from dataclasses import dataclass from random import Random from typing import Dict, Optional from ..factory import ProceduralDataset, register_dataset from .contrib.logic_puzzle.generate import generate_puzzle @dataclass class ZebraConfig: """Configuration for zebra puzzle generation""" num_people: int = 4 num_characteristics: int = 4 seed: Optional[int] = None size: int = 500 def validate(self): """Validate configuration parameters""" assert 2 <= self.num_people <= 7, "num_people must be between 2 and 7" assert 2 <= self.num_characteristics <= 7, "num_characteristics must be between 2 and 7" class ZebraDataset(ProceduralDataset): """Generates [Zebra Puzzles](https://en.wikipedia.org/wiki/Zebra_Puzzle) with configurable parameters""" def __init__(self, config: ZebraConfig): super().__init__(config=config, seed=config.seed, size=config.size) def __getitem__(self, idx: int) -> dict: """Generate a single Zebra task Returns: dict with keys: - question: str, the task description - answer: str, a solution string - metadata: dict with generation parameters """ rng = Random(self.seed + idx) K = self.config.num_people M = self.config.num_characteristics instance, puzzle = generate_puzzle(rng, K, M) q = instance["questions"][0]["question"] answer = instance["questions"][0]["answer"] question = str(puzzle) + "\n" + q return { "question": question, "answer": answer, "metadata": { "num_people": K, "num_characteristics": M, }, } def score_answer(self, answer: Optional[str], entry: Dict[str, any]) -> float: """Determine if the solution provided solves the Zebra task. The function awards 1.0 for a correct answer. Args: answer (Optional[str]): The user's answer. entry (Dict[str, any]): The original dataset entry containing the correct answer. Returns: float: The computed score between 0.0 and 1.0. """ if answer == None: return 0.0 if answer.lower().replace("\n", "") != entry["answer"].lower().replace("\n", ""): return 0.01 else: return 1.0 # Yay register_dataset("zebra_puzzles", ZebraDataset, ZebraConfig)