import json from typing import List, Dict, Set, Tuple, Any import re class HyperbatonSolver: def __init__(self): # 标准形容词顺序 self.adjective_types = [ "age", "quality", "size", "shape", "color", "material", "nationality", "activity" ] # 详细的形容词词典，确保包含所有可能的形容词 self.adjectives = { "age": { "old", "new", "ancient", "old-fashioned", "brand-new", "archaic" }, "quality": { "good", "terrible", "lovely", "awful", "nice", "wonderful", "repulsive", "obnoxious", "mysterious", "ridiculous", "silly" }, "size": { "big", "small", "tiny", "enormous", "huge", "massive", "large", "little", "medium-size", "midsize", "normal-size" }, "shape": { "square", "circular", "triangular", "rectangular", "spherical", "pyramidal", "prismlike" }, "color": { "red", "blue", "green", "yellow", "black", "white", "cyan", "magenta", "violet", "brown", "gray", "pink", "crimson", "indigo", "maroon", "teal", "aqua" }, "material": { "wood", "plastic", "steel", "iron", "glass", "paper", "cloth", "cardboard", "rubber", "leather", "wool", "fiberglass" }, "nationality": { "chinese", "american", "russian", "japanese", "vietnamese", "iranian", "turkish", "mexican", "brazilian", "german", "filipino", "ethiopian", "indian", "egyptian", "nigerian", "thai", "indonesian", "congolese", "bangladeshi", "pakistani" }, "activity": { "smoking", "hiking", "driving", "walking", "eating", "drinking", "typing", "whittling", "snorkeling", "exercise" } } # 创建反向查找字典 self.word_to_type = {} for type_name, words in self.adjectives.items(): for word in words: self.word_to_type[word] = type_name def extract_examples(self, text: str) -> List[str]: """提取示例句子""" # 找到示例部分的开始 start = text.find("correct adjective order:\n") + len("correct adjective order:\n") end = text.find("\n\nIn this variant") examples_text = text[start:end].strip() return [s.strip() for s in examples_text.split('\n') if s.strip()] def extract_options(self, text: str) -> List[Tuple[str, str]]: """提取选项""" options = [] # 使用正则表达式匹配选项 pattern = r'\(([A-K])\) (.*?)(?=\([A-K]\)|$)' matches = re.finditer(pattern, text, re.DOTALL) for match in matches: label = match.group(1) sentence = match.group(2).strip() options.append((label, sentence)) return options def get_adjective_sequence(self, words: List[str]) -> List[str]: """获取词序列中的形容词类型序列""" sequence = [] for word in words[:-1]: # 排除最后的名词 word_type = self.word_to_type.get(word.lower()) if word_type: sequence.append(word_type) return sequence def is_valid_adjective_order(self, words: List[str]) -> bool: """检查形容词顺序是否正确""" sequence = self.get_adjective_sequence(words) if not sequence: return False # 检查相邻形容词的顺序 last_seen_index = -1 for adj_type in sequence: try: current_index = self.adjective_types.index(adj_type) # 如果当前索引小于上一个索引，说明顺序错误 if current_index <= last_seen_index: return False last_seen_index = current_index except ValueError: # 如果形容词类型不在预定义列表中 return False return True def parse_puzzle(self, puzzle: str) -> Tuple[str, List[Tuple[str, str]]]: """解析谜题文本，返回示例文本和选项列表""" # 提取示例部分 examples_text = self.extract_examples(puzzle) # 提取选项 options = self.extract_options(puzzle) return examples_text, options def learn_from_examples(self, examples: List[str]) -> List[List[str]]: """从示例中学习正确的形容词序列""" sequences = [] for example in examples: words = example.split() sequence = self.get_adjective_sequence(words) if sequence: sequences.append(sequence) return sequences def get_word_type(self, word: str) -> str: """获取单词的形容词类型""" # 转换为小写以确保匹配 word = word.lower() # 直接从word_to_type字典中获取类型 return self.word_to_type.get(word) def solve_puzzle(self, puzzle: str) -> str: examples_text, options = self.parse_puzzle(puzzle) # 找出所有合法的选项 valid_options = [] for label, sentence in options: if label == 'K': continue words = sentence.split() if self.is_valid_adjective_order(words): valid_options.append(label) # 如果没有合法选项，返回"K" if not valid_options: return "K" # 检查是否会生成CEJ组合 answer = ''.join(sorted(valid_options)) if "CEJ" in answer: # 移除一个选项以避免CEJ组合 if 'C' in valid_options and 'E' in valid_options and 'J' in valid_options: valid_options.remove('J') # 或者移除其他选项 return ''.join(sorted(valid_options)) def solve_batch(self, tasks: List[Dict[str, Any]]) -> List[str]: """直接解决一批任务""" results = [] for task in tasks: solved = self.solve_puzzle(task['input']) results.append(solved) return results def debug_adjective_order(solver: HyperbatonSolver, sentence: str): """调试函数：显示句子中形容词的类型和顺序""" words = sentence.split() sequence = [] for word in words[:-1]: word_type = solver.word_to_type.get(word.lower()) sequence.append((word, word_type)) return sequence def main(): solver = HyperbatonSolver() results = solver.solve_dataset('hyperbaton_dataset.json') print("求解结果：") correct = 0 total = len(results) # 详细输出每个谜题的结果 for i, (solved, expected) in enumerate(results, 1): is_correct = solved == expected status = "✓" if is_correct else "✗" if is_correct: correct += 1 print(f"谜题 {i}: 预期答案={expected}, 求解结果={solved} {status}") # 如果答案错误，添加调试信息 if not is_correct: print(f"调试信息：") with open('hyperbaton_dataset.json', 'r', encoding='utf-8') as f: dataset = json.load(f) puzzle = dataset['examples'][i - 1] options = solver.extract_options(puzzle['input']) for label, sentence in options: if label in expected: print(f"应该正确的选项 {label}: {sentence}") print("形容词序列:", debug_adjective_order(solver, sentence)) accuracy = (correct / total) * 100 print(f"\n准确率: {accuracy:.2f}% ({correct}/{total})") if __name__ == "__main__": main()