import os
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request, Depends, Security
from fastapi.responses import StreamingResponse, HTMLResponse, JSONResponse, FileResponse
from fastapi.security import APIKeyHeader
from pydantic import BaseModel
import httpx
from functools import lru_cache
from pathlib import Path
import json
import datetime
import time
from typing import Optional, Dict, List, Any, Generator
import asyncio
from starlette.status import HTTP_403_FORBIDDEN
import cloudscraper
from concurrent.futures import ThreadPoolExecutor
import uvloop
from fastapi.middleware.gzip import GZipMiddleware
from starlette.middleware.cors import CORSMiddleware

# Enable uvloop for faster event loop
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())

# Thread pool for CPU-bound operations
executor = ThreadPoolExecutor(max_workers=8)

# Load environment variables once at startup
load_dotenv()

# API key security scheme
api_key_header = APIKeyHeader(name="Authorization", auto_error=False)

# Initialize usage tracker
from usage_tracker import UsageTracker
usage_tracker = UsageTracker()

app = FastAPI()

# Add middleware for compression and CORS
app.add_middleware(GZipMiddleware, minimum_size=1000)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Environment variables (cached)
@lru_cache(maxsize=1)
def get_env_vars():
    return {
        'api_keys': os.getenv('API_KEYS', '').split(','),
        'secret_api_endpoint': os.getenv('SECRET_API_ENDPOINT'),
        'secret_api_endpoint_2': os.getenv('SECRET_API_ENDPOINT_2'),
        'secret_api_endpoint_3': os.getenv('SECRET_API_ENDPOINT_3'),
        'mistral_api': "https://api.mistral.ai",
        'mistral_key': os.getenv('MISTRAL_KEY'),
        'image_endpoint': os.getenv("IMAGE_ENDPOINT"),
        'endpoint_origin': os.getenv('ENDPOINT_ORIGIN')
    }

# Configuration for models - use sets for faster lookups
mistral_models = {
    "mistral-large-latest",
    "pixtral-large-latest",
    "mistral-moderation-latest",
    "ministral-3b-latest",
    "ministral-8b-latest",
    "open-mistral-nemo",
    "mistral-small-latest",
    "mistral-saba-latest",
    "codestral-latest"
}

alternate_models = {
    "gpt-4o-mini",
    "deepseek-v3",
    "llama-3.1-8b-instruct",
    "searchgpt",
    "llama-3.1-sonar-small-128k-online",
    "sonar-reasoning",
    "deepseek-r1-uncensored",
    "tinyswallow1.5b",
    "andy-3.5",
    "o3-mini-low",
    "hermes-3-llama-3.2-3b"
}

# Request payload model
class Payload(BaseModel):
    model: str
    messages: list
    stream: bool = False

# Server status global variable
server_status = True
available_model_ids: List[str] = []

# Create a reusable httpx client pool with connection pooling
@lru_cache(maxsize=1)
def get_async_client():
    return httpx.AsyncClient(
        timeout=60.0,
        limits=httpx.Limits(max_keepalive_connections=20, max_connections=100)
    )

# Create a cloudscraper pool
scraper_pool = []
MAX_SCRAPERS = 10

def get_scraper():
    if not scraper_pool:
        for _ in range(MAX_SCRAPERS):
            scraper_pool.append(cloudscraper.create_scraper())
    
    return scraper_pool[int(time.time() * 1000) % MAX_SCRAPERS]  # Simple round-robin

# API key validation - optimized to avoid string operations when possible
async def verify_api_key(api_key: str = Security(api_key_header)) -> bool:
    if not api_key:
        raise HTTPException(
            status_code=HTTP_403_FORBIDDEN,
            detail="No API key provided"
        )
    
    # Only clean if needed    
    if api_key.startswith('Bearer '):
        api_key = api_key[7:]  # Remove 'Bearer ' prefix
    
    # Get API keys from environment
    valid_api_keys = get_env_vars()['api_keys']
    if not valid_api_keys or valid_api_keys == ['']:
        raise HTTPException(
            status_code=HTTP_403_FORBIDDEN,
            detail="API keys not configured on server"
        )
    
    # Fast check with set operation
    if api_key not in set(valid_api_keys):
        raise HTTPException(
            status_code=HTTP_403_FORBIDDEN,
            detail="Invalid API key"
        )
    
    return True

# Pre-load and cache models.json
@lru_cache(maxsize=1)
def load_models_data():
    try:
        file_path = Path(__file__).parent / 'models.json'
        with open(file_path, 'r') as f:
            return json.load(f)
    except (FileNotFoundError, json.JSONDecodeError) as e:
        print(f"Error loading models.json: {str(e)}")
        return []

# Async wrapper for models data
async def get_models():
    models_data = load_models_data()
    if not models_data:
        raise HTTPException(status_code=500, detail="Error loading available models")
    return models_data

# Searcher function with optimized streaming - moved to a separate thread
async def generate_search_async(query: str, systemprompt: Optional[str] = None, stream: bool = True):
    loop = asyncio.get_running_loop()
    
    def _generate_search():
        headers = {"User-Agent": ""}
        
        # Use the provided system prompt, or default to "Be Helpful and Friendly"
        system_message = systemprompt or "Be Helpful and Friendly"
        
        # Create the prompt history with the user query and system message
        prompt = [
            {"role": "user", "content": query},
        ]
        
        prompt.insert(0, {"content": system_message, "role": "system"})
        
        # Prepare the payload for the API request
        payload = {
            "is_vscode_extension": True,
            "message_history": prompt,
            "requested_model": "searchgpt",
            "user_input": prompt[-1]["content"],
        }
        
        # Get endpoint from environment
        secret_api_endpoint_3 = get_env_vars()['secret_api_endpoint_3']
        if not secret_api_endpoint_3:
            raise ValueError("Search API endpoint not configured")
        
        # Send the request to the chat endpoint using a scraper from the pool
        response = get_scraper().post(
            secret_api_endpoint_3, 
            headers=headers, 
            json=payload, 
            stream=True
        )
        
        result = []
        streaming_text = ""
        
        # Process the streaming response
        for value in response.iter_lines(decode_unicode=True):
            if value.startswith("data: "):  
                try:
                    json_modified_value = json.loads(value[6:])
                    content = json_modified_value.get("choices", [{}])[0].get("delta", {}).get("content", "")

                    if content.strip():  # Only process non-empty content
                        cleaned_response = {
                            "created": json_modified_value.get("created"),
                            "id": json_modified_value.get("id"),
                            "model": "searchgpt",
                            "object": "chat.completion",
                            "choices": [
                                {
                                    "message": {
                                        "content": content
                                    }
                                }
                            ]
                        }
                        
                        if stream:
                            result.append(f"data: {json.dumps(cleaned_response)}\n\n")
                        
                        streaming_text += content
                except json.JSONDecodeError:
                    continue
        
        if not stream:
            result.append(streaming_text)
            
        return result
    
    # Run in thread pool to avoid blocking the event loop
    return await loop.run_in_executor(executor, _generate_search)

# Cache for frequently accessed static files
@lru_cache(maxsize=10)
def read_html_file(file_path):
    try:
        with open(file_path, "r") as file:
            return file.read()
    except FileNotFoundError:
        return None

# Basic routes
@app.get("/favicon.ico")
async def favicon():
    favicon_path = Path(__file__).parent / "favicon.ico"
    return FileResponse(favicon_path, media_type="image/x-icon")

@app.get("/ping")
async def ping():
    return {"message": "pong", "response_time": "0.000000 seconds"}

@app.get("/", response_class=HTMLResponse)
async def root():
    html_content = read_html_file("index.html")
    if html_content is None:
        return HTMLResponse(content="<h1>File not found</h1>", status_code=404)
    return HTMLResponse(content=html_content)

@app.get("/playground", response_class=HTMLResponse)
async def playground():
    html_content = read_html_file("playground.html")
    if html_content is None:
        return HTMLResponse(content="<h1>playground.html not found</h1>", status_code=404)
    return HTMLResponse(content=html_content)

# Model routes
@app.get("/api/v1/models")
@app.get("/models")
async def return_models():
    return await get_models()

# Search routes
@app.get("/searchgpt")
async def search_gpt(q: str, stream: Optional[bool] = False, systemprompt: Optional[str] = None):
    if not q:
        raise HTTPException(status_code=400, detail="Query parameter 'q' is required")
    
    usage_tracker.record_request(endpoint="/searchgpt")
    
    result = await generate_search_async(q, systemprompt=systemprompt, stream=stream)
    
    if stream:
        async def stream_generator():
            for chunk in result:
                yield chunk
        
        return StreamingResponse(
            stream_generator(),
            media_type="text/event-stream"
        )
    else:
        # For non-streaming, return the collected text
        return JSONResponse(content={"response": result[0] if result else ""})

# Chat completion endpoint
@app.post("/chat/completions")
@app.post("/api/v1/chat/completions")
async def get_completion(payload: Payload, request: Request, authenticated: bool = Depends(verify_api_key)):
    # Check server status
    if not server_status:
        return JSONResponse(
            status_code=503,
            content={"message": "Server is under maintenance. Please try again later."}
        )
    
    model_to_use = payload.model or "gpt-4o-mini"

    # Validate model availability - fast lookup with set
    if available_model_ids and model_to_use not in set(available_model_ids):
        raise HTTPException(
            status_code=400,
            detail=f"Model '{model_to_use}' is not available. Check /models for the available model list."
        )

    # Log request without blocking
    asyncio.create_task(log_request(request, model_to_use))
    usage_tracker.record_request(model=model_to_use, endpoint="/chat/completions")

    # Prepare payload
    payload_dict = payload.dict()
    payload_dict["model"] = model_to_use

    # Get environment variables
    env_vars = get_env_vars()

    # Select the appropriate endpoint (fast lookup with sets)
    if model_to_use in mistral_models:
        endpoint = env_vars['mistral_api']
        custom_headers = {
            "Authorization": f"Bearer {env_vars['mistral_key']}"
        }
    elif model_to_use in alternate_models:
        endpoint = env_vars['secret_api_endpoint_2']
        custom_headers = {}
    else:
        endpoint = env_vars['secret_api_endpoint']
        custom_headers = {}

    # Get a scraper from the pool
    scraper = get_scraper()

    async def stream_generator(payload_dict):
        try:
            # Send POST request with the correct headers
            response = scraper.post(
                f"{endpoint}/v1/chat/completions",
                json=payload_dict,
                headers=custom_headers,
                stream=True
            )

            # Handle response errors
            if response.status_code >= 400:
                error_messages = {
                    422: "Unprocessable entity. Check your payload.",
                    400: "Bad request. Verify input data.",
                    403: "Forbidden. You do not have access to this resource.",
                    404: "The requested resource was not found.",
                }
                detail = error_messages.get(response.status_code, f"Error code: {response.status_code}")
                raise HTTPException(status_code=response.status_code, detail=detail)

            # Stream response lines to the client - use buffer for efficiency
            buffer = []
            buffer_size = 0
            max_buffer = 8192  # 8KB buffer
            
            for line in response.iter_lines():
                if line:
                    decoded = line.decode('utf-8') + "\n"
                    buffer.append(decoded)
                    buffer_size += len(decoded)
                    
                    if buffer_size >= max_buffer:
                        yield ''.join(buffer)
                        buffer = []
                        buffer_size = 0
            
            # Flush remaining buffer
            if buffer:
                yield ''.join(buffer)

        except Exception as e:
            # Use a generic error message that doesn't expose internal details
            raise HTTPException(status_code=500, detail="An error occurred while processing your request")

    return StreamingResponse(stream_generator(payload_dict), media_type="application/json")

# Asynchronous logging function
async def log_request(request, model):
    # Get minimal data for logging
    current_time = (datetime.datetime.utcnow() + datetime.timedelta(hours=5, minutes=30)).strftime("%Y-%m-%d %I:%M:%S %p")
    ip_hash = hash(request.client.host) % 10000  # Hash the IP for privacy
    print(f"Time: {current_time}, IP Hash: {ip_hash}, Model: {model}")

# Image generation endpoint - optimized to use connection pool
@app.api_route("/images/generations", methods=["GET", "POST"])
async def generate_image(
    prompt: Optional[str] = None, 
    model: str = "flux",
    seed: Optional[int] = None,
    width: Optional[int] = None,
    height: Optional[int] = None,
    nologo: Optional[bool] = True,
    private: Optional[bool] = None,
    enhance: Optional[bool] = None,
    request: Request = None,
    authenticated: bool = Depends(verify_api_key)
):
    # Validate the image endpoint
    image_endpoint = get_env_vars()['image_endpoint']
    if not image_endpoint:
        raise HTTPException(status_code=500, detail="Image endpoint not configured in environment variables.")
    
    usage_tracker.record_request(endpoint="/images/generations")
    
    # Handle GET and POST prompts
    if request.method == "POST":
        try:
            body = await request.json()
            prompt = body.get("prompt", "").strip()
            if not prompt:
                raise HTTPException(status_code=400, detail="Prompt cannot be empty")
        except Exception:
            raise HTTPException(status_code=400, detail="Invalid JSON payload")
    elif request.method == "GET":
        if not prompt or not prompt.strip():
            raise HTTPException(status_code=400, detail="Prompt cannot be empty")
        prompt = prompt.strip()

    # Sanitize and encode the prompt
    encoded_prompt = httpx.QueryParams({'prompt': prompt}).get('prompt')

    # Construct the URL with the encoded prompt
    base_url = image_endpoint.rstrip('/')
    url = f"{base_url}/{encoded_prompt}"

    # Prepare query parameters with validation
    params = {}
    if model and isinstance(model, str):
        params['model'] = model
    if seed is not None and isinstance(seed, int):
        params['seed'] = seed
    if width is not None and isinstance(width, int) and 64 <= width <= 2048:
        params['width'] = width
    if height is not None and isinstance(height, int) and 64 <= height <= 2048:
        params['height'] = height
    if nologo is not None:
        params['nologo'] = str(nologo).lower()
    if private is not None:
        params['private'] = str(private).lower()
    if enhance is not None:
        params['enhance'] = str(enhance).lower()

    try:
        # Use the shared httpx client for connection pooling
        client = get_async_client()
        response = await client.get(url, params=params, follow_redirects=True)

        # Check for various error conditions
        if response.status_code != 200:
            error_messages = {
                404: "Image generation service not found",
                400: "Invalid parameters provided to image service",
                429: "Too many requests to image service",
            }
            detail = error_messages.get(response.status_code, f"Image generation failed with status code {response.status_code}")
            raise HTTPException(status_code=response.status_code, detail=detail)

        # Verify content type
        content_type = response.headers.get('content-type', '')
        if not content_type.startswith('image/'):
            raise HTTPException(
                status_code=500,
                detail="Unexpected content type received from image service"
            )

        # Use larger chunks for streaming for better performance
        async def stream_with_larger_chunks():
            chunks = []
            size = 0
            async for chunk in response.aiter_bytes(chunk_size=16384):  # Use 16KB chunks
                chunks.append(chunk)
                size += len(chunk)
                
                if size >= 65536:  # Yield every 64KB
                    yield b''.join(chunks)
                    chunks = []
                    size = 0
            
            if chunks:
                yield b''.join(chunks)

        return StreamingResponse(
            stream_with_larger_chunks(),
            media_type=content_type,
            headers={
                'Cache-Control': 'no-cache, no-store, must-revalidate',
                'Pragma': 'no-cache',
                'Expires': '0'
            }
        )

    except httpx.TimeoutException:
        raise HTTPException(status_code=504, detail="Image generation request timed out")
    except httpx.RequestError:
        raise HTTPException(status_code=500, detail="Failed to contact image service")
    except Exception:
        raise HTTPException(status_code=500, detail="Unexpected error during image generation")

# Meme endpoint with optimized networking
@app.get("/meme")
async def get_meme():
    try:
        # Use the shared client for connection pooling
        client = get_async_client()
        response = await client.get("https://meme-api.com/gimme")
        response_data = response.json()

        meme_url = response_data.get("url")
        if not meme_url:
            raise HTTPException(status_code=404, detail="No meme found")

        image_response = await client.get(meme_url, follow_redirects=True)
        
        # Use larger chunks for streaming
        async def stream_with_larger_chunks():
            chunks = []
            size = 0
            async for chunk in image_response.aiter_bytes(chunk_size=16384):
                chunks.append(chunk)
                size += len(chunk)
                
                if size >= 65536:
                    yield b''.join(chunks)
                    chunks = []
                    size = 0
            
            if chunks:
                yield b''.join(chunks)
                
        return StreamingResponse(
            stream_with_larger_chunks(), 
            media_type=image_response.headers.get("content-type", "image/png"),
            headers={'Cache-Control': 'max-age=3600'}  # Add caching
        )
    except Exception:
        raise HTTPException(status_code=500, detail="Failed to retrieve meme")

# Cache usage statistics
@lru_cache(maxsize=10)
def get_usage_summary(days=7):
    return usage_tracker.get_usage_summary(days)

@app.get("/usage")
async def get_usage(days: int = 7):
    """Retrieve usage statistics"""
    return get_usage_summary(days)

# Generate HTML for usage page
def generate_usage_html(usage_data):
    # Model Usage Table Rows
    model_usage_rows = "\n".join([
        f"""
        <tr>
            <td>{model}</td>
            <td>{model_data['total_requests']}</td>
            <td>{model_data['first_used']}</td>
            <td>{model_data['last_used']}</td>
        </tr>
        """ for model, model_data in usage_data['models'].items()
    ])
    
    # API Endpoint Usage Table Rows
    api_usage_rows = "\n".join([
        f"""
        <tr>
            <td>{endpoint}</td>
            <td>{endpoint_data['total_requests']}</td>
            <td>{endpoint_data['first_used']}</td>
            <td>{endpoint_data['last_used']}</td>
        </tr>
        """ for endpoint, endpoint_data in usage_data['api_endpoints'].items()
    ])
    
    # Daily Usage Table Rows
    daily_usage_rows = "\n".join([
        "\n".join([
            f"""
            <tr>
                <td>{date}</td>
                <td>{entity}</td>
                <td>{requests}</td>
            </tr>
            """ for entity, requests in date_data.items()
        ]) for date, date_data in usage_data['recent_daily_usage'].items()
    ])
    
    html_content = f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <title>Lokiai AI - Usage Statistics</title>
        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet">
        <style>
            :root {{
                --bg-dark: #0f1011;
                --bg-darker: #070708;
                --text-primary: #e6e6e6;
                --text-secondary: #8c8c8c;
                --border-color: #2c2c2c;
                --accent-color: #3a6ee0;
                --accent-hover: #4a7ef0;
            }}
            body {{
                font-family: 'Inter', sans-serif;
                background-color: var(--bg-dark);
                color: var(--text-primary);
                max-width: 1200px;
                margin: 0 auto;
                padding: 40px 20px;
                line-height: 1.6;
            }}
            .logo {{
                display: flex;
                align-items: center;
                justify-content: center;
                margin-bottom: 30px;
            }}
            .logo h1 {{
                font-weight: 600;
                font-size: 2.5em;
                color: var(--text-primary);
                margin-left: 15px;
            }}
            .logo img {{
                width: 60px;
                height: 60px;
                border-radius: 10px;
            }}
            .container {{
                background-color: var(--bg-darker);
                border-radius: 12px;
                padding: 30px;
                box-shadow: 0 15px 40px rgba(0,0,0,0.3);
                border: 1px solid var(--border-color);
            }}
            h2, h3 {{
                color: var(--text-primary);
                border-bottom: 2px solid var(--border-color);
                padding-bottom: 10px;
                font-weight: 500;
            }}
            .total-requests {{
                background-color: var(--accent-color);
                color: white;
                text-align: center;
                padding: 15px;
                border-radius: 8px;
                margin-bottom: 30px;
                font-weight: 600;
                letter-spacing: -0.5px;
            }}
            table {{
                width: 100%;
                border-collapse: separate;
                border-spacing: 0;
                margin-bottom: 30px;
                background-color: var(--bg-dark);
                border-radius: 8px;
                overflow: hidden;
            }}
            th, td {{
                border: 1px solid var(--border-color);
                padding: 12px;
                text-align: left;
                transition: background-color 0.3s ease;
            }}
            th {{
                background-color: #1e1e1e;
                color: var(--text-primary);
                font-weight: 600;
                text-transform: uppercase;
                font-size: 0.9em;
            }}
            tr:nth-child(even) {{
                background-color: rgba(255,255,255,0.05);
            }}
            tr:hover {{
                background-color: rgba(62,100,255,0.1);
            }}
            @media (max-width: 768px) {{
                .container {{
                    padding: 15px;
                }}
                table {{
                    font-size: 0.9em;
                }}
            }}
        </style>
    </head>
    <body>
        <div class="container">
            <div class="logo">
                <img src="data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjAwIiBoZWlnaHQ9IjIwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMTAwIDM1TDUwIDkwaDEwMHoiIGZpbGw9IiMzYTZlZTAiLz48Y2lyY2xlIGN4PSIxMDAiIGN5PSIxNDAiIHI9IjMwIiBmaWxsPSIjM2E2ZWUwIi8+PC9zdmc+" alt="Lokai AI Logo">
                <h1>Lokiai AI</h1>
            </div>
            
            <div class="total-requests">
                Total API Requests: {usage_data['total_requests']}
            </div>
            
            <h2>Model Usage</h2>
            <table>
                <tr>
                    <th>Model</th>
                    <th>Total Requests</th>
                    <th>First Used</th>
                    <th>Last Used</th>
                </tr>
                {model_usage_rows}
            </table>
            
            <h2>API Endpoint Usage</h2>
            <table>
                <tr>
                    <th>Endpoint</th>
                    <th>Total Requests</th>
                    <th>First Used</th>
                    <th>Last Used</th>
                </tr>
                {api_usage_rows}
            </table>
            
            <h2>Daily Usage (Last 7 Days)</h2>
            <table>
                <tr>
                    <th>Date</th>
                    <th>Entity</th>
                    <th>Requests</th>
                </tr>
                {daily_usage_rows}
            </table>
        </div>
    </body>
    </html>
    """
    return html_content

# Cache the usage page HTML
@lru_cache(maxsize=1)
def get_usage_page_html():
    usage_data = get_usage_summary()
    return generate_usage_html(usage_data)

@app.get("/usage/page", response_class=HTMLResponse)
async def usage_page():
    """Serve an HTML page showing usage statistics"""
    # Use cached HTML if available, regenerate if not
    html_content = get_usage_page_html()
    return HTMLResponse(content=html_content)

# Utility function for loading model IDs - optimized to run once at startup
def load_model_ids(json_file_path):
    try:
        with open(json_file_path, 'r') as f:
            models_data = json.load(f)
            # Extract 'id' from each model object and use a set for fast lookups
            return [model['id'] for model in models_data if 'id' in model]
    except Exception as e:
        print(f"Error loading model IDs: {str(e)}")
        return []

@app.on_event("startup")
async def startup_event():
    global available_model_ids
    available_model_ids = load_model_ids("models.json")
    print(f"Loaded {len(available_model_ids)} model IDs")
    
    # Preload scrapers
    for _ in range(MAX_SCRAPERS):
        scraper_pool.append(cloudscraper.create_scraper())
    
    # Validate critical environment variables
    env_vars = get_env_vars()
    missing_vars = []
    
    if not env_vars['secret_api_endpoint']:
        missing_vars.append('SECRET_API_ENDPOINT')
    if not env_vars['secret_api_endpoint_2']:
        missing_vars.append('SECRET_API_ENDPOINT_2')
    if not env_vars['secret_api_endpoint_3']:
        missing_vars.append('SECRET_API_ENDPOINT_3')
    
    if missing_vars:
        print(f"WARNING: The following required environment variables are missing: {', '.join(missing_vars)}")
    
    print("API started successfully with high-performance optimizations")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        app, 
        host="0.0.0.0", 
        port=7860,
        workers=4,  # Multiple workers for better CPU utilization
        loop="uvloop",  # Use uvloop for faster async operations
        http="httptools",  # Faster HTTP parsing
        log_level="warning",  # Reduce logging overhead
        limit_concurrency=100,  # Limit concurrent connections
        timeout_keep_alive=5  # Reduce idle connection time
    )