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import json |
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from pathlib import Path |
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from typing import Dict |
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import argparse |
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from datasets import Dataset, DatasetDict, load_dataset |
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class ARCToHFConverter: |
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"""Converts ARC-AGI task JSON files to HuggingFace Arrow format.""" |
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def __init__(self, input_dir: Path): |
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self.input_dir = Path(input_dir) |
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self.output_dir = self.input_dir.parent / f"hf_{self.input_dir.name}" |
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def load_task(self, json_path: Path) -> Dict: |
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"""Load single task JSON file.""" |
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with open(json_path, 'r') as f: |
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return json.load(f) |
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def convert_task(self, task_data: Dict, task_id: str) -> Dict: |
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"""Convert single task to HF schema. |
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Returns: |
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{ |
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"id": str, |
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"list": [ |
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[grid, grid, ...], # example inputs |
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[grid, grid, ...], # example outputs |
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[grid, ...] # test inputs |
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], |
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"label": [grid, ...] # test outputs |
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} |
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""" |
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return { |
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"id": task_id, |
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"list": [ |
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[ex["input"] for ex in task_data["train"]], |
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[ex["output"] for ex in task_data["train"]], |
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[ex["input"] for ex in task_data["test"]] |
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], |
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"label": [ex["output"] for ex in task_data["test"]] |
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} |
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def convert_directory(self, subdir_name: str) -> Dataset: |
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"""Convert all JSON files in a subdirectory to HF Dataset.""" |
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subdir = self.input_dir / subdir_name |
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json_files = sorted(subdir.glob("*.json")) |
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print(f"Converting {subdir_name}/ directory ({len(json_files)} tasks)...") |
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tasks = [] |
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for json_path in json_files: |
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task_id = json_path.stem |
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task_data = self.load_task(json_path) |
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converted = self.convert_task(task_data, task_id) |
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tasks.append(converted) |
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return Dataset.from_list(tasks) |
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def convert_all(self) -> DatasetDict: |
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"""Convert both training and evaluation subdirectories.""" |
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train_dataset = self.convert_directory("training") |
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test_dataset = self.convert_directory("evaluation") |
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return DatasetDict({ |
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"train": train_dataset, |
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"test": test_dataset |
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}) |
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def save(self, dataset_dict: DatasetDict): |
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"""Save dataset to disk in Parquet format for HuggingFace Hub.""" |
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self.output_dir.mkdir(parents=True, exist_ok=True) |
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data_dir = self.output_dir / "data" |
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data_dir.mkdir(exist_ok=True) |
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print(f"Saving train split to {data_dir / 'train-00000-of-00001.parquet'}...") |
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dataset_dict['train'].to_parquet(data_dir / 'train-00000-of-00001.parquet') |
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print(f"Saving test split to {data_dir / 'test-00000-of-00001.parquet'}...") |
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dataset_dict['test'].to_parquet(data_dir / 'test-00000-of-00001.parquet') |
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print(f"\n✓ Dataset saved to {self.output_dir}") |
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print(f" - Train: {len(dataset_dict['train'])} examples") |
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print(f" - Test: {len(dataset_dict['test'])} examples") |
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def look_at_data(): |
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print("Loading dataset from parquet files...") |
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dataset = load_dataset('parquet', data_files={ |
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'train': 'data/train-00000-of-00001.parquet', |
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'test': 'data/test-00000-of-00001.parquet' |
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}) |
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print("\nDataset loaded successfully!") |
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print(f"Splits: {list(dataset.keys())}") |
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print(f"Train size: {len(dataset['train'])}") |
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print(f"Test size: {len(dataset['test'])}") |
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print(f"\nFeatures: {dataset['train'].features}") |
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print(f"\nFirst example ID: {dataset['train'][0]['id']}") |
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def main(): |
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parser = argparse.ArgumentParser( |
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description="Convert ARC-AGI JSON tasks to HuggingFace dataset" |
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) |
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parser.add_argument( |
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"input_dir", |
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type=str, |
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help="Parent directory containing training/ and evaluation/ subdirectories" |
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) |
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args = parser.parse_args() |
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print(f"Input directory: {args.input_dir}") |
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converter = ARCToHFConverter(args.input_dir) |
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print(f"Output directory: {converter.output_dir}\n") |
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dataset_dict = converter.convert_all() |
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converter.save(dataset_dict) |
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if __name__ == "__main__": |
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main() |
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