"""
OpenDistillery Compound AI System
Advanced enterprise-grade compound AI system with multi-model orchestration,
intelligent routing, and production-ready performance optimization.
"""

import asyncio
import logging
import time
from typing import Dict, List, Any, Optional, Union, Callable
from dataclasses import dataclass, field
from enum import Enum
import json
import uuid
from datetime import datetime
import numpy as np
from concurrent.futures import ThreadPoolExecutor
import threading

# Third-party imports
import openai
from anthropic import Anthropic
import google.generativeai as genai
from openai import AsyncOpenAI

# MLX integration for Apple Silicon optimization
try:
    import mlx.core as mx
    import mlx.nn as nn
    from mlx.utils import tree_flatten, tree_unflatten
    MLX_AVAILABLE = True
except ImportError:
    MLX_AVAILABLE = False
    mx = None
    nn = None

# Enterprise security and monitoring
from cryptography.fernet import Fernet
import prometheus_client
from prometheus_client import Counter, Histogram, Gauge
import structlog

# Configure structured logging
logger = structlog.get_logger(__name__)

# Prometheus metrics
SYSTEM_REQUESTS = Counter('opendistillery_system_requests_total', 'Total system requests', ['system_id', 'strategy'])
PROCESSING_TIME = Histogram('opendistillery_processing_seconds', 'Processing time in seconds')
ACTIVE_SYSTEMS = Gauge('opendistillery_active_systems', 'Number of active compound systems')
MODEL_CALLS = Counter('opendistillery_model_calls_total', 'Total model API calls', ['model', 'provider'])

class ReasoningStrategy(Enum):
    REACT = "react"
    CHAIN_OF_THOUGHT = "chain_of_thought"
    TREE_OF_THOUGHTS = "tree_of_thoughts"
    GRAPH_OF_THOUGHTS = "graph_of_thoughts"
    SELF_CONSISTENCY = "self_consistency"
    ENSEMBLE = "ensemble"
    ADAPTIVE = "adaptive"

class SystemArchitecture(Enum):
    HIERARCHICAL = "hierarchical"
    MESH = "mesh"
    PIPELINE = "pipeline"
    HYBRID = "hybrid"
    SWARM = "swarm"

@dataclass
class ModelConfiguration:
    """Configuration for individual models in the compound system"""
    model_name: str
    provider: str  # openai, anthropic, google, local_mlx
    model_id: str
    api_key: Optional[str] = None
    base_url: Optional[str] = None
    max_tokens: int = 4096
    temperature: float = 0.7
    cost_per_token: float = 0.0
    fallback_models: List[str] = field(default_factory=list)
    performance_weights: Dict[str, float] = field(default_factory=dict)
    specializations: List[str] = field(default_factory=list)

@dataclass
class SystemRequirements:
    """Enterprise system requirements specification"""
    domain: str
    use_case: str
    latency_target_ms: int = 1000
    throughput_rps: int = 100
    accuracy_threshold: float = 0.95
    cost_budget_per_request: float = 0.10
    compliance_requirements: List[str] = field(default_factory=list)
    security_level: str = "enterprise"
    integration_points: List[str] = field(default_factory=list)

class ModelRouter:
    """Intelligent model routing based on request characteristics and performance"""
    
    def __init__(self):
        self.performance_history: Dict[str, List[float]] = {}
        self.cost_history: Dict[str, List[float]] = {}
        self.accuracy_history: Dict[str, List[float]] = {}
        self.model_loads: Dict[str, int] = {}
        
    def route_request(self, request: Dict[str, Any], available_models: List[ModelConfiguration]) -> ModelConfiguration:
        """Route request to optimal model based on performance, cost, and load"""
        if not available_models:
            raise ValueError("No available models for routing")
        
        # Score each model
        scores = {}
        for model in available_models:
            score = self._calculate_model_score(model, request)
            scores[model.model_name] = score
        
        # Select best model
        best_model = max(available_models, key=lambda m: scores[m.model_name])
        
        # Update load tracking
        self.model_loads[best_model.model_name] = self.model_loads.get(best_model.model_name, 0) + 1
        
        return best_model
    
    def _calculate_model_score(self, model: ModelConfiguration, request: Dict[str, Any]) -> float:
        """Calculate model suitability score"""
        score = 0.0
        
        # Performance score (40% weight)
        avg_latency = np.mean(self.performance_history.get(model.model_name, [1.0]))
        performance_score = 1.0 / (1.0 + avg_latency)
        score += performance_score * 0.4
        
        # Cost efficiency (30% weight)
        avg_cost = np.mean(self.cost_history.get(model.model_name, [model.cost_per_token]))
        cost_score = 1.0 / (1.0 + avg_cost * 1000)  # Normalize cost
        score += cost_score * 0.3
        
        # Load balancing (20% weight)
        current_load = self.model_loads.get(model.model_name, 0)
        load_score = 1.0 / (1.0 + current_load * 0.1)
        score += load_score * 0.2
        
        # Specialization match (10% weight)
        request_type = request.get("task_type", "")
        specialization_score = 1.0 if request_type in model.specializations else 0.5
        score += specialization_score * 0.1
        
        return score
    
    def update_performance_metrics(self, model_name: str, latency: float, cost: float, accuracy: float = None):
        """Update model performance metrics"""
        if model_name not in self.performance_history:
            self.performance_history[model_name] = []
            self.cost_history[model_name] = []
            self.accuracy_history[model_name] = []
        
        self.performance_history[model_name].append(latency)
        self.cost_history[model_name].append(cost)
        
        if accuracy is not None:
            self.accuracy_history[model_name].append(accuracy)
        
        # Keep only recent history
        max_history = 1000
        if len(self.performance_history[model_name]) > max_history:
            self.performance_history[model_name] = self.performance_history[model_name][-max_history:]
            self.cost_history[model_name] = self.cost_history[model_name][-max_history:]
            self.accuracy_history[model_name] = self.accuracy_history[model_name][-max_history:]

class MLXProcessor:
    """MLX-based local processing for Apple Silicon optimization"""
    
    def __init__(self):
        self.models = {}
        self.compiled_models = {}
        
    def load_model(self, model_path: str, model_name: str) -> bool:
        """Load model for MLX processing"""
        if not MLX_AVAILABLE:
            logger.warning("MLX not available, skipping local model loading")
            return False
        
        try:
            # Load model using MLX
            # This would be implemented based on specific model format
            logger.info(f"Loading MLX model: {model_name}")
            # Placeholder for actual MLX model loading
            self.models[model_name] = {"path": model_path, "loaded": True}
            return True
        except Exception as e:
            logger.error(f"Failed to load MLX model {model_name}: {str(e)}")
            return False
    
    async def process_local(self, model_name: str, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process request using local MLX model"""
        if not MLX_AVAILABLE or model_name not in self.models:
            raise ValueError(f"MLX model {model_name} not available")
        
        try:
            start_time = time.time()
            
            # Process using MLX
            # This would contain actual MLX inference logic
            result = {
                "response": f"MLX processed response for {input_data.get('prompt', '')}",
                "model_used": model_name,
                "processing_type": "local_mlx"
            }
            
            processing_time = time.time() - start_time
            logger.info(f"MLX processing completed in {processing_time:.3f}s")
            
            return result
            
        except Exception as e:
            logger.error(f"MLX processing failed: {str(e)}")
            raise

class CompoundAISystem:
    """
    Advanced compound AI system for enterprise workflow transformation
    """
    
    def __init__(self, 
                 system_id: str,
                 requirements: SystemRequirements,
                 architecture: SystemArchitecture = SystemArchitecture.HYBRID):
        self.system_id = system_id
        self.requirements = requirements
        self.architecture = architecture
        
        # Core components
        self.models: Dict[str, ModelConfiguration] = {}
        self.model_router = ModelRouter()
        self.mlx_processor = MLXProcessor()
        
        # Clients
        self.openai_client = None
        self.anthropic_client = None
        self.google_client = None
        
        # Performance tracking
        self.performance_metrics = {
            "requests_processed": 0,
            "average_latency": 0.0,
            "success_rate": 1.0,
            "total_cost": 0.0,
            "accuracy_scores": []
        }
        
        # Security
        self.encryption_key = Fernet.generate_key()
        self.cipher_suite = Fernet(self.encryption_key)
        
        # Threading for concurrent processing
        self.executor = ThreadPoolExecutor(max_workers=10)
        self.processing_lock = threading.RLock()
        
        ACTIVE_SYSTEMS.inc()
        logger.info(f"Initialized compound AI system: {system_id}")
    
    def add_model(self, config: ModelConfiguration) -> None:
        """Add a model to the compound system"""
        self.models[config.model_name] = config
        
        # Initialize API clients
        if config.provider == "openai" and not self.openai_client:
            self.openai_client = AsyncOpenAI(api_key=config.api_key)
        elif config.provider == "anthropic" and not self.anthropic_client:
            self.anthropic_client = Anthropic(api_key=config.api_key)
        elif config.provider == "google" and not self.google_client:
            genai.configure(api_key=config.api_key)
        elif config.provider == "local_mlx":
            self.mlx_processor.load_model(config.model_id, config.model_name)
        
        logger.info(f"Added model: {config.model_name} ({config.provider})")
    
    async def process_request(self, 
                            request: Dict[str, Any],
                            strategy: ReasoningStrategy = ReasoningStrategy.ADAPTIVE) -> Dict[str, Any]:
        """Process request through compound AI system"""
        start_time = time.time()
        request_id = request.get("request_id", str(uuid.uuid4()))
        
        SYSTEM_REQUESTS.labels(system_id=self.system_id, strategy=strategy.value).inc()
        
        try:
            with self.processing_lock:
                # Route to appropriate processing strategy
                if strategy == ReasoningStrategy.REACT:
                    result = await self._process_react(request)
                elif strategy == ReasoningStrategy.CHAIN_OF_THOUGHT:
                    result = await self._process_chain_of_thought(request)
                elif strategy == ReasoningStrategy.TREE_OF_THOUGHTS:
                    result = await self._process_tree_of_thoughts(request)
                elif strategy == ReasoningStrategy.GRAPH_OF_THOUGHTS:
                    result = await self._process_graph_of_thoughts(request)
                elif strategy == ReasoningStrategy.ENSEMBLE:
                    result = await self._process_ensemble(request)
                else:
                    result = await self._process_adaptive(request)
                
                # Update metrics
                processing_time = time.time() - start_time
                self._update_metrics(processing_time, result)
                
                PROCESSING_TIME.observe(processing_time)
                
                # Add system metadata
                result["system_metadata"] = {
                    "system_id": self.system_id,
                    "request_id": request_id,
                    "processing_time": processing_time,
                    "strategy_used": strategy.value,
                    "timestamp": datetime.now().isoformat()
                }
                
                return result
                
        except Exception as e:
            logger.error(f"Error processing request {request_id}: {str(e)}")
            return {
                "success": False,
                "error": str(e),
                "request_id": request_id
            }
    
    async def _process_react(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Process using ReAct (Reasoning + Acting) strategy"""
        from ..research.techniques.react_engine import ReactEngine
        
        react_engine = ReactEngine(self.models, self.model_router, self.mlx_processor)
        return await react_engine.execute(request)
    
    async def _process_chain_of_thought(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Process using Chain of Thought reasoning"""
        prompt = request.get("prompt", "")
        
        # Enhanced prompt with CoT instructions
        cot_prompt = f"""
        Think through this step by step:
        
        Problem: {prompt}
        
        Let me break this down:
        1. First, I'll analyze the problem
        2. Then, I'll consider possible approaches
        3. Next, I'll work through the solution
        4. Finally, I'll verify my answer
        
        Step 1 - Problem Analysis:
        """
        
        # Route to best model
        model = self.model_router.route_request(request, list(self.models.values()))
        
        # Process request
        result = await self._call_model(model, cot_prompt, request)
        
        return {
            "success": True,
            "response": result,
            "strategy": "chain_of_thought",
            "model_used": model.model_name
        }
    
    async def _process_tree_of_thoughts(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Process using Tree of Thoughts for exploratory reasoning"""
        from ..research.techniques.tree_of_thoughts import TreeOfThoughts
        
        tot_engine = TreeOfThoughts(self.models, self.model_router)
        return await tot_engine.solve(request)
    
    async def _process_graph_of_thoughts(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Process using Graph of Thoughts for multi-perspective reasoning"""
        from ..research.techniques.graph_of_thoughts import GraphOfThoughts
        
        got_engine = GraphOfThoughts(self.models, self.model_router)
        return await got_engine.process(request)
    
    async def _process_ensemble(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Process using ensemble of multiple strategies"""
        strategies = [
            self._process_chain_of_thought,
            self._process_react,
            self._process_tree_of_thoughts
        ]
        
        # Execute strategies in parallel
        tasks = [strategy(request) for strategy in strategies]
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Aggregate results
        valid_results = [r for r in results if isinstance(r, dict) and r.get("success", False)]
        
        if not valid_results:
            return {"success": False, "error": "All ensemble strategies failed"}
        
        # Weighted aggregation
        best_result = max(valid_results, key=lambda x: x.get("confidence", 0))
        
        return {
            "success": True,
            "response": best_result.get("response"),
            "strategy": "ensemble",
            "ensemble_results": valid_results,
            "confidence": best_result.get("confidence", 0.8)
        }
    
    async def _process_adaptive(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Adaptively choose best processing strategy"""
        # Simple adaptive logic - could be enhanced with ML
        complexity = len(request.get("prompt", "").split())
        
        if complexity > 100:
            return await self._process_tree_of_thoughts(request)
        elif complexity > 50:
            return await self._process_graph_of_thoughts(request)
        else:
            return await self._process_chain_of_thought(request)
    
    async def _call_model(self, model: ModelConfiguration, prompt: str, request: Dict[str, Any]) -> str:
        """Call specific model with error handling and retries"""
        start_time = time.time()
        
        try:
            if model.provider == "openai":
                response = await self._call_openai(model, prompt, request)
            elif model.provider == "anthropic":
                response = await self._call_anthropic(model, prompt, request)
            elif model.provider == "google":
                response = await self._call_google(model, prompt, request)
            elif model.provider == "local_mlx":
                result = await self.mlx_processor.process_local(model.model_name, {"prompt": prompt})
                response = result.get("response", "")
            else:
                raise ValueError(f"Unsupported provider: {model.provider}")
            
            # Update router metrics
            processing_time = time.time() - start_time
            cost = len(prompt) * model.cost_per_token
            self.model_router.update_performance_metrics(model.model_name, processing_time, cost)
            
            MODEL_CALLS.labels(model=model.model_name, provider=model.provider).inc()
            
            return response
            
        except Exception as e:
            logger.error(f"Model call failed for {model.model_name}: {str(e)}")
            
            # Try fallback models
            for fallback_name in model.fallback_models:
                if fallback_name in self.models:
                    try:
                        fallback_model = self.models[fallback_name]
                        return await self._call_model(fallback_model, prompt, request)
                    except Exception:
                        continue
            
            raise e
    
    async def _call_openai(self, model: ModelConfiguration, prompt: str, request: Dict[str, Any]) -> str:
        """Call OpenAI API"""
        if not self.openai_client:
            self.openai_client = AsyncOpenAI(api_key=model.api_key)
        
        response = await self.openai_client.chat.completions.create(
            model=model.model_id,
            messages=[{"role": "user", "content": prompt}],
            max_tokens=model.max_tokens,
            temperature=model.temperature
        )
        
        return response.choices[0].message.content
    
    async def _call_anthropic(self, model: ModelConfiguration, prompt: str, request: Dict[str, Any]) -> str:
        """Call Anthropic API"""
        if not self.anthropic_client:
            self.anthropic_client = Anthropic(api_key=model.api_key)
        
        message = await self.anthropic_client.messages.create(
            model=model.model_id,
            max_tokens=model.max_tokens,
            temperature=model.temperature,
            messages=[{"role": "user", "content": prompt}]
        )
        
        return message.content[0].text
    
    async def _call_google(self, model: ModelConfiguration, prompt: str, request: Dict[str, Any]) -> str:
        """Call Google Generative AI API"""
        model_instance = genai.GenerativeModel(model.model_id)
        response = await model_instance.generate_content_async(prompt)
        return response.text
    
    def _update_metrics(self, processing_time: float, result: Dict[str, Any]) -> None:
        """Update system performance metrics"""
        self.performance_metrics["requests_processed"] += 1
        
        # Update average latency
        current_avg = self.performance_metrics["average_latency"]
        total_requests = self.performance_metrics["requests_processed"]
        self.performance_metrics["average_latency"] = (
            (current_avg * (total_requests - 1) + processing_time) / total_requests
        )
        
        # Update success rate
        success = result.get("success", True)
        current_rate = self.performance_metrics["success_rate"]
        self.performance_metrics["success_rate"] = (
            (current_rate * (total_requests - 1) + (1.0 if success else 0.0)) / total_requests
        )
    
    def get_system_status(self) -> Dict[str, Any]:
        """Get comprehensive system status"""
        return {
            "system_id": self.system_id,
            "architecture": self.architecture.value,
            "requirements": {
                "domain": self.requirements.domain,
                "use_case": self.requirements.use_case,
                "latency_target_ms": self.requirements.latency_target_ms
            },
            "models": {
                "count": len(self.models),
                "providers": list(set(m.provider for m in self.models.values())),
                "models": [m.model_name for m in self.models.values()]
            },
            "performance": self.performance_metrics,
            "health": self._assess_health(),
            "capabilities": {
                "mlx_available": MLX_AVAILABLE,
                "reasoning_strategies": [s.value for s in ReasoningStrategy],
                "enterprise_features": True
            }
        }
    
    def _assess_health(self) -> Dict[str, Any]:
        """Assess system health"""
        latency_ok = self.performance_metrics["average_latency"] * 1000 <= self.requirements.latency_target_ms
        success_rate_ok = self.performance_metrics["success_rate"] >= 0.95
        
        overall_health = "healthy" if latency_ok and success_rate_ok else "degraded"
        
        return {
            "overall": overall_health,
            "latency_sla_met": latency_ok,
            "success_rate_acceptable": success_rate_ok,
            "models_operational": len(self.models) > 0
        }
    
    def cleanup(self) -> None:
        """Cleanup system resources"""
        self.executor.shutdown(wait=True)
        ACTIVE_SYSTEMS.dec()
        logger.info(f"Cleaned up compound AI system: {self.system_id}")

class SystemBuilder:
    """Builder for creating compound AI systems"""
    
    def __init__(self):
        self.systems: Dict[str, CompoundAISystem] = {}
    
    def create_system(self, 
                     system_id: str,
                     requirements: SystemRequirements,
                     architecture: SystemArchitecture = SystemArchitecture.HYBRID) -> CompoundAISystem:
        """Create a new compound AI system"""
        system = CompoundAISystem(system_id, requirements, architecture)
        self.systems[system_id] = system
        return system
    
    def create_financial_system(self, system_id: str) -> CompoundAISystem:
        """Create specialized financial services system"""
        requirements = SystemRequirements(
            domain="finance",
            use_case="risk_analysis_trading",
            latency_target_ms=50,
            throughput_rps=1000,
            accuracy_threshold=0.99,
            compliance_requirements=["SOX", "MiFID", "GDPR"]
        )
        
        system = self.create_system(system_id, requirements, SystemArchitecture.HIERARCHICAL)
        
        # Add financial models
        system.add_model(ModelConfiguration(
            model_name="financial_gpt4",
            provider="openai",
            model_id="gpt-4",
            max_tokens=8192,
            temperature=0.1,
            cost_per_token=0.00003,
            specializations=["risk_analysis", "financial_modeling", "trading"]
        ))
        
        system.add_model(ModelConfiguration(
            model_name="financial_claude",
            provider="anthropic",
            model_id="claude-3-opus-20240229",
            max_tokens=4096,
            temperature=0.0,
            cost_per_token=0.000015,
            specializations=["compliance", "regulatory_analysis"]
        ))
        
        return system
    
    def create_healthcare_system(self, system_id: str) -> CompoundAISystem:
        """Create specialized healthcare system"""
        requirements = SystemRequirements(
            domain="healthcare",
            use_case="clinical_decision_support",
            latency_target_ms=2000,
            accuracy_threshold=0.99,
            compliance_requirements=["HIPAA", "FDA", "GDPR"]
        )
        
        system = self.create_system(system_id, requirements, SystemArchitecture.MESH)
        
        # Add healthcare models
        system.add_model(ModelConfiguration(
            model_name="clinical_gpt4",
            provider="openai",
            model_id="gpt-4",
            temperature=0.0,
            specializations=["clinical_analysis", "diagnosis_support", "treatment_planning"]
        ))
        
        return system
    
    def get_system(self, system_id: str) -> Optional[CompoundAISystem]:
        """Get existing system"""
        return self.systems.get(system_id)
    
    def list_systems(self) -> List[str]:
        """List all system IDs"""
        return list(self.systems.keys())
    
    def delete_system(self, system_id: str) -> bool:
        """Delete a system"""
        if system_id in self.systems:
            self.systems[system_id].cleanup()
            del self.systems[system_id]
            return True
        return False

# Global system builder instance
system_builder = SystemBuilder()

# --- MISSING CLASSES FOR TESTS (STUBS) ---
from enum import Enum
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional

class ChainType(Enum):
    SEQUENTIAL = "sequential"
    PARALLEL = "parallel"
    CUSTOM = "custom"

@dataclass
class AgentConfiguration:
    agent_id: str
    agent_type: str
    primary_model: str
    backup_models: List[str] = field(default_factory=list)
    capabilities: List[Any] = field(default_factory=list)
    specialization: Optional[str] = None
    decision_threshold: float = 0.5

@dataclass
class SystemConfiguration:
    system_id: str
    requirements: Any
    architecture: Any
    models: Dict[str, Any] = field(default_factory=dict)
    agents: Dict[str, Any] = field(default_factory=dict)

class TaskQueue:
    def __init__(self):
        self.tasks = []
    def add_task(self, task):
        self.tasks.append(task)
    def get_task(self):
        return self.tasks.pop(0) if self.tasks else None

class ResourceManager:
    def __init__(self):
        self.resources = {}
    def allocate(self, resource, amount):
        self.resources[resource] = self.resources.get(resource, 0) + amount
    def release(self, resource, amount):
        if resource in self.resources:
            self.resources[resource] = max(0, self.resources[resource] - amount)

class EnterpriseIntegrationLayer:
    def __init__(self, config):
        self.config = config

class SecurityFramework:
    def __init__(self, config):
        self.config = config

class ComplianceMonitor:
    def __init__(self, config):
        self.config = config

class CompoundSystem:
    def __init__(self, *args, **kwargs):
        pass

class SystemRegistry:
    def __init__(self, *args, **kwargs):
        self.systems = {}
    def register(self, system_id, system):
        self.systems[system_id] = system
    def get(self, system_id):
        return self.systems.get(system_id)
    def list(self):
        return list(self.systems.keys())