do me a quick favor please i need ur help just write the same code in here all of it every word please just fix spacing but give me bakc the indentical code okay?> import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  # Suppress TF messages

import sys
import torch
import numpy as np
from typing import Dict, Optional, List
from dataclasses import dataclass
from datetime import datetime
import logging
from pathlib import Path
import asyncio
import json
from colorama import init, Fore, Back, Style

# Token Constants
TOKEN_CONSTANTS = {
    'SOL': "So11111111111111111111111111111111111111112",
    'USDC': "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
    'USDT': "Es9vMFrzaCERmJfrF4H2FYD4KCoNkY11McCe8BenwNYB",
    'RAY': "4k3Dyjzvzp8eMZWUXbBCjEvwSkkk59S5iCNLY3QrkX6R"
}

# Trading Parameters
TRADING_PARAMS = {
    'min_size': int(0.001 * 1e9),  # 0.001 SOL
    'max_size': int(1.0 * 1e9),    # 1.0 SOL
    'risk_threshold': 0.8,
    'max_retries': 3,
    'min_lamports': 1_000_000,     # Minimum SOL for gas
    'max_slippage': 0.01,          # 1% max slippage
    'target_profit': 0.02,         # 2% target profit
    'stop_loss': 0.01              # 1% stop loss
}

# CUDA Setup
def setup_cuda_environment():
    """Setup CUDA environment with proper paths"""
    cuda_version = "11.8"
    cuda_base = f"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v{cuda_version}"
    
    cuda_paths = {
        "CUDA_PATH": cuda_base,
        "CUDA_HOME": cuda_base,
        "CUDA_LAUNCH_BLOCKING": "1"
    }
    
    dll_paths = [
        f"{cuda_base}/bin",
        f"{cuda_base}/lib/x64"
    ]
    
    # Add DLL directories
    for path in dll_paths:
        if os.path.exists(path):
            os.add_dll_directory(path)
    
    # Update environment
    os.environ.update(cuda_paths)
    os.environ["PATH"] = os.environ["PATH"] + f";{cuda_base}/bin"
    
    return cuda_paths

CUDA_PATHS = setup_cuda_environment()

@dataclass
class MarketData:
    """Market data structure"""
    price: float
    volume: float
    liquidity: float
    timestamp: datetime
    bid_depth: Optional[float] = None
    ask_depth: Optional[float] = None
    recent_trades: Optional[List[Dict]] = None

@dataclass
class QuantumTradeResult:
    """Trade result structure"""
    success: bool
    signature: Optional[str]
    execution_time_us: int
    price_impact: float
    profit_loss: float
    timestamp: datetime
    error: Optional[str] = None
    metrics: Optional[Dict] = None

class EnhancedQuantumCore:
    """Main trading system"""
    
    def __init__(self):
        init()  # Initialize colorama
        self.setup_logging()
        self.verify_cuda()
        
        # Initialize Rust trader
        from solana_rust_bot import TurboTrader
        self.trader = TurboTrader()
        
        # Setup ML
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.setup_models()
        
        # Initialize state
        self.trade_history = []
        self.market_cache = {}
        self.performance_metrics = {
            'trades_executed': 0,
            'successful_trades': 0,
            'total_profit_loss': 0.0,
            'avg_execution_time': 0.0
        }
        
        self.logger.info(f"{Fore.GREEN}🚀 Enhanced Quantum Core Initialized{Style.RESET_ALL}")

    def setup_logging(self):
        """Setup UTF-8 capable logging"""
        log_dir = Path("logs")
        log_dir.mkdir(exist_ok=True)
        
        self.logger = logging.getLogger("QuantumCore")
        self.logger.setLevel(logging.INFO)
        
        # File handler with UTC-8
        fh = logging.FileHandler(
            log_dir / f"quantum_core_{datetime.now().strftime('%Y%m%d')}.log",
            encoding='utf-8'
        )
        fh.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s'))
        
        # Console handler
        ch = logging.StreamHandler(sys.stdout)
        ch.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s'))
        
        self.logger.addHandler(fh)
        self.logger.addHandler(ch)

    def verify_cuda(self):
        """Verify CUDA setup"""
        if torch.cuda.is_available():
            self.logger.info(f"{Fore.GREEN}✓ CUDA Status: Available")
            self.logger.info(f"✓ CUDA Version: {torch.version.cuda}")
            self.logger.info(f"✓ GPU Device: {torch.cuda.get_device_name()}")
            
            # Enable TF32 for better performance
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.benchmark = True
        else:
            self.logger.error(f"{Back.RED}{Fore.WHITE}❌ CUDA not available{Style.RESET_ALL}")
            raise RuntimeError("CUDA is required for this system")

    def setup_models(self):
        """Initialize ML models"""
        try:
            # Market prediction model
            self.market_model = torch.nn.Sequential(
                torch.nn.Linear(128, 256),
                torch.nn.ReLU(),
                torch.nn.Dropout(0.2),
                torch.nn.Linear(256, 64),
                torch.nn.ReLU(),
                torch.nn.Linear(64, 1)
            ).to(self.device)
            
            # Risk assessment model
            self.risk_model = torch.nn.Sequential(
                torch.nn.Linear(64, 128),
                torch.nn.ReLU(),
                torch.nn.Dropout(0.2),
                torch.nn.Linear(128, 32),
                torch.nn.ReLU(),
                torch.nn.Linear(32, 1),
                torch.nn.Sigmoid()
            ).to(self.device)
            
        except Exception as e:
            self.logger.error(f"Model initialization error: {e}")
            raise

    async def get_market_data(self, token_mint: str) -> MarketData:
    """Get current market data with proper Rust integration"""
    try:
        # Check cache first
        cache_key = f"{token_mint}_{datetime.now().minute}"
        if cache_key in self.market_cache:
            return self.market_cache[cache_key]
        
        # Get balance using executor
        balance = await asyncio.get_event_loop().run_in_executor(
            None,
            self.trader.get_balance
        )
        
        # Convert balance from string to float if needed
        if isinstance(balance, str):
            balance = float(balance)
        
        # Create market data object
        market_data = MarketData(
            price=balance,
            volume=0.0,  # Will be updated from trade data
            liquidity=balance * 1000,  # Estimate liquidity
            timestamp=datetime.now(),
            bid_depth=None,
            ask_depth=None,
            recent_trades=[]
        )
        
        # Cache the data
        self.market_cache[cache_key] = market_data
        
        self.logger.info(f"Market data fetched - Price: {balance:.6f} SOL")
        return market_data
        
    except Exception as e:
        self.logger.error(f"Market data error: {str(e)}")
        raise

async def execute_trade(self, 
                       input_mint: str,
                       output_mint: str,
                       amount_in: int) -> QuantumTradeResult:
    """Execute trade with proper Rust result handling"""
    try:
        # Get market data
        market_data = await self.get_market_data(output_mint)
        
        # Analyze market
        prediction = self.predict_market(market_data)
        risk_score = self.assess_trade_risk(market_data, amount_in)
        
        # Log analysis
        self.logger.info(f"Trade analysis - Risk: {risk_score:.4f}, Prediction: {prediction['predicted_move']:.4f}")
        
        # Check risk
        if risk_score > TRADING_PARAMS['risk_threshold']:
            return QuantumTradeResult(
                success=False,
                signature=None,
                execution_time_us=0,
                price_impact=0.0,
                profit_loss=0.0,
                timestamp=datetime.now(),
                error="Risk too high",
                metrics={'risk_score': risk_score}
            )
        
        # Execute trade
        start_time = datetime.now()
        
        try:
            # Execute through Rust with proper error handling
            result_json = await asyncio.get_event_loop().run_in_executor(
                None,
                self.trader.execute_trade,
                input_mint,
                output_mint,
                amount_in
            )
            
            # Parse result
            if isinstance(result_json, str):
                result = json.loads(result_json)
            else:
                result = result_json
                
        except Exception as e:
            self.logger.error(f"Rust execution error: {str(e)}")
            raise
            
        execution_time = (datetime.now() - start_time).microseconds
        
        # Update metrics
        self.performance_metrics['trades_executed'] += 1
        if result.get('success', False):
            self.performance_metrics['successful_trades'] += 1
            self.performance_metrics['total_profit_loss'] += float(result.get('profit_loss', 0))
        
        # Calculate average execution time
        n = self.performance_metrics['trades_executed']
        old_avg = self.performance_metrics['avg_execution_time']
        self.performance_metrics['avg_execution_time'] = (old_avg * (n-1) + execution_time) / n
        
        trade_result = QuantumTradeResult(
            success=result.get('success', False),
            signature=result.get('signature'),
            execution_time_us=execution_time,
            price_impact=float(result.get('price_impact', 0.0)),
            profit_loss=float(result.get('profit_loss', 0.0)),
            timestamp=datetime.now(),
            metrics={
                'risk_score': risk_score,
                'predicted_move': prediction['predicted_move'],
                'confidence': prediction['confidence'],
                'execution_time_ms': execution_time / 1000
            }
        )
        
        # Log result
        self.logger.info(
            f"Trade executed - Success: {trade_result.success}, "
            f"Impact: {trade_result.price_impact:.4f}%, "
            f"Time: {execution_time/1000:.2f}ms"
        )
        
        return trade_result
        
    except Exception as e:
        self.logger.error(f"Trade execution error: {str(e)}")
        return QuantumTradeResult(
            success=False,
            signature=None,
            execution_time_us=0,
            price_impact=0.0,
            profit_loss=0.0,
            timestamp=datetime.now(),
            error=str(e)
        )

# Update the test function to show more information
async def test_quantum_core():
    """Test the complete system"""
    print(f"\n{Fore.CYAN}🚀 Quantum Trading System Test{Style.RESET_ALL}")
    
    try:
        # Initialize system
        core = EnhancedQuantumCore()
        print("\n✨ System Initialized")
        
        # Get initial balance
        balance = await asyncio.get_event_loop().run_in_executor(
            None,
            core.trader.get_balance
        )
        print(f"\n💰 Initial Balance: {float(balance):.6f} SOL")
        
        # Test trade
        amount = int(0.1 * 1e9)  # 0.1 SOL
        print(f"\n💫 Testing SOL -> USDC trade ({amount/1e9:.3f} SOL)")
        
        # Get market data first
        market_data = await core.get_market_data(TOKEN_CONSTANTS['USDC'])
        print(f"\n📊 Market Data:")
        print(f"  • Price: {market_data.price:.6f} SOL")
        print(f"  • Liquidity: {market_data.liquidity:.2f}")
        
        # Execute trade
        result = await core.execute_trade(
            input_mint=TOKEN_CONSTANTS['SOL'],
            output_mint=TOKEN_CONSTANTS['USDC'],
            amount_in=amount
        )
        
        # Display results
        print(f"\n📈 Trade Result:")
        print(f"  • Success: {result.success}")
        print(f"  • Signature: {result.signature}")
        print(f"  • Price Impact: {result.price_impact:.4f}%")
        print(f"  • Execution Time: {result.execution_time_us/1000:.2f}ms")
        
        if result.metrics:
            print(f"\n🔍 Metrics:")
            for key, value in result.metrics.items():
                if isinstance(value, float):
                    print(f"  • {key}: {value:.4f}")
                else:
                    print(f"  • {key}: {value}")
        
        if result.error:
            print(f"\n❌ Error: {result.error}")
        
        # Save metrics
        core.save_performance_metrics()
        print("\n💾 Performance metrics saved")
        
    except Exception as e:
        print(f"\n❌ Test error: {str(e)}")
        print(f"Error type: {type(e)}")
        
    finally:
        # Cleanup
        torch.cuda.empty_cache()
        print(f"\n🧹 CUDA memory cleared")

if __name__ == "__main__":
    asyncio.run(test_quantum_core())