"""
Next-Generation Model Hub - Supporting Latest APIs
GPT-4 Turbo, Claude 3.5 Sonnet, Gemini Pro, Mixtral 8x7B, Command R+
"""

import asyncio
from typing import Dict, List, Any, Optional, AsyncGenerator
from dataclasses import dataclass
from enum import Enum
import openai
import anthropic
import google.generativeai as genai
from together import Together
import cohere

class ModelProvider(Enum):
    OPENAI = "openai"
    ANTHROPIC = "anthropic" 
    GOOGLE = "google"
    TOGETHER = "together"
    COHERE = "cohere"
    MISTRAL = "mistral"

@dataclass
class ModelCapabilities:
    """Define model capabilities for optimal routing"""
    max_tokens: int
    supports_vision: bool
    supports_function_calling: bool
    supports_streaming: bool
    cost_per_token: float
    reasoning_quality: float
    safety_rating: float
    speed_tier: str

class UnifiedModelHub:
    """
    Universal model hub supporting all major LLM providers
    Implements intelligent routing, load balancing, and fallback strategies
    """
    
    def __init__(self):
        self.models = self._initialize_models()
        self.usage_tracker = ModelUsageTracker()
        self.intelligent_router = IntelligentModelRouter()
        
    def _initialize_models(self) -> Dict[str, ModelCapabilities]:
        """Initialize latest model configurations"""
        return {
            # OpenAI Models
            "gpt-4-turbo-2024-04-09": ModelCapabilities(
                max_tokens=128000,
                supports_vision=True,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.00003,
                reasoning_quality=0.98,
                safety_rating=0.95,
                speed_tier="fast"
            ),
            "gpt-4o": ModelCapabilities(
                max_tokens=128000,
                supports_vision=True,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.000015,
                reasoning_quality=0.96,
                safety_rating=0.97,
                speed_tier="ultra-fast"
            ),
            
            # Anthropic Models
            "claude-3-5-sonnet-20241022": ModelCapabilities(
                max_tokens=200000,
                supports_vision=True,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.000015,
                reasoning_quality=0.99,
                safety_rating=0.99,
                speed_tier="fast"
            ),
            "claude-3-haiku-20240307": ModelCapabilities(
                max_tokens=200000,
                supports_vision=True,
                supports_function_calling=False,
                supports_streaming=True,
                cost_per_token=0.00000025,
                reasoning_quality=0.85,
                safety_rating=0.98,
                speed_tier="ultra-fast"
            ),
            
            # Google Models
            "gemini-1.5-pro-002": ModelCapabilities(
                max_tokens=2000000,
                supports_vision=True,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.0000035,
                reasoning_quality=0.94,
                safety_rating=0.93,
                speed_tier="medium"
            ),
            
            # Cohere Models
            "command-r-plus": ModelCapabilities(
                max_tokens=128000,
                supports_vision=False,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.000003,
                reasoning_quality=0.91,
                safety_rating=0.94,
                speed_tier="fast"
            ),
            
            # Together AI Models
            "meta-llama/Llama-3.1-405B-Instruct-Turbo": ModelCapabilities(
                max_tokens=131072,
                supports_vision=False,
                supports_function_calling=False,
                supports_streaming=True,
                cost_per_token=0.000005,
                reasoning_quality=0.93,
                safety_rating=0.88,
                speed_tier="medium"
            ),
            
            # Mistral Models
            "mistral-large-2402": ModelCapabilities(
                max_tokens=32000,
                supports_vision=False,
                supports_function_calling=True,
                supports_streaming=True,
                cost_per_token=0.000008,
                reasoning_quality=0.90,
                safety_rating=0.89,
                speed_tier="fast"
            )
        }
    
    async def optimal_completion(
        self,
        prompt: str,
        task_type: str = "general",
        quality_tier: str = "highest",
        budget_constraint: Optional[float] = None,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Intelligent model selection and execution
        Automatically chooses best model based on task requirements
        """
        
        # Select optimal model
        selected_model = await self.intelligent_router.select_model(
            prompt=prompt,
            task_type=task_type,
            quality_tier=quality_tier,
            budget_constraint=budget_constraint,
            available_models=self.models
        )
        
        # Execute with fallback strategy
        return await self._execute_with_fallback(
            model_name=selected_model,
            prompt=prompt,
            **kwargs
        )
    
    async def _execute_with_fallback(
        self,
        model_name: str,
        prompt: str,
        **kwargs
    ) -> Dict[str, Any]:
        """Execute with intelligent fallback on failure"""
        
        provider = self._get_provider(model_name)
        
        try:
            if provider == ModelProvider.OPENAI:
                return await self._call_openai(model_name, prompt, **kwargs)
            elif provider == ModelProvider.ANTHROPIC:
                return await self._call_anthropic(model_name, prompt, **kwargs)
            elif provider == ModelProvider.GOOGLE:
                return await self._call_google(model_name, prompt, **kwargs)
            elif provider == ModelProvider.COHERE:
                return await self._call_cohere(model_name, prompt, **kwargs)
            elif provider == ModelProvider.TOGETHER:
                return await self._call_together(model_name, prompt, **kwargs)
                
        except Exception as e:
            # Intelligent fallback
            fallback_model = await self.intelligent_router.get_fallback_model(
                failed_model=model_name,
                error=str(e),
                available_models=self.models
            )
            
            if fallback_model and fallback_model != model_name:
                return await self._execute_with_fallback(fallback_model, prompt, **kwargs)
            else:
                raise e
    
    async def _call_openai(self, model: str, prompt: str, **kwargs) -> Dict[str, Any]:
        """OpenAI API call with latest features"""
        
        client = openai.AsyncOpenAI()
        
        messages = [{"role": "user", "content": prompt}]
        
        response = await client.chat.completions.create(
            model=model,
            messages=messages,
            temperature=kwargs.get("temperature", 0.7),
            max_tokens=kwargs.get("max_tokens", 4000),
            top_p=kwargs.get("top_p", 1.0),
            frequency_penalty=kwargs.get("frequency_penalty", 0),
            presence_penalty=kwargs.get("presence_penalty", 0),
            stream=kwargs.get("stream", False)
        )
        
        return {
            "content": response.choices[0].message.content,
            "model": model,
            "provider": "openai",
            "usage": response.usage.dict() if response.usage else {},
            "finish_reason": response.choices[0].finish_reason
        }
    
    async def _call_anthropic(self, model: str, prompt: str, **kwargs) -> Dict[str, Any]:
        """Anthropic Claude API call"""
        
        client = anthropic.AsyncAnthropic()
        
        response = await client.messages.create(
            model=model,
            max_tokens=kwargs.get("max_tokens", 4000),
            temperature=kwargs.get("temperature", 0.7),
            messages=[{"role": "user", "content": prompt}]
        )
        
        return {
            "content": response.content[0].text,
            "model": model,
            "provider": "anthropic",
            "usage": {
                "input_tokens": response.usage.input_tokens,
                "output_tokens": response.usage.output_tokens
            },
            "finish_reason": response.stop_reason
        }
    
    async def _call_google(self, model: str, prompt: str, **kwargs) -> Dict[str, Any]:
        """Google Gemini API call"""
        
        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
        model_instance = genai.GenerativeModel(model)
        
        response = await model_instance.generate_content_async(
            prompt,
            generation_config=genai.types.GenerationConfig(
                temperature=kwargs.get("temperature", 0.7),
                max_output_tokens=kwargs.get("max_tokens", 4000),
                top_p=kwargs.get("top_p", 1.0)
            )
        )
        
        return {
            "content": response.text,
            "model": model,
            "provider": "google",
            "usage": {
                "input_tokens": response.usage_metadata.prompt_token_count,
                "output_tokens": response.usage_metadata.candidates_token_count
            },
            "finish_reason": response.candidates[0].finish_reason.name
        }
    
    def _get_provider(self, model_name: str) -> ModelProvider:
        """Determine provider from model name"""
        if "gpt-" in model_name:
            return ModelProvider.OPENAI
        elif "claude-" in model_name:
            return ModelProvider.ANTHROPIC
        elif "gemini-" in model_name:
            return ModelProvider.GOOGLE
        elif "command-" in model_name:
            return ModelProvider.COHERE
        elif "llama" in model_name.lower() or "mistral" in model_name.lower():
            return ModelProvider.TOGETHER
        else:
            return ModelProvider.OPENAI  # Default fallback

class IntelligentModelRouter:
    """AI-powered model selection and routing"""
    
    async def select_model(
        self,
        prompt: str,
        task_type: str,
        quality_tier: str,
        budget_constraint: Optional[float],
        available_models: Dict[str, ModelCapabilities]
    ) -> str:
        """Use ML to select optimal model for task"""
        
        # Analyze prompt characteristics
        prompt_analysis = self._analyze_prompt(prompt)
        
        # Score models based on requirements
        model_scores = {}
        
        for model_name, capabilities in available_models.items():
            score = self._calculate_model_score(
                capabilities=capabilities,
                prompt_analysis=prompt_analysis,
                task_type=task_type,
                quality_tier=quality_tier,
                budget_constraint=budget_constraint
            )
            model_scores[model_name] = score
        
        # Return highest scoring model
        return max(model_scores.items(), key=lambda x: x[1])[0]
    
    def _analyze_prompt(self, prompt: str) -> Dict[str, Any]:
        """Analyze prompt to determine optimal model characteristics"""
        
        return {
            "length": len(prompt.split()),
            "complexity": self._estimate_complexity(prompt),
            "requires_reasoning": self._requires_reasoning(prompt),
            "requires_creativity": self._requires_creativity(prompt),
            "requires_safety": self._requires_safety_focus(prompt),
            "requires_speed": self._requires_speed(prompt)
        }
    
    def _calculate_model_score(
        self,
        capabilities: ModelCapabilities,
        prompt_analysis: Dict[str, Any],
        task_type: str,
        quality_tier: str,
        budget_constraint: Optional[float]
    ) -> float:
        """Calculate weighted score for model selection"""
        
        score = 0.0
        
        # Quality weighting
        if quality_tier == "highest":
            score += capabilities.reasoning_quality * 0.4
        elif quality_tier == "balanced":
            score += capabilities.reasoning_quality * 0.2
            score += (1.0 / max(capabilities.cost_per_token * 1000, 0.001)) * 0.2
        elif quality_tier == "fastest":
            speed_score = {"ultra-fast": 1.0, "fast": 0.8, "medium": 0.5, "slow": 0.2}
            score += speed_score.get(capabilities.speed_tier, 0.5) * 0.4
        
        # Task-specific weighting
        if task_type == "reasoning" and prompt_analysis["requires_reasoning"]:
            score += capabilities.reasoning_quality * 0.3
        
        if task_type == "safety_critical":
            score += capabilities.safety_rating * 0.5
        
        # Budget constraint
        if budget_constraint:
            estimated_cost = capabilities.cost_per_token * prompt_analysis["length"] * 2
            if estimated_cost > budget_constraint:
                score *= 0.1  # Heavy penalty for over-budget
        
        return score
    
    def _estimate_complexity(self, prompt: str) -> float:
        """Estimate prompt complexity (0-1 scale)"""
        factors = [
            len(prompt.split()) / 1000,  # Length factor
            prompt.count("?") * 0.1,     # Question complexity
            len(set(prompt.lower().split())) / max(len(prompt.split()), 1),  # Vocabulary diversity
        ]
        return min(sum(factors), 1.0)
    
    def _requires_reasoning(self, prompt: str) -> bool:
        """Detect if prompt requires complex reasoning"""
        reasoning_keywords = [
            "analyze", "reason", "logic", "deduce", "infer", "conclude",
            "step by step", "chain of thought", "thinking", "solve"
        ]
        return any(keyword in prompt.lower() for keyword in reasoning_keywords)
    
    def _requires_creativity(self, prompt: str) -> bool:
        """Detect if prompt requires creativity"""
        creative_keywords = [
            "creative", "novel", "innovative", "original", "brainstorm",
            "generate", "create", "design", "imagine", "story"
        ]
        return any(keyword in prompt.lower() for keyword in creative_keywords)

class ModelUsageTracker:
    """Track model usage, costs, and performance metrics"""
    
    def __init__(self):
        self.usage_stats = {}
        self.performance_metrics = {}
        
    async def log_usage(
        self,
        model: str,
        tokens_used: int,
        cost: float,
        latency: float,
        quality_score: Optional[float] = None
    ):
        """Log model usage for analytics"""
        
        if model not in self.usage_stats:
            self.usage_stats[model] = {
                "total_requests": 0,
                "total_tokens": 0,
                "total_cost": 0.0,
                "total_latency": 0.0,
                "quality_scores": []
            }
        
        stats = self.usage_stats[model]
        stats["total_requests"] += 1
        stats["total_tokens"] += tokens_used
        stats["total_cost"] += cost
        stats["total_latency"] += latency
        
        if quality_score:
            stats["quality_scores"].append(quality_score)