added rearc impl

reasoning_gym/arc/rearc_utils/utils.py

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