"""
AI Agent Service for Verifiable AI Agent Orchestration
Implements core orchestration logic and state management for AI agent workflows
"""

import asyncio
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from uuid import uuid4
import json
import logging

from sqlmodel import Session, select, update, delete
from sqlalchemy.exc import SQLAlchemyError

from ..domain.agent import (
    AIAgentWorkflow, AgentStep, AgentExecution, AgentStepExecution,
    AgentStatus, VerificationLevel, StepType,
    AgentExecutionRequest, AgentExecutionResponse, AgentExecutionStatus
)
from ..domain.job import Job
# Mock CoordinatorClient for now
class CoordinatorClient:
    """Mock coordinator client for agent orchestration"""
    pass

logger = logging.getLogger(__name__)


class AgentStateManager:
    """Manages persistent state for AI agent executions"""
    
    def __init__(self, session: Session):
        self.session = session
    
    async def create_execution(
        self, 
        workflow_id: str, 
        client_id: str,
        verification_level: VerificationLevel = VerificationLevel.BASIC
    ) -> AgentExecution:
        """Create a new agent execution record"""
        
        execution = AgentExecution(
            workflow_id=workflow_id,
            client_id=client_id,
            verification_level=verification_level
        )
        
        self.session.add(execution)
        self.session.commit()
        self.session.refresh(execution)
        
        logger.info(f"Created agent execution: {execution.id}")
        return execution
    
    async def update_execution_status(
        self, 
        execution_id: str, 
        status: AgentStatus,
        **kwargs
    ) -> AgentExecution:
        """Update execution status and related fields"""
        
        stmt = (
            update(AgentExecution)
            .where(AgentExecution.id == execution_id)
            .values(
                status=status,
                updated_at=datetime.utcnow(),
                **kwargs
            )
        )
        
        self.session.execute(stmt)
        self.session.commit()
        
        # Get updated execution
        execution = self.session.get(AgentExecution, execution_id)
        logger.info(f"Updated execution {execution_id} status to {status}")
        return execution
    
    async def get_execution(self, execution_id: str) -> Optional[AgentExecution]:
        """Get execution by ID"""
        return self.session.get(AgentExecution, execution_id)
    
    async def get_workflow(self, workflow_id: str) -> Optional[AIAgentWorkflow]:
        """Get workflow by ID"""
        return self.session.get(AIAgentWorkflow, workflow_id)
    
    async def get_workflow_steps(self, workflow_id: str) -> List[AgentStep]:
        """Get all steps for a workflow"""
        stmt = (
            select(AgentStep)
            .where(AgentStep.workflow_id == workflow_id)
            .order_by(AgentStep.step_order)
        )
        return self.session.exec(stmt).all()
    
    async def create_step_execution(
        self, 
        execution_id: str, 
        step_id: str
    ) -> AgentStepExecution:
        """Create a step execution record"""
        
        step_execution = AgentStepExecution(
            execution_id=execution_id,
            step_id=step_id
        )
        
        self.session.add(step_execution)
        self.session.commit()
        self.session.refresh(step_execution)
        
        return step_execution
    
    async def update_step_execution(
        self, 
        step_execution_id: str,
        **kwargs
    ) -> AgentStepExecution:
        """Update step execution"""
        
        stmt = (
            update(AgentStepExecution)
            .where(AgentStepExecution.id == step_execution_id)
            .values(
                updated_at=datetime.utcnow(),
                **kwargs
            )
        )
        
        self.session.execute(stmt)
        self.session.commit()
        
        step_execution = self.session.get(AgentStepExecution, step_execution_id)
        return step_execution


class AgentVerifier:
    """Handles verification of agent executions"""
    
    def __init__(self, cuda_accelerator=None):
        self.cuda_accelerator = cuda_accelerator
    
    async def verify_step_execution(
        self, 
        step_execution: AgentStepExecution,
        verification_level: VerificationLevel
    ) -> Dict[str, Any]:
        """Verify a single step execution"""
        
        verification_result = {
            "verified": False,
            "proof": None,
            "verification_time": 0.0,
            "verification_level": verification_level
        }
        
        try:
            if verification_level == VerificationLevel.ZERO_KNOWLEDGE:
                # Use ZK proof verification
                verification_result = await self._zk_verify_step(step_execution)
            elif verification_level == VerificationLevel.FULL:
                # Use comprehensive verification
                verification_result = await self._full_verify_step(step_execution)
            else:
                # Basic verification
                verification_result = await self._basic_verify_step(step_execution)
                
        except Exception as e:
            logger.error(f"Step verification failed: {e}")
            verification_result["error"] = str(e)
        
        return verification_result
    
    async def _basic_verify_step(self, step_execution: AgentStepExecution) -> Dict[str, Any]:
        """Basic verification of step execution"""
        start_time = datetime.utcnow()
        
        # Basic checks: execution completed, has output, no errors
        verified = (
            step_execution.status == AgentStatus.COMPLETED and
            step_execution.output_data is not None and
            step_execution.error_message is None
        )
        
        verification_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "verified": verified,
            "proof": None,
            "verification_time": verification_time,
            "verification_level": VerificationLevel.BASIC,
            "checks": ["completion", "output_presence", "error_free"]
        }
    
    async def _full_verify_step(self, step_execution: AgentStepExecution) -> Dict[str, Any]:
        """Full verification with additional checks"""
        start_time = datetime.utcnow()
        
        # Basic verification first
        basic_result = await self._basic_verify_step(step_execution)
        
        if not basic_result["verified"]:
            return basic_result
        
        # Additional checks: performance, resource usage
        additional_checks = []
        
        # Check execution time is reasonable
        if step_execution.execution_time and step_execution.execution_time < 3600:  # < 1 hour
            additional_checks.append("reasonable_execution_time")
        else:
            basic_result["verified"] = False
        
        # Check memory usage
        if step_execution.memory_usage and step_execution.memory_usage < 8192:  # < 8GB
            additional_checks.append("reasonable_memory_usage")
        
        verification_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "verified": basic_result["verified"],
            "proof": None,
            "verification_time": verification_time,
            "verification_level": VerificationLevel.FULL,
            "checks": basic_result["checks"] + additional_checks
        }
    
    async def _zk_verify_step(self, step_execution: AgentStepExecution) -> Dict[str, Any]:
        """Zero-knowledge proof verification"""
        start_time = datetime.utcnow()
        
        # For now, fall back to full verification
        # TODO: Implement ZK proof generation and verification
        result = await self._full_verify_step(step_execution)
        result["verification_level"] = VerificationLevel.ZERO_KNOWLEDGE
        result["note"] = "ZK verification not yet implemented, using full verification"
        
        return result


class AIAgentOrchestrator:
    """Orchestrates execution of AI agent workflows"""
    
    def __init__(self, session: Session, coordinator_client: CoordinatorClient):
        self.session = session
        self.coordinator = coordinator_client
        self.state_manager = AgentStateManager(session)
        self.verifier = AgentVerifier()
    
    async def execute_workflow(
        self, 
        request: AgentExecutionRequest,
        client_id: str
    ) -> AgentExecutionResponse:
        """Execute an AI agent workflow with verification"""
        
        # Get workflow
        workflow = await self.state_manager.get_workflow(request.workflow_id)
        if not workflow:
            raise ValueError(f"Workflow not found: {request.workflow_id}")
        
        # Create execution
        execution = await self.state_manager.create_execution(
            workflow_id=request.workflow_id,
            client_id=client_id,
            verification_level=request.verification_level
        )
        
        try:
            # Start execution
            await self.state_manager.update_execution_status(
                execution.id,
                status=AgentStatus.RUNNING,
                started_at=datetime.utcnow(),
                total_steps=len(workflow.steps)
            )
            
            # Execute steps asynchronously
            asyncio.create_task(
                self._execute_steps_async(execution.id, request.inputs)
            )
            
            # Return initial response
            return AgentExecutionResponse(
                execution_id=execution.id,
                workflow_id=workflow.id,
                status=execution.status,
                current_step=0,
                total_steps=len(workflow.steps),
                started_at=execution.started_at,
                estimated_completion=self._estimate_completion(execution),
                current_cost=0.0,
                estimated_total_cost=self._estimate_cost(workflow)
            )
            
        except Exception as e:
            await self._handle_execution_failure(execution.id, e)
            raise
    
    async def get_execution_status(self, execution_id: str) -> AgentExecutionStatus:
        """Get current execution status"""
        
        execution = await self.state_manager.get_execution(execution_id)
        if not execution:
            raise ValueError(f"Execution not found: {execution_id}")
        
        return AgentExecutionStatus(
            execution_id=execution.id,
            workflow_id=execution.workflow_id,
            status=execution.status,
            current_step=execution.current_step,
            total_steps=execution.total_steps,
            step_states=execution.step_states,
            final_result=execution.final_result,
            error_message=execution.error_message,
            started_at=execution.started_at,
            completed_at=execution.completed_at,
            total_execution_time=execution.total_execution_time,
            total_cost=execution.total_cost,
            verification_proof=execution.verification_proof
        )
    
    async def _execute_steps_async(
        self, 
        execution_id: str, 
        inputs: Dict[str, Any]
    ) -> None:
        """Execute workflow steps in dependency order"""
        
        try:
            execution = await self.state_manager.get_execution(execution_id)
            workflow = await self.state_manager.get_workflow(execution.workflow_id)
            steps = await self.state_manager.get_workflow_steps(workflow.id)
            
            # Build execution DAG
            step_order = self._build_execution_order(steps, workflow.dependencies)
            
            current_inputs = inputs.copy()
            step_results = {}
            
            for step_id in step_order:
                step = next(s for s in steps if s.id == step_id)
                
                # Execute step
                step_result = await self._execute_single_step(
                    execution_id, step, current_inputs
                )
                
                step_results[step_id] = step_result
                
                # Update inputs for next steps
                if step_result.output_data:
                    current_inputs.update(step_result.output_data)
                
                # Update execution progress
                await self.state_manager.update_execution_status(
                    execution_id,
                    current_step=execution.current_step + 1,
                    completed_steps=execution.completed_steps + 1,
                    step_states=step_results
                )
            
            # Mark execution as completed
            await self._complete_execution(execution_id, step_results)
            
        except Exception as e:
            await self._handle_execution_failure(execution_id, e)
    
    async def _execute_single_step(
        self, 
        execution_id: str, 
        step: AgentStep, 
        inputs: Dict[str, Any]
    ) -> AgentStepExecution:
        """Execute a single step"""
        
        # Create step execution record
        step_execution = await self.state_manager.create_step_execution(
            execution_id, step.id
        )
        
        try:
            # Update step status to running
            await self.state_manager.update_step_execution(
                step_execution.id,
                status=AgentStatus.RUNNING,
                started_at=datetime.utcnow(),
                input_data=inputs
            )
            
            # Execute the step based on type
            if step.step_type == StepType.INFERENCE:
                result = await self._execute_inference_step(step, inputs)
            elif step.step_type == StepType.TRAINING:
                result = await self._execute_training_step(step, inputs)
            elif step.step_type == StepType.DATA_PROCESSING:
                result = await self._execute_data_processing_step(step, inputs)
            else:
                result = await self._execute_custom_step(step, inputs)
            
            # Update step execution with results
            await self.state_manager.update_step_execution(
                step_execution.id,
                status=AgentStatus.COMPLETED,
                completed_at=datetime.utcnow(),
                output_data=result.get("output"),
                execution_time=result.get("execution_time", 0.0),
                gpu_accelerated=result.get("gpu_accelerated", False),
                memory_usage=result.get("memory_usage")
            )
            
            # Verify step if required
            if step.requires_proof:
                verification_result = await self.verifier.verify_step_execution(
                    step_execution, step.verification_level
                )
                
                await self.state_manager.update_step_execution(
                    step_execution.id,
                    step_proof=verification_result,
                    verification_status="verified" if verification_result["verified"] else "failed"
                )
            
            return step_execution
            
        except Exception as e:
            # Mark step as failed
            await self.state_manager.update_step_execution(
                step_execution.id,
                status=AgentStatus.FAILED,
                completed_at=datetime.utcnow(),
                error_message=str(e)
            )
            raise
    
    async def _execute_inference_step(
        self, 
        step: AgentStep, 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute inference step"""
        
        # TODO: Integrate with actual ML inference service
        # For now, simulate inference execution
        
        start_time = datetime.utcnow()
        
        # Simulate processing time
        await asyncio.sleep(0.1)
        
        execution_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "output": {"prediction": "simulated_result", "confidence": 0.95},
            "execution_time": execution_time,
            "gpu_accelerated": False,
            "memory_usage": 128.5
        }
    
    async def _execute_training_step(
        self, 
        step: AgentStep, 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute training step"""
        
        # TODO: Integrate with actual ML training service
        start_time = datetime.utcnow()
        
        # Simulate training time
        await asyncio.sleep(0.5)
        
        execution_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "output": {"model_updated": True, "training_loss": 0.123},
            "execution_time": execution_time,
            "gpu_accelerated": True,  # Training typically uses GPU
            "memory_usage": 512.0
        }
    
    async def _execute_data_processing_step(
        self, 
        step: AgentStep, 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute data processing step"""
        
        start_time = datetime.utcnow()
        
        # Simulate processing time
        await asyncio.sleep(0.05)
        
        execution_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "output": {"processed_records": 1000, "data_validated": True},
            "execution_time": execution_time,
            "gpu_accelerated": False,
            "memory_usage": 64.0
        }
    
    async def _execute_custom_step(
        self, 
        step: AgentStep, 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute custom step"""
        
        start_time = datetime.utcnow()
        
        # Simulate custom processing
        await asyncio.sleep(0.2)
        
        execution_time = (datetime.utcnow() - start_time).total_seconds()
        
        return {
            "output": {"custom_result": "completed", "metadata": inputs},
            "execution_time": execution_time,
            "gpu_accelerated": False,
            "memory_usage": 256.0
        }
    
    def _build_execution_order(
        self, 
        steps: List[AgentStep], 
        dependencies: Dict[str, List[str]]
    ) -> List[str]:
        """Build execution order based on dependencies"""
        
        # Simple topological sort
        step_ids = [step.id for step in steps]
        ordered_steps = []
        remaining_steps = step_ids.copy()
        
        while remaining_steps:
            # Find steps with no unmet dependencies
            ready_steps = []
            for step_id in remaining_steps:
                step_deps = dependencies.get(step_id, [])
                if all(dep in ordered_steps for dep in step_deps):
                    ready_steps.append(step_id)
            
            if not ready_steps:
                raise ValueError("Circular dependency detected in workflow")
            
            # Add ready steps to order
            for step_id in ready_steps:
                ordered_steps.append(step_id)
                remaining_steps.remove(step_id)
        
        return ordered_steps
    
    async def _complete_execution(
        self, 
        execution_id: str, 
        step_results: Dict[str, Any]
    ) -> None:
        """Mark execution as completed"""
        
        completed_at = datetime.utcnow()
        execution = await self.state_manager.get_execution(execution_id)
        
        total_execution_time = (
            completed_at - execution.started_at
        ).total_seconds() if execution.started_at else 0.0
        
        await self.state_manager.update_execution_status(
            execution_id,
            status=AgentStatus.COMPLETED,
            completed_at=completed_at,
            total_execution_time=total_execution_time,
            final_result={"step_results": step_results}
        )
    
    async def _handle_execution_failure(
        self, 
        execution_id: str, 
        error: Exception
    ) -> None:
        """Handle execution failure"""
        
        await self.state_manager.update_execution_status(
            execution_id,
            status=AgentStatus.FAILED,
            completed_at=datetime.utcnow(),
            error_message=str(error)
        )
    
    def _estimate_completion(
        self, 
        execution: AgentExecution
    ) -> Optional[datetime]:
        """Estimate completion time"""
        
        if not execution.started_at:
            return None
        
        # Simple estimation: 30 seconds per step
        estimated_duration = execution.total_steps * 30
        return execution.started_at + timedelta(seconds=estimated_duration)
    
    def _estimate_cost(
        self, 
        workflow: AIAgentWorkflow
    ) -> Optional[float]:
        """Estimate total execution cost"""
        
        # Simple cost model: $0.01 per step + base cost
        base_cost = 0.01
        per_step_cost = 0.01
        return base_cost + (len(workflow.steps) * per_step_cost)
