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#!/usr/bin/env python3
"""
Deep Think推理系统原型
实现三档推理模式的并行假设生成、自我批判和收敛机制
"""

import numpy as np
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import time
import math

class ThinkMode(Enum):
    STANDARD = "standard"    # 标准推理
    EXTENDED = "extended"    # 扩展推理
    EXTRA_HIGH = "extra_high"  # 极限推理

@dataclass
class Hypothesis:
    """推理假设"""
    content: str
    confidence: float
    generation_time: float
    critique_score: float = 0.0
    sub_hypotheses: List['Hypothesis'] = field(default_factory=list)

@dataclass
class DeepThinkConfig:
    """Deep Think配置"""
    mode: ThinkMode
    num_hypotheses: int
    max_iterations: int
    critique_threshold: float
    temperature: float
    time_budget: float  # 秒
    
    @classmethod
    def from_mode(cls, mode: ThinkMode) -> 'DeepThinkConfig':
        configs = {
            ThinkMode.STANDARD: cls(
                mode=mode, num_hypotheses=3, max_iterations=2,
                critique_threshold=0.7, temperature=0.3, time_budget=30,
            ),
            ThinkMode.EXTENDED: cls(
                mode=mode, num_hypotheses=5, max_iterations=4,
                critique_threshold=0.8, temperature=0.5, time_budget=120,
            ),
            ThinkMode.EXTRA_HIGH: cls(
                mode=mode, num_hypotheses=8, max_iterations=8,
                critique_threshold=0.9, temperature=0.7, time_budget=600,
            ),
        }
        return configs[mode]

class DeepThinkEngine:
    """Deep Think推理引擎"""
    
    def __init__(self, model: Callable):
        self.model = model
        self.thinking_log: List[Dict] = []
    
    def generate_hypotheses(self, problem: str, config: DeepThinkConfig) -> List[Hypothesis]:
        """并行生成多个假设"""
        hypotheses = []
        for i in range(config.num_hypotheses):
            start = time.time()
            # 模拟不同角度的推理
            perspective = f"角度{i+1}: "
            content = self.model(problem, config.temperature)
            elapsed = time.time() - start
            
            hypotheses.append(Hypothesis(
                content=content,
                confidence=random.uniform(0.3, 0.9),
                generation_time=elapsed,
            ))
        
        return hypotheses
    
    def self_critique(self, hypothesis: Hypothesis, config: DeepThinkConfig) -> float:
        """自我批判：评估假设的质量"""
        # 模拟批判评分
        scores = {
            "logical_consistency": random.uniform(0.5, 1.0),
            "factual_accuracy": random.uniform(0.4, 1.0),
            "completeness": random.uniform(0.3, 1.0),
            "novelty": random.uniform(0.2, 1.0),
        }
        
        weighted_score = (
            scores["logical_consistency"] * 0.35 +
            scores["factual_accuracy"] * 0.30 +
            scores["completeness"] * 0.20 +
            scores["novelty"] * 0.15
        )
        
        hypothesis.critique_score = weighted_score
        return weighted_score
    
    def converge(self, hypotheses: List[Hypothesis], config: DeepThinkConfig) -> Hypothesis:
        """收敛：从多个假设中选出最佳答案"""
        # 筛选超过批判阈值的假设
        valid = [h for h in hypotheses if h.critique_score >= config.critique_threshold]
        
        if not valid:
            # 所有假设都低于阈值，选置信度最高的
            valid = hypotheses
        
        # 按综合评分排序
        valid.sort(key=lambda h: h.critique_score * 0.6 + h.confidence * 0.4, reverse=True)
        
        # 合并最佳假设
        best = valid[0]
        for h in valid[1:3]:
            best.content += f"\n[补充：{h.content[:100]}...]"
            best.confidence = max(best.confidence, h.confidence)
        
        return best
    
    def solve(self, problem: str, mode: ThinkMode = ThinkMode.EXTENDED) -> Dict:
        """
        完整推理流程：生成假设 → 自我批判 → 迭代优化 → 收敛
        """
        config = DeepThinkConfig.from_mode(mode)
        start_time = time.time()
        
        self.thinking_log.append({
            "problem": problem,
            "mode": mode.value,
            "start_time": start_time,
        })
        
        # 迭代推理
        best_hypothesis = None
        for iteration in range(config.max_iterations):
            iteration_start = time.time()
            
            # 生成假设
            hypotheses = self.generate_hypotheses(problem, config)
            
            # 自我批判
            for h in hypotheses:
                self.self_critique(h, config)
            
            # 收敛
            best_hypothesis = self.converge(hypotheses, config)
            
            iteration_time = time.time() - iteration_start
            self.thinking_log[-1][f"iteration_{iteration}"] = {
                "num_hypotheses": len(hypotheses),
                "best_score": best_hypothesis.critique_score,
                "time": iteration_time,
            }
            
            # 检查是否达到阈值或超时
            elapsed = time.time() - start_time
            if (best_hypothesis.critique_score >= config.critique_threshold * 1.1 
                or elapsed > config.time_budget):
                break
        
        total_time = time.time() - start_time
        self.thinking_log[-1]["total_time"] = total_time
        self.thinking_log[-1]["final_confidence"] = best_hypothesis.confidence
        self.thinking_log[-1]["iterations_used"] = iteration + 1
        
        return {
            "answer": best_hypothesis.content,
            "confidence": best_hypothesis.confidence,
            "mode": mode.value,
            "total_time": total_time,
            "iterations": iteration + 1,
            "thinking_log": self.thinking_log[-1],
        }
    
    def benchmark_modes(self, problems: List[str]) -> Dict:
        """
        基准测试：对比三档推理模式
        """
        results = {}
        for mode in ThinkMode:
            mode_results = []
            total_time = 0
            for problem in problems:
                result = self.solve(problem, mode)
                mode_results.append(result)
                total_time += result["total_time"]
            
            avg_confidence = np.mean([r["confidence"] for r in mode_results])
            avg_iterations = np.mean([r["iterations"] for r in mode_results])
            
            results[mode.value] = {
                "avg_confidence": avg_confidence,
                "avg_iterations": avg_iterations,
                "total_time": total_time,
                "avg_time_per_problem": total_time / len(problems),
                "throughput": len(problems) / total_time * 60,  # 每分钟处理数
            }
        
        return results

# 模拟模型
def mock_model(prompt, temperature=0.5):
    return f"推理结果：基于{int(temperature*10)}个备选路径，导出最优解..."

# 运行基准测试
import random
engine = DeepThinkEngine(mock_model)

print("=" * 60)
print("Deep Think推理模式基准测试")
print("=" * 60)

test_problems = [
    "证明：对于任意n≥3，不存在整数解满足xⁿ + yⁿ = zⁿ",
    "设计一个支持百万级并发的分布式KV存储系统",
    "分析Transformer注意力机制在超长上下文下的计算复杂度优化方案",
]

results = engine.benchmark_modes(test_problems)

for mode, metrics in results.items():
    print(f"\n{mode.upper()}模式：")
    print(f"  平均置信度：{metrics['avg_confidence']:.3f}")
    print(f"  平均迭代次数：{metrics['avg_iterations']:.1f}")
    print(f"  总耗时：{metrics['total_time']:.1f}秒")
    print(f"  吞吐量：{metrics['throughput']:.1f} 问题/分钟")