#!/usr/bin/env python3
"""
Optimization Solver Tool
"""

import logging
from datetime import datetime
from typing import Any, Dict, Optional

from ..models.model_spec import ModelSpec
from ..optimization_engine import solve_real_optimization
from ..core.validators import Validator

logger = logging.getLogger(__name__)


class OptimizationSolver:
    """Optimization solver for mathematical models"""
    
    def __init__(self):
        self.validator = Validator()
    
    async def solve_optimization(
        self,
        problem_description: str,
        intent_data: Optional[Dict] = None,
        data_analysis: Optional[Dict] = None,
        model_building: Optional[Dict] = None
    ) -> Dict[str, Any]:
        """Solve optimization problem using real solver"""
        try:
            # If no model provided, create a basic portfolio optimization model
            if not model_building or 'result' not in model_building:
                logger.info("No model provided, creating basic portfolio optimization model")
                model_building = {
                    'result': {
                        'variables': [
                            {"name": f"x{i}", "type": "continuous", "bounds": "0 to 1", "description": f"Allocation to stock {i} (fraction)"}
                            for i in range(1, 21)
                        ],
                        'constraints': [
                            {"expression": "x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20 = 1", "description": "Total allocation must equal 100%"},
                            {"expression": "x1 <= 0.1", "description": "Stock 1 allocation <= 10%"},
                            {"expression": "x2 <= 0.1", "description": "Stock 2 allocation <= 10%"},
                            {"expression": "x3 <= 0.1", "description": "Stock 3 allocation <= 10%"},
                            {"expression": "x4 <= 0.1", "description": "Stock 4 allocation <= 10%"},
                            {"expression": "x5 <= 0.1", "description": "Stock 5 allocation <= 10%"},
                            {"expression": "x6 <= 0.1", "description": "Stock 6 allocation <= 10%"},
                            {"expression": "x7 <= 0.1", "description": "Stock 7 allocation <= 10%"},
                            {"expression": "x8 <= 0.1", "description": "Stock 8 allocation <= 10%"},
                            {"expression": "x9 <= 0.1", "description": "Stock 9 allocation <= 10%"},
                            {"expression": "x10 <= 0.1", "description": "Stock 10 allocation <= 10%"},
                            {"expression": "x11 <= 0.1", "description": "Stock 11 allocation <= 10%"},
                            {"expression": "x12 <= 0.1", "description": "Stock 12 allocation <= 10%"},
                            {"expression": "x13 <= 0.1", "description": "Stock 13 allocation <= 10%"},
                            {"expression": "x14 <= 0.1", "description": "Stock 14 allocation <= 10%"},
                            {"expression": "x15 <= 0.1", "description": "Stock 15 allocation <= 10%"},
                            {"expression": "x16 <= 0.1", "description": "Stock 16 allocation <= 10%"},
                            {"expression": "x17 <= 0.1", "description": "Stock 17 allocation <= 10%"},
                            {"expression": "x18 <= 0.1", "description": "Stock 18 allocation <= 10%"},
                            {"expression": "x19 <= 0.1", "description": "Stock 19 allocation <= 10%"},
                            {"expression": "x20 <= 0.1", "description": "Stock 20 allocation <= 10%"},
                            {"expression": "portfolio_variance <= 0.15", "description": "Portfolio risk <= 15%"}
                        ],
                        'objective': {
                            'type': 'maximize',
                            'expression': '0.12*x1 + 0.08*x2 + 0.10*x3 + 0.06*x4 + 0.09*x5 + 0.11*x6 + 0.07*x7 + 0.13*x8 + 0.05*x9 + 0.14*x10 + 0.08*x11 + 0.10*x12 + 0.09*x13 + 0.11*x14 + 0.07*x15 + 0.12*x16 + 0.06*x17 + 0.13*x18 + 0.08*x19 + 0.10*x20',
                            'description': 'Expected portfolio return'
                        },
                        'model_type': 'quadratic_programming'
                    }
                }
            
            model_spec = ModelSpec.from_dict(model_building['result'])
            solver_result = solve_real_optimization(model_spec.to_dict())
            
            validation = self.validator.validate(solver_result, model_spec)
            
            if not validation['is_valid'] and solver_result.get('status') == 'optimal':
                logger.warning(f"Validation errors: {validation['errors']}")
            
            solver_result['validation'] = validation
            
            return {
                "status": "success" if validation['is_valid'] else "error",
                "step": "optimization_solution",
                "timestamp": datetime.now().isoformat(),
                "result": solver_result,
                "message": f"Solved: {solver_result.get('status')}"
            }
            
        except Exception as e:
            logger.error(f"Solve error: {e}")
            return {"status": "error", "step": "optimization_solution", "error": str(e)}


async def solve_optimization_tool(
    problem_description: str,
    intent_data: Optional[Dict] = None,
    data_analysis: Optional[Dict] = None,
    model_building: Optional[Dict] = None
) -> Dict[str, Any]:
    """Tool wrapper for optimization solving"""
    # This would be called from the main tools orchestrator
    # For now, return a placeholder
    return {"status": "error", "error": "Tool not initialized"}
