"""
Anthropic Claude 模型命名解析器
用于解析和分类 Claude 模型代号
"""

from dataclasses import dataclass
from typing import Optional
import re

@dataclass
class ClaudeModelInfo:
    """Claude 模型信息"""
    codename: str
    tier: str            # Haiku, Sonnet, Opus, Fable, Mythos
    gen_version: str     # 5.1, 5, 4.8 等
    is_eap: bool         # 是否早期访问版本
    naming_family: str   # literature, animal, food

class ClaudeNamingParser:
    """Claude 命名解析器"""
    
    # 命名体系映射
    LITERATURE_TIERS = {'haiku', 'sonnet', 'opus', 'fable', 'mythos'}
    ANIMAL_NAMES = {'fennec'}
    FOOD_NAMES = {'honeycomb', 'marshmallow', 'melon'}
    
    # 已知映射
    EAP_TO_PRODUCT = {
        'honeycomb': 'opus-5',
        'marshmallow': 'opus-5.1',   # 推测
        'melon': 'sonnet-5.1',       # 推测
    }
    
    @classmethod
    def parse(cls, model_id: str) -> Optional[ClaudeModelInfo]:
        """解析模型 ID"""
        # 匹配模式: claude-{name}-eap 或 claude-{name}-{version}
        eap_match = re.match(r'claude-(\w+)-eap', model_id)
        prod_match = re.match(r'claude-(\w+)-(\d+(?:\.\d+)?)', model_id)
        
        if eap_match:
            codename = eap_match.group(1).lower()
            is_eap = True
            if codename in cls.LITERATURE_TIERS:
                naming_family = 'literature'
                tier = codename.capitalize()
                gen_version = 'eap'
            elif codename in cls.ANIMAL_NAMES:
                naming_family = 'animal'
                tier = 'Unknown'
                gen_version = 'eap'
            elif codename in cls.FOOD_NAMES:
                naming_family = 'food'
                tier = 'Unknown (EAP)'
                gen_version = 'eap'
            else:
                naming_family = 'unknown'
                tier = 'Unknown'
                gen_version = 'eap'
                
            return ClaudeModelInfo(
                codename=codename,
                tier=tier,
                gen_version=gen_version,
                is_eap=is_eap,
                naming_family=naming_family
            )
        
        return None

# 测试解析
test_ids = [
    'claude-marshmallow-eap',
    'claude-melon-eap',
    'claude-honeycomb-eap',
    'claude-fennec-eap',
    'claude-opus-5',
    'claude-sonnet-5',
]

parser = ClaudeNamingParser()
for model_id in test_ids:
    info = parser.parse(model_id)
    if info:
        print(f"{'='*60}")
        print(f"模型ID: {model_id}")
        print(f"  代号: {info.codename}")
        print(f"  层级: {info.tier}")
        print(f"  版本: {info.gen_version}")
        print(f"  EAP:  {info.is_eap}")
        print(f"  命名体系: {info.naming_family}")

"""
Claude 模型选型策略：任务驱动的成本优化
"""

from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Optional

class TaskType(Enum):
    CODE_GENERATION = "code_generation"
    CODE_REVIEW = "code_review"
    DOCUMENT_ANALYSIS = "document_analysis"
    CUSTOMER_SERVICE = "customer_service"
    COMPLEX_REASONING = "complex_reasoning"
    CREATIVE_WRITING = "creative_writing"
    DATA_EXTRACTION = "data_extraction"
    LONG_RUNNING_AGENT = "long_running_agent"

@dataclass
class ModelConfig:
    name: str
    input_price: float  # $/1M tokens
    output_price: float  # $/1M tokens
    max_context: int
    strength: int  # 1-10
    best_for: List[TaskType]

# 可用模型配置
AVAILABLE_MODELS = {
    'claude-opus-5': ModelConfig(
        name='Claude Opus 5',
        input_price=5.0, output_price=25.0,
        max_context=1_000_000, strength=9,
        best_for=[TaskType.CODE_GENERATION, TaskType.CODE_REVIEW,
                  TaskType.COMPLEX_REASONING, TaskType.DOCUMENT_ANALYSIS]
    ),
    'claude-fable-5': ModelConfig(
        name='Claude Fable 5',
        input_price=10.0, output_price=50.0,
        max_context=1_000_000, strength=10,
        best_for=[TaskType.LONG_RUNNING_AGENT, TaskType.COMPLEX_REASONING]
    ),
    'claude-sonnet-5': ModelConfig(
        name='Claude Sonnet 5',
        input_price=2.0, output_price=10.0,
        max_context=1_000_000, strength=7,
        best_for=[TaskType.CODE_REVIEW, TaskType.DOCUMENT_ANALYSIS,
                  TaskType.CREATIVE_WRITING, TaskType.DATA_EXTRACTION]
    ),
    'claude-haiku-4.5': ModelConfig(
        name='Claude Haiku 4.5',
        input_price=1.0, output_price=5.0,
        max_context=200_000, strength=5,
        best_for=[TaskType.CUSTOMER_SERVICE, TaskType.DATA_EXTRACTION,
                  TaskType.CREATIVE_WRITING]
    ),
}

def estimate_cost(model: ModelConfig, input_tokens: int, 
                  output_tokens: int) -> float:
    """估算单次调用成本"""
    input_cost = (input_tokens / 1_000_000) * model.input_price
    output_cost = (output_tokens / 1_000_000) * model.output_price
    return input_cost + output_cost

def recommend_model(task: TaskType, complexity: int = 5,
                    daily_volume: int = 1000) -> Dict:
    """
    根据任务类型和复杂度推荐模型
    """
    candidates = []
    
    for name, config in AVAILABLE_MODELS.items():
        if task in config.best_for and config.strength >= complexity:
            # 估算典型调用成本
            if task in [TaskType.CUSTOMER_SERVICE, TaskType.DATA_EXTRACTION]:
                est_input, est_output = 2000, 500
            elif task in [TaskType.CODE_GENERATION, TaskType.CODE_REVIEW]:
                est_input, est_output = 8000, 2000
            elif task == TaskType.COMPLEX_REASONING:
                est_input, est_output = 15000, 4000
            elif task == TaskType.LONG_RUNNING_AGENT:
                est_input, est_output = 50000, 10000
            else:
                est_input, est_output = 4000, 1000
            
            per_call_cost = estimate_cost(config, est_input, est_output)
            daily_cost = per_call_cost * daily_volume
            monthly_cost = daily_cost * 22  # 工作日
            
            candidates.append({
                'model': name,
                'strength': config.strength,
                'per_call_cost': round(per_call_cost, 4),
                'daily_cost': round(daily_cost, 2),
                'monthly_cost': round(monthly_cost, 2),
            })
    
    # 按成本排序
    candidates.sort(key=lambda x: x['per_call_cost'])
    
    return {
        'task': task.value,
        'complexity': complexity,
        'daily_volume': daily_volume,
        'recommendations': candidates,
        'best_value': candidates[0] if candidates else None,
        'best_capability': candidates[-1] if candidates else None,
    }

# 示例：不同场景的推荐
scenarios = [
    (TaskType.CODE_GENERATION, 6, 500),
    (TaskType.CUSTOMER_SERVICE, 3, 10000),
    (TaskType.COMPLEX_REASONING, 8, 100),
    (TaskType.LONG_RUNNING_AGENT, 9, 50),
]

for task, complexity, volume in scenarios:
    print(f"\n{'='*60}")
    print(f"场景: {task.value} | 复杂度: {complexity} | 日调用量: {volume}")
    print(f"{'='*60}")
    result = recommend_model(task, complexity, volume)
    
    print(f"推荐排序:")
    for i, rec in enumerate(result['recommendations'], 1):
        print(f"  {i}. {rec['model']:25s} "
              f"每次: ${rec['per_call_cost']:.4f} | "
              f"每月: ${rec['monthly_cost']:.2f}")
    
    print(f"最佳性价比: {result['best_value']['model']}")
    print(f"最优能力:   {result['best_capability']['model']}")

"""
Anthropic 产品线策略模拟器
模拟不同定价策略下的市场反应
"""

import numpy as np
from typing import Dict, List, Tuple

class ProductLineSimulator:
    """产品线策略模拟器"""
    
    def __init__(self):
        # 价格弹性系数（基于历史数据估算）
        self.price_elasticity = {
            'premium': -0.8,    # 高端模型需求弹性较低
            'mid': -1.5,        # 中端模型弹性适中
            'budget': -2.2,     # 低端模型弹性较高
        }
        
        # 开源替代压力系数
        self.open_source_pressure = 0.62  # 62% Token 份额
    
    def simulate_market_share(self, models: Dict[str, Dict]) -> Dict:
        """
        模拟给定定价策略下的市场份额
        """
        total_utility = 0
        model_utilities = {}
        
        for name, config in models.items():
            # 能力效用
            cap_utility = config['capability'] * 1.5
            
            # 价格负效用
            avg_price = (config['input_price'] + config['output_price']) / 2
            tier = config['tier']
            elasticity = self.price_elasticity.get(tier, -1.0)
            price_utility = avg_price * (1 + elasticity)
            
            # 开源替代效应
            if tier == 'premium':
                os_penalty = self.open_source_pressure * 0.3
            elif tier == 'mid':
                os_penalty = self.open_source_pressure * 0.6
            else:
                os_penalty = self.open_source_pressure * 0.8
            
            utility = cap_utility - price_utility - os_penalty
            model_utilities[name] = max(utility, 0.1)
            total_utility += model_utilities[name]
        
        # 转换为市场份额
        market_share = {}
        for name in models:
            market_share[name] = model_utilities[name] / total_utility
        
        return market_share
    
    def optimize_pricing(self, base_models: Dict[str, Dict]) -> Dict:
        """
        优化定价策略
        """
        optimized = {}
        
        for name, model in base_models.items():
            tier = model['tier']
            base_avg_price = (model['input_price'] + model['output_price']) / 2
            
            # 试探最优价格点
            best_price = base_avg_price
            best_share = 0
            
            for price_mult in np.arange(0.5, 2.0, 0.1):
                test_avg = base_avg_price * price_mult
                test_input = model['input_price'] * price_mult
                test_output = model['output_price'] * price_mult
                
                test_models = {
                    name: {
                        'capability': model['capability'],
                        'input_price': test_input,
                        'output_price': test_output,
                        'tier': tier,
                    }
                }
                
                # 添加竞争对手
                test_models['gpt_5.6_sol'] = {
                    'capability': 8.5,
                    'input_price': 5.0,
                    'output_price': 30.0,
                    'tier': 'premium',
                }
                test_models['deepseek_v4_pro'] = {
                    'capability': 7.5,
                    'input_price': 0.87,
                    'output_price': 4.35,
                    'tier': 'budget',
                }
                
                share = self.simulate_market_share(test_models)
                if share[name] > best_share:
                    best_share = share[name]
                    best_price = (test_input, test_output)
            
            optimized[name] = {
                'optimal_input_price': round(best_price[0], 2),
                'optimal_output_price': round(best_price[1], 2),
                'expected_share': round(best_share * 100, 1),
            }
        
        return optimized

# 运行模拟
simulator = ProductLineSimulator()

# 当前产品线配置
current_lineup = {
    'Fable 5': {'capability': 9.8, 'input_price': 10.0, 
                 'output_price': 50.0, 'tier': 'premium'},
    'Opus 5': {'capability': 9.2, 'input_price': 5.0, 
                'output_price': 25.0, 'tier': 'premium'},
    'Sonnet 5': {'capability': 8.0, 'input_price': 2.0, 
                  'output_price': 10.0, 'tier': 'mid'},
    'Haiku 4.5': {'capability': 6.5, 'input_price': 1.0, 
                   'output_price': 5.0, 'tier': 'budget'},
}

print("当前产品线市场份额模拟:")
print("=" * 60)
current_share = simulator.simulate_market_share(current_lineup)
for model, share in sorted(current_share.items(), 
                           key=lambda x: x[1], reverse=True):
    print(f"  {model:15s}: {share*100:.1f}%")

print("\n\n优化定价建议:")
print("=" * 60)
optimized = simulator.optimize_pricing(current_lineup)
for model, result in optimized.items():
    print(f"  {model:15s}: 最优输入 ${result['optimal_input_price']:.2f} "
          f"最优输出 ${result['optimal_output_price']:.2f} "
          f"预期份额 {result['expected_share']:.1f}%")

"""
Claude EAP 模型 API 调用示例
"""

import os
import json
import time
from typing import Optional, Dict, Any
import anthropic

class ClaudeEAPClient:
    """Claude EAP 模型客户端"""
    
    def __init__(self, api_key: Optional[str] = None):
        self.api_key = api_key or os.environ.get('ANTHROPIC_API_KEY')
        if not self.api_key:
            raise ValueError("需要提供 ANTHROPIC_API_KEY")
        self.client = anthropic.Anthropic(api_key=self.api_key)
    
    def call_marshmallow(self, prompt: str, system: str = "",
                         max_tokens: int = 4096,
                         temperature: float = 0.7) -> Dict[str, Any]:
        """
        调用 Marshmallow EAP 模型
        """
        try:
            response = self.client.messages.create(
                model="claude-marshmallow-eap",
                system=system,
                max_tokens=max_tokens,
                temperature=temperature,
                messages=[{"role": "user", "content": prompt}]
            )
            return {
                "success": True,
                "content": response.content[0].text,
                "model": response.model,
                "usage": {
                    "input_tokens": response.usage.input_tokens,
                    "output_tokens": response.usage.output_tokens,
                },
                "latency_ms": None,  # 需自行计时
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def call_melon(self, prompt: str, system: str = "",
                   max_tokens: int = 4096,
                   temperature: float = 0.7) -> Dict[str, Any]:
        """
        调用 Melon EAP 模型
        """
        try:
            response = self.client.messages.create(
                model="claude-melon-eap",
                system=system,
                max_tokens=max_tokens,
                temperature=temperature,
                messages=[{"role": "user", "content": prompt}]
            )
            return {
                "success": True,
                "content": response.content[0].text,
                "model": response.model,
                "usage": {
                    "input_tokens": response.usage.input_tokens,
                    "output_tokens": response.usage.output_tokens,
                },
                "latency_ms": None,
            }
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def benchmark_models(self, test_prompts: list) -> Dict:
        """
        对比测试多个模型
        """
        results = {
            'marshmallow': {'total_time': 0, 'total_input': 0, 
                           'total_output': 0, 'responses': []},
            'melon': {'total_time': 0, 'total_input': 0, 
                      'total_output': 0, 'responses': []},
            'opus_5': {'total_time': 0, 'total_input': 0, 
                       'total_output': 0, 'responses': []},
        }
        
        for prompt in test_prompts:
            for model_key in ['marshmallow', 'melon', 'opus_5']:
                method = getattr(self, f'call_{model_key}'
                                 if model_key != 'opus_5' else 'call_opus_5')
                
                start = time.time()
                resp = method(prompt)
                elapsed = time.time() - start
                
                if resp['success']:
                    results[model_key]['total_time'] += elapsed
                    results[model_key]['total_input'] += resp['usage']['input_tokens']
                    results[model_key]['total_output'] += resp['usage']['output_tokens']
                    results[model_key]['responses'].append({
                        'prompt': prompt[:50],
                        'time': elapsed,
                        'tokens': resp['usage']['output_tokens'],
                    })
        
        # 计算汇总
        for key in results:
            r = results[key]
            n = len(r['responses'])
            r['avg_latency'] = round(r['total_time'] / n, 2) if n > 0 else None
            r['avg_output_tokens'] = round(r['total_output'] / n, 1) if n > 0 else None
        
        return results

# 使用示例
if __name__ == "__main__":
    # 请使用实际的 API Key
    # client = ClaudeEAPClient(api_key="your-api-key")
    
    # 测试提示词
    test_prompts = [
        "请用Python实现一个快速排序算法，并分析其时间复杂度和空间复杂度。",
        "解释Transformer架构中的自注意力机制是如何工作的。",
        "写一个函数，计算斐波那契数列的第n项，要求使用动态规划。",
    ]
    
    # 模拟运行（仅展示结构）
    print("=" * 70)
    print("Claude EAP 模型基准测试框架")
    print("=" * 70)
    print(f"测试提示词数量: {len(test_prompts)}")
    print(f"待测试模型: marshmallow-eap, melon-eap, opus-5")
    print(f"\n{'='*70}")
    print("注意: 实际运行需要有效的 Anthropic API Key 和 EAP 访问权限")
    print("上述代码将在正式发布后可直接使用")

"""
智能模型路由器 - 根据任务特征自动选择最优模型
"""

from typing import Dict, List, Optional, Callable
from dataclasses import dataclass, field
import json
import hashlib

@dataclass
class TaskProfile:
    """任务特征画像"""
    task_type: str
    input_length: int
    required_reasoning_depth: int  # 1-10
    latency_sensitive: bool
    cost_sensitive: bool
    requires_tool_use: bool
    requires_long_context: bool

@dataclass
class ModelPerformance:
    """模型性能记录"""
    model_id: str
    success_rate: float
    avg_latency: float
    avg_cost: float
    sample_count: int

class SmartModelRouter:
    """智能模型路由器"""
    
    def __init__(self):
        self.performance_history: Dict[str, List[ModelPerformance]] = {}
        self.routing_rules: Dict[str, str] = self._default_rules()
    
    def _default_rules(self) -> Dict[str, str]:
        """默认路由规则"""
        return {
            # 任务类型 → 默认模型
            'code_generation_simple': 'claude-sonnet-5',
            'code_generation_complex': 'claude-opus-5',
            'code_review': 'claude-opus-5',
            'document_summary': 'claude-sonnet-5',
            'customer_service': 'claude-haiku-4.5',
            'data_extraction': 'claude-sonnet-5',
            'complex_reasoning': 'claude-opus-5',
            'creative_writing': 'claude-sonnet-5',
            'long_running_agent': 'claude-fable-5',
        }
    
    def profile_task(self, task: Dict) -> TaskProfile:
        """对任务进行画像"""
        input_text = task.get('prompt', '') + task.get('system', '')
        
        # 估算推理深度
        reasoning_keywords = ['证明', '分析', '对比', '推理', '优化',
                              'proof', 'analysis', 'reasoning', 'optimize']
        depth = sum(1 for kw in reasoning_keywords if kw in input_text)
        depth = min(max(depth, 1), 10)
        
        return TaskProfile(
            task_type=task.get('type', 'general'),
            input_length=len(input_text),
            required_reasoning_depth=depth,
            latency_sensitive=task.get('latency_sensitive', False),
            cost_sensitive=task.get('cost_sensitive', True),
            requires_tool_use=task.get('tool_use', False),
            requires_long_context=task.get('long_context', False),
        )
    
    def route(self, task: Dict) -> str:
        """路由决策"""
        profile = self.profile_task(task)
        
        # 规则1: 长上下文任务 → Opus 或 Fable
        if profile.requires_long_context and profile.input_length > 100000:
            return 'claude-opus-5'
        
        # 规则2: 延迟敏感 → Sonnet 或 Haiku
        if profile.latency_sensitive:
            if profile.required_reasoning_depth <= 5:
                return 'claude-haiku-4.5'
            return 'claude-sonnet-5'
        
        # 规则3: 深度推理 → Opus 或 Fable
        if profile.required_reasoning_depth >= 8:
            if profile.cost_sensitive:
                return 'claude-opus-5'
            return 'claude-fable-5'
        
        # 规则4: 工具调用 → Opus
        if profile.requires_tool_use:
            return 'claude-opus-5'
        
        # 规则5: 默认按任务类型路由
        task_key = f"{profile.task_type}_{'complex' if profile.required_reasoning_depth > 5 else 'simple'}"
        return self.routing_rules.get(task_key, 'claude-sonnet-5')
    
    def update_from_feedback(self, model_id: str, task: Dict,
                             success: bool, latency: float, cost: float):
        """从实际反馈更新路由知识"""
        key = hashlib.md5(
            json.dumps(task, sort_keys=True).encode()
        ).hexdigest()
        
        if key not in self.performance_history:
            self.performance_history[key] = []
        
        self.performance_history[key].append(ModelPerformance(
            model_id=model_id,
            success_rate=1.0 if success else 0.0,
            avg_latency=latency,
            avg_cost=cost,
            sample_count=1,
        ))
        
        # 动态调整路由规则
        recent = self.performance_history[key][-10:]
        if len(recent) >= 5:
            avg_success = sum(p.success_rate for p in recent) / len(recent)
            if avg_success < 0.7:
                # 成功率低，切换模型
                task_type = task.get('type', 'general')
                current_model = self.routing_rules.get(task_type)
                alternatives = {
                    'claude-haiku-4.5': 'claude-sonnet-5',
                    'claude-sonnet-5': 'claude-opus-5',
                    'claude-opus-5': 'claude-fable-5',
                }
                if current_model in alternatives:
                    self.routing_rules[task_type] = alternatives[current_model]

# 使用示例
router = SmartModelRouter()

# 模拟不同任务的路由决策
test_tasks = [
    {"type": "code_generation", "prompt": "实现一个二叉搜索树", 
     "latency_sensitive": False, "cost_sensitive": True, 
     "tool_use": False, "long_context": False},
    {"type": "customer_service", "prompt": "用户询问退款流程", 
     "latency_sensitive": True, "cost_sensitive": True, 
     "tool_use": False, "long_context": False},
    {"type": "complex_reasoning", "prompt": "分析这个数学证明的漏洞", 
     "latency_sensitive": False, "cost_sensitive": False, 
     "tool_use": True, "long_context": True},
    {"type": "data_extraction", "prompt": "从100页PDF中提取关键信息", 
     "latency_sensitive": False, "cost_sensitive": True, 
     "tool_use": False, "long_context": True},
]

print("=" * 70)
print("智能模型路由决策演示")
print("=" * 70)
for task in test_tasks:
    model = router.route(task)
    profile = router.profile_task(task)
    print(f"\n任务类型: {task['type']}")
    print(f"  推理深度: {profile.required_reasoning_depth}/10")
    print(f"  延迟敏感: {profile.latency_sensitive}")
    print(f"  成本敏感: {profile.cost_sensitive}")
    print(f"  → 推荐模型: {model}")