, 6, 8, 8, 8, 7, 7, 8, 6, 6, 8, 7, 7, 8, 8, 8, 6, 7, 6, 4, 4, 5, 5],\n [5, 5, 5, 5, 4, 4, 6, 7, 6, 8, 8, 8, 7, 7, 8, 6, 6, 8, 7, 7, 8, 8, 8, 6, 7, 6, 4, 4, 5, 5],\n [3, 5, 5, 5, 4, 4, 7, 6, 8, 6, 8, 8, 7, 7, 6, 6, 6, 6, 7, 7, 8, 8, 6, 8, 6, 7, 4, 4, 5, 5],\n [2, 3, 5, 5, 6, 7, 7, 6, 8, 8, 6, 8, 8, 6, 7, 2, 2, 7, 6, 8, 8, 6, 8, 8, 6, 7, 7, 6, 5, 5],\n [3, 3, 3, 5, 7, 6, 6, 7, 8, 8, 8, 6, 6, 6, 5, 7, 7, 5, 6, 6, 6, 8, 8, 8, 7, 6, 6, 7, 5, 3],\n [3, 8, 2, 8, 5, 5, 5, 5, 7, 7, 8, 6, 5, 4, 7, 7, 7, 7, 4, 5, 6, 8, 7, 7, 5, 5, 5, 5, 8, 2],\n [8, 3, 8, 2, 3, 5, 5, 5, 7, 7, 6, 6, 4, 5, 7, 7, 7, 7, 5, 4, 6, 6, 7, 7, 5, 5, 5, 3, 2, 8],\n [2, 8, 1, 8, 3, 3, 5, 5, 8, 6, 7, 5, 7, 7, 5, 4, 4, 5, 7, 7, 5, 7, 6, 8, 5, 5, 3, 3, 8, 1],\n [8, 2, 8, 1, 3, 2, 3, 5, 6, 6, 2, 7, 7, 7, 4, 5, 5, 4, 7, 7, 7, 2, 6, 6, 5, 3, 2, 3, 1, 8],\n [1, 8, 2, 7, 7, 1, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 7, 6, 6, 7, 5, 5, 5, 5, 6, 6, 1, 7, 7, 2],\n [8, 1, 7, 2, 2, 7, 6, 1, 3, 5, 5, 5, 6, 7, 7, 6, 6, 7, 7, 6, 5, 5, 5, 3, 1, 6, 7, 2, 2, 7],\n [2, 7, 1, 8, 6, 6, 2, 2, 2, 3, 5, 5, 6, 7, 4, 4, 4, 4, 7, 6, 5, 5, 3, 2, 2, 2, 6, 6, 8, 1],\n [7, 2, 8, 1, 6, 1, 7, 2, 3, 3, 3, 5, 7, 6, 4, 4, 4, 4, 6, 7, 5, 3, 3, 3, 2, 7, 1, 6, 1, 8],\n [7, 2, 6, 6, 6, 2, 6, 4, 1, 8, 2, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 2, 8, 1, 4, 6, 2, 6, 6, 6],\n [1, 7, 6, 1, 2, 6, 4, 6, 8, 1, 8, 2, 3, 5, 5, 5, 5, 5, 5, 3, 2, 8, 1, 8, 6, 4, 6, 2, 1, 6]\n ],\n \"output\": [\n [6, 1, 2, 2, 4, 6, 2, 6, 8, 2, 8, 3, 3, 2, 3, 5, 5, 3, 2, 3, 3, 8, 2, 8, 6, 2, 6, 4, 2, 2],\n [6, 6, 2, 7, 6, 4, 6, 2, 2, 8, 3, 8, 3, 3, 5, 5, 5, 5, 3, 3, 8, 3, 8, 2, 2, 6, 4, 6, 7, 2],\n [1, 7, 6, 1, 2, 6, 4, 6, 8, 1, 8, 2, 3, 5, 5, 5, 5, 5, 5, 3, 2, 8, 1, 8, 6, 4, 6, 2, 1, 6],\n [7, 2, 6, 6, 6, 2, 6, 4, 1, 8, 2, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 2, 8, 1, 4, 6, 2, 6, 6, 6],\n [7, 2, 8, 1, 6, 1, 7, 2, 3, 3, 3, 5, 7, 6, 4, 4, 4, 4, 6, 7, 5, 3, 3, 3, 2, 7, 1, 6, 1, 8],\n [2, 7, 1, 8, 6, 6, 2, 2, 2, 3, 5, 5, 6, 7, 4, 4, 4, 4, 7, 6, 5, 5, 3, 2, 2, 2, 6, 6, 8, 1],\n [8, 1, 7, 2, 2, 7, 6, 1, 3, 5, 5, 5, 6, 7, 7, 6, 6, 7, 7, 6, 5, 5, 5, 3, 1, 6, 7, 2, 2, 7],\n [1, 8, 2, 7, 7, 1, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 7, 6, 6, 7, 5, 5, 5, 5, 6, 6, 1, 7, 7, 2],\n [8, 2, 8, 1, 3, 2, 3, 5, 6, 6, 2, 7, 7, 7, 4, 5, 5, 4, 7, 7, 7, 2, 6, 6, 5, 3, 2, 3, 1, 8],\n [2, 8, 1, 8, 3, 3, 5, 5, 8, 6, 7, 5, 7, 7, 5, 4, 4, 5, 7, 7, 5, 7, 6, 8, 5, 5, 3, 3, 8, 1],\n [8, 3, 8, 2, 3, 5, 5, 5, 7, 7, 6, 6, 4, 5, 7, 7, 7, 7, 5, 4, 6, 6, 7, 7, 5, 5, 5, 3, 2, 8],\n [3, 8, 2, 8, 5, 5, 5, 5, 7, 7, 8, 6, 5, 4, 7, 7, 7, 7, 4, 5, 6, 8, 7, 7, 5, 5, 5, 5, 8, 2],\n [3, 3, 3, 5, 7, 6, 6, 7, 8, 8, 8, 6, 6, 6, 5, 7, 7, 5, 6, 6, 6, 8, 8, 8, 7, 6, 6, 7, 5, 3],\n [2, 3, 5, 5, 6, 7, 7, 6, 8, 8, 6, 8, 8, 6, 7, 2, 2, 7, 6, 8, 8, 6, 8, 8, 6, 7, 7, 6, 5, 5],\n [3, 5, 5, 5, 4, 4, 7, 6, 8, 6, 8, 8, 7, 7, 6, 6, 6, 6, 7, 7, 8, 8, 6, 8, 6, 7, 4, 4, 5, 5],\n [5, 5, 5, 5, 4, 4, 6, 7, 6, 8, 8, 8, 7, 7, 8, 6, 6, 8, 7, 7, 8, 8, 8, 6, 7, 6, 4, 4, 5, 5],\n [5, 5, 5, 5, 4, 4, 6, 7, 6, 8, 8, 8, 7, 7, 8, 6, 6, 8, 7, 7, 8, 8, 8, 6, 7, 6, 4, 4, 5, 5],\n [3, 5, 5, 5, 4, 4, 7, 6, 8, 6, 8, 8, 7, 7, 6, 6, 6, 6, 7, 7, 8, 8, 6, 8, 6, 7, 4, 4, 5, 5],\n [2, 3, 5, 5, 6, 7, 7, 6, 8, 8, 6, 8, 8, 6, 7, 2, 2, 7, 6, 8, 8, 6, 8, 8, 6, 7, 7, 6, 5, 5],\n [3, 3, 3, 5, 7, 6, 6, 7, 8, 8, 8, 6, 6, 6, 5, 7, 7, 5, 6, 6, 6, 8, 8, 8, 7, 6, 6, 7, 5, 3],\n [3, 8, 2, 8, 5, 5, 5, 5, 7, 7, 8, 6, 5, 4, 7, 7, 7, 7, 4, 5, 6, 8, 7, 7, 5, 5, 5, 5, 8, 2],\n [8, 3, 8, 2, 3, 5, 5, 5, 7, 7, 6, 6, 4, 5, 7, 7, 7, 7, 5, 4, 6, 6, 7, 7, 5, 5, 5, 3, 2, 8],\n [2, 8, 1, 8, 3, 3, 5, 5, 8, 6, 7, 5, 7, 7, 5, 4, 4, 5, 7, 7, 5, 7, 6, 8, 5, 5, 3, 3, 8, 1],\n [8, 2, 8, 1, 3, 2, 3, 5, 6, 6, 2, 7, 7, 7, 4, 5, 5, 4, 7, 7, 7, 2, 6, 6, 5, 3, 2, 3, 1, 8],\n [1, 8, 2, 7, 7, 1, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 7, 6, 6, 7, 5, 5, 5, 5, 6, 6, 1, 7, 7, 2],\n [8, 1, 7, 2, 2, 7, 6, 1, 3, 5, 5, 5, 6, 7, 7, 6, 6, 7, 7, 6, 5, 5, 5, 3, 1, 6, 7, 2, 2, 7],\n [2, 7, 1, 8, 6, 6, 2, 2, 2, 3, 5, 5, 6, 7, 4, 4, 4, 4, 7, 6, 5, 5, 3, 2, 2, 2, 6, 6, 8, 1],\n [7, 2, 8, 1, 6, 1, 7, 2, 3, 3, 3, 5, 7, 6, 4, 4, 4, 4, 6, 7, 5, 3, 3, 3, 2, 7, 1, 6, 1, 8],\n [7, 2, 6, 6, 6, 2, 6, 4, 1, 8, 2, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 2, 8, 1, 4, 6, 2, 6, 6, 6],\n [1, 7, 6, 1, 2, 6, 4, 6, 8, 1, 8, 2, 3, 5, 5, 5, 5, 5, 5, 3, 2, 8, 1, 8, 6, 4, 6, 2, 1, 6]\n ]\n}\n\n{\n \"input\": [\n [3, 2, 7, 8, 4, 7, 7, 4, 1, 1, 1, 7, 6, 4, 6, 4, 4, 6, 4, 6, 7, 1, 1, 1, 4, 7, 7, 4, 8, 7],\n [2, 3, 8, 7, 7, 4, 4, 7, 1, 1, 7, 1, 6, 6, 4, 6, 6, 4, 6, 6, 1, 7, 1, 1, 7, 4, 4, 7, 7, 8],\n [1, 4, 3, 2, 7, 4, 4, 7, 5, 5, 1, 1, 6, 4, 1, 1, 1, 1, 4, 6, 1, 1, 5, 5, 7, 4, 4, 7, 2, 3],\n [4, 1, 2, 3, 4, 7, 7, 4, 9, 9, 9, 9, 4, 6, 1, 1, 1, 1, 6, 4, 1, 1, 5, 5, 4, 7, 7, 4, 3, 2],\n [6, 9, 9, 9, 9, 9, 7, 8, 9, 9, 9, 9, 1, 6, 5, 8, 8, 5, 6, 1, 4, 6, 6, 6, 8, 7, 2, 3, 8, 8],\n [6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 1, 1, 8, 5, 5, 8, 1, 1, 6, 4, 6, 4, 7, 8, 3, 2, 8, 8],\n [8, 8, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 2, 1, 6, 6, 1, 2, 2, 1, 1, 4, 6, 2, 3, 4, 1, 6, 6],\n [8, 8, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 6, 4, 3, 2, 1, 4, 6, 6],\n [1, 1, 5, 9, 9, 9, 9, 9, 9, 6, 3, 3, 8, 8, 8, 2, 2, 8, 8, 8, 3, 3, 6, 4, 4, 6, 4, 6, 5, 5],\n [1, 1, 5, 9, 9, 9, 9, 9, 9, 4, 3, 3, 8, 8, 2, 8, 8, 2, 8, 8, 3, 3, 4, 7, 6, 4, 6, 6, 5, 5],\n [1, 7, 1, 1, 6, 4, 1, 1, 8, 6, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 6, 8, 1, 1, 4, 6, 1, 1],\n [7, 1, 1, 1, 4, 6, 1, 1, 6, 8, 7, 4, 2, 8, 8, 8, 8, 8, 8, 2, 4, 7, 8, 6, 1, 1, 6, 4, 1, 1],\n [6, 6, 6, 4, 1, 1, 2, 2, 5, 4, 6, 6, 4, 6, 3, 3, 3, 3, 6, 4, 6, 6, 4, 5, 2, 2, 1, 1, 4, 6],\n [4, 6, 4, 6, 6, 1, 2, 2, 4, 5, 6, 6, 7, 4, 3, 3, 3, 3, 4, 7, 6, 6, 5, 4, 2, 2, 1, 6, 6, 4],\n [6, 4, 1, 1, 5, 8, 1, 1, 6, 6, 5, 4, 8, 6, 4, 6, 6, 4, 6, 8, 4, 5, 6, 6, 1, 1, 8, 5, 1, 1],\n [4, 6, 1, 1, 8, 5, 6, 1, 6, 6, 4, 5, 6, 8, 7, 4, 4, 7, 8, 6, 5, 4, 6, 6, 1, 6, 5, 8, 1, 1],\n [4, 6, 1, 1, 8, 5, 6, 1, 6, 6, 4, 5, 6, 8, 7, 4, 4, 7, 8, 6, 5, 4, 6, 6, 1, 6, 5, 8, 1, 1],\n [6, 4, 1, 1, 5, 8, 1, 1, 6, 6, 5, 4, 8, 6, 4, 6, 6, 4, 6, 8, 4, 5, 6, 6, 1, 1, 8, 5, 1, 1],\n [4, 6, 4, 6, 6, 1, 2, 2, 4, 5, 6, 6, 7, 4, 3, 3, 3, 3, 4, 7, 6, 6, 5, 4, 2, 2, 1, 6, 6, 4],\n [6, 6, 6, 4, 1, 1, 2, 2, 5, 4, 6, 6, 4, 6, 3, 3, 3, 3, 6, 4, 6, 6, 4, 5, 2, 2, 1, 1, 4, 6],\n [7, 1, 1, 1, 4, 6, 1, 1, 6, 8, 7, 4, 2, 8, 8, 8, 8, 8, 8, 2, 4, 7, 8, 6, 1, 1, 6, 4, 1, 1],\n [1, 7, 1, 1, 6, 4, 1, 1, 8, 6, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 6, 8, 1, 1, 4, 6, 1, 1],\n [1, 1, 5, 5, 6, 6, 4, 6, 7, 4, 3, 3, 8, 8, 2, 8, 8, 2, 8, 8, 3, 3, 4, 7, 6, 4, 6, 6, 5, 5],\n [1, 1, 5, 5, 6, 4, 6, 4, 4, 6, 3, 3, 8, 8, 8, 2, 2, 8, 8, 8, 3, 3, 6, 4, 4, 6, 4, 6, 5, 5],\n [8, 8, 6, 6, 4, 1, 2, 3, 4, 6, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 6, 4, 3, 2, 1, 4, 6, 6],\n [8, 8, 6, 6, 1, 4, 3, 2, 6, 4, 9, 9, 9, 9, 9, 9, 6, 1, 2, 2, 1, 1, 4, 6, 2, 3, 4, 1, 6, 6],\n [6, 6, 8, 8, 2, 3, 8, 7, 4, 6, 9, 9, 9, 9, 9, 9, 5, 8, 1, 1, 6, 4, 6, 4, 7, 8, 3, 2, 8, 8],\n [6, 6, 8, 8, 3, 2, 7, 8, 6, 6, 9, 9, 9, 9, 9, 9, 8, 5, 6, 1, 4, 6, 6, 6, 8, 7, 2, 3, 8, 8],\n [4, 1, 2, 3, 4, 7, 7, 4, 5, 5, 1, 1, 4, 6, 1, 1, 1, 1, 6, 4, 1, 1, 5, 5, 4, 7, 7, 4, 3, 2],\n [1, 4, 3, 2, 7, 4, 4, 7, 5, 5, 1, 1, 6, 4, 1, 1, 1, 1, 4, 6, 1, 1, 5, 5, 7, 4, 4, 7, 2, 3]\n ],\n \"output\": [\n [3, 2, 7, 8, 4, 7, 7, 4, 1, 1, 1, 7, 6, 4, 6, 4, 4, 6, 4, 6, 7, 1, 1, 1, 4, 7, 7, 4, 8, 7],\n [2, 3, 8, 7, 7, 4, 4, 7, 1, 1, 7, 1, 6, 6, 4, 6, 6, 4, 6, 6, 1, 7, 1, 1, 7, 4, 4, 7, 7, 8],\n [1, 4, 3, 2, 7, 4, 4, 7, 5, 5, 1, 1, 6, 4, 1, 1, 1, 1, 4, 6, 1, 1, 5, 5, 7, 4, 4, 7, 2, 3],\n [4, 1, 2, 3, 4, 7, 7, 4, 5, 5, 1, 1, 4, 6, 1, 1, 1, 1, 6, 4, 1, 1, 5, 5, 4, 7, 7, 4, 3, 2],\n [6, 6, 8, 8, 3, 2, 7, 8, 6, 6, 6, 4, 1, 6, 5, 8, 8, 5, 6, 1, 4, 6, 6, 6, 8, 7, 2, 3, 8, 8],\n [6, 6, 8, 8, 2, 3, 8, 7, 4, 6, 4, 6, 1, 1, 8, 5, 5, 8, 1, 1, 6, 4, 6, 4, 7, 8, 3, 2, 8, 8],\n [8, 8, 6, 6, 1, 4, 3, 2, 6, 4, 1, 1, 2, 2, 1, 6, 6, 1, 2, 2, 1, 1, 4, 6, 2, 3, 4, 1, 6, 6],\n [8, 8, 6, 6, 4, 1, 2, 3, 4, 6, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 6, 4, 3, 2, 1, 4, 6, 6],\n [1, 1, 5, 5, 6, 4, 6, 4, 4, 6, 3, 3, 8, 8, 8, 2, 2, 8, 8, 8, 3, 3, 6, 4, 4, 6, 4, 6, 5, 5],\n [1, 1, 5, 5, 6, 6, 4, 6, 7, 4, 3, 3, 8, 8, 2, 8, 8, 2, 8, 8, 3, 3, 4, 7, 6, 4, 6, 6, 5, 5],\n [1, 7, 1, 1, 6, 4, 1, 1, 8, 6, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 6, 8, 1, 1, 4, 6, 1, 1],\n [7, 1, 1, 1, 4, 6, 1, 1, 6, 8, 7, 4, 2, 8, 8, 8, 8, 8, 8, 2, 4, 7, 8, 6, 1, 1, 6, 4, 1, 1],\n [6, 6, 6, 4, 1, 1, 2, 2, 5, 4, 6, 6, 4, 6, 3, 3, 3, 3, 6, 4, 6, 6, 4, 5, 2, 2, 1, 1, 4, 6],\n [4, 6, 4, 6, 6, 1, 2, 2, 4, 5, 6, 6, 7, 4, 3, 3, 3, 3, 4, 7, 6, 6, 5, 4, 2, 2, 1, 6, 6, 4],\n [6, 4, 1, 1, 5, 8, 1, 1, 6, 6, 5, 4, 8, 6, 4, 6, 6, 4, 6, 8, 4, 5, 6, 6, 1, 1, 8, 5, 1, 1],\n [4, 6, 1, 1, 8, 5, 6, 1, 6, 6, 4, 5, 6, 8, 7, 4, 4, 7, 8, 6, 5, 4, 6, 6, 1, 6, 5, 8, 1, 1],\n [4, 6, 1, 1, 8, 5, 6, 1, 6, 6, 4, 5, 6, 8, 7, 4, 4, 7, 8, 6, 5, 4, 6, 6, 1, 6, 5, 8, 1, 1],\n [6, 4, 1, 1, 5, 8, 1, 1, 6, 6, 5, 4, 8, 6, 4, 6, 6, 4, 6, 8, 4, 5, 6, 6, 1, 1, 8, 5, 1, 1],\n [4, 6, 4, 6, 6, 1, 2, 2, 4, 5, 6, 6, 7, 4, 3, 3, 3, 3, 4, 7, 6, 6, 5, 4, 2, 2, 1, 6, 6, 4],\n [6, 6, 6, 4, 1, 1, 2, 2, 5, 4, 6, 6, 4, 6, 3, 3, 3, 3, 6, 4, 6, 6, 4, 5, 2, 2, 1, 1, 4, 6],\n [7, 1, 1, 1, 4, 6, 1, 1, 6, 8, 7, 4, 2, 8, 8, 8, 8, 8, 8, 2, 4, 7, 8, 6, 1, 1, 6, 4, 1, 1],\n [1, 7, 1, 1, 6, 4, 1, 1, 8, 6, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 6, 8, 1, 1, 4, 6, 1, 1],\n [1, 1, 5, 5, 6, 6, 4, 6, 7, 4, 3, 3, 8, 8, 2, 8, 8, 2, 8, 8, 3, 3, 4, 7, 6, 4, 6, 6, 5, 5],\n [1, 1, 5, 5, 6, 4, 6, 4, 4, 6, 3, 3, 8, 8, 8, 2, 2, 8, 8, 8, 3, 3, 6, 4, 4, 6, 4, 6, 5, 5],\n [8, 8, 6, 6, 4, 1, 2, 3, 4, 6, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 6, 4, 3, 2, 1, 4, 6, 6],\n [8, 8, 6, 6, 1, 4, 3, 2, 6, 4, 1, 1, 2, 2, 1, 6, 6, 1, 2, 2, 1, 1, 4, 6, 2, 3, 4, 1, 6, 6],\n [6, 6, 8, 8, 2, 3, 8, 7, 4, 6, 4, 6, 1, 1, 8, 5, 5, 8, 1, 1, 6, 4, 6, 4, 7, 8, 3, 2, 8, 8],\n [6, 6, 8, 8, 3, 2, 7, 8, 6, 6, 6, 4, 1, 6, 5, 8, 8, 5, 6, 1, 4, 6, 6, 6, 8, 7, 2, 3, 8, 8],\n [4, 1, 2, 3, 4, 7, 7, 4, 5, 5, 1, 1, 4, 6, 1, 1, 1, 1, 6, 4, 1, 1, 5, 5, 4, 7, 7, 4, 3, 2],\n [1, 4, 3, 2, 7, 4, 4, 7, 5, 5, 1, 1, 6, 4, 1, 1, 1, 1, 4, 6, 1, 1, 5, 5, 7, 4, 4, 7, 2, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 8, 2, 8, 8, 7, 6, 4, 4, 6, 6, 3, 3, 2, 7, 7, 2, 3, 3, 6, 6, 4, 4, 6, 7, 8, 8, 2, 8],\n [3, 3, 2, 6, 8, 8, 6, 7, 6, 4, 6, 6, 1, 3, 7, 2, 2, 7, 3, 1, 6, 6, 4, 6, 7, 6, 8, 8, 6, 2],\n [8, 2, 3, 3, 7, 6, 8, 8, 8, 8, 4, 4, 2, 7, 6, 7, 7, 6, 7, 2, 4, 4, 8, 8, 8, 8, 6, 7, 3, 3],\n [2, 3, 3, 3, 6, 7, 8, 8, 8, 8, 6, 4, 7, 2, 3, 6, 6, 3, 2, 7, 4, 6, 8, 8, 8, 8, 7, 6, 3, 3],\n [5, 7, 8, 8, 3, 3, 6, 2, 3, 1, 2, 7, 1, 1, 5, 5, 5, 5, 1, 1, 7, 2, 1, 3, 2, 6, 9, 9, 9, 9],\n [7, 5, 8, 8, 3, 3, 2, 8, 3, 3, 7, 2, 1, 1, 5, 5, 5, 5, 1, 1, 2, 7, 3, 3, 8, 2, 9, 9, 9, 9],\n [8, 8, 5, 7, 3, 2, 3, 3, 2, 7, 6, 3, 6, 6, 1, 1, 1, 1, 6, 6, 3, 6, 7, 2, 3, 3, 9, 9, 9, 9],\n [8, 8, 7, 5, 2, 8, 3, 3, 7, 2, 7, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 7, 2, 7, 3, 3, 9, 9, 9, 9],\n [4, 6, 8, 8, 3, 3, 2, 7, 1, 7, 6, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 6, 7, 1, 7, 2, 9, 9, 9, 9],\n [4, 4, 8, 8, 1, 3, 7, 2, 8, 1, 5, 5, 5, 5, 7, 5, 5, 7, 5, 5, 5, 5, 1, 8, 2, 7, 9, 9, 9, 9],\n [6, 6, 4, 6, 2, 7, 6, 7, 1, 3, 1, 7, 5, 7, 5, 5, 5, 5, 7, 5, 7, 1, 3, 1, 9, 9, 9, 9, 9, 9],\n [6, 6, 4, 4, 7, 2, 3, 6, 3, 3, 8, 1, 7, 5, 5, 5, 5, 5, 5, 7, 1, 8, 3, 3, 9, 9, 9, 9, 9, 9],\n [3, 1, 2, 7, 1, 1, 6, 6, 8, 5, 5, 4, 1, 7, 5, 5, 5, 5, 7, 9, 9, 9, 9, 9, 6, 6, 1, 1, 7, 2],\n [3, 3, 7, 2, 1, 1, 6, 6, 5, 8, 4, 5, 8, 1, 5, 6, 6, 5, 1, 9, 9, 9, 9, 9, 6, 6, 1, 1, 2, 7],\n [2, 7, 6, 3, 5, 5, 1, 1, 5, 4, 8, 5, 3, 3, 1, 7, 7, 1, 3, 9, 9, 9, 9, 9, 1, 1, 5, 5, 3, 6],\n [7, 2, 7, 6, 5, 5, 1, 1, 4, 5, 5, 8, 3, 1, 8, 1, 1, 8, 1, 3, 8, 5, 5, 4, 1, 1, 5, 5, 6, 7],\n [7, 2, 7, 6, 5, 5, 1, 1, 4, 5, 5, 8, 3, 1, 8, 1, 1, 8, 1, 3, 8, 5, 5, 4, 1, 1, 5, 5, 6, 7],\n [2, 7, 6, 3, 5, 5, 1, 1, 5, 4, 8, 5, 3, 3, 1, 7, 7, 1, 3, 3, 5, 8, 4, 5, 1, 1, 5, 5, 3, 6],\n [3, 3, 7, 2, 1, 1, 6, 6, 5, 8, 4, 5, 8, 1, 5, 6, 6, 5, 1, 8, 5, 4, 8, 5, 6, 6, 1, 1, 2, 7],\n [3, 1, 2, 7, 1, 1, 6, 6, 8, 5, 5, 4, 1, 7, 5, 5, 5, 5, 7, 1, 4, 5, 5, 8, 6, 6, 1, 1, 7, 2],\n [6, 6, 4, 4, 7, 2, 3, 6, 3, 3, 8, 1, 7, 5, 5, 5, 5, 5, 5, 7, 1, 8, 3, 3, 6, 3, 2, 7, 4, 4],\n [6, 6, 4, 6, 2, 7, 6, 7, 1, 3, 1, 7, 5, 7, 5, 5, 5, 5, 7, 5, 7, 1, 3, 1, 7, 6, 7, 2, 6, 4],\n [4, 4, 8, 8, 1, 3, 7, 2, 8, 1, 5, 5, 5, 5, 7, 5, 5, 7, 5, 5, 5, 5, 1, 8, 2, 7, 3, 1, 8, 8],\n [4, 6, 8, 8, 3, 3, 2, 7, 1, 7, 6, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 6, 7, 1, 7, 2, 3, 3, 8, 8],\n [8, 8, 7, 5, 2, 8, 3, 3, 7, 2, 7, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 7, 2, 7, 3, 3, 8, 2, 5, 7],\n [8, 8, 5, 7, 3, 2, 3, 3, 2, 7, 6, 3, 6, 6, 1, 1, 1, 1, 6, 6, 3, 6, 7, 2, 3, 3, 2, 3, 7, 5],\n [7, 5, 8, 8, 3, 3, 2, 8, 3, 3, 7, 2, 1, 1, 5, 5, 5, 5, 1, 1, 2, 7, 3, 3, 8, 2, 3, 3, 8, 8],\n [5, 7, 8, 8, 3, 3, 6, 2, 3, 1, 2, 7, 1, 1, 5, 5, 5, 5, 1, 1, 7, 2, 1, 3, 2, 6, 3, 3, 8, 8],\n [2, 3, 3, 3, 6, 7, 8, 8, 8, 8, 6, 4, 7, 2, 3, 6, 6, 3, 2, 7, 4, 6, 8, 8, 8, 8, 7, 6, 3, 3],\n [8, 2, 3, 3, 7, 6, 8, 8, 8, 8, 4, 4, 2, 7, 6, 7, 7, 6, 7, 2, 4, 4, 8, 8, 8, 8, 6, 7, 3, 3]\n ],\n \"output\": [\n [3, 3, 8, 2, 8, 8, 7, 6, 4, 4, 6, 6, 3, 3, 2, 7, 7, 2, 3, 3, 6, 6, 4, 4, 6, 7, 8, 8, 2, 8],\n [3, 3, 2, 6, 8, 8, 6, 7, 6, 4, 6, 6, 1, 3, 7, 2, 2, 7, 3, 1, 6, 6, 4, 6, 7, 6, 8, 8, 6, 2],\n [8, 2, 3, 3, 7, 6, 8, 8, 8, 8, 4, 4, 2, 7, 6, 7, 7, 6, 7, 2, 4, 4, 8, 8, 8, 8, 6, 7, 3, 3],\n [2, 3, 3, 3, 6, 7, 8, 8, 8, 8, 6, 4, 7, 2, 3, 6, 6, 3, 2, 7, 4, 6, 8, 8, 8, 8, 7, 6, 3, 3],\n [5, 7, 8, 8, 3, 3, 6, 2, 3, 1, 2, 7, 1, 1, 5, 5, 5, 5, 1, 1, 7, 2, 1, 3, 2, 6, 3, 3, 8, 8],\n [7, 5, 8, 8, 3, 3, 2, 8, 3, 3, 7, 2, 1, 1, 5, 5, 5, 5, 1, 1, 2, 7, 3, 3, 8, 2, 3, 3, 8, 8],\n [8, 8, 5, 7, 3, 2, 3, 3, 2, 7, 6, 3, 6, 6, 1, 1, 1, 1, 6, 6, 3, 6, 7, 2, 3, 3, 2, 3, 7, 5],\n [8, 8, 7, 5, 2, 8, 3, 3, 7, 2, 7, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 7, 2, 7, 3, 3, 8, 2, 5, 7],\n [4, 6, 8, 8, 3, 3, 2, 7, 1, 7, 6, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 6, 7, 1, 7, 2, 3, 3, 8, 8],\n [4, 4, 8, 8, 1, 3, 7, 2, 8, 1, 5, 5, 5, 5, 7, 5, 5, 7, 5, 5, 5, 5, 1, 8, 2, 7, 3, 1, 8, 8],\n [6, 6, 4, 6, 2, 7, 6, 7, 1, 3, 1, 7, 5, 7, 5, 5, 5, 5, 7, 5, 7, 1, 3, 1, 7, 6, 7, 2, 6, 4],\n [6, 6, 4, 4, 7, 2, 3, 6, 3, 3, 8, 1, 7, 5, 5, 5, 5, 5, 5, 7, 1, 8, 3, 3, 6, 3, 2, 7, 4, 4],\n [3, 1, 2, 7, 1, 1, 6, 6, 8, 5, 5, 4, 1, 7, 5, 5, 5, 5, 7, 1, 4, 5, 5, 8, 6, 6, 1, 1, 7, 2],\n [3, 3, 7, 2, 1, 1, 6, 6, 5, 8, 4, 5, 8, 1, 5, 6, 6, 5, 1, 8, 5, 4, 8, 5, 6, 6, 1, 1, 2, 7],\n [2, 7, 6, 3, 5, 5, 1, 1, 5, 4, 8, 5, 3, 3, 1, 7, 7, 1, 3, 3, 5, 8, 4, 5, 1, 1, 5, 5, 3, 6],\n [7, 2, 7, 6, 5, 5, 1, 1, 4, 5, 5, 8, 3, 1, 8, 1, 1, 8, 1, 3, 8, 5, 5, 4, 1, 1, 5, 5, 6, 7],\n [7, 2, 7, 6, 5, 5, 1, 1, 4, 5, 5, 8, 3, 1, 8, 1, 1, 8, 1, 3, 8, 5, 5, 4, 1, 1, 5, 5, 6, 7],\n [2, 7, 6, 3, 5, 5, 1, 1, 5, 4, 8, 5, 3, 3, 1, 7, 7, 1, 3, 3, 5, 8, 4, 5, 1, 1, 5, 5, 3, 6],\n [3, 3, 7, 2, 1, 1, 6, 6, 5, 8, 4, 5, 8, 1, 5, 6, 6, 5, 1, 8, 5, 4, 8, 5, 6, 6, 1, 1, 2, 7],\n [3, 1, 2, 7, 1, 1, 6, 6, 8, 5, 5, 4, 1, 7, 5, 5, 5, 5, 7, 1, 4, 5, 5, 8, 6, 6, 1, 1, 7, 2],\n [6, 6, 4, 4, 7, 2, 3, 6, 3, 3, 8, 1, 7, 5, 5, 5, 5, 5, 5, 7, 1, 8, 3, 3, 6, 3, 2, 7, 4, 4],\n [6, 6, 4, 6, 2, 7, 6, 7, 1, 3, 1, 7, 5, 7, 5, 5, 5, 5, 7, 5, 7, 1, 3, 1, 7, 6, 7, 2, 6, 4],\n [4, 4, 8, 8, 1, 3, 7, 2, 8, 1, 5, 5, 5, 5, 7, 5, 5, 7, 5, 5, 5, 5, 1, 8, 2, 7, 3, 1, 8, 8],\n [4, 6, 8, 8, 3, 3, 2, 7, 1, 7, 6, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 6, 7, 1, 7, 2, 3, 3, 8, 8],\n [8, 8, 7, 5, 2, 8, 3, 3, 7, 2, 7, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 7, 2, 7, 3, 3, 8, 2, 5, 7],\n [8, 8, 5, 7, 3, 2, 3, 3, 2, 7, 6, 3, 6, 6, 1, 1, 1, 1, 6, 6, 3, 6, 7, 2, 3, 3, 2, 3, 7, 5],\n [7, 5, 8, 8, 3, 3, 2, 8, 3, 3, 7, 2, 1, 1, 5, 5, 5, 5, 1, 1, 2, 7, 3, 3, 8, 2, 3, 3, 8, 8],\n [5, 7, 8, 8, 3, 3, 6, 2, 3, 1, 2, 7, 1, 1, 5, 5, 5, 5, 1, 1, 7, 2, 1, 3, 2, 6, 3, 3, 8, 8],\n [2, 3, 3, 3, 6, 7, 8, 8, 8, 8, 6, 4, 7, 2, 3, 6, 6, 3, 2, 7, 4, 6, 8, 8, 8, 8, 7, 6, 3, 3],\n [8, 2, 3, 3, 7, 6, 8, 8, 8, 8, 4, 4, 2, 7, 6, 7, 7, 6, 7, 2, 4, 4, 8, 8, 8, 8, 6, 7, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [1, 6, 5, 5, 8, 8, 8, 8, 1, 3, 5, 2, 3, 6, 7, 3, 3, 7, 6, 3, 2, 5, 3, 1, 8, 8, 8, 8, 5, 5],\n [6, 1, 5, 2, 8, 8, 8, 8, 3, 1, 2, 5, 6, 3, 3, 6, 6, 3, 3, 6, 5, 2, 1, 3, 8, 8, 8, 8, 2, 5],\n [6, 3, 1, 6, 8, 8, 8, 8, 5, 3, 1, 3, 7, 3, 2, 2, 2, 2, 3, 7, 3, 1, 3, 5, 8, 8, 8, 8, 6, 1],\n [3, 6, 6, 1, 8, 8, 8, 8, 3, 5, 3, 1, 3, 6, 8, 2, 2, 9, 9, 9, 1, 3, 5, 3, 8, 8, 8, 8, 1, 6],\n [7, 7, 3, 3, 1, 6, 2, 5, 3, 6, 7, 3, 2, 1, 5, 5, 5, 9, 9, 9, 3, 7, 6, 9, 9, 2, 6, 1, 3, 3],\n [7, 7, 3, 3, 6, 1, 5, 5, 6, 3, 3, 6, 1, 2, 5, 5, 5, 9, 9, 9, 6, 3, 3, 9, 9, 5, 1, 6, 3, 3],\n [3, 3, 7, 7, 6, 3, 1, 6, 7, 3, 2, 8, 2, 1, 2, 1, 1, 9, 9, 9, 8, 2, 3, 7, 6, 1, 3, 6, 7, 7],\n [3, 3, 7, 7, 3, 6, 6, 1, 3, 6, 2, 2, 1, 2, 1, 2, 2, 9, 9, 9, 2, 2, 6, 3, 1, 6, 6, 3, 7, 7],\n [1, 3, 5, 3, 3, 6, 7, 3, 2, 2, 8, 8, 8, 4, 8, 8, 8, 9, 9, 9, 8, 8, 2, 2, 3, 7, 6, 3, 3, 5],\n [3, 1, 3, 5, 6, 3, 3, 6, 2, 2, 8, 8, 4, 8, 8, 8, 8, 9, 9, 9, 8, 8, 2, 2, 6, 3, 3, 6, 5, 3],\n [9, 9, 9, 3, 7, 3, 2, 2, 5, 3, 2, 2, 8, 8, 8, 4, 4, 8, 8, 8, 2, 2, 3, 5, 2, 2, 3, 7, 3, 1],\n [9, 9, 9, 1, 3, 6, 8, 2, 3, 8, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8, 2, 2, 8, 3, 2, 8, 6, 3, 1, 3],\n [3, 6, 7, 3, 2, 1, 2, 1, 6, 8, 6, 7, 2, 2, 8, 8, 8, 8, 2, 2, 7, 6, 8, 6, 1, 2, 1, 2, 3, 7],\n [6, 3, 3, 6, 1, 2, 1, 2, 8, 6, 7, 6, 2, 2, 8, 8, 8, 8, 2, 2, 6, 7, 6, 8, 2, 1, 2, 1, 6, 3],\n [7, 3, 2, 8, 5, 5, 2, 1, 6, 7, 6, 8, 8, 3, 2, 2, 2, 2, 3, 8, 8, 6, 7, 6, 1, 2, 5, 5, 8, 2],\n [3, 6, 2, 2, 5, 5, 1, 2, 7, 6, 8, 6, 3, 5, 2, 2, 2, 2, 5, 3, 6, 8, 6, 7, 2, 1, 5, 5, 2, 2],\n [3, 6, 2, 2, 5, 5, 1, 2, 7, 6, 8, 6, 3, 5, 2, 2, 2, 2, 5, 3, 6, 8, 6, 7, 2, 1, 5, 5, 2, 2],\n [7, 3, 2, 8, 5, 5, 2, 1, 6, 7, 6, 8, 8, 3, 2, 2, 2, 2, 3, 8, 8, 6, 7, 6, 1, 2, 5, 5, 8, 2],\n [6, 3, 3, 6, 1, 2, 1, 2, 8, 6, 7, 6, 2, 2, 8, 8, 8, 8, 2, 2, 6, 7, 6, 8, 2, 1, 2, 1, 6, 3],\n [3, 6, 7, 3, 2, 1, 2, 1, 6, 8, 6, 7, 2, 2, 8, 8, 8, 8, 2, 2, 7, 6, 8, 6, 1, 2, 1, 2, 3, 7],\n [2, 5, 3, 1, 3, 6, 8, 2, 3, 8, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8, 2, 2, 8, 3, 2, 8, 6, 3, 1, 3],\n [5, 2, 1, 3, 7, 3, 2, 2, 5, 3, 2, 2, 8, 8, 8, 4, 4, 8, 8, 8, 2, 2, 3, 5, 2, 2, 3, 7, 3, 1],\n [3, 1, 3, 5, 6, 3, 3, 6, 2, 2, 8, 8, 4, 8, 8, 8, 8, 8, 8, 4, 8, 8, 2, 2, 6, 3, 3, 6, 5, 3],\n [1, 3, 5, 3, 3, 6, 7, 3, 2, 2, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 2, 2, 3, 7, 6, 3, 3, 5],\n [3, 3, 7, 7, 3, 6, 6, 1, 3, 6, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 6, 3, 1, 6, 6, 3, 7, 7],\n [3, 3, 7, 7, 6, 3, 1, 6, 7, 3, 2, 8, 2, 1, 2, 1, 1, 2, 1, 2, 8, 2, 3, 7, 6, 1, 3, 6, 7, 7],\n [7, 7, 3, 3, 6, 1, 5, 5, 6, 3, 3, 6, 1, 2, 5, 5, 5, 5, 2, 1, 6, 3, 3, 6, 5, 5, 1, 6, 3, 3],\n [7, 7, 3, 3, 1, 6, 2, 5, 3, 6, 7, 3, 2, 1, 5, 5, 5, 5, 1, 2, 3, 7, 6, 3, 5, 2, 6, 1, 3, 3],\n [3, 6, 6, 1, 8, 8, 8, 8, 3, 5, 3, 1, 3, 6, 8, 2, 2, 8, 6, 3, 1, 3, 5, 3, 8, 8, 8, 8, 1, 6],\n [6, 3, 1, 6, 8, 8, 8, 8, 5, 3, 1, 3, 7, 3, 2, 2, 2, 2, 3, 7, 3, 1, 3, 5, 8, 8, 8, 8, 6, 1]\n ],\n \"output\": [\n [1, 6, 5, 5, 8, 8, 8, 8, 1, 3, 5, 2, 3, 6, 7, 3, 3, 7, 6, 3, 2, 5, 3, 1, 8, 8, 8, 8, 5, 5],\n [6, 1, 5, 2, 8, 8, 8, 8, 3, 1, 2, 5, 6, 3, 3, 6, 6, 3, 3, 6, 5, 2, 1, 3, 8, 8, 8, 8, 2, 5],\n [6, 3, 1, 6, 8, 8, 8, 8, 5, 3, 1, 3, 7, 3, 2, 2, 2, 2, 3, 7, 3, 1, 3, 5, 8, 8, 8, 8, 6, 1],\n [3, 6, 6, 1, 8, 8, 8, 8, 3, 5, 3, 1, 3, 6, 8, 2, 2, 8, 6, 3, 1, 3, 5, 3, 8, 8, 8, 8, 1, 6],\n [7, 7, 3, 3, 1, 6, 2, 5, 3, 6, 7, 3, 2, 1, 5, 5, 5, 5, 1, 2, 3, 7, 6, 3, 5, 2, 6, 1, 3, 3],\n [7, 7, 3, 3, 6, 1, 5, 5, 6, 3, 3, 6, 1, 2, 5, 5, 5, 5, 2, 1, 6, 3, 3, 6, 5, 5, 1, 6, 3, 3],\n [3, 3, 7, 7, 6, 3, 1, 6, 7, 3, 2, 8, 2, 1, 2, 1, 1, 2, 1, 2, 8, 2, 3, 7, 6, 1, 3, 6, 7, 7],\n [3, 3, 7, 7, 3, 6, 6, 1, 3, 6, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 6, 3, 1, 6, 6, 3, 7, 7],\n [1, 3, 5, 3, 3, 6, 7, 3, 2, 2, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 2, 2, 3, 7, 6, 3, 3, 5],\n [3, 1, 3, 5, 6, 3, 3, 6, 2, 2, 8, 8, 4, 8, 8, 8, 8, 8, 8, 4, 8, 8, 2, 2, 6, 3, 3, 6, 5, 3],\n [5, 2, 1, 3, 7, 3, 2, 2, 5, 3, 2, 2, 8, 8, 8, 4, 4, 8, 8, 8, 2, 2, 3, 5, 2, 2, 3, 7, 3, 1],\n [2, 5, 3, 1, 3, 6, 8, 2, 3, 8, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8, 2, 2, 8, 3, 2, 8, 6, 3, 1, 3],\n [3, 6, 7, 3, 2, 1, 2, 1, 6, 8, 6, 7, 2, 2, 8, 8, 8, 8, 2, 2, 7, 6, 8, 6, 1, 2, 1, 2, 3, 7],\n [6, 3, 3, 6, 1, 2, 1, 2, 8, 6, 7, 6, 2, 2, 8, 8, 8, 8, 2, 2, 6, 7, 6, 8, 2, 1, 2, 1, 6, 3],\n [7, 3, 2, 8, 5, 5, 2, 1, 6, 7, 6, 8, 8, 3, 2, 2, 2, 2, 3, 8, 8, 6, 7, 6, 1, 2, 5, 5, 8, 2],\n [3, 6, 2, 2, 5, 5, 1, 2, 7, 6, 8, 6, 3, 5, 2, 2, 2, 2, 5, 3, 6, 8, 6, 7, 2, 1, 5, 5, 2, 2],\n [3, 6, 2, 2, 5, 5, 1, 2, 7, 6, 8, 6, 3, 5, 2, 2, 2, 2, 5, 3, 6, 8, 6, 7, 2, 1, 5, 5, 2, 2],\n [7, 3, 2, 8, 5, 5, 2, 1, 6, 7, 6, 8, 8, 3, 2, 2, 2, 2, 3, 8, 8, 6, 7, 6, 1, 2, 5, 5, 8, 2],\n [6, 3, 3, 6, 1, 2, 1, 2, 8, 6, 7, 6, 2, 2, 8, 8, 8, 8, 2, 2, 6, 7, 6, 8, 2, 1, 2, 1, 6, 3],\n [3, 6, 7, 3, 2, 1, 2, 1, 6, 8, 6, 7, 2, 2, 8, 8, 8, 8, 2, 2, 7, 6, 8, 6, 1, 2, 1, 2, 3, 7],\n [2, 5, 3, 1, 3, 6, 8, 2, 3, 8, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8, 2, 2, 8, 3, 2, 8, 6, 3, 1, 3],\n [5, 2, 1, 3, 7, 3, 2, 2, 5, 3, 2, 2, 8, 8, 8, 4, 4, 8, 8, 8, 2, 2, 3, 5, 2, 2, 3, 7, 3, 1],\n [3, 1, 3, 5, 6, 3, 3, 6, 2, 2, 8, 8, 4, 8, 8, 8, 8, 8, 8, 4, 8, 8, 2, 2, 6, 3, 3, 6, 5, 3],\n [1, 3, 5, 3, 3, 6, 7, 3, 2, 2, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 2, 2, 3, 7, 6, 3, 3, 5],\n [3, 3, 7, 7, 3, 6, 6, 1, 3, 6, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 6, 3, 1, 6, 6, 3, 7, 7],\n [3, 3, 7, 7, 6, 3, 1, 6, 7, 3, 2, 8, 2, 1, 2, 1, 1, 2, 1, 2, 8, 2, 3, 7, 6, 1, 3, 6, 7, 7],\n [7, 7, 3, 3, 6, 1, 5, 5, 6, 3, 3, 6, 1, 2, 5, 5, 5, 5, 2, 1, 6, 3, 3, 6, 5, 5, 1, 6, 3, 3],\n [7, 7, 3, 3, 1, 6, 2, 5, 3, 6, 7, 3, 2, 1, 5, 5, 5, 5, 1, 2, 3, 7, 6, 3, 5, 2, 6, 1, 3, 3],\n [3, 6, 6, 1, 8, 8, 8, 8, 3, 5, 3, 1, 3, 6, 8, 2, 2, 8, 6, 3, 1, 3, 5, 3, 8, 8, 8, 8, 1, 6],\n [6, 3, 1, 6, 8, 8, 8, 8, 5, 3, 1, 3, 7, 3, 2, 2, 2, 2, 3, 7, 3, 1, 3, 5, 8, 8, 8, 8, 6, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 8, 8, 8, 5, 7, 4, 1, 7, 5, 1, 6, 9, 9, 9, 9, 9, 9, 9, 3, 6, 1, 5, 7, 1, 4, 7, 5, 8, 8],\n [4, 4, 8, 8, 7, 5, 1, 4, 7, 7, 6, 1, 9, 9, 9, 9, 9, 9, 9, 4, 1, 6, 7, 7, 4, 1, 5, 7, 8, 8],\n [8, 8, 4, 8, 4, 1, 5, 7, 8, 6, 7, 5, 9, 9, 9, 9, 9, 9, 9, 4, 5, 7, 6, 8, 7, 5, 1, 4, 8, 4],\n [8, 6, 4, 4, 1, 4, 7, 5, 6, 8, 7, 7, 9, 9, 9, 9, 9, 9, 9, 6, 7, 7, 8, 6, 5, 7, 4, 1, 4, 4],\n [8, 5, 5, 3, 4, 8, 8, 8, 3, 4, 4, 6, 9, 9, 9, 9, 9, 9, 9, 8, 6, 4, 4, 3, 8, 8, 8, 4, 3, 5],\n [5, 8, 3, 5, 4, 4, 8, 8, 4, 3, 6, 2, 9, 9, 9, 9, 9, 9, 9, 2, 2, 6, 3, 4, 8, 8, 4, 4, 5, 3],\n [5, 3, 8, 5, 6, 8, 4, 8, 4, 6, 1, 1, 9, 9, 9, 9, 9, 9, 9, 3, 1, 1, 6, 4, 8, 4, 8, 6, 5, 8],\n [3, 5, 5, 8, 8, 8, 4, 4, 6, 2, 8, 1, 9, 9, 9, 9, 9, 9, 9, 5, 1, 8, 2, 6, 4, 4, 8, 8, 8, 5],\n [7, 7, 8, 6, 3, 4, 4, 6, 3, 2, 2, 3, 8, 8, 6, 3, 3, 6, 8, 8, 3, 2, 2, 3, 6, 4, 4, 3, 6, 8],\n [5, 7, 6, 8, 4, 3, 6, 2, 2, 3, 3, 1, 8, 8, 3, 6, 6, 3, 8, 8, 1, 3, 3, 2, 2, 6, 3, 4, 8, 6],\n [1, 6, 7, 7, 4, 6, 1, 8, 4, 2, 3, 2, 6, 3, 8, 8, 8, 8, 3, 6, 2, 3, 2, 4, 8, 1, 6, 4, 7, 7],\n [6, 1, 5, 7, 6, 2, 1, 1, 2, 4, 9, 9, 9, 6, 8, 8, 8, 8, 6, 3, 3, 2, 4, 2, 1, 1, 2, 6, 7, 5],\n [3, 4, 4, 6, 8, 2, 3, 5, 4, 8, 9, 9, 9, 2, 1, 3, 3, 1, 2, 3, 7, 8, 8, 4, 5, 3, 2, 8, 6, 4],\n [4, 3, 6, 2, 6, 8, 5, 3, 8, 4, 9, 9, 9, 3, 3, 2, 2, 3, 3, 2, 8, 7, 4, 8, 3, 5, 8, 6, 2, 6],\n [4, 6, 1, 1, 8, 4, 8, 2, 8, 7, 4, 8, 4, 2, 3, 2, 2, 3, 2, 4, 8, 4, 7, 8, 2, 8, 4, 8, 1, 1],\n [6, 2, 8, 1, 4, 8, 6, 8, 7, 8, 8, 4, 2, 4, 2, 3, 3, 2, 4, 2, 4, 8, 8, 7, 8, 6, 8, 4, 1, 8],\n [6, 2, 8, 1, 4, 8, 6, 8, 7, 8, 8, 4, 2, 4, 2, 3, 3, 2, 4, 2, 4, 8, 8, 7, 9, 9, 9, 9, 9, 9],\n [4, 6, 1, 1, 8, 4, 8, 2, 8, 7, 4, 8, 4, 2, 3, 2, 2, 3, 2, 4, 8, 4, 7, 8, 9, 9, 9, 9, 9, 9],\n [4, 3, 6, 2, 6, 8, 5, 3, 8, 4, 7, 8, 2, 3, 3, 2, 2, 3, 3, 2, 8, 7, 4, 8, 9, 9, 9, 9, 9, 9],\n [3, 4, 4, 6, 8, 2, 3, 5, 4, 8, 8, 7, 3, 2, 1, 3, 3, 1, 2, 3, 7, 8, 8, 4, 9, 9, 9, 9, 9, 9],\n [6, 1, 5, 7, 6, 2, 1, 1, 2, 4, 2, 3, 3, 6, 8, 8, 8, 8, 6, 3, 3, 2, 4, 2, 9, 9, 9, 9, 9, 9],\n [1, 6, 7, 7, 4, 6, 1, 8, 4, 2, 3, 2, 6, 3, 8, 8, 8, 8, 3, 6, 2, 3, 2, 4, 9, 9, 9, 9, 9, 9],\n [5, 7, 6, 8, 4, 3, 6, 2, 2, 3, 3, 1, 8, 8, 3, 6, 6, 3, 8, 8, 1, 3, 3, 2, 9, 9, 9, 9, 9, 9],\n [7, 7, 8, 6, 3, 4, 4, 6, 3, 2, 2, 3, 8, 8, 6, 3, 3, 6, 8, 8, 3, 2, 2, 3, 6, 4, 4, 3, 6, 8],\n [3, 5, 5, 8, 8, 8, 4, 4, 6, 2, 8, 1, 5, 3, 2, 8, 8, 2, 3, 5, 1, 8, 2, 6, 4, 4, 8, 8, 8, 5],\n [5, 3, 8, 5, 6, 8, 4, 8, 4, 6, 1, 1, 3, 5, 8, 6, 6, 8, 5, 3, 1, 1, 6, 4, 8, 4, 8, 6, 5, 8],\n [5, 8, 3, 5, 4, 4, 8, 8, 4, 3, 6, 2, 2, 8, 4, 8, 8, 4, 8, 2, 2, 6, 3, 4, 8, 8, 4, 4, 5, 3],\n [8, 5, 5, 3, 4, 8, 8, 8, 3, 4, 4, 6, 8, 6, 8, 4, 4, 8, 6, 8, 6, 4, 4, 3, 8, 8, 8, 4, 3, 5],\n [8, 6, 4, 4, 1, 4, 7, 5, 6, 8, 7, 7, 6, 2, 1, 1, 1, 1, 2, 6, 7, 7, 8, 6, 5, 7, 4, 1, 4, 4],\n [8, 8, 4, 8, 4, 1, 5, 7, 8, 6, 7, 5, 4, 6, 1, 8, 8, 1, 6, 4, 5, 7, 6, 8, 7, 5, 1, 4, 8, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 8, 8, 8, 5, 7, 4, 1, 7, 5, 1, 6, 3, 4, 4, 6, 6, 4, 4, 3, 6, 1, 5, 7, 1, 4, 7, 5, 8, 8], [4, 4, 8, 8, 7, 5, 1, 4, 7, 7, 6, 1, 4, 3, 6, 2, 2, 6, 3, 4, 1, 6, 7, 7, 4, 1, 5, 7, 8, 8], [8, 8, 4, 8, 4, 1, 5, 7, 8, 6, 7, 5, 4, 6, 1, 8, 8, 1, 6, 4, 5, 7, 6, 8, 7, 5, 1, 4, 8, 4], [8, 6, 4, 4, 1, 4, 7, 5, 6, 8, 7, 7, 6, 2, 1, 1, 1, 1, 2, 6, 7, 7, 8, 6, 5, 7, 4, 1, 4, 4], [8, 5, 5, 3, 4, 8, 8, 8, 3, 4, 4, 6, 8, 6, 8, 4, 4, 8, 6, 8, 6, 4, 4, 3, 8, 8, 8, 4, 3, 5], [5, 8, 3, 5, 4, 4, 8, 8, 4, 3, 6, 2, 2, 8, 4, 8, 8, 4, 8, 2, 2, 6, 3, 4, 8, 8, 4, 4, 5, 3], [5, 3, 8, 5, 6, 8, 4, 8, 4, 6, 1, 1, 3, 5, 8, 6, 6, 8, 5, 3, 1, 1, 6, 4, 8, 4, 8, 6, 5, 8], [3, 5, 5, 8, 8, 8, 4, 4, 6, 2, 8, 1, 5, 3, 2, 8, 8, 2, 3, 5, 1, 8, 2, 6, 4, 4, 8, 8, 8, 5], [7, 7, 8, 6, 3, 4, 4, 6, 3, 2, 2, 3, 8, 8, 6, 3, 3, 6, 8, 8, 3, 2, 2, 3, 6, 4, 4, 3, 6, 8], [5, 7, 6, 8, 4, 3, 6, 2, 2, 3, 3, 1, 8, 8, 3, 6, 6, 3, 8, 8, 1, 3, 3, 2, 2, 6, 3, 4, 8, 6], [1, 6, 7, 7, 4, 6, 1, 8, 4, 2, 3, 2, 6, 3, 8, 8, 8, 8, 3, 6, 2, 3, 2, 4, 8, 1, 6, 4, 7, 7], [6, 1, 5, 7, 6, 2, 1, 1, 2, 4, 2, 3, 3, 6, 8, 8, 8, 8, 6, 3, 3, 2, 4, 2, 1, 1, 2, 6, 7, 5], [3, 4, 4, 6, 8, 2, 3, 5, 4, 8, 8, 7, 3, 2, 1, 3, 3, 1, 2, 3, 7, 8, 8, 4, 5, 3, 2, 8, 6, 4], [4, 3, 6, 2, 6, 8, 5, 3, 8, 4, 7, 8, 2, 3, 3, 2, 2, 3, 3, 2, 8, 7, 4, 8, 3, 5, 8, 6, 2, 6], [4, 6, 1, 1, 8, 4, 8, 2, 8, 7, 4, 8, 4, 2, 3, 2, 2, 3, 2, 4, 8, 4, 7, 8, 2, 8, 4, 8, 1, 1], [6, 2, 8, 1, 4, 8, 6, 8, 7, 8, 8, 4, 2, 4, 2, 3, 3, 2, 4, 2, 4, 8, 8, 7, 8, 6, 8, 4, 1, 8], [6, 2, 8, 1, 4, 8, 6, 8, 7, 8, 8, 4, 2, 4, 2, 3, 3, 2, 4, 2, 4, 8, 8, 7, 8, 6, 8, 4, 1, 8], [4, 6, 1, 1, 8, 4, 8, 2, 8, 7, 4, 8, 4, 2, 3, 2, 2, 3, 2, 4, 8, 4, 7, 8, 2, 8, 4, 8, 1, 1], [4, 3, 6, 2, 6, 8, 5, 3, 8, 4, 7, 8, 2, 3, 3, 2, 2, 3, 3, 2, 8, 7, 4, 8, 3, 5, 8, 6, 2, 6], [3, 4, 4, 6, 8, 2, 3, 5, 4, 8, 8, 7, 3, 2, 1, 3, 3, 1, 2, 3, 7, 8, 8, 4, 5, 3, 2, 8, 6, 4], [6, 1, 5, 7, 6, 2, 1, 1, 2, 4, 2, 3, 3, 6, 8, 8, 8, 8, 6, 3, 3, 2, 4, 2, 1, 1, 2, 6, 7, 5], [1, 6, 7, 7, 4, 6, 1, 8, 4, 2, 3, 2, 6, 3, 8, 8, 8, 8, 3, 6, 2, 3, 2, 4, 8, 1, 6, 4, 7, 7], [5, 7, 6, 8, 4, 3, 6, 2, 2, 3, 3, 1, 8, 8, 3, 6, 6, 3, 8, 8, 1, 3, 3, 2, 2, 6, 3, 4, 8, 6], [7, 7, 8, 6, 3, 4, 4, 6, 3, 2, 2, 3, 8, 8, 6, 3, 3, 6, 8, 8, 3, 2, 2, 3, 6, 4, 4, 3, 6, 8], [3, 5, 5, 8, 8, 8, 4, 4, 6, 2, 8, 1, 5, 3, 2, 8, 8, 2, 3, 5, 1, 8, 2, 6, 4, 4, 8, 8, 8, 5], [5, 3, 8, 5, 6, 8, 4, 8, 4, 6, 1, 1, 3, 5, 8, 6, 6, 8, 5, 3, 1, 1, 6, 4, 8, 4, 8, 6, 5, 8], [5, 8, 3, 5, 4, 4, 8, 8, 4, 3, 6, 2, 2, 8, 4, 8, 8, 4, 8, 2, 2, 6, 3, 4, 8, 8, 4, 4, 5, 3], [8, 5, 5, 3, 4, 8, 8, 8, 3, 4, 4, 6, 8, 6, 8, 4, 4, 8, 6, 8, 6, 4, 4, 3, 8, 8, 8, 4, 3, 5], [8, 6, 4, 4, 1, 4, 7, 5, 6, 8, 7, 7, 6, 2, 1, 1, 1, 1, 2, 6, 7, 7, 8, 6, 5, 7, 4, 1, 4, 4], [8, 8, 4, 8, 4, 1, 5, 7, 8, 6, 7, 5, 4, 6, 1, 8, 8, 1, 6, 4, 5, 7, 6, 8, 7, 5, 1, 4, 8, 4]], "task_id": "f9d67f8b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 0, 0, 0],\n [0, 3, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0],\n [0, 8, 0, 0, 2, 2],\n [0, 0, 0, 0, 2, 2],\n [6, 6, 6, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 2],\n [0, 0, 0, 0, 0, 2],\n [0, 0, 0, 8, 2, 2],\n [0, 0, 0, 0, 2, 2],\n [0, 0, 0, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 2, 0, 0, 0],\n [6, 0, 2, 0, 0, 0],\n [6, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 3, 0],\n [0, 0, 0, 0, 3, 0],\n [8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 6, 2],\n [0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 8, 3],\n [0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [2, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 4, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 8, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 2], [0, 0, 0, 0, 0, 0, 2, 3, 3, 3], [0, 0, 0, 0, 0, 0, 2, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 8, 6], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]], "task_id": "5ffb2104"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 6, 4, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 6, 4, 3, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 3, 3, 4, 6, 4, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 4, 6, 4, 2, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 4, 6, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 4, 6, 4, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 6, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 8],\n [8, 8, 0, 0, 0, 8, 8],\n [8, 0, 0, 0, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 4, 6, 6, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 3, 4, 6, 6, 4, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 4, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 3, 0, 6, 6, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 4, 6, 6, 4, 1, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 7, 7, 4, 4, 2, 0, 0, 0, 0, 1, 1, 4, 6, 6, 4, 1, 1, 0, 0],\n [0, 0, 0, 2, 4, 4, 7, 7, 4, 4, 2, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 2, 0, 0, 7, 7, 0, 0, 2, 0, 0, 0, 0, 1, 3, 4, 6, 6, 4, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 6, 6, 4, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 6, 6, 4, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 6, 6, 4, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 6, 6, 4, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 4, 7, 7, 4, 4, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 4, 7, 7, 4, 4, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 7, 7, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 0, 8, 8, 8],\n [0, 0, 8, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 3, 7, 7, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 3, 7, 7, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 3, 7, 7, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 7, 7, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 7, 7, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 7, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 0, 8, 8],\n [0, 0, 8, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 6, 6, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 4, 2, 6, 6, 2, 4, 1, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 2, 6, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 4, 4, 6, 6, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 3, 1, 4, 2, 6, 6, 2, 4, 1, 3, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 3, 1, 4, 2, 6, 6, 2, 4, 1, 3, 0, 0, 0, 0, 1, 3, 3, 3, 3, 3, 3, 1, 0, 0],\n [0, 3, 1, 1, 2, 6, 6, 2, 1, 1, 3, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 0, 0, 0, 0, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 8, 8]], "task_id": "2037f2c7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 2, 0, 0],\n [2, 0, 0, 0, 2, 0, 0],\n [2, 0, 2, 0, 2, 0, 0],\n [2, 0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 0, 0],\n [2, 8, 8, 8, 2, 0, 0],\n [2, 8, 2, 8, 2, 0, 0],\n [2, 8, 8, 8, 2, 0, 0],\n [2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 2, 2, 2, 2, 0, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 0, 0, 2, 0, 0, 2, 0, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 0, 0],\n [2, 4, 4, 4, 4, 4, 2, 0, 0],\n [2, 4, 4, 4, 4, 4, 2, 0, 0],\n [2, 4, 4, 2, 4, 4, 2, 0, 0],\n [2, 4, 4, 4, 4, 4, 2, 0, 0],\n [2, 4, 4, 4, 4, 4, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 2, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0]\n ],\n \"output\": [\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 8, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0],\n [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0],\n [0, 0, 0, 0, 0, 2, 4, 4, 2, 4, 4, 2, 0],\n [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0],\n [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 8, 8, 8, 2, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 2, 8, 2, 8, 2, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 2, 8, 8, 8, 2, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 2, 3, 3, 3, 2, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0, 2, 8, 8, 8, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 8, 2, 8, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 8, 8, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 4, 4, 2, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "00dbd492"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 6, 6, 6, 9, 9, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 6, 6, 6, 9, 9, 9, 0],\n [0, 0, 5, 6, 6, 6, 9, 9, 9, 0],\n [0, 0, 5, 6, 6, 6, 9, 9, 9, 0],\n [0, 0, 5, 6, 6, 6, 9, 9, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 4, 3, 3, 4, 4, 4, 0, 0, 0],\n [5, 7, 3, 7, 7, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 4, 3, 3, 4, 4, 4, 0, 0, 0],\n [5, 7, 3, 7, 7, 3, 3, 0, 0, 0],\n [5, 4, 3, 3, 4, 4, 4, 0, 0, 0],\n [5, 7, 3, 7, 7, 3, 3, 0, 0, 0],\n [5, 4, 3, 3, 4, 4, 4, 0, 0, 0],\n [5, 7, 3, 7, 7, 3, 3, 0, 0, 0],\n [5, 4, 3, 3, 4, 4, 4, 0, 0, 0],\n [5, 7, 3, 7, 7, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [5, 6, 3, 6, 3, 0, 0, 0, 0, 0],\n [5, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 6, 3, 6, 3, 0, 3, 3, 3, 3],\n [5, 2, 2, 2, 2, 0, 3, 3, 3, 3],\n [5, 6, 3, 6, 3, 0, 7, 7, 7, 7],\n [5, 2, 2, 2, 2, 0, 3, 3, 3, 3],\n [5, 6, 3, 6, 3, 0, 7, 7, 7, 7],\n [5, 2, 2, 2, 2, 0, 7, 7, 7, 7],\n [5, 6, 3, 6, 3, 0, 0, 0, 0, 0],\n [5, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 0, 5, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 8, 6, 8],\n [0, 0, 0, 0, 0, 0, 5, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 0, 5, 8, 6, 8], [4, 4, 4, 4, 4, 0, 5, 3, 3, 3], [2, 2, 2, 2, 2, 0, 5, 8, 6, 8], [2, 2, 2, 2, 2, 0, 5, 3, 3, 3], [2, 2, 2, 2, 2, 0, 5, 8, 6, 8], [1, 1, 1, 1, 1, 0, 5, 3, 3, 3], [0, 0, 0, 0, 0, 0, 5, 8, 6, 8], [0, 0, 0, 0, 0, 0, 5, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "9c1e755f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 0, 0],\n [1, 1, 1, 1, 1],\n [0, 1, 0, 1, 0],\n [1, 1, 1, 0, 1],\n [0, 0, 0, 1, 0],\n [8, 0, 8, 0, 0],\n [8, 0, 0, 8, 0],\n [8, 0, 0, 0, 8],\n [8, 8, 0, 0, 0],\n [8, 8, 0, 0, 0],\n [0, 6, 0, 0, 6],\n [6, 0, 0, 6, 6],\n [0, 6, 6, 6, 0],\n [6, 6, 0, 6, 6],\n [0, 0, 6, 0, 6]\n ],\n \"output\": [\n [1, 6, 8, 0, 6],\n [6, 1, 1, 6, 6],\n [8, 6, 6, 6, 8],\n [6, 6, 1, 6, 6],\n [8, 8, 6, 1, 6]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 0, 1],\n [0, 1, 0, 0, 1],\n [0, 1, 0, 0, 0],\n [1, 0, 0, 1, 1],\n [1, 0, 0, 1, 1],\n [0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0],\n [0, 8, 0, 0, 0],\n [8, 0, 0, 0, 8],\n [8, 0, 8, 8, 0],\n [0, 0, 6, 0, 6],\n [6, 0, 6, 0, 0],\n [6, 0, 0, 0, 6],\n [6, 0, 0, 0, 6],\n [0, 6, 6, 6, 6]\n ],\n \"output\": [\n [1, 0, 6, 0, 6],\n [6, 1, 6, 8, 1],\n [6, 1, 0, 0, 6],\n [6, 0, 0, 1, 6],\n [1, 6, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 1, 0],\n [1, 1, 1, 0, 0],\n [0, 1, 1, 1, 0],\n [0, 1, 0, 0, 1],\n [1, 0, 0, 1, 1],\n [8, 0, 8, 8, 0],\n [8, 0, 8, 8, 8],\n [8, 8, 8, 0, 8],\n [0, 8, 0, 8, 8],\n [8, 0, 8, 8, 8],\n [6, 0, 6, 0, 6],\n [0, 0, 0, 0, 6],\n [6, 6, 6, 6, 6],\n [0, 0, 6, 0, 0],\n [0, 6, 0, 6, 0]\n ],\n \"output\": [\n [6, 0, 6, 1, 6],\n [1, 1, 1, 8, 6],\n [6, 6, 6, 6, 6],\n [0, 1, 6, 8, 1],\n [1, 6, 8, 6, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 1, 1, 1],\n [0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1],\n [0, 1, 1, 1, 0],\n [0, 8, 8, 0, 0],\n [8, 0, 0, 8, 0],\n [0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0],\n [8, 0, 8, 0, 8],\n [0, 6, 0, 6, 6],\n [0, 0, 6, 6, 6],\n [0, 6, 0, 0, 0],\n [0, 6, 6, 0, 6],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 6, 1, 6, 6],\n [8, 1, 6, 6, 6],\n [0, 6, 1, 1, 8],\n [0, 6, 6, 1, 6],\n [8, 1, 1, 1, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 0, 0],\n [0, 0, 1, 1, 0],\n [1, 1, 0, 0, 1],\n [0, 1, 1, 1, 1],\n [0, 0, 0, 0, 1],\n [0, 8, 0, 0, 8],\n [8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0],\n [0, 0, 8, 8, 8],\n [6, 6, 0, 0, 0],\n [0, 6, 6, 6, 0],\n [0, 0, 6, 0, 6],\n [0, 0, 6, 6, 6],\n [6, 6, 6, 6, 6]\n ],\n \"output\": [\n [6, 6, 1, 0, 8],\n [8, 6, 6, 6, 0],\n [1, 1, 6, 0, 6],\n [0, 1, 6, 6, 6],\n [6, 6, 6, 6, 6]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 1, 1, 1],\n [1, 0, 1, 0, 0],\n [0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0],\n [1, 0, 1, 0, 1],\n [0, 0, 8, 0, 0],\n [0, 0, 8, 8, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 8, 0, 0],\n [8, 8, 0, 8, 0],\n [0, 6, 0, 6, 0],\n [0, 0, 6, 0, 6],\n [0, 6, 0, 0, 6],\n [0, 0, 6, 0, 6],\n [6, 0, 6, 6, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 6, 1, 6, 1], [1, 0, 6, 8, 6], [8, 6, 1, 8, 6], [0, 1, 6, 1, 6], [6, 8, 6, 6, 1]], "task_id": "6a11f6da"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 3, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 3, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 3, 3, 8, 6, 6, 6, 8, 3, 3, 3, 8, 3, 3, 3, 8, 3, 3, 3, 8, 0, 0],\n [3, 3, 3, 8, 6, 6, 6, 8, 3, 3, 3, 8, 3, 3, 3, 8, 3, 3, 3, 8, 0, 0],\n [3, 3, 3, 8, 6, 6, 6, 8, 3, 3, 3, 8, 3, 3, 3, 8, 3, 3, 3, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 3, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 3, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 0, 8, 0, 0, 8, 0, 0, 8, 2, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 3, 3, 8, 3, 3, 8, 6, 6, 8, 3, 3, 8, 3, 3, 8, 3, 3, 8, 0, 0, 8],\n [0, 0, 8, 3, 3, 8, 3, 3, 8, 6, 6, 8, 3, 3, 8, 3, 3, 8, 3, 3, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 2, 2, 8, 2, 2, 8, 2, 2, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 2, 8, 2, 2, 8, 2, 2, 8, 2, 2, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 0, 0],\n [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 0, 0],\n [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 0, 0],\n [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 0, 0],\n [2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 2, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 2, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 2, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 6, 6, 6, 6, 8, 2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 0, 0, 0], [2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 6, 6, 6, 6, 8, 2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 0, 0, 0], [2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 6, 6, 6, 6, 8, 2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 0, 0, 0], [2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 6, 6, 6, 6, 8, 2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [2, 2, 2, 2, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0]], "task_id": "e760a62e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 4, 1, 6, 0, 0, 1, 6, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [0, 1, 4, 1, 0, 0, 1, 6, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [0, 6, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 1],\n [0, 1, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 0, 0, 1, 1, 6, 1],\n [0, 0, 1, 1, 1, 6, 1, 1, 1, 6, 1, 1, 0, 0, 6, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 4, 1],\n [0, 0, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1],\n [1, 6, 1, 1, 1],\n [1, 1, 1, 4, 1],\n [1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 4, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 6, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 6, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 6, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 4, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 4, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 4, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 6, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 4, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 4, 1, 1, 6, 1, 0, 0, 0, 1, 1, 1, 6, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 6, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 4, 1, 1, 1],\n [1, 1, 1, 6, 1],\n [6, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 6, 1, 4, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 1, 4, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 6, 1, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 4, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 6, 0, 0, 0, 1, 1, 1, 4, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 4, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 6, 1, 1, 1, 1, 0, 1, 1, 6, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 6, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [1, 1, 1, 6, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [6, 1, 1, 1, 0, 1, 1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 4, 1, 6, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 6, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 4, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 4, 1, 0, 0, 0, 1, 4, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 6, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 1, 1, 1, 1, 4, 1, 4, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 4],\n [1, 4, 1, 1, 1],\n [1, 1, 1, 6, 1],\n [1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 4, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 6, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 6, 1, 1, 1],\n [1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 4, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 6, 1, 6, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 4, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 4, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 6, 1, 1],\n [1, 1, 1, 1, 4, 0, 0, 0, 0, 1, 1, 4, 1, 1, 1],\n [1, 4, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 1, 6, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 4, 1, 1, 6, 1, 4, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 4, 1, 1, 1, 1, 6, 1, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1],\n [1, 4, 1, 1, 1],\n [1, 1, 1, 1, 1],\n [1, 1, 4, 1, 1],\n [1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 4, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 1, 1, 6, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 4, 1, 0, 0, 1, 6, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 6, 1, 1, 0, 0, 1, 4, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 4, 1, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 6, 1, 0, 0, 0, 1, 1, 6, 1, 4, 1, 1, 1, 0, 0, 0, 1, 1, 1, 6, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 6, 1, 0, 0, 0, 6, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 4, 1, 1, 1, 0, 0, 0, 1, 1, 1, 4, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 6, 1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 4, 1], [1, 1, 1, 1], [1, 6, 1, 1], [1, 1, 1, 1]], "task_id": "7bb29440"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 2, 2, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 2, 2, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0],\n [0, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 1, 1, 8, 8, 8, 3, 3, 8, 8, 8, 8, 0],\n [0, 8, 8, 1, 1, 8, 8, 8, 3, 3, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 2, 2, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 2, 2, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 3],\n [0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 5, 5, 8, 8, 4, 4, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 5, 5, 8, 8, 4, 4, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 3, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 3, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 4],\n [3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 2, 2, 8, 8, 8, 8, 6, 6, 8, 8, 0],\n [0, 0, 8, 8, 2, 2, 8, 8, 8, 8, 6, 6, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 1, 1, 8, 8, 8, 8, 3, 3, 8, 8, 0],\n [0, 0, 8, 8, 1, 1, 8, 8, 8, 8, 3, 3, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 6], [1, 3]], "task_id": "19bb5feb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 8],\n [3, 0, 0, 4, 0, 0],\n [3, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 6],\n [1, 1, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 4, 8, 8],\n [3, 0, 0, 4, 0, 6],\n [1, 1, 5, 5, 0, 6],\n [2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 3, 3, 0, 0, 4, 4, 0, 0],\n [2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 5, 0, 0, 6, 6, 0],\n [2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [2, 0, 7, 7, 0, 0, 0, 8, 0, 0, 0]\n ],\n \"output\": [\n [2, 3, 3, 4, 4, 0, 0, 0, 0, 0, 0],\n [2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 5, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [2, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 5, 5, 0, 0, 6, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 2],\n [0, 9, 0, 0, 8, 8, 0, 0, 0, 2],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 2],\n [0, 0, 0, 0, 0, 0, 5, 5, 6, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 2],\n [0, 0, 0, 0, 0, 0, 9, 8, 8, 2],\n [0, 0, 0, 0, 0, 0, 9, 0, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 3, 3, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 6, 0, 0],\n [0, 8, 8, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 6],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 4, 0],\n [3, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 5, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 0, 3, 3, 0, 7, 0, 6, 6, 6], [3, 8, 8, 0, 0, 7, 0, 6, 4, 0], [0, 0, 9, 0, 3, 3, 0, 0, 4, 0], [0, 0, 9, 0, 0, 1, 1, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "6ad5bdfd"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0],\n [7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 7, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 7, 7, 7, 8, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 0, 8, 2, 0, 0, 0],\n [7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 7, 0],\n [0, 7, 0, 0, 0, 0, 0, 7, 7, 8, 7, 7, 7, 7, 2, 0, 0, 0, 0],\n [0, 7, 0, 0, 7, 0, 0, 0, 0, 4, 2, 2, 2, 2, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 8, 2, 0, 0, 0, 0, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 2, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 7, 7, 7, 7, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 4, 2, 2, 2, 2, 2, 3, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0],\n [7, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 7, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0]\n ],\n \"output\": [\n [0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 6, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0],\n [7, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 8, 7, 7, 2, 0, 7, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 7, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 3, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 2, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 8, 7, 7, 6, 7, 0, 0, 0, 2, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 4, 2, 2, 3, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [7, 0, 4, 3, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 8, 7, 7, 7, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 2, 3, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 8, 7, 7, 7, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 2, 3, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 7, 7, 8, 7, 7, 7, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 4, 2, 2, 2, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 8, 7, 7, 7, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 2, 2, 2, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 8, 7, 7, 7, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 4, 2, 2, 2, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 7, 8, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 4, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 7, 7, 6, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 7, 8, 7, 7, 2, 0, 0, 0, 0],\n [0, 0, 4, 2, 2, 3, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 6, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 7, 0, 0, 7, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 2, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 0, 0, 8, 6, 7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 2, 7, 0, 7, 0, 7, 4, 3, 0, 7, 0, 0, 7, 7, 7, 8, 7, 2, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0, 7, 0, 8, 2, 0, 7, 7, 0, 0, 0, 0, 0, 4, 2, 3, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 7, 8, 7, 7, 2, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 4, 2, 2, 3, 0, 7, 0, 0, 2, 7, 7, 7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 7, 7, 7, 7, 8, 7, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0, 0, 0, 4, 2, 3, 0, 0, 0], [7, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 8, 2, 0, 0, 0, 8, 7, 7, 7, 2, 0, 0, 0, 0, 0, 0, 0, 2, 7, 0, 0, 0, 0], [0, 0, 4, 3, 0, 7, 0, 4, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [7, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 2, 7, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 7, 7, 7, 7, 0, 0, 0, 0, 7, 0, 0, 2, 0, 0, 0, 7, 0], [0, 0, 2, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 7, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 2, 7, 7, 7, 7, 0], [0, 0, 2, 0, 0, 0, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 8, 2, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4, 3, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 6, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0, 0]], "task_id": "891232d6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 1, 8, 1, 8, 8, 8, 8, 8],\n [8, 1, 8, 8, 1, 8, 8, 8, 8, 8],\n [8, 1, 8, 8, 1, 8, 8, 8, 8, 8],\n [8, 1, 1, 1, 1, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 8, 8, 1],\n [8, 8, 8, 8, 8, 8, 1, 8, 8, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 8, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 2, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 2, 8, 8, 8, 8, 8, 8],\n [8, 1, 1, 2, 1, 8, 8, 8, 8, 8],\n [8, 1, 2, 2, 1, 8, 8, 8, 8, 8],\n [8, 1, 2, 2, 1, 8, 8, 8, 8, 8],\n [8, 1, 1, 1, 1, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 2, 2, 1],\n [8, 8, 8, 8, 8, 8, 1, 2, 2, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 2, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 2, 8]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 1, 1, 1, 1, 5, 5, 5],\n [5, 5, 1, 5, 5, 1, 5, 5, 5],\n [5, 5, 5, 5, 5, 1, 5, 5, 5],\n [5, 5, 1, 1, 1, 1, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 1, 1, 1, 1, 5, 5, 5],\n [5, 5, 1, 2, 2, 1, 5, 5, 5],\n [2, 2, 2, 2, 2, 1, 5, 5, 5],\n [5, 5, 1, 1, 1, 1, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [9, 1, 9, 1, 1, 9, 9, 9, 9],\n [9, 1, 9, 9, 1, 9, 9, 9, 9],\n [9, 1, 9, 9, 1, 9, 9, 9, 9],\n [9, 1, 1, 1, 1, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 1, 1, 1, 1, 9, 9, 9],\n [9, 9, 1, 9, 9, 1, 9, 9, 9],\n [9, 9, 1, 9, 9, 9, 9, 9, 9],\n [9, 9, 1, 1, 1, 1, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9]\n ],\n \"output\": [\n [9, 1, 2, 1, 1, 9, 9, 9, 9],\n [9, 1, 2, 2, 1, 9, 9, 9, 9],\n [9, 1, 2, 2, 1, 9, 9, 9, 9],\n [9, 1, 1, 1, 1, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 1, 1, 1, 1, 9, 9, 9],\n [9, 9, 1, 2, 2, 1, 9, 9, 9],\n [9, 9, 1, 2, 2, 2, 2, 2, 2],\n [9, 9, 1, 1, 1, 1, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4],\n [4, 1, 4, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4],\n [4, 1, 4, 4, 1, 4, 4, 4, 1, 4, 4, 1, 4, 4, 4],\n [4, 1, 4, 4, 1, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4],\n [4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 4, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4], [4, 1, 2, 1, 1, 4, 4, 4, 1, 2, 2, 2, 2, 2, 2], [4, 1, 2, 2, 1, 4, 4, 4, 1, 2, 2, 1, 4, 4, 4], [4, 1, 2, 2, 1, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4], [4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 1, 2, 2, 1, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 1, 2, 2, 1, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 1, 2, 1, 1, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4]], "task_id": "292dd178"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 1, 9, 9, 6, 6, 4, 4, 6, 6, 9, 9, 1, 9, 9],\n [9, 9, 2, 2, 6, 6, 2, 2, 2, 2, 6, 6, 2, 2, 9, 9],\n [1, 2, 1, 7, 6, 2, 6, 6, 6, 6, 2, 6, 7, 1, 2, 1],\n [9, 2, 7, 1, 4, 2, 6, 3, 3, 3, 3, 4, 1, 7, 2, 9],\n [9, 6, 6, 4, 8, 6, 6, 3, 3, 3, 3, 8, 4, 6, 6, 9],\n [6, 6, 2, 2, 6, 8, 6, 3, 3, 3, 3, 6, 2, 2, 6, 6],\n [6, 2, 6, 6, 6, 6, 8, 3, 3, 3, 3, 6, 6, 6, 2, 6],\n [4, 2, 6, 2, 8, 2, 8, 6, 6, 8, 2, 8, 2, 6, 2, 4],\n [4, 2, 6, 2, 8, 2, 8, 6, 6, 8, 2, 8, 2, 6, 2, 4],\n [6, 2, 6, 6, 6, 6, 8, 8, 8, 8, 6, 6, 6, 6, 2, 6],\n [6, 6, 2, 2, 6, 8, 6, 2, 2, 6, 8, 6, 2, 2, 6, 6],\n [9, 6, 6, 4, 8, 6, 6, 8, 8, 6, 6, 8, 4, 6, 6, 9],\n [9, 2, 7, 1, 4, 2, 6, 2, 2, 6, 2, 4, 1, 7, 2, 9],\n [1, 2, 1, 7, 6, 2, 6, 6, 6, 6, 2, 6, 7, 1, 2, 1],\n [9, 9, 2, 2, 6, 6, 2, 2, 2, 2, 6, 6, 2, 2, 9, 9],\n [9, 9, 1, 9, 9, 6, 6, 4, 4, 6, 6, 9, 9, 1, 9, 9]\n ],\n \"output\": [\n [2, 2, 6, 2],\n [8, 8, 6, 6],\n [2, 2, 6, 8],\n [8, 8, 8, 6]\n ]\n}\n\n{\n \"input\": [\n [1, 7, 7, 1, 2, 8, 6, 2, 2, 6, 8, 2, 1, 7, 7, 1],\n [7, 5, 1, 1, 8, 2, 4, 8, 8, 4, 2, 8, 1, 1, 5, 7],\n [7, 1, 5, 8, 6, 4, 8, 6, 6, 8, 4, 6, 8, 5, 1, 7],\n [1, 1, 8, 8, 2, 8, 6, 4, 4, 6, 8, 2, 8, 8, 1, 1],\n [2, 8, 6, 2, 9, 2, 9, 2, 2, 9, 2, 9, 2, 6, 8, 2],\n [8, 2, 4, 8, 2, 2, 2, 9, 9, 2, 2, 2, 8, 4, 2, 8],\n [6, 4, 8, 6, 9, 2, 9, 9, 9, 9, 2, 9, 6, 8, 4, 6],\n [2, 8, 6, 4, 2, 9, 9, 2, 2, 9, 9, 2, 4, 6, 8, 2],\n [2, 8, 6, 4, 2, 9, 9, 2, 2, 9, 9, 2, 4, 6, 8, 2],\n [6, 4, 8, 6, 9, 2, 9, 9, 3, 3, 3, 3, 6, 8, 4, 6],\n [8, 2, 4, 8, 2, 2, 2, 9, 3, 3, 3, 3, 8, 4, 2, 8],\n [2, 8, 6, 2, 9, 2, 9, 2, 3, 3, 3, 3, 2, 6, 8, 2],\n [1, 1, 8, 8, 2, 8, 6, 4, 3, 3, 3, 3, 8, 8, 1, 1],\n [7, 1, 5, 8, 6, 4, 8, 6, 6, 8, 4, 6, 8, 5, 1, 7],\n [7, 5, 1, 1, 8, 2, 4, 8, 8, 4, 2, 8, 1, 1, 5, 7],\n [1, 7, 7, 1, 2, 8, 6, 2, 2, 6, 8, 2, 1, 7, 7, 1]\n ],\n \"output\": [\n [9, 9, 2, 9],\n [9, 2, 2, 2],\n [2, 9, 2, 9],\n [4, 6, 8, 2]\n ]\n}\n\n{\n \"input\": [\n [7, 8, 9, 7, 9, 7, 1, 7, 7, 1, 7, 9, 7, 9, 8, 7],\n [8, 9, 9, 8, 7, 7, 9, 9, 9, 9, 7, 7, 8, 9, 9, 8],\n [9, 9, 7, 9, 1, 9, 1, 7, 7, 1, 9, 1, 9, 7, 9, 9],\n [7, 8, 9, 1, 7, 9, 7, 2, 2, 7, 9, 7, 1, 9, 8, 7],\n [9, 7, 1, 7, 5, 7, 1, 1, 1, 1, 7, 5, 7, 1, 7, 9],\n [7, 7, 9, 9, 7, 5, 1, 2, 2, 1, 5, 7, 9, 9, 7, 7],\n [1, 9, 1, 7, 1, 1, 1, 5, 5, 1, 1, 1, 7, 1, 9, 1],\n [7, 9, 7, 2, 1, 2, 5, 2, 2, 5, 2, 1, 2, 7, 9, 7],\n [7, 9, 7, 2, 1, 2, 5, 2, 2, 5, 2, 1, 2, 7, 9, 7],\n [1, 9, 1, 7, 1, 1, 1, 5, 5, 1, 1, 1, 7, 1, 9, 1],\n [7, 7, 9, 9, 7, 5, 1, 2, 2, 1, 5, 7, 9, 9, 7, 7],\n [9, 7, 1, 3, 3, 3, 3, 1, 1, 1, 7, 5, 7, 1, 7, 9],\n [7, 8, 9, 3, 3, 3, 3, 2, 2, 7, 9, 7, 1, 9, 8, 7],\n [9, 9, 7, 3, 3, 3, 3, 7, 7, 1, 9, 1, 9, 7, 9, 9],\n [8, 9, 9, 3, 3, 3, 3, 9, 9, 9, 7, 7, 8, 9, 9, 8],\n [7, 8, 9, 7, 9, 7, 1, 7, 7, 1, 7, 9, 7, 9, 8, 7]\n ],\n \"output\": [\n [7, 5, 7, 1],\n [1, 7, 9, 7],\n [9, 1, 9, 1],\n [8, 7, 7, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 2, 9, 4, 6, 6, 5, 5, 6, 6, 4, 9, 2, 9, 9],\n [9, 2, 9, 1, 6, 6, 1, 4, 4, 1, 6, 6, 1, 9, 2, 9],\n [2, 9, 1, 1, 6, 1, 4, 6, 6, 4, 1, 6, 1, 1, 9, 2],\n [9, 1, 1, 5, 5, 4, 6, 4, 4, 6, 4, 5, 5, 1, 1, 9],\n [4, 6, 6, 5, 1, 7, 7, 7, 7, 7, 7, 1, 5, 6, 6, 4],\n [6, 6, 1, 4, 7, 5, 1, 1, 1, 1, 5, 7, 4, 1, 6, 6],\n [6, 1, 4, 6, 7, 1, 5, 9, 9, 5, 1, 7, 6, 4, 1, 6],\n [5, 4, 6, 4, 7, 1, 9, 7, 7, 9, 1, 7, 4, 6, 4, 5],\n [5, 4, 6, 3, 3, 3, 3, 7, 7, 9, 1, 7, 4, 6, 4, 5],\n [6, 1, 4, 3, 3, 3, 3, 9, 9, 5, 1, 7, 6, 4, 1, 6],\n [6, 6, 1, 3, 3, 3, 3, 1, 1, 1, 5, 7, 4, 1, 6, 6],\n [4, 6, 6, 3, 3, 3, 3, 7, 7, 7, 7, 1, 5, 6, 6, 4],\n [9, 1, 1, 5, 5, 4, 6, 4, 4, 6, 4, 5, 5, 1, 1, 9],\n [2, 9, 1, 1, 6, 1, 4, 6, 6, 4, 1, 6, 1, 1, 9, 2],\n [9, 2, 9, 1, 6, 6, 1, 4, 4, 1, 6, 6, 1, 9, 2, 9],\n [9, 9, 2, 9, 4, 6, 6, 5, 5, 6, 6, 4, 9, 2, 9, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 7, 1, 9], [6, 7, 1, 5], [4, 7, 5, 1], [5, 1, 7, 7]], "task_id": "67b4a34d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 0, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 0, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 2, 1, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 2, 1, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 2, 1, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 8, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 8, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 8, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "94be5b80"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 0, 3, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 3, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 6, 0, 6, 0, 6, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 6, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 6, 0, 6, 0, 6, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 6, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 6, 0, 6, 0, 6, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 6, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 4, 0, 4, 0, 4, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 0, 4, 0, 4, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 2, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [4, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [5, 0, 2, 2, 2, 2, 0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 5, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 5, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 4, 0, 4, 0, 4, 0, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 4, 0, 4, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 4, 0, 4, 0, 4, 0, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 4, 0, 4, 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 3, 5, 0, 5, 0, 5, 0, 5, 0, 5, 3, 0, 0, 0, 0], [0, 0, 2, 8, 0, 2, 0, 0, 0, 0, 0, 3, 0, 5, 0, 5, 0, 5, 0, 5, 0, 3, 0, 0, 0, 0], [0, 0, 2, 0, 8, 2, 0, 0, 0, 0, 0, 3, 5, 0, 5, 0, 5, 0, 5, 0, 5, 3, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 3, 0, 5, 0, 5, 0, 5, 0, 5, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 0, 5, 0, 5, 0, 5, 0, 5, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 5, 0, 5, 0, 5, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "df8cc377"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [3, 3, 3, 8, 3, 3, 3, 3, 6, 3, 3],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [5, 5, 5, 8, 5, 5, 5, 5, 6, 5, 5],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 6, 0],\n [3, 8, 3, 6, 3],\n [0, 8, 0, 6, 0],\n [5, 8, 5, 6, 5],\n [0, 8, 0, 6, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [2, 2, 1, 2, 2, 8, 2, 3, 2, 2, 2],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [5, 5, 1, 5, 5, 8, 5, 3, 5, 5, 5],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 3, 0, 0, 0]\n ],\n \"output\": [\n [0, 1, 0, 8, 0, 3, 0],\n [2, 1, 2, 8, 2, 3, 2],\n [0, 1, 0, 8, 0, 3, 0],\n [5, 1, 5, 8, 5, 3, 5],\n [0, 1, 0, 8, 0, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [3, 3, 4, 3, 3, 3, 3, 3, 3],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 0],\n [3, 4, 3],\n [0, 4, 0],\n [8, 8, 8],\n [0, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [7, 7, 3, 7, 7, 7, 7, 1, 7, 7, 7],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 0, 1, 0],\n [7, 3, 7, 1, 7],\n [0, 3, 0, 1, 0],\n [2, 2, 2, 1, 2],\n [0, 3, 0, 1, 0],\n [8, 8, 8, 8, 8],\n [0, 3, 0, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [6, 6, 6, 6, 6, 2, 6, 7, 6, 6, 4, 6, 6],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [1, 1, 1, 1, 1, 2, 1, 7, 1, 1, 4, 1, 1],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 7, 8, 8, 4, 8, 8],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 7, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 3, 0, 2, 0, 7, 0, 4, 0], [6, 6, 6, 2, 6, 7, 6, 4, 6], [0, 3, 0, 2, 0, 7, 0, 4, 0], [1, 1, 1, 2, 1, 7, 1, 4, 1], [0, 3, 0, 2, 0, 7, 0, 4, 0], [8, 8, 8, 8, 8, 7, 8, 4, 8], [0, 3, 0, 2, 0, 7, 0, 4, 0]], "task_id": "ce8d95cc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 2, 1, 1, 1, 8, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 2, 1, 1, 1, 8, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 2, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 2, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 8, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [1, 8, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 1, 1, 1, 8, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 8, 0, 0, 0, 8, 0, 1, 0],\n [0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 0, 0, 0, 1, 0, 1, 0],\n [1, 8, 1, 1, 1, 8, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 8, 1, 1, 1, 8, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 1, 0, 8, 0, 0, 0, 8, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0], [1, 2, 1, 8, 1, 1, 1, 8, 1, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "72a961c9"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2],\n [0, 0, 0, 2, 0],\n [0, 0, 2, 0, 2],\n [0, 2, 2, 2, 2],\n [0, 0, 0, 0, 2],\n [0, 0, 2, 2, 0],\n [0, 0, 0, 0, 2],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 2, 0, 8, 8, 8, 8],\n [0, 0, 0, 2, 0, 8, 0, 8, 8, 8],\n [0, 0, 2, 0, 2, 0, 8, 0, 8, 8],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 2, 0, 8, 8, 8, 8],\n [0, 0, 2, 2, 0, 8, 0, 0, 8, 8],\n [0, 0, 0, 0, 2, 0, 8, 8, 8, 8],\n [0, 0, 0, 2, 2, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 0, 0],\n [2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 0],\n [2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 0, 2, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [8, 8, 8, 0, 8, 8, 0, 0, 2, 0, 0, 0],\n [8, 8, 0, 0, 0, 8, 0, 2, 2, 2, 0, 0],\n [8, 8, 0, 0, 8, 8, 0, 0, 2, 2, 0, 0],\n [8, 8, 8, 8, 0, 0, 2, 2, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [8, 8, 8, 8, 8, 0, 2, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0],\n [0, 2, 2],\n [0, 0, 2]\n ],\n \"output\": [\n [0, 2, 0, 8, 0, 8],\n [0, 2, 2, 0, 0, 8],\n [0, 0, 2, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0],\n [2, 0, 0],\n [2, 2, 0]\n ],\n \"output\": [\n [8, 0, 0, 2, 2, 0],\n [8, 8, 0, 2, 0, 0],\n [8, 0, 0, 2, 2, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0],\n [0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 2],\n [0, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 2, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 2, 0, 8, 0, 8, 8, 8, 8], [0, 0, 0, 2, 2, 2, 0, 0, 0, 8, 8, 8], [0, 0, 0, 0, 2, 2, 0, 0, 8, 8, 8, 8], [0, 2, 2, 2, 0, 0, 8, 8, 0, 0, 0, 8], [0, 0, 0, 2, 2, 2, 0, 0, 0, 8, 8, 8], [0, 0, 0, 0, 0, 2, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 2, 2, 0, 0, 8, 8, 8, 8], [0, 0, 0, 0, 2, 2, 0, 0, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8]], "task_id": "6f473927"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 2, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 0, 2, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 8, 0, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 2, 2, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 2, 0, 0, 2, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 2, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 0, 2, 0, 0, 2, 0, 8, 8, 0, 0, 8, 8, 8, 0, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 2, 2, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 2, 2, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 2, 0, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 2, 2, 2, 2, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 2, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 2, 2, 2, 2, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 8, 8, 0, 0, 2, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 8, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 8, 8, 0, 0, 2, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 8],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 8], [0, 0, 8, 8, 0, 0, 2, 0, 2, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8], [0, 0, 8, 0, 0, 2, 2, 0, 2, 2, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 8], [0, 0, 8, 8, 0, 0, 2, 0, 2, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 8], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 2, 2, 2, 0, 0, 8], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "18419cfa"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [5, 5, 5, 5, 5, 5, 5, 2, 5, 5, 5, 2, 5],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0],\n [7, 7, 2, 7, 7, 7, 7, 7, 7, 7, 7, 2, 7],\n [0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 0, 3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 2, 8, 8, 8, 8], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0]], "task_id": "45bbe264"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 5, 1, 5, 5, 2, 5, 5, 5, 1],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [5, 2, 5, 5, 5, 5, 5, 5, 2, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [5, 2, 5, 5, 5, 5, 1, 5, 5, 2]\n ],\n \"output\": [\n [1, 5, 1, 5, 5, 2, 5, 5, 5, 1],\n [1, 1, 1, 5, 2, 2, 2, 5, 2, 2],\n [1, 1, 1, 5, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 5, 2, 2, 2, 5, 2, 2],\n [5, 2, 5, 5, 5, 5, 5, 5, 2, 5],\n [2, 2, 2, 5, 1, 1, 1, 5, 2, 2],\n [2, 2, 2, 1, 1, 1, 1, 5, 2, 2],\n [2, 2, 2, 5, 1, 1, 1, 5, 2, 2],\n [2, 2, 2, 5, 1, 1, 1, 5, 2, 2],\n [5, 2, 5, 5, 5, 5, 1, 5, 5, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 5, 5, 5, 1, 5, 5, 5, 5, 2],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [5, 1, 5, 5, 5, 2, 5, 5, 5, 1],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [1, 5, 5, 5, 2, 5, 5, 5, 5, 1]\n ],\n \"output\": [\n [2, 5, 5, 5, 1, 5, 5, 5, 5, 2],\n [1, 1, 5, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 5, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 5, 1, 1, 1, 1, 5, 1, 1],\n [5, 1, 5, 5, 5, 2, 5, 5, 5, 1],\n [1, 1, 5, 2, 2, 2, 2, 5, 1, 1],\n [1, 1, 5, 2, 2, 2, 2, 2, 1, 1],\n [1, 1, 5, 2, 2, 2, 2, 5, 1, 1],\n [1, 5, 5, 5, 2, 5, 5, 5, 5, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 5, 2, 5, 2, 5, 5, 5, 5, 1],\n [0, 0, 0, 5, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [5, 2, 5, 5, 5, 1, 5, 5, 2, 5],\n [0, 0, 0, 5, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 5, 0, 0, 0],\n [2, 2, 5, 5, 1, 5, 5, 5, 1, 5],\n [0, 0, 0, 5, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [5, 5, 1, 5, 5, 1, 5, 5, 2, 5]\n ],\n \"output\": [\n [1, 5, 2, 5, 2, 5, 5, 5, 5, 1],\n [2, 2, 2, 5, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 5, 2, 2, 5, 2, 2, 2],\n [5, 2, 5, 5, 5, 1, 5, 5, 2, 5],\n [2, 2, 2, 5, 1, 1, 2, 2, 2, 2],\n [2, 2, 2, 1, 1, 1, 5, 2, 2, 2],\n [2, 2, 5, 5, 1, 5, 5, 5, 1, 5],\n [2, 2, 2, 5, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 5, 1, 1, 5, 1, 1, 1],\n [5, 5, 1, 5, 5, 1, 5, 5, 2, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 1, 5, 5, 5, 5, 5, 5, 1, 5],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 1, 0, 0, 0, 0],\n [5, 5, 5, 5, 1, 5, 5, 2, 1, 5],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [2, 5, 5, 5, 2, 5, 5, 1, 2, 5],\n [0, 0, 1, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [5, 2, 5, 5, 5, 5, 1, 5, 5, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 1, 5, 5, 5, 5, 5, 5, 1, 5], [1, 1, 5, 1, 1, 5, 1, 1, 1, 1], [1, 1, 5, 1, 1, 1, 1, 1, 1, 1], [5, 5, 5, 5, 1, 5, 5, 2, 1, 5], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 5, 2, 2, 5, 2, 2, 2, 2], [2, 5, 5, 5, 2, 5, 5, 1, 2, 5], [2, 2, 1, 2, 2, 2, 1, 1, 1, 1], [2, 2, 5, 2, 2, 5, 1, 1, 1, 1], [5, 2, 5, 5, 5, 5, 1, 5, 5, 1]], "task_id": "7c8af763"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 6, 6, 6, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 3, 6, 3, 8, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 6, 3, 8, 3, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 5, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 6, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 8, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 8, 0, 0, 6, 0],\n [0, 0, 3, 0, 0, 8, 0, 0, 6, 0],\n [5, 3, 5, 3, 5, 8, 5, 5, 3, 0],\n [0, 0, 3, 6, 6, 8, 6, 3, 6, 3],\n [0, 0, 5, 0, 0, 8, 0, 0, 3, 0],\n [0, 0, 5, 0, 0, 3, 0, 0, 6, 0],\n [8, 8, 5, 8, 3, 8, 3, 8, 6, 8],\n [0, 0, 5, 0, 0, 3, 0, 0, 6, 0],\n [0, 0, 5, 0, 0, 8, 0, 0, 6, 0],\n [0, 0, 5, 0, 0, 8, 0, 0, 6, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [5, 5, 5, 0, 5, 5, 5, 6, 5, 5, 5, 5, 3, 5, 3, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 4, 0, 0, 0],\n [6, 6, 6, 0, 6, 6, 3, 6, 3, 6, 6, 6, 6, 5, 4, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 3, 0, 0, 0],\n [4, 4, 4, 0, 4, 4, 4, 6, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 3, 2, 3, 2, 2, 2, 0, 2, 2, 2, 2],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 4, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [8, 8, 8, 2, 8, 3, 8, 3, 0, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 3, 4, 0, 4, 4, 4, 3, 4, 3, 4, 4, 4, 4], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 3, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 3, 0, 3, 0, 0, 0, 4, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 3, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0], [1, 1, 1, 2, 1, 1, 8, 1, 0, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1], [0, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [2, 2, 3, 2, 3, 2, 8, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2], [0, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]], "task_id": "f8be4b64"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 1, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 1, 1, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 1, 0],\n [0, 2, 0, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e7dd8335"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "103eff5b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 2, 0, 2, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 0, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 0, 2, 2, 0, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 0, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 0, 2, 2, 0, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 0, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 0, 2, 2, 0, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 0, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 2, 0, 2, 2, 0, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 3, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 3, 0, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 3, 3, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 0, 1, 0, 1, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 0, 1, 0, 1, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 0, 1, 0, 1, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 3, 0, 0, 3, 0, 0, 3, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 0, 3, 3, 0, 3, 3, 0, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 3, 0, 0, 3, 0, 0, 3, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 0, 3, 3, 0, 3, 3, 0, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8],\n [8, 8, 8, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 8, 8, 8],\n [8, 8, 8, 4, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 4, 8, 8, 8],\n [8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8],\n [8, 8, 8, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 8, 8, 8],\n [8, 8, 8, 4, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 4, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 1, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 1, 1, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 3, 0, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 0, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 3, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 0, 1, 0, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 1, 0, 1, 1, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 1, 1, 0, 1, 1, 0, 8, 8, 8, 8, 4, 0, 4, 0, 4, 0, 8, 8, 8], [8, 8, 8, 0, 1, 0, 0, 1, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 8], [8, 8, 8, 1, 0, 1, 1, 0, 1, 8, 8, 8, 8, 4, 0, 4, 0, 4, 0, 8, 8, 8], [8, 8, 8, 1, 1, 0, 1, 1, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 8], [8, 8, 8, 0, 1, 0, 0, 1, 0, 8, 8, 8, 8, 4, 0, 4, 0, 4, 0, 8, 8, 8], [8, 8, 8, 1, 0, 1, 1, 0, 1, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 8], [8, 8, 8, 1, 1, 0, 1, 1, 0, 8, 8, 8, 8, 4, 0, 4, 0, 4, 0, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 0, 3, 3, 3, 0, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 0, 3, 0, 3, 0, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 3, 0, 3, 3, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 0, 3, 3, 3, 0, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 0, 3, 0, 3, 0, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 3, 0, 3, 3, 3, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], "task_id": "a57f2f04"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 2, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 2, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 1, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 1, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 4, 4, 4, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 2, 4, 4, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 4, 4, 4, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 4, 4, 4, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 4, 4, 2, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 4, 4, 4, 4, 4, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 1, 4, 4, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 2, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 2, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 4, 2, 4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 4, 4, 4, 4, 2, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 2, 4, 4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 4, 1, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 3, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 6, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 6, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 6, 4, 6, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 1, 4, 4, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [0, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 3, 4, 4, 4, 3, 3, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 4, 3, 4, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 4, 4, 4, 6, 4, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 4, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 6, 4, 6, 4, 4, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "52fd389e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 4, 0, 3, 3, 0, 4, 0, 2, 2, 2, 0, 8, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 8, 0, 4, 0, 0, 0, 0, 4, 0, 2, 0, 2, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 8, 0, 4, 0, 0, 0, 0, 4, 0, 2, 0, 2, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 8, 0, 4, 4, 4, 4, 4, 4, 0, 2, 0, 2, 0, 8, 0, 0, 0, 7, 0, 1, 1, 1, 1, 1, 0, 7],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 8, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 1, 0, 0, 0, 1, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 1, 1, 1, 1, 1, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 8, 0, 8, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 8, 0, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 7, 0, 7, 7, 7, 7, 7, 0, 7],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 7, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 7, 7, 7, 7, 7, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 2, 0, 1, 6, 6, 6, 6, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 1, 6, 0, 0, 6, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 1, 6, 0, 0, 6, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 4, 0, 0, 0, 2, 0, 1, 6, 6, 6, 6, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 1, 1, 0, 0, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 1, 1, 0, 0, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 3, 3, 0, 1, 0],\n [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 3, 3, 0, 1, 0],\n [0, 0, 1, 0, 2, 0, 8, 8, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 2, 0, 8, 8, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0],\n [0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 0, 1, 1, 0, 0, 0, 0, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 0, 1, 1, 0, 0, 0, 0, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 3, 0, 4, 0, 0, 6, 0, 0],\n [0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 6, 0, 0],\n [0, 0, 4, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 1, 0, 8, 0, 0, 4, 4, 4, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 8, 0, 0, 4, 0, 4, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 0, 0, 0, 2, 2, 0, 3, 0, 0, 1, 0, 8, 0, 0, 4, 4, 4, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 0, 0, 0, 2, 2, 0, 3, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 1, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 0, 0, 6, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 6, 0, 0], [0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 8, 0, 0, 8, 8, 8, 0, 0, 8, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 8, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 8, 0, 0, 8, 8, 8, 0, 0, 8, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "7d1f7ee8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 2, 5, 0, 0, 2, 0, 5, 0, 0, 0],\n [2, 0, 0, 0, 5, 0, 2, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [2, 5, 5, 5, 5, 5, 5, 5, 2, 5, 5, 5, 5],\n [0, 2, 0, 0, 5, 0, 2, 0, 0, 5, 0, 0, 0],\n [0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 2, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 2, 0, 0, 5, 0, 2, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 2],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 0, 0, 5, 3, 3, 0, 3, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 5, 0, 0, 0, 0, 3, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5],\n [3, 3, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 3, 0, 3, 3, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0],\n [3, 3, 0, 3, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 3, 0],\n [5, 5, 3, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 3],\n [0, 0, 0, 3, 3, 5, 0, 3, 3, 0, 0, 5, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, 5, 0, 0, 0, 0, 3],\n [3, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 5, 0, 0, 0, 3, 3],\n [3, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [3, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 5, 0, 0, 0, 0, 0, 5, 3, 0, 3, 0, 3],\n [3, 0, 0, 0, 0, 5, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 4, 0, 0, 5, 0, 4, 0, 5],\n [0, 4, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 4, 4, 0, 0, 0, 4, 0, 0, 0, 5, 0, 0, 4, 5, 0, 0, 0, 5, 0, 0, 4, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 4],\n [0, 0, 0, 5, 0, 4, 4, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 4, 0, 4, 4, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [4, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 5, 4, 0, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 4, 0, 5],\n [0, 0, 0, 5, 4, 0, 0, 5, 0, 0, 0, 5, 4, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 4, 0, 0, 5, 0, 0, 0, 5, 0, 0, 4, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 4, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 4, 0, 4, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 4, 0, 5, 4, 0, 0, 4, 0, 0, 4, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 4, 0, 5, 4, 4, 0, 5, 0, 0, 0, 5, 0, 4, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 4, 0, 4, 0, 4, 4, 0, 0, 5, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 5, 4],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 5, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 5, 0, 0, 1, 0, 0, 5, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [1, 0, 0, 0, 1, 5, 1, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 1, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 1],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 5, 5, 5, 5, 5, 5, 1, 5, 5, 1, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 1, 0, 1, 0, 0, 5, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 5, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 1, 0],\n [0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 1, 0],\n [5, 5, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 1, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 5, 1, 0, 1, 0, 0, 5, 0, 0, 0],\n [1, 5, 5, 5, 5, 5, 5, 5, 1, 5, 5, 5, 1, 5, 5, 5, 5, 1, 1, 5, 5, 5, 5, 5, 1, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 1],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 1, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5, 1, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0]], "task_id": "95a58926"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 8, 0, 2, 2, 2, 2, 2, 2],\n [0, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 8, 0, 2, 1, 1, 1, 1, 2],\n [0, 0, 8, 7, 7, 4, 4, 4, 4, 7, 7, 8, 0, 2, 1, 3, 3, 1, 2],\n [0, 0, 8, 7, 7, 4, 3, 3, 4, 7, 7, 8, 0, 2, 1, 3, 3, 1, 2],\n [0, 0, 8, 7, 7, 4, 3, 3, 4, 7, 7, 8, 0, 2, 1, 1, 1, 1, 2],\n [0, 0, 8, 7, 7, 4, 3, 3, 4, 7, 7, 8, 0, 2, 2, 2, 2, 2, 2],\n [0, 0, 8, 7, 7, 4, 3, 3, 4, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 7, 7, 4, 3, 3, 4, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 7, 7, 4, 4, 4, 4, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 3, 3, 3, 3, 3, 3],\n [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 3, 1, 1, 1, 1, 3],\n [0, 0, 3, 4, 4, 7, 7, 7, 7, 4, 4, 3, 0, 3, 1, 2, 2, 1, 3],\n [0, 0, 3, 4, 4, 7, 8, 8, 7, 4, 4, 3, 0, 3, 1, 2, 2, 1, 3],\n [0, 0, 3, 4, 4, 7, 8, 8, 7, 4, 4, 3, 0, 3, 1, 1, 1, 1, 3],\n [0, 0, 3, 4, 4, 7, 8, 8, 7, 4, 4, 3, 0, 3, 3, 3, 3, 3, 3],\n [0, 0, 3, 4, 4, 7, 8, 8, 7, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 7, 8, 8, 7, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 7, 7, 7, 7, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 8, 8, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 8, 8, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 2, 2, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 2, 2, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 8, 8, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 8, 8, 8, 8, 8, 8, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 3, 3, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 3, 3, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 1, 1, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 1, 1, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 3, 3, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 3, 3, 3, 3, 3, 3, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 2, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 1, 1, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 1, 1, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 2, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 2, 2, 2, 2, 2, 2, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 2, 1, 1, 1, 1, 2, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 2, 1, 1, 1, 1, 2, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 2, 2, 2, 2, 2, 2, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 6, 0],\n [0, 0, 0, 0, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 8, 8, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 8, 8, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 3, 3, 3, 3, 3, 3, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 3, 6, 6, 6, 6, 3, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 3, 6, 6, 6, 6, 3, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 3, 3, 3, 3, 3, 3, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 0],\n [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 6, 6, 6, 6, 6, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 6, 8, 8, 8, 6, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 6, 6, 6, 6, 6, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 1, 8, 8, 8, 8, 8, 8, 1, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 1, 8, 8, 8, 8, 8, 8, 1, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 6, 6, 6, 6, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 6, 1, 1, 1, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 6, 6, 6, 6, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 4, 2, 2, 2, 2, 2, 2, 4, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 4, 2, 2, 2, 2, 2, 2, 4, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 6, 6, 6, 6, 6, 6, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 6, 1, 1, 1, 1, 6, 3, 3, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 3, 3, 6, 1, 4, 4, 1, 6, 3, 3, 0, 0, 5, 4, 4, 4, 4, 5],\n [0, 3, 3, 6, 1, 1, 1, 1, 6, 3, 3, 0, 0, 5, 4, 8, 8, 4, 5],\n [0, 3, 3, 6, 6, 6, 6, 6, 6, 3, 3, 0, 0, 5, 4, 8, 8, 4, 5],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 5, 4, 4, 4, 4, 5],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 2, 4, 4, 4, 4, 4, 4, 4, 2, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 2, 4, 3, 3, 3, 3, 3, 4, 2, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 2, 4, 4, 4, 4, 4, 4, 4, 2, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 1, 6, 6, 6, 6, 1, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8], [0, 4, 4, 1, 6, 3, 3, 6, 1, 4, 4, 0, 0, 8, 4, 4, 4, 4, 8], [0, 4, 4, 1, 6, 6, 6, 6, 1, 4, 4, 0, 0, 8, 4, 5, 5, 4, 8], [0, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 0, 0, 8, 4, 5, 5, 4, 8], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 4, 4, 4, 4, 8], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 2, 8, 6, 6, 6, 6, 6, 8, 2, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0]], "task_id": "8dae5dfc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8],\n [0, 2, 2],\n [0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 4, 4, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 2, 2, 2, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],\n [8, 8, 8, 8, 0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [8, 8, 8, 8, 0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2],\n [0, 3, 3, 3],\n [0, 0, 8, 8],\n [0, 0, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 8, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 8, 0, 0],\n [0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 3, 3, 3, 0, 1, 1, 1, 1, 1],\n [2, 2, 2, 0, 0, 0, 0, 3, 3, 3, 0, 1, 1, 1, 1, 1],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 1, 1, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1],\n [0, 0, 8, 8, 8],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 0, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 8, 8],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 4, 4, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 4, 4, 4, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 3, 3, 3], [0, 4, 4, 4, 4], [0, 0, 0, 8, 8]], "task_id": "2753e76c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 8, 8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c6e1b8da"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 2, 2, 1, 0, 0, 1, 2, 1, 0],\n [0, 1, 2, 2, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 1, 2, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0],\n [0, 1, 2, 3, 2, 2, 3, 2, 1, 0, 0, 0, 0],\n [0, 1, 2, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 2, 2, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 2, 2, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 2, 3, 3, 3, 2, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0], [0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0], [0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 2, 1, 0, 0, 0, 0], [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 1, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 3, 3, 3, 2, 1, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 1, 2, 3, 2, 3, 2, 1, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 3, 2, 3, 2, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 2, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0]], "task_id": "516b51b7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 3, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 3, 3, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 3, 3, 3, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 3, 3, 3, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 3, 3, 3, 0, 3, 2, 2, 0, 2, 2, 2, 0], [3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2]], "task_id": "351d6448"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [7, 6, 7],\n [2, 7, 6],\n [1, 2, 7]\n ],\n \"output\": [\n [7, 2, 1, 1, 2, 7, 7, 2, 1],\n [6, 7, 2, 2, 7, 6, 6, 7, 2],\n [7, 6, 7, 7, 6, 7, 7, 6, 7],\n [7, 6, 7, 7, 6, 7, 7, 6, 7],\n [6, 7, 2, 2, 7, 6, 6, 7, 2],\n [7, 2, 1, 1, 2, 7, 7, 2, 1],\n [7, 2, 1, 1, 2, 7, 7, 2, 1],\n [6, 7, 2, 2, 7, 6, 6, 7, 2],\n [7, 6, 7, 7, 6, 7, 7, 6, 7]\n ]\n}\n\n{\n \"input\": [\n [6, 1, 7],\n [1, 6, 7],\n [4, 7, 4]\n ],\n \"output\": [\n [4, 7, 4, 4, 7, 4, 4, 7, 4],\n [7, 6, 1, 1, 6, 7, 7, 6, 1],\n [7, 1, 6, 6, 1, 7, 7, 1, 6],\n [7, 1, 6, 6, 1, 7, 7, 1, 6],\n [7, 6, 1, 1, 6, 7, 7, 6, 1],\n [4, 7, 4, 4, 7, 4, 4, 7, 4],\n [4, 7, 4, 4, 7, 4, 4, 7, 4],\n [7, 6, 1, 1, 6, 7, 7, 6, 1],\n [7, 1, 6, 6, 1, 7, 7, 1, 6]\n ]\n}\n\n{\n \"input\": [\n [1, 9, 4],\n [9, 1, 6],\n [6, 9, 4]\n ],\n \"output\": [\n [4, 9, 6, 6, 9, 4, 4, 9, 6],\n [6, 1, 9, 9, 1, 6, 6, 1, 9],\n [4, 9, 1, 1, 9, 4, 4, 9, 1],\n [4, 9, 1, 1, 9, 4, 4, 9, 1],\n [6, 1, 9, 9, 1, 6, 6, 1, 9],\n [4, 9, 6, 6, 9, 4, 4, 9, 6],\n [4, 9, 6, 6, 9, 4, 4, 9, 6],\n [6, 1, 9, 9, 1, 6, 6, 1, 9],\n [4, 9, 1, 1, 9, 4, 4, 9, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 6],\n [6, 3, 6],\n [6, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 6, 6, 8, 8, 8, 8, 6], [6, 3, 6, 6, 3, 6, 6, 3, 6], [6, 8, 8, 8, 8, 6, 6, 8, 8], [6, 8, 8, 8, 8, 6, 6, 8, 8], [6, 3, 6, 6, 3, 6, 6, 3, 6], [8, 8, 6, 6, 8, 8, 8, 8, 6], [8, 8, 6, 6, 8, 8, 8, 8, 6], [6, 3, 6, 6, 3, 6, 6, 3, 6], [6, 8, 8, 8, 8, 6, 6, 8, 8]], "task_id": "c48954c1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 1, 0, 2, 2, 2, 0, 1, 2, 1],\n [1, 2, 2, 0, 2, 2, 2, 0, 1, 1, 2],\n [2, 2, 2, 0, 1, 2, 2, 0, 2, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 1, 0, 2, 1, 2, 0, 2, 2, 2],\n [1, 2, 2, 0, 1, 2, 1, 0, 2, 2, 2],\n [2, 1, 2, 0, 2, 2, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 1, 1, 1, 0, 2, 1, 1],\n [1, 2, 1, 0, 1, 2, 1, 0, 1, 1, 2]\n ],\n \"output\": [\n [2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 2, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 1, 1, 2, 0, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 1, 0, 2, 1, 2, 0, 1, 2, 1],\n [1, 2, 2, 0, 1, 2, 1, 0, 1, 1, 2],\n [2, 1, 2, 0, 2, 2, 1, 0, 2, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1],\n [2, 2, 2, 0, 2, 2, 2, 0, 1, 2, 2],\n [2, 2, 2, 0, 1, 2, 2, 0, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 2, 0, 2, 1, 2, 0, 2, 1, 1],\n [2, 1, 2, 0, 2, 1, 2, 0, 1, 1, 1],\n [1, 2, 2, 0, 1, 2, 2, 0, 1, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 0, 1, 1, 1, 0, 2, 2, 2],\n [2, 1, 1, 0, 1, 1, 1, 0, 2, 1, 2],\n [1, 2, 2, 0, 1, 2, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 2, 0, 1, 2, 1, 0, 1, 1, 1],\n [2, 2, 1, 0, 2, 1, 1, 0, 1, 1, 1],\n [2, 2, 2, 0, 1, 2, 1, 0, 1, 1, 1]\n ],\n \"output\": [\n [2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 1, 2, 0, 1, 2, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 2, 0, 1, 2, 1, 0, 1, 2, 1],\n [2, 1, 2, 0, 2, 1, 1, 0, 2, 1, 1],\n [1, 2, 2, 0, 1, 2, 2, 0, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 1, 2, 2, 0, 2, 1, 2],\n [2, 1, 2, 0, 2, 2, 1, 0, 2, 1, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 1, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1],\n [2, 2, 1, 0, 2, 2, 2, 0, 1, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 1, 1, 1, 0, 2, 1, 1],\n [2, 1, 2, 0, 1, 1, 1, 0, 1, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 0, 1, 2, 1, 0, 2, 1, 1],\n [1, 2, 1, 0, 1, 2, 1, 0, 1, 1, 1],\n [2, 1, 1, 0, 2, 2, 2, 0, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 1],\n [2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 0, 2, 1, 1, 0, 2, 1, 1],\n [1, 2, 1, 0, 1, 2, 1, 0, 1, 2, 1],\n [2, 2, 2, 0, 2, 1, 2, 0, 2, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1],\n [2, 2, 2, 0, 2, 2, 1, 0, 1, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 0, 2, 1, 2, 0, 2, 2, 1],\n [1, 2, 2, 0, 1, 2, 2, 0, 1, 2, 1],\n [2, 1, 2, 0, 2, 1, 2, 0, 2, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 2, 0, 1, 1, 1, 0, 1, 1, 1],\n [1, 2, 1, 0, 1, 1, 1, 0, 1, 1, 2],\n [1, 2, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 0, 2, 1, 1, 0, 2, 2, 2],\n [1, 1, 1, 0, 1, 2, 1, 0, 2, 2, 2],\n [1, 1, 2, 0, 1, 2, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 0, 1, 1, 2, 0, 1, 1, 1], [1, 1, 2, 0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 1, 0, 2, 1, 1, 0, 1, 1, 2], [1, 2, 1, 0, 1, 2, 1, 0, 1, 2, 1], [2, 1, 2, 0, 1, 2, 2, 0, 1, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2], [2, 2, 2, 0, 1, 2, 2, 0, 1, 2, 2], [2, 2, 2, 0, 2, 1, 2, 0, 2, 1, 2]], "task_id": "dc2aa30b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 0, 2, 0, 0, 0, 0, 0, 5, 2, 0, 0],\n [0, 5, 0, 2, 0, 5, 5, 0, 0, 2, 2, 0, 0],\n [5, 0, 0, 2, 0, 0, 2, 5, 0, 2, 0, 5, 0],\n [0, 0, 0, 2, 0, 5, 2, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 5, 0, 2, 0, 0, 2, 0, 0, 5],\n [0, 0, 0, 2, 5, 0, 2, 0, 0, 2, 0, 0, 0],\n [5, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 5, 5],\n [0, 0, 5, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0],\n [0, 0, 5, 2, 0, 5, 2, 0, 5, 2, 0, 0, 0],\n [0, 5, 2, 2, 5, 2, 2, 5, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 5, 5, 0, 2, 5, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0],\n [5, 0, 2, 0, 0, 2, 0, 5, 5, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 5, 0, 0, 0, 5, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 5, 0, 0, 0, 5, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 2, 2],\n [0, 0, 5, 5, 2, 0, 0, 5, 5, 0, 0, 0, 2, 0],\n [0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 2, 2, 0, 5, 0, 0, 0, 5, 0, 2, 0],\n [0, 5, 5, 2, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0],\n [5, 0, 5, 2, 0, 5, 0, 0, 0, 0, 0, 0, 2, 5],\n [5, 0, 0, 2, 5, 2, 5, 0, 0, 0, 0, 0, 2, 0],\n [5, 0, 0, 2, 0, 2, 0, 0, 0, 0, 5, 0, 2, 0],\n [0, 0, 5, 2, 0, 2, 5, 0, 0, 0, 5, 5, 2, 0],\n [0, 0, 2, 2, 0, 2, 0, 5, 0, 5, 5, 2, 2, 0],\n [5, 0, 2, 0, 0, 2, 0, 5, 5, 0, 2, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [5, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0],\n [0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0, 0],\n [0, 5, 5, 2, 0, 0, 0, 2, 0, 2, 0, 0, 5, 5, 0]\n ],\n \"output\": [\n [0, 5, 0, 0, 0, 2, 0, 5, 0, 2, 0, 0, 2, 5, 5],\n [0, 5, 0, 0, 0, 2, 0, 0, 0, 2, 5, 0, 2, 5, 0],\n [5, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 5, 2, 0, 0, 0, 2, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 5, 2, 5, 2, 2, 0, 0],\n [0, 5, 5, 5, 5, 2, 0, 0, 2, 2, 5, 2, 0, 5, 0],\n [5, 5, 0, 0, 0, 2, 0, 5, 2, 5, 5, 2, 0, 0, 5],\n [0, 5, 0, 0, 0, 2, 0, 0, 2, 0, 5, 2, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 0, 0, 2, 5, 0, 2, 0, 0, 0],\n [0, 5, 0, 2, 2, 2, 0, 5, 2, 0, 5, 2, 5, 0, 0],\n [0, 5, 5, 2, 0, 0, 5, 2, 2, 0, 5, 2, 0, 0, 0],\n [0, 0, 5, 2, 0, 0, 0, 2, 0, 5, 5, 2, 5, 0, 0],\n [0, 5, 5, 2, 0, 0, 0, 2, 0, 2, 2, 2, 5, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 0, 5],\n [0, 0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 5, 5, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 5, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 5, 2, 5, 0, 0, 0, 0, 0, 5, 5], [0, 0, 0, 0, 2, 5, 0, 5, 0, 5, 5, 5, 0], [0, 0, 0, 0, 2, 0, 5, 0, 0, 0, 0, 0, 0], [5, 0, 5, 0, 2, 0, 5, 5, 0, 0, 0, 0, 5], [0, 0, 5, 5, 2, 5, 0, 0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 2, 0, 0, 0, 5, 0, 5, 0, 2], [0, 0, 5, 5, 2, 5, 0, 5, 0, 0, 0, 0, 2], [5, 0, 0, 0, 2, 5, 5, 0, 5, 0, 0, 0, 2], [0, 5, 0, 0, 2, 0, 0, 0, 2, 5, 5, 0, 2], [0, 5, 0, 5, 2, 0, 0, 5, 2, 5, 0, 5, 2], [0, 5, 0, 2, 2, 0, 0, 2, 2, 5, 5, 2, 2], [5, 0, 5, 2, 5, 0, 5, 2, 0, 2, 2, 2, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 5, 5, 0]], "task_id": "712bf12e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 8, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 8, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]], "task_id": "cb227835"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 4, 0, 2, 0],\n [0, 0, 5, 0, 0, 0, 0, 4, 0, 2, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2],\n [2],\n [2],\n [2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 2, 0, 0, 0, 0, 6, 6, 6, 0],\n [0, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 0, 0],\n [6, 6, 6, 0, 0, 0, 0, 0, 2, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [7, 7],\n [7, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 1, 2],\n [1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 8, 4, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 8], [8, 8], [8, 4]], "task_id": "cd3c21df"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2]], "task_id": "20981f0e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 4],\n [1, 1, 0, 2, 2, 0, 3, 3, 0, 4],\n [1, 1, 0, 2, 2, 0, 3, 3, 0, 4]\n ],\n \"output\": [\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 7, 7, 0, 2, 2, 2],\n [8, 8, 8, 0, 7, 7, 0, 2, 2, 2]\n ],\n \"output\": [\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 3, 3, 3],\n [4, 4, 4, 4, 0, 2, 0, 3, 3, 3],\n [4, 4, 4, 4, 0, 2, 0, 3, 3, 3]\n ],\n \"output\": [\n [4, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 8, 8, 0, 6, 0, 0, 0, 0],\n [7, 0, 8, 8, 0, 6, 0, 3, 3, 0],\n [7, 0, 8, 8, 0, 6, 0, 3, 3, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [8, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "03560426"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 0, 9],\n [0, 5, 0, 8, 0],\n [0, 0, 7, 0, 0],\n [0, 8, 0, 5, 0],\n [9, 0, 0, 0, 1]\n ],\n \"output\": [\n [1, 5, 9],\n [8, 7, 8],\n [9, 5, 1]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 0, 0, 7],\n [0, 2, 0, 4, 0],\n [0, 0, 3, 0, 0],\n [0, 4, 0, 2, 0],\n [7, 0, 0, 0, 6]\n ],\n \"output\": [\n [6, 2, 7],\n [4, 3, 4],\n [7, 2, 6]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 1],\n [0, 3, 0, 6, 0],\n [0, 0, 4, 0, 0],\n [0, 6, 0, 3, 0],\n [1, 0, 0, 0, 2]\n ],\n \"output\": [\n [2, 3, 1],\n [6, 4, 6],\n [1, 3, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 0, 0, 0, 5],\n [0, 6, 0, 4, 0],\n [0, 0, 2, 0, 0],\n [0, 4, 0, 6, 0],\n [5, 0, 0, 0, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 6, 5], [4, 2, 4], [5, 6, 7]], "task_id": "ca8de6ea"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 7, 6, 5, 0, 0, 0, 0, 1, 4, 5, 6, 0, 0, 8],\n [7, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 6],\n [0, 9, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 2],\n [5, 5, 5, 5, 4, 0, 0, 0, 4, 0, 9, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 3, 6, 0, 0, 0, 7, 6, 0, 0, 9, 4, 0, 0, 4],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 9, 0, 8, 7, 0, 0, 0, 0, 0],\n [0, 6, 1, 0, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [1, 0, 5, 4, 0, 0, 8, 0, 0, 0, 0, 2, 2, 0, 6],\n [3, 0, 6, 0, 2, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0],\n [4, 1, 0, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 4, 0],\n [0, 2, 0, 0, 7, 0, 0, 9, 7, 6, 0, 0, 5, 3, 0],\n [4, 0, 4, 1, 0, 0, 8, 1, 8, 0, 0, 9, 4, 7, 7],\n [0, 8, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 1, 6]\n ],\n \"output\": [\n [0, 7, 6, 5, 0, 0, 0, 0, 1, 4, 5, 6, 0, 0, 8],\n [7, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 6],\n [0, 9, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 2],\n [5, 5, 5, 5, 4, 0, 0, 0, 4, 0, 9, 0, 9, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 2, 0, 0],\n [2, 3, 6, 0, 5, 0, 7, 6, 5, 0, 9, 4, 0, 0, 4],\n [0, 0, 0, 0, 5, 7, 0, 0, 5, 0, 0, 3, 0, 0, 0],\n [0, 9, 0, 0, 5, 0, 9, 0, 5, 7, 0, 0, 0, 0, 0],\n [0, 6, 1, 0, 5, 5, 5, 5, 5, 0, 0, 0, 7, 0, 0],\n [1, 0, 5, 4, 0, 0, 8, 0, 0, 0, 0, 2, 2, 0, 6],\n [3, 0, 6, 0, 2, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0],\n [4, 1, 0, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 4, 0],\n [0, 2, 0, 0, 7, 0, 0, 9, 7, 6, 0, 0, 5, 3, 0],\n [4, 0, 4, 1, 0, 0, 8, 1, 8, 0, 0, 9, 4, 7, 7],\n [0, 8, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 1, 6]\n ]\n}\n\n{\n \"input\": [\n [3, 4, 0, 5, 0, 0, 3, 0, 5, 8, 0, 7, 0, 0, 0],\n [0, 0, 4, 5, 8, 8, 0, 0, 0, 0, 7, 3, 3, 0, 0],\n [0, 8, 3, 5, 0, 0, 5, 0, 0, 1, 0, 2, 0, 0, 9],\n [5, 5, 5, 5, 6, 1, 0, 9, 0, 0, 3, 3, 0, 6, 0],\n [3, 7, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 4, 0, 0, 5, 5, 6, 0, 0, 0, 0, 0, 1, 5, 0],\n [0, 2, 1, 0, 0, 0, 0, 0, 4, 9, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 7, 2, 2, 0, 0, 9, 8],\n [1, 0, 0, 0, 1, 0, 3, 7, 0, 0, 0, 7, 0, 0, 3],\n [0, 0, 1, 2, 0, 9, 3, 4, 0, 0, 1, 0, 0, 2, 9],\n [0, 9, 0, 0, 8, 0, 0, 0, 4, 0, 0, 6, 0, 8, 4],\n [7, 7, 6, 0, 0, 0, 0, 8, 3, 0, 0, 0, 8, 2, 7],\n [0, 9, 0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 1, 6],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0, 9, 8, 0],\n [4, 0, 0, 0, 9, 0, 1, 1, 7, 9, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [3, 4, 0, 5, 0, 0, 3, 0, 5, 8, 0, 7, 0, 0, 0],\n [0, 0, 4, 5, 8, 8, 0, 0, 0, 0, 7, 3, 3, 0, 0],\n [0, 8, 3, 5, 0, 0, 5, 0, 0, 1, 0, 2, 0, 0, 9],\n [5, 5, 5, 5, 6, 1, 0, 9, 0, 0, 3, 3, 0, 6, 0],\n [3, 7, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 4, 0, 0, 5, 5, 6, 0, 0, 0, 0, 0, 1, 5, 0],\n [0, 2, 1, 0, 0, 0, 0, 0, 4, 9, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 7, 2, 2, 0, 0, 9, 8],\n [1, 0, 0, 0, 1, 5, 5, 5, 5, 5, 0, 7, 0, 0, 3],\n [0, 0, 1, 2, 0, 5, 3, 4, 0, 5, 1, 0, 0, 2, 9],\n [0, 9, 0, 0, 8, 5, 0, 0, 4, 5, 0, 6, 0, 8, 4],\n [7, 7, 6, 0, 0, 5, 0, 8, 3, 5, 0, 0, 8, 2, 7],\n [0, 9, 0, 0, 2, 5, 5, 5, 5, 5, 0, 0, 0, 1, 6],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0, 9, 8, 0],\n [4, 0, 0, 0, 9, 0, 1, 1, 7, 9, 0, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 2, 5, 0, 0, 0, 2, 6, 9, 0, 0, 5, 0, 0],\n [0, 7, 0, 5, 0, 8, 5, 8, 0, 7, 0, 0, 0, 8, 8],\n [0, 6, 6, 5, 7, 0, 3, 5, 0, 0, 0, 4, 7, 0, 0],\n [5, 5, 5, 5, 8, 0, 1, 9, 0, 0, 0, 0, 5, 0, 0],\n [8, 0, 0, 0, 0, 0, 1, 0, 3, 9, 8, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 6, 6, 4, 0, 9, 0, 0, 1, 7, 0],\n [8, 0, 6, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 9],\n [3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 4, 0, 2, 0, 3, 2, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 5, 0, 8],\n [0, 9, 4, 4, 0, 0, 4, 0, 6, 6, 0, 7, 0, 0, 0],\n [7, 0, 0, 0, 9, 0, 0, 8, 0, 0, 0, 5, 0, 0, 0],\n [0, 6, 0, 0, 1, 0, 0, 7, 7, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 4, 0, 5, 0, 0, 0, 0, 7, 0, 5, 0, 0],\n [8, 0, 9, 8, 5, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0]\n ],\n \"output\": [\n [4, 0, 2, 5, 0, 0, 0, 2, 6, 9, 0, 0, 5, 0, 0],\n [0, 7, 0, 5, 0, 8, 5, 8, 0, 7, 0, 0, 0, 8, 8],\n [0, 6, 6, 5, 7, 0, 3, 5, 0, 0, 0, 4, 7, 0, 0],\n [5, 5, 5, 5, 8, 0, 1, 9, 0, 0, 0, 0, 5, 0, 0],\n [8, 0, 0, 0, 0, 0, 1, 0, 3, 9, 8, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 6, 6, 4, 0, 9, 0, 0, 1, 7, 0],\n [8, 0, 6, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 9],\n [3, 0, 0, 2, 0, 0, 5, 5, 5, 5, 5, 8, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 5, 4, 0, 2, 5, 3, 2, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 7, 0, 5, 0, 5, 0, 8],\n [0, 9, 4, 4, 0, 0, 5, 0, 6, 6, 5, 7, 0, 0, 0],\n [7, 0, 0, 0, 9, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 6, 0, 0, 1, 0, 0, 7, 7, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 4, 0, 5, 0, 0, 0, 0, 7, 0, 5, 0, 0],\n [8, 0, 9, 8, 5, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 7, 3, 5, 0, 0, 0, 0, 0, 0, 0, 3, 5, 4, 0],\n [1, 0, 3, 5, 2, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0],\n [1, 0, 0, 5, 6, 0, 0, 9, 9, 0, 5, 0, 0, 0, 9],\n [5, 5, 5, 5, 0, 0, 2, 1, 0, 0, 3, 0, 0, 0, 0],\n [3, 0, 0, 3, 1, 8, 5, 0, 5, 2, 0, 0, 5, 0, 0],\n [4, 0, 9, 2, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 9, 5, 4, 0, 8, 0, 0, 5, 5],\n [0, 7, 0, 0, 0, 5, 5, 7, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 3, 0, 7, 3, 7, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 4, 0, 7, 3, 0, 2],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0],\n [0, 0, 5, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 0],\n [0, 0, 3, 0, 0, 5, 4, 7, 0, 0, 0, 0, 0, 3, 5],\n [8, 0, 0, 1, 7, 1, 0, 8, 0, 8, 2, 0, 0, 0, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 7, 3, 5, 0, 0, 0, 0, 0, 0, 0, 3, 5, 4, 0], [1, 0, 3, 5, 2, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0], [1, 0, 0, 5, 6, 0, 0, 9, 9, 0, 5, 0, 0, 0, 9], [5, 5, 5, 5, 0, 0, 2, 1, 0, 0, 3, 0, 0, 0, 0], [3, 0, 0, 3, 1, 8, 5, 0, 5, 2, 0, 0, 5, 0, 0], [4, 0, 9, 2, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 9, 5, 4, 0, 8, 0, 0, 5, 5], [0, 7, 0, 0, 0, 5, 5, 7, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 3, 0, 7, 3, 7, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 9, 0, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 5, 0, 7, 3, 5, 2], [0, 2, 2, 0, 0, 0, 0, 0, 0, 5, 1, 0, 3, 5, 0], [0, 0, 5, 2, 2, 2, 0, 0, 0, 5, 1, 0, 0, 5, 0], [0, 0, 3, 0, 0, 5, 4, 7, 0, 5, 5, 5, 5, 5, 5], [8, 0, 0, 1, 7, 1, 0, 8, 0, 8, 2, 0, 0, 0, 4]], "task_id": "e2092e0c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 7, 7, 0, 7, 7, 2, 7, 0, 0, 0, 0, 7],\n [7, 0, 0, 0, 0, 7, 2, 7, 0, 0, 7, 7, 0],\n [7, 0, 7, 7, 0, 7, 2, 7, 0, 0, 7, 0, 0],\n [0, 7, 0, 0, 0, 0, 2, 7, 0, 7, 0, 7, 0],\n [7, 7, 0, 7, 7, 0, 2, 0, 7, 0, 0, 7, 0]\n ],\n \"output\": [\n [1, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 1, 1],\n [1, 0, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 7, 7, 0, 7, 2, 7, 7, 0, 7, 0, 7],\n [0, 0, 0, 7, 0, 7, 2, 0, 7, 7, 7, 0, 7],\n [7, 0, 7, 0, 0, 0, 2, 7, 7, 0, 0, 0, 0],\n [7, 7, 7, 0, 0, 0, 2, 7, 7, 0, 0, 7, 7],\n [0, 7, 7, 0, 7, 7, 2, 7, 7, 7, 0, 0, 7]\n ],\n \"output\": [\n [1, 1, 1, 1, 0, 1],\n [0, 1, 1, 1, 0, 1],\n [1, 1, 1, 0, 0, 0],\n [1, 1, 1, 0, 1, 1],\n [1, 1, 1, 0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [7, 0, 7, 7, 0, 7, 2, 7, 7, 0, 0, 0, 0],\n [7, 0, 0, 7, 0, 0, 2, 0, 0, 0, 7, 0, 0],\n [0, 7, 7, 0, 0, 0, 2, 0, 0, 7, 7, 0, 0],\n [0, 7, 7, 7, 7, 0, 2, 7, 0, 0, 0, 7, 0],\n [7, 0, 7, 0, 7, 7, 2, 7, 7, 7, 7, 7, 7]\n ],\n \"output\": [\n [1, 1, 1, 1, 0, 1],\n [1, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 0, 0, 7, 0, 2, 0, 7, 7, 7, 7, 7],\n [7, 0, 0, 0, 7, 7, 2, 7, 0, 0, 7, 7, 7],\n [0, 7, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0],\n [7, 7, 0, 7, 7, 7, 2, 7, 0, 7, 0, 0, 0],\n [7, 7, 0, 7, 7, 0, 2, 7, 7, 7, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 1],\n [0, 1, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 7, 0, 0, 0, 0, 2, 0, 7, 7, 0, 0, 7],\n [0, 7, 0, 0, 0, 0, 2, 7, 0, 0, 7, 0, 7],\n [7, 7, 7, 0, 0, 7, 2, 0, 7, 7, 7, 0, 7],\n [0, 0, 0, 0, 0, 0, 2, 7, 7, 7, 7, 0, 0],\n [0, 0, 7, 7, 7, 0, 2, 0, 7, 7, 0, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 0, 0, 1], [1, 1, 0, 1, 0, 1], [1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1]], "task_id": "195ba7dc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 2, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2],\n [2, 0, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 0],\n [0, 3, 0],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 0, 3],\n [3, 0, 3],\n [3, 0, 3],\n [3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [6, 6, 6, 6, 6],\n [6, 0, 0, 0, 6],\n [6, 0, 0, 0, 6],\n [6, 0, 0, 0, 6],\n [6, 6, 6, 6, 6]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 0, 0, 8], [8, 8, 8, 8, 8, 8, 8]], "task_id": "fc754716"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 2, 2, 2, 1, 0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 2, 2, 2, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1],\n [0, 0, 0, 0, 0, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 1, 2, 2, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 2, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 2, 1, 0, 1, 3, 1, 1, 1, 3, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 1, 1, 1, 1, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 3, 3, 3, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 3, 1, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 4, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 4, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 1, 1, 1, 3, 1, 0, 0, 0, 1, 4, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 2, 2, 1, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 1, 2, 2, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 3, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 4, 4, 1, 1, 1, 1, 4, 4, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 4, 4, 1, 1, 1, 1, 4, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 4, 4, 1, 0, 0, 1, 3, 1, 1, 1, 3, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 6, 6, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 6, 6, 1, 0, 0, 1, 6, 6, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 6, 6, 1, 1, 1, 1, 6, 6, 1, 1, 1, 6, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 6, 6, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 4, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 6, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0, 1, 4, 4, 4, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 4, 4, 4, 4, 1, 0], [0, 0, 1, 4, 4, 1, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 0], [0, 0, 1, 4, 4, 1, 0, 0, 1, 4, 4, 4, 1, 0, 0, 1, 4, 4, 4, 4, 1, 0, 0, 1, 4, 4, 4, 4, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0, 1, 4, 4, 4, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 4, 4, 4, 4, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 4, 4, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 1, 4, 4, 1, 1, 1, 4, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 4, 4, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 4, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 8, 8, 8, 1, 1, 1, 8, 8, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 1, 8, 8, 8, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0], [0, 1, 8, 1, 1, 1, 8, 1, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 1, 6, 6, 1, 0], [0, 1, 1, 1, 0, 1, 8, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 1, 6, 6, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 1, 6, 6, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 6, 6, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 6, 6, 1, 0], [0, 0, 1, 6, 6, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0]], "task_id": "09c534e7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 4, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 2], [0, 0, 0, 4, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2], [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "ac0c5833"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 3],\n [5, 5, 5],\n [0, 0, 0]\n ],\n \"output\": [\n [2, 2, 3],\n [5, 5, 5],\n [0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 6, 4, 2, 4],\n [8, 4, 3, 3, 4],\n [5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 6, 4, 2, 4],\n [8, 4, 3, 3, 4],\n [5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 9, 9, 6, 1, 8, 4],\n [4, 6, 7, 8, 9, 7, 1],\n [9, 3, 1, 4, 1, 3, 6],\n [5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 9, 9, 6, 1, 8, 4],\n [4, 6, 7, 8, 9, 7, 1],\n [9, 3, 1, 4, 1, 3, 6],\n [5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 1, 2, 8, 4, 9, 8, 2, 1],\n [4, 4, 3, 1, 2, 7, 6, 7, 9],\n [2, 1, 6, 9, 7, 8, 4, 3, 6],\n [9, 8, 6, 3, 4, 2, 9, 1, 7],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 1, 2, 8, 4, 9, 8, 2, 1], [4, 4, 3, 1, 2, 7, 6, 7, 9], [2, 1, 6, 9, 7, 8, 4, 3, 6], [9, 8, 6, 3, 4, 2, 9, 1, 7], [5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0]], "task_id": "27a77e38"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 0, 0, 8, 0, 0, 0, 8, 8, 0, 8, 0],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 8, 8, 8, 8, 8, 0, 8],\n [0, 8, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0],\n [8, 0, 0, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8, 8],\n [0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 8, 0, 0, 8],\n [8, 0, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 8, 0]\n ],\n \"output\": [\n [8, 0, 0, 8, 3, 0, 0, 8, 8, 0, 8, 0],\n [8, 0, 8, 3, 3, 3, 8, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 3, 8, 8, 8, 8, 8, 0, 8],\n [0, 8, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0],\n [8, 0, 0, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8, 8],\n [0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 3, 0, 8, 0, 0, 8],\n [8, 0, 8, 0, 0, 3, 3, 3, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 8, 3, 8, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8],\n [0, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8],\n [8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 8, 0, 0, 0, 8, 0, 0],\n [8, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8],\n [8, 8, 8, 0, 8, 8, 0, 0, 8, 8, 8, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 0],\n [0, 8, 0, 8, 0, 0, 0, 8, 8, 0, 8, 8],\n [8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [8, 0, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 0, 8, 8, 8, 8, 0, 8, 0, 8, 8, 8],\n [0, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8],\n [8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 8, 0, 0, 0, 8, 0, 0],\n [8, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8],\n [8, 8, 8, 0, 8, 8, 0, 0, 8, 8, 8, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 0],\n [0, 8, 0, 8, 0, 3, 0, 8, 8, 0, 8, 8],\n [8, 8, 8, 8, 3, 3, 3, 0, 8, 0, 8, 0],\n [0, 8, 8, 3, 3, 3, 8, 8, 0, 0, 0, 0],\n [8, 0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 0, 0, 0, 8, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8],\n [8, 8, 8, 0, 0, 8, 8, 0, 0, 0, 8, 8],\n [0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 8, 0],\n [8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 0, 8],\n [8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8],\n [8, 8, 0, 0, 0, 8, 0, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 0, 8]\n ],\n \"output\": [\n [8, 8, 0, 0, 0, 8, 0, 0, 0, 3, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 8],\n [8, 8, 8, 0, 0, 8, 8, 3, 3, 3, 8, 8],\n [0, 8, 0, 8, 8, 8, 8, 0, 3, 8, 8, 8],\n [0, 3, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [3, 3, 3, 8, 8, 0, 8, 0, 8, 8, 0, 0],\n [0, 3, 8, 8, 0, 8, 8, 0, 8, 8, 8, 0],\n [8, 8, 8, 3, 8, 8, 8, 8, 0, 8, 0, 8],\n [8, 8, 3, 3, 3, 8, 8, 8, 0, 8, 8, 8],\n [8, 8, 3, 3, 3, 8, 0, 8, 8, 8, 8, 8],\n [8, 0, 0, 3, 0, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 0, 8, 0, 8, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 8, 0, 8, 8, 0, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 0, 8, 8, 0, 8, 8],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 0],\n [0, 8, 0, 8, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 8, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 8],\n [8, 8, 8, 0, 8, 0, 0, 0, 0, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0], [0, 8, 8, 8, 0, 3, 8, 0, 8, 0, 0, 0], [8, 8, 8, 8, 3, 3, 3, 8, 8, 8, 8, 8], [8, 0, 0, 0, 8, 3, 8, 8, 0, 0, 8, 0], [0, 8, 8, 8, 0, 8, 0, 8, 8, 3, 8, 8], [0, 0, 8, 8, 8, 0, 0, 0, 3, 3, 3, 0], [8, 0, 8, 8, 0, 8, 8, 0, 8, 3, 0, 0], [0, 8, 0, 8, 0, 0, 8, 8, 8, 8, 8, 8], [0, 3, 3, 8, 8, 0, 0, 8, 0, 8, 0, 0], [3, 3, 3, 3, 8, 0, 8, 8, 0, 8, 8, 0], [0, 3, 3, 8, 8, 0, 8, 8, 0, 8, 8, 8], [8, 8, 8, 0, 8, 0, 0, 0, 0, 8, 8, 8]], "task_id": "7e02026e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 4, 4, 4, 4],\n [2, 0, 0, 2, 4, 0, 0, 4],\n [2, 0, 0, 2, 4, 0, 0, 4],\n [2, 2, 2, 2, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1],\n [1, 1, 1, 1],\n [2, 2, 2, 2],\n [2, 0, 0, 2],\n [2, 0, 0, 2],\n [2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2],\n [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 2, 0, 0, 2],\n [0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 2, 0, 0, 2],\n [0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2],\n [4, 0, 0, 4, 3, 0, 0, 3, 2, 0, 0, 2],\n [4, 0, 0, 4, 3, 0, 0, 3, 2, 0, 0, 2],\n [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 3, 3], [3, 0, 0, 3], [3, 0, 0, 3], [3, 3, 3, 3], [2, 2, 2, 2], [2, 0, 0, 2], [2, 0, 0, 2], [2, 2, 2, 2], [1, 1, 1, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1, 1, 1, 1]], "task_id": "a680ac02"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 5, 5, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 3, 0, 0, 1, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 4, 5, 2, 0, 0, 0],\n [0, 0, 0, 4, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 4, 5, 2, 0, 0, 0, 0, 0],\n [0, 0, 4, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 5, 5, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 6, 0, 0, 0, 0, 0], [0, 1, 5, 5, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 2, 5, 4, 5, 2, 0, 0, 0], [0, 0, 0, 0, 4, 6, 0, 0, 0, 3, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 5, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0]], "task_id": "ac605cbb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 2],\n [2, 2, 2, 2],\n [2, 0, 0, 2],\n [0, 2, 0, 2]\n ],\n \"output\": [\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2],\n [2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1],\n [0, 1, 1, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0],\n [4, 4, 4, 4],\n [0, 4, 4, 0],\n [4, 4, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 4, 4, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 4, 4, 0],\n [4, 4, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 0, 3],\n [3, 0, 0, 3],\n [0, 0, 0, 3],\n [3, 3, 0, 3]\n ],\n \"output\": [\n [3, 3, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 3, 0, 3],\n [3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 3, 0, 3],\n [3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3],\n [3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3],\n [3, 3, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 3, 0, 3],\n [3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 3, 0, 3]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 0],\n [1, 1, 0, 0],\n [1, 1, 1, 1],\n [1, 0, 0, 1]\n ],\n \"output\": [\n [1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1],\n [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 2],\n [2, 2, 0, 2],\n [2, 2, 0, 0],\n [0, 0, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2, 0, 2], [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2], [2, 2, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2, 0, 2], [2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], "task_id": "5b6cbef5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 7, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [7, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 4, 0],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [0, 4, 0, 0, 8, 9, 0, 0, 0, 7, 0, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 7, 0, 0, 0, 0, 9, 4, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 9, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 0, 0, 4, 4, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 6, 0, 7],\n [7, 0, 0, 0, 0, 2, 0, 9, 0, 4, 0, 0, 0, 0, 6, 0, 9],\n [0, 0, 0, 3, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 6, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 4, 3, 0, 0, 0, 6, 4, 0],\n [6, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 4, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 2, 0, 0, 6, 0, 0],\n [0, 8, 0, 0, 0, 2, 0, 0, 0, 4, 7, 0, 0, 0, 6, 0, 0],\n [0, 0, 6, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 6],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 2, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 6],\n [0, 0, 0, 8, 4, 0, 8, 0, 6, 0, 0, 2, 0],\n [0, 0, 7, 0, 4, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 4, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 6, 1, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 3, 0, 6, 0, 0, 0, 0],\n [0, 3, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 2],\n [0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 2, 3, 0, 9, 0, 4, 0, 0, 0, 3, 0, 0, 8],\n [0, 0, 3, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 9, 4, 9, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 1, 0, 1, 0, 6, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 3, 0, 0, 3, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 1, 9, 0, 0, 0, 7, 0, 0, 1, 2, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 6, 0, 0, 4, 9, 0, 3, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 2, 3, 0, 9, 8, 4, 0, 3, 0, 3, 0, 0, 8], [0, 0, 3, 0, 0, 8, 4, 4, 8, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 1, 8, 0, 0, 3, 9, 4, 9, 0, 0], [0, 0, 0, 6, 0, 8, 0, 0, 1, 0, 1, 3, 6, 0, 0, 0, 7], [0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 5, 3, 0, 1, 3, 0, 7, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 5, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 5, 1, 9, 1, 0, 0, 7, 0, 0, 1, 2, 0], [0, 0, 0, 3, 0, 5, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 5, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 6, 0, 9, 4, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 6, 0, 0, 4, 0, 2, 0, 0, 0], [0, 0, 0, 1, 0, 5, 0, 0, 6, 0, 0, 4, 9, 0, 3, 0, 3], [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0]], "task_id": "17b80ad2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 6, 6, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [6, 6, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 1, 1, 1, 0, 0, 0],\n [6, 6, 0, 1, 1, 1, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 3, 3, 3],\n [0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [3, 3, 3, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 0, 4, 4, 4, 0, 0],\n [3, 3, 0, 0, 0, 0, 4, 4, 0, 0],\n [3, 3, 0, 0, 0, 0, 4, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 4, 0, 0, 0],\n [8, 0, 0, 0, 4, 4, 4, 4, 0, 0],\n [8, 8, 8, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 4, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 4, 0, 0],\n [8, 0, 0, 0, 0, 4, 4, 4, 4, 0],\n [8, 8, 8, 0, 0, 0, 0, 4, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 9, 9, 9, 0],\n [7, 7, 7, 7, 0, 0, 9, 9, 9, 0],\n [0, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 9, 9, 9, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 9, 9, 0, 0],\n [0, 7, 7, 0, 0, 9, 9, 9, 0, 0],\n [7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 9, 9, 9, 9, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 2, 2, 2, 0],\n [0, 0, 0, 3, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 0, 0, 0, 3, 3, 0], [0, 0, 0, 2, 0, 3, 3, 3, 3, 0], [0, 0, 2, 2, 0, 0, 0, 3, 0, 0]], "task_id": "4acc7107"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 2, 0, 3, 3, 0],\n [1, 2, 1, 3, 3, 1],\n [1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 0],\n [0, 0, 0, 0, 0, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 8, 0, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 2, 2, 2, 0],\n [8, 5, 5, 8, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0],\n [3, 0, 3, 3, 3],\n [3, 3, 3, 3, 3]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0],\n [3, 8, 3, 3, 3],\n [3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 6, 6, 0, 0, 7, 7, 7, 0, 0],\n [1, 5, 1, 6, 6, 1, 1, 7, 7, 7, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 1, 1, 0, 2, 2, 2, 2, 0], [5, 6, 5, 1, 1, 5, 2, 2, 2, 2, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]], "task_id": "67c52801"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 5, 5, 5, 0, 0, 0],\n [0, 5, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 1, 1, 5, 0, 0, 0],\n [0, 5, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 5, 5, 0, 0, 5, 0],\n [5, 0, 0, 0, 5, 5, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 1, 5],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 1, 1, 5, 0, 0, 0],\n [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 1, 0, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 5, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [5, 0, 0, 0, 5, 5, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [1, 0, 0, 0, 1, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 1, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 5, 0, 0], [0, 0, 5, 1, 1, 1, 1, 0, 0, 0]], "task_id": "ce039d91"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 2, 0, 0],\n [2, 2, 2, 2, 0],\n [0, 0, 2, 2, 0],\n [0, 0, 2, 2, 0],\n [4, 4, 4, 4, 4],\n [1, 0, 0, 1, 0],\n [1, 1, 1, 0, 1],\n [0, 0, 1, 1, 1],\n [1, 1, 1, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 0],\n [3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3],\n [3, 3, 3, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0],\n [0, 2, 0, 2, 2],\n [2, 2, 2, 2, 2],\n [4, 4, 4, 4, 4],\n [0, 1, 1, 0, 0],\n [1, 1, 0, 1, 0],\n [1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1]\n ],\n \"output\": [\n [3, 3, 3, 3, 3],\n [3, 3, 0, 3, 0],\n [3, 3, 0, 3, 3],\n [3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0],\n [0, 2, 0, 0, 0],\n [2, 2, 2, 0, 2],\n [4, 4, 4, 4, 4],\n [1, 1, 0, 0, 1],\n [1, 1, 0, 1, 1],\n [1, 0, 1, 0, 1],\n [0, 1, 0, 1, 1]\n ],\n \"output\": [\n [3, 3, 0, 0, 3],\n [3, 3, 3, 3, 3],\n [3, 3, 3, 0, 3],\n [3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 2],\n [0, 2, 2, 0, 2],\n [0, 0, 0, 2, 2],\n [0, 0, 2, 2, 0],\n [4, 4, 4, 4, 4],\n [1, 0, 1, 0, 0],\n [1, 0, 0, 1, 0],\n [0, 0, 0, 0, 1],\n [0, 1, 1, 1, 0]\n ],\n \"output\": [\n [3, 3, 3, 0, 3],\n [3, 3, 3, 3, 3],\n [0, 0, 0, 3, 3],\n [0, 3, 3, 3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 2, 2, 2],\n [0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0],\n [2, 2, 2, 0, 2],\n [4, 4, 4, 4, 4],\n [0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1],\n [1, 0, 0, 1, 1],\n [0, 0, 0, 0, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 3, 3, 3, 3], [0, 0, 0, 3, 3], [3, 3, 0, 3, 3], [3, 3, 3, 0, 3]], "task_id": "506d28a5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 0, 5, 5, 0, 8, 0, 0, 0, 0],\n [0, 2, 2, 0, 8, 0, 0, 5, 0, 8, 0, 0, 0, 0],\n [0, 2, 2, 0, 8, 5, 5, 5, 5, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 5, 0, 0, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 3, 3, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 3, 3, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 1, 1, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 1, 1, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2, 0, 8, 0, 2, 2, 0, 8, 0, 2, 2, 0],\n [0, 0, 2, 0, 8, 0, 0, 2, 0, 8, 0, 0, 2, 0],\n [2, 2, 2, 2, 8, 2, 2, 2, 2, 8, 2, 2, 2, 2],\n [0, 2, 0, 0, 8, 0, 2, 0, 0, 8, 0, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 3, 3, 0, 8, 0, 3, 3, 0, 8, 0, 3, 3, 0],\n [0, 0, 3, 0, 8, 0, 0, 3, 0, 8, 0, 0, 3, 0],\n [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3],\n [0, 3, 0, 0, 8, 0, 3, 0, 0, 8, 0, 3, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 1, 1, 0, 8, 0, 1, 1, 0, 8, 0, 1, 1, 0],\n [0, 0, 1, 0, 8, 0, 0, 1, 0, 8, 0, 0, 1, 0],\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1],\n [0, 1, 0, 0, 8, 0, 1, 0, 0, 8, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 4, 4, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 4, 4, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 2, 2, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 2, 2, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 8, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 8, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 1, 1, 0, 3, 6, 6, 0, 6, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 1, 1, 0, 3, 0, 6, 6, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 6, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [4, 4, 0, 4, 3, 4, 4, 0, 4, 3, 4, 4, 0, 4, 3, 4, 4, 0, 4],\n [0, 4, 4, 0, 3, 0, 4, 4, 0, 3, 0, 4, 4, 0, 3, 0, 4, 4, 0],\n [0, 0, 4, 0, 3, 0, 0, 4, 0, 3, 0, 0, 4, 0, 3, 0, 0, 4, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [2, 2, 0, 2, 3, 2, 2, 0, 2, 3, 2, 2, 0, 2, 3, 2, 2, 0, 2],\n [0, 2, 2, 0, 3, 0, 2, 2, 0, 3, 0, 2, 2, 0, 3, 0, 2, 2, 0],\n [0, 0, 2, 0, 3, 0, 0, 2, 0, 3, 0, 0, 2, 0, 3, 0, 0, 2, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [8, 8, 0, 8, 3, 8, 8, 0, 8, 3, 8, 8, 0, 8, 3, 8, 8, 0, 8],\n [0, 8, 8, 0, 3, 0, 8, 8, 0, 3, 0, 8, 8, 0, 3, 0, 8, 8, 0],\n [0, 0, 8, 0, 3, 0, 0, 8, 0, 3, 0, 0, 8, 0, 3, 0, 0, 8, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [1, 1, 0, 1, 3, 1, 1, 0, 1, 3, 1, 1, 0, 1, 3, 1, 1, 0, 1],\n [0, 1, 1, 0, 3, 0, 1, 1, 0, 3, 0, 1, 1, 0, 3, 0, 1, 1, 0],\n [0, 0, 1, 0, 3, 0, 0, 1, 0, 3, 0, 0, 1, 0, 3, 0, 0, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 2, 2, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 2, 2, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 1, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 3, 3, 0, 5, 0, 0, 0, 0, 5, 1, 1, 1, 1, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 3, 3, 0, 5, 0, 0, 0, 0, 5, 0, 1, 1, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 1, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 4, 4, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 4, 4, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 6, 6, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 6, 6, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 7, 7, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 7, 7, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0], [2, 2, 2, 2, 5, 2, 2, 2, 2, 5, 2, 2, 2, 2, 5, 2, 2, 2, 2, 5, 2, 2, 2, 2], [0, 2, 2, 0, 5, 0, 2, 2, 0, 5, 0, 2, 2, 0, 5, 0, 2, 2, 0, 5, 0, 2, 2, 0], [0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0, 5, 0, 0, 2, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0], [3, 3, 3, 3, 5, 3, 3, 3, 3, 5, 3, 3, 3, 3, 5, 3, 3, 3, 3, 5, 3, 3, 3, 3], [0, 3, 3, 0, 5, 0, 3, 3, 0, 5, 0, 3, 3, 0, 5, 0, 3, 3, 0, 5, 0, 3, 3, 0], [0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0, 5, 0, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0], [4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4], [0, 4, 4, 0, 5, 0, 4, 4, 0, 5, 0, 4, 4, 0, 5, 0, 4, 4, 0, 5, 0, 4, 4, 0], [0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0, 5, 0, 0, 4, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0], [6, 6, 6, 6, 5, 6, 6, 6, 6, 5, 6, 6, 6, 6, 5, 6, 6, 6, 6, 5, 6, 6, 6, 6], [0, 6, 6, 0, 5, 0, 6, 6, 0, 5, 0, 6, 6, 0, 5, 0, 6, 6, 0, 5, 0, 6, 6, 0], [0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0, 5, 0, 0, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0], [7, 7, 7, 7, 5, 7, 7, 7, 7, 5, 7, 7, 7, 7, 5, 7, 7, 7, 7, 5, 7, 7, 7, 7], [0, 7, 7, 0, 5, 0, 7, 7, 0, 5, 0, 7, 7, 0, 5, 0, 7, 7, 0, 5, 0, 7, 7, 0], [0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0, 5, 0, 0, 7, 0]], "task_id": "5a5a2103"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 2, 0],\n [2, 0, 0, 0],\n [0, 2, 0, 2],\n [2, 2, 2, 2],\n [0, 0, 2, 0],\n [0, 0, 2, 2],\n [7, 7, 7, 7],\n [0, 6, 6, 0],\n [0, 0, 0, 0],\n [6, 6, 6, 6],\n [6, 6, 0, 6],\n [0, 6, 6, 6],\n [0, 0, 6, 0]\n ],\n \"output\": [\n [8, 0, 0, 8],\n [0, 8, 8, 8],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [8, 0, 0, 0],\n [8, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 2],\n [2, 0, 2, 2],\n [2, 2, 0, 0],\n [0, 2, 0, 2],\n [0, 2, 2, 0],\n [2, 0, 0, 2],\n [7, 7, 7, 7],\n [6, 0, 6, 6],\n [0, 6, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 6],\n [6, 6, 0, 0],\n [6, 0, 6, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 8, 8],\n [8, 0, 8, 0],\n [0, 0, 0, 8],\n [0, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2],\n [2, 0, 0, 0],\n [0, 2, 2, 2],\n [0, 0, 0, 2],\n [2, 0, 2, 0],\n [0, 2, 2, 0],\n [7, 7, 7, 7],\n [6, 0, 6, 6],\n [6, 0, 0, 6],\n [0, 6, 6, 6],\n [6, 0, 0, 0],\n [6, 0, 0, 6],\n [0, 0, 6, 0]\n ],\n \"output\": [\n [0, 8, 0, 0],\n [0, 8, 8, 0],\n [8, 0, 0, 0],\n [0, 8, 8, 0],\n [0, 8, 0, 0],\n [8, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 0],\n [0, 2, 2, 0],\n [2, 2, 0, 0],\n [2, 0, 0, 0],\n [0, 0, 0, 2],\n [2, 2, 0, 0],\n [7, 7, 7, 7],\n [6, 6, 6, 6],\n [6, 0, 6, 6],\n [6, 6, 0, 0],\n [0, 0, 0, 0],\n [6, 6, 0, 0],\n [0, 0, 6, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 8, 8],\n [0, 8, 8, 8],\n [0, 0, 8, 0],\n [0, 0, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 2],\n [0, 2, 2, 0],\n [2, 0, 0, 2],\n [0, 2, 2, 0],\n [2, 0, 2, 2],\n [0, 0, 0, 2],\n [7, 7, 7, 7],\n [6, 6, 0, 6],\n [6, 6, 6, 0],\n [0, 0, 0, 0],\n [6, 6, 0, 6],\n [6, 0, 6, 0],\n [0, 0, 6, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0], [0, 0, 0, 8], [0, 8, 8, 0], [0, 0, 0, 0], [0, 8, 0, 0], [8, 8, 0, 0]], "task_id": "0c9aba6e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 1, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 1, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 1, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 6, 8, 8, 8, 1, 8, 8, 8, 8],\n [8, 8, 8, 6, 8, 1, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 1, 8, 6, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8, 6, 8, 8, 8, 8],\n [8, 1, 8, 8, 8, 8, 8, 6, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4],\n [4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 1, 4, 4, 4, 4, 6, 4, 4, 4, 4],\n [4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5],\n [5, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 1, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 6, 5, 5, 6, 5, 5],\n [5, 1, 5, 5, 5, 6, 5, 5, 5, 5, 5, 6],\n [5, 5, 1, 5, 6, 5, 5, 5, 5, 5, 6, 5],\n [5, 5, 5, 6, 5, 5, 5, 5, 5, 6, 5, 5],\n [5, 5, 6, 5, 1, 5, 5, 5, 6, 5, 5, 5],\n [5, 6, 5, 5, 5, 1, 5, 6, 5, 5, 5, 5],\n [6, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 6, 5, 1, 5, 5, 5, 5],\n [5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 6, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5],\n [6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8],\n [8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 6],\n [8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8],\n [8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8],\n [8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 6, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 6, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 6, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8],\n [8, 6, 8, 8, 8, 8, 8, 8, 6, 8, 1, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 1, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 1, 6, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 6, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 6, 4, 4, 4, 4], [4, 4, 1, 4, 4, 4, 6, 4, 4, 4, 6, 4, 4, 4, 4, 4], [4, 4, 4, 1, 4, 6, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 6, 4, 4, 4, 6, 4, 4, 4, 4, 4, 1, 4], [4, 4, 4, 6, 4, 1, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 6, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 6, 4, 4, 4, 6, 4, 1, 4, 4, 4, 4, 4, 4, 6, 4], [6, 4, 4, 4, 6, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 6, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4], [4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]], "task_id": "55783887"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 8, 1, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 8, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 4, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 8, 1, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 1, 8, 0, 0, 0, 0],\n [0, 0, 0, 1, 8, 8, 0, 0, 0, 0],\n [0, 4, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 1, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 4, 0, 0],\n [0, 0, 0, 0, 1, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "ecaa0ec1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 9, 6, 4, 4, 5, 4, 9, 9, 8, 7, 1, 1, 7, 8, 9, 9, 4, 5, 4, 4, 6, 9, 6],\n [9, 7, 6, 6, 7, 7, 9, 8, 9, 4, 9, 8, 8, 9, 4, 9, 8, 9, 7, 7, 6, 6, 7, 9],\n [6, 6, 9, 5, 4, 9, 9, 9, 4, 4, 9, 9, 9, 9, 4, 4, 9, 9, 9, 4, 5, 9, 6, 6],\n [4, 6, 5, 4, 5, 9, 8, 4, 4, 1, 4, 9, 9, 4, 1, 4, 4, 8, 9, 5, 4, 5, 6, 4],\n [4, 7, 4, 5, 7, 7, 7, 9, 9, 4, 8, 7, 7, 8, 4, 9, 9, 7, 7, 7, 5, 4, 7, 4],\n [5, 7, 9, 9, 7, 9, 1, 8, 9, 9, 7, 7, 7, 7, 9, 9, 8, 1, 9, 7, 9, 9, 7, 5],\n [4, 9, 9, 8, 7, 1, 1, 9, 9, 6, 7, 4, 4, 7, 6, 9, 9, 1, 1, 7, 8, 9, 9, 4],\n [9, 8, 9, 4, 9, 8, 9, 1, 6, 9, 7, 6, 6, 7, 9, 6, 1, 9, 8, 9, 4, 9, 8, 9],\n [9, 9, 4, 4, 9, 9, 9, 6, 1, 7, 7, 9, 9, 7, 7, 1, 6, 9, 9, 9, 4, 4, 9, 9],\n [8, 4, 4, 1, 4, 9, 6, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 6, 9, 4, 1, 4, 4, 8],\n [7, 9, 9, 4, 8, 7, 7, 7, 7, 9, 6, 7, 7, 6, 9, 7, 7, 7, 7, 8, 4, 9, 9, 7],\n [1, 8, 9, 9, 7, 7, 4, 6, 9, 6, 7, 1, 1, 7, 6, 2, 2, 4, 7, 7, 9, 9, 8, 1],\n [1, 8, 9, 9, 7, 7, 4, 6, 9, 6, 7, 1, 1, 7, 6, 2, 2, 4, 2, 2, 2, 9, 8, 1],\n [7, 9, 9, 4, 8, 7, 7, 7, 7, 9, 6, 7, 7, 6, 9, 2, 2, 7, 2, 2, 2, 9, 9, 7],\n [8, 4, 4, 1, 4, 9, 6, 9, 7, 6, 9, 6, 6, 9, 2, 2, 2, 2, 2, 2, 2, 4, 4, 8],\n [9, 9, 4, 4, 9, 9, 9, 6, 1, 7, 7, 9, 9, 7, 2, 2, 2, 2, 2, 2, 2, 4, 9, 9],\n [9, 8, 9, 4, 9, 8, 9, 1, 6, 9, 7, 6, 6, 7, 9, 2, 2, 9, 8, 9, 4, 9, 8, 9],\n [4, 9, 9, 8, 7, 1, 1, 9, 9, 6, 7, 4, 4, 7, 6, 9, 9, 1, 1, 7, 8, 9, 9, 4],\n [5, 7, 9, 9, 7, 9, 1, 8, 9, 2, 2, 2, 2, 2, 2, 9, 8, 1, 9, 7, 9, 9, 7, 5],\n [4, 7, 4, 5, 7, 7, 7, 9, 9, 2, 2, 2, 2, 2, 2, 9, 9, 7, 7, 7, 5, 4, 7, 4],\n [4, 6, 5, 4, 5, 9, 8, 4, 4, 2, 2, 2, 2, 2, 2, 4, 4, 8, 9, 5, 4, 5, 6, 4],\n [6, 6, 9, 5, 4, 9, 9, 9, 4, 2, 2, 2, 2, 2, 2, 4, 9, 9, 9, 4, 5, 9, 6, 6],\n [9, 7, 6, 6, 7, 7, 9, 8, 9, 2, 2, 2, 2, 2, 2, 9, 8, 9, 7, 7, 6, 6, 7, 9],\n [6, 9, 6, 4, 4, 5, 4, 9, 9, 8, 7, 1, 1, 7, 8, 9, 9, 4, 5, 4, 4, 6, 9, 6]\n ],\n \"output\": [\n [6, 9, 6, 4, 4, 5, 4, 9, 9, 8, 7, 1, 1, 7, 8, 9, 9, 4, 5, 4, 4, 6, 9, 6],\n [9, 7, 6, 6, 7, 7, 9, 8, 9, 4, 9, 8, 8, 9, 4, 9, 8, 9, 7, 7, 6, 6, 7, 9],\n [6, 6, 9, 5, 4, 9, 9, 9, 4, 4, 9, 9, 9, 9, 4, 4, 9, 9, 9, 4, 5, 9, 6, 6],\n [4, 6, 5, 4, 5, 9, 8, 4, 4, 1, 4, 9, 9, 4, 1, 4, 4, 8, 9, 5, 4, 5, 6, 4],\n [4, 7, 4, 5, 7, 7, 7, 9, 9, 4, 8, 7, 7, 8, 4, 9, 9, 7, 7, 7, 5, 4, 7, 4],\n [5, 7, 9, 9, 7, 9, 1, 8, 9, 9, 7, 7, 7, 7, 9, 9, 8, 1, 9, 7, 9, 9, 7, 5],\n [4, 9, 9, 8, 7, 1, 1, 9, 9, 6, 7, 4, 4, 7, 6, 9, 9, 1, 1, 7, 8, 9, 9, 4],\n [9, 8, 9, 4, 9, 8, 9, 1, 6, 9, 7, 6, 6, 7, 9, 6, 1, 9, 8, 9, 4, 9, 8, 9],\n [9, 9, 4, 4, 9, 9, 9, 6, 1, 7, 7, 9, 9, 7, 7, 1, 6, 9, 9, 9, 4, 4, 9, 9],\n [8, 4, 4, 1, 4, 9, 6, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 6, 9, 4, 1, 4, 4, 8],\n [7, 9, 9, 4, 8, 7, 7, 7, 7, 9, 6, 7, 7, 6, 9, 7, 7, 7, 7, 8, 4, 9, 9, 7],\n [1, 8, 9, 9, 7, 7, 4, 6, 9, 6, 7, 1, 1, 7, 6, 9, 6, 4, 7, 7, 9, 9, 8, 1],\n [1, 8, 9, 9, 7, 7, 4, 6, 9, 6, 7, 1, 1, 7, 6, 9, 6, 4, 7, 7, 9, 9, 8, 1],\n [7, 9, 9, 4, 8, 7, 7, 7, 7, 9, 6, 7, 7, 6, 9, 7, 7, 7, 7, 8, 4, 9, 9, 7],\n [8, 4, 4, 1, 4, 9, 6, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 6, 9, 4, 1, 4, 4, 8],\n [9, 9, 4, 4, 9, 9, 9, 6, 1, 7, 7, 9, 9, 7, 7, 1, 6, 9, 9, 9, 4, 4, 9, 9],\n [9, 8, 9, 4, 9, 8, 9, 1, 6, 9, 7, 6, 6, 7, 9, 6, 1, 9, 8, 9, 4, 9, 8, 9],\n [4, 9, 9, 8, 7, 1, 1, 9, 9, 6, 7, 4, 4, 7, 6, 9, 9, 1, 1, 7, 8, 9, 9, 4],\n [5, 7, 9, 9, 7, 9, 1, 8, 9, 9, 7, 7, 7, 7, 9, 9, 8, 1, 9, 7, 9, 9, 7, 5],\n [4, 7, 4, 5, 7, 7, 7, 9, 9, 4, 8, 7, 7, 8, 4, 9, 9, 7, 7, 7, 5, 4, 7, 4],\n [4, 6, 5, 4, 5, 9, 8, 4, 4, 1, 4, 9, 9, 4, 1, 4, 4, 8, 9, 5, 4, 5, 6, 4],\n [6, 6, 9, 5, 4, 9, 9, 9, 4, 4, 9, 9, 9, 9, 4, 4, 9, 9, 9, 4, 5, 9, 6, 6],\n [9, 7, 6, 6, 7, 7, 9, 8, 9, 4, 9, 8, 8, 9, 4, 9, 8, 9, 7, 7, 6, 6, 7, 9],\n [6, 9, 6, 4, 4, 5, 4, 9, 9, 8, 7, 1, 1, 7, 8, 9, 9, 4, 5, 4, 4, 6, 9, 6]\n ]\n}\n\n{\n \"input\": [\n [9, 5, 4, 1, 4, 6, 7, 1, 5, 7, 1, 7, 7, 1, 7, 5, 1, 7, 6, 4, 1, 4, 5, 9],\n [5, 4, 4, 4, 9, 9, 1, 7, 1, 6, 7, 5, 5, 7, 6, 1, 7, 1, 9, 9, 4, 4, 4, 5],\n [4, 4, 6, 5, 6, 4, 5, 1, 6, 7, 6, 4, 4, 6, 7, 6, 1, 5, 4, 6, 5, 6, 4, 4],\n [1, 4, 5, 1, 4, 4, 2, 2, 7, 5, 7, 6, 6, 7, 5, 7, 6, 7, 4, 4, 1, 5, 4, 1],\n [4, 9, 6, 4, 6, 6, 2, 2, 6, 7, 6, 6, 6, 6, 7, 6, 7, 1, 6, 6, 4, 6, 9, 4],\n [6, 9, 4, 4, 6, 4, 2, 2, 4, 6, 6, 1, 1, 6, 6, 4, 5, 7, 4, 6, 4, 4, 9, 6],\n [2, 2, 2, 2, 2, 7, 2, 2, 8, 5, 3, 6, 6, 3, 5, 8, 8, 5, 7, 1, 7, 5, 1, 7],\n [2, 2, 2, 2, 2, 5, 8, 6, 6, 3, 3, 8, 8, 3, 3, 6, 6, 8, 5, 7, 6, 1, 7, 1],\n [5, 1, 6, 7, 6, 4, 8, 6, 5, 5, 8, 6, 6, 8, 5, 5, 6, 8, 4, 6, 7, 6, 1, 5],\n [7, 6, 7, 5, 7, 6, 5, 3, 5, 5, 8, 8, 8, 8, 5, 5, 3, 5, 6, 7, 5, 7, 6, 7],\n [1, 7, 6, 7, 6, 6, 3, 3, 8, 8, 5, 5, 5, 5, 8, 8, 3, 3, 6, 6, 7, 6, 7, 1],\n [7, 5, 4, 6, 6, 1, 6, 8, 6, 8, 5, 8, 8, 5, 8, 6, 8, 6, 1, 6, 6, 4, 5, 7],\n [7, 5, 4, 6, 6, 1, 6, 8, 6, 8, 5, 8, 8, 5, 8, 6, 8, 6, 1, 6, 6, 4, 5, 7],\n [1, 7, 6, 7, 6, 6, 3, 3, 8, 8, 5, 5, 5, 5, 8, 8, 3, 3, 6, 6, 7, 6, 7, 1],\n [7, 6, 7, 5, 7, 6, 5, 3, 5, 5, 8, 8, 8, 8, 5, 5, 3, 5, 6, 7, 5, 7, 6, 7],\n [5, 1, 6, 7, 6, 4, 8, 6, 5, 5, 8, 6, 6, 8, 5, 5, 6, 8, 4, 6, 7, 6, 1, 5],\n [1, 7, 1, 6, 7, 5, 8, 6, 6, 3, 3, 8, 8, 3, 3, 6, 6, 8, 5, 7, 6, 1, 7, 1],\n [7, 1, 5, 7, 1, 7, 5, 8, 8, 5, 3, 6, 6, 3, 5, 8, 8, 5, 7, 1, 7, 5, 1, 7],\n [6, 9, 4, 4, 6, 4, 7, 5, 4, 6, 6, 1, 1, 6, 6, 4, 5, 7, 4, 6, 4, 4, 9, 6],\n [4, 9, 6, 4, 6, 6, 1, 7, 6, 7, 6, 6, 6, 6, 7, 6, 7, 1, 6, 6, 4, 6, 9, 4],\n [1, 4, 5, 1, 4, 4, 7, 6, 7, 5, 7, 6, 6, 7, 5, 7, 6, 7, 4, 4, 1, 5, 4, 1],\n [4, 4, 6, 5, 6, 4, 5, 1, 6, 7, 6, 4, 4, 6, 7, 6, 1, 5, 4, 6, 5, 6, 4, 4],\n [5, 4, 4, 4, 9, 9, 1, 7, 1, 6, 7, 5, 5, 7, 6, 1, 7, 1, 9, 9, 4, 4, 4, 5],\n [9, 5, 4, 1, 4, 6, 7, 1, 5, 7, 1, 7, 7, 1, 7, 5, 1, 7, 6, 4, 1, 4, 5, 9]\n ],\n \"output\": [\n [9, 5, 4, 1, 4, 6, 7, 1, 5, 7, 1, 7, 7, 1, 7, 5, 1, 7, 6, 4, 1, 4, 5, 9],\n [5, 4, 4, 4, 9, 9, 1, 7, 1, 6, 7, 5, 5, 7, 6, 1, 7, 1, 9, 9, 4, 4, 4, 5],\n [4, 4, 6, 5, 6, 4, 5, 1, 6, 7, 6, 4, 4, 6, 7, 6, 1, 5, 4, 6, 5, 6, 4, 4],\n [1, 4, 5, 1, 4, 4, 7, 6, 7, 5, 7, 6, 6, 7, 5, 7, 6, 7, 4, 4, 1, 5, 4, 1],\n [4, 9, 6, 4, 6, 6, 1, 7, 6, 7, 6, 6, 6, 6, 7, 6, 7, 1, 6, 6, 4, 6, 9, 4],\n [6, 9, 4, 4, 6, 4, 7, 5, 4, 6, 6, 1, 1, 6, 6, 4, 5, 7, 4, 6, 4, 4, 9, 6],\n [7, 1, 5, 7, 1, 7, 5, 8, 8, 5, 3, 6, 6, 3, 5, 8, 8, 5, 7, 1, 7, 5, 1, 7],\n [1, 7, 1, 6, 7, 5, 8, 6, 6, 3, 3, 8, 8, 3, 3, 6, 6, 8, 5, 7, 6, 1, 7, 1],\n [5, 1, 6, 7, 6, 4, 8, 6, 5, 5, 8, 6, 6, 8, 5, 5, 6, 8, 4, 6, 7, 6, 1, 5],\n [7, 6, 7, 5, 7, 6, 5, 3, 5, 5, 8, 8, 8, 8, 5, 5, 3, 5, 6, 7, 5, 7, 6, 7],\n [1, 7, 6, 7, 6, 6, 3, 3, 8, 8, 5, 5, 5, 5, 8, 8, 3, 3, 6, 6, 7, 6, 7, 1],\n [7, 5, 4, 6, 6, 1, 6, 8, 6, 8, 5, 8, 8, 5, 8, 6, 8, 6, 1, 6, 6, 4, 5, 7],\n [7, 5, 4, 6, 6, 1, 6, 8, 6, 8, 5, 8, 8, 5, 8, 6, 8, 6, 1, 6, 6, 4, 5, 7],\n [1, 7, 6, 7, 6, 6, 3, 3, 8, 8, 5, 5, 5, 5, 8, 8, 3, 3, 6, 6, 7, 6, 7, 1],\n [7, 6, 7, 5, 7, 6, 5, 3, 5, 5, 8, 8, 8, 8, 5, 5, 3, 5, 6, 7, 5, 7, 6, 7],\n [5, 1, 6, 7, 6, 4, 8, 6, 5, 5, 8, 6, 6, 8, 5, 5, 6, 8, 4, 6, 7, 6, 1, 5],\n [1, 7, 1, 6, 7, 5, 8, 6, 6, 3, 3, 8, 8, 3, 3, 6, 6, 8, 5, 7, 6, 1, 7, 1],\n [7, 1, 5, 7, 1, 7, 5, 8, 8, 5, 3, 6, 6, 3, 5, 8, 8, 5, 7, 1, 7, 5, 1, 7],\n [6, 9, 4, 4, 6, 4, 7, 5, 4, 6, 6, 1, 1, 6, 6, 4, 5, 7, 4, 6, 4, 4, 9, 6],\n [4, 9, 6, 4, 6, 6, 1, 7, 6, 7, 6, 6, 6, 6, 7, 6, 7, 1, 6, 6, 4, 6, 9, 4],\n [1, 4, 5, 1, 4, 4, 7, 6, 7, 5, 7, 6, 6, 7, 5, 7, 6, 7, 4, 4, 1, 5, 4, 1],\n [4, 4, 6, 5, 6, 4, 5, 1, 6, 7, 6, 4, 4, 6, 7, 6, 1, 5, 4, 6, 5, 6, 4, 4],\n [5, 4, 4, 4, 9, 9, 1, 7, 1, 6, 7, 5, 5, 7, 6, 1, 7, 1, 9, 9, 4, 4, 4, 5],\n [9, 5, 4, 1, 4, 6, 7, 1, 5, 7, 1, 7, 7, 1, 7, 5, 1, 7, 6, 4, 1, 4, 5, 9]\n ]\n}\n\n{\n \"input\": [\n [7, 9, 5, 5, 3, 5, 6, 9, 9, 9, 5, 6, 6, 5, 9, 9, 9, 6, 5, 3, 5, 5, 9, 7],\n [9, 7, 5, 1, 5, 3, 9, 3, 8, 6, 6, 2, 2, 2, 6, 8, 3, 9, 3, 5, 1, 5, 7, 9],\n [5, 5, 1, 3, 9, 3, 9, 8, 9, 5, 3, 2, 2, 2, 5, 9, 8, 9, 3, 9, 3, 1, 5, 5],\n [5, 1, 3, 7, 9, 9, 9, 6, 5, 9, 6, 2, 2, 2, 9, 5, 6, 9, 9, 9, 7, 3, 1, 5],\n [2, 2, 2, 2, 2, 9, 5, 6, 3, 6, 3, 2, 2, 2, 6, 3, 6, 5, 9, 3, 9, 9, 5, 3],\n [2, 2, 2, 2, 2, 3, 6, 6, 3, 9, 8, 2, 2, 2, 9, 3, 6, 6, 3, 9, 9, 3, 3, 5],\n [2, 2, 2, 2, 2, 6, 9, 7, 9, 9, 4, 2, 2, 2, 9, 9, 7, 9, 6, 5, 9, 9, 9, 6],\n [2, 2, 2, 2, 2, 6, 7, 9, 9, 1, 1, 4, 4, 1, 1, 9, 9, 7, 6, 6, 6, 8, 3, 9],\n [2, 2, 2, 2, 2, 3, 9, 9, 1, 7, 4, 3, 3, 4, 7, 1, 9, 9, 3, 3, 5, 9, 8, 9],\n [2, 2, 2, 2, 2, 9, 9, 1, 7, 1, 9, 7, 7, 9, 1, 7, 2, 2, 2, 2, 9, 5, 6, 9],\n [5, 6, 3, 6, 3, 8, 4, 1, 4, 9, 3, 9, 9, 3, 9, 4, 2, 2, 2, 2, 6, 3, 6, 5],\n [6, 6, 3, 9, 8, 9, 4, 4, 3, 7, 9, 7, 7, 9, 7, 3, 2, 2, 2, 2, 9, 3, 6, 6],\n [6, 6, 3, 9, 8, 9, 4, 4, 3, 7, 9, 7, 7, 9, 7, 3, 2, 2, 2, 2, 9, 3, 6, 6],\n [5, 6, 3, 6, 3, 8, 2, 2, 2, 2, 2, 2, 9, 3, 9, 4, 2, 2, 2, 2, 6, 3, 6, 5],\n [9, 6, 5, 9, 6, 9, 2, 2, 2, 2, 2, 2, 7, 9, 1, 7, 2, 2, 2, 2, 9, 5, 6, 9],\n [9, 8, 9, 5, 3, 3, 2, 2, 2, 2, 2, 2, 3, 4, 7, 1, 9, 9, 3, 3, 5, 9, 8, 9],\n [9, 3, 8, 6, 6, 6, 2, 2, 2, 2, 2, 2, 4, 1, 1, 9, 9, 7, 6, 6, 6, 8, 3, 9],\n [6, 9, 9, 9, 5, 6, 9, 7, 9, 9, 4, 4, 4, 4, 9, 9, 7, 9, 6, 5, 9, 9, 9, 6],\n [5, 3, 3, 9, 9, 3, 6, 6, 3, 9, 8, 9, 9, 8, 9, 3, 6, 6, 3, 9, 9, 3, 3, 5],\n [3, 5, 9, 9, 3, 9, 5, 6, 3, 6, 3, 8, 8, 3, 6, 3, 6, 5, 9, 3, 9, 9, 5, 3],\n [5, 1, 3, 7, 9, 9, 9, 6, 5, 9, 6, 9, 9, 6, 9, 5, 6, 9, 9, 9, 7, 3, 1, 5],\n [5, 5, 1, 3, 9, 3, 9, 8, 9, 5, 3, 3, 3, 3, 5, 9, 8, 9, 3, 9, 3, 1, 5, 5],\n [9, 7, 5, 1, 5, 3, 9, 3, 8, 6, 6, 6, 6, 6, 6, 8, 3, 9, 3, 5, 1, 5, 7, 9],\n [7, 9, 5, 5, 3, 5, 6, 9, 9, 9, 5, 6, 6, 5, 9, 9, 9, 6, 5, 3, 5, 5, 9, 7]\n ],\n \"output\": [\n [7, 9, 5, 5, 3, 5, 6, 9, 9, 9, 5, 6, 6, 5, 9, 9, 9, 6, 5, 3, 5, 5, 9, 7],\n [9, 7, 5, 1, 5, 3, 9, 3, 8, 6, 6, 6, 6, 6, 6, 8, 3, 9, 3, 5, 1, 5, 7, 9],\n [5, 5, 1, 3, 9, 3, 9, 8, 9, 5, 3, 3, 3, 3, 5, 9, 8, 9, 3, 9, 3, 1, 5, 5],\n [5, 1, 3, 7, 9, 9, 9, 6, 5, 9, 6, 9, 9, 6, 9, 5, 6, 9, 9, 9, 7, 3, 1, 5],\n [3, 5, 9, 9, 3, 9, 5, 6, 3, 6, 3, 8, 8, 3, 6, 3, 6, 5, 9, 3, 9, 9, 5, 3],\n [5, 3, 3, 9, 9, 3, 6, 6, 3, 9, 8, 9, 9, 8, 9, 3, 6, 6, 3, 9, 9, 3, 3, 5],\n [6, 9, 9, 9, 5, 6, 9, 7, 9, 9, 4, 4, 4, 4, 9, 9, 7, 9, 6, 5, 9, 9, 9, 6],\n [9, 3, 8, 6, 6, 6, 7, 9, 9, 1, 1, 4, 4, 1, 1, 9, 9, 7, 6, 6, 6, 8, 3, 9],\n [9, 8, 9, 5, 3, 3, 9, 9, 1, 7, 4, 3, 3, 4, 7, 1, 9, 9, 3, 3, 5, 9, 8, 9],\n [9, 6, 5, 9, 6, 9, 9, 1, 7, 1, 9, 7, 7, 9, 1, 7, 1, 9, 9, 6, 9, 5, 6, 9],\n [5, 6, 3, 6, 3, 8, 4, 1, 4, 9, 3, 9, 9, 3, 9, 4, 1, 4, 8, 3, 6, 3, 6, 5],\n [6, 6, 3, 9, 8, 9, 4, 4, 3, 7, 9, 7, 7, 9, 7, 3, 4, 4, 9, 8, 9, 3, 6, 6],\n [6, 6, 3, 9, 8, 9, 4, 4, 3, 7, 9, 7, 7, 9, 7, 3, 4, 4, 9, 8, 9, 3, 6, 6],\n [5, 6, 3, 6, 3, 8, 4, 1, 4, 9, 3, 9, 9, 3, 9, 4, 1, 4, 8, 3, 6, 3, 6, 5],\n [9, 6, 5, 9, 6, 9, 9, 1, 7, 1, 9, 7, 7, 9, 1, 7, 1, 9, 9, 6, 9, 5, 6, 9],\n [9, 8, 9, 5, 3, 3, 9, 9, 1, 7, 4, 3, 3, 4, 7, 1, 9, 9, 3, 3, 5, 9, 8, 9],\n [9, 3, 8, 6, 6, 6, 7, 9, 9, 1, 1, 4, 4, 1, 1, 9, 9, 7, 6, 6, 6, 8, 3, 9],\n [6, 9, 9, 9, 5, 6, 9, 7, 9, 9, 4, 4, 4, 4, 9, 9, 7, 9, 6, 5, 9, 9, 9, 6],\n [5, 3, 3, 9, 9, 3, 6, 6, 3, 9, 8, 9, 9, 8, 9, 3, 6, 6, 3, 9, 9, 3, 3, 5],\n [3, 5, 9, 9, 3, 9, 5, 6, 3, 6, 3, 8, 8, 3, 6, 3, 6, 5, 9, 3, 9, 9, 5, 3],\n [5, 1, 3, 7, 9, 9, 9, 6, 5, 9, 6, 9, 9, 6, 9, 5, 6, 9, 9, 9, 7, 3, 1, 5],\n [5, 5, 1, 3, 9, 3, 9, 8, 9, 5, 3, 3, 3, 3, 5, 9, 8, 9, 3, 9, 3, 1, 5, 5],\n [9, 7, 5, 1, 5, 3, 9, 3, 8, 6, 6, 6, 6, 6, 6, 8, 3, 9, 3, 5, 1, 5, 7, 9],\n [7, 9, 5, 5, 3, 5, 6, 9, 9, 9, 5, 6, 6, 5, 9, 9, 9, 6, 5, 3, 5, 5, 9, 7]\n ]\n}\n\n{\n \"input\": [\n [5, 9, 8, 6, 6, 5, 9, 5, 6, 6, 9, 9, 9, 9, 6, 6, 5, 9, 5, 6, 6, 8, 9, 5],\n [9, 5, 8, 5, 5, 5, 5, 1, 4, 6, 9, 6, 6, 9, 6, 4, 1, 5, 5, 5, 5, 8, 5, 9],\n [8, 8, 3, 3, 6, 8, 6, 4, 5, 9, 2, 2, 2, 2, 2, 5, 4, 6, 8, 6, 3, 3, 8, 8],\n [6, 5, 3, 3, 9, 6, 6, 6, 9, 5, 2, 2, 2, 2, 2, 9, 6, 6, 6, 9, 3, 3, 5, 6],\n [6, 5, 6, 9, 9, 9, 9, 9, 9, 5, 6, 6, 6, 6, 5, 9, 9, 9, 9, 9, 9, 6, 5, 6],\n [5, 5, 8, 6, 9, 9, 9, 6, 1, 9, 6, 6, 6, 6, 9, 1, 6, 9, 9, 9, 6, 8, 5, 5],\n [9, 5, 6, 6, 9, 9, 8, 7, 6, 6, 4, 7, 7, 4, 6, 6, 7, 8, 9, 9, 6, 6, 5, 9],\n [5, 1, 4, 6, 9, 6, 7, 4, 7, 4, 4, 8, 8, 4, 4, 7, 4, 7, 6, 9, 6, 4, 1, 5],\n [6, 4, 5, 9, 9, 1, 6, 7, 6, 8, 6, 8, 8, 6, 8, 6, 7, 6, 1, 9, 9, 5, 4, 6],\n [6, 6, 9, 5, 5, 9, 6, 4, 8, 6, 6, 6, 6, 6, 6, 8, 4, 6, 9, 5, 5, 9, 6, 6],\n [9, 9, 9, 5, 6, 6, 4, 4, 6, 6, 4, 6, 6, 2, 2, 2, 2, 2, 6, 6, 5, 9, 9, 9],\n [9, 6, 1, 9, 6, 6, 7, 8, 8, 6, 6, 1, 1, 2, 2, 2, 2, 2, 6, 6, 9, 1, 6, 9],\n [9, 6, 1, 9, 6, 6, 7, 8, 8, 6, 6, 1, 1, 2, 2, 2, 2, 2, 6, 6, 9, 1, 6, 9],\n [9, 9, 9, 5, 6, 6, 4, 4, 6, 6, 4, 6, 6, 2, 2, 2, 2, 2, 6, 6, 5, 9, 9, 9],\n [6, 6, 9, 5, 5, 9, 6, 4, 8, 6, 6, 6, 6, 2, 2, 2, 2, 2, 9, 5, 5, 9, 6, 6],\n [6, 4, 5, 9, 9, 1, 6, 7, 6, 8, 6, 8, 8, 6, 8, 6, 7, 6, 1, 9, 9, 5, 4, 6],\n [5, 1, 4, 6, 9, 6, 7, 4, 7, 4, 4, 8, 8, 4, 4, 7, 4, 7, 6, 9, 6, 4, 1, 5],\n [9, 5, 6, 6, 9, 9, 8, 7, 6, 6, 4, 7, 7, 4, 6, 6, 7, 8, 9, 9, 6, 6, 5, 9],\n [5, 5, 8, 6, 9, 9, 9, 6, 1, 9, 6, 6, 6, 6, 9, 1, 6, 9, 9, 9, 6, 8, 5, 5],\n [6, 5, 6, 9, 9, 9, 9, 9, 9, 5, 6, 6, 6, 6, 5, 9, 9, 9, 9, 9, 9, 6, 5, 6],\n [6, 5, 3, 3, 9, 6, 6, 6, 9, 5, 5, 9, 9, 5, 5, 9, 6, 6, 6, 9, 3, 3, 5, 6],\n [8, 8, 3, 3, 6, 8, 6, 4, 5, 9, 9, 1, 1, 9, 9, 5, 4, 6, 8, 6, 3, 3, 8, 8],\n [9, 5, 8, 5, 5, 5, 5, 1, 4, 6, 9, 6, 6, 9, 6, 4, 1, 5, 5, 5, 5, 8, 5, 9],\n [5, 9, 8, 6, 6, 5, 9, 5, 6, 6, 9, 9, 9, 9, 6, 6, 5, 9, 5, 6, 6, 8, 9, 5]\n ],\n \"output\": [\n [5, 9, 8, 6, 6, 5, 9, 5, 6, 6, 9, 9, 9, 9, 6, 6, 5, 9, 5, 6, 6, 8, 9, 5],\n [9, 5, 8, 5, 5, 5, 5, 1, 4, 6, 9, 6, 6, 9, 6, 4, 1, 5, 5, 5, 5, 8, 5, 9],\n [8, 8, 3, 3, 6, 8, 6, 4, 5, 9, 9, 1, 1, 9, 9, 5, 4, 6, 8, 6, 3, 3, 8, 8],\n [6, 5, 3, 3, 9, 6, 6, 6, 9, 5, 5, 9, 9, 5, 5, 9, 6, 6, 6, 9, 3, 3, 5, 6],\n [6, 5, 6, 9, 9, 9, 9, 9, 9, 5, 6, 6, 6, 6, 5, 9, 9, 9, 9, 9, 9, 6, 5, 6],\n [5, 5, 8, 6, 9, 9, 9, 6, 1, 9, 6, 6, 6, 6, 9, 1, 6, 9, 9, 9, 6, 8, 5, 5],\n [9, 5, 6, 6, 9, 9, 8, 7, 6, 6, 4, 7, 7, 4, 6, 6, 7, 8, 9, 9, 6, 6, 5, 9],\n [5, 1, 4, 6, 9, 6, 7, 4, 7, 4, 4, 8, 8, 4, 4, 7, 4, 7, 6, 9, 6, 4, 1, 5],\n [6, 4, 5, 9, 9, 1, 6, 7, 6, 8, 6, 8, 8, 6, 8, 6, 7, 6, 1, 9, 9, 5, 4, 6],\n [6, 6, 9, 5, 5, 9, 6, 4, 8, 6, 6, 6, 6, 6, 6, 8, 4, 6, 9, 5, 5, 9, 6, 6],\n [9, 9, 9, 5, 6, 6, 4, 4, 6, 6, 4, 6, 6, 4, 6, 6, 4, 4, 6, 6, 5, 9, 9, 9],\n [9, 6, 1, 9, 6, 6, 7, 8, 8, 6, 6, 1, 1, 6, 6, 8, 8, 7, 6, 6, 9, 1, 6, 9],\n [9, 6, 1, 9, 6, 6, 7, 8, 8, 6, 6, 1, 1, 6, 6, 8, 8, 7, 6, 6, 9, 1, 6, 9],\n [9, 9, 9, 5, 6, 6, 4, 4, 6, 6, 4, 6, 6, 4, 6, 6, 4, 4, 6, 6, 5, 9, 9, 9],\n [6, 6, 9, 5, 5, 9, 6, 4, 8, 6, 6, 6, 6, 6, 6, 8, 4, 6, 9, 5, 5, 9, 6, 6],\n [6, 4, 5, 9, 9, 1, 6, 7, 6, 8, 6, 8, 8, 6, 8, 6, 7, 6, 1, 9, 9, 5, 4, 6],\n [5, 1, 4, 6, 9, 6, 7, 4, 7, 4, 4, 8, 8, 4, 4, 7, 4, 7, 6, 9, 6, 4, 1, 5],\n [9, 5, 6, 6, 9, 9, 8, 7, 6, 6, 4, 7, 7, 4, 6, 6, 7, 8, 9, 9, 6, 6, 5, 9],\n [5, 5, 8, 6, 9, 9, 9, 6, 1, 9, 6, 6, 6, 6, 9, 1, 6, 9, 9, 9, 6, 8, 5, 5],\n [6, 5, 6, 9, 9, 9, 9, 9, 9, 5, 6, 6, 6, 6, 5, 9, 9, 9, 9, 9, 9, 6, 5, 6],\n [6, 5, 3, 3, 9, 6, 6, 6, 9, 5, 5, 9, 9, 5, 5, 9, 6, 6, 6, 9, 3, 3, 5, 6],\n [8, 8, 3, 3, 6, 8, 6, 4, 5, 9, 9, 1, 1, 9, 9, 5, 4, 6, 8, 6, 3, 3, 8, 8],\n [9, 5, 8, 5, 5, 5, 5, 1, 4, 6, 9, 6, 6, 9, 6, 4, 1, 5, 5, 5, 5, 8, 5, 9],\n [5, 9, 8, 6, 6, 5, 9, 5, 6, 6, 9, 9, 9, 9, 6, 6, 5, 9, 5, 6, 6, 8, 9, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 8, 8, 6, 6, 1, 3, 6, 9, 6, 4, 4, 4, 4, 6, 9, 6, 3, 1, 6, 6, 8, 8, 5],\n [8, 1, 5, 8, 5, 5, 6, 6, 6, 3, 3, 6, 6, 3, 3, 6, 6, 6, 5, 5, 8, 5, 1, 8],\n [8, 5, 6, 5, 8, 5, 9, 6, 9, 5, 4, 4, 4, 4, 5, 9, 6, 9, 5, 8, 5, 6, 5, 8],\n [6, 8, 5, 1, 6, 8, 6, 3, 5, 9, 5, 4, 4, 5, 9, 5, 3, 6, 8, 6, 1, 5, 8, 6],\n [6, 5, 8, 6, 3, 1, 4, 3, 4, 5, 5, 6, 6, 5, 5, 4, 3, 4, 1, 3, 6, 8, 5, 6],\n [1, 5, 5, 8, 1, 6, 4, 6, 4, 4, 6, 3, 3, 6, 4, 4, 6, 4, 6, 1, 8, 5, 5, 1],\n [3, 6, 9, 6, 4, 4, 8, 5, 8, 3, 5, 6, 6, 5, 3, 8, 5, 8, 4, 4, 6, 9, 6, 3],\n [6, 6, 6, 3, 3, 6, 5, 5, 5, 8, 1, 5, 5, 1, 8, 5, 5, 5, 6, 3, 3, 6, 6, 6],\n [9, 6, 9, 5, 4, 4, 8, 5, 5, 8, 5, 6, 6, 5, 8, 5, 5, 8, 4, 4, 5, 9, 6, 9],\n [6, 3, 5, 9, 5, 4, 3, 8, 8, 1, 3, 1, 1, 3, 1, 8, 8, 3, 4, 5, 9, 5, 3, 6],\n [4, 3, 4, 5, 5, 6, 5, 2, 2, 2, 1, 8, 8, 1, 3, 2, 2, 2, 6, 5, 5, 4, 3, 4],\n [4, 6, 4, 4, 6, 3, 6, 2, 2, 2, 8, 8, 8, 8, 1, 2, 2, 2, 3, 6, 4, 4, 6, 4],\n [4, 6, 4, 4, 6, 3, 6, 2, 2, 2, 8, 8, 8, 8, 1, 2, 2, 2, 3, 6, 4, 4, 6, 4],\n [4, 3, 4, 5, 5, 6, 5, 2, 2, 2, 1, 8, 8, 1, 3, 2, 2, 2, 6, 5, 5, 4, 3, 4],\n [6, 3, 5, 9, 5, 4, 3, 2, 2, 2, 3, 1, 1, 3, 1, 2, 2, 2, 4, 5, 9, 5, 3, 6],\n [9, 6, 9, 5, 4, 4, 8, 2, 2, 2, 5, 6, 6, 5, 8, 5, 5, 8, 4, 4, 5, 9, 6, 9],\n [6, 6, 6, 3, 3, 6, 5, 5, 5, 8, 1, 5, 5, 1, 8, 5, 5, 5, 6, 3, 3, 6, 6, 6],\n [3, 6, 9, 6, 4, 4, 8, 5, 8, 3, 5, 6, 6, 5, 3, 8, 5, 8, 4, 4, 6, 9, 6, 3],\n [1, 5, 5, 8, 1, 6, 4, 6, 4, 4, 6, 3, 3, 6, 4, 4, 6, 4, 6, 1, 8, 5, 5, 1],\n [6, 5, 8, 6, 3, 1, 4, 3, 4, 5, 5, 6, 6, 5, 5, 4, 3, 4, 1, 3, 6, 8, 5, 6],\n [6, 8, 5, 1, 6, 8, 6, 3, 5, 9, 5, 4, 4, 5, 9, 5, 3, 6, 8, 6, 1, 5, 8, 6],\n [8, 5, 6, 5, 8, 5, 9, 6, 9, 5, 4, 4, 4, 4, 5, 9, 6, 9, 5, 8, 5, 6, 5, 8],\n [8, 1, 5, 8, 5, 5, 6, 6, 6, 3, 3, 6, 6, 3, 3, 6, 6, 6, 5, 5, 8, 5, 1, 8],\n [5, 8, 8, 6, 6, 1, 3, 6, 9, 6, 4, 4, 4, 4, 6, 9, 6, 3, 1, 6, 6, 8, 8, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 8, 8, 6, 6, 1, 3, 6, 9, 6, 4, 4, 4, 4, 6, 9, 6, 3, 1, 6, 6, 8, 8, 5], [8, 1, 5, 8, 5, 5, 6, 6, 6, 3, 3, 6, 6, 3, 3, 6, 6, 6, 5, 5, 8, 5, 1, 8], [8, 5, 6, 5, 8, 5, 9, 6, 9, 5, 4, 4, 4, 4, 5, 9, 6, 9, 5, 8, 5, 6, 5, 8], [6, 8, 5, 1, 6, 8, 6, 3, 5, 9, 5, 4, 4, 5, 9, 5, 3, 6, 8, 6, 1, 5, 8, 6], [6, 5, 8, 6, 3, 1, 4, 3, 4, 5, 5, 6, 6, 5, 5, 4, 3, 4, 1, 3, 6, 8, 5, 6], [1, 5, 5, 8, 1, 6, 4, 6, 4, 4, 6, 3, 3, 6, 4, 4, 6, 4, 6, 1, 8, 5, 5, 1], [3, 6, 9, 6, 4, 4, 8, 5, 8, 3, 5, 6, 6, 5, 3, 8, 5, 8, 4, 4, 6, 9, 6, 3], [6, 6, 6, 3, 3, 6, 5, 5, 5, 8, 1, 5, 5, 1, 8, 5, 5, 5, 6, 3, 3, 6, 6, 6], [9, 6, 9, 5, 4, 4, 8, 5, 5, 8, 5, 6, 6, 5, 8, 5, 5, 8, 4, 4, 5, 9, 6, 9], [6, 3, 5, 9, 5, 4, 3, 8, 8, 1, 3, 1, 1, 3, 1, 8, 8, 3, 4, 5, 9, 5, 3, 6], [4, 3, 4, 5, 5, 6, 5, 1, 5, 3, 1, 8, 8, 1, 3, 5, 1, 5, 6, 5, 5, 4, 3, 4], [4, 6, 4, 4, 6, 3, 6, 5, 6, 1, 8, 8, 8, 8, 1, 6, 5, 6, 3, 6, 4, 4, 6, 4], [4, 6, 4, 4, 6, 3, 6, 5, 6, 1, 8, 8, 8, 8, 1, 6, 5, 6, 3, 6, 4, 4, 6, 4], [4, 3, 4, 5, 5, 6, 5, 1, 5, 3, 1, 8, 8, 1, 3, 5, 1, 5, 6, 5, 5, 4, 3, 4], [6, 3, 5, 9, 5, 4, 3, 8, 8, 1, 3, 1, 1, 3, 1, 8, 8, 3, 4, 5, 9, 5, 3, 6], [9, 6, 9, 5, 4, 4, 8, 5, 5, 8, 5, 6, 6, 5, 8, 5, 5, 8, 4, 4, 5, 9, 6, 9], [6, 6, 6, 3, 3, 6, 5, 5, 5, 8, 1, 5, 5, 1, 8, 5, 5, 5, 6, 3, 3, 6, 6, 6], [3, 6, 9, 6, 4, 4, 8, 5, 8, 3, 5, 6, 6, 5, 3, 8, 5, 8, 4, 4, 6, 9, 6, 3], [1, 5, 5, 8, 1, 6, 4, 6, 4, 4, 6, 3, 3, 6, 4, 4, 6, 4, 6, 1, 8, 5, 5, 1], [6, 5, 8, 6, 3, 1, 4, 3, 4, 5, 5, 6, 6, 5, 5, 4, 3, 4, 1, 3, 6, 8, 5, 6], [6, 8, 5, 1, 6, 8, 6, 3, 5, 9, 5, 4, 4, 5, 9, 5, 3, 6, 8, 6, 1, 5, 8, 6], [8, 5, 6, 5, 8, 5, 9, 6, 9, 5, 4, 4, 4, 4, 5, 9, 6, 9, 5, 8, 5, 6, 5, 8], [8, 1, 5, 8, 5, 5, 6, 6, 6, 3, 3, 6, 6, 3, 3, 6, 6, 6, 5, 5, 8, 5, 1, 8], [5, 8, 8, 6, 6, 1, 3, 6, 9, 6, 4, 4, 4, 4, 6, 9, 6, 3, 1, 6, 6, 8, 8, 5]], "task_id": "929ab4e9"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 2, 0],\n [0, 2, 2, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0],\n [2, 0, 2, 2, 0, 0, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 6, 6, 0],\n [0, 6, 6, 0, 0, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 6, 6, 0, 0, 0, 0],\n [2, 0, 6, 6, 0, 0, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 6, 6, 0, 0, 0, 6, 6, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 6, 6, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 2, 0, 0, 0, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 0, 0, 2],\n [2, 2, 0, 0, 0, 2],\n [0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2],\n [0, 2, 2, 2, 0, 0]\n ],\n \"output\": [\n [6, 6, 0, 0, 0, 2],\n [6, 6, 0, 0, 0, 2],\n [0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 2],\n [0, 6, 6, 6, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 6, 6, 0],\n [0, 0, 0, 0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 2, 2, 2, 0],\n [2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 2, 2, 0],\n [0, 2, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 2, 0, 0, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 2, 2, 2, 0], [6, 6, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 6, 6, 0], [0, 6, 0, 0, 0, 6, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 2, 0, 0, 0, 2]], "task_id": "ae58858e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 3, 3, 3, 3, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 3, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 3, 0],\n [8, 0, 0, 2, 2, 2, 2, 2, 3, 3, 3, 3, 0],\n [8, 0, 0, 2, 0, 8, 0, 0, 0, 0, 2, 0, 0],\n [8, 8, 8, 2, 8, 8, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [4, 4, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [4, 4, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2],\n [2, 8, 8, 8, 8, 8, 8, 2],\n [2, 8, 3, 3, 3, 3, 8, 2],\n [2, 8, 3, 4, 4, 3, 8, 2],\n [2, 8, 3, 4, 4, 3, 8, 2],\n [2, 8, 3, 3, 3, 3, 8, 2],\n [2, 8, 8, 8, 8, 8, 8, 2],\n [2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 8, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 8, 8, 8, 3, 8, 8, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 8, 8, 8, 8, 8, 3],\n [3, 8, 4, 4, 4, 4, 4, 8, 3],\n [3, 8, 4, 1, 1, 1, 4, 8, 3],\n [3, 8, 4, 1, 2, 1, 4, 8, 3],\n [3, 8, 4, 1, 1, 1, 4, 8, 3],\n [3, 8, 4, 4, 4, 4, 4, 8, 3],\n [3, 8, 8, 8, 8, 8, 8, 8, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 6, 0, 8, 8, 8, 8, 8, 8, 0, 6, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 6, 0, 8, 0, 0, 0, 0, 8, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 8, 3, 3, 3, 3, 8, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 8, 3, 0, 0, 0, 8, 0, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 8, 3, 0, 0, 0, 8, 0, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 8, 8, 8, 8, 8, 8, 0, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 3, 0, 0, 0, 0, 0, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 3, 6, 6, 6, 6, 6, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 3, 3, 3, 3, 3, 3, 3, 3, 6], [6, 3, 8, 8, 8, 8, 8, 8, 3, 6], [6, 3, 8, 4, 4, 4, 4, 8, 3, 6], [6, 3, 8, 4, 2, 2, 4, 8, 3, 6], [6, 3, 8, 4, 2, 2, 4, 8, 3, 6], [6, 3, 8, 4, 4, 4, 4, 8, 3, 6], [6, 3, 8, 8, 8, 8, 8, 8, 3, 6], [6, 3, 3, 3, 3, 3, 3, 3, 3, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6]], "task_id": "c658a4bd"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 2, 8, 8, 2, 3, 3, 3, 3, 3],\n [3, 3, 2, 2, 2, 8, 8, 2, 2, 3, 3, 3, 3],\n [3, 3, 2, 8, 8, 8, 8, 8, 2, 3, 3, 3, 3],\n [3, 3, 2, 8, 2, 2, 2, 8, 2, 3, 3, 3, 3],\n [3, 3, 2, 8, 2, 3, 2, 8, 2, 3, 3, 3, 3],\n [3, 3, 2, 8, 2, 3, 2, 8, 2, 2, 3, 3, 3],\n [3, 2, 2, 8, 2, 3, 2, 8, 8, 2, 3, 3, 3],\n [3, 2, 8, 8, 2, 3, 2, 8, 2, 2, 3, 3, 3],\n [3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 3, 1, 1, 0, 0, 1, 8, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 6, 1, 1, 0],\n [0, 7, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0],\n [0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4],\n [2, 2, 2, 3, 3, 3, 4, 4, 8, 8, 8, 4, 4],\n [2, 2, 2, 3, 4, 4, 4, 4, 8, 6, 8, 4, 4],\n [3, 3, 3, 3, 4, 4, 4, 4, 8, 6, 8, 8, 4],\n [7, 7, 3, 4, 4, 4, 4, 4, 8, 6, 6, 8, 4],\n [7, 7, 3, 4, 4, 4, 4, 8, 8, 6, 8, 8, 4],\n [7, 7, 3, 3, 3, 3, 4, 8, 6, 6, 8, 4, 4],\n [7, 7, 7, 7, 7, 3, 4, 8, 6, 6, 8, 8, 4],\n [7, 7, 7, 3, 3, 3, 4, 8, 6, 6, 6, 8, 4],\n [7, 7, 7, 3, 4, 4, 4, 8, 6, 6, 8, 8, 4],\n [7, 7, 3, 3, 4, 4, 4, 8, 8, 8, 8, 4, 4],\n [7, 7, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [7, 7, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 9, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 6, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [9, 9, 9, 9, 6, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 6, 9, 9, 6, 8, 8, 3, 3, 3, 3, 3, 8],\n [8, 6, 6, 6, 6, 8, 8, 3, 8, 8, 8, 3, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 8, 8, 3, 3, 8],\n [8, 8, 8, 8, 8, 3, 3, 3, 8, 8, 3, 8, 8],\n [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 3, 8],\n [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8],\n [8, 8, 8, 8, 8, 3, 3, 8, 8, 8, 8, 3, 8],\n [2, 2, 2, 8, 8, 8, 3, 8, 3, 3, 3, 3, 8],\n [7, 7, 2, 8, 8, 8, 3, 3, 3, 8, 8, 8, 8],\n [7, 7, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [7, 7, 7, 7, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [7, 7, 7, 7, 7, 2, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0],\n [0, 0, 7, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7], [5, 5, 5, 3, 7, 7, 6, 6, 6, 6, 6, 7, 7], [5, 5, 3, 3, 7, 7, 6, 8, 8, 8, 6, 7, 7], [3, 3, 3, 7, 7, 7, 6, 6, 8, 8, 6, 6, 7], [7, 7, 7, 7, 7, 7, 7, 6, 8, 8, 8, 6, 7], [7, 7, 7, 7, 7, 7, 7, 6, 8, 8, 8, 6, 7], [7, 7, 7, 7, 7, 7, 7, 6, 8, 8, 8, 6, 7], [7, 7, 7, 7, 7, 6, 6, 6, 8, 8, 8, 6, 7], [7, 7, 7, 7, 7, 6, 8, 8, 8, 8, 8, 6, 7], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7], [4, 4, 4, 4, 4, 4, 4, 4, 6, 7, 7, 7, 7], [4, 4, 4, 4, 4, 4, 4, 6, 6, 7, 7, 7, 7], [4, 4, 4, 4, 4, 4, 4, 6, 7, 7, 7, 7, 7]], "task_id": "477d2879"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 0, 3, 5, 5, 5, 0, 3, 9, 9, 9, 0, 3, 4, 4, 4, 4],\n [8, 0, 8, 0, 3, 5, 5, 5, 5, 3, 9, 9, 0, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 8, 3, 5, 5, 0, 0, 3, 0, 0, 0, 0, 3, 0, 4, 4, 0],\n [0, 8, 0, 0, 3, 0, 5, 5, 5, 3, 9, 0, 0, 0, 3, 4, 4, 4, 4]\n ],\n \"output\": [\n [9, 9, 9, 4],\n [9, 9, 8, 9],\n [5, 4, 4, 8],\n [9, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 3, 5, 5, 0, 0, 3, 0, 9, 9, 9, 3, 4, 0, 4, 0],\n [8, 8, 8, 8, 3, 0, 5, 0, 5, 3, 0, 9, 0, 9, 3, 4, 0, 4, 0],\n [8, 8, 0, 8, 3, 5, 0, 5, 5, 3, 0, 0, 0, 9, 3, 0, 4, 0, 4],\n [0, 8, 8, 0, 3, 0, 0, 0, 5, 3, 9, 0, 0, 9, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 9, 9, 9],\n [4, 9, 4, 9],\n [8, 4, 5, 9],\n [9, 8, 8, 9]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 0, 0, 3, 5, 5, 5, 0, 3, 9, 0, 9, 9, 3, 4, 4, 0, 4],\n [8, 8, 0, 8, 3, 5, 5, 5, 5, 3, 0, 9, 0, 0, 3, 0, 0, 4, 4],\n [8, 0, 0, 0, 3, 0, 5, 0, 5, 3, 9, 0, 0, 9, 3, 4, 0, 0, 4],\n [8, 0, 8, 8, 3, 5, 0, 5, 0, 3, 0, 0, 0, 0, 3, 0, 0, 4, 0]\n ],\n \"output\": [\n [9, 4, 9, 9],\n [8, 9, 4, 4],\n [9, 5, 0, 9],\n [8, 0, 4, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 3, 5, 0, 0, 5, 3, 9, 0, 0, 9, 3, 4, 0, 0, 4],\n [0, 8, 8, 0, 3, 5, 5, 0, 5, 3, 9, 9, 0, 9, 3, 0, 0, 4, 4],\n [8, 8, 8, 0, 3, 0, 5, 5, 0, 3, 9, 9, 0, 0, 3, 4, 0, 0, 0],\n [8, 8, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 4, 4, 4, 0]\n ],\n \"output\": [\n [9, 0, 8, 9],\n [9, 9, 4, 9],\n [9, 9, 8, 0],\n [4, 4, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 9, 3, 4, 0, 4, 0],\n [0, 8, 0, 0, 3, 5, 5, 0, 0, 3, 0, 9, 9, 0, 3, 4, 0, 0, 4],\n [8, 8, 8, 0, 3, 5, 0, 0, 5, 3, 9, 9, 9, 0, 3, 4, 0, 4, 0],\n [0, 0, 0, 0, 3, 5, 5, 5, 5, 3, 0, 0, 9, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 8, 4, 9],\n [4, 9, 9, 4],\n [9, 9, 9, 5],\n [5, 5, 9, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 0, 3, 5, 5, 5, 5, 3, 9, 9, 0, 9, 3, 4, 0, 0, 4],\n [8, 0, 8, 0, 3, 0, 5, 0, 5, 3, 0, 0, 0, 9, 3, 4, 0, 4, 4],\n [8, 8, 0, 8, 3, 0, 0, 0, 0, 3, 9, 9, 0, 9, 3, 0, 4, 0, 4],\n [8, 8, 0, 8, 3, 5, 5, 0, 0, 3, 9, 9, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [9, 9, 8, 9],\n [4, 5, 4, 9],\n [9, 9, 0, 9],\n [9, 9, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 0, 3, 5, 0, 5, 5, 3, 9, 0, 0, 0, 3, 0, 0, 0, 0],\n [8, 0, 8, 8, 3, 5, 5, 5, 5, 3, 0, 0, 9, 9, 3, 4, 4, 0, 4],\n [8, 0, 0, 8, 3, 5, 0, 5, 5, 3, 0, 0, 0, 9, 3, 0, 4, 0, 4],\n [0, 0, 8, 8, 3, 0, 0, 5, 5, 3, 9, 9, 9, 0, 3, 0, 4, 4, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 8, 8, 5], [4, 4, 9, 9], [8, 4, 5, 9], [9, 9, 9, 8]], "task_id": "281123b4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 6, 0, 0],\n [5, 4, 4, 4, 0],\n [0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 6, 0, 0],\n [5, 4, 4, 4, 0],\n [0, 0, 6, 0, 0],\n [0, 0, 6, 0, 0],\n [0, 4, 4, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 8, 8, 0, 0, 0],\n [5, 0, 0, 7, 0, 0, 0],\n [5, 0, 0, 4, 4, 0, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 8, 8, 0, 0, 0],\n [5, 0, 0, 7, 0, 0, 0],\n [5, 0, 0, 4, 4, 0, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 4, 4, 0, 0],\n [5, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0],\n [0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 4, 4, 0, 0],\n [5, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0],\n [0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0],\n [0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 4, 4, 0, 0],\n [0, 0, 8, 8, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 3, 3, 0],\n [5, 0, 0, 3, 2, 0],\n [5, 0, 0, 2, 3, 0],\n [5, 0, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 3, 3, 0],\n [5, 0, 0, 3, 2, 0],\n [5, 0, 0, 2, 3, 0],\n [5, 0, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 0],\n [0, 0, 0, 3, 3, 0],\n [0, 0, 0, 3, 2, 0],\n [0, 0, 0, 2, 3, 0],\n [0, 0, 0, 8, 8, 0],\n [0, 0, 0, 3, 3, 0],\n [0, 0, 0, 3, 2, 0],\n [0, 0, 0, 2, 3, 0],\n [0, 0, 0, 8, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 6, 8, 0, 0],\n [0, 0, 8, 3, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 6, 8, 0, 0],\n [0, 0, 8, 3, 0, 0],\n [0, 0, 6, 8, 0, 0],\n [0, 0, 6, 8, 0, 0],\n [0, 0, 6, 8, 0, 0],\n [0, 0, 6, 8, 0, 0],\n [0, 0, 6, 8, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 4, 4, 4, 0, 0],\n [5, 0, 0, 8, 0, 0, 0],\n [5, 0, 0, 6, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 0, 4, 4, 4, 0, 0], [5, 0, 0, 8, 0, 0, 0], [5, 0, 0, 6, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0], [0, 0, 4, 4, 4, 0, 0], [0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0], [0, 0, 4, 4, 4, 0, 0], [0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0]], "task_id": "12422b43"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 9, 5, 5, 9, 4, 8, 8, 4, 5, 7, 7, 3, 6, 6, 6, 7, 7, 1, 3, 7, 7, 5, 4, 8, 8, 4, 9, 5, 5],\n [9, 8, 5, 8, 4, 9, 8, 8, 5, 4, 7, 7, 1, 6, 6, 6, 9, 7, 7, 1, 7, 7, 4, 5, 8, 8, 9, 4, 8, 5],\n [5, 5, 2, 2, 8, 8, 1, 7, 7, 7, 7, 7, 7, 6, 6, 6, 9, 9, 7, 7, 7, 7, 7, 7, 7, 1, 8, 8, 2, 2],\n [5, 8, 2, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 2],\n [9, 4, 8, 8, 8, 2, 4, 4, 3, 1, 7, 7, 9, 3, 2, 5, 5, 2, 3, 9, 7, 7, 1, 3, 4, 4, 2, 8, 8, 8],\n [4, 9, 8, 8, 2, 8, 4, 1, 1, 7, 7, 9, 3, 8, 5, 2, 2, 5, 8, 3, 9, 7, 7, 1, 1, 4, 8, 2, 8, 8],\n [8, 8, 1, 7, 4, 4, 8, 7, 7, 7, 9, 9, 2, 5, 5, 4, 4, 5, 5, 2, 9, 9, 7, 7, 7, 8, 4, 4, 7, 1],\n [8, 8, 7, 7, 4, 1, 7, 8, 7, 9, 9, 9, 5, 2, 4, 5, 5, 4, 2, 5, 9, 9, 9, 7, 8, 7, 1, 4, 7, 7],\n [4, 5, 7, 7, 3, 1, 7, 7, 7, 7, 4, 4, 9, 1, 2, 2, 2, 2, 1, 9, 4, 4, 7, 7, 7, 7, 1, 3, 7, 7],\n [5, 4, 7, 7, 1, 7, 7, 9, 7, 7, 4, 9, 1, 7, 2, 2, 2, 2, 7, 1, 9, 4, 7, 7, 9, 7, 7, 1, 7, 7],\n [7, 7, 7, 7, 7, 7, 9, 9, 4, 4, 4, 5, 2, 2, 8, 3, 3, 8, 2, 2, 5, 4, 4, 4, 9, 9, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 9, 9, 9, 4, 9, 5, 5, 2, 2, 3, 8, 8, 3, 2, 2, 5, 5, 9, 4, 9, 9, 9, 7, 7, 7],\n [3, 1, 7, 7, 9, 3, 2, 5, 9, 1, 2, 2, 5, 5, 1, 1, 1, 1, 5, 5, 2, 2, 1, 9, 5, 2, 3, 9, 7, 7],\n [1, 7, 7, 9, 3, 8, 5, 2, 1, 7, 2, 2, 5, 2, 1, 7, 7, 1, 2, 5, 2, 2, 7, 1, 2, 5, 8, 3, 6, 6],\n [7, 7, 9, 9, 2, 5, 5, 4, 2, 2, 8, 3, 1, 1, 8, 2, 2, 8, 1, 1, 3, 8, 2, 2, 4, 5, 5, 2, 6, 6],\n [7, 9, 9, 9, 5, 2, 4, 5, 2, 2, 3, 8, 1, 7, 2, 3, 3, 2, 7, 1, 8, 3, 2, 2, 5, 4, 2, 5, 6, 6],\n [7, 9, 9, 9, 5, 2, 4, 5, 2, 2, 3, 8, 1, 7, 2, 3, 3, 2, 7, 1, 8, 3, 2, 2, 5, 4, 2, 5, 6, 6],\n [7, 7, 9, 9, 2, 5, 5, 4, 2, 2, 8, 3, 1, 1, 8, 2, 2, 8, 1, 1, 3, 8, 2, 2, 4, 5, 5, 2, 6, 6],\n [1, 7, 7, 9, 3, 8, 5, 2, 1, 7, 2, 2, 5, 2, 1, 7, 7, 1, 2, 5, 2, 2, 7, 1, 2, 5, 8, 3, 6, 6],\n [3, 1, 7, 7, 9, 3, 2, 5, 9, 1, 2, 2, 5, 5, 1, 1, 1, 1, 5, 5, 2, 2, 1, 9, 5, 2, 3, 9, 7, 7],\n [7, 7, 7, 7, 7, 9, 9, 9, 4, 9, 5, 5, 2, 2, 3, 8, 8, 3, 2, 2, 5, 5, 9, 4, 9, 9, 9, 7, 7, 6],\n [7, 7, 7, 7, 7, 7, 9, 9, 4, 4, 4, 5, 2, 2, 8, 3, 3, 8, 2, 2, 5, 4, 4, 4, 9, 9, 7, 7, 7, 6],\n [5, 4, 7, 7, 1, 7, 7, 9, 7, 7, 4, 9, 1, 7, 2, 2, 2, 2, 7, 1, 9, 4, 7, 7, 9, 7, 7, 1, 7, 6],\n [4, 5, 7, 7, 3, 1, 7, 7, 7, 7, 4, 4, 9, 1, 2, 2, 2, 2, 1, 9, 4, 4, 7, 7, 7, 7, 1, 3, 7, 6],\n [8, 8, 7, 7, 4, 1, 7, 8, 7, 9, 9, 9, 5, 2, 4, 5, 5, 4, 2, 5, 9, 9, 9, 6, 6, 7, 1, 4, 7, 6],\n [8, 8, 1, 7, 4, 4, 8, 7, 7, 7, 9, 9, 2, 5, 5, 4, 4, 5, 5, 2, 9, 9, 7, 6, 6, 8, 4, 4, 7, 6],\n [4, 9, 8, 8, 2, 8, 4, 1, 1, 7, 7, 9, 3, 8, 5, 2, 2, 5, 8, 3, 9, 7, 7, 6, 6, 4, 8, 2, 8, 8],\n [9, 4, 8, 8, 8, 2, 4, 4, 3, 1, 7, 7, 9, 3, 2, 5, 5, 2, 3, 9, 7, 7, 1, 6, 6, 4, 2, 8, 8, 8],\n [5, 8, 2, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 7, 7, 7, 7, 6, 6, 7, 8, 8, 9, 2],\n [5, 5, 2, 2, 8, 8, 1, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 7, 7, 7, 7, 7, 6, 6, 1, 8, 8, 2, 2]\n ],\n \"output\": [\n [8, 9, 5, 5, 9, 4, 8, 8, 4, 5, 7, 7, 3, 1, 7, 7, 7, 7, 1, 3, 7, 7, 5, 4, 8, 8, 4, 9, 5, 5],\n [9, 8, 5, 8, 4, 9, 8, 8, 5, 4, 7, 7, 1, 7, 7, 9, 9, 7, 7, 1, 7, 7, 4, 5, 8, 8, 9, 4, 8, 5],\n [5, 5, 2, 2, 8, 8, 1, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 1, 8, 8, 2, 2],\n [5, 8, 2, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 2],\n [9, 4, 8, 8, 8, 2, 4, 4, 3, 1, 7, 7, 9, 3, 2, 5, 5, 2, 3, 9, 7, 7, 1, 3, 4, 4, 2, 8, 8, 8],\n [4, 9, 8, 8, 2, 8, 4, 1, 1, 7, 7, 9, 3, 8, 5, 2, 2, 5, 8, 3, 9, 7, 7, 1, 1, 4, 8, 2, 8, 8],\n [8, 8, 1, 7, 4, 4, 8, 7, 7, 7, 9, 9, 2, 5, 5, 4, 4, 5, 5, 2, 9, 9, 7, 7, 7, 8, 4, 4, 7, 1],\n [8, 8, 7, 7, 4, 1, 7, 8, 7, 9, 9, 9, 5, 2, 4, 5, 5, 4, 2, 5, 9, 9, 9, 7, 8, 7, 1, 4, 7, 7],\n [4, 5, 7, 7, 3, 1, 7, 7, 7, 7, 4, 4, 9, 1, 2, 2, 2, 2, 1, 9, 4, 4, 7, 7, 7, 7, 1, 3, 7, 7],\n [5, 4, 7, 7, 1, 7, 7, 9, 7, 7, 4, 9, 1, 7, 2, 2, 2, 2, 7, 1, 9, 4, 7, 7, 9, 7, 7, 1, 7, 7],\n [7, 7, 7, 7, 7, 7, 9, 9, 4, 4, 4, 5, 2, 2, 8, 3, 3, 8, 2, 2, 5, 4, 4, 4, 9, 9, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 9, 9, 9, 4, 9, 5, 5, 2, 2, 3, 8, 8, 3, 2, 2, 5, 5, 9, 4, 9, 9, 9, 7, 7, 7],\n [3, 1, 7, 7, 9, 3, 2, 5, 9, 1, 2, 2, 5, 5, 1, 1, 1, 1, 5, 5, 2, 2, 1, 9, 5, 2, 3, 9, 7, 7],\n [1, 7, 7, 9, 3, 8, 5, 2, 1, 7, 2, 2, 5, 2, 1, 7, 7, 1, 2, 5, 2, 2, 7, 1, 2, 5, 8, 3, 9, 7],\n [7, 7, 9, 9, 2, 5, 5, 4, 2, 2, 8, 3, 1, 1, 8, 2, 2, 8, 1, 1, 3, 8, 2, 2, 4, 5, 5, 2, 9, 9],\n [7, 9, 9, 9, 5, 2, 4, 5, 2, 2, 3, 8, 1, 7, 2, 3, 3, 2, 7, 1, 8, 3, 2, 2, 5, 4, 2, 5, 9, 9],\n [7, 9, 9, 9, 5, 2, 4, 5, 2, 2, 3, 8, 1, 7, 2, 3, 3, 2, 7, 1, 8, 3, 2, 2, 5, 4, 2, 5, 9, 9],\n [7, 7, 9, 9, 2, 5, 5, 4, 2, 2, 8, 3, 1, 1, 8, 2, 2, 8, 1, 1, 3, 8, 2, 2, 4, 5, 5, 2, 9, 9],\n [1, 7, 7, 9, 3, 8, 5, 2, 1, 7, 2, 2, 5, 2, 1, 7, 7, 1, 2, 5, 2, 2, 7, 1, 2, 5, 8, 3, 9, 7],\n [3, 1, 7, 7, 9, 3, 2, 5, 9, 1, 2, 2, 5, 5, 1, 1, 1, 1, 5, 5, 2, 2, 1, 9, 5, 2, 3, 9, 7, 7],\n [7, 7, 7, 7, 7, 9, 9, 9, 4, 9, 5, 5, 2, 2, 3, 8, 8, 3, 2, 2, 5, 5, 9, 4, 9, 9, 9, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 9, 9, 4, 4, 4, 5, 2, 2, 8, 3, 3, 8, 2, 2, 5, 4, 4, 4, 9, 9, 7, 7, 7, 7],\n [5, 4, 7, 7, 1, 7, 7, 9, 7, 7, 4, 9, 1, 7, 2, 2, 2, 2, 7, 1, 9, 4, 7, 7, 9, 7, 7, 1, 7, 7],\n [4, 5, 7, 7, 3, 1, 7, 7, 7, 7, 4, 4, 9, 1, 2, 2, 2, 2, 1, 9, 4, 4, 7, 7, 7, 7, 1, 3, 7, 7],\n [8, 8, 7, 7, 4, 1, 7, 8, 7, 9, 9, 9, 5, 2, 4, 5, 5, 4, 2, 5, 9, 9, 9, 7, 8, 7, 1, 4, 7, 7],\n [8, 8, 1, 7, 4, 4, 8, 7, 7, 7, 9, 9, 2, 5, 5, 4, 4, 5, 5, 2, 9, 9, 7, 7, 7, 8, 4, 4, 7, 1],\n [4, 9, 8, 8, 2, 8, 4, 1, 1, 7, 7, 9, 3, 8, 5, 2, 2, 5, 8, 3, 9, 7, 7, 1, 1, 4, 8, 2, 8, 8],\n [9, 4, 8, 8, 8, 2, 4, 4, 3, 1, 7, 7, 9, 3, 2, 5, 5, 2, 3, 9, 7, 7, 1, 3, 4, 4, 2, 8, 8, 8],\n [5, 8, 2, 9, 8, 8, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 2],\n [5, 5, 2, 2, 8, 8, 1, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 1, 8, 8, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [9, 3, 7, 7, 3, 3, 9, 4, 3, 3, 5, 5, 7, 1, 2, 3, 3, 2, 1, 7, 5, 6, 6, 6, 6, 6, 6, 3, 7, 7],\n [3, 9, 7, 9, 3, 9, 4, 9, 3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 1, 1, 5, 6, 6, 6, 6, 6, 6, 3, 9, 7],\n [7, 7, 5, 3, 9, 4, 8, 5, 5, 5, 3, 3, 2, 3, 1, 1, 1, 1, 3, 2, 3, 3, 5, 5, 5, 8, 4, 9, 3, 5],\n [7, 9, 3, 3, 4, 9, 5, 8, 5, 5, 3, 3, 3, 3, 1, 7, 7, 1, 3, 3, 3, 3, 5, 5, 8, 5, 9, 4, 3, 3],\n [3, 3, 9, 4, 3, 5, 7, 2, 7, 1, 2, 3, 7, 6, 6, 6, 4, 7, 3, 7, 3, 2, 1, 7, 2, 7, 5, 3, 4, 9],\n [3, 9, 4, 9, 5, 5, 2, 1, 1, 1, 3, 3, 3, 6, 6, 6, 7, 4, 7, 3, 3, 3, 1, 1, 1, 2, 5, 5, 9, 4],\n [9, 4, 8, 5, 7, 2, 4, 4, 2, 3, 1, 1, 7, 6, 6, 6, 9, 5, 4, 7, 1, 1, 3, 2, 4, 4, 2, 7, 5, 8],\n [4, 9, 5, 8, 2, 1, 4, 2, 3, 3, 1, 7, 4, 6, 6, 6, 1, 9, 7, 4, 7, 1, 3, 3, 2, 4, 1, 2, 8, 5],\n [3, 3, 5, 5, 7, 1, 2, 3, 3, 3, 1, 4, 8, 5, 8, 3, 3, 8, 5, 8, 4, 1, 3, 3, 3, 2, 1, 7, 5, 5],\n [3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 4, 1, 5, 8, 3, 2, 2, 3, 8, 5, 1, 4, 3, 3, 3, 3, 1, 1, 5, 5],\n [5, 5, 3, 3, 2, 3, 1, 1, 1, 4, 9, 7, 8, 3, 7, 7, 7, 7, 3, 8, 7, 9, 4, 1, 1, 1, 3, 2, 3, 3],\n [5, 5, 3, 3, 3, 3, 1, 7, 4, 1, 7, 9, 3, 2, 7, 5, 5, 7, 2, 3, 9, 7, 1, 4, 7, 1, 3, 3, 3, 3],\n [7, 1, 2, 3, 7, 3, 7, 4, 8, 5, 8, 3, 8, 1, 4, 8, 8, 4, 1, 8, 3, 8, 5, 8, 4, 7, 3, 7, 3, 2],\n [1, 1, 3, 3, 3, 7, 4, 7, 5, 8, 3, 2, 1, 1, 8, 4, 4, 8, 1, 6, 6, 6, 6, 6, 6, 4, 7, 3, 3, 3],\n [2, 3, 1, 1, 7, 4, 5, 9, 8, 3, 7, 7, 4, 8, 7, 7, 7, 7, 8, 6, 6, 6, 6, 6, 6, 5, 4, 7, 1, 1],\n [3, 3, 1, 7, 4, 7, 9, 1, 3, 2, 7, 5, 8, 4, 7, 1, 1, 7, 4, 8, 5, 7, 2, 3, 1, 9, 7, 4, 7, 1],\n [3, 3, 1, 7, 4, 7, 9, 1, 3, 2, 7, 5, 8, 4, 7, 1, 1, 7, 4, 8, 5, 7, 2, 3, 1, 9, 7, 4, 7, 1],\n [2, 3, 1, 1, 7, 4, 5, 9, 8, 3, 7, 7, 4, 8, 7, 7, 7, 7, 8, 4, 7, 7, 3, 8, 9, 5, 4, 7, 1, 1],\n [1, 1, 3, 3, 3, 7, 4, 7, 5, 8, 3, 2, 1, 1, 8, 4, 4, 8, 1, 1, 2, 3, 8, 5, 7, 4, 7, 3, 3, 3],\n [7, 1, 2, 3, 7, 3, 7, 4, 8, 5, 8, 3, 8, 1, 4, 8, 8, 4, 1, 8, 3, 8, 5, 8, 4, 7, 3, 7, 3, 2],\n [5, 5, 3, 3, 3, 3, 1, 7, 4, 1, 7, 9, 3, 2, 7, 5, 5, 7, 2, 3, 9, 7, 1, 4, 7, 1, 3, 3, 3, 3],\n [5, 5, 3, 3, 2, 3, 1, 1, 1, 4, 9, 7, 8, 3, 7, 7, 7, 7, 3, 8, 7, 9, 4, 1, 1, 1, 3, 2, 3, 3],\n [3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 4, 1, 5, 8, 3, 2, 2, 3, 8, 5, 1, 4, 3, 3, 3, 3, 1, 1, 5, 5],\n [3, 3, 5, 5, 7, 1, 2, 3, 3, 3, 1, 4, 8, 5, 8, 3, 3, 8, 5, 8, 4, 1, 3, 3, 3, 2, 1, 7, 5, 5],\n [4, 9, 5, 8, 2, 1, 4, 2, 3, 3, 1, 7, 4, 7, 9, 1, 1, 9, 7, 4, 7, 1, 3, 3, 2, 4, 1, 2, 8, 5],\n [9, 4, 8, 5, 7, 2, 4, 4, 2, 3, 1, 1, 7, 4, 5, 9, 9, 5, 4, 7, 1, 1, 3, 2, 4, 4, 2, 7, 5, 8],\n [3, 9, 4, 9, 5, 5, 2, 1, 1, 1, 3, 3, 3, 7, 4, 7, 7, 4, 7, 3, 3, 3, 1, 1, 1, 2, 5, 5, 9, 4],\n [3, 3, 9, 4, 3, 6, 6, 6, 7, 1, 2, 3, 7, 3, 7, 4, 4, 7, 3, 7, 3, 2, 1, 7, 2, 7, 5, 3, 4, 9],\n [7, 9, 3, 3, 4, 6, 6, 6, 5, 5, 3, 3, 3, 3, 1, 7, 7, 1, 3, 3, 3, 3, 5, 5, 8, 5, 9, 4, 3, 3],\n [7, 7, 5, 3, 9, 6, 6, 6, 5, 5, 3, 3, 2, 3, 1, 1, 1, 1, 3, 2, 3, 3, 5, 5, 5, 8, 4, 9, 3, 5]\n ],\n \"output\": [\n [9, 3, 7, 7, 3, 3, 9, 4, 3, 3, 5, 5, 7, 1, 2, 3, 3, 2, 1, 7, 5, 5, 3, 3, 4, 9, 3, 3, 7, 7],\n [3, 9, 7, 9, 3, 9, 4, 9, 3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 1, 1, 5, 5, 3, 3, 9, 4, 9, 3, 9, 7],\n [7, 7, 5, 3, 9, 4, 8, 5, 5, 5, 3, 3, 2, 3, 1, 1, 1, 1, 3, 2, 3, 3, 5, 5, 5, 8, 4, 9, 3, 5],\n [7, 9, 3, 3, 4, 9, 5, 8, 5, 5, 3, 3, 3, 3, 1, 7, 7, 1, 3, 3, 3, 3, 5, 5, 8, 5, 9, 4, 3, 3],\n [3, 3, 9, 4, 3, 5, 7, 2, 7, 1, 2, 3, 7, 3, 7, 4, 4, 7, 3, 7, 3, 2, 1, 7, 2, 7, 5, 3, 4, 9],\n [3, 9, 4, 9, 5, 5, 2, 1, 1, 1, 3, 3, 3, 7, 4, 7, 7, 4, 7, 3, 3, 3, 1, 1, 1, 2, 5, 5, 9, 4],\n [9, 4, 8, 5, 7, 2, 4, 4, 2, 3, 1, 1, 7, 4, 5, 9, 9, 5, 4, 7, 1, 1, 3, 2, 4, 4, 2, 7, 5, 8],\n [4, 9, 5, 8, 2, 1, 4, 2, 3, 3, 1, 7, 4, 7, 9, 1, 1, 9, 7, 4, 7, 1, 3, 3, 2, 4, 1, 2, 8, 5],\n [3, 3, 5, 5, 7, 1, 2, 3, 3, 3, 1, 4, 8, 5, 8, 3, 3, 8, 5, 8, 4, 1, 3, 3, 3, 2, 1, 7, 5, 5],\n [3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 4, 1, 5, 8, 3, 2, 2, 3, 8, 5, 1, 4, 3, 3, 3, 3, 1, 1, 5, 5],\n [5, 5, 3, 3, 2, 3, 1, 1, 1, 4, 9, 7, 8, 3, 7, 7, 7, 7, 3, 8, 7, 9, 4, 1, 1, 1, 3, 2, 3, 3],\n [5, 5, 3, 3, 3, 3, 1, 7, 4, 1, 7, 9, 3, 2, 7, 5, 5, 7, 2, 3, 9, 7, 1, 4, 7, 1, 3, 3, 3, 3],\n [7, 1, 2, 3, 7, 3, 7, 4, 8, 5, 8, 3, 8, 1, 4, 8, 8, 4, 1, 8, 3, 8, 5, 8, 4, 7, 3, 7, 3, 2],\n [1, 1, 3, 3, 3, 7, 4, 7, 5, 8, 3, 2, 1, 1, 8, 4, 4, 8, 1, 1, 2, 3, 8, 5, 7, 4, 7, 3, 3, 3],\n [2, 3, 1, 1, 7, 4, 5, 9, 8, 3, 7, 7, 4, 8, 7, 7, 7, 7, 8, 4, 7, 7, 3, 8, 9, 5, 4, 7, 1, 1],\n [3, 3, 1, 7, 4, 7, 9, 1, 3, 2, 7, 5, 8, 4, 7, 1, 1, 7, 4, 8, 5, 7, 2, 3, 1, 9, 7, 4, 7, 1],\n [3, 3, 1, 7, 4, 7, 9, 1, 3, 2, 7, 5, 8, 4, 7, 1, 1, 7, 4, 8, 5, 7, 2, 3, 1, 9, 7, 4, 7, 1],\n [2, 3, 1, 1, 7, 4, 5, 9, 8, 3, 7, 7, 4, 8, 7, 7, 7, 7, 8, 4, 7, 7, 3, 8, 9, 5, 4, 7, 1, 1],\n [1, 1, 3, 3, 3, 7, 4, 7, 5, 8, 3, 2, 1, 1, 8, 4, 4, 8, 1, 1, 2, 3, 8, 5, 7, 4, 7, 3, 3, 3],\n [7, 1, 2, 3, 7, 3, 7, 4, 8, 5, 8, 3, 8, 1, 4, 8, 8, 4, 1, 8, 3, 8, 5, 8, 4, 7, 3, 7, 3, 2],\n [5, 5, 3, 3, 3, 3, 1, 7, 4, 1, 7, 9, 3, 2, 7, 5, 5, 7, 2, 3, 9, 7, 1, 4, 7, 1, 3, 3, 3, 3],\n [5, 5, 3, 3, 2, 3, 1, 1, 1, 4, 9, 7, 8, 3, 7, 7, 7, 7, 3, 8, 7, 9, 4, 1, 1, 1, 3, 2, 3, 3],\n [3, 3, 5, 5, 1, 1, 3, 3, 3, 3, 4, 1, 5, 8, 3, 2, 2, 3, 8, 5, 1, 4, 3, 3, 3, 3, 1, 1, 5, 5],\n [3, 3, 5, 5, 7, 1, 2, 3, 3, 3, 1, 4, 8, 5, 8, 3, 3, 8, 5, 8, 4, 1, 3, 3, 3, 2, 1, 7, 5, 5],\n [4, 9, 5, 8, 2, 1, 4, 2, 3, 3, 1, 7, 4, 7, 9, 1, 1, 9, 7, 4, 7, 1, 3, 3, 2, 4, 1, 2, 8, 5],\n [9, 4, 8, 5, 7, 2, 4, 4, 2, 3, 1, 1, 7, 4, 5, 9, 9, 5, 4, 7, 1, 1, 3, 2, 4, 4, 2, 7, 5, 8],\n [3, 9, 4, 9, 5, 5, 2, 1, 1, 1, 3, 3, 3, 7, 4, 7, 7, 4, 7, 3, 3, 3, 1, 1, 1, 2, 5, 5, 9, 4],\n [3, 3, 9, 4, 3, 5, 7, 2, 7, 1, 2, 3, 7, 3, 7, 4, 4, 7, 3, 7, 3, 2, 1, 7, 2, 7, 5, 3, 4, 9],\n [7, 9, 3, 3, 4, 9, 5, 8, 5, 5, 3, 3, 3, 3, 1, 7, 7, 1, 3, 3, 3, 3, 5, 5, 8, 5, 9, 4, 3, 3],\n [7, 7, 5, 3, 9, 4, 8, 5, 5, 5, 3, 3, 2, 3, 1, 1, 1, 1, 3, 2, 3, 3, 5, 5, 5, 8, 4, 9, 3, 5]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 2, 4, 2, 9, 9, 3, 2, 8, 8, 5, 5, 3, 3, 3, 3, 5, 5, 8, 8, 2, 3, 9, 9, 2, 4, 2, 8],\n [8, 9, 2, 8, 2, 2, 9, 5, 2, 3, 8, 8, 5, 7, 3, 9, 9, 3, 7, 5, 8, 8, 3, 2, 5, 9, 2, 2, 8, 2],\n [8, 2, 4, 9, 9, 9, 5, 1, 8, 8, 3, 3, 3, 3, 9, 4, 4, 9, 3, 3, 3, 3, 8, 8, 1, 5, 9, 9, 9, 4],\n [2, 8, 9, 8, 9, 5, 1, 8, 8, 8, 3, 9, 3, 9, 4, 8, 8, 4, 9, 3, 9, 3, 8, 8, 8, 1, 5, 9, 8, 9],\n [4, 2, 9, 9, 1, 9, 8, 1, 5, 5, 3, 3, 8, 5, 5, 5, 5, 5, 5, 8, 3, 3, 5, 5, 1, 8, 9, 1, 9, 9],\n [2, 2, 9, 5, 9, 5, 1, 8, 5, 7, 3, 9, 5, 5, 5, 5, 5, 5, 5, 5, 9, 3, 7, 5, 8, 1, 5, 9, 5, 9],\n [9, 9, 5, 1, 8, 1, 5, 5, 3, 3, 9, 4, 5, 5, 2, 5, 5, 2, 5, 5, 4, 9, 3, 3, 5, 5, 1, 8, 1, 5],\n [9, 5, 1, 8, 1, 8, 5, 7, 3, 9, 4, 8, 5, 5, 5, 7, 7, 5, 5, 5, 8, 4, 9, 3, 7, 5, 8, 1, 8, 1],\n [3, 2, 8, 8, 5, 5, 6, 6, 7, 2, 5, 2, 2, 4, 2, 7, 7, 2, 4, 2, 2, 5, 2, 7, 3, 3, 5, 5, 8, 8],\n [2, 3, 8, 8, 5, 7, 6, 6, 2, 7, 2, 2, 4, 9, 7, 2, 2, 7, 9, 4, 2, 2, 7, 2, 9, 3, 7, 5, 8, 8],\n [8, 8, 3, 3, 3, 3, 6, 6, 5, 2, 5, 4, 2, 7, 8, 7, 7, 8, 7, 2, 4, 5, 2, 5, 4, 9, 3, 3, 3, 3],\n [8, 8, 3, 9, 3, 9, 6, 6, 2, 2, 4, 5, 7, 2, 7, 5, 5, 7, 2, 7, 5, 4, 2, 2, 8, 4, 9, 3, 9, 3],\n [5, 5, 3, 3, 8, 5, 5, 5, 6, 6, 6, 6, 6, 9, 3, 3, 3, 3, 9, 9, 7, 2, 4, 2, 5, 5, 5, 8, 3, 3],\n [5, 7, 3, 9, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 3, 7, 7, 3, 7, 9, 2, 7, 9, 4, 5, 5, 5, 5, 9, 3],\n [3, 3, 9, 4, 5, 5, 2, 5, 6, 6, 6, 6, 6, 3, 8, 8, 8, 8, 3, 3, 7, 8, 7, 2, 5, 2, 5, 5, 4, 9],\n [3, 9, 4, 8, 5, 5, 5, 7, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 7, 3, 5, 7, 2, 7, 7, 5, 5, 5, 8, 4],\n [3, 9, 4, 8, 5, 5, 5, 7, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 7, 3, 5, 7, 2, 7, 7, 5, 5, 5, 8, 4],\n [3, 3, 9, 4, 5, 5, 2, 5, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 3, 3, 7, 8, 7, 2, 5, 2, 5, 5, 4, 9],\n [5, 7, 3, 9, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 3, 7, 9, 2, 7, 9, 4, 5, 5, 5, 5, 9, 3],\n [5, 5, 3, 3, 8, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 9, 9, 7, 2, 4, 2, 5, 5, 5, 8, 3, 3],\n [8, 8, 3, 9, 3, 9, 4, 8, 2, 6, 6, 6, 6, 6, 6, 5, 5, 7, 2, 7, 5, 4, 2, 2, 8, 4, 9, 3, 9, 3],\n [8, 8, 3, 3, 3, 3, 9, 4, 5, 6, 6, 6, 6, 6, 6, 7, 7, 8, 7, 2, 4, 5, 2, 5, 4, 9, 3, 3, 3, 3],\n [2, 3, 8, 8, 5, 7, 3, 9, 6, 6, 6, 6, 6, 9, 7, 2, 2, 7, 9, 4, 2, 2, 7, 2, 9, 3, 7, 5, 8, 8],\n [3, 2, 8, 8, 5, 5, 3, 3, 6, 6, 6, 6, 6, 4, 2, 7, 7, 2, 4, 2, 2, 5, 2, 7, 3, 3, 5, 5, 8, 8],\n [9, 5, 1, 8, 1, 8, 5, 7, 6, 6, 6, 6, 6, 5, 5, 7, 7, 5, 5, 5, 8, 4, 9, 3, 7, 5, 8, 1, 8, 1],\n [9, 9, 5, 1, 8, 1, 5, 5, 6, 6, 6, 6, 6, 5, 2, 5, 5, 2, 5, 5, 4, 9, 3, 3, 5, 5, 1, 8, 1, 5],\n [2, 2, 9, 5, 9, 5, 1, 8, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 9, 3, 7, 5, 8, 1, 5, 9, 5, 9],\n [4, 2, 9, 9, 1, 9, 8, 1, 5, 5, 3, 3, 8, 5, 5, 5, 5, 5, 5, 8, 3, 3, 5, 5, 1, 8, 9, 1, 9, 9],\n [2, 8, 9, 8, 9, 5, 1, 8, 8, 8, 3, 9, 3, 9, 4, 8, 8, 4, 9, 3, 9, 3, 8, 8, 8, 1, 5, 9, 8, 9],\n [8, 2, 4, 9, 9, 9, 5, 1, 8, 8, 3, 3, 3, 3, 9, 4, 4, 9, 3, 3, 3, 3, 8, 8, 1, 5, 9, 9, 9, 4]\n ],\n \"output\": [\n [8, 8, 8, 2, 4, 2, 9, 9, 3, 2, 8, 8, 5, 5, 3, 3, 3, 3, 5, 5, 8, 8, 2, 3, 9, 9, 2, 4, 2, 8],\n [8, 9, 2, 8, 2, 2, 9, 5, 2, 3, 8, 8, 5, 7, 3, 9, 9, 3, 7, 5, 8, 8, 3, 2, 5, 9, 2, 2, 8, 2],\n [8, 2, 4, 9, 9, 9, 5, 1, 8, 8, 3, 3, 3, 3, 9, 4, 4, 9, 3, 3, 3, 3, 8, 8, 1, 5, 9, 9, 9, 4],\n [2, 8, 9, 8, 9, 5, 1, 8, 8, 8, 3, 9, 3, 9, 4, 8, 8, 4, 9, 3, 9, 3, 8, 8, 8, 1, 5, 9, 8, 9],\n [4, 2, 9, 9, 1, 9, 8, 1, 5, 5, 3, 3, 8, 5, 5, 5, 5, 5, 5, 8, 3, 3, 5, 5, 1, 8, 9, 1, 9, 9],\n [2, 2, 9, 5, 9, 5, 1, 8, 5, 7, 3, 9, 5, 5, 5, 5, 5, 5, 5, 5, 9, 3, 7, 5, 8, 1, 5, 9, 5, 9],\n [9, 9, 5, 1, 8, 1, 5, 5, 3, 3, 9, 4, 5, 5, 2, 5, 5, 2, 5, 5, 4, 9, 3, 3, 5, 5, 1, 8, 1, 5],\n [9, 5, 1, 8, 1, 8, 5, 7, 3, 9, 4, 8, 5, 5, 5, 7, 7, 5, 5, 5, 8, 4, 9, 3, 7, 5, 8, 1, 8, 1],\n [3, 2, 8, 8, 5, 5, 3, 3, 7, 2, 5, 2, 2, 4, 2, 7, 7, 2, 4, 2, 2, 5, 2, 7, 3, 3, 5, 5, 8, 8],\n [2, 3, 8, 8, 5, 7, 3, 9, 2, 7, 2, 2, 4, 9, 7, 2, 2, 7, 9, 4, 2, 2, 7, 2, 9, 3, 7, 5, 8, 8],\n [8, 8, 3, 3, 3, 3, 9, 4, 5, 2, 5, 4, 2, 7, 8, 7, 7, 8, 7, 2, 4, 5, 2, 5, 4, 9, 3, 3, 3, 3],\n [8, 8, 3, 9, 3, 9, 4, 8, 2, 2, 4, 5, 7, 2, 7, 5, 5, 7, 2, 7, 5, 4, 2, 2, 8, 4, 9, 3, 9, 3],\n [5, 5, 3, 3, 8, 5, 5, 5, 2, 4, 2, 7, 9, 9, 3, 3, 3, 3, 9, 9, 7, 2, 4, 2, 5, 5, 5, 8, 3, 3],\n [5, 7, 3, 9, 5, 5, 5, 5, 4, 9, 7, 2, 9, 7, 3, 7, 7, 3, 7, 9, 2, 7, 9, 4, 5, 5, 5, 5, 9, 3],\n [3, 3, 9, 4, 5, 5, 2, 5, 2, 7, 8, 7, 3, 3, 8, 8, 8, 8, 3, 3, 7, 8, 7, 2, 5, 2, 5, 5, 4, 9],\n [3, 9, 4, 8, 5, 5, 5, 7, 7, 2, 7, 5, 3, 7, 8, 8, 8, 8, 7, 3, 5, 7, 2, 7, 7, 5, 5, 5, 8, 4],\n [3, 9, 4, 8, 5, 5, 5, 7, 7, 2, 7, 5, 3, 7, 8, 8, 8, 8, 7, 3, 5, 7, 2, 7, 7, 5, 5, 5, 8, 4],\n [3, 3, 9, 4, 5, 5, 2, 5, 2, 7, 8, 7, 3, 3, 8, 8, 8, 8, 3, 3, 7, 8, 7, 2, 5, 2, 5, 5, 4, 9],\n [5, 7, 3, 9, 5, 5, 5, 5, 4, 9, 7, 2, 9, 7, 3, 7, 7, 3, 7, 9, 2, 7, 9, 4, 5, 5, 5, 5, 9, 3],\n [5, 5, 3, 3, 8, 5, 5, 5, 2, 4, 2, 7, 9, 9, 3, 3, 3, 3, 9, 9, 7, 2, 4, 2, 5, 5, 5, 8, 3, 3],\n [8, 8, 3, 9, 3, 9, 4, 8, 2, 2, 4, 5, 7, 2, 7, 5, 5, 7, 2, 7, 5, 4, 2, 2, 8, 4, 9, 3, 9, 3],\n [8, 8, 3, 3, 3, 3, 9, 4, 5, 2, 5, 4, 2, 7, 8, 7, 7, 8, 7, 2, 4, 5, 2, 5, 4, 9, 3, 3, 3, 3],\n [2, 3, 8, 8, 5, 7, 3, 9, 2, 7, 2, 2, 4, 9, 7, 2, 2, 7, 9, 4, 2, 2, 7, 2, 9, 3, 7, 5, 8, 8],\n [3, 2, 8, 8, 5, 5, 3, 3, 7, 2, 5, 2, 2, 4, 2, 7, 7, 2, 4, 2, 2, 5, 2, 7, 3, 3, 5, 5, 8, 8],\n [9, 5, 1, 8, 1, 8, 5, 7, 3, 9, 4, 8, 5, 5, 5, 7, 7, 5, 5, 5, 8, 4, 9, 3, 7, 5, 8, 1, 8, 1],\n [9, 9, 5, 1, 8, 1, 5, 5, 3, 3, 9, 4, 5, 5, 2, 5, 5, 2, 5, 5, 4, 9, 3, 3, 5, 5, 1, 8, 1, 5],\n [2, 2, 9, 5, 9, 5, 1, 8, 5, 7, 3, 9, 5, 5, 5, 5, 5, 5, 5, 5, 9, 3, 7, 5, 8, 1, 5, 9, 5, 9],\n [4, 2, 9, 9, 1, 9, 8, 1, 5, 5, 3, 3, 8, 5, 5, 5, 5, 5, 5, 8, 3, 3, 5, 5, 1, 8, 9, 1, 9, 9],\n [2, 8, 9, 8, 9, 5, 1, 8, 8, 8, 3, 9, 3, 9, 4, 8, 8, 4, 9, 3, 9, 3, 8, 8, 8, 1, 5, 9, 8, 9],\n [8, 2, 4, 9, 9, 9, 5, 1, 8, 8, 3, 3, 3, 3, 9, 4, 4, 9, 3, 3, 3, 3, 8, 8, 1, 5, 9, 9, 9, 4]\n ]\n}\n\n{\n \"input\": [\n [2, 9, 2, 8, 9, 2, 5, 5, 7, 7, 3, 9, 8, 8, 4, 4, 4, 4, 8, 6, 6, 6, 6, 6, 6, 6, 2, 9, 8, 2],\n [9, 4, 8, 8, 2, 1, 5, 5, 7, 1, 9, 1, 8, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 1, 2, 8, 8],\n [2, 8, 4, 2, 5, 5, 9, 8, 3, 9, 9, 4, 4, 4, 3, 1, 1, 3, 4, 6, 6, 6, 6, 6, 6, 6, 5, 5, 2, 4],\n [8, 8, 2, 1, 5, 5, 8, 9, 9, 1, 4, 4, 4, 4, 1, 1, 1, 1, 4, 6, 6, 6, 6, 6, 6, 6, 5, 5, 1, 2],\n [9, 2, 5, 5, 8, 9, 8, 3, 8, 8, 4, 4, 7, 1, 1, 8, 8, 1, 1, 7, 4, 4, 8, 8, 3, 8, 9, 8, 5, 5],\n [2, 1, 5, 5, 9, 2, 3, 8, 8, 4, 4, 4, 1, 3, 8, 5, 5, 8, 3, 1, 4, 4, 4, 8, 8, 3, 2, 9, 5, 5],\n [5, 5, 9, 8, 8, 3, 5, 8, 4, 4, 3, 1, 1, 8, 3, 3, 3, 3, 8, 1, 1, 3, 4, 4, 8, 5, 3, 8, 8, 9],\n [5, 5, 8, 6, 6, 6, 6, 6, 4, 4, 1, 1, 8, 5, 3, 3, 3, 3, 5, 8, 1, 1, 4, 4, 7, 8, 8, 3, 9, 8],\n [7, 7, 3, 6, 6, 6, 6, 6, 2, 2, 8, 9, 8, 8, 7, 7, 7, 7, 8, 8, 9, 8, 2, 2, 4, 4, 8, 8, 9, 3],\n [7, 1, 9, 1, 8, 4, 4, 4, 2, 9, 9, 5, 8, 8, 7, 7, 7, 7, 8, 8, 5, 9, 9, 2, 4, 4, 4, 8, 1, 9],\n [3, 9, 9, 4, 4, 4, 3, 1, 8, 9, 4, 4, 7, 7, 2, 2, 2, 2, 7, 7, 4, 4, 9, 8, 1, 3, 4, 4, 4, 9],\n [9, 1, 4, 4, 4, 4, 1, 1, 9, 5, 4, 3, 7, 7, 2, 2, 2, 2, 7, 7, 3, 4, 5, 9, 1, 1, 4, 4, 4, 4],\n [8, 8, 4, 4, 7, 1, 1, 8, 8, 8, 7, 7, 1, 8, 1, 3, 3, 1, 8, 1, 7, 7, 8, 8, 8, 1, 1, 7, 4, 4],\n [6, 6, 6, 6, 6, 6, 6, 5, 8, 8, 7, 7, 8, 8, 3, 3, 3, 3, 8, 8, 7, 7, 8, 8, 5, 8, 3, 1, 4, 4],\n [6, 6, 6, 6, 6, 6, 6, 3, 7, 7, 2, 2, 1, 3, 5, 3, 3, 5, 3, 1, 2, 2, 7, 7, 3, 3, 8, 1, 1, 3],\n [6, 6, 6, 6, 6, 6, 6, 3, 7, 7, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 7, 7, 3, 3, 5, 8, 1, 1],\n [6, 6, 6, 6, 6, 6, 6, 3, 7, 7, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 7, 7, 3, 3, 5, 8, 1, 1],\n [6, 6, 6, 6, 6, 6, 6, 3, 7, 7, 2, 2, 1, 3, 5, 3, 3, 5, 3, 1, 2, 2, 7, 7, 3, 3, 8, 1, 1, 3],\n [6, 6, 6, 6, 6, 6, 6, 5, 8, 8, 7, 7, 8, 8, 3, 3, 3, 3, 8, 8, 7, 7, 8, 8, 5, 8, 3, 1, 4, 4],\n [8, 8, 4, 4, 7, 1, 1, 8, 8, 8, 7, 7, 1, 8, 1, 3, 3, 1, 8, 1, 7, 7, 8, 8, 8, 1, 1, 7, 4, 4],\n [9, 1, 4, 4, 6, 6, 6, 6, 6, 5, 4, 3, 7, 7, 2, 2, 2, 2, 7, 7, 3, 4, 5, 9, 1, 1, 4, 4, 4, 4],\n [3, 9, 9, 4, 6, 6, 6, 6, 6, 9, 4, 4, 7, 7, 2, 2, 2, 2, 7, 7, 4, 4, 9, 8, 1, 3, 4, 4, 4, 9],\n [7, 1, 9, 1, 6, 6, 6, 6, 6, 9, 9, 5, 8, 8, 7, 7, 7, 7, 8, 8, 5, 9, 9, 2, 4, 4, 4, 8, 1, 9],\n [7, 7, 3, 9, 6, 6, 6, 6, 6, 2, 8, 9, 8, 8, 7, 7, 7, 7, 8, 8, 9, 8, 2, 2, 4, 4, 8, 8, 9, 3],\n [5, 5, 8, 9, 6, 6, 6, 6, 6, 4, 1, 1, 8, 5, 3, 3, 3, 3, 5, 8, 1, 1, 4, 4, 7, 8, 8, 3, 9, 8],\n [5, 5, 9, 8, 6, 6, 6, 6, 6, 4, 3, 1, 1, 8, 3, 3, 3, 3, 8, 1, 1, 3, 4, 4, 8, 5, 3, 8, 8, 9],\n [2, 1, 5, 5, 6, 6, 6, 6, 6, 4, 4, 4, 1, 3, 8, 5, 5, 8, 3, 1, 4, 4, 4, 8, 8, 3, 2, 9, 5, 5],\n [9, 2, 5, 5, 8, 9, 8, 3, 8, 8, 4, 4, 7, 1, 1, 8, 8, 1, 1, 7, 4, 4, 8, 8, 3, 8, 9, 8, 5, 5],\n [8, 8, 2, 1, 5, 5, 8, 9, 9, 1, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 9, 9, 8, 5, 5, 1, 2],\n [2, 8, 4, 2, 5, 5, 9, 8, 3, 9, 9, 4, 4, 4, 3, 1, 1, 3, 4, 4, 4, 9, 9, 3, 8, 9, 5, 5, 2, 4]\n ],\n \"output\": [\n [2, 9, 2, 8, 9, 2, 5, 5, 7, 7, 3, 9, 8, 8, 4, 4, 4, 4, 8, 8, 9, 3, 7, 7, 5, 5, 2, 9, 8, 2],\n [9, 4, 8, 8, 2, 1, 5, 5, 7, 1, 9, 1, 8, 4, 4, 4, 4, 4, 4, 8, 1, 9, 1, 7, 5, 5, 1, 2, 8, 8],\n [2, 8, 4, 2, 5, 5, 9, 8, 3, 9, 9, 4, 4, 4, 3, 1, 1, 3, 4, 4, 4, 9, 9, 3, 8, 9, 5, 5, 2, 4],\n [8, 8, 2, 1, 5, 5, 8, 9, 9, 1, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 9, 9, 8, 5, 5, 1, 2],\n [9, 2, 5, 5, 8, 9, 8, 3, 8, 8, 4, 4, 7, 1, 1, 8, 8, 1, 1, 7, 4, 4, 8, 8, 3, 8, 9, 8, 5, 5],\n [2, 1, 5, 5, 9, 2, 3, 8, 8, 4, 4, 4, 1, 3, 8, 5, 5, 8, 3, 1, 4, 4, 4, 8, 8, 3, 2, 9, 5, 5],\n [5, 5, 9, 8, 8, 3, 5, 8, 4, 4, 3, 1, 1, 8, 3, 3, 3, 3, 8, 1, 1, 3, 4, 4, 8, 5, 3, 8, 8, 9],\n [5, 5, 8, 9, 3, 8, 8, 7, 4, 4, 1, 1, 8, 5, 3, 3, 3, 3, 5, 8, 1, 1, 4, 4, 7, 8, 8, 3, 9, 8],\n [7, 7, 3, 9, 8, 8, 4, 4, 2, 2, 8, 9, 8, 8, 7, 7, 7, 7, 8, 8, 9, 8, 2, 2, 4, 4, 8, 8, 9, 3],\n [7, 1, 9, 1, 8, 4, 4, 4, 2, 9, 9, 5, 8, 8, 7, 7, 7, 7, 8, 8, 5, 9, 9, 2, 4, 4, 4, 8, 1, 9],\n [3, 9, 9, 4, 4, 4, 3, 1, 8, 9, 4, 4, 7, 7, 2, 2, 2, 2, 7, 7, 4, 4, 9, 8, 1, 3, 4, 4, 4, 9],\n [9, 1, 4, 4, 4, 4, 1, 1, 9, 5, 4, 3, 7, 7, 2, 2, 2, 2, 7, 7, 3, 4, 5, 9, 1, 1, 4, 4, 4, 4],\n [8, 8, 4, 4, 7, 1, 1, 8, 8, 8, 7, 7, 1, 8, 1, 3, 3, 1, 8, 1, 7, 7, 8, 8, 8, 1, 1, 7, 4, 4],\n [8, 4, 4, 4, 1, 3, 8, 5, 8, 8, 7, 7, 8, 8, 3, 3, 3, 3, 8, 8, 7, 7, 8, 8, 5, 8, 3, 1, 4, 4],\n [4, 4, 3, 1, 1, 8, 3, 3, 7, 7, 2, 2, 1, 3, 5, 3, 3, 5, 3, 1, 2, 2, 7, 7, 3, 3, 8, 1, 1, 3],\n [4, 4, 1, 1, 8, 5, 3, 3, 7, 7, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 7, 7, 3, 3, 5, 8, 1, 1],\n [4, 4, 1, 1, 8, 5, 3, 3, 7, 7, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 7, 7, 3, 3, 5, 8, 1, 1],\n [4, 4, 3, 1, 1, 8, 3, 3, 7, 7, 2, 2, 1, 3, 5, 3, 3, 5, 3, 1, 2, 2, 7, 7, 3, 3, 8, 1, 1, 3],\n [8, 4, 4, 4, 1, 3, 8, 5, 8, 8, 7, 7, 8, 8, 3, 3, 3, 3, 8, 8, 7, 7, 8, 8, 5, 8, 3, 1, 4, 4],\n [8, 8, 4, 4, 7, 1, 1, 8, 8, 8, 7, 7, 1, 8, 1, 3, 3, 1, 8, 1, 7, 7, 8, 8, 8, 1, 1, 7, 4, 4],\n [9, 1, 4, 4, 4, 4, 1, 1, 9, 5, 4, 3, 7, 7, 2, 2, 2, 2, 7, 7, 3, 4, 5, 9, 1, 1, 4, 4, 4, 4],\n [3, 9, 9, 4, 4, 4, 3, 1, 8, 9, 4, 4, 7, 7, 2, 2, 2, 2, 7, 7, 4, 4, 9, 8, 1, 3, 4, 4, 4, 9],\n [7, 1, 9, 1, 8, 4, 4, 4, 2, 9, 9, 5, 8, 8, 7, 7, 7, 7, 8, 8, 5, 9, 9, 2, 4, 4, 4, 8, 1, 9],\n [7, 7, 3, 9, 8, 8, 4, 4, 2, 2, 8, 9, 8, 8, 7, 7, 7, 7, 8, 8, 9, 8, 2, 2, 4, 4, 8, 8, 9, 3],\n [5, 5, 8, 9, 3, 8, 8, 7, 4, 4, 1, 1, 8, 5, 3, 3, 3, 3, 5, 8, 1, 1, 4, 4, 7, 8, 8, 3, 9, 8],\n [5, 5, 9, 8, 8, 3, 5, 8, 4, 4, 3, 1, 1, 8, 3, 3, 3, 3, 8, 1, 1, 3, 4, 4, 8, 5, 3, 8, 8, 9],\n [2, 1, 5, 5, 9, 2, 3, 8, 8, 4, 4, 4, 1, 3, 8, 5, 5, 8, 3, 1, 4, 4, 4, 8, 8, 3, 2, 9, 5, 5],\n [9, 2, 5, 5, 8, 9, 8, 3, 8, 8, 4, 4, 7, 1, 1, 8, 8, 1, 1, 7, 4, 4, 8, 8, 3, 8, 9, 8, 5, 5],\n [8, 8, 2, 1, 5, 5, 8, 9, 9, 1, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 9, 9, 8, 5, 5, 1, 2],\n [2, 8, 4, 2, 5, 5, 9, 8, 3, 9, 9, 4, 4, 4, 3, 1, 1, 3, 4, 4, 4, 9, 9, 3, 8, 9, 5, 5, 2, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 4, 4, 8, 1, 1, 5, 5, 8, 6, 6, 6, 6, 6, 6, 6, 6, 8, 3, 8, 3, 1, 5, 8, 5, 5, 1, 1, 8, 4],\n [4, 8, 8, 8, 1, 1, 5, 8, 5, 6, 6, 6, 6, 6, 6, 6, 6, 8, 8, 3, 7, 3, 1, 5, 8, 5, 1, 1, 8, 8],\n [4, 8, 5, 2, 5, 5, 2, 2, 1, 6, 6, 6, 6, 6, 6, 6, 6, 3, 8, 8, 1, 2, 3, 1, 2, 2, 5, 5, 2, 5],\n [8, 8, 2, 5, 5, 8, 2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 8, 2, 8, 8, 1, 7, 3, 3, 2, 8, 5, 5, 2],\n [1, 1, 5, 5, 1, 2, 4, 3, 8, 6, 6, 6, 6, 6, 6, 6, 6, 9, 2, 7, 8, 8, 3, 8, 3, 4, 2, 1, 5, 5],\n [1, 1, 5, 8, 2, 2, 3, 4, 3, 6, 6, 6, 6, 6, 6, 6, 6, 5, 7, 2, 2, 8, 8, 3, 4, 3, 2, 2, 8, 5],\n [5, 5, 2, 2, 4, 3, 7, 7, 8, 8, 3, 8, 9, 5, 4, 3, 3, 4, 5, 9, 8, 3, 8, 8, 7, 7, 3, 4, 2, 2],\n [5, 8, 2, 3, 3, 4, 7, 7, 8, 2, 8, 8, 5, 9, 3, 3, 3, 3, 9, 5, 8, 8, 2, 6, 6, 6, 6, 6, 3, 2],\n [8, 5, 1, 3, 8, 3, 8, 8, 2, 1, 5, 7, 4, 5, 2, 2, 2, 2, 5, 4, 7, 5, 1, 6, 6, 6, 6, 6, 3, 1],\n [5, 1, 3, 7, 3, 8, 8, 2, 1, 5, 7, 1, 5, 5, 2, 9, 9, 2, 5, 5, 1, 7, 5, 6, 6, 6, 6, 6, 7, 3],\n [1, 3, 2, 1, 8, 8, 3, 8, 5, 7, 9, 9, 2, 2, 5, 5, 5, 5, 2, 2, 9, 9, 7, 6, 6, 6, 6, 6, 1, 2],\n [3, 7, 1, 8, 8, 2, 8, 8, 7, 1, 9, 4, 2, 9, 5, 5, 5, 5, 9, 2, 4, 9, 1, 6, 6, 6, 6, 6, 8, 1],\n [8, 3, 8, 8, 7, 2, 9, 5, 4, 5, 2, 2, 4, 3, 5, 7, 7, 5, 3, 4, 2, 2, 5, 6, 6, 6, 6, 6, 8, 8],\n [3, 8, 8, 2, 2, 7, 5, 9, 5, 5, 2, 9, 3, 1, 7, 3, 3, 7, 1, 3, 9, 2, 5, 6, 6, 6, 6, 6, 2, 8],\n [8, 8, 3, 8, 9, 5, 4, 3, 2, 2, 5, 5, 5, 7, 1, 7, 7, 1, 7, 5, 5, 5, 2, 6, 6, 6, 6, 9, 8, 3],\n [8, 2, 8, 8, 5, 6, 6, 6, 6, 6, 6, 6, 7, 3, 7, 1, 1, 7, 3, 7, 5, 5, 9, 6, 6, 6, 6, 5, 8, 8],\n [8, 2, 8, 8, 5, 6, 6, 6, 6, 6, 6, 6, 7, 3, 7, 1, 1, 7, 3, 7, 5, 5, 9, 6, 6, 6, 6, 5, 8, 8],\n [8, 8, 3, 8, 9, 6, 6, 6, 6, 6, 6, 6, 5, 7, 1, 7, 7, 1, 7, 5, 5, 5, 2, 6, 6, 6, 6, 9, 8, 3],\n [3, 8, 8, 2, 2, 6, 6, 6, 6, 6, 6, 6, 3, 1, 7, 3, 3, 7, 1, 3, 9, 2, 5, 5, 9, 5, 7, 2, 2, 8],\n [8, 3, 8, 8, 7, 6, 6, 6, 6, 6, 6, 6, 4, 3, 5, 7, 7, 5, 3, 6, 6, 6, 6, 4, 5, 9, 2, 7, 8, 8],\n [3, 7, 1, 8, 8, 2, 8, 8, 7, 1, 9, 4, 2, 9, 5, 5, 5, 5, 9, 6, 6, 6, 6, 7, 8, 8, 2, 8, 8, 1],\n [1, 3, 2, 1, 8, 8, 3, 8, 5, 7, 9, 9, 2, 2, 5, 5, 5, 5, 2, 2, 9, 9, 7, 5, 8, 3, 8, 8, 1, 2],\n [5, 1, 3, 7, 3, 8, 8, 2, 1, 5, 7, 1, 5, 5, 2, 9, 9, 2, 5, 5, 1, 7, 5, 1, 2, 8, 8, 3, 7, 3],\n [8, 5, 1, 3, 8, 3, 8, 8, 2, 1, 5, 7, 4, 5, 2, 2, 2, 2, 5, 4, 7, 5, 1, 2, 8, 8, 3, 8, 3, 1],\n [5, 8, 2, 3, 3, 4, 7, 7, 8, 2, 8, 8, 5, 9, 3, 3, 3, 3, 9, 5, 8, 8, 2, 8, 7, 7, 4, 3, 3, 2],\n [5, 5, 2, 2, 4, 3, 7, 7, 8, 8, 3, 8, 9, 5, 4, 3, 3, 4, 5, 9, 8, 3, 8, 8, 7, 7, 3, 4, 2, 2],\n [1, 1, 5, 8, 2, 2, 3, 4, 3, 8, 8, 2, 2, 7, 5, 9, 9, 5, 7, 2, 2, 8, 8, 3, 4, 3, 2, 2, 8, 5],\n [1, 1, 5, 5, 1, 2, 4, 3, 8, 3, 8, 8, 7, 2, 9, 5, 5, 9, 2, 7, 8, 8, 3, 8, 3, 4, 2, 1, 5, 5],\n [8, 8, 2, 5, 5, 8, 2, 3, 3, 7, 1, 8, 8, 2, 8, 8, 8, 8, 2, 8, 8, 1, 7, 3, 3, 2, 8, 5, 5, 2],\n [4, 8, 5, 2, 5, 5, 2, 2, 1, 3, 2, 1, 8, 8, 3, 8, 8, 3, 8, 8, 1, 2, 3, 1, 2, 2, 5, 5, 2, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 4, 4, 8, 1, 1, 5, 5, 8, 5, 1, 3, 8, 3, 8, 8, 8, 8, 3, 8, 3, 1, 5, 8, 5, 5, 1, 1, 8, 4], [4, 8, 8, 8, 1, 1, 5, 8, 5, 1, 3, 7, 3, 8, 8, 2, 2, 8, 8, 3, 7, 3, 1, 5, 8, 5, 1, 1, 8, 8], [4, 8, 5, 2, 5, 5, 2, 2, 1, 3, 2, 1, 8, 8, 3, 8, 8, 3, 8, 8, 1, 2, 3, 1, 2, 2, 5, 5, 2, 5], [8, 8, 2, 5, 5, 8, 2, 3, 3, 7, 1, 8, 8, 2, 8, 8, 8, 8, 2, 8, 8, 1, 7, 3, 3, 2, 8, 5, 5, 2], [1, 1, 5, 5, 1, 2, 4, 3, 8, 3, 8, 8, 7, 2, 9, 5, 5, 9, 2, 7, 8, 8, 3, 8, 3, 4, 2, 1, 5, 5], [1, 1, 5, 8, 2, 2, 3, 4, 3, 8, 8, 2, 2, 7, 5, 9, 9, 5, 7, 2, 2, 8, 8, 3, 4, 3, 2, 2, 8, 5], [5, 5, 2, 2, 4, 3, 7, 7, 8, 8, 3, 8, 9, 5, 4, 3, 3, 4, 5, 9, 8, 3, 8, 8, 7, 7, 3, 4, 2, 2], [5, 8, 2, 3, 3, 4, 7, 7, 8, 2, 8, 8, 5, 9, 3, 3, 3, 3, 9, 5, 8, 8, 2, 8, 7, 7, 4, 3, 3, 2], [8, 5, 1, 3, 8, 3, 8, 8, 2, 1, 5, 7, 4, 5, 2, 2, 2, 2, 5, 4, 7, 5, 1, 2, 8, 8, 3, 8, 3, 1], [5, 1, 3, 7, 3, 8, 8, 2, 1, 5, 7, 1, 5, 5, 2, 9, 9, 2, 5, 5, 1, 7, 5, 1, 2, 8, 8, 3, 7, 3], [1, 3, 2, 1, 8, 8, 3, 8, 5, 7, 9, 9, 2, 2, 5, 5, 5, 5, 2, 2, 9, 9, 7, 5, 8, 3, 8, 8, 1, 2], [3, 7, 1, 8, 8, 2, 8, 8, 7, 1, 9, 4, 2, 9, 5, 5, 5, 5, 9, 2, 4, 9, 1, 7, 8, 8, 2, 8, 8, 1], [8, 3, 8, 8, 7, 2, 9, 5, 4, 5, 2, 2, 4, 3, 5, 7, 7, 5, 3, 4, 2, 2, 5, 4, 5, 9, 2, 7, 8, 8], [3, 8, 8, 2, 2, 7, 5, 9, 5, 5, 2, 9, 3, 1, 7, 3, 3, 7, 1, 3, 9, 2, 5, 5, 9, 5, 7, 2, 2, 8], [8, 8, 3, 8, 9, 5, 4, 3, 2, 2, 5, 5, 5, 7, 1, 7, 7, 1, 7, 5, 5, 5, 2, 2, 3, 4, 5, 9, 8, 3], [8, 2, 8, 8, 5, 9, 3, 3, 2, 9, 5, 5, 7, 3, 7, 1, 1, 7, 3, 7, 5, 5, 9, 2, 3, 3, 9, 5, 8, 8], [8, 2, 8, 8, 5, 9, 3, 3, 2, 9, 5, 5, 7, 3, 7, 1, 1, 7, 3, 7, 5, 5, 9, 2, 3, 3, 9, 5, 8, 8], [8, 8, 3, 8, 9, 5, 4, 3, 2, 2, 5, 5, 5, 7, 1, 7, 7, 1, 7, 5, 5, 5, 2, 2, 3, 4, 5, 9, 8, 3], [3, 8, 8, 2, 2, 7, 5, 9, 5, 5, 2, 9, 3, 1, 7, 3, 3, 7, 1, 3, 9, 2, 5, 5, 9, 5, 7, 2, 2, 8], [8, 3, 8, 8, 7, 2, 9, 5, 4, 5, 2, 2, 4, 3, 5, 7, 7, 5, 3, 4, 2, 2, 5, 4, 5, 9, 2, 7, 8, 8], [3, 7, 1, 8, 8, 2, 8, 8, 7, 1, 9, 4, 2, 9, 5, 5, 5, 5, 9, 2, 4, 9, 1, 7, 8, 8, 2, 8, 8, 1], [1, 3, 2, 1, 8, 8, 3, 8, 5, 7, 9, 9, 2, 2, 5, 5, 5, 5, 2, 2, 9, 9, 7, 5, 8, 3, 8, 8, 1, 2], [5, 1, 3, 7, 3, 8, 8, 2, 1, 5, 7, 1, 5, 5, 2, 9, 9, 2, 5, 5, 1, 7, 5, 1, 2, 8, 8, 3, 7, 3], [8, 5, 1, 3, 8, 3, 8, 8, 2, 1, 5, 7, 4, 5, 2, 2, 2, 2, 5, 4, 7, 5, 1, 2, 8, 8, 3, 8, 3, 1], [5, 8, 2, 3, 3, 4, 7, 7, 8, 2, 8, 8, 5, 9, 3, 3, 3, 3, 9, 5, 8, 8, 2, 8, 7, 7, 4, 3, 3, 2], [5, 5, 2, 2, 4, 3, 7, 7, 8, 8, 3, 8, 9, 5, 4, 3, 3, 4, 5, 9, 8, 3, 8, 8, 7, 7, 3, 4, 2, 2], [1, 1, 5, 8, 2, 2, 3, 4, 3, 8, 8, 2, 2, 7, 5, 9, 9, 5, 7, 2, 2, 8, 8, 3, 4, 3, 2, 2, 8, 5], [1, 1, 5, 5, 1, 2, 4, 3, 8, 3, 8, 8, 7, 2, 9, 5, 5, 9, 2, 7, 8, 8, 3, 8, 3, 4, 2, 1, 5, 5], [8, 8, 2, 5, 5, 8, 2, 3, 3, 7, 1, 8, 8, 2, 8, 8, 8, 8, 2, 8, 8, 1, 7, 3, 3, 2, 8, 5, 5, 2], [4, 8, 5, 2, 5, 5, 2, 2, 1, 3, 2, 1, 8, 8, 3, 8, 8, 3, 8, 8, 1, 2, 3, 1, 2, 2, 5, 5, 2, 5]], "task_id": "47996f11"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [4, 0, 0, 0, 4],\n [0, 4, 0, 4, 0],\n [0, 0, 4, 0, 0],\n [2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 4, 0, 4, 0, 4],\n [0, 0, 0, 0, 4, 0],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 4],\n [0, 0, 4, 0, 0, 0, 4, 0],\n [0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 4, 0],\n [0, 0, 0],\n [0, 0, 0],\n [2, 2, 2],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [0, 0, 0],\n [0, 4, 0],\n [0, 0, 0],\n [2, 2, 2],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 4, 0, 0, 0, 0, 0], [0, 4, 0, 0, 4, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "73c3b0d8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 1, 1],\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 1, 1],\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [5, 5, 2, 5, 5, 2, 5, 5, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [1, 1, 2, 2, 2, 2, 2, 2, 1, 1],\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [1, 1, 2, 2, 2, 2, 2, 2, 1, 1],\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [0, 0, 5, 5, 2, 2, 5, 5, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0]\n ],\n \"output\": [\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0],\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0],\n [1, 2, 2, 2, 2, 2, 2, 2, 2, 1],\n [1, 2, 2, 2, 2, 2, 2, 2, 2, 1],\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0],\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0],\n [1, 2, 2, 2, 2, 2, 2, 2, 2, 1],\n [1, 2, 2, 2, 2, 2, 2, 2, 2, 1],\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0],\n [0, 5, 5, 2, 5, 5, 2, 5, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 5, 5, 0, 0, 5, 5, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 1, 1, 0, 0, 1, 0, 0], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [1, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [1, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [0, 5, 5, 2, 2, 5, 5, 2, 5, 5], [0, 0, 0, 1, 1, 0, 0, 1, 0, 0]], "task_id": "137f0df0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 8, 8, 8, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 8, 8, 8, 8, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 8, 1, 1, 1, 8, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 3, 1, 1, 1, 1, 1, 4, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1],\n [1, 1, 8, 8, 1, 1, 1, 1, 1],\n [1, 1, 1, 8, 1, 8, 8, 1, 1],\n [1, 1, 1, 8, 1, 8, 1, 1, 1],\n [1, 1, 1, 8, 8, 8, 1, 1, 1],\n [1, 1, 8, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 1, 1, 1, 1, 1, 8, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 1, 1, 1, 1, 5, 1],\n [1, 1, 1, 1, 2, 1, 1, 1, 1],\n [1, 1, 1, 1, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 2, 1, 1],\n [1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 1, 1, 1, 5, 1, 1, 5, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 5, 5, 1, 1, 5, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 5, 5, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 4, 1],\n [1, 1, 1, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 5, 1, 1],\n [1, 1, 1, 1, 5, 5, 1, 5, 1, 1],\n [1, 1, 5, 5, 1, 1, 5, 5, 1, 1],\n [1, 1, 1, 5, 1, 1, 5, 1, 1, 1],\n [1, 2, 1, 1, 1, 1, 1, 1, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 1, 1, 1, 1, 1, 1, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1], [1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1], [1, 1, 3, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1], [1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "94133066"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 9],\n [0, 0, 0],\n [9, 9, 0]\n ],\n \"output\": [\n [9, 9, 9, 9, 0, 0],\n [0, 0, 0, 9, 0, 9],\n [9, 9, 0, 9, 0, 9],\n [0, 9, 9, 9, 0, 9],\n [0, 0, 0, 9, 0, 9],\n [9, 9, 9, 0, 0, 9]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 3],\n [0, 3, 3],\n [3, 3, 3]\n ],\n \"output\": [\n [3, 0, 3, 3, 3, 3],\n [0, 3, 3, 0, 3, 3],\n [3, 3, 3, 3, 0, 3],\n [3, 3, 3, 3, 0, 3],\n [3, 3, 0, 3, 3, 0],\n [3, 0, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3],\n [0, 0, 3],\n [3, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 0],\n [0, 0, 3, 3, 0, 0],\n [3, 0, 0, 3, 0, 3],\n [0, 0, 3, 3, 0, 3],\n [3, 0, 0, 0, 0, 3],\n [3, 3, 3, 0, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 8],\n [8, 0, 0],\n [8, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 8, 0, 0],\n [8, 0, 0, 0, 0, 0],\n [8, 0, 0, 8, 8, 8],\n [0, 0, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 0],\n [8, 0, 8, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 7],\n [0, 0, 0],\n [7, 7, 0]\n ],\n \"output\": [\n [0, 7, 7, 7, 0, 0],\n [0, 0, 0, 7, 0, 7],\n [7, 7, 0, 0, 0, 7],\n [0, 7, 7, 7, 0, 0],\n [0, 0, 0, 7, 0, 7],\n [7, 7, 0, 0, 0, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 0],\n [6, 6, 0],\n [0, 0, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 6, 0, 0, 0, 6], [6, 6, 0, 6, 6, 0], [0, 0, 6, 6, 6, 0], [6, 0, 0, 0, 6, 6], [0, 6, 6, 0, 6, 6], [0, 6, 6, 6, 0, 0]], "task_id": "ed98d772"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 8, 8, 8, 8, 0],\n [0, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0],\n [0, 8, 8, 8, 8, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 3, 3, 3, 3, 0],\n [0, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0],\n [0, 0, 2, 0, 0, 0, 0, 3, 0, 3, 0],\n [0, 2, 2, 2, 2, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 2, 0, 2, 0, 2, 0, 0, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 2, 2, 2, 2, 0],\n [0, 8, 0, 8, 8, 0, 2, 0, 0, 2, 0],\n [0, 8, 0, 0, 8, 0, 2, 0, 2, 2, 0],\n [0, 8, 8, 8, 8, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 3, 3, 3, 3, 0],\n [0, 8, 0, 0, 8, 0, 3, 0, 3, 3, 0],\n [0, 8, 8, 0, 8, 0, 3, 3, 0, 3, 0],\n [0, 8, 8, 8, 8, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 2, 0, 2, 0, 2, 0, 0, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 2, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 8, 8, 0, 0],\n [0, 3, 3, 3, 3, 0, 8, 8, 8, 8, 0],\n [0, 3, 0, 3, 3, 0, 8, 0, 0, 8, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 8, 8, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 8, 8, 0],\n [0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 2, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0],\n [0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0], [0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0], [0, 8, 0, 0, 8, 0, 8, 0, 0, 8, 0], [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 3, 0, 0, 2, 2, 0, 0], [0, 3, 0, 0, 3, 0, 2, 0, 0, 0, 0], [0, 3, 0, 0, 3, 0, 2, 0, 0, 2, 0], [0, 0, 3, 3, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "fea12743"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 6, 0, 5, 0, 0, 5, 0],\n [0, 5, 0, 5, 5, 5, 0, 5, 0],\n [5, 0, 0, 0, 0, 5, 5, 8, 0],\n [0, 5, 0, 5, 0, 5, 0, 0, 5],\n [0, 5, 5, 0, 0, 0, 5, 0, 5],\n [5, 0, 5, 5, 5, 5, 0, 0, 5],\n [5, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 0]\n ],\n \"output\": [\n [5, 6, 6, 6, 5, 0, 0, 5, 8],\n [0, 5, 6, 5, 5, 5, 0, 5, 8],\n [5, 6, 6, 6, 6, 5, 5, 8, 8],\n [0, 5, 6, 5, 6, 5, 8, 8, 5],\n [0, 5, 5, 6, 6, 6, 5, 8, 5],\n [5, 0, 5, 5, 5, 5, 8, 8, 5],\n [5, 0, 0, 0, 5, 5, 8, 5, 0],\n [0, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 1, 0, 5, 0, 5, 0, 0, 5],\n [5, 0, 0, 5, 0, 3, 5, 0, 5],\n [0, 5, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 0],\n [5, 0, 0, 5, 0, 0, 0, 0, 5],\n [0, 5, 5, 0, 5, 5, 0, 5, 0],\n [0, 7, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 5, 5, 0, 0, 5],\n [0, 5, 0, 0, 0, 0, 5, 5, 0]\n ],\n \"output\": [\n [5, 1, 1, 5, 3, 5, 0, 0, 5],\n [5, 1, 1, 5, 3, 3, 5, 0, 5],\n [0, 5, 5, 0, 5, 3, 5, 0, 0],\n [0, 0, 5, 0, 5, 3, 3, 5, 0],\n [5, 0, 0, 5, 3, 3, 3, 3, 5],\n [7, 5, 5, 0, 5, 5, 3, 5, 0],\n [7, 7, 7, 5, 0, 0, 5, 0, 0],\n [7, 7, 5, 0, 5, 5, 0, 0, 5],\n [7, 5, 0, 0, 0, 0, 5, 5, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 5, 0],\n [5, 5, 4, 0, 0, 5, 5],\n [0, 0, 0, 5, 5, 0, 0],\n [0, 5, 0, 5, 0, 5, 0],\n [5, 0, 5, 0, 0, 5, 0],\n [0, 5, 0, 5, 0, 8, 5],\n [3, 0, 5, 5, 5, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 4, 4, 5, 0],\n [5, 5, 4, 4, 4, 5, 5],\n [4, 4, 4, 5, 5, 0, 0],\n [4, 5, 4, 5, 8, 5, 0],\n [5, 0, 5, 8, 8, 5, 0],\n [3, 5, 0, 5, 8, 8, 5],\n [3, 3, 5, 5, 5, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 3, 0, 5, 0],\n [5, 5, 5, 0, 0, 0, 5, 5, 0],\n [0, 8, 5, 5, 0, 5, 0, 5, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 5],\n [5, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0],\n [5, 5, 0, 5, 0, 5, 0, 0, 5],\n [0, 5, 0, 0, 5, 0, 5, 5, 0],\n [0, 0, 5, 5, 0, 6, 0, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 5, 3, 3, 3, 5, 0], [5, 5, 5, 3, 3, 3, 5, 5, 0], [8, 8, 5, 5, 3, 5, 0, 5, 0], [8, 8, 5, 0, 5, 0, 0, 5, 5], [5, 8, 5, 0, 0, 5, 0, 0, 0], [8, 8, 8, 5, 0, 0, 5, 0, 0], [5, 5, 8, 5, 0, 5, 0, 0, 5], [0, 5, 8, 8, 5, 6, 5, 5, 0], [0, 0, 5, 5, 6, 6, 6, 6, 5]], "task_id": "e69241bd"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0],\n [8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0],\n [0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "64a7c07e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 8, 8, 0, 0, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 0, 0, 8, 0, 0],\n [0, 8, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 0, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 4, 4, 0, 4, 0, 0, 8, 8, 0, 0, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 0, 8, 0, 4, 4, 0, 8, 8, 0, 0, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 4, 0, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 0, 0, 8, 0, 0],\n [0, 8, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 0, 8, 0, 0, 8, 0],\n [0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 0, 0, 8, 0, 0],\n [0, 4, 4, 0, 4, 4, 0, 8, 0, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 6, 6, 8, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 6, 6, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 8, 8, 8, 8, 8, 8, 4, 4, 4, 0],\n [0, 4, 0, 4, 8, 8, 8, 8, 8, 8, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 8, 8, 4, 4, 0, 4, 4, 0],\n [0, 4, 4, 4, 4, 4, 8, 8, 4, 4, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 6, 6, 8, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 6, 6, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 8, 8, 4, 4, 4, 4, 4, 0],\n [0, 4, 4, 4, 4, 0, 8, 8, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 8, 0, 8, 8, 8, 8, 4, 4, 4, 0],\n [0, 4, 4, 4, 8, 8, 8, 0, 8, 8, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 0, 0, 0, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 4, 4, 0, 8, 0, 0, 4, 4, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 6, 6, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 0, 4, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 0, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 8, 0, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0],\n [0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0],\n [0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 8, 8, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0],\n [0, 4, 4, 0, 4, 4, 0, 4, 0, 0, 8, 8, 0, 4, 4, 0, 4, 0, 0, 4, 4, 0],\n [0, 4, 4, 0, 4, 0, 0, 4, 4, 0, 8, 0, 0, 0, 4, 0, 4, 4, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 6, 6, 0, 8, 8, 8, 8, 0, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 8, 0, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 0, 0, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 4, 0, 4, 4, 0], [0, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 4, 8, 8, 8, 8, 0, 8, 4, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 0, 4, 4, 4, 4, 8, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 6, 6, 0, 8, 8, 8, 8, 0, 0, 8, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 8, 0, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 4, 4, 4, 4, 0, 0, 4, 4, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 0, 4, 0], [0, 4, 4, 4, 0, 4, 4, 0, 0, 8, 8, 0, 8, 4, 4, 4, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0], [0, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "7d419a02"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0], [4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0]], "task_id": "9772c176"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 8, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 8, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 2, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 2, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 8, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 8, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 2, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 2, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 8, 2, 0],\n [0, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 3, 3, 3, 3, 8, 2, 0],\n [0, 2, 8, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 8, 0, 0],\n [0, 2, 8, 3, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 8, 3, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 8, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 8, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 4, 2, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 3, 4, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 1, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 3, 1, 0], [0, 1, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 3, 1, 0], [0, 1, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 3, 1, 0], [0, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 3, 0, 0], [0, 1, 3, 4, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 0, 0, 0], [0, 0, 3, 4, 2, 1, 3, 3, 3, 3, 3, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 4, 2, 1, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 4, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 4, 2, 1, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 2, 1, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "b457fec5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0],\n [0, 7]\n ],\n \"output\": [\n [2, 0, 2, 0, 2, 0],\n [0, 7, 0, 7, 0, 7],\n [2, 0, 2, 0, 2, 0],\n [0, 7, 0, 7, 0, 7],\n [2, 0, 2, 0, 2, 0],\n [0, 7, 0, 7, 0, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 6],\n [6, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 6, 0, 0, 6, 0, 0, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 6, 0, 0, 6, 0, 0, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 6, 0, 0, 6, 0, 0, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0],\n [0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 8, 0, 0, 0, 2, 8, 0, 0, 0, 2, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 8, 0, 0, 0, 2, 8, 0, 0, 0, 2, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 8, 0, 0, 0, 2, 8, 0, 0, 0, 2, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 0, 5, 0],\n [0, 0, 0, 0],\n [0, 5, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 0, 4, 0],\n [0, 0, 0, 0],\n [4, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0], [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0], [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0]], "task_id": "310f3251"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 6, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 3, 0, 0, 3, 0, 0],\n [1, 6, 1, 1, 6, 1, 1, 6, 1],\n [0, 0, 3, 0, 0, 3, 0, 0, 3],\n [3, 0, 0, 3, 0, 0, 3, 0, 0],\n [1, 6, 1, 1, 6, 1, 1, 6, 1],\n [0, 0, 3, 0, 0, 3, 0, 0, 3],\n [3, 0, 0, 3, 0, 0, 3, 0, 0],\n [1, 6, 1, 1, 6, 1, 1, 6, 1],\n [0, 0, 3, 0, 0, 3, 0, 0, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0],\n [0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 5, 1, 1, 1, 5, 1, 1, 1, 5, 1],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3],\n [1, 1, 5, 1, 1, 1, 5, 1, 1, 1, 5, 1],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3],\n [1, 1, 5, 1, 1, 1, 5, 1, 1, 1, 5, 1],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 4, 0]\n ],\n \"output\": [\n [0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 0],\n [4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3],\n [1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 0],\n [4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3],\n [1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 0],\n [4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 1, 1, 2, 1, 1, 2, 1, 1], [0, 3, 0, 0, 3, 0, 0, 3, 0], [0, 0, 3, 0, 0, 3, 0, 0, 0], [2, 1, 1, 2, 1, 1, 2, 1, 1], [0, 3, 0, 0, 3, 0, 0, 3, 0], [0, 0, 3, 0, 0, 3, 0, 0, 0], [2, 1, 1, 2, 1, 1, 2, 1, 1], [0, 3, 0, 0, 3, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c92b942c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 1, 8, 8, 1, 8, 8, 8, 1, 3, 1, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1],\n [8, 3, 1, 3, 8, 1, 8, 8, 8, 1, 3, 1, 3],\n [1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 3, 1, 3, 8, 1, 8, 8, 8, 1, 8, 1, 8],\n [8, 8, 1, 8, 8, 1, 8, 8, 3, 1, 3, 1, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1],\n [8, 8, 1, 8, 8, 1, 8, 8, 3, 1, 3, 1, 8],\n [8, 8, 1, 8, 3, 1, 3, 8, 8, 1, 8, 1, 8],\n [1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 1, 8, 3, 1, 3, 8, 8, 1, 8, 1, 8],\n [8, 8, 1, 8, 8, 1, 8, 8, 8, 1, 8, 1, 8],\n [8, 8, 1, 8, 8, 1, 8, 8, 8, 1, 8, 1, 8]\n ]\n}\n\n{\n \"input\": [\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 1, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 1, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9]\n ],\n \"output\": [\n [9, 9, 9, 1, 9, 9, 1, 9, 9],\n [9, 9, 9, 1, 9, 9, 1, 9, 9],\n [9, 9, 3, 1, 3, 9, 1, 9, 9],\n [1, 1, 1, 2, 1, 1, 1, 1, 1],\n [9, 9, 3, 1, 3, 9, 1, 9, 9],\n [9, 9, 9, 1, 9, 3, 1, 3, 9],\n [1, 1, 1, 1, 1, 1, 2, 1, 1],\n [9, 9, 9, 1, 9, 3, 1, 3, 9],\n [9, 9, 9, 1, 9, 9, 1, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 1, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n ],\n \"output\": [\n [7, 7, 1, 7, 7, 7, 1, 3, 1, 3, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1],\n [7, 7, 1, 7, 7, 7, 1, 3, 1, 3, 7],\n [7, 3, 1, 3, 7, 7, 1, 7, 1, 7, 7],\n [1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 3, 1, 3, 7, 7, 1, 7, 1, 7, 7],\n [7, 7, 1, 7, 7, 7, 1, 7, 1, 7, 7],\n [7, 7, 1, 7, 7, 7, 1, 7, 1, 7, 7],\n [7, 7, 1, 7, 7, 3, 1, 3, 1, 7, 7],\n [1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1],\n [7, 7, 1, 7, 7, 3, 1, 3, 1, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 1, 8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [8, 8, 1, 8, 1, 8, 3, 1, 3, 8, 8, 8, 8, 1], [1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1], [8, 8, 1, 8, 1, 8, 3, 1, 3, 8, 8, 8, 8, 1], [8, 3, 1, 3, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 3, 1, 3, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [8, 8, 1, 8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [8, 8, 1, 8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [8, 8, 1, 8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 1], [8, 8, 1, 3, 1, 3, 8, 1, 8, 8, 8, 8, 8, 1], [1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 8, 1, 3, 1, 3, 8, 1, 8, 8, 8, 8, 3, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]], "task_id": "140c817e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 7, 1, 2],\n [8, 7, 1, 0],\n [8, 7, 1, 0],\n [8, 7, 0, 0],\n [8, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 1, 3],\n [2, 1, 0],\n [2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 8, 3],\n [4, 8, 0],\n [4, 8, 0],\n [4, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 3, 2, 1, 8], [6, 3, 2, 1, 0], [6, 3, 2, 0, 0], [6, 3, 0, 0, 0], [6, 3, 0, 0, 0], [6, 0, 0, 0, 0]], "task_id": "b7999b51"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [2, 2, 2, 2, 3, 2, 3, 2, 2, 2],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [2, 2, 2, 2, 3, 2, 3, 2, 2, 2],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [2, 2, 2, 2, 1, 2, 1, 2, 2, 2],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 3, 2, 3, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 3, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 1, 1, 1, 0],\n [1, 1, 3, 2, 3, 1, 1, 1, 1, 2, 1, 1],\n [0, 0, 3, 3, 3, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0],\n [1, 1, 1, 2, 1, 1, 1, 1, 3, 2, 3, 1],\n [0, 0, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 3, 2, 3, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 1, 1, 1, 0, 0],\n [2, 2, 2, 3, 2, 3, 2, 2, 2, 1, 2, 1, 2, 2],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 0],\n [2, 2, 2, 1, 2, 1, 2, 2, 2, 3, 2, 3, 2, 2],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 3, 3, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 3, 2, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 3, 3, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 3, 2, 3, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 3, 3, 3, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1], [1, 3, 2, 3, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1], [0, 3, 3, 3, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 3, 3, 3, 0, 0, 1, 1, 1], [1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 3, 2, 3, 1, 1, 1, 2, 1], [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 3, 3, 3, 0, 0, 1, 1, 1], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0]], "task_id": "ac3e2b04"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 5, 5, 5, 0],\n [5, 5, 0, 5, 5, 5],\n [5, 5, 0, 5, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [4, 0, 4, 4, 4, 0],\n [4, 0, 0, 0, 0, 0],\n [2, 0, 2, 2, 0, 2],\n [2, 0, 0, 0, 0, 2],\n [0, 0, 0, 2, 0, 0],\n [0, 8, 0, 8, 0, 0],\n [0, 8, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 0]\n ],\n \"output\": [\n [2, 5, 5, 5, 5, 2],\n [5, 5, 4, 5, 5, 5],\n [5, 5, 0, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 0, 5, 5, 5],\n [0, 5, 0, 5, 0, 5],\n [0, 0, 0, 5, 5, 0],\n [0, 4, 4, 0, 4, 0],\n [0, 0, 0, 0, 0, 4],\n [0, 4, 0, 4, 0, 4],\n [2, 2, 2, 0, 0, 0],\n [0, 2, 2, 0, 2, 0],\n [2, 2, 2, 0, 2, 0],\n [8, 0, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [5, 5, 4, 5, 5, 5],\n [0, 5, 8, 5, 8, 5],\n [2, 4, 2, 5, 5, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 5],\n [5, 0, 5, 0, 5, 0],\n [0, 0, 0, 4, 0, 4],\n [0, 0, 0, 4, 0, 0],\n [4, 0, 0, 4, 0, 4],\n [0, 0, 2, 0, 0, 2],\n [2, 2, 0, 2, 2, 0],\n [2, 2, 0, 0, 0, 2],\n [8, 8, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 0],\n [8, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 8, 5, 4, 8, 4],\n [8, 8, 5, 4, 8, 5],\n [5, 8, 5, 4, 5, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 0, 0],\n [0, 5, 5, 0, 5, 5],\n [0, 5, 5, 5, 5, 5],\n [4, 4, 4, 0, 4, 4],\n [0, 0, 0, 4, 4, 0],\n [4, 4, 4, 0, 4, 0],\n [2, 0, 2, 2, 0, 0],\n [2, 2, 0, 2, 0, 0],\n [2, 2, 2, 0, 2, 0],\n [0, 0, 8, 0, 8, 8],\n [8, 8, 8, 0, 0, 0],\n [0, 8, 0, 0, 8, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 4, 4],\n [8, 5, 5, 4, 5, 5],\n [4, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 5],\n [0, 0, 5, 5, 5, 0],\n [4, 4, 0, 4, 4, 4],\n [0, 0, 0, 4, 4, 0],\n [4, 0, 4, 4, 0, 0],\n [2, 0, 2, 2, 0, 2],\n [2, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 2],\n [8, 8, 8, 8, 0, 8],\n [0, 0, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 8]\n ],\n \"output\": [\n [5, 4, 8, 4, 4, 4],\n [2, 5, 0, 4, 4, 5],\n [4, 0, 5, 5, 5, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 5, 5, 0],\n [0, 5, 0, 5, 5, 5],\n [5, 5, 0, 5, 5, 5],\n [4, 0, 0, 0, 4, 4],\n [0, 0, 0, 4, 4, 0],\n [4, 0, 4, 0, 0, 4],\n [0, 2, 2, 2, 2, 0],\n [2, 2, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 0],\n [8, 0, 0, 8, 0, 8],\n [8, 0, 0, 0, 8, 0],\n [8, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [4, 5, 2, 5, 5, 4],\n [8, 5, 2, 5, 5, 5],\n [5, 5, 4, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 5, 0, 0, 5],\n [0, 5, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0],\n [0, 0, 0, 4, 0, 4],\n [0, 0, 0, 0, 0, 0],\n [4, 0, 0, 4, 0, 0],\n [2, 0, 2, 0, 2, 2],\n [2, 2, 0, 2, 2, 2],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 8, 8, 0, 0],\n [0, 8, 0, 0, 8, 8],\n [0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 0, 5, 4, 2, 5], [2, 5, 0, 2, 8, 5], [5, 5, 5, 4, 2, 2]], "task_id": "3d31c5b3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 4, 6, 4, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 4, 4, 0, 6, 0, 4, 4, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 4, 4, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 4, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 4, 6, 4, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 4, 4, 0, 6, 0, 4, 4, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 4, 4, 0, 6, 0, 4, 4, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 4, 0, 6, 0, 4, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 4, 6, 4, 0, 0, 0, 6, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [3, 0, 3, 3, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 3, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 3, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 0, 2, 0, 3, 3, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [3, 0, 3, 3, 2, 3, 3, 0, 3, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 1, 0, 2, 0, 1, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 1, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [3, 0, 3, 3, 2, 3, 3, 0, 3, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 0, 2, 0, 3, 3, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 3, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 3, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 0, 2, 0, 3, 3, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [3, 0, 3, 3, 2, 3, 3, 0, 3, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 1, 0, 2, 0, 1, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 1, 0, 2, 0, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 1, 1, 2, 1, 1, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 8, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 8, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 8, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 2, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 1, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 0, 8, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 8, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 1, 0, 8, 0, 1, 1, 0, 0],\n [0, 0, 4, 4, 4, 8, 4, 4, 4, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 8, 0, 4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 4, 4, 4, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 8, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 8, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 8, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 2, 0, 8, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 8, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 8, 0, 0, 2, 0, 0, 8, 0, 0, 1, 1, 0, 8, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 8, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 0, 8, 0, 1, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 0, 8, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 8, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 1, 0, 8, 0, 1, 1, 0, 0], [0, 0, 4, 4, 4, 8, 4, 4, 4, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 8, 0, 4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 4, 0, 8, 0, 4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 8, 4, 4, 4, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]], "task_id": "2546ccf6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 8, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0],\n [0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 1, 1, 0, 0, 0],\n [3, 3, 1, 1, 4, 0, 0],\n [0, 3, 2, 0, 4, 4, 0],\n [0, 2, 2, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 8, 0, 0],\n [8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0],\n [0, 8, 8, 0, 8, 8, 0],\n [0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 4, 0, 0],\n [2, 2, 0, 0, 4, 4, 0],\n [0, 1, 1, 0, 1, 1, 0],\n [0, 1, 1, 0, 1, 1, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0],\n [8, 8, 0, 8, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 4, 0, 0, 0, 0],\n [1, 1, 4, 4, 0, 0, 0],\n [3, 3, 0, 2, 0, 0, 0],\n [0, 3, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 8, 0, 8, 8, 0],\n [8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 0, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 4, 0, 1, 1, 0], [2, 2, 4, 4, 1, 1, 0], [0, 3, 3, 0, 2, 0, 0], [0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], "task_id": "626c0bcc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 9, 2, 9, 6, 4, 6, 2, 1, 2, 6, 6, 1, 1, 9, 4, 4, 9, 1, 1, 6, 6, 2, 1, 2, 6, 4, 6, 9, 2],\n [5, 5, 9, 5, 4, 6, 6, 6, 3, 6, 6, 6, 1, 1, 9, 9, 9, 9, 1, 1, 6, 6, 6, 3, 6, 6, 6, 4, 5, 9],\n [9, 9, 5, 9, 6, 6, 6, 4, 3, 2, 6, 2, 9, 9, 8, 4, 4, 8, 9, 9, 2, 6, 2, 3, 4, 6, 6, 6, 9, 5],\n [9, 9, 5, 2, 2, 6, 4, 6, 2, 3, 3, 1, 4, 9, 9, 8, 8, 9, 9, 4, 1, 3, 3, 2, 6, 4, 6, 2, 2, 5],\n [9, 8, 1, 8, 2, 9, 5, 9, 1, 1, 9, 4, 6, 6, 6, 6, 6, 6, 6, 6, 4, 9, 1, 1, 9, 5, 9, 2, 8, 1],\n [8, 9, 1, 1, 5, 5, 9, 2, 1, 1, 9, 9, 9, 9, 6, 6, 6, 6, 9, 9, 9, 9, 1, 1, 2, 9, 5, 5, 1, 1],\n [1, 1, 9, 8, 9, 9, 5, 9, 9, 9, 8, 9, 9, 1, 9, 6, 6, 9, 1, 9, 9, 8, 9, 9, 9, 5, 9, 9, 8, 9],\n [8, 1, 8, 9, 9, 9, 5, 2, 4, 9, 4, 8, 1, 9, 9, 6, 6, 9, 9, 1, 8, 4, 9, 4, 2, 5, 9, 9, 9, 8],\n [1, 3, 3, 2, 1, 1, 9, 4, 9, 9, 3, 4, 8, 8, 3, 8, 8, 3, 8, 8, 4, 3, 9, 9, 4, 9, 1, 1, 2, 3],\n [2, 6, 2, 3, 1, 1, 9, 9, 4, 4, 4, 8, 8, 8, 6, 3, 3, 6, 8, 8, 8, 4, 4, 4, 9, 9, 1, 1, 3, 2],\n [6, 6, 6, 3, 9, 9, 8, 4, 8, 9, 4, 9, 3, 6, 8, 8, 8, 8, 6, 3, 9, 4, 9, 8, 4, 8, 9, 9, 3, 6],\n [6, 6, 2, 1, 4, 9, 9, 8, 9, 3, 4, 9, 8, 3, 8, 8, 8, 8, 3, 8, 9, 4, 3, 9, 8, 9, 9, 4, 1, 2],\n [1, 1, 9, 4, 6, 9, 9, 1, 9, 9, 4, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 4, 9, 9, 1, 9, 9, 6, 4, 9],\n [1, 1, 9, 9, 6, 9, 1, 9, 9, 9, 1, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 1, 9, 9, 9, 1, 9, 6, 9, 9],\n [9, 9, 8, 9, 6, 6, 9, 9, 4, 1, 9, 9, 3, 9, 4, 9, 9, 4, 9, 3, 9, 9, 1, 4, 9, 9, 6, 6, 9, 8],\n [4, 9, 4, 8, 6, 6, 6, 6, 1, 4, 9, 9, 9, 8, 4, 9, 9, 4, 8, 9, 9, 9, 4, 1, 6, 6, 6, 6, 8, 4],\n [4, 9, 4, 8, 6, 6, 6, 6, 1, 4, 9, 9, 9, 8, 4, 9, 9, 4, 8, 9, 9, 9, 4, 1, 6, 6, 6, 6, 8, 4],\n [9, 9, 8, 9, 6, 6, 9, 9, 4, 1, 9, 9, 3, 9, 4, 9, 9, 4, 9, 3, 9, 9, 1, 4, 9, 9, 6, 6, 9, 8],\n [1, 1, 9, 9, 6, 9, 1, 9, 9, 9, 1, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 1, 9, 9, 9, 1, 9, 6, 9, 9],\n [1, 1, 9, 4, 6, 9, 9, 1, 9, 9, 4, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 4, 9, 9, 1, 9, 9, 6, 4, 9],\n [6, 6, 2, 1, 4, 9, 9, 8, 9, 3, 4, 9, 8, 3, 8, 8, 8, 8, 3, 8, 9, 7, 7, 7, 7, 7, 7, 7, 7, 2],\n [6, 6, 6, 3, 9, 9, 8, 4, 8, 9, 4, 9, 3, 6, 8, 8, 8, 8, 6, 3, 9, 7, 7, 7, 7, 7, 7, 7, 7, 6],\n [2, 6, 2, 3, 1, 1, 9, 9, 4, 4, 4, 8, 8, 8, 6, 3, 3, 6, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 2],\n [1, 3, 3, 2, 1, 1, 9, 4, 9, 9, 3, 4, 8, 8, 3, 8, 8, 3, 8, 8, 4, 7, 7, 7, 7, 7, 7, 7, 7, 3],\n [8, 1, 8, 9, 9, 9, 5, 2, 4, 9, 4, 8, 1, 9, 9, 6, 6, 9, 9, 1, 8, 7, 7, 7, 7, 7, 7, 7, 7, 8],\n [1, 1, 9, 8, 9, 9, 5, 9, 9, 9, 8, 9, 9, 1, 9, 6, 6, 9, 1, 9, 9, 7, 7, 7, 7, 7, 7, 7, 7, 9],\n [8, 9, 1, 1, 5, 5, 9, 2, 1, 1, 9, 9, 9, 9, 6, 6, 6, 6, 9, 9, 9, 9, 1, 1, 2, 9, 5, 5, 1, 1],\n [9, 8, 1, 8, 2, 9, 5, 9, 1, 1, 9, 4, 6, 6, 6, 6, 6, 6, 6, 6, 4, 9, 1, 1, 9, 5, 9, 2, 8, 1],\n [9, 9, 5, 2, 2, 6, 4, 6, 2, 3, 3, 1, 4, 9, 9, 8, 8, 9, 9, 4, 1, 3, 3, 2, 6, 4, 6, 2, 2, 5],\n [9, 9, 5, 9, 6, 6, 6, 4, 3, 2, 6, 2, 9, 9, 8, 4, 4, 8, 9, 9, 2, 6, 2, 3, 4, 6, 6, 6, 9, 5]\n ],\n \"output\": [\n [4, 3, 9, 8, 9, 9, 4, 1],\n [4, 9, 8, 4, 8, 9, 9, 3],\n [4, 4, 4, 9, 9, 1, 1, 3],\n [3, 9, 9, 4, 9, 1, 1, 2],\n [4, 9, 4, 2, 5, 9, 9, 9],\n [8, 9, 9, 9, 5, 9, 9, 8]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 2, 2, 8, 9, 9, 8, 8, 5, 5, 5, 1, 3, 3, 1, 1, 3, 3, 1, 5, 7, 7, 7, 7, 7, 9, 8, 2, 2],\n [1, 1, 2, 1, 9, 8, 8, 9, 5, 2, 5, 5, 3, 1, 1, 9, 9, 1, 1, 3, 5, 7, 7, 7, 7, 7, 8, 9, 1, 2],\n [2, 2, 1, 3, 9, 8, 8, 9, 8, 5, 2, 5, 3, 1, 9, 2, 2, 9, 1, 3, 5, 7, 7, 7, 7, 7, 8, 9, 3, 1],\n [2, 9, 1, 3, 8, 9, 9, 8, 5, 8, 5, 8, 1, 9, 1, 9, 9, 1, 9, 1, 8, 7, 7, 7, 7, 7, 9, 8, 3, 1],\n [8, 3, 8, 1, 3, 3, 1, 2, 1, 3, 3, 1, 9, 4, 9, 8, 8, 9, 4, 9, 1, 7, 7, 7, 7, 7, 3, 3, 1, 8],\n [3, 8, 3, 8, 1, 1, 2, 2, 3, 1, 1, 9, 2, 8, 8, 9, 9, 8, 8, 2, 9, 7, 7, 7, 7, 7, 1, 1, 8, 3],\n [8, 3, 8, 3, 9, 2, 1, 3, 3, 1, 9, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 7, 7, 7, 7, 7, 2, 9, 3, 8],\n [1, 8, 3, 8, 2, 2, 1, 3, 1, 9, 2, 9, 9, 9, 2, 9, 9, 2, 9, 9, 9, 2, 9, 1, 3, 1, 2, 2, 8, 3],\n [8, 5, 8, 5, 1, 3, 3, 1, 3, 2, 6, 2, 9, 9, 6, 9, 9, 6, 9, 9, 2, 6, 2, 3, 1, 3, 3, 1, 5, 8],\n [5, 2, 5, 8, 3, 1, 1, 9, 1, 1, 2, 2, 9, 9, 4, 6, 6, 4, 9, 9, 2, 2, 1, 1, 9, 1, 1, 3, 8, 5],\n [5, 5, 2, 5, 3, 1, 9, 2, 6, 3, 1, 2, 6, 4, 9, 9, 9, 9, 4, 6, 2, 1, 3, 6, 2, 9, 1, 3, 5, 2],\n [5, 5, 5, 8, 1, 9, 1, 9, 3, 6, 1, 3, 9, 6, 9, 9, 9, 9, 6, 9, 3, 1, 6, 3, 9, 1, 9, 1, 8, 5],\n [1, 3, 3, 1, 9, 2, 9, 9, 8, 4, 3, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3, 4, 8, 9, 9, 2, 9, 1, 3],\n [3, 1, 1, 9, 4, 8, 9, 9, 4, 8, 2, 3, 1, 1, 2, 6, 6, 2, 1, 1, 3, 2, 8, 4, 9, 9, 8, 4, 9, 1],\n [3, 1, 9, 1, 9, 8, 8, 2, 3, 2, 8, 4, 6, 3, 1, 2, 2, 1, 3, 6, 4, 8, 2, 3, 2, 8, 8, 9, 1, 9],\n [1, 9, 2, 9, 8, 9, 4, 9, 2, 3, 4, 8, 3, 6, 1, 3, 3, 1, 6, 3, 8, 4, 3, 2, 9, 4, 9, 8, 9, 2],\n [1, 9, 2, 9, 8, 9, 4, 9, 2, 3, 4, 8, 3, 6, 1, 3, 3, 1, 6, 3, 8, 4, 3, 2, 9, 4, 9, 8, 9, 2],\n [3, 1, 9, 1, 9, 8, 8, 2, 3, 2, 8, 4, 6, 3, 1, 2, 2, 1, 3, 6, 4, 8, 2, 3, 2, 8, 8, 9, 1, 9],\n [3, 1, 1, 9, 4, 8, 9, 9, 4, 8, 2, 3, 1, 1, 2, 6, 6, 2, 1, 1, 3, 2, 8, 4, 9, 9, 8, 4, 9, 1],\n [1, 3, 3, 1, 9, 2, 9, 9, 8, 4, 3, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3, 4, 8, 9, 9, 2, 9, 1, 3],\n [5, 5, 5, 8, 1, 9, 1, 9, 3, 6, 1, 3, 9, 6, 9, 9, 9, 9, 6, 9, 3, 1, 6, 3, 9, 1, 9, 1, 8, 5],\n [5, 5, 2, 5, 3, 1, 9, 2, 6, 3, 1, 2, 6, 4, 9, 9, 9, 9, 4, 6, 2, 1, 3, 6, 2, 9, 1, 3, 5, 2],\n [5, 2, 5, 8, 3, 1, 1, 9, 1, 1, 2, 2, 9, 9, 4, 6, 6, 4, 9, 9, 2, 2, 1, 1, 9, 1, 1, 3, 8, 5],\n [8, 5, 8, 5, 1, 3, 3, 1, 3, 2, 6, 2, 9, 9, 6, 9, 9, 6, 9, 9, 2, 6, 2, 3, 1, 3, 3, 1, 5, 8],\n [1, 8, 3, 8, 2, 2, 1, 3, 1, 9, 2, 9, 9, 9, 2, 9, 9, 2, 9, 9, 9, 2, 9, 1, 3, 1, 2, 2, 8, 3],\n [8, 3, 8, 3, 9, 2, 1, 3, 3, 1, 9, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 9, 1, 3, 3, 1, 2, 9, 3, 8],\n [3, 8, 3, 8, 1, 1, 2, 2, 3, 1, 1, 9, 2, 8, 8, 9, 9, 8, 8, 2, 9, 1, 1, 3, 2, 2, 1, 1, 8, 3],\n [8, 3, 8, 1, 3, 3, 1, 2, 1, 3, 3, 1, 9, 4, 9, 8, 8, 9, 4, 9, 1, 3, 3, 1, 2, 1, 3, 3, 1, 8],\n [2, 9, 1, 3, 8, 9, 9, 8, 5, 8, 5, 8, 1, 9, 1, 9, 9, 1, 9, 1, 8, 5, 8, 5, 8, 9, 9, 8, 3, 1],\n [2, 2, 1, 3, 9, 8, 8, 9, 8, 5, 2, 5, 3, 1, 9, 2, 2, 9, 1, 3, 5, 2, 5, 8, 9, 8, 8, 9, 3, 1]\n ],\n \"output\": [\n [5, 5, 8, 8, 9],\n [5, 2, 5, 9, 8],\n [2, 5, 8, 9, 8],\n [5, 8, 5, 8, 9],\n [3, 3, 1, 2, 1],\n [1, 1, 3, 2, 2],\n [9, 1, 3, 3, 1]\n ]\n}\n\n{\n \"input\": [\n [4, 3, 3, 8, 8, 8, 4, 4, 2, 3, 8, 2, 6, 2, 2, 6, 6, 2, 2, 6, 2, 8, 3, 2, 4, 4, 8, 8, 8, 3],\n [4, 3, 8, 8, 8, 8, 4, 4, 3, 8, 2, 8, 9, 6, 6, 6, 6, 6, 6, 9, 8, 2, 8, 3, 4, 4, 8, 8, 8, 8],\n [8, 8, 3, 3, 4, 4, 8, 8, 3, 8, 8, 3, 2, 6, 9, 6, 6, 9, 6, 2, 3, 8, 8, 3, 8, 8, 4, 4, 3, 3],\n [8, 3, 4, 4, 4, 4, 8, 8, 8, 3, 3, 2, 6, 6, 2, 9, 9, 2, 6, 6, 2, 3, 3, 8, 8, 8, 4, 4, 4, 4],\n [5, 9, 4, 4, 4, 3, 8, 8, 6, 9, 2, 6, 6, 2, 9, 8, 8, 9, 2, 6, 6, 2, 9, 6, 8, 8, 3, 4, 4, 4],\n [9, 5, 5, 4, 4, 3, 8, 3, 2, 6, 6, 6, 8, 6, 8, 9, 9, 8, 6, 8, 6, 6, 6, 2, 3, 8, 3, 4, 4, 5],\n [4, 5, 5, 9, 3, 8, 3, 3, 2, 6, 9, 2, 8, 6, 6, 2, 2, 6, 6, 8, 2, 9, 6, 2, 3, 3, 8, 3, 9, 5],\n [4, 4, 9, 5, 8, 8, 4, 4, 6, 6, 6, 9, 6, 8, 8, 6, 6, 8, 8, 6, 9, 6, 6, 6, 4, 4, 8, 8, 5, 9],\n [2, 3, 3, 8, 6, 2, 2, 6, 9, 9, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 9, 9, 6, 2, 2, 6, 8, 3],\n [3, 8, 8, 3, 9, 6, 6, 6, 9, 5, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 5, 9, 6, 6, 6, 9, 3, 8],\n [8, 2, 8, 3, 2, 6, 9, 6, 4, 8, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5, 8, 4, 6, 9, 6, 2, 3, 8],\n [2, 8, 3, 2, 6, 6, 2, 9, 8, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 8, 9, 2, 6, 6, 2, 3],\n [6, 9, 2, 6, 6, 8, 8, 6, 9, 9, 9, 6, 9, 9, 9, 8, 8, 9, 9, 9, 6, 9, 9, 9, 6, 8, 8, 6, 6, 2],\n [2, 6, 6, 6, 2, 6, 6, 8, 9, 9, 9, 9, 9, 5, 8, 8, 8, 8, 5, 9, 9, 9, 9, 9, 8, 6, 6, 2, 6, 6],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 4, 8, 5, 9, 9, 5, 8, 4, 9, 9, 9, 9, 8, 6, 8, 9, 2, 9],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 8, 4, 9, 9, 9, 9, 4, 8, 9, 9, 9, 6, 6, 2, 9, 8, 9, 6],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 8, 4, 9, 9, 9, 9, 4, 8, 9, 9, 9, 6, 6, 2, 9, 8, 9, 6],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 4, 8, 5, 9, 9, 5, 8, 4, 9, 9, 9, 9, 8, 6, 8, 9, 2, 9],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 5, 8, 8, 8, 8, 5, 9, 9, 9, 9, 9, 8, 6, 6, 2, 6, 6],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 6, 9, 9, 9, 8, 8, 9, 9, 9, 6, 9, 9, 9, 6, 8, 8, 6, 6, 2],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 8, 9, 2, 6, 6, 2, 3],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5, 8, 4, 6, 9, 6, 2, 3, 8],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 5, 9, 6, 6, 6, 9, 3, 8],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 9, 9, 6, 2, 2, 6, 8, 3],\n [4, 4, 9, 5, 8, 8, 4, 4, 6, 6, 6, 9, 6, 8, 8, 6, 6, 8, 8, 6, 9, 6, 6, 6, 4, 4, 8, 8, 5, 9],\n [4, 5, 5, 9, 3, 8, 3, 3, 2, 6, 9, 2, 8, 6, 6, 2, 2, 6, 6, 8, 2, 9, 6, 2, 3, 3, 8, 3, 9, 5],\n [9, 5, 5, 4, 4, 3, 8, 3, 2, 6, 6, 6, 8, 6, 8, 9, 9, 8, 6, 8, 6, 6, 6, 2, 3, 8, 3, 4, 4, 5],\n [5, 9, 4, 4, 4, 3, 8, 8, 6, 9, 2, 6, 6, 2, 9, 8, 8, 9, 2, 6, 6, 2, 9, 6, 8, 8, 3, 4, 4, 4],\n [8, 3, 4, 4, 4, 4, 8, 8, 8, 3, 3, 2, 6, 6, 2, 9, 9, 2, 6, 6, 2, 3, 3, 8, 8, 8, 4, 4, 4, 4],\n [8, 8, 3, 3, 4, 4, 8, 8, 3, 8, 8, 3, 2, 6, 9, 6, 6, 9, 6, 2, 3, 8, 8, 3, 8, 8, 4, 4, 3, 3]\n ],\n \"output\": [\n [2, 6, 9, 2, 9, 8, 6, 8, 9, 9],\n [6, 6, 6, 9, 8, 9, 2, 6, 6, 9],\n [6, 6, 6, 9, 8, 9, 2, 6, 6, 9],\n [2, 6, 9, 2, 9, 8, 6, 8, 9, 9],\n [2, 6, 6, 6, 2, 6, 6, 8, 9, 9],\n [6, 9, 2, 6, 6, 8, 8, 6, 9, 9],\n [2, 8, 3, 2, 6, 6, 2, 9, 8, 4],\n [8, 2, 8, 3, 2, 6, 9, 6, 4, 8],\n [3, 8, 8, 3, 9, 6, 6, 6, 9, 5],\n [2, 3, 3, 8, 6, 2, 2, 6, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [3, 1, 8, 8, 6, 8, 8, 6, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 6, 8, 8, 6, 8, 8],\n [2, 3, 8, 3, 8, 6, 1, 8, 8, 9, 9, 8, 2, 9, 8, 8, 8, 8, 9, 2, 8, 9, 9, 8, 8, 1, 6, 8, 3, 8],\n [1, 8, 3, 1, 8, 1, 6, 8, 4, 8, 9, 8, 9, 8, 9, 8, 8, 9, 8, 9, 8, 9, 8, 4, 8, 6, 1, 8, 1, 3],\n [8, 2, 2, 3, 6, 8, 8, 6, 8, 4, 8, 8, 9, 8, 9, 9, 9, 9, 8, 9, 8, 8, 4, 8, 6, 8, 8, 6, 3, 2],\n [3, 8, 8, 3, 3, 1, 3, 8, 9, 2, 9, 9, 9, 8, 9, 8, 8, 9, 8, 9, 9, 9, 2, 9, 8, 3, 1, 3, 3, 8],\n [8, 3, 1, 8, 2, 3, 8, 8, 9, 9, 8, 8, 8, 4, 8, 9, 9, 8, 4, 8, 8, 8, 9, 9, 8, 8, 3, 2, 8, 1],\n [8, 1, 3, 8, 2, 8, 3, 1, 9, 8, 9, 9, 9, 5, 4, 8, 8, 4, 5, 9, 9, 9, 8, 9, 1, 3, 8, 2, 8, 3],\n [3, 8, 8, 3, 8, 1, 2, 3, 9, 8, 8, 9, 5, 9, 8, 9, 9, 8, 9, 5, 9, 8, 8, 9, 3, 2, 1, 8, 3, 8],\n [8, 8, 4, 8, 9, 9, 9, 9, 2, 9, 8, 8, 5, 2, 5, 1, 1, 5, 2, 5, 8, 8, 9, 2, 9, 9, 9, 9, 8, 4],\n [8, 9, 8, 4, 2, 9, 8, 8, 8, 2, 8, 3, 2, 5, 4, 5, 5, 4, 5, 2, 3, 8, 2, 8, 8, 8, 9, 2, 4, 8],\n [8, 9, 9, 8, 9, 8, 9, 8, 8, 8, 2, 9, 5, 4, 5, 2, 2, 5, 4, 5, 9, 2, 8, 8, 8, 9, 8, 9, 8, 9],\n [9, 8, 8, 8, 9, 8, 9, 9, 8, 8, 8, 2, 1, 5, 2, 5, 5, 2, 5, 1, 2, 8, 8, 8, 9, 9, 8, 9, 8, 8],\n [9, 2, 9, 9, 9, 8, 9, 5, 8, 8, 6, 8, 2, 9, 3, 8, 8, 3, 9, 2, 8, 6, 8, 8, 5, 9, 8, 9, 9, 9],\n [9, 9, 8, 8, 8, 4, 5, 9, 8, 8, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 8, 8, 9, 5, 4, 8, 8, 8],\n [9, 8, 9, 9, 9, 8, 4, 8, 6, 4, 8, 8, 8, 8, 2, 9, 9, 2, 8, 8, 8, 8, 4, 6, 8, 4, 8, 9, 9, 9],\n [9, 8, 8, 9, 8, 9, 8, 9, 8, 6, 8, 8, 8, 8, 8, 2, 2, 8, 8, 8, 8, 8, 6, 8, 9, 8, 9, 8, 9, 8],\n [9, 8, 8, 9, 8, 9, 8, 9, 8, 6, 8, 8, 8, 8, 8, 2, 2, 8, 8, 8, 8, 8, 6, 8, 9, 8, 9, 8, 9, 8],\n [9, 8, 9, 9, 9, 8, 4, 8, 6, 4, 8, 8, 8, 8, 2, 9, 9, 2, 8, 8, 8, 8, 4, 6, 8, 4, 8, 9, 9, 9],\n [9, 9, 8, 8, 8, 4, 5, 9, 8, 8, 4, 6, 8, 2, 8, 8, 8, 8, 2, 8, 6, 4, 8, 8, 9, 5, 4, 8, 8, 8],\n [9, 2, 9, 9, 9, 8, 9, 5, 8, 8, 6, 8, 2, 9, 3, 8, 8, 3, 9, 2, 8, 6, 8, 8, 5, 9, 8, 9, 9, 9],\n [9, 8, 8, 8, 9, 8, 9, 9, 8, 8, 8, 2, 1, 5, 2, 5, 5, 2, 5, 1, 2, 8, 8, 8, 9, 9, 8, 9, 8, 8],\n [8, 9, 9, 8, 9, 8, 9, 8, 8, 8, 2, 9, 5, 4, 5, 2, 2, 5, 4, 5, 9, 2, 8, 8, 8, 9, 8, 9, 8, 9],\n [8, 9, 8, 4, 2, 9, 8, 8, 8, 2, 8, 3, 2, 5, 4, 5, 5, 4, 5, 2, 3, 8, 2, 8, 8, 8, 9, 2, 4, 8],\n [8, 8, 4, 8, 9, 9, 9, 9, 2, 9, 8, 8, 5, 2, 5, 1, 1, 5, 2, 5, 8, 8, 9, 2, 9, 9, 9, 9, 8, 4],\n [3, 8, 8, 3, 8, 7, 7, 7, 7, 8, 8, 9, 5, 9, 8, 9, 9, 8, 9, 5, 9, 8, 8, 9, 3, 2, 1, 8, 3, 8],\n [8, 1, 3, 8, 2, 7, 7, 7, 7, 8, 9, 9, 9, 5, 4, 8, 8, 4, 5, 9, 9, 9, 8, 9, 1, 3, 8, 2, 8, 3],\n [8, 3, 1, 8, 2, 7, 7, 7, 7, 9, 8, 8, 8, 4, 8, 9, 9, 8, 4, 8, 8, 8, 9, 9, 8, 8, 3, 2, 8, 1],\n [3, 8, 8, 3, 3, 7, 7, 7, 7, 2, 9, 9, 9, 8, 9, 8, 8, 9, 8, 9, 9, 9, 2, 9, 8, 3, 1, 3, 3, 8],\n [8, 2, 2, 3, 6, 8, 8, 6, 8, 4, 8, 8, 9, 8, 9, 9, 9, 9, 8, 9, 8, 8, 4, 8, 6, 8, 8, 6, 3, 2],\n [1, 8, 3, 1, 8, 1, 6, 8, 4, 8, 9, 8, 9, 8, 9, 8, 8, 9, 8, 9, 8, 9, 8, 4, 8, 6, 1, 8, 1, 3]\n ],\n \"output\": [\n [1, 2, 3, 9],\n [8, 3, 1, 9],\n [3, 8, 8, 9],\n [1, 3, 8, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 1, 6, 9, 9, 9, 9, 4, 4, 5, 1, 4, 4, 5, 5, 5, 5, 4, 4, 1, 5, 4, 4, 9, 9, 9, 9, 6, 1],\n [1, 1, 6, 3, 9, 9, 1, 9, 5, 3, 1, 5, 1, 4, 3, 5, 5, 3, 4, 1, 5, 1, 3, 5, 9, 1, 9, 9, 3, 6],\n [2, 6, 1, 2, 9, 1, 9, 9, 5, 4, 3, 4, 5, 3, 4, 1, 1, 4, 3, 5, 4, 3, 4, 5, 9, 9, 1, 9, 2, 1],\n [6, 3, 1, 2, 9, 9, 9, 9, 4, 5, 5, 4, 5, 5, 4, 4, 4, 4, 5, 5, 4, 5, 5, 4, 9, 9, 9, 9, 2, 1],\n [8, 8, 8, 8, 2, 2, 3, 6, 4, 1, 5, 5, 8, 4, 4, 4, 4, 4, 4, 8, 5, 5, 1, 4, 6, 3, 2, 2, 8, 8],\n [8, 8, 5, 8, 1, 1, 6, 1, 4, 4, 3, 5, 9, 3, 4, 4, 4, 4, 3, 9, 5, 3, 4, 4, 1, 6, 1, 1, 8, 5],\n [8, 5, 8, 8, 3, 6, 1, 2, 5, 3, 4, 4, 4, 9, 3, 4, 4, 3, 9, 4, 4, 4, 3, 5, 2, 1, 6, 3, 8, 8],\n [8, 8, 8, 8, 6, 2, 1, 2, 5, 5, 1, 4, 9, 4, 9, 8, 8, 9, 4, 9, 4, 1, 5, 5, 2, 1, 2, 6, 8, 8],\n [4, 5, 5, 4, 4, 4, 5, 5, 1, 9, 1, 9, 8, 6, 8, 6, 6, 8, 6, 8, 9, 1, 9, 1, 5, 5, 4, 4, 4, 5],\n [4, 3, 4, 5, 1, 4, 3, 5, 8, 3, 9, 9, 6, 8, 8, 8, 8, 8, 8, 6, 9, 9, 3, 8, 5, 3, 4, 1, 5, 4],\n [5, 1, 3, 5, 5, 3, 4, 1, 9, 9, 3, 9, 8, 8, 8, 6, 6, 8, 8, 8, 9, 3, 9, 9, 1, 4, 3, 5, 5, 3],\n [1, 5, 4, 4, 5, 5, 4, 4, 9, 3, 8, 1, 6, 8, 6, 8, 8, 6, 8, 6, 1, 8, 3, 9, 4, 4, 5, 5, 4, 4],\n [4, 1, 5, 5, 8, 9, 4, 9, 9, 9, 8, 8, 1, 9, 9, 9, 9, 9, 9, 1, 8, 8, 9, 9, 9, 4, 9, 8, 5, 5],\n [4, 4, 3, 5, 4, 3, 9, 4, 9, 9, 2, 8, 8, 3, 9, 1, 1, 9, 3, 8, 8, 2, 9, 9, 4, 9, 3, 4, 5, 3],\n [5, 3, 4, 4, 4, 4, 3, 9, 8, 2, 9, 9, 3, 9, 3, 9, 9, 3, 9, 3, 9, 9, 2, 8, 9, 3, 4, 4, 4, 4],\n [5, 5, 1, 4, 4, 4, 4, 8, 8, 8, 9, 9, 9, 9, 8, 1, 1, 8, 9, 9, 9, 9, 8, 8, 8, 4, 4, 4, 4, 1],\n [5, 5, 1, 4, 4, 4, 4, 8, 8, 8, 9, 9, 9, 9, 8, 1, 1, 8, 9, 7, 7, 9, 8, 8, 8, 4, 4, 4, 4, 1],\n [5, 3, 4, 4, 4, 4, 3, 9, 8, 2, 9, 9, 3, 9, 3, 9, 9, 3, 9, 7, 7, 9, 2, 8, 9, 3, 4, 4, 4, 4],\n [4, 4, 3, 5, 4, 3, 9, 4, 9, 9, 2, 8, 8, 3, 9, 1, 1, 9, 3, 7, 7, 2, 9, 9, 4, 9, 3, 4, 5, 3],\n [4, 1, 5, 5, 8, 9, 4, 9, 9, 9, 8, 8, 1, 9, 9, 9, 9, 9, 9, 7, 7, 8, 9, 9, 9, 4, 9, 8, 5, 5],\n [1, 5, 4, 4, 5, 5, 4, 4, 9, 3, 8, 1, 6, 8, 6, 8, 8, 6, 8, 7, 7, 8, 3, 9, 4, 4, 5, 5, 4, 4],\n [5, 1, 3, 5, 5, 3, 4, 1, 9, 9, 3, 9, 8, 8, 8, 6, 6, 8, 8, 7, 7, 3, 9, 9, 1, 4, 3, 5, 5, 3],\n [4, 3, 4, 5, 1, 4, 3, 5, 8, 3, 9, 9, 6, 8, 8, 8, 8, 8, 8, 7, 7, 9, 3, 8, 5, 3, 4, 1, 5, 4],\n [4, 5, 5, 4, 4, 4, 5, 5, 1, 9, 1, 9, 8, 6, 8, 6, 6, 8, 6, 7, 7, 1, 9, 1, 5, 5, 4, 4, 4, 5],\n [8, 8, 8, 8, 6, 2, 1, 2, 5, 5, 1, 4, 9, 4, 9, 8, 8, 9, 4, 7, 7, 1, 5, 5, 2, 1, 2, 6, 8, 8],\n [8, 5, 8, 8, 3, 6, 1, 2, 5, 3, 4, 4, 4, 9, 3, 4, 4, 3, 9, 7, 7, 4, 3, 5, 2, 1, 6, 3, 8, 8],\n [8, 8, 5, 8, 1, 1, 6, 1, 4, 4, 3, 5, 9, 3, 4, 4, 4, 4, 3, 9, 5, 3, 4, 4, 1, 6, 1, 1, 8, 5],\n [8, 8, 8, 8, 2, 2, 3, 6, 4, 1, 5, 5, 8, 4, 4, 4, 4, 4, 4, 8, 5, 5, 1, 4, 6, 3, 2, 2, 8, 8],\n [6, 3, 1, 2, 9, 9, 9, 9, 4, 5, 5, 4, 5, 5, 4, 4, 4, 4, 5, 5, 4, 5, 5, 4, 9, 9, 9, 9, 2, 1],\n [2, 6, 1, 2, 9, 1, 9, 9, 5, 4, 3, 4, 5, 3, 4, 1, 1, 4, 3, 5, 4, 3, 4, 5, 9, 9, 1, 9, 2, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 9], [3, 9], [8, 8], [1, 8], [6, 1], [8, 9], [6, 9], [8, 9], [9, 4], [4, 4]], "task_id": "de493100"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 2, 5, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 1, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 1, 0],\n [0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 5, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 0],\n [2, 5, 0],\n [2, 1, 0]\n ],\n \"output\": [\n [0, 1, 2],\n [0, 5, 2],\n [0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 5, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 5, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 3, 3, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 2, 2, 3, 0], [0, 0, 0, 0, 0, 2, 3, 3, 0], [0, 0, 0, 0, 5, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "90347967"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 2, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 2, 3, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 2, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 4, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 2, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 4, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 0, 2, 0, 4, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 2, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 2, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 2, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 2, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 2, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 4, 2, 7, 0, 7, 7, 7, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 2, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 2, 0, 0, 8, 0],\n [0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 2, 1, 1, 1, 1, 0, 0, 0, 4, 2, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 2, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 2, 8, 8, 8, 0], [0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 2, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 4, 4, 2, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 2, 0, 7, 0, 0, 0, 0, 3, 2, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "88207623"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 4, 0, 0, 0, 0, 0, 0],\n [0, 4, 8, 4, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 2, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 6, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 6, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 3, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 3, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 3, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 5, 8, 5, 0, 0, 0],\n [0, 5, 8, 5, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 8, 5, 8, 0, 0, 0],\n [0, 8, 5, 8, 0, 0, 0],\n [0, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 8, 0, 0, 0, 3, 2, 3, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 6, 6, 0, 0, 4, 4, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 8, 1, 0, 0, 0, 2, 3, 2, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 1, 1, 0, 0, 5, 5, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 0, 0, 0, 0, 0],\n [0, 3, 3, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 3, 3, 0, 0, 0, 0, 0], [0, 2, 2, 3, 0, 0, 0, 0, 0], [0, 2, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 6, 6, 6, 0], [0, 0, 0, 0, 0, 6, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "45737921"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0]\n ],\n \"output\": [\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0],\n [0, 0, 0],\n [0, 8, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3],\n [0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0],\n [3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 0, 0, 0],\n [0, 0, 0, 0],\n [9, 0, 0, 0],\n [0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3], [9, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 9, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "fb791726"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 8, 9, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 8, 4, 0, 0, 2, 0, 8, 6, 0, 3, 0, 0, 8, 9, 0, 0, 0, 5],\n [9, 6, 0, 0, 0, 8, 0, 0, 1, 0, 0, 8, 0, 0, 8, 0, 0, 8, 2, 0, 0, 4, 0],\n [7, 7, 0, 0, 5, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 1, 8, 0, 0, 2, 9, 0],\n [0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 1, 4, 2, 8, 0, 3, 0, 0, 0, 8, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 2, 0, 9, 0, 8, 0, 3, 0, 4, 0, 8, 0, 0, 0, 0, 2, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 5, 0, 0, 8, 0, 0, 0, 2, 2, 8, 0, 0, 0, 8, 3],\n [0, 0, 6, 0, 0, 8, 9, 1, 0, 7, 0, 8, 0, 2, 0, 2, 2, 8, 0, 0, 0, 7, 0],\n [0, 5, 0, 0, 9, 8, 0, 0, 0, 4, 0, 8, 0, 0, 0, 2, 0, 8, 8, 0, 0, 5, 3],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 1, 0, 7, 0, 8, 0, 0, 2, 3, 9, 8, 4, 0, 0, 9, 0, 8, 0, 0, 0, 4, 0],\n [0, 6, 0, 4, 0, 8, 0, 1, 9, 0, 8, 8, 0, 0, 0, 0, 0, 8, 0, 8, 2, 0, 0],\n [3, 2, 0, 9, 4, 8, 0, 0, 0, 6, 0, 8, 0, 3, 8, 0, 0, 8, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 0, 8, 0, 0, 0, 9, 0],\n [0, 0, 2, 0, 0, 8, 3, 4, 0, 0, 0, 8, 9, 0, 0, 0, 0, 8, 8, 0, 0, 0, 3],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 4, 0, 8, 2, 9, 0, 6, 0, 8, 0, 0, 0, 2, 0, 8, 0, 0, 0, 3, 0],\n [0, 6, 1, 0, 0, 8, 3, 0, 0, 0, 0, 8, 0, 2, 1, 0, 0, 8, 0, 0, 9, 0, 0],\n [0, 0, 0, 5, 5, 8, 0, 0, 0, 2, 5, 8, 0, 0, 0, 0, 1, 8, 5, 0, 3, 0, 6],\n [0, 0, 0, 9, 0, 8, 1, 0, 0, 8, 0, 8, 2, 0, 7, 0, 0, 8, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 8, 6, 0, 0, 8, 0, 8, 8, 0, 0, 0, 2, 8, 0, 0, 0, 4, 7]\n ],\n \"output\": [\n [0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2],\n [0, 0, 0, 2, 2],\n [0, 2, 0, 2, 2],\n [0, 0, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 7, 6, 0, 3, 0, 2, 6, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 4, 3, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 0, 6, 0, 9, 3, 2, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0],\n [3, 6, 0, 8, 0, 3, 4, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 3, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0, 3, 0, 0, 7, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [2, 0, 0, 8, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 8, 0],\n [0, 0, 6, 0, 3, 3, 0, 0, 6, 0, 0, 3, 4, 0, 0, 0, 0, 3, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 4, 3, 0, 0, 3, 0, 0, 9, 0, 2, 3, 0, 0, 3, 0, 3, 3, 0, 2, 0, 0, 0],\n [8, 0, 1, 5, 0, 3, 0, 5, 0, 0, 2, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 3, 0, 2, 0, 4, 0, 3, 0, 5, 0, 1, 0, 3, 0, 0, 0, 0, 9],\n [0, 0, 0, 9, 0, 3, 0, 6, 0, 1, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1],\n [0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 3, 0, 0, 6, 0, 0],\n [0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 3],\n [6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 9, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 3, 0, 7, 0, 0, 7, 3, 0, 0, 0, 0, 0, 3, 0, 0, 9, 0, 5],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 7, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 8, 0, 3, 0, 0, 0, 7, 0, 3, 0, 0, 7, 0, 0, 3, 0, 0, 7, 1, 0],\n [0, 8, 0, 0, 0, 3, 0, 0, 7, 0, 0, 3, 2, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 7, 3, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0]\n ],\n \"output\": [\n [0, 7, 0, 0, 7],\n [0, 0, 0, 7, 0],\n [0, 0, 0, 7, 0],\n [0, 0, 7, 0, 0],\n [0, 0, 0, 0, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 5, 5, 0, 0, 5, 0, 1, 0, 5, 0, 0, 4, 5, 0, 0, 0, 5, 6, 0, 0],\n [1, 9, 0, 5, 0, 6, 4, 5, 0, 0, 4, 5, 4, 4, 0, 5, 0, 0, 1, 5, 0, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 4, 0, 5, 0, 4, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 8, 5, 0, 0, 4, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 4, 0],\n [0, 5, 0, 5, 0, 3, 9, 5, 0, 1, 0, 5, 0, 1, 0, 5, 0, 1, 0, 5, 0, 1, 0],\n [1, 0, 0, 5, 0, 0, 7, 5, 0, 0, 0, 5, 0, 0, 3, 5, 0, 6, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 8, 0, 4, 5, 0, 9, 0, 5, 0, 7, 5, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 4, 5, 0, 0, 0, 5, 0, 0, 8, 5, 0, 0, 6, 5, 0, 6, 0, 5, 4, 0, 6],\n [0, 1, 0, 5, 2, 0, 0, 5, 7, 0, 0, 5, 0, 2, 0, 5, 0, 7, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 4, 5, 4, 0, 0, 5, 0, 0, 0],\n [0, 3, 8, 5, 0, 3, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 4, 0, 8],\n [0, 0, 0, 5, 8, 0, 0, 5, 1, 0, 3, 5, 0, 7, 0, 5, 0, 8, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 7, 0, 5, 0, 0, 0, 5, 0, 2, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2],\n [0, 3, 0, 5, 2, 6, 1, 5, 0, 8, 0, 5, 2, 0, 9, 5, 0, 7, 0, 5, 0, 0, 7],\n [0, 0, 0, 5, 8, 0, 0, 5, 0, 0, 0, 5, 0, 8, 0, 5, 0, 0, 4, 5, 2, 0, 4],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 7, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 3, 0, 5, 0, 0, 8, 5, 0, 0, 0],\n [0, 0, 1, 5, 0, 4, 0, 5, 3, 0, 3, 5, 0, 0, 0, 5, 3, 0, 0, 5, 0, 3, 0],\n [0, 0, 2, 5, 0, 0, 3, 5, 4, 0, 0, 5, 0, 8, 0, 5, 0, 0, 0, 5, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 4],\n [4, 4, 0],\n [0, 4, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 3, 0, 0, 0, 2, 0, 0, 0, 8, 3, 2, 1, 0, 0, 0, 0, 2, 0, 8, 0, 0, 0, 2, 8, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 7, 4, 0, 2, 0, 7, 4, 0, 4, 2, 0, 9, 0, 5, 0],\n [0, 0, 0, 0, 0, 2, 0, 9, 1, 0, 5, 2, 0, 6, 6, 0, 0, 2, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 2, 0, 0, 8, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 3, 0, 2, 4, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 2, 4, 0, 0, 9, 0, 2, 0, 9, 0, 2, 5, 2, 0, 0, 3, 7, 0, 2, 0, 0, 0, 4, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [6, 0, 0, 0, 0, 2, 6, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 2, 0, 0, 3, 0, 0, 2, 9, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0],\n [7, 0, 8, 3, 0, 2, 0, 0, 4, 4, 6, 2, 0, 0, 9, 7, 7, 2, 2, 0, 9, 0, 0, 2, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 0, 0, 4, 0, 0, 2, 0, 0, 5, 0, 0],\n [0, 0, 3, 0, 3, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 5, 5, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 6, 4, 0, 2, 0, 0, 8, 0, 8, 2, 0, 0, 0, 0, 0, 2, 0, 0, 8, 0, 6, 2, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 7, 2, 0, 0, 0, 0, 0, 2, 1, 0, 0, 1, 6, 2, 0, 0, 0, 0, 0, 2, 0, 4, 0, 0, 3],\n [0, 0, 0, 0, 0, 2, 8, 0, 0, 0, 0, 2, 0, 0, 1, 1, 0, 2, 0, 9, 0, 0, 0, 2, 0, 2, 0, 8, 0],\n [0, 0, 5, 0, 0, 2, 0, 0, 8, 8, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 8, 2, 0, 0, 2, 0, 0, 2, 1, 0, 0, 0, 0, 2, 0, 0, 0, 8, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 6, 2, 0, 0, 0, 7, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [8, 0, 0, 0, 0, 2, 0, 3, 2, 0, 0, 2, 0, 0, 0, 0, 3, 2, 7, 0, 0, 0, 7, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 4, 6, 0, 6, 1, 2, 0, 8, 2, 0, 8, 2, 0, 0, 0, 8, 0, 2, 0, 5, 0, 6, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 2, 5, 3, 4, 0, 0, 2, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 1, 2, 0, 0, 6, 0, 0, 2, 0, 0, 1, 4, 0, 2, 0, 0, 3, 8, 0, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 4, 0, 0, 2, 6, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 2, 4, 0, 3, 0, 8, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 5],\n [0, 4, 0, 0, 0, 2, 0, 2, 1, 0, 0, 2, 3, 0, 0, 4, 0, 2, 0, 0, 0, 0, 8, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 9, 0, 2, 0, 0, 0, 6, 5, 2, 0, 5, 0, 0, 0, 2, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 6, 0, 6, 0, 2, 0, 0, 0, 1, 9, 2, 7, 0, 5, 7, 3, 2, 0, 0, 1, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0, 8], [0, 0, 0, 0, 0], [8, 0, 0, 0, 0], [0, 0, 8, 8, 0], [0, 0, 0, 0, 8]], "task_id": "c3202e5a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 8, 8, 3, 8, 0, 0, 8, 1, 0, 8, 0, 3, 8, 0, 1, 8, 0, 8, 8, 0, 0, 8],\n [1, 2, 8, 0, 8, 8, 1, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 0, 0, 3],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 8, 0, 8],\n [8, 8, 8, 8, 1, 3, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 3, 4, 3, 8, 8, 3],\n [0, 8, 8, 8, 2, 3, 0, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0, 8, 8, 3, 8, 8, 8, 8],\n [8, 3, 8, 2, 4, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8],\n [0, 8, 8, 8, 2, 8, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 1, 8, 1, 8, 1, 0, 0, 8],\n [0, 1, 8, 1, 0, 8, 0, 0, 1, 8, 0, 3, 0, 8, 8, 0, 0, 8, 8, 0, 8, 8, 8, 2],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 8, 0, 0, 8],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8],\n [0, 8, 8, 2, 0, 8, 0, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0, 8, 2, 8, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8],\n [8, 3, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 0, 8, 8, 1, 8, 8, 8, 3, 8, 8, 8, 8],\n [0, 8, 8, 8, 1, 8, 0, 0, 8, 8, 0, 8, 0, 2, 8, 0, 0, 8, 8, 8, 8, 8, 8, 2],\n [0, 8, 8, 8, 0, 8, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 0, 8],\n [8, 8, 0, 8, 8, 8, 8, 8, 8, 1, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8],\n [0, 8, 1, 4, 1, 8, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 1, 8, 8, 3, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 3, 0, 8, 0, 0, 8, 0, 0, 8, 0, 8, 8, 0, 2, 8],\n [0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 0, 3, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0, 8],\n [8, 3, 0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 1, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 2, 4, 2, 8, 3, 8, 8, 3, 8, 8],\n [0, 0, 8, 8, 0, 8, 3, 8, 3, 8, 1, 8, 0, 0, 3, 2, 8, 8, 0, 8, 8, 0, 0, 8]\n ],\n \"output\": [\n [2]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 2, 2, 0, 2, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 2, 0, 8, 0, 0],\n [0, 2, 0, 2, 2, 0, 0, 2, 0, 2, 2, 3, 0, 0, 6, 2, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 1, 0, 2, 2, 0],\n [0, 2, 2, 7, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2],\n [2, 8, 6, 2, 2, 0, 3, 4, 3, 2, 2, 2, 2, 0, 2, 2, 2, 2, 1, 0, 2, 7],\n [2, 2, 2, 2, 2, 0, 2, 3, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 4, 1, 2, 2],\n [2, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 1, 0, 2, 2, 0, 2, 1, 2, 0, 0],\n [2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [2, 1, 0, 2, 3, 2, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 1, 0],\n [2, 2, 2, 3, 4, 3, 2, 2, 2, 3, 2, 2, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 2, 0, 2, 3, 2, 0, 2, 2, 1, 0, 2, 2, 2, 0, 2, 0, 0, 0, 2, 3, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 6, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 7, 2, 0, 2, 0, 0, 0, 0, 6, 4, 6, 2, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 6, 2, 2, 0, 2, 2, 2, 0, 6],\n [2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 2, 2, 2, 2, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7, 2, 2, 2],\n [0, 0, 0, 2, 2, 8, 4, 8, 0, 2, 0, 2, 0, 0, 2, 2, 0, 7, 4, 7, 0, 0],\n [2, 2, 2, 0, 2, 0, 8, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 7, 2, 2, 2],\n [0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 6, 0, 2, 0, 2, 0, 0],\n [2, 1, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 2, 0, 2, 7, 0],\n [0, 0, 0, 2, 2, 2, 0, 8, 0, 0, 2, 1, 0, 0, 2, 2, 0, 2, 0, 2, 0, 0]\n ],\n \"output\": [\n [3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0],\n [0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 0, 5],\n [5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 3, 4, 3, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 3, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 5, 8, 4, 8, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 0, 0],\n [5, 5, 0, 5, 8, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 4, 8, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 8, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0],\n [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 9, 9, 0, 9, 0, 9, 0, 6, 0, 9, 0, 0, 9, 9, 0, 9, 0, 0, 9, 0],\n [0, 0, 9, 9, 9, 9, 3, 9, 9, 9, 0, 9, 0, 0, 9, 9, 0, 6, 0, 9, 9, 0],\n [9, 9, 2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9],\n [9, 2, 4, 2, 9, 9, 9, 0, 9, 9, 0, 9, 0, 3, 9, 9, 9, 1, 9, 9, 2, 9],\n [9, 9, 2, 9, 9, 9, 6, 9, 9, 9, 6, 9, 9, 9, 2, 0, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 0, 9, 9, 0, 0, 9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 9],\n [0, 0, 9, 9, 9, 9, 0, 9, 9, 9, 0, 9, 3, 0, 9, 9, 0, 9, 0, 9, 9, 0],\n [9, 9, 9, 0, 9, 9, 9, 3, 9, 9, 9, 9, 0, 9, 9, 9, 9, 0, 9, 9, 9, 9],\n [6, 9, 9, 0, 9, 9, 3, 4, 3, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 0, 9, 9, 9, 3, 9, 3, 9, 9, 9, 9, 9, 9, 6, 9, 9, 0, 0, 3],\n [0, 0, 0, 1, 9, 9, 0, 9, 9, 9, 0, 9, 0, 0, 9, 6, 4, 6, 0, 9, 9, 0],\n [9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 0, 2, 9, 9, 9, 6, 9, 9, 0, 9, 1],\n [0, 0, 9, 9, 9, 9, 0, 9, 9, 9, 0, 9, 0, 0, 9, 9, 0, 9, 0, 9, 0, 0],\n [0, 0, 9, 2, 9, 9, 3, 9, 0, 6, 0, 9, 0, 0, 9, 9, 0, 9, 0, 9, 9, 0],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 0, 9, 0, 9, 9, 9, 2, 9],\n [0, 6, 3, 9, 9, 9, 0, 9, 9, 0, 0, 9, 0, 3, 9, 9, 0, 2, 0, 0, 9, 0],\n [9, 9, 9, 9, 9, 9, 0, 3, 9, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [0, 0, 9, 9, 9, 9, 0, 9, 9, 2, 0, 9, 0, 0, 9, 9, 0, 9, 0, 9, 9, 0],\n [9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 6, 0, 9, 9, 9, 9, 9, 9, 9, 6, 9],\n [9, 9, 9, 9, 1, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9, 3, 9, 9, 9, 9, 9, 9],\n [0, 0, 0, 1, 4, 1, 0, 9, 9, 0, 0, 9, 0, 0, 0, 9, 0, 9, 2, 9, 0, 0],\n [0, 0, 9, 9, 1, 9, 0, 9, 9, 9, 0, 9, 0, 0, 9, 9, 0, 9, 0, 9, 9, 0],\n [3, 9, 9, 9, 9, 0, 9, 9, 0, 9, 9, 9, 2, 9, 0, 9, 9, 9, 9, 0, 0, 9],\n [9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 2, 4, 2, 9, 9, 0, 9, 9, 9, 9, 9],\n [0, 0, 9, 9, 9, 9, 0, 0, 9, 1, 0, 9, 2, 0, 9, 9, 0, 9, 6, 9, 9, 0],\n [9, 9, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2]], "task_id": "642d658d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 8, 2, 2, 0, 0, 2, 8, 2, 0, 0, 2, 2, 8],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 2, 0, 0, 2, 8, 2, 0, 0, 2, 2, 8, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 0, 0, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 0, 2, 2, 0, 0, 3, 3, 3],\n [2, 0, 2, 0, 2, 0, 2, 0, 3, 3, 3],\n [0, 2, 0, 0, 0, 2, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [2, 0, 2, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 2, 2, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 2, 0, 2, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3]\n ],\n \"output\": [\n [8, 2, 0, 0, 2, 8, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [8, 0, 2, 0, 0, 0, 0, 0, 2, 0, 8],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 2, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 2, 0, 2, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 2, 2, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [2, 0, 2, 0, 2, 0, 2, 0, 3, 3, 3],\n [0, 2, 0, 0, 0, 2, 0, 0, 3, 3, 3],\n [2, 2, 0, 0, 2, 2, 0, 0, 3, 3, 3]\n ],\n \"output\": [\n [8, 0, 2, 0, 0, 0, 0, 0, 2, 0, 8],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [8, 2, 0, 0, 2, 8, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2],\n [2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2],\n [2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 2, 2, 2, 0, 2, 8, 2, 2, 0, 2, 2, 8, 2, 0, 2, 2, 2, 8], [2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0], [2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2], [2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [8, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 8], [2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0], [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2], [8, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 8, 2, 0, 2, 0, 2, 8]], "task_id": "456873bc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 0, 5, 0, 0, 0, 5, 5],\n [5, 0, 0, 5, 5, 0, 5, 0, 5, 5],\n [0, 5, 5, 0, 5, 5, 0, 5, 0, 0],\n [2, 0, 5, 5, 2, 0, 5, 0, 2, 5],\n [5, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [0, 0, 2, 5, 5, 5, 2, 0, 5, 0],\n [5, 5, 0, 0, 0, 5, 5, 5, 5, 5],\n [0, 5, 0, 5, 5, 0, 5, 0, 5, 5],\n [0, 5, 5, 0, 5, 0, 5, 0, 5, 5],\n [5, 5, 0, 0, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [5, 2, 0, 2, 5, 2, 0, 2, 5, 2],\n [5, 5, 2, 5, 5, 5, 2, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 0, 8, 0, 8, 8, 0, 0],\n [2, 0, 8, 8, 0, 8, 0, 0, 0, 8],\n [2, 2, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 8, 0, 0, 0, 0, 0],\n [8, 8, 2, 2, 0, 0, 0, 8, 8, 0],\n [8, 8, 8, 2, 2, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 8, 8, 8, 0],\n [8, 0, 8, 0, 0, 2, 2, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 2, 2, 8, 0],\n [0, 8, 0, 8, 0, 8, 8, 2, 2, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 2, 2, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 2, 2, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 2, 2, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 2, 2, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 2, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 2, 2, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 9, 0, 0, 0, 0, 0, 0, 0],\n [9, 9, 9, 0, 9, 0, 0, 9, 0, 0],\n [9, 0, 0, 0, 9, 0, 9, 0, 0, 0],\n [0, 0, 9, 9, 9, 0, 9, 0, 0, 0],\n [0, 2, 2, 2, 0, 2, 2, 2, 9, 2],\n [2, 2, 0, 2, 2, 2, 9, 2, 2, 2],\n [9, 0, 0, 9, 9, 9, 0, 9, 9, 0],\n [0, 0, 0, 0, 9, 0, 9, 0, 0, 9],\n [0, 9, 9, 0, 0, 0, 0, 9, 9, 0],\n [9, 0, 9, 0, 0, 9, 0, 9, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 0, 2, 2, 2, 0, 2], [2, 2, 9, 2, 2, 2, 9, 2, 2, 2], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9]], "task_id": "782b5218"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 3, 3, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 3, 3, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 0, 6, 0, 7, 0, 8, 8, 8, 0, 0, 8, 8, 8],\n [0, 1, 0, 6, 0, 7, 0, 8, 8, 8, 0, 0, 8, 8, 8],\n [0, 1, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 1, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 1, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 1, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 8, 8, 8, 8, 0, 8, 8, 0, 0],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 0, 0, 8, 8, 0, 8, 8, 8, 8],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 3, 0, 2, 0, 4, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 2, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 4, 4, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 8, 8, 8, 0, 0, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 1, 0, 3, 0, 2, 0, 4, 0, 6, 0, 7, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 2, 2, 4, 4, 4, 0, 0, 0, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 0, 6, 6, 6, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 2, 0, 0, 0, 6, 6, 6, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 6, 6, 6, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0]], "task_id": "9b365c51"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "b9630600"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [8, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [2, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [8, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 2, 2, 2, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [6, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [6, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [6, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [4, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [6, 0, 0, 0, 6, 6, 0, 0, 0, 0],\n [6, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [6, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [4, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 5, 5, 0, 5, 0, 0],\n [2, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [3, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [4, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [7, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 2, 2, 0, 2, 0, 0], [2, 0, 0, 2, 2, 2, 2, 2, 0, 0], [3, 0, 0, 3, 0, 0, 0, 0, 0, 0], [3, 0, 0, 3, 3, 3, 0, 0, 0, 0], [3, 0, 0, 0, 0, 3, 0, 0, 0, 0], [4, 0, 0, 4, 4, 4, 4, 0, 0, 0], [7, 0, 0, 7, 7, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c7d4e6ad"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 8, 8, 8, 0, 8, 0, 6, 0],\n [0, 8, 0, 0, 6, 6, 6, 6, 8, 0],\n [6, 6, 0, 1, 1, 1, 1, 0, 6, 6],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [8, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 6, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 6, 8],\n [0, 8, 1, 1, 1, 8, 6, 8, 0, 0],\n [6, 8, 6, 0, 6, 0, 8, 0, 6, 8],\n [8, 6, 0, 6, 0, 6, 6, 8, 0, 8]\n ],\n \"output\": [\n [6, 6, 8, 8, 8, 0, 8, 0, 6, 0],\n [0, 8, 0, 0, 6, 6, 6, 6, 8, 0],\n [6, 6, 0, 1, 1, 1, 1, 0, 6, 6],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 1, 8],\n [0, 8, 1, 1, 1, 1, 1, 1, 0, 0],\n [6, 8, 6, 0, 6, 0, 8, 0, 6, 8],\n [8, 6, 0, 6, 0, 6, 6, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [9, 0, 0, 0, 0, 7, 7, 0, 9, 0],\n [0, 0, 9, 0, 0, 0, 9, 9, 9, 0],\n [7, 7, 0, 3, 3, 3, 3, 7, 9, 7],\n [0, 3, 7, 3, 3, 3, 3, 9, 3, 7],\n [0, 3, 9, 3, 3, 0, 0, 0, 3, 9],\n [9, 3, 3, 3, 3, 0, 0, 9, 3, 0],\n [3, 3, 3, 3, 3, 9, 0, 0, 3, 7],\n [3, 3, 3, 3, 3, 0, 9, 9, 3, 0],\n [0, 9, 0, 3, 3, 3, 9, 9, 9, 9],\n [7, 9, 7, 9, 0, 0, 7, 7, 0, 0]\n ],\n \"output\": [\n [9, 0, 0, 0, 0, 7, 7, 0, 9, 0],\n [0, 0, 9, 0, 0, 0, 9, 9, 9, 0],\n [7, 7, 0, 3, 3, 3, 3, 7, 9, 7],\n [0, 3, 7, 3, 3, 3, 3, 9, 3, 7],\n [0, 3, 9, 3, 3, 3, 3, 0, 3, 9],\n [9, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 9, 0, 3, 3, 3, 3, 9, 9, 9],\n [7, 9, 7, 9, 0, 0, 7, 7, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 0, 0, 4, 1],\n [4, 4, 0, 4, 2, 2, 1, 4, 4, 4],\n [4, 0, 2, 2, 2, 2, 2, 2, 1, 0],\n [0, 4, 2, 2, 2, 0, 0, 1, 1, 0],\n [0, 0, 1, 2, 2, 2, 1, 0, 1, 0],\n [0, 4, 0, 2, 2, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [4, 1, 4, 1, 2, 2, 4, 4, 1, 4],\n [0, 4, 4, 4, 2, 1, 1, 4, 4, 1],\n [4, 0, 4, 4, 0, 4, 1, 1, 4, 0]\n ],\n \"output\": [\n [1, 1, 0, 1, 1, 0, 0, 0, 4, 1],\n [4, 4, 0, 4, 2, 2, 1, 4, 4, 4],\n [4, 0, 2, 2, 2, 2, 2, 2, 1, 0],\n [0, 4, 2, 2, 2, 2, 2, 2, 1, 0],\n [0, 0, 1, 2, 2, 2, 2, 0, 1, 0],\n [0, 4, 0, 2, 2, 2, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [4, 1, 4, 1, 2, 2, 4, 4, 1, 4],\n [0, 4, 4, 4, 2, 2, 1, 4, 4, 1],\n [4, 0, 4, 4, 0, 4, 1, 1, 4, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 6, 6, 6, 6, 0, 6, 6, 0],\n [2, 6, 0, 6, 9, 0, 6, 0, 2, 6],\n [2, 6, 6, 9, 9, 9, 9, 0, 6, 6],\n [2, 0, 0, 9, 9, 0, 9, 6, 0, 2],\n [9, 9, 9, 9, 9, 9, 6, 0, 0, 0],\n [9, 9, 9, 9, 9, 9, 9, 9, 0, 0],\n [0, 0, 9, 9, 9, 9, 6, 6, 0, 0],\n [2, 9, 9, 9, 9, 9, 9, 6, 2, 6],\n [0, 0, 2, 9, 0, 6, 9, 0, 2, 6],\n [6, 0, 0, 2, 0, 6, 0, 6, 6, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 6, 6, 6, 6, 0, 6, 6, 0], [2, 6, 0, 6, 9, 9, 6, 0, 2, 6], [2, 6, 6, 9, 9, 9, 9, 0, 6, 6], [2, 0, 0, 9, 9, 9, 9, 6, 0, 2], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 9, 9, 9, 9, 9, 9, 0, 0], [2, 9, 9, 9, 9, 9, 9, 9, 9, 6], [0, 0, 2, 9, 0, 6, 9, 0, 2, 6], [6, 0, 0, 2, 0, 6, 0, 6, 6, 2]], "task_id": "c35c1b4c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 0],\n [0, 7, 7],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 3, 0, 0],\n [0, 0, 3, 3, 0, 0],\n [0, 0, 7, 7, 7, 7],\n [0, 0, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 0],\n [0, 8, 5, 5],\n [0, 0, 0, 5],\n [0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 8, 8, 5, 5, 5, 5],\n [0, 0, 8, 8, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0],\n [0, 6, 6, 6, 0],\n [0, 0, 1, 6, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 6, 6, 6, 6, 6, 6, 0, 0], [0, 0, 6, 6, 6, 6, 6, 6, 0, 0], [0, 0, 0, 0, 1, 1, 6, 6, 0, 0], [0, 0, 0, 0, 1, 1, 6, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "60c09cac"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 3, 3],\n [0, 3, 0, 3],\n [0, 0, 0, 0],\n [3, 0, 3, 3],\n [3, 0, 0, 0],\n [0, 3, 0, 3],\n [0, 5, 0, 5],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [5, 0, 5, 0],\n [5, 0, 0, 0],\n [5, 5, 0, 5]\n ],\n \"output\": [\n [0, 4, 4, 4],\n [0, 4, 0, 4],\n [0, 0, 0, 0],\n [4, 0, 4, 4],\n [4, 0, 0, 0],\n [4, 4, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 0, 3],\n [3, 0, 3, 3],\n [0, 3, 0, 0],\n [0, 0, 3, 0],\n [3, 0, 3, 0],\n [0, 0, 0, 3],\n [5, 0, 0, 0],\n [0, 5, 5, 5],\n [5, 0, 0, 5],\n [0, 5, 5, 5],\n [5, 5, 5, 0],\n [5, 0, 0, 0]\n ],\n \"output\": [\n [4, 4, 0, 4],\n [4, 4, 4, 4],\n [4, 4, 0, 4],\n [0, 4, 4, 4],\n [4, 4, 4, 0],\n [4, 0, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 0, 0],\n [3, 0, 0, 0],\n [0, 0, 0, 3],\n [0, 0, 3, 3],\n [3, 0, 0, 0],\n [3, 3, 3, 3],\n [0, 5, 0, 0],\n [5, 5, 0, 0],\n [5, 0, 5, 0],\n [5, 5, 5, 5],\n [5, 5, 5, 0],\n [5, 0, 5, 0]\n ],\n \"output\": [\n [4, 4, 0, 0],\n [4, 4, 0, 0],\n [4, 0, 4, 4],\n [4, 4, 4, 4],\n [4, 4, 4, 0],\n [4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 0, 0],\n [0, 3, 3, 3],\n [3, 3, 0, 3],\n [0, 3, 3, 0],\n [3, 0, 3, 0],\n [3, 0, 0, 0],\n [0, 5, 5, 5],\n [5, 5, 5, 5],\n [5, 5, 5, 0],\n [5, 5, 5, 5],\n [5, 0, 0, 0],\n [0, 5, 5, 0]\n ],\n \"output\": [\n [4, 4, 4, 4],\n [4, 4, 4, 4],\n [4, 4, 4, 4],\n [4, 4, 4, 4],\n [4, 0, 4, 0],\n [4, 4, 4, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 3, 0, 3],\n [0, 3, 0, 3],\n [0, 0, 0, 3],\n [3, 3, 0, 3],\n [3, 0, 3, 3],\n [0, 3, 3, 3],\n [0, 0, 0, 0],\n [5, 0, 0, 5],\n [0, 0, 5, 0],\n [5, 0, 0, 5],\n [5, 5, 5, 5],\n [5, 5, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 0, 4], [4, 4, 0, 4], [0, 0, 4, 4], [4, 4, 0, 4], [4, 4, 4, 4], [4, 4, 4, 4]], "task_id": "d19f7514"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 4, 4, 6, 6, 6, 6, 6],\n [0, 4, 0, 4, 6, 0, 0, 0, 6],\n [0, 4, 4, 4, 6, 0, 0, 0, 6],\n [0, 0, 0, 0, 6, 6, 6, 6, 6]\n ],\n \"output\": [\n [4, 0, 0],\n [6, 6, 6],\n [6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 0, 0, 0, 0, 0, 0],\n [7, 0, 7, 7, 0, 8, 8, 8, 8],\n [7, 0, 0, 7, 0, 8, 0, 0, 8],\n [7, 7, 7, 7, 0, 8, 8, 8, 8]\n ],\n \"output\": [\n [7, 7, 7],\n [8, 8, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [9, 9, 9, 9, 0, 5, 5, 5, 5],\n [9, 0, 0, 9, 0, 5, 0, 0, 5],\n [9, 9, 0, 9, 0, 5, 0, 5, 5],\n [0, 9, 9, 9, 0, 5, 5, 5, 0]\n ],\n \"output\": [\n [9, 9, 9],\n [5, 5, 5],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3, 3, 0, 1, 1, 1, 0],\n [3, 0, 0, 3, 0, 1, 0, 1, 0],\n [3, 0, 0, 3, 0, 1, 0, 1, 0],\n [3, 3, 3, 3, 0, 1, 1, 1, 0]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 0, 0],\n [1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 0, 6, 6, 6, 0, 0],\n [8, 0, 8, 0, 6, 0, 6, 0, 0],\n [8, 8, 8, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0],\n [6, 0, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 0, 8, 8, 8, 0, 0],\n [7, 0, 7, 0, 8, 0, 8, 8, 0],\n [7, 0, 7, 0, 8, 0, 0, 8, 0],\n [7, 7, 7, 0, 8, 8, 8, 8, 0]\n ],\n \"output\": [\n [7, 7, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 0, 0, 0, 7, 7, 7],\n [4, 0, 4, 4, 4, 0, 7, 0, 7],\n [4, 0, 0, 0, 4, 0, 7, 7, 7],\n [4, 4, 4, 4, 4, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 4], [4, 0, 0], [7, 0, 0]], "task_id": "8ba14f53"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 2, 4, 2],\n [2, 2, 6, 6],\n [6, 4, 2, 4]\n ],\n \"output\": [\n [4, 2, 4, 6, 6, 4, 2, 4],\n [6, 6, 2, 2, 2, 2, 6, 6],\n [2, 4, 2, 6, 6, 2, 4, 2],\n [2, 4, 2, 6, 6, 2, 4, 2],\n [6, 6, 2, 2, 2, 2, 6, 6],\n [4, 2, 4, 6, 6, 4, 2, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 9, 9],\n [9, 5, 5, 5],\n [5, 7, 5, 7]\n ],\n \"output\": [\n [7, 5, 7, 5, 5, 7, 5, 7],\n [5, 5, 5, 9, 9, 5, 5, 5],\n [9, 9, 5, 5, 5, 5, 9, 9],\n [9, 9, 5, 5, 5, 5, 9, 9],\n [5, 5, 5, 9, 9, 5, 5, 5],\n [7, 5, 7, 5, 5, 7, 5, 7]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 5, 5],\n [5, 8, 5, 8],\n [8, 8, 5, 8]\n ],\n \"output\": [\n [8, 5, 8, 8, 8, 8, 5, 8],\n [8, 5, 8, 5, 5, 8, 5, 8],\n [5, 5, 3, 3, 3, 3, 5, 5],\n [5, 5, 3, 3, 3, 3, 5, 5],\n [8, 5, 8, 5, 5, 8, 5, 8],\n [8, 5, 8, 8, 8, 8, 5, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 5, 7, 8],\n [7, 7, 8, 8],\n [5, 5, 8, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 8, 5, 5, 5, 5, 8, 5], [8, 8, 7, 7, 7, 7, 8, 8], [8, 7, 5, 8, 8, 5, 7, 8], [8, 7, 5, 8, 8, 5, 7, 8], [8, 8, 7, 7, 7, 7, 8, 8], [5, 8, 5, 5, 5, 5, 8, 5]], "task_id": "0c786b71"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0],\n [2, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 2, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 1, 2, 1, 3, 3, 1, 1, 1, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 1, 1, 1, 3, 3, 1, 2, 1, 3, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3, 1, 2, 1, 0, 0, 0, 0],\n [2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 1, 2, 1, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 3, 1, 1, 1, 3, 3, 3, 1, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 1, 1, 3, 3, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 2, 1, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 1, 3, 3, 3, 3, 3, 3, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 2, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 2, 3, 3, 3, 3, 3, 3, 2, 3, 0, 0, 0, 2, 3, 3, 2, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 2, 3, 3, 3, 0, 2, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 2],\n [0, 0, 3, 2, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 3, 2, 3, 3, 3, 0, 0, 2, 0, 2, 3, 3, 3, 3],\n [0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 0, 2, 0, 0, 3, 3, 3, 3, 3],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3],\n [0, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 1, 1, 1, 3, 3, 1, 2, 1, 1, 1, 0, 0, 0, 0, 3, 1, 1, 1, 3],\n [0, 1, 2, 1, 3, 3, 1, 1, 1, 2, 1, 0, 0, 0, 2, 3, 1, 2, 1, 3],\n [0, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 1, 1, 1, 3],\n [0, 0, 3, 3, 3, 3, 1, 2, 1, 3, 3, 0, 2, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 3, 3, 3, 3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 3, 3, 3, 1, 1],\n [0, 0, 1, 1, 1, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 1, 2],\n [0, 0, 1, 2, 1, 3, 1, 1, 1, 3, 3, 0, 0, 0, 1, 1, 1, 3, 1, 1],\n [0, 1, 1, 1, 1, 3, 1, 2, 1, 3, 3, 0, 0, 2, 1, 2, 1, 3, 3, 3],\n [0, 1, 2, 1, 3, 3, 1, 1, 1, 3, 3, 0, 2, 0, 1, 1, 1, 3, 3, 3],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3],\n [0, 2, 0, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 1, 2, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 1, 1, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 1, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 1, 1, 1, 1, 2, 1, 3, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 2, 0, 0, 0, 0, 3, 3, 3, 3, 3, 2, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 3, 2, 2, 3, 3, 3, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 3, 3, 1, 2, 1, 0, 0, 0, 3, 3, 3, 3, 1, 2, 1, 0],\n [0, 3, 3, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 1, 2, 2, 1, 3, 3, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 0, 2],\n [0, 0, 0, 3, 3, 3, 3, 2, 3, 3, 3, 0, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 2, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0],\n [0, 0, 0, 3, 3, 2, 2, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 2, 3, 2, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 3, 2, 2, 3, 3, 3, 3, 3, 0, 0, 0, 2, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 1, 3, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 3, 3, 3, 1, 1, 1, 1, 2, 1, 0, 3, 1, 1, 2, 1, 1, 1, 3, 3, 3, 3, 3, 0, 2], [0, 0, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 2, 3, 1, 2, 1, 1, 3, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 1, 2, 1, 3, 1, 1, 1, 3, 3, 0, 0, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 3, 3, 1, 2, 1, 3, 3, 3, 0, 0, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 0], [0, 0, 0, 3, 1, 1, 1, 1, 3, 3, 3, 0, 0, 3, 1, 2, 1, 3, 3, 3, 3, 3, 3, 1, 2, 1, 0], [0, 0, 0, 3, 1, 2, 2, 1, 3, 3, 3, 0, 0, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 0], [0, 0, 0, 3, 1, 1, 1, 1, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 1, 2, 1, 2, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 1, 2, 2, 1, 3, 3, 3, 3, 0, 0, 0, 2, 0]], "task_id": "a04b2602"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2],\n [2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2],\n [2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 0, 2]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2],\n [2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 0, 2]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 2],\n [2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 0, 0]], "task_id": "e6de6e8f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 3, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5],\n [5, 5, 5, 5],\n [5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 5, 5, 8, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1],\n [4, 1, 4, 1, 1, 1, 1, 1, 4, 1]\n ],\n \"output\": [\n [1, 1, 1],\n [1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1]], "task_id": "7039b2d7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 4, 1, 1, 1, 8, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 8, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 8, 0, 8, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 8, 0, 0],\n [1, 3, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 0, 0, 0, 8, 0],\n [4, 4, 4, 0, 8, 0, 8],\n [0, 4, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 2, 2, 0],\n [3, 3, 0, 0, 2, 0, 2],\n [0, 3, 0, 0, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 2, 1, 1, 1, 4, 1],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 1, 1, 3, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 4, 0],\n [2, 2, 2, 0, 4, 4, 4],\n [0, 2, 2, 0, 0, 4, 4],\n [0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 3, 3, 0],\n [0, 6, 0, 0, 3, 0, 3],\n [6, 6, 6, 0, 0, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 5, 1, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [1, 2, 1, 1, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 0, 0, 0, 0, 4],\n [5, 5, 0, 0, 0, 4, 0],\n [0, 0, 5, 0, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 3, 0, 3],\n [2, 0, 2, 0, 0, 3, 3],\n [2, 2, 2, 0, 0, 0, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 6, 6, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 2, 0],\n [0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 1, 1, 3, 1, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 1, 1, 1, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 0, 3, 3, 3], [2, 2, 0, 0, 0, 3, 0], [0, 0, 2, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 6, 6], [8, 8, 8, 0, 6, 6, 0], [0, 0, 8, 0, 0, 6, 6]], "task_id": "7d18a6fb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 1, 1, 1, 0, 2, 2, 2, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 1, 0, 1, 0, 2, 2, 2, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 2, 2, 2, 0, 6, 6, 6, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 2, 2, 2, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 7, 0, 7, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 2, 2, 2, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "4c177718"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 2, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 8, 2, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 8, 8, 2, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 2, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 2, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c97c0139"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 6, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 9, 2, 4, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 3, 0, 7, 8, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 9, 2, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 3, 0, 7, 8, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 5, 0, 6, 0, 0, 0, 0, 9, 0, 0, 0, 9],\n [5, 5, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 9, 0, 1],\n [4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 5, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 3, 0, 0, 0, 0, 9],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 6, 0, 0, 1, 0, 0, 8]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 6, 0, 0, 0, 0, 9, 0, 0, 0, 9],\n [5, 5, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 9, 0, 1],\n [4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 5, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 1, 0, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 7],\n [5, 5, 5, 5, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 6, 2, 0],\n [0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 8, 7, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 7], [5, 5, 5, 5, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 6, 2, 0], [0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 7, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 7, 0, 0, 7, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 6, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0]], "task_id": "1e81d6f9"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8],\n [8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8],\n [8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8],\n [8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 3, 3, 3, 8, 8, 3, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8],\n [8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8],\n [8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8],\n [8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 8, 8],\n [8, 8, 8, 3, 3, 3, 8, 8, 3, 3, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 1, 1, 1, 4, 4, 4, 1, 1, 1],\n [1, 1, 4, 1, 1, 1, 4, 4, 4, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1],\n [1, 4, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1],\n [1, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1],\n [1, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1],\n [1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 4, 4, 4, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 4, 8, 4, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 1, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8], [8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8], [8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 4, 8, 4, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], "task_id": "4364c1c4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 0, 2, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 8, 0, 1, 0, 0, 6, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1, 0, 8, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "72207abc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 5, 5, 2, 5, 5, 5, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 5, 5, 5, 2, 5, 5, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 5, 5, 5, 2, 5, 5, 5, 5, 5, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 5, 2, 5, 5, 5, 5, 5, 5, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e4075551"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 0, 9, 0],\n [9, 0, 0, 9, 0],\n [0, 9, 9, 9, 9],\n [4, 0, 0, 4, 0],\n [4, 4, 0, 4, 4],\n [4, 4, 4, 0, 4]\n ],\n \"output\": [\n [0, 6, 0, 0, 0],\n [0, 6, 0, 0, 6],\n [6, 0, 0, 6, 0]\n ]\n}\n\n{\n \"input\": [\n [9, 0, 0, 9, 9],\n [0, 0, 0, 0, 0],\n [0, 0, 9, 0, 9],\n [0, 0, 4, 4, 0],\n [4, 4, 4, 0, 0],\n [4, 0, 4, 0, 4]\n ],\n \"output\": [\n [6, 0, 6, 0, 6],\n [6, 6, 6, 0, 0],\n [6, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 9, 0, 0, 0],\n [0, 9, 9, 0, 9],\n [9, 0, 0, 0, 9],\n [4, 4, 0, 4, 0],\n [0, 4, 4, 4, 0],\n [4, 4, 0, 0, 0]\n ],\n \"output\": [\n [6, 0, 0, 6, 0],\n [0, 0, 0, 6, 6],\n [0, 6, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 9, 9, 0],\n [9, 9, 0, 9, 9],\n [0, 9, 0, 0, 0],\n [4, 4, 0, 0, 0],\n [4, 0, 4, 4, 4],\n [0, 4, 0, 0, 4]\n ],\n \"output\": [\n [6, 6, 6, 6, 0],\n [0, 6, 6, 0, 0],\n [0, 0, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 9, 9, 0, 0],\n [9, 0, 0, 0, 9],\n [9, 0, 0, 0, 0],\n [0, 0, 4, 0, 4],\n [4, 4, 0, 4, 0],\n [4, 0, 4, 4, 0]\n ],\n \"output\": [\n [0, 6, 0, 0, 6],\n [0, 6, 0, 6, 6],\n [0, 0, 6, 6, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 0, 9, 0],\n [0, 0, 9, 0, 9],\n [0, 0, 0, 9, 9],\n [4, 4, 4, 0, 4],\n [4, 0, 4, 4, 4],\n [4, 4, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 6, 6, 6], [6, 0, 0, 6, 0], [6, 6, 0, 6, 6]], "task_id": "31d5ba1a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 3, 1, 3, 0, 0, 0, 0, 0, 0],\n [1, 0, 3, 1, 1, 1, 3, 0, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 1, 0, 1, 0],\n [0, 0, 0, 3, 1, 0, 1, 1, 0, 0, 1, 0],\n [1, 0, 3, 1, 0, 1, 0, 0, 1, 0, 0, 1],\n [0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1]\n ],\n \"output\": [\n [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1],\n [1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 3, 8, 3, 0, 0, 0, 0, 0, 0],\n [1, 0, 3, 8, 8, 8, 3, 0, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 8, 3, 0, 1, 0, 1, 0],\n [0, 0, 0, 3, 8, 8, 8, 8, 0, 0, 1, 0],\n [1, 0, 3, 8, 8, 8, 8, 8, 8, 0, 0, 1],\n [0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 0, 3, 0, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 3, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 3, 1, 0, 0, 1, 0, 1, 1],\n [0, 1, 1, 3, 1, 1, 1, 1, 0, 1, 0, 0],\n [0, 1, 0, 0, 3, 1, 0, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 3, 0, 0, 1, 0, 0, 1],\n [0, 0, 1, 0, 0, 1, 3, 0, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 3, 1, 1, 0, 0, 0, 0, 1],\n [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1],\n [0, 1, 3, 0, 0, 0, 3, 0, 0, 1, 1, 1]\n ],\n \"output\": [\n [1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0],\n [1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 0, 3, 0, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 3, 8, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 3, 8, 8, 0, 1, 0, 1, 1],\n [0, 1, 1, 3, 8, 8, 8, 1, 0, 1, 0, 0],\n [0, 1, 0, 0, 3, 8, 8, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 3, 8, 0, 1, 0, 0, 1],\n [0, 0, 1, 0, 0, 1, 3, 0, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 3, 1, 1, 0, 0, 0, 0, 1],\n [0, 1, 0, 8, 8, 8, 1, 1, 0, 0, 1, 1],\n [0, 1, 3, 8, 8, 8, 3, 0, 0, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 3, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 3, 1, 3, 0, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 0, 3, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 1, 1, 3, 0, 0, 0, 3, 1, 1, 0],\n [0, 1, 0, 1, 1, 0, 1, 3, 1, 1, 1, 0, 0, 3, 1, 3],\n [1, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 1, 0, 0, 3, 0],\n [0, 0, 1, 0, 1, 3, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 3, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1],\n [0, 1, 1, 1, 0, 0, 1, 1, 3, 1, 0, 1, 0, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 3, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 3, 8, 3, 0, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 0, 3, 8, 8, 8, 8, 0, 1, 0, 1, 1, 1, 0, 0],\n [1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 1, 1, 3, 0, 0, 0, 3, 8, 8, 8],\n [0, 1, 0, 1, 1, 0, 1, 3, 8, 1, 1, 0, 0, 3, 8, 3],\n [1, 0, 0, 0, 0, 1, 3, 8, 8, 0, 0, 1, 0, 0, 3, 0],\n [0, 0, 1, 0, 1, 3, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 1, 3, 8, 8, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 8, 8, 0, 0, 0, 0, 1, 1, 1],\n [0, 1, 1, 1, 0, 0, 1, 1, 3, 1, 0, 1, 0, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [1, 0, 1, 3, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0],\n [0, 3, 0, 1, 0, 3, 1, 0, 1, 1, 0, 1, 0, 3, 1, 0, 0, 3],\n [0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 3, 0, 3, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 3, 1, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0],\n [1, 3, 0, 0, 1, 1, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 3, 0, 0, 0, 1, 1, 3, 0, 1, 1, 0, 1, 0, 0, 1, 0],\n [0, 0, 0, 3, 0, 1, 1, 3, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0],\n [0, 0, 3, 1, 1, 0, 3, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 0, 1, 1, 3, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0], [1, 0, 1, 3, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0], [0, 3, 8, 8, 8, 3, 1, 0, 1, 1, 0, 1, 0, 3, 8, 8, 8, 3], [0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 3, 8, 3, 0], [0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 3, 1, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0], [1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0], [1, 3, 0, 0, 1, 1, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 3, 0, 0, 0, 1, 1, 3, 8, 1, 1, 0, 1, 0, 0, 1, 0], [0, 8, 8, 3, 0, 1, 1, 3, 8, 8, 0, 0, 1, 1, 1, 0, 1, 0], [0, 8, 3, 1, 1, 0, 3, 8, 8, 8, 0, 1, 0, 1, 1, 0, 0, 0], [0, 8, 1, 0, 0, 1, 1, 3, 8, 8, 1, 1, 1, 1, 1, 0, 1, 0], [1, 1, 1, 1, 1, 0, 1, 0, 8, 8, 0, 0, 0, 0, 0, 1, 0, 1]], "task_id": "896d5239"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 1, 8, 8, 0, 8, 8, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 8, 0, 8, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 1, 0, 8, 1, 1, 1, 1, 0],\n [0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 1, 1, 0, 8, 8, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0, 8, 8, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0, 8, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 8, 0, 8, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 8, 8, 0, 8, 8, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 8, 8, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 8, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 1, 1, 1, 0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0],\n [0, 8, 1, 1, 1, 1, 0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 1, 1, 1, 0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0],\n [0, 8, 1, 1, 1, 1, 0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 1, 0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0],\n [0, 8, 8, 1, 1, 1, 0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 1, 1, 1, 1, 0, 8, 1, 1, 1, 8, 0, 1, 1, 1, 1, 8, 0],\n [0, 8, 8, 1, 1, 1, 0, 8, 8, 1, 8, 8, 0, 1, 1, 1, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 2, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 2, 2, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 2, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 2, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 2, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 2, 2, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 2, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 2, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 3, 2, 0, 3, 3, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0],\n [0, 3, 2, 3, 2, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 2, 3, 2, 3, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 2, 3, 3, 3, 2, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 3, 2, 0],\n [0, 3, 2, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 2, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 2, 3, 3, 3, 2, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 2, 3, 2, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 2, 2, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 2, 2, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 2, 3, 2, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 2, 3, 3, 3, 2, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 3, 2, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 2, 3, 0],\n [0, 2, 3, 3, 3, 3, 0, 3, 3, 2, 3, 3, 0, 3, 3, 3, 3, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 6, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 6, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 6, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 6, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 6, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 8, 8, 8, 0, 6, 6, 6, 6, 6, 0, 8, 8, 8, 6, 6, 0],\n [0, 6, 6, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 6, 6, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 6, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 6, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 6, 8, 8, 8, 8, 0, 6, 6, 8, 6, 6, 0, 8, 8, 8, 8, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 6, 6, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 6, 6, 0],\n [0, 6, 6, 8, 8, 8, 0, 6, 6, 6, 6, 6, 0, 8, 8, 8, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 1, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 1, 0, 4, 4, 1, 4, 4, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 1, 1, 0, 4, 1, 1, 1, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 1, 1, 0, 4, 1, 1, 1, 4, 0, 1, 1, 4, 1, 1, 0],\n [0, 4, 4, 4, 4, 1, 0, 4, 4, 1, 4, 4, 0, 1, 4, 4, 4, 1, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 1, 4, 4, 4, 1, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 1, 1, 4, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 1, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 1, 0],\n [0, 1, 1, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 1, 1, 0],\n [0, 1, 4, 4, 4, 4, 0, 1, 4, 4, 4, 4, 0, 4, 4, 4, 4, 1, 0],\n [0, 4, 4, 4, 4, 4, 0, 1, 1, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 4, 4, 4, 0, 4, 1, 1, 1, 4, 0, 4, 4, 4, 1, 1, 0], [0, 1, 4, 4, 4, 4, 0, 4, 4, 1, 4, 4, 0, 4, 4, 4, 4, 1, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 1, 1, 4, 1, 1, 0, 4, 4, 4, 4, 4, 0], [0, 1, 4, 4, 4, 4, 0, 1, 4, 4, 4, 1, 0, 4, 4, 4, 4, 1, 0], [0, 1, 1, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 1, 1, 0], [0, 1, 4, 4, 4, 4, 0, 1, 4, 4, 4, 1, 0, 4, 4, 4, 4, 1, 0], [0, 4, 4, 4, 4, 4, 0, 1, 1, 4, 1, 1, 0, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 0], [0, 1, 4, 4, 4, 4, 0, 4, 4, 1, 4, 4, 0, 4, 4, 4, 4, 1, 0], [0, 1, 1, 4, 4, 4, 0, 4, 1, 1, 1, 4, 0, 4, 4, 4, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "4e45f183"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 7, 7, 7, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 7, 7, 7, 0, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 0, 7, 0, 7], [0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 0, 0, 7], [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "009d5c81"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [9, 9, 8, 8, 7, 7, 6, 6, 5, 5]\n ],\n \"output\": [\n [9, 9, 0, 0, 0, 0, 0, 0, 0, 9],\n [9, 9, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 8, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 8, 8, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 7],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [9, 9, 8, 8, 7, 7, 6, 6, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [5, 6, 6, 7, 7, 7, 8, 9, 9, 9]\n ],\n \"output\": [\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 6, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 6, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 7],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 7],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 9, 9, 9],\n [0, 0, 0, 0, 0, 0, 0, 9, 9, 9],\n [5, 6, 6, 7, 7, 7, 8, 9, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [8, 8, 4, 4, 4, 5, 5, 3, 3, 3]\n ],\n \"output\": [\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 4],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 5, 5, 0, 0, 5],\n [0, 0, 0, 0, 0, 5, 5, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [8, 8, 4, 4, 4, 5, 5, 3, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [3, 3, 4, 6, 6, 6, 9, 9, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 4, 0, 0, 0, 0, 0, 0, 4], [0, 0, 4, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 6, 6, 6, 0, 0, 0, 6], [0, 0, 0, 6, 6, 6, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 9, 9, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 7, 7], [3, 3, 4, 6, 6, 6, 9, 9, 7, 7]], "task_id": "a406ac07"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 5, 1, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 3, 5, 1, 6, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 5, 1, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 5, 1, 6, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 5, 1, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 4, 3, 0, 0, 0, 0, 0, 0, 0, 5, 7, 8, 6]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 7, 8, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 2, 4, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 8, 6, 0, 0, 0, 1, 2, 4, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 4, 3, 0, 0, 0, 0, 0, 0, 0, 5, 7, 8, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 7, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 7, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 3, 7, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 2, 0],\n [0, 4, 3, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 5, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 6, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 5, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 3, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 9, 5, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 6, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 1, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 5, 6, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 6, 7]], "task_id": "5af49b42"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 7, 0],\n [2, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 8, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 6, 0, 0, 0, 8],\n [0, 0, 6, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 8, 2, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 2, 0, 0, 7, 0],\n [2, 2, 2, 2, 2, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 2, 8, 0, 0, 6],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 7, 0, 0, 0, 6, 0, 0, 0, 8],\n [0, 0, 6, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 3, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 7, 0, 2, 0, 0, 0, 0],\n [0, 0, 8, 2, 2, 2, 2, 2, 7, 0, 0, 3],\n [2, 2, 2, 2, 2, 2, 6, 2, 2, 2, 2, 2],\n [0, 0, 2, 0, 2, 8, 0, 2, 2, 8, 0, 0],\n [2, 2, 2, 8, 2, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 2, 0, 2, 7, 0, 2, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 6, 0, 0],\n [0, 0, 7, 0, 2, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 7, 2, 2, 7, 0, 0],\n [0, 0, 0, 6, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 3, 0, 0, 2, 0, 0, 8, 3, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 0, 0, 7],\n [0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8],\n [0, 0, 3, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 2, 0, 7],\n [0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 3, 0],\n [0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 8],\n [0, 0, 3, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 7, 0, 0, 0, 0, 2, 0],\n [2, 2, 2, 2, 2, 2, 2, 3],\n [0, 2, 0, 0, 0, 0, 2, 0],\n [2, 2, 8, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 7, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 8, 0],\n [0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 7, 0, 0],\n [6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 7, 0, 0],\n [6, 2, 2, 2, 2, 2],\n [0, 2, 0, 2, 0, 0],\n [2, 2, 2, 2, 8, 0],\n [0, 2, 0, 2, 0, 0],\n [0, 2, 0, 2, 0, 8],\n [0, 2, 0, 2, 0, 0],\n [7, 2, 0, 2, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0, 0, 7, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 6, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 8, 0, 0, 0, 7, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 8, 0, 2, 0, 0, 7, 0, 0, 3], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 6, 0, 0, 2, 0, 6, 2, 0, 0, 0], [2, 2, 2, 2, 2, 2, 6, 0, 2, 0, 7, 0], [0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 6], [0, 0, 0, 3, 0, 2, 0, 8, 2, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 6, 0, 0], [0, 0, 7, 2, 0, 2, 0, 0, 2, 0, 0, 3], [0, 0, 8, 2, 2, 2, 7, 0, 2, 6, 0, 0], [0, 0, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0]], "task_id": "b942fd60"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "11e1fe23"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 1],\n [1, 1, 1, 1],\n [1, 8, 8, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 3, 3, 3],\n [2, 2, 2, 3],\n [2, 8, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 1, 2, 2],\n [1, 1, 1, 1],\n [8, 1, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8], [8, 3, 6, 8], [3, 3, 6, 6]], "task_id": "b7cb93ac"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 2, 1, 0, 0, 0, 8, 0],\n [0, 1, 1, 2, 1, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 2, 1, 3, 8, 3, 8, 0],\n [0, 1, 1, 2, 1, 3, 8, 3, 3, 0],\n [0, 2, 2, 2, 1, 3, 8, 8, 8, 0],\n [0, 1, 1, 1, 1, 3, 3, 3, 3, 0],\n [0, 7, 7, 7, 7, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 7, 0, 5, 5, 5, 0],\n [0, 7, 7, 4, 7, 0, 5, 0, 0, 0],\n [0, 4, 7, 4, 7, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 9, 0, 0, 0, 0],\n [0, 3, 3, 8, 8, 7, 0, 0, 0, 0],\n [0, 3, 8, 3, 8, 0, 0, 0, 0, 0],\n [0, 3, 8, 8, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 5, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 9, 9, 9, 9, 0],\n [0, 3, 3, 8, 8, 7, 7, 9, 9, 0],\n [0, 3, 8, 3, 8, 7, 9, 7, 9, 0],\n [0, 3, 8, 8, 3, 9, 7, 7, 9, 0],\n [0, 4, 1, 1, 4, 2, 5, 5, 2, 0],\n [0, 4, 1, 4, 1, 5, 2, 5, 2, 0],\n [0, 4, 4, 1, 1, 5, 5, 2, 2, 0],\n [0, 4, 4, 4, 4, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 2, 8, 8, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 6, 0, 0, 0, 0],\n [0, 8, 2, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 2, 8, 8, 0, 1, 0, 0, 0],\n [0, 0, 5, 4, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 2, 8, 8, 1, 1, 6, 1, 0],\n [0, 2, 2, 2, 2, 6, 6, 6, 6, 0],\n [0, 8, 2, 8, 8, 1, 1, 6, 1, 0],\n [0, 8, 2, 8, 8, 1, 1, 6, 1, 0],\n [0, 4, 5, 4, 4, 3, 3, 1, 3, 0],\n [0, 4, 5, 4, 4, 3, 3, 1, 3, 0],\n [0, 5, 5, 5, 5, 1, 1, 1, 1, 0],\n [0, 4, 5, 4, 4, 3, 3, 1, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 1, 1, 8, 0, 0, 0, 0],\n [0, 4, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 4, 0, 0, 0, 0, 0],\n [0, 1, 1, 4, 4, 5, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 1, 1, 8, 8, 5, 5, 0], [0, 4, 1, 1, 1, 8, 8, 8, 5, 0], [0, 1, 1, 1, 4, 5, 8, 8, 8, 0], [0, 1, 1, 4, 4, 5, 5, 8, 8, 0], [0, 7, 7, 6, 6, 0, 0, 3, 3, 0], [0, 7, 7, 7, 6, 0, 3, 3, 3, 0], [0, 6, 7, 7, 7, 3, 3, 3, 0, 0], [0, 6, 6, 7, 7, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "cfb2ce5a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 2, 8, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 1, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 2, 8, 8, 8, 8, 8, 8, 2, 8],\n [1, 8, 1, 1, 1, 1, 1, 1, 8, 1],\n [1, 8, 1, 1, 1, 1, 1, 1, 8, 1]\n ]\n}\n\n{\n \"input\": [\n [3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3],\n [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1],\n [3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3],\n [1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 3, 8, 2, 2, 2, 2, 2, 2, 2, 2, 3, 8, 2],\n [2, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 2],\n [2, 8, 3, 2, 2, 2, 2, 2, 2, 2, 2, 8, 3, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 3], [2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2], [1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1], [2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2]], "task_id": "62b74c02"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 1, 9, 1],\n [1, 9, 1, 4],\n [9, 1, 4, 6],\n [4, 1, 6, 6]\n ],\n \"output\": [\n [4, 1, 9, 1, 1, 4, 6, 6],\n [1, 9, 1, 4, 9, 1, 4, 6],\n [9, 1, 4, 6, 1, 9, 1, 1],\n [4, 1, 6, 6, 4, 1, 9, 4],\n [6, 6, 1, 4, 4, 9, 1, 4],\n [6, 4, 1, 9, 1, 1, 9, 1],\n [4, 1, 9, 1, 6, 4, 1, 9],\n [1, 9, 1, 4, 6, 6, 4, 1]\n ]\n}\n\n{\n \"input\": [\n [6, 2, 6, 2],\n [6, 6, 5, 5],\n [1, 1, 1, 2],\n [5, 1, 2, 1]\n ],\n \"output\": [\n [6, 2, 6, 2, 2, 5, 2, 1],\n [6, 6, 5, 5, 6, 5, 1, 2],\n [1, 1, 1, 2, 2, 6, 1, 1],\n [5, 1, 2, 1, 6, 6, 1, 5],\n [1, 2, 1, 5, 5, 1, 6, 6],\n [2, 1, 1, 1, 1, 1, 6, 2],\n [5, 5, 6, 6, 2, 1, 5, 6],\n [2, 6, 2, 6, 1, 2, 5, 2]\n ]\n}\n\n{\n \"input\": [\n [6, 7, 7, 6],\n [7, 1, 6, 6],\n [9, 1, 6, 6],\n [9, 1, 6, 1]\n ],\n \"output\": [\n [6, 7, 7, 6, 6, 6, 6, 1],\n [7, 1, 6, 6, 7, 6, 6, 6],\n [9, 1, 6, 6, 7, 1, 1, 1],\n [9, 1, 6, 1, 6, 7, 9, 9],\n [1, 6, 1, 9, 9, 9, 7, 6],\n [6, 6, 1, 9, 1, 1, 1, 7],\n [6, 6, 1, 7, 6, 6, 6, 7],\n [6, 7, 7, 6, 1, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [4, 9, 1, 8],\n [8, 4, 1, 8],\n [4, 8, 8, 1],\n [1, 1, 1, 8]\n ],\n \"output\": [\n [4, 9, 1, 8, 8, 8, 1, 8],\n [8, 4, 1, 8, 1, 1, 8, 1],\n [4, 8, 8, 1, 9, 4, 8, 1],\n [1, 1, 1, 8, 4, 8, 4, 1],\n [8, 1, 1, 1, 1, 4, 8, 4],\n [1, 8, 8, 4, 1, 8, 4, 9],\n [8, 1, 4, 8, 1, 8, 1, 1],\n [8, 1, 9, 4, 8, 1, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 2, 1],\n [6, 6, 7, 6],\n [7, 6, 2, 1],\n [1, 6, 2, 6]\n ],\n \"output\": [\n [1, 1, 2, 1, 1, 6, 1, 6],\n [6, 6, 7, 6, 2, 7, 2, 2],\n [7, 6, 2, 1, 1, 6, 6, 6],\n [1, 6, 2, 6, 1, 6, 7, 1],\n [6, 2, 6, 1, 1, 7, 6, 1],\n [1, 2, 6, 7, 6, 6, 6, 1],\n [6, 7, 6, 6, 2, 2, 7, 2],\n [1, 2, 1, 1, 6, 1, 6, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 6, 4, 4],\n [4, 6, 4, 4],\n [7, 6, 7, 9],\n [9, 4, 9, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 6, 4, 4, 4, 4, 9, 7], [4, 6, 4, 4, 4, 4, 7, 9], [7, 6, 7, 9, 6, 6, 6, 4], [9, 4, 9, 7, 4, 4, 7, 9], [7, 9, 4, 9, 9, 7, 4, 4], [9, 7, 6, 7, 4, 6, 6, 6], [4, 4, 6, 4, 9, 7, 4, 4], [4, 4, 6, 4, 7, 9, 4, 4]], "task_id": "7953d61e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 1, 3, 0, 0, 0, 0, 0, 0, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 0, 0, 0, 0, 0, 0, 5, 3, 1, 1, 5, 3, 1, 1, 0, 0, 0, 0, 0, 3, 1, 1, 5, 3],\n [3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 1, 1, 1, 3, 0, 0, 0, 0, 0, 1, 1, 3, 3, 3],\n [5, 1, 3, 0, 0, 0, 0, 0, 0, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 0, 0, 0, 0, 0, 5, 1, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1],\n [3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 0, 0, 0, 0, 0, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 0, 0, 0, 0, 0, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3],\n [0, 0, 0, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3]\n ],\n \"output\": [\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1, 3, 1, 3, 5, 1, 3, 5, 1],\n [5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1, 5, 1, 5, 3, 1, 5, 3, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3, 5, 1, 3, 5, 1, 5, 1, 3],\n [1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3, 1, 1, 5, 3],\n [3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 0, 0, 0, 0, 0, 0, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 7, 3, 6, 2, 5, 1, 0, 0, 0, 0, 0, 0, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 0, 0, 0, 0, 0, 0, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 0, 0, 0, 0, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 0, 0, 0, 0, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 0, 0, 0, 0, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1],\n [3, 7, 0, 0, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 0, 0, 0, 0, 7, 4],\n [7, 6, 0, 0, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 0, 0, 0, 0, 6, 5],\n [4, 5, 0, 0, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 0, 0, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 0, 0, 0, 0, 0, 0, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 0, 0, 0, 0, 0, 0, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1],\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 0, 0, 0, 0, 0, 0, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6]\n ],\n \"output\": [\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6],\n [1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7, 3, 6, 2, 5, 1, 1, 4, 7],\n [3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5, 6, 5, 6, 7, 1, 3, 4, 5],\n [7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6, 2, 6, 2, 5, 1, 7, 3, 6],\n [4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7, 5, 7, 5, 3, 1, 4, 2, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4, 1, 3, 7, 4],\n [7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5, 4, 4, 3, 2, 1, 7, 6, 5],\n [4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6, 7, 5, 6, 7, 1, 4, 5, 6]\n ]\n}\n\n{\n \"input\": [\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 0, 0, 0, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 0, 0, 0, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 5, 1, 5, 1, 5, 1, 5],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [3, 1, 7, 5, 7, 5, 3, 0, 0, 0, 0, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 0, 0, 0, 0, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [0, 0, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 5, 1, 5],\n [0, 0, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 0, 0, 0, 7, 1, 3],\n [0, 0, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 5, 1, 5],\n [0, 0, 7, 5, 7, 5, 3, 1, 3, 1, 0, 0, 7, 5, 3, 1, 3, 1, 7, 5, 7, 0, 0, 0, 3, 1, 7],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 0, 0, 0, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3]\n ],\n \"output\": [\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3],\n [5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7, 5, 7, 5, 3, 1, 3, 1, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3, 5, 3, 5, 7, 1, 7, 1, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3],\n [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6],\n [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9],\n [2, 7, 3, 8, 4, 0, 0, 0, 0, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3],\n [4, 7, 1, 4, 1, 0, 0, 0, 0, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1],\n [3, 8, 4, 9, 4, 0, 0, 0, 0, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7],\n [1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1],\n [5, 0, 0, 0, 0, 0, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 0, 0, 3],\n [4, 0, 0, 0, 0, 0, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 0, 0, 6],\n [3, 0, 0, 0, 0, 0, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 0, 0, 9],\n [2, 0, 0, 0, 0, 0, 5, 1, 2, 7, 3, 8, 4, 0, 0, 0, 0, 0, 0, 8, 4, 9, 5, 1, 2, 7, 3],\n [4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 0, 0, 0, 0, 0, 0, 4, 1, 4, 7, 1, 4, 7, 1],\n [3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4],\n [2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3],\n [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6],\n [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9],\n [2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 0, 0, 0, 0, 3],\n [4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 0, 0, 0, 0, 1],\n [3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 0, 0, 0, 0, 4],\n [2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 0, 0, 0, 0, 7],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3],\n [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6],\n [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3], [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6], [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9], [2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3], [4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1], [3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4], [2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3], [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6], [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9], [2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3], [4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1], [3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4], [2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3], [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6], [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9], [2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3, 8, 4, 9, 5, 1, 2, 7, 3], [4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1, 4, 1, 4, 7, 1, 4, 7, 1], [3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4, 9, 4, 9, 5, 1, 3, 8, 4], [2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7, 5, 7, 5, 3, 1, 2, 9, 7], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3, 2, 4, 3, 2, 1, 5, 4, 3], [4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6, 7, 7, 8, 9, 1, 4, 5, 6], [3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9, 3, 1, 4, 7, 1, 3, 6, 9]], "task_id": "c663677b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0],\n [2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 1, 0],\n [2, 2, 2, 2, 2, 2, 3, 0, 2, 2, 2, 2, 2, 3, 0, 0, 2, 2, 2, 2, 1, 0],\n [0, 0, 0, 0, 0, 2, 3, 0, 2, 1, 0, 0, 2, 3, 0, 0, 2, 1, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 3, 0, 3, 3, 3, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 2, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 3, 3, 3, 0, 3, 3, 3, 2, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 2, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 3, 3, 3, 0, 3, 3, 3, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 3, 0, 0, 0, 0, 3, 3],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 3, 0, 2, 1, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 0, 2, 1, 0, 2, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 1, 0, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 1, 0, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 3, 0, 0, 0, 2, 1, 0, 2, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 2, 3, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 2, 3, 0, 0, 2, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 1, 0, 0, 1, 0, 0, 3, 0, 0, 2, 3, 0, 0, 2, 1, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 2, 2, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], "task_id": "96a8c0cd"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 0, 0, 0],\n [0, 0, 8, 8, 0, 8],\n [0, 8, 0, 0, 0, 0],\n [8, 8, 0, 0, 8, 8],\n [8, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 8]\n ],\n \"output\": [\n [5, 5, 5, 0, 0, 0],\n [0, 0, 2, 2, 0, 2],\n [0, 2, 0, 0, 0, 0],\n [5, 2, 0, 0, 5, 5],\n [5, 0, 2, 2, 5, 2],\n [0, 0, 0, 0, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 0, 8, 8, 0],\n [8, 0, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 8],\n [8, 8, 0, 8, 0, 8],\n [8, 0, 0, 8, 0, 8]\n ],\n \"output\": [\n [2, 5, 0, 2, 5, 0],\n [2, 0, 5, 2, 5, 0],\n [0, 0, 5, 5, 2, 2],\n [0, 5, 0, 0, 2, 2],\n [2, 5, 0, 2, 0, 5],\n [2, 0, 0, 2, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 0],\n [8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8],\n [8, 8, 0, 8, 8, 0],\n [8, 8, 0, 0, 0, 8]\n ],\n \"output\": [\n [0, 2, 0, 5, 5, 2],\n [2, 2, 5, 2, 2, 0],\n [5, 0, 2, 0, 5, 0],\n [0, 5, 2, 5, 0, 5],\n [2, 2, 0, 2, 2, 0],\n [5, 2, 0, 0, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 0, 0, 8],\n [8, 8, 8, 0, 0, 0],\n [8, 8, 8, 0, 8, 0],\n [8, 0, 8, 8, 0, 8],\n [8, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0]\n ],\n \"output\": [\n [0, 2, 2, 0, 0, 5],\n [2, 2, 5, 0, 0, 0],\n [2, 5, 2, 0, 5, 0],\n [2, 0, 2, 5, 0, 5],\n [2, 2, 0, 0, 0, 0],\n [5, 2, 2, 5, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 8],\n [8, 8, 8, 0, 8, 8],\n [8, 8, 8, 8, 0, 8],\n [8, 0, 0, 0, 8, 8],\n [0, 8, 0, 0, 0, 8],\n [8, 8, 8, 0, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 5, 0, 2], [5, 2, 5, 0, 5, 2], [2, 5, 5, 5, 0, 2], [2, 0, 0, 0, 5, 2], [0, 2, 0, 0, 0, 2], [5, 5, 5, 0, 5, 2]], "task_id": "a8610ef7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 0, 0, 2, 5, 2, 2, 0, 5, 2, 5, 5, 0, 2, 2, 5, 2, 2, 5, 5, 0, 2, 0, 0, 2, 0, 0, 0],\n [5, 0, 0, 5, 2, 2, 5, 2, 5, 0, 0, 2, 2, 5, 5, 2, 2, 5, 0, 5, 2, 0, 0, 0, 5, 0, 5, 5, 0, 2],\n [5, 0, 2, 2, 8, 8, 8, 8, 8, 8, 8, 5, 0, 2, 4, 4, 4, 4, 5, 0, 0, 2, 3, 3, 3, 3, 3, 0, 0, 2],\n [0, 5, 0, 5, 8, 8, 8, 8, 8, 8, 8, 2, 0, 0, 4, 4, 4, 4, 0, 0, 2, 0, 3, 3, 3, 3, 3, 0, 2, 0],\n [5, 0, 5, 0, 8, 8, 8, 8, 8, 8, 8, 2, 2, 0, 4, 4, 4, 4, 2, 2, 0, 2, 3, 3, 3, 3, 3, 5, 0, 5],\n [0, 0, 0, 5, 8, 8, 8, 8, 8, 8, 8, 2, 0, 0, 4, 4, 4, 4, 0, 0, 2, 2, 3, 3, 3, 3, 3, 0, 0, 2],\n [0, 0, 0, 2, 5, 5, 5, 2, 2, 0, 0, 0, 2, 5, 0, 5, 2, 0, 2, 0, 5, 0, 5, 2, 0, 2, 0, 5, 5, 2],\n [0, 0, 2, 2, 5, 5, 0, 0, 2, 0, 5, 0, 5, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 5, 2, 0, 0, 0, 5, 2, 0, 5, 2, 0, 0, 5, 2, 0, 0],\n [0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 5, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 5, 0, 5, 0],\n [0, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 0, 0, 7, 7, 7, 7, 7, 0, 0, 5, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 1, 0, 5, 0, 3, 3, 3, 3, 3, 3, 3, 2, 0, 7, 7, 7, 7, 7, 2, 5, 5, 5],\n [0, 0, 5, 2, 1, 1, 1, 1, 1, 5, 2, 0, 3, 3, 3, 3, 3, 3, 3, 0, 2, 7, 7, 7, 7, 7, 0, 2, 5, 2],\n [2, 5, 0, 2, 1, 1, 1, 1, 1, 2, 0, 0, 3, 3, 3, 3, 3, 3, 3, 2, 5, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 5, 0, 2, 2, 2, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 7, 7, 7, 7, 7, 2, 0, 2, 2],\n [0, 0, 2, 0, 0, 5, 0, 2, 0, 2, 0, 5, 5, 0, 0, 2, 0, 5, 2, 2, 2, 2, 0, 5, 2, 0, 0, 2, 2, 0],\n [0, 0, 5, 2, 0, 0, 2, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 2, 2, 0, 0, 0, 0, 5, 5, 0, 2, 0, 0, 5],\n [0, 2, 2, 0, 8, 8, 8, 8, 8, 0, 2, 0, 5, 4, 4, 4, 4, 4, 2, 0, 0, 2, 0, 0, 5, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 8, 8, 8, 8, 8, 2, 2, 5, 0, 4, 4, 4, 4, 4, 0, 2, 5, 0, 1, 1, 1, 1, 1, 2, 0, 2],\n [2, 2, 0, 0, 8, 8, 8, 8, 8, 5, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 1, 1, 1, 1, 1, 5, 0, 0],\n [2, 5, 5, 0, 8, 8, 8, 8, 8, 0, 5, 0, 5, 4, 4, 4, 4, 4, 0, 5, 0, 2, 1, 1, 1, 1, 1, 0, 0, 0],\n [2, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 5, 2, 5, 0, 0, 2, 5, 0, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 5, 2, 5, 5, 2, 2, 0, 2, 0, 0, 2, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],\n [2, 0, 0, 0, 2, 5, 0, 0, 5, 5, 2, 0, 2, 2, 0, 0, 5, 5, 0, 0, 0, 5, 0, 2, 0, 5, 0, 0, 2, 5],\n [0, 0, 5, 0, 0, 0, 0, 2, 0, 5, 5, 0, 2, 5, 0, 0, 0, 2, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 2, 0, 2, 0, 5, 2, 5, 0, 5, 2, 0, 0, 0, 0, 0, 0, 5, 2, 2, 5, 2, 0, 0, 0, 0, 0, 5, 5, 0],\n [0, 0, 0, 5, 5, 0, 2, 2, 2, 0, 0, 2, 0, 2, 0, 0, 5, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 5, 2, 2, 2, 0, 0, 0, 5],\n [2, 2, 2, 0, 0, 0, 0, 2, 0, 5, 5, 0, 0, 0, 5, 0, 2, 0, 5, 0, 0, 0, 5, 0, 2, 0, 2, 2, 2, 5],\n [5, 0, 0, 2, 2, 5, 2, 2, 0, 0, 0, 0, 2, 5, 0, 2, 0, 5, 0, 0, 5, 5, 5, 0, 0, 2, 0, 0, 0, 5]\n ],\n \"output\": [\n [8, 4, 3],\n [1, 3, 7],\n [8, 4, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 8, 0, 0, 2, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 8, 8, 2, 0, 0, 0, 0, 0, 0],\n [8, 0, 2, 3, 3, 3, 3, 3, 3, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 9, 9, 9, 9, 9, 0, 0],\n [8, 0, 8, 3, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 8, 0, 8, 9, 9, 9, 9, 9, 8, 8],\n [2, 8, 0, 3, 3, 3, 3, 3, 3, 8, 8, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 9, 9, 9, 9, 9, 0, 0],\n [8, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 2, 2, 2, 8, 8, 8, 8, 0, 2, 8, 2, 0, 9, 9, 9, 9, 9, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 2, 8, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 9, 9, 9, 9, 9, 8, 8],\n [0, 8, 8, 8, 0, 0, 2, 0, 8, 0, 0, 0, 2, 8, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 8, 8, 2, 0, 0, 2, 0, 0, 2, 0, 0, 8, 2, 8, 0],\n [8, 0, 0, 0, 0, 0, 8, 2, 8, 2, 8, 0, 0, 0, 0, 0, 0, 2, 8, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 2, 6, 6, 6, 6, 0, 8, 0, 0, 4, 4, 4, 4, 4, 4, 2, 0, 0, 0, 8, 0, 0, 2, 0, 0, 0, 2, 0],\n [8, 0, 8, 6, 6, 6, 6, 0, 8, 0, 8, 4, 4, 4, 4, 4, 4, 2, 0, 2, 2, 2, 0, 1, 1, 1, 1, 1, 8, 0],\n [0, 2, 0, 6, 6, 6, 6, 8, 0, 2, 2, 4, 4, 4, 4, 4, 4, 8, 0, 8, 0, 0, 0, 1, 1, 1, 1, 1, 0, 2],\n [0, 2, 8, 6, 6, 6, 6, 8, 0, 8, 0, 4, 4, 4, 4, 4, 4, 0, 8, 2, 2, 0, 2, 1, 1, 1, 1, 1, 0, 8],\n [0, 0, 2, 6, 6, 6, 6, 0, 0, 0, 2, 4, 4, 4, 4, 4, 4, 0, 0, 8, 0, 8, 8, 1, 1, 1, 1, 1, 8, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 2, 8, 0, 8, 8, 2, 8, 0, 8, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 2],\n [2, 8, 0, 6, 6, 6, 6, 0, 2, 0, 0, 0, 0, 2, 8, 0, 0, 0, 2, 8, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 0, 0, 0, 2],\n [0, 0, 2, 0, 8, 0, 0, 0, 2, 8, 0, 8, 0, 0, 0, 8, 0, 8, 8, 8, 0, 8, 0, 0, 8, 0, 2, 2, 0, 2],\n [8, 0, 0, 0, 0, 0, 8, 8, 2, 2, 8, 0, 8, 2, 2, 8, 0, 0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 2],\n [2, 2, 0, 0, 0, 0, 2, 8, 0, 8, 0, 0, 2, 2, 8, 0, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 8],\n [0, 8, 8, 0, 0, 8, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 8, 2, 0, 0],\n [0, 0, 2, 8, 2, 0, 2, 0, 0, 8, 0, 0, 0, 2, 0, 8, 0, 0, 0, 2, 8, 8, 0, 8, 0, 2, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 2, 0, 8, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 8, 8, 0],\n [8, 2, 0, 0, 0, 8, 0, 8, 0, 8, 2, 0, 0, 0, 8, 0, 0, 8, 0, 2, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0],\n [0, 8, 0, 2, 2, 8, 2, 8, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 8, 0, 8, 0, 0, 8, 2],\n [0, 0, 2, 8, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 2, 2, 0, 0, 8, 0, 2, 0, 0, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 8, 0, 0, 8, 0],\n [8, 2, 0, 2, 8, 8, 0, 0, 0, 2, 0, 0, 0, 8, 8, 0, 8, 0, 0, 0, 8, 2, 8, 8, 0, 2, 8, 2, 2, 2],\n [2, 0, 8, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 2, 0, 0, 8, 0, 8, 0, 0, 2, 8, 0, 0]\n ],\n \"output\": [\n [3, 1, 9],\n [6, 4, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 0, 9, 1, 1, 0, 1, 9, 1, 0, 9, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 9, 0, 1, 1, 9, 9, 9],\n [0, 0, 0, 0, 9, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 9, 9, 0, 0, 1, 1, 1, 1, 9, 0],\n [1, 1, 1, 0, 0, 1, 1, 9, 1, 0, 1, 0, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 9],\n [0, 1, 9, 0, 0, 0, 0, 1, 0, 0, 1, 1, 4, 4, 4, 4, 4, 4, 0, 9, 0, 0, 8, 8, 8, 8, 1, 0, 1, 0],\n [0, 0, 1, 1, 0, 9, 0, 9, 0, 0, 0, 9, 4, 4, 4, 4, 4, 4, 9, 0, 1, 1, 8, 8, 8, 8, 0, 1, 9, 0],\n [1, 1, 0, 8, 8, 8, 8, 8, 8, 1, 0, 0, 4, 4, 4, 4, 4, 4, 1, 0, 0, 0, 8, 8, 8, 8, 1, 0, 9, 0],\n [1, 0, 9, 8, 8, 8, 8, 8, 8, 0, 0, 9, 4, 4, 4, 4, 4, 4, 0, 0, 1, 9, 8, 8, 8, 8, 1, 0, 1, 0],\n [9, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 9, 9, 0, 9, 0, 0, 1, 0, 1, 9, 1, 0, 0, 9, 1],\n [0, 9, 1, 1, 0, 1, 9, 1, 0, 1, 0, 9, 1, 0, 0, 0, 9, 9, 1, 0, 1, 1, 0, 0, 0, 0, 0, 9, 0, 1],\n [1, 1, 0, 9, 9, 0, 0, 9, 0, 0, 0, 0, 7, 7, 7, 7, 1, 1, 1, 0, 1, 0, 3, 3, 3, 3, 3, 0, 1, 0],\n [0, 1, 0, 0, 3, 3, 3, 1, 9, 1, 0, 0, 7, 7, 7, 7, 0, 1, 0, 9, 0, 0, 3, 3, 3, 3, 3, 1, 1, 9],\n [1, 0, 1, 1, 3, 3, 3, 1, 0, 0, 1, 0, 7, 7, 7, 7, 0, 0, 9, 0, 0, 0, 3, 3, 3, 3, 3, 0, 1, 0],\n [0, 1, 1, 0, 3, 3, 3, 9, 0, 1, 0, 9, 1, 1, 0, 0, 0, 1, 9, 1, 1, 1, 3, 3, 3, 3, 3, 0, 0, 9],\n [0, 0, 0, 1, 0, 9, 9, 9, 0, 9, 9, 1, 9, 9, 0, 0, 1, 0, 1, 0, 0, 9, 0, 0, 0, 0, 9, 0, 9, 0],\n [0, 1, 0, 1, 0, 9, 1, 0, 1, 9, 1, 9, 0, 0, 1, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 0, 9, 9, 1, 0],\n [1, 0, 9, 0, 1, 9, 0, 0, 0, 0, 9, 9, 1, 1, 1, 9, 0, 1, 9, 1, 4, 4, 4, 4, 4, 9, 0, 1, 0, 0],\n [9, 0, 0, 0, 9, 0, 9, 0, 0, 9, 0, 0, 9, 0, 0, 0, 1, 0, 0, 9, 4, 4, 4, 4, 4, 0, 1, 0, 0, 0],\n [9, 0, 9, 2, 2, 2, 2, 2, 9, 9, 1, 9, 8, 8, 8, 8, 0, 9, 0, 9, 4, 4, 4, 4, 4, 0, 0, 0, 0, 1],\n [0, 0, 1, 2, 2, 2, 2, 2, 1, 0, 1, 0, 8, 8, 8, 8, 1, 9, 9, 1, 4, 4, 4, 4, 4, 1, 0, 9, 9, 0],\n [0, 1, 0, 2, 2, 2, 2, 2, 0, 1, 0, 1, 8, 8, 8, 8, 0, 9, 1, 0, 4, 4, 4, 4, 4, 0, 1, 1, 1, 1],\n [1, 0, 0, 2, 2, 2, 2, 2, 0, 0, 1, 0, 8, 8, 8, 8, 0, 9, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0],\n [9, 1, 9, 0, 9, 0, 9, 9, 1, 9, 9, 9, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0],\n [9, 0, 9, 0, 0, 1, 0, 0, 9, 1, 1, 9, 9, 1, 0, 9, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 9, 1, 1, 1, 1, 0, 0, 9, 1, 0, 1, 1, 1, 9, 1, 9, 0, 9, 1, 1, 1, 1, 0, 0, 0],\n [1, 0, 0, 0, 1, 9, 9, 1, 1, 0, 1, 0, 0, 9, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 9, 1, 1],\n [0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 9, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 9, 9, 0, 1, 0, 1, 1, 0, 1],\n [0, 0, 0, 9, 0, 1, 9, 1, 1, 1, 1, 0, 9, 9, 0, 0, 0, 0, 0, 0, 9, 0, 1, 0, 0, 0, 0, 9, 0, 1],\n [1, 0, 1, 9, 0, 9, 0, 0, 0, 0, 9, 1, 0, 0, 0, 0, 9, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0],\n [1, 0, 0, 0, 0, 9, 9, 0, 1, 0, 9, 0, 9, 0, 1, 1, 1, 0, 0, 1, 0, 0, 9, 0, 1, 0, 9, 9, 9, 1]\n ],\n \"output\": [\n [8, 4, 8],\n [3, 7, 3],\n [2, 8, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 0, 0, 0, 8, 5, 0, 0, 8, 8, 8, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5, 8, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 5, 5, 0, 8, 0, 0, 5, 8, 0, 0, 5, 8, 0, 8, 0, 8, 0, 8, 0, 0, 5, 0, 8, 8, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 8, 0, 5, 8, 0, 0, 0, 5, 0, 5, 8, 8, 5, 8, 5, 0, 5, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 5, 5, 5, 8, 8, 0, 0, 0, 5, 8, 3, 3, 3, 3, 3, 5, 0, 8, 0, 8, 8, 0, 8, 8, 0, 0, 5],\n [0, 5, 0, 5, 2, 2, 2, 2, 2, 2, 0, 5, 8, 3, 3, 3, 3, 3, 8, 8, 8, 3, 3, 3, 3, 3, 3, 0, 0, 5],\n [8, 8, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 3, 3, 5, 8, 0, 3, 3, 3, 3, 3, 3, 0, 8, 0],\n [8, 5, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 8, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3, 3, 5, 0, 0],\n [5, 0, 8, 8, 2, 2, 2, 2, 2, 2, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 8, 0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [0, 0, 5, 0, 5, 5, 0, 8, 0, 8, 8, 0, 0, 5, 8, 0, 0, 0, 0, 5, 0, 0, 1, 1, 1, 1, 1, 5, 5, 5],\n [8, 0, 8, 4, 4, 4, 4, 4, 5, 0, 5, 8, 7, 7, 7, 7, 7, 0, 0, 8, 5, 0, 1, 1, 1, 1, 1, 0, 5, 0],\n [8, 5, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 5, 0, 0, 1, 1, 1, 1, 1, 0, 5, 0],\n [0, 8, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 7, 7, 7, 7, 5, 0, 0, 5, 8, 1, 1, 1, 1, 1, 5, 5, 0],\n [0, 8, 5, 4, 4, 4, 4, 4, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 8, 0, 8, 0, 1, 1, 1, 1, 1, 5, 5, 0],\n [0, 5, 8, 4, 4, 4, 4, 4, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 5, 8],\n [8, 8, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 8, 0, 5, 0, 5, 8, 0, 0, 0, 5],\n [0, 8, 0, 5, 0, 0, 0, 5, 5, 8, 5, 5, 3, 3, 3, 3, 3, 3, 3, 8, 0, 5, 0, 7, 7, 7, 7, 5, 0, 5],\n [0, 0, 5, 5, 0, 5, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 8, 8, 8, 7, 7, 7, 7, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 5, 8, 3, 3, 3, 3, 3, 3, 3, 8, 5, 0, 8, 7, 7, 7, 7, 0, 5, 5],\n [0, 5, 0, 8, 0, 5, 1, 1, 1, 1, 5, 0, 3, 3, 3, 3, 3, 3, 3, 5, 0, 5, 0, 7, 7, 7, 7, 5, 0, 0],\n [0, 0, 5, 0, 0, 8, 1, 1, 1, 1, 0, 0, 5, 8, 0, 0, 5, 8, 8, 0, 0, 8, 0, 7, 7, 7, 7, 8, 0, 0],\n [5, 0, 5, 8, 0, 0, 8, 0, 5, 0, 0, 0, 0, 0, 5, 8, 0, 0, 5, 8, 0, 0, 5, 0, 8, 8, 8, 0, 0, 5],\n [0, 5, 0, 5, 5, 4, 4, 4, 5, 0, 5, 0, 6, 6, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 5, 5],\n [0, 8, 0, 5, 5, 4, 4, 4, 0, 8, 5, 0, 6, 6, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2, 2, 2, 5, 0, 8, 5],\n [8, 0, 0, 0, 0, 4, 4, 4, 5, 0, 8, 0, 6, 6, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 5, 5, 0, 8, 6, 6, 6, 6, 6, 6, 5, 0, 2, 2, 2, 2, 2, 2, 0, 0, 8, 0],\n [5, 5, 0, 0, 0, 5, 5, 8, 5, 8, 0, 0, 6, 6, 6, 6, 6, 6, 8, 5, 8, 0, 0, 8, 5, 0, 8, 5, 0, 0],\n [0, 5, 8, 5, 0, 8, 5, 5, 5, 0, 8, 8, 0, 0, 5, 0, 8, 5, 5, 0, 0, 0, 5, 8, 0, 0, 0, 0, 8, 5],\n [0, 0, 0, 0, 8, 0, 0, 5, 8, 8, 8, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0, 8, 0, 5, 5, 0, 0, 8],\n [8, 0, 5, 0, 0, 0, 0, 0, 5, 8, 8, 8, 0, 0, 5, 5, 5, 5, 8, 5, 0, 0, 5, 8, 5, 8, 5, 5, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 3, 3], [4, 7, 1], [1, 3, 7], [4, 6, 2]], "task_id": "0a1d4ef5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 1, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 1, 1, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0],\n [0, 1, 2, 2, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 1, 2, 2], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 1, 1, 2, 2, 0, 0, 0], [0, 0, 2, 2, 2, 2, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "69889d6e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 8, 0, 1, 1, 1, 0, 0, 1, 8, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 1, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1],\n [0, 1, 1, 8, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [0, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0],\n [1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 8, 0],\n [0, 0, 0, 1, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 8, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 1, 8, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0],\n [1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 8, 1, 0, 0],\n [1, 1, 1, 1, 0, 8, 1, 1, 8, 0, 0, 1, 8, 0, 0],\n [1, 8, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 8, 1, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 8, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 8, 1, 8, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 1, 1],\n [1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [8, 1, 1, 0, 0, 0, 0, 0, 1, 8, 1, 8],\n [1, 1, 1, 0, 8, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 1],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 8, 1, 0, 1, 1, 1, 1],\n [0, 1, 8, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0]], "task_id": "a934301b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0],\n [2, 8, 2, 8, 0, 8, 2, 8, 0, 8, 2, 8, 2, 8, 0, 8, 0],\n [2, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0],\n [2, 8, 2, 8, 0, 8, 2, 8, 0, 8, 2, 8, 2, 8, 0, 8, 0],\n [2, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 6, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 1, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 8, 8, 8, 6, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 6],\n [0, 8, 0, 8, 6, 8, 6, 8, 0, 8, 6, 8, 0, 8, 6, 8, 6],\n [0, 8, 8, 8, 6, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 6],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 8, 8, 0, 8, 8, 8, 1, 8, 8, 8, 0, 8, 8, 8, 0],\n [1, 8, 1, 8, 0, 8, 1, 8, 1, 8, 0, 8, 0, 8, 0, 8, 0],\n [1, 8, 8, 8, 0, 8, 8, 8, 1, 8, 8, 8, 0, 8, 8, 8, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 7, 8, 8, 8, 7],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 7, 8, 7, 8, 7],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 7, 8, 8, 8, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [3, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 3, 8, 8, 8, 0],\n [3, 8, 3, 8, 0, 8, 3, 8, 0, 8, 3, 8, 3, 8, 0, 8, 0],\n [3, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 3, 8, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 4, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 4, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 2, 8, 0, 8, 2, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 2, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 8, 8, 8, 4, 8, 8, 8, 0, 8, 8, 8, 4, 8, 8, 8, 0], [0, 8, 0, 8, 4, 8, 4, 8, 0, 8, 4, 8, 4, 8, 0, 8, 0], [0, 8, 8, 8, 4, 8, 8, 8, 0, 8, 8, 8, 4, 8, 8, 8, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 8, 8, 8, 4, 8, 8, 8, 0, 8, 8, 8, 4, 8, 8, 8, 0], [0, 8, 0, 8, 4, 8, 4, 8, 0, 8, 4, 8, 4, 8, 0, 8, 0], [0, 8, 8, 8, 4, 8, 8, 8, 0, 8, 8, 8, 4, 8, 8, 8, 0], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 8, 8, 8, 2, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0], [0, 8, 0, 8, 2, 8, 2, 8, 0, 8, 2, 8, 2, 8, 0, 8, 0], [0, 8, 8, 8, 2, 8, 8, 8, 0, 8, 8, 8, 2, 8, 8, 8, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0]], "task_id": "97239e3d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ],\n \"output\": [\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2],\n [2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0],\n [3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 3, 3, 0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8], [8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 8, 8, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "4f537728"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 2, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 2, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 2, 1, 0, 1, 8, 2, 1, 0, 1, 8, 2, 1, 0, 1, 8, 2, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 8, 1, 0, 1, 1, 4, 1, 0, 1, 1, 8, 1, 0, 1, 1, 8, 1, 0],\n [0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0, 1, 8, 8, 1, 0],\n [0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 8, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 8, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 1, 3, 2, 0, 2, 1, 3, 2, 0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 1, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 6, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 2, 3, 8, 2, 0, 2, 3, 8, 2, 0, 2, 3, 8, 2, 0, 2, 3, 8, 2, 0],\n [0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0, 2, 3, 3, 2, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 7, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 4, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 3, 8, 8, 0, 8, 4, 8, 8, 0, 8, 3, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 7, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 7, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 7, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 3, 8, 8, 0, 8, 3, 8, 8, 0, 8, 3, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 8, 4, 4, 8, 0, 8, 7, 4, 8, 0, 8, 4, 4, 8, 0],\n [0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0, 8, 4, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0],\n [0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0],\n [0, 5, 5, 2, 5, 0, 5, 5, 4, 5, 0, 5, 5, 2, 5, 0, 5, 5, 4, 5, 0, 5, 5, 2, 5, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0],\n [0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0],\n [0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 6, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 6, 2, 5, 0, 5, 2, 3, 5, 0],\n [0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0], [0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0], [0, 5, 5, 8, 5, 0, 5, 5, 4, 5, 0, 5, 5, 4, 5, 0, 5, 5, 4, 5, 0, 5, 5, 2, 5, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0], [0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 3, 5, 0, 5, 2, 2, 5, 0, 5, 2, 2, 5, 0], [0, 5, 5, 8, 5, 0, 5, 5, 8, 5, 0, 5, 5, 8, 5, 0, 5, 5, 8, 5, 0, 5, 5, 2, 5, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 6, 2, 5, 0, 5, 6, 2, 5, 0, 5, 6, 3, 5, 0, 5, 6, 3, 5, 0, 5, 2, 3, 5, 0], [0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0, 5, 5, 2, 5, 0], [0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "a096bf4d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 0, 5, 5, 5, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 5, 5, 5, 5],\n [5, 0, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 0, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 5],\n [5, 0, 5, 5, 0, 0, 5, 5, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 3, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 1, 5, 5, 5, 3, 5, 5, 5, 5],\n [5, 5, 5, 5, 2, 3, 5, 5, 5, 5],\n [5, 1, 5, 5, 2, 5, 5, 5, 5, 5],\n [5, 1, 5, 5, 2, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 4, 5],\n [5, 1, 5, 5, 2, 3, 5, 5, 4, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 4, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 5, 5, 5, 5, 5, 0, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 0, 5, 5],\n [5, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 5],\n [5, 5, 5, 5, 0, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [1, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 3, 5, 5],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 4, 5],\n [5, 5, 5, 5, 2, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 0, 0, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 0, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 1, 2, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 4],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 4],\n [5, 5, 5, 5, 5, 5, 5, 5, 3, 4],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 2, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 4],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 0, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 0, 5, 5, 5, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 0, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5, 3, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 4, 5, 5, 5, 5], [5, 5, 2, 5, 5, 5, 5, 5, 5, 5], [5, 5, 2, 5, 5, 5, 5, 5, 5, 5], [1, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 5, 5, 5, 5, 4, 5, 5, 5, 5]], "task_id": "575b1a71"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 3, 3, 3, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 3, 3, 3, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 3, 3, 3, 4, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 4, 3, 3, 3, 4, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 7, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5],\n [0, 0, 7, 7, 7, 7, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5],\n [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5],\n [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5],\n [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 0, 0, 0],\n [5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 0, 0, 0],\n [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 2, 2, 2, 2, 2, 2, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 2, 2, 2, 2, 2, 2, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 8, 8, 8, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 8, 8, 8, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 8, 8, 8, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 8, 8, 8, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 8, 8, 8, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "13713586"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0],\n [5, 5, 5],\n [0, 5, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0],\n [5, 5, 0],\n [0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0],\n [0, 5, 0],\n [0, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0],\n [0, 5, 0],\n [5, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 5, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0], [5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 5], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0]], "task_id": "8719f442"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 1, 1, 8, 8, 8, 1, 1, 4, 4, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 6, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 6, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 4, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 4, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 4, 8, 8, 0, 0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 1, 1, 2, 2, 8, 4, 8, 2, 2, 1, 1, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 2, 8, 4, 4, 4, 4, 8, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "40f6cd08"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 7, 7, 7, 0],\n [8, 8, 0, 0, 5, 5, 0, 0],\n [0, 8, 8, 0, 0, 5, 5, 0],\n [0, 1, 1, 0, 8, 0, 0, 1],\n [0, 7, 0, 1, 8, 0, 0, 0],\n [8, 0, 0, 0, 1, 0, 7, 0],\n [0, 8, 8, 8, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 0, 7, 7, 7, 0],\n [8, 8, 0, 0, 5, 5, 0, 0],\n [0, 8, 8, 0, 0, 5, 5, 0],\n [0, 3, 3, 0, 3, 0, 0, 3],\n [0, 3, 0, 3, 3, 0, 0, 0],\n [3, 0, 0, 0, 3, 0, 3, 0],\n [0, 8, 8, 8, 3, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 8, 1, 1, 1, 0],\n [1, 5, 1, 7, 1, 1, 0, 0],\n [0, 8, 0, 7, 7, 7, 8, 8],\n [0, 8, 8, 0, 0, 0, 8, 0],\n [0, 7, 0, 0, 8, 5, 5, 0],\n [1, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 8, 7, 7, 8, 0, 0],\n [0, 0, 8, 7, 7, 0, 8, 8],\n [0, 8, 8, 0, 8, 0, 8, 8]\n ],\n \"output\": [\n [0, 0, 3, 3, 1, 1, 1, 0],\n [3, 3, 3, 7, 1, 1, 0, 0],\n [0, 8, 0, 7, 7, 7, 8, 8],\n [0, 8, 8, 0, 0, 0, 8, 0],\n [0, 3, 0, 0, 3, 3, 3, 0],\n [3, 0, 0, 0, 0, 0, 0, 3],\n [3, 0, 8, 7, 7, 3, 0, 0],\n [0, 0, 8, 7, 7, 0, 8, 8],\n [0, 8, 8, 0, 3, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 7, 7, 1, 0, 8, 0, 5],\n [1, 7, 7, 1, 1, 0, 1, 0],\n [8, 8, 0, 0, 7, 7, 7, 7],\n [0, 1, 0, 0, 0, 0, 1, 1],\n [5, 0, 8, 0, 1, 0, 1, 1]\n ],\n \"output\": [\n [3, 7, 7, 1, 0, 3, 0, 3],\n [3, 7, 7, 1, 1, 0, 3, 0],\n [3, 3, 0, 0, 7, 7, 7, 7],\n [0, 3, 0, 0, 0, 0, 1, 1],\n [3, 0, 3, 0, 3, 0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 5],\n [1, 0, 0],\n [7, 7, 7]\n ],\n \"output\": [\n [3, 0, 3],\n [3, 0, 0],\n [7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 1, 5, 5, 0, 5],\n [1, 1, 0, 0, 0, 1, 1, 0],\n [0, 7, 7, 0, 0, 0, 0, 5],\n [1, 1, 0, 5, 0, 1, 0, 0],\n [0, 1, 0, 5, 5, 5, 0, 1],\n [0, 7, 0, 0, 7, 0, 0, 7],\n [1, 0, 1, 0, 0, 0, 1, 7],\n [0, 0, 1, 1, 0, 1, 0, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 3, 0, 3, 3, 3, 0, 3], [3, 3, 0, 0, 0, 3, 3, 0], [0, 3, 3, 0, 0, 0, 0, 3], [1, 1, 0, 5, 0, 3, 0, 0], [0, 1, 0, 5, 5, 5, 0, 3], [0, 3, 0, 0, 3, 0, 0, 7], [3, 0, 1, 0, 0, 0, 3, 7], [0, 0, 1, 1, 0, 3, 0, 7]], "task_id": "12eac192"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 3, 3],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [2, 2, 2, 2],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [3, 3, 0, 0]\n ],\n \"output\": [\n [0, 3, 3, 3],\n [0, 4, 0, 0],\n [0, 4, 0, 0],\n [0, 4, 0, 0],\n [2, 2, 2, 2],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [3, 3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 6, 6, 6, 6],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6]\n ],\n \"output\": [\n [0, 6, 6, 6, 6],\n [0, 0, 4, 4, 4],\n [0, 0, 4, 4, 4],\n [0, 0, 4, 4, 4],\n [0, 0, 4, 4, 4],\n [0, 0, 4, 4, 4],\n [2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0]\n ],\n \"output\": [\n [0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2],\n [0, 4, 4, 0, 0],\n [0, 4, 4, 0, 0],\n [0, 4, 4, 0, 0],\n [0, 4, 4, 0, 0],\n [0, 4, 4, 0, 0],\n [1, 1, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n [0, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 0, 4, 4, 0],\n [0, 0, 0, 4, 4, 0],\n [0, 0, 0, 4, 4, 0],\n [0, 0, 0, 4, 4, 0],\n [0, 0, 0, 4, 4, 0],\n [0, 3, 3, 3, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 7, 7, 7, 7], [0, 4, 4, 0, 0], [0, 4, 4, 0, 0], [0, 4, 4, 0, 0], [0, 4, 4, 0, 0], [2, 2, 2, 2, 2], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 7, 7, 0, 0]], "task_id": "770cc55f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2],\n [8, 2, 2, 2],\n [2, 2, 8, 2],\n [8, 2, 8, 8]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [8, 2, 2, 2, 2, 2, 2, 8, 8, 2, 2, 2, 2, 2, 2, 8, 8, 2, 2, 2],\n [2, 2, 8, 2, 2, 8, 2, 2, 2, 2, 8, 2, 2, 8, 2, 2, 2, 2, 8, 2],\n [8, 2, 8, 8, 8, 8, 2, 8, 8, 2, 8, 8, 8, 8, 2, 8, 8, 2, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [9, 5, 1, 5],\n [1, 5, 9, 1],\n [9, 1, 5, 5],\n [5, 5, 5, 1]\n ],\n \"output\": [\n [9, 5, 1, 5, 5, 1, 5, 9, 9, 5, 1, 5, 5, 1, 5, 9, 9, 5, 1, 5],\n [1, 5, 9, 1, 1, 9, 5, 1, 1, 5, 9, 1, 1, 9, 5, 1, 1, 5, 9, 1],\n [9, 1, 5, 5, 5, 5, 1, 9, 9, 1, 5, 5, 5, 5, 1, 9, 9, 1, 5, 5],\n [5, 5, 5, 1, 1, 5, 5, 5, 5, 5, 5, 1, 1, 5, 5, 5, 5, 5, 5, 1]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 2, 5],\n [2, 3, 3, 2],\n [5, 2, 5, 3],\n [3, 5, 3, 2]\n ],\n \"output\": [\n [5, 5, 2, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 2, 5, 5, 5, 5, 2, 5],\n [2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2],\n [5, 2, 5, 3, 3, 5, 2, 5, 5, 2, 5, 3, 3, 5, 2, 5, 5, 2, 5, 3],\n [3, 5, 3, 2, 2, 3, 5, 3, 3, 5, 3, 2, 2, 3, 5, 3, 3, 5, 3, 2]\n ]\n}\n\n{\n \"input\": [\n [4, 1, 1, 4],\n [7, 7, 4, 7],\n [1, 4, 1, 1],\n [4, 1, 1, 1]\n ],\n \"output\": [\n [4, 1, 1, 4, 4, 1, 1, 4, 4, 1, 1, 4, 4, 1, 1, 4, 4, 1, 1, 4],\n [7, 7, 4, 7, 7, 4, 7, 7, 7, 7, 4, 7, 7, 4, 7, 7, 7, 7, 4, 7],\n [1, 4, 1, 1, 1, 1, 4, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 4, 1, 1],\n [4, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 4, 4],\n [5, 5, 5, 2],\n [2, 5, 5, 5],\n [5, 5, 2, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 4, 4, 4, 4, 5, 5, 5, 5, 4, 4, 4, 4, 5, 5, 5, 5, 4, 4], [5, 5, 5, 2, 2, 5, 5, 5, 5, 5, 5, 2, 2, 5, 5, 5, 5, 5, 5, 2], [2, 5, 5, 5, 5, 5, 5, 2, 2, 5, 5, 5, 5, 5, 5, 2, 2, 5, 5, 5], [5, 5, 2, 4, 4, 2, 5, 5, 5, 5, 2, 4, 4, 2, 5, 5, 5, 5, 2, 4]], "task_id": "bc4146bd"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "0b17323b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 0, 0, 0, 0, 3, 1, 4, 2, 5, 3, 1, 4, 2, 0, 0, 0, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 0, 0, 0, 0, 3, 1, 4, 2, 5, 3, 1, 4, 2, 0, 0, 0, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 0, 0, 0, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 0, 0, 0, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 0, 0, 0, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 0, 0, 0, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3, 1, 4, 2, 5, 3]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 0, 0, 0, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 0, 0, 0, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 0, 0, 0, 0, 0, 0, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 0, 0, 0, 0, 0, 0, 4],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [1, 4, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 0, 0, 0, 0, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 0, 0, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 0, 0, 0, 0, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 0, 0, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 0, 0, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 0, 0, 7, 3, 6, 2, 0, 0, 0, 0, 0, 0, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 0, 0, 0, 0, 0, 0, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 0, 0, 0, 0, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 0, 0, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4, 7, 3, 6, 2, 5, 1, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 0, 0, 0, 0, 0, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 0, 0, 0, 0, 0, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 0, 0, 0, 0, 0, 0, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 0, 0, 0, 0, 0, 0, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 0, 0, 0, 0, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 0, 0, 0, 0, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8, 3, 6, 1, 4, 7, 2, 5, 8]], "task_id": "ca8f78db"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 7, 7, 0, 2, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 8, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 9, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 7, 0, 0, 8, 0],\n [0, 0, 1, 0, 0, 2, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 7, 7, 7, 0, 2, 0],\n [7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 7, 0, 0, 7, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 6, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [9, 7, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 9, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 4, 6, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 3, 1, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [9, 7, 0, 0, 0, 0, 4, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 4, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 8, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 8, 7, 0, 0, 0, 0, 0, 0],\n [6, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 1, 0, 0, 0, 0],\n [0, 0, 2, 6, 5, 0, 3, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 1],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 6, 7, 0, 0, 0, 0, 0, 0, 0, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 8, 7, 0, 0, 0, 2, 0, 0],\n [6, 8, 0, 0, 3, 0, 0, 0, 0, 0, 8, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 6, 0, 1, 0, 2, 0, 0],\n [0, 0, 2, 6, 3, 0, 3, 0, 0, 0, 2, 2, 2, 0],\n [2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 8, 0, 2, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3],\n [0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 3, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [5, 0, 0, 0, 3, 0, 3, 0, 4, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 6, 2, 0, 1],\n [0, 0, 0, 8, 3, 8, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 8, 0, 3, 7, 0, 0, 0, 0, 0, 2, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 8, 8, 8, 1, 3, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 8, 8, 8, 0, 0, 0, 0],\n [1, 0, 0, 7, 0, 0, 0, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 5, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 4, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [1, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 4, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [0, 0, 2, 0, 6, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 3, 0],\n [8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8],\n [0, 0, 4, 0, 6, 0, 0, 0, 0, 4, 0, 8, 8, 8, 0, 0, 3, 0],\n [1, 0, 0, 7, 6, 0, 0, 7, 2, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [8, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 3, 0],\n [0, 5, 0, 0, 6, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 3, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 6, 6, 6, 0, 6],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 3, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0],\n [0, 0, 9, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 3, 3],\n [3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 6, 4, 0, 0, 0, 8, 0, 0, 8, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 8, 4, 0, 0, 3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 7, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 7, 4, 4, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 7, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 2, 4],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],\n [0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 6, 0, 0, 0, 2, 0],\n [7, 7, 7, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 9, 9, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 2, 0],\n [3, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 8, 0, 2, 0, 0], [4, 7, 0, 0, 0, 0, 0, 4, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0], [1, 7, 0, 0, 0, 7, 4, 4, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4], [0, 7, 0, 0, 0, 7, 4, 4, 4, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0], [0, 7, 0, 0, 6, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0], [0, 7, 7, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 8, 8, 0, 2, 4], [8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 7, 2, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8, 1, 4, 0, 0], [0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 6, 8, 0, 0, 2, 0], [7, 7, 7, 0, 0, 0, 0, 4, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7], [7, 7, 7, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 7, 6, 6, 9, 9, 0, 4, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 7, 0, 8, 0, 0, 2, 0], [3, 7, 4, 0, 0, 0, 0, 4, 0, 0, 0, 8, 0, 0, 8, 0, 4, 0, 0], [0, 7, 0, 0, 0, 0, 0, 4, 0, 0, 0, 6, 0, 0, 8, 0, 0, 0, 0]], "task_id": "e9bb6954"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 6, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 6, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 4, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 4, 4, 1, 1, 0, 0, 0, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 0], [0, 0, 0, 6, 6, 4, 4, 1, 1, 0, 0, 0, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 0], [0, 0, 0, 2, 2, 4, 4, 3, 3, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 1, 1, 0], [0, 0, 0, 2, 2, 4, 4, 3, 3, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 1, 1, 0], [0, 0, 0, 2, 2, 4, 4, 3, 3, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 1, 1, 0], [0, 0, 0, 2, 2, 2, 3, 3, 3, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 1, 1, 0], [0, 0, 0, 2, 2, 2, 3, 3, 3, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 4, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 0], [0, 0, 0, 6, 6, 6, 1, 1, 1, 0, 0, 0, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 0], [0, 0, 0, 6, 6, 6, 1, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 0], [0, 0, 0, 6, 6, 4, 4, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 0], [0, 0, 0, 2, 2, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "639f5a19"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 6, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6]\n ],\n \"output\": [\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 7, 0, 7, 0, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 0, 7, 0],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7]\n ],\n \"output\": [\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 0, 0, 7, 7, 0, 7, 0],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 0, 0, 7, 7, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 7, 0, 7, 0, 0, 7, 7],\n [7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7, 0, 7, 7]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ],\n \"output\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ],\n \"output\": [\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1],\n [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 0],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 0, 0, 6, 6, 0, 6, 6, 0, 6, 0, 0, 6, 6],\n [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 0], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 6], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 0, 0, 6, 0, 0, 6, 0, 0, 6, 6, 0, 6, 6], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 0, 0, 6, 6, 0, 6, 0, 0, 6, 6, 0, 6, 6], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [6, 0, 0, 6, 6, 0, 6, 6, 0, 6, 0, 0, 6, 6], [6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6]], "task_id": "85b81ff1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0],\n [0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0],\n [0, 1, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0],\n [0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 1, 0, 0, 0],\n [0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 8, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 1, 1, 8, 1, 1, 1, 1, 1, 0], [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0], [0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 1, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 8, 1, 1, 0, 0, 0], [0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 1, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 1, 1, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0]], "task_id": "551d5bf1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "55059096"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 6, 0, 0, 0, 7],\n [0, 0, 0, 4, 0, 0],\n [2, 0, 0, 0, 9, 0],\n [0, 0, 3, 0, 0, 0],\n [0, 0, 0, 5, 0, 0],\n [1, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [6, 4, 7],\n [2, 3, 9],\n [1, 5, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 7, 0, 0, 8, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1],\n [5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 7, 8],\n [5, 2, 1],\n [3, 9, 6]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 7],\n [0, 3, 0, 0, 9, 0],\n [0, 0, 5, 0, 0, 0],\n [0, 0, 0, 6, 0, 0],\n [4, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [2, 1, 7],\n [3, 5, 9],\n [4, 6, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 9, 0, 0],\n [5, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 6, 9], [4, 1, 8], [3, 2, 7]], "task_id": "5783df64"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 5, 5, 5, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 5, 5, 5, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 5, 5, 5, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 5, 5, 5, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 5, 5, 5, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 1, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 1, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 4, 4, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 4, 4, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 6, 6, 6, 6, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 6, 4, 4, 6, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 6, 4, 4, 6, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 6, 6, 6, 6, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 2, 2, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 2, 2, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 2, 2, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 2, 2, 2, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 2, 2, 2, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 2, 2, 2, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 0],\n [0, 0, 2, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "3a301edc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0],\n [1, 1, 0],\n [8, 8, 8],\n [8, 8, 8],\n [0, 1, 1],\n [0, 1, 1],\n [8, 8, 0],\n [1, 1, 1],\n [8, 8, 0],\n [8, 8, 0],\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1],\n [8, 8, 8],\n [8, 8, 8]\n ],\n \"output\": [\n [2, 2, 0],\n [2, 2, 0],\n [8, 8, 8],\n [8, 8, 8],\n [0, 1, 1],\n [0, 1, 1],\n [2, 2, 0],\n [1, 1, 1],\n [8, 8, 0],\n [8, 8, 0],\n [2, 2, 2],\n [2, 2, 2],\n [2, 2, 2],\n [8, 8, 8],\n [8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 8, 0],\n [0, 8, 8, 8, 0],\n [1, 1, 1, 0, 0],\n [0, 8, 8, 8, 0],\n [0, 8, 8, 8, 0],\n [1, 1, 1, 1, 1],\n [0, 8, 8, 8, 8],\n [0, 8, 8, 8, 8],\n [1, 1, 1, 1, 0],\n [1, 1, 1, 1, 0],\n [0, 8, 8, 8, 0],\n [0, 1, 1, 1, 1],\n [0, 1, 1, 1, 1],\n [8, 8, 8, 0, 0],\n [0, 0, 0, 1, 1],\n [8, 8, 8, 0, 0],\n [8, 8, 8, 0, 0],\n [0, 0, 1, 1, 0],\n [0, 0, 1, 1, 0]\n ],\n \"output\": [\n [0, 2, 2, 2, 0],\n [0, 2, 2, 2, 0],\n [1, 1, 1, 0, 0],\n [0, 8, 8, 8, 0],\n [0, 8, 8, 8, 0],\n [2, 2, 2, 2, 2],\n [0, 8, 8, 8, 8],\n [0, 8, 8, 8, 8],\n [1, 1, 1, 1, 0],\n [1, 1, 1, 1, 0],\n [0, 2, 2, 2, 0],\n [0, 1, 1, 1, 1],\n [0, 1, 1, 1, 1],\n [8, 8, 8, 0, 0],\n [0, 0, 0, 2, 2],\n [8, 8, 8, 0, 0],\n [8, 8, 8, 0, 0],\n [0, 0, 1, 1, 0],\n [0, 0, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 0],\n [1, 1, 1, 0],\n [8, 8, 8, 8],\n [0, 0, 1, 1],\n [0, 0, 1, 1],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [0, 1, 1, 0],\n [8, 8, 0, 0],\n [1, 1, 1, 1],\n [0, 8, 8, 0],\n [0, 8, 8, 0],\n [1, 1, 1, 1],\n [8, 8, 8, 0],\n [8, 8, 8, 0],\n [0, 1, 1, 1],\n [0, 1, 1, 1],\n [8, 8, 8, 0],\n [0, 1, 1, 0],\n [8, 8, 8, 8]\n ],\n \"output\": [\n [2, 2, 2, 0],\n [2, 2, 2, 0],\n [8, 8, 8, 8],\n [0, 0, 1, 1],\n [0, 0, 1, 1],\n [2, 2, 2, 2],\n [2, 2, 2, 2],\n [0, 1, 1, 0],\n [8, 8, 0, 0],\n [2, 2, 2, 2],\n [0, 8, 8, 0],\n [0, 8, 8, 0],\n [1, 1, 1, 1],\n [2, 2, 2, 0],\n [2, 2, 2, 0],\n [0, 1, 1, 1],\n [0, 1, 1, 1],\n [8, 8, 8, 0],\n [0, 2, 2, 0],\n [8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 0, 0],\n [0, 8, 8, 8, 8],\n [1, 1, 1, 1, 0],\n [0, 8, 8, 0, 0],\n [0, 0, 1, 1, 1],\n [0, 0, 1, 1, 1],\n [8, 8, 8, 8, 0],\n [0, 1, 1, 0, 0],\n [0, 1, 1, 0, 0],\n [8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8],\n [1, 1, 1, 0, 0],\n [0, 8, 8, 8, 0],\n [0, 0, 0, 1, 1]\n ],\n \"output\": [\n [2, 2, 2, 0, 0],\n [0, 8, 8, 8, 8],\n [1, 1, 1, 1, 0],\n [0, 2, 2, 0, 0],\n [0, 0, 1, 1, 1],\n [0, 0, 1, 1, 1],\n [8, 8, 8, 8, 0],\n [0, 2, 2, 0, 0],\n [0, 2, 2, 0, 0],\n [8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8],\n [1, 1, 1, 0, 0],\n [0, 2, 2, 2, 0],\n [0, 0, 0, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 0],\n [0, 1, 1, 1],\n [0, 8, 8, 0],\n [1, 1, 1, 1],\n [8, 8, 8, 0],\n [8, 8, 8, 0],\n [0, 1, 1, 1],\n [0, 8, 8, 0],\n [0, 8, 8, 0],\n [1, 1, 1, 1],\n [1, 1, 1, 1],\n [8, 8, 8, 8],\n [1, 1, 0, 0],\n [0, 8, 8, 8],\n [1, 1, 1, 0],\n [8, 8, 8, 8],\n [0, 1, 1, 0],\n [0, 1, 1, 0],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [0, 1, 1, 1],\n [0, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 0], [0, 1, 1, 1], [0, 8, 8, 0], [2, 2, 2, 2], [8, 8, 8, 0], [8, 8, 8, 0], [0, 1, 1, 1], [0, 2, 2, 0], [0, 2, 2, 0], [1, 1, 1, 1], [1, 1, 1, 1], [8, 8, 8, 8], [2, 2, 0, 0], [0, 8, 8, 8], [1, 1, 1, 0], [2, 2, 2, 2], [0, 1, 1, 0], [0, 1, 1, 0], [8, 8, 8, 8], [8, 8, 8, 8], [0, 2, 2, 2], [0, 2, 2, 2]], "task_id": "22a4bbc2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 2, 3, 5, 7, 2, 4, 6, 0, 0, 0, 0, 0, 0, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 1, 5, 7, 2, 4, 6, 1, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 0, 0, 0, 0, 0, 0, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 2, 4, 6, 1, 3, 5, 0, 0, 0, 0, 0, 0, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 0, 0, 0, 0, 0, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 0, 0, 0, 0, 0, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 0, 0, 0, 0, 0, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 0, 0, 0, 0, 0, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 0, 0, 0, 0, 0, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 0, 0, 0, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 0, 0, 0, 0, 0, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 0, 0, 0, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 0, 0, 0, 0, 0, 0, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 0, 0, 0, 0, 0, 0, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 0, 0, 0, 0, 0, 0, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 2, 4, 6, 0, 0, 0, 0, 0, 0, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 4, 6, 1, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 6, 1, 3, 0, 0, 0, 0, 0, 0, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 1, 3, 5, 0, 0, 0, 0, 0, 0, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 2, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 1, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4],\n [1, 1, 4, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6],\n [1, 1, 4, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1],\n [1, 1, 4, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3],\n [1, 1, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5],\n [1, 1, 4, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7],\n [1, 1, 4, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2],\n [1, 1, 4, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, 7, 2, 4]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 2, 1, 3, 5, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 0, 0, 0, 0, 0, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 0, 0, 0, 0, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 0, 0, 0, 0, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 0, 0, 0, 0, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 0, 0, 0, 0, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 0, 0, 0, 0, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 0, 0, 0, 0, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 2, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5],\n [1, 1, 4, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1],\n [1, 1, 4, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3],\n [1, 1, 4, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 2, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 1, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 0, 0, 0, 0, 0, 0, 0, 8, 1, 3, 5, 7, 9, 0, 0, 0, 0, 0, 0, 5, 7, 9, 2, 4, 6, 8, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 9, 2, 4, 6, 0, 0, 0, 0, 7, 9, 2, 4, 6, 8, 1, 3],\n [1, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5],\n [1, 1, 4, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 0, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 1, 4, 3, 5, 7, 9, 0, 0, 0, 0, 0, 0, 0, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 1, 4, 5, 7, 9, 2, 0, 0, 0, 0, 0, 0, 0, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 1, 4, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1],\n [1, 1, 4, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3],\n [1, 1, 4, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5],\n [1, 1, 4, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 1, 4, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 1, 4, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 1, 4, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 2, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 1, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 1, 4, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 1, 4, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 1, 4, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1],\n [1, 1, 4, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3],\n [1, 1, 4, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5],\n [1, 1, 4, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 1, 4, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 1, 4, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 1, 4, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1],\n [1, 1, 4, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3],\n [1, 1, 4, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5],\n [1, 1, 4, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7],\n [1, 1, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9],\n [1, 1, 4, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2],\n [1, 1, 4, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4],\n [1, 1, 4, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6],\n [1, 1, 4, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8],\n [1, 1, 4, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1, 3, 5, 7, 9, 2, 4, 6, 8, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 4, 3, 2, 1, 9, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1],\n [1, 1, 4, 2, 7, 9, 1, 3, 5, 7, 9, 0, 0, 0, 0, 0, 0, 0, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3],\n [1, 1, 4, 1, 9, 1, 3, 5, 7, 9, 1, 0, 0, 0, 0, 0, 0, 0, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5],\n [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 0, 0, 0, 0, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 0, 0, 0, 0, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9],\n [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1],\n [1, 0, 0, 0, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3],\n [1, 0, 0, 0, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5],\n [0, 0, 0, 0, 0, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7],\n [0, 0, 0, 0, 0, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9],\n [0, 0, 0, 0, 0, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1],\n [0, 0, 0, 0, 0, 9, 1, 3, 5, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3],\n [0, 0, 0, 0, 0, 1, 3, 5, 7, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5],\n [0, 0, 0, 0, 0, 3, 5, 7, 9, 0, 0, 0, 0, 0, 0, 0, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7],\n [0, 0, 0, 0, 0, 5, 7, 9, 1, 0, 0, 0, 0, 0, 0, 0, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9],\n [1, 1, 4, 3, 5, 7, 9, 1, 3, 0, 0, 0, 0, 0, 0, 0, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1],\n [1, 1, 4, 5, 7, 9, 1, 3, 5, 0, 0, 0, 0, 0, 0, 0, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3],\n [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5],\n [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9],\n [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1],\n [1, 1, 4, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3],\n [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5],\n [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7],\n [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [1, 1, 4, 3, 2, 1, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1], [1, 1, 4, 2, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3], [1, 1, 4, 1, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5], [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7], [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9], [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1], [1, 1, 4, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3], [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5], [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7], [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9], [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1], [1, 1, 4, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3], [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5], [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7], [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9], [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1], [1, 1, 4, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3], [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5], [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7], [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9], [1, 1, 4, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1], [1, 1, 4, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3], [1, 1, 4, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5], [1, 1, 4, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7], [1, 1, 4, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9]], "task_id": "4aab4007"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 2, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [2, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [0, 2, 8, 0, 0, 8, 0, 0, 2, 8, 0, 0],\n [0, 2, 8, 0, 0, 8, 0, 0, 2, 8, 0, 0],\n [0, 2, 8, 0, 0, 8, 0, 0, 2, 8, 0, 0],\n [2, 2, 8, 0, 2, 2, 2, 2, 2, 8, 0, 0],\n [0, 2, 8, 0, 2, 8, 0, 0, 2, 8, 0, 0],\n [0, 2, 2, 2, 2, 8, 0, 0, 2, 8, 0, 0],\n [0, 0, 8, 0, 2, 8, 0, 0, 2, 2, 2, 2],\n [0, 0, 8, 0, 2, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 2, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 2, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 2, 8, 0, 0, 0, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 2],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 2, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 2, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 8, 2, 0, 0, 8, 2, 0, 0],\n [0, 0, 8, 0, 0, 8, 2, 0, 0, 8, 2, 2, 2],\n [0, 0, 8, 0, 0, 8, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 8, 0, 0, 8, 2, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 2, 0, 0, 8, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 8, 8, 0, 8, 8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0], [8, 8, 2, 8, 8, 8, 8, 2, 8, 8, 8, 0, 8, 8], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [8, 8, 2, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8], [0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0], [8, 8, 8, 8, 2, 8, 8, 2, 8, 8, 8, 2, 8, 8], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0]], "task_id": "f9a67cb5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 6, 6, 6, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 6, 4, 8, 4, 8, 4, 6, 4, 8, 4, 8],\n [6, 8, 8, 6, 8, 6, 8, 8, 8, 8, 8, 8, 6, 6, 8, 8, 6, 8, 8],\n [8, 4, 8, 4, 8, 6, 6, 4, 8, 4, 6, 4, 8, 4, 8, 6, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6],\n [8, 4, 8, 4, 8, 4, 6, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 8, 8, 6, 8, 6],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 6, 4, 8, 4, 6, 4, 8],\n [8, 8, 8, 8, 6, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 6, 8, 4, 8, 4, 6, 4, 6, 6, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 6, 8, 6, 6, 8, 6, 8, 8, 6, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 6, 6, 6, 4, 8, 4, 6, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 6, 8, 8, 6, 8, 8, 6, 8, 6, 8, 8, 8, 8, 8],\n [8, 4, 8, 6, 8, 6, 8, 4, 8, 4, 8, 4, 6, 4, 8, 4, 6, 4, 8],\n [8, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8, 4, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 6, 7, 7, 6, 7, 6, 7, 7, 7, 6],\n [7, 8, 7, 7, 6, 7, 7, 8, 6, 7, 8, 7],\n [7, 7, 7, 6, 7, 7, 7, 7, 6, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 6, 7, 8, 7, 7, 8, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [6, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7],\n [7, 6, 7, 7, 8, 7, 6, 6, 6, 7, 8, 7],\n [7, 6, 7, 7, 7, 7, 6, 6, 7, 7, 7, 6],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 7, 6, 8, 7, 7, 8, 6],\n [7, 7, 7, 7, 7, 7, 6, 7, 7, 6, 7, 7]\n ],\n \"output\": [\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 7, 7, 8, 7, 7, 8, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 7, 7, 8, 7, 7, 8, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 7, 7, 8, 7, 7, 8, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 8, 7, 7, 8, 7, 7, 8, 7, 7, 8, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 6, 3, 3, 3, 3, 3, 3, 6, 6, 3, 3, 3, 3, 3, 3],\n [3, 1, 1, 3, 6, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3],\n [6, 1, 1, 3, 6, 1, 3, 1, 1, 3, 1, 6, 3, 1, 6, 3],\n [6, 6, 3, 3, 6, 6, 6, 3, 6, 3, 3, 3, 6, 3, 6, 3],\n [3, 1, 1, 6, 1, 1, 3, 1, 1, 3, 1, 1, 3, 6, 1, 6],\n [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 6, 1, 3, 1, 1, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 3, 3, 3, 3],\n [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 6, 1, 6, 1, 1, 3],\n [3, 1, 6, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6],\n [6, 6, 1, 3, 6, 6, 3, 6, 1, 3, 6, 6, 3, 1, 1, 6],\n [6, 1, 1, 3, 6, 6, 3, 6, 1, 3, 1, 6, 3, 6, 1, 6],\n [3, 3, 6, 3, 6, 3, 6, 3, 3, 3, 6, 3, 3, 3, 3, 6],\n [3, 1, 1, 3, 1, 6, 3, 1, 6, 3, 6, 1, 3, 1, 1, 6],\n [6, 6, 1, 6, 1, 1, 3, 6, 1, 3, 6, 6, 3, 6, 1, 6],\n [3, 3, 3, 3, 3, 6, 3, 3, 3, 6, 3, 6, 6, 3, 3, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], "task_id": "f823c43c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 1, 0, 8, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 1, 0, 2, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 1, 0, 0, 4],\n [3, 0, 1, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 1, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 1, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 1, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 1, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4]\n ],\n \"output\": [\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 1, 4, 0, 4],\n [3, 3, 1, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 3, 1, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 1, 4, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 1, 4, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [3, 3, 1, 0, 0, 0, 0, 0, 0, 4],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8],\n [2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8], [2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8]], "task_id": "642248e4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 4, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 9, 9, 9, 9],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3], [8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0]], "task_id": "705a3229"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 1],\n [5, 2, 0],\n [5, 5, 5]\n ],\n \"output\": [\n [5, 0, 1, 0, 0, 0, 0, 0, 0],\n [5, 2, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 1, 0, 0, 0, 0, 0, 0],\n [5, 2, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0],\n [5, 0, 1, 5, 0, 1, 5, 0, 1],\n [5, 2, 0, 5, 2, 0, 5, 2, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [2, 5, 1],\n [0, 5, 0],\n [2, 5, 1]\n ],\n \"output\": [\n [0, 0, 0, 2, 5, 1, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 2, 5, 1, 0, 0, 0],\n [0, 0, 0, 2, 5, 1, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 2, 5, 1, 0, 0, 0],\n [0, 0, 0, 2, 5, 1, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 2, 5, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 3],\n [5, 5, 2, 0],\n [0, 2, 5, 5],\n [3, 0, 5, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 3, 0, 5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 2, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 5, 0, 2, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 5, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, 5, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 2, 0, 5, 5, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 5, 0, 2, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 3, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5],\n [5, 2, 3, 5],\n [5, 3, 3, 5],\n [5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 2, 3, 5, 5, 2, 3, 5, 5, 2, 3, 5, 5, 2, 3, 5],\n [5, 3, 3, 5, 5, 3, 3, 5, 5, 3, 3, 5, 5, 3, 3, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 2, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 3, 5],\n [5, 3, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 3, 5],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 2, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 3, 5],\n [5, 3, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 3, 5],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 2, 3, 5, 5, 2, 3, 5, 5, 2, 3, 5, 5, 2, 3, 5],\n [5, 3, 3, 5, 5, 3, 3, 5, 5, 3, 3, 5, 5, 3, 3, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 5, 0, 1],\n [0, 2, 2, 2, 0],\n [5, 0, 5, 0, 5],\n [0, 2, 2, 2, 0],\n [1, 0, 5, 0, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1], [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5], [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "ad7e01d0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 1, 1, 3, 2, 0, 0, 0, 0, 0],\n [2, 2, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0],\n [2, 2, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0],\n [0, 2, 3, 1, 1, 3, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2],\n [0, 0, 2, 2],\n [0, 2, 3, 1],\n [2, 2, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 2, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 3, 3, 3, 3, 5, 0, 0, 0],\n [0, 0, 2, 2, 3, 1, 1, 3, 2, 2, 0, 0],\n [0, 0, 2, 2, 3, 1, 1, 3, 2, 2, 0, 0],\n [0, 0, 0, 5, 3, 3, 3, 3, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 2, 2, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2],\n [0, 5, 5, 2],\n [0, 5, 3, 3],\n [2, 2, 3, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0],\n [0, 0, 0, 0, 7, 2, 2, 3, 3, 2, 2, 7],\n [0, 0, 0, 0, 7, 2, 8, 8, 8, 8, 2, 7],\n [0, 0, 0, 0, 0, 3, 8, 0, 0, 8, 3, 0],\n [0, 0, 0, 0, 0, 3, 8, 0, 0, 8, 3, 0],\n [0, 0, 0, 0, 7, 2, 8, 8, 8, 8, 2, 7],\n [0, 0, 0, 0, 7, 2, 2, 3, 3, 2, 2, 7],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0]\n ],\n \"output\": [\n [0, 7, 7, 0],\n [7, 2, 2, 3],\n [7, 2, 8, 8],\n [0, 3, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 0, 5, 5, 0, 0, 1, 0, 0],\n [0, 0, 0, 5, 3, 8, 8, 3, 5, 0, 0, 0],\n [0, 0, 0, 3, 2, 8, 8, 2, 3, 0, 0, 0],\n [0, 0, 5, 8, 8, 6, 6, 8, 8, 5, 0, 0],\n [0, 0, 5, 8, 8, 6, 6, 8, 8, 5, 0, 0],\n [0, 0, 0, 3, 2, 8, 8, 2, 3, 0, 0, 0],\n [0, 0, 0, 5, 3, 8, 8, 3, 5, 0, 0, 0],\n [0, 0, 1, 0, 0, 5, 5, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 0, 5], [0, 5, 3, 8], [0, 3, 2, 8], [5, 8, 8, 6]], "task_id": "73182012"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [7, 0, 0, 0, 4, 0, 0, 9, 0],\n [7, 7, 0, 0, 4, 9, 9, 0, 9],\n [0, 0, 0, 0, 4, 0, 9, 9, 0],\n [0, 0, 7, 0, 4, 0, 0, 0, 0],\n [7, 0, 7, 7, 4, 9, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [2, 0, 2, 0, 4, 0, 0, 0, 0],\n [2, 0, 0, 2, 4, 0, 0, 8, 8],\n [2, 0, 0, 2, 4, 8, 0, 0, 8],\n [0, 0, 0, 2, 4, 0, 8, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 8, 8]\n ],\n \"output\": [\n [7, 0, 9, 0],\n [7, 7, 8, 8],\n [8, 9, 9, 8],\n [0, 8, 7, 2],\n [7, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 7, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 9, 0, 9],\n [0, 7, 7, 0, 4, 9, 9, 0, 9],\n [7, 0, 7, 7, 4, 0, 0, 0, 9],\n [7, 0, 7, 7, 4, 9, 0, 0, 9],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 2, 2, 4, 8, 8, 8, 0],\n [0, 2, 0, 2, 4, 0, 0, 0, 8],\n [2, 2, 2, 2, 4, 0, 0, 8, 8],\n [0, 0, 2, 2, 4, 8, 0, 0, 0],\n [0, 0, 2, 0, 4, 0, 8, 8, 0]\n ],\n \"output\": [\n [8, 8, 8, 2],\n [0, 9, 0, 8],\n [9, 7, 8, 8],\n [8, 0, 7, 7],\n [7, 8, 8, 7]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 0, 4, 9, 0, 0, 0],\n [7, 7, 7, 7, 4, 0, 9, 0, 9],\n [7, 7, 7, 7, 4, 0, 0, 9, 0],\n [0, 7, 0, 7, 4, 9, 9, 9, 9],\n [7, 7, 0, 7, 4, 9, 0, 0, 9],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 0, 2, 4, 0, 0, 0, 8],\n [2, 2, 2, 0, 4, 0, 8, 0, 0],\n [2, 0, 2, 2, 4, 0, 0, 0, 8],\n [0, 0, 2, 2, 4, 0, 8, 0, 0],\n [0, 2, 2, 0, 4, 8, 8, 0, 0]\n ],\n \"output\": [\n [7, 7, 7, 8],\n [7, 8, 7, 7],\n [7, 7, 7, 8],\n [9, 8, 9, 7],\n [8, 8, 2, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 0, 0, 4, 9, 0, 9, 0],\n [7, 7, 0, 0, 4, 9, 0, 0, 0],\n [0, 0, 0, 0, 4, 9, 0, 9, 9],\n [0, 7, 7, 7, 4, 0, 0, 0, 0],\n [0, 0, 7, 7, 4, 0, 0, 9, 9],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 2, 0, 4, 8, 8, 0, 0],\n [2, 2, 0, 2, 4, 8, 0, 8, 8],\n [2, 0, 2, 2, 4, 0, 8, 0, 8],\n [2, 0, 2, 2, 4, 0, 8, 8, 0],\n [2, 0, 0, 0, 4, 0, 0, 8, 0]\n ],\n \"output\": [\n [8, 8, 9, 0],\n [8, 7, 8, 8],\n [9, 8, 9, 8],\n [2, 8, 8, 7],\n [2, 0, 8, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 4, 0, 9, 0, 0],\n [7, 0, 7, 7, 4, 9, 9, 9, 9],\n [7, 0, 7, 7, 4, 9, 9, 0, 0],\n [7, 7, 0, 0, 4, 0, 0, 9, 0],\n [7, 0, 0, 7, 4, 9, 9, 9, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 2, 2, 4, 8, 0, 0, 0],\n [2, 2, 2, 2, 4, 8, 8, 8, 8],\n [2, 0, 0, 2, 4, 8, 8, 8, 0],\n [2, 2, 0, 0, 4, 0, 8, 8, 8],\n [2, 2, 2, 0, 4, 0, 8, 8, 0]\n ],\n \"output\": [\n [8, 9, 2, 2],\n [8, 8, 8, 8],\n [8, 8, 8, 7],\n [7, 8, 8, 8],\n [7, 8, 8, 7]\n ]\n}\n\n{\n \"input\": [\n [7, 0, 7, 7, 4, 0, 9, 9, 9],\n [0, 7, 7, 0, 4, 9, 9, 9, 0],\n [0, 0, 0, 0, 4, 9, 0, 0, 0],\n [7, 0, 0, 7, 4, 9, 9, 9, 0],\n [7, 0, 7, 7, 4, 9, 0, 9, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 0, 0, 4, 0, 0, 8, 0],\n [2, 0, 2, 2, 4, 8, 0, 8, 8],\n [0, 2, 0, 0, 4, 0, 0, 8, 8],\n [2, 0, 2, 2, 4, 8, 0, 0, 8],\n [2, 2, 2, 0, 4, 8, 8, 0, 0]\n ],\n \"output\": [\n [7, 9, 8, 7],\n [8, 7, 8, 8],\n [9, 2, 8, 8],\n [8, 9, 9, 8],\n [8, 8, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 7, 0, 0, 4, 0, 9, 9, 0],\n [7, 0, 0, 0, 4, 0, 9, 0, 9],\n [0, 7, 7, 0, 4, 9, 9, 9, 9],\n [7, 7, 0, 0, 4, 9, 0, 9, 9],\n [7, 0, 0, 0, 4, 9, 9, 0, 9],\n [4, 4, 4, 4, 4, 4, 4, 4, 4],\n [2, 2, 0, 2, 4, 8, 8, 0, 8],\n [0, 0, 2, 0, 4, 8, 8, 0, 0],\n [0, 0, 2, 0, 4, 8, 0, 8, 8],\n [0, 0, 0, 2, 4, 8, 8, 8, 0],\n [0, 0, 2, 2, 4, 8, 8, 8, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 9, 8], [8, 8, 2, 9], [8, 7, 8, 8], [8, 8, 8, 9], [8, 8, 8, 9]], "task_id": "e99362f0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 5, 0, 5, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 3, 0, 5, 2, 0, 5, 3, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 3, 0, 5, 5, 0, 5, 5, 0, 5, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 4, 4],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 2, 2, 0, 3, 3, 0, 0, 0],\n [0, 3, 0, 2, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 5, 3, 0, 5, 3, 0, 5, 7, 0, 5, 5, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 5, 2, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 5, 2, 0, 5, 2, 0, 5, 3, 0, 5, 3, 0, 5, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 0, 3, 3, 0, 7, 7, 0, 0, 0, 0, 2, 2],\n [0, 3, 0, 0, 3, 0, 7, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 2, 2, 0, 3, 3, 0, 3, 3, 0, 7, 7],\n [2, 2, 0, 2, 2, 0, 0, 3, 0, 0, 3, 0, 7, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 2, 0, 5, 2, 0, 5, 2, 0, 5, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 3, 0, 5, 3, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 4, 0, 5, 3, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 4, 0, 5, 5, 0, 5, 5, 0, 5, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 0, 2, 2, 0, 2, 2, 0, 8, 8], [2, 0, 0, 2, 0, 0, 2, 0, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 3, 3, 0, 0, 0, 0, 0, 0], [4, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 8, 8], [4, 4, 0, 0, 0, 0, 0, 0, 0, 8, 8]], "task_id": "c64f1187"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 2, 5, 0, 0, 0, 0],\n [0, 0, 5, 2, 2, 5, 0, 0, 0, 0],\n [0, 0, 5, 2, 2, 5, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 5, 2, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 5, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 5, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 5, 2, 5, 5, 0, 0, 0, 0, 0],\n [0, 5, 2, 2, 5, 0, 0, 0, 0, 0],\n [0, 5, 2, 2, 5, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 2, 5, 0],\n [0, 0, 0, 0, 0, 5, 2, 2, 5, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 2, 0, 0, 0, 0, 0, 0], [0, 5, 5, 2, 5, 0, 0, 0, 0, 0], [0, 5, 2, 2, 5, 0, 0, 0, 0, 0], [0, 5, 2, 2, 5, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 5, 5, 2, 5, 0], [0, 0, 0, 0, 0, 5, 2, 2, 5, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 0]], "task_id": "4e469f39"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 3, 0, 3, 0, 0, 0, 0, 0, 3, 0],\n [0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 2],\n [0, 2, 0, 3, 0, 3, 3, 3, 2, 3, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0],\n [0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 0],\n [0, 3, 2, 0, 0, 0, 0, 0, 0, 3, 2],\n [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 3, 3, 3, 2, 0, 0, 0, 2],\n [0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 3, 0, 0, 2, 0],\n [0, 2, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 3, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 2, 0, 0, 3, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0]], "task_id": "e5c44e8f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 2],\n [0, 2, 0],\n [2, 2, 2]\n ],\n \"output\": [\n [2, 0, 2, 2, 0, 2, 2, 0, 2],\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 2, 2, 0, 2, 2, 0, 2],\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 2, 2, 0, 2, 2, 0, 2],\n [0, 2, 0, 0, 2, 0, 0, 2, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 0],\n [1, 1]\n ],\n \"output\": [\n [1, 0, 1, 0],\n [1, 1, 1, 1],\n [1, 0, 1, 0],\n [1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 3],\n [3, 0]\n ],\n \"output\": [\n [0, 3, 0, 3],\n [3, 0, 3, 0],\n [0, 3, 0, 3],\n [3, 0, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8],\n [8, 8, 8],\n [8, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 8, 0, 0, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0],\n [2, 0, 2, 0],\n [0, 2, 0, 2],\n [2, 2, 2, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 0, 0],\n [7, 7, 7, 7],\n [0, 7, 0, 0],\n [0, 7, 0, 7]\n ],\n \"output\": [\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0],\n [0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 8, 0, 0],\n [8, 8, 8, 8, 8],\n [0, 8, 8, 0, 0],\n [8, 8, 8, 8, 8],\n [0, 8, 8, 0, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8]], "task_id": "ccd554ac"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 4, 0, 0, 4, 9, 0, 0, 9, 9],\n [4, 9, 9, 4, 9, 9, 0, 0, 9, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 9],\n [9, 4, 5, 9, 0, 9, 9, 5, 0, 4],\n [4, 4, 5, 0, 0, 4, 0, 5, 4, 4],\n [9, 4, 5, 4, 9, 0, 9, 5, 0, 0],\n [0, 9, 5, 0, 4, 0, 0, 5, 0, 4],\n [0, 4, 5, 5, 5, 5, 5, 5, 4, 4],\n [9, 0, 9, 9, 4, 0, 9, 0, 0, 0],\n [9, 9, 9, 0, 9, 4, 9, 9, 0, 0]\n ],\n \"output\": [\n [9, 4, 0, 0, 4, 9, 0, 0, 9, 9],\n [4, 9, 9, 4, 9, 9, 0, 0, 9, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 0, 9],\n [9, 4, 5, 4, 0, 4, 4, 5, 0, 4],\n [4, 4, 5, 0, 0, 9, 0, 5, 4, 4],\n [9, 4, 5, 9, 4, 0, 4, 5, 0, 0],\n [0, 9, 5, 0, 9, 0, 0, 5, 0, 4],\n [0, 4, 5, 5, 5, 5, 5, 5, 4, 4],\n [9, 0, 9, 9, 4, 0, 9, 0, 0, 0],\n [9, 9, 9, 0, 9, 4, 9, 9, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 6, 0, 6, 0, 8, 0, 8],\n [8, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 0, 8, 8, 6, 6, 0, 5, 8],\n [6, 5, 6, 6, 6, 8, 0, 6, 5, 8],\n [0, 5, 6, 6, 8, 6, 0, 6, 5, 8],\n [6, 5, 8, 8, 8, 6, 8, 0, 5, 8],\n [6, 5, 6, 8, 6, 8, 6, 8, 5, 8],\n [0, 5, 6, 0, 6, 8, 8, 8, 5, 8],\n [8, 5, 5, 5, 5, 5, 5, 5, 5, 6],\n [8, 8, 8, 0, 8, 8, 6, 0, 6, 6]\n ],\n \"output\": [\n [0, 0, 8, 6, 0, 6, 0, 8, 0, 8],\n [8, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 0, 6, 6, 8, 8, 0, 5, 8],\n [6, 5, 8, 8, 8, 6, 0, 8, 5, 8],\n [0, 5, 8, 8, 6, 8, 0, 8, 5, 8],\n [6, 5, 6, 6, 6, 8, 6, 0, 5, 8],\n [6, 5, 8, 6, 8, 6, 8, 6, 5, 8],\n [0, 5, 8, 0, 8, 6, 6, 6, 5, 8],\n [8, 5, 5, 5, 5, 5, 5, 5, 5, 6],\n [8, 8, 8, 0, 8, 8, 6, 0, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 3, 3, 3, 2, 0, 2, 0],\n [3, 5, 5, 5, 5, 5, 5, 5, 5, 3],\n [3, 5, 3, 2, 2, 2, 2, 0, 5, 2],\n [0, 5, 0, 3, 0, 3, 2, 2, 5, 2],\n [3, 5, 2, 0, 2, 3, 2, 2, 5, 3],\n [3, 5, 3, 3, 0, 2, 3, 3, 5, 3],\n [3, 5, 3, 3, 3, 0, 3, 2, 5, 2],\n [0, 5, 3, 0, 3, 3, 3, 0, 5, 3],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 3],\n [2, 0, 3, 3, 3, 2, 3, 2, 3, 0]\n ],\n \"output\": [\n [0, 0, 3, 3, 3, 3, 2, 0, 2, 0],\n [3, 5, 5, 5, 5, 5, 5, 5, 5, 3],\n [3, 5, 2, 3, 3, 3, 3, 0, 5, 2],\n [0, 5, 0, 2, 0, 2, 3, 3, 5, 2],\n [3, 5, 3, 0, 3, 2, 3, 3, 5, 3],\n [3, 5, 2, 2, 0, 3, 2, 2, 5, 3],\n [3, 5, 2, 2, 2, 0, 2, 3, 5, 2],\n [0, 5, 2, 0, 2, 2, 2, 0, 5, 3],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 3],\n [2, 0, 3, 3, 3, 2, 3, 2, 3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 0, 1, 1, 7, 0, 0, 7, 7, 7],\n [1, 5, 5, 5, 5, 5, 5, 5, 5, 7],\n [1, 5, 0, 0, 1, 0, 1, 7, 5, 7],\n [0, 5, 7, 1, 7, 0, 1, 7, 5, 1],\n [7, 5, 7, 7, 0, 1, 7, 1, 5, 1],\n [7, 5, 0, 1, 7, 0, 7, 7, 5, 1],\n [1, 5, 7, 7, 1, 1, 1, 1, 5, 0],\n [0, 5, 1, 7, 7, 7, 7, 0, 5, 7],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 1, 7, 1, 0, 7, 0, 0, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 0, 1, 1, 7, 0, 0, 7, 7, 7], [1, 5, 5, 5, 5, 5, 5, 5, 5, 7], [1, 5, 0, 0, 7, 0, 7, 1, 5, 7], [0, 5, 1, 7, 1, 0, 7, 1, 5, 1], [7, 5, 1, 1, 0, 7, 1, 7, 5, 1], [7, 5, 0, 7, 1, 0, 1, 1, 5, 1], [1, 5, 1, 1, 7, 7, 7, 7, 5, 0], [0, 5, 7, 1, 1, 1, 1, 0, 5, 7], [0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 1, 7, 1, 0, 7, 0, 0, 7, 7]], "task_id": "7ee1c6ea"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 3, 3, 6, 0, 0],\n [0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 6, 0],\n [0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 3, 3],\n [0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 6, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 6],\n [0, 0, 0, 8, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0],\n [3, 3, 3, 3, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 6, 0, 0, 3, 0, 0],\n [0, 0, 3, 3, 3, 3, 8, 0],\n [0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 8, 0], [0, 0, 6, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 8, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e5790162"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 6, 5, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 6, 5, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 5, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 5, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 6, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 5, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 5, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 3, 8, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 3, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 6, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 4, 6, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 6, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 6, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 6, 2, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 6, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 5, 0, 0, 0, 0, 0], [4, 4, 4, 4, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]], "task_id": "29700607"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0]\n ],\n \"output\": [\n [0, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [0, 8, 0, 8, 0]\n ],\n \"output\": [\n [0, 8, 0, 8, 0],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [0, 8, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0]], "task_id": "9ddd00f0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 6, 7, 7, 7, 0, 0, 6, 0, 0, 6, 0, 0, 6, 6, 0, 0, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 7, 0, 0, 7, 0, 0, 6, 7, 6, 0, 7, 0],\n [6, 0, 0, 7, 6, 0, 0, 0, 0, 7, 0, 0, 6, 6, 0, 0, 0, 6, 7, 6],\n [7, 0, 7, 2, 2, 2, 2, 0, 7, 0, 7, 7, 0, 7, 0, 0, 6, 7, 0, 6],\n [0, 7, 0, 2, 2, 2, 2, 7, 6, 7, 0, 0, 6, 7, 6, 0, 7, 0, 6, 0],\n [7, 0, 0, 2, 2, 2, 2, 7, 0, 0, 0, 0, 6, 7, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 2, 2, 2, 2, 0, 7, 0, 0, 9, 9, 9, 7, 7, 0, 7, 7, 0],\n [7, 0, 0, 0, 7, 0, 0, 7, 6, 0, 6, 9, 9, 9, 7, 0, 6, 0, 0, 0],\n [7, 6, 0, 6, 6, 7, 0, 6, 0, 6, 7, 9, 9, 9, 6, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 6, 0, 7, 6, 6, 7, 6, 9, 9, 9, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 6, 0, 0, 0, 0, 7, 0, 6, 0, 6, 0, 0, 7, 0, 7, 0, 0],\n [0, 6, 7, 7, 0, 7, 7, 7, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 6, 6],\n [6, 7, 7, 0, 7, 6, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 7],\n [7, 6, 6, 0, 6, 7, 0, 6, 0, 6, 7, 6, 0, 0, 6, 7, 0, 0, 7, 6],\n [6, 0, 0, 6, 0, 7, 4, 4, 4, 4, 4, 0, 0, 7, 6, 0, 6, 0, 0, 0],\n [7, 0, 7, 0, 0, 0, 4, 4, 4, 4, 4, 7, 0, 7, 6, 0, 0, 0, 0, 7],\n [6, 6, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [6, 7, 6, 6, 6, 0, 4, 4, 4, 4, 4, 6, 7, 7, 6, 7, 0, 0, 0, 6],\n [7, 0, 0, 0, 6, 6, 4, 4, 4, 4, 4, 6, 0, 6, 0, 0, 0, 0, 6, 7],\n [0, 0, 7, 7, 6, 0, 0, 6, 7, 6, 6, 0, 6, 0, 6, 0, 7, 7, 0, 0]\n ],\n \"output\": [\n [4, 4, 4],\n [4, 4, 4],\n [4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 0, 1, 0, 1, 1, 8, 1, 1, 1, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 8, 0, 5, 5, 5, 5, 5, 8, 1],\n [0, 0, 0, 0, 0, 8, 1, 0, 0, 8, 1, 1, 1, 5, 5, 5, 5, 5, 8, 0],\n [1, 8, 0, 1, 8, 0, 0, 8, 8, 8, 8, 1, 8, 5, 5, 5, 5, 5, 1, 0],\n [0, 8, 0, 9, 9, 9, 9, 8, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 8, 0],\n [8, 1, 8, 9, 9, 9, 9, 8, 1, 1, 0, 1, 1, 0, 8, 0, 8, 8, 0, 8],\n [0, 0, 0, 9, 9, 9, 9, 0, 1, 1, 8, 8, 3, 3, 8, 1, 1, 0, 0, 1],\n [8, 1, 1, 8, 1, 8, 0, 1, 0, 0, 0, 3, 3, 3, 1, 0, 8, 1, 8, 8],\n [0, 1, 8, 8, 1, 1, 0, 8, 8, 3, 3, 3, 3, 3, 8, 0, 0, 8, 1, 0],\n [0, 1, 1, 0, 1, 0, 0, 0, 8, 3, 3, 3, 3, 3, 1, 1, 8, 8, 1, 0],\n [8, 0, 8, 0, 8, 0, 0, 0, 0, 3, 3, 3, 3, 3, 1, 1, 1, 0, 8, 8],\n [0, 0, 0, 0, 8, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 0, 1, 8, 1],\n [0, 8, 8, 0, 8, 8, 1, 8, 0, 3, 3, 3, 8, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 8, 0, 1, 0, 0, 1, 0, 0, 0, 8, 1, 1, 1, 0],\n [0, 0, 1, 0, 1, 0, 1, 8, 8, 1, 0, 0, 8, 0, 1, 0, 1, 1, 0, 0],\n [0, 4, 4, 4, 4, 4, 8, 4, 0, 0, 0, 1, 0, 8, 0, 8, 0, 1, 8, 0],\n [1, 4, 4, 4, 4, 4, 4, 4, 0, 1, 1, 0, 8, 0, 0, 0, 0, 8, 1, 8],\n [1, 4, 4, 4, 4, 4, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 8],\n [0, 1, 0, 0, 0, 1, 8, 1, 0, 8, 0, 1, 0, 0, 8, 0, 0, 8, 1, 0],\n [8, 0, 1, 0, 0, 1, 0, 8, 0, 1, 1, 0, 1, 8, 0, 8, 0, 0, 1, 0]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 3, 3],\n [3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 2, 0, 0, 0, 0, 0, 2, 0, 3, 2, 2, 3, 3, 2, 0, 0, 0, 0],\n [2, 2, 0, 0, 2, 0, 0, 0, 3, 3, 2, 2, 0, 3, 0, 0, 3, 2, 2, 3],\n [0, 2, 8, 8, 8, 8, 8, 8, 0, 0, 0, 2, 3, 3, 0, 2, 6, 6, 0, 2],\n [3, 8, 8, 8, 8, 8, 8, 8, 3, 0, 0, 3, 2, 3, 6, 6, 6, 6, 6, 2],\n [0, 8, 8, 8, 8, 8, 8, 8, 3, 2, 0, 2, 3, 9, 6, 6, 6, 6, 6, 3],\n [2, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 2, 0, 0, 6, 6, 6, 6, 6, 0],\n [0, 2, 0, 8, 8, 8, 8, 8, 8, 2, 2, 0, 6, 6, 6, 6, 6, 6, 6, 2],\n [0, 0, 2, 3, 8, 8, 8, 8, 8, 8, 0, 3, 0, 6, 6, 6, 6, 6, 6, 3],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 3, 3, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 2, 3, 2, 2, 3, 3, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, 0, 2, 2],\n [3, 0, 0, 3, 2, 0, 3, 0, 0, 2, 4, 4, 4, 0, 0, 0, 2, 0, 2, 3],\n [0, 0, 0, 0, 2, 0, 2, 0, 0, 4, 4, 4, 4, 0, 0, 3, 0, 2, 0, 2],\n [3, 2, 0, 0, 0, 3, 0, 0, 4, 4, 4, 4, 4, 3, 2, 3, 2, 0, 2, 0],\n [3, 2, 2, 0, 2, 0, 0, 0, 4, 4, 4, 4, 4, 3, 2, 0, 3, 0, 2, 2],\n [2, 3, 0, 0, 2, 2, 0, 3, 0, 4, 4, 4, 4, 3, 2, 0, 0, 0, 3, 2],\n [2, 2, 0, 3, 0, 2, 0, 3, 0, 2, 3, 2, 2, 2, 0, 2, 2, 3, 0, 3],\n [2, 2, 0, 0, 0, 0, 0, 3, 2, 3, 0, 2, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 3, 3, 2, 3, 3, 3, 2, 0, 0, 3, 0, 2, 3, 3],\n [0, 0, 3, 2, 0, 0, 2, 2, 2, 0, 3, 0, 0, 2, 0, 3, 0, 3, 0, 0]\n ],\n \"output\": [\n [8, 8, 8],\n [8, 8, 8],\n [8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 9, 9, 7, 0, 0, 9, 7, 7, 7, 0, 7, 0, 0, 7, 0, 9, 0, 0, 0],\n [0, 0, 7, 3, 3, 3, 3, 0, 7, 7, 0, 0, 0, 0, 6, 6, 6, 6, 0, 9],\n [0, 7, 9, 3, 3, 3, 3, 3, 3, 0, 9, 9, 7, 0, 6, 6, 6, 6, 0, 7],\n [0, 9, 0, 3, 3, 3, 3, 3, 3, 3, 7, 0, 7, 6, 6, 6, 6, 6, 0, 0],\n [9, 0, 0, 7, 0, 9, 7, 7, 0, 7, 7, 0, 0, 0, 9, 6, 6, 6, 7, 7],\n [0, 0, 9, 7, 0, 9, 9, 0, 0, 7, 0, 0, 9, 0, 0, 6, 6, 6, 0, 7],\n [0, 9, 0, 9, 0, 0, 7, 0, 0, 9, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0],\n [0, 9, 0, 0, 9, 7, 0, 0, 0, 9, 7, 0, 9, 9, 0, 7, 0, 0, 0, 0],\n [0, 7, 8, 8, 8, 8, 9, 9, 0, 7, 0, 0, 9, 7, 7, 0, 0, 9, 7, 7],\n [9, 0, 9, 8, 8, 8, 7, 7, 0, 7, 0, 0, 9, 0, 0, 9, 0, 7, 0, 0],\n [0, 0, 9, 8, 8, 8, 0, 9, 0, 9, 0, 0, 7, 5, 5, 0, 0, 9, 0, 9],\n [0, 0, 9, 8, 8, 8, 9, 0, 0, 0, 0, 9, 5, 5, 5, 7, 0, 0, 0, 9],\n [9, 0, 0, 8, 8, 8, 0, 7, 9, 9, 7, 0, 5, 5, 5, 5, 0, 0, 0, 7],\n [9, 9, 9, 7, 9, 8, 8, 0, 9, 7, 0, 5, 5, 5, 5, 5, 9, 0, 7, 0],\n [0, 0, 7, 7, 0, 7, 8, 0, 0, 0, 7, 5, 5, 5, 5, 5, 5, 9, 0, 9],\n [9, 7, 7, 0, 9, 0, 7, 9, 7, 0, 9, 5, 5, 5, 5, 5, 5, 0, 0, 9],\n [0, 7, 7, 0, 0, 7, 9, 0, 7, 0, 9, 7, 5, 5, 5, 5, 5, 9, 7, 9],\n [0, 0, 7, 7, 7, 0, 0, 9, 0, 9, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 9, 0, 0, 0, 9, 9, 5, 5, 5, 0, 9, 0, 9, 0],\n [0, 0, 9, 0, 7, 0, 0, 9, 7, 0, 0, 7, 0, 0, 7, 9, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5], [5, 5, 5], [5, 5, 5]], "task_id": "3194b014"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0],\n [0, 3, 0, 0, 0, 3, 0, 0],\n [3, 0, 0, 0, 0, 0, 3, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 2, 3, 0, 0, 0],\n [0, 3, 2, 2, 2, 3, 0, 0],\n [3, 2, 2, 2, 2, 2, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 0, 4, 0, 0],\n [0, 0, 4, 0, 4, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 2, 2, 2, 4, 0, 0],\n [0, 0, 4, 2, 4, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 2, 2, 2, 2, 2, 2, 2, 8, 0, 0],\n [8, 2, 8, 2, 2, 2, 2, 2, 8, 2, 8, 0],\n [0, 0, 0, 8, 2, 2, 2, 8, 2, 2, 2, 8],\n [0, 0, 0, 0, 8, 2, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1],\n [0, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 2, 2, 1],\n [0, 0, 1, 2, 2, 2, 1, 0],\n [0, 0, 0, 1, 2, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 6, 0, 0, 0, 0, 0, 6],\n [0, 6, 0, 6, 0, 0, 0, 6, 0],\n [6, 0, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 6, 2, 2, 2, 2, 2, 6], [0, 6, 2, 6, 2, 2, 2, 6, 0], [6, 2, 2, 2, 6, 2, 6, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0]], "task_id": "aa18de87"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 6, 6, 0, 9, 7, 0],\n [0, 8, 3, 0, 6, 3, 0, 9, 7, 0],\n [0, 3, 8, 0, 3, 6, 0, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 2, 2, 0, 6, 1, 0],\n [0, 2, 3, 0, 5, 5, 0, 1, 1, 0],\n [0, 2, 3, 0, 5, 5, 0, 1, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 3, 6, 7, 0],\n [0, 3, 5, 1, 0],\n [0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 5, 5, 0, 4, 4, 0],\n [0, 1, 1, 0, 3, 3, 0, 4, 4, 0],\n [0, 3, 3, 0, 5, 5, 0, 4, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 7, 1, 0, 9, 9, 0],\n [0, 2, 2, 0, 7, 7, 0, 1, 9, 0],\n [0, 2, 2, 0, 7, 1, 0, 9, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 1, 5, 4, 0],\n [0, 2, 7, 9, 0],\n [0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 5, 0, 8, 4, 0, 7, 7, 0],\n [0, 5, 3, 0, 8, 8, 0, 7, 6, 0],\n [0, 3, 3, 0, 8, 4, 0, 6, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 2, 2, 0, 1, 3, 0],\n [0, 4, 3, 0, 2, 2, 0, 1, 1, 0],\n [0, 3, 3, 0, 1, 2, 0, 1, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 3, 8, 7, 0],\n [0, 3, 2, 1, 0],\n [0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 3, 3, 0, 4, 4, 0],\n [0, 3, 1, 0, 8, 3, 0, 4, 4, 0],\n [0, 1, 1, 0, 3, 8, 0, 8, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 3, 5, 0, 2, 2, 0],\n [0, 6, 6, 0, 5, 5, 0, 2, 2, 0],\n [0, 2, 2, 0, 5, 3, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0], [0, 1, 3, 4, 0], [0, 2, 5, 2, 0], [0, 0, 0, 0, 0]], "task_id": "af24b4cc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [2, 8],\n [3, 5]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 4, 4, 4, 5, 5, 5, 2, 2, 2, 2, 2, 2],\n [4, 4, 4, 4, 4, 5, 5, 5, 2, 2, 2, 2, 2, 2],\n [4, 4, 4, 4, 4, 5, 5, 5, 2, 2, 2, 2, 2, 2],\n [4, 4, 4, 4, 4, 5, 5, 5, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2],\n [1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2]\n ],\n \"output\": [\n [4, 5, 2],\n [1, 3, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n [1, 2, 8],\n [5, 6, 3]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 7, 7, 7, 7, 7, 9, 9, 9, 9, 8, 8, 8],\n [8, 8, 8, 7, 7, 7, 7, 7, 9, 9, 9, 9, 8, 8, 8],\n [8, 8, 8, 7, 7, 7, 7, 7, 9, 9, 9, 9, 8, 8, 8],\n [8, 8, 8, 7, 7, 7, 7, 7, 9, 9, 9, 9, 8, 8, 8],\n [3, 3, 3, 1, 1, 1, 1, 1, 6, 6, 6, 6, 4, 4, 4],\n [3, 3, 3, 1, 1, 1, 1, 1, 6, 6, 6, 6, 4, 4, 4],\n [3, 3, 3, 1, 1, 1, 1, 1, 6, 6, 6, 6, 4, 4, 4],\n [3, 3, 3, 1, 1, 1, 1, 1, 6, 6, 6, 6, 4, 4, 4],\n [3, 3, 3, 1, 1, 1, 1, 1, 6, 6, 6, 6, 4, 4, 4],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5],\n [2, 2, 2, 4, 4, 4, 4, 4, 1, 1, 1, 1, 5, 5, 5]\n ],\n \"output\": [\n [8, 7, 9, 8],\n [3, 1, 6, 4],\n [2, 4, 1, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 7, 7, 7, 7, 4, 4, 4, 4, 4, 8, 8],\n [8, 8, 8, 8, 7, 7, 7, 7, 4, 4, 4, 4, 4, 8, 8],\n [3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 2, 8, 8],\n [3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 2, 8, 8],\n [3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 2, 8, 8],\n [4, 4, 4, 4, 5, 5, 5, 5, 3, 3, 3, 3, 3, 9, 9],\n [4, 4, 4, 4, 5, 5, 5, 5, 3, 3, 3, 3, 3, 9, 9],\n [4, 4, 4, 4, 5, 5, 5, 5, 3, 3, 3, 3, 3, 9, 9],\n [4, 4, 4, 4, 5, 5, 5, 5, 3, 3, 3, 3, 3, 9, 9],\n [4, 4, 4, 4, 5, 5, 5, 5, 3, 3, 3, 3, 3, 9, 9],\n [2, 2, 2, 2, 6, 6, 6, 6, 1, 1, 1, 1, 1, 7, 7],\n [2, 2, 2, 2, 6, 6, 6, 6, 1, 1, 1, 1, 1, 7, 7],\n [1, 1, 1, 1, 5, 5, 5, 5, 2, 2, 2, 2, 2, 8, 8],\n [1, 1, 1, 1, 5, 5, 5, 5, 2, 2, 2, 2, 2, 8, 8],\n [1, 1, 1, 1, 5, 5, 5, 5, 2, 2, 2, 2, 2, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 7, 4, 8], [3, 1, 2, 8], [4, 5, 3, 9], [2, 6, 1, 7], [1, 5, 2, 8]], "task_id": "e1baa8a4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 7, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 2, 4, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 8, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 8, 1, 1, 1, 1, 7, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 3, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1],\n [1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 4, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1],\n [1, 2, 8, 2, 1, 1, 2, 7, 2, 1, 1],\n [1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8],\n [8, 2, 8, 8, 8, 8],\n [2, 1, 2, 8, 8, 8],\n [2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8],\n [8, 8, 2, 8, 2, 8],\n [8, 8, 8, 4, 2, 8],\n [8, 8, 2, 2, 2, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 8, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3],\n [3, 2, 1, 2, 3, 3, 3, 3, 2, 2, 3, 3],\n [3, 3, 2, 3, 3, 3, 3, 3, 2, 8, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 7, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 7, 4, 4, 0, 0, 0, 0, 8, 2, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 2, 1, 2, 4, 4, 4, 4, 4, 4, 2, 7, 2, 4], [4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 2, 6, 4, 4, 4, 4, 4], [4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 4, 2, 2, 4], [2, 3, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 2], [2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2]], "task_id": "414297c0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 0, 0, 4, 0, 0, 8],\n [0, 6, 0, 4, 0, 0, 8],\n [0, 6, 0, 4, 8, 8, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [0, 2, 2],\n [2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6, 4, 8, 8, 0],\n [0, 6, 0, 4, 0, 8, 8],\n [0, 6, 6, 4, 8, 0, 0]\n ],\n \"output\": [\n [2, 2, 2],\n [0, 2, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6, 4, 8, 0, 8],\n [6, 0, 6, 4, 0, 0, 0],\n [0, 6, 6, 4, 8, 0, 8]\n ],\n \"output\": [\n [2, 0, 2],\n [2, 0, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 6, 4, 0, 0, 0],\n [6, 6, 0, 4, 8, 0, 8],\n [6, 6, 6, 4, 0, 8, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [2, 2, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6, 4, 8, 0, 8],\n [0, 6, 0, 4, 0, 8, 0],\n [0, 0, 0, 4, 8, 0, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [0, 2, 0],\n [2, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 6, 6, 4, 0, 0, 8],\n [0, 6, 0, 4, 8, 8, 8],\n [6, 0, 6, 4, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 2], [2, 2, 2], [2, 0, 2]], "task_id": "e133d23d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 5, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 6, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 0, 0, 1, 0, 0, 2, 0, 0],\n [0, 0, 3, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 1, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 6, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 7, 7, 0, 0, 0, 0],\n [0, 0, 2, 0, 7, 0, 7, 0, 0, 0],\n [0, 2, 0, 0, 7, 0, 0, 7, 0, 0],\n [2, 0, 0, 0, 7, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 2, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 7, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 6, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 6, 0, 1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 7, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 3, 3, 1, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 1, 0, 0, 0, 0, 0], [0, 3, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0], [3, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1d398264"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 0, 0, 4, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 0, 4, 4, 4, 4],\n [0, 4, 4, 4, 4, 5, 4, 4, 0, 0, 0, 4, 4, 4, 0, 4, 0, 4, 0],\n [0, 0, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 0, 4],\n [0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 0, 5, 4, 4],\n [4, 0, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 4, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 4, 4, 0, 4],\n [4, 4, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 5, 4, 4, 0, 5, 4],\n [4, 4, 4, 0, 0, 0, 0, 0, 0, 4, 5, 4, 4, 4, 0, 4, 0, 0, 5],\n [0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 5, 4, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 0, 4, 0, 4, 0, 4, 4, 5],\n [4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 5, 5, 4, 0, 4, 0, 4, 4, 5],\n [4, 4, 4, 4, 4, 5, 0, 4, 0, 4, 0, 4, 4, 0, 4, 0, 5, 4, 4],\n [5, 4, 4, 0, 4, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4],\n [4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 0, 4, 4],\n [5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 0, 0, 4, 4, 4, 0],\n [0, 0, 4, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4, 4],\n [4, 0, 0, 4, 4, 5, 4, 5, 4, 5, 4, 0, 4, 4, 0, 4, 4, 5, 0],\n [4, 0, 0, 4, 4, 0, 0, 0, 5, 4, 4, 0, 0, 4, 4, 5, 4, 4, 0]\n ],\n \"output\": [\n [4, 0, 0, 4, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 0, 4, 4, 4, 4],\n [0, 4, 4, 4, 4, 5, 4, 4, 0, 0, 0, 4, 4, 4, 0, 4, 0, 4, 0],\n [0, 0, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 0, 4],\n [0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 0, 5, 4, 4],\n [4, 0, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 4, 4, 0, 0, 8, 8, 8, 0, 4, 0, 0, 4, 0, 0, 4, 4, 0, 4],\n [4, 4, 0, 0, 0, 8, 8, 8, 0, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 0, 8, 8, 8, 0, 4, 4, 4, 4, 5, 4, 4, 0, 5, 4],\n [4, 4, 4, 0, 0, 8, 8, 8, 0, 4, 5, 4, 4, 4, 0, 4, 0, 0, 5],\n [0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 5, 4, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 0, 4, 0, 4, 0, 4, 4, 5],\n [4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 5, 5, 4, 0, 4, 0, 4, 4, 5],\n [4, 4, 4, 4, 4, 5, 0, 4, 0, 4, 0, 4, 4, 0, 4, 0, 5, 4, 4],\n [5, 4, 4, 0, 4, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4],\n [4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 0, 4, 4],\n [5, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 0, 0, 4, 4, 4, 0],\n [0, 0, 4, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4, 4],\n [4, 0, 0, 4, 4, 5, 4, 5, 4, 5, 4, 0, 4, 4, 0, 4, 4, 5, 0],\n [4, 0, 0, 4, 4, 0, 0, 0, 5, 4, 4, 0, 0, 4, 4, 5, 4, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2],\n [2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 2, 2],\n [2, 2, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2],\n [2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2],\n [0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2],\n [2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2],\n [0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2],\n [2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0],\n [2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [2, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2],\n [0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0]\n ],\n \"output\": [\n [0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2],\n [2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 2, 2],\n [2, 2, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2],\n [2, 0, 2, 2, 0, 0, 8, 8, 0, 2, 0, 0, 0, 2, 2],\n [0, 2, 0, 2, 2, 0, 8, 8, 0, 0, 2, 2, 0, 2, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2],\n [2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2],\n [0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2],\n [2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0],\n [2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [2, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2],\n [0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3, 3],\n [0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0],\n [3, 3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 0, 0, 3, 3],\n [0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 0],\n [3, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0],\n [3, 3, 3, 0, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3, 3, 0],\n [3, 0, 0, 3, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 0, 3, 0],\n [0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3]\n ],\n \"output\": [\n [0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3, 3],\n [0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0],\n [3, 3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 0, 0, 3, 3],\n [0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 0],\n [3, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0],\n [3, 3, 3, 0, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3, 3, 0],\n [3, 0, 0, 3, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 8, 8, 8, 8, 0, 0, 3, 3],\n [3, 0, 0, 0, 3, 0, 3, 0, 8, 8, 8, 8, 0, 3, 3, 3],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 0, 3, 0],\n [0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 7],\n [7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 0, 0],\n [7, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0],\n [7, 0, 0, 7, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 7, 7, 0],\n [7, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7],\n [7, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7],\n [0, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7, 7, 7, 7, 7, 7],\n [7, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7],\n [7, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7],\n [7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7],\n [0, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 7, 0, 0, 0, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0],\n [7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 7],\n [0, 7, 7, 0, 0, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 7],\n [7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0],\n [7, 0, 7, 7, 0, 7, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0],\n [7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 7, 7, 0, 7, 7],\n [0, 0, 7, 7, 0, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 0, 0, 7],\n [7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 7, 7, 7, 7, 7],\n [0, 7, 7, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 7, 0, 0, 0, 7, 7, 7, 0, 0, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 7], [7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 0, 0], [7, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0], [7, 0, 0, 7, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 7, 7, 0], [7, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7], [7, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7, 7, 7, 7, 7, 7], [7, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 0, 7, 7, 7], [7, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7], [7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7], [0, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 7, 0, 0, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0], [7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 7], [0, 7, 7, 0, 0, 7, 0, 0, 7, 7, 0, 8, 8, 8, 8, 0, 7, 7, 0, 0, 7], [7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 8, 8, 8, 8, 0, 0, 7, 0, 7, 0], [7, 0, 7, 7, 0, 7, 0, 7, 0, 7, 0, 8, 8, 8, 8, 0, 7, 7, 7, 0, 0], [7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 7, 7, 0, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 0, 0, 7], [7, 0, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0], [0, 7, 7, 0, 0, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 7, 0, 7, 7]], "task_id": "e88171ec"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 4, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 2, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0], [0, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "0e671a1a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 0],\n [0, 0, 9],\n [0, 9, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 9, 9, 0],\n [0, 0, 0, 0, 0, 0, 9, 0, 9],\n [0, 0, 9, 0, 0, 9, 0, 0, 0],\n [9, 9, 0, 9, 9, 0, 0, 0, 0],\n [9, 0, 9, 9, 0, 9, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 9],\n [9, 9, 0, 0, 0, 0, 9, 9, 0],\n [9, 0, 9, 0, 0, 0, 9, 0, 9]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 0],\n [0, 8, 8],\n [0, 8, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 8, 0, 0],\n [8, 0, 8, 0, 0, 0, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [7, 0, 7],\n [7, 7, 7],\n [0, 7, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 7, 0, 0, 0, 7, 0, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0],\n [0, 1, 0],\n [1, 0, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1], [1, 0, 1, 0, 0, 0, 1, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]], "task_id": "8e2edd66"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 6],\n [3, 3, 3],\n [6, 6, 4]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 6, 4, 4, 6, 4, 4, 6],\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [6, 6, 4, 6, 6, 4, 6, 6, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 4, 3],\n [2, 3, 4],\n [2, 3, 4]\n ],\n \"output\": [\n [2, 4, 3, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0],\n [2, 4, 3, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0],\n [2, 4, 3, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1],\n [6, 2, 2],\n [2, 2, 6]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [6, 2, 2, 6, 2, 2, 6, 2, 2],\n [2, 2, 6, 2, 2, 6, 2, 2, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 1, 6],\n [3, 6, 1],\n [3, 1, 6]\n ],\n \"output\": [\n [3, 1, 6, 0, 0, 0, 0, 0, 0],\n [3, 6, 1, 0, 0, 0, 0, 0, 0],\n [3, 1, 6, 0, 0, 0, 0, 0, 0],\n [3, 1, 6, 0, 0, 0, 0, 0, 0],\n [3, 6, 1, 0, 0, 0, 0, 0, 0],\n [3, 1, 6, 0, 0, 0, 0, 0, 0],\n [3, 1, 6, 0, 0, 0, 0, 0, 0],\n [3, 6, 1, 0, 0, 0, 0, 0, 0],\n [3, 1, 6, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 3],\n [4, 4, 3],\n [4, 4, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 6, 6, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3], [0, 0, 0, 0, 0, 0, 6, 6, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3], [0, 0, 0, 0, 0, 0, 6, 6, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3], [0, 0, 0, 0, 0, 0, 4, 4, 3]], "task_id": "15696249"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 0, 3, 1],\n [0, 0, 0, 3, 1],\n [0, 0, 0, 3, 1],\n [0, 0, 0, 3, 1],\n [0, 0, 0, 3, 1]\n ],\n \"output\": [\n [5, 0, 3, 0, 0],\n [0, 0, 1, 0, 0],\n [0, 0, 3, 0, 0],\n [0, 0, 1, 0, 0],\n [0, 0, 3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 0, 9, 8],\n [5, 0, 0, 0, 0, 9, 8],\n [5, 0, 0, 0, 0, 9, 8],\n [0, 0, 0, 0, 0, 9, 8],\n [0, 0, 0, 0, 0, 9, 8],\n [0, 0, 0, 0, 0, 9, 8],\n [0, 0, 0, 0, 0, 9, 8]\n ],\n \"output\": [\n [5, 0, 0, 0, 9, 0, 0],\n [5, 0, 0, 0, 9, 0, 0],\n [5, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 9, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 9, 6, 7],\n [5, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7],\n [0, 0, 0, 0, 9, 6, 7]\n ],\n \"output\": [\n [5, 0, 0, 9, 0, 0, 0],\n [5, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0, 2, 3],\n [5, 0, 0, 0, 0, 0, 2, 3],\n [5, 0, 0, 0, 0, 0, 2, 3],\n [5, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3],\n [0, 0, 0, 0, 0, 0, 2, 3]\n ],\n \"output\": [\n [5, 0, 0, 0, 0, 2, 0, 0],\n [5, 0, 0, 0, 0, 2, 0, 0],\n [5, 0, 0, 0, 0, 2, 0, 0],\n [5, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4],\n [0, 0, 0, 2, 8, 4]\n ],\n \"output\": [\n [5, 0, 2, 0, 0, 0],\n [0, 0, 8, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 8, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 8, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 8, 0, 0, 0],\n [0, 0, 4, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 8, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0, 4, 8, 3],\n [5, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3],\n [0, 0, 0, 0, 0, 0, 4, 8, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 0, 0, 0, 0, 4, 0, 0, 0], [5, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0]], "task_id": "e7b06bea"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 6],\n [3, 8, 8],\n [8, 3, 3]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 9, 9, 6],\n [0, 0, 0, 0, 0, 0, 3, 8, 8],\n [0, 0, 0, 0, 0, 0, 8, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 5, 5],\n [8, 8, 8],\n [5, 9, 9]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 5, 5, 8, 5, 5],\n [0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 5, 9, 9, 5, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [7, 1, 7],\n [1, 7, 7],\n [7, 1, 7]\n ],\n \"output\": [\n [0, 0, 0, 7, 1, 7, 0, 0, 0],\n [0, 0, 0, 1, 7, 7, 0, 0, 0],\n [0, 0, 0, 7, 1, 7, 0, 0, 0],\n [7, 1, 7, 0, 0, 0, 0, 0, 0],\n [1, 7, 7, 0, 0, 0, 0, 0, 0],\n [7, 1, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 1, 7, 0, 0, 0],\n [0, 0, 0, 1, 7, 7, 0, 0, 0],\n [0, 0, 0, 7, 1, 7, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 2, 7],\n [2, 2, 7],\n [5, 5, 7]\n ],\n \"output\": [\n [3, 2, 7, 0, 0, 0, 0, 0, 0],\n [2, 2, 7, 0, 0, 0, 0, 0, 0],\n [5, 5, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 6, 6],\n [5, 1, 6],\n [5, 5, 5]\n ],\n \"output\": [\n [1, 6, 6, 0, 0, 0, 0, 0, 0],\n [5, 1, 6, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 6, 6, 0, 0, 0],\n [0, 0, 0, 5, 1, 6, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 2],\n [2, 2, 2],\n [2, 4, 2]\n ],\n \"output\": [\n [4, 4, 2, 4, 4, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 0, 0, 0],\n [2, 4, 2, 2, 4, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 4, 2, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 7, 9],\n [9, 9, 7],\n [7, 9, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 9, 7, 9, 0, 0, 0], [0, 0, 0, 9, 9, 7, 0, 0, 0], [0, 0, 0, 7, 9, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 9, 7, 9], [0, 0, 0, 0, 0, 0, 9, 9, 7], [0, 0, 0, 0, 0, 0, 7, 9, 7], [9, 7, 9, 0, 0, 0, 9, 7, 9], [9, 9, 7, 0, 0, 0, 9, 9, 7], [7, 9, 7, 0, 0, 0, 7, 9, 7]], "task_id": "48f8583b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 3, 3, 0, 0, 1, 1],\n [1, 0, 2, 2, 0, 1, 1, 1, 1, 1, 3, 3, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [4, 4, 4, 4],\n [3, 3, 0, 0],\n [3, 3, 0, 0],\n [0, 2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 1, 0, 0, 8, 8, 8, 8, 0, 0, 3, 3, 8, 8, 8],\n [8, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 0, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 0, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 4, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 4, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 3, 3],\n [1, 1, 2, 4],\n [0, 2, 2, 4]\n ]\n}\n\n{\n \"input\": [\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 0, 1, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 1, 1, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 0, 1, 1, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 0, 0, 2, 2, 0, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 0, 0, 0, 2, 0, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 0, 0, 0, 2, 0, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n ],\n \"output\": [\n [0, 1, 0, 0, 0],\n [1, 1, 2, 2, 0],\n [0, 1, 1, 2, 0],\n [0, 0, 0, 2, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 2, 0, 1, 1, 1, 1, 0, 0, 3, 1, 1, 1, 1, 1],\n [1, 2, 2, 0, 1, 1, 1, 1, 0, 0, 3, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 0, 4, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 6, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 3], [2, 2, 3], [4, 4, 4], [6, 4, 0], [6, 6, 0]], "task_id": "7c9b52a0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 7, 0],\n [0, 0, 1, 0, 0, 1, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 7, 0, 1, 0, 0],\n [0, 7, 0, 0, 7, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0, 0], [0, 0, 6, 0, 6, 0, 0, 6, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 5, 0, 0, 6, 6, 6, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0], [0, 0, 5, 5, 5, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "3391f8c0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 8, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 3, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 8, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 8, 0, 3, 0, 8, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 3, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0], [8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 0], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "f5c89df1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 0, 4, 0, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 0, 4, 0, 4, 0],\n [0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 2, 2, 2, 2, 2, 0, 7, 7, 7, 7, 7, 0],\n [0, 4, 0, 0, 0, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 0, 0, 0, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 0, 0, 0, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 4, 4, 4, 4, 0, 2, 2, 2, 2, 2, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 0, 4, 0, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0, 4, 0, 4, 0, 4, 0],\n [0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 2, 2, 2, 2, 2, 0, 7, 7, 7, 7, 7, 0],\n [0, 4, 0, 4, 0, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 4, 4, 4, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 0, 4, 0, 4, 0, 2, 0, 0, 0, 2, 0, 7, 0, 0, 0, 7, 0],\n [0, 4, 4, 4, 4, 4, 0, 2, 2, 2, 2, 2, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 0],\n [0, 2, 0, 2, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 0, 0, 8, 0],\n [0, 2, 2, 2, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 8, 0, 8, 0],\n [0, 2, 0, 0, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 0, 0, 8, 0],\n [0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 3, 3, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 3, 0, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 0],\n [0, 2, 0, 2, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 0, 0, 8, 0],\n [0, 2, 2, 2, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 8, 0, 8, 0],\n [0, 2, 0, 0, 0, 2, 0, 4, 0, 0, 0, 4, 0, 8, 0, 0, 0, 8, 0],\n [0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 8, 0, 8, 0, 3, 0, 3, 3, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 3, 0, 3, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 2, 2, 0, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0],\n [0, 8, 0, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 0, 0, 0, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 0, 6, 0, 6, 0, 2, 0, 0, 0, 2, 0, 4, 0, 0, 0, 4, 0],\n [0, 6, 0, 6, 0, 6, 0, 2, 0, 0, 0, 2, 0, 4, 0, 4, 0, 4, 0],\n [0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0, 4, 0, 0, 0, 4, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 5, 5, 5, 5, 5, 0, 2, 2, 2, 2, 2, 0],\n [0, 1, 0, 1, 0, 1, 0, 5, 0, 0, 0, 5, 0, 2, 0, 0, 0, 2, 0],\n [0, 1, 0, 1, 0, 1, 0, 5, 0, 0, 0, 5, 0, 2, 0, 0, 0, 2, 0],\n [0, 1, 1, 0, 1, 1, 0, 5, 0, 0, 0, 5, 0, 2, 0, 0, 0, 2, 0],\n [0, 1, 1, 1, 1, 1, 0, 5, 5, 5, 5, 5, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 2, 0, 2, 0, 6, 0, 6, 0, 6, 0, 2, 0, 2, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 0, 6, 0, 6, 0, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 0, 0, 2, 0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 0, 0, 8, 0],\n [0, 8, 0, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 0, 6, 0, 6, 0, 2, 0, 2, 0, 2, 0, 4, 0, 0, 0, 4, 0],\n [0, 6, 0, 6, 0, 6, 0, 2, 2, 2, 2, 2, 0, 4, 0, 4, 0, 4, 0],\n [0, 6, 0, 0, 0, 6, 0, 2, 0, 0, 0, 2, 0, 4, 0, 0, 0, 4, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 5, 5, 5, 5, 5, 0, 2, 2, 2, 2, 2, 0],\n [0, 1, 0, 1, 0, 1, 0, 5, 0, 0, 0, 5, 0, 2, 0, 2, 0, 2, 0],\n [0, 1, 0, 1, 0, 1, 0, 5, 0, 0, 0, 5, 0, 2, 2, 2, 2, 2, 0],\n [0, 1, 1, 0, 1, 1, 0, 5, 0, 0, 0, 5, 0, 2, 0, 0, 0, 2, 0],\n [0, 1, 1, 1, 1, 1, 0, 5, 5, 5, 5, 5, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 3, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 8, 0, 1, 0, 1, 0, 1, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 0, 1, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 8, 0, 1, 0, 0, 0, 1, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 2, 0, 2, 0, 2, 0],\n [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 0, 2, 0, 2, 0, 2, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 8, 0, 1, 0, 1, 0, 1, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 0, 1, 0],\n [0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 8, 0, 1, 0, 0, 0, 1, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0],\n [0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 0, 1, 0],\n [0, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0],\n [0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 7, 7, 7, 7, 7, 0],\n [0, 1, 0, 0, 0, 1, 0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 3, 3, 0, 7, 0, 7, 0, 7, 0],\n [0, 1, 0, 0, 0, 1, 0, 8, 8, 8, 0, 8, 0, 3, 0, 3, 0, 3, 0, 7, 0, 7, 0, 7, 0],\n [0, 1, 0, 0, 0, 1, 0, 8, 0, 8, 0, 8, 0, 3, 3, 0, 0, 3, 0, 7, 0, 7, 0, 7, 0],\n [0, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 6, 0, 6, 0, 6, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 6, 0, 6, 6, 6, 0, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 6, 0, 0, 0, 6, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0],\n [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 8, 0, 0, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 8, 0, 0, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 8, 0, 0, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 2, 0],\n [0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 6, 0, 0, 0, 6, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 6, 0, 0, 0, 6, 0],\n [0, 2, 0, 0, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 0, 0, 2, 0, 6, 0, 0, 0, 6, 0],\n [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 7, 7, 7, 7, 7, 0], [0, 1, 0, 0, 0, 1, 0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 3, 3, 0, 7, 0, 7, 0, 7, 0], [0, 1, 0, 0, 0, 1, 0, 8, 8, 8, 0, 8, 0, 3, 0, 3, 0, 3, 0, 7, 0, 7, 0, 7, 0], [0, 1, 0, 0, 0, 1, 0, 8, 0, 8, 0, 8, 0, 3, 3, 0, 0, 3, 0, 7, 0, 7, 0, 7, 0], [0, 1, 1, 1, 1, 1, 0, 8, 8, 8, 8, 8, 0, 3, 3, 3, 3, 3, 0, 7, 7, 7, 7, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0], [0, 6, 0, 6, 0, 6, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 2, 0, 2, 0], [0, 6, 0, 6, 6, 6, 0, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 2, 2, 0, 2, 0], [0, 6, 0, 0, 0, 6, 0, 2, 0, 2, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 2, 0, 2, 0], [0, 6, 6, 6, 6, 6, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0], [0, 8, 0, 0, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 0, 0, 3, 3, 0, 2, 0, 2, 0, 2, 0], [0, 8, 8, 8, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 0, 3, 0, 3, 0, 2, 2, 2, 0, 2, 0], [0, 8, 0, 8, 0, 8, 0, 4, 0, 0, 0, 4, 0, 3, 3, 0, 0, 3, 0, 2, 0, 2, 0, 2, 0], [0, 8, 8, 8, 8, 8, 0, 4, 4, 4, 4, 4, 0, 3, 3, 3, 3, 3, 0, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0], [0, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 2, 0, 2, 0, 6, 0, 6, 0, 6, 0], [0, 2, 2, 2, 0, 2, 0, 8, 8, 8, 0, 8, 0, 2, 2, 2, 0, 2, 0, 6, 0, 6, 6, 6, 0], [0, 2, 0, 2, 0, 2, 0, 8, 0, 8, 0, 8, 0, 2, 0, 2, 0, 2, 0, 6, 0, 0, 0, 6, 0], [0, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 0, 2, 2, 2, 2, 2, 0, 6, 6, 6, 6, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "42918530"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 3, 3, 3, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 5, 2],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [0, 5, 3],\n [0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 5, 0, 0],\n [0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 3, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0]], "task_id": "c074846d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 5, 6, 6, 0],\n [8, 8, 8, 5, 6, 0, 0],\n [8, 8, 8, 5, 6, 0, 0],\n [8, 8, 8, 5, 0, 0, 0],\n [8, 8, 8, 5, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 5, 6, 0, 0, 0, 0],\n [8, 8, 5, 6, 0, 0, 0, 0],\n [8, 8, 5, 0, 0, 0, 0, 0],\n [8, 8, 5, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 5, 6, 6, 6],\n [8, 8, 8, 8, 5, 6, 6, 0],\n [8, 8, 8, 8, 5, 6, 6, 0],\n [8, 8, 8, 8, 5, 6, 0, 0],\n [8, 8, 8, 8, 5, 6, 0, 0],\n [8, 8, 8, 8, 5, 0, 0, 0],\n [8, 8, 8, 8, 5, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 5, 6, 6, 6, 0, 0], [8, 8, 8, 5, 6, 6, 0, 0, 0], [8, 8, 8, 5, 6, 6, 0, 0, 0], [8, 8, 8, 5, 6, 0, 0, 0, 0], [8, 8, 8, 5, 6, 0, 0, 0, 0], [8, 8, 8, 5, 0, 0, 0, 0, 0], [8, 8, 8, 5, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "5207a7b5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 0, 0, 0],\n [8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0],\n [0, 6, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 0],\n [0, 6, 6, 6, 6, 0],\n [0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "bf32578f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 4, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [4, 0, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 4, 0, 0, 0, 0, 0, 4],\n [4, 0, 4, 0, 4, 4, 4, 0, 4],\n [4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 0, 4, 0, 0, 0, 4, 0, 4],\n [4, 0, 4, 4, 4, 4, 4, 0, 4],\n [4, 0, 0, 0, 0, 0, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [5, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 5],\n [5, 0, 5, 0, 5, 0, 5, 0, 5],\n [5, 0, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 5, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 5, 5, 0, 5, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 0, 3, 0, 3, 0, 3],\n [3, 0, 3, 0, 0, 0, 3, 0, 3],\n [3, 0, 3, 3, 3, 3, 3, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 8],\n [0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 0, 8, 0, 8, 8, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 0],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 0],\n [8, 0, 8, 0, 8, 0, 8, 8, 8],\n [8, 0, 8, 0, 8, 0, 0, 0, 0],\n [8, 0, 8, 0, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 7],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [7, 0, 7, 0, 7, 0, 7, 0, 7],\n [7, 0, 7, 0, 7, 0, 7, 0, 0],\n [7, 0, 7, 0, 7, 0, 7, 7, 7],\n [7, 0, 7, 0, 7, 0, 0, 0, 0],\n [7, 0, 7, 0, 7, 7, 7, 7, 7],\n [7, 0, 7, 0, 0, 0, 0, 0, 0],\n [7, 0, 7, 7, 7, 7, 7, 7, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 0],\n [0, 0, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 0, 6, 6, 6, 6, 6, 6, 6], [6, 0, 6, 0, 0, 0, 0, 0, 0], [6, 0, 6, 0, 6, 6, 6, 6, 6], [6, 0, 6, 0, 6, 0, 0, 0, 0], [6, 0, 6, 0, 6, 0, 6, 6, 6], [6, 0, 6, 0, 6, 0, 6, 0, 0], [6, 0, 6, 0, 6, 0, 6, 0, 6], [6, 0, 6, 0, 6, 0, 6, 0, 6], [6, 0, 6, 0, 6, 0, 6, 0, 6]], "task_id": "8b28cd80"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0, 0, 4, 0, 0, 0, 1, 0],\n [0, 1, 0, 0, 8, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 8, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 1, 0, 0, 0, 0],\n [4, 8, 8, 2, 2, 2, 8, 8, 4, 8],\n [0, 0, 0, 1, 2, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 8, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 8, 0, 0, 1, 0, 0],\n [1, 0, 0, 0, 4, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 2, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8, 4, 8, 8],\n [0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0], [4, 8, 8, 4, 8, 8, 2, 2, 2, 8, 8, 4, 8, 8, 4, 8, 8], [0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "fe9372f3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 7, 9],\n [9, 6, 7],\n [7, 6, 6]\n ],\n \"output\": [\n [9, 7, 9, 9, 7, 9, 9, 7, 9],\n [9, 6, 7, 9, 6, 7, 9, 6, 7],\n [7, 6, 6, 7, 6, 6, 7, 6, 6],\n [9, 7, 9, 9, 7, 9, 9, 7, 9],\n [9, 6, 7, 9, 6, 7, 9, 6, 7],\n [7, 6, 6, 7, 6, 6, 7, 6, 6],\n [9, 7, 9, 9, 7, 9, 9, 7, 9],\n [9, 6, 7, 9, 6, 7, 9, 6, 7],\n [7, 6, 6, 7, 6, 6, 7, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [3, 4, 4],\n [3, 3, 3],\n [3, 4, 4]\n ],\n \"output\": [\n [3, 4, 4, 3, 4, 4],\n [3, 3, 3, 3, 3, 3],\n [3, 4, 4, 3, 4, 4],\n [3, 4, 4, 3, 4, 4],\n [3, 3, 3, 3, 3, 3],\n [3, 4, 4, 3, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 2, 1],\n [1, 8, 3],\n [2, 1, 3]\n ],\n \"output\": [\n [8, 2, 1, 8, 2, 1, 8, 2, 1, 8, 2, 1],\n [1, 8, 3, 1, 8, 3, 1, 8, 3, 1, 8, 3],\n [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3],\n [8, 2, 1, 8, 2, 1, 8, 2, 1, 8, 2, 1],\n [1, 8, 3, 1, 8, 3, 1, 8, 3, 1, 8, 3],\n [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3],\n [8, 2, 1, 8, 2, 1, 8, 2, 1, 8, 2, 1],\n [1, 8, 3, 1, 8, 3, 1, 8, 3, 1, 8, 3],\n [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3],\n [8, 2, 1, 8, 2, 1, 8, 2, 1, 8, 2, 1],\n [1, 8, 3, 1, 8, 3, 1, 8, 3, 1, 8, 3],\n [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7],\n [7, 2, 2],\n [7, 7, 2]\n ],\n \"output\": [\n [7, 7, 7, 7, 7, 7],\n [7, 2, 2, 7, 2, 2],\n [7, 7, 2, 7, 7, 2],\n [7, 7, 7, 7, 7, 7],\n [7, 2, 2, 7, 2, 2],\n [7, 7, 2, 7, 7, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 2],\n [3, 3, 2],\n [2, 2, 1]\n ],\n \"output\": [\n [2, 3, 2, 2, 3, 2, 2, 3, 2],\n [3, 3, 2, 3, 3, 2, 3, 3, 2],\n [2, 2, 1, 2, 2, 1, 2, 2, 1],\n [2, 3, 2, 2, 3, 2, 2, 3, 2],\n [3, 3, 2, 3, 3, 2, 3, 3, 2],\n [2, 2, 1, 2, 2, 1, 2, 2, 1],\n [2, 3, 2, 2, 3, 2, 2, 3, 2],\n [3, 3, 2, 3, 3, 2, 3, 3, 2],\n [2, 2, 1, 2, 2, 1, 2, 2, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 3, 2],\n [2, 1, 4],\n [3, 1, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 3, 2, 4, 3, 2, 4, 3, 2, 4, 3, 2], [2, 1, 4, 2, 1, 4, 2, 1, 4, 2, 1, 4], [3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2], [4, 3, 2, 4, 3, 2, 4, 3, 2, 4, 3, 2], [2, 1, 4, 2, 1, 4, 2, 1, 4, 2, 1, 4], [3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2], [4, 3, 2, 4, 3, 2, 4, 3, 2, 4, 3, 2], [2, 1, 4, 2, 1, 4, 2, 1, 4, 2, 1, 4], [3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2], [4, 3, 2, 4, 3, 2, 4, 3, 2, 4, 3, 2], [2, 1, 4, 2, 1, 4, 2, 1, 4, 2, 1, 4], [3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2]], "task_id": "a59b95c0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 8, 1, 1, 0, 0, 0, 8, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 1, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 8, 1, 1, 0, 1, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 1, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 1, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 4, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 0, 4, 0, 0], [0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0], [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "93c31fbe"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1c56ad9f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 2, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 3, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "bf89d739"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 0, 1, 0, 0, 3, 3, 3],\n [2, 0, 2, 0, 1, 1, 1, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 1, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 1, 1, 1, 0, 3, 0, 3],\n [2, 2, 2, 0, 0, 0, 0, 0, 3, 0, 3],\n [0, 2, 0, 0, 1, 1, 1, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 1, 0, 1, 0, 0, 3, 0],\n [0, 0, 0, 0, 1, 0, 1, 0, 3, 3, 3],\n [2, 2, 2, 0, 1, 1, 1, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0, 0, 1, 0, 0, 3, 3, 3],\n [2, 0, 2, 0, 1, 1, 1, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 1, 0, 0, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 3, 0, 0, 0, 1, 1, 1],\n [0, 2, 0, 0, 3, 3, 3, 0, 0, 1, 0],\n [2, 0, 2, 0, 0, 0, 3, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 3, 3, 3, 0, 1, 0, 1],\n [2, 2, 2, 0, 0, 3, 0, 0, 0, 1, 0],\n [0, 0, 2, 0, 0, 3, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 0, 3, 0, 3, 0, 1, 0, 0],\n [0, 2, 0, 0, 0, 3, 0, 0, 1, 1, 1],\n [0, 2, 0, 0, 3, 0, 3, 0, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 3, 0, 0, 0, 1, 0, 0, 0, 5, 0, 5],\n [0, 0, 2, 0, 3, 0, 0, 0, 0, 1, 1, 0, 5, 0, 5],\n [0, 0, 2, 0, 3, 3, 3, 0, 1, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 0, 0, 3, 0, 0, 0, 1, 0, 1, 0, 5, 5, 5],\n [2, 0, 0, 0, 0, 3, 3, 0, 1, 0, 1, 0, 0, 0, 5],\n [2, 2, 2, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 3, 0, 3, 0, 1, 0, 1, 0, 0, 5, 0],\n [2, 2, 2, 0, 0, 3, 0, 0, 1, 1, 1, 0, 5, 5, 5],\n [0, 0, 2, 0, 3, 0, 3, 0, 0, 1, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 3, 0, 3, 0, 0, 1, 0, 0, 5, 0, 0],\n [0, 2, 0, 0, 3, 3, 3, 0, 1, 1, 1, 0, 5, 5, 5],\n [2, 0, 2, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0, 0, 3, 0, 0, 1, 0, 0, 0, 5, 0, 5],\n [2, 2, 2, 0, 3, 3, 3, 0, 1, 1, 1, 0, 0, 5, 0],\n [0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 5, 0, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 3, 3, 0, 0, 1, 0, 1, 0, 0, 5, 0],\n [0, 2, 0, 0, 0, 0, 3, 0, 1, 0, 1, 0, 5, 5, 5],\n [2, 2, 2, 0, 3, 3, 0, 0, 1, 0, 1, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 3, 0, 3, 0, 0, 1, 0, 0, 5, 5, 0],\n [0, 0, 2, 0, 3, 0, 3, 0, 1, 1, 1, 0, 0, 5, 0],\n [2, 2, 0, 0, 3, 0, 3, 0, 0, 1, 0, 0, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 0, 2, 0, 0, 3, 0, 0, 1, 1, 0, 0, 5, 5, 0], [2, 0, 2, 0, 3, 3, 3, 0, 0, 1, 0, 0, 0, 0, 5], [2, 0, 2, 0, 0, 3, 0, 0, 1, 1, 1, 0, 5, 5, 0]], "task_id": "e78887d1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1],\n [2, 2, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 1, 0, 1],\n [2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 1, 0, 1],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 1, 1, 1],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0],\n [2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0], [2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 0, 0, 1, 0, 1, 0, 2, 0, 0, 0, 2, 0, 0], [0, 2, 0, 2, 0, 2, 0, 0, 1, 0, 1, 0, 2, 2, 2, 2, 2, 0, 0], [0, 2, 0, 2, 0, 2, 0, 0, 1, 1, 1, 0, 2, 0, 0, 0, 2, 0, 0], [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "bd14c3bf"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 8, 8, 8, 0, 8],\n [0, 8, 0, 0, 2, 2, 2, 8, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8],\n [0, 8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 8],\n [0, 8, 0, 8, 8, 2, 2, 2, 8, 8, 0, 8],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 8, 0, 8],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 8, 0, 8],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 8, 0, 8],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [8, 8, 0, 0, 8, 8, 0, 8, 8, 0, 8, 0],\n [8, 2, 2, 0, 8, 2, 2, 2, 8, 0, 8, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 8],\n [8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 8],\n [8, 8, 2, 2, 0, 2, 2, 2, 8, 8, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0], [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0], [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0], [0, 8, 0, 8, 8, 0, 0, 8, 0, 8, 8, 0, 0, 8, 8], [0, 8, 0, 8, 2, 2, 0, 8, 0, 8, 2, 2, 2, 2, 8], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 8], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 8], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 8], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 8], [0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 8]], "task_id": "c87289bb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 9],\n [0, 0, 1, 1, 0, 0, 0, 0, 9],\n [0, 0, 0, 1, 0, 7, 0, 0, 0],\n [6, 6, 6, 0, 0, 7, 7, 0, 0],\n [6, 0, 6, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 7, 0, 0],\n [0, 6, 0, 6, 0, 0, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 9],\n [0, 0, 9, 9, 0, 0, 0, 0, 9],\n [0, 0, 0, 9, 0, 7, 0, 0, 0],\n [6, 6, 6, 0, 0, 7, 7, 0, 0],\n [6, 0, 6, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 9],\n [1, 1, 1, 0, 0, 1, 0, 0, 0, 9, 9],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 3, 0, 0, 0, 6, 0, 1, 1, 0, 0],\n [3, 3, 3, 0, 0, 6, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 7, 7, 7, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 7, 0, 0, 0, 6, 0, 0, 0, 0, 9],\n [7, 7, 7, 0, 0, 6, 0, 0, 0, 9, 9],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],\n [0, 3, 0, 0, 0, 6, 0, 9, 9, 0, 0],\n [3, 3, 3, 0, 0, 6, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 7, 7, 7, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 8, 8, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 6, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 6, 6, 6],\n [8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 8, 8, 8, 0, 8, 8, 0, 0], [0, 2, 2, 2, 0, 0, 8, 0, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0], [0, 4, 4, 0, 0, 0, 6, 0, 0, 0, 0, 2, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 6, 0], [0, 0, 8, 0, 0, 0, 4, 0, 0, 0, 6, 6, 6], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "2a5f8217"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 8, 8, 8, 0, 8, 8, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 8, 0, 8, 8, 0, 0, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 6, 6],\n [0, 8, 8, 8, 0, 8, 8, 0, 8, 0, 0, 6, 0, 6, 6, 0, 0, 0, 6],\n [0, 8, 0, 0, 8, 8, 0, 8, 8, 0, 0, 6, 6, 6, 0, 6, 6, 0, 6],\n [0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 0, 6, 0, 0, 6, 6, 0, 6, 6],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 6, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 4, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0, 0, 2],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 2, 0, 2, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 0, 2, 2, 2, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 2, 2, 0, 0, 2],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 4, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0],\n [0, 7, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0],\n [0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 6, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 6, 6, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "f21745ec"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [7, 5, 7],\n [5, 5, 7],\n [7, 7, 5]\n ],\n \"output\": [\n [7, 5, 7, 7, 5, 7, 7, 5, 7, 7, 5, 7],\n [7, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 7],\n [5, 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 5]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 8],\n [5, 8, 8],\n [5, 8, 8]\n ],\n \"output\": [\n [8, 7, 7, 7, 7, 8, 8, 7, 7, 7, 7, 8],\n [8, 8, 5, 5, 8, 8, 8, 8, 5, 5, 8, 8],\n [8, 8, 5, 5, 8, 8, 8, 8, 5, 5, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8],\n [5, 5, 7],\n [5, 7, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [7, 5, 5, 5, 5, 7, 7, 5, 5, 5, 5, 7],\n [8, 7, 5, 5, 7, 8, 8, 7, 5, 5, 7, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 7],\n [7, 5, 5],\n [5, 7, 8]\n ],\n \"output\": [\n [7, 8, 8, 8, 8, 7, 7, 8, 8, 8, 8, 7],\n [5, 5, 7, 7, 5, 5, 5, 5, 7, 7, 5, 5],\n [8, 7, 5, 5, 7, 8, 8, 7, 5, 5, 7, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 5, 7],\n [5, 7, 5],\n [8, 8, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 5, 8, 8, 5, 7, 7, 5, 8, 8, 5, 7], [5, 7, 5, 5, 7, 5, 5, 7, 5, 5, 7, 5], [5, 8, 8, 8, 8, 5, 5, 8, 8, 8, 8, 5]], "task_id": "59341089"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 0],\n [3, 4, 0],\n [3, 4, 2]\n ],\n \"output\": [\n [2, 4, 3, 3, 4, 2],\n [0, 4, 3, 3, 4, 0],\n [0, 0, 3, 3, 0, 0],\n [0, 0, 3, 3, 0, 0],\n [0, 4, 3, 3, 4, 0],\n [2, 4, 3, 3, 4, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 6, 0, 0],\n [4, 6, 0, 3],\n [4, 6, 3, 0],\n [4, 3, 3, 0]\n ],\n \"output\": [\n [0, 3, 3, 4, 4, 3, 3, 0],\n [0, 3, 6, 4, 4, 6, 3, 0],\n [3, 0, 6, 4, 4, 6, 0, 3],\n [0, 0, 6, 0, 0, 6, 0, 0],\n [0, 0, 6, 0, 0, 6, 0, 0],\n [3, 0, 6, 4, 4, 6, 0, 3],\n [0, 3, 6, 4, 4, 6, 3, 0],\n [0, 3, 3, 4, 4, 3, 3, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 0, 0, 0, 0],\n [0, 2, 1, 0, 9, 0, 0, 0],\n [0, 2, 1, 0, 9, 0, 0, 0],\n [0, 2, 1, 0, 9, 1, 1, 1],\n [9, 2, 0, 0, 9, 0, 0, 0],\n [9, 2, 0, 0, 9, 0, 0, 9],\n [1, 2, 0, 0, 9, 0, 0, 9],\n [9, 9, 0, 0, 9, 0, 0, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 0, 0, 9, 0, 0, 9, 9, 9, 9, 0, 0, 9, 0, 0, 9], [9, 0, 0, 9, 0, 0, 2, 1, 1, 2, 0, 0, 9, 0, 0, 9], [9, 0, 0, 9, 0, 0, 2, 9, 9, 2, 0, 0, 9, 0, 0, 9], [0, 0, 0, 9, 0, 0, 2, 9, 9, 2, 0, 0, 9, 0, 0, 0], [1, 1, 1, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 1, 1, 1], [0, 0, 0, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 0, 0, 0], [0, 0, 0, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 0, 0, 0], [0, 0, 0, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 0, 0, 0], [1, 1, 1, 9, 0, 1, 2, 0, 0, 2, 1, 0, 9, 1, 1, 1], [0, 0, 0, 9, 0, 0, 2, 9, 9, 2, 0, 0, 9, 0, 0, 0], [9, 0, 0, 9, 0, 0, 2, 9, 9, 2, 0, 0, 9, 0, 0, 9], [9, 0, 0, 9, 0, 0, 2, 1, 1, 2, 0, 0, 9, 0, 0, 9], [9, 0, 0, 9, 0, 0, 9, 9, 9, 9, 0, 0, 9, 0, 0, 9]], "task_id": "833dafe3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2],\n [2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2],\n [2, 0, 1, 2, 2, 2, 0, 0, 8, 2, 0],\n [0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0],\n [1, 2, 2, 0, 0, 2, 8, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 2, 1, 2, 0, 0, 0, 2, 8, 0],\n [0, 2, 0, 0, 1, 2, 2, 2, 0, 2, 8],\n [0, 2, 0, 2, 2, 0, 2, 2, 2, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0],\n [2, 1, 2, 2, 2, 2, 2, 8, 2, 2, 0],\n [2, 2, 2, 0, 2, 0, 0, 2, 0, 0, 2],\n [0, 0, 2, 0, 2, 0, 2, 2, 2, 2, 0]\n ],\n \"output\": [\n [2, 2, 2, 0, 0],\n [2, 2, 0, 0, 2],\n [2, 0, 0, 0, 2],\n [2, 2, 2, 0, 2],\n [2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2],\n [2, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0],\n [2, 1, 0, 2, 0, 0, 0, 0, 2, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 0, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 0, 2, 2, 0, 2, 2, 0],\n [1, 0, 2, 0, 8, 0, 2, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 0, 0, 0, 2],\n [2, 0, 2, 0, 0, 1, 2, 0, 0, 8],\n [2, 2, 0, 0, 2, 2, 0, 2, 2, 0]\n ],\n \"output\": [\n [0, 2, 0],\n [2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 2, 2, 0, 0, 0, 2, 0, 0, 2, 2],\n [2, 0, 1, 2, 0, 2, 0, 8, 0, 2, 0, 2],\n [1, 2, 2, 2, 2, 8, 2, 0, 0, 0, 2, 0],\n [2, 0, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0],\n [2, 2, 1, 0, 2, 2, 2, 8, 2, 0, 2, 2],\n [2, 0, 0, 2, 0, 2, 0, 2, 2, 2, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0],\n [2, 2, 2, 2],\n [0, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 2, 0, 2, 0, 0, 0, 8, 2, 0, 0, 2],\n [1, 2, 0, 2, 0, 2, 0, 8, 0, 0, 0, 2],\n [1, 0, 2, 2, 0, 2, 2, 8, 0, 0, 2, 2],\n [2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2],\n [0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 2, 2],\n [2, 0, 0, 2, 0, 0, 2, 2, 2, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0, 0, 0],\n [2, 0, 2, 0, 2, 0],\n [0, 2, 2, 0, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 1, 0, 2, 0, 2, 2, 8, 2, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2],\n [0, 1, 0, 2, 2, 0, 2, 8, 2, 0, 0, 0, 0, 2],\n [0, 2, 1, 0, 0, 0, 0, 0, 8, 2, 2, 0, 2, 2],\n [2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 2, 0, 0],\n [0, 1, 2, 2, 0, 0, 0, 8, 0, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 0],\n [2, 2, 2, 2, 1, 0, 0, 2, 0, 0, 8, 0, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 2, 2], [0, 2, 2, 0, 2], [0, 0, 0, 0, 0], [2, 2, 0, 0, 0], [0, 0, 2, 0, 0]], "task_id": "505fff84"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 1, 8, 0, 8, 1, 8, 0, 1, 0, 1, 1],\n [1, 0, 1, 1, 8, 6, 0, 1, 1, 6, 6, 8],\n [0, 1, 8, 8, 0, 8, 0, 1, 0, 6, 1, 0],\n [0, 8, 0, 8, 0, 0, 0, 0, 6, 8, 8, 6],\n [1, 8, 0, 8, 0, 0, 0, 6, 8, 8, 0, 0],\n [4, 6, 6, 8, 6, 0, 8, 0, 1, 1, 0, 8],\n [4, 4, 6, 8, 0, 1, 8, 1, 1, 1, 8, 6],\n [6, 4, 4, 0, 8, 0, 6, 0, 1, 0, 1, 0],\n [8, 8, 1, 1, 8, 8, 8, 0, 0, 0, 8, 0],\n [0, 6, 8, 8, 0, 0, 0, 1, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 6],\n [1, 0, 1, 8, 8, 0, 6, 0, 8, 8, 1, 8],\n [1, 0, 0, 8, 1, 6, 6, 0, 1, 0, 8, 8],\n [8, 1, 8, 8, 1, 1, 0, 1, 8, 0, 8, 8],\n [0, 1, 1, 0, 0, 6, 1, 8, 0, 0, 8, 1],\n [1, 8, 8, 8, 0, 8, 8, 6, 1, 8, 6, 0],\n [8, 0, 6, 1, 8, 1, 6, 6, 8, 0, 1, 1],\n [8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 1],\n [8, 1, 0, 0, 1, 1, 0, 8, 8, 0, 0, 8]\n ],\n \"output\": [\n [8, 1, 8, 0, 8, 1, 8, 0, 1, 0, 1, 1],\n [1, 0, 1, 1, 8, 6, 0, 1, 1, 6, 6, 4],\n [0, 1, 8, 8, 0, 8, 0, 1, 0, 6, 4, 4],\n [0, 8, 0, 8, 0, 0, 0, 0, 6, 4, 4, 6],\n [1, 8, 0, 8, 0, 0, 0, 6, 8, 8, 0, 0],\n [4, 6, 6, 8, 6, 0, 8, 0, 1, 1, 0, 8],\n [4, 4, 6, 8, 0, 1, 8, 1, 1, 1, 8, 6],\n [6, 4, 4, 0, 8, 0, 6, 0, 1, 0, 1, 0],\n [8, 8, 1, 1, 8, 8, 8, 0, 0, 0, 8, 0],\n [0, 6, 8, 8, 0, 0, 0, 1, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 6],\n [1, 0, 1, 8, 8, 0, 6, 0, 8, 8, 1, 8],\n [1, 0, 0, 8, 1, 6, 6, 0, 1, 0, 8, 8],\n [8, 1, 8, 8, 1, 1, 0, 1, 8, 0, 8, 8],\n [0, 1, 1, 0, 0, 6, 4, 4, 0, 0, 8, 1],\n [1, 8, 8, 8, 0, 4, 4, 6, 1, 8, 6, 0],\n [8, 0, 6, 1, 8, 4, 6, 6, 8, 0, 1, 1],\n [8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 1],\n [8, 1, 0, 0, 1, 1, 0, 8, 8, 0, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 6, 0, 0, 0, 0, 8, 0, 1, 8, 8, 8, 1, 1, 1],\n [0, 1, 8, 6, 1, 6, 0, 0, 0, 0, 6, 1, 1, 8, 8, 1],\n [0, 1, 8, 1, 0, 1, 0, 0, 6, 6, 1, 0, 0, 8, 1, 1],\n [0, 0, 1, 1, 1, 6, 1, 1, 0, 6, 6, 0, 1, 1, 8, 0],\n [0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 6, 6, 1],\n [0, 0, 0, 0, 0, 6, 0, 1, 8, 8, 0, 8, 1, 1, 0, 1],\n [1, 1, 1, 8, 1, 0, 0, 8, 1, 1, 0, 8, 8, 1, 1, 1],\n [8, 8, 1, 1, 0, 6, 1, 8, 1, 8, 6, 8, 8, 1, 8, 6],\n [1, 1, 6, 1, 8, 1, 0, 1, 1, 1, 6, 0, 6, 8, 1, 8],\n [0, 0, 8, 8, 6, 4, 6, 0, 1, 8, 0, 1, 0, 0, 1, 8],\n [0, 1, 8, 0, 4, 6, 6, 1, 0, 8, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 6, 4, 6, 4, 8, 0, 0, 0, 1, 0, 8, 6, 8],\n [6, 0, 1, 1, 1, 1, 1, 0, 8, 1, 0, 1, 1, 8, 0, 0],\n [0, 1, 1, 1, 1, 8, 1, 1, 1, 8, 8, 1, 8, 8, 8, 0],\n [0, 1, 0, 0, 6, 0, 0, 0, 8, 1, 6, 8, 8, 6, 0, 0],\n [1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 0, 8, 1, 1, 8, 1],\n [8, 1, 1, 0, 0, 6, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1],\n [6, 0, 1, 0, 1, 8, 8, 8, 8, 6, 1, 1, 8, 1, 8, 8]\n ],\n \"output\": [\n [1, 0, 6, 0, 0, 0, 0, 8, 0, 1, 8, 8, 8, 1, 1, 1],\n [0, 1, 8, 6, 1, 6, 0, 0, 4, 4, 6, 1, 1, 8, 8, 1],\n [0, 1, 8, 1, 0, 1, 0, 0, 6, 6, 4, 0, 0, 8, 1, 1],\n [0, 0, 1, 1, 1, 6, 1, 1, 4, 6, 6, 0, 1, 1, 8, 0],\n [0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 6, 6, 1],\n [0, 0, 0, 0, 0, 6, 0, 1, 8, 8, 0, 8, 1, 1, 0, 1],\n [1, 1, 1, 8, 1, 0, 0, 8, 1, 1, 0, 8, 8, 1, 1, 1],\n [8, 8, 1, 1, 0, 6, 1, 8, 1, 8, 6, 8, 8, 1, 8, 6],\n [1, 1, 6, 1, 8, 1, 0, 1, 1, 1, 6, 0, 6, 8, 1, 8],\n [0, 0, 8, 8, 6, 4, 6, 0, 1, 8, 0, 1, 0, 0, 1, 8],\n [0, 1, 8, 0, 4, 6, 6, 1, 0, 8, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 6, 4, 6, 4, 8, 0, 0, 0, 1, 0, 8, 6, 8],\n [6, 0, 1, 1, 1, 1, 1, 0, 8, 1, 0, 1, 1, 8, 0, 0],\n [0, 1, 1, 1, 1, 8, 1, 1, 1, 8, 8, 1, 8, 8, 8, 0],\n [0, 1, 0, 0, 6, 0, 0, 0, 8, 1, 6, 8, 8, 6, 0, 0],\n [1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 0, 8, 1, 1, 8, 1],\n [8, 1, 1, 0, 0, 6, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1],\n [6, 0, 1, 0, 1, 8, 8, 8, 8, 6, 1, 1, 8, 1, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 1, 8, 8, 6, 0, 0, 1, 0, 0, 1, 0, 8, 0, 1, 0, 0],\n [6, 8, 1, 0, 0, 8, 0, 4, 6, 6, 1, 0, 0, 0, 8, 0, 1],\n [0, 8, 1, 8, 0, 1, 0, 6, 4, 4, 0, 0, 8, 0, 0, 0, 8],\n [1, 1, 0, 1, 1, 0, 8, 4, 4, 4, 1, 8, 8, 1, 0, 1, 8],\n [1, 6, 6, 0, 0, 8, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 8, 6, 0, 0, 8, 0, 0, 1, 1, 0, 6, 0, 0, 0, 1, 8],\n [0, 8, 0, 8, 0, 0, 8, 8, 8, 1, 8, 0, 8, 0, 0, 0, 6],\n [0, 1, 0, 1, 6, 0, 0, 1, 1, 0, 0, 8, 1, 1, 6, 8, 6],\n [0, 0, 1, 0, 1, 8, 0, 8, 8, 0, 1, 1, 8, 1, 1, 8, 0],\n [0, 8, 0, 8, 1, 0, 6, 8, 8, 0, 0, 0, 0, 6, 8, 1, 1],\n [0, 0, 0, 0, 6, 0, 1, 1, 8, 1, 1, 8, 8, 0, 8, 8, 8],\n [8, 8, 8, 0, 6, 8, 1, 8, 1, 0, 0, 0, 1, 8, 1, 1, 6],\n [8, 8, 0, 0, 1, 0, 1, 8, 0, 1, 8, 0, 1, 0, 0, 0, 1],\n [0, 8, 8, 1, 8, 6, 8, 1, 6, 1, 0, 6, 0, 0, 8, 0, 6],\n [1, 0, 8, 8, 1, 0, 8, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 1, 0, 8, 8, 0, 0, 0, 8, 0, 6, 6],\n [1, 0, 0, 0, 0, 0, 1, 0, 8, 0, 1, 1, 6, 0, 6, 0, 1]\n ],\n \"output\": [\n [8, 1, 8, 8, 6, 0, 0, 1, 0, 0, 1, 0, 8, 0, 1, 0, 0],\n [6, 8, 1, 0, 0, 8, 0, 4, 6, 6, 1, 0, 0, 0, 8, 0, 1],\n [0, 8, 1, 8, 0, 1, 0, 6, 4, 4, 0, 0, 8, 0, 0, 0, 8],\n [1, 1, 0, 1, 1, 0, 8, 4, 4, 4, 1, 8, 8, 1, 0, 1, 8],\n [1, 6, 6, 0, 0, 8, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 8, 6, 0, 0, 8, 0, 0, 1, 1, 0, 6, 0, 0, 0, 1, 8],\n [0, 8, 0, 8, 0, 0, 8, 8, 8, 1, 8, 0, 8, 0, 0, 0, 6],\n [0, 1, 0, 1, 6, 0, 0, 1, 1, 0, 0, 8, 1, 1, 6, 8, 6],\n [0, 0, 1, 0, 1, 8, 0, 8, 8, 0, 1, 1, 8, 1, 1, 8, 0],\n [0, 8, 0, 8, 1, 0, 6, 8, 8, 0, 0, 0, 0, 6, 8, 1, 1],\n [0, 0, 0, 0, 6, 0, 1, 1, 8, 1, 1, 8, 8, 0, 8, 8, 8],\n [8, 8, 8, 0, 6, 8, 1, 8, 1, 0, 0, 0, 1, 8, 1, 1, 6],\n [8, 8, 0, 0, 1, 0, 1, 8, 0, 1, 8, 0, 1, 0, 0, 0, 1],\n [0, 8, 8, 1, 8, 6, 8, 1, 6, 1, 0, 6, 0, 0, 8, 0, 6],\n [1, 0, 8, 8, 1, 0, 8, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 1, 0, 8, 8, 0, 0, 0, 8, 4, 6, 6],\n [1, 0, 0, 0, 0, 0, 1, 0, 8, 0, 1, 1, 6, 0, 6, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 6, 8, 0, 0, 6, 1, 6, 6, 1, 1, 1, 0, 0, 1],\n [1, 0, 8, 1, 6, 8, 8, 1, 1, 0, 1, 0, 8, 0, 1],\n [0, 0, 6, 0, 1, 8, 0, 1, 1, 0, 0, 0, 1, 0, 1],\n [1, 1, 1, 8, 6, 6, 6, 8, 0, 0, 1, 8, 0, 8, 6],\n [1, 0, 8, 0, 8, 6, 0, 6, 8, 1, 1, 1, 1, 1, 8],\n [0, 0, 6, 0, 1, 0, 0, 8, 8, 1, 1, 8, 1, 6, 0],\n [0, 1, 8, 1, 0, 6, 8, 8, 8, 6, 0, 1, 6, 6, 0],\n [1, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 8, 1, 0, 8],\n [0, 1, 0, 8, 1, 1, 1, 8, 0, 0, 8, 1, 1, 8, 6],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 8, 1],\n [8, 0, 8, 8, 8, 4, 4, 4, 6, 1, 1, 8, 6, 8, 0],\n [1, 0, 8, 1, 1, 6, 4, 4, 8, 1, 8, 1, 0, 1, 1],\n [0, 6, 1, 0, 0, 6, 6, 4, 1, 1, 0, 0, 8, 8, 8],\n [8, 1, 1, 0, 0, 8, 8, 0, 8, 8, 0, 0, 1, 1, 1],\n [1, 1, 8, 8, 0, 1, 8, 8, 8, 8, 0, 0, 1, 6, 8],\n [0, 8, 1, 8, 0, 1, 8, 0, 6, 1, 6, 0, 6, 6, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 6, 8, 0, 0, 6, 1, 6, 6, 1, 1, 1, 0, 0, 1], [1, 0, 8, 1, 6, 8, 8, 1, 1, 0, 1, 0, 8, 0, 1], [0, 0, 6, 0, 1, 8, 0, 1, 1, 0, 0, 0, 1, 0, 1], [1, 1, 1, 4, 6, 6, 6, 8, 0, 0, 1, 8, 0, 8, 6], [1, 0, 8, 4, 4, 6, 0, 6, 8, 1, 1, 4, 4, 4, 8], [0, 0, 6, 4, 4, 4, 0, 8, 8, 1, 1, 4, 4, 6, 0], [0, 1, 8, 1, 0, 6, 8, 8, 8, 6, 0, 4, 6, 6, 0], [1, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 8, 1, 0, 8], [0, 1, 0, 8, 1, 1, 1, 8, 0, 0, 8, 1, 1, 8, 6], [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 8, 1], [8, 0, 8, 8, 8, 4, 4, 4, 6, 1, 1, 8, 6, 8, 0], [1, 0, 8, 1, 1, 6, 4, 4, 8, 1, 8, 1, 0, 1, 1], [0, 6, 1, 0, 0, 6, 6, 4, 1, 1, 0, 0, 8, 8, 8], [8, 1, 1, 0, 0, 8, 8, 0, 8, 8, 0, 4, 4, 4, 1], [1, 1, 8, 8, 0, 1, 8, 8, 8, 8, 0, 4, 4, 6, 8], [0, 8, 1, 8, 0, 1, 8, 0, 6, 1, 6, 4, 6, 6, 0]], "task_id": "79369cc6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 9, 9, 9, 4, 4, 9, 4, 3, 4, 4, 4, 4, 2, 4, 4, 4, 4, 2, 4, 4, 4, 4, 3, 4, 9, 4, 4, 9, 9],\n [9, 9, 9, 4, 9, 3, 9, 3, 4, 4, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 4, 4, 3, 9, 3, 9, 4, 9],\n [9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 3, 2, 2, 3, 4, 4, 4, 2, 3, 4, 4, 9, 3, 3, 4, 9],\n [9, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 2, 4, 3, 3, 4, 2, 4, 4, 4, 2, 4, 4, 3, 4, 3, 3, 4],\n [4, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 2, 4, 3, 9, 4, 4, 3, 3],\n [4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 4, 3, 2, 9, 9, 4, 4, 4, 3],\n [9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 9, 9, 9, 9, 3, 9],\n [4, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 3, 4, 4, 4, 3, 2, 2, 4, 9, 9, 9, 3, 4, 4],\n [3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 2, 0, 0, 0, 0, 0, 0, 0, 3, 7, 3, 4, 4, 2, 4, 4, 4],\n [4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 2, 0, 0, 0, 0, 0, 0, 0, 7, 2, 7, 2, 3, 3, 2, 2, 3],\n [4, 3, 2, 4, 4, 4, 3, 2, 3, 7, 3, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 3, 0, 0, 2, 3, 4, 4, 4, 2],\n [4, 2, 4, 4, 4, 2, 4, 3, 7, 7, 7, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 7, 0, 0, 3, 4, 2, 4, 4, 4],\n [4, 2, 4, 4, 3, 3, 3, 4, 7, 7, 2, 2, 3, 2, 7, 7, 7, 7, 2, 3, 2, 2, 0, 0, 4, 3, 3, 3, 4, 4],\n [2, 3, 4, 2, 3, 3, 3, 4, 2, 2, 7, 3, 2, 2, 7, 7, 7, 7, 2, 2, 3, 7, 0, 0, 4, 3, 3, 3, 2, 4],\n [4, 3, 3, 4, 3, 3, 4, 4, 2, 7, 7, 3, 7, 7, 2, 3, 3, 2, 7, 7, 3, 7, 0, 0, 4, 4, 3, 3, 4, 3],\n [4, 2, 2, 3, 4, 4, 4, 3, 3, 3, 2, 2, 7, 7, 3, 2, 2, 3, 7, 7, 2, 2, 3, 3, 3, 4, 4, 4, 3, 2],\n [4, 2, 2, 3, 4, 4, 4, 3, 3, 3, 2, 2, 7, 7, 3, 2, 2, 3, 7, 7, 2, 2, 3, 3, 3, 4, 4, 4, 3, 2],\n [4, 3, 3, 4, 3, 3, 4, 4, 2, 7, 7, 3, 7, 7, 2, 3, 3, 2, 7, 7, 3, 7, 7, 2, 4, 4, 3, 3, 4, 3],\n [2, 3, 4, 2, 3, 3, 3, 4, 2, 2, 7, 3, 2, 2, 7, 7, 7, 7, 2, 2, 3, 7, 2, 2, 4, 3, 3, 3, 2, 4],\n [4, 2, 4, 4, 3, 3, 3, 4, 7, 7, 2, 2, 3, 2, 7, 0, 0, 0, 0, 3, 2, 2, 7, 7, 4, 3, 3, 3, 4, 4],\n [4, 2, 4, 4, 4, 2, 4, 3, 7, 7, 7, 2, 2, 3, 3, 0, 0, 0, 0, 2, 2, 7, 7, 7, 3, 4, 2, 4, 4, 4],\n [4, 3, 2, 4, 4, 4, 3, 2, 3, 7, 3, 7, 2, 7, 7, 0, 0, 0, 0, 2, 7, 3, 7, 3, 2, 3, 4, 4, 4, 2],\n [4, 4, 3, 2, 2, 3, 3, 2, 7, 2, 7, 7, 7, 2, 7, 3, 3, 7, 2, 7, 7, 7, 2, 7, 2, 3, 3, 2, 2, 3],\n [3, 4, 4, 4, 4, 2, 4, 4, 3, 7, 3, 7, 7, 2, 2, 3, 3, 2, 2, 7, 7, 3, 7, 3, 4, 4, 2, 4, 4, 4],\n [4, 3, 4, 4, 3, 9, 9, 9, 4, 2, 2, 3, 4, 4, 4, 3, 3, 4, 4, 4, 3, 2, 2, 4, 9, 9, 9, 3, 4, 4],\n [9, 9, 9, 3, 9, 9, 9, 9, 4, 3, 3, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 9, 9, 9, 9, 3, 9],\n [4, 3, 3, 4, 4, 4, 9, 9, 2, 3, 4, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 4, 3, 2, 9, 9, 4, 4, 4, 3],\n [4, 9, 3, 3, 4, 4, 9, 3, 4, 2, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 2, 4, 3, 9, 4, 4, 3, 3],\n [9, 4, 4, 3, 3, 4, 3, 4, 4, 2, 4, 4, 4, 2, 4, 3, 3, 4, 2, 4, 4, 4, 2, 4, 4, 3, 4, 3, 3, 4],\n [9, 9, 9, 4, 3, 3, 9, 4, 4, 3, 2, 4, 4, 4, 3, 2, 2, 3, 4, 4, 4, 2, 3, 4, 4, 9, 3, 3, 4, 9]\n ],\n \"output\": [\n [3, 9, 9, 9, 4, 4, 9, 4, 3, 4, 4, 4, 4, 2, 4, 4, 4, 4, 2, 4, 4, 4, 4, 3, 4, 9, 4, 4, 9, 9],\n [9, 9, 9, 4, 9, 3, 9, 3, 4, 4, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 4, 4, 3, 9, 3, 9, 4, 9],\n [9, 9, 9, 4, 3, 3, 9, 4, 4, 3, 2, 4, 4, 4, 3, 2, 2, 3, 4, 4, 4, 2, 3, 4, 4, 9, 3, 3, 4, 9],\n [9, 4, 4, 3, 3, 4, 3, 4, 4, 2, 4, 4, 4, 2, 4, 3, 3, 4, 2, 4, 4, 4, 2, 4, 4, 3, 4, 3, 3, 4],\n [4, 9, 3, 3, 4, 4, 9, 3, 4, 2, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 2, 4, 3, 9, 4, 4, 3, 3],\n [4, 3, 3, 4, 4, 4, 9, 9, 2, 3, 4, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 4, 3, 2, 9, 9, 4, 4, 4, 3],\n [9, 9, 9, 3, 9, 9, 9, 9, 4, 3, 3, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 9, 9, 9, 9, 3, 9],\n [4, 3, 4, 4, 3, 9, 9, 9, 4, 2, 2, 3, 4, 4, 4, 3, 3, 4, 4, 4, 3, 2, 2, 4, 9, 9, 9, 3, 4, 4],\n [3, 4, 4, 4, 4, 2, 4, 4, 3, 7, 3, 7, 7, 2, 2, 3, 3, 2, 2, 7, 7, 3, 7, 3, 4, 4, 2, 4, 4, 4],\n [4, 4, 3, 2, 2, 3, 3, 2, 7, 2, 7, 7, 7, 2, 7, 3, 3, 7, 2, 7, 7, 7, 2, 7, 2, 3, 3, 2, 2, 3],\n [4, 3, 2, 4, 4, 4, 3, 2, 3, 7, 3, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 3, 7, 3, 2, 3, 4, 4, 4, 2],\n [4, 2, 4, 4, 4, 2, 4, 3, 7, 7, 7, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 7, 7, 7, 3, 4, 2, 4, 4, 4],\n [4, 2, 4, 4, 3, 3, 3, 4, 7, 7, 2, 2, 3, 2, 7, 7, 7, 7, 2, 3, 2, 2, 7, 7, 4, 3, 3, 3, 4, 4],\n [2, 3, 4, 2, 3, 3, 3, 4, 2, 2, 7, 3, 2, 2, 7, 7, 7, 7, 2, 2, 3, 7, 2, 2, 4, 3, 3, 3, 2, 4],\n [4, 3, 3, 4, 3, 3, 4, 4, 2, 7, 7, 3, 7, 7, 2, 3, 3, 2, 7, 7, 3, 7, 7, 2, 4, 4, 3, 3, 4, 3],\n [4, 2, 2, 3, 4, 4, 4, 3, 3, 3, 2, 2, 7, 7, 3, 2, 2, 3, 7, 7, 2, 2, 3, 3, 3, 4, 4, 4, 3, 2],\n [4, 2, 2, 3, 4, 4, 4, 3, 3, 3, 2, 2, 7, 7, 3, 2, 2, 3, 7, 7, 2, 2, 3, 3, 3, 4, 4, 4, 3, 2],\n [4, 3, 3, 4, 3, 3, 4, 4, 2, 7, 7, 3, 7, 7, 2, 3, 3, 2, 7, 7, 3, 7, 7, 2, 4, 4, 3, 3, 4, 3],\n [2, 3, 4, 2, 3, 3, 3, 4, 2, 2, 7, 3, 2, 2, 7, 7, 7, 7, 2, 2, 3, 7, 2, 2, 4, 3, 3, 3, 2, 4],\n [4, 2, 4, 4, 3, 3, 3, 4, 7, 7, 2, 2, 3, 2, 7, 7, 7, 7, 2, 3, 2, 2, 7, 7, 4, 3, 3, 3, 4, 4],\n [4, 2, 4, 4, 4, 2, 4, 3, 7, 7, 7, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 7, 7, 7, 3, 4, 2, 4, 4, 4],\n [4, 3, 2, 4, 4, 4, 3, 2, 3, 7, 3, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 3, 7, 3, 2, 3, 4, 4, 4, 2],\n [4, 4, 3, 2, 2, 3, 3, 2, 7, 2, 7, 7, 7, 2, 7, 3, 3, 7, 2, 7, 7, 7, 2, 7, 2, 3, 3, 2, 2, 3],\n [3, 4, 4, 4, 4, 2, 4, 4, 3, 7, 3, 7, 7, 2, 2, 3, 3, 2, 2, 7, 7, 3, 7, 3, 4, 4, 2, 4, 4, 4],\n [4, 3, 4, 4, 3, 9, 9, 9, 4, 2, 2, 3, 4, 4, 4, 3, 3, 4, 4, 4, 3, 2, 2, 4, 9, 9, 9, 3, 4, 4],\n [9, 9, 9, 3, 9, 9, 9, 9, 4, 3, 3, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 3, 3, 4, 9, 9, 9, 9, 3, 9],\n [4, 3, 3, 4, 4, 4, 9, 9, 2, 3, 4, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 4, 3, 2, 9, 9, 4, 4, 4, 3],\n [4, 9, 3, 3, 4, 4, 9, 3, 4, 2, 4, 4, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 2, 4, 3, 9, 4, 4, 3, 3],\n [9, 4, 4, 3, 3, 4, 3, 4, 4, 2, 4, 4, 4, 2, 4, 3, 3, 4, 2, 4, 4, 4, 2, 4, 4, 3, 4, 3, 3, 4],\n [9, 9, 9, 4, 3, 3, 9, 4, 4, 3, 2, 4, 4, 4, 3, 2, 2, 3, 4, 4, 4, 2, 3, 4, 4, 9, 3, 3, 4, 9]\n ]\n}\n\n{\n \"input\": [\n [9, 4, 9, 9, 9, 9, 9, 4, 7, 8, 5, 8, 7, 8, 7, 5, 5, 7, 8, 7, 8, 5, 8, 7, 4, 9, 9, 9, 9, 9],\n [4, 6, 9, 4, 9, 9, 9, 9, 8, 5, 7, 8, 7, 8, 8, 5, 5, 8, 8, 7, 8, 7, 5, 8, 9, 9, 9, 9, 4, 9],\n [9, 9, 6, 9, 4, 9, 4, 4, 5, 7, 7, 7, 8, 5, 8, 5, 5, 8, 5, 8, 7, 7, 7, 5, 4, 4, 9, 4, 9, 6],\n [9, 4, 9, 4, 6, 6, 9, 9, 8, 8, 7, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 7, 8, 8, 9, 9, 6, 6, 4, 9],\n [9, 9, 4, 6, 6, 6, 4, 6, 7, 7, 8, 5, 8, 8, 8, 7, 7, 8, 8, 8, 5, 8, 7, 7, 6, 4, 6, 6, 6, 4],\n [9, 9, 9, 6, 6, 6, 9, 9, 8, 8, 5, 5, 8, 7, 8, 8, 8, 8, 7, 8, 5, 5, 8, 8, 9, 9, 6, 6, 6, 9],\n [9, 9, 4, 9, 4, 9, 4, 6, 7, 8, 8, 8, 8, 8, 5, 8, 8, 5, 8, 8, 8, 8, 8, 7, 6, 4, 9, 4, 9, 4],\n [4, 9, 4, 9, 6, 9, 6, 4, 5, 5, 5, 5, 7, 8, 8, 5, 5, 8, 8, 7, 0, 0, 0, 0, 4, 6, 9, 6, 9, 4],\n [7, 8, 5, 8, 7, 8, 7, 5, 7, 5, 8, 8, 7, 7, 7, 8, 8, 7, 7, 7, 0, 0, 0, 0, 5, 7, 8, 7, 8, 5],\n [8, 5, 7, 8, 7, 8, 8, 5, 5, 7, 8, 8, 5, 8, 5, 7, 7, 5, 8, 5, 0, 0, 0, 0, 5, 8, 8, 7, 8, 7],\n [5, 7, 7, 7, 8, 5, 8, 5, 8, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 5, 0, 0, 0, 0, 5, 8, 5, 8, 7, 7],\n [8, 8, 7, 5, 5, 5, 8, 5, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 5, 8, 5, 5, 5, 7],\n [7, 7, 8, 5, 8, 8, 8, 7, 7, 5, 5, 7, 5, 7, 7, 7, 7, 7, 7, 5, 0, 0, 0, 0, 7, 8, 8, 8, 5, 8],\n [8, 8, 5, 5, 8, 7, 8, 8, 7, 8, 7, 7, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 8, 8, 7, 8, 5, 5],\n [7, 8, 8, 8, 8, 8, 5, 8, 7, 5, 7, 7, 7, 0, 0, 0, 0, 0, 0, 7, 7, 7, 5, 7, 8, 5, 8, 8, 8, 8],\n [5, 5, 5, 5, 7, 8, 8, 5, 8, 7, 7, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7, 7, 7, 8, 5, 8, 8, 7, 5, 5],\n [5, 5, 5, 5, 7, 8, 8, 5, 8, 7, 7, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7, 7, 7, 8, 5, 8, 8, 7, 5, 5],\n [7, 8, 8, 8, 8, 8, 5, 8, 7, 5, 7, 7, 7, 7, 8, 7, 7, 8, 7, 7, 7, 7, 5, 7, 8, 5, 8, 8, 8, 8],\n [8, 8, 5, 5, 8, 7, 8, 8, 7, 8, 7, 7, 7, 5, 7, 8, 8, 7, 5, 7, 7, 7, 8, 7, 8, 8, 7, 8, 5, 5],\n [7, 7, 8, 5, 8, 8, 8, 7, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 5, 7, 5, 5, 7, 7, 8, 8, 8, 5, 8],\n [8, 8, 7, 5, 5, 5, 8, 5, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 5, 7, 8, 8, 5, 8, 5, 5, 5, 7],\n [5, 7, 7, 7, 8, 5, 8, 5, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 5, 7, 8, 8, 8, 5, 8, 5, 8, 7, 7],\n [8, 5, 7, 8, 7, 8, 8, 5, 0, 0, 0, 0, 0, 0, 0, 7, 7, 5, 8, 5, 8, 8, 7, 5, 5, 8, 8, 7, 8, 7],\n [7, 8, 5, 8, 7, 8, 7, 5, 0, 0, 0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 8, 8, 5, 7, 5, 7, 8, 7, 8, 5],\n [4, 9, 4, 9, 6, 9, 6, 4, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 8, 7, 5, 5, 5, 5, 4, 6, 9, 6, 9, 4],\n [9, 9, 4, 9, 4, 9, 4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 7, 6, 4, 9, 4, 9, 4],\n [9, 9, 9, 6, 6, 6, 9, 9, 8, 8, 5, 5, 8, 0, 0, 0, 0, 0, 0, 8, 5, 5, 8, 8, 9, 9, 6, 6, 6, 9],\n [9, 9, 4, 6, 6, 6, 4, 6, 7, 7, 8, 5, 8, 0, 0, 0, 0, 0, 0, 8, 5, 8, 7, 7, 6, 4, 6, 6, 6, 4],\n [9, 4, 9, 4, 6, 6, 9, 9, 8, 8, 7, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5, 7, 8, 8, 9, 9, 6, 6, 4, 9],\n [9, 9, 6, 9, 4, 9, 4, 4, 5, 7, 7, 7, 8, 0, 0, 0, 0, 0, 0, 8, 7, 7, 7, 5, 4, 4, 9, 4, 9, 6]\n ],\n \"output\": [\n [9, 4, 9, 9, 9, 9, 9, 4, 7, 8, 5, 8, 7, 8, 7, 5, 5, 7, 8, 7, 8, 5, 8, 7, 4, 9, 9, 9, 9, 9],\n [4, 6, 9, 4, 9, 9, 9, 9, 8, 5, 7, 8, 7, 8, 8, 5, 5, 8, 8, 7, 8, 7, 5, 8, 9, 9, 9, 9, 4, 9],\n [9, 9, 6, 9, 4, 9, 4, 4, 5, 7, 7, 7, 8, 5, 8, 5, 5, 8, 5, 8, 7, 7, 7, 5, 4, 4, 9, 4, 9, 6],\n [9, 4, 9, 4, 6, 6, 9, 9, 8, 8, 7, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 7, 8, 8, 9, 9, 6, 6, 4, 9],\n [9, 9, 4, 6, 6, 6, 4, 6, 7, 7, 8, 5, 8, 8, 8, 7, 7, 8, 8, 8, 5, 8, 7, 7, 6, 4, 6, 6, 6, 4],\n [9, 9, 9, 6, 6, 6, 9, 9, 8, 8, 5, 5, 8, 7, 8, 8, 8, 8, 7, 8, 5, 5, 8, 8, 9, 9, 6, 6, 6, 9],\n [9, 9, 4, 9, 4, 9, 4, 6, 7, 8, 8, 8, 8, 8, 5, 8, 8, 5, 8, 8, 8, 8, 8, 7, 6, 4, 9, 4, 9, 4],\n [4, 9, 4, 9, 6, 9, 6, 4, 5, 5, 5, 5, 7, 8, 8, 5, 5, 8, 8, 7, 5, 5, 5, 5, 4, 6, 9, 6, 9, 4],\n [7, 8, 5, 8, 7, 8, 7, 5, 7, 5, 8, 8, 7, 7, 7, 8, 8, 7, 7, 7, 8, 8, 5, 7, 5, 7, 8, 7, 8, 5],\n [8, 5, 7, 8, 7, 8, 8, 5, 5, 7, 8, 8, 5, 8, 5, 7, 7, 5, 8, 5, 8, 8, 7, 5, 5, 8, 8, 7, 8, 7],\n [5, 7, 7, 7, 8, 5, 8, 5, 8, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 5, 7, 8, 8, 8, 5, 8, 5, 8, 7, 7],\n [8, 8, 7, 5, 5, 5, 8, 5, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 7, 7, 5, 7, 8, 8, 5, 8, 5, 5, 5, 7],\n [7, 7, 8, 5, 8, 8, 8, 7, 7, 5, 5, 7, 5, 7, 7, 7, 7, 7, 7, 5, 7, 5, 5, 7, 7, 8, 8, 8, 5, 8],\n [8, 8, 5, 5, 8, 7, 8, 8, 7, 8, 7, 7, 7, 5, 7, 8, 8, 7, 5, 7, 7, 7, 8, 7, 8, 8, 7, 8, 5, 5],\n [7, 8, 8, 8, 8, 8, 5, 8, 7, 5, 7, 7, 7, 7, 8, 7, 7, 8, 7, 7, 7, 7, 5, 7, 8, 5, 8, 8, 8, 8],\n [5, 5, 5, 5, 7, 8, 8, 5, 8, 7, 7, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7, 7, 7, 8, 5, 8, 8, 7, 5, 5],\n [5, 5, 5, 5, 7, 8, 8, 5, 8, 7, 7, 7, 7, 8, 7, 8, 8, 7, 8, 7, 7, 7, 7, 8, 5, 8, 8, 7, 5, 5],\n [7, 8, 8, 8, 8, 8, 5, 8, 7, 5, 7, 7, 7, 7, 8, 7, 7, 8, 7, 7, 7, 7, 5, 7, 8, 5, 8, 8, 8, 8],\n [8, 8, 5, 5, 8, 7, 8, 8, 7, 8, 7, 7, 7, 5, 7, 8, 8, 7, 5, 7, 7, 7, 8, 7, 8, 8, 7, 8, 5, 5],\n [7, 7, 8, 5, 8, 8, 8, 7, 7, 5, 5, 7, 5, 7, 7, 7, 7, 7, 7, 5, 7, 5, 5, 7, 7, 8, 8, 8, 5, 8],\n [8, 8, 7, 5, 5, 5, 8, 5, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 7, 7, 5, 7, 8, 8, 5, 8, 5, 5, 5, 7],\n [5, 7, 7, 7, 8, 5, 8, 5, 8, 8, 8, 7, 5, 7, 7, 7, 7, 7, 7, 5, 7, 8, 8, 8, 5, 8, 5, 8, 7, 7],\n [8, 5, 7, 8, 7, 8, 8, 5, 5, 7, 8, 8, 5, 8, 5, 7, 7, 5, 8, 5, 8, 8, 7, 5, 5, 8, 8, 7, 8, 7],\n [7, 8, 5, 8, 7, 8, 7, 5, 7, 5, 8, 8, 7, 7, 7, 8, 8, 7, 7, 7, 8, 8, 5, 7, 5, 7, 8, 7, 8, 5],\n [4, 9, 4, 9, 6, 9, 6, 4, 5, 5, 5, 5, 7, 8, 8, 5, 5, 8, 8, 7, 5, 5, 5, 5, 4, 6, 9, 6, 9, 4],\n [9, 9, 4, 9, 4, 9, 4, 6, 7, 8, 8, 8, 8, 8, 5, 8, 8, 5, 8, 8, 8, 8, 8, 7, 6, 4, 9, 4, 9, 4],\n [9, 9, 9, 6, 6, 6, 9, 9, 8, 8, 5, 5, 8, 7, 8, 8, 8, 8, 7, 8, 5, 5, 8, 8, 9, 9, 6, 6, 6, 9],\n [9, 9, 4, 6, 6, 6, 4, 6, 7, 7, 8, 5, 8, 8, 8, 7, 7, 8, 8, 8, 5, 8, 7, 7, 6, 4, 6, 6, 6, 4],\n [9, 4, 9, 4, 6, 6, 9, 9, 8, 8, 7, 5, 5, 5, 8, 5, 5, 8, 5, 5, 5, 7, 8, 8, 9, 9, 6, 6, 4, 9],\n [9, 9, 6, 9, 4, 9, 4, 4, 5, 7, 7, 7, 8, 5, 8, 5, 5, 8, 5, 8, 7, 7, 7, 5, 4, 4, 9, 4, 9, 6]\n ]\n}\n\n{\n \"input\": [\n [6, 4, 4, 6, 3, 6, 4, 4, 8, 6, 8, 6, 8, 8, 4, 8, 8, 4, 8, 8, 6, 8, 6, 8, 4, 4, 6, 3, 6, 4],\n [4, 6, 6, 6, 3, 4, 4, 3, 6, 4, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 4, 6, 3, 4, 4, 3, 6, 6],\n [4, 6, 4, 6, 3, 4, 6, 4, 8, 8, 8, 4, 4, 4, 4, 6, 6, 4, 4, 4, 4, 8, 8, 8, 4, 6, 4, 3, 6, 4],\n [6, 6, 6, 4, 3, 3, 6, 6, 6, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 6, 6, 6, 3, 3, 4, 6],\n [3, 3, 3, 3, 6, 6, 4, 4, 8, 8, 4, 8, 6, 8, 8, 6, 6, 8, 8, 6, 8, 4, 8, 8, 4, 4, 6, 6, 3, 3],\n [0, 0, 0, 3, 6, 4, 6, 4, 8, 4, 4, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 4, 4, 8, 4, 6, 4, 6, 3, 4],\n [0, 0, 0, 6, 4, 6, 4, 6, 4, 8, 4, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 4, 8, 4, 6, 4, 6, 4, 6, 6],\n [0, 0, 0, 6, 4, 4, 6, 3, 8, 8, 6, 8, 6, 8, 8, 4, 0, 0, 8, 6, 8, 6, 8, 8, 3, 6, 4, 4, 6, 4],\n [8, 6, 8, 6, 8, 8, 4, 8, 1, 6, 6, 1, 5, 5, 6, 1, 0, 0, 5, 5, 1, 6, 6, 1, 8, 4, 8, 8, 6, 8],\n [6, 4, 8, 8, 8, 4, 8, 8, 6, 1, 6, 5, 1, 1, 5, 6, 0, 0, 1, 1, 5, 6, 1, 6, 8, 8, 4, 8, 8, 8],\n [8, 8, 8, 4, 4, 4, 4, 6, 6, 6, 5, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 5, 6, 6, 6, 4, 4, 4, 4, 8],\n [6, 8, 4, 8, 8, 8, 8, 8, 1, 5, 6, 5, 6, 1, 6, 1, 1, 6, 1, 6, 5, 6, 5, 1, 8, 8, 8, 8, 8, 4],\n [8, 8, 4, 8, 6, 8, 8, 6, 5, 1, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 1, 5, 6, 8, 8, 6, 8, 4],\n [8, 4, 4, 8, 8, 8, 8, 8, 5, 1, 6, 1, 6, 1, 5, 6, 6, 5, 1, 6, 1, 6, 1, 5, 8, 8, 8, 8, 8, 4],\n [4, 8, 4, 8, 8, 8, 8, 8, 6, 5, 1, 6, 6, 5, 1, 6, 6, 1, 5, 6, 6, 1, 5, 6, 8, 8, 8, 8, 8, 4],\n [8, 8, 6, 8, 6, 8, 8, 4, 1, 6, 1, 1, 5, 6, 6, 1, 1, 6, 6, 5, 1, 1, 6, 1, 4, 8, 8, 6, 8, 6],\n [8, 8, 6, 8, 6, 8, 8, 4, 1, 6, 1, 1, 5, 6, 6, 1, 1, 6, 6, 5, 1, 1, 6, 1, 4, 8, 8, 6, 8, 6],\n [4, 8, 4, 8, 8, 8, 8, 8, 6, 5, 1, 6, 6, 5, 1, 6, 6, 1, 5, 6, 6, 1, 5, 6, 8, 8, 8, 8, 8, 4],\n [8, 4, 4, 8, 8, 8, 8, 8, 5, 1, 6, 1, 6, 1, 5, 6, 6, 5, 1, 6, 1, 6, 1, 5, 8, 8, 8, 8, 8, 4],\n [8, 8, 4, 8, 6, 8, 8, 6, 5, 1, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 1, 5, 6, 8, 8, 6, 8, 4],\n [6, 8, 4, 8, 8, 8, 8, 8, 1, 5, 6, 5, 6, 1, 6, 1, 1, 6, 1, 6, 5, 6, 5, 1, 8, 8, 8, 8, 8, 4],\n [8, 8, 8, 4, 4, 4, 4, 6, 6, 6, 5, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 5, 6, 6, 6, 4, 4, 4, 4, 8],\n [6, 4, 8, 8, 8, 4, 8, 8, 6, 1, 6, 5, 1, 1, 5, 6, 6, 5, 1, 1, 5, 6, 1, 6, 8, 8, 4, 8, 8, 8],\n [8, 6, 8, 6, 8, 8, 4, 8, 1, 6, 6, 1, 5, 5, 6, 1, 1, 6, 5, 5, 1, 6, 6, 1, 8, 4, 8, 8, 6, 8],\n [4, 3, 4, 6, 4, 4, 6, 3, 8, 8, 6, 8, 6, 8, 8, 4, 4, 8, 8, 6, 8, 6, 8, 8, 3, 6, 0, 0, 0, 4],\n [4, 4, 6, 6, 4, 6, 4, 6, 4, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 4, 6, 4, 0, 0, 0, 6],\n [6, 4, 4, 3, 6, 4, 6, 4, 8, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 8, 4, 6, 0, 0, 0, 4],\n [3, 3, 3, 3, 6, 6, 4, 4, 8, 8, 4, 8, 6, 8, 8, 6, 6, 8, 8, 6, 8, 4, 8, 8, 4, 4, 0, 0, 0, 3],\n [6, 6, 6, 4, 3, 3, 6, 6, 6, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 6, 6, 6, 0, 0, 0, 6],\n [4, 6, 4, 6, 3, 4, 6, 4, 8, 8, 8, 4, 4, 4, 4, 6, 6, 4, 4, 4, 4, 8, 8, 8, 4, 6, 4, 3, 6, 4]\n ],\n \"output\": [\n [6, 4, 4, 6, 3, 6, 4, 4, 8, 6, 8, 6, 8, 8, 4, 8, 8, 4, 8, 8, 6, 8, 6, 8, 4, 4, 6, 3, 6, 4],\n [4, 6, 6, 6, 3, 4, 4, 3, 6, 4, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 4, 6, 3, 4, 4, 3, 6, 6],\n [4, 6, 4, 6, 3, 4, 6, 4, 8, 8, 8, 4, 4, 4, 4, 6, 6, 4, 4, 4, 4, 8, 8, 8, 4, 6, 4, 3, 6, 4],\n [6, 6, 6, 4, 3, 3, 6, 6, 6, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 6, 6, 6, 3, 3, 4, 6],\n [3, 3, 3, 3, 6, 6, 4, 4, 8, 8, 4, 8, 6, 8, 8, 6, 6, 8, 8, 6, 8, 4, 8, 8, 4, 4, 6, 6, 3, 3],\n [6, 4, 4, 3, 6, 4, 6, 4, 8, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 8, 4, 6, 4, 6, 3, 4],\n [4, 4, 6, 6, 4, 6, 4, 6, 4, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 4, 6, 4, 6, 4, 6, 6],\n [4, 3, 4, 6, 4, 4, 6, 3, 8, 8, 6, 8, 6, 8, 8, 4, 4, 8, 8, 6, 8, 6, 8, 8, 3, 6, 4, 4, 6, 4],\n [8, 6, 8, 6, 8, 8, 4, 8, 1, 6, 6, 1, 5, 5, 6, 1, 1, 6, 5, 5, 1, 6, 6, 1, 8, 4, 8, 8, 6, 8],\n [6, 4, 8, 8, 8, 4, 8, 8, 6, 1, 6, 5, 1, 1, 5, 6, 6, 5, 1, 1, 5, 6, 1, 6, 8, 8, 4, 8, 8, 8],\n [8, 8, 8, 4, 4, 4, 4, 6, 6, 6, 5, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 5, 6, 6, 6, 4, 4, 4, 4, 8],\n [6, 8, 4, 8, 8, 8, 8, 8, 1, 5, 6, 5, 6, 1, 6, 1, 1, 6, 1, 6, 5, 6, 5, 1, 8, 8, 8, 8, 8, 4],\n [8, 8, 4, 8, 6, 8, 8, 6, 5, 1, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 1, 5, 6, 8, 8, 6, 8, 4],\n [8, 4, 4, 8, 8, 8, 8, 8, 5, 1, 6, 1, 6, 1, 5, 6, 6, 5, 1, 6, 1, 6, 1, 5, 8, 8, 8, 8, 8, 4],\n [4, 8, 4, 8, 8, 8, 8, 8, 6, 5, 1, 6, 6, 5, 1, 6, 6, 1, 5, 6, 6, 1, 5, 6, 8, 8, 8, 8, 8, 4],\n [8, 8, 6, 8, 6, 8, 8, 4, 1, 6, 1, 1, 5, 6, 6, 1, 1, 6, 6, 5, 1, 1, 6, 1, 4, 8, 8, 6, 8, 6],\n [8, 8, 6, 8, 6, 8, 8, 4, 1, 6, 1, 1, 5, 6, 6, 1, 1, 6, 6, 5, 1, 1, 6, 1, 4, 8, 8, 6, 8, 6],\n [4, 8, 4, 8, 8, 8, 8, 8, 6, 5, 1, 6, 6, 5, 1, 6, 6, 1, 5, 6, 6, 1, 5, 6, 8, 8, 8, 8, 8, 4],\n [8, 4, 4, 8, 8, 8, 8, 8, 5, 1, 6, 1, 6, 1, 5, 6, 6, 5, 1, 6, 1, 6, 1, 5, 8, 8, 8, 8, 8, 4],\n [8, 8, 4, 8, 6, 8, 8, 6, 5, 1, 6, 6, 6, 6, 6, 5, 5, 6, 6, 6, 6, 6, 1, 5, 6, 8, 8, 6, 8, 4],\n [6, 8, 4, 8, 8, 8, 8, 8, 1, 5, 6, 5, 6, 1, 6, 1, 1, 6, 1, 6, 5, 6, 5, 1, 8, 8, 8, 8, 8, 4],\n [8, 8, 8, 4, 4, 4, 4, 6, 6, 6, 5, 6, 6, 6, 1, 1, 1, 1, 6, 6, 6, 5, 6, 6, 6, 4, 4, 4, 4, 8],\n [6, 4, 8, 8, 8, 4, 8, 8, 6, 1, 6, 5, 1, 1, 5, 6, 6, 5, 1, 1, 5, 6, 1, 6, 8, 8, 4, 8, 8, 8],\n [8, 6, 8, 6, 8, 8, 4, 8, 1, 6, 6, 1, 5, 5, 6, 1, 1, 6, 5, 5, 1, 6, 6, 1, 8, 4, 8, 8, 6, 8],\n [4, 3, 4, 6, 4, 4, 6, 3, 8, 8, 6, 8, 6, 8, 8, 4, 4, 8, 8, 6, 8, 6, 8, 8, 3, 6, 4, 4, 6, 4],\n [4, 4, 6, 6, 4, 6, 4, 6, 4, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 4, 6, 4, 6, 4, 6, 6],\n [6, 4, 4, 3, 6, 4, 6, 4, 8, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 8, 4, 6, 4, 6, 3, 4],\n [3, 3, 3, 3, 6, 6, 4, 4, 8, 8, 4, 8, 6, 8, 8, 6, 6, 8, 8, 6, 8, 4, 8, 8, 4, 4, 6, 6, 3, 3],\n [6, 6, 6, 4, 3, 3, 6, 6, 6, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 6, 6, 6, 3, 3, 4, 6],\n [4, 6, 4, 6, 3, 4, 6, 4, 8, 8, 8, 4, 4, 4, 4, 6, 6, 4, 4, 4, 4, 8, 8, 8, 4, 6, 4, 3, 6, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 7, 8, 8, 7, 7, 8, 7, 9, 5, 5, 9, 9, 9, 5, 9, 9, 5, 9, 9, 9, 5, 5, 9, 7, 8, 7, 7, 8, 8],\n [7, 8, 8, 7, 7, 5, 7, 7, 5, 9, 9, 9, 5, 3, 9, 3, 3, 9, 3, 5, 9, 9, 9, 5, 7, 7, 5, 7, 7, 8],\n [8, 8, 5, 8, 7, 5, 7, 8, 5, 9, 3, 9, 3, 3, 9, 3, 3, 9, 3, 3, 9, 3, 9, 5, 8, 7, 5, 7, 8, 5],\n [8, 7, 8, 5, 7, 8, 8, 8, 9, 9, 9, 3, 5, 5, 9, 9, 9, 9, 5, 5, 3, 9, 9, 9, 8, 8, 8, 7, 5, 8],\n [7, 7, 7, 7, 5, 7, 8, 8, 9, 5, 3, 5, 3, 5, 9, 9, 9, 9, 5, 3, 5, 3, 5, 9, 8, 8, 7, 5, 7, 7],\n [7, 5, 5, 8, 7, 7, 8, 7, 9, 3, 3, 5, 5, 5, 3, 9, 9, 3, 5, 5, 5, 3, 3, 9, 7, 8, 7, 7, 8, 5],\n [8, 7, 7, 8, 8, 8, 8, 7, 5, 9, 9, 9, 9, 3, 9, 3, 3, 9, 3, 9, 9, 9, 9, 5, 7, 8, 8, 8, 8, 7],\n [7, 7, 8, 8, 8, 7, 7, 8, 9, 3, 3, 9, 9, 9, 3, 9, 9, 3, 9, 9, 9, 3, 3, 9, 8, 7, 7, 8, 8, 8],\n [9, 5, 5, 9, 9, 9, 5, 9, 3, 8, 8, 8, 8, 3, 7, 7, 7, 7, 3, 8, 8, 8, 8, 3, 9, 5, 9, 9, 9, 5],\n [5, 9, 9, 9, 5, 3, 9, 3, 8, 3, 8, 8, 7, 3, 8, 8, 8, 8, 3, 7, 8, 8, 3, 8, 3, 9, 3, 5, 9, 9],\n [5, 9, 3, 9, 3, 3, 9, 3, 8, 8, 7, 8, 7, 3, 7, 7, 7, 7, 3, 7, 8, 7, 8, 8, 3, 9, 3, 3, 9, 3],\n [9, 9, 9, 3, 5, 5, 9, 9, 8, 8, 8, 7, 8, 3, 3, 7, 7, 3, 3, 8, 7, 8, 8, 8, 9, 9, 5, 5, 3, 9],\n [9, 5, 3, 5, 3, 5, 9, 9, 8, 7, 7, 8, 3, 8, 8, 8, 8, 8, 8, 3, 8, 7, 7, 8, 9, 9, 5, 3, 5, 3],\n [9, 3, 3, 5, 5, 5, 3, 9, 3, 3, 3, 3, 8, 7, 3, 7, 7, 3, 7, 8, 3, 3, 3, 3, 9, 3, 5, 5, 5, 3],\n [5, 9, 9, 9, 9, 3, 9, 3, 7, 8, 7, 3, 8, 3, 8, 8, 8, 8, 3, 8, 3, 7, 8, 0, 0, 0, 0, 0, 9, 9],\n [9, 3, 3, 9, 9, 9, 3, 9, 7, 8, 7, 7, 8, 7, 8, 8, 8, 8, 7, 8, 7, 7, 8, 0, 0, 0, 0, 0, 9, 3],\n [9, 3, 3, 9, 9, 9, 3, 9, 7, 8, 7, 7, 8, 7, 8, 8, 8, 8, 7, 8, 7, 7, 8, 0, 0, 0, 0, 0, 9, 3],\n [5, 9, 9, 9, 9, 3, 9, 3, 7, 8, 7, 3, 8, 3, 8, 8, 8, 8, 3, 8, 3, 7, 8, 0, 0, 0, 0, 0, 9, 9],\n [9, 3, 3, 5, 5, 5, 3, 9, 3, 0, 0, 0, 0, 0, 3, 7, 7, 3, 7, 8, 3, 3, 3, 0, 0, 0, 0, 0, 5, 3],\n [9, 5, 3, 5, 3, 5, 9, 9, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 3, 8, 7, 7, 0, 0, 0, 0, 0, 5, 3],\n [9, 9, 9, 3, 5, 5, 9, 9, 8, 8, 8, 7, 8, 3, 3, 7, 7, 3, 3, 8, 7, 8, 8, 0, 0, 0, 0, 0, 3, 9],\n [5, 9, 3, 9, 3, 3, 9, 3, 8, 8, 7, 8, 7, 3, 7, 7, 7, 7, 3, 7, 8, 7, 8, 8, 3, 9, 3, 3, 9, 3],\n [5, 9, 9, 9, 5, 3, 9, 3, 8, 3, 8, 8, 7, 0, 0, 8, 8, 8, 3, 7, 8, 8, 3, 8, 3, 9, 3, 5, 9, 9],\n [9, 5, 5, 9, 9, 9, 5, 9, 3, 8, 8, 8, 8, 0, 0, 7, 7, 7, 3, 8, 8, 8, 8, 3, 9, 5, 9, 9, 9, 5],\n [7, 7, 8, 8, 8, 7, 7, 8, 9, 3, 3, 9, 9, 0, 0, 9, 9, 3, 9, 9, 9, 3, 3, 9, 8, 7, 7, 8, 8, 8],\n [8, 7, 7, 8, 8, 8, 8, 7, 5, 9, 9, 9, 9, 0, 0, 3, 3, 9, 3, 9, 9, 9, 9, 5, 7, 8, 8, 8, 8, 7],\n [7, 5, 5, 8, 7, 7, 8, 7, 9, 3, 3, 5, 5, 0, 0, 9, 9, 3, 5, 5, 5, 3, 3, 9, 7, 8, 7, 7, 8, 5],\n [7, 7, 7, 7, 5, 7, 8, 8, 9, 5, 3, 5, 3, 0, 0, 9, 9, 9, 5, 3, 5, 3, 5, 9, 8, 8, 7, 5, 7, 7],\n [8, 7, 8, 5, 7, 8, 8, 8, 9, 9, 9, 3, 5, 0, 0, 9, 9, 9, 5, 5, 3, 9, 9, 9, 8, 8, 8, 7, 5, 8],\n [8, 8, 5, 8, 7, 5, 7, 8, 5, 9, 3, 9, 3, 3, 9, 3, 3, 9, 3, 3, 9, 3, 9, 5, 8, 7, 5, 7, 8, 5]\n ],\n \"output\": [\n [8, 7, 8, 8, 7, 7, 8, 7, 9, 5, 5, 9, 9, 9, 5, 9, 9, 5, 9, 9, 9, 5, 5, 9, 7, 8, 7, 7, 8, 8],\n [7, 8, 8, 7, 7, 5, 7, 7, 5, 9, 9, 9, 5, 3, 9, 3, 3, 9, 3, 5, 9, 9, 9, 5, 7, 7, 5, 7, 7, 8],\n [8, 8, 5, 8, 7, 5, 7, 8, 5, 9, 3, 9, 3, 3, 9, 3, 3, 9, 3, 3, 9, 3, 9, 5, 8, 7, 5, 7, 8, 5],\n [8, 7, 8, 5, 7, 8, 8, 8, 9, 9, 9, 3, 5, 5, 9, 9, 9, 9, 5, 5, 3, 9, 9, 9, 8, 8, 8, 7, 5, 8],\n [7, 7, 7, 7, 5, 7, 8, 8, 9, 5, 3, 5, 3, 5, 9, 9, 9, 9, 5, 3, 5, 3, 5, 9, 8, 8, 7, 5, 7, 7],\n [7, 5, 5, 8, 7, 7, 8, 7, 9, 3, 3, 5, 5, 5, 3, 9, 9, 3, 5, 5, 5, 3, 3, 9, 7, 8, 7, 7, 8, 5],\n [8, 7, 7, 8, 8, 8, 8, 7, 5, 9, 9, 9, 9, 3, 9, 3, 3, 9, 3, 9, 9, 9, 9, 5, 7, 8, 8, 8, 8, 7],\n [7, 7, 8, 8, 8, 7, 7, 8, 9, 3, 3, 9, 9, 9, 3, 9, 9, 3, 9, 9, 9, 3, 3, 9, 8, 7, 7, 8, 8, 8],\n [9, 5, 5, 9, 9, 9, 5, 9, 3, 8, 8, 8, 8, 3, 7, 7, 7, 7, 3, 8, 8, 8, 8, 3, 9, 5, 9, 9, 9, 5],\n [5, 9, 9, 9, 5, 3, 9, 3, 8, 3, 8, 8, 7, 3, 8, 8, 8, 8, 3, 7, 8, 8, 3, 8, 3, 9, 3, 5, 9, 9],\n [5, 9, 3, 9, 3, 3, 9, 3, 8, 8, 7, 8, 7, 3, 7, 7, 7, 7, 3, 7, 8, 7, 8, 8, 3, 9, 3, 3, 9, 3],\n [9, 9, 9, 3, 5, 5, 9, 9, 8, 8, 8, 7, 8, 3, 3, 7, 7, 3, 3, 8, 7, 8, 8, 8, 9, 9, 5, 5, 3, 9],\n [9, 5, 3, 5, 3, 5, 9, 9, 8, 7, 7, 8, 3, 8, 8, 8, 8, 8, 8, 3, 8, 7, 7, 8, 9, 9, 5, 3, 5, 3],\n [9, 3, 3, 5, 5, 5, 3, 9, 3, 3, 3, 3, 8, 7, 3, 7, 7, 3, 7, 8, 3, 3, 3, 3, 9, 3, 5, 5, 5, 3],\n [5, 9, 9, 9, 9, 3, 9, 3, 7, 8, 7, 3, 8, 3, 8, 8, 8, 8, 3, 8, 3, 7, 8, 7, 3, 9, 3, 9, 9, 9],\n [9, 3, 3, 9, 9, 9, 3, 9, 7, 8, 7, 7, 8, 7, 8, 8, 8, 8, 7, 8, 7, 7, 8, 7, 9, 3, 9, 9, 9, 3],\n [9, 3, 3, 9, 9, 9, 3, 9, 7, 8, 7, 7, 8, 7, 8, 8, 8, 8, 7, 8, 7, 7, 8, 7, 9, 3, 9, 9, 9, 3],\n [5, 9, 9, 9, 9, 3, 9, 3, 7, 8, 7, 3, 8, 3, 8, 8, 8, 8, 3, 8, 3, 7, 8, 7, 3, 9, 3, 9, 9, 9],\n [9, 3, 3, 5, 5, 5, 3, 9, 3, 3, 3, 3, 8, 7, 3, 7, 7, 3, 7, 8, 3, 3, 3, 3, 9, 3, 5, 5, 5, 3],\n [9, 5, 3, 5, 3, 5, 9, 9, 8, 7, 7, 8, 3, 8, 8, 8, 8, 8, 8, 3, 8, 7, 7, 8, 9, 9, 5, 3, 5, 3],\n [9, 9, 9, 3, 5, 5, 9, 9, 8, 8, 8, 7, 8, 3, 3, 7, 7, 3, 3, 8, 7, 8, 8, 8, 9, 9, 5, 5, 3, 9],\n [5, 9, 3, 9, 3, 3, 9, 3, 8, 8, 7, 8, 7, 3, 7, 7, 7, 7, 3, 7, 8, 7, 8, 8, 3, 9, 3, 3, 9, 3],\n [5, 9, 9, 9, 5, 3, 9, 3, 8, 3, 8, 8, 7, 3, 8, 8, 8, 8, 3, 7, 8, 8, 3, 8, 3, 9, 3, 5, 9, 9],\n [9, 5, 5, 9, 9, 9, 5, 9, 3, 8, 8, 8, 8, 3, 7, 7, 7, 7, 3, 8, 8, 8, 8, 3, 9, 5, 9, 9, 9, 5],\n [7, 7, 8, 8, 8, 7, 7, 8, 9, 3, 3, 9, 9, 9, 3, 9, 9, 3, 9, 9, 9, 3, 3, 9, 8, 7, 7, 8, 8, 8],\n [8, 7, 7, 8, 8, 8, 8, 7, 5, 9, 9, 9, 9, 3, 9, 3, 3, 9, 3, 9, 9, 9, 9, 5, 7, 8, 8, 8, 8, 7],\n [7, 5, 5, 8, 7, 7, 8, 7, 9, 3, 3, 5, 5, 5, 3, 9, 9, 3, 5, 5, 5, 3, 3, 9, 7, 8, 7, 7, 8, 5],\n [7, 7, 7, 7, 5, 7, 8, 8, 9, 5, 3, 5, 3, 5, 9, 9, 9, 9, 5, 3, 5, 3, 5, 9, 8, 8, 7, 5, 7, 7],\n [8, 7, 8, 5, 7, 8, 8, 8, 9, 9, 9, 3, 5, 5, 9, 9, 9, 9, 5, 5, 3, 9, 9, 9, 8, 8, 8, 7, 5, 8],\n [8, 8, 5, 8, 7, 5, 7, 8, 5, 9, 3, 9, 3, 3, 9, 3, 3, 9, 3, 3, 9, 3, 9, 5, 8, 7, 5, 7, 8, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 5, 9, 8, 9, 5, 8, 5, 6, 5, 5, 2, 2, 5, 6, 6, 5, 2, 2, 5, 5, 6, 5, 8, 5, 9, 8, 9, 5],\n [8, 9, 9, 8, 8, 9, 8, 5, 6, 6, 5, 6, 2, 5, 2, 6, 6, 2, 5, 2, 6, 5, 6, 6, 5, 8, 9, 8, 8, 9],\n [5, 9, 9, 9, 9, 9, 8, 5, 5, 5, 2, 5, 6, 2, 2, 2, 2, 2, 2, 6, 5, 2, 5, 5, 5, 8, 9, 9, 9, 9],\n [9, 8, 9, 9, 9, 8, 9, 8, 5, 6, 5, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 5, 6, 5, 8, 9, 8, 9, 9, 9],\n [8, 8, 9, 9, 8, 8, 8, 9, 2, 2, 6, 6, 2, 5, 6, 2, 2, 6, 5, 2, 6, 6, 2, 2, 9, 8, 8, 8, 9, 9],\n [9, 9, 9, 8, 8, 5, 8, 9, 2, 5, 2, 2, 5, 2, 5, 6, 6, 5, 2, 5, 2, 2, 5, 2, 9, 8, 5, 8, 8, 9],\n [5, 8, 8, 9, 8, 8, 5, 5, 5, 2, 2, 2, 6, 5, 2, 6, 6, 2, 5, 6, 2, 2, 2, 5, 5, 5, 8, 8, 9, 8],\n [8, 5, 5, 8, 9, 9, 5, 9, 6, 6, 2, 2, 2, 6, 6, 6, 6, 6, 6, 2, 2, 2, 6, 6, 9, 5, 9, 9, 8, 5],\n [5, 6, 5, 5, 2, 2, 5, 6, 1, 7, 2, 2, 1, 1, 7, 2, 2, 7, 1, 1, 2, 2, 7, 1, 6, 5, 2, 2, 5, 5],\n [6, 6, 5, 6, 2, 5, 2, 6, 7, 1, 1, 7, 1, 1, 7, 2, 2, 7, 1, 1, 7, 1, 1, 7, 6, 2, 5, 2, 6, 5],\n [5, 5, 2, 5, 6, 2, 2, 2, 2, 1, 2, 7, 7, 1, 1, 7, 7, 1, 1, 7, 7, 2, 1, 2, 2, 2, 2, 6, 5, 2],\n [5, 6, 5, 6, 6, 2, 2, 2, 2, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 2, 2, 2, 2, 6, 6, 5],\n [2, 2, 6, 6, 2, 5, 6, 2, 1, 1, 7, 2, 1, 7, 7, 7, 7, 7, 7, 1, 2, 7, 1, 1, 2, 6, 5, 2, 6, 6],\n [2, 5, 2, 2, 5, 2, 5, 6, 1, 1, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 1, 1, 6, 5, 2, 5, 2, 2],\n [5, 2, 2, 2, 6, 5, 2, 6, 7, 7, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 7, 7, 6, 2, 5, 6, 2, 2],\n [6, 6, 2, 2, 2, 6, 6, 6, 2, 2, 7, 2, 7, 2, 2, 1, 1, 2, 2, 7, 2, 7, 2, 2, 6, 6, 6, 2, 2, 2],\n [6, 6, 2, 2, 2, 6, 6, 6, 2, 2, 7, 2, 7, 2, 2, 1, 1, 2, 2, 7, 2, 7, 2, 2, 6, 6, 6, 2, 2, 2],\n [5, 2, 2, 2, 6, 5, 2, 6, 7, 7, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 7, 7, 0, 0, 5, 6, 2, 2],\n [2, 5, 2, 2, 5, 2, 5, 6, 1, 1, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 1, 1, 0, 0, 2, 5, 2, 2],\n [2, 2, 6, 6, 2, 5, 6, 2, 1, 1, 7, 2, 1, 7, 7, 7, 7, 7, 7, 1, 2, 7, 1, 1, 0, 0, 5, 2, 6, 6],\n [5, 6, 5, 6, 6, 2, 2, 2, 2, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 2, 0, 0, 2, 6, 6, 5],\n [5, 5, 2, 5, 6, 2, 2, 2, 2, 1, 2, 7, 7, 1, 1, 7, 7, 1, 1, 7, 7, 2, 1, 2, 0, 0, 2, 6, 5, 2],\n [6, 6, 5, 6, 2, 5, 2, 6, 7, 1, 1, 7, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 5, 2, 6, 5],\n [5, 6, 5, 5, 2, 2, 5, 6, 1, 7, 2, 2, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 5, 5],\n [8, 5, 5, 8, 9, 9, 5, 9, 6, 6, 2, 2, 2, 6, 6, 6, 6, 6, 6, 2, 2, 2, 6, 6, 0, 0, 9, 9, 8, 5],\n [5, 8, 8, 9, 8, 8, 5, 5, 5, 2, 2, 2, 6, 5, 2, 6, 6, 2, 5, 6, 2, 2, 2, 5, 5, 5, 8, 8, 9, 8],\n [9, 9, 9, 8, 8, 5, 8, 9, 2, 5, 2, 2, 5, 2, 5, 6, 6, 5, 2, 5, 2, 2, 5, 2, 9, 8, 5, 8, 8, 9],\n [8, 8, 9, 9, 8, 8, 8, 9, 2, 2, 6, 6, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 9, 8, 8, 8, 9, 9],\n [9, 8, 9, 9, 9, 8, 9, 8, 5, 6, 5, 6, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 6, 5, 8, 9, 8, 9, 9, 9],\n [5, 9, 9, 9, 9, 9, 8, 5, 5, 5, 2, 5, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 8, 9, 9, 9, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 5, 9, 8, 9, 5, 8, 5, 6, 5, 5, 2, 2, 5, 6, 6, 5, 2, 2, 5, 5, 6, 5, 8, 5, 9, 8, 9, 5], [8, 9, 9, 8, 8, 9, 8, 5, 6, 6, 5, 6, 2, 5, 2, 6, 6, 2, 5, 2, 6, 5, 6, 6, 5, 8, 9, 8, 8, 9], [5, 9, 9, 9, 9, 9, 8, 5, 5, 5, 2, 5, 6, 2, 2, 2, 2, 2, 2, 6, 5, 2, 5, 5, 5, 8, 9, 9, 9, 9], [9, 8, 9, 9, 9, 8, 9, 8, 5, 6, 5, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 5, 6, 5, 8, 9, 8, 9, 9, 9], [8, 8, 9, 9, 8, 8, 8, 9, 2, 2, 6, 6, 2, 5, 6, 2, 2, 6, 5, 2, 6, 6, 2, 2, 9, 8, 8, 8, 9, 9], [9, 9, 9, 8, 8, 5, 8, 9, 2, 5, 2, 2, 5, 2, 5, 6, 6, 5, 2, 5, 2, 2, 5, 2, 9, 8, 5, 8, 8, 9], [5, 8, 8, 9, 8, 8, 5, 5, 5, 2, 2, 2, 6, 5, 2, 6, 6, 2, 5, 6, 2, 2, 2, 5, 5, 5, 8, 8, 9, 8], [8, 5, 5, 8, 9, 9, 5, 9, 6, 6, 2, 2, 2, 6, 6, 6, 6, 6, 6, 2, 2, 2, 6, 6, 9, 5, 9, 9, 8, 5], [5, 6, 5, 5, 2, 2, 5, 6, 1, 7, 2, 2, 1, 1, 7, 2, 2, 7, 1, 1, 2, 2, 7, 1, 6, 5, 2, 2, 5, 5], [6, 6, 5, 6, 2, 5, 2, 6, 7, 1, 1, 7, 1, 1, 7, 2, 2, 7, 1, 1, 7, 1, 1, 7, 6, 2, 5, 2, 6, 5], [5, 5, 2, 5, 6, 2, 2, 2, 2, 1, 2, 7, 7, 1, 1, 7, 7, 1, 1, 7, 7, 2, 1, 2, 2, 2, 2, 6, 5, 2], [5, 6, 5, 6, 6, 2, 2, 2, 2, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 2, 2, 2, 2, 6, 6, 5], [2, 2, 6, 6, 2, 5, 6, 2, 1, 1, 7, 2, 1, 7, 7, 7, 7, 7, 7, 1, 2, 7, 1, 1, 2, 6, 5, 2, 6, 6], [2, 5, 2, 2, 5, 2, 5, 6, 1, 1, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 1, 1, 6, 5, 2, 5, 2, 2], [5, 2, 2, 2, 6, 5, 2, 6, 7, 7, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 7, 7, 6, 2, 5, 6, 2, 2], [6, 6, 2, 2, 2, 6, 6, 6, 2, 2, 7, 2, 7, 2, 2, 1, 1, 2, 2, 7, 2, 7, 2, 2, 6, 6, 6, 2, 2, 2], [6, 6, 2, 2, 2, 6, 6, 6, 2, 2, 7, 2, 7, 2, 2, 1, 1, 2, 2, 7, 2, 7, 2, 2, 6, 6, 6, 2, 2, 2], [5, 2, 2, 2, 6, 5, 2, 6, 7, 7, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 7, 7, 6, 2, 5, 6, 2, 2], [2, 5, 2, 2, 5, 2, 5, 6, 1, 1, 1, 7, 7, 1, 1, 2, 2, 1, 1, 7, 7, 1, 1, 1, 6, 5, 2, 5, 2, 2], [2, 2, 6, 6, 2, 5, 6, 2, 1, 1, 7, 2, 1, 7, 7, 7, 7, 7, 7, 1, 2, 7, 1, 1, 2, 6, 5, 2, 6, 6], [5, 6, 5, 6, 6, 2, 2, 2, 2, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 2, 2, 2, 2, 6, 6, 5], [5, 5, 2, 5, 6, 2, 2, 2, 2, 1, 2, 7, 7, 1, 1, 7, 7, 1, 1, 7, 7, 2, 1, 2, 2, 2, 2, 6, 5, 2], [6, 6, 5, 6, 2, 5, 2, 6, 7, 1, 1, 7, 1, 1, 7, 2, 2, 7, 1, 1, 7, 1, 1, 7, 6, 2, 5, 2, 6, 5], [5, 6, 5, 5, 2, 2, 5, 6, 1, 7, 2, 2, 1, 1, 7, 2, 2, 7, 1, 1, 2, 2, 7, 1, 6, 5, 2, 2, 5, 5], [8, 5, 5, 8, 9, 9, 5, 9, 6, 6, 2, 2, 2, 6, 6, 6, 6, 6, 6, 2, 2, 2, 6, 6, 9, 5, 9, 9, 8, 5], [5, 8, 8, 9, 8, 8, 5, 5, 5, 2, 2, 2, 6, 5, 2, 6, 6, 2, 5, 6, 2, 2, 2, 5, 5, 5, 8, 8, 9, 8], [9, 9, 9, 8, 8, 5, 8, 9, 2, 5, 2, 2, 5, 2, 5, 6, 6, 5, 2, 5, 2, 2, 5, 2, 9, 8, 5, 8, 8, 9], [8, 8, 9, 9, 8, 8, 8, 9, 2, 2, 6, 6, 2, 5, 6, 2, 2, 6, 5, 2, 6, 6, 2, 2, 9, 8, 8, 8, 9, 9], [9, 8, 9, 9, 9, 8, 9, 8, 5, 6, 5, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 5, 6, 5, 8, 9, 8, 9, 9, 9], [5, 9, 9, 9, 9, 9, 8, 5, 5, 5, 2, 5, 6, 2, 2, 2, 2, 2, 2, 6, 5, 2, 5, 5, 5, 8, 9, 9, 9, 9]], "task_id": "af22c60d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 4, 5, 0, 0, 1, 0, 6, 5, 0, 0, 0],\n [9, 0, 4, 3, 0, 0, 9, 0, 4, 7, 9, 4, 6, 0, 2, 7, 0],\n [0, 7, 3, 0, 0, 0, 9, 0, 0, 9, 0, 0, 9, 9, 9, 5, 0],\n [0, 5, 5, 3, 0, 3, 0, 6, 0, 4, 7, 2, 3, 2, 0, 3, 0],\n [0, 8, 8, 0, 0, 0, 7, 0, 8, 8, 9, 0, 0, 6, 0, 0, 4],\n [0, 8, 8, 6, 4, 3, 1, 9, 8, 8, 0, 0, 0, 0, 0, 0, 7],\n [9, 0, 0, 9, 5, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1],\n [0, 2, 9, 9, 0, 0, 9, 0, 7, 1, 0, 0, 0, 9, 0, 0, 0],\n [0, 7, 0, 8, 8, 0, 4, 0, 6, 0, 8, 8, 9, 0, 0, 0, 0],\n [0, 2, 4, 8, 8, 0, 3, 0, 0, 6, 8, 8, 6, 5, 7, 9, 0],\n [0, 0, 9, 2, 0, 2, 0, 0, 0, 7, 9, 0, 0, 0, 5, 7, 1],\n [1, 0, 0, 3, 0, 1, 0, 4, 1, 4, 0, 0, 0, 0, 1, 0, 9],\n [1, 0, 6, 2, 1, 4, 6, 0, 0, 1, 9, 0, 3, 0, 1, 4, 0]\n ],\n \"output\": [\n [0, 0, 0, 7, 0],\n [6, 4, 3, 1, 9],\n [0, 4, 0, 6, 0],\n [0, 3, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 7, 9, 0, 7, 7, 0, 0, 1, 3, 0],\n [2, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 3, 5, 9, 1, 8, 8, 0, 2, 0],\n [0, 0, 0, 0, 8, 8, 1, 0, 0, 6, 8, 8, 3, 0, 0],\n [2, 0, 0, 0, 5, 0, 0, 0, 0, 0, 9, 2, 0, 0, 2],\n [0, 0, 9, 0, 4, 9, 9, 9, 0, 2, 9, 6, 1, 4, 0],\n [0, 0, 0, 0, 0, 0, 9, 4, 0, 0, 0, 0, 0, 0, 5],\n [1, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 5, 0, 6, 0],\n [2, 1, 0, 0, 6, 0, 6, 2, 7, 0, 4, 0, 0, 0, 7],\n [0, 9, 0, 0, 2, 0, 5, 0, 1, 0, 0, 0, 0, 5, 3],\n [4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n [3, 5, 9, 1],\n [1, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [9, 0, 0, 5, 0, 0, 0, 0, 4, 4],\n [9, 4, 0, 0, 0, 0, 0, 0, 5, 0],\n [2, 2, 0, 6, 0, 0, 5, 0, 5, 3],\n [2, 9, 0, 2, 6, 4, 0, 1, 0, 0],\n [0, 0, 2, 9, 0, 4, 9, 1, 1, 3],\n [8, 8, 1, 0, 9, 7, 7, 0, 8, 8],\n [8, 8, 4, 0, 0, 5, 6, 4, 8, 8],\n [0, 5, 9, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 5, 0, 0, 3, 0],\n [0, 9, 0, 0, 0, 0, 0, 7, 0, 9],\n [0, 0, 5, 1, 7, 0, 0, 0, 9, 9],\n [0, 0, 9, 0, 0, 1, 0, 0, 0, 7]\n ],\n \"output\": [\n [1, 0, 9, 7, 7, 0],\n [4, 0, 0, 5, 6, 4]\n ]\n}\n\n{\n \"input\": [\n [9, 2, 1, 5, 3, 4, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 8, 8, 3, 0, 7, 0, 7, 8, 8, 4, 0, 7, 2, 0, 0, 0],\n [1, 8, 8, 0, 2, 0, 0, 6, 8, 8, 0, 0, 0, 0, 0, 7, 0],\n [1, 0, 0, 0, 0, 4, 1, 3, 9, 1, 0, 7, 5, 9, 4, 7, 0],\n [0, 0, 3, 2, 2, 0, 2, 6, 0, 4, 9, 2, 4, 0, 3, 0, 5],\n [0, 6, 8, 8, 3, 0, 1, 9, 2, 8, 8, 0, 3, 0, 4, 0, 0],\n [0, 0, 8, 8, 0, 7, 9, 2, 9, 8, 8, 0, 9, 3, 0, 0, 9],\n [0, 0, 0, 4, 0, 7, 5, 7, 5, 0, 1, 3, 0, 2, 0, 0, 0],\n [0, 0, 9, 9, 3, 6, 4, 0, 4, 7, 2, 0, 9, 0, 0, 9, 0],\n [9, 1, 9, 0, 0, 7, 1, 5, 7, 1, 0, 5, 0, 5, 9, 6, 9],\n [0, 0, 3, 7, 2, 0, 8, 8, 9, 0, 0, 0, 0, 8, 8, 1, 0],\n [6, 7, 0, 4, 0, 4, 8, 8, 0, 4, 0, 2, 0, 8, 8, 5, 0]\n ],\n \"output\": [\n [3, 0, 7, 0, 7],\n [0, 2, 0, 0, 6],\n [3, 0, 1, 9, 2],\n [0, 7, 9, 2, 9],\n [9, 0, 0, 0, 0],\n [0, 4, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 7, 2, 7, 0, 2, 0, 0, 0, 4, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 6, 0, 0, 2, 0, 0, 7, 3, 1],\n [0, 0, 8, 8, 6, 5, 2, 8, 8, 1, 0, 2, 4, 5, 0, 0],\n [0, 0, 8, 8, 0, 0, 2, 8, 8, 0, 0, 7, 1, 0, 0, 7],\n [0, 0, 0, 0, 4, 0, 0, 0, 9, 0, 7, 0, 0, 0, 0, 0],\n [8, 8, 1, 3, 0, 8, 8, 0, 0, 0, 0, 9, 0, 3, 0, 1],\n [8, 8, 0, 0, 9, 8, 8, 0, 0, 0, 0, 0, 3, 0, 9, 2],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 9, 3, 4, 0, 0, 0, 0],\n [4, 0, 0, 9, 0, 9, 0, 0, 7, 3, 0, 6, 0, 4, 0, 5],\n [6, 0, 0, 0, 4, 0, 0, 3, 0, 0, 2, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 1, 2, 0, 4, 0, 0, 0, 0],\n [4, 5, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 5, 2, 0, 2],\n [0, 9, 0, 6, 0, 0, 0, 7, 2, 0, 9, 3, 0, 0, 0, 6]\n ],\n \"output\": [\n [6, 5, 2],\n [0, 0, 2],\n [1, 3, 0],\n [0, 0, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 6, 9, 0, 0, 0, 9, 0, 0, 7, 0, 9, 0, 0, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 9, 0, 0, 0, 0, 0, 2, 0, 1, 0, 5, 1],\n [2, 1, 0, 8, 8, 4, 1, 5, 0, 8, 8, 0, 1, 0, 4, 0, 0],\n [0, 7, 3, 8, 8, 0, 9, 0, 0, 8, 8, 0, 6, 0, 4, 7, 2],\n [2, 5, 0, 4, 0, 0, 0, 0, 7, 9, 0, 9, 5, 0, 4, 0, 1],\n [8, 8, 5, 9, 0, 4, 8, 8, 4, 0, 3, 7, 0, 0, 0, 0, 5],\n [8, 8, 7, 7, 0, 0, 8, 8, 6, 4, 7, 0, 6, 0, 0, 0, 4],\n [0, 6, 9, 0, 4, 0, 0, 3, 0, 9, 0, 3, 0, 0, 0, 3, 4],\n [0, 5, 2, 0, 0, 0, 0, 2, 9, 0, 0, 6, 0, 4, 5, 0, 0],\n [0, 7, 0, 3, 8, 8, 4, 5, 4, 3, 8, 8, 9, 5, 0, 3, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 7, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 3, 5, 0],\n [0, 9, 2, 0, 0, 0, 9, 8, 8, 0, 0, 6, 0, 8, 8, 0, 6],\n [0, 0, 0, 9, 0, 0, 0, 8, 8, 0, 7, 0, 4, 8, 8, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 1, 5, 0], [0, 9, 0, 0], [5, 9, 0, 4], [7, 7, 0, 0], [4, 5, 4, 3], [0, 0, 7, 0], [0, 0, 6, 0], [0, 7, 0, 4]], "task_id": "aab50785"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 3, 2, 2, 0, 0],\n [3, 3, 2, 2, 0, 0],\n [0, 0, 2, 2, 0, 0],\n [0, 0, 2, 2, 0, 0],\n [0, 0, 6, 6, 1, 1],\n [0, 0, 6, 6, 1, 1],\n [5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 3, 2, 0, 3, 2, 0, 3, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 1, 0, 6, 1, 0, 6, 1, 0, 6, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 3, 3, 6, 6],\n [1, 1, 3, 3, 6, 6],\n [0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 0, 0],\n [2, 2, 2, 2, 1, 1],\n [2, 2, 2, 2, 1, 1],\n [5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 6, 1, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 6, 1, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 1, 1],\n [1, 1, 0, 0, 1, 1],\n [0, 0, 2, 2, 0, 0],\n [0, 0, 2, 2, 0, 0],\n [3, 3, 2, 2, 0, 0],\n [3, 3, 2, 2, 0, 0],\n [5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 0, 3, 2, 0, 3, 2, 0, 3, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 1, 1],\n [1, 1, 0, 0, 1, 1],\n [0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 0, 0],\n [3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3],\n [5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 1, 0, 0],\n [0, 0, 1, 1, 0, 0],\n [3, 3, 0, 0, 3, 3],\n [3, 3, 0, 0, 3, 3],\n [0, 0, 2, 2, 0, 0],\n [0, 0, 2, 2, 0, 0],\n [5, 5, 5, 5, 5, 5],\n [2, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], [3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3], [0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "b4a43f3b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 4, 0, 5, 8, 0, 0, 2, 4],\n [4, 4, 0, 8, 8, 0, 0, 2, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 0, 9, 5, 0, 0, 3, 3],\n [9, 9, 0, 9, 5, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 4, 0, 4, 4, 0, 0, 8, 3],\n [2, 4, 0, 2, 2, 0, 0, 8, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 0, 9, 9],\n [1, 2, 0, 2, 1, 0, 0, 7, 9]\n ],\n \"output\": [\n [5, 8],\n [5, 8],\n [0, 0],\n [2, 3],\n [2, 3],\n [0, 0],\n [3, 3],\n [8, 8],\n [0, 0],\n [9, 9],\n [9, 7]\n ]\n}\n\n{\n \"input\": [\n [2, 4, 0, 4, 2, 0, 0, 8, 6],\n [4, 4, 0, 4, 4, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 2, 1, 0, 0, 5, 5],\n [2, 2, 0, 2, 1, 0, 0, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 7, 0, 8, 3, 0, 0, 3, 3],\n [3, 3, 0, 8, 8, 0, 0, 3, 7]\n ],\n \"output\": [\n [6, 8],\n [8, 8],\n [0, 0],\n [4, 5],\n [4, 5],\n [0, 0],\n [8, 8],\n [8, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 1, 0, 0, 4, 4],\n [2, 1, 0, 1, 2, 0, 0, 3, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 5, 2, 0, 0, 3, 3],\n [5, 5, 0, 5, 2, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 6, 8, 0, 0, 7, 7],\n [8, 8, 0, 6, 8, 0, 0, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4], [4, 3], [0, 0], [1, 3], [1, 3], [0, 0], [7, 4], [7, 4]], "task_id": "b0722778"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 8, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 8, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 6, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 6, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 8, 0, 0, 3, 0, 0, 0, 3, 0, 0, 8],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 3, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 3, 0, 2, 2, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 2, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 3, 8, 6, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 8, 3, 0, 6, 6],\n [0, 0, 0, 0, 7, 0, 8, 0, 0, 2, 2, 6],\n [7, 0, 0, 7, 0, 8, 0, 0, 0, 2, 2, 0],\n [0, 7, 7, 0, 8, 0, 0, 0, 8, 0, 0, 7],\n [0, 0, 2, 2, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [8, 0, 0, 7, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 8, 0, 0, 3, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 0, 0, 8, 8, 0, 0, 6, 0, 0],\n [0, 3, 0, 0, 8, 0, 0, 2, 2, 0, 0, 0],\n [6, 0, 3, 8, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 2, 2, 3, 0, 0, 3, 0, 0, 7, 0, 0],\n [0, 2, 2, 0, 3, 3, 0, 0, 0, 0, 7, 6],\n [3, 0, 0, 7, 3, 3, 0, 0, 0, 0, 6, 7],\n [0, 0, 0, 3, 7, 0, 3, 0, 0, 6, 0, 0],\n [0, 0, 3, 0, 0, 7, 0, 2, 2, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 7, 2, 2, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 8, 7, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 7, 0, 7, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 7, 0, 7]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 0, 7, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [6, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 7, 0],\n [0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 8, 0, 0, 3, 0]\n ],\n \"output\": [\n [6, 0, 0, 3, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [8, 0, 8, 7, 0, 0, 0, 6],\n [0, 0, 0, 8, 7, 0, 6, 0],\n [0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 3, 0, 0, 7, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 0, 0, 8, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 7, 0, 0, 6, 0, 0],\n [7, 0, 0, 6, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0],\n [8, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 7, 0, 0, 3, 0, 7], [0, 0, 0, 2, 2, 0, 7, 0], [0, 0, 0, 2, 2, 7, 0, 0], [0, 0, 6, 0, 7, 8, 0, 0], [8, 6, 0, 7, 0, 0, 8, 0], [6, 2, 2, 0, 0, 0, 0, 8], [0, 2, 2, 0, 0, 0, 0, 0], [3, 0, 0, 6, 0, 0, 0, 0]], "task_id": "85fa5666"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0],\n [6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0]], "task_id": "fd4b2b02"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 8, 0],\n [8, 8, 8, 0, 0, 0],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 0, 0],\n [8, 8, 8, 8, 8, 8],\n [0, 8, 8, 0, 8, 8],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 8, 0],\n [8, 8, 8, 8, 8, 0],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 0, 0],\n [8, 8, 8, 8, 0, 0],\n [8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [8, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 8, 0, 0],\n [8, 8, 8, 8, 0, 0],\n [8, 8, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 0, 8, 8], [8, 8, 0, 8, 8], [0, 0, 0, 0, 0], [8, 8, 0, 8, 8], [8, 8, 0, 8, 8]], "task_id": "b1fc8b8e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 7, 7, 0, 7, 7, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 8, 0],\n [0, 0, 8, 0, 0],\n [0, 8, 8, 8, 0],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 3, 0, 3, 0, 0],\n [4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [0, 1, 1, 0, 1, 1, 0],\n [1, 1, 0, 0, 0, 1, 1],\n [0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 6, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [6, 6, 0, 6, 6],\n [0, 6, 6, 6, 0],\n [0, 6, 0, 6, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0],\n [0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 0, 2, 0, 2, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 2, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0]], "task_id": "d56f2372"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [3, 2, 2, 3, 3, 3, 3, 8, 3, 3, 3, 3],\n [3, 2, 2, 3, 3, 3, 3, 8, 3, 3, 3, 3],\n [0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n [6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 4, 4, 3, 3, 3, 8, 3, 3],\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6],\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [8]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0],\n [1, 2, 2, 1, 6, 1, 1, 8, 8, 1, 1],\n [1, 2, 2, 1, 6, 1, 1, 8, 8, 1, 1],\n [1, 2, 2, 1, 6, 1, 1, 8, 8, 1, 1],\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0],\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0],\n [4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4],\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0],\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0],\n [0, 2, 2, 0, 6, 0, 0, 8, 8, 0, 0]\n ],\n \"output\": [\n [6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0],\n [2, 2, 2, 2, 3, 3, 2, 2, 5, 2, 2, 2],\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4],\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [1]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 0],\n [3, 3, 3],\n [0, 1, 0]\n ],\n \"output\": [\n [3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [1, 1, 1, 3, 3, 1, 1, 1, 1, 7, 1, 1, 1],\n [1, 1, 1, 3, 3, 1, 1, 1, 1, 7, 1, 1, 1],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 6, 6, 6],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 7, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7]], "task_id": "1a2e2828"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 7, 0, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 7, 7, 0],\n [0, 0, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 0, 7, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 0, 7, 7, 7, 0],\n [0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [7, 7, 7, 7, 7],\n [7, 0, 7, 7, 7],\n [7, 7, 7, 0, 7],\n [7, 0, 7, 7, 7],\n [7, 7, 7, 7, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8],\n [0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 8],\n [0, 8, 0, 8, 0, 8, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 0, 8],\n [0, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 8, 8],\n [0, 8, 0, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8],\n [0, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8],\n [8, 0, 8, 0, 8],\n [8, 8, 8, 8, 8],\n [8, 0, 8, 0, 8],\n [8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 6, 0, 0],\n [0, 6, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 6, 0, 6, 6, 6, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 0, 6, 6, 0, 6, 6, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6],\n [0, 6, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6],\n [0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6]\n ],\n \"output\": [\n [6, 6, 6, 6, 6],\n [6, 0, 6, 0, 6],\n [6, 6, 6, 6, 6],\n [6, 0, 6, 6, 6],\n [6, 6, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2],\n [2, 0, 2, 2, 2],\n [2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1],\n [1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1], [1, 0, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]], "task_id": "358ba94e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 2, 1, 1, 2, 2, 0, 0, 2, 1, 1, 2, 2, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 2, 1, 2, 1, 2, 0, 0, 2, 2, 1, 2, 2, 0, 0],\n [8, 8, 6, 8, 8, 8, 0, 0, 2, 1, 1, 2, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0],\n [8, 8, 8, 8, 5, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 8, 5, 5, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 5, 8, 5, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 4, 8, 8, 8, 8, 0, 0, 2, 1, 1, 1, 2, 0, 0, 2, 2, 1, 2, 2, 0, 0],\n [8, 4, 4, 4, 8, 8, 0, 0, 2, 2, 1, 2, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0],\n [8, 4, 8, 8, 8, 8, 0, 0, 2, 2, 1, 2, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [8, 8, 6, 8, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [8, 8, 8, 8, 5, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [8, 8, 5, 5, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 5, 8, 5, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [8, 4, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [8, 4, 4, 4, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [8, 4, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 4, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 8, 8, 2, 2, 8, 8],\n [0, 2, 2, 1, 2, 2, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 8, 2, 8, 8, 8, 8],\n [0, 2, 2, 1, 2, 2, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 1, 2, 1, 2, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 8, 8, 3, 3, 3, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 8, 8, 8, 3, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 1, 2, 2, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 8, 4, 4, 4, 8, 8],\n [0, 2, 2, 1, 1, 2, 0, 0, 2, 1, 1, 2, 2, 0, 0, 0, 8, 8, 4, 8, 8, 8],\n [0, 2, 2, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 8, 4, 8, 4, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 7, 7, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 7, 7, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 7, 8, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 2, 2, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 1, 1, 1, 2, 0, 0, 0, 8, 2, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 1, 2, 2, 2, 0, 0, 0, 8, 8, 3, 3, 3, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 3, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 7, 7, 7, 7, 7, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 7, 7, 7, 7, 7, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 8, 4, 4, 4, 8, 8],\n [0, 7, 7, 7, 7, 7, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 8, 8, 4, 8, 8, 8],\n [0, 7, 7, 7, 7, 7, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 8, 4, 8, 4, 8, 8],\n [0, 7, 7, 7, 7, 7, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 7, 7, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 7, 7, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 7, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [8, 1, 8, 1, 8, 8, 0, 0, 2, 1, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [8, 8, 1, 1, 8, 8, 0, 0, 2, 1, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 1, 2, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [8, 3, 3, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 3, 8, 8, 0, 0, 6, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 6, 6, 6, 6, 6, 0, 0, 2, 2, 1, 2, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 6, 6, 6, 6, 6, 0, 0, 2, 1, 1, 1, 2, 0, 0],\n [8, 6, 6, 6, 8, 8, 0, 0, 6, 6, 6, 6, 6, 0, 0, 2, 2, 1, 2, 2, 0, 0],\n [8, 8, 6, 8, 8, 8, 0, 0, 6, 6, 6, 6, 6, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 8, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 1, 8, 1, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 2, 1, 1, 2, 2, 0, 0],\n [8, 8, 1, 1, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 2, 1, 2, 1, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 1, 1, 2, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 2, 2, 2, 0, 0],\n [8, 3, 3, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 3, 3, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 1, 1, 1, 2, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 2, 2, 1, 2, 2, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [8, 6, 6, 6, 8, 8, 0, 0, 2, 1, 2, 1, 2, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [8, 8, 6, 8, 8, 8, 0, 0, 2, 2, 2, 2, 2, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [8, 6, 8, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 4, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 8, 8, 3, 8, 3, 8],\n [0, 2, 1, 1, 1, 2, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 8, 8, 3, 3, 3, 8],\n [0, 2, 2, 1, 2, 2, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 8, 8, 3, 8, 3, 8],\n [0, 2, 1, 1, 1, 2, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 8, 4, 8, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 4, 4, 8, 8, 8],\n [0, 2, 1, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 8, 8, 8, 8, 6, 8],\n [0, 2, 1, 2, 1, 2, 0, 0, 2, 1, 1, 2, 2, 0, 0, 0, 8, 8, 8, 6, 6, 6],\n [0, 2, 2, 1, 2, 2, 0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 8, 8, 8, 8, 6, 8],\n [0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 5, 8, 5, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 5, 5, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 5, 8, 5, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 3, 8, 3, 8], [0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 1, 2, 2, 0, 0, 0, 8, 8, 3, 3, 3, 8], [0, 3, 3, 3, 3, 3, 0, 0, 2, 1, 1, 1, 2, 0, 0, 0, 8, 8, 3, 8, 3, 8], [0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 1, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 3, 3, 3, 3, 3, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 8, 4, 8, 8], [0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 8, 4, 4, 8, 8, 8], [0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 8, 8, 8, 8, 6, 8], [0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 8, 8, 8, 6, 6, 6], [0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 8, 8, 8, 8, 6, 8], [0, 4, 4, 4, 4, 4, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 5, 8, 5, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 5, 5, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 5, 8, 5, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8]], "task_id": "b20f7c8b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1]], "task_id": "8ee62060"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 5, 2, 2, 2, 0],\n [1, 0, 0, 0, 5, 0, 2, 2, 2],\n [1, 1, 0, 0, 5, 0, 0, 2, 2],\n [1, 1, 1, 0, 5, 0, 0, 0, 2]\n ],\n \"output\": [\n [2, 2, 2, 1],\n [1, 2, 2, 2],\n [1, 1, 2, 2],\n [1, 1, 1, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 0, 5, 2, 2, 0, 0],\n [1, 1, 0, 0, 5, 0, 2, 2, 0],\n [1, 1, 1, 0, 5, 0, 2, 2, 0]\n ],\n \"output\": [\n [0, 0, 0, 1],\n [1, 0, 0, 0],\n [1, 1, 0, 0],\n [1, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 5, 0, 0, 3, 3],\n [1, 0, 0, 1, 5, 0, 3, 3, 0],\n [1, 0, 0, 1, 5, 0, 3, 3, 0],\n [1, 1, 0, 0, 5, 0, 0, 3, 3]\n ],\n \"output\": [\n [1, 1, 3, 3],\n [1, 3, 3, 1],\n [1, 3, 3, 1],\n [1, 1, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 0, 0, 0, 0],\n [1, 0, 0, 1, 5, 0, 6, 6, 0],\n [1, 0, 0, 1, 5, 0, 6, 6, 0],\n [1, 1, 1, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 6, 6, 1],\n [1, 6, 6, 1],\n [1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 1, 5, 0, 0, 0, 0],\n [1, 1, 1, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1],\n [1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 3, 3, 0, 0],\n [1, 0, 0, 1, 5, 3, 3, 0, 0],\n [1, 0, 0, 1, 5, 3, 0, 0, 0],\n [1, 0, 0, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 0, 0, 0, 0],\n [1, 0, 0, 0, 5, 0, 7, 7, 7],\n [1, 0, 1, 1, 5, 0, 7, 0, 0],\n [1, 0, 1, 0, 5, 0, 7, 0, 7]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 7, 7, 7],\n [1, 7, 1, 1],\n [1, 7, 1, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 5, 0, 0, 3, 3],\n [1, 0, 0, 1, 5, 0, 3, 3, 0],\n [0, 0, 0, 1, 5, 3, 3, 3, 0],\n [0, 1, 1, 1, 5, 3, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 3, 3], [1, 3, 3, 1], [3, 3, 3, 1], [3, 1, 1, 1]], "task_id": "bbb1b8b6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 8, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 8, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 8, 8, 8, 0, 0, 3, 3, 3, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 8, 8, 8, 0, 0, 3, 3, 3, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0, 0, 0, 8, 0, 0, 8, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 0, 0, 0, 8, 0, 0, 8, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 2, 0, 0], [3, 3, 3, 0, 0, 4, 4, 4, 0, 0, 0, 8, 8, 8, 0, 0, 8, 8, 8, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 4, 4, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "9b2a60aa"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 3, 2, 6, 3, 6, 8, 8, 8, 2, 3, 3, 3, 3, 3, 6, 3, 3, 8, 8, 1, 2, 2, 6, 3, 3, 2, 8, 1, 1],\n [8, 2, 1, 6, 3, 1, 8, 3, 1, 8, 3, 8, 8, 1, 3, 2, 8, 3, 8, 8, 3, 1, 3, 1, 8, 3, 2, 6, 2, 6],\n [1, 8, 3, 1, 8, 8, 8, 8, 8, 8, 1, 3, 2, 3, 3, 6, 6, 2, 3, 6, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3],\n [1, 2, 8, 1, 8, 8, 8, 8, 8, 8, 3, 8, 1, 2, 1, 1, 2, 3, 8, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 3],\n [1, 3, 8, 1, 8, 8, 8, 8, 8, 8, 3, 2, 3, 3, 8, 3, 1, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2, 6, 8, 3],\n [3, 3, 8, 2, 8, 8, 8, 8, 8, 8, 3, 3, 1, 3, 2, 3, 6, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 6, 2],\n [8, 2, 1, 1, 8, 8, 8, 8, 8, 8, 3, 3, 6, 1, 3, 1, 8, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 8],\n [1, 3, 3, 6, 8, 8, 8, 8, 8, 8, 8, 1, 2, 8, 2, 8, 2, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 8, 6],\n [3, 1, 3, 8, 3, 2, 3, 8, 1, 3, 1, 8, 1, 3, 1, 2, 3, 1, 8, 6, 2, 1, 3, 1, 1, 8, 3, 1, 6, 3],\n [2, 8, 6, 3, 1, 3, 8, 2, 1, 3, 2, 3, 3, 3, 3, 1, 8, 3, 3, 6, 2, 8, 2, 2, 6, 2, 1, 6, 2, 3],\n [8, 8, 2, 2, 3, 1, 1, 3, 2, 3, 3, 8, 2, 3, 3, 8, 8, 6, 6, 2, 1, 2, 6, 2, 3, 3, 3, 2, 6, 3],\n [6, 3, 2, 2, 8, 3, 2, 3, 3, 1, 3, 2, 2, 3, 2, 6, 3, 2, 2, 1, 1, 2, 1, 8, 6, 3, 2, 1, 8, 2],\n [8, 6, 2, 8, 2, 2, 2, 3, 3, 8, 1, 1, 3, 1, 6, 1, 3, 2, 8, 3, 8, 3, 3, 3, 3, 3, 3, 1, 8, 1],\n [8, 8, 2, 8, 8, 6, 8, 6, 3, 8, 6, 1, 3, 2, 8, 3, 6, 6, 2, 6, 3, 8, 3, 3, 3, 3, 3, 3, 8, 1],\n [1, 8, 2, 6, 2, 8, 1, 3, 6, 3, 8, 2, 2, 3, 6, 1, 6, 2, 8, 3, 8, 3, 3, 3, 3, 3, 3, 2, 2, 3],\n [3, 2, 8, 1, 1, 3, 2, 2, 2, 6, 8, 3, 8, 8, 1, 2, 8, 6, 1, 3, 1, 2, 3, 3, 3, 3, 3, 2, 2, 2],\n [8, 3, 8, 1, 2, 3, 8, 6, 3, 3, 3, 1, 6, 3, 1, 2, 1, 3, 2, 3, 2, 8, 3, 3, 3, 3, 3, 8, 3, 3],\n [6, 2, 3, 8, 6, 2, 2, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 6, 6, 2, 1, 6, 3, 1, 6, 8, 3, 1, 2, 3],\n [2, 1, 2, 1, 8, 2, 3, 2, 6, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 6, 2, 1, 2, 2, 2, 3, 3, 1],\n [1, 8, 8, 2, 8, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 8, 2, 3, 2, 3, 6, 6, 2, 3, 3, 3, 6, 3],\n [2, 6, 8, 3, 6, 1, 3, 8, 3, 6, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 1, 6, 3, 3, 6, 1, 3, 2, 2],\n [6, 8, 6, 2, 3, 2, 6, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 8, 1, 6, 3, 3, 3, 8, 1, 8, 2, 3],\n [6, 3, 1, 3, 6, 6, 1, 6, 3, 8, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 6, 3, 3, 8, 1, 8, 3, 8, 2, 1],\n [3, 2, 2, 3, 1, 1, 2, 3, 8, 6, 1, 3, 3, 1, 8, 3, 1, 8, 8, 3, 8, 3, 1, 8, 8, 1, 1, 2, 1, 8],\n [3, 2, 3, 6, 1, 8, 3, 6, 3, 3, 2, 2, 1, 3, 6, 3, 2, 3, 8, 3, 8, 3, 2, 2, 2, 2, 3, 3, 1, 6],\n [2, 8, 6, 2, 2, 1, 8, 3, 1, 6, 8, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 2, 8, 6, 8, 3, 6, 1, 3],\n [6, 2, 3, 2, 3, 3, 8, 3, 3, 6, 2, 2, 3, 3, 8, 8, 1, 3, 1, 2, 8, 3, 8, 3, 3, 3, 6, 1, 2, 2],\n [2, 3, 2, 1, 2, 6, 3, 1, 8, 3, 1, 6, 2, 3, 8, 2, 6, 1, 1, 1, 3, 6, 8, 1, 2, 8, 6, 2, 3, 2],\n [2, 1, 8, 2, 6, 3, 8, 2, 3, 6, 8, 8, 2, 8, 8, 3, 2, 3, 1, 6, 8, 2, 6, 3, 2, 3, 2, 1, 8, 3],\n [1, 6, 3, 1, 6, 6, 3, 1, 2, 8, 8, 1, 8, 1, 3, 3, 1, 2, 6, 8, 3, 1, 6, 8, 3, 8, 3, 1, 1, 8]\n ],\n \"output\": [\n [3, 3, 2, 6, 3, 6, 8, 8, 8, 2, 3, 3, 3, 3, 3, 6, 3, 3, 8, 8, 1, 2, 2, 6, 3, 3, 2, 8, 1, 1],\n [8, 2, 1, 6, 3, 1, 8, 3, 1, 8, 3, 8, 8, 1, 3, 2, 8, 3, 8, 8, 3, 1, 3, 1, 8, 3, 2, 6, 2, 6],\n [1, 8, 3, 1, 4, 4, 4, 4, 4, 4, 1, 3, 2, 3, 3, 6, 6, 2, 3, 6, 4, 4, 4, 4, 4, 4, 4, 3, 2, 3],\n [1, 2, 8, 1, 4, 4, 4, 4, 4, 4, 3, 8, 1, 2, 1, 1, 2, 3, 8, 3, 4, 4, 4, 4, 4, 4, 4, 2, 1, 3],\n [1, 3, 8, 1, 4, 4, 4, 4, 4, 4, 3, 2, 3, 3, 8, 3, 1, 1, 3, 3, 4, 4, 4, 4, 4, 4, 4, 6, 8, 3],\n [3, 3, 8, 2, 4, 4, 4, 4, 4, 4, 3, 3, 1, 3, 2, 3, 6, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 3, 6, 2],\n [8, 2, 1, 1, 4, 4, 4, 4, 4, 4, 3, 3, 6, 1, 3, 1, 8, 1, 2, 1, 4, 4, 4, 4, 4, 4, 4, 2, 3, 8],\n [1, 3, 3, 6, 4, 4, 4, 4, 4, 4, 8, 1, 2, 8, 2, 8, 2, 1, 3, 3, 4, 4, 4, 4, 4, 4, 4, 2, 8, 6],\n [3, 1, 3, 8, 3, 2, 3, 8, 1, 3, 1, 8, 1, 3, 1, 2, 3, 1, 8, 6, 2, 1, 3, 1, 1, 8, 3, 1, 6, 3],\n [2, 8, 6, 3, 1, 3, 8, 2, 1, 3, 2, 3, 3, 3, 3, 1, 8, 3, 3, 6, 2, 8, 2, 2, 6, 2, 1, 6, 2, 3],\n [8, 8, 2, 2, 3, 1, 1, 3, 2, 3, 3, 8, 2, 3, 3, 8, 8, 6, 6, 2, 1, 2, 6, 2, 3, 3, 3, 2, 6, 3],\n [6, 3, 2, 2, 8, 3, 2, 3, 3, 1, 3, 2, 2, 3, 2, 6, 3, 2, 2, 1, 1, 2, 1, 8, 6, 3, 2, 1, 8, 2],\n [8, 6, 2, 8, 2, 2, 2, 3, 3, 8, 1, 1, 3, 1, 6, 1, 3, 2, 8, 3, 8, 3, 4, 4, 4, 4, 4, 1, 8, 1],\n [8, 8, 2, 8, 8, 6, 8, 6, 3, 8, 6, 1, 3, 2, 8, 3, 6, 6, 2, 6, 3, 8, 4, 4, 4, 4, 4, 3, 8, 1],\n [1, 8, 2, 6, 2, 8, 1, 3, 6, 3, 8, 2, 2, 3, 6, 1, 6, 2, 8, 3, 8, 3, 4, 4, 4, 4, 4, 2, 2, 3],\n [3, 2, 8, 1, 1, 3, 2, 2, 2, 6, 8, 3, 8, 8, 1, 2, 8, 6, 1, 3, 1, 2, 4, 4, 4, 4, 4, 2, 2, 2],\n [8, 3, 8, 1, 2, 3, 8, 6, 3, 3, 3, 1, 6, 3, 1, 2, 1, 3, 2, 3, 2, 8, 4, 4, 4, 4, 4, 8, 3, 3],\n [6, 2, 3, 8, 6, 2, 2, 1, 8, 8, 4, 4, 4, 4, 4, 4, 4, 6, 6, 2, 1, 6, 3, 1, 6, 8, 3, 1, 2, 3],\n [2, 1, 2, 1, 8, 2, 3, 2, 6, 8, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 3, 6, 2, 1, 2, 2, 2, 3, 3, 1],\n [1, 8, 8, 2, 8, 2, 2, 2, 3, 1, 4, 4, 4, 4, 4, 4, 4, 8, 2, 3, 2, 3, 6, 6, 2, 3, 3, 3, 6, 3],\n [2, 6, 8, 3, 6, 1, 3, 8, 3, 6, 4, 4, 4, 4, 4, 4, 4, 2, 3, 3, 3, 1, 6, 3, 3, 6, 1, 3, 2, 2],\n [6, 8, 6, 2, 3, 2, 6, 3, 3, 1, 4, 4, 4, 4, 4, 4, 4, 3, 3, 8, 1, 6, 3, 3, 3, 8, 1, 8, 2, 3],\n [6, 3, 1, 3, 6, 6, 1, 6, 3, 8, 4, 4, 4, 4, 4, 4, 4, 1, 2, 1, 6, 3, 3, 8, 1, 8, 3, 8, 2, 1],\n [3, 2, 2, 3, 1, 1, 2, 3, 8, 6, 1, 3, 3, 1, 8, 3, 1, 8, 8, 3, 8, 3, 1, 8, 8, 1, 1, 2, 1, 8],\n [3, 2, 3, 6, 1, 8, 3, 6, 3, 3, 2, 2, 1, 3, 6, 3, 2, 3, 8, 3, 8, 3, 2, 2, 2, 2, 3, 3, 1, 6],\n [2, 8, 6, 2, 2, 1, 8, 3, 1, 6, 8, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 2, 8, 6, 8, 3, 6, 1, 3],\n [6, 2, 3, 2, 3, 3, 8, 3, 3, 6, 2, 2, 3, 3, 8, 8, 1, 3, 1, 2, 8, 3, 8, 3, 3, 3, 6, 1, 2, 2],\n [2, 3, 2, 1, 2, 6, 3, 1, 8, 3, 1, 6, 2, 3, 8, 2, 6, 1, 1, 1, 3, 6, 8, 1, 2, 8, 6, 2, 3, 2],\n [2, 1, 8, 2, 6, 3, 8, 2, 3, 6, 8, 8, 2, 8, 8, 3, 2, 3, 1, 6, 8, 2, 6, 3, 2, 3, 2, 1, 8, 3],\n [1, 6, 3, 1, 6, 6, 3, 1, 2, 8, 8, 1, 8, 1, 3, 3, 1, 2, 6, 8, 3, 1, 6, 8, 3, 8, 3, 1, 1, 8]\n ]\n}\n\n{\n \"input\": [\n [6, 8, 6, 8, 8, 8, 8, 6, 6, 3, 8, 6, 2, 3, 8, 1, 2, 8, 1, 3, 8, 3, 3, 6, 6, 1, 2, 6, 2, 2],\n [2, 3, 3, 2, 2, 3, 6, 2, 6, 2, 8, 1, 3, 8, 1, 8, 1, 8, 8, 8, 8, 2, 2, 3, 2, 1, 2, 8, 6, 3],\n [2, 3, 8, 2, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 8, 2, 6, 6, 2, 8, 3, 8, 8, 3, 2, 3, 3, 3, 2],\n [8, 6, 8, 2, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 1, 1, 2, 1, 2, 6, 8, 2, 6, 2, 1, 8, 3, 3, 8],\n [6, 8, 6, 2, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 8, 1, 1, 2, 1, 8, 3, 8, 2, 3, 6, 8, 2],\n [3, 8, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 2, 8, 6, 3, 2, 6, 1, 6, 6, 2, 8, 8, 3, 2, 6, 6],\n [2, 6, 3, 2, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 3, 8, 3, 3, 6, 3, 1, 8, 8, 1, 2, 3, 1, 8, 8],\n [3, 8, 1, 6, 1, 8, 1, 3, 8, 3, 2, 3, 2, 8, 1, 3, 1, 2, 2, 8, 1, 6, 3, 3, 3, 6, 2, 2, 8, 6],\n [8, 3, 3, 8, 3, 8, 2, 2, 8, 8, 8, 8, 8, 1, 1, 6, 3, 3, 6, 2, 2, 6, 1, 3, 3, 6, 3, 1, 3, 3],\n [2, 3, 3, 2, 3, 2, 6, 2, 3, 6, 8, 3, 3, 8, 3, 6, 1, 3, 3, 8, 8, 1, 6, 6, 8, 8, 1, 6, 2, 6],\n [3, 6, 3, 3, 3, 2, 3, 6, 1, 6, 3, 8, 2, 8, 2, 3, 2, 6, 3, 6, 6, 8, 3, 6, 6, 1, 6, 8, 8, 6],\n [8, 3, 3, 1, 2, 2, 6, 8, 2, 3, 6, 8, 3, 2, 2, 6, 3, 2, 1, 2, 6, 3, 6, 8, 8, 8, 1, 8, 1, 6],\n [1, 8, 8, 1, 6, 6, 8, 2, 8, 2, 1, 2, 8, 8, 1, 8, 2, 8, 3, 8, 3, 3, 8, 8, 2, 3, 3, 3, 3, 3],\n [8, 8, 3, 8, 3, 2, 8, 6, 3, 3, 1, 3, 2, 1, 6, 6, 8, 3, 6, 6, 3, 6, 3, 1, 8, 1, 2, 6, 3, 8],\n [8, 6, 6, 3, 2, 6, 6, 8, 6, 1, 3, 2, 8, 3, 1, 2, 8, 3, 6, 2, 8, 8, 3, 2, 2, 6, 1, 8, 6, 3],\n [1, 8, 1, 6, 2, 3, 2, 2, 1, 8, 2, 2, 8, 3, 6, 8, 8, 8, 2, 8, 8, 3, 3, 1, 3, 2, 2, 1, 3, 2],\n [8, 1, 3, 6, 8, 8, 6, 6, 3, 3, 2, 2, 3, 8, 8, 8, 8, 8, 8, 8, 1, 3, 3, 8, 2, 3, 6, 2, 8, 2],\n [3, 3, 3, 6, 3, 2, 2, 2, 6, 3, 2, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 2, 3, 2, 2, 2, 2, 3],\n [3, 2, 1, 2, 2, 8, 6, 3, 8, 8, 8, 3, 1, 8, 8, 8, 8, 8, 8, 8, 6, 1, 8, 3, 8, 3, 6, 8, 1, 8],\n [3, 6, 1, 3, 2, 3, 6, 6, 6, 3, 2, 1, 3, 8, 8, 8, 8, 8, 8, 8, 3, 3, 2, 1, 8, 3, 6, 3, 2, 3],\n [8, 1, 3, 8, 6, 2, 3, 3, 3, 3, 2, 8, 6, 8, 8, 8, 8, 8, 8, 8, 2, 8, 8, 3, 8, 2, 3, 1, 3, 2],\n [3, 6, 3, 2, 8, 6, 6, 3, 8, 3, 1, 2, 3, 8, 8, 8, 8, 8, 8, 8, 3, 6, 8, 6, 1, 2, 1, 3, 3, 6],\n [3, 8, 8, 2, 3, 8, 3, 6, 8, 8, 3, 1, 3, 3, 8, 8, 2, 2, 2, 2, 3, 8, 1, 1, 3, 3, 2, 3, 1, 3],\n [3, 3, 6, 8, 1, 6, 6, 2, 8, 6, 6, 1, 8, 1, 2, 2, 1, 6, 8, 3, 2, 6, 8, 6, 8, 8, 6, 2, 8, 3],\n [8, 3, 3, 1, 8, 3, 2, 3, 3, 3, 8, 3, 3, 3, 3, 2, 3, 8, 3, 1, 3, 6, 6, 6, 6, 6, 3, 6, 2, 3],\n [3, 6, 8, 3, 2, 1, 8, 6, 6, 8, 6, 6, 1, 6, 6, 1, 3, 3, 6, 2, 6, 1, 3, 3, 8, 1, 2, 2, 3, 3],\n [1, 8, 3, 6, 3, 2, 6, 8, 8, 1, 6, 6, 8, 6, 6, 6, 2, 6, 8, 3, 8, 1, 3, 8, 2, 6, 3, 2, 6, 6],\n [8, 8, 6, 8, 1, 1, 8, 2, 2, 3, 6, 2, 8, 3, 8, 2, 1, 1, 8, 6, 8, 6, 8, 6, 3, 3, 3, 3, 2, 3],\n [1, 3, 8, 1, 3, 1, 6, 3, 6, 8, 2, 3, 3, 8, 2, 2, 2, 1, 3, 2, 8, 8, 3, 8, 6, 6, 3, 8, 3, 8],\n [6, 2, 6, 2, 8, 2, 3, 3, 3, 3, 1, 3, 3, 3, 2, 6, 3, 8, 2, 3, 6, 3, 3, 2, 2, 3, 8, 8, 1, 3]\n ],\n \"output\": [\n [6, 8, 6, 8, 8, 8, 8, 6, 6, 3, 8, 6, 2, 3, 8, 1, 2, 8, 1, 3, 8, 3, 3, 6, 6, 1, 2, 6, 2, 2],\n [2, 3, 3, 2, 2, 3, 6, 2, 6, 2, 8, 1, 3, 8, 1, 8, 1, 8, 8, 8, 8, 2, 2, 3, 2, 1, 2, 8, 6, 3],\n [2, 3, 8, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 8, 2, 6, 6, 2, 8, 3, 8, 8, 3, 2, 3, 3, 3, 2],\n [8, 6, 8, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 1, 1, 2, 1, 2, 6, 8, 2, 6, 2, 1, 8, 3, 3, 8],\n [6, 8, 6, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 8, 1, 1, 2, 1, 8, 3, 8, 2, 3, 6, 8, 2],\n [3, 8, 3, 3, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 8, 6, 3, 2, 6, 1, 6, 6, 2, 8, 8, 3, 2, 6, 6],\n [2, 6, 3, 2, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 3, 8, 3, 3, 6, 3, 1, 8, 8, 1, 2, 3, 1, 8, 8],\n [3, 8, 1, 6, 1, 8, 1, 3, 8, 3, 2, 3, 2, 8, 1, 3, 1, 2, 2, 8, 1, 6, 3, 3, 3, 6, 2, 2, 8, 6],\n [8, 3, 3, 8, 3, 8, 2, 2, 8, 8, 8, 8, 8, 1, 1, 6, 3, 3, 6, 2, 2, 6, 1, 3, 3, 6, 3, 1, 3, 3],\n [2, 3, 3, 2, 3, 2, 6, 2, 3, 6, 8, 3, 3, 8, 3, 6, 1, 3, 3, 8, 8, 1, 6, 6, 8, 8, 1, 6, 2, 6],\n [3, 6, 3, 3, 3, 2, 3, 6, 1, 6, 3, 8, 2, 8, 2, 3, 2, 6, 3, 6, 6, 8, 3, 6, 6, 1, 6, 8, 8, 6],\n [8, 3, 3, 1, 2, 2, 6, 8, 2, 3, 6, 8, 3, 2, 2, 6, 3, 2, 1, 2, 6, 3, 6, 8, 8, 8, 1, 8, 1, 6],\n [1, 8, 8, 1, 6, 6, 8, 2, 8, 2, 1, 2, 8, 8, 1, 8, 2, 8, 3, 8, 3, 3, 8, 8, 2, 3, 3, 3, 3, 3],\n [8, 8, 3, 8, 3, 2, 8, 6, 3, 3, 1, 3, 2, 1, 6, 6, 8, 3, 6, 6, 3, 6, 3, 1, 8, 1, 2, 6, 3, 8],\n [8, 6, 6, 3, 2, 6, 6, 8, 6, 1, 3, 2, 8, 3, 1, 2, 8, 3, 6, 2, 8, 8, 3, 2, 2, 6, 1, 8, 6, 3],\n [1, 8, 1, 6, 2, 3, 2, 2, 1, 8, 2, 2, 8, 3, 6, 8, 8, 8, 2, 8, 8, 3, 3, 1, 3, 2, 2, 1, 3, 2],\n [8, 1, 3, 6, 8, 8, 6, 6, 3, 3, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 1, 3, 3, 8, 2, 3, 6, 2, 8, 2],\n [3, 3, 3, 6, 3, 2, 2, 2, 6, 3, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 8, 3, 3, 2, 3, 2, 2, 2, 2, 3],\n [3, 2, 1, 2, 2, 8, 6, 3, 8, 8, 8, 3, 1, 4, 4, 4, 4, 4, 4, 4, 6, 1, 8, 3, 8, 3, 6, 8, 1, 8],\n [3, 6, 1, 3, 2, 3, 6, 6, 6, 3, 2, 1, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 2, 1, 8, 3, 6, 3, 2, 3],\n [8, 1, 3, 8, 6, 2, 3, 3, 3, 3, 2, 8, 6, 4, 4, 4, 4, 4, 4, 4, 2, 8, 8, 3, 8, 2, 3, 1, 3, 2],\n [3, 6, 3, 2, 8, 6, 6, 3, 8, 3, 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 3, 6, 8, 6, 1, 2, 1, 3, 3, 6],\n [3, 8, 8, 2, 3, 8, 3, 6, 8, 8, 3, 1, 3, 3, 8, 8, 2, 2, 2, 2, 3, 8, 1, 1, 3, 3, 2, 3, 1, 3],\n [3, 3, 6, 8, 1, 6, 6, 2, 8, 6, 6, 1, 8, 1, 2, 2, 1, 6, 8, 3, 2, 6, 8, 6, 8, 8, 6, 2, 8, 3],\n [8, 3, 3, 1, 8, 3, 2, 3, 3, 3, 8, 3, 3, 3, 3, 2, 3, 8, 3, 1, 3, 6, 6, 6, 6, 6, 3, 6, 2, 3],\n [3, 6, 8, 3, 2, 1, 8, 6, 6, 8, 6, 6, 1, 6, 6, 1, 3, 3, 6, 2, 6, 1, 3, 3, 8, 1, 2, 2, 3, 3],\n [1, 8, 3, 6, 3, 2, 6, 8, 8, 1, 6, 6, 8, 6, 6, 6, 2, 6, 8, 3, 8, 1, 3, 8, 2, 6, 3, 2, 6, 6],\n [8, 8, 6, 8, 1, 1, 8, 2, 2, 3, 6, 2, 8, 3, 8, 2, 1, 1, 8, 6, 8, 6, 8, 6, 3, 3, 3, 3, 2, 3],\n [1, 3, 8, 1, 3, 1, 6, 3, 6, 8, 2, 3, 3, 8, 2, 2, 2, 1, 3, 2, 8, 8, 3, 8, 6, 6, 3, 8, 3, 8],\n [6, 2, 6, 2, 8, 2, 3, 3, 3, 3, 1, 3, 3, 3, 2, 6, 3, 8, 2, 3, 6, 3, 3, 2, 2, 3, 8, 8, 1, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 8, 3, 3, 3, 8, 1, 8, 3, 8, 8, 1, 6, 3, 1, 2, 3, 1, 1, 1, 3, 1, 2, 2, 6, 8, 2, 3, 8, 6],\n [1, 6, 6, 8, 8, 1, 3, 3, 6, 3, 8, 8, 2, 1, 1, 3, 6, 8, 8, 3, 1, 2, 1, 8, 2, 3, 8, 8, 8, 2],\n [8, 8, 3, 6, 3, 1, 6, 8, 3, 8, 6, 3, 1, 2, 8, 1, 8, 2, 1, 3, 2, 1, 1, 1, 1, 1, 1, 8, 3, 6],\n [1, 3, 1, 2, 8, 1, 8, 2, 2, 2, 1, 3, 1, 8, 3, 6, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 2, 6, 3],\n [8, 1, 2, 3, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 3, 8, 1, 1, 1, 1, 1, 1, 1, 8, 3, 6, 8],\n [6, 1, 3, 1, 8, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 3, 1, 6, 1, 1, 1, 1, 1, 6, 2, 8, 3],\n [3, 3, 3, 3, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 3, 1, 1, 6, 2, 8, 8, 8, 8, 8, 8, 1, 3],\n [3, 3, 1, 3, 2, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 3, 2, 6, 3, 2, 1, 8, 8, 8, 2, 8],\n [1, 8, 3, 2, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 2, 8, 3, 1, 3, 3, 6, 1, 3, 1],\n [8, 1, 2, 3, 1, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 6, 6, 1, 3, 8, 3, 3, 2, 8, 8, 8, 1],\n [3, 3, 1, 1, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 1, 8, 1, 8, 6, 2, 8, 1, 1, 1, 6, 3],\n [6, 1, 1, 2, 1, 6, 8, 3, 2, 2, 8, 1, 6, 3, 8, 2, 6, 3, 2, 1, 3, 3, 6, 1, 2, 8, 6, 2, 2, 2],\n [1, 3, 2, 8, 1, 2, 8, 8, 1, 3, 2, 2, 2, 2, 3, 3, 3, 8, 3, 2, 1, 1, 6, 6, 3, 8, 3, 3, 1, 6],\n [8, 1, 2, 6, 3, 1, 3, 2, 8, 1, 3, 3, 8, 1, 8, 3, 6, 6, 8, 3, 1, 8, 1, 3, 3, 2, 3, 3, 6, 3],\n [8, 8, 1, 3, 2, 8, 1, 1, 2, 1, 2, 1, 8, 1, 1, 3, 3, 3, 8, 8, 8, 3, 3, 3, 1, 8, 6, 1, 1, 3],\n [6, 8, 3, 2, 8, 1, 3, 1, 1, 3, 1, 1, 8, 3, 8, 8, 3, 3, 3, 6, 3, 3, 3, 1, 3, 6, 3, 2, 8, 6],\n [3, 8, 1, 1, 8, 2, 3, 3, 2, 8, 1, 6, 1, 6, 6, 8, 6, 3, 1, 8, 3, 8, 8, 8, 1, 1, 6, 3, 3, 1],\n [8, 8, 1, 2, 3, 6, 3, 3, 1, 8, 8, 3, 3, 6, 8, 1, 8, 1, 2, 2, 3, 8, 3, 8, 8, 2, 6, 8, 6, 1],\n [3, 1, 8, 2, 3, 3, 6, 3, 3, 6, 3, 1, 1, 3, 1, 1, 8, 1, 3, 1, 8, 2, 8, 1, 1, 2, 1, 3, 3, 8],\n [8, 8, 8, 1, 2, 8, 8, 3, 1, 1, 3, 3, 3, 3, 6, 3, 3, 3, 3, 3, 3, 1, 3, 8, 2, 8, 8, 1, 2, 8],\n [6, 8, 3, 8, 1, 2, 8, 3, 8, 3, 1, 8, 3, 3, 1, 3, 3, 3, 3, 3, 3, 8, 2, 8, 8, 8, 3, 3, 1, 1],\n [8, 1, 6, 3, 1, 2, 8, 8, 1, 1, 3, 8, 2, 3, 8, 3, 3, 3, 3, 3, 3, 3, 8, 3, 8, 3, 3, 1, 3, 1],\n [2, 1, 2, 2, 1, 8, 6, 3, 1, 3, 8, 8, 8, 2, 2, 3, 3, 3, 3, 3, 3, 8, 8, 1, 3, 1, 1, 3, 2, 6],\n [1, 1, 3, 6, 6, 3, 2, 2, 8, 2, 6, 3, 8, 6, 2, 3, 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 8, 3, 3, 3],\n [1, 3, 6, 1, 8, 3, 2, 1, 3, 3, 1, 3, 8, 3, 8, 3, 3, 3, 3, 3, 3, 3, 8, 3, 8, 3, 3, 8, 1, 2],\n [8, 8, 3, 8, 8, 2, 2, 2, 2, 8, 8, 6, 8, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 1, 6, 1, 8, 6, 8, 6],\n [3, 3, 1, 1, 1, 8, 8, 3, 3, 3, 2, 8, 2, 3, 3, 3, 3, 3, 6, 8, 8, 2, 2, 8, 1, 1, 2, 1, 8, 8],\n [6, 6, 3, 3, 3, 6, 3, 3, 8, 8, 8, 8, 1, 3, 6, 8, 3, 3, 8, 1, 1, 1, 6, 1, 2, 6, 2, 6, 3, 8],\n [1, 2, 8, 3, 2, 1, 3, 3, 3, 3, 1, 3, 8, 6, 6, 1, 8, 1, 6, 2, 1, 6, 3, 3, 3, 8, 3, 8, 1, 8],\n [8, 6, 2, 6, 3, 8, 2, 8, 8, 3, 1, 1, 6, 1, 1, 2, 1, 8, 1, 2, 1, 3, 1, 8, 3, 6, 3, 3, 3, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 8, 3, 3, 3, 8, 1, 8, 3, 8, 8, 1, 6, 3, 1, 2, 3, 1, 1, 1, 3, 1, 2, 2, 6, 8, 2, 3, 8, 6], [1, 6, 6, 8, 8, 1, 3, 3, 6, 3, 8, 8, 2, 1, 1, 3, 6, 8, 8, 3, 1, 2, 1, 8, 2, 3, 8, 8, 8, 2], [8, 8, 3, 6, 3, 1, 6, 8, 3, 8, 6, 3, 1, 2, 8, 1, 8, 2, 1, 3, 2, 4, 4, 4, 4, 4, 1, 8, 3, 6], [1, 3, 1, 2, 8, 1, 8, 2, 2, 2, 1, 3, 1, 8, 3, 6, 3, 1, 1, 3, 1, 4, 4, 4, 4, 4, 1, 2, 6, 3], [8, 1, 2, 3, 8, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 3, 8, 1, 1, 4, 4, 4, 4, 4, 8, 3, 6, 8], [6, 1, 3, 1, 8, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 3, 1, 6, 4, 4, 4, 4, 4, 6, 2, 8, 3], [3, 3, 3, 3, 8, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 3, 1, 1, 6, 2, 8, 8, 8, 8, 8, 8, 1, 3], [3, 3, 1, 3, 2, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 1, 1, 3, 2, 6, 3, 2, 1, 8, 8, 8, 2, 8], [1, 8, 3, 2, 8, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 8, 8, 8, 2, 8, 3, 1, 3, 3, 6, 1, 3, 1], [8, 1, 2, 3, 1, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 8, 6, 6, 1, 3, 8, 3, 3, 2, 8, 8, 8, 1], [3, 3, 1, 1, 2, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 8, 1, 8, 1, 8, 6, 2, 8, 1, 1, 1, 6, 3], [6, 1, 1, 2, 1, 6, 8, 3, 2, 2, 8, 1, 6, 3, 8, 2, 6, 3, 2, 1, 3, 3, 6, 1, 2, 8, 6, 2, 2, 2], [1, 3, 2, 8, 1, 2, 8, 8, 1, 3, 2, 2, 2, 2, 3, 3, 3, 8, 3, 2, 1, 1, 6, 6, 3, 8, 3, 3, 1, 6], [8, 1, 2, 6, 3, 1, 3, 2, 8, 1, 3, 3, 8, 1, 8, 3, 6, 6, 8, 3, 1, 8, 1, 3, 3, 2, 3, 3, 6, 3], [8, 8, 1, 3, 2, 8, 1, 1, 2, 1, 2, 1, 8, 1, 1, 3, 3, 3, 8, 8, 8, 3, 3, 3, 1, 8, 6, 1, 1, 3], [6, 8, 3, 2, 8, 1, 3, 1, 1, 3, 1, 1, 8, 3, 8, 8, 3, 3, 3, 6, 3, 3, 3, 1, 3, 6, 3, 2, 8, 6], [3, 8, 1, 1, 8, 2, 3, 3, 2, 8, 1, 6, 1, 6, 6, 8, 6, 3, 1, 8, 3, 8, 8, 8, 1, 1, 6, 3, 3, 1], [8, 8, 1, 2, 3, 6, 3, 3, 1, 8, 8, 3, 3, 6, 8, 1, 8, 1, 2, 2, 3, 8, 3, 8, 8, 2, 6, 8, 6, 1], [3, 1, 8, 2, 3, 3, 6, 3, 3, 6, 3, 1, 1, 3, 1, 1, 8, 1, 3, 1, 8, 2, 8, 1, 1, 2, 1, 3, 3, 8], [8, 8, 8, 1, 2, 8, 8, 3, 1, 1, 3, 3, 3, 3, 6, 4, 4, 4, 4, 4, 4, 1, 3, 8, 2, 8, 8, 1, 2, 8], [6, 8, 3, 8, 1, 2, 8, 3, 8, 3, 1, 8, 3, 3, 1, 4, 4, 4, 4, 4, 4, 8, 2, 8, 8, 8, 3, 3, 1, 1], [8, 1, 6, 3, 1, 2, 8, 8, 1, 1, 3, 8, 2, 3, 8, 4, 4, 4, 4, 4, 4, 3, 8, 3, 8, 3, 3, 1, 3, 1], [2, 1, 2, 2, 1, 8, 6, 3, 1, 3, 8, 8, 8, 2, 2, 4, 4, 4, 4, 4, 4, 8, 8, 1, 3, 1, 1, 3, 2, 6], [1, 1, 3, 6, 6, 3, 2, 2, 8, 2, 6, 3, 8, 6, 2, 4, 4, 4, 4, 4, 4, 3, 1, 1, 3, 3, 8, 3, 3, 3], [1, 3, 6, 1, 8, 3, 2, 1, 3, 3, 1, 3, 8, 3, 8, 4, 4, 4, 4, 4, 4, 3, 8, 3, 8, 3, 3, 8, 1, 2], [8, 8, 3, 8, 8, 2, 2, 2, 2, 8, 8, 6, 8, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2, 1, 6, 1, 8, 6, 8, 6], [3, 3, 1, 1, 1, 8, 8, 3, 3, 3, 2, 8, 2, 3, 3, 3, 3, 3, 6, 8, 8, 2, 2, 8, 1, 1, 2, 1, 8, 8], [6, 6, 3, 3, 3, 6, 3, 3, 8, 8, 8, 8, 1, 3, 6, 8, 3, 3, 8, 1, 1, 1, 6, 1, 2, 6, 2, 6, 3, 8], [1, 2, 8, 3, 2, 1, 3, 3, 3, 3, 1, 3, 8, 6, 6, 1, 8, 1, 6, 2, 1, 6, 3, 3, 3, 8, 3, 8, 1, 8], [8, 6, 2, 6, 3, 8, 2, 8, 8, 3, 1, 1, 6, 1, 1, 2, 1, 8, 1, 2, 1, 3, 1, 8, 3, 6, 3, 3, 3, 8]], "task_id": "25094a63"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 1],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 1, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0],\n [0, 2, 0, 0],\n [0, 0, 0, 0],\n [0, 2, 0, 0],\n [2, 2, 2, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 0],\n [0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2],\n [0, 2, 0, 2, 0],\n [2, 2, 2, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 2, 2, 2],\n [2, 2, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 2, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 2, 2, 2],\n [0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 2, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 2, 0, 0, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 0, 3, 1, 0, 1], [0, 0, 0, 0, 0, 0], [3, 0, 0, 1, 0, 1]], "task_id": "d5c634a2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 7, 0],\n [7, 7, 7],\n [0, 7, 0]\n ],\n \"output\": [\n [0, 0, 0, 7, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 7, 0, 0, 0],\n [7, 0, 7, 7, 0, 7, 7, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 7, 7, 0, 7, 7, 0, 7],\n [0, 0, 0, 7, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 7, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6],\n [0, 6, 0],\n [6, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 6, 6],\n [0, 0, 0, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 0],\n [0, 0, 4],\n [0, 0, 4]\n ],\n \"output\": [\n [0, 0, 4, 0, 0, 4, 0, 0, 0],\n [4, 4, 0, 4, 4, 0, 0, 0, 0],\n [4, 4, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3],\n [3, 3, 0],\n [0, 3, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 0, 3, 0, 3], [3, 3, 0, 3, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 3, 0, 0, 0], [3, 0, 3, 3, 0, 3, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0]], "task_id": "0692e18c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6, 0, 7],\n [0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7],\n [0, 0, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6, 0, 7, 7, 7, 0, 7, 7, 7, 0, 6, 6, 6, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0, 7, 0, 7, 0, 7, 0, 6, 0, 6, 0, 7, 0], [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0]], "task_id": "d304284e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], "task_id": "0f63c0b9"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 2, 0, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 3, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 3, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 4, 2, 2, 2, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 4, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 4, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 1, 0, 0, 0, 0]], "task_id": "9def23fe"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 2, 2, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 2, 2, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 2, 2, 2, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 2, 2, 2, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 2, 2, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 2, 2, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 2, 2, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 2, 2, 2, 1, 1, 1],\n [8, 2, 2, 8, 8, 8, 1, 1, 2, 2, 2, 1, 1, 1],\n [8, 2, 2, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 8, 2, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8],\n [1, 2, 2, 1, 1, 1, 8, 8, 2, 2, 8],\n [1, 2, 2, 1, 1, 1, 8, 8, 2, 2, 8],\n [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 1, 1, 8, 8, 8, 8, 8],\n [2, 2, 2, 2, 1, 1, 8, 8, 2, 8, 8],\n [1, 1, 1, 1, 1, 1, 8, 8, 2, 8, 8],\n [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 2, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8], [1, 1, 1, 1, 2, 2, 2, 2, 8, 8, 8], [1, 1, 1, 1, 2, 2, 2, 2, 8, 8, 8], [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8], [1, 1, 2, 2, 2, 2, 8, 8, 8, 8, 8], [1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 2, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8]], "task_id": "9b4c17c4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 1],\n [8, 6, 1],\n [4, 9, 6]\n ],\n \"output\": [\n [8, 8, 1, 8, 8, 1, 0, 0, 0],\n [8, 6, 1, 8, 6, 1, 0, 0, 0],\n [4, 9, 6, 4, 9, 6, 0, 0, 0],\n [8, 8, 1, 0, 0, 0, 0, 0, 0],\n [8, 6, 1, 0, 0, 0, 0, 0, 0],\n [4, 9, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 1],\n [4, 7, 1],\n [3, 3, 7]\n ],\n \"output\": [\n [7, 7, 1, 7, 7, 1, 0, 0, 0],\n [4, 7, 1, 4, 7, 1, 0, 0, 0],\n [3, 3, 7, 3, 3, 7, 0, 0, 0],\n [0, 0, 0, 7, 7, 1, 0, 0, 0],\n [0, 0, 0, 4, 7, 1, 0, 0, 0],\n [0, 0, 0, 3, 3, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 1],\n [0, 0, 0, 0, 0, 0, 4, 7, 1],\n [0, 0, 0, 0, 0, 0, 3, 3, 7]\n ]\n}\n\n{\n \"input\": [\n [4, 5, 4],\n [2, 2, 5],\n [5, 5, 4]\n ],\n \"output\": [\n [0, 0, 0, 4, 5, 4, 0, 0, 0],\n [0, 0, 0, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 5, 4],\n [0, 0, 0, 0, 0, 0, 2, 2, 5],\n [0, 0, 0, 0, 0, 0, 5, 5, 4],\n [4, 5, 4, 4, 5, 4, 0, 0, 0],\n [2, 2, 5, 2, 2, 5, 0, 0, 0],\n [5, 5, 4, 5, 5, 4, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 2, 3],\n [9, 9, 1],\n [2, 9, 4]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 1, 2, 3, 0, 0, 0],\n [9, 9, 1, 9, 9, 1, 0, 0, 0],\n [2, 9, 4, 2, 9, 4, 0, 0, 0],\n [0, 0, 0, 1, 2, 3, 0, 0, 0],\n [0, 0, 0, 9, 9, 1, 0, 0, 0],\n [0, 0, 0, 2, 9, 4, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 6, 7],\n [8, 7, 7],\n [2, 8, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 9, 6, 7], [0, 0, 0, 0, 0, 0, 8, 7, 7], [0, 0, 0, 0, 0, 0, 2, 8, 7], [0, 0, 0, 9, 6, 7, 9, 6, 7], [0, 0, 0, 8, 7, 7, 8, 7, 7], [0, 0, 0, 2, 8, 7, 2, 8, 7], [0, 0, 0, 0, 0, 0, 9, 6, 7], [0, 0, 0, 0, 0, 0, 8, 7, 7], [0, 0, 0, 0, 0, 0, 2, 8, 7]], "task_id": "27f8ce4f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 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0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 2, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 2, 2, 0, 2, 8, 8, 8, 8, 8, 0, 2, 0, 2, 2, 8, 8, 8, 8, 2, 2, 0, 2, 0, 8, 8, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 8, 8, 8, 8, 2, 2, 2, 2, 2, 8, 8, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 2, 2, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 2, 2, 2, 8, 0, 0, 0, 0, 0, 2, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 8, 2, 8, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 3, 3, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 3, 0, 0, 0, 0], [2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3], [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "05a7bcf2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2],\n [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2], [2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2]], "task_id": "42a15761"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0],\n [0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0],\n [8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 0, 4, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 0, 4, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c62e2108"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2]\n ],\n \"output\": [\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 2, 2, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 2, 2, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 2, 2, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 2, 2, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2],\n [2, 2, 0, 0, 0, 2, 2],\n [2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8],\n [8, 8, 0, 0, 0, 8, 8],\n [8, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0],\n [2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0],\n [2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8],\n [0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 0, 0],\n [2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 8, 8], [0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 8, 8], [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 0, 0], [8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 0, 2, 2, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0]], "task_id": "817e6c09"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 8, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 8, 0, 8, 0, 8, 8],\n [4, 4, 0, 4, 0, 4, 0, 4, 0, 0, 8, 8, 0, 8, 0, 8],\n [4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 8, 0, 8, 0, 8, 8],\n [4, 4, 0, 4, 0, 4, 0, 4, 0, 0, 8, 8, 8, 8, 8, 8],\n [4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 8, 0, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0], [0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 7, 0, 7, 0, 7, 0, 7, 7, 0], [0, 8, 0, 8, 0, 8, 0, 8, 8, 0, 7, 7, 0, 7, 0, 7, 0, 7, 0], [0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 7, 0, 7, 0, 7, 0, 7, 7, 0], [0, 8, 0, 8, 0, 8, 0, 8, 8, 0, 7, 7, 0, 7, 0, 7, 0, 7, 0], [0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 7, 0, 7, 0, 7, 0, 7, 7, 0], [0, 8, 0, 8, 0, 8, 0, 8, 8, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 4, 4, 4, 4, 4, 0], [0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 4, 0, 4, 0, 4, 0], [0, 6, 6, 0, 6, 0, 6, 0, 6, 0, 6, 6, 0, 4, 4, 0, 4, 4, 0], [0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 4, 0, 4, 0, 4, 0], [0, 6, 6, 0, 6, 0, 6, 0, 6, 0, 6, 6, 0, 4, 4, 0, 4, 4, 0], [0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 6, 0, 4, 4, 4, 4, 4, 0], [0, 6, 6, 0, 6, 0, 6, 0, 6, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0]], "task_id": "ba9d41b8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 3, 3, 0, 3, 3],\n [4, 0, 4, 4, 0, 0, 0, 3, 0, 3],\n [0, 0, 4, 0, 4, 0, 0, 0, 3, 0],\n [4, 4, 4, 0, 0, 3, 0, 0, 3, 3],\n [4, 4, 4, 4, 0, 3, 0, 3, 0, 3],\n [9, 9, 9, 0, 9, 0, 0, 8, 8, 8],\n [9, 9, 0, 0, 9, 8, 0, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 9, 0, 8, 0, 8, 0]\n ],\n \"output\": [\n [3, 3, 9, 3, 3],\n [9, 9, 3, 4, 3],\n [0, 0, 4, 3, 4],\n [3, 9, 8, 3, 3],\n [3, 8, 3, 8, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 4, 0, 0, 0, 0, 3],\n [0, 4, 4, 4, 4, 3, 3, 3, 3, 3],\n [0, 4, 0, 0, 0, 0, 3, 3, 3, 0],\n [4, 4, 0, 0, 0, 3, 3, 3, 0, 3],\n [0, 0, 4, 4, 0, 3, 3, 0, 0, 0],\n [9, 0, 9, 0, 9, 0, 0, 8, 8, 0],\n [0, 0, 0, 9, 0, 0, 0, 0, 8, 0],\n [9, 9, 0, 9, 0, 0, 8, 8, 8, 0],\n [0, 0, 9, 9, 9, 0, 0, 0, 0, 0],\n [9, 9, 0, 9, 0, 8, 8, 8, 8, 0]\n ],\n \"output\": [\n [9, 4, 9, 8, 3],\n [3, 3, 3, 3, 3],\n [9, 3, 3, 3, 0],\n [3, 3, 3, 9, 3],\n [3, 3, 8, 9, 0]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 0, 0, 0, 3, 0, 0, 0, 3],\n [0, 0, 4, 4, 4, 3, 3, 3, 3, 3],\n [4, 4, 0, 4, 0, 3, 3, 3, 3, 3],\n [4, 4, 4, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 8, 0, 8, 8],\n [9, 0, 9, 0, 9, 8, 0, 8, 0, 0],\n [0, 0, 9, 0, 0, 8, 0, 8, 8, 0],\n [9, 9, 9, 9, 0, 8, 0, 0, 0, 8],\n [0, 9, 9, 0, 0, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [3, 8, 0, 8, 3],\n [3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3],\n [3, 9, 9, 9, 8],\n [3, 3, 9, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 4, 4, 0, 0, 0, 0, 3, 3],\n [4, 4, 0, 0, 0, 3, 0, 3, 3, 0],\n [4, 0, 0, 4, 4, 0, 3, 3, 3, 0],\n [0, 0, 4, 0, 4, 3, 0, 0, 3, 0],\n [0, 0, 4, 4, 4, 3, 3, 3, 3, 3],\n [0, 9, 0, 9, 9, 0, 0, 0, 8, 0],\n [9, 0, 0, 9, 9, 0, 8, 8, 0, 8],\n [0, 0, 0, 9, 0, 0, 0, 8, 8, 0],\n [0, 0, 9, 9, 0, 8, 0, 8, 0, 0],\n [9, 9, 0, 9, 0, 0, 8, 0, 8, 8]\n ],\n \"output\": [\n [0, 9, 4, 3, 3],\n [3, 8, 3, 3, 9],\n [4, 3, 3, 3, 4],\n [3, 0, 9, 3, 4],\n [3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 4, 4, 0, 0, 3, 0, 3, 0],\n [0, 4, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 4, 4, 0, 3, 0, 3, 3, 3],\n [0, 4, 4, 4, 4, 3, 3, 3, 3, 3],\n [9, 0, 9, 9, 0, 0, 0, 0, 0, 0],\n [9, 0, 0, 0, 9, 0, 8, 0, 8, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 9, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 8, 8, 8]\n ],\n \"output\": [\n [9, 3, 9, 3, 0],\n [9, 3, 0, 8, 3],\n [0, 0, 3, 3, 3],\n [3, 8, 3, 3, 3],\n [3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 0, 0, 4, 0, 0, 3, 3, 0],\n [4, 0, 0, 0, 0, 3, 3, 3, 3, 0],\n [0, 4, 4, 0, 4, 3, 0, 0, 3, 3],\n [0, 4, 4, 0, 4, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 3],\n [0, 9, 9, 9, 9, 0, 8, 0, 0, 8],\n [0, 0, 9, 9, 9, 8, 0, 0, 0, 8],\n [9, 9, 9, 0, 0, 8, 8, 0, 8, 0],\n [9, 9, 9, 0, 9, 0, 8, 8, 8, 8],\n [0, 9, 9, 0, 9, 0, 8, 0, 0, 8]\n ],\n \"output\": [\n [4, 9, 3, 3, 9],\n [3, 3, 3, 3, 9],\n [3, 9, 9, 3, 3],\n [9, 9, 3, 8, 9],\n [0, 9, 9, 0, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 0, 4, 0, 0, 3, 0, 3, 3, 0],\n [4, 0, 0, 0, 0, 3, 3, 0, 0, 3],\n [0, 0, 4, 4, 4, 0, 0, 0, 3, 0],\n [0, 0, 4, 0, 4, 3, 3, 3, 3, 0],\n [4, 4, 4, 4, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 9, 9, 0, 0, 8, 0, 8],\n [0, 9, 0, 9, 9, 8, 0, 0, 0, 8],\n [0, 0, 0, 9, 9, 0, 0, 8, 8, 0],\n [0, 0, 9, 9, 9, 8, 0, 0, 0, 0],\n [9, 0, 9, 0, 0, 0, 0, 8, 8, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 0, 3, 3, 9], [3, 3, 0, 9, 3], [0, 0, 8, 3, 9], [3, 3, 3, 3, 9], [3, 4, 9, 8, 0]], "task_id": "ea9794b1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 1, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 1, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 6, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 6, 0, 0, 0, 0, 0, 6, 3, 0, 0, 0],\n [0, 0, 3, 0, 6, 0, 0, 0, 6, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 6, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 6, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 0, 6, 0, 0, 0, 6, 0, 3, 0, 0, 0],\n [0, 0, 3, 6, 0, 0, 0, 0, 0, 6, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 2, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 2, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 2, 0, 0, 0, 2, 1, 0, 0, 3, 2, 0, 0, 2, 3, 0],\n [0, 0, 1, 0, 2, 0, 2, 0, 1, 0, 0, 3, 0, 2, 2, 0, 3, 0],\n [0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 3, 0, 2, 2, 0, 3, 0],\n [0, 0, 1, 0, 2, 0, 2, 0, 1, 0, 0, 3, 2, 0, 0, 2, 3, 0],\n [0, 0, 1, 2, 0, 0, 0, 2, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 3, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 3, 3, 3, 3, 3, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 3, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 6, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 3, 1, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 3, 0, 1, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 6, 0, 0, 0, 0, 0, 6, 8, 0], [0, 3, 0, 1, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 6, 0, 0, 0, 6, 0, 8, 0], [0, 3, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 6, 0, 6, 0, 0, 8, 0], [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 8, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8, 0, 0, 0, 6, 0, 0, 0, 8, 0], [0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 4, 2, 0, 8, 0, 0, 0, 6, 0, 0, 0, 8, 0], [0, 0, 0, 2, 0, 4, 0, 0, 0, 0, 4, 0, 2, 0, 8, 0, 0, 0, 6, 0, 0, 0, 8, 0], [0, 0, 0, 2, 0, 0, 4, 0, 0, 4, 0, 0, 2, 0, 8, 0, 0, 6, 0, 6, 0, 0, 8, 0], [0, 0, 0, 2, 0, 0, 0, 4, 4, 0, 0, 0, 2, 0, 8, 0, 6, 0, 0, 0, 6, 0, 8, 0], [0, 0, 0, 2, 0, 0, 0, 4, 4, 0, 0, 0, 2, 0, 8, 6, 0, 0, 0, 0, 0, 6, 8, 0], [0, 0, 0, 2, 0, 0, 4, 0, 0, 4, 0, 0, 2, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 2, 0, 4, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "8cb8642d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 0, 1, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 1, 0, 0, 5, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 3, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 1, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 1, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 4, 4, 0, 1, 1, 1, 0, 5, 0, 0, 3, 0, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 5, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]\n ],\n \"output\": [\n [0, 2, 0, 4, 4, 0, 1, 1, 1, 0, 5, 0, 0, 1, 0, 0],\n [0, 2, 0, 4, 0, 0, 0, 1, 0, 0, 5, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 4, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 0, 0, 7, 0, 0, 5, 0, 0],\n [2, 0, 0, 7, 7, 7, 0, 5, 0, 0],\n [2, 2, 0, 0, 7, 0, 0, 5, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 3, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 0, 0, 7, 0, 0, 5, 0, 0],\n [2, 0, 0, 7, 7, 7, 0, 5, 0, 0],\n [2, 2, 0, 0, 7, 0, 0, 5, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 0, 2, 0, 2, 0, 0],\n [0, 0, 7, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 1, 0, 0, 4, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [2, 2, 0, 1, 1, 0, 4, 0, 0, 8, 8, 5, 0, 0, 3, 0, 0],\n [0, 0, 0, 1, 0, 0, 4, 4, 0, 0, 0, 5, 0, 0, 3, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 1, 0, 0, 4, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0], [2, 2, 0, 1, 1, 0, 4, 0, 0, 8, 8, 5, 0, 0, 8, 0, 0], [0, 0, 0, 1, 0, 0, 4, 4, 0, 0, 0, 5, 0, 0, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "845d6e51"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 6, 6, 5, 0, 5, 0],\n [6, 0, 0, 0, 5, 5, 0, 0],\n [6, 0, 6, 6, 0, 0, 5, 5],\n [0, 0, 6, 0, 0, 5, 5, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 4, 4],\n [0, 4, 0, 0],\n [4, 0, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 6, 6, 0, 5, 5, 5, 0],\n [0, 6, 0, 6, 5, 0, 0, 5],\n [0, 6, 6, 6, 5, 5, 5, 5],\n [6, 0, 0, 0, 0, 5, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 4],\n [0, 0, 4, 0],\n [0, 0, 0, 0],\n [0, 0, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 6, 6, 0, 5, 0, 5, 5],\n [6, 0, 0, 0, 0, 5, 5, 5],\n [6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 5, 5, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 4, 4, 4],\n [0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 6, 0, 0, 0, 5, 5],\n [0, 6, 6, 6, 5, 0, 5, 5],\n [6, 6, 0, 6, 5, 0, 5, 5],\n [6, 6, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 0, 6, 6, 5, 0, 0, 5],\n [0, 0, 0, 6, 5, 5, 5, 5],\n [0, 6, 6, 0, 5, 5, 0, 5],\n [6, 6, 0, 0, 5, 5, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 4, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 4]], "task_id": "e345f17b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 0, 0, 3, 1, 3],\n [3, 0, 0, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 0, 0, 5, 3, 2],\n [4, 0, 0, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 0, 0, 4, 4, 2],\n [4, 0, 0, 1, 5, 4, 3, 0, 0, 0, 0, 3, 2, 1, 5, 4, 3, 0, 0, 5, 4, 3],\n [3, 0, 0, 5, 3, 3, 5, 0, 0, 0, 0, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 0, 0, 0, 0, 3, 4, 4, 3, 1, 3, 4, 4, 0, 0, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 0, 0, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 0, 0, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 0, 0, 0, 0, 0, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 0, 0, 0, 0, 0, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 0, 0, 0, 0, 0, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 0, 0, 0, 0, 0, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2]\n ],\n \"output\": [\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2],\n [4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2, 3, 2, 4, 4, 2],\n [4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3, 2, 1, 5, 4, 3],\n [3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5, 4, 5, 3, 3, 5],\n [1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3, 4, 4, 3, 1, 3],\n [3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2, 2, 3, 5, 3, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 3, 3, 1, 0, 0, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 0, 0, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 0, 0, 0, 0, 0, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 0, 0, 0, 0, 0, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 0, 0, 0, 0, 1, 3, 3, 0, 0, 0, 0, 0, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 0, 0, 0, 0, 3, 1, 5, 0, 0, 0, 0, 0, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 0, 0, 0, 0, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 0, 0, 0, 0, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 0, 0, 0, 0, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 0, 0, 5, 3, 1, 5, 3],\n [0, 0, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 0, 0, 3, 3, 5, 3, 3],\n [0, 0, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [0, 0, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [0, 0, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1]\n ],\n \"output\": [\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1],\n [3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3, 1, 5, 3],\n [3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3],\n [1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 3, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3],\n [2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 0, 0, 0, 0, 0, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 0, 0, 0, 0, 0, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 0, 0, 0, 0, 0, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 0, 0, 0, 0, 0, 0, 0, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 0, 0, 0, 0, 0, 0, 0, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 0, 0, 0, 0, 0, 3, 1, 3, 2, 5, 5, 2, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3],\n [2, 3, 5, 1, 5, 3, 0, 0, 0, 0, 0, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 0, 0, 0, 0, 0, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 0, 0, 0, 0, 0, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 0, 0, 0, 3, 2, 5, 0, 0, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 0, 0, 0, 7, 3, 5, 0, 0, 5, 3],\n [2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 0, 0, 0, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 0, 0, 0, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1]\n ],\n \"output\": [\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3],\n [2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3],\n [2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1],\n [3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3, 7, 3, 5, 6, 6, 5, 3],\n [2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2, 3, 5, 1, 5, 3, 2, 2],\n [5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5, 5, 1, 7, 2, 7, 1, 5],\n [5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5, 6, 5, 2, 4, 4, 2, 5],\n [2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2, 6, 3, 7, 4, 1, 5, 2],\n [3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3],\n [1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1, 3, 2, 5, 5, 2, 3, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3],\n [3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3],\n [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 0, 0, 0, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5],\n [1, 7, 1, 7, 1, 0, 0, 7, 1, 7, 1, 7, 1, 7, 0, 0, 0, 7, 1, 7, 1, 7],\n [3, 3, 1, 5, 7, 0, 0, 1, 3, 3, 1, 5, 7, 7, 0, 0, 0, 3, 1, 5, 7, 7],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 5, 1, 5, 1, 5],\n [3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1, 0, 0, 0, 5, 1, 7, 7, 1],\n [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3],\n [3, 7, 1, 1, 7, 3, 5, 5, 3, 0, 0, 0, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 1, 1, 3, 7, 5, 5, 7, 3, 0, 0, 0, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5],\n [1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7],\n [3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 5, 1, 5, 1, 5],\n [3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1, 0, 0, 0, 5, 1, 7, 7, 1],\n [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 0, 0, 0, 3, 1, 3, 1, 3],\n [3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3, 0, 0, 0, 7, 1, 1, 7, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1],\n [3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5],\n [1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7],\n [3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3], [3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5], [1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7], [3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7], [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5], [3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1], [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3], [3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5], [1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7], [3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7], [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5], [3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1, 5, 3, 3, 5, 1, 7, 7, 1], [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3], [3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3, 5, 5, 3, 7, 1, 1, 7, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5, 5, 7, 3, 1, 1, 3, 7, 5], [1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7, 1, 7], [3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7, 5, 1, 3, 3, 1, 5, 7, 7]], "task_id": "e95e3d8e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 1, 0, 0, 1, 6],\n [0, 0, 1, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 0],\n [0, 1, 0, 0, 0, 1, 1],\n [0, 0, 1, 0, 0, 2, 0],\n [1, 0, 1, 0, 1, 0, 7],\n [1, 1, 1, 0, 4, 1, 0]\n ],\n \"output\": [\n [0, 0, 8],\n [8, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 2, 2, 0, 5],\n [0, 2, 2, 0, 0, 0, 2],\n [0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 9],\n [0, 9, 0, 0, 0, 0, 2],\n [0, 0, 2, 1, 0, 0, 8],\n [2, 0, 0, 2, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 4, 1, 3],\n [3, 3, 4, 3, 0, 3, 7],\n [3, 0, 0, 0, 1, 0, 3],\n [0, 0, 3, 0, 3, 0, 0],\n [3, 0, 0, 3, 3, 0, 3],\n [3, 0, 3, 0, 3, 0, 3],\n [3, 3, 3, 0, 4, 2, 3]\n ],\n \"output\": [\n [0, 8, 8],\n [0, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 0, 7, 0, 0],\n [1, 1, 9, 1, 0, 1, 0],\n [0, 0, 1, 1, 0, 2, 0],\n [0, 0, 0, 0, 3, 0, 1],\n [0, 4, 0, 1, 0, 0, 1],\n [0, 0, 1, 0, 2, 0, 8],\n [0, 0, 1, 0, 7, 3, 1]\n ],\n \"output\": [\n [0, 0, 8],\n [8, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 3, 5, 3, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 3],\n [3, 4, 3, 9, 3, 0, 3],\n [0, 0, 9, 3, 1, 3, 3],\n [0, 3, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 3]\n ],\n \"output\": [\n [0, 8, 8],\n [0, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 2, 0, 2],\n [0, 2, 2, 9, 2, 2, 0],\n [0, 5, 0, 2, 4, 6, 0],\n [2, 0, 0, 0, 0, 9, 2],\n [0, 0, 0, 2, 2, 0, 0],\n [8, 0, 2, 9, 0, 6, 3],\n [0, 2, 0, 2, 0, 2, 4]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 1, 5, 3],\n [0, 0, 2, 9, 0, 2, 0],\n [2, 2, 2, 4, 2, 0, 0],\n [0, 2, 0, 2, 7, 2, 0],\n [2, 2, 0, 0, 2, 2, 6],\n [0, 2, 2, 0, 2, 0, 0],\n [5, 0, 4, 2, 0, 2, 2]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 1, 1, 0, 1],\n [5, 1, 1, 0, 1, 1, 0],\n [0, 1, 0, 1, 0, 0, 1],\n [1, 0, 2, 0, 0, 6, 0],\n [6, 0, 1, 1, 5, 0, 0],\n [0, 0, 3, 0, 0, 0, 5],\n [0, 1, 0, 0, 2, 0, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8], [8, 8, 0], [0, 8, 0]], "task_id": "9110e3c5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 4, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 4, 4, 4, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 3, 4, 3, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 3, 3, 3, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8],\n [8, 8, 1, 8, 8, 8],\n [8, 1, 1, 1, 8, 8],\n [8, 2, 1, 2, 8, 8],\n [8, 2, 2, 2, 8, 8],\n [8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 0, 4, 5, 0, 0],\n [0, 3, 5, 5, 5, 5, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 1, 1, 5, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 8, 1, 1, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 8, 8, 8, 8, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3],\n [3, 4, 4, 4, 4, 3, 3],\n [3, 3, 2, 2, 4, 3, 3],\n [3, 6, 2, 2, 3, 3, 3],\n [3, 6, 6, 6, 6, 3, 3],\n [3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 2, 4, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 2, 2, 4, 4, 4, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 1, 8, 8, 8],\n [8, 3, 3, 1, 1, 1, 8],\n [8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 2, 2, 1],\n [3, 3, 2, 1],\n [3, 3, 1, 1],\n [1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 3, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 8, 8, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 7, 1, 1, 0, 0, 4, 8, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 2, 1, 1], [1, 2, 2, 2, 1], [1, 4, 4, 4, 1], [1, 6, 6, 6, 1], [1, 1, 6, 1, 1], [1, 1, 1, 1, 1]], "task_id": "e9b4f6fc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 6, 0, 0, 0, 6, 6, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 6, 6, 6, 6, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 6, 6, 0, 0],\n [0, 6, 6, 6, 0, 0, 6, 0, 6],\n [4, 0, 0, 6, 6, 6, 6, 0, 4],\n [0, 6, 6, 6, 0, 6, 6, 0, 0]\n ],\n \"output\": [\n [0, 6, 0, 0, 0, 6, 6, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 6, 6, 6, 6, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 6, 6, 0, 0],\n [0, 6, 6, 6, 0, 0, 6, 0, 6],\n [4, 8, 8, 7, 7, 7, 7, 8, 4],\n [0, 6, 6, 6, 0, 6, 6, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 6, 0, 6, 6, 0, 6, 0, 6],\n [4, 7, 8, 7, 8, 8, 8, 8, 4],\n [0, 6, 6, 6, 6, 6, 6, 6, 0],\n [0, 0, 6, 0, 6, 6, 0, 0, 6],\n [4, 8, 7, 7, 7, 7, 8, 8, 4],\n [0, 0, 0, 0, 6, 0, 0, 0, 6],\n [6, 0, 6, 0, 6, 0, 0, 6, 0],\n [4, 7, 8, 8, 7, 8, 7, 7, 4],\n [6, 6, 0, 6, 0, 6, 6, 0, 0]\n ],\n \"output\": [\n [0, 6, 0, 6, 6, 0, 6, 0, 6],\n [4, 6, 0, 6, 0, 0, 0, 0, 4],\n [0, 6, 6, 6, 6, 6, 6, 6, 0],\n [0, 0, 6, 0, 6, 6, 0, 0, 6],\n [4, 0, 6, 6, 6, 6, 0, 0, 4],\n [0, 0, 0, 0, 6, 0, 0, 0, 6],\n [6, 0, 6, 0, 6, 0, 0, 6, 0],\n [4, 6, 0, 0, 6, 0, 6, 6, 4],\n [6, 6, 0, 6, 0, 6, 6, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 6, 4, 6, 0, 0, 4, 6],\n [6, 0, 6, 0, 0, 6, 0, 0, 6],\n [0, 6, 6, 0, 0, 0, 0, 6, 0],\n [6, 6, 6, 0, 0, 0, 0, 6, 6],\n [6, 0, 0, 6, 6, 0, 0, 0, 6],\n [6, 6, 6, 4, 0, 6, 6, 4, 0]\n ],\n \"output\": [\n [6, 0, 6, 4, 6, 0, 0, 4, 6],\n [6, 0, 6, 8, 0, 6, 0, 8, 6],\n [0, 6, 6, 8, 0, 0, 0, 7, 0],\n [6, 6, 6, 8, 0, 0, 0, 7, 6],\n [6, 0, 0, 7, 6, 0, 0, 8, 6],\n [6, 6, 6, 4, 0, 6, 6, 4, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 6, 6, 0, 4, 6, 4, 0],\n [0, 6, 0, 0, 0, 6, 6, 6, 0],\n [0, 0, 0, 6, 0, 0, 6, 6, 6],\n [6, 6, 6, 0, 0, 0, 6, 0, 0],\n [0, 6, 0, 6, 0, 0, 6, 0, 0],\n [0, 6, 6, 0, 6, 6, 0, 6, 6],\n [6, 6, 6, 6, 0, 6, 0, 6, 6],\n [0, 6, 0, 6, 6, 6, 6, 6, 6],\n [6, 0, 0, 0, 6, 0, 0, 6, 0],\n [0, 4, 0, 0, 6, 4, 6, 4, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 4, 6, 6, 0, 4, 6, 4, 0], [0, 7, 0, 0, 0, 7, 6, 7, 0], [0, 8, 0, 6, 0, 8, 6, 7, 6], [6, 7, 6, 0, 0, 8, 6, 8, 0], [0, 7, 0, 6, 0, 8, 6, 8, 0], [0, 7, 6, 0, 6, 7, 0, 7, 6], [6, 7, 6, 6, 0, 7, 0, 7, 6], [0, 7, 0, 6, 6, 7, 6, 7, 6], [6, 8, 0, 0, 6, 8, 0, 7, 0], [0, 4, 0, 0, 6, 4, 6, 4, 0]], "task_id": "d2acf2cb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 1, 1, 9, 5, 6, 7, 1, 1, 4, 5, 7, 3, 9, 9, 1, 1, 9, 9, 3, 7, 5, 4, 1, 1, 7, 6, 5, 9, 1],\n [1, 3, 9, 5, 6, 5, 1, 7, 4, 1, 7, 5, 4, 3, 1, 3, 3, 1, 3, 4, 5, 7, 1, 4, 7, 1, 5, 6, 5, 9],\n [6, 9, 3, 1, 7, 1, 5, 6, 9, 9, 1, 4, 9, 1, 1, 4, 4, 1, 1, 9, 4, 1, 9, 9, 6, 5, 1, 7, 1, 3],\n [9, 1, 1, 3, 1, 7, 6, 5, 9, 9, 4, 1, 1, 3, 4, 1, 1, 4, 3, 1, 1, 4, 9, 9, 5, 6, 7, 1, 3, 1],\n [6, 6, 6, 7, 3, 1, 5, 9, 3, 4, 9, 1, 6, 7, 2, 5, 5, 2, 7, 6, 1, 9, 4, 3, 9, 5, 1, 3, 7, 6],\n [6, 6, 7, 6, 1, 3, 9, 1, 9, 3, 1, 3, 7, 6, 5, 2, 2, 5, 6, 7, 3, 1, 3, 9, 1, 9, 3, 1, 6, 7],\n [6, 7, 6, 6, 1, 9, 3, 1, 9, 1, 1, 4, 6, 9, 6, 7, 7, 6, 9, 6, 4, 1, 1, 9, 1, 3, 9, 1, 6, 6],\n [7, 6, 6, 6, 9, 6, 1, 3, 1, 3, 4, 1, 9, 6, 7, 6, 6, 7, 6, 9, 1, 4, 3, 1, 3, 1, 8, 8, 8, 8],\n [1, 4, 9, 9, 3, 9, 9, 1, 1, 1, 6, 1, 5, 2, 5, 5, 5, 5, 2, 5, 1, 6, 1, 1, 1, 9, 8, 8, 8, 8],\n [4, 1, 9, 9, 4, 3, 1, 3, 1, 1, 1, 6, 2, 5, 5, 5, 5, 5, 5, 2, 6, 1, 1, 1, 3, 1, 8, 8, 8, 8],\n [5, 7, 1, 4, 9, 1, 1, 4, 2, 2, 1, 1, 5, 5, 5, 2, 2, 5, 5, 5, 1, 1, 2, 2, 4, 1, 8, 8, 8, 8],\n [7, 5, 4, 1, 1, 3, 4, 1, 2, 1, 1, 1, 5, 5, 2, 5, 5, 2, 5, 5, 1, 1, 1, 2, 1, 4, 3, 1, 1, 4],\n [3, 4, 9, 1, 6, 7, 6, 9, 7, 6, 3, 3, 1, 1, 6, 1, 1, 6, 1, 1, 3, 3, 6, 7, 9, 6, 7, 6, 1, 9],\n [9, 3, 1, 3, 7, 6, 9, 6, 6, 7, 3, 3, 1, 1, 1, 6, 6, 1, 1, 1, 3, 3, 7, 6, 6, 9, 6, 7, 3, 1],\n [9, 1, 1, 4, 2, 5, 6, 7, 3, 3, 7, 6, 1, 2, 1, 1, 1, 1, 2, 1, 6, 7, 3, 3, 7, 6, 5, 2, 4, 1],\n [1, 3, 4, 1, 5, 2, 7, 6, 3, 3, 6, 7, 2, 2, 1, 1, 1, 1, 2, 2, 7, 6, 3, 3, 6, 7, 2, 5, 1, 4],\n [1, 3, 4, 1, 5, 2, 7, 6, 3, 3, 6, 7, 2, 2, 1, 1, 1, 1, 2, 2, 7, 6, 3, 3, 6, 7, 2, 5, 1, 4],\n [9, 1, 1, 4, 2, 5, 6, 7, 3, 3, 7, 6, 1, 2, 1, 1, 1, 1, 2, 1, 6, 7, 3, 3, 7, 6, 5, 2, 4, 1],\n [9, 3, 1, 3, 7, 6, 9, 6, 6, 7, 3, 3, 1, 1, 1, 6, 6, 1, 1, 1, 3, 3, 7, 6, 6, 9, 6, 7, 3, 1],\n [3, 4, 9, 1, 6, 7, 6, 9, 7, 6, 3, 3, 1, 1, 6, 1, 1, 6, 1, 1, 3, 3, 6, 7, 9, 6, 7, 6, 1, 9],\n [7, 5, 4, 1, 1, 3, 4, 1, 2, 1, 1, 1, 5, 5, 2, 5, 5, 2, 5, 5, 1, 1, 1, 2, 1, 4, 3, 1, 1, 4],\n [5, 7, 1, 4, 9, 1, 1, 4, 2, 2, 1, 1, 5, 5, 5, 2, 2, 5, 5, 5, 1, 1, 2, 2, 4, 1, 1, 9, 4, 1],\n [4, 1, 9, 9, 4, 3, 1, 3, 1, 1, 1, 6, 2, 5, 5, 5, 5, 5, 5, 2, 6, 1, 1, 1, 3, 1, 3, 4, 9, 9],\n [1, 4, 9, 9, 3, 9, 9, 1, 1, 1, 6, 1, 5, 2, 5, 5, 5, 5, 2, 5, 1, 6, 1, 1, 1, 9, 9, 3, 9, 9],\n [7, 6, 6, 6, 9, 6, 1, 3, 1, 3, 4, 1, 9, 6, 7, 6, 6, 7, 6, 9, 1, 4, 3, 1, 3, 1, 6, 9, 6, 6],\n [6, 7, 6, 6, 1, 9, 3, 1, 9, 1, 1, 4, 6, 9, 6, 7, 7, 6, 9, 6, 4, 1, 1, 9, 1, 3, 9, 1, 6, 6],\n [6, 6, 7, 6, 1, 3, 9, 1, 9, 3, 1, 3, 7, 6, 5, 2, 2, 5, 6, 7, 3, 1, 3, 9, 1, 9, 3, 1, 6, 7],\n [6, 6, 6, 7, 3, 1, 5, 9, 3, 4, 9, 1, 6, 7, 2, 5, 5, 2, 7, 6, 1, 9, 4, 3, 9, 5, 1, 3, 7, 6],\n [9, 1, 1, 3, 1, 7, 6, 5, 9, 9, 4, 1, 1, 3, 4, 1, 1, 4, 3, 1, 1, 4, 9, 9, 5, 6, 7, 1, 3, 1],\n [6, 9, 3, 1, 7, 1, 5, 6, 9, 9, 1, 4, 9, 1, 1, 4, 4, 1, 1, 9, 4, 1, 9, 9, 6, 5, 1, 7, 1, 3]\n ],\n \"output\": [\n [6, 9, 6, 6],\n [9, 3, 9, 9],\n [3, 4, 9, 9],\n [1, 9, 4, 1]\n ]\n}\n\n{\n \"input\": [\n [9, 9, 2, 3, 4, 4, 7, 5, 3, 3, 6, 6, 3, 5, 6, 4, 4, 6, 5, 3, 6, 6, 3, 3, 5, 7, 4, 4, 3, 2],\n [7, 9, 3, 5, 4, 4, 5, 7, 3, 3, 6, 6, 6, 3, 4, 6, 6, 4, 3, 6, 6, 6, 3, 3, 7, 5, 4, 4, 5, 3],\n [3, 2, 9, 9, 7, 5, 4, 4, 4, 1, 3, 3, 6, 4, 4, 7, 7, 4, 4, 6, 3, 8, 8, 8, 8, 8, 5, 7, 9, 9],\n [2, 3, 7, 9, 5, 7, 4, 4, 1, 4, 3, 3, 4, 6, 7, 4, 4, 7, 6, 4, 3, 8, 8, 8, 8, 8, 7, 5, 9, 7],\n [7, 7, 9, 3, 9, 9, 5, 3, 3, 6, 6, 4, 6, 7, 9, 9, 9, 9, 7, 6, 4, 8, 8, 8, 8, 8, 9, 9, 3, 9],\n [7, 7, 3, 9, 7, 9, 3, 2, 5, 3, 4, 6, 2, 6, 9, 9, 9, 9, 6, 2, 6, 8, 8, 8, 8, 8, 9, 7, 9, 3],\n [9, 3, 7, 7, 3, 2, 9, 9, 6, 4, 4, 7, 9, 2, 6, 7, 7, 6, 2, 9, 7, 4, 4, 6, 9, 9, 2, 3, 7, 7],\n [3, 9, 7, 7, 2, 3, 7, 9, 4, 6, 7, 4, 2, 9, 2, 6, 6, 2, 9, 2, 4, 7, 6, 4, 9, 7, 3, 2, 7, 7],\n [3, 3, 4, 1, 3, 5, 6, 4, 2, 4, 7, 7, 1, 6, 7, 2, 2, 7, 6, 1, 7, 7, 4, 2, 4, 6, 5, 3, 1, 4],\n [3, 3, 1, 4, 6, 3, 4, 6, 2, 2, 7, 1, 6, 1, 2, 7, 7, 2, 1, 6, 1, 7, 2, 2, 6, 4, 3, 6, 4, 1],\n [6, 6, 3, 3, 6, 4, 4, 7, 1, 1, 2, 4, 7, 2, 1, 6, 6, 1, 2, 7, 4, 2, 1, 1, 7, 4, 4, 6, 3, 3],\n [6, 6, 3, 3, 4, 6, 7, 4, 1, 3, 2, 2, 2, 7, 6, 1, 1, 6, 7, 2, 2, 2, 3, 1, 4, 7, 6, 4, 3, 3],\n [3, 6, 6, 4, 6, 2, 9, 2, 9, 9, 9, 7, 2, 4, 1, 7, 7, 1, 4, 2, 7, 9, 9, 9, 2, 9, 2, 6, 4, 6],\n [5, 3, 4, 6, 7, 6, 2, 9, 9, 9, 7, 9, 2, 2, 7, 7, 7, 7, 2, 2, 9, 7, 9, 9, 9, 2, 6, 7, 6, 4],\n [6, 4, 4, 7, 9, 9, 6, 2, 9, 7, 9, 9, 3, 1, 2, 4, 4, 2, 1, 3, 9, 9, 7, 9, 2, 6, 9, 9, 7, 4],\n [4, 6, 7, 4, 9, 9, 7, 6, 7, 9, 9, 9, 1, 1, 2, 2, 2, 2, 1, 1, 9, 9, 9, 7, 6, 7, 9, 9, 4, 7],\n [4, 6, 7, 4, 9, 9, 7, 6, 7, 9, 9, 9, 1, 1, 2, 2, 2, 2, 1, 1, 9, 9, 9, 7, 6, 7, 9, 9, 4, 7],\n [6, 4, 4, 7, 9, 9, 6, 2, 9, 7, 9, 9, 3, 1, 2, 4, 4, 2, 1, 3, 9, 9, 7, 9, 2, 6, 9, 9, 7, 4],\n [5, 3, 4, 6, 7, 6, 2, 9, 9, 9, 7, 9, 2, 2, 7, 7, 7, 7, 2, 2, 9, 7, 9, 9, 9, 2, 6, 7, 6, 4],\n [3, 6, 6, 4, 6, 2, 9, 2, 9, 9, 9, 7, 2, 4, 1, 7, 7, 1, 4, 2, 7, 9, 9, 9, 2, 9, 2, 6, 4, 6],\n [6, 6, 3, 3, 4, 6, 7, 4, 1, 3, 2, 2, 2, 7, 6, 1, 1, 6, 7, 2, 2, 2, 3, 1, 4, 7, 6, 4, 3, 3],\n [6, 6, 3, 3, 6, 4, 4, 7, 1, 1, 2, 4, 7, 2, 1, 6, 6, 1, 2, 7, 4, 2, 1, 1, 7, 4, 4, 6, 3, 3],\n [3, 3, 1, 4, 6, 3, 4, 6, 2, 2, 7, 1, 6, 1, 2, 7, 7, 2, 1, 6, 1, 7, 2, 2, 6, 4, 3, 6, 4, 1],\n [3, 3, 4, 1, 3, 5, 6, 4, 2, 4, 7, 7, 1, 6, 7, 2, 2, 7, 6, 1, 7, 7, 4, 2, 4, 6, 5, 3, 1, 4],\n [3, 9, 7, 7, 2, 3, 7, 9, 4, 6, 7, 4, 2, 9, 2, 6, 6, 2, 9, 2, 4, 7, 6, 4, 9, 7, 3, 2, 7, 7],\n [9, 3, 7, 7, 3, 2, 9, 9, 6, 4, 4, 7, 9, 2, 6, 7, 7, 6, 2, 9, 7, 4, 4, 6, 9, 9, 2, 3, 7, 7],\n [7, 7, 3, 9, 7, 9, 3, 2, 5, 3, 4, 6, 2, 6, 9, 9, 9, 9, 6, 2, 6, 4, 3, 5, 2, 3, 9, 7, 9, 3],\n [7, 7, 9, 3, 9, 9, 5, 3, 3, 6, 6, 4, 6, 7, 9, 9, 9, 9, 7, 6, 4, 6, 6, 3, 3, 5, 9, 9, 3, 9],\n [2, 3, 7, 9, 5, 7, 4, 4, 1, 4, 3, 3, 4, 6, 7, 4, 4, 7, 6, 4, 3, 3, 4, 1, 4, 4, 7, 5, 9, 7],\n [3, 2, 9, 9, 7, 5, 4, 4, 4, 1, 3, 3, 6, 4, 4, 7, 7, 4, 4, 6, 3, 3, 1, 4, 4, 4, 5, 7, 9, 9]\n ],\n \"output\": [\n [3, 1, 4, 4, 4],\n [3, 4, 1, 4, 4],\n [6, 6, 3, 3, 5],\n [4, 3, 5, 2, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 5, 3, 3, 6, 6, 5, 4, 1, 4, 9, 9, 4, 3, 9, 9, 9, 9, 3, 4, 9, 9, 4, 1, 4, 5, 6, 6, 3, 3],\n [5, 3, 3, 3, 6, 6, 4, 5, 4, 1, 9, 9, 3, 4, 9, 1, 1, 9, 4, 3, 9, 9, 1, 4, 5, 4, 6, 6, 3, 3],\n [1, 1, 3, 5, 5, 4, 6, 6, 9, 1, 1, 4, 9, 9, 4, 5, 5, 4, 9, 9, 4, 1, 1, 9, 6, 6, 4, 5, 5, 3],\n [1, 1, 5, 3, 4, 5, 6, 6, 1, 9, 4, 1, 9, 1, 4, 4, 4, 4, 1, 9, 1, 4, 9, 1, 6, 6, 5, 4, 3, 5],\n [6, 9, 9, 9, 3, 5, 3, 3, 4, 3, 9, 9, 9, 2, 6, 9, 9, 6, 2, 9, 9, 9, 3, 4, 3, 3, 5, 3, 9, 9],\n [9, 6, 9, 9, 5, 3, 3, 3, 3, 4, 9, 1, 9, 9, 9, 6, 6, 9, 9, 9, 1, 9, 4, 3, 3, 3, 3, 5, 9, 9],\n [9, 9, 6, 9, 1, 1, 3, 5, 9, 9, 4, 4, 6, 9, 9, 2, 2, 9, 9, 6, 4, 4, 9, 9, 5, 3, 1, 1, 9, 6],\n [9, 9, 9, 6, 1, 1, 5, 3, 9, 1, 5, 4, 9, 6, 9, 9, 9, 9, 6, 9, 4, 5, 1, 9, 3, 5, 1, 1, 6, 9],\n [1, 4, 9, 1, 4, 3, 9, 9, 5, 5, 7, 2, 4, 3, 2, 4, 4, 2, 3, 4, 2, 7, 5, 5, 9, 9, 3, 4, 1, 9],\n [4, 1, 1, 9, 3, 4, 9, 1, 4, 5, 2, 7, 3, 4, 4, 2, 2, 4, 4, 3, 7, 2, 5, 4, 1, 9, 4, 3, 9, 1],\n [9, 9, 1, 4, 9, 9, 4, 5, 6, 4, 5, 5, 2, 4, 4, 3, 3, 4, 4, 2, 5, 5, 4, 6, 5, 4, 9, 9, 4, 1],\n [9, 9, 4, 1, 9, 1, 4, 4, 4, 5, 4, 5, 4, 2, 3, 4, 4, 3, 2, 4, 5, 4, 5, 4, 4, 4, 1, 9, 1, 4],\n [4, 3, 9, 9, 9, 9, 6, 9, 5, 9, 7, 7, 5, 5, 7, 2, 2, 7, 5, 5, 7, 7, 9, 5, 9, 6, 9, 9, 9, 9],\n [3, 4, 9, 1, 2, 9, 9, 6, 9, 5, 7, 7, 4, 5, 2, 7, 7, 2, 5, 4, 7, 7, 5, 9, 6, 9, 9, 2, 1, 9],\n [9, 9, 4, 4, 6, 9, 9, 9, 7, 7, 5, 9, 5, 4, 5, 5, 5, 5, 4, 5, 9, 5, 7, 7, 9, 8, 8, 8, 8, 4],\n [9, 1, 5, 4, 9, 6, 2, 9, 7, 7, 9, 5, 4, 6, 4, 5, 5, 4, 6, 4, 5, 9, 7, 7, 9, 8, 8, 8, 8, 5],\n [9, 1, 5, 4, 9, 6, 2, 9, 7, 7, 9, 5, 4, 6, 4, 5, 5, 4, 6, 4, 5, 9, 7, 7, 9, 8, 8, 8, 8, 5],\n [9, 9, 4, 4, 6, 9, 9, 9, 7, 7, 5, 9, 5, 4, 5, 5, 5, 5, 4, 5, 9, 5, 7, 7, 9, 8, 8, 8, 8, 4],\n [3, 4, 9, 1, 2, 9, 9, 6, 9, 5, 7, 7, 4, 5, 2, 7, 7, 2, 5, 4, 7, 7, 5, 9, 6, 8, 8, 8, 8, 9],\n [4, 3, 9, 9, 9, 9, 6, 9, 5, 9, 7, 7, 5, 5, 7, 2, 2, 7, 5, 5, 7, 7, 9, 5, 9, 8, 8, 8, 8, 9],\n [9, 9, 4, 1, 9, 1, 4, 4, 4, 5, 4, 5, 4, 2, 3, 4, 4, 3, 2, 4, 5, 4, 5, 4, 4, 8, 8, 8, 8, 4],\n [9, 9, 1, 4, 9, 9, 4, 5, 6, 4, 5, 5, 2, 4, 4, 3, 3, 4, 4, 2, 5, 5, 4, 6, 5, 8, 8, 8, 8, 1],\n [4, 1, 1, 9, 3, 4, 9, 1, 4, 5, 2, 7, 3, 4, 4, 2, 2, 4, 4, 3, 7, 2, 5, 4, 1, 8, 8, 8, 8, 1],\n [1, 4, 9, 1, 4, 3, 9, 9, 5, 5, 7, 2, 4, 3, 2, 4, 4, 2, 3, 4, 2, 7, 5, 5, 9, 9, 3, 4, 1, 9],\n [9, 9, 9, 6, 1, 1, 5, 3, 9, 1, 5, 4, 9, 6, 9, 9, 9, 9, 6, 9, 4, 5, 1, 9, 3, 5, 1, 1, 6, 9],\n [9, 9, 6, 9, 1, 1, 3, 5, 9, 9, 4, 4, 6, 9, 9, 2, 2, 9, 9, 6, 4, 4, 9, 9, 5, 3, 1, 1, 9, 6],\n [9, 6, 9, 9, 5, 3, 3, 3, 3, 4, 9, 1, 9, 9, 9, 6, 6, 9, 9, 9, 1, 9, 4, 3, 3, 3, 3, 5, 9, 9],\n [6, 9, 9, 9, 3, 5, 3, 3, 4, 3, 9, 9, 9, 2, 6, 9, 9, 6, 2, 9, 9, 9, 3, 4, 3, 3, 5, 3, 9, 9],\n [1, 1, 5, 3, 4, 5, 6, 6, 1, 9, 4, 1, 9, 1, 4, 4, 4, 4, 1, 9, 1, 4, 9, 1, 6, 6, 5, 4, 3, 5],\n [1, 1, 3, 5, 5, 4, 6, 6, 9, 1, 1, 4, 9, 9, 4, 5, 5, 4, 9, 9, 4, 1, 1, 9, 6, 6, 4, 5, 5, 3]\n ],\n \"output\": [\n [9, 9, 6, 4],\n [2, 6, 9, 4],\n [2, 6, 9, 4],\n [9, 9, 6, 4],\n [9, 9, 2, 1],\n [6, 9, 9, 9],\n [4, 1, 9, 1],\n [4, 9, 9, 4],\n [9, 4, 3, 9]\n ]\n}\n\n{\n \"input\": [\n [1, 9, 4, 4, 9, 9, 2, 7, 6, 6, 9, 9, 7, 6, 7, 2, 2, 7, 6, 7, 9, 9, 6, 6, 7, 2, 9, 9, 4, 4],\n [7, 1, 4, 4, 9, 9, 7, 2, 6, 6, 9, 9, 6, 7, 2, 7, 7, 2, 7, 6, 9, 9, 6, 6, 2, 7, 9, 9, 4, 4],\n [2, 7, 1, 9, 2, 7, 9, 9, 4, 4, 6, 6, 7, 2, 5, 1, 1, 5, 2, 7, 6, 6, 4, 4, 9, 9, 7, 2, 9, 1],\n [7, 2, 7, 1, 7, 2, 9, 9, 4, 4, 6, 6, 2, 7, 5, 5, 5, 5, 7, 2, 6, 6, 4, 4, 9, 9, 2, 7, 1, 7],\n [9, 6, 7, 2, 1, 9, 4, 4, 7, 6, 7, 2, 9, 2, 6, 4, 4, 6, 2, 9, 2, 7, 6, 7, 4, 4, 9, 1, 2, 7],\n [6, 9, 2, 7, 7, 1, 4, 4, 6, 7, 2, 7, 9, 9, 4, 6, 6, 4, 9, 9, 7, 2, 7, 6, 4, 4, 1, 7, 7, 2],\n [7, 2, 9, 6, 2, 7, 1, 9, 7, 2, 5, 5, 4, 5, 9, 2, 2, 9, 5, 4, 5, 5, 2, 7, 9, 1, 7, 2, 6, 9],\n [2, 7, 6, 9, 7, 2, 7, 1, 2, 7, 1, 5, 5, 4, 9, 9, 9, 9, 4, 5, 5, 1, 7, 2, 1, 7, 2, 7, 9, 6],\n [6, 6, 4, 4, 7, 6, 7, 2, 3, 7, 1, 4, 9, 7, 7, 6, 6, 7, 7, 9, 4, 1, 7, 3, 2, 7, 6, 7, 4, 4],\n [6, 6, 4, 4, 6, 7, 2, 7, 4, 3, 4, 4, 7, 9, 6, 7, 7, 6, 9, 7, 4, 4, 3, 4, 7, 2, 7, 6, 4, 4],\n [9, 9, 6, 6, 7, 2, 5, 1, 3, 7, 3, 7, 7, 6, 9, 7, 7, 9, 6, 7, 7, 3, 7, 3, 1, 5, 2, 7, 6, 6],\n [9, 9, 6, 6, 2, 7, 5, 5, 7, 7, 4, 3, 6, 7, 7, 9, 9, 7, 7, 6, 3, 4, 7, 7, 5, 5, 7, 2, 6, 6],\n [7, 6, 7, 2, 9, 9, 4, 5, 6, 6, 5, 9, 3, 7, 4, 4, 4, 4, 7, 3, 9, 5, 6, 6, 5, 4, 9, 9, 2, 7],\n [6, 7, 2, 7, 2, 9, 5, 4, 6, 6, 9, 5, 4, 3, 4, 1, 1, 4, 3, 4, 5, 9, 6, 6, 4, 5, 9, 2, 7, 2],\n [7, 2, 5, 5, 6, 4, 9, 9, 5, 9, 6, 6, 7, 7, 3, 7, 7, 3, 7, 7, 6, 6, 9, 5, 9, 9, 4, 6, 5, 5],\n [2, 7, 1, 5, 4, 6, 2, 9, 9, 5, 6, 6, 7, 3, 4, 3, 3, 4, 3, 7, 6, 6, 5, 9, 9, 2, 6, 4, 5, 1],\n [2, 7, 1, 5, 4, 6, 2, 9, 9, 5, 6, 6, 7, 3, 4, 3, 3, 4, 3, 7, 6, 6, 5, 9, 9, 2, 6, 4, 5, 1],\n [7, 2, 5, 5, 6, 4, 9, 9, 5, 9, 6, 6, 7, 7, 3, 7, 7, 3, 7, 7, 6, 6, 9, 5, 9, 9, 4, 6, 5, 5],\n [6, 7, 2, 7, 2, 9, 5, 4, 6, 6, 9, 5, 4, 3, 4, 1, 1, 4, 3, 4, 5, 9, 6, 6, 4, 5, 9, 2, 7, 2],\n [7, 6, 7, 2, 9, 9, 4, 5, 6, 6, 5, 9, 8, 8, 8, 8, 8, 8, 8, 3, 9, 5, 6, 6, 5, 4, 9, 9, 2, 7],\n [9, 9, 6, 6, 2, 7, 5, 5, 7, 7, 4, 3, 8, 8, 8, 8, 8, 8, 8, 6, 3, 4, 7, 7, 5, 5, 7, 2, 6, 6],\n [9, 9, 6, 6, 7, 2, 5, 1, 3, 7, 3, 7, 8, 8, 8, 8, 8, 8, 8, 7, 7, 3, 7, 3, 1, 5, 2, 7, 6, 6],\n [6, 6, 4, 4, 6, 7, 2, 7, 4, 3, 4, 4, 7, 9, 6, 7, 7, 6, 9, 7, 4, 4, 3, 4, 7, 2, 7, 6, 4, 4],\n [6, 6, 4, 4, 7, 6, 7, 2, 3, 7, 1, 4, 9, 7, 7, 6, 6, 7, 7, 9, 4, 1, 7, 3, 2, 7, 6, 7, 4, 4],\n [2, 7, 6, 9, 7, 2, 7, 1, 2, 7, 1, 5, 5, 4, 9, 9, 9, 9, 4, 5, 5, 1, 7, 2, 1, 7, 2, 7, 9, 6],\n [7, 2, 9, 6, 2, 7, 1, 9, 7, 2, 5, 5, 4, 5, 9, 2, 2, 9, 5, 4, 5, 5, 2, 7, 9, 1, 7, 2, 6, 9],\n [6, 9, 2, 7, 7, 1, 4, 4, 6, 7, 2, 7, 9, 9, 4, 6, 6, 4, 9, 9, 7, 2, 7, 6, 4, 4, 1, 7, 7, 2],\n [9, 6, 7, 2, 1, 9, 4, 4, 7, 6, 7, 2, 9, 2, 6, 4, 4, 6, 2, 9, 2, 7, 6, 7, 4, 4, 9, 1, 2, 7],\n [7, 2, 7, 1, 7, 2, 9, 9, 4, 4, 6, 6, 2, 7, 5, 5, 5, 5, 7, 2, 6, 6, 4, 4, 9, 9, 2, 7, 1, 7],\n [2, 7, 1, 9, 2, 7, 9, 9, 4, 4, 6, 6, 7, 2, 5, 1, 1, 5, 2, 7, 6, 6, 4, 4, 9, 9, 7, 2, 9, 1]\n ],\n \"output\": [\n [3, 7, 4, 4, 4, 4, 7],\n [6, 7, 7, 9, 9, 7, 7],\n [7, 6, 9, 7, 7, 9, 6]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 1, 3, 5, 7, 7, 9, 6, 1, 6, 6, 4, 4, 7, 7, 7, 7, 4, 4, 6, 6, 1, 6, 9, 7, 7, 5, 3, 1],\n [4, 4, 3, 3, 7, 5, 9, 7, 6, 6, 6, 6, 4, 4, 7, 2, 2, 7, 4, 4, 6, 6, 6, 6, 7, 9, 5, 7, 3, 3],\n [3, 4, 4, 4, 7, 9, 5, 7, 5, 1, 6, 1, 7, 7, 9, 9, 9, 9, 7, 7, 1, 6, 1, 5, 7, 5, 9, 7, 4, 4],\n [4, 3, 4, 4, 9, 7, 7, 5, 1, 5, 6, 6, 7, 2, 1, 9, 9, 1, 2, 7, 6, 6, 5, 1, 5, 7, 7, 9, 4, 4],\n [9, 7, 7, 4, 4, 4, 3, 3, 4, 4, 7, 7, 9, 7, 3, 2, 2, 3, 7, 9, 7, 7, 4, 4, 3, 3, 4, 4, 4, 7],\n [7, 9, 4, 7, 4, 4, 3, 1, 4, 4, 7, 2, 7, 9, 2, 3, 3, 2, 9, 7, 2, 7, 4, 4, 1, 3, 4, 4, 7, 4],\n [7, 4, 9, 7, 3, 4, 4, 4, 7, 7, 9, 1, 7, 4, 9, 7, 7, 9, 4, 7, 1, 9, 7, 7, 4, 4, 4, 3, 7, 9],\n [4, 7, 7, 9, 4, 3, 4, 4, 7, 2, 9, 9, 4, 7, 7, 9, 9, 7, 7, 4, 9, 9, 2, 7, 4, 4, 3, 4, 9, 7],\n [6, 6, 5, 1, 4, 4, 7, 7, 7, 2, 2, 6, 4, 6, 2, 2, 2, 2, 6, 4, 6, 2, 2, 7, 7, 7, 4, 4, 1, 5],\n [1, 6, 1, 5, 4, 4, 7, 2, 3, 7, 6, 6, 6, 4, 2, 2, 2, 2, 4, 6, 6, 6, 7, 3, 2, 7, 4, 4, 5, 1],\n [6, 6, 6, 6, 7, 7, 9, 9, 9, 1, 7, 2, 2, 2, 4, 6, 6, 4, 2, 2, 2, 7, 1, 9, 9, 9, 7, 7, 6, 6],\n [6, 6, 1, 6, 7, 2, 1, 9, 1, 5, 3, 7, 2, 2, 6, 4, 4, 6, 2, 2, 7, 3, 5, 1, 9, 1, 2, 7, 6, 1],\n [4, 4, 7, 7, 9, 7, 7, 4, 9, 9, 1, 6, 7, 2, 6, 6, 6, 6, 2, 7, 6, 1, 9, 9, 4, 7, 7, 9, 7, 7],\n [4, 4, 7, 2, 7, 9, 4, 7, 9, 9, 6, 1, 3, 7, 6, 2, 2, 6, 7, 3, 1, 6, 9, 9, 7, 4, 9, 7, 2, 7],\n [8, 8, 8, 1, 3, 2, 9, 7, 1, 6, 9, 9, 5, 1, 7, 2, 2, 7, 1, 5, 9, 9, 6, 1, 7, 9, 2, 3, 1, 9],\n [8, 8, 8, 9, 2, 3, 7, 9, 6, 1, 9, 9, 1, 9, 3, 7, 7, 3, 9, 1, 9, 9, 1, 6, 9, 7, 3, 2, 9, 9],\n [8, 8, 8, 9, 2, 3, 7, 9, 6, 1, 9, 9, 1, 9, 3, 7, 7, 3, 9, 1, 9, 9, 1, 6, 9, 7, 3, 2, 9, 9],\n [8, 8, 8, 1, 3, 2, 9, 7, 1, 6, 9, 9, 5, 1, 7, 2, 2, 7, 1, 5, 9, 9, 6, 1, 7, 9, 2, 3, 1, 9],\n [8, 8, 8, 2, 7, 9, 4, 7, 9, 9, 6, 1, 3, 7, 6, 2, 2, 6, 7, 3, 1, 6, 9, 9, 7, 4, 9, 7, 2, 7],\n [8, 8, 8, 7, 9, 7, 7, 4, 9, 9, 1, 6, 7, 2, 6, 6, 6, 6, 2, 7, 6, 1, 9, 9, 4, 7, 7, 9, 7, 7],\n [8, 8, 8, 6, 7, 2, 1, 9, 1, 5, 3, 7, 2, 2, 6, 4, 4, 6, 2, 2, 7, 3, 5, 1, 9, 1, 2, 7, 6, 1],\n [8, 8, 8, 6, 7, 7, 9, 9, 9, 1, 7, 2, 2, 2, 4, 6, 6, 4, 2, 2, 2, 7, 1, 9, 9, 9, 7, 7, 6, 6],\n [8, 8, 8, 5, 4, 4, 7, 2, 3, 7, 6, 6, 6, 4, 2, 2, 2, 2, 4, 6, 6, 6, 7, 3, 2, 7, 4, 4, 5, 1],\n [6, 6, 5, 1, 4, 4, 7, 7, 7, 2, 2, 6, 4, 6, 2, 2, 2, 2, 6, 4, 6, 2, 2, 7, 7, 7, 4, 4, 1, 5],\n [4, 7, 7, 9, 4, 3, 4, 4, 7, 2, 9, 9, 4, 7, 7, 9, 9, 7, 7, 4, 9, 9, 2, 7, 4, 4, 3, 4, 9, 7],\n [7, 4, 9, 7, 3, 4, 4, 4, 7, 7, 9, 1, 7, 4, 9, 7, 7, 9, 4, 7, 1, 9, 7, 7, 4, 4, 4, 3, 7, 9],\n [7, 9, 4, 7, 4, 4, 3, 1, 4, 4, 7, 2, 7, 9, 2, 3, 3, 2, 9, 7, 2, 7, 4, 4, 1, 3, 4, 4, 7, 4],\n [9, 7, 7, 4, 4, 4, 3, 3, 4, 4, 7, 7, 9, 7, 3, 2, 2, 3, 7, 9, 7, 7, 4, 4, 3, 3, 4, 4, 4, 7],\n [4, 3, 4, 4, 9, 7, 7, 5, 1, 5, 6, 6, 7, 2, 1, 9, 9, 1, 2, 7, 6, 6, 5, 1, 5, 7, 7, 9, 4, 4],\n [3, 4, 4, 4, 7, 9, 5, 7, 5, 1, 6, 1, 7, 7, 9, 9, 9, 9, 7, 7, 1, 6, 1, 5, 7, 5, 9, 7, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 7, 9], [7, 2, 9], [7, 2, 9], [7, 7, 9], [4, 4, 7], [4, 4, 7], [6, 6, 1], [6, 6, 6], [1, 6, 1]], "task_id": "0934a4d8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 3, 0, 0, 3, 4, 4, 4, 4, 4],\n [2, 2, 2, 3, 0, 0, 3, 4, 4, 4, 4, 4],\n [2, 2, 2, 3, 0, 0, 3, 4, 4, 4, 4, 4],\n [2, 2, 2, 3, 0, 0, 3, 4, 4, 4, 4, 4],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 3, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 3, 0, 0, 0, 3, 4, 4, 4, 4],\n [2, 2, 2, 3, 0, 0, 0, 3, 4, 4, 4, 4],\n [2, 2, 2, 3, 0, 0, 0, 3, 4, 4, 4, 4],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 1, 3, 0, 0, 0, 3, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 8, 8, 8, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0]\n ],\n \"output\": [\n [2, 2, 2, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 4],\n [2, 2, 2, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 4],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [0, 0, 0, 3, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 1, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 8],\n [1, 1, 1, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 3, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4], [2, 2, 3, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4], [2, 2, 3, 0, 0, 0, 3, 0, 0, 3, 4, 4, 4, 4, 4], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 3, 7, 7, 7, 3, 7, 7, 3, 0, 0, 0, 0, 0], [0, 0, 3, 7, 7, 7, 3, 7, 7, 3, 0, 0, 0, 0, 0], [0, 0, 3, 7, 7, 7, 3, 7, 7, 3, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 3, 7, 7, 7, 3, 7, 7, 3, 0, 0, 0, 0, 0], [0, 0, 3, 7, 7, 7, 3, 7, 7, 3, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [1, 1, 3, 0, 0, 0, 3, 0, 0, 3, 8, 8, 8, 8, 8], [1, 1, 3, 0, 0, 0, 3, 0, 0, 3, 8, 8, 8, 8, 8], [1, 1, 3, 0, 0, 0, 3, 0, 0, 3, 8, 8, 8, 8, 8], [1, 1, 3, 0, 0, 0, 3, 0, 0, 3, 8, 8, 8, 8, 8]], "task_id": "e9c9d9a1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 2, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 6, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 4, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 4, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 9, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 3, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 7, 7, 5, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "070dd51e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3],\n [0, 0, 2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3],\n [2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 5, 5, 5, 5],\n [0, 0, 2, 2, 5, 5, 2, 2, 2, 2, 5, 5, 5, 5],\n [2, 5, 2, 2, 5, 5, 2, 2, 2, 2, 5, 5, 5, 5],\n [5, 3, 5, 5, 3, 3, 5, 5, 5, 5, 3, 3, 3, 3],\n [0, 0, 5, 5, 3, 3, 5, 5, 5, 5, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],\n [3, 4, 3, 3, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 3, 3, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], "task_id": "762cd429"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 3, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 3, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "da2b0fe3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 8],\n [8, 8, 8, 8, 8, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [3, 3, 3, 3, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 2, 2, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 2, 2, 2, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 3, 3, 3, 3, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [3, 3, 2],\n [2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1],\n [1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [3, 3, 3],\n [2, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [3, 3, 2],\n [2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 3], [2, 2, 2]], "task_id": "5289ad53"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0],\n [0, 4, 0, 4, 0, 4, 0],\n [0, 0, 5, 5, 5, 0, 0],\n [0, 6, 0, 6, 0, 6, 0],\n [0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 6, 0, 6, 0], [0, 0, 6, 0, 6, 0, 0], [0, 0, 5, 5, 5, 0, 0], [0, 4, 4, 4, 4, 4, 0], [0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0]], "task_id": "e21a174a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8],\n [1, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [1, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 8, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 8, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 8, 0, 0, 1, 1, 1, 0, 0, 8, 1, 1, 1, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [8, 1, 1, 1, 0, 8, 0, 1, 1, 1, 0, 8, 0, 0, 0, 0, 8, 1, 1, 8],\n [1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 8, 0, 8, 0, 8, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 1, 1, 1, 8, 0],\n [0, 1, 1, 1, 1, 1, 8, 0, 0, 1, 1, 1, 0, 0, 1, 1, 8, 1, 0, 0],\n [1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1],\n [0, 1, 1, 8, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 8, 0],\n [0, 0, 1, 1, 1, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 5, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 5, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 5, 0],\n [1, 1, 5, 1, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 5, 1, 0, 0, 0, 5, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [1, 1, 1, 1, 5, 1, 1, 1, 1, 1],\n [0, 5, 0, 1, 1, 1, 0, 0, 5, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0],\n [0, 0, 1, 1, 1, 0],\n [1, 1, 1, 5, 1, 1],\n [0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [1, 0, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0],\n [1, 1, 8, 1, 1, 1],\n [0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [1, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 5, 0],\n [0, 1, 1, 1, 5, 0, 0, 1, 1, 5, 1, 1, 1],\n [1, 1, 5, 1, 1, 1, 1, 1, 5, 0, 0, 0, 5],\n [0, 1, 1, 1, 0, 5, 0, 1, 1, 5, 1, 1, 1],\n [0, 5, 0, 5, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1],\n [0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [1, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [1, 1, 8, 1, 8, 1, 1],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 8, 8, 0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1], [0, 0, 0, 0, 8, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 8, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1], [0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 8, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 8, 0], [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1], [8, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0]], "task_id": "79fb03f4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0],\n [2, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0],\n [0, 2, 0, 2, 0],\n [2, 0, 0, 0, 2],\n [0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 2, 0],\n [2, 0, 1, 0, 0, 0, 2],\n [0, 0, 0, 1, 0, 0, 0],\n [1, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0], [0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0], [2, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]], "task_id": "c1990cce"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [3, 3, 3, 3, 1, 2, 2, 2],\n [3, 3, 3, 3, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 5, 5, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 5, 5, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [5, 5, 3, 3, 3, 3, 2, 2, 2],\n [5, 5, 3, 3, 3, 3, 2, 2, 2],\n [3, 3, 3, 3, 3, 3, 2, 2, 2],\n [3, 3, 3, 3, 3, 3, 2, 2, 2],\n [3, 3, 3, 3, 3, 3, 2, 2, 2],\n [3, 3, 3, 3, 3, 3, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 3, 3, 4, 6, 6, 6],\n [3, 3, 3, 3, 4, 6, 6, 6],\n [3, 3, 3, 3, 4, 6, 6, 6],\n [4, 4, 4, 4, 4, 6, 6, 6],\n [4, 4, 4, 4, 4, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 1, 1],\n [1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 6, 8, 3, 2, 2, 2, 2, 2, 2], [8, 8, 8, 3, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]], "task_id": "20818e16"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1],\n [0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0],\n [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0],\n [1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1],\n [0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2]\n ],\n \"output\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0],\n [0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1],\n [0, 1, 1, 0, 3, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 3, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1],\n [0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 0, 0, 1, 1, 0],\n [1, 0, 0, 0, 1, 0, 0, 1, 2, 1, 0, 1, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 1, 1, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 2, 1, 0, 0, 0, 1],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 0],\n [1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 3, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 2, 1],\n [0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 2],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1],\n [0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0]\n ],\n \"output\": [\n [0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0],\n [1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1],\n [1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 3, 3, 3, 2, 2, 3, 2, 2, 3, 2],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1],\n [0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 1, 1, 0, 0, 2, 0, 0, 1],\n [1, 0, 1, 0, 1, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 1, 1, 1, 0],\n [1, 1, 1, 0, 0, 0, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 0],\n [0, 1, 1, 0, 1, 0, 1, 0, 1, 0],\n [1, 0, 0, 0, 1, 0, 1, 1, 0, 1],\n [0, 1, 1, 1, 1, 0, 0, 1, 1, 1],\n [0, 1, 0, 1, 0, 0, 2, 1, 1, 0]\n ],\n \"output\": [\n [0, 1, 1, 1, 0, 0, 2, 0, 0, 1],\n [1, 0, 1, 0, 1, 1, 3, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 2, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 3, 1, 1, 0],\n [1, 1, 1, 0, 0, 0, 3, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 2, 0, 1, 0],\n [0, 1, 1, 0, 1, 0, 3, 0, 1, 0],\n [1, 0, 0, 0, 1, 0, 3, 1, 0, 1],\n [0, 1, 1, 1, 1, 0, 2, 1, 1, 1],\n [0, 1, 0, 1, 0, 0, 2, 1, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2],\n [1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1],\n [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1],\n [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1],\n [1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0],\n [1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1],\n [1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0],\n [1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1],\n [2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2], [1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 3, 1], [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 0, 0], [1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 3, 0, 0, 1], [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 3, 0, 1, 0, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 1, 0, 1, 0, 1], [1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 3, 0, 0, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 0, 0, 2, 1, 0, 1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 1, 0, 0, 0, 2, 1, 0, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 3, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 3, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1], [1, 1, 0, 0, 3, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [1, 0, 0, 3, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0], [1, 1, 3, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0], [1, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1], [2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0]], "task_id": "bcb3040b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 6, 4, 3, 4, 7, 3, 8, 3, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 3, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 6, 4, 3, 4, 7, 3, 8, 3, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 4, 6, 2, 1, 9, 2, 9, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 4, 6, 2, 1, 9, 2, 9, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 1, 4, 4, 6, 3, 1, 6, 3, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 4, 4, 6, 0, 0, 6, 0, 6],\n [4, 0, 4, 4, 6, 0, 0, 6, 0, 6],\n [4, 0, 4, 4, 6, 0, 0, 6, 0, 6],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 1, 4, 4, 6, 3, 1, 6, 3, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 3, 2, 3, 1, 1, 3, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 3, 2, 3, 1, 1, 3, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 2, 1, 2, 1, 1, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 1, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 2, 1, 2, 1, 1, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 1, 1, 1, 1, 4, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 4, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 1, 1, 1, 1, 4, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 6, 4, 6, 2, 4, 4, 3, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 6, 0, 6, 2, 0, 0, 3, 0], [2, 3, 6, 0, 6, 2, 0, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 6, 4, 6, 2, 4, 4, 3, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "2685904e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 7, 7, 7, 8, 8, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 7, 7, 7, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 8, 8, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0], [0, 0, 0, 0, 2, 2, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0], [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0], [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 7, 7, 7, 7, 7, 7, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "3490cc26"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 4, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [8, 8, 0, 1, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 1, 0, 1],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 1, 0, 0, 1, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 1, 0, 0],\n [8, 8, 0, 1, 0, 0, 1, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 1]\n ],\n \"output\": [\n [1, 4, 8, 8, 8, 8, 8, 8, 8, 8],\n [3, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 4, 0, 0, 0],\n [8, 8, 0, 1, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 4, 0, 4],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 3, 0, 0, 2, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 2, 0, 0],\n [8, 8, 0, 3, 0, 0, 2, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 6],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 8, 8],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 8, 8],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 8, 8],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 6],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 2],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 8, 8],\n [0, 4, 0, 0, 0, 0, 0, 0, 6, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [4, 0, 0, 0, 0, 0, 6, 6, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 8],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 8, 8],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 8, 8],\n [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 8, 8],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 3, 0, 0, 0, 1, 0, 1, 0, 0, 8, 8], [3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 8], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 8], [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [7, 0, 0, 0, 0, 7, 0, 0, 4, 0, 0, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 4, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 8, 8], [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 1], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 4]], "task_id": "58743b76"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 8, 8, 0, 0, 8, 8, 4, 0, 0, 1, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 8, 8, 0, 0, 8, 8, 4, 0, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 0, 8, 8, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 0, 8, 8, 0, 0, 4, 1, 1, 1, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [0, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 1, 1, 4, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 1, 1, 0, 4, 0, 1, 1, 4, 0, 1, 0],\n [0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 1, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0],\n [0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1],\n [0, 1, 0, 4, 1, 0, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 1, 4, 1, 0, 0],\n [1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 1, 1, 0],\n [1, 1, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 1, 0, 4, 1, 1, 0, 4, 1, 0, 1],\n [1, 0, 0, 4, 1, 1, 1, 4, 0, 1, 0, 4, 0, 1, 1, 4, 1, 1, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 1, 4, 8, 0, 8, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 8, 8, 0, 0, 8, 8, 4, 0, 0, 1, 4, 0, 8, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 8, 8, 0, 0, 8, 8, 4, 0, 1, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 0, 8, 8, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 0, 8, 8, 0, 0, 4, 8, 1, 8, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 4, 0, 8, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [0, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 1, 1, 4, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 1, 1, 0, 4, 0, 1, 1, 4, 0, 1, 0],\n [0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 1, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0],\n [0, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1],\n [0, 1, 0, 4, 1, 0, 1, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 1, 4, 1, 0, 0],\n [1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 1, 1, 0],\n [1, 1, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 1, 0, 4, 1, 1, 0, 4, 1, 0, 1],\n [1, 0, 0, 4, 1, 1, 1, 4, 0, 1, 0, 4, 0, 1, 1, 4, 1, 1, 1, 4, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 0, 0, 1, 4, 0, 1, 0],\n [1, 1, 0, 4, 0, 1, 0, 4, 1, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 0, 0, 1],\n [1, 1, 1, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 1, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 0, 0],\n [0, 0, 0, 4, 0, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1],\n [0, 1, 0, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 0, 4, 0, 1, 0, 4, 1, 1, 1, 4, 0, 0, 0, 4, 0, 1, 0, 4, 0, 1, 1],\n [0, 0, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 1],\n [0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 1, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 0, 0, 1, 4, 0, 0, 1],\n [1, 1, 1, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0],\n [1, 1, 0, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 0, 1, 1, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 0, 1, 0, 4, 1, 0, 1, 4, 1, 0, 1, 4, 0, 0, 0, 0, 6, 6, 4],\n [1, 1, 0, 4, 0, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 0, 6, 6, 4],\n [0, 0, 0, 4, 0, 0, 1, 4, 0, 0, 0, 4, 0, 1, 1, 4, 6, 6, 6, 6, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 0, 0, 4],\n [0, 0, 1, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 0, 6, 6, 0, 0, 4],\n [1, 1, 1, 4, 0, 0, 0, 4, 1, 1, 1, 4, 0, 0, 1, 4, 0, 0, 6, 6, 0, 0, 4],\n [0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [0, 0, 6, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 0, 0, 1, 4, 0, 1, 0],\n [6, 6, 0, 4, 0, 1, 0, 4, 1, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 0, 0, 1],\n [1, 6, 1, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 1, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 0, 0],\n [0, 0, 0, 4, 0, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1],\n [0, 1, 0, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 0, 4, 0, 1, 0, 4, 1, 1, 1, 4, 0, 0, 0, 4, 0, 1, 0, 4, 0, 1, 1],\n [0, 0, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 1],\n [0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 6, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 1, 4, 0, 0, 6, 4, 0, 0, 1],\n [6, 6, 1, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 0, 0, 4, 6, 6, 0, 4, 1, 0, 0],\n [1, 6, 0, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 0, 6, 1, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 0, 1, 0, 4, 1, 0, 1, 4, 1, 0, 1, 4, 0, 0, 0, 0, 6, 6, 4],\n [1, 1, 0, 4, 0, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 0, 0, 0, 6, 6, 4],\n [0, 0, 0, 4, 0, 0, 1, 4, 0, 0, 0, 4, 0, 1, 1, 4, 6, 6, 6, 6, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 0, 0, 4],\n [0, 0, 1, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 0, 6, 6, 0, 0, 4],\n [1, 1, 1, 4, 0, 0, 0, 4, 1, 1, 1, 4, 0, 0, 1, 4, 0, 0, 6, 6, 0, 0, 4],\n [0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0],\n [1, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 1],\n [0, 1, 1, 4, 1, 0, 0, 4, 1, 0, 1, 4, 0, 1, 0, 4, 0, 0, 0, 4, 1, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 1, 1, 1, 4, 0, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0, 4, 1, 0, 0],\n [1, 0, 0, 4, 1, 1, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 1, 0, 1, 4, 0, 1, 0],\n [1, 1, 0, 4, 0, 1, 1, 4, 1, 0, 0, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 1, 1, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0],\n [1, 1, 0, 4, 0, 0, 1, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 1, 4, 0, 1, 0],\n [0, 1, 1, 4, 1, 1, 0, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 1, 4, 1, 0, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 1, 0, 0],\n [1, 0, 1, 4, 1, 0, 1, 4, 0, 1, 1, 4, 0, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0],\n [0, 1, 1, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 0, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 3, 3, 0, 0, 0, 0, 4, 1, 1, 1, 4, 1, 0, 1, 4, 0, 0, 1, 4, 0, 1, 1],\n [4, 3, 3, 0, 0, 0, 0, 4, 1, 1, 1, 4, 0, 1, 1, 4, 1, 1, 0, 4, 1, 0, 0],\n [4, 0, 0, 3, 3, 0, 0, 4, 0, 1, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0],\n [4, 0, 0, 3, 3, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 0, 0, 0, 3, 3, 4, 0, 1, 1, 4, 0, 0, 0, 4, 0, 1, 0, 4, 0, 1, 0],\n [4, 0, 0, 0, 0, 3, 3, 4, 1, 0, 1, 4, 0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0]\n ],\n \"output\": [\n [3, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0],\n [1, 3, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 1],\n [0, 1, 3, 4, 1, 0, 0, 4, 1, 0, 1, 4, 0, 1, 0, 4, 0, 0, 0, 4, 1, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 3, 1, 1, 4, 0, 0, 1, 4, 3, 0, 0, 4, 1, 0, 0, 4, 1, 0, 0],\n [1, 0, 0, 4, 1, 3, 0, 4, 0, 1, 0, 4, 1, 3, 0, 4, 1, 0, 1, 4, 0, 1, 0],\n [1, 1, 0, 4, 0, 1, 3, 4, 1, 0, 0, 4, 0, 0, 3, 4, 1, 0, 1, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 1, 1, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0],\n [1, 1, 0, 4, 0, 0, 1, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 1, 4, 0, 1, 0],\n [0, 1, 1, 4, 1, 1, 0, 4, 0, 1, 0, 4, 0, 1, 0, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 1, 1, 4, 1, 0, 1, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 1, 0, 0],\n [1, 0, 1, 4, 1, 0, 1, 4, 0, 1, 1, 4, 0, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0],\n [0, 1, 1, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 0, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 3, 3, 0, 0, 0, 0, 4, 1, 1, 1, 4, 3, 0, 1, 4, 0, 0, 1, 4, 0, 1, 1],\n [4, 3, 3, 0, 0, 0, 0, 4, 1, 1, 1, 4, 0, 3, 1, 4, 1, 1, 0, 4, 1, 0, 0],\n [4, 0, 0, 3, 3, 0, 0, 4, 0, 1, 0, 4, 0, 0, 3, 4, 1, 1, 1, 4, 0, 0, 0],\n [4, 0, 0, 3, 3, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 0, 0, 0, 3, 3, 4, 0, 1, 1, 4, 0, 0, 0, 4, 0, 1, 0, 4, 0, 1, 0],\n [4, 0, 0, 0, 0, 3, 3, 4, 1, 0, 1, 4, 0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 1],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 1, 0, 4, 1, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 1, 4, 1, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0, 0, 7, 7, 4],\n [0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 1, 0, 4, 0, 0, 0, 0, 7, 7, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 7, 7, 7, 7, 7, 4],\n [0, 0, 0, 4, 0, 0, 0, 4, 1, 1, 1, 4, 0, 0, 0, 4, 7, 7, 7, 7, 7, 7, 4],\n [0, 1, 0, 4, 1, 0, 0, 4, 0, 1, 1, 4, 0, 1, 1, 4, 7, 7, 0, 0, 0, 0, 4],\n [1, 0, 0, 4, 1, 0, 1, 4, 1, 0, 0, 4, 0, 1, 0, 4, 7, 7, 0, 0, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 0, 0, 1, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 1, 0],\n [1, 0, 0, 4, 1, 1, 1, 4, 0, 0, 0, 4, 1, 1, 0, 4, 1, 0, 1, 4, 1, 0, 0],\n [0, 0, 0, 4, 1, 0, 0, 4, 1, 1, 0, 4, 1, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 1, 4, 0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 0, 4, 0, 0, 0, 4, 0, 0, 1],\n [1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 0, 0, 0],\n [0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0],\n [0, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0],\n [1, 1, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 1, 0, 1, 4, 0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 1, 1, 4, 0, 0, 1, 4, 1, 0, 1, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0],\n [0, 0, 0, 4, 1, 1, 1, 4, 1, 1, 1, 4, 0, 1, 1, 4, 1, 0, 1, 4, 1, 1, 0],\n [0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 1, 4, 1, 0, 0, 4, 0, 1, 1, 4, 0, 1, 0, 4, 4, 4, 4, 4, 4, 4, 4], [1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 0, 0, 0, 0, 7, 7, 4], [0, 1, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 1, 1, 0, 4, 0, 0, 0, 0, 7, 7, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 7, 7, 7, 7, 7, 4], [0, 0, 0, 4, 0, 0, 0, 4, 1, 1, 1, 4, 0, 0, 0, 4, 7, 7, 7, 7, 7, 7, 4], [0, 1, 0, 4, 1, 0, 0, 4, 0, 1, 1, 4, 0, 1, 1, 4, 7, 7, 0, 0, 0, 0, 4], [1, 0, 0, 4, 1, 0, 1, 4, 1, 0, 0, 4, 0, 1, 0, 4, 7, 7, 0, 0, 0, 0, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [1, 0, 0, 4, 0, 0, 7, 4, 1, 1, 0, 4, 1, 1, 0, 4, 0, 0, 1, 4, 1, 1, 0], [1, 0, 0, 4, 7, 7, 7, 4, 0, 0, 0, 4, 1, 1, 0, 4, 1, 0, 1, 4, 1, 0, 0], [0, 0, 0, 4, 7, 0, 0, 4, 1, 1, 0, 4, 1, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [1, 0, 1, 4, 0, 0, 0, 4, 1, 0, 1, 4, 1, 1, 0, 4, 0, 0, 0, 4, 0, 0, 1], [1, 0, 0, 4, 0, 0, 0, 4, 0, 0, 1, 4, 1, 1, 1, 4, 1, 1, 0, 4, 0, 0, 0], [0, 1, 1, 4, 0, 1, 0, 4, 1, 0, 1, 4, 0, 0, 1, 4, 1, 0, 0, 4, 1, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0], [0, 0, 1, 4, 0, 1, 0, 4, 1, 0, 0, 4, 1, 0, 0, 4, 1, 1, 0, 4, 1, 0, 0], [1, 1, 0, 4, 0, 0, 0, 4, 1, 0, 0, 4, 1, 0, 1, 4, 0, 0, 0, 4, 0, 1, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 1, 1, 4, 0, 0, 7, 4, 1, 0, 7, 4, 0, 1, 0, 4, 1, 1, 0, 4, 0, 1, 0], [0, 0, 0, 4, 7, 7, 7, 4, 7, 7, 7, 4, 0, 1, 1, 4, 1, 0, 1, 4, 1, 1, 0], [0, 0, 0, 4, 7, 0, 1, 4, 7, 1, 1, 4, 0, 0, 0, 4, 1, 0, 0, 4, 0, 1, 0]], "task_id": "15113be4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 1, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 1, 1, 0, 0, 0],\n [2, 2, 0, 1, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 3, 3, 0, 0, 1, 0, 2, 2],\n [2, 2, 0, 0, 0, 0, 1, 1, 0, 0, 2],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 1, 2, 2],\n [0, 2, 3, 3, 1, 1, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [2, 2, 2, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 6, 6, 6]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 8, 6, 6, 6],\n [0, 0, 2, 8, 8, 8, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 1, 0, 0, 0, 0, 0, 2, 2, 0],\n [1, 1, 0, 0, 2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 1, 1, 2, 2, 0, 2, 2],\n [1, 1, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 1, 1, 0, 0, 3, 0, 5, 5],\n [2, 2, 0, 0, 1, 0, 3, 3, 0, 0, 5],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 1, 1, 0, 0, 0, 0], [2, 2, 0, 1, 0, 3, 5, 5], [0, 0, 0, 1, 3, 3, 0, 5]], "task_id": "d017b73f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 1, 0, 0, 0, 0],\n [1, 3, 2, 0, 0, 0],\n [0, 2, 3, 1, 0, 0],\n [0, 0, 1, 4, 1, 0],\n [0, 0, 0, 1, 3, 2],\n [0, 0, 0, 0, 2, 3]\n ],\n \"output\": [\n [4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 3, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3]\n ]\n}\n\n{\n \"input\": [\n [2, 5, 0, 0, 0],\n [5, 2, 0, 0, 0],\n [0, 0, 2, 5, 0],\n [0, 0, 5, 2, 0],\n [0, 0, 0, 0, 2]\n ],\n \"output\": [\n [2, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 6],\n [0, 0, 0, 0, 0, 0, 6, 6, 4, 0],\n [0, 0, 0, 0, 0, 4, 8, 6, 0, 0],\n [0, 0, 0, 0, 6, 6, 4, 0, 0, 0],\n [0, 0, 0, 4, 8, 6, 0, 0, 0, 0],\n [0, 0, 6, 6, 4, 0, 0, 0, 0, 0],\n [0, 4, 8, 6, 0, 0, 0, 0, 0, 0],\n [6, 6, 4, 0, 0, 0, 0, 0, 0, 0],\n [8, 6, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "cad67732"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 3, 0, 3, 4, 0, 4],\n [0, 2, 0, 0, 3, 0, 0, 4, 0],\n [2, 2, 2, 3, 3, 3, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 8, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3],\n [3, 3, 3],\n [0, 3, 0],\n [0, 6, 6],\n [6, 6, 6],\n [0, 6, 0],\n [0, 8, 8],\n [8, 8, 8],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3, 0, 2, 2],\n [3, 3, 0, 2, 2, 0],\n [0, 3, 3, 0, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 6, 0],\n [6, 6, 0],\n [0, 0, 6],\n [0, 2, 0],\n [2, 2, 0],\n [0, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 8, 0, 3, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 2, 0, 8, 8, 0, 3, 3, 0, 6, 6], [2, 2, 0, 8, 8, 0, 3, 3, 0, 6, 6, 0], [2, 2, 0, 8, 8, 0, 3, 3, 0, 6, 6, 0]], "task_id": "12997ef3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 6, 6, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 6, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 7, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 4, 1, 4, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 4, 4, 1, 4, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 6, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 8, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 4, 1, 4, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 4, 4, 1, 4, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 5, 1, 1], [1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 1, 5, 1, 1], [1, 1, 1, 1, 1, 6, 1, 6, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 6, 6, 1, 6, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1], [1, 1, 1, 1, 9, 1, 1, 1, 1, 7, 7, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1], [1, 1, 1, 9, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 9, 9, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "fd096ab6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 4, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 4, 2, 4, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 4, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 4, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 4, 2, 4, 2, 4, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 4, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 4, 3, 4, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 3, 4, 3, 4, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 8, 8, 4, 8, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 4, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 8, 8, 4, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 4, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 4, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 8, 8, 4, 8, 4, 8, 4, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 4, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 4, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 4, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 4, 1, 4, 1, 4, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 1, 1, 1, 4, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 1, 1, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "5b692c0f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 2, 0, 5, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 3, 0, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 8, 8, 8, 8, 8, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2], [0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "3f23242b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "992798f6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 0, 0, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 5, 4, 5, 0, 0, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 1, 5, 0, 0, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 0, 0, 0, 0, 0, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 0, 0, 0, 0, 0, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 0, 0, 0, 0, 0, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 0, 0, 0, 1, 5, 5, 1, 5, 5, 0, 0, 0, 0, 0, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 0, 0, 0, 1, 4, 1, 4, 1, 4, 0, 0, 0, 0, 0, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 0, 0, 0, 0, 0, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1],\n [1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1, 5, 5, 1],\n [1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 0, 0, 0, 0, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 0, 0, 0, 0, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 0, 0, 0, 0, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 0, 0, 0, 0, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 0, 0, 0, 0, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 0, 0, 0, 0, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 3, 0, 0, 0, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 5, 0, 0, 0, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 0, 0, 0, 1, 2, 3, 5, 5, 3, 2, 0, 0, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 0, 0, 0, 0, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 0, 0, 0, 0, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 0, 0, 0, 0, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 0, 0, 0, 0, 1, 5, 0, 0, 0, 0, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 0, 0, 0, 0, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 0, 0, 0, 0, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5, 5, 3, 2, 1, 2, 3, 5],\n [1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3, 3, 2, 5, 1, 5, 2, 3],\n [1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2, 2, 5, 3, 1, 3, 5, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 0, 0, 0, 0, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 0, 0, 0, 0, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0, 0, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 0, 0, 0, 0, 0, 0, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 0, 0, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 0, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 0, 0, 0, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1, 5, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1],\n [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1],\n [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1],\n [1, 2, 8, 1, 5, 5, 0, 0, 0, 0, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 8, 1, 5, 5, 0, 0, 0, 0, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1],\n [1, 5, 2, 1, 8, 8, 0, 0, 0, 0, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1],\n [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 8, 5, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 0, 0, 1],\n [1, 8, 5, 1, 2, 0, 0, 0, 0, 0, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1],\n [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 0, 0, 0, 0, 1, 2, 5, 1, 5, 2, 1, 0, 0, 1],\n [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 0, 0, 0, 0, 1, 8, 2, 1, 2, 8, 1, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1],\n [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 0, 0, 0, 0, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1],\n [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 0, 0, 0, 0, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1],\n [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1], [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1], [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1], [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1], [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1, 8, 2, 1, 2, 8, 1, 5, 5, 1], [1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1, 2, 5, 1, 5, 2, 1, 8, 8, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1, 5, 8, 1, 8, 5, 1, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "1d0a4b61"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 0, 0, 0, 5, 5, 0, 0, 5, 5],\n [5, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [5, 0, 0, 5, 0, 5, 0, 0, 0, 5],\n [5, 5, 0, 5, 5, 5, 0, 5, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 8, 0, 0, 5, 5, 0, 0, 5, 5],\n [5, 5, 8, 0, 0, 5, 0, 0, 0, 5],\n [5, 5, 5, 8, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 8, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 8, 0, 0, 5, 5],\n [5, 5, 0, 0, 0, 5, 8, 0, 0, 5],\n [5, 0, 0, 5, 0, 5, 0, 8, 0, 5],\n [5, 5, 0, 5, 5, 5, 0, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 0, 5],\n [5, 5, 0, 0, 5, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 5, 5, 5],\n [5, 5, 0, 5, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 8, 5],\n [5, 5, 0, 0, 5, 0, 0, 8, 0, 5],\n [5, 0, 0, 0, 0, 0, 8, 0, 0, 5],\n [5, 5, 0, 0, 0, 8, 0, 0, 0, 5],\n [5, 5, 5, 0, 8, 0, 0, 0, 5, 5],\n [5, 0, 0, 8, 0, 0, 0, 0, 0, 5],\n [5, 0, 8, 0, 0, 0, 0, 5, 5, 5],\n [5, 5, 0, 5, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 5, 0, 0, 0, 0, 5, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 5, 5, 0, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 0, 5, 5, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 8, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 5, 8, 0, 0, 0, 5, 0, 0, 5],\n [5, 0, 0, 8, 0, 0, 0, 0, 0, 5],\n [5, 5, 0, 0, 8, 0, 0, 0, 0, 5],\n [5, 5, 0, 0, 0, 8, 0, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 8, 0, 0, 5],\n [5, 0, 0, 5, 5, 0, 0, 8, 0, 5],\n [5, 5, 5, 5, 5, 0, 5, 5, 8, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 0, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 5, 5, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 5, 0, 5, 5, 0, 0, 0, 5],\n [5, 5, 0, 0, 5, 5, 0, 0, 5, 5],\n [5, 5, 5, 0, 5, 5, 5, 0, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 8, 0, 0, 5, 5, 5, 5],\n [5, 5, 5, 0, 8, 0, 0, 5, 5, 5],\n [5, 5, 0, 0, 0, 8, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 8, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 0, 8, 0, 5],\n [5, 5, 5, 0, 5, 5, 0, 0, 8, 5],\n [5, 5, 0, 0, 5, 5, 0, 0, 5, 5],\n [5, 5, 5, 0, 5, 5, 5, 0, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 5, 5, 5],\n [5, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 5, 5, 0, 0, 5, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 0, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 0, 0, 0, 0, 8, 5, 5], [5, 5, 0, 0, 0, 0, 8, 0, 5, 5], [5, 5, 5, 0, 0, 8, 0, 0, 0, 5], [5, 0, 0, 0, 8, 0, 0, 5, 5, 5], [5, 0, 0, 8, 0, 0, 5, 5, 5, 5], [5, 0, 8, 0, 0, 0, 0, 0, 0, 5], [5, 8, 0, 5, 5, 0, 0, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]], "task_id": "aa300dc3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 9, 9, 0, 0],\n [0, 9, 0, 9, 0, 9, 0],\n [0, 0, 4, 4, 4, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 9, 0, 9, 0],\n [0, 0, 9, 9, 9, 0, 0],\n [0, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 0, 0], [0, 0, 2, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 0, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 4, 0, 4, 4, 4, 4, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e74e1818"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 8, 0, 0, 8, 2, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 0, 8, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 5, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 8, 8, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 8, 8, 8, 8, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 8, 8, 8, 8, 8, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 8, 8, 8, 8, 8, 2, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 2, 8, 8, 8, 8, 8, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 1, 1, 1, 5, 5, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 5, 5, 5, 5, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 5, 5, 5, 5, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 5, 5, 5, 5, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 5, 5, 5, 5, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 6, 0, 3, 0, 0],\n [0, 0, 2, 2, 2, 0, 3, 0, 0, 0, 2, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 2, 0, 0, 0, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 8, 0],\n [3, 0, 0, 0, 0, 0, 0, 4, 8, 0, 3, 0, 8, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 4, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 2, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 3, 3, 3, 3, 3, 2, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 2, 0, 0, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 8, 8, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 3, 0, 0, 0, 8, 0, 0, 8, 0, 3, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 3, 0, 6, 0, 0, 2, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 6, 0, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 6, 6, 6, 6, 6, 6, 3, 0, 0, 0],\n [0, 3, 3, 3, 6, 6, 6, 6, 6, 6, 3, 0, 0, 0],\n [0, 3, 6, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0, 0],\n [0, 3, 6, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0, 0],\n [0, 3, 3, 3, 6, 6, 6, 6, 6, 6, 3, 3, 0, 0],\n [0, 0, 0, 3, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0],\n [0, 0, 0, 3, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0],\n [0, 0, 3, 3, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0],\n [0, 0, 3, 6, 6, 6, 6, 6, 6, 6, 6, 3, 0, 0],\n [0, 0, 3, 6, 6, 6, 6, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 3, 6, 6, 6, 6, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 0, 3, 3, 3, 3, 3, 3, 0, 1],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 2, 0, 6, 0, 0, 8, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 6, 0, 3, 0, 0],\n [0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 0, 0, 0, 0, 8, 0, 3, 0, 0],\n [0, 2, 0, 0, 1, 0, 6, 0, 0, 0, 0, 2, 0, 0, 3, 8, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 3, 0, 0, 0, 8, 0, 0, 3, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 3, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 8, 0, 4, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 4, 0, 0, 0, 0, 8, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 8, 0, 3, 0, 0, 0, 0, 4, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 7, 0, 2, 0, 1, 0, 7, 0, 0],\n [0, 0, 8, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 0, 0, 0, 7, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0], [0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 3, 8, 8, 8, 8, 3, 0, 0], [0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 3, 3, 3, 8, 8, 8, 8, 3, 0, 0], [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 3, 8, 8, 8, 8, 8, 8, 3, 0, 0], [0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 3, 8, 8, 8, 8, 8, 8, 3, 0, 0], [0, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0, 0, 3, 8, 8, 8, 8, 8, 8, 3, 0, 0], [0, 2, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 8, 8, 3, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 8, 8, 8, 8, 8, 8, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 8, 8, 8, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 8, 8, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 8, 8, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0], [0, 0, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 8, 4, 4, 4, 4, 4, 4, 4, 4, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 8, 4, 4, 8, 8, 8, 8, 4, 4, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "4b6b68e5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 1, 1, 1, 2],\n [0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 1, 1, 1, 2],\n [4, 1, 0, 1, 4],\n [4, 1, 0, 1, 4],\n [4, 1, 0, 1, 4],\n [4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 2],\n [0, 0, 0, 1, 0],\n [0, 1, 0, 1, 0],\n [0, 1, 0, 0, 0],\n [2, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 2],\n [4, 4, 4, 1, 4],\n [4, 1, 4, 1, 4],\n [4, 1, 4, 4, 4],\n [2, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 4, 4, 4, 4, 0],\n [1, 1, 1, 1, 4, 0],\n [0, 0, 4, 4, 4, 0],\n [0, 0, 4, 1, 1, 1],\n [0, 0, 4, 4, 4, 2],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 2],\n [0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0],\n [2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 2],\n [4, 1, 1, 1, 1, 1],\n [4, 4, 4, 4, 4, 4],\n [1, 1, 1, 1, 1, 4],\n [2, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 2],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 0],\n [0, 1, 0, 1, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [2, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 2],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 4],\n [0, 0, 0, 1, 1, 0, 0, 0, 1, 4],\n [4, 4, 4, 1, 1, 4, 4, 4, 1, 4],\n [4, 1, 4, 1, 1, 4, 1, 4, 1, 4],\n [4, 1, 4, 4, 4, 4, 1, 4, 4, 4],\n [4, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [4, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [4, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [2, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 1, 0, 0, 0, 1, 0, 0, 0, 2],\n [0, 1, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 1, 0, 0, 0, 1, 4, 4, 4, 2], [4, 1, 4, 4, 4, 1, 4, 1, 0, 0], [4, 1, 4, 1, 4, 1, 4, 1, 0, 0], [4, 1, 4, 1, 4, 1, 4, 1, 0, 0], [4, 1, 4, 1, 4, 1, 4, 1, 0, 0], [4, 4, 4, 1, 4, 4, 4, 1, 0, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0, 0]], "task_id": "b15fca0b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 5, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 3, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 5, 8, 8, 0],\n [0, 0, 3, 2, 0, 0, 0, 0, 3, 5, 3, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 8, 8],\n [3, 5, 3, 8],\n [0, 3, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 6, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 6, 6, 8, 0, 0],\n [0, 0, 5, 5, 5, 0, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0],\n [8, 8, 8],\n [5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 8, 0, 0, 0, 0, 0, 0, 0, 5, 9, 0, 0],\n [0, 0, 8, 2, 0, 0, 0, 0, 0, 7, 7, 5, 9, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 5, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 5, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 9],\n [7, 7, 5, 9],\n [0, 5, 7, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 8, 5, 5, 8, 0],\n [0, 0, 0, 5, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 8, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 3, 0],\n [0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 0],\n [0, 0, 0, 7, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 8, 3], [3, 4, 3], [0, 4, 0], [0, 4, 0]], "task_id": "f5aa3634"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 0, 3, 0, 0, 3],\n [2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [2, 3, 3, 0, 0, 2, 2],\n [0, 3, 3, 0, 0, 2, 2],\n [0, 0, 0, 0, 3, 3, 0],\n [3, 0, 2, 0, 3, 3, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 0],\n [0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 0, 0, 2],\n [2, 2, 0, 0, 0],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 2, 2]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 3, 0, 0, 0],\n [2, 0, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 0, 2, 2, 0],\n [3, 3, 0, 0, 2, 2, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 0, 0, 3],\n [0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [3, 0, 0, 0, 3, 3, 0],\n [0, 0, 3, 0, 3, 3, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 2],\n [3, 3, 0, 2, 2],\n [3, 3, 0, 0, 0],\n [0, 0, 2, 2, 0],\n [3, 0, 2, 2, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0],\n [0, 3, 3, 0, 0],\n [2, 0, 0, 0, 0],\n [0, 0, 0, 0, 3],\n [3, 3, 0, 0, 0],\n [3, 3, 0, 2, 2],\n [0, 0, 0, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 0], [0, 1, 0], [0, 0, 0]], "task_id": "3b4c2228"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 4, 4, 4, 1, 4, 4, 4, 4, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4],\n [4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 2, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 2, 2, 2, 8, 6, 6, 6, 6, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4],\n [2, 1, 1, 1, 1, 2, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4],\n [2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 4, 2, 2, 2, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4],\n [4, 4, 2, 2, 1, 2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4],\n [4, 4, 2, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4],\n [4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 1, 1, 1, 4, 4],\n [4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 1, 1, 1, 4, 4],\n [4, 4, 4, 1, 1, 1, 1, 4, 4, 1, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 1, 1, 1, 4, 4],\n [4, 4, 4, 1, 1, 1, 1, 4, 4, 1, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4],\n [4, 4, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4],\n [4, 4, 2, 8, 8, 8, 8, 2, 2, 2, 2, 4, 4, 4, 4, 2, 8, 6, 6, 6, 8, 8, 8, 2, 4],\n [4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 2, 8, 6, 6, 6, 8, 8, 8, 2, 4],\n [4, 4, 2, 8, 8, 8, 8, 6, 6, 8, 2, 4, 4, 4, 4, 2, 8, 6, 6, 6, 8, 8, 8, 2, 4],\n [4, 4, 2, 8, 8, 8, 8, 6, 6, 8, 2, 4, 4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4],\n [4, 4, 2, 2, 2, 2, 8, 8, 8, 8, 2, 4, 4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4],\n [4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 2, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4],\n [4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 2, 1, 1, 1, 2, 4, 4, 4, 4, 2, 2, 2, 4],\n [4, 4, 2, 2, 2, 2, 2, 4, 4, 4, 4, 2, 1, 2, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],\n [4, 4, 4, 2, 8, 8, 8, 8, 8, 2, 2, 2, 4, 4, 4],\n [4, 4, 4, 2, 8, 6, 6, 8, 8, 8, 8, 2, 4, 4, 4],\n [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4],\n [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4],\n [4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 1, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 8, 6, 6, 6, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 2, 2, 2, 8, 8, 8, 8, 8, 6, 6, 6, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 4, 4, 4, 4, 2, 1, 1, 1, 1, 2, 4, 4, 4], [4, 4, 4, 2, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 4, 4, 4, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], [4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2], [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2], [4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 2, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2], [4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 1, 1, 1, 2, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 4, 4], [4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 2, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 8, 6, 6, 8, 2, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 8, 8, 8, 8, 2, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]], "task_id": "aa4ec2a5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 2, 3, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 3, 2, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 3, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 8, 4, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 1, 0, 4, 0, 4, 0],\n [0, 0, 4, 8, 0, 0, 1, 0, 0, 4, 8, 0],\n [0, 0, 8, 8, 8, 0, 1, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 6, 6, 5, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 5, 6, 6, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 5, 5, 6, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 8, 0, 1, 0, 2, 0, 0, 2, 0, 0], [0, 0, 0, 2, 2, 2, 2, 0, 1, 0, 8, 8, 8, 8, 0, 0], [0, 0, 0, 8, 0, 0, 8, 0, 1, 0, 2, 0, 0, 2, 0, 0], [0, 0, 2, 2, 2, 0, 0, 0, 1, 0, 0, 0, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]], "task_id": "2b01abd0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0],\n [0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0],\n [0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0]], "task_id": "21f83797"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 2, 2, 2, 0, 2, 2, 0, 5, 5, 0, 0],\n [0, 2, 2, 0, 0, 2, 2, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 5, 5, 0, 5, 0],\n [5, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0],\n [5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 2, 2, 2, 0, 2, 0],\n [2, 2, 0, 5, 5, 0, 2, 2, 2, 0, 2, 0],\n [2, 2, 0, 0, 5, 0, 0, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],\n [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5],\n [0, 5, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5],\n [5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],\n [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 5, 0, 0, 5],\n [0, 2, 0, 0, 2, 2, 2, 0, 5, 0, 0, 5],\n [2, 2, 2, 0, 0, 2, 0, 0, 5, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],\n [1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 5, 5, 0, 0, 0, 0, 5, 0],\n [0, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0], [0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0], [0, 2, 2, 0, 2, 0, 0, 5, 5, 0, 2, 0], [0, 0, 2, 0, 2, 0, 0, 5, 5, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1acc24af"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 3, 3, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 3, 2, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 2, 3, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 3, 3, 4, 0, 0, 0, 4, 3, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 0, 4, 3, 3, 3, 2, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 2, 3, 3, 4, 0],\n [0, 0, 0, 0, 4, 3, 3, 3, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0, 8],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 4, 8, 8, 8, 4],\n [0, 0, 8, 0, 2, 8, 8, 4, 0, 0, 8, 0, 0, 0, 8],\n [0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 4, 2, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 2, 0, 2, 4],\n [0, 0, 4, 8, 4, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 4, 8, 8, 8, 4, 0],\n [0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 1, 1, 1, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 2, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 2, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 1, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 1, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 2, 0, 0, 2, 4, 0],\n [0, 0, 0, 4, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 0],\n [0, 0, 0, 4, 1, 1, 1, 1, 1, 1, 4, 0, 4, 1, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 3, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 3, 3, 0],\n [0, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 3, 3, 0, 3, 0],\n [0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 0],\n [0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0],\n [0, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 3, 4, 0, 0, 4, 3, 4, 0, 0, 0, 0, 0, 0], [0, 4, 2, 0, 2, 3, 3, 2, 0, 3, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 3, 3, 4, 0], [0, 4, 3, 2, 0, 0, 2, 3, 3, 4, 0, 3, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 4, 2, 0, 3, 0], [0, 4, 3, 2, 0, 0, 2, 4, 0, 0, 0, 0, 3, 0, 3, 0], [0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0], [0, 3, 0, 0, 2, 3, 3, 4, 0, 0, 4, 3, 2, 0, 3, 0], [0, 4, 3, 3, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 4, 3, 4, 0, 0, 0, 0, 4, 3, 3, 3, 4, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "15663ba9"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 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0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 6, 6, 6, 6, 6, 8, 8, 8, 6, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 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[0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 8, 8, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 8, 8, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 8, 8, 6, 6, 6, 0, 0, 0],\n [4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 8, 8, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "f3b10344"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 0, 0],\n [0, 8, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2],\n [2, 0, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3],\n [0, 3, 0],\n [3, 0, 0]\n ],\n \"output\": [\n [1, 1, 0],\n [1, 0, 1],\n [0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0],\n [5, 5, 0],\n [5, 0, 0]\n ],\n \"output\": [\n [0, 4, 4],\n [0, 0, 4],\n [0, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5],\n [0, 0, 5],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [4, 4, 0],\n [4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0],\n [0, 8, 0],\n [8, 0, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [2, 0, 2],\n [0, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 8],\n [0, 8, 0],\n [0, 8, 0]\n ],\n \"output\": [\n [0, 2, 0],\n [2, 0, 2],\n [2, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 0],\n [3, 3, 3],\n [0, 0, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 1, 1], [0, 0, 0], [1, 1, 0]], "task_id": "6ea4a07e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 3, 3],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 3, 3, 0],\n [0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 3, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 2, 0, 5, 5, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 2, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 0, 0, 3, 3],\n [0, 1, 0, 3, 3, 0],\n [0, 1, 1, 0, 3, 0],\n [0, 4, 0, 5, 5, 0],\n [4, 0, 4, 0, 5, 0],\n [0, 4, 0, 0, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 1, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 5, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2, 3, 0, 0],\n [2, 2, 2, 0, 3, 3],\n [0, 2, 0, 0, 3, 0],\n [0, 0, 5, 8, 0, 8],\n [5, 5, 5, 0, 8, 0],\n [0, 5, 0, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 1, 0],\n [0, 0, 2, 2, 0, 0, 3, 0, 1, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 3, 0, 0, 1, 0, 0],\n [0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2, 0, 1, 1],\n [2, 0, 2, 1, 0, 0],\n [0, 2, 0, 0, 1, 0],\n [4, 0, 0, 0, 5, 0],\n [4, 4, 4, 5, 0, 5],\n [0, 4, 0, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 0],\n [0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 3, 3, 0],\n [0, 0, 2, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0],\n [0, 0, 6, 6, 0, 0, 1, 0, 0, 4, 0, 4, 0],\n [0, 6, 0, 6, 0, 0, 1, 0, 0, 0, 4, 0, 0],\n [0, 6, 6, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 2, 3, 3, 3], [0, 2, 0, 0, 3, 3], [2, 0, 2, 3, 0, 0], [0, 6, 6, 0, 4, 0], [6, 0, 6, 4, 0, 4], [6, 6, 0, 0, 4, 0]], "task_id": "0bb8deee"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 3, 9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 9, 3, 3, 0, 0, 0, 0],\n [0, 9, 3, 3, 9, 3, 0, 3, 3, 3, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 3, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0],\n [3, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 3, 3, 0, 0, 3, 9, 3, 0, 0, 0, 3, 3, 9, 3],\n [3, 9, 3, 0, 0, 3, 3, 9, 0, 0, 0, 9, 3, 3, 3],\n [3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 3],\n [9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 3, 3],\n [3, 3, 9, 0, 3, 3, 3, 9, 3, 0, 0, 3, 3, 9, 3],\n [0, 0, 0, 0, 9, 3, 9, 3, 3, 0, 0, 3, 3, 3, 9],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 9, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3, 9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 9, 3, 3, 0, 0, 0, 0],\n [0, 9, 3, 3, 9, 3, 0, 3, 3, 3, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 3, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0],\n [3, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 3],\n [3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 9, 3, 3, 3],\n [3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 3],\n [9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 3, 3],\n [3, 3, 9, 0, 3, 3, 3, 9, 3, 0, 0, 3, 3, 9, 3],\n [0, 0, 0, 0, 9, 3, 9, 3, 3, 0, 0, 3, 3, 3, 9],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 9, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 3, 9, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 3, 9, 3, 0, 0, 3, 3, 3, 9, 3, 0, 0, 0, 0],\n [0, 3, 3, 9, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 9, 3, 9, 9, 3, 3],\n [0, 3, 9, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 9, 3, 3, 3, 3],\n [0, 3, 3, 3, 9, 3, 0, 0, 3, 3, 3, 3, 3, 3, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 9, 3, 9, 9, 3, 3],\n [0, 3, 9, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 3, 3, 9, 3, 3, 3, 3],\n [0, 3, 3, 3, 9, 3, 0, 0, 3, 3, 3, 3, 3, 3, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 9, 3],\n [0, 3, 3, 9, 3, 3, 0, 3, 3, 3, 3, 0, 3, 9, 3],\n [0, 3, 9, 3, 3, 3, 0, 3, 9, 9, 3, 0, 3, 3, 9],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 9, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 9, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [3, 3, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 3, 9, 0],\n [3, 9, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 3, 3, 0],\n [3, 3, 3, 9, 3, 0, 0, 3, 9, 3, 9, 0, 3, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 9, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 9, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 9, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 9, 9, 3, 0, 3, 3, 9],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 9, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 9, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [3, 3, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 3, 9, 0],\n [3, 9, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 3, 3, 0],\n [3, 3, 3, 9, 3, 0, 0, 3, 9, 3, 9, 0, 3, 9, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 9, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 9, 9, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 9, 3, 3, 3, 3, 0, 0, 0, 3, 3, 9, 3, 9],\n [3, 3, 3, 3, 3, 9, 3, 0, 0, 0, 3, 3, 3, 3, 3],\n [3, 9, 3, 9, 3, 3, 3, 0, 0, 0, 3, 9, 3, 3, 9],\n [3, 3, 3, 3, 9, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3],\n [3, 3, 9, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 9, 3, 0, 0],\n [0, 3, 9, 3, 9, 0, 3, 9, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 9, 0, 3, 3, 3, 9, 3, 3, 3, 0, 0],\n [0, 9, 3, 9, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 9, 3, 3, 3, 3, 0, 0, 0, 3, 3, 9, 3, 9],\n [3, 3, 3, 3, 3, 9, 3, 0, 0, 0, 3, 3, 3, 3, 3],\n [3, 9, 3, 9, 3, 3, 3, 0, 0, 0, 3, 9, 3, 3, 9],\n [3, 3, 3, 3, 9, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3],\n [3, 3, 9, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 9, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 3, 3, 3, 3, 9, 3, 0, 3, 9, 3, 3, 3, 0],\n [0, 0, 3, 9, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 3, 3, 3, 3, 3, 9, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 9, 3, 3, 0],\n [3, 9, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 9, 3, 9, 3, 0, 3, 3, 9, 9, 3, 0, 0, 0, 0],\n [3, 3, 9, 3, 3, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 0, 3, 9, 3, 9, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 9],\n [3, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [3, 9, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 9],\n [9, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 9, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 9, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 9, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 9, 3, 9, 3, 0, 3, 3, 9, 9, 3, 0, 0, 0, 0], [3, 3, 9, 3, 3, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0], [3, 3, 3, 3, 3, 0, 3, 9, 3, 9, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 9], [3, 3, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3], [3, 9, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 9], [9, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3]], "task_id": "54db823b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [0, 3, 0, 4, 0, 2, 0, 4, 0, 6, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [1, 0, 0, 4, 0, 1, 0, 4, 1, 0, 1],\n [0, 1, 0, 4, 1, 1, 1, 4, 1, 0, 1],\n [1, 1, 1, 4, 1, 0, 1, 4, 0, 1, 0]\n ],\n \"output\": [\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [0, 3, 0, 4, 0, 2, 0, 4, 0, 6, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [3, 0, 0, 4, 0, 2, 0, 4, 6, 0, 6],\n [0, 3, 0, 4, 2, 2, 2, 4, 6, 0, 6],\n [3, 3, 3, 4, 2, 0, 2, 4, 0, 6, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 1, 0, 0],\n [0, 7, 0, 4, 0, 1, 1],\n [0, 0, 0, 4, 0, 1, 0],\n [4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 1, 1, 0],\n [0, 3, 0, 4, 0, 1, 0],\n [0, 0, 0, 4, 1, 1, 1],\n [4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 1, 1, 0],\n [0, 8, 0, 4, 0, 1, 1],\n [0, 0, 0, 4, 1, 0, 1]\n ],\n \"output\": [\n [0, 0, 0, 4, 7, 0, 0],\n [0, 7, 0, 4, 0, 7, 7],\n [0, 0, 0, 4, 0, 7, 0],\n [4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 3, 3, 0],\n [0, 3, 0, 4, 0, 3, 0],\n [0, 0, 0, 4, 3, 3, 3],\n [4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 4, 8, 8, 0],\n [0, 8, 0, 4, 0, 8, 8],\n [0, 0, 0, 4, 8, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 4, 0, 0, 0],\n [0, 1, 0, 4, 0, 6, 0],\n [1, 1, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4],\n [0, 0, 1, 4, 0, 0, 0],\n [0, 1, 1, 4, 0, 2, 0],\n [1, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4],\n [1, 1, 0, 4, 0, 0, 0],\n [0, 1, 0, 4, 0, 8, 0],\n [1, 1, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 0, 0, 4, 0, 0, 0], [0, 6, 0, 4, 0, 6, 0], [6, 6, 0, 4, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4], [0, 0, 2, 4, 0, 0, 0], [0, 2, 2, 4, 0, 2, 0], [2, 0, 0, 4, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4], [8, 8, 0, 4, 0, 0, 0], [0, 8, 0, 4, 0, 8, 0], [8, 8, 8, 4, 0, 0, 0]], "task_id": "ef26cbf6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0],\n [2, 0, 0, 0, 0, 3, 0, 1, 4, 1],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [1, 4, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 4, 0, 4, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 0, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 0, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 4, 0, 3, 3, 0],\n [0, 1, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 4],\n [3, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0],\n [0, 3, 0, 0, 0, 4, 3, 2, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 3, 0],\n [0, 0, 4, 0, 0, 4, 0, 1, 0, 1]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 3, 0],\n [0, 1, 0, 0, 2, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 3, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 4, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0],\n [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 3, 0, 0, 2, 4, 0, 0, 0],\n [0, 3, 0, 2, 0, 0, 0, 0, 0, 3],\n [4, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0],\n [3, 0, 1, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 1, 0, 0, 3],\n [0, 0, 0, 0, 2, 4, 0, 2, 4, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 2, 3, 0, 0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 0, 0, 0, 0, 0, 0], [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]], "task_id": "f3cdc58f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 7, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "423a55dc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 4, 0],\n [0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 4, 4, 4, 0],\n [0, 4, 4, 4, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 4, 0, 0, 0, 0, 0, 4, 0, 4, 4, 4],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0],\n [4, 4, 4, 0, 4, 0, 0, 0, 0, 0, 4, 0, 4, 4, 4],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 0],\n [0, 4, 0, 4, 4, 0, 0],\n [0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0],\n [4, 0, 4, 4, 0, 0, 0, 4, 4, 0, 4],\n [0, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 4, 0, 4, 0],\n [4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4],\n [0, 4, 0, 4, 0, 0, 0, 4, 0, 4, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 4, 0, 0, 0],\n [4, 0, 4, 4, 4, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0],\n [4, 0, 4, 4, 4, 0, 0, 0],\n [4, 0, 0, 0, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4],\n [4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0],\n [4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4],\n [4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 0, 0, 0],\n [0, 4, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 4, 4, 0, 0, 4, 0, 0, 0],\n [0, 0, 4, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0], [0, 4, 4, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 4, 4, 0], [4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4], [0, 4, 4, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 4, 4, 0], [0, 4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "2697da3f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 5, 5, 1, 0, 6, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 6, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 3, 3, 1, 0, 2, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 2, 3, 3, 3, 3, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 3, 0, 3, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 3, 0, 5, 5, 1, 0, 3, 0, 3, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 3, 0, 3, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 3, 0, 3, 5, 5, 5, 5, 0, 3, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 3, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 0, 8], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8], [8, 0, 2, 2, 2, 2, 2, 2, 8, 0, 8, 0, 8], [8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8], [8, 0, 8, 0, 2, 2, 1, 0, 8, 0, 8, 0, 8], [8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8], [8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8], [8, 0, 8, 0, 8, 2, 2, 2, 2, 0, 8, 0, 8], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8], [8, 0, 8, 2, 2, 2, 2, 2, 2, 2, 2, 0, 8], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]], "task_id": "08573cc6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 8, 8, 0, 0, 8, 8, 8, 8],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8],\n [0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8],\n [0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 0, 0, 0, 8, 8, 8, 8, 0],\n [8, 8, 8, 8, 8, 0, 8, 0, 0, 8, 0],\n [8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 1, 1, 1],\n [0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 1],\n [0, 0, 2, 2, 2, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 0, 1, 1, 1, 1, 0],\n [3, 3, 3, 3, 3, 0, 1, 0, 0, 1, 0],\n [3, 0, 0, 0, 3, 0, 1, 0, 0, 1, 0],\n [3, 3, 3, 3, 3, 0, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 0, 4, 0, 0, 4, 0, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 3, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 2, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0, 2, 0],\n [0, 3, 0, 0, 3, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 3, 3, 3, 3, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 8, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 8, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 4, 4, 0, 0, 2, 0, 2, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 3, 3, 3, 3], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 3, 0, 0, 3, 0, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3]], "task_id": "0a2355a6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 8, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 6, 7, 7, 4, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 6, 7, 7, 4, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 6, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 7, 6, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7],\n [4, 4, 4, 4, 4, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 8, 7, 7, 7, 9, 9, 9],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 1, 7, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 8, 8, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4]\n ],\n \"output\": [\n [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 4, 1, 6, 1, 1, 1, 1, 0, 0, 1, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 9, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 7, 7, 7, 7],\n [0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 7, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 3, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 3, 1, 1, 7, 0, 0, 0, 0, 0, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 1, 3, 3, 7, 9, 5, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 9, 6, 3, 3, 0, 9, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 9, 6, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 9, 6, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 6, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 6, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 6, 6, 6, 6],\n [3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [5, 5, 5, 5, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [6, 6, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [1, 1, 1, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [6, 6, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 2, 0, 0, 0, 3, 3, 3, 3, 3, 3, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 9, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 9, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 1, 3, 3, 9, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 0, 0, 0, 0, 0, 2, 6, 9, 6, 7, 6, 4, 6, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 4, 6, 6, 0, 2, 0, 0, 0, 0, 0, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 4, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 2, 6, 6, 6, 7, 6, 4, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 4, 6, 6, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 4, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 6, 6, 6, 7, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 7, 6, 6, 6, 6, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 9, 9, 9, 9, 9, 9, 9, 9], [3, 3, 3, 3, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7], [6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 6, 5, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 5, 6, 4, 6, 6, 6, 6, 6, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 5, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 5, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 5, 6, 4, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "256b0a75"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 0, 8, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 1, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0],\n [8, 8, 8, 8],\n [0, 0, 8, 0],\n [0, 8, 8, 0],\n [0, 1, 1, 1],\n [0, 1, 0, 0],\n [1, 1, 1, 0],\n [0, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 6, 6, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 2, 0, 0],\n [0, 3, 3, 3, 0, 2, 0, 0],\n [3, 3, 0, 0, 2, 2, 2, 2],\n [0, 3, 3, 3, 0, 0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 6, 0],\n [0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 1, 0, 0, 0, 3, 0, 0, 4, 0, 0, 4],\n [1, 1, 1, 1, 3, 3, 0, 0, 4, 4, 4, 4],\n [0, 1, 1, 0, 0, 3, 0, 0, 0, 4, 4, 0],\n [0, 1, 0, 0, 3, 3, 3, 3, 0, 0, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 1, 1, 0], [1, 1, 0, 0], [0, 1, 1, 1], [0, 0, 0, 1], [7, 0, 0, 0], [7, 7, 7, 7], [7, 0, 0, 0], [7, 7, 0, 0], [3, 3, 3, 0], [3, 0, 3, 3], [3, 0, 0, 0], [3, 3, 3, 0]], "task_id": "50aad11f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0]], "task_id": "f45f5ca7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 9, 1, 1, 9, 5, 8, 8, 2, 6, 3, 3, 6, 2, 8, 8, 5, 9, 1, 1, 9, 8, 8],\n [8, 5, 9, 1, 5, 5, 8, 2, 8, 6, 6, 6, 6, 6, 6, 8, 2, 8, 5, 5, 1, 9, 5, 8],\n [9, 9, 8, 8, 8, 9, 8, 8, 3, 2, 5, 2, 2, 5, 2, 3, 8, 8, 9, 8, 8, 8, 9, 9],\n [1, 1, 8, 9, 5, 7, 2, 6, 2, 6, 5, 5, 5, 5, 6, 2, 6, 2, 7, 5, 9, 8, 1, 1],\n [1, 5, 8, 5, 9, 8, 6, 6, 5, 5, 3, 6, 6, 3, 5, 5, 6, 6, 8, 9, 5, 8, 5, 1],\n [9, 5, 9, 7, 8, 8, 3, 6, 2, 5, 6, 8, 8, 6, 5, 2, 6, 3, 8, 8, 7, 9, 5, 9],\n [5, 8, 8, 2, 6, 3, 2, 9, 7, 8, 2, 5, 5, 2, 8, 7, 9, 2, 3, 6, 2, 8, 8, 5],\n [8, 2, 8, 6, 6, 6, 9, 2, 9, 5, 7, 2, 2, 7, 5, 9, 2, 9, 6, 6, 6, 8, 2, 8],\n [8, 8, 3, 2, 5, 2, 7, 9, 7, 5, 7, 7, 7, 7, 5, 7, 9, 7, 2, 5, 2, 3, 8, 8],\n [2, 6, 2, 6, 5, 5, 8, 5, 5, 7, 7, 5, 5, 7, 7, 5, 5, 8, 5, 5, 6, 2, 6, 2],\n [6, 6, 5, 5, 3, 6, 2, 7, 7, 7, 7, 9, 9, 7, 7, 7, 7, 2, 6, 3, 5, 5, 6, 6],\n [3, 6, 2, 5, 6, 8, 5, 2, 7, 5, 9, 9, 9, 9, 5, 7, 2, 5, 8, 6, 5, 2, 6, 3],\n [3, 6, 2, 5, 6, 8, 5, 2, 7, 5, 9, 9, 9, 9, 5, 7, 2, 5, 8, 6, 5, 2, 6, 3],\n [6, 6, 5, 5, 3, 6, 2, 7, 7, 7, 7, 9, 9, 7, 7, 7, 7, 2, 6, 3, 5, 5, 6, 6],\n [2, 6, 2, 6, 5, 5, 8, 5, 5, 7, 7, 5, 5, 7, 7, 5, 5, 8, 5, 5, 6, 2, 6, 2],\n [8, 8, 3, 2, 5, 0, 0, 0, 0, 0, 7, 7, 7, 7, 5, 7, 9, 7, 2, 5, 2, 3, 8, 8],\n [8, 2, 8, 6, 6, 0, 0, 0, 0, 0, 7, 2, 2, 7, 5, 9, 2, 9, 6, 6, 6, 8, 2, 8],\n [5, 8, 8, 2, 6, 0, 0, 0, 0, 0, 2, 5, 5, 2, 8, 7, 9, 2, 3, 6, 2, 8, 8, 5],\n [9, 5, 9, 7, 8, 0, 0, 0, 0, 0, 6, 8, 8, 6, 5, 2, 6, 3, 8, 8, 7, 9, 5, 9],\n [1, 5, 8, 5, 9, 0, 0, 0, 0, 0, 3, 6, 6, 3, 5, 5, 6, 6, 8, 9, 5, 8, 5, 1],\n [1, 1, 8, 9, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 6, 2, 6, 2, 7, 5, 9, 8, 1, 1],\n [9, 9, 8, 8, 8, 0, 0, 0, 0, 0, 5, 2, 2, 5, 2, 3, 8, 8, 9, 8, 8, 8, 9, 9],\n [8, 5, 9, 1, 5, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 8, 2, 8, 5, 5, 1, 9, 5, 8],\n [8, 8, 9, 1, 1, 9, 5, 8, 8, 2, 6, 3, 3, 6, 2, 8, 8, 5, 9, 1, 1, 9, 8, 8]\n ],\n \"output\": [\n [2, 7, 9, 7, 5],\n [6, 9, 2, 9, 5],\n [3, 2, 9, 7, 8],\n [8, 3, 6, 2, 5],\n [8, 6, 6, 5, 5],\n [7, 2, 6, 2, 6],\n [9, 8, 8, 3, 2],\n [5, 8, 2, 8, 6]\n ]\n}\n\n{\n \"input\": [\n [7, 6, 9, 9, 3, 7, 3, 9, 4, 3, 3, 3, 3, 3, 3, 4, 9, 3, 7, 3, 9, 9, 6, 7],\n [6, 3, 7, 3, 9, 9, 9, 9, 3, 4, 5, 9, 9, 5, 4, 3, 9, 9, 9, 9, 3, 7, 3, 6],\n [9, 7, 9, 3, 7, 7, 4, 3, 4, 6, 9, 4, 4, 9, 6, 4, 3, 4, 7, 7, 3, 9, 7, 9],\n [9, 3, 3, 9, 6, 9, 3, 4, 6, 9, 5, 9, 9, 5, 9, 6, 4, 3, 9, 6, 9, 3, 3, 9],\n [3, 9, 7, 6, 6, 6, 3, 5, 9, 5, 0, 0, 0, 6, 5, 9, 5, 3, 6, 6, 6, 7, 9, 3],\n [7, 9, 7, 9, 6, 3, 3, 9, 4, 9, 0, 0, 0, 9, 9, 4, 9, 3, 3, 6, 9, 7, 9, 7],\n [3, 9, 4, 3, 3, 3, 8, 7, 9, 2, 0, 0, 0, 8, 2, 9, 7, 8, 3, 3, 3, 4, 9, 3],\n [9, 9, 3, 4, 5, 9, 7, 7, 5, 9, 0, 0, 0, 2, 9, 5, 7, 7, 9, 5, 4, 3, 9, 9],\n [4, 3, 4, 6, 9, 4, 9, 5, 5, 2, 7, 7, 7, 7, 2, 5, 5, 9, 4, 9, 6, 4, 3, 4],\n [3, 4, 6, 9, 5, 9, 2, 9, 2, 8, 8, 8, 8, 8, 8, 2, 9, 2, 9, 5, 9, 6, 4, 3],\n [3, 5, 9, 5, 6, 9, 8, 2, 7, 8, 9, 9, 9, 9, 8, 7, 2, 8, 9, 6, 5, 9, 5, 3],\n [3, 9, 4, 9, 9, 4, 9, 7, 7, 8, 9, 7, 7, 9, 8, 7, 7, 9, 4, 9, 9, 4, 9, 3],\n [3, 9, 4, 9, 9, 4, 9, 7, 7, 8, 9, 7, 7, 9, 8, 7, 7, 9, 4, 9, 9, 4, 9, 3],\n [3, 5, 9, 5, 6, 9, 8, 2, 7, 8, 9, 9, 9, 9, 8, 7, 2, 8, 9, 6, 5, 9, 5, 3],\n [3, 4, 6, 9, 5, 9, 2, 9, 2, 8, 8, 8, 8, 8, 8, 2, 9, 2, 9, 5, 9, 6, 4, 3],\n [4, 3, 4, 6, 9, 4, 9, 5, 5, 2, 7, 7, 7, 7, 2, 5, 5, 9, 4, 9, 6, 4, 3, 4],\n [9, 9, 3, 4, 5, 9, 7, 7, 5, 9, 2, 7, 7, 2, 9, 5, 7, 7, 9, 5, 4, 3, 9, 9],\n [3, 9, 4, 3, 3, 3, 8, 7, 9, 2, 8, 9, 9, 8, 2, 9, 7, 8, 3, 3, 3, 4, 9, 3],\n [7, 9, 7, 9, 6, 3, 3, 9, 4, 9, 9, 4, 4, 9, 9, 4, 9, 3, 3, 6, 9, 7, 9, 7],\n [3, 9, 7, 6, 6, 6, 3, 5, 9, 5, 6, 9, 9, 6, 5, 9, 5, 3, 6, 6, 6, 7, 9, 3],\n [9, 3, 3, 9, 6, 9, 3, 4, 6, 9, 5, 9, 9, 5, 9, 6, 4, 3, 9, 6, 9, 3, 3, 9],\n [9, 7, 9, 3, 7, 7, 4, 3, 4, 6, 9, 4, 4, 9, 6, 4, 3, 4, 7, 7, 3, 9, 7, 9],\n [6, 3, 7, 3, 9, 9, 9, 9, 3, 4, 5, 9, 9, 5, 4, 3, 9, 9, 9, 9, 3, 7, 3, 6],\n [7, 6, 9, 9, 3, 7, 3, 9, 4, 3, 3, 3, 3, 3, 3, 4, 9, 3, 7, 3, 9, 9, 6, 7]\n ],\n \"output\": [\n [6, 9, 9],\n [9, 4, 4],\n [8, 9, 9],\n [2, 7, 7]\n ]\n}\n\n{\n \"input\": [\n [6, 8, 8, 1, 8, 5, 1, 5, 8, 2, 2, 1, 1, 2, 2, 8, 5, 1, 5, 8, 1, 8, 8, 6],\n [8, 8, 5, 4, 5, 1, 5, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 5, 1, 5, 4, 5, 8, 8],\n [8, 5, 5, 4, 5, 4, 8, 2, 1, 6, 8, 1, 1, 8, 6, 1, 2, 8, 4, 5, 4, 5, 5, 8],\n [1, 4, 4, 4, 8, 8, 2, 1, 6, 2, 8, 2, 2, 8, 2, 6, 1, 2, 8, 8, 4, 4, 4, 1],\n [8, 5, 5, 8, 1, 6, 2, 2, 8, 8, 6, 5, 5, 6, 8, 8, 2, 2, 6, 1, 8, 5, 5, 8],\n [5, 1, 4, 8, 6, 6, 1, 2, 1, 2, 5, 5, 5, 5, 2, 1, 2, 1, 6, 6, 8, 4, 1, 5],\n [1, 5, 8, 2, 2, 1, 6, 8, 8, 6, 6, 8, 8, 6, 6, 8, 8, 6, 1, 2, 2, 8, 5, 1],\n [5, 2, 2, 1, 2, 2, 8, 2, 2, 6, 2, 2, 2, 2, 6, 2, 2, 8, 2, 2, 1, 2, 2, 5],\n [8, 2, 1, 6, 8, 1, 8, 2, 8, 1, 6, 6, 6, 6, 1, 8, 2, 8, 1, 8, 6, 1, 2, 8],\n [2, 1, 6, 2, 8, 2, 6, 6, 1, 7, 7, 8, 8, 7, 7, 1, 6, 6, 2, 8, 2, 6, 1, 2],\n [2, 2, 8, 8, 6, 5, 6, 2, 6, 7, 2, 7, 7, 2, 7, 6, 2, 6, 5, 6, 8, 8, 2, 2],\n [1, 2, 1, 2, 5, 5, 8, 2, 6, 8, 7, 1, 1, 7, 8, 6, 2, 8, 5, 5, 2, 1, 2, 1],\n [1, 2, 1, 2, 5, 5, 8, 2, 6, 8, 7, 1, 1, 7, 8, 6, 2, 8, 5, 5, 2, 1, 2, 1],\n [2, 2, 8, 8, 6, 5, 6, 2, 6, 7, 2, 7, 7, 2, 7, 6, 2, 6, 5, 6, 8, 8, 2, 2],\n [2, 1, 6, 2, 8, 2, 6, 6, 1, 7, 7, 8, 8, 7, 7, 1, 6, 6, 2, 8, 2, 6, 1, 2],\n [8, 2, 1, 6, 8, 1, 8, 2, 8, 1, 6, 6, 6, 6, 1, 8, 2, 0, 0, 0, 0, 0, 0, 8],\n [5, 2, 2, 1, 2, 2, 8, 2, 2, 6, 2, 2, 2, 2, 6, 2, 2, 0, 0, 0, 0, 0, 0, 5],\n [1, 5, 8, 2, 2, 1, 6, 8, 8, 6, 6, 8, 8, 6, 6, 8, 8, 0, 0, 0, 0, 0, 0, 1],\n [5, 1, 4, 8, 6, 6, 1, 2, 1, 2, 5, 5, 5, 5, 2, 1, 2, 0, 0, 0, 0, 0, 0, 5],\n [8, 5, 5, 8, 1, 6, 2, 2, 8, 8, 6, 5, 5, 6, 8, 8, 2, 0, 0, 0, 0, 0, 0, 8],\n [1, 4, 4, 4, 8, 8, 2, 1, 6, 2, 8, 2, 2, 8, 2, 6, 1, 2, 8, 8, 4, 4, 4, 1],\n [8, 5, 5, 4, 5, 4, 8, 2, 1, 6, 8, 1, 1, 8, 6, 1, 2, 8, 4, 5, 4, 5, 5, 8],\n [8, 8, 5, 4, 5, 1, 5, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 5, 1, 5, 4, 5, 8, 8],\n [6, 8, 8, 1, 8, 5, 1, 5, 8, 2, 2, 1, 1, 2, 2, 8, 5, 1, 5, 8, 1, 8, 8, 6]\n ],\n \"output\": [\n [8, 1, 8, 6, 1, 2],\n [8, 2, 2, 1, 2, 2],\n [6, 1, 2, 2, 8, 5],\n [1, 6, 6, 8, 4, 1],\n [2, 6, 1, 8, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [5, 4, 6, 4, 6, 6, 5, 5, 5, 9, 5, 6, 6, 5, 9, 5, 5, 5, 6, 6, 4, 6, 4, 5],\n [4, 1, 9, 4, 9, 1, 5, 1, 9, 6, 5, 1, 1, 5, 6, 9, 1, 5, 1, 9, 4, 9, 1, 4],\n [6, 9, 1, 4, 4, 5, 5, 9, 2, 5, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 9, 6],\n [4, 4, 4, 4, 4, 9, 9, 6, 5, 5, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4],\n [6, 9, 4, 4, 1, 5, 5, 5, 9, 2, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 9, 6],\n [6, 1, 5, 9, 5, 6, 6, 1, 5, 6, 6, 9, 9, 6, 6, 5, 1, 6, 6, 5, 9, 5, 1, 6],\n [5, 5, 5, 9, 5, 6, 5, 5, 8, 5, 7, 7, 7, 7, 5, 8, 5, 5, 6, 5, 9, 5, 5, 5],\n [5, 1, 9, 6, 5, 1, 5, 2, 7, 3, 8, 5, 5, 8, 3, 7, 2, 5, 1, 5, 6, 9, 1, 5],\n [5, 9, 2, 5, 9, 5, 8, 7, 3, 7, 5, 2, 2, 5, 7, 3, 7, 8, 5, 9, 5, 2, 9, 5],\n [9, 6, 5, 5, 2, 6, 5, 3, 7, 2, 5, 7, 7, 5, 2, 7, 3, 5, 6, 2, 5, 5, 6, 9],\n [5, 5, 9, 2, 1, 6, 7, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 7, 6, 1, 2, 9, 5, 5],\n [6, 1, 5, 6, 6, 9, 7, 5, 2, 7, 5, 3, 3, 5, 7, 2, 5, 7, 9, 6, 6, 5, 1, 6],\n [6, 1, 5, 6, 6, 9, 7, 5, 2, 7, 5, 3, 3, 5, 7, 2, 5, 7, 9, 6, 6, 5, 1, 6],\n [5, 5, 9, 2, 1, 6, 7, 8, 5, 5, 5, 5, 5, 5, 5, 5, 8, 7, 6, 1, 2, 9, 5, 5],\n [9, 6, 5, 5, 2, 6, 5, 3, 7, 2, 5, 7, 7, 5, 2, 7, 3, 5, 6, 2, 5, 5, 6, 9],\n [5, 9, 2, 5, 9, 5, 8, 7, 3, 7, 5, 2, 2, 5, 7, 3, 7, 8, 5, 9, 5, 2, 9, 5],\n [5, 1, 9, 6, 5, 1, 5, 2, 7, 3, 8, 5, 5, 8, 3, 7, 2, 5, 1, 5, 6, 9, 1, 5],\n [5, 5, 5, 9, 5, 6, 5, 5, 8, 5, 7, 7, 7, 7, 5, 8, 5, 5, 6, 5, 9, 5, 5, 5],\n [6, 1, 5, 9, 5, 6, 6, 1, 5, 6, 6, 9, 9, 6, 6, 5, 1, 6, 6, 5, 9, 5, 1, 6],\n [6, 9, 4, 4, 1, 5, 5, 5, 9, 2, 1, 6, 6, 1, 2, 9, 5, 5, 5, 1, 4, 4, 9, 6],\n [4, 4, 4, 4, 4, 9, 9, 6, 5, 5, 2, 6, 6, 2, 5, 5, 6, 9, 9, 4, 4, 4, 4, 4],\n [6, 9, 1, 4, 4, 5, 5, 9, 2, 5, 9, 5, 5, 9, 5, 2, 9, 5, 5, 4, 4, 1, 9, 6],\n [4, 1, 9, 4, 9, 1, 5, 1, 9, 6, 5, 1, 1, 5, 6, 9, 1, 5, 1, 9, 4, 9, 1, 4],\n [5, 4, 6, 4, 6, 6, 5, 5, 5, 9, 5, 6, 6, 5, 9, 5, 5, 5, 6, 6, 4, 6, 4, 5]\n ],\n \"output\": [\n [5, 9, 5, 2, 9, 5, 5, 4],\n [6, 2, 5, 5, 6, 9, 9, 4],\n [6, 1, 2, 9, 5, 5, 5, 1]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 6, 3, 8, 7, 3, 5, 4, 4, 5, 1, 1, 5, 4, 4, 5, 3, 7, 8, 3, 6, 7, 7],\n [7, 3, 3, 6, 3, 7, 5, 4, 5, 8, 4, 3, 3, 4, 8, 5, 4, 5, 7, 3, 6, 3, 3, 7],\n [6, 3, 8, 7, 7, 2, 4, 5, 8, 1, 8, 4, 4, 8, 1, 8, 5, 4, 2, 7, 7, 8, 3, 6],\n [3, 6, 7, 3, 6, 7, 4, 8, 1, 4, 8, 4, 4, 8, 4, 1, 8, 4, 7, 6, 3, 7, 6, 3],\n [8, 3, 7, 6, 8, 6, 5, 4, 8, 8, 5, 8, 8, 5, 8, 8, 4, 5, 6, 8, 6, 7, 3, 8],\n [7, 7, 2, 7, 6, 2, 1, 3, 4, 4, 8, 4, 4, 8, 4, 4, 3, 1, 2, 6, 7, 2, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 2, 2, 6, 6, 6, 6, 1, 1, 5, 4, 4, 5, 3],\n [0, 0, 0, 0, 0, 0, 0, 1, 9, 6, 2, 7, 7, 2, 6, 9, 1, 6, 3, 4, 8, 5, 4, 5],\n [0, 0, 0, 0, 0, 0, 0, 9, 7, 9, 1, 6, 6, 1, 9, 7, 9, 6, 4, 8, 1, 8, 5, 4],\n [0, 0, 0, 0, 0, 0, 0, 6, 9, 2, 7, 6, 6, 7, 2, 9, 6, 6, 4, 8, 4, 1, 8, 4],\n [0, 0, 0, 0, 0, 0, 0, 2, 1, 7, 1, 6, 6, 1, 7, 1, 2, 6, 8, 5, 8, 8, 4, 5],\n [0, 0, 0, 0, 0, 0, 0, 7, 6, 6, 6, 6, 6, 6, 6, 6, 7, 2, 4, 8, 4, 4, 3, 1],\n [1, 3, 4, 4, 8, 4, 2, 7, 6, 6, 6, 6, 6, 6, 6, 6, 7, 2, 4, 8, 4, 4, 3, 1],\n [5, 4, 8, 8, 5, 8, 6, 2, 1, 7, 1, 6, 6, 1, 7, 1, 2, 6, 8, 5, 8, 8, 4, 5],\n [4, 8, 1, 4, 8, 4, 6, 6, 9, 2, 7, 6, 6, 7, 2, 9, 6, 6, 4, 8, 4, 1, 8, 4],\n [4, 5, 8, 1, 8, 4, 6, 9, 7, 9, 1, 6, 6, 1, 9, 7, 9, 6, 4, 8, 1, 8, 5, 4],\n [5, 4, 5, 8, 4, 3, 6, 1, 9, 6, 2, 7, 7, 2, 6, 9, 1, 6, 3, 4, 8, 5, 4, 5],\n [3, 5, 4, 4, 5, 1, 1, 6, 6, 6, 6, 2, 2, 6, 6, 6, 6, 1, 1, 5, 4, 4, 5, 3],\n [7, 7, 2, 7, 6, 2, 1, 3, 4, 4, 8, 4, 4, 8, 4, 4, 3, 1, 2, 6, 7, 2, 7, 7],\n [8, 3, 7, 6, 8, 6, 5, 4, 8, 8, 5, 8, 8, 5, 8, 8, 4, 5, 6, 8, 6, 7, 3, 8],\n [3, 6, 7, 3, 6, 7, 4, 8, 1, 4, 8, 4, 4, 8, 4, 1, 8, 4, 7, 6, 3, 7, 6, 3],\n [6, 3, 8, 7, 7, 2, 4, 5, 8, 1, 8, 4, 4, 8, 1, 8, 5, 4, 2, 7, 7, 8, 3, 6],\n [7, 3, 3, 6, 3, 7, 5, 4, 5, 8, 4, 3, 3, 4, 8, 5, 4, 5, 7, 3, 6, 3, 3, 7],\n [7, 7, 6, 3, 8, 7, 3, 5, 4, 4, 5, 1, 1, 5, 4, 4, 5, 3, 7, 8, 3, 6, 7, 7]\n ],\n \"output\": [\n [3, 5, 4, 4, 5, 1, 1],\n [5, 4, 5, 8, 4, 3, 6],\n [4, 5, 8, 1, 8, 4, 6],\n [4, 8, 1, 4, 8, 4, 6],\n [5, 4, 8, 8, 5, 8, 6],\n [1, 3, 4, 4, 8, 4, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 4, 9, 3, 4, 8, 5, 6, 3, 5, 6, 3, 3, 6, 5, 3, 6, 5, 8, 4, 3, 9, 4, 8],\n [4, 9, 3, 8, 9, 1, 6, 7, 6, 6, 3, 2, 2, 3, 6, 6, 7, 6, 1, 9, 8, 3, 9, 4],\n [9, 3, 1, 3, 4, 1, 3, 6, 3, 3, 3, 5, 5, 3, 3, 3, 6, 3, 1, 4, 3, 1, 3, 9],\n [3, 8, 3, 1, 3, 8, 5, 6, 3, 7, 5, 7, 7, 5, 7, 3, 6, 5, 8, 3, 1, 3, 8, 3],\n [4, 9, 4, 3, 4, 8, 6, 3, 3, 5, 3, 3, 3, 3, 5, 3, 3, 6, 8, 4, 3, 4, 9, 4],\n [8, 1, 1, 8, 8, 3, 3, 2, 5, 7, 3, 7, 7, 3, 7, 5, 2, 3, 3, 8, 8, 1, 1, 8],\n [5, 6, 3, 5, 6, 3, 9, 9, 9, 8, 5, 9, 9, 5, 8, 9, 9, 9, 3, 6, 5, 3, 6, 5],\n [6, 7, 6, 6, 3, 2, 9, 9, 9, 4, 4, 4, 4, 4, 4, 9, 9, 9, 2, 3, 6, 6, 7, 6],\n [3, 6, 3, 3, 3, 5, 9, 9, 5, 8, 9, 9, 9, 9, 8, 5, 9, 9, 5, 3, 3, 3, 6, 3],\n [5, 6, 3, 7, 5, 7, 8, 4, 8, 6, 5, 8, 8, 5, 0, 0, 0, 8, 7, 5, 7, 3, 6, 5],\n [6, 3, 3, 5, 3, 3, 5, 4, 9, 5, 5, 5, 5, 5, 0, 0, 0, 5, 3, 3, 5, 3, 3, 6],\n [3, 2, 5, 7, 3, 7, 9, 4, 9, 8, 5, 6, 6, 5, 0, 0, 0, 9, 7, 3, 7, 5, 2, 3],\n [3, 2, 5, 7, 3, 7, 9, 4, 9, 8, 5, 6, 6, 5, 8, 9, 4, 9, 7, 3, 7, 5, 2, 3],\n [6, 3, 3, 5, 3, 3, 5, 4, 9, 5, 5, 5, 5, 5, 5, 9, 4, 5, 3, 3, 5, 3, 3, 6],\n [5, 6, 3, 7, 5, 7, 8, 4, 8, 6, 5, 8, 8, 5, 6, 8, 4, 8, 7, 5, 7, 3, 6, 5],\n [3, 6, 3, 3, 3, 5, 9, 9, 5, 8, 9, 9, 9, 9, 8, 5, 9, 9, 5, 3, 3, 3, 6, 3],\n [6, 7, 6, 6, 3, 2, 9, 9, 9, 4, 4, 4, 4, 4, 4, 9, 9, 9, 2, 3, 6, 6, 7, 6],\n [5, 6, 3, 5, 6, 3, 9, 9, 9, 8, 5, 9, 9, 5, 8, 9, 9, 9, 3, 6, 5, 3, 6, 5],\n [8, 1, 1, 8, 8, 3, 3, 2, 5, 7, 3, 7, 7, 3, 7, 5, 2, 3, 3, 8, 8, 1, 1, 8],\n [4, 9, 4, 3, 4, 8, 6, 3, 3, 5, 3, 3, 3, 3, 5, 3, 3, 6, 8, 4, 3, 4, 9, 4],\n [3, 8, 3, 1, 3, 8, 5, 6, 3, 7, 5, 7, 7, 5, 7, 3, 6, 5, 8, 3, 1, 3, 8, 3],\n [9, 3, 1, 3, 4, 1, 3, 6, 3, 3, 3, 5, 5, 3, 3, 3, 6, 3, 1, 4, 3, 1, 3, 9],\n [4, 9, 3, 8, 9, 1, 6, 7, 6, 6, 3, 2, 2, 3, 6, 6, 7, 6, 1, 9, 8, 3, 9, 4],\n [8, 4, 9, 3, 4, 8, 5, 6, 3, 5, 6, 3, 3, 6, 5, 3, 6, 5, 8, 4, 3, 9, 4, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 8, 4], [5, 9, 4], [8, 9, 4]], "task_id": "e66aafb8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 5, 5, 5],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 3, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 5, 4, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 5, 5, 5],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 3, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 5, 4, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 5, 2, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 5, 6, 5, 5, 3, 5, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [0, 5, 2, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 4, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 5, 6, 5, 5, 3, 5, 0, 6, 6, 6, 6, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 5, 5, 5, 4, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 5, 5, 5, 6, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 5, 5, 5, 4, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 6, 0, 0, 6, 0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 5, 5, 5, 6, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0]], "task_id": "1da012fc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 0, 0, 0, 4, 5, 6, 1, 2, 3],\n [6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 0, 0, 0, 4, 5, 6, 1, 2, 3],\n [6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 0, 0, 0, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 0, 0, 0, 0, 6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 0, 0, 0, 0, 6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 0, 0, 0, 0, 6, 1, 2, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 0, 0, 0, 0, 6, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 0, 0, 0, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 0, 0, 0, 4, 5, 6, 1, 2, 0, 0, 0, 0, 0, 2, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 0, 0, 0, 4, 5, 6, 1, 2, 0, 0, 0, 0, 0, 2, 3, 3, 4, 5, 6, 1, 2, 3],\n [6, 0, 0, 0, 4, 5, 6, 1, 2, 0, 0, 0, 0, 0, 2, 3, 4, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 0, 0, 0, 0, 0, 2, 3, 4, 5, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 0, 0, 0, 2, 3, 4, 5, 6, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 0, 0, 0, 2, 3, 4, 5, 6, 1, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 0, 0, 0, 2, 3, 4, 5, 6, 1, 2, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 3]\n ],\n \"output\": [\n [5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 1, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 2, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 4, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 5, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 6, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 1, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 2, 3],\n [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 1, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 0, 0, 0, 0, 0, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 0, 0, 0, 0, 0, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 6, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 7, 1, 2, 0, 0, 0, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 0, 0, 0, 0, 0, 6, 7, 1, 1, 2, 0, 0, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 0, 0, 0, 0, 0, 6, 7, 1, 2, 2, 0, 0, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 0, 0, 0, 0, 0, 6, 7, 1, 2, 3, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 4]\n ],\n \"output\": [\n [3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 3, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 4, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 5, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 6, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 7, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 1, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 2, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 3, 4],\n [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 6, 7, 8, 0, 0, 0, 0, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 6, 7, 8, 0, 0, 0, 0, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 7, 8, 0, 0, 0, 0, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 8, 0, 0, 0, 0, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 0, 0, 0, 0, 4, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 0, 0, 0, 0, 4, 5, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 0, 0, 0, 0, 4, 5, 6, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 0, 0, 0, 0, 4, 5, 6, 7, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 8, 1, 2, 3, 0, 0, 0, 0, 8, 1, 2, 3],\n [6, 7, 8, 1, 0, 0, 4, 5, 6, 7, 8, 1, 1, 2, 3, 0, 0, 0, 0, 8, 1, 2, 3],\n [6, 7, 8, 1, 0, 0, 4, 5, 6, 7, 8, 1, 2, 2, 3, 0, 0, 0, 0, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 3, 0, 0, 0, 0, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 6, 7, 0, 0, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 7, 0, 0, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 3]\n ],\n \"output\": [\n [5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 3, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 4, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 5, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 6, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 7, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 8, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 2, 3],\n [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 0, 0, 0, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 0, 0, 0, 4, 5, 6, 7, 8, 9, 1, 0, 0, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 1, 0, 0, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 1, 0, 0, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 0, 0, 0, 0, 0, 7, 8, 9, 1, 0, 0, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 0, 0, 0, 0, 0, 7, 8, 9, 1, 0, 0, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 0, 0, 0, 0, 0, 8, 8, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 0, 0, 0, 0, 0, 8, 9, 9, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 1, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 2, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 3, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 4, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 5, 6],\n [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 8, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 4, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 5, 6], [3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 6]], "task_id": "1e97544e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 1, 1, 1, 2, 0],\n [2, 1, 1, 1, 3, 1, 2, 0],\n [2, 1, 3, 3, 3, 1, 2, 0],\n [2, 1, 3, 0, 3, 1, 2, 0],\n [2, 1, 3, 3, 3, 1, 2, 0],\n [2, 1, 1, 1, 1, 1, 2, 0],\n [2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 2, 1, 3, 3, 3, 3, 1, 2, 2, 2, 2, 0],\n [0, 0, 2, 1, 3, 0, 0, 3, 1, 1, 1, 1, 2, 0],\n [0, 0, 2, 1, 3, 0, 0, 3, 3, 3, 3, 1, 2, 0],\n [0, 0, 2, 1, 3, 3, 3, 3, 3, 0, 3, 1, 2, 0],\n [0, 0, 2, 1, 1, 1, 1, 1, 3, 0, 3, 1, 2, 0],\n [0, 0, 2, 2, 2, 2, 2, 1, 3, 3, 3, 1, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 3, 1, 2],\n [0, 0, 0, 1, 0, 1, 1, 1, 0, 2, 1, 1, 1, 2],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 3, 3, 3, 3, 3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 3, 3, 3, 0, 3, 1, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 2, 1, 1, 1, 3, 0, 3, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 2, 2, 2, 1, 3, 3, 3, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 3, 3, 1, 2, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 3, 3, 3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 3, 3, 3, 1], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 1, 3, 0, 3, 1], [0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 0, 2, 1, 1, 1, 2, 0, 2, 1, 1, 3, 0, 3, 1], [0, 0, 0, 0, 2, 1, 3, 3, 3, 1, 1, 1, 2, 0, 0, 0, 0, 2, 1, 3, 1, 2, 0, 2, 1, 3, 3, 3, 3, 1], [0, 0, 0, 0, 2, 1, 3, 0, 3, 3, 3, 1, 2, 0, 0, 0, 0, 2, 1, 3, 1, 2, 0, 2, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 2, 1, 3, 3, 3, 3, 3, 1, 2, 0, 0, 0, 0, 2, 1, 1, 1, 2, 0, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 2, 1, 1, 1, 1, 3, 3, 1, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 1, 3, 3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 3, 3, 3, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 1, 1, 3, 3, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 3, 3, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "d931c21c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 2, 0, 1, 0],\n [0, 0, 0, 0, 0, 0],\n [2, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 1],\n [2, 1, 0],\n [3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 2],\n [8, 0, 8],\n [0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 2, 0],\n [6, 0, 6]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 3, 0, 4, 0],\n [0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 4], [7, 0, 1], [7, 0, 1]], "task_id": "68b67ca3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 8, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 8, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 3, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 3, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 8, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 8, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 8, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 3],\n [8, 0, 0, 0, 0, 0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 3, 0, 0, 0, 0, 8, 0, 0, 3, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 8, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 8, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 8, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 8, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 8, 0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 8, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 3, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 3, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 3, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "58e15b12"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 3, 3, 4, 4, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 3, 3, 4, 4, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 1, 1, 8, 8, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 1, 1, 8, 8, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 3, 3, 3, 3, 4, 4, 4, 4, 2],\n [2, 3, 3, 3, 3, 4, 4, 4, 4, 2],\n [2, 3, 3, 3, 3, 4, 4, 4, 4, 2],\n [2, 3, 3, 3, 3, 4, 4, 4, 4, 2],\n [2, 1, 1, 1, 1, 8, 8, 8, 8, 2],\n [2, 1, 1, 1, 1, 8, 8, 8, 8, 2],\n [2, 1, 1, 1, 1, 8, 8, 8, 8, 2],\n [2, 1, 1, 1, 1, 8, 8, 8, 8, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 3, 5, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 6, 8, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2],\n [2, 3, 3, 5, 5, 2],\n [2, 3, 3, 5, 5, 2],\n [2, 6, 6, 8, 8, 2],\n [2, 6, 6, 8, 8, 2],\n [2, 2, 2, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 2, 0, 3, 3, 6, 6, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 3, 3, 6, 6, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 4, 4, 1, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 4, 4, 1, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 3, 3, 3, 3, 6, 6, 6, 6, 2], [2, 3, 3, 3, 3, 6, 6, 6, 6, 2], [2, 3, 3, 3, 3, 6, 6, 6, 6, 2], [2, 3, 3, 3, 3, 6, 6, 6, 6, 2], [2, 4, 4, 4, 4, 1, 1, 1, 1, 2], [2, 4, 4, 4, 4, 1, 1, 1, 1, 2], [2, 4, 4, 4, 4, 1, 1, 1, 1, 2], [2, 4, 4, 4, 4, 1, 1, 1, 1, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]], "task_id": "e7a25a18"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 0, 5, 0, 0, 0, 0, 4, 0, 0],\n [3, 3, 3, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 5, 4, 0, 0, 0, 4, 0, 4],\n [2, 1, 2, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 4, 0, 0, 0, 4, 0, 4],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0],\n [3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0],\n [2, 2, 2, 2, 1, 2, 2],\n [0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 1, 0, 5, 0, 4, 0, 0, 0, 4, 0],\n [3, 3, 3, 3, 3, 5, 4, 4, 0, 0, 0, 4, 4],\n [0, 2, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 4, 4, 0, 0, 0, 4, 4],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 4, 0, 0, 0, 4, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 0, 1, 0],\n [3, 3, 3, 3, 3, 3, 3],\n [0, 2, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 1, 0],\n [3, 3, 3, 3, 3, 3, 3],\n [0, 2, 0, 0, 0, 1, 0],\n [0, 2, 0, 0, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 0, 1, 0, 5, 0, 0, 4, 0, 0, 4, 0],\n [2, 1, 2, 1, 2, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 3, 1, 3, 5, 4, 0, 4, 0, 0, 4, 4],\n [0, 1, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 4, 0, 4, 0, 0, 4, 4],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 4, 0, 0, 4, 0]\n ],\n \"output\": [\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0],\n [2, 2, 1, 2, 2, 1, 2],\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0],\n [3, 3, 1, 3, 3, 1, 3],\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 4, 0, 4, 0, 0, 5, 0, 2, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 1, 2, 1, 3, 1],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [4, 0, 4, 0, 4, 0, 4, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 4, 0, 4, 0, 0, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [0, 0, 2, 0, 3, 0, 0],\n [0, 0, 2, 0, 3, 0, 0],\n [0, 0, 2, 0, 3, 0, 0],\n [0, 0, 2, 0, 3, 0, 0],\n [1, 1, 2, 1, 3, 1, 1],\n [0, 0, 2, 0, 3, 0, 0],\n [0, 0, 2, 0, 3, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 0, 4, 0, 4, 0, 5, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 3, 2, 3, 3, 3, 2, 3],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 1, 1, 1, 2, 1, 1, 1],\n [4, 4, 0, 4, 0, 4, 4, 5, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],\n [4, 4, 0, 4, 0, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 4, 0, 4, 0, 4, 0, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [3, 2, 3, 3, 3, 2, 3], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0], [1, 1, 1, 2, 1, 1, 1], [0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0]], "task_id": "b0f4d537"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "332efdb3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 6, 6, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 6, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 6, 8, 8, 8, 8, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 6, 6, 8, 8, 8, 8, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 6, 6, 6, 8, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 2, 2, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 4, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 2, 2, 4, 4, 4, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 2, 2, 4, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "16b78196"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8],\n [8, 0, 0, 0, 0, 8],\n [0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 3, 3],\n [8, 0, 8, 0, 3, 3],\n [0, 8, 0, 8, 8, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 0, 0],\n [0, 0, 8, 0, 0, 8],\n [8, 0, 0, 0, 0, 8],\n [0, 0, 8, 0, 8, 0],\n [3, 3, 0, 0, 0, 0],\n [8, 0, 8, 3, 3, 0],\n [0, 8, 0, 8, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 8, 3],\n [0, 8, 0, 0, 8, 0, 0, 3],\n [8, 8, 0, 8, 0, 0, 8, 3],\n [8, 8, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 8, 8, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 0, 0, 8, 3],\n [0, 8, 0, 0, 8, 3, 0, 0],\n [8, 8, 0, 8, 0, 0, 8, 3],\n [8, 8, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8],\n [8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 8, 3, 3],\n [8, 0, 0, 8, 0, 0, 3, 3],\n [8, 8, 0, 0, 0, 0, 3, 3],\n [8, 8, 0, 0, 8, 8, 0, 8],\n [0, 0, 0, 8, 0, 8, 0, 3],\n [0, 8, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 8, 8, 0, 8, 3],\n [8, 0, 0, 8, 8, 8, 0, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 8, 3, 3],\n [8, 0, 0, 8, 3, 3, 0, 0],\n [8, 8, 3, 3, 0, 0, 0, 0],\n [8, 8, 0, 0, 8, 8, 0, 8],\n [0, 0, 0, 8, 0, 8, 3, 0],\n [0, 8, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 8, 3],\n [8, 0, 0, 8, 8, 8, 0, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 8, 8, 8, 8, 8, 0, 8],\n [8, 8, 8, 0, 0, 8, 8, 0, 8],\n [0, 8, 8, 0, 8, 8, 0, 0, 8],\n [0, 8, 0, 0, 0, 0, 0, 3, 3],\n [0, 8, 0, 8, 0, 0, 0, 3, 3],\n [8, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 8, 0, 8, 8, 0, 3, 3],\n [0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 8, 8, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 8, 8, 8, 8, 8, 0, 8], [8, 8, 8, 0, 0, 8, 8, 0, 8], [0, 8, 8, 0, 8, 8, 0, 0, 8], [0, 8, 3, 3, 0, 0, 0, 0, 0], [0, 8, 0, 8, 3, 3, 0, 0, 0], [8, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 8, 8, 3, 3, 0], [0, 8, 8, 8, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 8, 8, 8, 0]], "task_id": "9c56f360"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 3, 4],\n [0, 0, 2, 1],\n [2, 1, 4, 0],\n [0, 3, 1, 2]\n ],\n \"output\": [\n [1, 2, 3, 4],\n [3, 4, 2, 1],\n [2, 1, 4, 3],\n [4, 3, 1, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 2, 3],\n [4, 1, 0, 2],\n [0, 3, 4, 0],\n [3, 0, 1, 4]\n ],\n \"output\": [\n [1, 4, 2, 3],\n [4, 1, 3, 2],\n [2, 3, 4, 1],\n [3, 2, 1, 4]\n ]\n}\n\n{\n \"input\": [\n [3, 0, 2, 1],\n [1, 0, 0, 0],\n [4, 3, 0, 2],\n [0, 1, 4, 3]\n ],\n \"output\": [\n [3, 4, 2, 1],\n [1, 2, 3, 4],\n [4, 3, 1, 2],\n [2, 1, 4, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 2, 3],\n [0, 3, 1, 0],\n [3, 0, 4, 1],\n [0, 4, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 1, 2, 3], [2, 3, 1, 4], [3, 2, 4, 1], [1, 4, 3, 2]], "task_id": "4cd1b7b2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 3, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 3, 0, 0, 0],\n [0, 3, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 3, 0, 0, 0, 3, 0, 0],\n [0, 3, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 3, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 3, 8, 3, 3, 8, 3, 0, 3, 0, 0, 0, 3],\n [0, 0, 8, 8, 3, 3, 3, 8, 0, 0, 3, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 0, 0],\n [3, 3, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 3, 6, 6, 6, 0, 3, 6, 6, 6, 3, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 3, 0],\n [0, 0, 8, 3, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 8, 8, 3, 3, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 1, 1, 1, 2, 3, 3, 0, 3, 0, 0],\n [0, 1, 1, 3, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 3, 0, 3, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 1, 3, 3, 0, 0, 0, 3],\n [0, 1, 3, 3, 3, 1, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 1, 3, 3, 3, 0, 3, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 3, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 3, 2, 1, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [1, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 3, 1, 3, 2, 3, 3, 0, 0, 0, 0],\n [0, 8, 1, 8, 8, 3, 0, 8, 8, 8, 8, 8, 0, 1, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 3, 0],\n [0, 1, 1, 3, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 1, 3, 3, 0, 1, 1, 2, 3, 1, 0, 0, 0, 0],\n [1, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 1, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [3, 8, 8, 8, 3, 3, 1, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 3, 0, 3, 0, 1, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 1, 1, 2, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 3],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 3, 3, 2, 2, 3, 0, 0, 3, 0, 0],\n [0, 2, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 2, 3, 1, 3, 0, 0, 2, 2, 1, 2, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 2, 2, 2, 2, 3, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 3, 3, 2, 2, 3, 2, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 3, 2, 3, 2, 0, 0, 2, 3, 2, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 3, 3, 0, 0, 3, 2, 3, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 2, 3, 3, 0, 0, 2, 0, 0],\n [0, 3, 1, 1, 3, 0, 3, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 3, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 3, 2, 2, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 3, 1, 1, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 2, 2, 8, 3, 2, 0, 2, 2, 8, 2, 2, 0, 3, 2, 8, 2, 2, 0, 0, 0, 3],\n [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 0, 0],\n [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0],\n [3, 8, 8, 3, 3, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 3, 8, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 3, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 2, 3, 8, 2, 2, 0, 2, 2, 3, 2, 3, 0, 2, 2, 8, 2, 2, 0, 0, 0, 3],\n [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 0, 0],\n [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 3, 0, 8, 8, 8, 3, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 3, 8, 0, 8, 3, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3],\n [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0],\n [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 3, 0],\n [0, 3, 3, 3, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0],\n [0, 3, 8, 8, 8, 8, 0, 8, 8, 3, 3, 8, 0, 8, 8, 3, 8, 8, 0, 3, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 8, 8, 3, 8, 8, 0, 3, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 2, 2, 8, 2, 2, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "0607ce86"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 1, 0, 1, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 1, 0, 1, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0],\n [0, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "5b526a93"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 2, 0, 2, 4, 0, 0, 0, 5, 0, 0, 0],\n [1, 0, 1, 0, 2, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 2, 2, 2, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 3, 3, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 0, 2, 2, 0],\n [0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 2, 0, 6, 0, 6, 4, 0, 5, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 3, 3, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 0, 6, 0, 6, 4, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 1, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 3, 3, 3, 3, 0, 0],\n [0, 6, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 2, 0, 6, 0, 6, 4, 0, 0, 0, 0, 5, 0, 0],\n [2, 0, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 1, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 6, 0, 6, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 1, 1, 0, 4, 0, 0, 5, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 1, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 6, 0, 6, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 6, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 3, 3, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 3, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [6, 0, 6, 0, 2, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 2, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 2, 2, 2, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 5, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0], [3, 3, 3, 3, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 0, 0], [0, 3, 3, 3, 3, 0, 0], [2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], "task_id": "136b0064"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0, 1, 3, 3, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0, 1, 3, 3, 3, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0, 1, 0, 3, 0, 1, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4, 2, 0, 0, 0, 2, 4, 0, 4],\n [0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 2, 0, 4, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n [0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0, 0, 0, 8, 0, 2, 0, 8, 0],\n [0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0, 0, 0, 8, 0, 2, 2, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0, 0, 0, 8, 2, 2, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0], [8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0], [0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0], [8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0], [0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0], [8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0, 0, 0, 3, 8, 8, 0, 3, 0], [0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0, 0, 0, 3, 0, 0, 8, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0, 0, 0, 3, 0, 8, 0, 3, 0]], "task_id": "92e50de0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 4, 4, 0, 6, 0, 6, 6, 0, 6, 0, 8, 8],\n [0, 4, 4, 0, 6, 0, 6, 6, 0, 6, 0, 8, 8],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 8, 8, 0, 6, 0, 6, 6, 0, 6, 0, 3, 3],\n [0, 8, 8, 0, 6, 0, 6, 6, 0, 6, 0, 3, 3],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 6, 6, 0, 6, 0, 8, 8, 0, 6, 0, 4, 4],\n [0, 6, 6, 0, 6, 0, 8, 8, 0, 6, 0, 4, 4],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 6, 6, 0, 6, 0, 6, 6, 0, 6, 0, 6, 6],\n [0, 6, 6, 0, 6, 0, 6, 6, 0, 6, 0, 6, 6],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0],\n [4, 4, 0],\n [8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 3, 3, 0, 3, 0, 1, 1, 0, 3, 0, 2, 2, 0, 3, 0],\n [0, 3, 3, 0, 3, 0, 1, 1, 0, 3, 0, 2, 2, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 2, 2, 0, 3, 0, 1, 1, 0, 3, 0, 3, 3, 0, 3, 0],\n [0, 2, 2, 0, 3, 0, 1, 1, 0, 3, 0, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [0, 1, 1, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 3, 0],\n [0, 1, 1, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0]\n ],\n \"output\": [\n [2, 2, 0],\n [1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 4, 4, 0, 2, 0, 1, 1, 0, 2, 0, 1, 1, 0, 2, 0, 8, 8],\n [0, 4, 4, 0, 2, 0, 1, 1, 0, 2, 0, 1, 1, 0, 2, 0, 8, 8],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 2, 0, 2, 0, 4, 4, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2],\n [0, 2, 2, 0, 2, 0, 4, 4, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 4, 4, 0, 2, 0, 4, 4, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2],\n [0, 4, 4, 0, 2, 0, 4, 4, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0],\n [1, 1, 0, 0],\n [4, 4, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 4, 4, 0, 8, 0, 6, 6, 0, 8, 0, 3, 3, 0, 8, 0, 2, 2, 0, 8, 0],\n [0, 4, 4, 0, 8, 0, 6, 6, 0, 8, 0, 3, 3, 0, 8, 0, 2, 2, 0, 8, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 6, 6, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 3, 3, 0, 8, 0],\n [0, 6, 6, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0, 3, 3, 0, 8, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 8, 8, 0, 8, 0, 6, 6, 0, 8, 0, 2, 2, 0, 8, 0, 8, 8, 0, 8, 0],\n [0, 8, 8, 0, 8, 0, 6, 6, 0, 8, 0, 2, 2, 0, 8, 0, 8, 8, 0, 8, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0],\n [0, 2, 2, 0, 8, 0, 2, 2, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0],\n [0, 2, 2, 0, 8, 0, 2, 2, 0, 8, 0, 8, 8, 0, 8, 0, 8, 8, 0, 8, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 0, 0, 0], [3, 3, 0, 0], [6, 6, 6, 0], [2, 2, 2, 2]], "task_id": "81c0276b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 8, 8, 8, 9],\n [8, 8, 2, 8, 8],\n [8, 2, 2, 2, 8],\n [8, 8, 2, 8, 8],\n [9, 8, 8, 8, 9]\n ],\n \"output\": [\n [9, 8, 8, 8, 9, 9, 2, 8, 9, 2],\n [8, 8, 2, 8, 8, 9, 2, 8, 9, 2],\n [8, 2, 2, 2, 8, 9, 2, 8, 9, 2],\n [8, 8, 2, 8, 8, 9, 2, 8, 9, 2],\n [9, 8, 8, 8, 9, 9, 2, 8, 9, 2],\n [9, 9, 9, 9, 9, 2, 2, 8, 9, 2],\n [2, 2, 2, 2, 2, 2, 8, 8, 9, 2],\n [8, 8, 8, 8, 8, 8, 8, 9, 9, 2],\n [9, 9, 9, 9, 9, 9, 9, 9, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 3, 3, 2],\n [3, 3, 5, 3, 3],\n [3, 5, 5, 5, 3],\n [3, 3, 5, 3, 3],\n [2, 3, 3, 3, 2]\n ],\n \"output\": [\n [2, 3, 3, 3, 2, 2, 5, 3, 2, 5],\n [3, 3, 5, 3, 3, 2, 5, 3, 2, 5],\n [3, 5, 5, 5, 3, 2, 5, 3, 2, 5],\n [3, 3, 5, 3, 3, 2, 5, 3, 2, 5],\n [2, 3, 3, 3, 2, 2, 5, 3, 2, 5],\n [2, 2, 2, 2, 2, 5, 5, 3, 2, 5],\n [5, 5, 5, 5, 5, 5, 3, 3, 2, 5],\n [3, 3, 3, 3, 3, 3, 3, 2, 2, 5],\n [2, 2, 2, 2, 2, 2, 2, 2, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 1, 1, 1, 5],\n [1, 1, 9, 1, 1],\n [1, 9, 9, 9, 1],\n [1, 1, 9, 1, 1],\n [5, 1, 1, 1, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 1, 1, 1, 5, 5, 9, 1, 5, 9], [1, 1, 9, 1, 1, 5, 9, 1, 5, 9], [1, 9, 9, 9, 1, 5, 9, 1, 5, 9], [1, 1, 9, 1, 1, 5, 9, 1, 5, 9], [5, 1, 1, 1, 5, 5, 9, 1, 5, 9], [5, 5, 5, 5, 5, 9, 9, 1, 5, 9], [9, 9, 9, 9, 9, 9, 1, 1, 5, 9], [1, 1, 1, 1, 1, 1, 1, 5, 5, 9], [5, 5, 5, 5, 5, 5, 5, 5, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9, 1]], "task_id": "3979b1a8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 5, 0, 0, 5, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 5, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 5, 0, 0, 5, 2, 2, 0, 0],\n [0, 2, 2, 0, 0, 5, 0, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 5, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 5, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 5, 0, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 0, 5, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 5, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 5, 0, 0, 5, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 2, 2, 5, 0, 0, 0, 2, 2, 2, 0],\n [0, 2, 2, 2, 0, 0, 5, 0, 2, 2, 2, 0],\n [0, 2, 2, 2, 5, 0, 0, 5, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 5, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 5, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 5, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 5, 0, 0, 0, 2, 2, 2, 0], [0, 0, 2, 2, 0, 0, 0, 5, 2, 2, 2, 0], [0, 0, 2, 2, 0, 0, 5, 0, 2, 2, 2, 0], [0, 0, 2, 2, 0, 5, 0, 0, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "d37a1ef5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 1, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 8, 1, 0, 1],\n [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [1, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 1, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 8, 1, 4, 1],\n [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4],\n [1, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8],\n [0, 0, 8, 0, 0, 0, 0, 0, 8, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 8, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 8, 1, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 1, 0, 0],\n [0, 0, 0, 4, 1, 4, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 8, 0, 0, 0],\n [1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8],\n [0, 0, 8, 0, 0, 0, 0, 0, 8, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 8, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0],\n [0, 0, 0, 8, 1, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 1, 0, 0],\n [0, 0, 0, 4, 1, 4, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 1, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 8, 0, 0, 0],\n [1, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 8, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [8, 0, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 1, 0, 8],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 4, 4, 4, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 1],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 8, 0],\n [4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [8, 4, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 1, 0, 8],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 4, 4, 4, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8, 1],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 4, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 8, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 1, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 8, 8, 0, 0, 0, 1, 0, 0],\n [0, 0, 8, 0, 0, 8, 8, 0, 0, 0, 1, 8, 1, 0, 0, 8, 0, 0, 0, 1, 0, 0],\n [0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0],\n [0, 1, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8],\n [8, 0, 0, 0, 4, 8, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 8, 1, 0],\n [0, 0, 0, 0, 4, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 1, 0, 0, 0, 0],\n [1, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 8, 0],\n [0, 8, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 8, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 8, 0, 8, 1, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 8, 0], [0, 0, 0, 1, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 8, 8, 0, 0, 0, 1, 0, 0], [0, 0, 8, 0, 0, 8, 8, 0, 0, 0, 1, 8, 1, 0, 0, 8, 0, 0, 0, 1, 0, 0], [0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0], [0, 1, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8], [8, 0, 0, 0, 4, 8, 8, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 8, 1, 0], [0, 0, 0, 0, 4, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 8], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 8, 8, 8, 0, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0, 1, 0, 0, 0, 0], [1, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 0, 0, 0, 0, 4, 8, 4], [0, 8, 0, 0, 8, 1, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 0, 0, 0, 4, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 1, 4], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 8, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 1, 0, 8, 0, 8, 1, 0, 1, 0, 0, 0]], "task_id": "bb52a14b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 0, 4, 4, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 0, 4, 4, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 0, 4, 4, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 4, 4, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 4, 4],\n [0, 0, 0, 0, 4, 0, 4, 4, 0, 4],\n [0, 0, 0, 0, 0, 4, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "9bebae7a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 3, 4, 0],\n [0, 7, 6, 0],\n [0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 4],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [7, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 5, 6, 0],\n [0, 8, 3, 0],\n [0, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 6],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [8, 0, 0, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0],\n [0, 2, 3, 0],\n [0, 4, 9, 0],\n [0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 0, 0, 3], [0, 0, 0, 0], [0, 0, 0, 0], [4, 0, 0, 9]], "task_id": "66e6c45b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 6, 0, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 0, 0],\n [0, 7, 7, 0, 0, 0],\n [1, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 0],\n [1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 3, 3, 0, 0],\n [0, 3, 3, 3, 0, 0],\n [3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 7, 0, 0, 0, 1, 1, 1, 0, 0],\n [1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0],\n [4, 0, 4, 0, 0, 0, 6, 0, 6, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 1, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 7, 7, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 0, 3, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0],\n [1, 1, 1, 0, 0, 0, 0, 7, 7, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 1, 0, 0, 7, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 7, 7, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 3, 3, 3, 3, 0, 0], [6, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 0, 0, 8, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0]], "task_id": "604001fa"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 7, 3, 7, 1, 7, 2, 2, 1, 7, 6, 5, 5, 7, 5, 7, 7, 5, 7, 5, 5, 6, 7, 1, 2, 2, 7, 1, 7, 3],\n [7, 6, 9, 9, 7, 7, 5, 5, 7, 6, 5, 1, 7, 7, 7, 7, 7, 7, 7, 7, 1, 5, 6, 7, 5, 5, 7, 7, 9, 9],\n [3, 9, 9, 7, 2, 5, 2, 7, 6, 5, 7, 6, 5, 7, 9, 9, 9, 9, 7, 5, 6, 7, 5, 6, 7, 2, 5, 2, 7, 9],\n [7, 9, 7, 9, 2, 5, 7, 7, 5, 1, 6, 6, 7, 7, 9, 4, 4, 9, 7, 7, 6, 6, 1, 5, 7, 7, 5, 2, 9, 7],\n [1, 7, 2, 2, 9, 9, 9, 6, 5, 7, 5, 7, 3, 3, 9, 9, 9, 9, 3, 3, 7, 5, 7, 5, 6, 9, 9, 9, 2, 2],\n [7, 0, 0, 0, 0, 0, 0, 5, 7, 7, 7, 7, 3, 6, 9, 6, 6, 9, 6, 3, 7, 7, 7, 7, 5, 9, 6, 9, 5, 5],\n [2, 0, 0, 0, 0, 0, 0, 9, 5, 7, 9, 9, 9, 9, 3, 6, 6, 3, 9, 9, 9, 9, 7, 5, 9, 9, 9, 9, 7, 2],\n [2, 0, 0, 0, 0, 0, 0, 7, 7, 7, 9, 4, 9, 6, 6, 3, 3, 6, 6, 9, 4, 9, 7, 7, 7, 9, 5, 6, 7, 7],\n [1, 0, 0, 0, 0, 0, 0, 7, 2, 6, 2, 5, 7, 7, 3, 7, 7, 3, 7, 7, 5, 2, 6, 2, 7, 5, 7, 5, 5, 6],\n [7, 0, 0, 0, 0, 0, 0, 7, 6, 6, 2, 4, 7, 3, 7, 7, 7, 7, 3, 7, 4, 2, 6, 6, 7, 7, 7, 7, 1, 5],\n [6, 0, 0, 0, 0, 0, 0, 9, 2, 2, 6, 2, 3, 7, 6, 7, 7, 6, 7, 3, 2, 6, 2, 2, 9, 9, 7, 5, 6, 7],\n [5, 0, 0, 0, 0, 0, 0, 4, 5, 4, 2, 5, 7, 7, 7, 6, 6, 7, 7, 7, 5, 2, 4, 5, 4, 9, 7, 7, 6, 6],\n [5, 7, 5, 7, 3, 3, 9, 9, 7, 7, 3, 7, 2, 5, 2, 1, 1, 2, 5, 2, 7, 3, 7, 0, 0, 0, 0, 3, 7, 5],\n [7, 7, 7, 7, 3, 6, 9, 6, 7, 3, 7, 7, 5, 5, 1, 1, 1, 1, 5, 5, 7, 7, 3, 0, 0, 0, 0, 3, 7, 7],\n [5, 7, 9, 9, 9, 9, 3, 6, 3, 7, 6, 7, 2, 1, 2, 5, 5, 2, 1, 2, 7, 6, 7, 0, 0, 0, 0, 9, 9, 9],\n [7, 7, 9, 4, 9, 6, 6, 3, 7, 7, 7, 6, 1, 1, 5, 2, 2, 5, 1, 1, 6, 7, 7, 0, 0, 0, 0, 9, 4, 9],\n [7, 7, 9, 4, 9, 6, 6, 3, 7, 7, 7, 6, 1, 1, 5, 2, 2, 5, 1, 1, 6, 7, 7, 7, 3, 6, 6, 9, 4, 9],\n [5, 7, 9, 9, 9, 9, 3, 6, 3, 7, 6, 7, 2, 1, 2, 5, 5, 2, 1, 2, 7, 6, 7, 3, 6, 3, 9, 9, 9, 9],\n [7, 7, 7, 7, 3, 6, 9, 6, 7, 3, 7, 7, 5, 5, 1, 1, 1, 1, 5, 5, 7, 7, 3, 7, 6, 9, 6, 3, 7, 7],\n [5, 7, 5, 7, 3, 3, 9, 9, 7, 7, 3, 7, 2, 5, 2, 1, 1, 2, 5, 2, 7, 3, 7, 7, 9, 9, 3, 3, 7, 5],\n [5, 1, 6, 6, 7, 7, 9, 4, 5, 4, 2, 5, 7, 7, 7, 6, 6, 7, 7, 7, 5, 2, 4, 5, 4, 9, 7, 7, 6, 6],\n [6, 5, 7, 6, 5, 7, 9, 9, 2, 2, 6, 2, 3, 7, 6, 7, 7, 6, 7, 3, 2, 6, 2, 2, 9, 9, 7, 5, 6, 7],\n [7, 6, 5, 1, 7, 7, 7, 7, 6, 6, 2, 4, 7, 3, 0, 0, 0, 0, 0, 0, 4, 2, 6, 6, 7, 7, 7, 7, 1, 5],\n [1, 7, 6, 5, 5, 7, 5, 7, 2, 6, 2, 5, 7, 7, 0, 0, 0, 0, 0, 0, 5, 2, 6, 2, 7, 5, 7, 5, 5, 6],\n [2, 5, 7, 7, 6, 5, 9, 7, 7, 7, 9, 4, 9, 6, 6, 3, 3, 6, 0, 0, 4, 9, 7, 7, 7, 9, 5, 6, 7, 7],\n [2, 5, 2, 7, 9, 9, 9, 9, 5, 7, 9, 9, 9, 9, 3, 6, 6, 3, 0, 0, 9, 9, 7, 5, 9, 9, 9, 9, 7, 2],\n [7, 7, 5, 5, 9, 6, 9, 5, 7, 7, 7, 7, 3, 6, 9, 6, 6, 9, 0, 0, 7, 7, 7, 7, 5, 9, 6, 9, 5, 5],\n [1, 7, 2, 2, 9, 9, 9, 6, 5, 7, 5, 7, 3, 3, 9, 9, 9, 9, 0, 0, 7, 5, 7, 5, 6, 9, 9, 9, 2, 2],\n [7, 9, 7, 9, 2, 5, 7, 7, 5, 1, 6, 6, 7, 7, 9, 4, 4, 9, 0, 0, 6, 6, 1, 5, 7, 7, 5, 2, 9, 7],\n [3, 9, 9, 7, 2, 5, 2, 7, 6, 5, 7, 6, 5, 7, 9, 9, 9, 9, 0, 0, 6, 7, 5, 6, 7, 2, 5, 2, 7, 9]\n ],\n \"output\": [\n [6, 7, 3, 7, 1, 7, 2, 2, 1, 7, 6, 5, 5, 7, 5, 7, 7, 5, 7, 5, 5, 6, 7, 1, 2, 2, 7, 1, 7, 3],\n [7, 6, 9, 9, 7, 7, 5, 5, 7, 6, 5, 1, 7, 7, 7, 7, 7, 7, 7, 7, 1, 5, 6, 7, 5, 5, 7, 7, 9, 9],\n [3, 9, 9, 7, 2, 5, 2, 7, 6, 5, 7, 6, 5, 7, 9, 9, 9, 9, 7, 5, 6, 7, 5, 6, 7, 2, 5, 2, 7, 9],\n [7, 9, 7, 9, 2, 5, 7, 7, 5, 1, 6, 6, 7, 7, 9, 4, 4, 9, 7, 7, 6, 6, 1, 5, 7, 7, 5, 2, 9, 7],\n [1, 7, 2, 2, 9, 9, 9, 6, 5, 7, 5, 7, 3, 3, 9, 9, 9, 9, 3, 3, 7, 5, 7, 5, 6, 9, 9, 9, 2, 2],\n [7, 7, 5, 5, 9, 6, 9, 5, 7, 7, 7, 7, 3, 6, 9, 6, 6, 9, 6, 3, 7, 7, 7, 7, 5, 9, 6, 9, 5, 5],\n [2, 5, 2, 7, 9, 9, 9, 9, 5, 7, 9, 9, 9, 9, 3, 6, 6, 3, 9, 9, 9, 9, 7, 5, 9, 9, 9, 9, 7, 2],\n [2, 5, 7, 7, 6, 5, 9, 7, 7, 7, 9, 4, 9, 6, 6, 3, 3, 6, 6, 9, 4, 9, 7, 7, 7, 9, 5, 6, 7, 7],\n [1, 7, 6, 5, 5, 7, 5, 7, 2, 6, 2, 5, 7, 7, 3, 7, 7, 3, 7, 7, 5, 2, 6, 2, 7, 5, 7, 5, 5, 6],\n [7, 6, 5, 1, 7, 7, 7, 7, 6, 6, 2, 4, 7, 3, 7, 7, 7, 7, 3, 7, 4, 2, 6, 6, 7, 7, 7, 7, 1, 5],\n [6, 5, 7, 6, 5, 7, 9, 9, 2, 2, 6, 2, 3, 7, 6, 7, 7, 6, 7, 3, 2, 6, 2, 2, 9, 9, 7, 5, 6, 7],\n [5, 1, 6, 6, 7, 7, 9, 4, 5, 4, 2, 5, 7, 7, 7, 6, 6, 7, 7, 7, 5, 2, 4, 5, 4, 9, 7, 7, 6, 6],\n [5, 7, 5, 7, 3, 3, 9, 9, 7, 7, 3, 7, 2, 5, 2, 1, 1, 2, 5, 2, 7, 3, 7, 7, 9, 9, 3, 3, 7, 5],\n [7, 7, 7, 7, 3, 6, 9, 6, 7, 3, 7, 7, 5, 5, 1, 1, 1, 1, 5, 5, 7, 7, 3, 7, 6, 9, 6, 3, 7, 7],\n [5, 7, 9, 9, 9, 9, 3, 6, 3, 7, 6, 7, 2, 1, 2, 5, 5, 2, 1, 2, 7, 6, 7, 3, 6, 3, 9, 9, 9, 9],\n [7, 7, 9, 4, 9, 6, 6, 3, 7, 7, 7, 6, 1, 1, 5, 2, 2, 5, 1, 1, 6, 7, 7, 7, 3, 6, 6, 9, 4, 9],\n [7, 7, 9, 4, 9, 6, 6, 3, 7, 7, 7, 6, 1, 1, 5, 2, 2, 5, 1, 1, 6, 7, 7, 7, 3, 6, 6, 9, 4, 9],\n [5, 7, 9, 9, 9, 9, 3, 6, 3, 7, 6, 7, 2, 1, 2, 5, 5, 2, 1, 2, 7, 6, 7, 3, 6, 3, 9, 9, 9, 9],\n [7, 7, 7, 7, 3, 6, 9, 6, 7, 3, 7, 7, 5, 5, 1, 1, 1, 1, 5, 5, 7, 7, 3, 7, 6, 9, 6, 3, 7, 7],\n [5, 7, 5, 7, 3, 3, 9, 9, 7, 7, 3, 7, 2, 5, 2, 1, 1, 2, 5, 2, 7, 3, 7, 7, 9, 9, 3, 3, 7, 5],\n [5, 1, 6, 6, 7, 7, 9, 4, 5, 4, 2, 5, 7, 7, 7, 6, 6, 7, 7, 7, 5, 2, 4, 5, 4, 9, 7, 7, 6, 6],\n [6, 5, 7, 6, 5, 7, 9, 9, 2, 2, 6, 2, 3, 7, 6, 7, 7, 6, 7, 3, 2, 6, 2, 2, 9, 9, 7, 5, 6, 7],\n [7, 6, 5, 1, 7, 7, 7, 7, 6, 6, 2, 4, 7, 3, 7, 7, 7, 7, 3, 7, 4, 2, 6, 6, 7, 7, 7, 7, 1, 5],\n [1, 7, 6, 5, 5, 7, 5, 7, 2, 6, 2, 5, 7, 7, 3, 7, 7, 3, 7, 7, 5, 2, 6, 2, 7, 5, 7, 5, 5, 6],\n [2, 5, 7, 7, 6, 5, 9, 7, 7, 7, 9, 4, 9, 6, 6, 3, 3, 6, 6, 9, 4, 9, 7, 7, 7, 9, 5, 6, 7, 7],\n [2, 5, 2, 7, 9, 9, 9, 9, 5, 7, 9, 9, 9, 9, 3, 6, 6, 3, 9, 9, 9, 9, 7, 5, 9, 9, 9, 9, 7, 2],\n [7, 7, 5, 5, 9, 6, 9, 5, 7, 7, 7, 7, 3, 6, 9, 6, 6, 9, 6, 3, 7, 7, 7, 7, 5, 9, 6, 9, 5, 5],\n [1, 7, 2, 2, 9, 9, 9, 6, 5, 7, 5, 7, 3, 3, 9, 9, 9, 9, 3, 3, 7, 5, 7, 5, 6, 9, 9, 9, 2, 2],\n [7, 9, 7, 9, 2, 5, 7, 7, 5, 1, 6, 6, 7, 7, 9, 4, 4, 9, 7, 7, 6, 6, 1, 5, 7, 7, 5, 2, 9, 7],\n [3, 9, 9, 7, 2, 5, 2, 7, 6, 5, 7, 6, 5, 7, 9, 9, 9, 9, 7, 5, 6, 7, 5, 6, 7, 2, 5, 2, 7, 9]\n ]\n}\n\n{\n \"input\": [\n [9, 2, 9, 6, 2, 4, 1, 6, 8, 7, 7, 1, 5, 6, 7, 3, 3, 7, 6, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [2, 9, 6, 6, 4, 4, 1, 1, 7, 8, 5, 5, 6, 6, 7, 6, 6, 7, 6, 6, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [9, 6, 6, 6, 1, 1, 4, 0, 0, 0, 0, 7, 7, 7, 3, 6, 6, 3, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [6, 6, 6, 2, 6, 1, 6, 0, 0, 0, 0, 5, 3, 6, 6, 5, 5, 6, 6, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6],\n [2, 4, 1, 6, 2, 1, 2, 0, 0, 0, 0, 3, 1, 8, 6, 1, 1, 6, 8, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 6, 8, 8, 9, 8, 8, 9, 8, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 2, 2, 9, 9, 7, 7, 3, 6, 6, 9, 6, 9, 9, 6, 9, 6, 6, 3, 7, 7, 9, 9, 2, 2, 6, 4],\n [6, 1, 6, 2, 1, 2, 9, 2, 3, 6, 6, 5, 1, 8, 9, 8, 8, 9, 8, 1, 5, 6, 6, 3, 2, 9, 2, 1, 2, 6],\n [8, 7, 7, 1, 5, 6, 7, 3, 8, 5, 5, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 5, 5, 8, 3, 7, 6, 5, 1, 7],\n [7, 8, 5, 5, 6, 6, 7, 6, 5, 8, 4, 3, 6, 9, 3, 6, 6, 3, 9, 6, 3, 4, 8, 5, 6, 7, 6, 6, 5, 5],\n [7, 5, 8, 7, 7, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8, 4, 5, 6, 3, 7, 7, 7, 8],\n [1, 5, 7, 5, 3, 0, 0, 0, 0, 0, 0, 0, 3, 6, 3, 1, 1, 3, 6, 3, 4, 5, 3, 8, 5, 6, 6, 3, 5, 7],\n [5, 6, 7, 3, 1, 0, 0, 0, 0, 0, 0, 0, 8, 8, 6, 8, 8, 6, 8, 8, 3, 3, 6, 9, 1, 6, 8, 1, 3, 7],\n [6, 6, 7, 6, 8, 0, 0, 0, 0, 0, 0, 0, 8, 1, 7, 8, 8, 7, 1, 8, 6, 3, 9, 6, 8, 9, 8, 8, 6, 7],\n [7, 7, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 6, 7, 7, 7, 7, 7, 7, 6, 3, 3, 3, 3, 9, 6, 9, 6, 6, 3],\n [3, 6, 6, 5, 1, 0, 0, 0, 0, 0, 0, 0, 8, 8, 7, 8, 8, 7, 8, 8, 1, 3, 6, 3, 8, 9, 8, 1, 5, 6],\n [3, 6, 6, 5, 1, 8, 9, 8, 3, 6, 3, 1, 8, 8, 7, 8, 8, 7, 8, 8, 1, 3, 6, 3, 8, 9, 8, 1, 5, 6],\n [7, 7, 3, 6, 6, 9, 6, 9, 3, 3, 3, 3, 6, 7, 7, 7, 7, 7, 7, 6, 3, 3, 3, 3, 9, 6, 9, 6, 6, 3],\n [6, 6, 7, 6, 8, 8, 9, 8, 6, 9, 3, 6, 8, 1, 7, 8, 8, 7, 1, 8, 6, 3, 9, 6, 8, 9, 8, 8, 6, 7],\n [5, 6, 7, 3, 1, 8, 6, 1, 9, 6, 3, 3, 8, 8, 6, 8, 8, 6, 8, 8, 3, 3, 6, 9, 1, 6, 8, 1, 3, 7],\n [1, 5, 7, 5, 3, 6, 6, 5, 8, 3, 5, 4, 3, 6, 3, 1, 1, 3, 6, 3, 4, 5, 3, 8, 5, 6, 6, 3, 5, 7],\n [7, 5, 8, 7, 7, 7, 3, 6, 5, 4, 8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8, 4, 5, 6, 3, 7, 7, 7, 8],\n [7, 8, 5, 5, 6, 6, 7, 6, 5, 8, 4, 3, 6, 9, 3, 6, 6, 3, 9, 6, 3, 4, 8, 5, 6, 7, 6, 6, 5, 5],\n [8, 7, 7, 1, 5, 6, 7, 3, 8, 5, 5, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 5, 5, 8, 3, 7, 6, 5, 1, 7],\n [6, 1, 6, 2, 1, 2, 9, 2, 3, 6, 6, 5, 1, 8, 9, 8, 8, 9, 8, 1, 5, 6, 6, 3, 2, 9, 2, 1, 2, 6],\n [1, 1, 4, 6, 2, 2, 9, 9, 7, 7, 3, 6, 6, 9, 6, 9, 9, 6, 9, 6, 6, 3, 7, 7, 9, 9, 2, 2, 6, 4],\n [4, 4, 1, 1, 1, 2, 2, 2, 6, 6, 7, 6, 8, 8, 9, 8, 8, 9, 8, 8, 6, 7, 6, 6, 2, 2, 2, 1, 1, 1],\n [2, 4, 1, 6, 2, 1, 2, 1, 5, 6, 7, 3, 1, 8, 6, 1, 1, 6, 8, 1, 3, 7, 6, 5, 1, 2, 1, 2, 6, 1],\n [6, 6, 6, 2, 6, 1, 6, 2, 1, 5, 7, 5, 3, 6, 6, 5, 5, 6, 6, 3, 5, 7, 5, 1, 2, 6, 1, 6, 2, 6],\n [9, 6, 6, 6, 1, 1, 4, 6, 7, 5, 8, 7, 7, 7, 3, 6, 6, 3, 7, 7, 7, 8, 5, 7, 6, 4, 1, 1, 6, 6]\n ],\n \"output\": [\n [9, 2, 9, 6, 2, 4, 1, 6, 8, 7, 7, 1, 5, 6, 7, 3, 3, 7, 6, 5, 1, 7, 7, 8, 6, 1, 4, 2, 6, 9],\n [2, 9, 6, 6, 4, 4, 1, 1, 7, 8, 5, 5, 6, 6, 7, 6, 6, 7, 6, 6, 5, 5, 8, 7, 1, 1, 4, 4, 6, 6],\n [9, 6, 6, 6, 1, 1, 4, 6, 7, 5, 8, 7, 7, 7, 3, 6, 6, 3, 7, 7, 7, 8, 5, 7, 6, 4, 1, 1, 6, 6],\n [6, 6, 6, 2, 6, 1, 6, 2, 1, 5, 7, 5, 3, 6, 6, 5, 5, 6, 6, 3, 5, 7, 5, 1, 2, 6, 1, 6, 2, 6],\n [2, 4, 1, 6, 2, 1, 2, 1, 5, 6, 7, 3, 1, 8, 6, 1, 1, 6, 8, 1, 3, 7, 6, 5, 1, 2, 1, 2, 6, 1],\n [4, 4, 1, 1, 1, 2, 2, 2, 6, 6, 7, 6, 8, 8, 9, 8, 8, 9, 8, 8, 6, 7, 6, 6, 2, 2, 2, 1, 1, 1],\n [1, 1, 4, 6, 2, 2, 9, 9, 7, 7, 3, 6, 6, 9, 6, 9, 9, 6, 9, 6, 6, 3, 7, 7, 9, 9, 2, 2, 6, 4],\n [6, 1, 6, 2, 1, 2, 9, 2, 3, 6, 6, 5, 1, 8, 9, 8, 8, 9, 8, 1, 5, 6, 6, 3, 2, 9, 2, 1, 2, 6],\n [8, 7, 7, 1, 5, 6, 7, 3, 8, 5, 5, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 5, 5, 8, 3, 7, 6, 5, 1, 7],\n [7, 8, 5, 5, 6, 6, 7, 6, 5, 8, 4, 3, 6, 9, 3, 6, 6, 3, 9, 6, 3, 4, 8, 5, 6, 7, 6, 6, 5, 5],\n [7, 5, 8, 7, 7, 7, 3, 6, 5, 4, 8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8, 4, 5, 6, 3, 7, 7, 7, 8],\n [1, 5, 7, 5, 3, 6, 6, 5, 8, 3, 5, 4, 3, 6, 3, 1, 1, 3, 6, 3, 4, 5, 3, 8, 5, 6, 6, 3, 5, 7],\n [5, 6, 7, 3, 1, 8, 6, 1, 9, 6, 3, 3, 8, 8, 6, 8, 8, 6, 8, 8, 3, 3, 6, 9, 1, 6, 8, 1, 3, 7],\n [6, 6, 7, 6, 8, 8, 9, 8, 6, 9, 3, 6, 8, 1, 7, 8, 8, 7, 1, 8, 6, 3, 9, 6, 8, 9, 8, 8, 6, 7],\n [7, 7, 3, 6, 6, 9, 6, 9, 3, 3, 3, 3, 6, 7, 7, 7, 7, 7, 7, 6, 3, 3, 3, 3, 9, 6, 9, 6, 6, 3],\n [3, 6, 6, 5, 1, 8, 9, 8, 3, 6, 3, 1, 8, 8, 7, 8, 8, 7, 8, 8, 1, 3, 6, 3, 8, 9, 8, 1, 5, 6],\n [3, 6, 6, 5, 1, 8, 9, 8, 3, 6, 3, 1, 8, 8, 7, 8, 8, 7, 8, 8, 1, 3, 6, 3, 8, 9, 8, 1, 5, 6],\n [7, 7, 3, 6, 6, 9, 6, 9, 3, 3, 3, 3, 6, 7, 7, 7, 7, 7, 7, 6, 3, 3, 3, 3, 9, 6, 9, 6, 6, 3],\n [6, 6, 7, 6, 8, 8, 9, 8, 6, 9, 3, 6, 8, 1, 7, 8, 8, 7, 1, 8, 6, 3, 9, 6, 8, 9, 8, 8, 6, 7],\n [5, 6, 7, 3, 1, 8, 6, 1, 9, 6, 3, 3, 8, 8, 6, 8, 8, 6, 8, 8, 3, 3, 6, 9, 1, 6, 8, 1, 3, 7],\n [1, 5, 7, 5, 3, 6, 6, 5, 8, 3, 5, 4, 3, 6, 3, 1, 1, 3, 6, 3, 4, 5, 3, 8, 5, 6, 6, 3, 5, 7],\n [7, 5, 8, 7, 7, 7, 3, 6, 5, 4, 8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8, 4, 5, 6, 3, 7, 7, 7, 8],\n [7, 8, 5, 5, 6, 6, 7, 6, 5, 8, 4, 3, 6, 9, 3, 6, 6, 3, 9, 6, 3, 4, 8, 5, 6, 7, 6, 6, 5, 5],\n [8, 7, 7, 1, 5, 6, 7, 3, 8, 5, 5, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 5, 5, 8, 3, 7, 6, 5, 1, 7],\n [6, 1, 6, 2, 1, 2, 9, 2, 3, 6, 6, 5, 1, 8, 9, 8, 8, 9, 8, 1, 5, 6, 6, 3, 2, 9, 2, 1, 2, 6],\n [1, 1, 4, 6, 2, 2, 9, 9, 7, 7, 3, 6, 6, 9, 6, 9, 9, 6, 9, 6, 6, 3, 7, 7, 9, 9, 2, 2, 6, 4],\n [4, 4, 1, 1, 1, 2, 2, 2, 6, 6, 7, 6, 8, 8, 9, 8, 8, 9, 8, 8, 6, 7, 6, 6, 2, 2, 2, 1, 1, 1],\n [2, 4, 1, 6, 2, 1, 2, 1, 5, 6, 7, 3, 1, 8, 6, 1, 1, 6, 8, 1, 3, 7, 6, 5, 1, 2, 1, 2, 6, 1],\n [6, 6, 6, 2, 6, 1, 6, 2, 1, 5, 7, 5, 3, 6, 6, 5, 5, 6, 6, 3, 5, 7, 5, 1, 2, 6, 1, 6, 2, 6],\n [9, 6, 6, 6, 1, 1, 4, 6, 7, 5, 8, 7, 7, 7, 3, 6, 6, 3, 7, 7, 7, 8, 5, 7, 6, 4, 1, 1, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [2, 8, 2, 8, 6, 8, 1, 1, 4, 6, 6, 6, 2, 4, 2, 8, 8, 2, 4, 0, 0, 0, 0, 0, 0, 1, 8, 6, 8, 2],\n [8, 5, 5, 5, 8, 2, 1, 2, 6, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 0, 0, 0, 0, 0, 0, 1, 2, 8, 5, 5],\n [2, 5, 4, 4, 1, 1, 8, 8, 6, 4, 1, 6, 2, 4, 4, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 8, 1, 1, 4, 4],\n [8, 5, 4, 2, 1, 2, 8, 1, 6, 4, 6, 1, 8, 2, 8, 2, 2, 8, 2, 0, 0, 0, 0, 0, 0, 8, 2, 1, 2, 4],\n [6, 8, 1, 1, 3, 7, 6, 3, 2, 4, 2, 8, 9, 6, 3, 3, 3, 3, 6, 0, 0, 0, 0, 0, 0, 6, 7, 3, 1, 1],\n [8, 2, 1, 2, 7, 8, 3, 6, 4, 4, 4, 2, 6, 3, 1, 3, 3, 1, 3, 0, 0, 0, 0, 0, 0, 3, 8, 7, 2, 1],\n [1, 1, 8, 8, 6, 3, 3, 6, 2, 4, 4, 8, 3, 1, 1, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 3, 3, 6, 8, 8],\n [1, 2, 8, 1, 3, 6, 6, 3, 8, 2, 8, 2, 3, 3, 3, 1, 1, 3, 3, 0, 0, 0, 2, 8, 3, 6, 6, 3, 1, 8],\n [4, 6, 6, 6, 2, 4, 2, 8, 4, 4, 3, 3, 1, 4, 1, 2, 2, 1, 4, 0, 0, 0, 4, 4, 8, 2, 4, 2, 6, 6],\n [6, 4, 4, 4, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 8, 8, 8, 8, 2, 0, 0, 0, 4, 4, 2, 4, 4, 4, 4, 4],\n [6, 4, 1, 6, 2, 4, 4, 8, 3, 2, 2, 4, 1, 8, 1, 8, 8, 1, 8, 0, 0, 0, 2, 3, 8, 4, 4, 2, 6, 1],\n [6, 4, 6, 1, 8, 2, 8, 2, 3, 2, 4, 7, 2, 8, 8, 8, 8, 8, 8, 0, 0, 0, 2, 3, 2, 8, 2, 8, 1, 6],\n [2, 4, 2, 8, 9, 6, 3, 3, 1, 4, 1, 2, 9, 9, 3, 3, 3, 3, 9, 0, 0, 0, 4, 1, 3, 3, 6, 9, 8, 2],\n [4, 4, 4, 2, 6, 3, 1, 3, 4, 2, 8, 8, 9, 3, 8, 3, 3, 8, 3, 9, 8, 8, 2, 4, 3, 1, 3, 6, 2, 4],\n [2, 4, 4, 8, 3, 1, 1, 3, 1, 8, 1, 8, 3, 8, 2, 8, 8, 2, 8, 3, 8, 1, 8, 1, 3, 1, 1, 3, 8, 4],\n [8, 2, 8, 2, 3, 3, 3, 1, 2, 8, 8, 8, 3, 3, 8, 8, 8, 8, 3, 3, 8, 8, 8, 2, 1, 3, 3, 3, 2, 8],\n [8, 2, 8, 2, 3, 3, 3, 1, 2, 8, 8, 8, 3, 3, 8, 8, 8, 8, 3, 3, 8, 8, 8, 2, 1, 3, 3, 3, 2, 8],\n [2, 4, 4, 8, 3, 1, 1, 3, 1, 8, 1, 8, 3, 8, 2, 8, 8, 2, 8, 3, 8, 1, 8, 1, 3, 1, 1, 3, 8, 4],\n [4, 4, 4, 2, 6, 3, 1, 3, 4, 2, 8, 8, 9, 3, 8, 3, 3, 8, 3, 9, 8, 8, 2, 4, 3, 1, 3, 6, 2, 4],\n [2, 4, 2, 8, 9, 6, 3, 3, 1, 4, 1, 2, 9, 9, 3, 3, 3, 3, 9, 9, 2, 1, 4, 1, 3, 3, 6, 9, 8, 2],\n [6, 4, 6, 1, 8, 2, 8, 2, 3, 2, 4, 7, 2, 8, 8, 8, 8, 8, 8, 2, 7, 4, 2, 3, 2, 8, 2, 8, 1, 6],\n [6, 4, 1, 6, 2, 4, 4, 8, 3, 2, 2, 4, 1, 8, 1, 8, 8, 1, 8, 1, 4, 2, 2, 3, 8, 4, 4, 2, 6, 1],\n [6, 4, 4, 4, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 8, 8, 8, 8, 2, 4, 2, 2, 4, 4, 2, 4, 4, 4, 4, 4],\n [4, 6, 6, 6, 2, 4, 2, 8, 4, 4, 3, 3, 1, 4, 1, 2, 2, 1, 4, 1, 3, 3, 4, 4, 8, 2, 4, 2, 6, 6],\n [1, 2, 8, 1, 3, 6, 6, 3, 8, 2, 8, 2, 3, 3, 3, 1, 1, 3, 3, 3, 2, 8, 2, 8, 3, 6, 6, 3, 1, 8],\n [1, 1, 8, 8, 6, 3, 3, 6, 2, 4, 4, 8, 3, 1, 1, 3, 3, 1, 1, 3, 0, 0, 0, 2, 6, 3, 3, 6, 8, 8],\n [8, 2, 1, 2, 7, 8, 3, 6, 4, 4, 4, 2, 6, 3, 1, 3, 3, 1, 3, 6, 0, 0, 0, 4, 6, 3, 0, 0, 2, 1],\n [6, 8, 1, 1, 3, 7, 6, 3, 2, 4, 2, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 6, 0, 0, 1, 1],\n [8, 5, 4, 2, 1, 2, 8, 1, 6, 4, 6, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 1, 8, 2, 1, 2, 4],\n [2, 5, 4, 4, 1, 1, 8, 8, 6, 4, 1, 6, 2, 4, 4, 8, 8, 4, 4, 2, 0, 0, 0, 6, 8, 8, 1, 1, 4, 4]\n ],\n \"output\": [\n [2, 8, 2, 8, 6, 8, 1, 1, 4, 6, 6, 6, 2, 4, 2, 8, 8, 2, 4, 2, 6, 6, 6, 4, 1, 1, 8, 6, 8, 2],\n [8, 5, 5, 5, 8, 2, 1, 2, 6, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 6, 2, 1, 2, 8, 5, 5],\n [2, 5, 4, 4, 1, 1, 8, 8, 6, 4, 1, 6, 2, 4, 4, 8, 8, 4, 4, 2, 6, 1, 4, 6, 8, 8, 1, 1, 4, 4],\n [8, 5, 4, 2, 1, 2, 8, 1, 6, 4, 6, 1, 8, 2, 8, 2, 2, 8, 2, 8, 1, 6, 4, 6, 1, 8, 2, 1, 2, 4],\n [6, 8, 1, 1, 3, 7, 6, 3, 2, 4, 2, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 2, 4, 2, 3, 6, 7, 3, 1, 1],\n [8, 2, 1, 2, 7, 8, 3, 6, 4, 4, 4, 2, 6, 3, 1, 3, 3, 1, 3, 6, 2, 4, 4, 4, 6, 3, 8, 7, 2, 1],\n [1, 1, 8, 8, 6, 3, 3, 6, 2, 4, 4, 8, 3, 1, 1, 3, 3, 1, 1, 3, 8, 4, 4, 2, 6, 3, 3, 6, 8, 8],\n [1, 2, 8, 1, 3, 6, 6, 3, 8, 2, 8, 2, 3, 3, 3, 1, 1, 3, 3, 3, 2, 8, 2, 8, 3, 6, 6, 3, 1, 8],\n [4, 6, 6, 6, 2, 4, 2, 8, 4, 4, 3, 3, 1, 4, 1, 2, 2, 1, 4, 1, 3, 3, 4, 4, 8, 2, 4, 2, 6, 6],\n [6, 4, 4, 4, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 8, 8, 8, 8, 2, 4, 2, 2, 4, 4, 2, 4, 4, 4, 4, 4],\n [6, 4, 1, 6, 2, 4, 4, 8, 3, 2, 2, 4, 1, 8, 1, 8, 8, 1, 8, 1, 4, 2, 2, 3, 8, 4, 4, 2, 6, 1],\n [6, 4, 6, 1, 8, 2, 8, 2, 3, 2, 4, 7, 2, 8, 8, 8, 8, 8, 8, 2, 7, 4, 2, 3, 2, 8, 2, 8, 1, 6],\n [2, 4, 2, 8, 9, 6, 3, 3, 1, 4, 1, 2, 9, 9, 3, 3, 3, 3, 9, 9, 2, 1, 4, 1, 3, 3, 6, 9, 8, 2],\n [4, 4, 4, 2, 6, 3, 1, 3, 4, 2, 8, 8, 9, 3, 8, 3, 3, 8, 3, 9, 8, 8, 2, 4, 3, 1, 3, 6, 2, 4],\n [2, 4, 4, 8, 3, 1, 1, 3, 1, 8, 1, 8, 3, 8, 2, 8, 8, 2, 8, 3, 8, 1, 8, 1, 3, 1, 1, 3, 8, 4],\n [8, 2, 8, 2, 3, 3, 3, 1, 2, 8, 8, 8, 3, 3, 8, 8, 8, 8, 3, 3, 8, 8, 8, 2, 1, 3, 3, 3, 2, 8],\n [8, 2, 8, 2, 3, 3, 3, 1, 2, 8, 8, 8, 3, 3, 8, 8, 8, 8, 3, 3, 8, 8, 8, 2, 1, 3, 3, 3, 2, 8],\n [2, 4, 4, 8, 3, 1, 1, 3, 1, 8, 1, 8, 3, 8, 2, 8, 8, 2, 8, 3, 8, 1, 8, 1, 3, 1, 1, 3, 8, 4],\n [4, 4, 4, 2, 6, 3, 1, 3, 4, 2, 8, 8, 9, 3, 8, 3, 3, 8, 3, 9, 8, 8, 2, 4, 3, 1, 3, 6, 2, 4],\n [2, 4, 2, 8, 9, 6, 3, 3, 1, 4, 1, 2, 9, 9, 3, 3, 3, 3, 9, 9, 2, 1, 4, 1, 3, 3, 6, 9, 8, 2],\n [6, 4, 6, 1, 8, 2, 8, 2, 3, 2, 4, 7, 2, 8, 8, 8, 8, 8, 8, 2, 7, 4, 2, 3, 2, 8, 2, 8, 1, 6],\n [6, 4, 1, 6, 2, 4, 4, 8, 3, 2, 2, 4, 1, 8, 1, 8, 8, 1, 8, 1, 4, 2, 2, 3, 8, 4, 4, 2, 6, 1],\n [6, 4, 4, 4, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 8, 8, 8, 8, 2, 4, 2, 2, 4, 4, 2, 4, 4, 4, 4, 4],\n [4, 6, 6, 6, 2, 4, 2, 8, 4, 4, 3, 3, 1, 4, 1, 2, 2, 1, 4, 1, 3, 3, 4, 4, 8, 2, 4, 2, 6, 6],\n [1, 2, 8, 1, 3, 6, 6, 3, 8, 2, 8, 2, 3, 3, 3, 1, 1, 3, 3, 3, 2, 8, 2, 8, 3, 6, 6, 3, 1, 8],\n [1, 1, 8, 8, 6, 3, 3, 6, 2, 4, 4, 8, 3, 1, 1, 3, 3, 1, 1, 3, 8, 4, 4, 2, 6, 3, 3, 6, 8, 8],\n [8, 2, 1, 2, 7, 8, 3, 6, 4, 4, 4, 2, 6, 3, 1, 3, 3, 1, 3, 6, 2, 4, 4, 4, 6, 3, 8, 7, 2, 1],\n [6, 8, 1, 1, 3, 7, 6, 3, 2, 4, 2, 8, 9, 6, 3, 3, 3, 3, 6, 9, 8, 2, 4, 2, 3, 6, 7, 3, 1, 1],\n [8, 5, 4, 2, 1, 2, 8, 1, 6, 4, 6, 1, 8, 2, 8, 2, 2, 8, 2, 8, 1, 6, 4, 6, 1, 8, 2, 1, 2, 4],\n [2, 5, 4, 4, 1, 1, 8, 8, 6, 4, 1, 6, 2, 4, 4, 8, 8, 4, 4, 2, 6, 1, 4, 6, 8, 8, 1, 1, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 4, 8, 8, 8, 6, 5, 0, 0, 0, 0, 9, 2, 5, 5, 2, 9, 5, 3, 6, 5, 5, 6, 8, 8, 8, 4, 8],\n [8, 3, 8, 4, 8, 8, 8, 8, 5, 0, 0, 0, 0, 9, 9, 5, 5, 9, 9, 9, 8, 5, 8, 5, 8, 8, 8, 8, 4, 8],\n [8, 8, 4, 8, 8, 8, 9, 8, 6, 5, 5, 5, 2, 9, 5, 9, 9, 5, 9, 2, 5, 5, 5, 6, 8, 9, 8, 8, 8, 4],\n [4, 4, 8, 8, 6, 8, 8, 5, 3, 8, 5, 5, 5, 5, 9, 8, 8, 9, 5, 5, 5, 5, 8, 3, 5, 8, 8, 6, 8, 8],\n [8, 8, 8, 6, 8, 4, 9, 1, 5, 9, 2, 5, 5, 9, 8, 9, 9, 8, 9, 5, 5, 2, 9, 5, 1, 9, 4, 8, 6, 8],\n [8, 8, 8, 8, 4, 1, 8, 1, 9, 9, 9, 5, 9, 2, 2, 9, 9, 2, 2, 9, 5, 9, 9, 9, 1, 8, 1, 4, 8, 8],\n [8, 8, 9, 8, 9, 8, 8, 9, 2, 9, 5, 9, 8, 0, 0, 0, 5, 8, 2, 8, 9, 5, 9, 2, 9, 8, 8, 9, 8, 9],\n [6, 8, 8, 5, 1, 1, 9, 8, 5, 5, 9, 8, 9, 0, 0, 0, 2, 5, 9, 9, 8, 9, 5, 5, 8, 9, 1, 1, 5, 8],\n [5, 5, 0, 0, 0, 0, 0, 0, 4, 9, 4, 4, 6, 0, 0, 0, 6, 4, 6, 6, 4, 4, 9, 4, 5, 2, 9, 5, 3, 6],\n [5, 8, 0, 0, 0, 0, 0, 0, 9, 6, 6, 4, 6, 0, 0, 0, 5, 7, 5, 6, 4, 6, 6, 9, 5, 9, 9, 9, 8, 5],\n [6, 5, 0, 0, 0, 0, 0, 0, 4, 6, 9, 4, 4, 0, 0, 0, 7, 6, 7, 4, 4, 9, 6, 4, 9, 5, 9, 2, 5, 5],\n [3, 8, 0, 0, 0, 0, 0, 0, 4, 4, 4, 1, 6, 0, 0, 0, 4, 7, 5, 6, 1, 4, 4, 4, 8, 9, 5, 5, 5, 5],\n [5, 9, 0, 0, 0, 0, 0, 0, 6, 6, 4, 6, 2, 0, 0, 0, 4, 4, 5, 2, 6, 4, 6, 6, 9, 8, 9, 5, 5, 2],\n [9, 9, 0, 0, 0, 0, 0, 0, 6, 5, 7, 5, 5, 0, 0, 0, 4, 2, 2, 5, 5, 7, 5, 6, 9, 2, 2, 9, 5, 9],\n [2, 9, 5, 9, 8, 2, 8, 5, 4, 7, 6, 7, 4, 2, 4, 4, 4, 4, 2, 4, 7, 6, 7, 4, 5, 8, 2, 8, 9, 5],\n [5, 5, 9, 8, 9, 9, 5, 2, 6, 5, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 5, 6, 2, 5, 9, 9, 8, 9],\n [5, 5, 9, 8, 9, 9, 5, 2, 6, 5, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 5, 6, 2, 5, 9, 9, 8, 9],\n [2, 9, 5, 9, 8, 2, 8, 5, 4, 7, 6, 7, 4, 2, 4, 4, 4, 4, 2, 4, 7, 6, 7, 4, 5, 8, 2, 8, 9, 5],\n [9, 9, 9, 5, 9, 2, 2, 9, 6, 5, 7, 5, 5, 2, 2, 4, 4, 2, 2, 5, 5, 7, 5, 6, 9, 2, 2, 9, 0, 0],\n [5, 9, 2, 5, 5, 9, 8, 9, 6, 6, 4, 6, 2, 5, 4, 4, 4, 4, 5, 2, 6, 4, 6, 6, 9, 8, 9, 5, 0, 0],\n [3, 8, 5, 5, 5, 5, 9, 8, 4, 4, 4, 1, 6, 5, 7, 4, 4, 7, 5, 6, 1, 4, 4, 4, 8, 9, 5, 5, 0, 0],\n [6, 5, 5, 5, 2, 9, 5, 9, 4, 6, 9, 4, 4, 7, 6, 7, 7, 6, 7, 4, 4, 9, 6, 4, 9, 5, 9, 2, 0, 0],\n [5, 8, 5, 8, 9, 9, 9, 5, 9, 6, 6, 4, 6, 5, 7, 5, 5, 7, 5, 6, 4, 6, 6, 9, 5, 9, 9, 9, 0, 0],\n [5, 5, 6, 3, 5, 9, 2, 5, 4, 9, 4, 4, 6, 6, 4, 6, 6, 4, 6, 6, 4, 4, 9, 4, 5, 2, 9, 5, 0, 0],\n [6, 8, 8, 5, 1, 1, 9, 8, 5, 5, 9, 8, 9, 9, 5, 2, 2, 5, 9, 9, 8, 9, 5, 5, 8, 9, 1, 1, 0, 0],\n [8, 8, 9, 8, 9, 8, 8, 9, 2, 9, 5, 9, 8, 2, 8, 5, 5, 8, 2, 8, 9, 5, 9, 2, 9, 8, 8, 9, 0, 0],\n [8, 8, 8, 8, 4, 1, 8, 1, 9, 9, 9, 5, 9, 2, 2, 9, 9, 2, 2, 9, 5, 9, 9, 9, 1, 8, 1, 4, 8, 8],\n [8, 8, 8, 6, 8, 4, 9, 1, 5, 9, 2, 5, 5, 9, 8, 9, 9, 8, 9, 5, 5, 2, 9, 5, 1, 9, 4, 8, 6, 8],\n [4, 4, 8, 8, 6, 8, 8, 5, 3, 8, 5, 5, 5, 5, 9, 8, 8, 9, 5, 5, 5, 5, 8, 3, 5, 8, 8, 6, 8, 8],\n [8, 8, 4, 8, 8, 8, 9, 8, 6, 5, 5, 5, 2, 9, 5, 9, 9, 5, 9, 2, 5, 5, 5, 6, 8, 9, 8, 8, 8, 4]\n ],\n \"output\": [\n [8, 8, 8, 4, 8, 8, 8, 6, 5, 5, 6, 3, 5, 9, 2, 5, 5, 2, 9, 5, 3, 6, 5, 5, 6, 8, 8, 8, 4, 8],\n [8, 3, 8, 4, 8, 8, 8, 8, 5, 8, 5, 8, 9, 9, 9, 5, 5, 9, 9, 9, 8, 5, 8, 5, 8, 8, 8, 8, 4, 8],\n [8, 8, 4, 8, 8, 8, 9, 8, 6, 5, 5, 5, 2, 9, 5, 9, 9, 5, 9, 2, 5, 5, 5, 6, 8, 9, 8, 8, 8, 4],\n [4, 4, 8, 8, 6, 8, 8, 5, 3, 8, 5, 5, 5, 5, 9, 8, 8, 9, 5, 5, 5, 5, 8, 3, 5, 8, 8, 6, 8, 8],\n [8, 8, 8, 6, 8, 4, 9, 1, 5, 9, 2, 5, 5, 9, 8, 9, 9, 8, 9, 5, 5, 2, 9, 5, 1, 9, 4, 8, 6, 8],\n [8, 8, 8, 8, 4, 1, 8, 1, 9, 9, 9, 5, 9, 2, 2, 9, 9, 2, 2, 9, 5, 9, 9, 9, 1, 8, 1, 4, 8, 8],\n [8, 8, 9, 8, 9, 8, 8, 9, 2, 9, 5, 9, 8, 2, 8, 5, 5, 8, 2, 8, 9, 5, 9, 2, 9, 8, 8, 9, 8, 9],\n [6, 8, 8, 5, 1, 1, 9, 8, 5, 5, 9, 8, 9, 9, 5, 2, 2, 5, 9, 9, 8, 9, 5, 5, 8, 9, 1, 1, 5, 8],\n [5, 5, 6, 3, 5, 9, 2, 5, 4, 9, 4, 4, 6, 6, 4, 6, 6, 4, 6, 6, 4, 4, 9, 4, 5, 2, 9, 5, 3, 6],\n [5, 8, 5, 8, 9, 9, 9, 5, 9, 6, 6, 4, 6, 5, 7, 5, 5, 7, 5, 6, 4, 6, 6, 9, 5, 9, 9, 9, 8, 5],\n [6, 5, 5, 5, 2, 9, 5, 9, 4, 6, 9, 4, 4, 7, 6, 7, 7, 6, 7, 4, 4, 9, 6, 4, 9, 5, 9, 2, 5, 5],\n [3, 8, 5, 5, 5, 5, 9, 8, 4, 4, 4, 1, 6, 5, 7, 4, 4, 7, 5, 6, 1, 4, 4, 4, 8, 9, 5, 5, 5, 5],\n [5, 9, 2, 5, 5, 9, 8, 9, 6, 6, 4, 6, 2, 5, 4, 4, 4, 4, 5, 2, 6, 4, 6, 6, 9, 8, 9, 5, 5, 2],\n [9, 9, 9, 5, 9, 2, 2, 9, 6, 5, 7, 5, 5, 2, 2, 4, 4, 2, 2, 5, 5, 7, 5, 6, 9, 2, 2, 9, 5, 9],\n [2, 9, 5, 9, 8, 2, 8, 5, 4, 7, 6, 7, 4, 2, 4, 4, 4, 4, 2, 4, 7, 6, 7, 4, 5, 8, 2, 8, 9, 5],\n [5, 5, 9, 8, 9, 9, 5, 2, 6, 5, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 5, 6, 2, 5, 9, 9, 8, 9],\n [5, 5, 9, 8, 9, 9, 5, 2, 6, 5, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 5, 6, 2, 5, 9, 9, 8, 9],\n [2, 9, 5, 9, 8, 2, 8, 5, 4, 7, 6, 7, 4, 2, 4, 4, 4, 4, 2, 4, 7, 6, 7, 4, 5, 8, 2, 8, 9, 5],\n [9, 9, 9, 5, 9, 2, 2, 9, 6, 5, 7, 5, 5, 2, 2, 4, 4, 2, 2, 5, 5, 7, 5, 6, 9, 2, 2, 9, 5, 9],\n [5, 9, 2, 5, 5, 9, 8, 9, 6, 6, 4, 6, 2, 5, 4, 4, 4, 4, 5, 2, 6, 4, 6, 6, 9, 8, 9, 5, 5, 2],\n [3, 8, 5, 5, 5, 5, 9, 8, 4, 4, 4, 1, 6, 5, 7, 4, 4, 7, 5, 6, 1, 4, 4, 4, 8, 9, 5, 5, 5, 5],\n [6, 5, 5, 5, 2, 9, 5, 9, 4, 6, 9, 4, 4, 7, 6, 7, 7, 6, 7, 4, 4, 9, 6, 4, 9, 5, 9, 2, 5, 5],\n [5, 8, 5, 8, 9, 9, 9, 5, 9, 6, 6, 4, 6, 5, 7, 5, 5, 7, 5, 6, 4, 6, 6, 9, 5, 9, 9, 9, 8, 5],\n [5, 5, 6, 3, 5, 9, 2, 5, 4, 9, 4, 4, 6, 6, 4, 6, 6, 4, 6, 6, 4, 4, 9, 4, 5, 2, 9, 5, 3, 6],\n [6, 8, 8, 5, 1, 1, 9, 8, 5, 5, 9, 8, 9, 9, 5, 2, 2, 5, 9, 9, 8, 9, 5, 5, 8, 9, 1, 1, 5, 8],\n [8, 8, 9, 8, 9, 8, 8, 9, 2, 9, 5, 9, 8, 2, 8, 5, 5, 8, 2, 8, 9, 5, 9, 2, 9, 8, 8, 9, 8, 9],\n [8, 8, 8, 8, 4, 1, 8, 1, 9, 9, 9, 5, 9, 2, 2, 9, 9, 2, 2, 9, 5, 9, 9, 9, 1, 8, 1, 4, 8, 8],\n [8, 8, 8, 6, 8, 4, 9, 1, 5, 9, 2, 5, 5, 9, 8, 9, 9, 8, 9, 5, 5, 2, 9, 5, 1, 9, 4, 8, 6, 8],\n [4, 4, 8, 8, 6, 8, 8, 5, 3, 8, 5, 5, 5, 5, 9, 8, 8, 9, 5, 5, 5, 5, 8, 3, 5, 8, 8, 6, 8, 8],\n [8, 8, 4, 8, 8, 8, 9, 8, 6, 5, 5, 5, 2, 9, 5, 9, 9, 5, 9, 2, 5, 5, 5, 6, 8, 9, 8, 8, 8, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 9, 3, 5, 9, 9, 3, 9, 4, 4, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 4, 4, 9, 3, 9, 9, 5, 3],\n [9, 3, 5, 5, 9, 7, 3, 5, 4, 6, 2, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 2, 6, 4, 5, 3, 7, 9, 5, 5],\n [3, 5, 5, 9, 3, 3, 9, 9, 2, 2, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 2, 2, 9, 9, 3, 3, 9, 5],\n [5, 5, 9, 5, 9, 5, 9, 5, 6, 2, 6, 2, 6, 4, 6, 1, 1, 6, 4, 6, 2, 6, 2, 6, 5, 9, 5, 9, 5, 9],\n [9, 9, 3, 9, 1, 8, 8, 9, 3, 3, 3, 6, 4, 2, 1, 4, 4, 1, 2, 4, 6, 3, 3, 3, 9, 8, 8, 1, 9, 3],\n [9, 7, 3, 5, 8, 5, 8, 1, 3, 3, 3, 4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 3, 3, 3, 1, 8, 5, 8, 5, 3],\n [3, 3, 9, 9, 8, 8, 5, 9, 3, 3, 3, 6, 1, 2, 2, 4, 4, 2, 2, 1, 6, 3, 3, 3, 9, 5, 8, 8, 9, 9],\n [9, 5, 9, 5, 9, 1, 9, 9, 6, 4, 6, 1, 4, 4, 4, 9, 9, 4, 4, 4, 1, 6, 4, 6, 9, 9, 1, 9, 5, 9],\n [4, 4, 2, 6, 3, 3, 3, 6, 7, 5, 5, 8, 3, 6, 6, 6, 6, 6, 6, 3, 8, 5, 5, 7, 6, 3, 3, 3, 6, 2],\n [4, 6, 2, 2, 3, 3, 3, 4, 5, 5, 5, 1, 6, 9, 4, 3, 3, 4, 9, 6, 1, 5, 5, 5, 4, 3, 3, 3, 2, 2],\n [2, 2, 2, 6, 3, 3, 3, 6, 5, 5, 7, 5, 6, 4, 4, 9, 9, 4, 4, 6, 5, 7, 5, 5, 6, 3, 3, 3, 6, 2],\n [6, 2, 6, 2, 6, 4, 6, 1, 8, 1, 5, 8, 6, 3, 9, 4, 4, 9, 3, 6, 8, 5, 1, 8, 1, 6, 4, 6, 2, 6],\n [3, 3, 3, 6, 4, 2, 1, 4, 3, 0, 0, 6, 1, 8, 2, 8, 8, 2, 8, 1, 6, 6, 6, 3, 4, 1, 2, 4, 6, 3],\n [3, 3, 3, 4, 2, 2, 2, 4, 6, 0, 0, 3, 8, 6, 2, 2, 2, 2, 6, 8, 3, 4, 9, 6, 4, 2, 2, 2, 4, 3],\n [3, 3, 3, 6, 1, 2, 2, 4, 6, 0, 0, 9, 2, 2, 2, 8, 8, 2, 2, 2, 9, 4, 4, 6, 4, 2, 2, 1, 6, 3],\n [6, 4, 6, 1, 4, 4, 4, 9, 6, 0, 0, 4, 8, 2, 8, 6, 6, 8, 2, 8, 4, 9, 3, 6, 9, 4, 4, 4, 1, 6],\n [6, 4, 6, 1, 4, 4, 4, 9, 6, 0, 0, 4, 8, 2, 8, 6, 6, 8, 2, 8, 4, 9, 3, 6, 9, 4, 4, 4, 1, 6],\n [3, 3, 3, 6, 1, 2, 2, 4, 6, 4, 4, 9, 2, 2, 2, 8, 8, 2, 2, 2, 9, 4, 4, 6, 4, 2, 2, 1, 6, 3],\n [3, 3, 3, 4, 2, 2, 2, 4, 6, 9, 4, 3, 8, 6, 2, 2, 2, 2, 6, 8, 3, 4, 9, 6, 4, 2, 2, 2, 4, 3],\n [3, 3, 3, 6, 4, 2, 1, 4, 3, 6, 6, 6, 1, 8, 2, 8, 8, 2, 8, 1, 6, 6, 6, 3, 4, 1, 2, 4, 6, 3],\n [6, 2, 6, 2, 6, 4, 6, 1, 8, 1, 5, 8, 6, 3, 9, 4, 4, 9, 3, 6, 8, 5, 1, 8, 1, 6, 4, 6, 2, 6],\n [2, 2, 2, 6, 3, 3, 3, 6, 5, 5, 7, 5, 6, 4, 4, 9, 9, 4, 4, 6, 5, 7, 5, 5, 6, 3, 3, 3, 6, 2],\n [4, 6, 2, 2, 3, 3, 3, 4, 5, 5, 5, 1, 6, 9, 4, 3, 3, 4, 9, 6, 1, 5, 5, 5, 4, 3, 3, 3, 2, 2],\n [4, 4, 2, 6, 3, 3, 3, 6, 7, 5, 5, 8, 3, 6, 6, 6, 6, 6, 6, 3, 8, 5, 5, 7, 6, 3, 3, 3, 6, 2],\n [9, 5, 9, 5, 9, 1, 9, 9, 6, 4, 6, 1, 4, 4, 4, 9, 9, 4, 4, 4, 1, 6, 4, 6, 9, 9, 1, 9, 5, 9],\n [3, 3, 9, 9, 8, 8, 5, 9, 3, 3, 3, 6, 1, 2, 2, 4, 4, 2, 2, 1, 6, 3, 3, 3, 9, 5, 8, 8, 9, 9],\n [9, 7, 3, 5, 8, 5, 8, 1, 3, 3, 3, 4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 3, 3, 3, 1, 0, 0, 0, 0, 0],\n [9, 9, 3, 9, 1, 8, 8, 9, 3, 3, 3, 6, 4, 2, 1, 4, 4, 1, 2, 4, 6, 3, 3, 3, 9, 0, 0, 0, 0, 0],\n [5, 5, 9, 5, 9, 5, 9, 5, 6, 2, 6, 2, 6, 4, 6, 1, 1, 6, 4, 6, 2, 6, 2, 6, 5, 0, 0, 0, 0, 0],\n [3, 5, 5, 9, 3, 3, 9, 9, 2, 2, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 2, 2, 9, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 9, 3, 5, 9, 9, 3, 9, 4, 4, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 4, 4, 9, 3, 9, 9, 5, 3], [9, 3, 5, 5, 9, 7, 3, 5, 4, 6, 2, 2, 3, 3, 3, 4, 4, 3, 3, 3, 2, 2, 6, 4, 5, 3, 7, 9, 5, 5], [3, 5, 5, 9, 3, 3, 9, 9, 2, 2, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 2, 2, 9, 9, 3, 3, 9, 5], [5, 5, 9, 5, 9, 5, 9, 5, 6, 2, 6, 2, 6, 4, 6, 1, 1, 6, 4, 6, 2, 6, 2, 6, 5, 9, 5, 9, 5, 9], [9, 9, 3, 9, 1, 8, 8, 9, 3, 3, 3, 6, 4, 2, 1, 4, 4, 1, 2, 4, 6, 3, 3, 3, 9, 8, 8, 1, 9, 3], [9, 7, 3, 5, 8, 5, 8, 1, 3, 3, 3, 4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 3, 3, 3, 1, 8, 5, 8, 5, 3], [3, 3, 9, 9, 8, 8, 5, 9, 3, 3, 3, 6, 1, 2, 2, 4, 4, 2, 2, 1, 6, 3, 3, 3, 9, 5, 8, 8, 9, 9], [9, 5, 9, 5, 9, 1, 9, 9, 6, 4, 6, 1, 4, 4, 4, 9, 9, 4, 4, 4, 1, 6, 4, 6, 9, 9, 1, 9, 5, 9], [4, 4, 2, 6, 3, 3, 3, 6, 7, 5, 5, 8, 3, 6, 6, 6, 6, 6, 6, 3, 8, 5, 5, 7, 6, 3, 3, 3, 6, 2], [4, 6, 2, 2, 3, 3, 3, 4, 5, 5, 5, 1, 6, 9, 4, 3, 3, 4, 9, 6, 1, 5, 5, 5, 4, 3, 3, 3, 2, 2], [2, 2, 2, 6, 3, 3, 3, 6, 5, 5, 7, 5, 6, 4, 4, 9, 9, 4, 4, 6, 5, 7, 5, 5, 6, 3, 3, 3, 6, 2], [6, 2, 6, 2, 6, 4, 6, 1, 8, 1, 5, 8, 6, 3, 9, 4, 4, 9, 3, 6, 8, 5, 1, 8, 1, 6, 4, 6, 2, 6], [3, 3, 3, 6, 4, 2, 1, 4, 3, 6, 6, 6, 1, 8, 2, 8, 8, 2, 8, 1, 6, 6, 6, 3, 4, 1, 2, 4, 6, 3], [3, 3, 3, 4, 2, 2, 2, 4, 6, 9, 4, 3, 8, 6, 2, 2, 2, 2, 6, 8, 3, 4, 9, 6, 4, 2, 2, 2, 4, 3], [3, 3, 3, 6, 1, 2, 2, 4, 6, 4, 4, 9, 2, 2, 2, 8, 8, 2, 2, 2, 9, 4, 4, 6, 4, 2, 2, 1, 6, 3], [6, 4, 6, 1, 4, 4, 4, 9, 6, 3, 9, 4, 8, 2, 8, 6, 6, 8, 2, 8, 4, 9, 3, 6, 9, 4, 4, 4, 1, 6], [6, 4, 6, 1, 4, 4, 4, 9, 6, 3, 9, 4, 8, 2, 8, 6, 6, 8, 2, 8, 4, 9, 3, 6, 9, 4, 4, 4, 1, 6], [3, 3, 3, 6, 1, 2, 2, 4, 6, 4, 4, 9, 2, 2, 2, 8, 8, 2, 2, 2, 9, 4, 4, 6, 4, 2, 2, 1, 6, 3], [3, 3, 3, 4, 2, 2, 2, 4, 6, 9, 4, 3, 8, 6, 2, 2, 2, 2, 6, 8, 3, 4, 9, 6, 4, 2, 2, 2, 4, 3], [3, 3, 3, 6, 4, 2, 1, 4, 3, 6, 6, 6, 1, 8, 2, 8, 8, 2, 8, 1, 6, 6, 6, 3, 4, 1, 2, 4, 6, 3], [6, 2, 6, 2, 6, 4, 6, 1, 8, 1, 5, 8, 6, 3, 9, 4, 4, 9, 3, 6, 8, 5, 1, 8, 1, 6, 4, 6, 2, 6], [2, 2, 2, 6, 3, 3, 3, 6, 5, 5, 7, 5, 6, 4, 4, 9, 9, 4, 4, 6, 5, 7, 5, 5, 6, 3, 3, 3, 6, 2], [4, 6, 2, 2, 3, 3, 3, 4, 5, 5, 5, 1, 6, 9, 4, 3, 3, 4, 9, 6, 1, 5, 5, 5, 4, 3, 3, 3, 2, 2], [4, 4, 2, 6, 3, 3, 3, 6, 7, 5, 5, 8, 3, 6, 6, 6, 6, 6, 6, 3, 8, 5, 5, 7, 6, 3, 3, 3, 6, 2], [9, 5, 9, 5, 9, 1, 9, 9, 6, 4, 6, 1, 4, 4, 4, 9, 9, 4, 4, 4, 1, 6, 4, 6, 9, 9, 1, 9, 5, 9], [3, 3, 9, 9, 8, 8, 5, 9, 3, 3, 3, 6, 1, 2, 2, 4, 4, 2, 2, 1, 6, 3, 3, 3, 9, 5, 8, 8, 9, 9], [9, 7, 3, 5, 8, 5, 8, 1, 3, 3, 3, 4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 3, 3, 3, 1, 8, 5, 8, 5, 3], [9, 9, 3, 9, 1, 8, 8, 9, 3, 3, 3, 6, 4, 2, 1, 4, 4, 1, 2, 4, 6, 3, 3, 3, 9, 8, 8, 1, 9, 3], [5, 5, 9, 5, 9, 5, 9, 5, 6, 2, 6, 2, 6, 4, 6, 1, 1, 6, 4, 6, 2, 6, 2, 6, 5, 9, 5, 9, 5, 9], [3, 5, 5, 9, 3, 3, 9, 9, 2, 2, 2, 6, 3, 3, 3, 6, 6, 3, 3, 3, 6, 2, 2, 2, 9, 9, 3, 3, 9, 5]], "task_id": "981571dc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 7, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 3, 2, 4, 0, 0],\n [0, 0, 0, 7, 7, 3, 2, 4, 0, 0],\n [0, 0, 0, 7, 3, 3, 2, 0, 0, 0],\n [0, 0, 0, 7, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 7, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 7, 4, 2, 0, 0],\n [0, 0, 0, 3, 3, 7, 4, 2, 0, 0],\n [0, 0, 0, 3, 7, 7, 4, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 1, 8, 0, 0],\n [0, 0, 3, 3, 2, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 2, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 3, 2, 0, 0],\n [0, 0, 1, 1, 8, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 8, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [9, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 6, 0, 0, 0, 9, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 4, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 7, 0, 0, 0, 0],\n [0, 0, 0, 7, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [9, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 6, 0, 0, 0, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 9, 0, 0, 0, 0],\n [0, 0, 0, 7, 7, 6, 0, 0, 0, 0],\n [0, 0, 0, 6, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 9, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 4, 0, 0, 0, 9, 9, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 9, 0, 0, 0],\n [0, 0, 0, 2, 8, 8, 9, 0, 0, 0],\n [0, 0, 0, 2, 4, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 4, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 9, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 9, 0, 0, 0, 0, 0, 0, 0, 0], [2, 4, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 9, 9, 9, 8, 0, 0, 0], [0, 0, 0, 4, 9, 9, 8, 0, 0, 0], [0, 0, 0, 4, 2, 4, 0, 0, 0, 0], [0, 0, 0, 4, 4, 2, 0, 0, 0, 0], [0, 0, 0, 4, 2, 2, 0, 0, 0, 0], [0, 0, 0, 8, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "0becf7df"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 2, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 3, 3, 3, 3, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 3, 2, 2, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 3, 2, 1, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 3, 2, 2, 2, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 3, 3, 3, 3, 3, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 3, 3, 4, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 4, 4, 4, 4, 4, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 3, 3, 3, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 3, 3, 3, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 3, 3, 2, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 3, 3, 3, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 3, 3, 3, 3, 3, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 4, 4, 4, 4, 4, 4, 4, 0, 8, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 2, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 2, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 2, 2, 2, 0, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 2, 3, 2, 0, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 2, 2, 2, 0, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 0, 0, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "9356391f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 3, 3],\n [3, 3, 3],\n [3, 3, 3]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8],\n [8, 8]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 2],\n [2, 2, 2, 2],\n [2, 2, 2, 2],\n [2, 2, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0]], "task_id": "695367ec"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 2, 5, 2, 5, 2, 5, 1],\n [2, 5, 2, 5, 2, 5, 2, 1],\n [5, 2, 5, 2, 5, 2, 5, 1],\n [2, 5, 2, 5, 2, 5, 2, 1],\n [5, 2, 5, 2, 5, 2, 5, 1],\n [2, 5, 2, 5, 2, 5, 2, 1],\n [5, 2, 5, 2, 5, 2, 5, 1],\n [1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [2, 5, 2, 5, 2, 5, 2, 5],\n [5, 2, 5, 2, 5, 2, 5, 2],\n [2, 5, 2, 5, 2, 5, 2, 5],\n [5, 2, 5, 2, 5, 2, 5, 2],\n [2, 5, 2, 5, 2, 5, 2, 5],\n [5, 2, 5, 2, 5, 2, 5, 2],\n [2, 5, 2, 5, 2, 5, 2, 5],\n [5, 2, 5, 2, 5, 2, 5, 2]\n ]\n}\n\n{\n \"input\": [\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5],\n [3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6],\n [7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5, 7, 6, 3, 5]\n ]\n}\n\n{\n \"input\": [\n [7, 6, 3, 7, 6, 3, 7, 6, 4],\n [6, 3, 7, 6, 3, 7, 6, 3, 4],\n [7, 6, 3, 7, 6, 3, 7, 6, 4],\n [6, 3, 7, 6, 3, 7, 6, 3, 4],\n [7, 6, 3, 7, 6, 3, 7, 6, 4],\n [6, 3, 7, 6, 3, 7, 6, 3, 4],\n [7, 6, 3, 7, 6, 3, 7, 6, 4],\n [6, 3, 7, 6, 3, 7, 6, 3, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [6, 3, 7, 6, 3, 7, 6, 3, 7],\n [3, 7, 6, 3, 7, 6, 3, 7, 6],\n [6, 3, 7, 6, 3, 7, 6, 3, 7],\n [3, 7, 6, 3, 7, 6, 3, 7, 6],\n [6, 3, 7, 6, 3, 7, 6, 3, 7],\n [3, 7, 6, 3, 7, 6, 3, 7, 6],\n [6, 3, 7, 6, 3, 7, 6, 3, 7],\n [3, 7, 6, 3, 7, 6, 3, 7, 6],\n [6, 3, 7, 6, 3, 7, 6, 3, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 8, 6, 8, 6, 3],\n [8, 6, 8, 6, 8, 3],\n [6, 8, 6, 8, 6, 3],\n [8, 6, 8, 6, 8, 3],\n [6, 8, 6, 8, 6, 3],\n [3, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 6, 8, 6, 8, 6], [6, 8, 6, 8, 6, 8], [8, 6, 8, 6, 8, 6], [6, 8, 6, 8, 6, 8], [8, 6, 8, 6, 8, 6], [6, 8, 6, 8, 6, 8]], "task_id": "50a16a69"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1],\n [0, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 2, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 0, 2],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0],\n [2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 1, 1, 1],\n [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 3, 3, 3, 0, 0, 8, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 8, 8, 8, 8, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 3, 0, 3, 8, 0, 0, 8, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 3, 3, 3, 8, 8, 8, 8, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 8, 0, 0],\n [3, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 8, 8, 8, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 2, 2, 2, 2],\n [0, 8, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [8, 8, 8, 3, 3, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0]], "task_id": "ac2e8ecf"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 0, 0],\n [0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 0, 0],\n [0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 0, 0],\n [0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 0, 0],\n [0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 0, 0],\n [0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 0, 0],\n [0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5],\n [5, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 5],\n [5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5],\n [5, 2, 2, 2, 2, 5],\n [5, 2, 5, 5, 2, 5],\n [5, 2, 5, 5, 2, 5],\n [5, 2, 2, 2, 2, 5],\n [5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 5, 5, 5, 5, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 5, 2, 2, 5, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 5, 5, 5, 5, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0],\n [0, 0, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0],\n [0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0],\n [0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0],\n [0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 5, 0, 0, 0, 0], [0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "a3f84088"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 4, 4, 5, 2, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 5, 2, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 4, 0, 2, 0, 0, 4, 4, 4, 2, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 2, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 8, 8, 8, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 4, 0, 2, 5, 0, 5, 0, 0, 0, 0, 0, 5],\n [5, 0, 0, 0, 0, 2, 4, 4, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 4, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0],\n [0, 2, 0, 5, 4, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 8, 8, 8, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 2],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 4, 4, 4, 2, 0],\n [0, 0, 0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 4, 4, 4, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 4, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 4, 8, 8, 8, 4, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 2, 0, 4, 0, 2, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 4, 5, 0, 2, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 8, 8, 8, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 4, 4, 4, 0, 0, 5, 0],\n [0, 0, 0, 5, 4, 4, 0, 0, 0, 0, 0, 0, 4, 0, 5, 0, 0, 0, 0],\n [0, 5, 0, 5, 0, 4, 0, 5, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 2, 0, 4, 0, 2, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 4, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 4, 4, 8, 8, 8, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 4, 0, 5, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 5, 5],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0],\n [5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 2, 0, 0, 5, 0, 0, 4, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 5], [0, 0, 4, 5, 2, 0, 0, 4, 4, 4, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 2, 0, 4, 0, 2, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 5, 5], [0, 0, 0, 0, 4, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 0, 4, 4, 8, 8, 8, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 4, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 4, 0, 2, 4, 4, 4, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0], [0, 0, 5, 0, 5, 4, 4, 4, 0, 0, 2, 0, 4, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0], [5, 0, 0, 0, 5, 0, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5], [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0]], "task_id": "212895b5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 6, 1, 1, 1, 1, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 1, 1, 1, 1, 1, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 1, 1, 1, 1, 1, 2, 3, 6, 5, 6, 3, 1, 1, 1, 1, 1, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 1, 1, 1, 1, 1, 3, 4, 1, 6, 1, 4, 1, 1, 1, 1, 1, 4, 3, 4, 1, 6, 1, 4],\n [5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 1, 1, 1, 1, 1, 5, 4, 5, 2, 1, 2, 5],\n [6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 1, 1, 1, 1, 1, 6, 5, 6, 3, 2, 3, 6],\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4],\n [5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5],\n [6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6],\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 1, 1, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 1, 1, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 1, 1, 1, 1, 1, 3],\n [4, 3, 1, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 1, 1, 1, 1, 1, 4],\n [5, 4, 1, 1, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 1, 1, 1, 1, 1, 5],\n [6, 5, 1, 1, 1, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 1, 1, 1, 1, 1, 6],\n [1, 6, 1, 1, 1, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 1, 1, 1, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4]\n ],\n \"output\": [\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4],\n [5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5],\n [6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6],\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4],\n [5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5],\n [6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6],\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4],\n [5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5],\n [6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6],\n [1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1],\n [2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2, 1, 2, 5, 4, 5, 2],\n [3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3, 2, 3, 6, 5, 6, 3],\n [4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4, 3, 4, 1, 6, 1, 4]\n ]\n}\n\n{\n \"input\": [\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3],\n [4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4],\n [5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 1, 1, 1, 3, 5],\n [6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 1, 1, 1, 4, 6],\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 1, 1, 1, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 1, 1, 1, 1, 1, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3],\n [4, 2, 2, 4, 1, 1, 1, 1, 1, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4],\n [5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5],\n [6, 4, 4, 1, 1, 1, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6],\n [7, 5, 5, 1, 1, 1, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3],\n [4, 2, 1, 1, 1, 1, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 1, 1, 1, 1, 1, 2, 2, 4],\n [5, 3, 1, 1, 1, 1, 1, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 1, 1, 1, 1, 1, 3, 3, 5],\n [6, 4, 1, 1, 1, 1, 1, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 1, 1, 1, 1, 1, 4, 4, 6],\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7]\n ],\n \"output\": [\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3],\n [4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4],\n [5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5],\n [6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6],\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3],\n [4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4],\n [5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5],\n [6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6],\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7],\n [1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1, 5, 4, 5, 1, 6, 6, 1],\n [2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2, 6, 5, 6, 2, 7, 7, 2],\n [3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3, 7, 6, 7, 3, 1, 1, 3],\n [4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4, 1, 7, 1, 4, 2, 2, 4],\n [5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5, 2, 1, 2, 5, 3, 3, 5],\n [6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6, 3, 2, 3, 6, 4, 4, 6],\n [7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7, 4, 3, 4, 7, 5, 5, 7]\n ]\n}\n\n{\n \"input\": [\n [7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3],\n [4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4],\n [5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5],\n [6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6],\n [7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 1, 1, 1, 1, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 1, 1, 1, 1, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3],\n [4, 1, 8, 1, 1, 1, 1, 1, 4, 1, 8, 1, 1, 1, 1, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4],\n [5, 2, 1, 2, 1, 1, 1, 2, 5, 1, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5],\n [6, 3, 2, 3, 1, 1, 1, 3, 6, 1, 1, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6],\n [7, 4, 3, 4, 1, 1, 1, 4, 7, 1, 1, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 1, 1, 1, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 1, 1, 1, 6, 5, 6, 1, 1, 1, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 1, 1, 1, 7, 6, 7, 1, 1, 1, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 1, 1, 1, 8, 3],\n [4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4]\n ],\n \"output\": [\n [7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3],\n [4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4],\n [5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5],\n [6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6],\n [7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3],\n [4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4],\n [5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5, 2, 1, 2, 5],\n [6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6, 3, 2, 3, 6],\n [7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7, 4, 3, 4, 7],\n [8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8, 5, 4, 5, 8],\n [1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1, 6, 5, 6, 1],\n [2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2, 7, 6, 7, 2],\n [3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3, 8, 7, 8, 3],\n [4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4, 1, 8, 1, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4],\n [8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5],\n [9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6],\n [1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7],\n [2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8],\n [3, 8, 6, 6, 8, 3, 9, 8, 1, 1, 1, 1, 1, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9],\n [4, 9, 7, 7, 9, 4, 1, 9, 1, 1, 1, 1, 1, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1],\n [5, 1, 8, 8, 1, 5, 2, 1, 1, 1, 1, 1, 1, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2],\n [6, 2, 9, 9, 2, 6, 3, 2, 1, 1, 1, 1, 1, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3],\n [7, 3, 1, 1, 1, 1, 1, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4],\n [8, 4, 2, 2, 1, 1, 1, 4, 5, 8, 4, 1, 1, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5],\n [9, 5, 3, 1, 1, 1, 1, 1, 6, 9, 5, 1, 1, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6],\n [1, 6, 4, 1, 1, 1, 1, 1, 7, 1, 6, 1, 1, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7],\n [2, 7, 5, 1, 1, 1, 1, 1, 8, 2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8],\n [3, 8, 6, 1, 1, 1, 1, 1, 9, 3, 8, 6, 6, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9],\n [4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1],\n [5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2],\n [6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3],\n [7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4],\n [8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5],\n [9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6],\n [1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4], [8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5], [9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6], [1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7], [2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8], [3, 8, 6, 6, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9], [4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1], [5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2], [6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3], [7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4], [8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5], [9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6], [1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7], [2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8, 7, 8, 2, 7, 5, 5, 7, 2, 8], [3, 8, 6, 6, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9, 8, 9, 3, 8, 6, 6, 8, 3, 9], [4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1, 9, 1, 4, 9, 7, 7, 9, 4, 1], [5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2, 1, 2, 5, 1, 8, 8, 1, 5, 2], [6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3, 2, 3, 6, 2, 9, 9, 2, 6, 3], [7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4, 3, 4, 7, 3, 1, 1, 3, 7, 4], [8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5, 4, 5, 8, 4, 2, 2, 4, 8, 5], [9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6, 5, 6, 9, 5, 3, 3, 5, 9, 6], [1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7, 6, 7, 1, 6, 4, 4, 6, 1, 7]], "task_id": "ea959feb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 0, 5, 0],\n [5, 0, 0, 5, 0],\n [5, 0, 5, 5, 5],\n [5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5],\n [0, 0, 0, 5, 0],\n [5, 5, 5, 5, 0],\n [0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0],\n [0, 0, 0, 5, 0],\n [0, 5, 5, 5, 5],\n [5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0]\n ],\n \"output\": [\n [5, 0, 0, 5, 0],\n [5, 0, 0, 5, 0],\n [5, 0, 5, 5, 5],\n [5, 5, 5, 0, 0],\n [0, 0, 5, 0, 0],\n [0, 0, 5, 5, 5],\n [0, 0, 0, 5, 8],\n [5, 5, 5, 5, 8],\n [0, 5, 8, 8, 8],\n [0, 5, 8, 8, 8],\n [0, 5, 5, 5, 8],\n [0, 0, 0, 5, 8],\n [0, 5, 5, 5, 5],\n [5, 5, 0, 0, 0],\n [7, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 5, 7, 7, 5, 8, 8, 8],\n [0, 0, 5, 5, 5, 5, 8, 8, 8],\n [5, 5, 5, 8, 8, 8, 8, 8, 8],\n [0, 5, 8, 8, 8, 8, 8, 8, 8],\n [0, 5, 8, 8, 8, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0],\n [5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 5, 7, 7, 5, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 7, 7, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0],\n [5, 5, 5, 0, 0, 0, 5, 8, 8, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 5, 8, 8, 8, 8, 8],\n [0, 0, 0, 5, 5, 5, 5, 8, 8, 8, 8, 8],\n [5, 5, 5, 5, 0, 0, 5, 8, 8, 8, 8, 8],\n [0, 0, 0, 5, 0, 0, 5, 5, 5, 8, 8, 8],\n [0, 0, 0, 5, 0, 0, 0, 0, 5, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0],\n [5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 5, 8, 8, 8, 8, 8, 5, 0, 0, 5, 0], [7, 5, 8, 8, 8, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 8, 8, 5, 0, 5, 5, 5, 0, 0], [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0]], "task_id": "62ab2642"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [7, 8, 4, 8, 8, 3, 5, 6, 2, 3, 7, 5, 6, 9, 7, 7, 9, 6, 3, 1],\n [5, 5, 8, 4, 5, 8, 3, 1, 7, 9, 2, 2, 9, 5, 9, 3, 9, 6, 4, 9],\n [1, 6, 6, 6, 5, 7, 2, 4, 4, 5, 1, 1, 5, 3, 5, 5, 9, 7, 6, 6],\n [6, 3, 8, 6, 5, 8, 4, 4, 5, 4, 3, 5, 8, 2, 1, 5, 6, 2, 1, 7],\n [3, 5, 4, 1, 5, 9, 3, 4, 6, 5, 5, 2, 4, 8, 2, 3, 9, 7, 1, 3],\n [2, 8, 9, 4, 7, 9, 8, 8, 1, 8, 1, 3, 7, 4, 7, 1, 2, 6, 5, 6],\n [3, 3, 6, 5, 2, 7, 4, 8, 0, 0, 2, 6, 5, 4, 5, 1, 8, 7, 9, 8],\n [2, 6, 3, 3, 5, 5, 7, 8, 0, 0, 5, 9, 9, 5, 4, 9, 9, 4, 6, 1],\n [2, 3, 4, 5, 9, 1, 1, 7, 3, 8, 7, 3, 3, 9, 6, 8, 7, 4, 3, 3],\n [9, 4, 2, 7, 2, 9, 5, 7, 8, 8, 3, 1, 2, 4, 8, 6, 8, 3, 9, 6],\n [1, 1, 9, 7, 3, 6, 4, 3, 3, 2, 5, 3, 9, 5, 1, 1, 9, 7, 5, 3],\n [7, 8, 8, 3, 6, 9, 7, 6, 9, 9, 3, 4, 1, 7, 6, 3, 6, 2, 1, 4],\n [3, 5, 7, 4, 3, 3, 4, 5, 7, 2, 9, 2, 5, 3, 4, 5, 6, 9, 9, 6],\n [4, 2, 5, 5, 7, 1, 4, 7, 9, 9, 9, 5, 2, 3, 2, 8, 5, 9, 7, 7],\n [8, 5, 7, 5, 8, 1, 2, 4, 4, 3, 1, 9, 9, 9, 9, 8, 1, 5, 1, 7],\n [5, 4, 5, 1, 8, 2, 6, 4, 4, 5, 2, 5, 8, 8, 8, 2, 4, 6, 5, 7],\n [4, 5, 7, 7, 9, 5, 5, 7, 2, 2, 2, 4, 3, 4, 7, 3, 8, 2, 8, 1],\n [9, 5, 9, 6, 8, 1, 6, 2, 1, 9, 4, 8, 8, 7, 5, 1, 3, 1, 6, 4],\n [7, 7, 1, 6, 6, 7, 3, 4, 7, 5, 8, 4, 8, 4, 4, 3, 5, 6, 4, 2],\n [4, 3, 7, 8, 3, 4, 5, 5, 8, 4, 7, 9, 4, 9, 4, 9, 3, 9, 8, 7]\n ],\n \"output\": [\n [7, 8, 4, 8, 8, 3, 5, 6, 2, 0, 7, 5, 6, 9, 7, 7, 9, 6, 3, 1],\n [5, 5, 8, 4, 5, 8, 3, 1, 0, 0, 2, 2, 9, 5, 9, 3, 9, 6, 4, 9],\n [1, 6, 6, 6, 5, 7, 2, 4, 0, 0, 1, 1, 5, 3, 5, 5, 9, 7, 6, 6],\n [6, 3, 8, 6, 5, 8, 4, 4, 0, 0, 3, 5, 8, 2, 1, 5, 6, 2, 1, 7],\n [3, 5, 4, 1, 5, 9, 3, 4, 0, 0, 5, 2, 4, 8, 2, 3, 9, 7, 1, 3],\n [2, 8, 9, 4, 7, 9, 8, 8, 0, 0, 1, 3, 7, 4, 7, 1, 2, 6, 5, 6],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 3, 4, 5, 9, 1, 1, 7, 0, 0, 7, 3, 3, 9, 6, 8, 7, 4, 3, 3],\n [9, 4, 2, 7, 2, 9, 5, 7, 0, 0, 3, 1, 2, 4, 8, 6, 8, 3, 9, 6],\n [1, 1, 9, 7, 3, 6, 4, 3, 0, 2, 5, 3, 9, 5, 1, 1, 9, 7, 5, 3],\n [7, 8, 8, 3, 6, 9, 7, 6, 0, 0, 3, 4, 1, 7, 6, 3, 6, 2, 1, 4],\n [3, 5, 7, 4, 3, 3, 4, 5, 0, 2, 9, 2, 5, 3, 4, 5, 6, 9, 9, 6],\n [4, 2, 5, 5, 7, 1, 4, 7, 0, 0, 9, 5, 2, 3, 2, 8, 5, 9, 7, 7],\n [8, 5, 7, 5, 8, 1, 2, 4, 0, 0, 1, 9, 9, 9, 9, 8, 1, 5, 1, 7],\n [5, 4, 5, 1, 8, 2, 6, 4, 0, 0, 2, 5, 8, 8, 8, 2, 4, 6, 5, 7],\n [4, 5, 7, 7, 9, 5, 5, 7, 2, 2, 2, 4, 3, 4, 7, 3, 8, 2, 8, 1],\n [9, 5, 9, 6, 8, 1, 6, 2, 0, 0, 4, 8, 8, 7, 5, 1, 3, 1, 6, 4],\n [7, 7, 1, 6, 6, 7, 3, 4, 0, 0, 8, 4, 8, 4, 4, 3, 5, 6, 4, 2],\n [4, 3, 7, 8, 3, 4, 5, 5, 0, 0, 7, 9, 4, 9, 4, 9, 3, 9, 8, 7]\n ]\n}\n\n{\n \"input\": [\n [5, 2, 5, 2, 5, 4, 3, 8, 2, 7, 3, 7, 5, 6, 2, 1, 9, 3, 2, 1],\n [1, 2, 3, 6, 5, 2, 2, 5, 8, 9, 8, 5, 8, 7, 9, 2, 6, 5, 5, 5],\n [3, 1, 9, 7, 9, 8, 8, 7, 1, 9, 7, 3, 9, 7, 5, 9, 8, 4, 9, 8],\n [5, 7, 7, 8, 3, 4, 4, 4, 4, 6, 2, 9, 4, 3, 6, 8, 4, 6, 7, 1],\n [6, 3, 8, 3, 5, 9, 7, 5, 4, 6, 1, 9, 3, 9, 7, 7, 6, 7, 8, 1],\n [6, 2, 4, 9, 8, 3, 9, 1, 4, 8, 9, 9, 6, 5, 9, 1, 9, 7, 5, 7],\n [6, 2, 8, 3, 4, 6, 4, 8, 9, 3, 9, 3, 6, 9, 2, 2, 9, 1, 9, 3],\n [2, 4, 7, 7, 8, 1, 4, 7, 6, 2, 8, 9, 8, 8, 7, 4, 8, 4, 9, 1],\n [2, 1, 5, 9, 2, 6, 8, 3, 6, 4, 5, 8, 6, 3, 1, 4, 5, 1, 5, 1],\n [9, 4, 9, 5, 1, 2, 8, 2, 1, 4, 2, 9, 9, 6, 1, 9, 9, 7, 2, 1],\n [6, 6, 2, 3, 7, 3, 7, 5, 4, 3, 2, 4, 4, 7, 7, 7, 6, 7, 6, 7],\n [2, 4, 3, 1, 4, 8, 0, 0, 9, 6, 3, 2, 4, 4, 8, 7, 2, 9, 4, 2],\n [3, 5, 7, 8, 2, 4, 0, 0, 1, 6, 4, 7, 4, 7, 2, 3, 9, 4, 5, 2],\n [8, 1, 4, 3, 9, 6, 9, 9, 8, 5, 4, 3, 5, 2, 6, 8, 9, 9, 4, 8],\n [1, 2, 6, 9, 8, 9, 1, 4, 3, 3, 6, 2, 3, 7, 3, 1, 8, 1, 4, 5],\n [3, 8, 4, 4, 4, 9, 6, 1, 6, 7, 9, 4, 2, 6, 2, 9, 3, 1, 5, 1],\n [2, 7, 5, 8, 8, 8, 6, 3, 4, 6, 3, 7, 9, 2, 1, 1, 7, 2, 5, 9],\n [2, 1, 7, 2, 1, 3, 5, 5, 3, 6, 2, 8, 3, 6, 9, 5, 5, 9, 8, 4],\n [3, 3, 3, 6, 6, 3, 6, 5, 9, 4, 7, 2, 4, 4, 7, 7, 6, 1, 2, 9],\n [2, 5, 8, 9, 7, 9, 7, 2, 3, 2, 2, 6, 6, 7, 9, 8, 9, 1, 1, 6]\n ],\n \"output\": [\n [5, 2, 5, 2, 5, 4, 0, 0, 2, 7, 3, 7, 5, 6, 2, 1, 9, 3, 2, 1],\n [1, 2, 3, 6, 5, 2, 2, 0, 8, 9, 8, 5, 8, 7, 9, 2, 6, 5, 5, 5],\n [3, 1, 9, 7, 9, 8, 0, 0, 1, 9, 7, 3, 9, 7, 5, 9, 8, 4, 9, 8],\n [5, 7, 7, 8, 3, 4, 0, 0, 4, 6, 2, 9, 4, 3, 6, 8, 4, 6, 7, 1],\n [6, 3, 8, 3, 5, 9, 0, 0, 4, 6, 1, 9, 3, 9, 7, 7, 6, 7, 8, 1],\n [6, 2, 4, 9, 8, 3, 0, 0, 4, 8, 9, 9, 6, 5, 9, 1, 9, 7, 5, 7],\n [6, 2, 8, 3, 4, 6, 0, 0, 9, 3, 9, 3, 6, 9, 2, 2, 9, 1, 9, 3],\n [2, 4, 7, 7, 8, 1, 0, 0, 6, 2, 8, 9, 8, 8, 7, 4, 8, 4, 9, 1],\n [2, 1, 5, 9, 2, 6, 0, 0, 6, 4, 5, 8, 6, 3, 1, 4, 5, 1, 5, 1],\n [9, 4, 9, 5, 1, 2, 0, 2, 1, 4, 2, 9, 9, 6, 1, 9, 9, 7, 2, 1],\n [6, 6, 2, 3, 7, 3, 0, 0, 4, 3, 2, 4, 4, 7, 7, 7, 6, 7, 6, 7],\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2],\n [8, 1, 4, 3, 9, 6, 0, 0, 8, 5, 4, 3, 5, 2, 6, 8, 9, 9, 4, 8],\n [1, 2, 6, 9, 8, 9, 0, 0, 3, 3, 6, 2, 3, 7, 3, 1, 8, 1, 4, 5],\n [3, 8, 4, 4, 4, 9, 0, 0, 6, 7, 9, 4, 2, 6, 2, 9, 3, 1, 5, 1],\n [2, 7, 5, 8, 8, 8, 0, 0, 4, 6, 3, 7, 9, 2, 1, 1, 7, 2, 5, 9],\n [2, 1, 7, 2, 1, 3, 0, 0, 3, 6, 2, 8, 3, 6, 9, 5, 5, 9, 8, 4],\n [3, 3, 3, 6, 6, 3, 0, 0, 9, 4, 7, 2, 4, 4, 7, 7, 6, 1, 2, 9],\n [2, 5, 8, 9, 7, 9, 0, 2, 3, 2, 2, 6, 6, 7, 9, 8, 9, 1, 1, 6]\n ]\n}\n\n{\n \"input\": [\n [3, 5, 9, 3, 6, 9, 3, 3, 7, 3, 7, 9, 6, 5, 3, 1, 6, 5, 1, 6],\n [1, 9, 9, 3, 1, 2, 9, 4, 4, 7, 9, 9, 7, 3, 2, 7, 5, 1, 4, 6],\n [9, 3, 7, 2, 4, 4, 8, 3, 3, 6, 3, 7, 7, 5, 4, 5, 4, 4, 5, 1],\n [8, 2, 1, 1, 1, 4, 3, 2, 2, 9, 7, 2, 2, 1, 2, 6, 4, 8, 7, 6],\n [3, 1, 3, 1, 7, 6, 4, 7, 5, 1, 8, 2, 6, 2, 0, 0, 5, 2, 5, 5],\n [7, 8, 7, 3, 4, 2, 5, 6, 2, 2, 3, 1, 5, 6, 0, 0, 5, 3, 8, 6],\n [7, 1, 4, 8, 2, 3, 3, 6, 6, 3, 7, 7, 9, 3, 5, 7, 6, 4, 3, 8],\n [6, 3, 3, 7, 4, 8, 2, 8, 7, 6, 4, 1, 8, 5, 4, 3, 4, 5, 4, 1],\n [8, 1, 2, 6, 9, 5, 9, 4, 6, 8, 2, 6, 8, 9, 1, 3, 8, 4, 7, 4],\n [6, 7, 7, 2, 9, 8, 7, 5, 6, 3, 7, 7, 3, 7, 9, 1, 5, 1, 2, 2],\n [1, 2, 2, 5, 8, 8, 4, 2, 3, 5, 2, 2, 6, 7, 2, 6, 3, 6, 3, 9],\n [3, 6, 9, 1, 8, 4, 6, 5, 7, 9, 2, 9, 7, 4, 4, 2, 9, 4, 4, 6],\n [2, 6, 5, 2, 6, 6, 8, 7, 6, 6, 1, 5, 2, 9, 1, 3, 3, 4, 9, 7],\n [2, 9, 8, 1, 7, 3, 2, 9, 6, 6, 9, 7, 7, 2, 2, 6, 7, 5, 9, 5],\n [4, 4, 1, 5, 2, 7, 6, 3, 5, 7, 2, 1, 6, 1, 5, 8, 8, 8, 8, 7],\n [7, 5, 2, 3, 8, 6, 9, 2, 3, 9, 9, 5, 6, 6, 7, 8, 9, 5, 8, 4],\n [6, 7, 1, 9, 2, 1, 2, 1, 6, 4, 5, 8, 1, 1, 1, 6, 7, 9, 8, 8],\n [7, 6, 5, 8, 3, 5, 9, 6, 4, 9, 6, 8, 7, 9, 1, 7, 9, 9, 1, 2],\n [1, 4, 8, 4, 2, 2, 6, 3, 5, 6, 6, 7, 5, 8, 7, 3, 6, 1, 7, 7],\n [5, 3, 3, 4, 8, 9, 8, 9, 5, 3, 2, 2, 5, 8, 1, 8, 4, 6, 7, 3]\n ],\n \"output\": [\n [3, 5, 9, 3, 6, 9, 3, 3, 7, 3, 7, 9, 6, 5, 0, 0, 6, 5, 1, 6],\n [1, 9, 9, 3, 1, 2, 9, 4, 4, 7, 9, 9, 7, 3, 2, 0, 5, 1, 4, 6],\n [9, 3, 7, 2, 4, 4, 8, 3, 3, 6, 3, 7, 7, 5, 0, 0, 4, 4, 5, 1],\n [8, 2, 1, 1, 1, 4, 3, 2, 2, 9, 7, 2, 2, 1, 2, 0, 4, 8, 7, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 1, 4, 8, 2, 3, 3, 6, 6, 3, 7, 7, 9, 3, 0, 0, 6, 4, 3, 8],\n [6, 3, 3, 7, 4, 8, 2, 8, 7, 6, 4, 1, 8, 5, 0, 0, 4, 5, 4, 1],\n [8, 1, 2, 6, 9, 5, 9, 4, 6, 8, 2, 6, 8, 9, 0, 0, 8, 4, 7, 4],\n [6, 7, 7, 2, 9, 8, 7, 5, 6, 3, 7, 7, 3, 7, 0, 0, 5, 1, 2, 2],\n [1, 2, 2, 5, 8, 8, 4, 2, 3, 5, 2, 2, 6, 7, 2, 0, 3, 6, 3, 9],\n [3, 6, 9, 1, 8, 4, 6, 5, 7, 9, 2, 9, 7, 4, 0, 2, 9, 4, 4, 6],\n [2, 6, 5, 2, 6, 6, 8, 7, 6, 6, 1, 5, 2, 9, 0, 0, 3, 4, 9, 7],\n [2, 9, 8, 1, 7, 3, 2, 9, 6, 6, 9, 7, 7, 2, 2, 0, 7, 5, 9, 5],\n [4, 4, 1, 5, 2, 7, 6, 3, 5, 7, 2, 1, 6, 1, 0, 0, 8, 8, 8, 7],\n [7, 5, 2, 3, 8, 6, 9, 2, 3, 9, 9, 5, 6, 6, 0, 0, 9, 5, 8, 4],\n [6, 7, 1, 9, 2, 1, 2, 1, 6, 4, 5, 8, 1, 1, 0, 0, 7, 9, 8, 8],\n [7, 6, 5, 8, 3, 5, 9, 6, 4, 9, 6, 8, 7, 9, 0, 0, 9, 9, 1, 2],\n [1, 4, 8, 4, 2, 2, 6, 3, 5, 6, 6, 7, 5, 8, 0, 0, 6, 1, 7, 7],\n [5, 3, 3, 4, 8, 9, 8, 9, 5, 3, 2, 2, 5, 8, 0, 0, 4, 6, 7, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 5, 7, 5, 9, 2, 3, 1, 7, 8, 6, 9, 4, 2, 4, 2, 5, 1, 2, 7],\n [9, 1, 7, 5, 5, 1, 2, 7, 1, 9, 1, 7, 4, 8, 4, 7, 2, 9, 5, 8],\n [9, 1, 8, 6, 9, 4, 7, 6, 9, 5, 5, 5, 8, 1, 4, 8, 4, 7, 8, 5],\n [4, 2, 9, 5, 5, 2, 8, 2, 7, 7, 9, 6, 1, 4, 9, 8, 3, 3, 9, 2],\n [1, 9, 2, 3, 8, 4, 3, 1, 1, 3, 4, 6, 3, 5, 1, 1, 2, 6, 4, 6],\n [1, 4, 3, 5, 6, 1, 1, 1, 1, 6, 7, 3, 9, 5, 8, 3, 5, 6, 2, 9],\n [7, 7, 1, 9, 2, 4, 6, 9, 1, 5, 5, 8, 5, 2, 2, 2, 4, 2, 1, 2],\n [2, 9, 6, 1, 2, 9, 4, 8, 2, 7, 8, 2, 5, 7, 3, 2, 2, 4, 7, 7],\n [4, 2, 6, 6, 8, 9, 2, 1, 3, 2, 4, 5, 8, 3, 7, 4, 8, 5, 2, 1],\n [3, 2, 4, 8, 9, 8, 9, 5, 2, 1, 6, 8, 1, 2, 5, 7, 9, 1, 8, 5],\n [1, 3, 7, 2, 7, 3, 5, 2, 3, 9, 3, 2, 7, 2, 1, 7, 9, 8, 5, 7],\n [1, 6, 4, 6, 6, 5, 5, 9, 2, 3, 2, 4, 6, 7, 3, 9, 9, 9, 6, 6],\n [8, 6, 5, 2, 2, 3, 2, 1, 4, 9, 6, 9, 4, 9, 7, 7, 1, 5, 9, 3],\n [3, 7, 7, 5, 9, 8, 7, 4, 2, 2, 9, 5, 0, 0, 8, 6, 7, 6, 6, 7],\n [6, 9, 5, 6, 3, 7, 5, 7, 9, 8, 9, 5, 0, 0, 4, 2, 3, 3, 2, 6],\n [2, 8, 5, 3, 5, 6, 7, 1, 4, 7, 4, 4, 9, 5, 1, 3, 4, 4, 4, 7],\n [7, 8, 4, 7, 9, 5, 7, 8, 7, 4, 6, 8, 5, 3, 3, 1, 4, 1, 9, 7],\n [4, 5, 9, 9, 4, 5, 5, 7, 9, 8, 5, 8, 8, 6, 2, 8, 3, 7, 2, 4],\n [1, 6, 1, 4, 3, 6, 5, 4, 1, 7, 4, 7, 8, 6, 5, 9, 5, 4, 9, 9],\n [2, 9, 2, 6, 7, 8, 9, 9, 6, 4, 5, 8, 7, 5, 4, 3, 6, 3, 9, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 5, 7, 5, 9, 2, 3, 1, 7, 8, 6, 9, 0, 2, 4, 2, 5, 1, 2, 7], [9, 1, 7, 5, 5, 1, 2, 7, 1, 9, 1, 7, 0, 0, 4, 7, 2, 9, 5, 8], [9, 1, 8, 6, 9, 4, 7, 6, 9, 5, 5, 5, 0, 0, 4, 8, 4, 7, 8, 5], [4, 2, 9, 5, 5, 2, 8, 2, 7, 7, 9, 6, 0, 0, 9, 8, 3, 3, 9, 2], [1, 9, 2, 3, 8, 4, 3, 1, 1, 3, 4, 6, 0, 0, 1, 1, 2, 6, 4, 6], [1, 4, 3, 5, 6, 1, 1, 1, 1, 6, 7, 3, 0, 0, 8, 3, 5, 6, 2, 9], [7, 7, 1, 9, 2, 4, 6, 9, 1, 5, 5, 8, 0, 2, 2, 2, 4, 2, 1, 2], [2, 9, 6, 1, 2, 9, 4, 8, 2, 7, 8, 2, 0, 0, 3, 2, 2, 4, 7, 7], [4, 2, 6, 6, 8, 9, 2, 1, 3, 2, 4, 5, 0, 0, 7, 4, 8, 5, 2, 1], [3, 2, 4, 8, 9, 8, 9, 5, 2, 1, 6, 8, 0, 2, 5, 7, 9, 1, 8, 5], [1, 3, 7, 2, 7, 3, 5, 2, 3, 9, 3, 2, 0, 2, 1, 7, 9, 8, 5, 7], [1, 6, 4, 6, 6, 5, 5, 9, 2, 3, 2, 4, 0, 0, 3, 9, 9, 9, 6, 6], [8, 6, 5, 2, 2, 3, 2, 1, 4, 9, 6, 9, 0, 0, 7, 7, 1, 5, 9, 3], [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0], [2, 8, 5, 3, 5, 6, 7, 1, 4, 7, 4, 4, 0, 0, 1, 3, 4, 4, 4, 7], [7, 8, 4, 7, 9, 5, 7, 8, 7, 4, 6, 8, 0, 0, 3, 1, 4, 1, 9, 7], [4, 5, 9, 9, 4, 5, 5, 7, 9, 8, 5, 8, 0, 0, 2, 8, 3, 7, 2, 4], [1, 6, 1, 4, 3, 6, 5, 4, 1, 7, 4, 7, 0, 0, 5, 9, 5, 4, 9, 9], [2, 9, 2, 6, 7, 8, 9, 9, 6, 4, 5, 8, 0, 0, 4, 3, 6, 3, 9, 5]], "task_id": "319f2597"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 1],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 1, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 2, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 1],\n [0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2],\n [0, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 2, 2, 2],\n [0, 2, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0],\n [1, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 1],\n [0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "0d87d2a6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 0, 5, 2, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 0, 0, 0, 5, 2, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 5, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 2, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5, 0, 0, 0],\n [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 5, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 5, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n [1, 0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 5, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 2],\n [1, 0, 0, 0, 0, 2, 0, 5, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 5, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0],\n [1, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2],\n [0, 0, 5, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 1, 5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0],\n [1, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 1, 0, 5, 0, 2, 0, 0, 2, 0, 0, 2, 0], [1, 0, 0, 0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 1, 5, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 5, 0, 0, 2, 0, 0, 0, 2, 0, 0], [1, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 2, 0, 0, 0, 2, 0, 0, 0, 2]], "task_id": "dd2401ed"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 4, 0],\n [0, 0, 4, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 4],\n [1, 0, 0, 0, 4, 1, 0],\n [1, 4, 0, 4, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0]\n ],\n \"output\": [\n [4, 4, 4],\n [0, 0, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 6, 0, 6, 0, 1],\n [0, 0, 0, 1, 0, 6, 0, 0, 1],\n [0, 0, 0, 1, 0, 0, 0, 6, 1],\n [0, 6, 0, 1, 6, 0, 0, 0, 1],\n [0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [6, 6, 6],\n [6, 6, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 3, 0, 0, 0],\n [3, 0, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 3, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 3, 0],\n [0, 0, 1, 0, 0, 3, 1, 0, 0],\n [0, 0, 1, 3, 0, 0, 1, 0, 0],\n [0, 3, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 3, 0, 1, 3, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 0, 0],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 2, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 0, 2, 0, 0, 0, 1, 0, 0],\n [1, 0, 0, 0, 2, 0, 1, 2, 0],\n [1, 0, 0, 0, 0, 2, 1, 0, 0],\n [1, 2, 0, 0, 0, 0, 1, 0, 2],\n [1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2], [2, 0, 0], [0, 0, 0]], "task_id": "c8b7cc0f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 1, 0, 0, 0, 0],\n [0, 4, 0, 4, 1, 4, 0, 0, 0],\n [4, 0, 0, 0, 1, 0, 4, 0, 0],\n [0, 4, 4, 0, 1, 0, 0, 0, 0],\n [4, 0, 4, 0, 1, 4, 4, 4, 4],\n [0, 4, 4, 4, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8],\n [8, 8, 0, 8],\n [8, 8, 0, 0],\n [0, 8, 8, 0],\n [8, 8, 8, 8],\n [0, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 4, 4, 1, 0, 0, 4, 4],\n [0, 4, 4, 4, 1, 0, 0, 0, 0],\n [0, 4, 0, 0, 1, 4, 0, 4, 0],\n [0, 4, 4, 4, 1, 4, 4, 0, 4],\n [0, 4, 4, 4, 1, 4, 0, 4, 4],\n [0, 4, 0, 4, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 8],\n [0, 8, 8, 8],\n [8, 8, 8, 0],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 4, 0, 1, 4, 0, 4, 4],\n [4, 0, 4, 0, 1, 4, 4, 4, 0],\n [4, 4, 0, 4, 1, 4, 0, 4, 0],\n [0, 4, 0, 0, 1, 4, 0, 0, 4],\n [0, 0, 4, 4, 1, 4, 4, 4, 0],\n [4, 4, 0, 4, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 8],\n [8, 8, 8, 0],\n [8, 8, 8, 8],\n [8, 8, 0, 8],\n [8, 8, 8, 8],\n [8, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 1, 4, 4, 0, 0],\n [0, 0, 4, 4, 1, 0, 4, 0, 0],\n [4, 0, 4, 4, 1, 0, 4, 4, 0],\n [4, 4, 4, 0, 1, 4, 4, 0, 0],\n [4, 0, 4, 4, 1, 4, 0, 0, 4],\n [0, 0, 0, 0, 1, 4, 4, 4, 4]\n ],\n \"output\": [\n [8, 8, 0, 8],\n [0, 8, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 8, 0],\n [8, 0, 8, 8],\n [8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 0, 4, 1, 0, 4, 0, 4],\n [0, 0, 4, 4, 1, 0, 4, 0, 0],\n [4, 0, 4, 4, 1, 4, 0, 4, 0],\n [0, 4, 0, 4, 1, 4, 0, 4, 4],\n [4, 4, 0, 4, 1, 0, 4, 4, 0],\n [0, 4, 4, 4, 1, 0, 4, 0, 4]\n ],\n \"output\": [\n [8, 8, 0, 8],\n [0, 8, 8, 8],\n [8, 0, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [0, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 4, 0, 1, 0, 0, 4, 0],\n [4, 0, 4, 4, 1, 4, 4, 0, 4],\n [0, 0, 0, 4, 1, 4, 4, 0, 4],\n [4, 0, 4, 0, 1, 4, 4, 4, 0],\n [0, 4, 0, 0, 1, 4, 4, 4, 4],\n [4, 4, 0, 4, 1, 0, 4, 0, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0], [8, 8, 8, 8], [8, 8, 0, 8], [8, 8, 8, 0], [8, 8, 8, 8], [8, 8, 0, 8]], "task_id": "5d2a5c43"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 4, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 8, 0, 8, 8],\n [8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0],\n [8, 8, 0],\n [0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8, 0, 8, 8, 0],\n [8, 8, 0, 8, 8, 0, 8, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 4, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 0, 8, 8, 0], [8, 0, 8, 8, 0, 8], [8, 8, 0, 8, 8, 0]], "task_id": "4852f2fa"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 0, 0, 0, 0, 0, 5],\n [5, 0, 5, 0, 5, 0, 0, 5, 0],\n [5, 5, 5, 0, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 4, 4, 4, 9, 9, 9],\n [3, 3, 3, 4, 4, 4, 9, 9, 9],\n [3, 3, 3, 4, 4, 4, 9, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 5, 0],\n [5, 0, 0, 5, 5, 5, 0, 0, 0]\n ],\n \"output\": [\n [9, 9, 9, 1, 1, 1, 4, 4, 4],\n [9, 9, 9, 1, 1, 1, 4, 4, 4],\n [9, 9, 9, 1, 1, 1, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [6, 6, 6, 3, 3, 3, 1, 1, 1],\n [6, 6, 6, 3, 3, 3, 1, 1, 1],\n [6, 6, 6, 3, 3, 3, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 0, 0, 5, 5, 5]\n ],\n \"output\": [\n [4, 4, 4, 6, 6, 6, 3, 3, 3],\n [4, 4, 4, 6, 6, 6, 3, 3, 3],\n [4, 4, 4, 6, 6, 6, 3, 3, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 5, 5, 5, 5],\n [0, 0, 0, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 9, 9, 9, 6, 6, 6], [1, 1, 1, 9, 9, 9, 6, 6, 6], [1, 1, 1, 9, 9, 9, 6, 6, 6]], "task_id": "17cae0c1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "696d4842"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 8, 8, 1, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 8, 8, 1, 3, 3, 3, 3, 3, 2, 4, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 8, 8, 1, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 8, 8, 1, 3, 3, 3, 3, 3, 2, 4, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 2, 4, 2, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0],\n [0, 4, 4, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 2, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 8, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 1, 2, 3, 3, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 8, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [8, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 2, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 2, 1, 2, 3, 3, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 0],\n [3, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 8, 8, 0, 0, 1, 1, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 1, 2, 1, 0, 0],\n [8, 4, 4, 4, 8, 3, 0, 0, 8, 8, 0, 0, 1, 2, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0],\n [8, 8, 8, 8, 8, 3, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 8, 4, 4, 4, 8, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 1, 1, 1, 0, 0, 0, 0],\n [3, 1, 2, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0],\n [3, 1, 1, 1, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 1, 1, 1, 0, 3, 3, 3, 0, 0, 0, 2, 1, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 2, 3, 3, 3, 3, 3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 2, 1, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 6, 8, 8, 6, 3, 3, 3, 3, 3, 3],\n [3, 3, 2, 1, 1, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 6, 8, 8, 6, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 1, 2, 3, 3, 3, 3, 3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 1, 2, 1, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 6, 8, 8, 6, 3, 3, 3, 3, 3, 3],\n [3, 3, 2, 1, 1, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 6, 8, 8, 6, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 6, 8, 8, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 8, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 2, 2, 8, 3, 3, 3, 3, 3, 1, 6, 1, 3, 3, 3, 3, 3, 3, 3, 1, 4, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 4, 1, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 8, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 2, 2, 8, 3, 3, 3, 3, 3, 1, 6, 1, 3, 3, 3, 3, 3, 3, 3, 1, 4, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 8, 8, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 4, 1, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], "task_id": "3ed85e70"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 2, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 2, 5, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "692cd3b6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 6, 0, 0, 5, 0, 3, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 0, 3, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 8, 0, 0, 0, 0, 5, 0, 9, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 8, 0, 0, 9, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 5, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 6, 0, 0, 5, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 1, 0, 0, 3],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 9, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 9, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 9, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 9, 0, 0, 7, 0, 0, 0, 5, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 4, 0, 0, 5, 0, 0, 0, 0, 7, 0, 0, 4, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 9, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 9, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 9, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 4, 0, 0], [0, 9, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "d47aa2ff"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e619ca6e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4],\n [0, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 1, 1, 1, 1, 1, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 1, 1, 1, 1, 1, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 1, 1, 1, 1, 1, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 6, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 3, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 6, 5, 5, 1, 1, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 0], [0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 3, 3, 3, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 3, 3, 3, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 3, 3, 3, 0], [0, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1c02dbbe"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 8, 8, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 3, 3, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0],\n [0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 0, 8, 8, 8, 0],\n [0, 0, 8, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 8, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 8, 8, 8, 0, 8, 0, 0, 0, 8, 0, 8, 8, 8, 0, 8, 0],\n [0, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 0, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 3, 0, 3, 3, 3, 0],\n [0, 0, 2, 0, 0, 2, 2, 2, 0, 0, 0, 3, 3, 3, 0, 3, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 3, 0, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0],\n [0, 2, 2, 2, 0, 2, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7, 0],\n [0, 2, 0, 2, 2, 2, 0, 0, 0, 7, 7, 7, 0, 7, 0, 7, 0],\n [0, 2, 2, 2, 2, 2, 0, 0, 0, 7, 0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 8, 0, 8, 0, 8, 0, 0, 8, 0, 8, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 8, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 8, 0, 8, 8],\n [0, 0, 8, 8, 0, 0, 8, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 0, 3, 0, 0, 3, 0, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 7, 7, 7, 7],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 7, 0, 7, 0, 7, 7],\n [0, 0, 7, 7, 0, 0, 7, 0, 0, 0, 7, 7, 7, 7, 7, 7],\n [0, 0, 7, 7, 7, 7, 7, 0, 0, 0, 7, 0, 7, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8, 8, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 8, 8, 8, 8, 8, 0],\n [0, 8, 0, 0, 8, 8, 8, 8, 0, 0, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 0, 8, 8, 0, 0, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 2, 0, 2, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 2, 0, 2, 2, 2, 0, 3, 0, 3, 3, 0, 0, 3, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 7, 0, 7, 7, 7, 7, 7, 0], [0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 7, 0, 7, 7, 7, 7, 7, 0], [0, 2, 0, 0, 2, 2, 2, 2, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0], [0, 2, 2, 2, 2, 0, 2, 2, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 7, 7, 7, 7, 7, 7, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "37d3e8b2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 4, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 3, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 4, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 2, 2, 2, 2, 2, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 4, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 2, 2, 2, 2, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 2, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 3, 4, 2, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 2, 4, 2, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 2, 4, 3, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 2, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 2, 2, 2, 2, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 4, 4, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 2, 2, 2, 2, 2, 2, 3, 0, 0, 0],\n [0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0],\n [0, 0, 0, 3, 2, 2, 2, 2, 2, 2, 2, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 0, 3, 2, 2, 2, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 2, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 3, 4, 2, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 2, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 2, 2, 2, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 4, 4, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 2, 2, 2, 2, 2, 4, 2, 3, 0, 0, 0], [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "b7fb29bc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 0],\n [8, 0, 8],\n [0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 8, 0, 8],\n [0, 8, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8],\n [8, 0, 8, 8, 0, 8],\n [0, 8, 0, 0, 8, 0],\n [8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [7, 0],\n [0, 7]\n ],\n \"output\": [\n [0, 7, 0, 7],\n [7, 0, 7, 0],\n [0, 7, 0, 7],\n [7, 0, 7, 0]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 0, 0],\n [0, 4, 4, 4],\n [0, 0, 4, 0],\n [0, 4, 0, 0]\n ],\n \"output\": [\n [0, 4, 4, 4, 0, 4, 4, 4],\n [4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 0, 4, 4, 4, 0, 4],\n [4, 0, 4, 4, 4, 0, 4, 4],\n [0, 4, 4, 4, 0, 4, 4, 4],\n [4, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 0, 4, 4, 4, 0, 4],\n [4, 0, 4, 4, 4, 0, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 1, 0, 1],\n [1, 0, 0, 1],\n [0, 0, 1, 0],\n [1, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1, 1, 1]], "task_id": "48131b3c"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 1, 0, 0, 0, 0, 0],\n [0, 4, 4, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 3, 1, 0, 0, 0, 0, 0],\n [0, 4, 4, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 7, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 3, 1, 0, 0],\n [0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 3, 3, 0, 0, 0, 0, 0],\n [0, 1, 0, 3, 3, 8, 8, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 0, 0], [0, 0, 0, 0, 1, 0, 3, 3, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 2, 2, 3, 3, 0, 0, 0, 0, 0], [0, 1, 0, 3, 3, 8, 8, 0, 0, 0]], "task_id": "2c737e39"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 7, 8, 7, 1, 8, 4, 7, 7, 7, 4, 1, 1, 4, 7, 7, 7, 4, 8, 1, 7, 8, 7, 1],\n [7, 7, 7, 7, 1, 1, 7, 4, 7, 1, 7, 4, 4, 7, 1, 7, 4, 7, 1, 1, 7, 7, 7, 7],\n [8, 7, 1, 7, 8, 1, 7, 7, 7, 1, 7, 7, 7, 7, 1, 7, 7, 7, 1, 8, 7, 1, 7, 8],\n [7, 7, 7, 1, 7, 1, 7, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 7, 1, 7, 1, 7, 7, 7],\n [1, 1, 8, 7, 7, 8, 4, 7, 7, 4, 1, 1, 1, 1, 4, 7, 7, 4, 8, 7, 7, 8, 1, 1],\n [8, 1, 1, 1, 8, 7, 1, 4, 7, 4, 1, 1, 1, 1, 4, 7, 4, 1, 7, 8, 1, 1, 1, 8],\n [4, 7, 7, 7, 4, 1, 5, 5, 5, 5, 1, 4, 4, 1, 5, 5, 5, 5, 1, 4, 7, 7, 7, 4],\n [7, 4, 7, 1, 7, 4, 5, 4, 5, 4, 4, 1, 1, 4, 4, 5, 4, 5, 4, 7, 1, 7, 4, 7],\n [7, 7, 7, 1, 7, 7, 5, 5, 1, 4, 1, 5, 5, 1, 4, 1, 5, 5, 7, 7, 1, 7, 7, 7],\n [7, 1, 1, 4, 4, 4, 5, 4, 4, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 1, 1, 7],\n [4, 7, 7, 4, 1, 1, 1, 4, 1, 5, 5, 4, 4, 5, 5, 1, 4, 1, 1, 1, 4, 7, 7, 4],\n [1, 4, 7, 4, 1, 1, 4, 1, 5, 5, 4, 5, 5, 4, 5, 5, 1, 4, 1, 1, 4, 7, 4, 1],\n [1, 4, 7, 4, 1, 1, 4, 1, 5, 5, 4, 5, 5, 4, 5, 5, 1, 4, 1, 1, 4, 7, 4, 1],\n [4, 7, 7, 4, 1, 1, 1, 4, 1, 5, 5, 4, 4, 5, 5, 1, 4, 1, 1, 1, 4, 7, 7, 4],\n [7, 1, 1, 4, 4, 4, 5, 4, 4, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 1, 1, 7],\n [7, 7, 7, 1, 7, 7, 5, 5, 1, 4, 1, 5, 5, 1, 4, 1, 5, 5, 7, 7, 1, 7, 7, 7],\n [7, 4, 7, 1, 7, 4, 5, 4, 5, 4, 4, 1, 1, 4, 4, 5, 4, 5, 4, 7, 1, 7, 4, 7],\n [4, 7, 7, 7, 4, 1, 3, 3, 3, 5, 1, 4, 4, 1, 5, 5, 5, 5, 1, 4, 7, 7, 7, 4],\n [8, 1, 1, 1, 8, 7, 3, 3, 3, 4, 1, 1, 1, 1, 4, 7, 4, 1, 7, 8, 1, 1, 1, 8],\n [1, 1, 8, 7, 7, 8, 3, 3, 3, 4, 1, 1, 1, 1, 4, 7, 7, 4, 8, 7, 7, 8, 1, 1],\n [7, 7, 7, 1, 7, 1, 7, 1, 1, 4, 4, 4, 4, 4, 4, 1, 1, 7, 1, 7, 1, 7, 7, 7],\n [8, 7, 1, 7, 8, 1, 7, 7, 7, 1, 7, 7, 7, 7, 1, 7, 7, 7, 1, 8, 7, 1, 7, 8],\n [7, 7, 7, 7, 1, 1, 7, 4, 7, 1, 7, 4, 4, 7, 1, 7, 4, 7, 1, 1, 7, 7, 7, 7],\n [1, 7, 8, 7, 1, 8, 4, 7, 7, 7, 4, 1, 1, 4, 7, 7, 7, 4, 8, 1, 7, 8, 7, 1]\n ],\n \"output\": [\n [5, 5, 5],\n [1, 4, 7],\n [4, 7, 7]\n ]\n}\n\n{\n \"input\": [\n [1, 7, 7, 7, 1, 1, 2, 9, 9, 5, 2, 9, 9, 2, 5, 9, 9, 2, 1, 1, 7, 7, 7, 1],\n [7, 2, 7, 1, 1, 2, 9, 9, 5, 5, 2, 2, 2, 2, 5, 5, 9, 9, 2, 1, 1, 7, 2, 7],\n [7, 7, 7, 7, 7, 1, 9, 5, 5, 5, 2, 9, 9, 2, 5, 5, 5, 9, 1, 7, 7, 7, 7, 7],\n [7, 1, 7, 1, 7, 2, 5, 5, 5, 2, 9, 9, 9, 9, 2, 5, 5, 5, 2, 7, 1, 7, 1, 7],\n [1, 1, 7, 7, 7, 7, 2, 2, 2, 9, 5, 9, 9, 5, 9, 2, 2, 2, 7, 7, 7, 7, 1, 1],\n [1, 2, 1, 2, 7, 1, 9, 2, 9, 9, 9, 5, 5, 9, 9, 9, 2, 9, 1, 7, 2, 1, 2, 1],\n [2, 9, 9, 5, 2, 9, 7, 9, 9, 7, 9, 9, 9, 9, 7, 9, 9, 7, 9, 2, 5, 9, 9, 2],\n [9, 9, 5, 5, 2, 2, 9, 4, 7, 9, 4, 7, 7, 4, 9, 7, 4, 9, 2, 2, 5, 5, 9, 9],\n [9, 5, 5, 5, 2, 9, 9, 7, 9, 7, 4, 7, 7, 4, 7, 9, 7, 9, 9, 2, 5, 5, 5, 9],\n [5, 5, 5, 2, 9, 9, 7, 9, 7, 9, 9, 4, 4, 9, 9, 7, 9, 7, 9, 9, 2, 5, 5, 5],\n [2, 2, 2, 9, 5, 9, 9, 3, 3, 3, 3, 3, 3, 9, 9, 4, 4, 9, 9, 5, 9, 2, 2, 2],\n [9, 2, 9, 9, 9, 5, 9, 3, 3, 3, 3, 3, 3, 7, 4, 7, 7, 9, 5, 9, 9, 9, 2, 9],\n [9, 2, 9, 9, 9, 5, 9, 7, 7, 4, 7, 4, 4, 7, 4, 7, 7, 9, 5, 9, 9, 9, 2, 9],\n [2, 2, 2, 9, 5, 9, 9, 4, 4, 9, 9, 7, 7, 9, 9, 4, 4, 9, 9, 5, 9, 2, 2, 2],\n [5, 5, 5, 2, 9, 9, 7, 9, 7, 9, 9, 4, 4, 9, 9, 7, 9, 7, 9, 9, 2, 5, 5, 5],\n [9, 5, 5, 5, 2, 9, 9, 7, 9, 7, 4, 7, 7, 4, 7, 9, 7, 9, 9, 2, 5, 5, 5, 9],\n [9, 9, 5, 5, 2, 2, 9, 4, 7, 9, 4, 7, 7, 4, 9, 7, 4, 9, 2, 2, 5, 5, 9, 9],\n [2, 9, 9, 5, 2, 9, 7, 9, 9, 7, 9, 9, 9, 9, 7, 9, 9, 7, 9, 2, 5, 9, 9, 2],\n [1, 2, 1, 2, 7, 1, 9, 2, 9, 9, 9, 5, 5, 9, 9, 9, 2, 9, 1, 7, 2, 1, 2, 1],\n [1, 1, 7, 7, 7, 7, 2, 2, 2, 9, 5, 9, 9, 5, 9, 2, 2, 2, 7, 7, 7, 7, 1, 1],\n [7, 1, 7, 1, 7, 2, 5, 5, 5, 2, 9, 9, 9, 9, 2, 5, 5, 5, 2, 7, 1, 7, 1, 7],\n [7, 7, 7, 7, 7, 1, 9, 5, 5, 5, 2, 9, 9, 2, 5, 5, 5, 9, 1, 7, 7, 7, 7, 7],\n [7, 2, 7, 1, 1, 2, 9, 9, 5, 5, 2, 2, 2, 2, 5, 5, 9, 9, 2, 1, 1, 7, 2, 7],\n [1, 7, 7, 7, 1, 1, 2, 9, 9, 5, 2, 9, 9, 2, 5, 9, 9, 2, 1, 1, 7, 7, 7, 1]\n ],\n \"output\": [\n [4, 4, 9, 9, 7, 7],\n [7, 7, 4, 7, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 8, 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 2, 2, 2, 2, 7, 7, 7, 8, 8, 7, 7],\n [7, 1, 8, 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 1, 1, 7, 2, 2, 7, 1, 1, 8, 1, 7],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 1, 1, 7, 2, 1, 8, 7, 8, 8, 8],\n [8, 1, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 7, 7, 1, 1, 1, 2, 8, 7, 8, 7, 1, 8],\n [7, 1, 8, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 7, 7, 7, 1, 2, 8, 8, 7, 8, 1, 7],\n [7, 7, 1, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 7, 7, 7, 7, 8, 8, 8, 1, 7, 7],\n [7, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 8, 8, 8, 8, 8, 7, 2, 2, 2, 2, 7],\n [2, 2, 7, 1, 1, 7, 8, 1, 9, 1, 9, 9, 9, 9, 1, 9, 1, 8, 7, 1, 1, 7, 2, 2],\n [2, 7, 1, 1, 7, 7, 8, 9, 8, 8, 9, 1, 1, 9, 8, 8, 9, 8, 7, 7, 1, 1, 7, 2],\n [2, 1, 1, 1, 7, 7, 8, 1, 8, 1, 9, 9, 9, 9, 1, 8, 1, 8, 7, 7, 1, 1, 1, 2],\n [2, 1, 7, 7, 7, 2, 8, 9, 9, 9, 8, 9, 9, 8, 9, 9, 9, 8, 2, 7, 7, 7, 1, 2],\n [7, 7, 7, 7, 2, 2, 1, 9, 1, 9, 9, 9, 9, 9, 9, 1, 9, 1, 2, 2, 7, 7, 7, 7],\n [7, 7, 7, 7, 2, 2, 1, 9, 1, 9, 9, 9, 9, 9, 9, 1, 9, 1, 2, 2, 7, 7, 7, 7],\n [2, 1, 7, 7, 7, 2, 8, 9, 9, 9, 8, 9, 9, 8, 9, 9, 9, 8, 2, 7, 7, 7, 1, 2],\n [2, 1, 1, 1, 7, 7, 8, 1, 8, 1, 9, 9, 9, 9, 1, 8, 1, 8, 7, 7, 1, 1, 1, 2],\n [2, 7, 1, 1, 7, 7, 8, 9, 8, 8, 9, 1, 1, 9, 8, 8, 9, 8, 7, 7, 1, 1, 7, 2],\n [2, 2, 7, 1, 1, 7, 8, 1, 9, 1, 9, 9, 9, 9, 1, 9, 1, 8, 7, 1, 1, 7, 2, 2],\n [7, 2, 2, 2, 2, 7, 8, 8, 8, 8, 8, 1, 1, 8, 8, 8, 8, 8, 7, 2, 2, 2, 2, 7],\n [7, 7, 1, 8, 8, 8, 7, 7, 7, 7, 2, 2, 2, 2, 7, 7, 7, 7, 8, 8, 8, 1, 7, 7],\n [7, 1, 8, 7, 8, 8, 2, 1, 7, 7, 7, 2, 2, 7, 7, 7, 1, 2, 8, 8, 7, 8, 1, 7],\n [8, 1, 7, 8, 7, 8, 2, 1, 1, 1, 7, 7, 7, 7, 1, 1, 1, 2, 8, 7, 8, 7, 1, 8],\n [8, 8, 8, 7, 8, 1, 2, 7, 1, 1, 7, 7, 7, 7, 1, 1, 7, 2, 1, 8, 7, 8, 8, 8],\n [7, 1, 8, 1, 1, 7, 2, 2, 7, 1, 1, 7, 7, 1, 1, 7, 2, 2, 7, 1, 1, 8, 1, 7],\n [7, 7, 8, 8, 7, 7, 7, 2, 2, 2, 2, 7, 7, 2, 2, 2, 2, 7, 7, 7, 8, 8, 7, 7]\n ],\n \"output\": [\n [8, 7, 7, 7, 2, 2, 2, 2],\n [1, 1, 7, 2, 2, 7, 1, 1],\n [7, 8, 1, 2, 7, 1, 1, 7],\n [8, 7, 8, 2, 1, 1, 1, 7],\n [7, 8, 8, 2, 1, 7, 7, 7],\n [8, 8, 8, 7, 7, 7, 7, 2],\n [2, 2, 7, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 9, 9, 2, 4, 9, 6, 4, 6, 1, 4, 1, 1, 4, 1, 6, 4, 6, 9, 4, 2, 9, 9, 2],\n [9, 9, 4, 9, 4, 9, 4, 6, 1, 1, 6, 1, 1, 6, 1, 1, 6, 4, 9, 4, 9, 4, 9, 9],\n [9, 4, 2, 2, 9, 2, 6, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 6, 2, 9, 2, 2, 4, 9],\n [2, 9, 2, 4, 4, 4, 1, 1, 1, 6, 4, 6, 6, 4, 6, 1, 1, 1, 4, 4, 4, 2, 9, 2],\n [4, 4, 9, 4, 4, 4, 4, 6, 1, 4, 6, 6, 6, 6, 4, 1, 6, 4, 4, 4, 4, 9, 4, 4],\n [9, 9, 2, 4, 4, 9, 1, 1, 6, 6, 6, 4, 4, 6, 6, 6, 1, 1, 9, 4, 4, 2, 9, 9],\n [6, 4, 6, 1, 4, 1, 2, 4, 9, 2, 9, 2, 2, 9, 2, 9, 4, 2, 1, 4, 1, 6, 4, 6],\n [4, 6, 1, 1, 6, 1, 4, 4, 9, 2, 2, 4, 4, 2, 2, 9, 4, 4, 1, 6, 1, 1, 6, 4],\n [6, 1, 1, 1, 1, 6, 9, 9, 9, 2, 2, 2, 2, 2, 2, 9, 9, 9, 6, 1, 1, 1, 1, 6],\n [1, 1, 1, 6, 4, 6, 2, 2, 2, 2, 9, 9, 9, 9, 2, 2, 2, 2, 6, 4, 6, 1, 1, 1],\n [4, 6, 1, 4, 6, 6, 9, 2, 2, 9, 9, 9, 9, 9, 9, 2, 2, 9, 6, 6, 4, 1, 6, 4],\n [1, 1, 6, 6, 6, 4, 2, 4, 2, 9, 9, 9, 9, 9, 9, 2, 4, 2, 4, 6, 6, 6, 1, 1],\n [1, 3, 3, 3, 3, 3, 3, 3, 2, 9, 9, 9, 9, 9, 9, 2, 4, 2, 4, 6, 6, 6, 1, 1],\n [4, 3, 3, 3, 3, 3, 3, 3, 2, 9, 9, 9, 9, 9, 9, 2, 2, 9, 6, 6, 4, 1, 6, 4],\n [1, 3, 3, 3, 3, 3, 3, 3, 2, 2, 9, 9, 9, 9, 2, 2, 2, 2, 6, 4, 6, 1, 1, 1],\n [6, 3, 3, 3, 3, 3, 3, 3, 9, 2, 2, 2, 2, 2, 2, 9, 9, 9, 6, 1, 1, 1, 1, 6],\n [4, 3, 3, 3, 3, 3, 3, 3, 9, 2, 2, 4, 4, 2, 2, 9, 4, 4, 1, 6, 1, 1, 6, 4],\n [6, 3, 3, 3, 3, 3, 3, 3, 9, 2, 9, 2, 2, 9, 2, 9, 4, 2, 1, 4, 1, 6, 4, 6],\n [9, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 4, 4, 6, 6, 6, 1, 1, 9, 4, 4, 2, 9, 9],\n [4, 3, 3, 3, 3, 3, 3, 3, 1, 4, 6, 6, 6, 6, 4, 1, 6, 4, 4, 4, 4, 9, 4, 4],\n [2, 9, 2, 4, 4, 4, 1, 1, 1, 6, 4, 6, 6, 4, 6, 1, 1, 1, 4, 4, 4, 2, 9, 2],\n [9, 4, 2, 2, 9, 2, 6, 1, 1, 1, 1, 6, 6, 1, 1, 1, 1, 6, 2, 9, 2, 2, 4, 9],\n [9, 9, 4, 9, 4, 9, 4, 6, 1, 1, 6, 1, 1, 6, 1, 1, 6, 4, 9, 4, 9, 4, 9, 9],\n [2, 9, 9, 2, 4, 9, 6, 4, 6, 1, 4, 1, 1, 4, 1, 6, 4, 6, 9, 4, 2, 9, 9, 2]\n ],\n \"output\": [\n [1, 6, 6, 6, 4, 2, 4],\n [6, 1, 4, 6, 6, 9, 2],\n [1, 1, 6, 4, 6, 2, 2],\n [1, 1, 1, 1, 6, 9, 9],\n [6, 1, 1, 6, 1, 4, 4],\n [4, 6, 1, 4, 1, 2, 4],\n [9, 2, 4, 4, 9, 1, 1],\n [4, 9, 4, 4, 4, 4, 6]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 7, 2, 2, 2, 5, 9, 9, 2, 2, 5, 5, 2, 2, 9, 9, 5, 2, 2, 2, 7, 2, 2],\n [2, 7, 2, 2, 7, 2, 9, 2, 9, 2, 2, 9, 9, 2, 2, 9, 2, 9, 2, 7, 2, 2, 7, 2],\n [7, 2, 1, 7, 1, 7, 9, 9, 5, 2, 5, 5, 5, 5, 2, 5, 9, 9, 7, 1, 7, 1, 2, 7],\n [2, 2, 7, 1, 2, 7, 2, 2, 2, 5, 5, 5, 5, 5, 5, 2, 2, 2, 7, 2, 1, 7, 2, 2],\n [2, 7, 1, 2, 7, 7, 2, 2, 5, 5, 5, 9, 9, 5, 5, 5, 2, 2, 7, 7, 2, 1, 7, 2],\n [2, 2, 7, 7, 7, 2, 5, 9, 5, 5, 9, 2, 2, 9, 5, 5, 9, 5, 2, 7, 7, 7, 2, 2],\n [5, 9, 9, 2, 2, 5, 6, 6, 6, 8, 8, 6, 6, 8, 8, 6, 6, 6, 5, 2, 2, 9, 9, 5],\n [9, 2, 9, 2, 2, 9, 6, 8, 6, 6, 8, 8, 8, 8, 6, 6, 8, 6, 9, 2, 2, 9, 2, 9],\n [9, 9, 5, 2, 5, 5, 6, 6, 6, 8, 4, 8, 8, 4, 8, 6, 6, 6, 5, 5, 2, 5, 9, 9],\n [2, 2, 2, 5, 5, 5, 8, 6, 8, 4, 8, 8, 8, 8, 4, 8, 6, 8, 5, 5, 5, 2, 2, 2],\n [2, 2, 5, 5, 5, 9, 8, 8, 4, 8, 8, 8, 8, 8, 8, 4, 8, 8, 9, 5, 5, 5, 2, 2],\n [5, 9, 5, 5, 9, 2, 6, 8, 8, 8, 8, 6, 6, 8, 8, 8, 8, 6, 2, 9, 5, 5, 9, 5],\n [5, 9, 5, 5, 9, 2, 6, 8, 8, 8, 8, 6, 6, 8, 8, 8, 8, 6, 2, 9, 5, 5, 9, 5],\n [2, 2, 5, 5, 5, 9, 8, 8, 4, 8, 8, 8, 8, 8, 8, 4, 8, 8, 9, 5, 5, 5, 2, 2],\n [2, 2, 2, 3, 3, 3, 3, 3, 8, 4, 8, 8, 8, 8, 4, 8, 6, 8, 5, 5, 5, 2, 2, 2],\n [9, 9, 5, 3, 3, 3, 3, 3, 6, 8, 4, 8, 8, 4, 8, 6, 6, 6, 5, 5, 2, 5, 9, 9],\n [9, 2, 9, 3, 3, 3, 3, 3, 6, 6, 8, 8, 8, 8, 6, 6, 8, 6, 9, 2, 2, 9, 2, 9],\n [5, 9, 9, 3, 3, 3, 3, 3, 6, 8, 8, 6, 6, 8, 8, 6, 6, 6, 5, 2, 2, 9, 9, 5],\n [2, 2, 7, 3, 3, 3, 3, 3, 5, 5, 9, 2, 2, 9, 5, 5, 9, 5, 2, 7, 7, 7, 2, 2],\n [2, 7, 1, 2, 7, 7, 2, 2, 5, 5, 5, 9, 9, 5, 5, 5, 2, 2, 7, 7, 2, 1, 7, 2],\n [2, 2, 7, 1, 2, 7, 2, 2, 2, 5, 5, 5, 5, 5, 5, 2, 2, 2, 7, 2, 1, 7, 2, 2],\n [7, 2, 1, 7, 1, 7, 9, 9, 5, 2, 5, 5, 5, 5, 2, 5, 9, 9, 7, 1, 7, 1, 2, 7],\n [2, 7, 2, 2, 7, 2, 9, 2, 9, 2, 2, 9, 9, 2, 2, 9, 2, 9, 2, 7, 2, 2, 7, 2],\n [2, 2, 7, 2, 2, 2, 5, 9, 9, 2, 2, 5, 5, 2, 2, 9, 9, 5, 2, 2, 2, 7, 2, 2]\n ],\n \"output\": [\n [5, 5, 5, 8, 6],\n [2, 5, 5, 6, 6],\n [2, 2, 9, 6, 8],\n [2, 2, 5, 6, 6],\n [7, 7, 2, 5, 9]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 9, 7, 7, 9, 2, 5, 8, 2, 8, 5, 5, 8, 2, 8, 5, 2, 9, 7, 7, 9, 9, 9],\n [9, 9, 7, 4, 9, 4, 5, 2, 5, 2, 5, 5, 5, 5, 2, 5, 2, 5, 4, 9, 4, 7, 9, 9],\n [9, 7, 7, 7, 4, 7, 8, 5, 2, 8, 8, 2, 2, 8, 8, 2, 5, 8, 7, 4, 7, 7, 7, 9],\n [7, 4, 7, 4, 9, 9, 2, 2, 8, 5, 5, 5, 5, 5, 5, 8, 2, 2, 9, 9, 4, 7, 4, 7],\n [7, 9, 4, 9, 4, 7, 8, 5, 8, 5, 5, 3, 3, 3, 3, 8, 5, 8, 7, 4, 9, 4, 9, 7],\n [9, 4, 7, 9, 7, 9, 5, 5, 2, 5, 8, 3, 3, 3, 3, 2, 5, 5, 9, 7, 9, 7, 4, 9],\n [2, 5, 8, 2, 8, 5, 2, 1, 6, 6, 2, 3, 3, 3, 3, 6, 1, 2, 5, 8, 2, 8, 5, 2],\n [5, 2, 5, 2, 5, 5, 1, 1, 6, 6, 6, 3, 3, 3, 3, 6, 1, 1, 5, 5, 2, 5, 2, 5],\n [8, 5, 2, 8, 8, 2, 6, 6, 1, 2, 1, 3, 3, 3, 3, 1, 6, 6, 2, 8, 8, 2, 5, 8],\n [2, 2, 8, 5, 5, 5, 6, 6, 2, 1, 6, 3, 3, 3, 3, 2, 6, 6, 5, 5, 5, 8, 2, 2],\n [8, 5, 8, 5, 5, 8, 2, 6, 1, 6, 1, 6, 6, 1, 6, 1, 6, 2, 8, 5, 5, 8, 5, 8],\n [5, 5, 2, 5, 8, 8, 6, 2, 6, 6, 6, 1, 1, 6, 6, 6, 2, 6, 8, 8, 5, 2, 5, 5],\n [5, 5, 2, 5, 8, 8, 6, 2, 6, 6, 6, 1, 1, 6, 6, 6, 2, 6, 8, 8, 5, 2, 5, 5],\n [8, 5, 8, 5, 5, 8, 2, 6, 1, 6, 1, 6, 6, 1, 6, 1, 6, 2, 8, 5, 5, 8, 5, 8],\n [2, 2, 8, 5, 5, 5, 6, 6, 2, 1, 6, 6, 6, 6, 1, 2, 6, 6, 5, 5, 5, 8, 2, 2],\n [8, 5, 2, 8, 8, 2, 6, 6, 1, 2, 1, 6, 6, 1, 2, 1, 6, 6, 2, 8, 8, 2, 5, 8],\n [5, 2, 5, 2, 5, 5, 1, 1, 6, 6, 6, 2, 2, 6, 6, 6, 1, 1, 5, 5, 2, 5, 2, 5],\n [2, 5, 8, 2, 8, 5, 2, 1, 6, 6, 2, 6, 6, 2, 6, 6, 1, 2, 5, 8, 2, 8, 5, 2],\n [9, 4, 7, 9, 7, 9, 5, 5, 2, 5, 8, 8, 8, 8, 5, 2, 5, 5, 9, 7, 9, 7, 4, 9],\n [7, 9, 4, 9, 4, 7, 8, 5, 8, 5, 5, 8, 8, 5, 5, 8, 5, 8, 7, 4, 9, 4, 9, 7],\n [7, 4, 7, 4, 9, 9, 2, 2, 8, 5, 5, 5, 5, 5, 5, 8, 2, 2, 9, 9, 4, 7, 4, 7],\n [9, 7, 7, 7, 4, 7, 8, 5, 2, 8, 8, 2, 2, 8, 8, 2, 5, 8, 7, 4, 7, 7, 7, 9],\n [9, 9, 7, 4, 9, 4, 5, 2, 5, 2, 5, 5, 5, 5, 2, 5, 2, 5, 4, 9, 4, 7, 9, 9],\n [9, 9, 9, 7, 7, 9, 2, 5, 8, 2, 8, 5, 5, 8, 2, 8, 5, 2, 9, 7, 7, 9, 9, 9]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 5, 5], [8, 8, 8, 5], [6, 6, 2, 6], [2, 2, 6, 6], [6, 6, 1, 2], [6, 6, 6, 1]], "task_id": "f4081712"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 3, 0, 0, 8, 0],\n [2, 2, 2, 3, 3, 3, 8, 8, 8],\n [0, 2, 0, 0, 3, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 0],\n [3, 0, 3],\n [0, 3, 0],\n [0, 1, 0],\n [1, 0, 1],\n [0, 1, 0],\n [0, 8, 0],\n [8, 0, 8],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [0, 2, 0],\n [2, 0, 2],\n [1, 0, 1],\n [0, 1, 0],\n [1, 0, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 1, 0, 1, 0],\n [0, 4, 4, 4, 0, 0, 2, 0, 2, 0, 3, 0, 3, 0, 1, 1, 1, 0],\n [0, 4, 0, 4, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 0, 4, 2, 0, 2, 3, 0, 3, 1, 0, 1], [4, 4, 4, 2, 2, 2, 3, 3, 3, 1, 1, 1], [4, 0, 4, 2, 0, 2, 3, 0, 3, 1, 0, 1]], "task_id": "67636eac"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 1, 0, 0, 0, 0, 2, 2, 0],\n [0, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e1d2900e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 8, 8, 8, 3, 8, 0, 0, 0, 8, 8, 3, 3, 8, 8, 8, 0, 0, 0],\n [0, 0, 8, 3, 3, 3, 8, 3, 3, 3, 0, 0, 0, 3, 8, 8, 8, 8, 8, 3, 0, 0, 0],\n [0, 0, 8, 8, 3, 8, 8, 8, 3, 8, 0, 0, 0, 8, 8, 8, 3, 8, 8, 8, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 3, 8, 8, 8, 0, 0, 0, 8, 8, 3, 3, 3, 8, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 3, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 8, 3, 8, 8, 8, 8, 8, 3, 8, 0, 0, 0, 8, 8, 8, 8, 3, 8, 8, 0, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 3, 3, 8, 3, 8, 3, 8, 0, 8, 3, 8, 8, 8, 8, 8, 3, 0, 0, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 8, 8, 8, 0, 3, 3, 3, 8, 8, 3, 8, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 3, 8, 8, 3, 3, 3, 8, 0, 0, 0, 0],\n [0, 8, 3, 8, 8, 8, 3, 8, 8, 8, 0, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 3, 3, 3, 8, 8, 0, 8, 3, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0],\n [0, 8, 3, 8, 8, 8, 3, 8, 3, 8, 0, 3, 3, 3, 8, 8, 3, 3, 3, 0, 0, 0, 0],\n [0, 3, 8, 8, 8, 8, 8, 8, 8, 3, 0, 8, 3, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 3, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 3, 8, 8, 8, 8, 8, 3],\n [3, 3, 3, 8, 8, 3, 8, 8],\n [8, 3, 8, 8, 3, 3, 3, 8],\n [8, 8, 8, 8, 8, 3, 8, 8],\n [8, 3, 8, 8, 8, 8, 3, 8],\n [3, 3, 3, 8, 8, 3, 3, 3],\n [8, 3, 8, 8, 8, 8, 3, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 8, 8, 3, 8, 8, 8, 3, 8, 0, 0, 3, 8, 8, 8, 3, 8, 8, 8, 3, 8, 0, 0],\n [0, 8, 8, 3, 3, 3, 8, 8, 8, 3, 0, 0, 8, 8, 3, 8, 8, 8, 8, 3, 3, 3, 0, 0],\n [0, 8, 8, 8, 3, 8, 8, 8, 8, 8, 0, 0, 8, 3, 3, 3, 8, 8, 8, 8, 3, 8, 0, 0],\n [0, 8, 3, 8, 8, 8, 8, 3, 8, 8, 0, 0, 8, 8, 3, 8, 3, 8, 3, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 3, 8, 3, 3, 3, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 0, 0],\n [0, 3, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 8, 3, 8, 3, 8, 8, 3, 8, 8, 8, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 8, 3, 8, 0, 0, 8, 8, 8, 8, 8, 3, 3, 3, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8, 3, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 3, 3, 8, 8, 3, 3, 3, 0, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 8, 3, 8, 0, 8, 8, 8, 3, 8, 8, 8, 8, 3, 3, 8, 0, 0],\n [0, 3, 8, 8, 8, 3, 8, 8, 8, 8, 0, 8, 8, 3, 3, 3, 8, 8, 8, 8, 8, 8, 0, 0],\n [0, 8, 3, 8, 8, 8, 8, 3, 8, 3, 0, 8, 8, 8, 3, 8, 8, 8, 3, 8, 8, 3, 0, 0],\n [0, 3, 3, 3, 8, 8, 3, 3, 3, 8, 0, 3, 8, 8, 8, 8, 8, 3, 3, 3, 8, 8, 0, 0],\n [0, 8, 3, 8, 8, 8, 8, 3, 8, 8, 0, 8, 8, 3, 8, 3, 8, 8, 3, 8, 8, 8, 0, 0],\n [0, 8, 8, 8, 8, 3, 8, 8, 8, 8, 0, 8, 3, 3, 3, 8, 8, 8, 8, 8, 3, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8, 3, 3, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 3, 8, 8, 8, 8, 3, 8],\n [8, 3, 3, 3, 8, 8, 3, 3, 3],\n [8, 8, 3, 8, 8, 8, 8, 3, 8],\n [3, 8, 8, 8, 3, 8, 8, 8, 8],\n [8, 3, 8, 8, 8, 8, 3, 8, 3],\n [3, 3, 3, 8, 8, 3, 3, 3, 8],\n [8, 3, 8, 8, 8, 8, 3, 8, 8],\n [8, 8, 8, 8, 3, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 8, 8, 3, 8, 8, 3, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 0, 0, 8, 3, 3, 3, 8, 8, 3, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 3, 8, 8, 8, 3, 0, 0, 8, 8, 3, 8, 8, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 3, 8, 8, 3, 0, 0, 8, 8, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 3, 8, 8, 8, 3, 8, 8, 3, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 8, 8, 3, 3, 3, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 0, 0, 8, 3, 8, 8, 3, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8, 3, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 3, 3, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 3, 8, 8, 3, 8, 8],\n [8, 3, 3, 3, 8, 8, 3, 8],\n [8, 8, 3, 8, 8, 3, 3, 3],\n [8, 8, 8, 8, 8, 8, 3, 8],\n [3, 8, 8, 8, 3, 8, 8, 3],\n [8, 8, 8, 3, 3, 3, 8, 8],\n [8, 3, 8, 8, 3, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 8, 3, 8, 0, 0, 0, 3, 8, 8, 3, 8, 8, 3, 8],\n [0, 0, 3, 3, 3, 8, 3, 3, 3, 0, 0, 0, 8, 8, 3, 8, 8, 3, 3, 3],\n [0, 0, 8, 3, 8, 8, 8, 3, 8, 0, 0, 0, 8, 3, 3, 3, 8, 8, 3, 8],\n [0, 0, 8, 8, 8, 3, 8, 8, 8, 0, 0, 0, 8, 8, 3, 8, 8, 8, 8, 8],\n [0, 0, 8, 3, 8, 8, 8, 8, 8, 0, 0, 0, 3, 8, 8, 8, 8, 3, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 3, 3, 3, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 3, 8, 8, 3, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 8, 8, 8, 8, 8, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 3, 8, 8, 3, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 3, 3, 3, 8, 8, 8, 3, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 3, 8, 8, 8, 3, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 8, 8, 3, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 8, 8, 3, 3, 3, 8, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 3, 8, 8, 3, 8, 8, 8, 8],\n [8, 8, 3, 3, 3, 8, 8, 8, 3, 8, 8],\n [8, 8, 8, 3, 8, 8, 8, 3, 3, 3, 8],\n [8, 3, 8, 8, 8, 8, 8, 8, 3, 8, 8],\n [3, 3, 3, 8, 8, 3, 8, 8, 8, 8, 8],\n [8, 3, 8, 8, 3, 3, 3, 8, 8, 3, 8],\n [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 3, 8, 8, 8, 3, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 3, 3, 8, 8, 8, 3, 3, 3, 0, 0, 8, 8, 8, 8, 8, 8, 3, 3, 8, 8],\n [0, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 0, 0, 3, 8, 8, 3, 8, 8, 8, 8, 8, 8],\n [0, 8, 3, 8, 8, 8, 3, 8, 3, 8, 8, 0, 0, 8, 8, 3, 3, 3, 8, 8, 8, 3, 8],\n [0, 8, 8, 8, 8, 3, 3, 3, 8, 8, 3, 0, 0, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8],\n [0, 3, 3, 8, 8, 8, 3, 8, 8, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 3, 8, 0, 0, 8, 8, 8, 3, 8, 8, 3, 8, 8, 8],\n [0, 3, 8, 8, 3, 8, 8, 8, 3, 8, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 8, 8, 8, 8, 8, 8, 3, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 0, 0, 3, 3, 8, 8, 3, 8, 8, 8, 3, 8, 0],\n [8, 8, 3, 8, 8, 3, 3, 3, 8, 8, 0, 0, 8, 8, 8, 3, 3, 3, 8, 8, 8, 3, 0],\n [8, 3, 3, 3, 8, 8, 3, 8, 3, 8, 0, 0, 3, 8, 8, 8, 3, 8, 8, 8, 8, 8, 0],\n [8, 8, 3, 8, 8, 8, 8, 3, 3, 3, 0, 0, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 0],\n [8, 8, 8, 8, 3, 8, 8, 8, 3, 8, 0, 0, 3, 3, 3, 8, 8, 8, 3, 3, 3, 8, 0],\n [8, 3, 8, 3, 3, 3, 8, 8, 8, 8, 0, 0, 8, 3, 8, 3, 8, 8, 8, 3, 8, 8, 0],\n [3, 3, 3, 8, 3, 8, 8, 8, 8, 3, 0, 0, 8, 8, 3, 3, 3, 8, 8, 8, 8, 8, 0],\n [8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 0, 0, 8, 8, 8, 3, 8, 8, 8, 3, 8, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 8, 8, 8, 8, 8, 3, 8, 8, 8], [8, 8, 3, 8, 8, 3, 3, 3, 8, 8], [8, 3, 3, 3, 8, 8, 3, 8, 3, 8], [8, 8, 3, 8, 8, 8, 8, 3, 3, 3], [8, 8, 8, 8, 3, 8, 8, 8, 3, 8], [8, 3, 8, 3, 3, 3, 8, 8, 8, 8], [3, 3, 3, 8, 3, 8, 8, 8, 8, 3], [8, 3, 8, 8, 8, 8, 8, 3, 8, 8]], "task_id": "2c0b0aff"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 0, 0, 0, 0, 0, 9, 2, 3, 0, 2, 3, 3, 0],\n [2, 2, 2, 3, 0, 0, 3, 5, 7, 0, 0, 0, 2, 7, 0],\n [0, 3, 2, 2, 0, 0, 0, 7, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 9, 0, 0, 2, 9, 2],\n [8, 0, 0, 3, 0, 0, 1, 2, 8, 2, 0, 0, 0, 0, 0],\n [3, 0, 0, 3, 2, 0, 0, 0, 7, 0, 2, 0, 3, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 5, 6, 0, 2, 0, 0],\n [0, 1, 0, 2, 3, 6, 0, 0, 2, 3, 0, 2, 0, 6, 0],\n [0, 2, 8, 0, 3, 0, 0, 0, 6, 0, 7, 0, 0, 3, 0],\n [0, 2, 3, 0, 8, 0, 0, 3, 0, 1, 0, 0, 6, 0, 0],\n [7, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 6, 7, 0],\n [0, 0, 2, 0, 5, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 9, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0],\n [0, 0, 2, 0, 2, 3, 3, 0, 0, 0, 1, 0, 0, 6, 2],\n [0, 2, 9, 0, 0, 5, 2, 3, 0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 0, 0, 0, 0, 9, 2, 3, 0, 2, 3, 3, 0],\n [2, 2, 2, 3, 0, 0, 3, 5, 7, 0, 0, 0, 2, 7, 0],\n [0, 3, 2, 2, 0, 0, 0, 7, 0, 5, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 2, 1, 1, 1, 0, 9, 0, 0, 2, 9, 2],\n [8, 0, 0, 3, 0, 1, 1, 2, 8, 2, 0, 0, 0, 0, 0],\n [3, 0, 0, 3, 2, 1, 1, 1, 7, 0, 2, 0, 3, 0, 0],\n [1, 1, 3, 0, 0, 0, 3, 0, 0, 5, 6, 0, 2, 0, 0],\n [1, 1, 1, 2, 3, 6, 0, 0, 2, 3, 0, 2, 0, 6, 0],\n [1, 2, 8, 0, 3, 0, 0, 0, 6, 1, 7, 0, 0, 3, 0],\n [0, 2, 3, 0, 8, 0, 0, 3, 1, 1, 1, 0, 6, 0, 0],\n [7, 0, 3, 0, 0, 2, 0, 0, 1, 1, 1, 0, 6, 7, 0],\n [0, 0, 2, 0, 5, 2, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 9, 0, 2, 0, 0, 0, 0, 0, 2, 1, 1, 3, 0, 0],\n [0, 0, 2, 0, 2, 3, 3, 0, 0, 1, 1, 1, 0, 6, 2],\n [0, 2, 9, 0, 0, 5, 2, 3, 0, 1, 1, 1, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 6, 2, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 6, 0, 4, 0, 0],\n [6, 3, 0, 1, 0, 4, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 4, 0, 6, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0],\n [6, 0, 3, 0, 0, 0, 0, 0, 0, 3, 2, 2, 0, 0, 4],\n [4, 2, 0, 2, 0, 2, 0, 0, 0, 0, 6, 0, 0, 6, 0],\n [0, 0, 0, 0, 2, 6, 0, 6, 0, 0, 4, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 4, 0, 0, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 3, 3, 4, 0, 6, 6, 0],\n [4, 6, 0, 3, 1, 3, 0, 0, 4, 0, 0, 2, 6, 0, 0],\n [0, 0, 3, 2, 0, 4, 0, 6, 0, 0, 4, 3, 6, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 3, 0, 3, 0, 0, 2, 2, 0],\n [6, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 4, 3],\n [0, 0, 0, 0, 0, 3, 4, 0, 0, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 6, 2, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 1, 1, 1, 2, 0, 0, 0, 2, 6, 0, 4, 0, 0],\n [6, 3, 1, 1, 1, 4, 1, 1, 1, 0, 0, 6, 0, 0, 0],\n [0, 0, 4, 1, 6, 0, 1, 1, 1, 0, 0, 0, 3, 0, 0],\n [6, 0, 3, 0, 0, 0, 1, 1, 1, 3, 2, 2, 0, 0, 4],\n [4, 2, 0, 2, 0, 2, 0, 0, 0, 0, 6, 0, 0, 6, 0],\n [0, 0, 0, 0, 2, 6, 0, 6, 0, 0, 4, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 4, 0, 0, 0, 4, 6, 0, 0, 0],\n [0, 0, 0, 6, 1, 6, 0, 0, 3, 3, 4, 0, 6, 6, 0],\n [4, 6, 0, 3, 1, 3, 0, 0, 4, 0, 0, 2, 6, 0, 0],\n [0, 0, 3, 2, 1, 4, 0, 6, 0, 0, 4, 3, 6, 0, 0],\n [0, 4, 1, 1, 1, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 3, 0, 3, 1, 1, 2, 2, 0],\n [6, 0, 1, 1, 1, 0, 2, 0, 0, 1, 1, 1, 0, 4, 3],\n [0, 0, 0, 0, 0, 3, 4, 0, 0, 2, 1, 1, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 9, 0, 0, 0, 0, 0, 0, 0, 8, 3, 9, 3, 0, 8],\n [0, 0, 0, 4, 0, 4, 0, 0, 3, 0, 2, 7, 7, 0, 2],\n [0, 3, 3, 0, 9, 0, 9, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 9, 0, 4, 0, 3, 0, 3, 3, 0, 1, 0],\n [0, 1, 0, 0, 8, 8, 0, 3, 0, 2, 9, 3, 0, 0, 0],\n [0, 9, 0, 8, 0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 3],\n [0, 0, 7, 2, 2, 4, 7, 0, 9, 0, 0, 0, 0, 0, 8],\n [0, 4, 0, 0, 7, 0, 0, 0, 8, 0, 3, 3, 2, 7, 0],\n [0, 3, 3, 0, 2, 0, 1, 0, 2, 3, 3, 0, 0, 0, 4],\n [0, 0, 0, 3, 0, 8, 0, 0, 0, 7, 0, 3, 0, 1, 0],\n [0, 8, 0, 0, 3, 0, 9, 9, 0, 0, 7, 3, 9, 0, 0],\n [4, 4, 3, 0, 3, 0, 7, 8, 0, 4, 0, 7, 3, 0, 9],\n [7, 0, 1, 3, 3, 0, 7, 0, 1, 7, 0, 0, 4, 0, 9],\n [3, 0, 0, 0, 7, 8, 8, 0, 0, 8, 0, 9, 0, 0, 0],\n [0, 0, 7, 0, 0, 9, 8, 0, 0, 4, 8, 3, 0, 0, 0]\n ],\n \"output\": [\n [3, 9, 0, 0, 0, 0, 0, 0, 0, 8, 3, 9, 3, 0, 8],\n [0, 0, 0, 4, 0, 4, 0, 0, 3, 0, 2, 7, 7, 0, 2],\n [0, 3, 3, 0, 9, 0, 9, 0, 0, 0, 0, 2, 1, 1, 1],\n [1, 1, 1, 0, 9, 0, 4, 0, 3, 0, 3, 3, 1, 1, 1],\n [1, 1, 1, 0, 8, 8, 0, 3, 0, 2, 9, 3, 1, 1, 1],\n [1, 9, 1, 8, 0, 0, 0, 0, 3, 0, 0, 7, 0, 0, 3],\n [0, 0, 7, 2, 2, 4, 7, 0, 9, 0, 0, 0, 0, 0, 8],\n [0, 4, 0, 0, 7, 1, 1, 1, 8, 0, 3, 3, 2, 7, 0],\n [0, 3, 3, 0, 2, 1, 1, 1, 2, 3, 3, 0, 1, 1, 4],\n [0, 0, 0, 3, 0, 8, 1, 1, 0, 7, 0, 3, 1, 1, 1],\n [0, 8, 0, 0, 3, 0, 9, 9, 0, 0, 7, 3, 9, 1, 1],\n [4, 4, 3, 1, 3, 0, 7, 8, 1, 4, 0, 7, 3, 0, 9],\n [7, 1, 1, 3, 3, 0, 7, 1, 1, 7, 0, 0, 4, 0, 9],\n [3, 1, 1, 1, 7, 8, 8, 1, 1, 8, 0, 9, 0, 0, 0],\n [0, 0, 7, 0, 0, 9, 8, 0, 0, 4, 8, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 7, 0, 0, 6, 0, 7, 0, 0, 0, 0, 0, 3],\n [2, 0, 4, 0, 3, 7, 0, 0, 7, 0, 7, 0, 0, 0, 8],\n [0, 0, 0, 7, 8, 0, 6, 2, 7, 0, 1, 0, 2, 7, 2],\n [0, 1, 0, 0, 2, 0, 0, 2, 6, 0, 0, 0, 0, 7, 8],\n [6, 0, 0, 6, 0, 1, 0, 0, 0, 2, 0, 0, 8, 6, 4],\n [0, 0, 4, 6, 6, 0, 0, 4, 8, 0, 0, 8, 0, 8, 7],\n [8, 7, 6, 0, 0, 0, 0, 7, 7, 4, 4, 8, 0, 0, 7],\n [3, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 7, 0, 8, 0],\n [0, 0, 8, 6, 8, 6, 7, 6, 1, 6, 6, 0, 4, 0, 7],\n [0, 8, 7, 0, 7, 8, 0, 7, 0, 8, 0, 0, 8, 0, 4],\n [4, 4, 0, 0, 0, 3, 0, 0, 2, 0, 0, 3, 8, 4, 8],\n [0, 0, 8, 0, 1, 0, 8, 3, 7, 6, 7, 8, 0, 8, 7],\n [0, 0, 0, 0, 8, 0, 0, 6, 0, 3, 0, 0, 3, 0, 0],\n [0, 6, 0, 0, 0, 0, 6, 3, 1, 0, 3, 0, 0, 1, 3],\n [4, 6, 0, 0, 0, 0, 8, 0, 0, 0, 2, 2, 0, 0, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 7, 0, 0, 6, 0, 7, 0, 0, 0, 0, 0, 3], [2, 0, 4, 0, 3, 7, 0, 0, 7, 1, 7, 1, 0, 0, 8], [1, 1, 1, 7, 8, 0, 6, 2, 7, 1, 1, 1, 2, 7, 2], [1, 1, 1, 0, 2, 1, 1, 2, 6, 1, 1, 1, 0, 7, 8], [6, 1, 1, 6, 1, 1, 1, 0, 0, 2, 0, 0, 8, 6, 4], [0, 0, 4, 6, 6, 1, 1, 4, 8, 0, 0, 8, 0, 8, 7], [8, 7, 6, 1, 1, 0, 0, 7, 7, 4, 4, 8, 0, 0, 7], [3, 0, 1, 1, 1, 0, 3, 1, 1, 1, 0, 7, 0, 8, 0], [0, 0, 8, 6, 8, 6, 7, 6, 1, 6, 6, 0, 4, 0, 7], [0, 8, 7, 0, 7, 8, 0, 7, 1, 8, 0, 0, 8, 0, 4], [4, 4, 0, 1, 1, 3, 0, 0, 2, 0, 0, 3, 8, 4, 8], [0, 0, 8, 1, 1, 1, 8, 3, 7, 6, 7, 8, 0, 8, 7], [0, 0, 0, 1, 8, 1, 0, 6, 1, 3, 0, 0, 3, 1, 1], [0, 6, 0, 0, 0, 0, 6, 3, 1, 1, 3, 0, 1, 1, 3], [4, 6, 0, 0, 0, 0, 8, 1, 1, 1, 2, 2, 1, 1, 6]], "task_id": "f0df5ff0"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5],\n [0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0],\n [5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5, 0],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 0, 0],\n [5, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0],\n [0, 5, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 5, 5, 0, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5]\n ],\n \"output\": [\n [5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5],\n [0, 5, 5, 3, 5, 5, 5, 3, 5, 3, 0, 5, 0, 3, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0],\n [5, 5, 0, 3, 0, 3, 0, 3, 0, 3, 0, 5, 0, 5, 5, 3],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0],\n [5, 5, 5, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 5, 0, 3],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 5, 5, 3, 0, 3, 0, 3, 0, 3, 0, 3, 5, 5, 5, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5],\n [0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 0, 3],\n [5, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0],\n [0, 5, 5, 3, 0, 5, 0, 5, 0, 3, 0, 3, 5, 5, 0, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 5, 5, 5, 0, 5, 0, 5, 5, 5],\n [5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 5, 5, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 0, 0, 1, 0, 0, 0, 0, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 5, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5],\n [5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5]\n ],\n \"output\": [\n [0, 1, 5, 1, 5, 5, 5, 1, 5, 1, 5, 5, 5],\n [5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 5, 5, 5],\n [5, 1, 5, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 1, 0, 1, 0, 1, 0, 1, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 5, 5, 1, 0, 1, 0, 1, 0, 1, 0, 1, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 5, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 1, 5, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5],\n [5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 0, 5, 0, 5, 5, 5, 5, 1, 5, 1, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 5, 5, 5],\n [0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0], [2, 5, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 5, 5], [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5], [5, 0, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5], [2, 5, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 5, 2], [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 5, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 5, 5], [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 5, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 5], [0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5], [5, 5, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 5, 5], [0, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 0], [2, 5, 5, 0, 2, 0, 2, 0, 5, 5, 2, 5, 5, 0, 5, 5, 5], [0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5]], "task_id": "d492a647"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 8, 0, 8, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 7, 0, 7, 0, 8, 8, 8, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 8, 0, 8, 0, 0, 7, 0, 0, 8, 0, 8, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 7, 0, 7, 0, 8, 8, 8, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 8, 8, 8, 0, 0, 7, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 8, 0, 8, 0, 7, 7, 7, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 8, 8, 8, 0, 0, 7, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 8, 8, 8, 0, 0, 7, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 8, 8, 8, 0],\n [0, 1, 1, 1, 0, 8, 0, 8, 0, 7, 7, 7, 0, 0, 7, 0, 0, 7, 7, 7, 0, 8, 0, 8, 0],\n [0, 1, 0, 1, 0, 8, 8, 8, 0, 0, 7, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 8, 0, 8, 0, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 8, 8, 8, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 8, 0, 0, 7, 7, 7, 0, 0, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 8, 8, 8, 0, 7, 7, 7, 0, 8, 8, 8, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [0, 1, 0, 1, 0, 0, 8, 0, 0, 7, 7, 7, 0, 0, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 7, 0, 7, 0, 0, 0, 0, 0, 7, 7, 7, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 7, 7, 7, 0, 0, 1, 0, 0, 7, 7, 7, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 7, 0, 7, 0, 0, 0, 0, 0, 7, 7, 7, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 8, 8, 8, 0, 0, 7, 0, 0, 0, 7, 0, 0, 8, 8, 8, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 0, 0, 7, 7, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 8, 0, 0],\n [0, 0, 1, 0, 0, 8, 8, 8, 0, 0, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 0, 8, 8, 8, 0],\n [0, 1, 1, 1, 0, 0, 8, 0, 0, 7, 7, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 8, 8, 8, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 8, 0, 8, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 0, 8, 0, 8, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 8, 8, 8, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 8, 0, 8, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 8, 0, 8, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 7, 0, 7, 0, 1, 1, 0, 0],\n [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 7, 0, 0, 1, 1, 1, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 7, 0, 0, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 8, 8, 8, 0, 0, 7, 0, 0, 7, 0, 0, 0, 8, 8, 8, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 8, 0, 8, 0, 7, 7, 7, 0, 7, 7, 7, 0, 8, 0, 8, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 0, 8, 0, 8, 0, 0, 7, 0, 0, 0, 0, 7, 0, 8, 0, 8, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 8, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 1, 0, 8, 8, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 8, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0],\n [0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 8, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 8, 0, 0], [0, 1, 0, 1, 0, 8, 8, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 7, 7, 7, 0, 8, 8, 0, 0], [0, 1, 1, 1, 0, 0, 0, 8, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 7, 7, 7, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 7, 7, 7, 0], [0, 0, 1, 0, 0, 0, 7, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 7, 0, 7, 0], [0, 0, 1, 1, 0, 7, 7, 7, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 7, 7, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 7, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 7, 7, 7, 0, 8, 8, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 7, 0, 0], [0, 1, 1, 1, 0, 0, 7, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 8, 0, 0, 7, 7, 7, 0, 0, 7, 0, 0, 7, 7, 0, 0, 0, 8, 0, 0], [0, 1, 1, 1, 0, 8, 8, 0, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 7, 0, 0, 8, 8, 0, 0], [0, 0, 1, 0, 0, 0, 0, 8, 0, 7, 7, 7, 0, 0, 7, 0, 0, 0, 7, 7, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "d94c3b52"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 0, 0, 0],\n [0, 0, 0, 3, 3, 4, 4, 0, 0, 0],\n [0, 0, 0, 8, 8, 6, 6, 0, 0, 0],\n [0, 0, 0, 8, 8, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 9, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 9, 9, 9, 0, 0, 0],\n [0, 8, 8, 8, 9, 9, 9, 0, 0, 0],\n [0, 8, 8, 8, 9, 9, 9, 0, 0, 0],\n [0, 7, 7, 7, 6, 6, 6, 0, 0, 0],\n [0, 7, 7, 7, 6, 6, 6, 0, 0, 0],\n [0, 7, 7, 7, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 9, 0, 0, 7, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 2, 0, 0, 6, 0],\n [7, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 3, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 7, 7, 8, 8, 8, 0, 0, 0, 0, 9, 7, 0, 0], [0, 7, 7, 7, 8, 8, 8, 0, 0, 0, 0, 2, 6, 0, 0], [0, 7, 7, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e9ac8c9e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0],\n [1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1]\n ],\n \"output\": [\n [1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 8, 8, 1, 1, 8, 1, 1, 0],\n [1, 1, 8, 8, 1, 1, 8, 1, 1, 8, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],\n [1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],\n [1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1],\n [1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1],\n [0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1]\n ],\n \"output\": [\n [1, 1, 1, 8, 8, 1, 1, 1, 8, 1, 0, 1, 1],\n [1, 1, 0, 1, 1, 1, 1, 1, 8, 8, 1, 0, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],\n [1, 1, 8, 8, 1, 0, 1, 1, 0, 1, 1, 1, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 0, 1, 8, 1, 1, 1],\n [1, 8, 8, 1, 1, 1, 0, 1, 1, 8, 8, 1, 1],\n [1, 8, 8, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],\n [1, 8, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1],\n [8, 8, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n [8, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 8, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 8, 8, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1],\n [0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1],\n [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 0, 1],\n [1, 1, 1, 0, 1, 1, 1, 1, 8, 1, 1, 1, 0],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1],\n [8, 1, 8, 8, 8, 1, 1, 1, 0, 1, 1, 0, 1],\n [1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8],\n [1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 0, 1, 8],\n [0, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 8, 1],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 8, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1],\n [0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0],\n [0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0],\n [1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0],\n [1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0],\n [1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1],\n [0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],\n [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0],\n [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 8, 8, 1], [0, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 8, 8], [1, 1, 8, 8, 8, 1, 1, 0, 1, 1, 0, 1, 8], [0, 1, 1, 8, 1, 1, 1, 1, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1], [1, 8, 1, 0, 1, 1, 8, 1, 1, 1, 0, 1, 8], [1, 8, 8, 1, 0, 1, 8, 1, 1, 1, 1, 1, 8], [1, 8, 1, 0, 1, 1, 1, 1, 8, 8, 8, 1, 8], [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 8, 1, 8], [1, 0, 1, 8, 8, 1, 1, 1, 1, 8, 8, 1, 1], [0, 1, 1, 8, 1, 1, 1, 1, 0, 1, 8, 1, 1], [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 8, 1, 0], [1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]], "task_id": "e0fb7511"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0],\n [5, 5, 5],\n [0, 5, 0]\n ],\n \"output\": [\n [0, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 0, 0],\n [1, 2, 1, 2, 1, 2],\n [2, 1, 2, 1, 2, 1],\n [0, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0],\n [0, 5, 0],\n [0, 0, 5]\n ],\n \"output\": [\n [1, 2, 0, 0, 0, 0],\n [2, 1, 0, 0, 0, 0],\n [0, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 0, 0],\n [0, 0, 0, 0, 1, 2],\n [0, 0, 0, 0, 2, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0],\n [0, 5, 5],\n [5, 5, 0]\n ],\n \"output\": [\n [0, 0, 1, 2, 0, 0],\n [0, 0, 2, 1, 0, 0],\n [0, 0, 1, 2, 1, 2],\n [0, 0, 2, 1, 2, 1],\n [1, 2, 1, 2, 0, 0],\n [2, 1, 2, 1, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 5, 0],\n [5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 0, 0], [0, 0, 2, 1, 0, 0], [1, 2, 1, 2, 1, 2], [2, 1, 2, 1, 2, 1]], "task_id": "2072aba6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 1, 0, 0],\n [0, 0, 1, 3, 2, 2, 3, 1, 0, 0],\n [0, 0, 1, 3, 2, 2, 3, 1, 0, 0],\n [0, 0, 1, 3, 3, 3, 3, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 6, 6, 6, 6, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 6, 4, 4, 4, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 6, 4, 4, 4, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 6, 4, 4, 4, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 6, 6, 6, 6, 6, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 6, 6, 6, 6, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 4, 4, 4, 4, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 4, 2, 2, 4, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 4, 2, 2, 4, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 4, 4, 4, 4, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 6, 6, 6, 6, 6, 6, 8, 1, 0],\n [0, 0, 0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 1, 0],\n [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 9, 9, 9, 9, 9, 9, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 8, 8, 8, 8, 8, 8, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 8, 7, 7, 7, 7, 8, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 8, 7, 7, 7, 7, 8, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 8, 7, 7, 7, 7, 8, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 8, 8, 8, 8, 8, 8, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 9, 9, 9, 9, 9, 9, 9, 9, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 0], [0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "99306f82"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [7, 0, 0, 0, 0, 6, 0, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 7, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [6, 0, 0, 5, 5, 5, 0, 6, 0, 9],\n [0, 0, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 6, 0],\n [9, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 9, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 9, 0, 0],\n [4, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 2],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 9, 9, 9, 0, 0, 0, 0],\n [0, 0, 0, 9, 9, 9, 0, 0, 0, 0],\n [0, 0, 0, 9, 9, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 4, 0, 0, 0, 3, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 5, 0, 0, 8, 0, 0],\n [0, 0, 4, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 4],\n [0, 8, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 0, 8, 0, 0, 3, 0, 0, 4, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 0, 0, 0, 0, 4, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 6],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 0, 0, 2, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 0, 0, 0, 5, 5, 0, 0, 0, 3],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 0, 0, 0],\n [0, 2, 0, 0, 5, 5, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0],\n [3, 0, 7, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 7, 0, 2, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "6df30ad6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 5, 0],\n [0, 0, 5, 0, 0, 5, 0, 5, 0],\n [0, 0, 5, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2],\n [2, 0, 2],\n [2, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 0, 5, 0],\n [0, 0, 5, 0, 0, 5, 0, 5, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3],\n [3, 0, 3],\n [3, 3, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 0, 5, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 0],\n [0, 0, 5, 0, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 1],\n [0, 1, 1],\n [1, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 0, 5, 0],\n [0, 0, 5, 0, 0, 5, 5, 5, 0],\n [0, 0, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 1],\n [1, 1, 1],\n [1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 0],\n [0, 0, 5, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 0],\n [0, 2, 2],\n [0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 0],\n [0, 0, 5, 5, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 0],\n [0, 2, 2],\n [2, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 5, 0, 0, 5, 5, 5, 0],\n [0, 5, 5, 0, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 0], [3, 3, 3], [3, 0, 3]], "task_id": "ed74f2f2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 3, 0, 8, 0, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 4],\n [0, 8, 4, 0, 0, 0, 0, 0, 0, 8, 0, 9, 0],\n [7, 8, 0, 0, 6, 0, 0, 7, 5, 8, 4, 8, 3],\n [2, 8, 4, 2, 0, 0, 0, 8, 0, 8, 0, 0, 0],\n [0, 8, 9, 1, 9, 6, 0, 0, 0, 8, 0, 0, 7],\n [1, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 4, 0, 4, 8, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 7, 8, 0, 6, 0, 0, 0, 3],\n [5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 4, 0, 0, 6, 0, 7, 7, 7, 7, 7],\n [0, 0, 2, 2, 4, 0, 0, 0, 7, 0, 0, 0, 7],\n [8, 8, 0, 3, 0, 0, 0, 1, 7, 0, 8, 4, 7],\n [7, 0, 0, 0, 0, 5, 0, 5, 7, 0, 6, 8, 7],\n [0, 0, 7, 0, 0, 6, 0, 0, 7, 7, 7, 7, 7],\n [3, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 2],\n [0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 9, 1],\n [0, 0, 0, 8, 0, 8, 6, 0, 0, 0, 0, 0, 1]\n ],\n \"output\": [\n [4, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 0, 7, 5],\n [4, 2, 0, 0, 0, 8, 0],\n [9, 1, 9, 6, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 4, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 9, 0, 0, 0, 9, 5, 0, 4, 0, 0, 0, 0, 5, 0, 0, 0, 4, 0, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 9, 0, 0, 9, 0, 3, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2],\n [0, 0, 8, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 8, 0, 0, 7, 0, 0, 0, 2, 0, 0, 1, 0, 0, 4, 0, 7, 9, 8],\n [0, 0, 7, 0, 2, 0, 1, 5, 3, 0, 6, 5, 2, 5, 0, 0, 1, 1, 5, 0, 0, 0, 0],\n [9, 0, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 7],\n [8, 0, 4, 0, 2, 0, 9, 5, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9],\n [1, 0, 0, 0, 2, 3, 0, 0, 0, 2, 0, 0, 2, 0, 0, 6, 4, 4, 8, 0, 0, 0, 0],\n [8, 0, 0, 0, 2, 0, 0, 1, 4, 0, 0, 8, 2, 0, 0, 0, 4, 2, 7, 0, 9, 1, 6],\n [0, 4, 4, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 8, 0, 3, 0, 6, 0],\n [0, 7, 0, 0, 0, 8, 0, 3, 7, 0, 9, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 2, 5, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 4, 0, 4, 6, 0, 0, 0, 1, 1, 0, 0, 6, 0, 4],\n [6, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 4, 0, 0, 6, 0, 6, 0, 4, 0, 0, 0, 4],\n [0, 0, 3, 7, 9, 0, 3, 0, 0, 0, 6, 4, 0, 0, 3, 0, 0, 8, 0, 7, 0, 5, 4],\n [0, 7, 3, 0, 0, 0, 3, 0, 2, 8, 0, 4, 4, 0, 3, 4, 0, 3, 0, 0, 8, 0, 4],\n [0, 8, 3, 0, 9, 0, 3, 0, 3, 3, 0, 4, 0, 0, 3, 7, 7, 5, 0, 0, 1, 0, 4],\n [0, 0, 3, 3, 3, 3, 3, 6, 5, 0, 0, 4, 3, 0, 0, 0, 0, 9, 0, 0, 0, 0, 4],\n [0, 9, 2, 0, 0, 2, 3, 0, 0, 0, 9, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 4],\n [0, 5, 8, 0, 0, 0, 1, 0, 6, 0, 9, 4, 3, 0, 0, 0, 0, 0, 0, 9, 0, 0, 4],\n [9, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ],\n \"output\": [\n [6, 0, 0, 0, 1, 1, 0, 0, 6, 0],\n [0, 0, 6, 0, 6, 0, 4, 0, 0, 0],\n [0, 0, 3, 0, 0, 8, 0, 7, 0, 5],\n [4, 0, 3, 4, 0, 3, 0, 0, 8, 0],\n [0, 0, 3, 7, 7, 5, 0, 0, 1, 0],\n [3, 0, 0, 0, 0, 9, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0, 0, 9, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 6, 0, 0, 2, 0, 1, 6, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 9, 0, 0, 3, 0, 0, 3, 0, 2, 0, 0, 2],\n [0, 3, 0, 0, 8, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 7],\n [5, 0, 7, 0, 0, 6, 1, 1, 0, 0, 0, 0, 0, 1, 0, 6, 0, 0, 0, 2, 0],\n [0, 0, 4, 0, 5, 0, 0, 1, 0, 6, 0, 4, 0, 1, 0, 3, 0, 0, 0, 5, 0],\n [0, 9, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 6, 1, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 5, 0, 6, 7, 0, 1, 0, 9, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 0, 5, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 1, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0],\n [0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 3, 0, 0],\n [0, 0, 7, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 9, 8, 3, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 9, 1, 0, 0, 0, 7, 7, 0, 0, 7, 0, 7, 0, 0, 0],\n [0, 0, 6, 0, 7, 0, 7, 7, 7, 7, 7, 7, 9, 3, 6, 2, 0, 0, 5, 0, 3],\n [0, 5, 0, 0, 0, 2, 7, 0, 2, 8, 0, 7, 0, 5, 8, 0, 0, 0, 3, 0, 6],\n [0, 0, 0, 6, 0, 6, 7, 4, 0, 0, 8, 7, 5, 5, 0, 6, 0, 0, 7, 0, 0],\n [0, 8, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 3, 2, 0, 5, 0],\n [0, 9, 0, 0, 0, 2, 0, 2, 0, 5, 6, 4, 0, 0, 0, 0, 0, 4, 7, 0, 0],\n [2, 0, 0, 0, 0, 0, 9, 0, 0, 5, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 7],\n [0, 6, 3, 0, 0, 0, 0, 9, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 0, 9, 7, 0, 0, 0, 2, 3, 8, 5, 0, 0, 0, 0],\n [9, 6, 0, 2, 0, 4, 0, 0, 0, 7, 0, 0, 0, 6, 0, 0, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0],\n [0, 6, 0, 4, 0],\n [0, 0, 0, 5, 6],\n [0, 9, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 2, 4, 0, 0, 0, 9, 0],\n [0, 0, 3, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 5, 0, 0, 4, 0, 4],\n [0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 6, 0],\n [0, 0, 0, 6, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 5, 4, 0, 0, 3, 0, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 8, 0, 0, 3, 0, 9, 0, 0, 0, 0, 0, 0, 6, 7, 3, 0, 3, 0, 0, 9, 0],\n [6, 3, 0, 0, 0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 2, 7, 0],\n [7, 0, 0, 8, 3, 0, 0, 3, 0, 3, 9, 0, 0, 0, 6, 7, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [5, 7, 0, 0, 8, 8, 7, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 3, 4, 0, 0, 2, 0, 1],\n [7, 0, 0, 0, 9, 0, 0, 3, 0, 1, 0, 0, 0, 8, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 3, 0, 2, 3, 0, 3, 3, 0, 0, 2],\n [0, 0, 7, 0, 0, 0, 7, 3, 0, 0, 0, 0, 0, 5, 0, 0, 1, 0, 3, 0, 0, 7, 0, 7, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 7, 0, 0, 0, 0, 0, 0, 0, 8, 1, 3, 4, 0, 0, 6, 0, 8],\n [0, 0, 0, 3, 0, 4, 2, 3, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 3, 0, 9, 9, 0, 0, 9],\n [5, 0, 1, 0, 0, 0, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 0, 0, 7, 0, 5, 0, 3, 0, 0, 0, 0],\n [0, 5, 4, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 6],\n [4, 0, 0, 0, 7, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 5, 0, 0, 7, 7, 7, 7, 7, 7],\n [9, 0, 1, 0, 0, 0, 8, 0, 0, 1, 0, 0, 5, 8, 8, 0, 0, 0, 0, 7, 0, 0, 8, 0, 7],\n [2, 0, 0, 0, 6, 2, 8, 0, 0, 9, 0, 0, 0, 8, 5, 0, 0, 0, 0, 7, 0, 0, 0, 0, 7],\n [8, 0, 1, 0, 8, 0, 8, 0, 7, 0, 0, 6, 0, 8, 0, 0, 0, 0, 7, 7, 0, 0, 0, 2, 7],\n [5, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 5, 6, 8, 4, 0, 8, 0, 5, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 4, 0, 8, 0, 9, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 2, 8, 0, 0, 2],\n [8, 0, 0, 0, 0, 0, 8, 0, 0, 2, 0, 2, 0, 8, 0, 6, 0, 0, 0, 3, 0, 3, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 3, 0, 0, 5, 1, 0, 0, 0, 0],\n [0, 0, 6, 0, 9, 6, 0, 5, 9, 0, 0, 0, 0, 0, 0, 1, 0, 7, 0, 1, 5, 3, 0, 0, 6],\n [5, 0, 0, 0, 8, 8, 0, 9, 8, 0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 0, 0, 2, 0],\n [8, 0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 4, 7, 0, 2, 5, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 3, 5, 4, 0, 0], [0, 9, 0, 0, 0, 0, 0, 0, 6, 7], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 9, 0, 0, 0, 6, 7, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 8, 0, 0, 4, 0], [0, 0, 0, 6, 0, 0, 0, 3, 0, 2], [0, 0, 0, 0, 0, 5, 0, 0, 1, 0], [7, 0, 0, 0, 0, 0, 0, 0, 8, 1], [0, 0, 0, 0, 0, 4, 0, 2, 0, 0]], "task_id": "1a6449f1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0],\n [5, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0],\n [0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0],\n [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0],\n [0, 5, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0],\n [0],\n [0],\n [0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0],\n [0, 5, 5],\n [0, 0, 5]\n ],\n \"output\": [\n [0],\n [0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 5],\n [0, 5, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 5, 5, 0],\n [0, 5, 0, 0, 5, 0, 0],\n [0, 5, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [0],\n [0],\n [0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 0, 5, 0, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 0, 5, 0, 5, 0, 5, 5, 0, 0, 0, 0],\n [5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 5, 5],\n [0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0],\n [0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [0],\n [0],\n [0],\n [0],\n [0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 5, 5, 0],\n [0, 5, 5, 5, 0, 0, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 5, 5, 5, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 0, 0, 0],\n [0, 0, 5, 0, 0, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0], [0], [0]], "task_id": "e872b94a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 8, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 8, 0, 0, 8, 0, 0, 4, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 6, 0, 6, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 0, 6, 0, 6, 6, 0, 6, 1, 0, 1, 1, 0, 1, 3, 0, 3, 3, 0, 3],\n [0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 0, 6, 6, 0, 0, 6, 6, 1, 1, 0, 0, 1, 1, 3, 3, 0, 0, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 4, 4, 0, 0, 0, 0, 8, 8, 0, 0, 2, 2, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 4, 4, 0, 0, 0, 0, 8, 8, 0, 0, 2, 2, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 0, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 0, 0, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 4, 4, 4, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 0, 0, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e41c6fd3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 0, 0, 5, 5],\n [5, 5, 0, 5, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 0, 5],\n [0, 5, 0, 5, 0, 5, 5, 0, 5, 0],\n [5, 0, 0, 0, 0, 5, 0, 0, 5, 5],\n [5, 5, 5, 0, 5, 0, 0, 0, 0, 5],\n [0, 5, 0, 0, 0, 0, 5, 5, 5, 0],\n [5, 0, 0, 0, 0, 5, 0, 0, 5, 5],\n [5, 0, 0, 0, 0, 0, 5, 5, 0, 0]\n ],\n \"output\": [\n [5, 5, 0, 0, 1, 1, 1, 5, 0, 0],\n [0, 0, 5, 5, 1, 1, 1, 0, 5, 5],\n [5, 5, 0, 5, 1, 1, 1, 0, 5, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 0, 5],\n [0, 5, 0, 5, 0, 5, 5, 0, 5, 0],\n [5, 0, 0, 0, 0, 5, 1, 1, 5, 5],\n [5, 5, 5, 0, 5, 0, 1, 1, 0, 5],\n [0, 5, 1, 1, 1, 0, 5, 5, 5, 0],\n [5, 0, 1, 1, 1, 5, 0, 0, 5, 5],\n [5, 0, 1, 1, 1, 0, 5, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 5, 0, 0, 5, 0, 0, 0, 0, 0],\n [5, 5, 0, 0, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 5, 5, 0, 0, 5, 5, 5],\n [0, 0, 5, 0, 5, 5, 0, 0, 5, 0],\n [0, 5, 0, 0, 0, 0, 0, 0, 5, 0],\n [5, 0, 5, 0, 0, 5, 5, 5, 0, 5],\n [0, 0, 0, 5, 0, 5, 5, 0, 5, 0],\n [0, 0, 5, 0, 5, 5, 5, 0, 0, 0],\n [5, 0, 5, 5, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0]\n ],\n \"output\": [\n [0, 5, 1, 1, 5, 0, 0, 0, 0, 0],\n [5, 5, 1, 1, 0, 5, 5, 0, 5, 0],\n [1, 1, 0, 5, 5, 0, 0, 5, 5, 5],\n [1, 1, 5, 0, 5, 5, 1, 1, 5, 0],\n [0, 5, 0, 1, 1, 0, 1, 1, 5, 0],\n [5, 0, 5, 1, 1, 5, 5, 5, 0, 5],\n [1, 1, 0, 5, 0, 5, 5, 0, 5, 0],\n [1, 1, 5, 0, 5, 5, 5, 0, 0, 0],\n [5, 0, 5, 5, 0, 5, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 5, 0, 0, 0, 5, 0, 0, 0, 5],\n [0, 0, 5, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 5, 5, 0, 5],\n [5, 0, 0, 5, 0, 5, 0, 0, 0, 0],\n [5, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 5, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 5, 0, 0, 5, 0]\n ],\n \"output\": [\n [0, 0, 5, 1, 1, 0, 0, 5, 0, 5],\n [0, 5, 0, 1, 1, 5, 1, 1, 1, 5],\n [1, 1, 5, 0, 5, 0, 1, 1, 1, 0],\n [1, 1, 0, 0, 5, 0, 1, 1, 1, 0],\n [0, 5, 5, 0, 0, 5, 5, 5, 0, 5],\n [5, 0, 0, 5, 0, 5, 0, 0, 1, 1],\n [5, 5, 5, 5, 0, 5, 5, 5, 1, 1],\n [1, 1, 1, 5, 0, 0, 0, 0, 5, 0],\n [1, 1, 1, 0, 5, 5, 5, 5, 5, 5],\n [1, 1, 1, 0, 0, 5, 0, 0, 5, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 0, 0, 5, 0, 5, 0, 5, 0],\n [5, 0, 0, 5, 0, 5, 5, 0, 0, 0],\n [5, 5, 0, 5, 5, 0, 0, 5, 5, 0],\n [5, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [5, 0, 0, 0, 5, 5, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 5, 5, 0, 0],\n [0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [5, 0, 5, 0, 5, 0, 5, 0, 0, 5],\n [0, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 1, 1, 0, 5, 0, 5, 0, 5, 0], [5, 1, 1, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 5, 5, 1, 1, 5, 5, 0], [5, 1, 1, 1, 0, 1, 1, 5, 0, 0], [5, 1, 1, 1, 5, 5, 0, 0, 0, 5], [0, 1, 1, 1, 1, 1, 5, 5, 1, 1], [0, 0, 5, 5, 1, 1, 5, 5, 1, 1], [5, 0, 5, 0, 5, 0, 5, 0, 0, 5], [0, 5, 5, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0]], "task_id": "31adaf00"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 1, 1],\n [0, 1, 1, 1],\n [1, 1, 1, 0],\n [0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0],\n [0, 2, 2, 2, 0],\n [2, 0, 2, 0, 2],\n [0, 2, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 0, 0, 0, 3],\n [0, 3, 0, 3, 0, 3, 0],\n [0, 0, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 0, 3, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 8, 0, 8], [8, 8, 8, 8, 8], [0, 0, 8, 0, 8], [0, 8, 8, 8, 0], [8, 0, 8, 0, 8], [8, 8, 8, 8, 8], [0, 8, 8, 8, 0]], "task_id": "73ccf9c2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 8, 8, 1, 6, 4, 6, 6, 4, 6, 1, 8, 8, 6, 6],\n [6, 8, 6, 6, 6, 4, 6, 1, 1, 6, 4, 6, 6, 6, 8, 6],\n [8, 6, 8, 8, 3, 3, 3, 6, 6, 6, 6, 4, 8, 8, 6, 8],\n [8, 6, 8, 9, 3, 3, 3, 6, 6, 6, 1, 6, 9, 8, 6, 8],\n [1, 6, 4, 6, 3, 3, 3, 7, 7, 2, 7, 2, 6, 4, 6, 1],\n [6, 4, 6, 1, 3, 3, 3, 7, 7, 7, 5, 7, 1, 6, 4, 6],\n [4, 6, 6, 6, 2, 7, 5, 7, 7, 5, 7, 2, 6, 6, 6, 4],\n [6, 1, 6, 6, 7, 7, 7, 5, 5, 7, 7, 7, 6, 6, 1, 6],\n [6, 1, 6, 6, 7, 7, 7, 5, 5, 7, 7, 7, 6, 6, 1, 6],\n [4, 6, 6, 6, 2, 7, 5, 7, 7, 5, 7, 2, 6, 6, 6, 4],\n [6, 4, 6, 1, 7, 5, 7, 7, 7, 7, 5, 7, 1, 6, 4, 6],\n [1, 6, 4, 6, 2, 7, 2, 7, 7, 2, 7, 2, 6, 4, 6, 1],\n [8, 6, 8, 9, 6, 1, 6, 6, 6, 6, 1, 6, 9, 8, 6, 8],\n [8, 6, 8, 8, 4, 6, 6, 6, 3, 3, 6, 4, 8, 8, 6, 8],\n [6, 8, 6, 6, 6, 4, 6, 1, 3, 3, 4, 6, 6, 6, 8, 6],\n [6, 6, 8, 8, 1, 6, 4, 6, 6, 4, 6, 1, 8, 8, 6, 6]\n ],\n \"output\": [\n [6, 6, 8, 8, 1, 6, 4, 6, 6, 4, 6, 1, 8, 8, 6, 6],\n [6, 8, 6, 6, 6, 4, 6, 1, 1, 6, 4, 6, 6, 6, 8, 6],\n [8, 6, 8, 8, 4, 6, 6, 6, 6, 6, 6, 4, 8, 8, 6, 8],\n [8, 6, 8, 9, 6, 1, 6, 6, 6, 6, 1, 6, 9, 8, 6, 8],\n [1, 6, 4, 6, 2, 7, 2, 7, 7, 2, 7, 2, 6, 4, 6, 1],\n [6, 4, 6, 1, 7, 5, 7, 7, 7, 7, 5, 7, 1, 6, 4, 6],\n [4, 6, 6, 6, 2, 7, 5, 7, 7, 5, 7, 2, 6, 6, 6, 4],\n [6, 1, 6, 6, 7, 7, 7, 5, 5, 7, 7, 7, 6, 6, 1, 6],\n [6, 1, 6, 6, 7, 7, 7, 5, 5, 7, 7, 7, 6, 6, 1, 6],\n [4, 6, 6, 6, 2, 7, 5, 7, 7, 5, 7, 2, 6, 6, 6, 4],\n [6, 4, 6, 1, 7, 5, 7, 7, 7, 7, 5, 7, 1, 6, 4, 6],\n [1, 6, 4, 6, 2, 7, 2, 7, 7, 2, 7, 2, 6, 4, 6, 1],\n [8, 6, 8, 9, 6, 1, 6, 6, 6, 6, 1, 6, 9, 8, 6, 8],\n [8, 6, 8, 8, 4, 6, 6, 6, 6, 6, 6, 4, 8, 8, 6, 8],\n [6, 8, 6, 6, 6, 4, 6, 1, 1, 6, 4, 6, 6, 6, 8, 6],\n [6, 6, 8, 8, 1, 6, 4, 6, 6, 4, 6, 1, 8, 8, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [4, 9, 2, 2, 9, 7, 9, 6, 6, 9, 7, 9, 2, 2, 9, 4],\n [9, 4, 2, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 2, 4, 9],\n [2, 2, 2, 4, 9, 9, 7, 9, 9, 7, 9, 9, 4, 2, 2, 2],\n [2, 9, 4, 9, 6, 6, 9, 7, 7, 9, 6, 6, 9, 4, 9, 2],\n [9, 7, 9, 6, 1, 7, 2, 1, 1, 2, 7, 1, 6, 9, 7, 9],\n [7, 6, 9, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 9, 6, 7],\n [9, 9, 7, 9, 2, 7, 1, 1, 1, 1, 7, 2, 9, 7, 9, 9],\n [6, 6, 9, 7, 1, 7, 1, 1, 1, 1, 7, 3, 3, 9, 6, 6],\n [6, 6, 9, 7, 1, 7, 1, 1, 1, 1, 7, 3, 3, 9, 6, 6],\n [9, 9, 7, 9, 2, 7, 1, 1, 1, 1, 7, 3, 3, 7, 9, 9],\n [7, 6, 9, 6, 7, 7, 7, 7, 7, 7, 7, 3, 3, 9, 6, 7],\n [9, 7, 3, 3, 3, 7, 2, 1, 1, 2, 7, 1, 6, 9, 7, 9],\n [2, 9, 3, 3, 3, 6, 9, 7, 7, 9, 6, 6, 9, 4, 9, 2],\n [2, 2, 3, 3, 3, 9, 7, 9, 9, 7, 9, 9, 4, 2, 2, 2],\n [9, 4, 2, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 2, 4, 9],\n [4, 9, 2, 2, 9, 7, 9, 6, 6, 9, 7, 9, 2, 2, 9, 4]\n ],\n \"output\": [\n [4, 9, 2, 2, 9, 7, 9, 6, 6, 9, 7, 9, 2, 2, 9, 4],\n [9, 4, 2, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 2, 4, 9],\n [2, 2, 2, 4, 9, 9, 7, 9, 9, 7, 9, 9, 4, 2, 2, 2],\n [2, 9, 4, 9, 6, 6, 9, 7, 7, 9, 6, 6, 9, 4, 9, 2],\n [9, 7, 9, 6, 1, 7, 2, 1, 1, 2, 7, 1, 6, 9, 7, 9],\n [7, 6, 9, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 9, 6, 7],\n [9, 9, 7, 9, 2, 7, 1, 1, 1, 1, 7, 2, 9, 7, 9, 9],\n [6, 6, 9, 7, 1, 7, 1, 1, 1, 1, 7, 1, 7, 9, 6, 6],\n [6, 6, 9, 7, 1, 7, 1, 1, 1, 1, 7, 1, 7, 9, 6, 6],\n [9, 9, 7, 9, 2, 7, 1, 1, 1, 1, 7, 2, 9, 7, 9, 9],\n [7, 6, 9, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 9, 6, 7],\n [9, 7, 9, 6, 1, 7, 2, 1, 1, 2, 7, 1, 6, 9, 7, 9],\n [2, 9, 4, 9, 6, 6, 9, 7, 7, 9, 6, 6, 9, 4, 9, 2],\n [2, 2, 2, 4, 9, 9, 7, 9, 9, 7, 9, 9, 4, 2, 2, 2],\n [9, 4, 2, 9, 7, 6, 9, 6, 6, 9, 6, 7, 9, 2, 4, 9],\n [4, 9, 2, 2, 9, 7, 9, 6, 6, 9, 7, 9, 2, 2, 9, 4]\n ]\n}\n\n{\n \"input\": [\n [2, 7, 7, 7, 1, 1, 1, 1, 1, 3, 3, 1, 7, 7, 7, 2],\n [7, 7, 2, 2, 1, 6, 1, 1, 1, 3, 3, 1, 2, 2, 7, 7],\n [7, 2, 7, 7, 1, 1, 6, 2, 2, 6, 1, 1, 7, 7, 2, 7],\n [7, 2, 7, 7, 1, 1, 2, 6, 6, 2, 1, 1, 7, 7, 2, 7],\n [1, 1, 1, 1, 2, 9, 2, 9, 9, 2, 9, 2, 1, 1, 1, 1],\n [1, 6, 1, 1, 9, 9, 2, 6, 6, 2, 9, 9, 1, 1, 6, 1],\n [1, 1, 6, 2, 2, 2, 9, 9, 9, 9, 2, 2, 2, 6, 1, 1],\n [1, 1, 2, 6, 9, 6, 9, 2, 2, 9, 6, 9, 6, 2, 1, 1],\n [1, 1, 2, 6, 9, 6, 9, 2, 2, 9, 6, 9, 6, 2, 1, 1],\n [1, 1, 6, 2, 2, 2, 9, 9, 9, 9, 2, 2, 2, 6, 1, 1],\n [1, 6, 1, 1, 9, 9, 2, 6, 6, 2, 9, 9, 1, 1, 6, 1],\n [1, 1, 1, 1, 2, 9, 2, 3, 3, 2, 9, 2, 1, 1, 1, 1],\n [7, 2, 7, 7, 1, 1, 2, 3, 3, 2, 1, 1, 7, 7, 2, 7],\n [7, 2, 7, 7, 1, 1, 6, 3, 3, 6, 1, 1, 7, 7, 2, 7],\n [7, 7, 2, 2, 1, 6, 1, 1, 1, 1, 6, 1, 2, 2, 7, 7],\n [2, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7, 2]\n ],\n \"output\": [\n [2, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7, 2],\n [7, 7, 2, 2, 1, 6, 1, 1, 1, 1, 6, 1, 2, 2, 7, 7],\n [7, 2, 7, 7, 1, 1, 6, 2, 2, 6, 1, 1, 7, 7, 2, 7],\n [7, 2, 7, 7, 1, 1, 2, 6, 6, 2, 1, 1, 7, 7, 2, 7],\n [1, 1, 1, 1, 2, 9, 2, 9, 9, 2, 9, 2, 1, 1, 1, 1],\n [1, 6, 1, 1, 9, 9, 2, 6, 6, 2, 9, 9, 1, 1, 6, 1],\n [1, 1, 6, 2, 2, 2, 9, 9, 9, 9, 2, 2, 2, 6, 1, 1],\n [1, 1, 2, 6, 9, 6, 9, 2, 2, 9, 6, 9, 6, 2, 1, 1],\n [1, 1, 2, 6, 9, 6, 9, 2, 2, 9, 6, 9, 6, 2, 1, 1],\n [1, 1, 6, 2, 2, 2, 9, 9, 9, 9, 2, 2, 2, 6, 1, 1],\n [1, 6, 1, 1, 9, 9, 2, 6, 6, 2, 9, 9, 1, 1, 6, 1],\n [1, 1, 1, 1, 2, 9, 2, 9, 9, 2, 9, 2, 1, 1, 1, 1],\n [7, 2, 7, 7, 1, 1, 2, 6, 6, 2, 1, 1, 7, 7, 2, 7],\n [7, 2, 7, 7, 1, 1, 6, 2, 2, 6, 1, 1, 7, 7, 2, 7],\n [7, 7, 2, 2, 1, 6, 1, 1, 1, 1, 6, 1, 2, 2, 7, 7],\n [2, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 6, 6, 4, 6, 7, 1, 6, 6, 1, 7, 6, 4, 6, 6, 1],\n [6, 1, 4, 1, 7, 6, 1, 1, 1, 1, 6, 7, 1, 4, 1, 6],\n [3, 3, 3, 6, 1, 1, 6, 7, 7, 6, 1, 1, 6, 4, 4, 6],\n [3, 3, 3, 1, 6, 1, 7, 1, 1, 7, 1, 6, 1, 6, 1, 4],\n [3, 3, 3, 6, 4, 4, 4, 8, 8, 4, 4, 4, 6, 1, 7, 6],\n [3, 3, 3, 1, 4, 4, 5, 8, 8, 5, 4, 4, 1, 1, 6, 7],\n [1, 1, 6, 7, 4, 5, 8, 5, 5, 8, 5, 4, 7, 6, 1, 1],\n [6, 1, 7, 1, 8, 8, 3, 3, 5, 5, 8, 8, 1, 7, 1, 6],\n [6, 1, 7, 1, 8, 8, 3, 3, 5, 5, 8, 8, 1, 7, 1, 6],\n [1, 1, 6, 7, 4, 5, 3, 3, 5, 8, 5, 4, 7, 6, 1, 1],\n [7, 6, 1, 1, 4, 4, 3, 3, 8, 5, 4, 4, 1, 1, 6, 7],\n [6, 7, 1, 6, 4, 4, 4, 8, 8, 4, 4, 4, 6, 1, 7, 6],\n [4, 1, 6, 1, 6, 1, 7, 1, 1, 7, 1, 6, 1, 6, 1, 4],\n [6, 4, 4, 6, 1, 1, 6, 7, 7, 6, 1, 1, 6, 4, 4, 6],\n [6, 1, 4, 1, 7, 6, 1, 1, 1, 1, 6, 7, 1, 4, 1, 6],\n [1, 6, 6, 4, 6, 7, 1, 6, 6, 1, 7, 6, 4, 6, 6, 1]\n ],\n \"output\": [\n [1, 6, 6, 4, 6, 7, 1, 6, 6, 1, 7, 6, 4, 6, 6, 1],\n [6, 1, 4, 1, 7, 6, 1, 1, 1, 1, 6, 7, 1, 4, 1, 6],\n [6, 4, 4, 6, 1, 1, 6, 7, 7, 6, 1, 1, 6, 4, 4, 6],\n [4, 1, 6, 1, 6, 1, 7, 1, 1, 7, 1, 6, 1, 6, 1, 4],\n [6, 7, 1, 6, 4, 4, 4, 8, 8, 4, 4, 4, 6, 1, 7, 6],\n [7, 6, 1, 1, 4, 4, 5, 8, 8, 5, 4, 4, 1, 1, 6, 7],\n [1, 1, 6, 7, 4, 5, 8, 5, 5, 8, 5, 4, 7, 6, 1, 1],\n [6, 1, 7, 1, 8, 8, 5, 5, 5, 5, 8, 8, 1, 7, 1, 6],\n [6, 1, 7, 1, 8, 8, 5, 5, 5, 5, 8, 8, 1, 7, 1, 6],\n [1, 1, 6, 7, 4, 5, 8, 5, 5, 8, 5, 4, 7, 6, 1, 1],\n [7, 6, 1, 1, 4, 4, 5, 8, 8, 5, 4, 4, 1, 1, 6, 7],\n [6, 7, 1, 6, 4, 4, 4, 8, 8, 4, 4, 4, 6, 1, 7, 6],\n [4, 1, 6, 1, 6, 1, 7, 1, 1, 7, 1, 6, 1, 6, 1, 4],\n [6, 4, 4, 6, 1, 1, 6, 7, 7, 6, 1, 1, 6, 4, 4, 6],\n [6, 1, 4, 1, 7, 6, 1, 1, 1, 1, 6, 7, 1, 4, 1, 6],\n [1, 6, 6, 4, 6, 7, 1, 6, 6, 1, 7, 6, 4, 6, 6, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 8, 2, 8, 9, 7, 9, 7, 7, 9, 7, 9, 8, 2, 8, 1],\n [8, 8, 1, 1, 7, 9, 1, 1, 1, 1, 9, 7, 1, 1, 8, 8],\n [2, 1, 1, 2, 9, 1, 9, 9, 9, 9, 1, 9, 2, 1, 1, 2],\n [8, 1, 2, 8, 7, 1, 9, 9, 9, 9, 1, 7, 8, 2, 1, 8],\n [9, 7, 9, 7, 8, 6, 8, 6, 6, 8, 6, 8, 7, 9, 7, 9],\n [7, 3, 3, 3, 6, 4, 6, 8, 8, 6, 4, 6, 1, 1, 9, 7],\n [9, 3, 3, 3, 8, 6, 6, 6, 6, 6, 6, 8, 9, 9, 1, 9],\n [7, 3, 3, 3, 6, 8, 6, 8, 8, 6, 8, 6, 9, 9, 1, 7],\n [7, 3, 3, 3, 6, 8, 6, 8, 8, 6, 8, 6, 9, 9, 1, 7],\n [9, 1, 9, 9, 8, 6, 6, 6, 6, 6, 6, 8, 9, 9, 1, 9],\n [7, 3, 3, 3, 3, 4, 6, 8, 8, 6, 4, 6, 1, 1, 9, 7],\n [9, 3, 3, 3, 3, 6, 8, 6, 6, 8, 6, 8, 7, 9, 7, 9],\n [8, 1, 2, 8, 7, 1, 9, 9, 9, 9, 1, 7, 8, 2, 1, 8],\n [2, 1, 1, 2, 9, 1, 9, 9, 9, 9, 1, 9, 2, 1, 1, 2],\n [8, 8, 1, 1, 7, 9, 1, 1, 1, 1, 9, 7, 1, 1, 8, 8],\n [1, 8, 2, 8, 9, 7, 9, 7, 7, 9, 7, 9, 8, 2, 8, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 8, 2, 8, 9, 7, 9, 7, 7, 9, 7, 9, 8, 2, 8, 1], [8, 8, 1, 1, 7, 9, 1, 1, 1, 1, 9, 7, 1, 1, 8, 8], [2, 1, 1, 2, 9, 1, 9, 9, 9, 9, 1, 9, 2, 1, 1, 2], [8, 1, 2, 8, 7, 1, 9, 9, 9, 9, 1, 7, 8, 2, 1, 8], [9, 7, 9, 7, 8, 6, 8, 6, 6, 8, 6, 8, 7, 9, 7, 9], [7, 9, 1, 1, 6, 4, 6, 8, 8, 6, 4, 6, 1, 1, 9, 7], [9, 1, 9, 9, 8, 6, 6, 6, 6, 6, 6, 8, 9, 9, 1, 9], [7, 1, 9, 9, 6, 8, 6, 8, 8, 6, 8, 6, 9, 9, 1, 7], [7, 1, 9, 9, 6, 8, 6, 8, 8, 6, 8, 6, 9, 9, 1, 7], [9, 1, 9, 9, 8, 6, 6, 6, 6, 6, 6, 8, 9, 9, 1, 9], [7, 9, 1, 1, 6, 4, 6, 8, 8, 6, 4, 6, 1, 1, 9, 7], [9, 7, 9, 7, 8, 6, 8, 6, 6, 8, 6, 8, 7, 9, 7, 9], [8, 1, 2, 8, 7, 1, 9, 9, 9, 9, 1, 7, 8, 2, 1, 8], [2, 1, 1, 2, 9, 1, 9, 9, 9, 9, 1, 9, 2, 1, 1, 2], [8, 8, 1, 1, 7, 9, 1, 1, 1, 1, 9, 7, 1, 1, 8, 8], [1, 8, 2, 8, 9, 7, 9, 7, 7, 9, 7, 9, 8, 2, 8, 1]], "task_id": "903d1b4a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 0, 2, 2, 2],\n [2, 0, 2, 0, 2, 2, 2],\n [2, 2, 2, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 2, 0, 0],\n [0, 2, 0, 0, 0, 2, 2],\n [2, 2, 2, 0, 2, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 0, 2, 2, 0],\n [0, 2, 2, 0, 2, 0, 2],\n [0, 0, 2, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 0],\n [2, 0, 2, 0, 0, 2, 0],\n [2, 2, 2, 0, 2, 0, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 0, 2, 0, 2],\n [2, 0, 2, 0, 0, 2, 0],\n [0, 2, 0, 0, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 0, 2, 0],\n [2, 2, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 2, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 0, 2, 0, 0, 0, 2], [0, 2, 0, 0, 0, 2, 2], [2, 2, 2, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 2, 2, 2], [2, 2, 2, 0, 2, 0, 2], [2, 0, 0, 0, 2, 0, 2]], "task_id": "1990f7a8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n [4, 4],\n [4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n [2, 2],\n [2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0]\n ],\n \"output\": [\n [2, 2],\n [2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2],\n [2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 2, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4], [4, 4]], "task_id": "8597cfd7"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 2, 2],\n [2, 3, 3, 3, 2],\n [2, 3, 8, 3, 2],\n [2, 3, 3, 3, 2],\n [2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 3, 1],\n [1, 3, 6, 6, 3, 1],\n [1, 3, 6, 6, 3, 1],\n [1, 3, 3, 3, 3, 1],\n [1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8],\n [8, 3, 3, 3, 3, 3, 3, 8],\n [8, 3, 4, 4, 4, 4, 3, 8],\n [8, 3, 4, 7, 7, 4, 3, 8],\n [8, 3, 4, 7, 7, 4, 3, 8],\n [8, 3, 4, 4, 4, 4, 3, 8],\n [8, 3, 3, 3, 3, 3, 3, 8],\n [8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8], [8, 6, 6, 6, 6, 6, 8], [8, 6, 7, 7, 7, 6, 8], [8, 6, 7, 3, 7, 6, 8], [8, 6, 7, 7, 7, 6, 8], [8, 6, 6, 6, 6, 6, 8], [8, 8, 8, 8, 8, 8, 8]], "task_id": "3ee1011a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0],\n [3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3],\n [0, 0, 2, 0, 3, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [3, 3, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0],\n [8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8],\n [0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 8, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [8, 8, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 4, 0, 3, 0, 0, 0],\n [4, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4],\n [0, 0, 0, 0, 3, 0, 4, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [4, 4, 4, 4, 3, 3, 3, 3, 3, 4, 4, 4],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [6, 6, 7, 6, 6, 6, 6, 6, 6, 7, 6, 6],\n [0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 7, 0, 0, 0, 6, 0, 0, 7, 0, 0],\n [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0]], "task_id": "917bccba"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2],\n [2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 1, 3, 3, 1, 2, 2, 2, 2],\n [2, 2, 2, 2, 1, 3, 1, 1, 2, 2, 2, 2],\n [2, 2, 2, 2, 3, 3, 3, 1, 2, 2, 2, 2],\n [2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 4, 1, 4, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 1, 4, 4, 2, 2, 2, 2, 2, 2],\n [2, 4, 1, 4, 1, 1, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 1, 4, 1, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 4, 1, 4, 4, 4, 2, 2],\n [2, 2, 2, 2, 2, 4, 4, 1, 4, 4, 2, 2],\n [2, 2, 2, 2, 2, 1, 1, 4, 1, 4, 2, 2],\n [2, 2, 2, 2, 2, 1, 4, 1, 4, 4, 2, 2],\n [2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 4, 1, 4, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 1, 4, 4, 2, 2, 2, 2, 2, 2],\n [2, 4, 1, 4, 1, 1, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 1, 4, 1, 2, 2, 2, 2, 2, 2],\n [2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2],\n [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 2, 3, 3, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 2, 3, 3, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 2, 3, 3, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1],\n [1, 3, 3, 3, 2, 3, 3, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1],\n [1, 1, 1, 3, 3, 2, 3, 3, 3, 1, 1, 1],\n [1, 1, 1, 3, 2, 3, 2, 2, 2, 1, 1, 1],\n [1, 1, 1, 3, 3, 2, 3, 3, 3, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 1, 1, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 1, 2, 1, 8, 8, 8, 8, 8, 8, 8],\n [8, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 1, 1, 1, 2, 8, 8, 8, 8, 8, 8, 8], [8, 1, 1, 2, 1, 8, 8, 8, 8, 8, 8, 8], [8, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 2, 1, 1, 1, 8, 8, 8, 8, 8, 8], [8, 8, 1, 2, 1, 1, 8, 8, 8, 8, 8, 8], [8, 8, 2, 2, 2, 1, 8, 8, 8, 8, 8, 8]], "task_id": "9f27f097"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "task_id": "8a371977"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [4, 4, 5],\n [5, 5, 5],\n [5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 5, 5, 5],\n [5, 3, 3, 3, 3, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5],\n [3, 3, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 7, 7, 7, 7, 7, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [7, 7, 7, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5],\n [5, 7, 7, 7, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5],\n [7, 7, 7, 7, 7, 5, 5],\n [5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 6, 6, 6, 6, 6, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 6, 6, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 6, 6, 6, 6, 6, 6, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]], "task_id": "32e9702f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0],\n [0, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5],\n [0, 5, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 2, 5, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 0, 5],\n [0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 5, 0, 5, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 2, 5, 5, 0, 5, 5, 5, 5, 0],\n [5, 5, 2, 2, 2, 5, 0, 0, 0, 0, 5, 2, 2, 5, 5, 5, 0, 0, 5],\n [5, 0, 5, 5, 2, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 5],\n [0, 0, 2, 5, 2, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5],\n [0, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0],\n [5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 0, 5, 5],\n [5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5],\n [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0]\n ],\n \"output\": [\n [0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0],\n [0, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5],\n [5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 5, 7, 7, 7, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5],\n [0, 5, 0, 5, 2, 4, 7, 5, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 7, 2, 7, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 0, 5],\n [0, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 5, 0, 5, 0, 2, 7, 7, 0, 0, 5, 0, 5, 5],\n [0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 2, 4, 7, 0, 5, 5, 5, 5, 0],\n [5, 5, 2, 2, 2, 5, 0, 0, 0, 0, 7, 2, 2, 5, 5, 5, 0, 0, 5],\n [5, 0, 7, 4, 2, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 5],\n [0, 0, 2, 7, 2, 5, 0, 5, 0, 0, 0, 0, 5, 7, 7, 7, 0, 0, 5],\n [0, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 7, 4, 7, 0, 5, 0],\n [5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 7, 2, 7, 0, 5, 5],\n [5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5],\n [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 2, 2, 5, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 2, 5, 5, 0, 5, 0, 0, 0],\n [5, 0, 5, 2, 2, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5],\n [0, 5, 5, 5, 2, 5, 0, 0, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 2, 2, 2, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 5],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 2, 2, 5],\n [0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 2],\n [5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 2, 5, 2, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 0, 2, 2, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0],\n [0, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 0],\n [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5]\n ],\n \"output\": [\n [5, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5],\n [0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 2, 2, 7, 5, 5, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 2, 4, 7, 0, 5, 0, 0, 0],\n [5, 0, 7, 2, 2, 5, 5, 0, 0, 5, 5, 7, 7, 7, 5, 0, 5, 5, 5],\n [0, 5, 7, 4, 2, 5, 0, 0, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 2, 2, 2, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 5],\n [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5, 0, 0, 0, 0, 0, 2, 2, 7],\n [0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 7, 4, 7],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 0, 5, 7, 7, 2],\n [5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5],\n [0, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [0, 0, 5, 5, 7, 7, 7, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 2, 4, 2, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 0, 2, 2, 7, 5, 5, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0],\n [0, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 0],\n [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 2, 5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 0, 2, 2, 5, 5, 0],\n [5, 5, 5, 2, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5],\n [0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 2, 5, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 0, 0, 5, 5, 5, 5, 5],\n [5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5],\n [0, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 2, 5, 5, 5, 0, 5, 5],\n [5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 2, 5, 0, 0, 0, 5],\n [0, 5, 5, 5, 2, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0],\n [5, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 5],\n [5, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5]\n ],\n \"output\": [\n [0, 0, 7, 7, 7, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 2, 4, 7, 5, 5, 5, 5, 0, 5, 0, 0, 0, 2, 2, 7, 5, 0],\n [5, 5, 7, 2, 7, 0, 5, 5, 5, 0, 5, 5, 0, 5, 2, 4, 2, 5, 5],\n [0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 7, 2, 7, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 0, 0, 5, 5, 5, 5, 5],\n [5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5],\n [5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0],\n [0, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5],\n [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5],\n [0, 5, 2, 2, 7, 5, 0, 0, 5, 0, 0, 7, 2, 7, 5, 5, 0, 5, 5],\n [5, 5, 7, 4, 7, 0, 0, 5, 0, 0, 0, 7, 4, 2, 5, 0, 0, 0, 5],\n [0, 5, 7, 7, 2, 0, 0, 5, 5, 5, 5, 7, 7, 7, 0, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0],\n [5, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 5, 0],\n [5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0],\n [5, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 5, 5],\n [5, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 5]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0],\n [0, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 0, 5],\n [0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5],\n [0, 5, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 2, 5, 5, 5, 0, 5],\n [5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 2, 2, 5, 5, 0, 0],\n [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 5],\n [5, 2, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5],\n [5, 5, 2, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5],\n [5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 0],\n [5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 0, 5, 5],\n [5, 5, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5],\n [5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 5, 2, 5],\n [5, 5, 5, 5, 5, 2, 5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 2, 5, 5],\n [5, 0, 5, 0, 5, 5, 2, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 2, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5],\n [0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5],\n [5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 7, 7, 7, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 2, 4, 7, 5, 0, 5], [5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 2, 2, 7, 5, 0, 0], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 5], [7, 2, 7, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5], [7, 4, 2, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [7, 7, 7, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 0, 5, 5], [5, 5, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5], [5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 5], [5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 0, 5, 5, 7, 2, 7], [5, 5, 5, 5, 7, 2, 7, 5, 5, 5, 0, 5, 0, 0, 5, 0, 2, 4, 7], [5, 0, 5, 0, 7, 4, 2, 5, 0, 0, 5, 0, 0, 5, 0, 5, 7, 7, 7], [5, 5, 5, 5, 7, 2, 7, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5], [5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5]], "task_id": "9caba7c3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 5, 5],\n [5, 1, 7],\n [4, 5, 2]\n ],\n \"output\": [\n [6, 6, 5, 5, 5],\n [6, 6, 5, 5, 5],\n [5, 5, 1, 7, 7],\n [4, 4, 5, 2, 2],\n [4, 4, 5, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [1, 3, 5],\n [1, 2, 8],\n [8, 3, 8]\n ],\n \"output\": [\n [1, 1, 3, 5, 5],\n [1, 1, 3, 5, 5],\n [1, 1, 2, 8, 8],\n [8, 8, 3, 8, 8],\n [8, 8, 3, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [2, 3, 7],\n [2, 1, 6],\n [1, 5, 7]\n ],\n \"output\": [\n [2, 2, 3, 7, 7],\n [2, 2, 3, 7, 7],\n [2, 2, 1, 6, 6],\n [1, 1, 5, 7, 7],\n [1, 1, 5, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 2, 5],\n [7, 3, 6],\n [7, 6, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 2, 5, 5], [1, 1, 2, 5, 5], [7, 7, 3, 6, 6], [7, 7, 6, 5, 5], [7, 7, 6, 5, 5]], "task_id": "e633a9e5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 1, 0, 3, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 8],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 8, 0, 0, 8],\n [0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 8, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 8],\n [0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 2],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 3],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 2],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 8],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 4],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0],\n [3, 1, 1, 1, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 4],\n [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 8], [0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 8], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 3, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 3, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 4], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 4, 0, 0], [3, 1, 1, 1, 1, 1, 1, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 1, 1, 1, 1, 4], [3, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e681b708"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n [5, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 4, 4, 2, 2, 2, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 8, 8, 8, 8, 8, 8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 8, 8, 8, 8, 8, 8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 6, 6, 6, 6, 6, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 6, 6, 6, 6, 6, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 6, 6, 6, 6, 6, 6, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 4, 4, 4, 0, 5, 0, 0, 3, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5],\n [0, 0, 0, 0, 5, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 0, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 4, 4, 4, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 8, 4, 4, 3, 3, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 5, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 0, 5, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 5, 0, 0, 0, 8, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 5, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 4, 4, 4, 4, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 3, 3, 1, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 3, 3, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 0, 8, 8, 8, 8, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 8, 8, 8, 8, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 8, 8, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 8, 8, 0, 0, 8, 0, 0, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 5, 0, 1, 0, 0, 0, 8, 8, 0, 0, 8, 8, 8, 8, 0],\n [0, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 2, 2, 2, 2, 4, 4, 4, 4, 3, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 2, 2, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 3, 8, 8, 8, 8, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 4, 4, 4, 4, 4, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 8, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 7, 7, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 7, 7, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 1, 1, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "184a9768"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 8, 8, 8, 0, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 8, 8, 0, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 8, 0, 0, 8, 8, 8, 0, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0],\n [0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 0],\n [0, 0, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 8, 0, 8, 8, 8, 0],\n [0, 8, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 2, 2, 2, 0, 2, 0, 0, 0],\n [0, 2, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 2, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0],\n [0, 8, 0, 8, 0, 8, 8, 8, 0, 0, 8, 8, 0],\n [0, 8, 8, 8, 0, 8, 0, 0, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 0],\n [0, 8, 8, 8, 0, 0, 8, 0, 0, 8, 0, 8, 0],\n [0, 0, 8, 8, 0, 8, 0, 8, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 0, 8, 0, 0, 0, 8, 0],\n [0, 8, 8, 0, 0, 8, 8, 0, 0, 8, 8, 8, 0],\n [0, 0, 8, 0, 0, 0, 8, 8, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 0, 0], [0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1c0d0a4b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 0, 2, 0, 2, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 3, 0, 3, 0, 0, 0],\n [8, 0, 0, 8, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 4, 5, 4, 0, 0, 0, 2, 5, 2, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 7, 7, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 3, 5, 3, 0, 0, 0],\n [8, 7, 7, 8, 0, 0, 0, 3, 3, 3, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [0, 8, 0, 8, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [0, 8, 5, 8, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 0, 0],\n [4, 0, 4, 0, 0, 0],\n [4, 0, 4, 0, 0, 0],\n [4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 0, 0],\n [4, 7, 4, 0, 0, 0],\n [4, 7, 4, 0, 0, 0],\n [4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 4, 0, 0, 3, 3, 3, 0, 0],\n [0, 4, 4, 4, 4, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 3, 0, 3, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 7, 7, 4, 0, 0, 3, 3, 3, 0, 0],\n [0, 4, 4, 4, 4, 0, 0, 3, 7, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 7, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 3, 5, 3, 0, 0, 2, 7, 7, 2],\n [0, 0, 0, 3, 3, 3, 0, 0, 2, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 5, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 4, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 4, 4, 4, 0, 0, 2, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 2, 0, 2, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 2, 2, 2, 0, 0, 3, 3, 3],\n [3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 5, 4, 0, 0, 2, 2, 2, 2, 0, 0], [0, 4, 4, 4, 0, 0, 2, 7, 7, 2, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 0, 2, 2, 2, 0, 0, 0, 0, 0], [3, 7, 3, 0, 2, 5, 2, 0, 0, 0, 0, 0], [3, 7, 3, 0, 2, 2, 2, 0, 0, 3, 3, 3], [3, 3, 3, 0, 0, 0, 0, 0, 0, 3, 5, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "84f2aca1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 6],\n [6, 4]\n ],\n \"output\": [\n [8, 6, 8, 6, 8, 6],\n [6, 4, 6, 4, 6, 4],\n [6, 8, 6, 8, 6, 8],\n [4, 6, 4, 6, 4, 6],\n [8, 6, 8, 6, 8, 6],\n [6, 4, 6, 4, 6, 4]\n ]\n}\n\n{\n \"input\": [\n [7, 9],\n [4, 3]\n ],\n \"output\": [\n [7, 9, 7, 9, 7, 9],\n [4, 3, 4, 3, 4, 3],\n [9, 7, 9, 7, 9, 7],\n [3, 4, 3, 4, 3, 4],\n [7, 9, 7, 9, 7, 9],\n [4, 3, 4, 3, 4, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 2],\n [7, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8], [2, 3, 2, 3, 2, 3], [8, 7, 8, 7, 8, 7], [3, 2, 3, 2, 3, 2], [7, 8, 7, 8, 7, 8]], "task_id": "00576224"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [3, 1, 0, 3, 3, 3, 3, 3, 0, 3],\n [1, 0, 0, 3, 3, 0, 1, 3, 1, 1],\n [0, 1, 1, 1, 0, 3, 0, 0, 0, 3],\n [0, 1, 3, 3, 0, 3, 1, 3, 0, 0],\n [1, 3, 1, 1, 0, 1, 3, 0, 0, 0],\n [0, 1, 1, 3, 0, 0, 3, 1, 1, 3],\n [3, 0, 1, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 3, 1, 0, 0, 1, 3],\n [3, 3, 1, 0, 0, 1, 1, 0, 0, 1],\n [0, 1, 3, 0, 1, 1, 1, 1, 1, 3]\n ],\n \"output\": [\n [3, 1, 2, 3, 3, 3, 3, 3, 2, 3],\n [1, 2, 2, 3, 3, 5, 1, 3, 1, 1],\n [2, 1, 1, 1, 5, 3, 2, 2, 2, 3],\n [2, 1, 3, 3, 5, 3, 1, 3, 2, 2],\n [1, 3, 1, 1, 5, 1, 3, 2, 2, 2],\n [2, 1, 1, 3, 5, 5, 3, 1, 1, 3],\n [3, 2, 1, 5, 5, 5, 5, 5, 3, 2],\n [2, 2, 2, 3, 3, 1, 5, 5, 1, 3],\n [3, 3, 1, 2, 2, 1, 1, 5, 5, 1],\n [2, 1, 3, 2, 1, 1, 1, 1, 1, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 3, 1, 0, 1, 1, 3],\n [1, 3, 0, 0, 1, 1, 3, 1, 0, 0],\n [1, 0, 1, 0, 0, 1, 3, 0, 3, 3],\n [0, 0, 3, 3, 1, 3, 3, 3, 0, 1],\n [0, 0, 3, 3, 0, 0, 0, 0, 3, 1],\n [3, 3, 0, 0, 3, 0, 0, 0, 3, 0],\n [0, 0, 3, 3, 3, 0, 3, 0, 3, 3],\n [3, 1, 1, 1, 3, 0, 1, 1, 1, 3],\n [0, 0, 1, 3, 1, 0, 0, 3, 3, 3],\n [0, 3, 3, 0, 3, 3, 1, 3, 1, 1]\n ],\n \"output\": [\n [2, 3, 3, 2, 3, 1, 2, 1, 1, 3],\n [1, 3, 2, 2, 1, 1, 3, 1, 2, 2],\n [1, 2, 1, 2, 2, 1, 3, 5, 3, 3],\n [2, 2, 3, 3, 1, 3, 3, 3, 5, 1],\n [2, 2, 3, 3, 5, 5, 5, 5, 3, 1],\n [3, 3, 5, 5, 3, 5, 5, 5, 3, 2],\n [2, 2, 3, 3, 3, 5, 3, 5, 3, 3],\n [3, 1, 1, 1, 3, 5, 1, 1, 1, 3],\n [2, 2, 1, 3, 1, 5, 5, 3, 3, 3],\n [2, 3, 3, 2, 3, 3, 1, 3, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 3, 0, 0, 1, 3, 3, 1],\n [0, 1, 1, 1, 1, 3, 0, 0, 1, 1],\n [0, 3, 1, 0, 1, 0, 3, 0, 3, 0],\n [3, 3, 3, 0, 0, 3, 3, 3, 0, 0],\n [1, 1, 3, 1, 3, 0, 0, 0, 1, 0],\n [1, 0, 1, 0, 3, 0, 3, 3, 0, 3],\n [0, 0, 0, 0, 1, 1, 3, 0, 1, 0],\n [3, 0, 1, 3, 3, 1, 0, 3, 0, 0],\n [1, 1, 0, 0, 1, 3, 3, 1, 1, 3],\n [0, 0, 1, 1, 0, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 3, 2, 3, 2, 2, 1, 3, 3, 1],\n [2, 1, 1, 1, 1, 3, 5, 5, 1, 1],\n [2, 3, 1, 5, 1, 5, 3, 5, 3, 2],\n [3, 3, 3, 5, 5, 3, 3, 3, 2, 2],\n [1, 1, 3, 1, 3, 5, 5, 5, 1, 2],\n [1, 2, 1, 2, 3, 5, 3, 3, 5, 3],\n [2, 2, 2, 2, 1, 1, 3, 5, 1, 2],\n [3, 2, 1, 3, 3, 1, 5, 3, 2, 2],\n [1, 1, 5, 5, 1, 3, 3, 1, 1, 3],\n [2, 2, 1, 1, 2, 1, 2, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 3, 1, 1, 3],\n [0, 0, 3, 1, 0, 1, 1, 0, 0, 3],\n [0, 1, 0, 0, 1, 3, 3, 1, 3, 1],\n [0, 1, 3, 0, 0, 0, 0, 0, 1, 0],\n [0, 1, 3, 1, 0, 1, 0, 3, 0, 1],\n [1, 0, 0, 3, 1, 3, 1, 0, 1, 0],\n [1, 0, 0, 3, 0, 1, 0, 3, 0, 0],\n [0, 1, 0, 1, 1, 0, 3, 1, 0, 3],\n [0, 3, 1, 1, 3, 0, 0, 3, 1, 0],\n [1, 1, 3, 3, 0, 0, 1, 3, 0, 3]\n ],\n \"output\": [\n [2, 2, 2, 2, 2, 2, 3, 1, 1, 3],\n [2, 2, 3, 1, 2, 1, 1, 5, 5, 3],\n [2, 1, 5, 5, 1, 3, 3, 1, 3, 1],\n [2, 1, 3, 5, 5, 5, 5, 5, 1, 2],\n [2, 1, 3, 1, 5, 1, 5, 3, 5, 1],\n [1, 5, 5, 3, 1, 3, 1, 5, 1, 2],\n [1, 5, 5, 3, 5, 1, 5, 3, 2, 2],\n [2, 1, 5, 1, 1, 2, 3, 1, 2, 3],\n [2, 3, 1, 1, 3, 2, 2, 3, 1, 2],\n [1, 1, 3, 3, 2, 2, 1, 3, 2, 3]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 0, 0, 1, 0, 1, 1, 1, 1, 3],\n [0, 0, 0, 3, 0, 3, 0, 1, 0, 0],\n [0, 1, 0, 3, 3, 0, 1, 3, 3, 3],\n [3, 1, 3, 1, 1, 0, 3, 3, 0, 1],\n [1, 1, 3, 0, 1, 3, 0, 1, 1, 0],\n [0, 3, 0, 1, 3, 0, 1, 1, 0, 3],\n [1, 1, 3, 0, 0, 3, 0, 3, 3, 3],\n [3, 1, 1, 1, 1, 3, 1, 0, 3, 1],\n [3, 0, 0, 0, 3, 3, 1, 0, 1, 1],\n [1, 0, 3, 1, 1, 0, 0, 0, 1, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 2, 2, 1, 2, 1, 1, 1, 1, 3], [2, 2, 2, 3, 2, 3, 5, 1, 2, 2], [2, 1, 2, 3, 3, 5, 1, 3, 3, 3], [3, 1, 3, 1, 1, 5, 3, 3, 5, 1], [1, 1, 3, 5, 1, 3, 5, 1, 1, 2], [2, 3, 5, 1, 3, 5, 1, 1, 5, 3], [1, 1, 3, 5, 5, 3, 5, 3, 3, 3], [3, 1, 1, 1, 1, 3, 1, 2, 3, 1], [3, 2, 2, 2, 3, 3, 1, 2, 1, 1], [1, 2, 3, 1, 1, 2, 2, 2, 1, 2]], "task_id": "84db8fc4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 3, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 4, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 4, 4, 4, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 3, 4, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 4, 4, 4, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 3, 4, 0, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 2, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 4, 4, 2, 4, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 4, 4, 0],\n [0, 4, 4, 2, 4],\n [0, 0, 4, 4, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 0, 0, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 4, 4, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 4, 4, 1, 4, 4, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 0, 0, 4, 4, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0],\n [0, 0, 0, 4, 4, 4, 0],\n [0, 0, 4, 4, 1, 4, 4],\n [0, 0, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 4, 4, 0, 4, 4, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 4, 2, 4, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 4, 4, 4, 4, 4, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 4, 4, 0, 0, 0], [0, 0, 4, 2, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0]], "task_id": "2f0c5170"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 5, 0, 0, 8, 8, 8, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 8, 0, 0, 5, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 1, 0, 5, 0, 0, 1, 0, 0, 0],\n [0, 8, 0, 5, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 5, 0, 8, 8, 8, 0, 0, 0, 0, 1, 0, 0, 5, 0, 1, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 0],\n [0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 0, 0, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 5, 0, 5, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 5, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 2, 0],\n [6, 6, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 3, 0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 3, 5, 0, 5, 0, 0, 0, 0, 3, 0, 0, 8, 5, 0, 0, 8, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 5, 0, 3, 0, 0, 8, 0, 5, 0, 8, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 8, 0, 0, 0, 8, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 8, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 0, 0, 0, 0, 5, 0, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 2, 0, 5, 0, 0, 0, 0, 2, 0, 0, 4, 0, 5, 0, 5, 0, 0, 0, 4],\n [0, 2, 0, 5, 0, 5, 0, 0, 2, 0, 0, 4, 0, 0, 0, 0, 5, 0, 5, 4],\n [0, 2, 0, 0, 0, 0, 0, 5, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 4, 0, 0, 5, 0, 5, 0, 0, 4],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 0, 0, 0],\n [3, 3, 3, 3, 0, 0],\n [2, 2, 2, 2, 2, 0],\n [4, 4, 4, 4, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 5, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 5, 0, 5, 0, 0, 0, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [6, 6, 0, 0, 0, 0, 5, 0, 6, 6, 6, 6, 0, 8, 8, 8, 8, 8, 8, 8, 8],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 8, 0, 0, 0, 0, 0, 0, 8],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 8, 0, 0, 0, 5, 5, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 5, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 5, 0, 0, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 5, 0, 0, 8],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0, 5, 8],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 8, 0, 5, 0, 5, 0, 0, 8],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 2, 2, 0, 5, 0, 0, 5, 2, 2, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 0, 2, 2, 0, 0, 0, 5, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2, 0, 0, 0, 0, 0], [6, 6, 6, 6, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8]], "task_id": "d4c90558"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0],\n [0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1, 1, 0, 3, 3, 3, 0, 1, 1, 1, 0, 0],\n [0, 0, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 1, 0, 3, 0, 3, 0, 1, 0, 1, 0, 0],\n [0, 0, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1, 1, 0, 3, 3, 3, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 2, 2, 2, 0, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 0, 2, 2, 2, 0, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 1, 1, 1, 0, 2, 2, 2, 0, 3, 3, 3, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 0, 2, 0, 1, 0, 1, 0, 2, 0, 2, 0, 3, 0, 3, 0, 2, 0, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 1, 1, 1, 0, 2, 2, 2, 0, 3, 3, 3, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 1, 2, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 1, 8, 1, 8, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 8, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 4, 1, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 1, 1, 4, 4, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 8, 8, 8, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0, 1, 0, 0, 0, 8, 0, 0, 0],\n [0, 1, 8, 1, 8, 0, 1, 1, 1, 0, 8, 8, 8, 0, 1, 1, 1, 0, 8, 8, 8, 0, 0],\n [0, 8, 8, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 4, 1, 0, 8, 8, 8, 0, 8, 8, 8, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 1, 1, 4, 4, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 8, 8, 8, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 4, 4, 4, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 4, 4, 4, 0, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0],\n [0, 2, 1, 2, 2, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 8, 1, 4, 4, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 3, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 1, 3, 1, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0],\n [0, 8, 1, 1, 1, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 1, 1, 1, 0, 2, 2, 2, 0, 2, 2, 2, 0], [0, 2, 1, 2, 2, 0, 0, 2, 0, 2, 0, 1, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 8, 1, 4, 4, 0, 0, 2, 0, 2, 0, 1, 0, 1, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 3, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 1, 3, 1, 0, 0, 8, 8, 8, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4, 4, 0], [0, 8, 1, 1, 1, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 4, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 4, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 1, 0, 1, 0, 4, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 1, 0, 1, 0, 4, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 1, 1, 1, 0, 3, 3, 3, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 3, 0, 3, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 3, 0, 3, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "33b52de3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 1, 1],\n [1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0],\n [0, 1, 0, 2, 2],\n [1, 1, 0, 2, 2]\n ],\n \"output\": [\n [1, 0],\n [1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 0, 1, 1],\n [1, 1, 0, 1, 0],\n [0, 0, 0, 0, 0],\n [1, 1, 0, 2, 2],\n [0, 1, 0, 2, 2]\n ],\n \"output\": [\n [0, 1],\n [1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 1],\n [0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2],\n [1, 1, 0, 2, 2]\n ],\n \"output\": [\n [1, 0],\n [1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 1],\n [0, 1, 0, 1, 1],\n [0, 0, 0, 0, 0],\n [1, 0, 0, 2, 2],\n [1, 1, 0, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1], [1, 0]], "task_id": "be03b35f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 3, 2, 0, 0, 2, 3, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 1, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 1, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 3, 2, 0, 0, 2, 3, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 1, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 1, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 3, 2, 3, 3, 2, 3, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 1, 2, 1, 1, 2, 4, 2, 1, 1, 2, 4, 2, 1, 1, 2, 1, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 3, 2, 3, 3, 2, 3, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 1, 2, 1, 1, 2, 4, 2, 1, 1, 2, 4, 2, 1, 1, 2, 1, 2, 0, 0, 2, 4],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2],\n [0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4, 2, 0, 0, 2, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 0, 3, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 8, 3, 2, 2, 3, 8, 3, 8, 8],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 8, 3, 2, 2, 3, 8, 3, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 4, 3, 2, 2, 3, 4, 3, 2, 2, 3, 4, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 4, 3, 2, 2, 3, 4, 3, 2, 2, 3, 4, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 4, 4, 0, 0, 0, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 8, 3, 2, 2, 3, 8, 3, 8, 8],\n [0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 2, 2, 3, 0, 3, 8, 8, 3, 8, 3, 2, 2, 3, 8, 3, 8, 8],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 3, 3, 3],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 4, 3, 2, 2, 3, 4, 3, 2, 2, 3, 4, 3, 4, 4, 3, 0, 3, 2, 2],\n [0, 3, 2, 2, 3, 0, 3, 4, 4, 3, 4, 3, 2, 2, 3, 4, 3, 2, 2, 3, 4, 3, 4, 4, 3, 0, 3, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 3, 1, 3, 3, 3, 1, 2, 1, 3, 3, 3, 1, 2, 1, 3, 3, 3, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 1, 3, 1, 3, 3, 3, 1, 2, 1, 3, 3, 3, 1, 2, 1, 3, 3, 3, 1, 3, 1, 0, 0, 0, 1, 2, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 3, 3, 3, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4, 3, 3, 3, 4, 0, 0, 4],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 0, 3, 3, 3, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 3, 3, 3, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 4, 3, 3, 4, 3, 3, 3, 4, 0, 0, 4], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 2, 2, 2, 4, 0, 0, 4, 8, 8, 8, 4, 8, 8, 4, 8, 8, 8, 4, 0, 0, 4, 2, 2, 2, 4, 0, 0, 4], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 0, 4]], "task_id": "b7f8a4d8"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 8, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 2, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 8, 0, 0, 8, 0, 0, 0, 0],\n [0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0]\n ],\n \"output\": [\n [2, 8, 2, 2, 8, 2, 2, 2, 0, 0, 0, 0],\n [2, 8, 8, 2, 8, 2, 2, 8, 0, 0, 0, 0],\n [2, 8, 8, 8, 8, 8, 8, 2, 0, 0, 0, 0],\n [8, 8, 2, 2, 8, 8, 8, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 8, 0, 0],\n [0, 0, 0, 8, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 8, 2, 2, 8, 0, 0],\n [0, 8, 8, 8, 8, 0, 0],\n [0, 2, 2, 8, 2, 0, 0],\n [0, 2, 8, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8],\n [8, 8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 8, 8, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 8, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 8, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8], [8, 8, 2, 8, 8, 2, 2, 2, 0, 0, 0, 0], [2, 8, 2, 8, 8, 2, 8, 8, 0, 0, 0, 0], [2, 2, 8, 2, 8, 2, 2, 8, 0, 0, 0, 0], [2, 2, 2, 8, 8, 2, 2, 8, 0, 0, 0, 0]], "task_id": "8fbca751"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 2, 2, 3, 0, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 4, 4, 4, 7, 7, 0, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 0, 0, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 7],\n [8, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [9, 0, 9, 9, 9, 9, 5, 5, 5, 0, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [8, 8, 8, 8, 8, 8, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [9, 9, 9, 9, 9, 9, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 9, 9, 0, 9, 9, 9, 9, 9, 9],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 0, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 3, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 0, 9, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0]], "task_id": "cf133acc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 8, 8, 8, 8, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1],\n [1, 8, 2, 2, 8, 1, 1, 1, 8, 8, 8, 8, 1, 1, 8, 1],\n [1, 8, 2, 8, 8, 8, 1, 1, 8, 2, 2, 8, 1, 1, 1, 1],\n [1, 8, 8, 8, 8, 1, 1, 1, 8, 2, 8, 8, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 8, 8, 8, 8, 8, 1, 1, 1],\n [1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1],\n [1, 1, 8, 8, 8, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 8, 2, 2, 8, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1],\n [1, 1, 8, 2, 8, 8, 1, 1, 8, 8, 8, 8, 1, 1, 1, 1],\n [8, 1, 8, 8, 8, 8, 1, 1, 8, 2, 2, 8, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 8, 1, 1, 8, 1],\n [1, 1, 1, 1, 1, 1, 8, 1, 8, 8, 8, 8, 1, 1, 1, 1],\n [1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 8, 8, 1],\n [1, 8, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8],\n [8, 2, 2, 8],\n [8, 8, 2, 8],\n [8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 8, 1, 8, 1, 1, 1],\n [1, 8, 8, 8, 8, 1, 8, 1, 1, 8, 8, 1, 1, 1, 1, 1],\n [1, 8, 2, 2, 8, 8, 1, 1, 8, 1, 1, 8, 8, 1, 1, 1],\n [1, 8, 2, 2, 8, 1, 1, 8, 1, 1, 1, 8, 1, 1, 8, 1],\n [1, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 8, 1],\n [1, 8, 1, 8, 1, 1, 1, 8, 1, 1, 8, 1, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8],\n [1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 8, 2, 2, 8],\n [1, 8, 1, 1, 1, 8, 2, 8, 8, 1, 1, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 8, 8, 2, 8, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 8, 8, 8, 8],\n [1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8],\n [1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 8, 1, 8, 8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8, 8],\n [8, 2, 8, 8],\n [8, 8, 2, 8],\n [8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 8, 1, 8, 1, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 8, 8, 1],\n [1, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 8, 1],\n [8, 8, 2, 8, 2, 8, 1, 1, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1],\n [1, 8, 8, 2, 8, 8, 8, 1, 1, 8, 2, 8, 2, 8, 8, 1, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 8, 8, 2, 8, 8, 1, 8, 1, 1],\n [1, 8, 8, 8, 8, 8, 1, 1, 1, 8, 2, 2, 8, 8, 8, 1, 1, 1],\n [1, 8, 1, 8, 1, 1, 8, 1, 1, 8, 8, 8, 8, 8, 8, 1, 8, 8],\n [8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1],\n [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1],\n [8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 1, 8, 1, 1, 1, 1],\n [8, 8, 1, 1, 1, 8, 2, 8, 2, 8, 1, 1, 8, 1, 1, 1, 1, 1],\n [1, 1, 8, 1, 1, 8, 8, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 8],\n [1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 1, 1, 8, 8, 1, 1, 8, 1],\n [1, 8, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 8, 1],\n [1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1],\n [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1],\n [1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8, 8],\n [8, 2, 8, 2, 8],\n [8, 8, 2, 8, 8],\n [8, 8, 2, 2, 8],\n [8, 8, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 8, 8, 8, 8, 8],\n [1, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 8, 8, 8, 2, 8, 8, 8],\n [1, 1, 8, 2, 2, 8, 8, 1, 1, 1, 1, 1, 1, 8, 8, 8, 2, 8],\n [1, 1, 8, 2, 8, 8, 8, 1, 8, 1, 8, 1, 1, 8, 8, 8, 8, 8],\n [1, 8, 8, 8, 8, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 1, 8, 8, 8, 8, 8, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1],\n [1, 8, 1, 8, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 1, 8, 8, 1],\n [1, 1, 1, 1, 8, 1, 8, 1, 1, 8, 2, 2, 8, 8, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 8, 1, 1, 8, 2, 8, 8, 8, 8, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 8, 8, 8, 2, 8, 1, 1, 1, 1],\n [8, 2, 2, 8, 8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 1, 1],\n [8, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 2, 8, 1, 1, 1, 8, 8, 8, 8, 8, 1, 1, 1, 8, 1],\n [8, 8, 8, 8, 8, 1, 1, 1, 8, 8, 2, 2, 8, 1, 1, 1, 1, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 1, 1, 1, 8, 8],\n [8, 8, 1, 1, 1, 1, 1, 1, 8, 2, 8, 8, 8, 1, 1, 1, 1, 1],\n [8, 1, 8, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8, 8], [8, 8, 2, 2, 8], [8, 8, 2, 2, 8], [8, 2, 8, 8, 8], [8, 8, 8, 8, 8]], "task_id": "aee291af"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0],\n [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 3, 2, 0],\n [1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 2, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 0, 2, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0],\n [3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 3, 2, 0],\n [3, 0, 3, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 3, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 3, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n [0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 0, 4, 4, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 4, 0, 0, 0],\n [0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 7, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 7, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 7, 0, 0, 0, 4, 0], [0, 4, 4, 4, 0, 7, 7, 7, 0, 4, 4, 4], [0, 4, 0, 4, 0, 7, 0, 7, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 3, 3, 3, 0, 0, 0, 0, 0, 3, 3, 3], [0, 3, 0, 3, 0, 0, 0, 0, 0, 3, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "fafd9572"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 0, 0, 0, 0, 0, 3, 3, 0, 0],\n [0, 1, 1, 1, 0, 3, 3, 3, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 1, 1, 1, 0, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 6, 6],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 6, 0],\n [0, 1, 1, 1, 1, 0, 0, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 0, 0, 1, 1, 0, 0, 0, 0, 2, 2, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0], [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 3, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "963f59bc"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 8, 5, 8, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 5, 5],\n [5, 8, 8, 8, 5, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 3, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 3, 3, 3, 5, 7, 7, 7, 7, 5, 5, 7, 7],\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 1, 5, 1, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 1, 1, 1, 5, 5, 5],\n [5, 6, 6, 6, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5],\n [5, 6, 5, 6, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5],\n [5, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n [1, 1, 1],\n [1, 5, 1],\n [1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 1, 5, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 3, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 5, 5, 5, 5, 2, 2, 2, 5],\n [5, 7, 7, 7, 5, 7, 7, 7, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5],\n [5, 7, 5, 5, 5, 5, 5, 7, 5, 5, 5, 5, 5, 5, 2, 2, 2, 5],\n [5, 7, 5, 4, 4, 4, 5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 4, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 7, 5, 4, 4, 4, 5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 7, 5, 5, 5, 5, 5, 7, 5, 5, 5, 8, 8, 8, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 7, 5, 5, 5, 8, 5, 8, 5, 5, 5, 5],\n [5, 7, 5, 5, 5, 5, 5, 7, 5, 5, 5, 8, 8, 8, 5, 5, 5, 5]\n ],\n \"output\": [\n [4, 4, 4],\n [4, 5, 4],\n [4, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 5, 2, 2, 2, 5],\n [5, 5, 6, 6, 6, 5, 5, 5, 7, 5, 2, 5, 2, 5],\n [5, 5, 6, 5, 6, 5, 5, 5, 7, 5, 2, 2, 2, 5],\n [5, 5, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 7, 7, 5, 5, 7, 7],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 5, 8, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 8, 8, 8, 5, 5],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 1, 5, 1, 5, 5, 5, 5, 5, 5, 5, 5],\n [5, 5, 5, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 2], [2, 5, 2], [2, 2, 2]], "task_id": "bf699163"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 4, 4, 4, 3, 4, 4, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 0, 0, 0, 3, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 0, 4, 4, 3, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 0, 4, 0, 3, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 4, 0, 4, 0, 4, 0, 3, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 0, 4, 4, 3, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 0, 0, 0, 3, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 4, 4, 4, 4, 3, 4, 4, 4, 4, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4],\n [0, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0]\n ],\n \"output\": [\n [4, 0, 4, 4, 4, 4, 3, 4, 4, 4],\n [4, 0, 4, 0, 0, 0, 3, 0, 0, 0],\n [4, 0, 4, 0, 4, 4, 3, 4, 4, 0],\n [4, 0, 4, 0, 4, 0, 3, 0, 4, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [4, 0, 4, 0, 4, 0, 3, 0, 4, 0],\n [4, 0, 4, 0, 4, 4, 3, 4, 4, 0],\n [4, 0, 4, 0, 0, 0, 3, 0, 0, 0],\n [4, 0, 4, 4, 4, 4, 3, 4, 4, 4],\n [4, 0, 0, 0, 0, 0, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4], [4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [4, 0, 4, 4, 4, 4, 3, 4, 4, 4, 4, 0], [4, 0, 4, 0, 0, 0, 3, 0, 0, 0, 4, 0], [4, 0, 4, 0, 4, 4, 3, 4, 4, 0, 4, 0], [4, 0, 4, 0, 4, 0, 3, 0, 4, 0, 4, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 0, 4, 0, 4, 0, 3, 0, 4, 0, 4, 0], [4, 0, 4, 0, 4, 4, 3, 4, 4, 0, 4, 0], [4, 0, 4, 0, 0, 0, 3, 0, 0, 0, 4, 0]], "task_id": "759f3fd3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 1, 2, 0, 0, 1, 4, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 0, 0, 0],\n [0, 7, 6, 7, 0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 0],\n [0, 6, 7, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 7, 6, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 1, 4, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 6, 7, 1, 4, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 6, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 1, 0, 0, 0, 5, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 5, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 9, 0, 0, 4, 6, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 9, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 5, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 9, 4, 6],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 9, 0, 0, 0, 0, 0, 3, 7, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 3, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 8, 6, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 6, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 8, 6, 0, 0, 0, 8, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 3, 7, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 3, 7],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 6, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 6, 8, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 6, 8, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 6, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 8, 6, 0, 2, 5, 2, 5, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 2, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 5, 2, 5, 2, 0, 0, 0],\n [0, 0, 2, 1, 0, 0, 0, 5, 2, 5, 2, 5, 0, 0, 0],\n [0, 0, 1, 2, 0, 0, 0, 2, 5, 2, 5, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 3, 0, 0, 0, 0, 8, 4, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 4, 8, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 4, 8, 0, 0, 0, 0, 0],\n [0, 0, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 6, 8, 6, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 6, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 6, 8, 6, 2, 5, 2, 5, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 5, 2, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 2, 5, 2], [0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 5, 2, 5, 2, 5], [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 5, 2, 5, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 8, 4, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 8, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 4, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 1]], "task_id": "d282b262"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 2, 0, 1, 1, 8, 8, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 8, 8, 2, 2, 0, 1, 1, 8, 8, 1, 1, 1, 1, 0, 0, 0, 0],\n [0, 2, 8, 8, 8, 0, 8, 8, 1, 1, 1, 1, 8, 8, 0, 0, 0, 0],\n [0, 2, 8, 2, 2, 0, 8, 8, 1, 1, 1, 1, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1],\n [8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1],\n [1, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8],\n [1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 8, 3, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 0, 3, 3, 8, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 8, 8, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3],\n [3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 3],\n [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8],\n [3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 3],\n [3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 8, 8, 8, 3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 8, 2, 0, 4, 4, 8, 8, 4, 4, 0, 0, 0, 0, 0],\n [8, 2, 2, 0, 4, 4, 8, 8, 4, 4, 0, 0, 0, 0, 0],\n [8, 8, 8, 0, 8, 8, 4, 4, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 4, 4, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [4, 4, 4, 4, 8, 4, 4, 4, 4],\n [4, 4, 4, 8, 4, 4, 4, 4, 4],\n [4, 4, 4, 8, 8, 8, 4, 4, 4],\n [4, 8, 4, 4, 4, 4, 4, 8, 4],\n [8, 4, 4, 4, 4, 4, 8, 4, 4],\n [8, 8, 8, 4, 4, 4, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 8, 2, 8, 0, 1, 8, 1, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 2, 2, 0, 8, 1, 1, 1, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 8, 8, 0, 1, 8, 1, 1, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 2, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1], [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 8, 1, 1, 1, 1], [1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8], [8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1], [1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8], [8, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 8], [1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1], [1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 8, 8, 1, 1, 1, 1]], "task_id": "5833af48"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 8, 0, 0, 4, 0, 5, 5, 0],\n [8, 8, 0, 8, 4, 0, 0, 5, 5],\n [0, 0, 0, 0, 4, 0, 0, 5, 0],\n [8, 8, 0, 0, 4, 0, 5, 5, 5],\n [8, 0, 0, 8, 4, 0, 0, 0, 5]\n ],\n \"output\": [\n [2, 0, 2, 0],\n [2, 2, 2, 0],\n [0, 0, 2, 0],\n [2, 0, 2, 2],\n [2, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 4, 5, 0, 5, 0],\n [0, 8, 0, 8, 4, 5, 0, 5, 5],\n [0, 8, 0, 8, 4, 0, 0, 0, 5],\n [0, 8, 0, 8, 4, 0, 5, 0, 5],\n [0, 0, 0, 8, 4, 0, 0, 5, 0]\n ],\n \"output\": [\n [2, 2, 2, 0],\n [2, 2, 2, 0],\n [0, 2, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0, 0, 4, 0, 5, 5, 0],\n [8, 8, 0, 8, 4, 5, 0, 0, 5],\n [8, 8, 0, 0, 4, 5, 0, 0, 5],\n [0, 8, 0, 8, 4, 0, 0, 5, 0],\n [0, 0, 8, 0, 4, 0, 5, 0, 5]\n ],\n \"output\": [\n [0, 0, 2, 0],\n [0, 2, 0, 0],\n [0, 2, 0, 2],\n [0, 2, 2, 2],\n [0, 2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 4, 0, 5, 5, 5],\n [0, 8, 8, 8, 4, 0, 5, 0, 0],\n [8, 0, 0, 0, 4, 0, 5, 0, 5],\n [8, 0, 8, 8, 4, 5, 5, 5, 0],\n [0, 8, 8, 0, 4, 5, 0, 0, 5]\n ],\n \"output\": [\n [0, 2, 2, 0],\n [0, 0, 2, 2],\n [2, 2, 0, 2],\n [0, 2, 0, 2],\n [2, 2, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 0, 0, 4, 5, 0, 0, 0],\n [0, 8, 0, 8, 4, 5, 5, 0, 5],\n [8, 8, 0, 8, 4, 0, 0, 5, 5],\n [8, 8, 8, 8, 4, 5, 0, 5, 5],\n [0, 0, 8, 8, 4, 5, 0, 5, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[2, 2, 0, 0], [2, 0, 0, 0], [2, 2, 2, 0], [0, 2, 0, 0], [2, 0, 0, 2]], "task_id": "34b99a2b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 0, 0, 0]], "task_id": "f3e62deb"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 9, 9, 0, 0, 0, 7, 4, 4, 0, 0, 0, 0],\n [0, 0, 8, 8, 9, 0, 0, 0, 7, 4, 7, 0, 0, 0, 0],\n [0, 0, 8, 8, 9, 0, 0, 0, 7, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 0, 0, 0],\n [0, 0, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 3, 3],\n [1, 2, 2],\n [1, 3, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 5, 5, 0, 0, 9, 9, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 9, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 7, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 2, 0, 7, 1, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 0, 7, 7, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5],\n [6, 8, 8],\n [6, 5, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 7, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 5, 7, 5, 0, 0, 0, 1, 6, 2, 0, 0, 0, 0],\n [0, 0, 5, 7, 7, 0, 0, 0, 6, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 6, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 3, 3, 0, 0, 0, 9, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 9, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 9, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 6, 2],\n [6, 1, 1],\n [2, 6, 1]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 6, 6, 0, 0, 0, 7, 1, 7, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 1, 7, 7, 0, 0, 0, 0],\n [0, 0, 6, 3, 6, 0, 0, 0, 7, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 5, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0],\n [0, 0, 9, 4, 4, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[9, 4, 4], [4, 4, 4], [2, 2, 2]], "task_id": "9a4bb226"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 8, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 8, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 8, 1, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 8, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e7639916"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 3, 3, 3, 3, 3, 2, 2, 2, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 3, 3, 0, 0, 0, 0, 2, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 2, 2, 0, 2]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 5, 0, 5],\n [0, 5, 0, 0, 0, 0, 5],\n [5, 5, 5, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3, 0, 0, 3, 0, 2, 0, 0, 0, 2, 2, 2],\n [0, 3, 3, 3, 3, 0, 3, 2, 0, 0, 0, 0, 0, 2],\n [0, 0, 3, 0, 3, 3, 3, 0, 0, 2, 2, 0, 2, 2],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0],\n [5, 5, 0, 0, 0, 0, 0],\n [5, 5, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 0, 3, 3, 0, 2, 2, 2, 2, 0, 0, 0],\n [3, 0, 0, 0, 3, 3, 0, 0, 2, 2, 2, 2, 0, 2],\n [3, 3, 0, 3, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2],\n [3, 3, 3, 0, 0, 0, 3, 2, 2, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0],\n [0, 0, 0, 5, 5, 5, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 3, 0, 3, 2, 0, 2, 0, 0, 0, 2],\n [3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n [0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 2, 0, 2],\n [0, 3, 3, 0, 3, 0, 3, 0, 2, 2, 2, 0, 2, 0]\n ],\n \"output\": [\n [0, 0, 0, 5, 0, 5, 0],\n [0, 5, 0, 5, 0, 0, 5],\n [5, 0, 0, 5, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 3, 0, 0, 3, 3, 0, 0, 2, 2, 0, 2, 2],\n [3, 0, 0, 0, 3, 3, 0, 2, 0, 0, 0, 0, 2, 0],\n [0, 3, 0, 3, 0, 3, 0, 0, 2, 2, 2, 0, 2, 2],\n [0, 3, 0, 0, 3, 3, 3, 0, 2, 0, 0, 0, 2, 2]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 5, 0, 0, 5, 0, 0], [0, 5, 5, 5, 0, 0, 5], [5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 5, 0, 0, 0]], "task_id": "66f2d22f"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1],\n [6, 6, 6],\n [6, 1, 6]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1],\n [6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6],\n [6, 6, 1, 1, 6, 6],\n [6, 6, 1, 1, 6, 6]\n ]\n}\n\n{\n \"input\": [\n [4, 4, 7],\n [8, 7, 7],\n [8, 8, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 4, 4, 7, 7, 7],\n [4, 4, 4, 4, 4, 4, 7, 7, 7],\n [4, 4, 4, 4, 4, 4, 7, 7, 7],\n [8, 8, 8, 7, 7, 7, 7, 7, 7],\n [8, 8, 8, 7, 7, 7, 7, 7, 7],\n [8, 8, 8, 7, 7, 7, 7, 7, 7],\n [8, 8, 8, 8, 8, 8, 4, 4, 4],\n [8, 8, 8, 8, 8, 8, 4, 4, 4],\n [8, 8, 8, 8, 8, 8, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [4, 2, 8],\n [2, 2, 5],\n [8, 5, 4]\n ],\n \"output\": [\n [4, 4, 4, 4, 2, 2, 2, 2, 8, 8, 8, 8],\n [4, 4, 4, 4, 2, 2, 2, 2, 8, 8, 8, 8],\n [4, 4, 4, 4, 2, 2, 2, 2, 8, 8, 8, 8],\n [4, 4, 4, 4, 2, 2, 2, 2, 8, 8, 8, 8],\n [2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5],\n [2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5],\n [2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5],\n [2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5],\n [8, 8, 8, 8, 5, 5, 5, 5, 4, 4, 4, 4],\n [8, 8, 8, 8, 5, 5, 5, 5, 4, 4, 4, 4],\n [8, 8, 8, 8, 5, 5, 5, 5, 4, 4, 4, 4],\n [8, 8, 8, 8, 5, 5, 5, 5, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8],\n [8, 8, 8],\n [8, 8, 8]\n ],\n \"output\": [\n [8, 8, 8],\n [8, 8, 8],\n [8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [3, 3, 3],\n [3, 3, 3],\n [3, 3, 3]\n ],\n \"output\": [\n [3, 3, 3],\n [3, 3, 3],\n [3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [3, 6, 6],\n [3, 6, 6],\n [3, 3, 3]\n ],\n \"output\": [\n [3, 3, 6, 6, 6, 6],\n [3, 3, 6, 6, 6, 6],\n [3, 3, 6, 6, 6, 6],\n [3, 3, 6, 6, 6, 6],\n [3, 3, 3, 3, 3, 3],\n [3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 4],\n [4, 4, 4],\n [2, 4, 2]\n ],\n \"output\": [\n [2, 2, 2, 2, 4, 4],\n [2, 2, 2, 2, 4, 4],\n [4, 4, 4, 4, 4, 4],\n [4, 4, 4, 4, 4, 4],\n [2, 2, 4, 4, 2, 2],\n [2, 2, 4, 4, 2, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [7, 1, 7],\n [3, 3, 6],\n [8, 8, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6]], "task_id": "d4b1c2b1"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],\n [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n ],\n \"output\": [\n [9, 9, 0],\n [9, 9, 9],\n [9, 9, 9]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [0, 8, 8],\n [8, 0, 8],\n [8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n ],\n \"output\": [\n [0, 7, 0],\n [0, 7, 7],\n [7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 0, 6], [6, 6, 6], [6, 6, 0]], "task_id": "e57337a4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 3, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [2, 0, 2, 3, 0, 3, 4, 0, 4],\n [0, 2, 0, 0, 3, 0, 0, 4, 0],\n [2, 2, 2, 3, 3, 3, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 0, 0, 8, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3],\n [3, 3, 3],\n [0, 3, 0],\n [0, 6, 6],\n [6, 6, 6],\n [0, 6, 0],\n [0, 8, 8],\n [8, 8, 8],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 3, 0, 2, 2],\n [3, 3, 0, 2, 2, 0],\n [0, 3, 3, 0, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 6, 0],\n [6, 6, 0],\n [0, 0, 6],\n [0, 2, 0],\n [2, 2, 0],\n [0, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 7, 0], [7, 7, 7], [0, 7, 0], [0, 6, 0], [6, 6, 6], [0, 6, 0], [0, 3, 0], [3, 3, 3], [0, 3, 0], [0, 2, 0], [2, 2, 2], [0, 2, 0]], "task_id": "12997ef3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 8, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 2, 0, 5, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 5, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 1, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 6, 2, 0, 0, 0, 0],\n [0, 0, 0, 2, 7, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 3, 0, 0, 1, 0, 0, 2, 0, 0],\n [0, 0, 3, 0, 1, 0, 2, 0, 0, 0],\n [0, 0, 0, 3, 1, 2, 0, 0, 0, 0],\n [2, 2, 2, 2, 6, 2, 2, 2, 2, 2],\n [0, 0, 0, 2, 7, 7, 0, 0, 0, 0],\n [0, 0, 2, 0, 7, 0, 7, 0, 0, 0],\n [0, 2, 0, 0, 7, 0, 0, 7, 0, 0],\n [2, 0, 0, 0, 7, 0, 0, 0, 7, 0],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 7],\n [0, 0, 0, 0, 7, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 2, 0, 0, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 2, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 2, 0, 0],\n [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8, 0, 8, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 8, 0, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 4, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 9, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "1d398264"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [9, 9, 0, 9, 0],\n [9, 0, 0, 9, 0],\n [0, 9, 9, 9, 9],\n [4, 0, 0, 4, 0],\n [4, 4, 0, 4, 4],\n [4, 4, 4, 0, 4]\n ],\n \"output\": [\n [0, 6, 0, 0, 0],\n [0, 6, 0, 0, 6],\n [6, 0, 0, 6, 0]\n ]\n}\n\n{\n \"input\": [\n [9, 0, 0, 9, 9],\n [0, 0, 0, 0, 0],\n [0, 0, 9, 0, 9],\n [0, 0, 4, 4, 0],\n [4, 4, 4, 0, 0],\n [4, 0, 4, 0, 4]\n ],\n \"output\": [\n [6, 0, 6, 0, 6],\n [6, 6, 6, 0, 0],\n [6, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 9, 0, 0, 0],\n [0, 9, 9, 0, 9],\n [9, 0, 0, 0, 9],\n [4, 4, 0, 4, 0],\n [0, 4, 4, 4, 0],\n [4, 4, 0, 0, 0]\n ],\n \"output\": [\n [6, 0, 0, 6, 0],\n [0, 0, 0, 6, 6],\n [0, 6, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 9, 9, 0],\n [9, 9, 0, 9, 9],\n [0, 9, 0, 0, 0],\n [4, 4, 0, 0, 0],\n [4, 0, 4, 4, 4],\n [0, 4, 0, 0, 4]\n ],\n \"output\": [\n [6, 6, 6, 6, 0],\n [0, 6, 6, 0, 0],\n [0, 0, 0, 0, 6]\n ]\n}\n\n{\n \"input\": [\n [0, 9, 9, 0, 0],\n [9, 0, 0, 0, 9],\n [9, 0, 0, 0, 0],\n [0, 0, 4, 0, 4],\n [4, 4, 0, 4, 0],\n [4, 0, 4, 4, 0]\n ],\n \"output\": [\n [0, 6, 0, 0, 6],\n [0, 6, 0, 6, 6],\n [0, 0, 6, 6, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 9, 0, 9],\n [0, 9, 0, 0, 0],\n [9, 0, 9, 0, 0],\n [4, 0, 4, 4, 4],\n [0, 4, 4, 0, 4],\n [4, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[6, 0, 0, 6, 0], [0, 0, 6, 0, 6], [0, 0, 6, 0, 0]], "task_id": "31d5ba1a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [2, 2, 0, 3, 0, 0, 3],\n [2, 2, 0, 2, 2, 0, 0],\n [0, 0, 0, 2, 2, 0, 0],\n [2, 3, 3, 0, 0, 2, 2],\n [0, 3, 3, 0, 0, 2, 2],\n [0, 0, 0, 0, 3, 3, 0],\n [3, 0, 2, 0, 3, 3, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 0],\n [0, 3, 3, 0, 0],\n [0, 0, 0, 0, 0],\n [2, 2, 0, 0, 2],\n [2, 2, 0, 0, 0],\n [0, 0, 0, 2, 2],\n [0, 0, 0, 2, 2]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3, 3, 0, 0, 0],\n [2, 0, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [3, 3, 0, 0, 2, 2, 0],\n [3, 3, 0, 0, 2, 2, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 3, 3, 0, 0, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 3, 0, 0, 0, 3],\n [0, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [3, 0, 0, 0, 3, 3, 0],\n [0, 0, 3, 0, 3, 3, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 2],\n [3, 3, 0, 2, 2],\n [3, 3, 0, 0, 0],\n [0, 0, 2, 2, 0],\n [3, 0, 2, 2, 0]\n ],\n \"output\": [\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 0, 3, 2, 0],\n [2, 0, 0, 0, 0, 0, 0, 3],\n [0, 0, 2, 2, 0, 0, 0, 0],\n [3, 0, 2, 2, 0, 3, 3, 0],\n [0, 0, 0, 0, 0, 3, 3, 0],\n [0, 3, 3, 0, 0, 0, 0, 0],\n [0, 3, 3, 0, 2, 0, 3, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 0, 0], [0, 1, 0], [0, 0, 1]], "task_id": "3b4c2228"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 4, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 8, 0, 8, 8],\n [8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0],\n [8, 8, 0],\n [0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [0, 8, 0, 0, 8, 0, 0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 0, 0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8, 0, 8, 8, 0],\n [8, 8, 0, 8, 8, 0, 8, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 8, 0, 0, 0, 0, 0, 0],\n [8, 8, 0, 0, 4, 0, 0, 0, 0],\n [0, 8, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8, 0, 8, 8, 0, 8, 8, 0],\n [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 4, 0, 0, 0, 0, 0],\n [4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 4, 0, 0, 8, 8, 8, 0, 0],\n [0, 0, 0, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0]], "task_id": "4852f2fa"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 1, 1, 1, 0, 2, 2, 2, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 4, 4, 4, 0, 0],\n [0, 0, 1, 0, 1, 0, 2, 2, 2, 0, 0, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 2, 2, 2, 0, 6, 6, 6, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 6, 0, 6, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 6, 6, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 0, 2, 2, 2, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 7, 0, 7, 0, 0],\n [0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 7, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 2, 2, 2, 0, 3, 3, 3, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 3, 0, 3, 0, 0],\n [0, 0, 1, 1, 1, 0, 0, 2, 0, 0, 3, 3, 3, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "4c177718"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 4, 1, 0, 0, 0, 0],\n [0, 4, 0, 4, 1, 4, 0, 0, 0],\n [4, 0, 0, 0, 1, 0, 4, 0, 0],\n [0, 4, 4, 0, 1, 0, 0, 0, 0],\n [4, 0, 4, 0, 1, 4, 4, 4, 4],\n [0, 4, 4, 4, 1, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 8, 8],\n [8, 8, 0, 8],\n [8, 8, 0, 0],\n [0, 8, 8, 0],\n [8, 8, 8, 8],\n [0, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 4, 4, 1, 0, 0, 4, 4],\n [0, 4, 4, 4, 1, 0, 0, 0, 0],\n [0, 4, 0, 0, 1, 4, 0, 4, 0],\n [0, 4, 4, 4, 1, 4, 4, 0, 4],\n [0, 4, 4, 4, 1, 4, 0, 4, 4],\n [0, 4, 0, 4, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 8, 8],\n [0, 8, 8, 8],\n [8, 8, 8, 0],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 4, 0, 1, 4, 0, 4, 4],\n [4, 0, 4, 0, 1, 4, 4, 4, 0],\n [4, 4, 0, 4, 1, 4, 0, 4, 0],\n [0, 4, 0, 0, 1, 4, 0, 0, 4],\n [0, 0, 4, 4, 1, 4, 4, 4, 0],\n [4, 4, 0, 4, 1, 4, 0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 8],\n [8, 8, 8, 0],\n [8, 8, 8, 8],\n [8, 8, 0, 8],\n [8, 8, 8, 8],\n [8, 8, 0, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 4, 1, 4, 4, 0, 0],\n [0, 0, 4, 4, 1, 0, 4, 0, 0],\n [4, 0, 4, 4, 1, 0, 4, 4, 0],\n [4, 4, 4, 0, 1, 4, 4, 0, 0],\n [4, 0, 4, 4, 1, 4, 0, 0, 4],\n [0, 0, 0, 0, 1, 4, 4, 4, 4]\n ],\n \"output\": [\n [8, 8, 0, 8],\n [0, 8, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 8, 0],\n [8, 0, 8, 8],\n [8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [4, 0, 0, 4, 1, 0, 4, 0, 4],\n [0, 0, 4, 4, 1, 0, 4, 0, 0],\n [4, 0, 4, 4, 1, 4, 0, 4, 0],\n [0, 4, 0, 4, 1, 4, 0, 4, 4],\n [4, 4, 0, 4, 1, 0, 4, 4, 0],\n [0, 4, 4, 4, 1, 0, 4, 0, 4]\n ],\n \"output\": [\n [8, 8, 0, 8],\n [0, 8, 8, 8],\n [8, 0, 8, 8],\n [8, 8, 8, 8],\n [8, 8, 8, 8],\n [0, 8, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [4, 4, 4, 0, 1, 0, 0, 4, 4],\n [4, 4, 0, 0, 1, 0, 0, 0, 4],\n [4, 0, 0, 4, 1, 0, 4, 0, 0],\n [0, 4, 4, 4, 1, 0, 4, 4, 4],\n [0, 4, 0, 4, 1, 0, 0, 4, 0],\n [0, 0, 4, 0, 1, 0, 4, 4, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[8, 8, 8, 8], [8, 8, 0, 8], [8, 8, 0, 8], [0, 8, 8, 8], [0, 8, 8, 8], [0, 8, 8, 0]], "task_id": "5d2a5c43"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [8, 0, 0],\n [0, 8, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 2, 2],\n [2, 0, 2],\n [2, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 3],\n [0, 3, 0],\n [3, 0, 0]\n ],\n \"output\": [\n [1, 1, 0],\n [1, 0, 1],\n [0, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [5, 0, 0],\n [5, 5, 0],\n [5, 0, 0]\n ],\n \"output\": [\n [0, 4, 4],\n [0, 0, 4],\n [0, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [5, 5, 5],\n [0, 0, 5],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [4, 4, 0],\n [4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 8, 0],\n [0, 8, 0],\n [8, 0, 0]\n ],\n \"output\": [\n [2, 0, 2],\n [2, 0, 2],\n [0, 2, 2]\n ]\n}\n\n{\n \"input\": [\n [8, 0, 8],\n [0, 8, 0],\n [0, 8, 0]\n ],\n \"output\": [\n [0, 2, 0],\n [2, 0, 2],\n [2, 0, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 5, 0],\n [5, 5, 0],\n [0, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 0, 4], [0, 0, 4], [4, 4, 0]], "task_id": "6ea4a07e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 4, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [4, 0, 4, 4, 4, 4, 4, 4, 4],\n [4, 0, 4, 0, 0, 0, 0, 0, 4],\n [4, 0, 4, 0, 4, 4, 4, 0, 4],\n [4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 0, 4, 0, 4, 0, 4, 0, 4],\n [4, 0, 4, 0, 0, 0, 4, 0, 4],\n [4, 0, 4, 4, 4, 4, 4, 0, 4],\n [4, 0, 0, 0, 0, 0, 0, 0, 4],\n [4, 4, 4, 4, 4, 4, 4, 4, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [5, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [5, 5, 5, 5, 5, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 5],\n [5, 0, 5, 0, 5, 0, 5, 0, 5],\n [5, 0, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 5, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 0, 5, 0, 5, 0, 5],\n [0, 0, 0, 0, 5, 0, 5, 0, 5],\n [5, 5, 5, 5, 5, 0, 5, 0, 5]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 0, 3, 0, 3, 0, 3],\n [3, 0, 3, 0, 0, 0, 3, 0, 3],\n [3, 0, 3, 3, 3, 3, 3, 0, 3],\n [3, 0, 0, 0, 0, 0, 0, 0, 3],\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 8],\n [0, 0, 0]\n ],\n \"output\": [\n [8, 0, 8, 0, 8, 0, 8, 8, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 0],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 8],\n [8, 0, 8, 0, 8, 0, 8, 0, 0],\n [8, 0, 8, 0, 8, 0, 8, 8, 8],\n [8, 0, 8, 0, 8, 0, 0, 0, 0],\n [8, 0, 8, 0, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 7],\n [0, 0, 0],\n [0, 0, 0]\n ],\n \"output\": [\n [7, 0, 7, 0, 7, 0, 7, 0, 7],\n [7, 0, 7, 0, 7, 0, 7, 0, 0],\n [7, 0, 7, 0, 7, 0, 7, 7, 7],\n [7, 0, 7, 0, 7, 0, 0, 0, 0],\n [7, 0, 7, 0, 7, 7, 7, 7, 7],\n [7, 0, 7, 0, 0, 0, 0, 0, 0],\n [7, 0, 7, 7, 7, 7, 7, 7, 7],\n [7, 0, 0, 0, 0, 0, 0, 0, 0],\n [7, 7, 7, 7, 7, 7, 7, 7, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0],\n [0, 0, 0],\n [3, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 3, 3, 3, 3, 0, 3], [0, 0, 0, 0, 0, 0, 3, 0, 3], [3, 3, 3, 3, 3, 0, 3, 0, 3], [0, 0, 0, 0, 3, 0, 3, 0, 3], [3, 3, 3, 0, 3, 0, 3, 0, 3], [3, 0, 3, 0, 3, 0, 3, 0, 3], [3, 0, 3, 0, 3, 0, 3, 0, 3]], "task_id": "8b28cd80"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 4, 1, 0, 0, 1, 6],\n [0, 0, 1, 0, 0, 0, 0],\n [1, 1, 0, 0, 1, 1, 0],\n [0, 1, 0, 0, 0, 1, 1],\n [0, 0, 1, 0, 0, 2, 0],\n [1, 0, 1, 0, 1, 0, 7],\n [1, 1, 1, 0, 4, 1, 0]\n ],\n \"output\": [\n [0, 0, 8],\n [8, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 0, 0, 2, 2, 0, 5],\n [0, 2, 2, 0, 0, 0, 2],\n [0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 0, 9],\n [0, 9, 0, 0, 0, 0, 2],\n [0, 0, 2, 1, 0, 0, 8],\n [2, 0, 0, 2, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 4, 0, 0, 4, 1, 3],\n [3, 3, 4, 3, 0, 3, 7],\n [3, 0, 0, 0, 1, 0, 3],\n [0, 0, 3, 0, 3, 0, 0],\n [3, 0, 0, 3, 3, 0, 3],\n [3, 0, 3, 0, 3, 0, 3],\n [3, 3, 3, 0, 4, 2, 3]\n ],\n \"output\": [\n [0, 8, 8],\n [0, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 0, 1, 0, 7, 0, 0],\n [1, 1, 9, 1, 0, 1, 0],\n [0, 0, 1, 1, 0, 2, 0],\n [0, 0, 0, 0, 3, 0, 1],\n [0, 4, 0, 1, 0, 0, 1],\n [0, 0, 1, 0, 2, 0, 8],\n [0, 0, 1, 0, 7, 3, 1]\n ],\n \"output\": [\n [0, 0, 8],\n [8, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 3, 0, 3, 5, 3, 0],\n [0, 0, 3, 3, 0, 0, 0],\n [8, 0, 0, 0, 0, 0, 3],\n [3, 4, 3, 9, 3, 0, 3],\n [0, 0, 9, 3, 1, 3, 3],\n [0, 3, 3, 3, 0, 3, 0],\n [0, 0, 0, 0, 0, 0, 3]\n ],\n \"output\": [\n [0, 8, 8],\n [0, 8, 0],\n [0, 8, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 2, 2, 0, 2],\n [0, 2, 2, 9, 2, 2, 0],\n [0, 5, 0, 2, 4, 6, 0],\n [2, 0, 0, 0, 0, 9, 2],\n [0, 0, 0, 2, 2, 0, 0],\n [8, 0, 2, 9, 0, 6, 3],\n [0, 2, 0, 2, 0, 2, 4]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 2, 0, 1, 5, 3],\n [0, 0, 2, 9, 0, 2, 0],\n [2, 2, 2, 4, 2, 0, 0],\n [0, 2, 0, 2, 7, 2, 0],\n [2, 2, 0, 0, 2, 2, 6],\n [0, 2, 2, 0, 2, 0, 0],\n [5, 0, 4, 2, 0, 2, 2]\n ],\n \"output\": [\n [0, 0, 0],\n [8, 8, 8],\n [0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [3, 0, 3, 0, 0, 0, 3],\n [3, 0, 9, 5, 0, 0, 5],\n [0, 3, 0, 3, 0, 2, 9],\n [8, 3, 0, 3, 0, 0, 7],\n [0, 3, 5, 0, 0, 3, 3],\n [0, 0, 3, 3, 0, 0, 0],\n [0, 0, 3, 0, 4, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 8], [0, 8, 0], [0, 8, 0]], "task_id": "9110e3c5"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 2, 2, 1, 1, 1, 8, 8, 2, 2, 8, 8],\n [1, 1, 2, 2, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 2, 2, 2, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 2, 2, 2, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 2, 2, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 2, 2, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 2, 2, 2, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 2, 2, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 2, 2, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 2, 2, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 2, 2, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 2, 2, 2, 1, 1, 1],\n [8, 2, 2, 8, 8, 8, 1, 1, 2, 2, 2, 1, 1, 1],\n [8, 2, 2, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 2],\n [2, 2, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 2, 2],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 2, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 2, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1]\n ],\n \"output\": [\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 1, 2, 2, 1, 1, 1, 1, 1],\n [1, 1, 1, 1, 1, 2, 1, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1],\n [8, 8, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 2, 2, 8, 8, 8],\n [8, 8, 8, 8, 2, 2, 8, 8, 8],\n [8, 2, 8, 8, 8, 8, 8, 8, 8],\n [8, 8, 8, 8, 8, 8, 8, 8, 8]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 1, 1, 1, 1, 2], [1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 8, 8, 8, 8, 8, 8, 8], [2, 2, 8, 8, 8, 8, 8, 8, 8], [2, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8]], "task_id": "9b4c17c4"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 0, 8, 8, 8, 0],\n [0, 8, 0, 8, 8, 0],\n [8, 8, 8, 0, 0, 0],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [8, 8, 8, 8, 0, 0],\n [8, 8, 8, 8, 8, 8],\n [0, 8, 8, 0, 8, 8],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 8, 0],\n [8, 8, 8, 8, 8, 0],\n [0, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 8, 8, 0, 0],\n [8, 8, 8, 8, 0, 0],\n [8, 8, 8, 8, 8, 8],\n [0, 0, 8, 8, 8, 8],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [8, 8, 0, 8, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 8, 0, 0],\n [0, 8, 8, 8, 0, 0],\n [8, 8, 8, 0, 8, 0],\n [0, 8, 8, 8, 8, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8],\n [0, 0, 0, 0, 0],\n [0, 8, 0, 0, 8],\n [8, 8, 0, 8, 8]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 8, 0, 8, 0, 0],\n [8, 8, 8, 8, 8, 0],\n [0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 8, 8],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 8, 0, 0, 8], [8, 8, 0, 8, 8], [0, 0, 0, 0, 0], [0, 8, 0, 0, 8], [8, 8, 0, 8, 8]], "task_id": "b1fc8b8e"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 1, 5, 2, 2, 2, 0],\n [1, 0, 0, 0, 5, 0, 2, 2, 2],\n [1, 1, 0, 0, 5, 0, 0, 2, 2],\n [1, 1, 1, 0, 5, 0, 0, 0, 2]\n ],\n \"output\": [\n [2, 2, 2, 1],\n [1, 2, 2, 2],\n [1, 1, 2, 2],\n [1, 1, 1, 2]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 0, 5, 2, 2, 0, 0],\n [1, 1, 0, 0, 5, 0, 2, 2, 0],\n [1, 1, 1, 0, 5, 0, 2, 2, 0]\n ],\n \"output\": [\n [0, 0, 0, 1],\n [1, 0, 0, 0],\n [1, 1, 0, 0],\n [1, 1, 1, 0]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 0, 0, 5, 0, 0, 3, 3],\n [1, 0, 0, 1, 5, 0, 3, 3, 0],\n [1, 0, 0, 1, 5, 0, 3, 3, 0],\n [1, 1, 0, 0, 5, 0, 0, 3, 3]\n ],\n \"output\": [\n [1, 1, 3, 3],\n [1, 3, 3, 1],\n [1, 3, 3, 1],\n [1, 1, 3, 3]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 0, 0, 0, 0],\n [1, 0, 0, 1, 5, 0, 6, 6, 0],\n [1, 0, 0, 1, 5, 0, 6, 6, 0],\n [1, 1, 1, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 6, 6, 1],\n [1, 6, 6, 1],\n [1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 1, 5, 2, 2, 0, 0],\n [1, 0, 0, 1, 5, 0, 0, 0, 0],\n [1, 1, 1, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1],\n [1, 1, 1, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 3, 3, 0, 0],\n [1, 0, 0, 1, 5, 3, 3, 0, 0],\n [1, 0, 0, 1, 5, 3, 0, 0, 0],\n [1, 0, 0, 1, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1],\n [1, 0, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [1, 1, 1, 1, 5, 0, 0, 0, 0],\n [1, 0, 0, 0, 5, 0, 7, 7, 7],\n [1, 0, 1, 1, 5, 0, 7, 0, 0],\n [1, 0, 1, 0, 5, 0, 7, 0, 7]\n ],\n \"output\": [\n [1, 1, 1, 1],\n [1, 7, 7, 7],\n [1, 7, 1, 1],\n [1, 7, 1, 7]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [1, 1, 1, 1, 5, 2, 0, 0, 0],\n [0, 1, 1, 0, 5, 2, 2, 2, 2],\n [0, 1, 1, 0, 5, 2, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[1, 1, 1, 1], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]], "task_id": "bbb1b8b6"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 3, 3, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 3, 3, 3, 5, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 5, 2, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0],\n [0, 5, 2],\n [0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0],\n [0, 5, 3],\n [0, 2, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 5, 0, 0],\n [0, 0, 0, 0, 2, 0, 0],\n [0, 0, 0, 0, 2, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 5, 0, 0],\n [0, 0, 0, 0, 3, 0, 0],\n [0, 0, 0, 0, 3, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "c074846d"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 1],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 1, 0, 1]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0],\n [0, 2, 0, 0],\n [0, 0, 0, 0],\n [0, 2, 0, 0],\n [2, 2, 2, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [2, 2, 2, 0, 0],\n [0, 2, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2],\n [0, 2, 0, 2, 0],\n [2, 2, 2, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 2, 2, 2],\n [2, 2, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 2, 0],\n [0, 2, 2, 2, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 2, 2, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 2, 2, 2],\n [0, 2, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 0, 2, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 0, 0],\n [0, 2, 0, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 2, 0, 0, 0, 0, 0],\n [2, 2, 2, 0, 0, 2, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 0, 2, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 2],\n [0, 2, 0, 0, 0, 2, 0],\n [2, 2, 2, 0, 0, 0, 0]\n ],\n \"output\": [\n [3, 0, 3, 1, 0, 0],\n [0, 0, 0, 0, 0, 0],\n [3, 0, 3, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[3, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0], [3, 0, 0, 1, 0, 0]], "task_id": "d5c634a2"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 2, 0, 0, 2, 0, 0, 0, 0],\n [0, 0, 2, 2, 2, 2, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],\n [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n [0, 0, 1, 1, 0, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 0, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 3, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 5, 0, 0, 3, 0, 5, 0, 0, 0],\n [0, 5, 5, 5, 3, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [0, 0, 6, 6, 0, 6, 6, 0, 0, 0],\n [0, 0, 6, 6, 0, 6, 6, 0, 0, 0],\n [0, 0, 0, 6, 0, 6, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 6, 3, 6, 0, 0, 0, 0], [0, 0, 6, 6, 3, 6, 6, 0, 0, 0], [0, 0, 6, 6, 3, 6, 6, 0, 0, 0], [0, 0, 0, 6, 3, 6, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]], "task_id": "da2b0fe3"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0],\n [0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0],\n [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0],\n [0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0],\n [0, 3, 3, 3, 3, 3, 0],\n [0, 0, 0, 3, 0, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 0, 2, 2, 2, 0, 0],\n [0, 1, 1, 1, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 3, 0, 3, 3, 0, 3, 0, 0],\n [0, 0, 3, 3, 3, 3, 3, 3, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 2, 0, 0, 2, 0, 0, 0],\n [0, 0, 0, 2, 2, 2, 2, 0, 0, 0],\n [0, 0, 0, 0, 2, 2, 0, 0, 0, 0],\n [0, 0, 4, 4, 4, 4, 4, 4, 0, 0],\n [0, 0, 0, 8, 0, 0, 8, 0, 0, 0],\n [0, 0, 0, 0, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 6, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 6, 6, 0, 0, 0], [0, 0, 0, 8, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 3, 0, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "e21a174a"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [6, 6, 6, 6, 5, 0, 5, 0],\n [6, 0, 0, 0, 5, 5, 0, 0],\n [6, 0, 6, 6, 0, 0, 5, 5],\n [0, 0, 6, 0, 0, 5, 5, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 4, 4],\n [0, 4, 0, 0],\n [4, 0, 0, 4]\n ]\n}\n\n{\n \"input\": [\n [0, 6, 6, 0, 5, 5, 5, 0],\n [0, 6, 0, 6, 5, 0, 0, 5],\n [0, 6, 6, 6, 5, 5, 5, 5],\n [6, 0, 0, 0, 0, 5, 0, 5]\n ],\n \"output\": [\n [0, 0, 0, 4],\n [0, 0, 4, 0],\n [0, 0, 0, 0],\n [0, 0, 4, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 6, 6, 0, 5, 0, 5, 5],\n [6, 0, 0, 0, 0, 5, 5, 5],\n [6, 0, 0, 0, 0, 0, 0, 0],\n [0, 6, 6, 6, 5, 5, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 4, 4, 4],\n [0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [6, 0, 6, 0, 0, 0, 5, 5],\n [0, 6, 6, 6, 5, 0, 5, 5],\n [6, 6, 0, 6, 5, 0, 5, 5],\n [6, 6, 0, 0, 5, 0, 0, 0]\n ],\n \"output\": [\n [0, 4, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 4, 4]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 6, 0, 0, 0, 5, 0, 5],\n [0, 6, 0, 0, 0, 0, 0, 5],\n [6, 0, 0, 0, 5, 5, 0, 0],\n [6, 6, 0, 6, 0, 0, 0, 5]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[4, 0, 4, 0], [4, 0, 4, 0], [0, 0, 4, 4], [0, 0, 4, 0]], "task_id": "e345f17b"} {"prompt": "Here are the example input and output pairs from which you should learn the underlying rule to later predict the output for the given test input:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 0, 4, 0, 0, 0, 0],\n [0, 0, 0, 4, 4, 4, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 0, 8, 0, 0, 0, 0],\n [0, 0, 0, 8, 8, 8, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 0, 6, 0, 0, 0],\n [0, 0, 0, 0, 6, 6, 6, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 0, 6, 0, 0, 0, 0, 0],\n [0, 0, 6, 6, 6, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 0, 8, 0, 0, 0, 0, 0],\n [0, 0, 8, 8, 8, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 0, 8],\n [0, 0, 0, 0, 0, 0, 0, 8, 8, 8],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ]\n}\n----------------------------------------\nNow, solve the following puzzle based on its input grid by applying the rules you have learned from the training data.:\n----------------------------------------\n{\n \"input\": [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 0, 3, 0, 0, 0, 0],\n [0, 0, 0, 3, 3, 3, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n ],\n \"output\": [\n []\n ]\n}\n----------------------------------------\nWhat is the output grid? Your final answer should provide the output grid in the form as in the example input and output pairs, e.g. :\n```json\n[\n [0, 0],\n [0, 0]\n]\n```", "answer": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [3, 0, 3, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "task_id": "f3e62deb"} ================================================ FILE: eval/eval/arc_agi_1.py ================================================ import json import re from collections import defaultdict import numpy as np def parse_model_output(output): try: return json.loads(output) except json.JSONDecodeError: json_match = re.findall(r"```(?:json|python)\s*(.*?)\s*```", output, re.DOTALL) if json_match: try: return json.loads(json_match[-1]) except json.JSONDecodeError: print("Error: Invalid JSON format in the ```json``` block") return None else: array_match = re.findall(r"(\[\[(?:[\d,\[\]\s\n]*)\]\])", output, re.DOTALL) if array_match: try: return json.loads(array_match[-1]) except json.JSONDecodeError: print("Error: Invalid JSON format in the last array-like structure") return None else: print("Error: No valid JSON array found in the output") return None def solution_score(predicted, ground_truth): if not predicted or not ground_truth: return 0.0 return 1.0 if predicted == ground_truth else 0.0 def compute_scores_arc_agi_1(jobs, cache_path): taskid2score = defaultdict(list) for job in jobs: assert ( len(job.get("gen", [])) == 1 ), "Each job should contain exactly one generation output" answer = job.get("answer") pred_raw = job["gen"][0] parsed_pred = parse_model_output(pred_raw) if parsed_pred is not None: solu_score = solution_score(parsed_pred, answer) else: solu_score = 0.0 job.update({"acc": solu_score}) taskid2score[job["task_id"]].append(solu_score) save_cache(jobs, cache_path) assert len(taskid2score) == 400, 'The ARC-AGI-1 dataset should have 400 tasks' return sum(np.mean(x) for x in taskid2score.values()) / len(taskid2score) if jobs else 0.0 def save_cache(jobs, cache_path): with open(cache_path, "w", encoding="utf-8") as g: for job in jobs: g.write(json.dumps(job, ensure_ascii=False) + "\n") g.flush() ================================================ FILE: eval/eval/eval.py ================================================ import json import argparse from tqdm import tqdm import os import yaml ALL_TASKS = {} from arc_agi_1 import compute_scores_arc_agi_1 ALL_TASKS['arc_agi_1'] = compute_scores_arc_agi_1 def get_after_think(text): parts = text.split("\n\n\n", 1) if len(parts) > 1: return parts[1] else: return text def main(): parser = argparse.ArgumentParser( description="Evaluate model outputs using a YAML configuration." ) parser.add_argument( "--config", type=str, required=True, help="Path to the YAML configuration file" ) args = parser.parse_args() config_file_path = args.config try: with open(config_file_path, "r", encoding="utf-8") as f: config = yaml.safe_load(f) except FileNotFoundError: print( f"Error: Configuration file '{config_file_path}' not found. Please check the path." ) return except yaml.YAMLError as e: print(f"Error: Failed to parse YAML file '{config_file_path}':\n{e}") return except Exception as e: print(f"An unknown error occurred while loading the configuration file: {e}") return eval_input_path = config.get("eval_input_path") details_path = config.get("details_path") task_name = config.get("task_name") if eval_input_path is None: print( "Error: Required parameter 'eval_input_path' is missing in the YAML configuration file." ) return if details_path is None: print( "Error: Required parameter 'details_path' is missing in the YAML configuration file." ) return if task_name is None: print( "Error: Required parameter 'task_name' is missing in the YAML configuration file." ) return if task_name not in ALL_TASKS: print( f"Error: Invalid value '{task_name}' for 'task_name'. It must be one of the following: {ALL_TASKS.keys()}" ) return print("\n--- Evaluation Configuration Information ---") print(f"Model Output File Path: {eval_input_path}") print(f"Results Details Path: {details_path}") print(f"Task Name: {task_name}") print("--------------------\n") os.makedirs(os.path.dirname(details_path), exist_ok=True) with open(eval_input_path, "r", encoding="utf-8") as f: data = [json.loads(line) for line in f] for item in data: temp = get_after_think(item["gen"][0]) item["gen"][0] = temp acc = ALL_TASKS[task_name](data, details_path) print(f"Task: {task_name}, Accuracy: {acc}") print("Evaluation complete!") if __name__ == "__main__": main() ================================================ FILE: eval/eval_res/ARCAGI-Qwen3-235B-A22B-Instruct-2507_eval_result.txt ================================================ --- Evaluation Configuration Information --- Model Output File Path: output/ARCAGI-Qwen3-235B-A22B-Instruct-2507.jsonl Results Details Path: output/ARCAGI-Qwen3-235B-A22B-Instruct-2507_details.jsonl Task Name: arc_agi_1 -------------------- Task: arc_agi_1, Accuracy: 0.4075 Evaluation complete! ================================================ FILE: eval/generate_api_answers/infer_multithread.py ================================================ import json import argparse from tqdm import tqdm import copy import concurrent.futures import threading import os import collections import yaml from utils_vllm import get_content file_lock = threading.Lock() def count_completed_samples(output_file): prompt_counts = collections.defaultdict(int) if os.path.exists(output_file) and os.path.getsize(output_file) > 0: with open(output_file, "r", encoding="utf-8") as f: for line in f: try: item = json.loads(line) prompt = item["prompt"] gen_count = len(item.get("gen", [])) prompt_counts[prompt] += gen_count except json.JSONDecodeError: continue return prompt_counts def process_item( item, output_file, base_url, model_name, temperature, top_p, max_tokens, top_k, presence_penalty, ): result = copy.deepcopy(item) response = get_content( item["prompt"], base_url, model_name, temperature, top_p, max_tokens, top_k, presence_penalty, ) if "gen" not in result: result["gen"] = [] result["gen"].append(response) with file_lock: with open(output_file, "a", encoding="utf-8") as g: g.write(json.dumps(result, ensure_ascii=False) + "\n") g.flush() return result def main(): parser = argparse.ArgumentParser( description="Run inference on model with prompts from a jsonl file, configurable via YAML." ) parser.add_argument( "--config", type=str, required=True, help="Path to the YAML configuration file." ) args = parser.parse_args() config_file_path = args.config try: with open(config_file_path, "r", encoding="utf-8") as f: config = yaml.safe_load(f) except FileNotFoundError: print( f"Error: Configuration file '{config_file_path}' not found. Please check the path." ) return except yaml.YAMLError as e: print(f"Error: Failed to parse YAML file '{config_file_path}':\n{e}") return except Exception as e: print(f"An unknown error occurred while loading the configuration file: {e}") return input_file = config.get("input_file") output_file = config.get("output_file") if input_file is None: print( "Error: Required parameter 'input_file' is missing in the YAML configuration file." ) return if output_file is None: print( "Error: Required parameter 'output_file' is missing in the YAML configuration file." ) return n_samples = config.get("n_samples", 1) max_workers = config.get("max_workers", 128) base_url = config.get("base_url", "http://10.77.249.36:8030/v1") model_name = config.get("model_name", "Qwen/QwQ-32B") top_p = config.get("top_p", 0.7) temperature = config.get("temperature", 0.8) top_k = config.get("top_k", 20) max_tokens = config.get("max_tokens", 32768) presence_penalty = config.get("presence_penalty", 1.0) print("\n--- Configuration Information ---") print(f"Input File: {input_file}") print(f"Output File: {output_file}") print(f"Number of Samples per Prompt: {n_samples}") print(f"Maximum Workers: {max_workers}") print(f"VLLM Server Base URL: {base_url}") print(f"VLLM Server Model Name: {model_name}") print(f"Top-p: {top_p}") print(f"Temperature: {temperature}") print(f"Top-k: {top_k}") print(f"Max Generation Tokens: {max_tokens}") print(f"Presence Penalty: {presence_penalty}") print("----------------\n") with open(input_file, "r", encoding="utf-8") as f: data = [json.loads(l) for l in f] if os.path.exists(output_file): completed_counts = count_completed_samples(output_file) total_completed = sum(completed_counts.values()) print(f"Found {total_completed} completed samples from previous run") else: with open(output_file, "w", encoding="utf-8") as g: completed_counts = dict() expanded_data = [] for item in data: prompt = item["prompt"] completed = completed_counts.get(prompt, 0) remaining = n_samples - completed for _ in range(remaining): expanded_data.append(copy.deepcopy(item)) total_tasks = len(expanded_data) print(f"Total remaining samples to process: {total_tasks}") completed_count = 0 with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: future_to_item = { executor.submit( process_item, item, output_file, base_url, model_name, temperature, top_p, max_tokens, top_k, presence_penalty, ): i for i, item in enumerate(expanded_data) } with tqdm(total=len(expanded_data), desc="Processing samples") as pbar: for future in concurrent.futures.as_completed(future_to_item): idx = future_to_item[future] try: future.result() completed_count += 1 except Exception as exc: print(f"Error processing sample {idx}: {exc}") pbar.update(1) print(f"Completed {completed_count}/{len(expanded_data)} samples") print(f"Results saved to {output_file}") if __name__ == "__main__": main() ================================================ FILE: eval/generate_api_answers/utils_vllm.py ================================================ import os import time import random import openai import logging from packaging.version import parse as parse_version IS_OPENAI_V1 = parse_version(openai.__version__) >= parse_version("1.0.0") if IS_OPENAI_V1: from openai import APIError, APIConnectionError, RateLimitError else: from openai.error import APIError, APIConnectionError, RateLimitError class ClientError(RuntimeError): pass def get_content( query, base_url, model_name, temperature, top_p, max_tokens, top_k, presence_penalty ): API_KEY = os.environ.get("OPENAI_API_KEY", "EMPTY") API_REQUEST_TIMEOUT = int(os.getenv("OPENAI_API_REQUEST_TIMEOUT", "99999")) if IS_OPENAI_V1: import httpx client = openai.OpenAI( api_key=API_KEY, base_url=base_url, timeout=httpx.Timeout(API_REQUEST_TIMEOUT), ) else: client = None messages = [{"role": "user", "content": query}] call_args = dict( model=model_name, messages=messages, temperature=temperature, top_p=top_p, max_tokens=max_tokens, presence_penalty=presence_penalty, ) if IS_OPENAI_V1: call_args["extra_body"] = {} extra_args_dict = call_args["extra_body"] else: extra_args_dict = call_args extra_args_dict.update( { "top_k": top_k, } ) if IS_OPENAI_V1: call_func = client.chat.completions.create call_args["timeout"] = API_REQUEST_TIMEOUT else: call_func = openai.ChatCompletion.create call_args["api_key"] = API_KEY call_args["api_base"] = base_url call_args["request_timeout"] = API_REQUEST_TIMEOUT result = "" try: completion = call_func( **call_args, ) result = completion.choices[0].message.content except AttributeError as e: err_msg = getattr(completion, "message", "") if err_msg: time.sleep(random.randint(25, 35)) raise ClientError(err_msg) from e raise ClientError(err_msg) from e except (APIConnectionError, RateLimitError) as e: err_msg = e.message if IS_OPENAI_V1 else e.user_message time.sleep(random.randint(25, 35)) raise ClientError(err_msg) from e except APIError as e: err_msg = e.message if IS_OPENAI_V1 else e.user_message if ( "maximum context length" in err_msg ): # or "Expecting value: line 1 column 1 (char 0)" in err_msg: logging.warn(f"max length exceeded. Error: {err_msg}") return {"gen": "", "end_reason": "max length exceeded"} time.sleep(1) raise ClientError(err_msg) from e return result if __name__ == "__main__": conversation_history = [] user_input = "Hello!" res = get_content(user_input, "http://10.77.249.36:8030/v1", "Qwen/QwQ") print(f"Response: {res}") user_input = "How are you?" res = get_content(user_input, "http://10.77.249.36:8030/v1", "Qwen/QwQ") print(f"Response: {res}") ================================================ FILE: eval/output/ARCAGI-Qwen3-235B-A22B-Instruct-2507.jsonl ================================================ [File too large to display: 12.1 MB] ================================================ FILE: eval/output/ARCAGI-Qwen3-235B-A22B-Instruct-2507_details.jsonl ================================================ [File too large to display: 12.1 MB] ================================================ FILE: eval/requirements.txt ================================================ # common openai>=0.28.1,<=1.65.5 packaging numpy tqdm datasets==2.14.6 pyyaml ================================================ FILE: examples/README.md ================================================ # Examples > [!IMPORTANT] > The examples in this directory should be considered deprecated at the moment and they are not updated for Qwen3. > ================================================ FILE: examples/demo/cli_demo.py ================================================ # Copyright (c) Alibaba Cloud. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """A simple command-line interactive chat demo.""" import argparse import os import platform import shutil from copy import deepcopy from threading import Thread import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer from transformers.trainer_utils import set_seed DEFAULT_CKPT_PATH = "Qwen/Qwen2.5-7B-Instruct" _WELCOME_MSG = """\ Welcome to use Qwen2.5-Instruct model, type text to start chat, type :h to show command help. (欢迎使用 Qwen2.5-Instruct 模型，输入内容即可进行对话，:h 显示命令帮助。) Note: This demo is governed by the original license of Qwen2.5. We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, including hate speech, violence, pornography, deception, etc. (注：本演示受Qwen2.5的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。) """ _HELP_MSG = """\ Commands: :help / :h Show this help message 显示帮助信息 :exit / :quit / :q Exit the demo 退出Demo :clear / :cl Clear screen 清屏 :clear-history / :clh Clear history 清除对话历史 :history / :his Show history 显示对话历史 :seed Show current random seed 显示当前随机种子 :seed Set random seed to 设置随机种子 :conf Show current generation config 显示生成配置 :conf = Change generation config 修改生成配置 :reset-conf Reset generation config 重置生成配置 """ _ALL_COMMAND_NAMES = [ "help", "h", "exit", "quit", "q", "clear", "cl", "clear-history", "clh", "history", "his", "seed", "conf", "reset-conf", ] def _setup_readline(): try: import readline except ImportError: return _matches = [] def _completer(text, state): nonlocal _matches if state == 0: _matches = [ cmd_name for cmd_name in _ALL_COMMAND_NAMES if cmd_name.startswith(text) ] if 0 <= state < len(_matches): return _matches[state] return None readline.set_completer(_completer) readline.parse_and_bind("tab: complete") def _load_model_tokenizer(args): tokenizer = AutoTokenizer.from_pretrained( args.checkpoint_path, resume_download=True, ) if args.cpu_only: device_map = "cpu" else: device_map = "auto" model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, torch_dtype="auto", device_map=device_map, resume_download=True, ).eval() model.generation_config.max_new_tokens = 2048 # For chat. return model, tokenizer def _gc(): import gc gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() def _clear_screen(): if platform.system() == "Windows": os.system("cls") else: os.system("clear") def _print_history(history): terminal_width = shutil.get_terminal_size()[0] print(f"History ({len(history)})".center(terminal_width, "=")) for index, (query, response) in enumerate(history): print(f"User[{index}]: {query}") print(f"Qwen[{index}]: {response}") print("=" * terminal_width) def _get_input() -> str: while True: try: message = input("User> ").strip() except UnicodeDecodeError: print("[ERROR] Encoding error in input") continue except KeyboardInterrupt: exit(1) if message: return message print("[ERROR] Query is empty") def _chat_stream(model, tokenizer, query, history): conversation = [] for query_h, response_h in history: conversation.append({"role": "user", "content": query_h}) conversation.append({"role": "assistant", "content": response_h}) conversation.append({"role": "user", "content": query}) input_text = tokenizer.apply_chat_template( conversation, add_generation_prompt=True, tokenize=False, ) inputs = tokenizer([input_text], return_tensors="pt").to(model.device) streamer = TextIteratorStreamer( tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True ) generation_kwargs = { **inputs, "streamer": streamer, } thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() for new_text in streamer: yield new_text def main(): parser = argparse.ArgumentParser( description="Qwen2.5-Instruct command-line interactive chat demo." ) parser.add_argument( "-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH, help="Checkpoint name or path, default to %(default)r", ) parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed") parser.add_argument( "--cpu-only", action="store_true", help="Run demo with CPU only" ) args = parser.parse_args() history, response = [], "" model, tokenizer = _load_model_tokenizer(args) orig_gen_config = deepcopy(model.generation_config) _setup_readline() _clear_screen() print(_WELCOME_MSG) seed = args.seed while True: query = _get_input() # Process commands. if query.startswith(":"): command_words = query[1:].strip().split() if not command_words: command = "" else: command = command_words[0] if command in ["exit", "quit", "q"]: break elif command in ["clear", "cl"]: _clear_screen() print(_WELCOME_MSG) _gc() continue elif command in ["clear-history", "clh"]: print(f"[INFO] All {len(history)} history cleared") history.clear() _gc() continue elif command in ["help", "h"]: print(_HELP_MSG) continue elif command in ["history", "his"]: _print_history(history) continue elif command in ["seed"]: if len(command_words) == 1: print(f"[INFO] Current random seed: {seed}") continue else: new_seed_s = command_words[1] try: new_seed = int(new_seed_s) except ValueError: print( f"[WARNING] Fail to change random seed: {new_seed_s!r} is not a valid number" ) else: print(f"[INFO] Random seed changed to {new_seed}") seed = new_seed continue elif command in ["conf"]: if len(command_words) == 1: print(model.generation_config) else: for key_value_pairs_str in command_words[1:]: eq_idx = key_value_pairs_str.find("=") if eq_idx == -1: print("[WARNING] format: =") continue conf_key, conf_value_str = ( key_value_pairs_str[:eq_idx], key_value_pairs_str[eq_idx + 1 :], ) try: conf_value = eval(conf_value_str) except Exception as e: print(e) continue else: print( f"[INFO] Change config: model.generation_config.{conf_key} = {conf_value}" ) setattr(model.generation_config, conf_key, conf_value) continue elif command in ["reset-conf"]: print("[INFO] Reset generation config") model.generation_config = deepcopy(orig_gen_config) print(model.generation_config) continue else: # As normal query. pass # Run chat. set_seed(seed) _clear_screen() print(f"\nUser: {query}") print(f"\nQwen: ", end="") try: partial_text = "" for new_text in _chat_stream(model, tokenizer, query, history): print(new_text, end="", flush=True) partial_text += new_text response = partial_text print() except KeyboardInterrupt: print("[WARNING] Generation interrupted") continue history.append((query, response)) if __name__ == "__main__": main() ================================================ FILE: examples/demo/web_demo.py ================================================ # Copyright (c) Alibaba Cloud. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """A simple web interactive chat demo based on gradio.""" from argparse import ArgumentParser from threading import Thread import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer DEFAULT_CKPT_PATH = "Qwen/Qwen2.5-7B-Instruct" def _get_args(): parser = ArgumentParser(description="Qwen2.5-Instruct web chat demo.") parser.add_argument( "-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH, help="Checkpoint name or path, default to %(default)r", ) parser.add_argument( "--cpu-only", action="store_true", help="Run demo with CPU only" ) parser.add_argument( "--share", action="store_true", default=False, help="Create a publicly shareable link for the interface.", ) parser.add_argument( "--inbrowser", action="store_true", default=False, help="Automatically launch the interface in a new tab on the default browser.", ) parser.add_argument( "--server-port", type=int, default=8000, help="Demo server port." ) parser.add_argument( "--server-name", type=str, default="127.0.0.1", help="Demo server name." ) args = parser.parse_args() return args def _load_model_tokenizer(args): tokenizer = AutoTokenizer.from_pretrained( args.checkpoint_path, resume_download=True, ) if args.cpu_only: device_map = "cpu" else: device_map = "auto" model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, torch_dtype="auto", device_map=device_map, resume_download=True, ).eval() model.generation_config.max_new_tokens = 2048 # For chat. return model, tokenizer def _chat_stream(model, tokenizer, query, history): conversation = [] for query_h, response_h in history: conversation.append({"role": "user", "content": query_h}) conversation.append({"role": "assistant", "content": response_h}) conversation.append({"role": "user", "content": query}) input_text = tokenizer.apply_chat_template( conversation, add_generation_prompt=True, tokenize=False, ) inputs = tokenizer([input_text], return_tensors="pt").to(model.device) streamer = TextIteratorStreamer( tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True ) generation_kwargs = { **inputs, "streamer": streamer, } thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() for new_text in streamer: yield new_text def _gc(): import gc gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() def _launch_demo(args, model, tokenizer): def predict(_query, _chatbot, _task_history): print(f"User: {_query}") _chatbot.append((_query, "")) full_response = "" response = "" for new_text in _chat_stream(model, tokenizer, _query, history=_task_history): response += new_text _chatbot[-1] = (_query, response) yield _chatbot full_response = response print(f"History: {_task_history}") _task_history.append((_query, full_response)) print(f"Qwen: {full_response}") def regenerate(_chatbot, _task_history): if not _task_history: yield _chatbot return item = _task_history.pop(-1) _chatbot.pop(-1) yield from predict(item[0], _chatbot, _task_history) def reset_user_input(): return gr.update(value="") def reset_state(_chatbot, _task_history): _task_history.clear() _chatbot.clear() _gc() return _chatbot with gr.Blocks() as demo: gr.Markdown("""\

""") gr.Markdown( """\

This WebUI is based on Qwen2.5-Instruct, developed by Alibaba Cloud. \ (本WebUI基于Qwen2.5-Instruct打造，实现聊天机器人功能。)
""" ) gr.Markdown("""\
Qwen2.5-7B-Instruct 🤖 | 🤗  ｜ Qwen2.5-32B-Instruct 🤖 | 🤗  ｜ Qwen2.5-72B-Instruct 🤖 | 🤗  ｜  Github
""") chatbot = gr.Chatbot(label="Qwen", elem_classes="control-height") query = gr.Textbox(lines=2, label="Input") task_history = gr.State([]) with gr.Row(): empty_btn = gr.Button("🧹 Clear History (清除历史)") submit_btn = gr.Button("🚀 Submit (发送)") regen_btn = gr.Button("🤔️ Regenerate (重试)") submit_btn.click( predict, [query, chatbot, task_history], [chatbot], show_progress=True ) submit_btn.click(reset_user_input, [], [query]) empty_btn.click( reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True ) regen_btn.click( regenerate, [chatbot, task_history], [chatbot], show_progress=True ) gr.Markdown("""\ Note: This demo is governed by the original license of Qwen2.5. \ We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \ including hate speech, violence, pornography, deception, etc. \ (注：本演示受Qwen2.5的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\ 包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""") demo.queue().launch( share=args.share, inbrowser=args.inbrowser, server_port=args.server_port, server_name=args.server_name, ) def main(): args = _get_args() model, tokenizer = _load_model_tokenizer(args) _launch_demo(args, model, tokenizer) if __name__ == "__main__": main() ================================================ FILE: examples/gcu-support/README.md ================================================ # Qwen2.5 推理 ## 1、配置运行环境 **安装驱动** ``` # 为软件包具体版本号。 chmod +x TopsRider_i3x__deb_amd64.run ./TopsRider_i3x__deb_amd64.run -y ``` **创建并启动 docker** ``` # 创建 docker 容器，将在基础镜像 artifact.enflame.cn/enflame_docker_images/ubuntu/qic_ubuntu_2004_gcc9:1.4.4 的基础上创建 docker。 # 当前工程所在路径 # -e ENFLAME_VISIBLE_DEVICES=2 进行 GCU 资源隔离，如需多卡可以改为 0,1,2,3 等 docker run -itd -e ENFLAME_VISIBLE_DEVICES=2 --name qwen-infer -v :/work -v /root/:/root/ --privileged --network host artifact.enflame.cn/enflame_docker_images/ubuntu/qic_ubuntu_2004_gcc9:1.4.4 bash ``` **进入 docker 安装环境** ``` # 进入 docker 容器 docker exec -it qwen-infer bash # 安装 SDK 框架，进入软件包所在地址。 # 为软件包具体版本号。 ./TopsRider_i3x__amd64.run -C torch-gcu-2 -y ./TopsRider_i3x__deb_amd64.run -C tops-sdk -y # 安装 python 库 pip3.8 install transformers==4.40.2 pip3.8 install accelerate ``` ## 2、推理 ``` # 进入本工程目录，包含运行代码、推理输入等文件。 . ├── README.md └── gcu_demo.py ``` **启动推理示例** ``` python3.8 gcu_demo.py ``` 执行 gcu_demo.py 推理示例，代码改编自 [仓库 README](https://github.com/QwenLM/Qwen2.5/blob/main/README.md) 中的给的 Huggingface quick start 用例。 **GCU PyTorch 原生推理支持** GCU 支持 pytorch 原生推理，在 pytorch 代码上只需做少许改动就可以在 GCU 上顺利运行： 1. 导入 *torch_gcu* 后端库，并载入 transfer_to_gcu ``` python try: import torch_gcu # 导入 torch_gcu from torch_gcu import transfer_to_gcu # transfer_to_gcu except Exception as e: print(e) ``` 2. device 名改为 *gcu* ``` python device = "gcu" ``` **GCU vLLM 推理** GCU 也支持 *vLLM* 原生推理，需要安装 GCU 版本的 *vLLM* 后，将设备名改为 gcu ``` python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2.5-7B-Instruct --model Qwen/Qwen2.5-7B-Instruct --device gcu ``` ================================================ FILE: examples/gcu-support/gcu_demo.py ================================================ try: import torch_gcu # 导入 torch_gcu from torch_gcu import transfer_to_gcu # transfer_to_gcu except Exception as e: print(e) from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct" device = "gcu" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ================================================ FILE: examples/llama-factory/finetune-zh.md ================================================ # 使用LLaMA-Factory微调Qwen模型 ## LLAMA-Factory简介 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)是一个简单易用且高效的大模型训练框架，支持上百种大模型的训练，框架特性主要包括： - 模型种类：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。 - 训练算法：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。 - 运算精度：16比特全参数微调、冻结微调、LoRA微调和基于AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ的2/3/4/5/6/8比特QLoRA 微调。 - 优化算法：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ和PiSSA。 - 加速算子：FlashAttention-2和Unsloth。 - 推理引擎：Transformers和vLLM。 - 实验面板：LlamaBoard、TensorBoard、Wandb、MLflow等等。 本文将介绍如何使用LLAMA-Factory对Qwen2系列大模型进行微调（Qwen1.5系列模型也适用），更多特性请参考[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)。 ## 安装LLaMA-Factory 下载并安装LLaMA-Factory： ```bash git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory pip install -e ".[torch,metrics]" ``` 安装完成后，执行`llamafactory-cli version`，若出现以下提示，则表明安装成功： ``` ---------------------------------------------------------- | Welcome to LLaMA Factory, version 0.8.4.dev0 | | | | Project page: https://github.com/hiyouga/LLaMA-Factory | ---------------------------------------------------------- ``` ## 准备训练数据 自定义的训练数据应保存为jsonl文件，每一行的格式如下： ```json { "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Tell me something about large language models." }, { "role": "assistant", "content": "Large language models are a type of language model that is trained on a large corpus of text data. They are capable of generating human-like text and are used in a variety of natural language processing tasks..." }, { "role": "user", "content": "How about Qwen2?" }, { "role": "assistant", "content": "Qwen2 is a large language model developed by Alibaba Cloud..." } ] } ``` 在LLaMA-Factory文件夹下的`data/dataset_info.json`文件中注册自定义的训练数据，在文件尾部添加如下配置信息： ``` "qwen_train_data": { "file_name": "PATH-TO-YOUR-TRAIN-DATA", "formatting": "sharegpt", "columns": { "messages": "messages" }, "tags": { "role_tag": "role", "content_tag": "content", "user_tag": "user", "assistant_tag": "assistant", "system_tag": "system" } } ``` ## 配置训练参数 设置训练参数的配置文件，我们提供了全量参数、LoRA、QLoRA训练所对应的示例文件，你可以根据自身需求自行修改，配置详情见本目录下对应的文件: - `qwen2-7b-full-sft.yaml`: 全量参数训练 - `qwen2-7b-lora-sft.yaml`: LoRA训练 - `qwen2-7b-qlora-sft.yaml`: QLoRA训练 全量参数训练时的deepspeed配置文件可参考[文件](https://github.com/hiyouga/LLaMA-Factory/tree/main/examples/deepspeed) 部分训练参数说明： | 参数 | 说明 | |-----------------------------|----------------------------------------------------------------------------------------------| | model_name_or_path | 模型名称或路径 | | stage | 训练阶段，可选: rm(reward modeling), pt(pretrain), sft(Supervised Fine-Tuning), PPO, DPO, KTO, ORPO | | do_train | true用于训练, false用于评估 | | finetuning_type | 微调方式。可选: freeze, LoRA, full | | lora_target | 采取LoRA方法的目标模块，默认值为all。 | | dataset | 使用的数据集，使用”,”分隔多个数据集 | | template | 数据集模板，请保证数据集模板与模型相对应。 | | output_dir | 输出路径 | | logging_steps | 日志输出步数间隔 | | save_steps | 模型断点保存间隔 | | overwrite_output_dir | 是否允许覆盖输出目录 | | per_device_train_batch_size | 每个设备上训练的批次大小 | | gradient_accumulation_steps | 梯度积累步数 | | learning_rate | 学习率 | | lr_scheduler_type | 学习率曲线，可选 linear, cosine, polynomial, constant 等。 | | num_train_epochs | 训练周期数 | | bf16 | 是否使用 bf16 格式 | ## 开始训练 全量参数训练： ```bash FORCE_TORCHRUN=1 llamafactory-cli train qwen2-7b-full-sft.yaml ``` LoRA训练： ```bash llamafactory-cli train qwen2-7b-lora-sft.yaml ``` QLoRA训练： ```bash llamafactory-cli train qwen2-7b-qlora-sft.yaml ``` 使用上述训练配置，各个方法实测的显存占用如下。训练中的显存占用与训练参数配置息息相关，可根据自身实际需求进行设置。 - 全量参数训练：42.18GB - LoRA训练：20.17GB - QLoRA训练: 10.97GB ## 合并模型权重 如果采用LoRA或者QLoRA进行训练，脚本只保存对应的LoRA权重，需要合并权重才能进行推理。**全量参数训练无需执行此步骤** ```bash llamafactory-cli export qwen2-7b-merge-lora.yaml ``` 权重合并的部分参数说明： | 参数 | 说明 | |----------------------|-------------| | model_name_or_path | 预训练模型的名称或路径 | | template | 模型模板 | | export_dir | 导出路径 | | export_size | 最大导出模型文件大小 | | export_device | 导出设备 | | export_legacy_format | 是否使用旧格式导出 | 注意： - 合并Qwen2模型权重，务必将template设为`qwen`；无论LoRA还是QLoRA训练，合并权重时，`finetuning_type`均为`lora`。 - adapter_name_or_path需要与微调中的适配器输出路径output_dir相对应。 ## 模型推理 训练完成，合并模型权重之后，即可加载完整的模型权重进行推理， 推理的示例脚本如下： ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model_name_or_path = YOUR-MODEL-PATH model = AutoModelForCausalLM.from_pretrained( model_name_or_path, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ================================================ FILE: examples/llama-factory/qwen2-7b-full-sft.yaml ================================================ ### model model_name_or_path: Qwen/Qwen2-7B-Instruct ### method stage: sft do_train: true finetuning_type: full deepspeed: PATH-TO-DS-CONFIG ### dataset dataset: qwen_train_data template: qwen cutoff_len: 1024 overwrite_cache: true preprocessing_num_workers: 16 ### output output_dir: saves/qwen2-7b/full/sft logging_steps: 10 save_steps: 100 plot_loss: true overwrite_output_dir: true ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 16 learning_rate: 1.0e-5 num_train_epochs: 1.0 lr_scheduler_type: cosine warmup_ratio: 0.1 bf16: true ddp_timeout: 180000000 ### eval val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 500 ================================================ FILE: examples/llama-factory/qwen2-7b-lora-sft.yaml ================================================ ### model model_name_or_path: Qwen/Qwen2-7B-Instruct ### method stage: sft do_train: true finetuning_type: lora lora_target: all lora_rank: 16 lora_alpha: 16 lora_dropout: 0.05 ### dataset dataset: qwen_train_data template: qwen cutoff_len: 1024 overwrite_cache: true preprocessing_num_workers: 16 ### output output_dir: saves/qwen2-7b/lora/sft logging_steps: 100 save_steps: 100 plot_loss: true overwrite_output_dir: true ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 16 learning_rate: 1.0e-4 num_train_epochs: 1.0 lr_scheduler_type: cosine warmup_ratio: 0.1 bf16: true ddp_timeout: 180000000 ### eval val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 500 ================================================ FILE: examples/llama-factory/qwen2-7b-merge-lora.yaml ================================================ ### Note: DO NOT use quantized model or quantization_bit when merging lora adapters ### model model_name_or_path: Qwen/Qwen2-7B-Instruct adapter_name_or_path: PATH-TO-LORA template: qwen finetuning_type: lora ### export export_dir: models/qwen2-7b-sft-lora-merged export_size: 2 export_device: cpu export_legacy_format: false ================================================ FILE: examples/llama-factory/qwen2-7b-qlora-sft.yaml ================================================ ### model model_name_or_path: Qwen/Qwen2-7B-Instruct ### method stage: sft do_train: true finetuning_type: lora lora_target: all quantization_bit: 4 quantization_method: bitsandbytes # choices: [bitsandbytes (4/8), hqq (2/3/4/5/6/8), eetq (8)] lora_rank: 16 lora_alpha: 16 lora_dropout: 0.05 ### dataset dataset: qwen_train_data template: qwen cutoff_len: 1024 overwrite_cache: true preprocessing_num_workers: 16 ### output output_dir: saves/qwen2-7b/qlora/sft logging_steps: 100 save_steps: 100 plot_loss: true overwrite_output_dir: true ### train per_device_train_batch_size: 1 gradient_accumulation_steps: 16 learning_rate: 1.0e-4 num_train_epochs: 1.0 lr_scheduler_type: cosine warmup_ratio: 0.1 bf16: true ddp_timeout: 180000000 ### eval val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 500 ================================================ FILE: examples/speed-benchmark/README.md ================================================ # Speed Benchmark This document introduces the speed benchmark testing process for the Qwen2.5 series models (original and quantized models). For detailed reports, please refer to the [Qwen2.5 Speed Benchmark](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## 1. Model Collections For models hosted on HuggingFace, refer to [Qwen2.5 Collections-HuggingFace](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e). For models hosted on ModelScope, refer to [Qwen2.5 Collections-ModelScope](https://modelscope.cn/collections/Qwen25-dbc4d30adb768). ## 2. Environment Setup For inference using HuggingFace transformers: ```shell conda create -n qwen_perf_transformers python=3.10 conda activate qwen_perf_transformers pip install torch==2.3.1 pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@v0.7.1 pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.5.8 pip install -r requirements-perf-transformers.txt ``` > [!Important] > - For `flash-attention`, you can use the prebulit wheels from [GitHub Releases](https://github.com/Dao-AILab/flash-attention/releases/tag/v2.5.8) or installing from source, which requires a compatible CUDA compiler. > - You don't actually need to install `flash-attention`. It has been intergrated into `torch` as a backend of `sdpa`. > - For `auto_gptq` to use efficent kernels, you need to install from source, because the prebuilt wheels require incompatible `torch` versions. Installing from source also requires a compatible CUDA compiler. > - For `autoawq` to use efficent kenerls, you need `autoawq-kernels`, which should be automatically installed. If not, run `pip install autoawq-kernels`. For inference using vLLM: ```shell conda create -n qwen_perf_vllm python=3.10 conda activate qwen_perf_vllm pip install -r requirements-perf-vllm.txt ``` ## 3. Execute Tests Below are two methods for executing tests: using a script or the Speed Benchmark tool. ### Method 1: Testing with Speed Benchmark Tool Use the Speed Benchmark tool developed by [EvalScope](https://github.com/modelscope/evalscope), which supports automatic model downloads from ModelScope and outputs test results. It also allows testing by specifying the model service URL. For details, please refer to the [📖 User Guide](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/speed_benchmark.html). **Install Dependencies** ```shell pip install 'evalscope[perf]' -U ``` #### HuggingFace Transformers Inference Execute the command as follows: ```shell CUDA_VISIBLE_DEVICES=0 evalscope perf \ --parallel 1 \ --model Qwen/Qwen2.5-0.5B-Instruct \ --attn-implementation flash_attention_2 \ --log-every-n-query 5 \ --connect-timeout 6000 \ --read-timeout 6000 \ --max-tokens 2048 \ --min-tokens 2048 \ --api local \ --dataset speed_benchmark ``` #### vLLM Inference ```shell CUDA_VISIBLE_DEVICES=0 evalscope perf \ --parallel 1 \ --model Qwen/Qwen2.5-0.5B-Instruct \ --log-every-n-query 1 \ --connect-timeout 60000 \ --read-timeout 60000\ --max-tokens 2048 \ --min-tokens 2048 \ --api local_vllm \ --dataset speed_benchmark ``` #### Parameter Explanation - `--parallel` sets the number of worker threads for concurrent requests, should be fixed at 1. - `--model` specifies the model file path or model ID, supporting automatic downloads from ModelScope, e.g., Qwen/Qwen2.5-0.5B-Instruct. - `--attn-implementation` sets the attention implementation method, with optional values: flash_attention_2|eager|sdpa. - `--log-every-n-query`: sets how often to log every n requests. - `--connect-timeout`: sets the connection timeout in seconds. - `--read-timeout`: sets the read timeout in seconds. - `--max-tokens`: sets the maximum output length in tokens. - `--min-tokens`: sets the minimum output length in tokens; both parameters set to 2048 means the model will output a fixed length of 2048. - `--api`: sets the inference interface; local inference options are local|local_vllm. - `--dataset`: sets the test dataset; options are speed_benchmark|speed_benchmark_long. #### Test Results Test results can be found in the `outputs/{model_name}/{timestamp}/speed_benchmark.json` file, which contains all request results and test parameters. ### Method 2: Testing with Scripts #### HuggingFace Transformers Inference - Using HuggingFace Hub ```shell python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --outputs_dir outputs/transformers ``` - Using ModelScope Hub ```shell python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --use_modelscope --outputs_dir outputs/transformers ``` Parameter Explanation: `--model_id_or_path`: Model ID or local path, optional values refer to the `Model Resources` section `--context_length`: Input length in tokens; optional values are 1, 6144, 14336, 30720, 63488, 129024; refer to the `Qwen2.5 Model Efficiency Evaluation Report` for specifics `--generate_length`: Number of tokens to generate; default is 2048 `--gpus`: Equivalent to the environment variable CUDA_VISIBLE_DEVICES, e.g., `0,1,2,3`, `4,5` `--use_modelscope`: If set, uses ModelScope to load the model; otherwise, uses HuggingFace `--outputs_dir`: Output directory, default is `outputs/transformers` #### vLLM Inference - Using HuggingFace Hub ```shell python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm ``` - Using ModelScope Hub ```shell python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --use_modelscope --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm ``` Parameter Explanation: `--model_id_or_path`: Model ID or local path, optional values refer to the `Model Resources` section `--context_length`: Input length in tokens; optional values are 1, 6144, 14336, 30720, 63488, 129024; refer to the `Qwen2.5 Model Efficiency Evaluation Report` for specifics `--generate_length`: Number of tokens to generate; default is 2048 `--max_model_len`: Maximum model length in tokens; default is 32768 `--gpus`: Equivalent to the environment variable CUDA_VISIBLE_DEVICES, e.g., `0,1,2,3`, `4,5` `--use_modelscope`: If set, uses ModelScope to load the model; otherwise, uses HuggingFace `--gpu_memory_utilization`: GPU memory utilization, range (0, 1]; default is 0.9 `--outputs_dir`: Output directory, default is `outputs/vllm` `--enforce_eager`: Whether to enforce eager mode; default is False #### Test Results Test results can be found in the `outputs` directory, which by default includes two folders for `transformers` and `vllm`, storing test results for HuggingFace transformers and vLLM respectively. ## Notes 1. Conduct multiple tests and take the average, with a typical value of 3 tests. 2. Ensure the GPU is idle before testing to avoid interference from other tasks. ================================================ FILE: examples/speed-benchmark/README_zh.md ================================================ # 效率评估 本文介绍Qwen2.5系列模型（原始模型和量化模型）的效率测试流程，详细报告可参考 [Qwen2.5模型效率评估报告](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html)。 ## 1. 模型资源 对于托管在HuggingFace上的模型，可参考 [Qwen2.5模型-HuggingFace](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e)。 对于托管在ModelScope上的模型，可参考 [Qwen2.5模型-ModelScope](https://modelscope.cn/collections/Qwen25-dbc4d30adb768)。 ## 2. 环境安装 使用HuggingFace transformers推理，安装环境如下： ```shell conda create -n qwen_perf_transformers python=3.10 conda activate qwen_perf_transformers pip install torch==2.3.1 pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@v0.7.1 pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.5.8 pip install -r requirements-perf-transformers.txt ``` > [!Important] > - 对于 `flash-attention`，您可以从 [GitHub 发布页面](https://github.com/Dao-AILab/flash-attention/releases/tag/v2.5.8) 使用预编译的 wheel 包进行安装，或者从源代码安装，后者需要一个兼容的 CUDA 编译器。 > - 实际上，您并不需要单独安装 `flash-attention`。它已经被集成到了 `torch` 中作为 `sdpa` 的后端实现。 > - 若要使 `auto_gptq` 使用高效的内核，您需要从源代码安装，因为预编译的 wheel 包依赖于与之不兼容的 `torch` 版本。从源代码安装同样需要一个兼容的 CUDA 编译器。 > - 若要使 `autoawq` 使用高效的内核，您需要安装 `autoawq-kernels`，该组件应当会自动安装。如果未自动安装，请运行 `pip install autoawq-kernels` 进行手动安装。 使用vLLM推理，安装环境如下： ```shell conda create -n qwen_perf_vllm python=3.10 conda activate qwen_perf_vllm pip install -r requirements-perf-vllm.txt ``` ## 3. 执行测试 下面介绍两种执行测试的方法，分别是使用脚本测试和使用Speed Benchmark工具进行测试。 ### 方法1：使用Speed Benchmark工具测试 使用[EvalScope](https://github.com/modelscope/evalscope)开发的Speed Benchmark工具进行测试，支持自动从modelscope下载模型并输出测试结果，也支持指定模型服务的url进行测试，具体请参考[📖使用指南](https://evalscope.readthedocs.io/zh-cn/latest/user_guides/stress_test/speed_benchmark.html)。 **安装依赖** ```shell pip install 'evalscope[perf]' -U ``` #### HuggingFace transformers推理 执行命令如下： ```shell CUDA_VISIBLE_DEVICES=0 evalscope perf \ --parallel 1 \ --model Qwen/Qwen2.5-0.5B-Instruct \ --attn-implementation flash_attention_2 \ --log-every-n-query 5 \ --connect-timeout 6000 \ --read-timeout 6000 \ --max-tokens 2048 \ --min-tokens 2048 \ --api local \ --dataset speed_benchmark ``` #### vLLM推理 ```shell CUDA_VISIBLE_DEVICES=0 evalscope perf \ --parallel 1 \ --model Qwen/Qwen2.5-0.5B-Instruct \ --log-every-n-query 1 \ --connect-timeout 60000 \ --read-timeout 60000\ --max-tokens 2048 \ --min-tokens 2048 \ --api local_vllm \ --dataset speed_benchmark ``` #### 参数说明 - `--parallel` 设置并发请求的worker数量，需固定为1。 - `--model` 测试模型文件路径，也可为模型ID，支持自动从modelscope下载模型，例如Qwen/Qwen2.5-0.5B-Instruct。 - `--attn-implementation` 设置attention实现方式，可选值为flash_attention_2|eager|sdpa。 - `--log-every-n-query`: 设置每n个请求打印一次日志。 - `--connect-timeout`: 设置连接超时时间，单位为秒。 - `--read-timeout`: 设置读取超时时间，单位为秒。 - `--max-tokens`: 设置最大输出长度，单位为token。 - `--min-tokens`: 设置最小输出长度，单位为token；两个参数同时设置为2048则模型固定输出长度为2048。 - `--api`: 设置推理接口，本地推理可选值为local|local_vllm。 - `--dataset`: 设置测试数据集，可选值为speed_benchmark|speed_benchmark_long。 #### 测试结果 测试结果详见`outputs/{model_name}/{timestamp}/speed_benchmark.json`文件，其中包含所有请求结果和测试参数。 ### 方法2：使用脚本测试 #### HuggingFace transformers推理 - 使用HuggingFace hub ```shell python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --outputs_dir outputs/transformers # 指定HF_ENDPOINT HF_ENDPOINT=https://hf-mirror.com python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --outputs_dir outputs/transformers ``` - 使用ModelScope hub ```shell python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --use_modelscope --outputs_dir outputs/transformers ``` 参数说明： `--model_id_or_path`: 模型ID或本地路径， 可选值参考`模型资源`章节 `--context_length`: 输入长度，单位为token数；可选值为1, 6144, 14336, 30720, 63488, 129024；具体可参考`Qwen2.5模型效率评估报告` `--generate_length`: 生成token数量；默认为2048 `--gpus`: 等价于环境变量CUDA_VISIBLE_DEVICES，例如`0,1,2,3`，`4,5` `--use_modelscope`: 如果设置该值，则使用ModelScope加载模型，否则使用HuggingFace `--outputs_dir`: 输出目录， 默认为`outputs/transformers` #### vLLM推理 - 使用HuggingFace hub ```shell python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm # 指定HF_ENDPOINT HF_ENDPOINT=https://hf-mirror.com python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm ``` - 使用ModelScope hub ```shell python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --use_modelscope --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm ``` 参数说明： `--model_id_or_path`: 模型ID或本地路径， 可选值参考`模型资源`章节 `--context_length`: 输入长度，单位为token数；可选值为1, 6144, 14336, 30720, 63488, 129024；具体可参考`Qwen2.5模型效率评估报告` `--generate_length`: 生成token数量；默认为2048 `--max_model_len`: 模型最大长度，单位为token数；默认为32768 `--gpus`: 等价于环境变量CUDA_VISIBLE_DEVICES，例如`0,1,2,3`，`4,5` `--use_modelscope`: 如果设置该值，则使用ModelScope加载模型，否则使用HuggingFace `--gpu_memory_utilization`: GPU内存利用率，取值范围为(0, 1]；默认为0.9 `--outputs_dir`: 输出目录， 默认为`outputs/vllm` `--enforce_eager`: 是否强制使用eager模式；默认为False #### 测试结果 测试结果详见`outputs`目录下的文件，默认包括`transformers`和`vllm`两个目录，分别存放HuggingFace transformers和vLLM的测试结果。 ## 注意事项 1. 多次测试，取平均值，典型值为3次 2. 测试前请确保GPU处于空闲状态，避免其他任务影响测试结果 ================================================ FILE: examples/speed-benchmark/requirements-perf-transformers.txt ================================================ # Note: install following requirements saparately # pip install torch==2.3.1 # pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@v0.7.1 # pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.5.8 transformers==4.46.0 autoawq==0.2.6 modelscope[framework] accelerate optimum>=1.20.0 ================================================ FILE: examples/speed-benchmark/requirements-perf-vllm.txt ================================================ vllm==0.6.3.post1 torch==2.4.0 modelscope[framework] accelerate ================================================ FILE: examples/speed-benchmark/speed_benchmark_transformers.py ================================================ # Copyright (c) Alibaba Cloud. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """ Qwen2.5 Speed Benchmark for transformers(pt) inference. """ import os import time import json import csv import torch from transformers.trainer_utils import set_seed class SpeedBenchmarkTransformers: SEED = 1024 BATCH_SIZE = 1 USE_FLASH_ATTN = True COMMENT = 'default' DEVICE_MAP = 'auto' TORCH_DTYPE = 'auto' OVERWRITE_RESULT = False DUMMY_INPUT = '我' def __init__(self, model_id_or_path, use_modelscope: bool = True, outputs_dir: str = 'outputs/transformers'): """ Speed benchmark for transformers(pt) inference. Args: model_id_or_path: The model id on ModelScope or HuggingFace hub, or local model path. use_modelscope: Use ModelScope, otherwise HuggingFace. outputs_dir: The output directory. Default is 'outputs/transformers'. """ set_seed(self.SEED) self.model_id_or_path = model_id_or_path self.outputs_dir = outputs_dir if use_modelscope: from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig else: from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path, trust_remote_code=True) attn_impl = 'flash_attention_2' if self.USE_FLASH_ATTN else 'eager' self.model = AutoModelForCausalLM.from_pretrained(model_id_or_path, torch_dtype=self.TORCH_DTYPE, device_map=self.DEVICE_MAP, attn_implementation=attn_impl ).eval() self.generation_config = GenerationConfig.from_pretrained(model_id_or_path, trust_remote_code=True) def run(self, context_length: int, generate_length: int) -> str: # Specify hyperparameters for generation self.generation_config.min_length = generate_length + context_length self.generation_config.max_new_tokens = generate_length print(f'Generation config: {self.generation_config}') # Prepare inputs batch_size = self.BATCH_SIZE context_str = self.DUMMY_INPUT * context_length inputs = self.tokenizer([context_str for _ in range(batch_size)], return_tensors='pt') assert inputs['input_ids'].shape[1] == context_length assert inputs['input_ids'].shape[0] == batch_size inputs = inputs.to(self.model.device) # Run inference print(f'Start running inference for model {self.model_id_or_path} with input length {context_length} ...') start_time = time.time() torch.cuda.synchronize() pred = self.model.generate(**inputs, generation_config=self.generation_config) torch.cuda.synchronize() time_cost = time.time() - start_time assert pred.shape[1] == self.generation_config.min_length m = 0 max_gpu_memory_cost = 0 for i in range(torch.cuda.device_count()): m += torch.cuda.max_memory_allocated(i) max_gpu_memory_cost = max(max_gpu_memory_cost, m) torch.cuda.empty_cache() # Prepare results tokens_per_second: float = generate_length / time_cost # Compute the maximum GPU memory cost (in GB) max_gpu_memory_cost_gb = max_gpu_memory_cost / 1024 / 1024 / 1024 data = { "model_id_or_path": self.model_id_or_path, "batch_size": batch_size, "context_length_per_experiment": context_length, "generate_length_per_experiment": generate_length, "use_flash_attn": self.USE_FLASH_ATTN, "comment": self.COMMENT, "tokens_per_second": round(tokens_per_second, 4), "max_gpu_memory_cost_gb": round(max_gpu_memory_cost_gb, 4), } data_json = json.dumps(data, indent=4, ensure_ascii=False) print(f'**Final result **\n{data_json}\n') # Dump results to CSV file from datetime import datetime now = datetime.now() timestamp: str = now.strftime("%m%d%H%M%S") model_id_or_path_str = self.model_id_or_path.split(os.sep)[-1] \ if os.path.isdir(self.model_id_or_path) else self.model_id_or_path.replace('/', '__') out_file: str = os.path.join(self.outputs_dir, f"{model_id_or_path_str}" f"_context_length-{context_length}_{timestamp}.csv") out_dir = os.path.dirname(out_file) os.makedirs(out_dir, exist_ok=True) self.save_result(data, out_file) return out_file @staticmethod def save_result(data: dict, out_file: str) -> None: with open(out_file, mode='w') as file: writer = csv.DictWriter(file, fieldnames=data.keys()) writer.writeheader() writer.writerows([data]) print(f"Results saved to {out_file}") def main(): import argparse # Parse args parser = argparse.ArgumentParser(description='Speed benchmark for transformers(pt) deployment') parser.add_argument('--model_id_or_path', type=str, help='The model path or id on ModelScope or HuggingFace hub') parser.add_argument('--context_length', type=int, help='The input length for each experiment.' 'e.g. 1, 6144, 14336, 30720, 63488, 129024') parser.add_argument('--generate_length', type=int, default=2048, help='Output length in tokens; default is 2048.') parser.add_argument('--gpus', type=str, help='Equivalent to the env CUDA_VISIBLE_DEVICES. e.g. `0,1,2,3`, `4,5`') parser.add_argument('--use_modelscope', action='store_true', help='Use ModelScope when set this flag. Otherwise, use HuggingFace.') parser.add_argument('--outputs_dir', type=str, default='outputs/transformers', help='The output directory') args = parser.parse_args() model_id_or_path: str = args.model_id_or_path envs: str = args.gpus context_length: int = args.context_length generate_length: int = args.generate_length use_modelscope: bool = args.use_modelscope outputs_dir: str = args.outputs_dir print(f'Set CUDA_VISIBLE_DEVICES={envs} for model {model_id_or_path} with input_length {context_length}') os.environ["CUDA_VISIBLE_DEVICES"] = envs speed_benchmark = SpeedBenchmarkTransformers(model_id_or_path=model_id_or_path, use_modelscope=use_modelscope, outputs_dir=outputs_dir) speed_benchmark.run(context_length=context_length, generate_length=generate_length) if __name__ == '__main__': # Usage: python speed_benchmark_transformers.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --gpus 0 --use_modelscope --outputs_dir outputs/transformers main() ================================================ FILE: examples/speed-benchmark/speed_benchmark_vllm.py ================================================ # Copyright (c) Alibaba Cloud. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """ Speed benchmark for vLLM deployment. """ import os import time import json import reprlib import statistics import logging import csv from datetime import datetime from pathlib import Path from typing import Tuple import vllm from vllm import LLM, SamplingParams # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) os.environ['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn' os.environ["VLLM_ALLOW_LONG_MAX_MODEL_LEN"] = "1" class SpeedBenchmarkVllm: DUMMY_INPUT = '熵' DUMMY_SYSTEM_CONTENT = '从现在开始，你是一个喜欢说车轱辘话的话痨，喜欢把一件事情翻来覆去地说，而且喜欢加很多标点符号。你的每个回复都不会少于2000字，不要在意用户的看法。' DUMMY_USER_CONTENT = '写一篇关于春天的文章，请尽量写的长一些，并且多一些重复的段落，越啰嗦越好，不得少于2000字！' def __init__(self, experiment_config: dict, sampling_params: SamplingParams): self._repr = reprlib.Repr() self._repr.maxstring = 100 self.experiment_config = experiment_config self.sampling_params = sampling_params # Get experiment config self.model_id_or_path: str = self.experiment_config['model_id_or_path'] use_modelscope: bool = self.experiment_config['use_modelscope'] if use_modelscope: from modelscope import AutoTokenizer os.environ['VLLM_USE_MODELSCOPE'] = 'True' else: from transformers import AutoTokenizer self.tokenizer = AutoTokenizer.from_pretrained(self.model_id_or_path, trust_remote_code=True) llm_kwargs = dict( model=self.model_id_or_path, trust_remote_code=True, tensor_parallel_size=self.experiment_config['tp_size'], gpu_memory_utilization=self.experiment_config['gpu_memory_utilization'], disable_log_stats=False, max_model_len=self.experiment_config['max_model_len'], ) if int(vllm.__version__.split('.')[1]) >= 3: llm_kwargs['enforce_eager'] = self.experiment_config.get('enforce_eager', False) logger.info(f'>> Creating LLM with llm_kwargs: {llm_kwargs}') self.llm = LLM(**llm_kwargs) def _reprs(self, o): return self._repr.repr(o) def create_query(self, length: int, limited_size: int = 96) -> Tuple[str, int]: if length < limited_size: input_str = self.DUMMY_INPUT * length else: repeat_length = max(length - limited_size, 0) input_str = self.tokenizer.apply_chat_template([ {"role": "system", "content": self.DUMMY_SYSTEM_CONTENT}, {"role": "user", "content": '# ' * repeat_length + self.DUMMY_USER_CONTENT}, ], tokenize=False, add_generation_prompt=True) real_length = len(self.tokenizer.tokenize(input_str)) return input_str, real_length def run_infer(self, query: str): start_time = time.time() output = self.llm.generate([query], self.sampling_params)[0] time_cost = time.time() - start_time generated_text = output.outputs[0].text real_out_length = len(self.tokenizer.tokenize(generated_text)) return time_cost, real_out_length, generated_text def run(self): context_length: int = self.experiment_config['context_length'] output_len: int = self.experiment_config['output_len'] # Construct input query query, real_length = self.create_query(length=context_length) logger.info(f'Got input query length: {real_length}') logger.info(f"Warmup run with {self.experiment_config['warmup']} iterations ...") for _ in range(self.experiment_config['warmup']): self.llm.generate([query], self.sampling_params) logger.info(f"Running inference with real length {real_length}, " f"out length {output_len}, " f"tp_size {self.experiment_config['tp_size']} ...") time_cost, real_out_length, generated_text = self.run_infer(query) if real_out_length < output_len: logger.warning(f'Generate result {real_out_length} too short, try again ...') query, real_length = self.create_query(length=context_length, limited_size=context_length + 1) time_cost, real_out_length, generated_text = self.run_infer(query) time_cost = round(time_cost, 4) logger.info(f'Inference time cost: {time_cost}s') logger.info(f'Input({real_length}): {self._reprs(query)}') logger.info(f'Output({real_out_length}): {self._reprs(generated_text)}') results: dict = self.collect_statistics(self.model_id_or_path, [time_cost, time_cost], output_len, context_length, self.experiment_config['tp_size']) self.print_table(results) # Dump results to CSV file outputs_dir = Path(self.experiment_config['outputs_dir']) outputs_dir.mkdir(parents=True, exist_ok=True) now = datetime.now() timestamp: str = now.strftime("%m%d%H%M%S") model_id_or_path_str = self.model_id_or_path.split(os.sep)[-1] \ if os.path.isdir(self.model_id_or_path) else self.model_id_or_path.replace('/', '__') out_file: str = os.path.join(outputs_dir, f"{model_id_or_path_str}" f"_context_length-{context_length}_{timestamp}.csv") self.save_result(results, out_file) @staticmethod def collect_statistics(model_id_or_path, data, out_length, in_length, tp_size) -> dict: avg_time = statistics.mean(data) throughput_data = [out_length / t for t in data] avg_throughput = statistics.mean(throughput_data) results = { 'Model ID': model_id_or_path, 'Input Length': in_length, 'Output Length': out_length, 'TP Size': tp_size, 'Average Time (s)': round(avg_time, 4), 'Average Throughput (tokens/s)': round(avg_throughput, 4), } return results @staticmethod def print_table(results): json_res = json.dumps(results, indent=4, ensure_ascii=False) logger.info(f"Final results:\n{json_res}") @staticmethod def save_result(data: dict, out_file: str) -> None: with open(out_file, mode='w') as file: writer = csv.DictWriter(file, fieldnames=data.keys()) writer.writeheader() writer.writerows([data]) logger.info(f"Results saved to {out_file}") def main(): import argparse # Define command line arguments parser = argparse.ArgumentParser(description='Speed benchmark for vLLM deployment') parser.add_argument('--model_id_or_path', type=str, help='The model id on ModelScope or HuggingFace hub') parser.add_argument('--context_length', type=int, help='The context length for each experiment, ' 'e.g. 1, 6144, 14336, 30720, 63488, 129024') parser.add_argument('--generate_length', type=int, default=2048, help='Output length in tokens; default is 2048.') parser.add_argument('--gpus', type=str, help='Equivalent to the env CUDA_VISIBLE_DEVICES. e.g. `0,1,2,3`, `4,5`') parser.add_argument('--gpu_memory_utilization', type=float, default=0.9, help='GPU memory utilization') parser.add_argument('--max_model_len', type=int, default=32768, help='The maximum model length, ' 'e.g. 4096, 8192, 32768, 65536, 131072') parser.add_argument('--enforce_eager', action='store_true', help='Enforce eager mode for vLLM') parser.add_argument('--outputs_dir', type=str, default='outputs/vllm', help='The output directory') parser.add_argument('--use_modelscope', action='store_true', help='Use ModelScope when set this flag. Otherwise, use HuggingFace.') # Parse args args = parser.parse_args() # Parse args model_id_or_path: str = args.model_id_or_path context_length: int = args.context_length output_len: int = args.generate_length envs: str = args.gpus gpu_memory_utilization: float = args.gpu_memory_utilization max_model_len: int = args.max_model_len enforce_eager: bool = args.enforce_eager outputs_dir = args.outputs_dir use_modelscope: bool = args.use_modelscope # Set vLLM sampling params sampling_params = SamplingParams( temperature=1.0, top_p=0.8, top_k=-1, repetition_penalty=0.1, presence_penalty=-2.0, frequency_penalty=-2.0, max_tokens=output_len, ) # Set experiment config experiment_config: dict = { 'model_id_or_path': model_id_or_path, 'context_length': context_length, 'output_len': output_len, 'tp_size': len(envs.split(',')), 'gpu_memory_utilization': gpu_memory_utilization, 'max_model_len': max_model_len, 'enforce_eager': enforce_eager, 'envs': envs, 'outputs_dir': outputs_dir, 'warmup': 0, 'use_modelscope': use_modelscope, } logger.info(f'Sampling params: {sampling_params}') logger.info(f'Experiment config: {experiment_config}') logger.info(f'Set CUDA_VISIBLE_DEVICES={envs} for model {model_id_or_path} with context_length {context_length}') os.environ["CUDA_VISIBLE_DEVICES"] = envs speed_benchmark_vllm = SpeedBenchmarkVllm(experiment_config=experiment_config, sampling_params=sampling_params) speed_benchmark_vllm.run() if __name__ == '__main__': # Usage: python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --use_modelscope --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm # HF_ENDPOINT=https://hf-mirror.com python speed_benchmark_vllm.py --model_id_or_path Qwen/Qwen2.5-0.5B-Instruct --context_length 1 --max_model_len 32768 --gpus 0 --gpu_memory_utilization 0.9 --outputs_dir outputs/vllm main()