import asyncio
import websockets
import json
import base64
import struct
import torch
from .quantum_core import QuantumCore
from .ml_engine import MLEngine
from .config import Config
import numpy as np
from concurrent.futures import ThreadPoolExecutor
from typing import Dict
import logging
from decimal import Decimal

logging.basicConfig(level=logging.INFO, format='%(asctime)s | %(message)s')

class QuantumEngine:
   def __init__(self):
       self.device = torch.device("cuda:0")
       self.batch_size = 312_500
       self.feature_size = 640
       self.hidden_size = 1280
       self.gpu_streams = [torch.cuda.Stream() for _ in range(16)]
       self.thread_pool = ThreadPoolExecutor(max_workers=24)
       
       self.model = self._build_model()
       self._warmup()
       
       self.min_profit = Decimal('0.008') 
       self.position_size = 0.05
       self.scale_factor = 1.0
       self.profitable_trades = 0

   def _build_model(self):
       model = torch.nn.Sequential(
           torch.nn.Linear(self.feature_size, self.hidden_size),
           torch.nn.ReLU(),
           torch.nn.BatchNorm1d(self.hidden_size),
           torch.nn.Linear(self.hidden_size, self.hidden_size),
           torch.nn.ReLU(),
           torch.nn.BatchNorm1d(self.hidden_size),
           torch.nn.Linear(self.hidden_size, self.feature_size)
       ).half().to(self.device)
       model.eval()
       return model

   def _warmup(self):
       dummy = torch.randn(1000, self.feature_size, device=self.device, dtype=torch.half)
       with torch.amp.autocast('cuda'):
           self.model(dummy)
       torch.cuda.synchronize()

   def process_data(self, data: np.ndarray) -> torch.Tensor:
       chunks = np.array_split(data, len(self.gpu_streams))
       outputs = []
       
       for stream, chunk in zip(self.gpu_streams, chunks):
           with torch.cuda.stream(stream):
               tensor = torch.from_numpy(chunk).half().to(self.device)
               with torch.amp.autocast('cuda'):
                   outputs.append(self.model(tensor))
       
       torch.cuda.synchronize()
       return torch.cat(outputs)

   async def execute_trade(self, pool_data: Dict) -> str:
       data = np.array([
           pool_data['volume'],
           pool_data['price'],
           pool_data['buy_vol'],
           pool_data['sell_vol']
       ]).reshape(1, -1)
       
       prediction = self.process_data(data)
       if prediction.mean().item() > 0.75:
           size = self.position_size * self.scale_factor
           
           tx = {
               "market_id": pool_data['token'],
               "size": size,
               "priority_fee": 75000,
               "compute_limit": 1400000
           }
           
           # Execute via rust backend
           sig = self.core.execute_trade(json.dumps(tx), size, 75000)
           if sig:
               self.profitable_trades += 1
               if self.profitable_trades % 5 == 0:
                   self.scale_factor *= 1.1
               logging.info(f"Trade executed: {sig}")
           return sig
       return None

   async def monitor(self):
       ws_url = "wss://marga-e56m84-fast-mainnet.helius-rpc.com/"
       
       while True:
           try:
               async with websockets.connect(ws_url) as ws:
                   await ws.send(json.dumps({
                       "jsonrpc": "2.0",
                       "id": 1,
                       "method": "programSubscribe",
                       "params": ["675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8", 
                                {"encoding": "base64"}]
                   }))

                   while True:
                       msg = await ws.recv()
                       data = json.loads(msg)
                       
                       if "params" in data and "result" in data["params"]:
                           account = data["params"]["result"]["value"]
                           raw_data = account["account"]["data"][0]
                           
                           decoded = base64.b64decode(raw_data)
                           pool_data = {
                               "volume": struct.unpack('<Q', decoded[8:16])[0] / 1e9,
                               "price": struct.unpack('<d', decoded[24:32])[0],
                               "buy_vol": struct.unpack('<Q', decoded[80:88])[0] / 1e9,
                               "sell_vol": struct.unpack('<Q', decoded[88:96])[0] / 1e9,
                               "token": decoded[32:64].hex()
                           }
                           
                           bs_ratio = pool_data["buy_vol"] / max(pool_data["sell_vol"], 1)
                           if pool_data["volume"] > 5000 and bs_ratio > 2:
                               await self.execute_trade(pool_data)

           except Exception as e:
               logging.error(f"Connection error: {e}")
               await asyncio.sleep(1)

if __name__ == "__main__":
   engine = QuantumEngine()
   asyncio.run(engine.monitor())
   
   # main.py updated with CUDA optimizations:
import asyncio
import torch
from .quantum_core import QuantumCore
from .ml_engine import MLEngine
from .config import Config

async def main():
    config = Config()
    engine = MLEngine(config)
    core = QuantumCore(config)
    await core.start()

if __name__ == "__main__":
    asyncio.run(main()) please fix and give it maximum logging