mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-19 12:58:07 +00:00
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
import random
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import matplotlib.pyplot as plt
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from dsl import *
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from matplotlib.colors import ListedColormap, Normalize
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global rng
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rng = []
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def unifint(rng: random.Random, diff_lb: float, diff_ub: float, bounds: Tuple[int, int]) -> int:
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"""
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rng
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diff_lb: lower bound for difficulty, must be in range [0, diff_ub]
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diff_ub: upper bound for difficulty, must be in range [diff_lb, 1]
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bounds: interval [a, b] determining the integer values that can be sampled
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"""
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a, b = bounds
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d = rng.uniform(diff_lb, diff_ub)
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if not hasattr(rng, "difficulty_samples"):
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rng.difficulty_samples = []
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rng.difficulty_samples.append(d)
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return min(max(a, round(a + (b - a) * d)), b)
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def is_grid(grid: Any) -> bool:
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"""
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returns True if and only if argument is a valid grid
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"""
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if not isinstance(grid, tuple):
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return False
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if not 0 < len(grid) <= 30:
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return False
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if not all(isinstance(r, tuple) for r in grid):
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return False
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if not all(0 < len(r) <= 30 for r in grid):
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return False
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if not len(set(len(r) for r in grid)) == 1:
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return False
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if not all(all(isinstance(x, int) for x in r) for r in grid):
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return False
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if not all(all(0 <= x <= 9 for x in r) for r in grid):
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return False
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return True
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def strip_prefix(string: str, prefix: str) -> str:
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"""
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removes prefix
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"""
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return string[len(prefix) :]
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def format_grid(grid: List[List[int]]) -> Grid:
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"""
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grid type casting
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"""
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return tuple(tuple(row) for row in grid)
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def format_example(example: dict) -> dict:
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"""
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example data type
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"""
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return {"input": format_grid(example["input"]), "output": format_grid(example["output"])}
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def format_task(task: dict) -> dict:
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"""
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task data type
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"""
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return {
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"train": [format_example(example) for example in task["train"]],
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"test": [format_example(example) for example in task["test"]],
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}
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def plot_task(task: List[dict], title: str = None) -> None:
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"""
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displays a task
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"""
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cmap = ListedColormap(
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["#000", "#0074D9", "#FF4136", "#2ECC40", "#FFDC00", "#AAAAAA", "#F012BE", "#FF851B", "#7FDBFF", "#870C25"]
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)
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norm = Normalize(vmin=0, vmax=9)
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args = {"cmap": cmap, "norm": norm}
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height = 2
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width = len(task)
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figure_size = (width * 3, height * 3)
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figure, axes = plt.subplots(height, width, figsize=figure_size)
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for column, example in enumerate(task):
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axes[0, column].imshow(example["input"], **args)
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axes[1, column].imshow(example["output"], **args)
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axes[0, column].axis("off")
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axes[1, column].axis("off")
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if title is not None:
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figure.suptitle(title, fontsize=20)
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plt.subplots_adjust(wspace=0.1, hspace=0.1)
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plt.show()
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def fix_bugs(dataset: dict) -> None:
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"""
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fixes bugs in the original ARC training dataset
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"""
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dataset["a8d7556c"]["train"][2]["output"] = fill(dataset["a8d7556c"]["train"][2]["output"], 2, {(8, 12), (9, 12)})
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dataset["6cf79266"]["train"][2]["output"] = fill(
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dataset["6cf79266"]["train"][2]["output"], 1, {(6, 17), (7, 17), (8, 15), (8, 16), (8, 17)}
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)
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dataset["469497ad"]["train"][1]["output"] = fill(
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dataset["469497ad"]["train"][1]["output"], 7, {(5, 12), (5, 13), (5, 14)}
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)
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dataset["9edfc990"]["train"][1]["output"] = fill(dataset["9edfc990"]["train"][1]["output"], 1, {(6, 13)})
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dataset["e5062a87"]["train"][1]["output"] = fill(
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dataset["e5062a87"]["train"][1]["output"], 2, {(1, 3), (1, 4), (1, 5), (1, 6)}
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)
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dataset["e5062a87"]["train"][0]["output"] = fill(
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dataset["e5062a87"]["train"][0]["output"], 2, {(5, 2), (6, 3), (3, 6), (4, 7)}
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)
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