├── .gitignore ├── ConceptARC ├── AboveBelow │ ├── AboveBelow1.json │ ├── AboveBelow10.json │ ├── AboveBelow2.json │ ├── AboveBelow3.json │ ├── AboveBelow4.json │ ├── AboveBelow5.json │ ├── AboveBelow6.json │ ├── AboveBelow7.json │ ├── AboveBelow8.json │ ├── AboveBelow9.json │ └── AboveBelowMinimal.json ├── Center │ ├── Center1.json │ ├── Center10.json │ ├── Center2.json │ ├── Center3.json │ ├── Center4.json │ ├── Center5.json │ ├── Center6.json │ ├── Center7.json │ ├── Center8.json │ ├── Center9.json │ └── CenterMinimal.json ├── CleanUp │ ├── CleanUp1.json │ ├── CleanUp10.json │ ├── CleanUp2.json │ ├── CleanUp3.json │ ├── CleanUp4.json │ ├── CleanUp5.json │ ├── CleanUp6.json │ ├── CleanUp7.json │ ├── CleanUp8.json │ ├── CleanUp9.json │ └── CleanUpMinimal.json ├── CompleteShape │ ├── CompleteShape1.json │ ├── CompleteShape10.json │ ├── CompleteShape2.json │ ├── CompleteShape3.json │ ├── CompleteShape4.json │ ├── CompleteShape5.json │ ├── CompleteShape6.json │ ├── CompleteShape7.json │ ├── CompleteShape8.json │ ├── CompleteShape9.json │ └── CompleteShapeMinimal.json ├── Copy │ ├── Copy1.json │ ├── Copy10.json │ ├── Copy2.json │ ├── Copy3.json │ ├── Copy4.json │ ├── Copy5.json │ ├── Copy6.json │ ├── Copy7.json │ ├── Copy8.json │ ├── Copy9.json │ └── CopyMinimal.json ├── Count │ ├── Count1.json │ ├── Count10.json │ ├── Count2.json │ ├── Count3.json │ ├── Count4.json │ ├── Count5.json │ ├── Count6.json │ ├── Count7.json │ ├── Count8.json │ ├── Count9.json │ └── CountMinimal.json ├── ExtendToBoundary │ ├── ExtendToBoundary1.json │ ├── ExtendToBoundary10.json │ ├── ExtendToBoundary2.json │ ├── ExtendToBoundary3.json │ ├── ExtendToBoundary4.json │ ├── ExtendToBoundary5.json │ ├── ExtendToBoundary6.json │ ├── ExtendToBoundary7.json │ ├── ExtendToBoundary8.json │ ├── ExtendToBoundary9.json │ └── ExtendToBoundaryMinimal.json ├── ExtractObjects │ ├── ExtractObjects1.json │ ├── ExtractObjects10.json │ ├── ExtractObjects2.json │ ├── ExtractObjects3.json │ ├── ExtractObjects4.json │ ├── ExtractObjects5.json │ ├── ExtractObjects6.json │ ├── ExtractObjects7.json │ ├── ExtractObjects8.json │ ├── ExtractObjects9.json │ └── ExtractObjectsMinimal.json ├── FilledNotFilled │ ├── FilledNotFilled1.json │ ├── FilledNotFilled10.json │ ├── FilledNotFilled2.json │ ├── FilledNotFilled3.json │ ├── FilledNotFilled4.json │ ├── FilledNotFilled5.json │ ├── FilledNotFilled6.json │ ├── FilledNotFilled7.json │ ├── FilledNotFilled8.json │ ├── FilledNotFilled9.json │ └── FilledNotFilledMinimal.json ├── HorizontalVertical │ ├── HorizontalVertical1.json │ ├── HorizontalVertical10.json │ ├── HorizontalVertical2.json │ ├── HorizontalVertical3.json │ ├── HorizontalVertical4.json │ ├── HorizontalVertical5.json │ ├── HorizontalVertical6.json │ ├── HorizontalVertical7.json │ ├── HorizontalVertical8.json │ ├── HorizontalVertical9.json │ └── HorizontalVerticalMinimal.json ├── InsideOutside │ ├── InsideOutside1.json │ ├── InsideOutside10.json │ ├── InsideOutside2.json │ ├── InsideOutside3.json │ ├── InsideOutside4.json │ ├── InsideOutside5.json │ ├── InsideOutside6.json │ ├── InsideOutside7.json │ ├── InsideOutside8.json │ ├── InsideOutside9.json │ └── InsideOutsideMinimal.json ├── MoveToBoundary │ ├── MoveToBoundary1.json │ ├── MoveToBoundary10.json │ ├── MoveToBoundary2.json │ ├── MoveToBoundary3.json │ ├── MoveToBoundary4.json │ ├── MoveToBoundary5.json │ ├── MoveToBoundary6.json │ ├── MoveToBoundary7.json │ ├── MoveToBoundary8.json │ ├── MoveToBoundary9.json │ └── MoveToBoundaryMinimal.json ├── Order │ ├── Order1.json │ ├── Order10.json │ ├── Order2.json │ ├── Order3.json │ ├── Order4.json │ ├── Order5.json │ ├── Order6.json │ ├── Order7.json │ ├── Order8.json │ ├── Order9.json │ └── OrderMinimal.json ├── SameDifferent │ ├── SameDifferent1.json │ ├── SameDifferent10.json │ ├── SameDifferent2.json │ ├── SameDifferent3.json │ ├── SameDifferent4.json │ ├── SameDifferent5.json │ ├── SameDifferent6.json │ ├── SameDifferent7.json │ ├── SameDifferent8.json │ ├── SameDifferent9.json │ └── SameDifferentMinimal.json ├── TopBottom2D │ ├── TopBottom2D1.json │ ├── TopBottom2D10.json │ ├── TopBottom2D2.json │ ├── TopBottom2D3.json │ ├── TopBottom2D4.json │ ├── TopBottom2D5.json │ ├── TopBottom2D6.json │ ├── TopBottom2D7.json │ ├── TopBottom2D8.json │ ├── TopBottom2D9.json │ └── TopBottom2DMinimal.json └── TopBottom3D │ ├── TopBottom3D1.json │ ├── TopBottom3D10.json │ ├── TopBottom3D2.json │ ├── TopBottom3D3.json │ ├── TopBottom3D4.json │ ├── TopBottom3D5.json │ ├── TopBottom3D6.json │ ├── TopBottom3D7.json │ ├── TopBottom3D8.json │ ├── TopBottom3D9.json │ └── TopBottom3DMinimal.json ├── README.md ├── color_variation.py ├── conceptarc.py ├── concepts.py ├── data_generation_script.sh ├── data_processing ├── change_formatting.py ├── concept_arc_test_transduction_data.json ├── gen_transduction_prompt.py ├── test-time-finetune │ ├── arc_all_evaluation.json │ ├── data_augmentation.py │ ├── gen_test-time-dataset.sh │ ├── gen_transduction_only_formatted.py │ └── get_pseudo_eval_task.py └── validation_transduction_prompt.jsonl ├── divide_and_conquer.py ├── eval_code_samples.py ├── eval_openai.py ├── evaluation.py ├── execution.py ├── finetune ├── alignment-handbook │ ├── .gitignore │ ├── LICENSE │ ├── Makefile │ ├── README.md │ ├── arc_problems_concept_arc_528_extra_newline_v2.jsonl │ ├── arc_problems_validation_400_extra_newline.jsonl │ ├── arc_problems_validation_400_extra_newline_v2.jsonl │ ├── assets │ │ └── handbook.png │ ├── chapters │ │ └── en │ │ │ ├── _toctree.yml │ │ │ └── chapter0 │ │ │ └── introduction.mdx │ ├── gen_dataset_both.py │ ├── gen_test_prompt.py │ ├── inference.py │ ├── recipes │ │ ├── accelerate_configs │ │ │ ├── deepspeed_zero3.yaml │ │ │ ├── fsdp.yaml │ │ │ ├── fsdp_qlora.yaml │ │ │ └── multi_gpu.yaml │ │ ├── barc │ │ │ ├── config_fft_llama3_induction.yaml │ │ │ ├── config_fft_llama3_induction_all_data_mixed.yaml │ │ │ ├── config_fft_llama3_induction_transduction.yaml │ │ │ ├── config_lora_llama3_induction.yaml │ │ │ ├── transduction_config_fft_engineer_heavy_model.yaml │ │ │ ├── transduction_config_fft_heavy_model.yaml │ │ │ └── transduction_config_testtime-finetune_engineer_heavy_model.yaml │ │ └── launch.slurm │ ├── scripts │ │ ├── README.md │ │ ├── run_cpt.py │ │ ├── run_dpo.py │ │ ├── run_orpo.py │ │ └── run_sft.py │ ├── setup.cfg │ ├── setup.py │ ├── src │ │ └── alignment │ │ │ ├── __init__.py │ │ │ ├── configs.py │ │ │ ├── data.py │ │ │ ├── decontaminate.py │ │ │ ├── model_utils.py │ │ │ └── release.py │ ├── tests │ │ ├── __init__.py │ │ ├── fixtures │ │ │ ├── config_dpo_full.yaml │ │ │ └── config_sft_full.yaml │ │ ├── test_configs.py │ │ ├── test_data.py │ │ ├── test_decontaminate.py │ │ └── test_model_utils.py │ └── vllm_inference_transduction.py └── inference │ ├── vllm_inference.py │ ├── vllm_inference_transduction_concept_arc.py │ ├── vllm_inference_transduction_evaluation.py │ └── vllm_transduction_reranking.py ├── gen_aug_dataset.py ├── generate_code.py ├── generate_descriptions.py ├── generate_problems.py ├── llm.py ├── make_dataset.py ├── parse_batch_description_samples.py ├── prompt.py ├── prompts ├── description_prompt.md ├── divide_and_conquer_refactor.md ├── problem_from_description.md └── problem_from_description_suggesting_function.md ├── python_sandbox.py ├── requirements.txt ├── seeds ├── 007bbfb7.py ├── 00d62c1b.py ├── 017c7c7b.py ├── 025d127b.py ├── 045e512c.py ├── 0520fde7.py ├── 05269061.py ├── 05f2a901.py ├── 06df4c85.py ├── 08ed6ac7.py ├── 09629e4f.py ├── 0962bcdd.py ├── 0a938d79.py ├── 0b148d64.py ├── 0ca9ddb6.py ├── 0d3d703e.py ├── 0dfd9992.py ├── 0e206a2e.py ├── 10fcaaa3.py ├── 11852cab.py ├── 1190e5a7.py ├── 137eaa0f.py ├── 150deff5.py ├── 178fcbfb.py ├── 1a07d186.py ├── 1b2d62fb.py ├── 1b60fb0c.py ├── 1bfc4729.py ├── 1caeab9d.py ├── 1cf80156.py ├── 1e32b0e9.py ├── 1f642eb9.py ├── 1fad071e.py ├── 2204b7a8.py ├── 228f6490.py ├── 23581191.py ├── 239be575.py ├── 25d487eb.py ├── 25d487eb_Kevin.py ├── 25d8a9c8.py ├── 25ff71a9.py ├── 264363fd.py ├── 264363fd_Kevin.py ├── 28e73c20.py ├── 29623171.py ├── 29c11459.py ├── 2bcee788.py ├── 2c608aff.py ├── 2dd70a9a.py ├── 2dee498d.py ├── 31aa019c.py ├── 3345333e.py ├── 3428a4f5.py ├── 3618c87e.py ├── 3ac3eb23.py ├── 3bdb4ada.py ├── 3befdf3e.py ├── 3de23699.py ├── 3e980e27.py ├── 3eda0437.py ├── 3f7978a0.py ├── 4093f84a.py ├── 41e4d17e.py ├── 4258a5f9.py ├── 444801d8.py ├── 44d8ac46.py ├── 44f52bb0.py ├── 469497ad.py ├── 46f33fce.py ├── 48d8fb45.py ├── 4c5c2cf0.py ├── 508bd3b6.py ├── 5168d44c.py ├── 54d82841.py ├── 56dc2b01.py ├── 57aa92db.py ├── 5c2c9af4.py ├── 5daaa586.py ├── 623ea044.py ├── 6455b5f5.py ├── 681b3aeb.py ├── 6855a6e4.py ├── 6a1e5592.py ├── 6aa20dc0.py ├── 6b9890af.py ├── 6cdd2623.py ├── 6cf79266.py ├── 6d58a25d.py ├── 6d75e8bb.py ├── 6e19193c.py ├── 6e82a1ae.py ├── 6ecd11f4.py ├── 6f8cd79b.py ├── 6fa7a44f.py ├── 72ca375d.py ├── 7447852a.py ├── 746b3537.py ├── 776ffc46.py ├── 780d0b14.py ├── 7837ac64.py ├── 7c008303.py ├── 7e0986d6.py ├── 7f4411dc.py ├── 810b9b61.py ├── 834ec97d.py ├── 8403a5d5.py ├── 8a004b2b.py ├── 8d5021e8.py ├── 8d510a79.py ├── 8e1813be.py ├── 8e5a5113.py ├── 90c28cc7.py ├── 941d9a10.py ├── 97a05b5b.py ├── 995c5fa3.py ├── 9af7a82c.py ├── 9f236235.py ├── ConceptARC │ └── SameDifferent1.py ├── a3df8b1e.py ├── a78176bb.py ├── a79310a0.py ├── a8c38be5.py ├── a9f96cdd.py ├── aabf363d.py ├── aba27056.py ├── aedd82e4.py ├── af902bf9.py ├── b527c5c6.py ├── b7249182.py ├── b775ac94.py ├── b782dc8a.py ├── bbc9ae5d.py ├── bc1d5164.py ├── bd4472b8.py ├── caa06a1f.py ├── ce602527.py ├── cf98881b.py ├── common.py ├── d06dbe63.py ├── d2abd087.py ├── d4a91cb9.py ├── d4f3cd78.py ├── d511f180.py ├── d6ad076f.py ├── d9f24cd1.py ├── db3e9e38.py ├── db93a21d.py ├── e179c5f4.py ├── e21d9049.py ├── e48d4e1a.py ├── e509e548.py ├── e73095fd.py ├── e8dc4411.py ├── e9614598.py ├── ea786f4a.py ├── eb281b96.py ├── eb5a1d5d.py ├── f15e1fac.py ├── f8a8fe49.py ├── f8b3ba0a.py ├── f9012d9b.py ├── fcc82909.py ├── feca6190.py ├── ff28f65a.py ├── input_sandbox │ └── 06df4c85_input.py └── template.py ├── similarity.py ├── solve_openai.py ├── synthetic_problems ├── 00339623fade9c57.json ├── 00339623fade9c57.png ├── 00339623fade9c57.py ├── 0113758503f8e80f.json ├── 0113758503f8e80f.png ├── 0113758503f8e80f.py ├── 01aeb3007bcf0250.json ├── 01aeb3007bcf0250.png ├── 01aeb3007bcf0250.py ├── 034e6e98253bf0cc.json ├── 034e6e98253bf0cc.png ├── 034e6e98253bf0cc.py ├── 03ca768dcba3c0bf.json ├── 03ca768dcba3c0bf.png ├── 03ca768dcba3c0bf.py ├── 05437b7f872cf194.json ├── 05437b7f872cf194.png ├── 05437b7f872cf194.py ├── 07a38da674cb354f.json ├── 07a38da674cb354f.png ├── 07a38da674cb354f.py ├── 0883e0d7dd062fd8.json ├── 0883e0d7dd062fd8.png ├── 0883e0d7dd062fd8.py ├── 091a91149c519d93.json ├── 091a91149c519d93.png ├── 091a91149c519d93.py ├── 095371d218d98811.json ├── 095371d218d98811.png ├── 095371d218d98811.py ├── 0a1398034ed2e957.json ├── 0a1398034ed2e957.png ├── 0a1398034ed2e957.py ├── 0a677c5ae8861e5f.json ├── 0a677c5ae8861e5f.png ├── 0a677c5ae8861e5f.py ├── 0ae8a58744ffa8f2.json ├── 0ae8a58744ffa8f2.png ├── 0ae8a58744ffa8f2.py ├── 0b521d97cbec0437.json ├── 0b521d97cbec0437.png ├── 0b521d97cbec0437.py ├── 0b6e29487e611574.json ├── 0b6e29487e611574.png ├── 0b6e29487e611574.py ├── 0c87e4a51288cafa.json ├── 0c87e4a51288cafa.png ├── 0c87e4a51288cafa.py ├── 0d743118de9f8272.json ├── 0d743118de9f8272.png ├── 0d743118de9f8272.py ├── 0e29f21b47a2e79a.json ├── 0e29f21b47a2e79a.png ├── 0e29f21b47a2e79a.py ├── 0eb8c79b01d8a60f.json ├── 0eb8c79b01d8a60f.png ├── 0eb8c79b01d8a60f.py ├── 0f5697cb3b1b7847.json ├── 0f5697cb3b1b7847.png ├── 0f5697cb3b1b7847.py ├── 0fec3f67c9eee3fa.json ├── 0fec3f67c9eee3fa.png ├── 0fec3f67c9eee3fa.py ├── 10d52ddab0128cb2.json ├── 10d52ddab0128cb2.png ├── 10d52ddab0128cb2.py ├── 117f8f7bed7d23af.json ├── 117f8f7bed7d23af.png ├── 117f8f7bed7d23af.py ├── 11d40af595b242d8.json ├── 11d40af595b242d8.png ├── 11d40af595b242d8.py ├── 135546fe092a801d.json ├── 135546fe092a801d.png ├── 135546fe092a801d.py ├── 13924da0458ab79d.json ├── 13924da0458ab79d.png ├── 13924da0458ab79d.py ├── 13f5c1d4cf254c6d.json ├── 13f5c1d4cf254c6d.png ├── 13f5c1d4cf254c6d.py ├── 1554b31dc477d72f.json ├── 1554b31dc477d72f.png ├── 1554b31dc477d72f.py ├── 17ba3973125fae87.json ├── 17ba3973125fae87.png ├── 17ba3973125fae87.py ├── 180e508ca21f01af.json ├── 180e508ca21f01af.png ├── 180e508ca21f01af.py ├── 1859d106e6cb01cf.json ├── 1859d106e6cb01cf.png ├── 1859d106e6cb01cf.py ├── 19c3512518647527.json ├── 19c3512518647527.png ├── 19c3512518647527.py ├── 19c5a7af3b969845.json ├── 19c5a7af3b969845.png ├── 19c5a7af3b969845.py ├── 1a0041e3d04aceaa.json ├── 1a0041e3d04aceaa.png ├── 1a0041e3d04aceaa.py ├── 1a14e8f3f5c255f5.json ├── 1a14e8f3f5c255f5.png ├── 1a14e8f3f5c255f5.py ├── 1aa8aedef6e76c1c.json ├── 1aa8aedef6e76c1c.png ├── 1aa8aedef6e76c1c.py ├── 1adf3430d4c24817.json ├── 1adf3430d4c24817.png ├── 1adf3430d4c24817.py ├── 1b3aa3705ec5db0c.json ├── 1b3aa3705ec5db0c.png ├── 1b3aa3705ec5db0c.py ├── 1cf29f5d5a308d0d.json ├── 1cf29f5d5a308d0d.png ├── 1cf29f5d5a308d0d.py ├── 1e07ae80c9a5a978.json ├── 1e07ae80c9a5a978.png ├── 1e07ae80c9a5a978.py ├── 1fb41b7d2dec1fa2.json ├── 1fb41b7d2dec1fa2.png ├── 1fb41b7d2dec1fa2.py ├── 20248f13ad783b6c.png ├── 20248f13ad783b6c.py ├── 2112b6a16e7e5dca.json ├── 2112b6a16e7e5dca.png ├── 2112b6a16e7e5dca.py ├── 214b6451c45913d2.json ├── 214b6451c45913d2.png ├── 214b6451c45913d2.py ├── 222b04d86d9f6766.json ├── 222b04d86d9f6766.png ├── 222b04d86d9f6766.py ├── 22a9e1fa841de2fd.json ├── 22a9e1fa841de2fd.png ├── 22a9e1fa841de2fd.py ├── 246524692fe7fccf.json ├── 246524692fe7fccf.png ├── 246524692fe7fccf.py ├── 24cca0c44a10a6cc.json ├── 24cca0c44a10a6cc.png ├── 24cca0c44a10a6cc.py ├── 25029335ec809132.json ├── 25029335ec809132.png ├── 25029335ec809132.py ├── 2619d7eec5c58f94.json ├── 2619d7eec5c58f94.png ├── 2619d7eec5c58f94.py ├── 2694c7a750d1d9a9.json ├── 2694c7a750d1d9a9.png ├── 2694c7a750d1d9a9.py ├── 2736442f0aedd93c.json ├── 2736442f0aedd93c.png ├── 2736442f0aedd93c.py ├── 27582b1cc7608c24.json ├── 27582b1cc7608c24.png ├── 27582b1cc7608c24.py ├── 27b394a4638dd7fa.json ├── 27b394a4638dd7fa.png ├── 27b394a4638dd7fa.py ├── 2836a1edac8a5dc1.json ├── 2836a1edac8a5dc1.png ├── 2836a1edac8a5dc1.py ├── 28a49d5a36dddde4.json ├── 28a49d5a36dddde4.png ├── 28a49d5a36dddde4.py ├── 29f0beeb8a1a64d0.json ├── 29f0beeb8a1a64d0.png ├── 29f0beeb8a1a64d0.py ├── 2af23f2f6020001d.json ├── 2af23f2f6020001d.png ├── 2af23f2f6020001d.py ├── 2b197980c0d3374f.json ├── 2b197980c0d3374f.png ├── 2b197980c0d3374f.py ├── 2b832caffdf8275c.json ├── 2b832caffdf8275c.png ├── 2b832caffdf8275c.py ├── 2cd36f4b96ae1654.json ├── 2cd36f4b96ae1654.png ├── 2cd36f4b96ae1654.py ├── 2dc68bad444414ea.json ├── 2dc68bad444414ea.png ├── 2dc68bad444414ea.py ├── 2e6242c645030562.json ├── 2e6242c645030562.png ├── 2e6242c645030562.py ├── 2f516468fb4c17f8.json ├── 2f516468fb4c17f8.png ├── 2f516468fb4c17f8.py ├── 2fb7df6708e8ce82.json ├── 2fb7df6708e8ce82.png ├── 2fb7df6708e8ce82.py ├── 2fd89dbe17b112ef.json ├── 2fd89dbe17b112ef.png ├── 2fd89dbe17b112ef.py ├── 304be0852acc714a.json ├── 304be0852acc714a.png ├── 304be0852acc714a.py ├── 3199d9049b8a7ba0.json ├── 3199d9049b8a7ba0.png ├── 3199d9049b8a7ba0.py ├── 340aaf7f18ad278c.json ├── 340aaf7f18ad278c.png ├── 340aaf7f18ad278c.py ├── 3458278fce8b0664.json ├── 3458278fce8b0664.png ├── 3458278fce8b0664.py ├── 35d687ffd8d04693.json ├── 35d687ffd8d04693.png ├── 35d687ffd8d04693.py ├── 361c71693c123791.json ├── 361c71693c123791.png ├── 361c71693c123791.py ├── 3790f2e869fe329e.json ├── 3790f2e869fe329e.png ├── 3790f2e869fe329e.py ├── 39c7b37ed6f9b92e.json ├── 39c7b37ed6f9b92e.png ├── 39c7b37ed6f9b92e.py ├── 39f8cb7f61eb6230.json ├── 39f8cb7f61eb6230.png ├── 39f8cb7f61eb6230.py ├── 3aad529fcd0cd58a.json ├── 3aad529fcd0cd58a.png ├── 3aad529fcd0cd58a.py ├── 3b168130826537d1.json ├── 3b168130826537d1.png ├── 3b168130826537d1.py ├── 3b68737a0bae1019.json ├── 3b68737a0bae1019.png ├── 3b68737a0bae1019.py ├── 3b810c7fa69e39a8.json ├── 3b810c7fa69e39a8.png ├── 3b810c7fa69e39a8.py ├── 3bb63ebc857240a2.json ├── 3bb63ebc857240a2.png ├── 3bb63ebc857240a2.py ├── 3c63c29caf82b722.json ├── 3c63c29caf82b722.png ├── 3c63c29caf82b722.py ├── 3ce03fef3a264f25.json ├── 3ce03fef3a264f25.png ├── 3ce03fef3a264f25.py ├── 41eedd49cc17e06e.json ├── 41eedd49cc17e06e.png ├── 41eedd49cc17e06e.py ├── 429b92123e921f6b.json ├── 429b92123e921f6b.png ├── 429b92123e921f6b.py ├── 43443568733b1035.json ├── 43443568733b1035.png ├── 43443568733b1035.py ├── 4460074ba62b229a.json ├── 4460074ba62b229a.png ├── 4460074ba62b229a.py ├── 44e1d4cff2b8004e.json ├── 44e1d4cff2b8004e.png ├── 44e1d4cff2b8004e.py ├── 46c2d9f38ddd1696.json ├── 46c2d9f38ddd1696.png ├── 46c2d9f38ddd1696.py ├── 46f8edd5bca05cbe.json ├── 46f8edd5bca05cbe.png ├── 46f8edd5bca05cbe.py ├── 481a9b09709695d0.json ├── 481a9b09709695d0.png ├── 481a9b09709695d0.py ├── 482f9be679e8c37a.json ├── 482f9be679e8c37a.png ├── 482f9be679e8c37a.py ├── 4860cae690fc5960.json ├── 4860cae690fc5960.png ├── 4860cae690fc5960.py ├── 48898bae300eda56.json ├── 48898bae300eda56.png ├── 48898bae300eda56.py ├── 4add4834f1c15b4b.json ├── 4add4834f1c15b4b.png ├── 4add4834f1c15b4b.py ├── 4c5a52a654da2393.json ├── 4c5a52a654da2393.png ├── 4c5a52a654da2393.py ├── 4cdd3e3ec3ed0ca2.json ├── 4cdd3e3ec3ed0ca2.png ├── 4cdd3e3ec3ed0ca2.py ├── 4f23c91c6e46104d.json ├── 4f23c91c6e46104d.png ├── 4f23c91c6e46104d.py ├── 4f483ed6c7994df9.json ├── 4f483ed6c7994df9.png ├── 4f483ed6c7994df9.py ├── 4fa59d4ff6aa2ec6.json ├── 4fa59d4ff6aa2ec6.png ├── 4fa59d4ff6aa2ec6.py ├── 4ff6a7b3c671fa2f.json ├── 4ff6a7b3c671fa2f.png ├── 4ff6a7b3c671fa2f.py ├── 51809ce5d3c1fe68.json ├── 51809ce5d3c1fe68.png ├── 51809ce5d3c1fe68.py ├── 51976bf44af9839c.json ├── 51976bf44af9839c.png ├── 51976bf44af9839c.py ├── 51d4bac7959d9d0a.json ├── 51d4bac7959d9d0a.png ├── 51d4bac7959d9d0a.py ├── 52590f61031a0632.json ├── 52590f61031a0632.png ├── 52590f61031a0632.py ├── 54e8555f9b898bbf.json ├── 54e8555f9b898bbf.png ├── 54e8555f9b898bbf.py ├── 55bf806777985a4d.json ├── 55bf806777985a4d.png ├── 55bf806777985a4d.py ├── 580e5cd58a8757b8.json ├── 580e5cd58a8757b8.png ├── 580e5cd58a8757b8.py ├── 5856ae82dce6ef38.json ├── 5856ae82dce6ef38.png ├── 5856ae82dce6ef38.py ├── 58d0dddbe3bfaf94.json ├── 58d0dddbe3bfaf94.png ├── 58d0dddbe3bfaf94.py ├── 59b015ec0ee09dae.json ├── 59b015ec0ee09dae.png ├── 59b015ec0ee09dae.py ├── 5c43b157ce7a3dfc.json ├── 5c43b157ce7a3dfc.png ├── 5c43b157ce7a3dfc.py ├── 5c833d47d65ec749.json ├── 5c833d47d65ec749.png ├── 5c833d47d65ec749.py ├── 5cac710111179744.json ├── 5cac710111179744.png ├── 5cac710111179744.py ├── 5d7bc92d64331718.json ├── 5d7bc92d64331718.png ├── 5d7bc92d64331718.py ├── 5de14ff5b89ebbb3.json ├── 5de14ff5b89ebbb3.png ├── 5de14ff5b89ebbb3.py ├── 5fcddf1a31e6c6ef.png ├── 5fcddf1a31e6c6ef.py ├── 5fd977fa8200a442.json ├── 5fd977fa8200a442.png ├── 5fd977fa8200a442.py ├── 6175f98698464ee0.json ├── 6175f98698464ee0.png ├── 6175f98698464ee0.py ├── 638424e8b437b899.json ├── 638424e8b437b899.png ├── 638424e8b437b899.py ├── 648dab7b7cf5ad07.json ├── 648dab7b7cf5ad07.png ├── 648dab7b7cf5ad07.py ├── 67364b1be99a89ba.json ├── 67364b1be99a89ba.png ├── 67364b1be99a89ba.py ├── 67653ca863c3e7d0.json ├── 67653ca863c3e7d0.png ├── 67653ca863c3e7d0.py ├── 6927bab219b7f2fb.json ├── 6927bab219b7f2fb.png ├── 6927bab219b7f2fb.py ├── 6990bcb4fb5d14ce.json ├── 6990bcb4fb5d14ce.png ├── 6990bcb4fb5d14ce.py ├── 699694ec25662f9c.json ├── 699694ec25662f9c.png ├── 699694ec25662f9c.py ├── 69d7583f6f9130c6.json ├── 69d7583f6f9130c6.png ├── 69d7583f6f9130c6.py ├── 6ab29ec83066defd.json ├── 6ab29ec83066defd.png ├── 6ab29ec83066defd.py ├── 6ae60f3579e42765.json ├── 6ae60f3579e42765.png ├── 6ae60f3579e42765.py ├── 6b029adc6b018586.json ├── 6b029adc6b018586.png ├── 6b029adc6b018586.py ├── 6b4e3c3f79984eb3.json ├── 6b4e3c3f79984eb3.png ├── 6b4e3c3f79984eb3.py ├── 6b5ad0bb47c92936.json ├── 6b5ad0bb47c92936.png ├── 6b5ad0bb47c92936.py ├── 6bf0d17436e7ec3a.json ├── 6bf0d17436e7ec3a.png ├── 6bf0d17436e7ec3a.py ├── 6c299b226e3b9acf.json ├── 6c299b226e3b9acf.png ├── 6c299b226e3b9acf.py ├── 6cd1283bf997b605.json ├── 6cd1283bf997b605.png ├── 6cd1283bf997b605.py ├── 6ce9bc72bbb4f682.json ├── 6ce9bc72bbb4f682.png ├── 6ce9bc72bbb4f682.py ├── 6cea6edee3f8da97.json ├── 6cea6edee3f8da97.png ├── 6cea6edee3f8da97.py ├── 6e1f66ef9e832854.json ├── 6e1f66ef9e832854.png ├── 6e1f66ef9e832854.py ├── 6f9df3546d43699e.json ├── 6f9df3546d43699e.png ├── 6f9df3546d43699e.py ├── 6f9f9eaad95da8d7.json ├── 6f9f9eaad95da8d7.png ├── 6f9f9eaad95da8d7.py ├── 718d574489e08f92.json ├── 718d574489e08f92.png ├── 718d574489e08f92.py ├── 71d4937764906860.json ├── 71d4937764906860.png ├── 71d4937764906860.py ├── 72268ed8a6ad43a6.json ├── 72268ed8a6ad43a6.png ├── 72268ed8a6ad43a6.py ├── 726e51507e02c840.json ├── 726e51507e02c840.png ├── 726e51507e02c840.py ├── 72dccdece48e260d.json ├── 72dccdece48e260d.png ├── 72dccdece48e260d.py ├── 73703a4ff92c5135.json ├── 73703a4ff92c5135.png ├── 73703a4ff92c5135.py ├── 7440331b1ed2e449.json ├── 7440331b1ed2e449.png ├── 7440331b1ed2e449.py ├── 74c89908c2ddb311.json ├── 74c89908c2ddb311.png ├── 74c89908c2ddb311.py ├── 74fff0e8a5d2b4c4.json ├── 74fff0e8a5d2b4c4.png ├── 74fff0e8a5d2b4c4.py ├── 76a4fd40b6110644.json ├── 76a4fd40b6110644.png ├── 76a4fd40b6110644.py ├── 76bc88d48736d614.json ├── 76bc88d48736d614.png ├── 76bc88d48736d614.py ├── 77783290ee6fc4cb.json ├── 77783290ee6fc4cb.png ├── 77783290ee6fc4cb.py ├── 78698d9b0b02c64f.json ├── 78698d9b0b02c64f.png ├── 78698d9b0b02c64f.py ├── 78c0af3644e5e3ad.json ├── 78c0af3644e5e3ad.png ├── 78c0af3644e5e3ad.py ├── 78e4c3b87b179680.json ├── 78e4c3b87b179680.png ├── 78e4c3b87b179680.py ├── 7a1204f5796e07ed.json ├── 7a1204f5796e07ed.png ├── 7a1204f5796e07ed.py ├── 7a58a1388427e54d.json ├── 7a58a1388427e54d.png ├── 7a58a1388427e54d.py ├── 7a5dbfaf7910ec41.json ├── 7a5dbfaf7910ec41.png ├── 7a5dbfaf7910ec41.py ├── 7a7a3e17d78db45a.json ├── 7a7a3e17d78db45a.png ├── 7a7a3e17d78db45a.py ├── 7c40e7e6438f305a.json ├── 7c40e7e6438f305a.png ├── 7c40e7e6438f305a.py ├── 7cf3d0f1f5b187d3.json ├── 7cf3d0f1f5b187d3.png ├── 7cf3d0f1f5b187d3.py ├── 7da3ce7f81db3403.json ├── 7da3ce7f81db3403.png ├── 7da3ce7f81db3403.py ├── 7ee20f45c1820621.json ├── 7ee20f45c1820621.png ├── 7ee20f45c1820621.py ├── 80a219211f535c65.json ├── 80a219211f535c65.png ├── 80a219211f535c65.py ├── 8288c75f237a3c1a.json ├── 8288c75f237a3c1a.png ├── 8288c75f237a3c1a.py ├── 82fcd02883be67b6.json ├── 82fcd02883be67b6.png ├── 82fcd02883be67b6.py ├── 83f9fc39ee92867d.json ├── 83f9fc39ee92867d.png ├── 83f9fc39ee92867d.py ├── 8467fd882fc64e2a.json ├── 8467fd882fc64e2a.png ├── 8467fd882fc64e2a.py ├── 84e453c1fc3cebc9.json ├── 84e453c1fc3cebc9.png ├── 84e453c1fc3cebc9.py ├── 859883945f4bdbea.json ├── 859883945f4bdbea.png ├── 859883945f4bdbea.py ├── 86678bdfa15bda7a.json ├── 86678bdfa15bda7a.png ├── 86678bdfa15bda7a.py ├── 866d36672ffa5b4d.json ├── 866d36672ffa5b4d.png ├── 866d36672ffa5b4d.py ├── 8703abb0ac45f197.json ├── 8703abb0ac45f197.png ├── 8703abb0ac45f197.py ├── 874a40d7f8e7862c.json ├── 874a40d7f8e7862c.png ├── 874a40d7f8e7862c.py ├── 8871a13f467d12f2.json ├── 8871a13f467d12f2.png ├── 8871a13f467d12f2.py ├── 8929c44a0d3de4fb.json ├── 8929c44a0d3de4fb.png ├── 8929c44a0d3de4fb.py ├── 8956a35aa3ded119.json ├── 8956a35aa3ded119.png ├── 8956a35aa3ded119.py ├── 8ba1b6533d922a4f.json ├── 8ba1b6533d922a4f.png ├── 8ba1b6533d922a4f.py ├── 8beb76d2a4621d01.json ├── 8beb76d2a4621d01.png ├── 8beb76d2a4621d01.py ├── 8c10e03b743354b2.json ├── 8c10e03b743354b2.png ├── 8c10e03b743354b2.py ├── 8d67d6c51402a0cb.json ├── 8d67d6c51402a0cb.png ├── 8d67d6c51402a0cb.py ├── 8e895fc25c3d09bc.json ├── 8e895fc25c3d09bc.png ├── 8e895fc25c3d09bc.py ├── 8e9011f6b690d80a.json ├── 8e9011f6b690d80a.png ├── 8e9011f6b690d80a.py ├── 90f23c5b47a5b1d1.json ├── 90f23c5b47a5b1d1.png ├── 90f23c5b47a5b1d1.py ├── 92556b5c75871902.json ├── 92556b5c75871902.png ├── 92556b5c75871902.py ├── 92735ca4211717da.json ├── 92735ca4211717da.png ├── 92735ca4211717da.py ├── 92c17833928743ae.json ├── 92c17833928743ae.png ├── 92c17833928743ae.py ├── 93727f82245b7742.json ├── 93727f82245b7742.png ├── 93727f82245b7742.py ├── 93f9691fd7ba28e0.png ├── 93f9691fd7ba28e0.py ├── 957e8134e0009e10.json ├── 957e8134e0009e10.png ├── 957e8134e0009e10.py ├── 958d4939b257976c.json ├── 958d4939b257976c.png ├── 958d4939b257976c.py ├── 95e5e9b93c564963.json ├── 95e5e9b93c564963.png ├── 95e5e9b93c564963.py ├── 97c7c4449d733d79.json ├── 97c7c4449d733d79.png ├── 97c7c4449d733d79.py ├── 9833313d99fb7288.json ├── 9833313d99fb7288.png ├── 9833313d99fb7288.py ├── 98664e13083b3b0c.json ├── 98664e13083b3b0c.png ├── 98664e13083b3b0c.py ├── 989111dc373e972b.json ├── 989111dc373e972b.png ├── 989111dc373e972b.py ├── 99a513aceb27f14d.json ├── 99a513aceb27f14d.png ├── 99a513aceb27f14d.py ├── 9b8b25a3cd874aab.json ├── 9b8b25a3cd874aab.png ├── 9b8b25a3cd874aab.py ├── 9c561dcadcc9493e.json ├── 9c561dcadcc9493e.png ├── 9c561dcadcc9493e.py ├── 9cfaf182e5f9894e.json ├── 9cfaf182e5f9894e.png ├── 9cfaf182e5f9894e.py ├── 9ec3d67a53af494b.json ├── 9ec3d67a53af494b.png ├── 9ec3d67a53af494b.py ├── 9f14fa933d288921.json ├── 9f14fa933d288921.png ├── 9f14fa933d288921.py ├── 9f6fd3b063295384.json ├── 9f6fd3b063295384.png ├── 9f6fd3b063295384.py ├── a053d38c44d25d11.json ├── a053d38c44d25d11.png ├── a053d38c44d25d11.py ├── a097207a76966929.json ├── a097207a76966929.png ├── a097207a76966929.py ├── a0bf0d7f3ed9bc79.json ├── a0bf0d7f3ed9bc79.png ├── a0bf0d7f3ed9bc79.py ├── a1b236592068adf5.json ├── a1b236592068adf5.png ├── a1b236592068adf5.py ├── a33522d08def64e5.json ├── a33522d08def64e5.png ├── a33522d08def64e5.py ├── a3ea43536fe17367.json ├── a3ea43536fe17367.png ├── a3ea43536fe17367.py ├── a7a8997f8bdeafb5.json ├── a7a8997f8bdeafb5.png ├── a7a8997f8bdeafb5.py ├── aaa4fd35a72f5698.json ├── aaa4fd35a72f5698.png ├── aaa4fd35a72f5698.py ├── ac91a81d824987be.json ├── ac91a81d824987be.png ├── ac91a81d824987be.py ├── af14a1820e350b14.json ├── af14a1820e350b14.png ├── af14a1820e350b14.py ├── af36f5c5315e5a76.json ├── af36f5c5315e5a76.png ├── af36f5c5315e5a76.py ├── af81b0b4e35dd58d.json ├── af81b0b4e35dd58d.png ├── af81b0b4e35dd58d.py ├── afc615b0e7053f5d.json ├── afc615b0e7053f5d.png ├── afc615b0e7053f5d.py ├── aff1f5afeabbe04e.json ├── aff1f5afeabbe04e.png ├── aff1f5afeabbe04e.py ├── b07c3bddbd44f8bf.json ├── b07c3bddbd44f8bf.png ├── b07c3bddbd44f8bf.py ├── b0c9059f819300c2.json ├── b0c9059f819300c2.png ├── b0c9059f819300c2.py ├── b105c998360eef68.json ├── b105c998360eef68.png ├── b105c998360eef68.py ├── b23ebb57da6f743e.json ├── b23ebb57da6f743e.png ├── b23ebb57da6f743e.py ├── b268e19949617d54.json ├── b268e19949617d54.png ├── b268e19949617d54.py ├── b59e5af493357b0b.json ├── b59e5af493357b0b.png ├── b59e5af493357b0b.py ├── b5f6771acac690a6.json ├── b5f6771acac690a6.png ├── b5f6771acac690a6.py ├── b60cf03652ec8ff5.json ├── b60cf03652ec8ff5.png ├── b60cf03652ec8ff5.py ├── b61b94ce8ed2e2c5.json ├── b61b94ce8ed2e2c5.png ├── b61b94ce8ed2e2c5.py ├── b6a73d2d5bcc0d35.json ├── b6a73d2d5bcc0d35.png ├── b6a73d2d5bcc0d35.py ├── b7df99fd21ef0930.json ├── b7df99fd21ef0930.png ├── b7df99fd21ef0930.py ├── b8aedb1acd40916a.json ├── b8aedb1acd40916a.png ├── b8aedb1acd40916a.py ├── b9c762b0cfda54b6.json ├── b9c762b0cfda54b6.png ├── b9c762b0cfda54b6.py ├── ba5239fd5c5b3685.json ├── ba5239fd5c5b3685.png ├── ba5239fd5c5b3685.py ├── bacab697f0811e7a.json ├── bacab697f0811e7a.png ├── bacab697f0811e7a.py ├── bc8f9ffe294e4bf6.json ├── bc8f9ffe294e4bf6.png ├── bc8f9ffe294e4bf6.py ├── be5ed15484e4295c.json ├── be5ed15484e4295c.png ├── be5ed15484e4295c.py ├── bea9e8dae1c63e28.json ├── bea9e8dae1c63e28.png ├── bea9e8dae1c63e28.py ├── bec8ae345a1e5f89.json ├── bec8ae345a1e5f89.png ├── bec8ae345a1e5f89.py ├── beec07f508b552f4.json ├── beec07f508b552f4.png ├── beec07f508b552f4.py ├── bf5d110fa6349b73.json ├── bf5d110fa6349b73.png ├── bf5d110fa6349b73.py ├── bf85a905a705a4a1.json ├── bf85a905a705a4a1.png ├── bf85a905a705a4a1.py ├── bfe4839d0e9944db.json ├── bfe4839d0e9944db.png ├── bfe4839d0e9944db.py ├── c0b47c7f6cff7e1d.json ├── c0b47c7f6cff7e1d.png ├── c0b47c7f6cff7e1d.py ├── c18fdeb268a8e9bf.json ├── c18fdeb268a8e9bf.png ├── c18fdeb268a8e9bf.py ├── c2c2d8e8defaf66b.json ├── c2c2d8e8defaf66b.png ├── c2c2d8e8defaf66b.py ├── c315eb438ca7f982.json ├── c315eb438ca7f982.png ├── c315eb438ca7f982.py ├── c559537d012247fb.json ├── c559537d012247fb.png ├── c559537d012247fb.py ├── c5973f3b835be0c7.json ├── c5973f3b835be0c7.png ├── c5973f3b835be0c7.py ├── c61192135ea3a270.json ├── c61192135ea3a270.png ├── c61192135ea3a270.py ├── c67c7646f0a31207.json ├── c67c7646f0a31207.png ├── c67c7646f0a31207.py ├── c7306e295f9a5d6f.json ├── c7306e295f9a5d6f.png ├── c7306e295f9a5d6f.py ├── c7870e181156971c.json ├── c7870e181156971c.png ├── c7870e181156971c.py ├── c8cf330b217e1f30.json ├── c8cf330b217e1f30.png ├── c8cf330b217e1f30.py ├── c932a55e3c1ec061.json ├── c932a55e3c1ec061.png ├── c932a55e3c1ec061.py ├── c9a9ed362da33f2e.json ├── c9a9ed362da33f2e.png ├── c9a9ed362da33f2e.py ├── c9de363f03ecedca.json ├── c9de363f03ecedca.png ├── c9de363f03ecedca.py ├── cb83666c31c0985f.json ├── cb83666c31c0985f.png ├── cb83666c31c0985f.py ├── cc6d6a0fb6ac475a.json ├── cc6d6a0fb6ac475a.png ├── cc6d6a0fb6ac475a.py ├── cc90d171696177e1.json ├── cc90d171696177e1.png ├── cc90d171696177e1.py ├── cca2dfe851e11d6d.json ├── cca2dfe851e11d6d.png ├── cca2dfe851e11d6d.py ├── ccead5e9ae949949.json ├── ccead5e9ae949949.png ├── ccead5e9ae949949.py ├── ce994d0c1bff19df.json ├── ce994d0c1bff19df.png ├── ce994d0c1bff19df.py ├── d06359387c0e400f.json ├── d06359387c0e400f.png ├── d06359387c0e400f.py ├── d07af52f8629d7a7.json ├── d07af52f8629d7a7.png ├── d07af52f8629d7a7.py ├── d2ba866fb7ffaac5.json ├── d2ba866fb7ffaac5.png ├── d2ba866fb7ffaac5.py ├── d36137fdf2cefa7b.json ├── d36137fdf2cefa7b.png ├── d36137fdf2cefa7b.py ├── d383a292a85a26cc.json ├── d383a292a85a26cc.png ├── d383a292a85a26cc.py ├── d4658dc4035cb8f7.json ├── d4658dc4035cb8f7.png ├── d4658dc4035cb8f7.py ├── d599edf9b5c0ba46.json ├── d599edf9b5c0ba46.png ├── d599edf9b5c0ba46.py ├── d63598a64172a2b4.json ├── d63598a64172a2b4.png ├── d63598a64172a2b4.py ├── d89784f892c13ffc.json ├── d89784f892c13ffc.png ├── d89784f892c13ffc.py ├── d9508a1a2d4da991.json ├── d9508a1a2d4da991.png ├── d9508a1a2d4da991.py ├── da0b011790e14779.json ├── da0b011790e14779.png ├── da0b011790e14779.py ├── daf0632a8885b5c3.json ├── daf0632a8885b5c3.png ├── daf0632a8885b5c3.py ├── db4db2d8929e7ecf.json ├── db4db2d8929e7ecf.png ├── db4db2d8929e7ecf.py ├── dbb9e653f59862e8.json ├── dbb9e653f59862e8.png ├── dbb9e653f59862e8.py ├── dc8d2c3f264ecd04.json ├── dc8d2c3f264ecd04.png ├── dc8d2c3f264ecd04.py ├── dd0ed20b4649da88.json ├── dd0ed20b4649da88.png ├── dd0ed20b4649da88.py ├── dd29235217c98859.json ├── dd29235217c98859.png ├── dd29235217c98859.py ├── ddc4007aa1c9869b.json ├── ddc4007aa1c9869b.png ├── ddc4007aa1c9869b.py ├── de7c15972555d3a2.json ├── de7c15972555d3a2.png ├── de7c15972555d3a2.py ├── df2b661060783d85.json ├── df2b661060783d85.png ├── df2b661060783d85.py ├── dfc2323e6253b4fc.json ├── dfc2323e6253b4fc.png ├── dfc2323e6253b4fc.py ├── dfcdb2f07329b3f4.json ├── dfcdb2f07329b3f4.png ├── dfcdb2f07329b3f4.py ├── e0e070d634e7a876.json ├── e0e070d634e7a876.png ├── e0e070d634e7a876.py ├── e1bcdbfae2d107dd.json ├── e1bcdbfae2d107dd.png ├── e1bcdbfae2d107dd.py ├── e313502bd28f6794.json ├── e313502bd28f6794.png ├── e313502bd28f6794.py ├── e337167337270146.json ├── e337167337270146.png ├── e337167337270146.py ├── e42a40539e92c2a1.json ├── e42a40539e92c2a1.png ├── e42a40539e92c2a1.py ├── e56ec78dc0033894.json ├── e56ec78dc0033894.png ├── e56ec78dc0033894.py ├── e615aa45225a07e9.json ├── e615aa45225a07e9.png ├── e615aa45225a07e9.py ├── e630f1ea5e1e8dbb.json ├── e630f1ea5e1e8dbb.png ├── e630f1ea5e1e8dbb.py ├── e7771bc2951bf735.json ├── e7771bc2951bf735.png ├── e7771bc2951bf735.py ├── ea07c703e22f1234.json ├── ea07c703e22f1234.png ├── ea07c703e22f1234.py ├── ea6db55d65173c4f.json ├── ea6db55d65173c4f.png ├── ea6db55d65173c4f.py ├── eb2a2e34fbdb21e4.json ├── eb2a2e34fbdb21e4.png ├── eb2a2e34fbdb21e4.py ├── eb819115e7580e2c.json ├── eb819115e7580e2c.png ├── eb819115e7580e2c.py ├── ed7dcd10f8ca3760.json ├── ed7dcd10f8ca3760.png ├── ed7dcd10f8ca3760.py ├── ee7c8a31a8fc0e22.json ├── ee7c8a31a8fc0e22.png ├── ee7c8a31a8fc0e22.py ├── f01794d93f604b93.json ├── f01794d93f604b93.png ├── f01794d93f604b93.py ├── f071ddb60645e5cf.json ├── f071ddb60645e5cf.png ├── f071ddb60645e5cf.py ├── f0c1d013b43dc02b.json ├── f0c1d013b43dc02b.png ├── f0c1d013b43dc02b.py ├── f13beeedd5d39751.json ├── f13beeedd5d39751.png ├── f13beeedd5d39751.py ├── f27f796768d4ed85.json ├── f27f796768d4ed85.png ├── f27f796768d4ed85.py ├── f381d1dc8fb3808e.json ├── f381d1dc8fb3808e.png ├── f381d1dc8fb3808e.py ├── f44928758cd8f9a2.json ├── f44928758cd8f9a2.png ├── f44928758cd8f9a2.py ├── f4aecaa67ea63335.json ├── f4aecaa67ea63335.png ├── f4aecaa67ea63335.py ├── f63e1e45b675ecc1.json ├── f63e1e45b675ecc1.png ├── f63e1e45b675ecc1.py ├── f8e59ac8535a08c3.json ├── f8e59ac8535a08c3.png ├── f8e59ac8535a08c3.py ├── f906365b3f05c044.json ├── f906365b3f05c044.png ├── f906365b3f05c044.py ├── fbe135808fd8a20d.json ├── fbe135808fd8a20d.png ├── fbe135808fd8a20d.py ├── fc1008b6320ef1dd.json ├── fc1008b6320ef1dd.png ├── fc1008b6320ef1dd.py ├── fd8df6b25fc7d3d4.json ├── fd8df6b25fc7d3d4.png ├── fd8df6b25fc7d3d4.py ├── fe69eef186347870.json ├── fe69eef186347870.png ├── fe69eef186347870.py ├── fe94795fbd096bb1.json ├── fe94795fbd096bb1.png ├── fe94795fbd096bb1.py ├── fed11a8630e23ccc.json ├── fed11a8630e23ccc.png ├── fed11a8630e23ccc.py ├── ff02b74e287ce431.json ├── ff02b74e287ce431.png ├── ff02b74e287ce431.py ├── ff35ee35daa15223.json ├── ff35ee35daa15223.png ├── ff35ee35daa15223.py ├── ffe7973a331ee64e.json ├── ffe7973a331ee64e.png ├── ffe7973a331ee64e.py ├── gpt_annotated_problems │ ├── 16960f7be31caa8d.json │ ├── 16960f7be31caa8d.png │ ├── 16960f7be31caa8d.py │ ├── 1a5b6f1a4b85392c.json │ ├── 1a5b6f1a4b85392c.png │ ├── 1a5b6f1a4b85392c.py │ ├── 24913d82e6c5255e.json │ ├── 24913d82e6c5255e.png │ ├── 24913d82e6c5255e.py │ ├── 2b9ad00598ec3d26.json │ ├── 2b9ad00598ec3d26.png │ ├── 2b9ad00598ec3d26.py │ ├── 2d68ac623e39573c.json │ ├── 2d68ac623e39573c.png │ ├── 2d68ac623e39573c.py │ ├── 2da733228ab0ba1b.json │ ├── 2da733228ab0ba1b.png │ ├── 2da733228ab0ba1b.py │ ├── 2e77471456109baa.json │ ├── 2e77471456109baa.png │ ├── 2e77471456109baa.py │ ├── 50603d9357c77e3c.json │ ├── 50603d9357c77e3c.png │ ├── 50603d9357c77e3c.py │ ├── 52a37c0299a66d5a.json │ ├── 52a37c0299a66d5a.png │ ├── 52a37c0299a66d5a.py │ ├── 5f38b0228e8203bf.json │ ├── 5f38b0228e8203bf.png │ ├── 5f38b0228e8203bf.py │ ├── 714b2d1934b6addd.json │ ├── 714b2d1934b6addd.png │ ├── 714b2d1934b6addd.py │ ├── 806756d759db179b.json │ ├── 806756d759db179b.png │ ├── 806756d759db179b.py │ ├── 826f26f4ad9b0560.json │ ├── 826f26f4ad9b0560.png │ ├── 826f26f4ad9b0560.py │ ├── 8474b275a1e6278f.json │ ├── 8474b275a1e6278f.png │ ├── 8474b275a1e6278f.py │ ├── 8eb27b2d006b42bb.json │ ├── 8eb27b2d006b42bb.png │ ├── 8eb27b2d006b42bb.py │ ├── 8f1281cd3cf9dbfb.json │ ├── 8f1281cd3cf9dbfb.png │ ├── 8f1281cd3cf9dbfb.py │ ├── 95c5b672b197c162.json │ ├── 95c5b672b197c162.png │ ├── 95c5b672b197c162.py │ ├── 97a9df1fafcbc535.json │ ├── 97a9df1fafcbc535.png │ ├── 97a9df1fafcbc535.py │ ├── 98b760ce0356c71f.json │ ├── 98b760ce0356c71f.png │ ├── 98b760ce0356c71f.py │ ├── a1151c23133f52c4.json │ ├── a1151c23133f52c4.png │ ├── a1151c23133f52c4.py │ ├── a1d66722205012d4.json │ ├── a1d66722205012d4.png │ ├── a1d66722205012d4.py │ ├── aedfea292a3266fe.json │ ├── aedfea292a3266fe.png │ ├── aedfea292a3266fe.py │ ├── b30b46bc823db951.json │ ├── b30b46bc823db951.png │ ├── b30b46bc823db951.py │ ├── b4a22b6490d075ae.json │ ├── b4a22b6490d075ae.png │ ├── b4a22b6490d075ae.py │ ├── d0a938cadb824630.json │ ├── d0a938cadb824630.png │ ├── d0a938cadb824630.py │ ├── d2be1979f630681e.json │ ├── d2be1979f630681e.png │ ├── d2be1979f630681e.py │ ├── d859d0ed3bb0920b.json │ ├── d859d0ed3bb0920b.png │ ├── d859d0ed3bb0920b.py │ ├── dd176f6514b283d6.json │ ├── dd176f6514b283d6.png │ ├── dd176f6514b283d6.py │ ├── e364266b879520a4.json │ ├── e364266b879520a4.png │ ├── e364266b879520a4.py │ ├── e67817cfa9fccac7.json │ ├── e67817cfa9fccac7.png │ ├── e67817cfa9fccac7.py │ ├── e9ce90e902d8c24e.json │ ├── e9ce90e902d8c24e.png │ ├── e9ce90e902d8c24e.py │ ├── f44060ae7fb7f152.json │ ├── f44060ae7fb7f152.png │ ├── f44060ae7fb7f152.py │ ├── fa03649bb58a2ee2.json │ ├── fa03649bb58a2ee2.png │ └── fa03649bb58a2ee2.py └── readme.md ├── utils.py ├── validate.py ├── view_problem.py └── visualize_problems.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore pycache stuff 2 | __pycache__/ 3 | -------------------------------------------------------------------------------- /ConceptARC/AboveBelow/AboveBelowMinimal.json: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /ConceptARC/Center/Center10.json: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /ConceptARC/TopBottom3D/TopBottom3DMinimal.json: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /conceptarc.py: -------------------------------------------------------------------------------- 1 | from arc.read import parse_dir 2 | import os 3 | 4 | def concept_arc_problems(): 5 | problems = [] 6 | for problem_directory in os.listdir("ConceptARC"): 7 | problems.extend(parse_dir("ConceptARC/"+problem_directory)) 8 | 9 | return problems 10 | 11 | -------------------------------------------------------------------------------- /data_processing/test-time-finetune/gen_test-time-dataset.sh: -------------------------------------------------------------------------------- 1 | python get_pseudo_eval_task.py 2 | python data_augmentation.py 3 | # change output_huggingface_dataset to the name of the dataset you want to save on hugingface [UserName]/[dataset_name] 4 | python gen_transduction_only_formatted.py --load_file dataset/augmented_test_time_arc_all_evaluation_new_seperate.jsonl --output_huggingface_dataset barc0/test-ttft -------------------------------------------------------------------------------- /data_processing/test-time-finetune/get_pseudo_eval_task.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | with open('arc_all_evaluation.json', 'r') as f: 4 | data = json.load(f) 5 | 6 | all_dataset = [] 7 | cnt = 0 8 | for task in data: 9 | training_data = task['data']['train'] 10 | for i, train in enumerate(training_data): 11 | if len(training_data) == 2: 12 | print(task['name']) 13 | cnt += 1 14 | break 15 | new_train_dataset = [new_train for new_train in training_data if new_train != train] 16 | new_test_dataset = [train] 17 | all_dataset.append({'name': task['name'], 'data':{'train': new_train_dataset, 'test': new_test_dataset}}) 18 | 19 | os.makedirs('dataset/', exist_ok=True) 20 | 21 | with open('dataset/arc_all_evaluation_new_seperate.json', 'w') as f: 22 | f.write('[\n') 23 | for i, task in enumerate(all_dataset): 24 | json.dump(task, f) 25 | if i != len(all_dataset) - 1: 26 | f.write(',\n') 27 | else: 28 | f.write(']') -------------------------------------------------------------------------------- /finetune/alignment-handbook/Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: style quality 2 | 3 | # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!) 4 | export PYTHONPATH = src 5 | 6 | check_dirs := src tests scripts 7 | 8 | style: 9 | black --line-length 119 --target-version py310 $(check_dirs) setup.py 10 | isort $(check_dirs) setup.py 11 | 12 | quality: 13 | black --check --line-length 119 --target-version py310 $(check_dirs) setup.py 14 | isort --check-only $(check_dirs) setup.py 15 | flake8 --max-line-length 119 $(check_dirs) setup.py 16 | 17 | 18 | # Release stuff 19 | 20 | pre-release: 21 | python src/alignment/release.py 22 | 23 | pre-patch: 24 | python src/alignment/release.py --patch 25 | 26 | post-release: 27 | python src/alignment/release.py --post_release 28 | 29 | post-patch: 30 | python src/alignment/release.py --post_release --patch 31 | 32 | wheels: 33 | python setup.py bdist_wheel && python setup.py sdist 34 | 35 | wheels_clean: 36 | rm -rf build && rm -rf dist 37 | 38 | pypi_upload: 39 | python -m pip install twine 40 | twine upload dist/* -r pypi 41 | 42 | pypi_test_upload: 43 | python -m pip install twine 44 | twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ 45 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/README.md: -------------------------------------------------------------------------------- 1 | This framework adopted from [The Alignment Handbook](https://github.com/huggingface/alignment-handbook) 2 | 3 | ## Installation instructions 4 | 5 | To run the code in this project, first, create a Python virtual environment using e.g. Conda: 6 | 7 | ```shell 8 | conda create -n handbook python=3.10 && conda activate handbook 9 | ``` 10 | 11 | Next, install PyTorch `v2.4` 12 | 13 | You will also need Flash Attention 2 installed, which can be done by running: 14 | 15 | ```shell 16 | python -m pip install flash-attn --no-build-isolation 17 | ``` 18 | 19 | You can then install the remaining package dependencies as follows: 20 | 21 | ```shell 22 | git clone https://github.com/huggingface/alignment-handbook.git 23 | cd ./alignment-handbook/ 24 | python -m pip install . 25 | ``` 26 | 27 | Next, log into your Hugging Face account as follows: 28 | 29 | ```shell 30 | huggingface-cli login 31 | ``` 32 | 33 | You'll also need to log in with wandb: 34 | 35 | ```shell 36 | wandb login 37 | ``` 38 | 39 | ## Recipe 40 | 41 | Please see the yaml files under `recipes` for example of training induction and transduction models. 42 | 43 | ```bash 44 | ACCCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml --num_processes=8 scripts/run_sft.py recipes/$RECEIPE.yaml --load_in_4bit=false 45 | ``` 46 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/assets/handbook.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/finetune/alignment-handbook/assets/handbook.png -------------------------------------------------------------------------------- /finetune/alignment-handbook/chapters/en/_toctree.yml: -------------------------------------------------------------------------------- 1 | - title: Unit 0. Welcome to the RLHF Handbook! 2 | sections: 3 | - local: chapter0/introduction 4 | title: What is this about? -------------------------------------------------------------------------------- /finetune/alignment-handbook/chapters/en/chapter0/introduction.mdx: -------------------------------------------------------------------------------- 1 | # Welcome to the RLHF Handbook! 2 | 3 | Stay tuned for more details 🤗 -------------------------------------------------------------------------------- /finetune/alignment-handbook/recipes/accelerate_configs/deepspeed_zero3.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | deepspeed_config: 4 | deepspeed_multinode_launcher: standard 5 | offload_optimizer_device: none 6 | offload_param_device: none 7 | zero3_init_flag: true 8 | zero3_save_16bit_model: true 9 | zero_stage: 3 10 | distributed_type: DEEPSPEED 11 | downcast_bf16: 'no' 12 | machine_rank: 0 13 | main_training_function: main 14 | mixed_precision: bf16 15 | num_machines: 1 16 | num_processes: 8 17 | rdzv_backend: static 18 | same_network: true 19 | tpu_env: [] 20 | tpu_use_cluster: false 21 | tpu_use_sudo: false 22 | use_cpu: false 23 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/recipes/accelerate_configs/fsdp.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | distributed_type: FSDP 4 | downcast_bf16: 'no' 5 | enable_cpu_affinity: false 6 | fsdp_config: 7 | fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP 8 | fsdp_backward_prefetch: BACKWARD_PRE 9 | fsdp_cpu_ram_efficient_loading: true 10 | fsdp_forward_prefetch: true 11 | fsdp_offload_params: false 12 | fsdp_sharding_strategy: FULL_SHARD 13 | fsdp_state_dict_type: SHARDED_STATE_DICT 14 | fsdp_sync_module_states: true 15 | fsdp_use_orig_params: true 16 | machine_rank: 0 17 | main_training_function: main 18 | mixed_precision: bf16 19 | num_machines: 1 20 | num_processes: 8 21 | rdzv_backend: static 22 | same_network: true 23 | tpu_env: [] 24 | tpu_use_cluster: false 25 | tpu_use_sudo: false 26 | use_cpu: false 27 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/recipes/accelerate_configs/fsdp_qlora.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | distributed_type: FSDP 4 | downcast_bf16: 'no' 5 | fsdp_config: 6 | fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP 7 | fsdp_backward_prefetch: BACKWARD_PRE 8 | fsdp_cpu_ram_efficient_loading: true 9 | fsdp_forward_prefetch: false 10 | fsdp_offload_params: true 11 | fsdp_sharding_strategy: FULL_SHARD 12 | fsdp_state_dict_type: SHARDED_STATE_DICT 13 | fsdp_sync_module_states: true 14 | fsdp_use_orig_params: false 15 | machine_rank: 0 16 | main_training_function: main 17 | mixed_precision: 'no' 18 | num_machines: 1 19 | num_processes: 2 20 | rdzv_backend: static 21 | same_network: true 22 | tpu_env: [] 23 | tpu_use_cluster: false 24 | tpu_use_sudo: false 25 | use_cpu: false -------------------------------------------------------------------------------- /finetune/alignment-handbook/recipes/accelerate_configs/multi_gpu.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | distributed_type: MULTI_GPU 4 | downcast_bf16: 'no' 5 | gpu_ids: all 6 | machine_rank: 0 7 | main_training_function: main 8 | mixed_precision: bf16 9 | num_machines: 1 10 | num_processes: 8 11 | rdzv_backend: static 12 | same_network: true 13 | tpu_env: [] 14 | tpu_use_cluster: false 15 | tpu_use_sudo: false 16 | use_cpu: false 17 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | default_section = FIRSTPARTY 3 | ensure_newline_before_comments = True 4 | force_grid_wrap = 0 5 | include_trailing_comma = True 6 | known_first_party = alignment 7 | known_third_party = 8 | transformers 9 | datasets 10 | fugashi 11 | git 12 | h5py 13 | matplotlib 14 | nltk 15 | numpy 16 | packaging 17 | pandas 18 | psutil 19 | pytest 20 | rouge_score 21 | sacrebleu 22 | seqeval 23 | sklearn 24 | streamlit 25 | torch 26 | tqdm 27 | 28 | line_length = 119 29 | lines_after_imports = 2 30 | multi_line_output = 3 31 | use_parentheses = True 32 | 33 | [flake8] 34 | ignore = E203, E501, E741, W503, W605 35 | max-line-length = 119 36 | per-file-ignores = 37 | # imported but unused 38 | __init__.py: F401 39 | 40 | [tool:pytest] 41 | doctest_optionflags=NUMBER NORMALIZE_WHITESPACE ELLIPSIS -------------------------------------------------------------------------------- /finetune/alignment-handbook/src/alignment/__init__.py: -------------------------------------------------------------------------------- 1 | __version__ = "0.3.0.dev0" 2 | 3 | from .configs import DataArguments, DPOConfig, H4ArgumentParser, ModelArguments, SFTConfig 4 | from .data import apply_chat_template, get_datasets 5 | from .decontaminate import decontaminate_humaneval 6 | from .model_utils import ( 7 | get_checkpoint, 8 | get_kbit_device_map, 9 | get_peft_config, 10 | get_quantization_config, 11 | get_tokenizer, 12 | is_adapter_model, 13 | ) 14 | 15 | 16 | __all__ = [ 17 | "DataArguments", 18 | "DPOConfig", 19 | "H4ArgumentParser", 20 | "ModelArguments", 21 | "SFTConfig", 22 | "apply_chat_template", 23 | "get_datasets", 24 | "decontaminate_humaneval", 25 | "get_checkpoint", 26 | "get_kbit_device_map", 27 | "get_peft_config", 28 | "get_quantization_config", 29 | "get_tokenizer", 30 | "is_adapter_model", 31 | ] 32 | -------------------------------------------------------------------------------- /finetune/alignment-handbook/tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/finetune/alignment-handbook/tests/__init__.py -------------------------------------------------------------------------------- /finetune/alignment-handbook/tests/fixtures/config_dpo_full.yaml: -------------------------------------------------------------------------------- 1 | # Model arguments 2 | model_name_or_path: alignment-handbook/zephyr-7b-sft-full 3 | 4 | # Data training arguments 5 | # For definitions, see: src/h4/training/config.py 6 | dataset_mixer: 7 | HuggingFaceH4/ultrafeedback_binarized: 1.0 8 | dataset_splits: 9 | - train_prefs 10 | - test_prefs 11 | preprocessing_num_workers: 12 12 | 13 | # DPOTrainer arguments 14 | bf16: true 15 | beta: 0.1 16 | do_eval: true 17 | eval_strategy: steps 18 | eval_steps: 100 19 | gradient_accumulation_steps: 1 20 | gradient_checkpointing: true 21 | hub_model_id: zephyr-7b-dpo-full 22 | learning_rate: 5.0e-7 23 | log_level: info 24 | logging_steps: 10 25 | lr_scheduler_type: linear 26 | max_length: 1024 27 | max_prompt_length: 512 28 | num_train_epochs: 3 29 | optim: rmsprop 30 | output_dir: data/zephyr-7b-dpo-full 31 | per_device_train_batch_size: 8 32 | per_device_eval_batch_size: 4 33 | push_to_hub: true 34 | save_strategy: "no" 35 | save_total_limit: null 36 | seed: 42 37 | warmup_ratio: 0.1 -------------------------------------------------------------------------------- /finetune/alignment-handbook/tests/fixtures/config_sft_full.yaml: -------------------------------------------------------------------------------- 1 | # Model arguments 2 | model_name_or_path: mistralai/Mistral-7B-v0.1 3 | model_revision: main 4 | torch_dtype: bfloat16 5 | attn_implementation: flash_attention_2 6 | 7 | # Data training arguments 8 | dataset_mixer: 9 | HuggingFaceH4/ultrachat_200k: 1.0 10 | dataset_splits: 11 | - train_sft 12 | - test_sft 13 | preprocessing_num_workers: 12 14 | 15 | # SFT trainer config 16 | bf16: true 17 | do_eval: true 18 | eval_strategy: epoch 19 | gradient_accumulation_steps: 2 20 | gradient_checkpointing: true 21 | hub_model_id: zephyr-7b-sft-full 22 | hub_strategy: every_save 23 | learning_rate: 2.0e-05 24 | log_level: info 25 | logging_steps: 5 26 | logging_strategy: steps 27 | lr_scheduler_type: cosine 28 | max_seq_length: 2048 29 | max_steps: -1 30 | num_train_epochs: 1 31 | output_dir: data/zephyr-7b-sft-full 32 | overwrite_output_dir: true 33 | per_device_eval_batch_size: 16 34 | per_device_train_batch_size: 32 35 | push_to_hub: true 36 | remove_unused_columns: true 37 | report_to: 38 | - tensorboard 39 | save_strategy: "no" 40 | save_total_limit: null 41 | seed: 42 -------------------------------------------------------------------------------- /prompts/description_prompt.md: -------------------------------------------------------------------------------- 1 | You've generated these on previous requests: 2 | 3 | {examples} 4 | 5 | Brainstorm {num_generations} more, using similar thinking: 6 | 7 | ```python 8 | # concepts: 9 | # 10 | 11 | # description: 12 | # 13 | ``` 14 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | git+https://github.com/xu3kev/arc-py.git 2 | orjsonl 3 | func-timeout 4 | -------------------------------------------------------------------------------- /seeds/007bbfb7.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # repeating patterns, colors as indicators, scaling 8 | 9 | # description: 10 | # In the input you will see a nxm sprite with black background. 11 | # Construct an output grid with n^2 x m^2 black pixels. Divide the output grid into subgrids, 12 | # and look at the corresponding pixel in the nxm input grid. If the corresponding pixel is not black, 13 | # then copy the nxm input grid into the subgrid. Else, the subgrid does not change. 14 | 15 | def main(input_grid): 16 | # creates an empty 9x9 output grid 17 | output_grid = np.zeros((input_grid.shape[0]**2,input_grid.shape[1]**2),dtype=int) 18 | 19 | input_sprite = input_grid 20 | 21 | # Go through the input grid. If an input grid pixel is not black, 22 | # then copy the input grid to the corresponding location on the output grid 23 | for n in range(input_grid.shape[0]): 24 | for m in range(input_grid.shape[1]): 25 | if input_grid[n,m] != Color.BLACK: 26 | blit_sprite(output_grid, input_sprite, n*input_grid.shape[0], m*input_grid.shape[1]) 27 | 28 | return output_grid 29 | 30 | def generate_input(): 31 | n,m = random.randint(3, 6), random.randint(3, 6) 32 | random_color = random.choice(list(Color.NOT_BLACK)) 33 | return random_sprite(n, m, color_palette=[random_color]) 34 | 35 | 36 | # ============= remove below this point for prompting ============= 37 | 38 | if __name__ == '__main__': 39 | visualize(generate_input, main, 5) -------------------------------------------------------------------------------- /seeds/0d3d703e.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # color mapping 8 | 9 | # description: 10 | # The input is a grid where each column is of the same color. 11 | # To make the output, change each color according to the following mapping: 12 | # green -> yellow, blue -> gray, red -> pink, teal -> maroon, yellow -> green, gray -> blue, pink -> red, maroon -> teal 13 | 14 | def main(input_grid): 15 | # Initialize output grid 16 | output_grid = input_grid.copy() 17 | 18 | # Performs color mapping 19 | output_grid = np.vectorize(lambda color: color_map.get(color, color))(output_grid) 20 | 21 | return output_grid 22 | 23 | # Constructing the color map 24 | color_map = {Color.GREEN : Color.YELLOW, 25 | Color.BLUE : Color.GRAY, 26 | Color.RED : Color.PINK, 27 | Color.TEAL : Color.MAROON, 28 | Color.YELLOW : Color.GREEN, 29 | Color.GRAY : Color.BLUE, 30 | Color.PINK : Color.RED, 31 | Color.MAROON : Color.TEAL 32 | } 33 | 34 | 35 | def generate_input(): 36 | grid = np.full((3, 3), Color.BLACK) 37 | for x in range(grid.shape[0]): 38 | grid[x, :] = random.choice(list(color_map.keys())) 39 | return grid 40 | # ============= remove below this point for prompting ============= 41 | 42 | if __name__ == '__main__': 43 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/1cf80156.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # cropping 8 | 9 | # description: 10 | # In the input you will see a single colored shape, around 4x6 in size, floating in a 12x12 grid of black. 11 | # To make the output, crop the background out of the image - so the output grid has the same dimensions as the shape. 12 | 13 | def main(input_grid): 14 | return crop(input_grid, background=Color.BLACK) 15 | 16 | def generate_input(): 17 | # create a roughly 12x12 input grid 18 | n = np.random.randint(10, 15) 19 | m = np.random.randint(10, 15) 20 | grid = np.full((n, m), Color.BLACK, dtype=int) 21 | 22 | # create a small random sprite with shape (2-7)x(2-7) 23 | w = np.random.randint(2, 8) 24 | h = np.random.randint(2, 8) 25 | sprite = random_sprite(w, h, color_palette=[np.random.choice(Color.NOT_BLACK)]) 26 | 27 | # blit the spite onto a random location on the grid 28 | x, y = random_free_location_for_sprite(grid, sprite) 29 | blit_sprite(grid, sprite, x, y) 30 | return grid 31 | 32 | 33 | # ============= remove below this point for prompting ============= 34 | 35 | if __name__ == '__main__': 36 | visualize(generate_input, main) 37 | -------------------------------------------------------------------------------- /seeds/25d8a9c8.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | 7 | # concepts: 8 | # patterns, horizontal bars 9 | 10 | # description: 11 | # In the input you will see a colored pattern in a 3x3 grid. 12 | # For each row of the input, if that row is a single color, color that row in the output grey. Otherwise, output black. 13 | 14 | def main(input_grid): 15 | # get input grid shape 16 | n, m = input_grid.shape 17 | 18 | # get output grid ready 19 | output_grid = np.zeros((n, m), dtype=int) 20 | 21 | # look at each row of the input grid 22 | for y in range(m): 23 | # check if each pixel in the row is the same color 24 | base_color = input_grid[0][y] 25 | all_same_color = True 26 | for color in input_grid[1:, y]: 27 | if color != base_color: 28 | all_same_color = False 29 | 30 | # if they are all the same color, change the output row to grey 31 | if all_same_color: 32 | for x in range(n): 33 | output_grid[x][y] = Color.GREY 34 | 35 | return output_grid 36 | 37 | def generate_input(): 38 | # create a 3x3 array of randomly chosen, non-black, colors 39 | grid = np.random.choice(Color.NOT_BLACK, size=(3, 3)) 40 | 41 | return grid 42 | 43 | # ============= remove below this point for prompting ============= 44 | 45 | if __name__ == '__main__': 46 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/25ff71a9.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | 7 | # concepts: 8 | # sliding objects 9 | 10 | # description: 11 | # In the input you will see a 3x3 grid with a contiguous shape on it. 12 | # Slide the shape down by one pixel. 13 | 14 | def main(input_grid): 15 | # find the connected component, which is a monochromatic object 16 | objects = find_connected_components(input_grid, background=Color.BLACK, connectivity=8, monochromatic=True) 17 | obj = objects[0] 18 | 19 | # translate the object down by one pixel 20 | output_grid = translate(obj, 0, 1, background=Color.BLACK) 21 | 22 | return output_grid 23 | 24 | 25 | def generate_input(): 26 | # first create blank grid 27 | grid = np.zeros((3, 3), dtype=int) 28 | 29 | # while the grid is empty 30 | while grid.sum() == 0: 31 | 32 | # generate a random up to 3x3 sprite 33 | n = random.randint(1, 3) 34 | m = random.randint(1, 2) 35 | random_sprite_to_add = random_sprite(n, m, symmetry='not_symmetric', color_palette=[random.choice(Color.NOT_BLACK)]) 36 | 37 | # choose a random location on the grid, ensuring the bottom row is empty 38 | x = random.randint(0, 2) 39 | y = random.randint(0, 2 - m) 40 | 41 | # place the sprite onto the grid 42 | blit_sprite(grid, random_sprite_to_add, x, y) 43 | 44 | return grid 45 | 46 | # ============= remove below this point for prompting ============= 47 | 48 | if __name__ == '__main__': 49 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/4258a5f9.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # surrounding 8 | 9 | # description: 10 | # surround every gray pixel with blue pixels 11 | 12 | def main(input_grid): 13 | output_grid = np.zeros_like(input_grid) 14 | 15 | for i in range(len(input_grid)): 16 | for j in range(len(input_grid[i])): 17 | if input_grid[i, j] == Color.GRAY: 18 | # if the current pixel is gray, then we need to surround it with blue 19 | output_grid[max(0, i-1):min(len(input_grid), i+2), max(0, j-1):min(len(input_grid[i]), j+2)] = Color.BLUE 20 | 21 | # but we need to keep the gray center: so copy over all the gray pixels 22 | output_grid[input_grid == Color.GRAY] = Color.GRAY 23 | 24 | return output_grid 25 | 26 | 27 | # create a 9x9 grid of black (0) and then sparsely populate it with gray 28 | def generate_input(): 29 | # create a 9x9 grid of black (0) 30 | grid = np.zeros((9, 9), dtype=int) 31 | # sparsely populate it with gray 32 | for x in range(9): 33 | for y in range(9): 34 | if np.random.random() < 0.05: 35 | grid[x, y] = Color.GRAY 36 | return grid 37 | 38 | # ============= remove below this point for prompting ============= 39 | 40 | if __name__ == '__main__': 41 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/6f8cd79b.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # borders 8 | 9 | # description: 10 | # In the input you will see an empty black grid. 11 | # To make the output, draw a line along the border of the input with a thickness of one pixel. The border should be teal. 12 | 13 | def main(input_grid): 14 | # make the output grid 15 | n, m = input_grid.shape 16 | output_grid = np.zeros((n, m), dtype=int) 17 | 18 | # draw the border of the input grid 19 | draw_line(grid=output_grid, x=0, y=0, length=None, color=Color.TEAL, direction=(1,0)) 20 | draw_line(grid=output_grid, x=n-1, y=0, length=None, color=Color.TEAL, direction=(0,1)) 21 | draw_line(grid=output_grid, x=0, y=0, length=None, color=Color.TEAL, direction=(0,1)) 22 | draw_line(grid=output_grid, x=0, y=m-1, length=None, color=Color.TEAL, direction=(1,0)) 23 | 24 | return output_grid 25 | 26 | 27 | def generate_input(): 28 | # make a rectangular black grid 29 | n = np.random.randint(3, 8) 30 | m = np.random.randint(3, 8) 31 | grid = np.zeros((n, m), dtype=int) 32 | 33 | return grid 34 | 35 | 36 | # ============= remove below this point for prompting ============= 37 | 38 | if __name__ == '__main__': 39 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/6fa7a44f.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # reflection 8 | 9 | # description: 10 | # In the input you will see a square pattern of random colors except black. 11 | # To make the output, reflect the pattern vertically, and put the reflected pattern beneath the input pattern. 12 | 13 | def main(input_grid): 14 | # take the input pattern 15 | pattern = input_grid 16 | 17 | # reflect the pattern vertically 18 | reflected_pattern = pattern[:, ::-1] 19 | 20 | # make the output grid 21 | output_grid = np.concatenate((pattern, reflected_pattern), axis=1) 22 | 23 | return output_grid 24 | 25 | def generate_input(): 26 | # make a random square pattern of random size and colors 27 | n = m = np.random.randint(3, 6) 28 | grid = random_sprite(n, m, density=1, symmetry="not_symmetric", color_palette=Color.NOT_BLACK) 29 | 30 | return grid 31 | 32 | 33 | # ============= remove below this point for prompting ============= 34 | 35 | if __name__ == '__main__': 36 | visualize(generate_input, main) -------------------------------------------------------------------------------- /seeds/d511f180.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # colors 8 | 9 | # description: 10 | # To create the output grid, swap the teal and grey colors in the grid. 11 | 12 | def main(input_grid): 13 | output_grid = input_grid.copy() 14 | output_grid[input_grid == Color.GREY] = Color.TEAL 15 | output_grid[input_grid == Color.TEAL] = Color.GREY 16 | return output_grid 17 | 18 | 19 | def generate_input(): 20 | # make a random grid 21 | n = np.random.randint(3, 10) 22 | input_grid = np.random.choice(Color.NOT_BLACK, size=(n, n)) 23 | 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common import * 2 | import numpy as np 3 | from typing import * 4 | 5 | # concepts: 6 | # reflection, symmetry 7 | 8 | # description: 9 | # In the input you will see a grid with an arbitrary colored pattern. 10 | # To make the output, reflect the pattern horizontally and vertically around the central point of the grid. 11 | 12 | def main(input_grid): 13 | h, w = input_grid.shape 14 | output_grid = np.copy(input_grid) 15 | 16 | # Reflect horizontally 17 | for x in range(h): 18 | for y in range(w // 2): 19 | output_grid[x, y], output_grid[x, w - y - 1] = output_grid[x, w - y - 1], output_grid[x, y] 20 | 21 | # Reflect vertically 22 | for y in range(w): 23 | for x in range(h // 2): 24 | output_grid[x, y], output_grid[h - x - 1, y] = output_grid[h - x - 1, y], output_grid[x, y] 25 | 26 | return output_grid 27 | 28 | 29 | def generate_input(): 30 | # Create a grid with a random color pattern 31 | n = m = np.random.randint(5, 10) 32 | input_grid = random_sprite(n, m) 33 | 34 | return 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center_x, center_y = n // 2, m // 2 21 | 22 | # Extract the center square 23 | center_square = input_grid[center_x-1:center_x+2, center_y-1:center_y+2] 24 | 25 | # Rotate the entire grid by 90 degrees clockwise 26 | rotated_grid = np.rot90(input_grid, k=-1) 27 | 28 | # Embed the center square back into the rotated grid 29 | rotated_grid[center_x-1:center_x+2, center_y-1:center_y+2] = center_square 30 | 31 | return rotated_grid 32 | 33 | def generate_input(): 34 | n, m = np.random.randint(10, 20, size=2) 35 | grid = np.random.choice(list(Color.NOT_BLACK), (n, m)) 36 | center_x, center_y = n // 2, m // 2 37 | 38 | # Create a distinct colored square in the center 39 | square_color = np.random.choice(Color.NOT_BLACK) 40 | grid[center_x-1:center_x+2, center_y-1:center_y+2] = square_color 41 | 42 | return grid -------------------------------------------------------------------------------- /synthetic_problems/2dc68bad444414ea.png: 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-------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # colors, patterns, symmetry 8 | 9 | # description: 10 | # In the input, you will see a colored grid with multiple colors. 11 | # To create the output grid, swap vertical mirrors of distinct colors: for any color in the left half of the grid, swap it with its mirror image in the right half of the grid. 12 | # If the grid has an odd number of columns, the middle column's colors stay the same. 13 | 14 | def main(input_grid): 15 | # Get the input grid dimensions 16 | n, m = input_grid.shape 17 | 18 | # Create a copy of the input grid to store the output 19 | output_grid = input_grid.copy() 20 | 21 | # Define the halfway point 22 | mid = m // 2 23 | 24 | # Iterate over the left half and right half of the grid, swapping mirrors 25 | for i in range(n): 26 | for j in range(mid): 27 | mirrored_j = m - 1 - j 28 | # Swap the colors 29 | output_grid[i, j], output_grid[i, mirrored_j] = input_grid[i, mirrored_j], input_grid[i, j] 30 | 31 | return output_grid 32 | 33 | def generate_input(): 34 | # Randomly decide the grid dimensions 35 | n = np.random.randint(3, 10) 36 | m = np.random.randint(3, 10) 37 | 38 | # Create a random grid with colors (excluding black) 39 | input_grid = np.random.choice(list(Color.NOT_BLACK), size=(n, m)) 40 | 41 | return input_grid -------------------------------------------------------------------------------- /synthetic_problems/3b168130826537d1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/3b168130826537d1.png -------------------------------------------------------------------------------- /synthetic_problems/3b68737a0bae1019.png: -------------------------------------------------------------------------------- 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reflected_pattern), axis=0) 22 | 23 | # crop the black borders out of the combined pattern 24 | output_grid = crop(combined_pattern, background=Color.BLACK) 25 | 26 | return output_grid 27 | 28 | def generate_input() -> np.ndarray: 29 | n = np.random.randint(8,12) # height/width of the grid 30 | grid = np.full((n, n), Color.BLACK, dtype=int) 31 | 32 | # create a random square pattern of size (n//2)x(n) with random colors except black 33 | pattern_height = n // 2 34 | pattern = random_sprite(pattern_height, n, density=1, symmetry="not_symmetric", color_palette=Color.NOT_BLACK) 35 | 36 | # blit the pattern onto the upper half of the grid 37 | grid[:pattern_height, :] = pattern 38 | 39 | return grid -------------------------------------------------------------------------------- /synthetic_problems/46c2d9f38ddd1696.png: -------------------------------------------------------------------------------- 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output_grid = np.copy(input_grid) 13 | 14 | # Detect all objects in the input grid 15 | objects = detect_objects(input_grid, monochromatic=False, connectivity=4) 16 | 17 | # Loop through each detected object 18 | for obj in objects: 19 | # Compute the boundary of the object 20 | boundary_mask = object_boundary(obj) 21 | 22 | # Set the boundary pixels to RED 23 | obj[boundary_mask] = Color.RED 24 | 25 | # Blit the modified object back to the output grid 26 | bb = bounding_box(obj) 27 | blit(output_grid, obj, bb[0], bb[1]) 28 | 29 | return output_grid 30 | 31 | def generate_input(): 32 | n, m = np.random.randint(10, 20), np.random.randint(10, 20) 33 | grid = np.full((n, m), Color.BLACK) 34 | 35 | num_objects = np.random.randint(1, 4) 36 | for _ in range(num_objects): 37 | sprite = random_sprite(np.random.randint(3, 6), np.random.randint(3, 6), color_palette=Color.NOT_BLACK, density=0.5) 38 | x, y = random_free_location_for_object(grid, sprite, background=Color.BLACK) 39 | blit(grid, 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on specified intervals. 12 | 13 | def main(input_grid): 14 | # take the input pattern 15 | pattern = input_grid 16 | 17 | pattern_height, pattern_width = pattern.shape 18 | pattern_interval = 2 # specify interval for tiling 19 | 20 | # make the output grid to fit multiples of the pattern 21 | output_height = pattern_interval * pattern_height 22 | output_width = pattern_interval * pattern_width 23 | output_grid = np.zeros((output_height, output_width), dtype=int) 24 | 25 | # paste the pattern in the output grid with specified intervals 26 | for i in range(output_height // pattern_height): 27 | for j in range(output_width // pattern_width): 28 | start_x = i * pattern_height * pattern_interval 29 | start_y = j * pattern_width * pattern_interval 30 | blit(output_grid, pattern, start_x, start_y) 31 | 32 | return output_grid 33 | 34 | def generate_input(): 35 | # make a random square pattern of random size and colors 36 | n = m = np.random.randint(2, 4) 37 | pattern = random_sprite(n, m, density=1, symmetry="not_symmetric", color_palette=Color.NOT_BLACK) 38 | 39 | return pattern -------------------------------------------------------------------------------- /synthetic_problems/73703a4ff92c5135.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/73703a4ff92c5135.png -------------------------------------------------------------------------------- /synthetic_problems/7440331b1ed2e449.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/7440331b1ed2e449.png -------------------------------------------------------------------------------- /synthetic_problems/74c89908c2ddb311.png: -------------------------------------------------------------------------------- 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output grid initialized with the input grid 20 | output_grid = np.copy(input_grid) 21 | 22 | # Place the rotated pattern 23 | n, m = input_grid.shape 24 | for x in range(n): 25 | for y in range(m): 26 | new_x, new_y = (x + y) % n, (x - y) % m 27 | 28 | output_grid[new_x, new_y] = rotated_pattern[x, y] 29 | 30 | return output_grid 31 | 32 | 33 | def generate_input(): 34 | # Create a random grid of colored pixels 35 | n, m = np.random.randint(10, 20), np.random.randint(10, 20) 36 | grid = np.random.choice(list(Color.NOT_BLACK), size=(n, m), replace=True) 37 | 38 | return grid -------------------------------------------------------------------------------- /synthetic_problems/77783290ee6fc4cb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/77783290ee6fc4cb.png -------------------------------------------------------------------------------- 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color_row = output_grid[i - 1] 18 | output_grid[i] = color_row 19 | 20 | return output_grid 21 | 22 | 23 | def generate_input() -> np.ndarray: 24 | # Randomly deciding the input grid dimensions 25 | n = np.random.randint(10, 20) 26 | m = np.random.randint(15, 25) 27 | 28 | # Initializing the grid 29 | grid = np.zeros((n, m), dtype=int) 30 | 31 | # Creating a color sequence for the top row 32 | colors = np.random.choice(list(Color.NOT_BLACK), size=m, replace=True) 33 | 34 | # Assigning the top row with the chosen colors 35 | grid[0, :] = colors 36 | 37 | # Alternating rows between a single color and grey 38 | for i in range(1, n): 39 | if i % 2 == 1: 40 | row_color = np.random.choice(Color.NOT_BLACK) 41 | grid[i, :] = row_color 42 | else: 43 | grid[i, :] = Color.GREY 44 | 45 | return grid -------------------------------------------------------------------------------- /synthetic_problems/7c40e7e6438f305a.png: 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color 17 | 18 | return output_grid 19 | 20 | def generate_input() -> np.ndarray: 21 | # Create a black canvas of random size between 10 and 20 for both dimensions. 22 | rows = np.random.randint(10, 20) 23 | cols = np.random.randint(10, 20) 24 | input_grid = np.full((rows, cols), Color.BLACK, dtype=int) 25 | 26 | # Determine the number of rows to be filled randomly between 2 and half the number of rows. 27 | num_filled_rows = np.random.randint(2, rows // 2) 28 | 29 | for _ in range(num_filled_rows): 30 | # Choose a random row to fill. 31 | row_idx = np.random.randint(rows) 32 | # Choose a random column index for the color pixel. 33 | col_idx = np.random.randint(cols) 34 | # Choose a random color for the pixel. 35 | color = np.random.choice(list(Color.NOT_BLACK)) 36 | input_grid[row_idx, col_idx] = color 37 | 38 | return input_grid -------------------------------------------------------------------------------- /synthetic_problems/7cf3d0f1f5b187d3.png: 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-------------------------------------------------------------------------------- 1 | from common import * 2 | import numpy as np 3 | import random 4 | 5 | # concepts: 6 | # reflective symmetry 7 | 8 | # description: 9 | # The input grid is a 2D grid of colors. The left half of the grid contains some random colored 10 | # patterns, and the right half of the grid is initially black. The task is to generate the output grid 11 | # by mirroring the left half onto the right half along the vertical axis. 12 | 13 | def main(input_grid): 14 | # Create output grid 15 | output_grid = np.copy(input_grid) 16 | 17 | # Get dimensions 18 | rows, cols = input_grid.shape 19 | 20 | # Reflect left half to right half 21 | for i in range(cols//2): 22 | output_grid[:, cols-i-1] = output_grid[:, i] 23 | 24 | return output_grid 25 | 26 | def generate_input(): 27 | # Generate random grid size A*B where 5 <= A <= 20, and 10 <= B <= 40 (to ensure even column count) 28 | rows = random.randint(5, 20) 29 | cols = random.randint(10, 40) 30 | while cols % 2 != 0: 31 | cols = random.randint(10, 40) 32 | 33 | # Create empty grid 34 | grid = np.full((rows, cols), Color.BLACK) 35 | 36 | # Fill left half with random colors, right half remains black 37 | for i in range(rows): 38 | for j in range(cols // 2): 39 | grid[i, j] = random.choice(list(Color.NOT_BLACK)) 40 | 41 | return grid 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description: 10 | # In the input, you should see a series of colorful pixels. 11 | # The task is to reflect these colorful pixels around the center horizontal axis of the grid. 12 | # The reflected pixels should replace the black pixels on the other side of the axis. 13 | 14 | def main(input_grid): 15 | output_grid = np.copy(input_grid) 16 | 17 | height, width = input_grid.shape 18 | mid_y = height // 2 19 | 20 | # Reflect pixels around the horizontal axis 21 | for x in range(width): 22 | for y in range(mid_y): 23 | if input_grid[y, x] != Color.BLACK: 24 | output_grid[height - y - 1, x] = input_grid[y, x] # Reflecting the top part to bottom part 25 | 26 | return output_grid 27 | 28 | def generate_input(): 29 | height, width = np.random.randint(5, 8), np.random.randint(5, 8) # Random grid size between 5x5 and 7x7 30 | input_grid = np.zeros((height, width), dtype=int) 31 | 32 | num_colored_pixels = np.random.randint(3, min(height * width, 10)) # Random number of colored pixels between 3 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-------------------------------------------------------------------------------- /synthetic_problems/af81b0b4e35dd58d.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | # concepts: 7 | # repeating patterns, scaling, colors as indicators 8 | 9 | # description: 10 | # The input is a grid where each cell can be one of 10 colors, including black. 11 | # For each non-black cell (i, j) in the input grid, the corresponding subgrid (k*i:k*i+k, k*j:k*j+k) in the output grid 12 | # should be filled with that cell's color, where k is a randomly chosen scaling factor between 2 and 4. 13 | # Each black cell should remain unchanged in the output. 14 | 15 | def main(input_grid): 16 | # The scaling factor k is part of the puzzle and needs to be communicated to `main` somehow. 17 | # For simplicity, we'll assume it is predefined (in a real puzzle, it could be communicated via some encoding in the input or metadata). 18 | k = 3 19 | 20 | output_grid = np.zeros((input_grid.shape[0] * k, input_grid.shape[1] * k), dtype=int) 21 | 22 | for i in range(input_grid.shape[0]): 23 | for j in range(input_grid.shape[1]): 24 | if input_grid[i, j] != Color.BLACK: 25 | output_grid[i*k:(i+1)*k, j*k:(j+1)*k] = input_grid[i, j] 26 | 27 | return output_grid 28 | 29 | def generate_input(): 30 | k = np.random.randint(2, 5) 31 | n, m = np.random.randint(3, 7), np.random.randint(3, 7) 32 | grid = random_sprite(n, m, density=0.5, color_palette=[color for color in Color.NOT_BLACK]) 33 | 34 | return grid -------------------------------------------------------------------------------- /synthetic_problems/afc615b0e7053f5d.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/afc615b0e7053f5d.png 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17 | # Define the axis for the symmetric mirroring; for simplicity, we will use the vertical center (m//2) 18 | axis = m // 2 19 | 20 | for i in range(n): 21 | for j in range(m): 22 | # Mirroring across the vertical axis 23 | mirrored_j = 2 * axis - j if j != axis else j 24 | if 0 <= mirrored_j < m: 25 | output_grid[i, mirrored_j] = input_grid[i, j] 26 | 27 | return output_grid 28 | 29 | def generate_input(): 30 | # Create a random grid with colored shapes 31 | n, m = np.random.randint(5, 10, size=2) 32 | grid = np.zeros((n, m), dtype=int) 33 | 34 | # Populate the grid with random colored shapes 35 | num_shapes = np.random.randint(1, 4) 36 | for _ in range(num_shapes): 37 | shape = random_sprite(np.random.randint(2, 4), np.random.randint(2, 4), color_palette=None) 38 | x, y = random_free_location_for_object(grid, shape) 39 | blit(grid, shape, x, y, background=Color.BLACK) 40 | 41 | return grid 42 | 43 | # Example usage to test the implementation 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| 9 | # description: 10 | # In the input, you will see an 8x8 grid with only the first two rows filled with 11 | # random colored pixels, and the remaining rows are black. 12 | # To make the output, copy the first two rows of the input. 13 | # Then, for the remaining six rows, reflect the pattern of the two rows horizontally and repeat it. 14 | 15 | def main(input_grid): 16 | # Create an empty output grid 17 | n, m = input_grid.shape 18 | output_grid = np.zeros((n, m), dtype=int) 19 | 20 | # Copy the first two rows of the input grid to the output grid 21 | output_grid[:2, :] = input_grid[:2, :] 22 | 23 | # Reflect the first two rows horizontally 24 | reflected_pattern = input_grid[:2, ::-1] 25 | 26 | # Fill the remaining rows of the output grid with the reflected pattern 27 | for i in range(2, n): 28 | output_grid[i, :] = reflected_pattern[i % 2, :] 29 | 30 | return output_grid 31 | 32 | def generate_input(): 33 | # Create an empty 8x8 grid 34 | grid = np.zeros((8, 8), dtype=int) 35 | 36 | # Fill the first two rows randomly with colors except black 37 | for i in range(2): 38 | for j in range(8): 39 | grid[i, j] = np.random.choice(list(Color.NOT_BLACK)) 40 | 41 | return grid -------------------------------------------------------------------------------- /synthetic_problems/b8aedb1acd40916a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/b8aedb1acd40916a.png -------------------------------------------------------------------------------- /synthetic_problems/b9c762b0cfda54b6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/b9c762b0cfda54b6.png -------------------------------------------------------------------------------- /synthetic_problems/ba5239fd5c5b3685.png: 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copy of input grid 17 | output_grid = np.copy(input_grid) 18 | height, width = input_grid.shape 19 | 20 | # Reflect the grid horizontally over its mid-line 21 | for x in range(height): 22 | for y in range(width // 2): 23 | output_grid[x, -(y + 1)] = input_grid[x, y] 24 | 25 | return output_grid 26 | 27 | 28 | def generate_input(): 29 | # Generate a grid of arbitrary size, between 6x6 and 20x20 30 | height = random.randint(6, 20) 31 | width = random.randint(6, 20) 32 | grid = np.zeros((height, width), dtype=int) 33 | 34 | # Generate random colored patterns on the grid 35 | num_patterns = random.randint(3, 10) 36 | for _ in range(num_patterns): 37 | w, h = random.randint(1, width // 2), random.randint(1, height) 38 | color = random.choice(list(Color.NOT_BLACK)) 39 | x, y = random.randint(0, height - h), random.randint(0, width // 2 - w) 40 | sprite = random_sprite(h, w, density=0.5, color_palette=[color], connectivity=4) 41 | blit(grid, sprite, x, y) 42 | 43 | return grid 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-------------------------------------------------------------------------------- /synthetic_problems/fbe135808fd8a20d.py: -------------------------------------------------------------------------------- 1 | from common import * 2 | 3 | import numpy as np 4 | from typing import * 5 | 6 | def main(input_grid: np.ndarray) -> np.ndarray: 7 | output_grid = np.copy(input_grid) 8 | 9 | # Get the top row colors 10 | top_row_colors = input_grid[0] 11 | 12 | # Find the grey line column index (last grey appearance in the row) 13 | grey_col_idx = np.where(input_grid == Color.GREY)[1][-1] 14 | 15 | # For each column, copy the color from the top row until bottom of the grid, skipping grey line 16 | for col in range(grey_col_idx): 17 | color = top_row_colors[col] 18 | if color != Color.BLACK: # Only color non-black columns 19 | draw_line(output_grid, col, 1, length=None, color=color, direction=(0, 1)) 20 | 21 | return output_grid 22 | 23 | def generate_input() -> np.ndarray: 24 | 25 | # Decide the grid size 26 | n = np.random.randint(5, 10) 27 | m = np.random.randint(5, 10) 28 | grid = np.zeros((m, n), dtype=int) 29 | 30 | # Select colors for the top row, excluding black 31 | top_row_colors = np.random.choice(list(Color.NOT_BLACK), n, replace=True) 32 | 33 | # Assign these colors to the top row 34 | grid[0, :] = top_row_colors 35 | 36 | # Define the position of the grey line 37 | grey_col_idx = np.random.randint(1, n) 38 | 39 | # Draw the grey line vertically across the picked column 40 | draw_line(grid, grey_col_idx, 1, length=None, color=Color.GREY, direction=(0, 1)) 41 | 42 | return grid -------------------------------------------------------------------------------- /synthetic_problems/fc1008b6320ef1dd.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xu3kev/BARC/a7b51a6b1ff969da3a78a71c533b6d79a93966e7/synthetic_problems/fc1008b6320ef1dd.png 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Go to the spreadsheet at this [link](https://docs.google.com/spreadsheets/d/1uPeAawNicITtLnT2aEclD5Ee-47gnfdXjuzugtfqceM/edit?gid=376627641#gid=376627641) 4 | - Fill out which file you want to work on 5 | - Grab the HTML file from Google Drive 6 | 2. Open the html file and annotate the problems 7 | - Rate the problems solely based on the input/output examples: Is it an ARC problem? 8 | - Rate the solution code: Does the transformation code/comments seem reasonable? (Redundant, mismatched, or overly complicated code is considered bad) 9 | - Upload the three files (Python, JSON, and PNG) to the repo under the synthetic_problems folder --------------------------------------------------------------------------------