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from typing import Optional
import os
import csv
import json
import numpy as np
from argdantic import ArgParser
from pydantic import BaseModel
from tqdm import tqdm
from huggingface_hub import hf_hub_download
from common import PuzzleDatasetMetadata
cli = ArgParser()
class DataProcessConfig(BaseModel):
source_repo: str = "imone/sudoku-hard-v2"
output_dir: str = "data/sudoku-extreme-full"
subsample_size: Optional[int] = None
min_difficulty: Optional[int] = None
num_aug: int = 0
def shuffle_sudoku(board: np.ndarray, solution: np.ndarray):
# Create a random digit mapping: a permutation of 1..9, with zero (blank) unchanged
digit_map = np.pad(np.random.permutation(np.arange(1, 10)), (1, 0))
# Randomly decide whether to transpose.
transpose_flag = np.random.rand() < 0.5
# Generate a valid row permutation:
# - Shuffle the 3 bands (each band = 3 rows) and for each band, shuffle its 3 rows.
bands = np.random.permutation(3)
row_perm = np.concatenate([b * 3 + np.random.permutation(3) for b in bands])
# Similarly for columns (stacks).
stacks = np.random.permutation(3)
col_perm = np.concatenate([s * 3 + np.random.permutation(3) for s in stacks])
# Build an 81->81 mapping. For each new cell at (i, j)
# (row index = i // 9, col index = i % 9),
# its value comes from old row = row_perm[i//9] and old col = col_perm[i%9].
mapping = np.array([row_perm[i // 9] * 9 + col_perm[i % 9] for i in range(81)])
def apply_transformation(x: np.ndarray) -> np.ndarray:
# Apply transpose flag
if transpose_flag:
x = x.T
# Apply the position mapping.
new_board = x.flatten()[mapping].reshape(9, 9).copy()
# Apply digit mapping
return digit_map[new_board]
return apply_transformation(board), apply_transformation(solution)
def convert_subset(set_name: str, config: DataProcessConfig):
# Read CSV
inputs = []
labels = []
with open(hf_hub_download(config.source_repo, f"{set_name}.csv", repo_type="dataset"), newline="") as csvfile:
reader = csv.reader(csvfile)
next(reader) # Skip header
for source, q, a, rating in reader:
if (config.min_difficulty is None) or (int(rating) >= config.min_difficulty):
assert len(q) == 81 and len(a) == 81
inputs.append(np.frombuffer(q.replace('.', '0').encode(), dtype=np.uint8).reshape(9, 9) - ord('0'))
labels.append(np.frombuffer(a.encode(), dtype=np.uint8).reshape(9, 9) - ord('0'))
# If subsample_size is specified for the training set,
# randomly sample the desired number of examples.
if set_name == "train" and config.subsample_size is not None:
total_samples = len(inputs)
if config.subsample_size < total_samples:
indices = np.random.choice(total_samples, size=config.subsample_size, replace=False)
inputs = [inputs[i] for i in indices]
labels = [labels[i] for i in indices]
# Generate dataset
num_augments = config.num_aug if set_name == "train" else 0
results = {k: [] for k in ["inputs", "labels", "puzzle_identifiers", "puzzle_indices", "group_indices"]}
puzzle_id = 0
example_id = 0
results["puzzle_indices"].append(0)
results["group_indices"].append(0)
for orig_inp, orig_out in zip(tqdm(inputs), labels):
for aug_idx in range(1 + num_augments):
# First index is not augmented
if aug_idx == 0:
inp, out = orig_inp, orig_out
else:
inp, out = shuffle_sudoku(orig_inp, orig_out)
# Push puzzle (only single example)
results["inputs"].append(inp)
results["labels"].append(out)
example_id += 1
puzzle_id += 1
results["puzzle_indices"].append(example_id)
results["puzzle_identifiers"].append(0)
# Push group
results["group_indices"].append(puzzle_id)
# To Numpy
def _seq_to_numpy(seq):
arr = np.concatenate(seq).reshape(len(seq), -1)
assert np.all((arr >= 0) & (arr <= 9))
return arr + 1
results = {
"inputs": _seq_to_numpy(results["inputs"]),
"labels": _seq_to_numpy(results["labels"]),
"group_indices": np.array(results["group_indices"], dtype=np.int32),
"puzzle_indices": np.array(results["puzzle_indices"], dtype=np.int32),
"puzzle_identifiers": np.array(results["puzzle_identifiers"], dtype=np.int32),
}
# Metadata
metadata = PuzzleDatasetMetadata(
seq_len=81,
vocab_size=10 + 1, # PAD + "0" ... "9"
pad_id=0,
ignore_label_id=0,
blank_identifier_id=0,
num_puzzle_identifiers=1,
total_groups=len(results["group_indices"]) - 1,
mean_puzzle_examples=1,
sets=["all"]
)
# Save metadata as JSON.
save_dir = os.path.join(config.output_dir, set_name)
os.makedirs(save_dir, exist_ok=True)
with open(os.path.join(save_dir, "dataset.json"), "w") as f:
json.dump(metadata.model_dump(), f)
# Save data
for k, v in results.items():
np.save(os.path.join(save_dir, f"all__{k}.npy"), v)
# Save IDs mapping (for visualization only)
with open(os.path.join(config.output_dir, "identifiers.json"), "w") as f:
json.dump(["<blank>"], f)
@cli.command(singleton=True)
def preprocess_data(config: DataProcessConfig):
convert_subset("train", config)
convert_subset("test", config)
if __name__ == "__main__":
cli()
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