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from typing import List, Optional, Tuple, Dict
from dataclasses import dataclass
from pathlib import Path
import os
import json
import hashlib
import numpy as np
from glob import glob
from argdantic import ArgParser
from pydantic import BaseModel
from common import PuzzleDatasetMetadata, dihedral_transform
cli = ArgParser()
class DataProcessConfig(BaseModel):
# ARC-1
dataset_dirs: List[str] = ["dataset/raw-data/ARC-AGI/data", "dataset/raw-data/ConceptARC/corpus"]
output_dir: str = "data/arc-aug-1000"
# ARC-2
# dataset_dirs: List[str] = ["dataset/raw-data/ARC-AGI-2/data"]
# output_dir: str = "data/arc-2-aug-1000"
seed: int = 42
num_aug: int = 1000
ARCMaxGridSize = 30
ARCAugmentRetriesFactor = 5
@dataclass
class ARCPuzzle:
id: str
examples: List[Tuple[np.ndarray, np.ndarray]]
def arc_grid_to_np(grid: List[List[int]]):
arr = np.array(grid)
# Shape check
assert arr.ndim == 2
assert arr.shape[0] <= ARCMaxGridSize and arr.shape[1] <= ARCMaxGridSize
# Element check
assert np.all((arr >= 0) & (arr <= 9))
return arr.astype(np.uint8)
def np_grid_to_seq_translational_augment(inp: np.ndarray, out: np.ndarray, do_translation: bool):
# PAD: 0, <eos>: 1, digits: 2 ... 11
# Compute random top-left pad
if do_translation:
pad_r = np.random.randint(0, ARCMaxGridSize - max(inp.shape[0], out.shape[0]) + 1)
pad_c = np.random.randint(0, ARCMaxGridSize - max(inp.shape[1], out.shape[1]) + 1)
else:
pad_r = pad_c = 0
# Pad grid
result = []
for grid in [inp, out]:
nrow, ncol = grid.shape
grid = np.pad(grid + 2, ((pad_r, ARCMaxGridSize - pad_r - nrow), (pad_c, ARCMaxGridSize - pad_c - ncol)), constant_values=0)
# Add <eos>
eos_row, eos_col = pad_r + nrow, pad_c + ncol
if eos_row < ARCMaxGridSize:
grid[eos_row, pad_c:eos_col] = 1
if eos_col < ARCMaxGridSize:
grid[pad_r:eos_row, eos_col] = 1
result.append(grid.flatten())
return result
def puzzle_hash(puzzle: dict):
# Hash the puzzle for checking equivalence
def _grid_hash(grid: np.ndarray):
buffer = [x.to_bytes(1) for x in grid.shape]
buffer.append(grid.tobytes())
return hashlib.sha256(b"".join(buffer)).hexdigest()
hashes = []
for example_type, example in puzzle.items():
for input, label in example.examples:
hashes.append(f"{_grid_hash(input)}|{_grid_hash(label)}")
hashes.sort()
return hashlib.sha256("|".join(hashes).encode()).hexdigest()
def convert_single_arc_puzzle(results: dict, default_name: str, puzzle: dict, aug_count: int, dest_mapping: Dict[str, Tuple[str, str]]):
# Remove "name"
name = puzzle.pop("name", default_name)
# Convert
dests = set(dest_mapping.values())
converted = {dest: ARCPuzzle(name, []) for dest in dests}
for example_type, examples in puzzle.items():
dest = dest_mapping[example_type]
converted[dest].examples.extend([(arc_grid_to_np(example["input"]), arc_grid_to_np(example["output"])) for example in examples])
group = [converted]
# Augment
if aug_count > 0:
hashes = {puzzle_hash(converted)}
for _trial in range(ARCAugmentRetriesFactor * aug_count):
# Augment plan
trans_id = np.random.randint(0, 8)
mapping = np.concatenate([np.arange(0, 1, dtype=np.uint8), np.random.permutation(np.arange(1, 10, dtype=np.uint8))]) # Permute colors, Excluding "0" (black)
aug_repr = f"t{trans_id}_{''.join(str(x) for x in mapping)}"
def _map_grid(grid: np.ndarray):
return dihedral_transform(mapping[grid], trans_id)
# Check duplicate
augmented = {dest: ARCPuzzle(f"{puzzle.id}_{aug_repr}", [(_map_grid(input), _map_grid(label)) for (input, label) in puzzle.examples]) for dest, puzzle in converted.items()}
h = puzzle_hash(augmented)
if h not in hashes:
hashes.add(h)
group.append(augmented)
if len(group) >= aug_count + 1:
break
if len(group) < aug_count + 1:
print (f"[Puzzle {name}] augmentation not full, only {len(group)}")
# Append
for dest in dests:
# Convert the examples
dest_split, dest_set = dest
results.setdefault(dest_split, {})
results[dest_split].setdefault(dest_set, [])
results[dest_split][dest_set].append([converted[dest] for converted in group])
def load_puzzles_arcagi(results: dict, dataset_path: str, config: DataProcessConfig):
train_examples_dest = ("train", "all")
test_examples_map = {
"evaluation": [(1.0, ("test", "all"))],
"_default": [(1.0, ("train", "all"))]
}
total_puzzles = 0
for subdir in os.scandir(dataset_path):
if subdir.is_dir():
# Load all puzzles in this directory
puzzles = []
for filename in glob(os.path.join(subdir.path, "*.json")):
with open(filename, "r") as f:
puzzles.append((Path(filename).stem, json.load(f)))
# Shuffle puzzles
np.random.shuffle(puzzles)
# Assign by fraction
for idx, (default_name, puzzle) in enumerate(puzzles):
fraction = idx / len(puzzles)
test_examples_dest = None
for f, dest in test_examples_map.get(subdir.name, test_examples_map["_default"]):
if fraction < f:
test_examples_dest = dest
break
assert test_examples_dest is not None
convert_single_arc_puzzle(results, default_name, puzzle, config.num_aug, {"train": train_examples_dest, "test": test_examples_dest})
total_puzzles += 1
print (f"[{dataset_path}] total puzzles: {total_puzzles}")
def convert_dataset(config: DataProcessConfig):
np.random.seed(config.seed)
# Read dataset
data = {}
for dataset_dir in config.dataset_dirs:
load_puzzles_arcagi(data, dataset_dir, config)
# Map global puzzle identifiers
num_identifiers = 1 # 0 is blank
identifier_map = {}
for split_name, split in data.items():
for subset_name, subset in split.items():
for group in subset:
for puzzle in group:
if puzzle.id not in identifier_map:
identifier_map[puzzle.id] = num_identifiers
num_identifiers += 1
print (f"Total puzzle IDs (including <blank>): {num_identifiers}")
# Save
for split_name, split in data.items():
os.makedirs(os.path.join(config.output_dir, split_name), exist_ok=True)
# Translational augmentations
enable_translational_augment = split_name == "train"
# Statistics
total_examples = 0
total_puzzles = 0
total_groups = 0
for subset_name, subset in split.items():
# Construct subset
results = {k: [] for k in ["inputs", "labels", "puzzle_identifiers", "puzzle_indices", "group_indices"]}
results["puzzle_indices"].append(0)
results["group_indices"].append(0)
example_id = 0
puzzle_id = 0
for group in subset:
for puzzle in group:
# Push puzzle
no_aug_id = np.random.randint(0, len(puzzle.examples))
for _idx_ex, (inp, out) in enumerate(puzzle.examples):
inp, out = np_grid_to_seq_translational_augment(inp, out, do_translation=enable_translational_augment and _idx_ex != no_aug_id)
results["inputs"].append(inp)
results["labels"].append(out)
example_id += 1
total_examples += 1
results["puzzle_indices"].append(example_id)
results["puzzle_identifiers"].append(identifier_map[puzzle.id])
puzzle_id += 1
total_puzzles += 1
# Push group
results["group_indices"].append(puzzle_id)
total_groups += 1
for k, v in results.items():
if k in {"inputs", "labels"}:
v = np.stack(v, 0)
else:
v = np.array(v, dtype=np.int32)
np.save(os.path.join(config.output_dir, split_name, f"{subset_name}__{k}.npy"), v)
# Metadata
metadata = PuzzleDatasetMetadata(
seq_len=ARCMaxGridSize * ARCMaxGridSize,
vocab_size=10 + 2, # PAD + EOS + "0" ... "9"
pad_id=0,
ignore_label_id=0,
blank_identifier_id=0,
num_puzzle_identifiers=num_identifiers,
total_groups=total_groups,
mean_puzzle_examples=total_examples / total_puzzles,
sets=list(split.keys())
)
# Save metadata as JSON.
with open(os.path.join(config.output_dir, split_name, "dataset.json"), "w") as f:
json.dump(metadata.model_dump(), f)
# Save IDs mapping
with open(os.path.join(config.output_dir, "identifiers.json"), "w") as f:
ids_mapping = {v: k for k, v in identifier_map.items()}
json.dump([ids_mapping.get(i, "<blank>") for i in range(num_identifiers)], f)
@cli.command(singleton=True)
def main(config: DataProcessConfig):
convert_dataset(config)
if __name__ == "__main__":
cli()
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