"""
AgentTokenEconomics.py
Agent Token经济学深度分析器
"""

from dataclasses import dataclass, field
from typing import List, Dict
import math


@dataclass
class ModelPricing:
    """模型定价"""
    name: str
    input_price: float  # $/M tokens
    output_price: float  # $/M tokens
    token_efficiency: float = 1.0  # 相对基准效率
    
    @property
    def effective_output_price(self) -> float:
        """考虑效率后的有效输出价格"""
        return self.output_price / self.token_efficiency


@dataclass
class AgentTask:
    """Agent任务定义"""
    name: str
    prompt_tokens: int
    completion_tokens: int
    num_steps: int
    tool_calls: int
    context_window_required: int


@dataclass
class AgentTaskCost:
    """Agent任务成本计算"""
    model: ModelPricing
    task: AgentTask
    
    def compute_cost(self) -> Dict[str, float]:
        """计算任务成本"""
        # 输入成本
        input_cost = (self.task.prompt_tokens / 1_000_000) * self.model.input_price
        
        # 输出成本（考虑效率）
        effective_output = self.model.effective_output_price
        output_cost = (self.task.completion_tokens / 1_000_000) * effective_output
        
        # 多步迭代成本（Agent每次迭代都会重新生成上下文）
        iteration_overhead = 1.5  # 多步迭代的开销系数
        total_input = input_cost * self.task.num_steps
        total_output = output_cost * self.task.num_steps * iteration_overhead
        
        # 工具调用成本（每次工具调用额外消耗Token）
        tool_overhead = self.task.tool_calls * 0.5  # 每次工具调用约500 Token
        tool_cost = (tool_overhead / 1_000_000) * effective_output
        
        total_cost = total_input + total_output + tool_cost
        
        return {
            "input_cost": total_input,
            "output_cost": total_output,
            "tool_cost": tool_cost,
            "total_cost": total_cost
        }


class AgentCostAnalyzer:
    """Agent成本分析器"""
    
    def __init__(self):
        self.models: List[ModelPricing] = []
        self.tasks: List[AgentTask] = []
    
    def add_model(self, model: ModelPricing):
        self.models.append(model)
    
    def add_task(self, task: AgentTask):
        self.tasks.append(task)
    
    def analyze(self) -> Dict:
        """分析所有模型在所有任务上的成本"""
        results = {}
        
        for task in self.tasks:
            task_results = {}
            for model in self.models:
                cost_calc = AgentTaskCost(model, task)
                costs = cost_calc.compute_cost()
                task_results[model.name] = costs
            results[task.name] = task_results
        
        return results
    
    def compute_savings(self, base_model: str, target_model: str) -> Dict:
        """计算成本节约比例"""
        savings = {}
        all_results = self.analyze()
        
        for task_name, model_results in all_results.items():
            if base_model in model_results and target_model in model_results:
                base_cost = model_results[base_model]["total_cost"]
                target_cost = model_results[target_model]["total_cost"]
                if base_cost > 0:
                    savings[task_name] = (base_cost - target_cost) / base_cost * 100
        
        return savings


def main():
    """分析Gemini 3.6 Flash的成本优势"""
    
    # 定义模型定价
    models = [
        ModelPricing("Gemini 3.5 Flash", 1.50, 9.00, 1.0),
        ModelPricing("Gemini 3.6 Flash", 1.50, 7.50, 1.65),  # 65% Token效率提升
        ModelPricing("Gemini 3.5 Flash Lite", 0.30, 2.50, 0.8),
        ModelPricing("Claude Sonnet 4.5", 21.00, 105.00, 1.2),
        ModelPricing("GPT-5.6 Luna", 1.00, 6.00, 1.0),
    ]
    
    # 定义典型Agent任务
    tasks = [
        AgentTask("Code Review Agent", 4000, 2000, 3, 5, 128000),
        AgentTask("Multi-step Coding Agent", 8000, 5000, 5, 8, 128000),
        AgentTask("Customer Support Agent", 2000, 1500, 4, 6, 32000),
        AgentTask("Data Analysis Agent", 6000, 3000, 3, 4, 64000),
        AgentTask("Document Generation Agent", 3000, 4000, 2, 2, 64000),
    ]
    
    analyzer = AgentCostAnalyzer()
    for m in models:
        analyzer.add_model(m)
    for t in tasks:
        analyzer.add_task(t)
    
    print("=" * 80)
    print("Gemini 3.6 Flash Agent成本分析")
    print("=" * 80)
    
    all_results = analyzer.analyze()
    
    print(f"\n[各模型在不同Agent任务上的成本对比]")
    print(f"{'任务':<25} {'3.5 Flash':<12} {'3.6 Flash':<12} {'Lite':<12} {'Sonnet 4.5':<12} {'GPT-5.6 Luna':<12}")
    print("-" * 80)
    
    for task_name, model_results in all_results.items():
        costs = []
        for model_name in ["Gemini 3.5 Flash", "Gemini 3.6 Flash", "Gemini 3.5 Flash Lite", 
                           "Claude Sonnet 4.5", "GPT-5.6 Luna"]:
            if model_name in model_results:
                c = model_results[model_name]["total_cost"]
                costs.append(f"${c:.4f}")
            else:
                costs.append("N/A")
        print(f"{task_name:<25} {costs[0]:<12} {costs[1]:<12} {costs[2]:<12} {costs[3]:<12} {costs[4]:<12}")
    
    # 计算节约比例
    savings = analyzer.compute_savings("Gemini 3.5 Flash", "Gemini 3.6 Flash")
    
    print(f"\n[Gemini 3.6 Flash vs 3.5 Flash 节约比例]")
    for task, pct in savings.items():
        print(f"  {task:<25}: {pct:.1f}%")
    
    avg_savings = sum(savings.values()) / len(savings)
    print(f"\n  平均节约: {avg_savings:.1f}%")
    
    # DeepSWE基准测试分析
    print(f"\n[DeepSWE基准测试 - 编码Agent任务]")
    print(f"  Token消耗降低: 65%")
    print(f"  输出价格降低: 17%")
    print(f"  有效成本降低: 1 - (1-0.65)×(1-0.17) = {1-(1-0.65)*(1-0.17):.0f}%")
    print(f"  Google官方数据: 71%")
    
    print(f"\n[结论]")
    print(f"  1. 3.6 Flash在Agent工作负载中成本优势显著")
    print(f"  2. 价格降低 + Token效率提升 = 超线性成本节约")
    print(f"  3. 与Sonnet 4.5相比，成本降低超过90%")
    print(f"  4. 生产部署中，Agent任务成本是模型选型的核心指标")


if __name__ == "__main__":
    main()

// TokenEfficiencyEngine.go
package main

import (
	"fmt"
	"math"
)

// KV Cache优化模拟器
type KVCacheManager struct {
	CacheSize     int
	HitRate       float64
	ComputeCost   float64 // 每Token计算成本
	MemoryCost    float64 // 每Token内存成本
}

func (k *KVCacheManager) ComputeWithCache(promptLen, generateLen, numSteps int) float64 {
	// 无缓存：每次重新计算
	noCache := float64(promptLen+generateLen) * float64(numSteps) * k.ComputeCost
	
	// 有缓存：首次计算+后续增量
	firstStep := float64(promptLen+generateLen) * k.ComputeCost
	subsequentSteps := float64(generateLen) * float64(numSteps-1) * k.ComputeCost * (1 - k.HitRate)
	cacheCost := firstStep + subsequentSteps
	
	return cacheCost
}

// TokenCostAnalyzer
type TokenCostAnalyzer struct {
	InputPrice  float64
	OutputPrice float64
	Efficiency  float64
}

func (t *TokenCostAnalyzer) EffectiveCost(inputTokens, outputTokens float64) float64 {
	// 考虑效率后的有效输出成本
	effectiveOutputPrice := t.OutputPrice / t.Efficiency
	return (inputTokens/1e6)*t.InputPrice + (outputTokens/1e6)*effectiveOutputPrice
}

func main() {
	// 分析Agent工作负载
	gemini35 := TokenCostAnalyzer{InputPrice: 1.50, OutputPrice: 9.00, Efficiency: 1.0}
	gemini36 := TokenCostAnalyzer{InputPrice: 1.50, OutputPrice: 7.50, Efficiency: 1.65}
	
	// 典型Agent任务：8K输入，5K输出，5步迭代，8次工具调用
	taskInput := 8000.0 * 5.0     // 多步迭代输入
	taskOutput := 5000.0 * 5.0 * 1.5 // 输出+迭代开销
	
	cost35 := gemini35.EffectiveCost(taskInput, taskOutput)
	cost36 := gemini36.EffectiveCost(taskInput, taskOutput)
	
	fmt.Println("=== Agent Token经济学分析 ===")
	fmt.Printf("3.5 Flash成本: $%.4f\n", cost35)
	fmt.Printf("3.6 Flash成本: $%.4f\n", cost36)
	fmt.Printf("节约比例: %.1f%%\n", (cost35-cost36)/cost35*100)
}

"""
SubAgentWorkflow.py
SubAgent工作流成本优化
"""

from dataclasses import dataclass
from typing import List


@dataclass
class SubAgentTask:
    """SubAgent任务"""
    task_type: str
    avg_input_tokens: int
    avg_output_tokens: int
    parallelism: int  # 并行度
    daily_volume: int  # 每日调用量


class SubAgentWorkflowOptimizer:
    """SubAgent工作流优化器"""
    
    def __init__(self, model_name: str, input_price: float, output_price: float):
        self.model_name = model_name
        self.input_price = input_price
        self.output_price = output_price
    
    def compute_daily_cost(self, tasks: List[SubAgentTask]) -> float:
        """计算每日成本"""
        total_cost = 0.0
        
        for task in tasks:
            input_cost = (task.avg_input_tokens / 1_000_000) * self.input_price
            output_cost = (task.avg_output_tokens / 1_000_000) * self.output_price
            per_call_cost = input_cost + output_cost
            daily_cost = per_call_cost * task.daily_volume
            total_cost += daily_cost
        
        return total_cost


def main():
    """对比SubAgent工作流成本"""
    
    tasks = [
        SubAgentTask("文本分类/路由", 500, 50, 100, 100000),
        SubAgentTask("数据提取", 2000, 500, 50, 50000),
        SubAgentTask("内容审核", 1000, 100, 200, 200000),
        SubAgentTask("摘要生成", 3000, 500, 20, 30000),
        SubAgentTask("实体识别", 1500, 200, 100, 80000),
    ]
    
    models = [
        ("Gemini 3.6 Flash", 1.50, 7.50),
        ("Gemini 3.5 Flash Lite", 0.30, 2.50),
        ("Claude Haiku 4.5", 7.00, 35.00),
    ]
    
    print("=" * 70)
    print("SubAgent工作流每日成本对比")
    print("=" * 70)
    
    print(f"\n{'模型':<25} {'每日成本':<15} {'相对成本':<15}")
    print("-" * 55)
    
    best_cost = float('inf')
    for name, inp, out in models:
        opt = SubAgentWorkflowOptimizer(name, inp, out)
        cost = opt.compute_daily_cost(tasks)
        if cost < best_cost:
            best_cost = cost
        print(f"{name:<25} ${cost:<10.2f} {cost/best_cost:.1f}x")
    
    print(f"\n[结论]")
    print(f"  Flash Lite是企业大规模SubAgent部署的最佳选择")
    print(f"  相比Claude Haiku 4.5，成本降低超过90%")


if __name__ == "__main__":
    main()

// CyberSecurityModel.go
package main

import (
	"fmt"
	"strings"
)

// Vulnerability 漏洞定义
type Vulnerability struct {
	ID          string
	Type        string
	Severity    string
	CVSS        float64
	Description string
	Affected    string
}

// Patch 修复方案
type Patch struct {
	VulnID      string
	File        string
	LineStart   int
	LineEnd     int
	Original    string
	Fixed       string
	Description string
}

// CyberModel 安全模型
type CyberModel struct {
	Name          string
	Capabilities  []string
	AccessLevel   string
	FineTunedOn   string
}

func (cm *CyberModel) AnalyzeVulnerability(code string) []Vulnerability {
	// 模拟漏洞分析
	vulns := make([]Vulnerability, 0)
	
	// 检测常见漏洞模式
	if strings.Contains(code, "strcpy(") {
		vulns = append(vulns, Vulnerability{
			ID: "CVE-2026-BUF-001",
			Type: "Buffer Overflow",
			Severity: "CRITICAL",
			CVSS: 9.8,
			Description: "Unsafe strcpy() usage without bounds checking",
			Affected: "memory corruption",
		})
	}
	
	if strings.Contains(code, "eval(") {
		vulns = append(vulns, Vulnerability{
			ID: "CVE-2026-INJ-001",
			Type: "Code Injection",
			Severity: "CRITICAL",
			CVSS: 9.3,
			Description: "Dynamic code evaluation with untrusted input",
			Affected: "remote code execution",
		})
	}
	
	if strings.Contains(code, "exec(") || strings.Contains(code, "system(") {
		vulns = append(vulns, Vulnerability{
			ID: "CVE-2026-CMD-001",
			Type: "Command Injection",
			Severity: "HIGH",
			CVSS: 8.1,
			Description: "Command execution via OS shell",
			Affected: "command injection",
		})
	}
	
	return vulns
}

func (cm *CyberModel) GeneratePatch(vuln Vulnerability, code string) Patch {
	// 生成修复方案
	patches := map[string]Patch{
		"CVE-2026-BUF-001": {
			VulnID: "CVE-2026-BUF-001",
			Description: "Replace strcpy() with strncpy() or use std::string",
			Original: "strcpy(dest, src);",
			Fixed:    "strncpy(dest, src, sizeof(dest)-1);\ndest[sizeof(dest)-1] = '\\0';",
		},
		"CVE-2026-INJ-001": {
			VulnID: "CVE-2026-INJ-001",
			Description: "Replace eval() with safe parsing",
			Original: "result = eval(user_input);",
			Fixed:    "import ast\nresult = ast.literal_eval(user_input);",
		},
	}
	
	if patch, ok := patches[vuln.ID]; ok {
		return patch
	}
	return Patch{}
}

func main() {
	model := &CyberModel{
		Name: "Gemini 3.5 Flash Cyber",
		Capabilities: []string{
			"Vulnerability Discovery",
			"Patch Generation",
			"Exploit Analysis",
			"Security Audit",
			"Compliance Checking",
		},
		AccessLevel: "Government & Trusted Partners Only",
		FineTunedOn: "CVE database, security advisories, exploit codebases",
	}
	
	fmt.Println("=== Gemini 3.5 Flash Cyber - 安全分析 ===")
	
	// 示例代码
	code := `
void process_input(char *user_input) {
    char buffer[256];
    strcpy(buffer, user_input);  // 不安全
    eval(user_input);  // 危险
}
`
	
	vulns := model.AnalyzeVulnerability(code)
	fmt.Printf("\n发现 %d 个漏洞:\n", len(vulns))
	for _, v := range vulns {
		fmt.Printf("  [%s] %s (CVSS: %.1f)\n", v.Severity, v.Description, v.CVSS)
		patch := model.GeneratePatch(v, code)
		if patch.Description != "" {
			fmt.Printf("    修复: %s\n", patch.Description)
		}
	}
}