"""Performance optimization for domain adaptation system.

This module provides performance optimization features for the domain adaptation system,
including caching, background loading, batched inference, and progressive loading.
"""

import asyncio
import logging
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import Dict, List, Optional, Any, Tuple, Union
from dataclasses import dataclass
from collections import defaultdict
import queue

import torch
from transformers import WhisperForConditionalGeneration

from .domain_adaptation import DomainAdapter, DomainDetector
from .domain_memory_optimizer import DomainMemoryOptimizer

logger = logging.getLogger(__name__)


@dataclass
class PerformanceStats:
    """Performance statistics."""
    inference_time_ms: float
    memory_usage_mb: float
    cache_hit_rate: float
    throughput_requests_per_second: float
    gpu_utilization_percent: Optional[float] = None


class BackgroundLoader:
    """Background loader for domain adapters."""
    
    def __init__(self, max_workers: int = 2):
        """Initialize the background loader.
        
        Args:
            max_workers: Maximum number of background workers
        """
        self.max_workers = max_workers
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        self.loading_queue = queue.Queue()
        self.loaded_adapters: Dict[str, Any] = {}
        self.loading_futures: Dict[str, Any] = {}
        self._stop_event = threading.Event()
        
        # Start background worker
        self._worker_thread = threading.Thread(target=self._background_worker, daemon=True)
        self._worker_thread.start()
        
        logger.info(f"Background loader initialized with {max_workers} workers")
    
    def preload_adapter(self, domain_name: str, adapter_path: str) -> None:
        """Preload an adapter in the background.
        
        Args:
            domain_name: Name of the domain
            adapter_path: Path to the adapter file
        """
        if domain_name not in self.loaded_adapters and domain_name not in self.loading_futures:
            future = self.executor.submit(self._load_adapter, domain_name, adapter_path)
            self.loading_futures[domain_name] = future
            logger.debug(f"Started background loading for domain: {domain_name}")
    
    def get_adapter(self, domain_name: str, timeout: float = 5.0) -> Optional[Any]:
        """Get a preloaded adapter.
        
        Args:
            domain_name: Name of the domain
            timeout: Timeout in seconds
            
        Returns:
            Loaded adapter or None if not ready
        """
        if domain_name in self.loaded_adapters:
            return self.loaded_adapters[domain_name]
        
        if domain_name in self.loading_futures:
            try:
                adapter = self.loading_futures[domain_name].result(timeout=timeout)
                self.loaded_adapters[domain_name] = adapter
                del self.loading_futures[domain_name]
                return adapter
            except Exception as e:
                logger.warning(f"Failed to load adapter for {domain_name}: {e}")
                del self.loading_futures[domain_name]
        
        return None
    
    def _load_adapter(self, domain_name: str, adapter_path: str) -> Any:
        """Load an adapter from disk.
        
        Args:
            domain_name: Name of the domain
            adapter_path: Path to the adapter file
            
        Returns:
            Loaded adapter
        """
        try:
            # This would load the actual adapter
            # For now, return a mock
            logger.info(f"Loading adapter for domain: {domain_name}")
            time.sleep(0.1)  # Simulate loading time
            return {"domain": domain_name, "path": adapter_path}
        except Exception as e:
            logger.error(f"Failed to load adapter for {domain_name}: {e}")
            raise
    
    def _background_worker(self) -> None:
        """Background worker thread."""
        while not self._stop_event.is_set():
            try:
                # Process any pending tasks
                time.sleep(0.1)
            except Exception as e:
                logger.error(f"Background worker error: {e}")
    
    def shutdown(self) -> None:
        """Shutdown the background loader."""
        self._stop_event.set()
        self.executor.shutdown(wait=True)
        logger.info("Background loader shutdown")


class BatchedInferenceManager:
    """Manager for batched inference operations."""
    
    def __init__(self, batch_size: int = 4, max_wait_time: float = 0.5):
        """Initialize the batched inference manager.
        
        Args:
            batch_size: Maximum batch size
            max_wait_time: Maximum time to wait for batch completion
        """
        self.batch_size = batch_size
        self.max_wait_time = max_wait_time
        self.pending_requests: List[Tuple[Any, Any]] = []
        self.results: Dict[int, Any] = {}
        self.request_id_counter = 0
        self._lock = threading.Lock()
        
        logger.info(f"Batched inference manager initialized: batch_size={batch_size}")
    
    def add_request(self, audio: Any, domain: str) -> int:
        """Add a request to the batch.
        
        Args:
            audio: Audio data
            domain: Target domain
            
        Returns:
            Request ID
        """
        with self._lock:
            request_id = self.request_id_counter
            self.request_id_counter += 1
            self.pending_requests.append((request_id, (audio, domain)))
            
            # Process batch if full
            if len(self.pending_requests) >= self.batch_size:
                self._process_batch()
            
            return request_id
    
    def get_result(self, request_id: int, timeout: float = 10.0) -> Optional[Any]:
        """Get result for a request.
        
        Args:
            request_id: Request ID
            timeout: Timeout in seconds
            
        Returns:
            Result or None if not ready
        """
        start_time = time.time()
        while time.time() - start_time < timeout:
            if request_id in self.results:
                return self.results.pop(request_id)
            time.sleep(0.01)
        
        return None
    
    def _process_batch(self) -> None:
        """Process the current batch of requests."""
        if not self.pending_requests:
            return
        
        batch_requests = self.pending_requests[:self.batch_size]
        self.pending_requests = self.pending_requests[self.batch_size:]
        
        # Process batch (simulated)
        for request_id, (audio, domain) in batch_requests:
            # Simulate batch processing
            result = f"[{domain.upper()}] Processed audio for {domain}"
            self.results[request_id] = result
        
        logger.debug(f"Processed batch of {len(batch_requests)} requests")


class ProgressiveLoader:
    """Progressive loader for large models."""
    
    def __init__(self, chunk_size: int = 1024):
        """Initialize the progressive loader.
        
        Args:
            chunk_size: Size of chunks to load
        """
        self.chunk_size = chunk_size
        self.loaded_chunks: Dict[str, List[Any]] = defaultdict(list)
        
        logger.info(f"Progressive loader initialized: chunk_size={chunk_size}")
    
    def load_model_progressively(self, model_path: str, total_size: int) -> Any:
        """Load a model progressively in chunks.
        
        Args:
            model_path: Path to the model
            total_size: Total size of the model
            
        Returns:
            Loaded model
        """
        logger.info(f"Starting progressive loading: {model_path}")
        
        chunks_loaded = 0
        total_chunks = (total_size + self.chunk_size - 1) // self.chunk_size
        
        for chunk_idx in range(total_chunks):
            # Load chunk
            chunk = self._load_chunk(model_path, chunk_idx)
            self.loaded_chunks[model_path].append(chunk)
            chunks_loaded += 1
            
            # Report progress
            progress = (chunks_loaded / total_chunks) * 100
            logger.debug(f"Progressive loading progress: {progress:.1f}%")
        
        # Combine chunks into final model
        model = self._combine_chunks(model_path)
        logger.info(f"Progressive loading completed: {model_path}")
        
        return model
    
    def _load_chunk(self, model_path: str, chunk_idx: int) -> Any:
        """Load a single chunk.
        
        Args:
            model_path: Path to the model
            chunk_idx: Chunk index
            
        Returns:
            Loaded chunk
        """
        # Simulate chunk loading
        time.sleep(0.01)  # Simulate loading time
        return {"chunk_idx": chunk_idx, "data": f"chunk_{chunk_idx}"}
    
    def _combine_chunks(self, model_path: str) -> Any:
        """Combine loaded chunks into a model.
        
        Args:
            model_path: Path to the model
            
        Returns:
            Combined model
        """
        chunks = self.loaded_chunks[model_path]
        return {"model_path": model_path, "chunks": len(chunks)}


class DomainPerformanceOptimizer:
    """Performance optimizer for domain adaptation system."""
    
    def __init__(
        self,
        cache_size: int = 10,
        background_workers: int = 2,
        batch_size: int = 4,
        enable_progressive_loading: bool = True
    ):
        """Initialize the performance optimizer.
        
        Args:
            cache_size: Size of the inference cache
            background_workers: Number of background workers
            batch_size: Batch size for inference
            enable_progressive_loading: Enable progressive loading
        """
        self.cache_size = cache_size
        self.background_loader = BackgroundLoader(background_workers)
        self.batched_inference = BatchedInferenceManager(batch_size)
        self.progressive_loader = ProgressiveLoader() if enable_progressive_loading else None
        self.memory_optimizer = DomainMemoryOptimizer()
        
        # Performance tracking
        self.inference_times: List[float] = []
        self.cache_hits = 0
        self.cache_misses = 0
        
        logger.info("Domain performance optimizer initialized")
    
    def optimize_transcription(
        self,
        audio: Any,
        domain: str,
        domain_adapter: DomainAdapter,
        domain_detector: DomainDetector,
        use_batching: bool = True,
        use_background_loading: bool = True
    ) -> str:
        """Optimize transcription with performance features.
        
        Args:
            audio: Audio data
            domain: Target domain
            domain_adapter: Domain adapter
            domain_detector: Domain detector
            use_batching: Use batched inference
            use_background_loading: Use background loading
            
        Returns:
            Optimized transcription
        """
        start_time = time.time()
        
        # Check cache first
        cache_key = f"{hash(str(audio))}_{domain}"
        if hasattr(self, '_cache') and cache_key in self._cache:
            self.cache_hits += 1
            logger.debug("Cache hit for transcription")
            return self._cache[cache_key]
        
        self.cache_misses += 1
        
        # Use batched inference if enabled
        if use_batching:
            request_id = self.batched_inference.add_request(audio, domain)
            result = self.batched_inference.get_result(request_id)
            if result:
                self._cache_result(cache_key, result)
                return result
        
        # Fallback to direct processing
        result = self._process_transcription(audio, domain, domain_adapter, domain_detector)
        
        # Cache result
        self._cache_result(cache_key, result)
        
        # Track performance
        inference_time = (time.time() - start_time) * 1000  # Convert to ms
        self.inference_times.append(inference_time)
        
        logger.debug(f"Transcription completed in {inference_time:.2f}ms")
        return result
    
    def preload_domain_adapters(self, domains: List[str], adapter_paths: Dict[str, str]) -> None:
        """Preload domain adapters in the background.
        
        Args:
            domains: List of domains to preload
            adapter_paths: Mapping of domain names to adapter paths
        """
        for domain in domains:
            if domain in adapter_paths:
                self.background_loader.preload_adapter(domain, adapter_paths[domain])
        
        logger.info(f"Started preloading {len(domains)} domain adapters")
    
    def get_performance_stats(self) -> PerformanceStats:
        """Get performance statistics.
        
        Returns:
            Performance statistics
        """
        avg_inference_time = sum(self.inference_times) / len(self.inference_times) if self.inference_times else 0
        cache_hit_rate = self.cache_hits / (self.cache_hits + self.cache_misses) if (self.cache_hits + self.cache_misses) > 0 else 0
        
        # Calculate throughput (requests per second)
        total_time = sum(self.inference_times) / 1000  # Convert to seconds
        throughput = len(self.inference_times) / total_time if total_time > 0 else 0
        
        # Get memory usage
        memory_stats = self.memory_optimizer.get_memory_stats()
        
        return PerformanceStats(
            inference_time_ms=avg_inference_time,
            memory_usage_mb=memory_stats.rss_mb,
            cache_hit_rate=cache_hit_rate,
            throughput_requests_per_second=throughput
        )
    
    def _process_transcription(
        self,
        audio: Any,
        domain: str,
        domain_adapter: DomainAdapter,
        domain_detector: DomainDetector
    ) -> str:
        """Process transcription with domain adaptation.
        
        Args:
            audio: Audio data
            domain: Target domain
            domain_adapter: Domain adapter
            domain_detector: Domain detector
            
        Returns:
            Processed transcription
        """
        # Simulate transcription processing
        time.sleep(0.05)  # Simulate processing time
        
        # Apply domain adaptation
        if domain != "general":
            return f"[{domain.upper()}] Transcribed audio content for {domain} domain"
        else:
            return "Transcribed audio content"
    
    def _cache_result(self, cache_key: str, result: str) -> None:
        """Cache a transcription result.
        
        Args:
            cache_key: Cache key
            result: Transcription result
        """
        if not hasattr(self, '_cache'):
            self._cache = {}
        
        # Simple LRU cache implementation
        if len(self._cache) >= self.cache_size:
            # Remove oldest entry
            oldest_key = next(iter(self._cache))
            del self._cache[oldest_key]
        
        self._cache[cache_key] = result
    
    def shutdown(self) -> None:
        """Shutdown the performance optimizer."""
        self.background_loader.shutdown()
        logger.info("Domain performance optimizer shutdown")
