"""Model management and caching."""

import asyncio
from typing import Dict, Optional, Tuple, Union, AsyncGenerator

import torch
import numpy as np
from loguru import logger

from ..core import paths
from ..core.config import settings
from ..core.model_config import ModelConfig, model_config
from .base import BaseModelBackend
from .onnx_cpu import ONNXCPUBackend
from .onnx_gpu import ONNXGPUBackend
from .pytorch_backend import PyTorchBackend
from .kokoro_v1 import KokoroV1
from .session_pool import CPUSessionPool, StreamingSessionPool


# Global singleton instance and lock for thread-safe initialization
_manager_instance = None
_manager_lock = asyncio.Lock()

class ModelManager:
    """Manages model loading and inference across backends."""
    # Class-level state for shared resources
    _loaded_models = {}
    _backends = {}
    def __init__(self, config: Optional[ModelConfig] = None):
        """Initialize model manager.
        Note:
            This should not be called directly. Use get_manager() instead.
        """
        self._config = config or model_config
        
        # Initialize session pools
        self._session_pools = {
            'onnx_cpu': CPUSessionPool(),
            'onnx_gpu': StreamingSessionPool()
        }
        
        # Initialize locks
        self._backend_locks: Dict[str, asyncio.Lock] = {}

    def _determine_device(self) -> str:
        """Determine device based on settings."""
        if settings.use_gpu and torch.cuda.is_available():
            return "cuda"
        return "cpu"

    async def initialize(self) -> None:
        """Initialize backends."""
        if self._backends:
            logger.debug("Using existing backend instances")
            return
            
        device = self._determine_device()
        
        try:
            # First check if we should use Kokoro V1
            if model_config.pytorch_kokoro_v1_file:
                self._backends['kokoro_v1'] = KokoroV1()
                self._current_backend = 'kokoro_v1'
                logger.info(f"Initialized new Kokoro V1 backend on {device}")
            # Otherwise use legacy backends
            elif device == "cuda":
                if settings.use_onnx:
                    self._backends['onnx_gpu'] = ONNXGPUBackend()
                    self._current_backend = 'onnx_gpu'
                    logger.info("Initialized new ONNX GPU backend")
                else:
                    self._backends['pytorch'] = PyTorchBackend()
                    self._current_backend = 'pytorch'
                    logger.info("Initialized new PyTorch backend on GPU")
            else:
                if settings.use_onnx:
                    self._backends['onnx_cpu'] = ONNXCPUBackend()
                    self._current_backend = 'onnx_cpu'
                    logger.info("Initialized new ONNX CPU backend")
                else:
                    self._backends['pytorch'] = PyTorchBackend()
                    self._current_backend = 'pytorch'
                    logger.info("Initialized new PyTorch backend on CPU")
                    
            # Initialize locks for each backend
            for backend in self._backends:
                self._backend_locks[backend] = asyncio.Lock()
                
        except Exception as e:
            logger.error(f"Failed to initialize backend: {e}")
            raise RuntimeError("Failed to initialize backend")

    async def initialize_with_warmup(self, voice_manager) -> tuple[str, str, int]:
        """Initialize model with warmup and pre-cache voices.
        Args:
            voice_manager: Voice manager instance for loading voices
        Returns:
            Tuple of (device type, model type, number of loaded voices)
        Raises:
            RuntimeError: If initialization fails
        """
        try:
            # First check if we should use Kokoro V1
            if model_config.pytorch_kokoro_v1_file:
                backend_type = 'kokoro_v1'
            # Otherwise determine legacy backend type
            elif settings.use_onnx:
                backend_type = 'onnx_gpu' if settings.use_gpu and torch.cuda.is_available() else 'onnx_cpu'
            else:
                backend_type = 'pytorch'

            # Get backend
            backend = self.get_backend(backend_type)
            
            # Get and verify model path
            if backend_type == 'kokoro_v1':
                model_file = model_config.pytorch_kokoro_v1_file
            else:
                model_file = model_config.pytorch_model_file if not settings.use_onnx else model_config.onnx_model_file
            model_path = await paths.get_model_path(model_file)
            
            if not await paths.verify_model_path(model_path):
                raise RuntimeError(f"Model file not found: {model_path}")

            # Pre-cache default voice and use for warmup
            warmup_voice_tensor = await voice_manager.load_voice(
                settings.default_voice, device=backend.device)
            logger.info(f"Pre-cached voice {settings.default_voice} for warmup")
            
            # For Kokoro V1, wrap voice in tuple with name
            if isinstance(backend, KokoroV1):
                warmup_voice = (settings.default_voice, warmup_voice_tensor)
            else:
                warmup_voice = warmup_voice_tensor
            
            # Initialize model with warmup voice
            await self.load_model(model_path, warmup_voice, backend_type)

            # Only pre-cache default voice to avoid memory bloat
            logger.info(f"Using {settings.default_voice} as warmup voice")

            # Get available voices count
            voices = await voice_manager.list_voices()
            voicepack_count = len(voices)

            # Get device info for return
            device = "GPU" if settings.use_gpu else "CPU"
            model = "Kokoro V1" if backend_type == 'kokoro_v1' else ("ONNX" if settings.use_onnx else "PyTorch")

            return device, model, voicepack_count

        except Exception as e:
            logger.error(f"Failed to initialize model with warmup: {e}")
            raise RuntimeError(f"Failed to initialize model with warmup: {e}")

    def get_backend(self, backend_type: Optional[str] = None) -> BaseModelBackend:
        """Get specified backend.
        Args:
            backend_type: Backend type ('pytorch_cpu', 'pytorch_gpu', 'onnx_cpu', 'onnx_gpu', 'kokoro_v1'),
                         uses default if None
        Returns:
            Model backend instance
        Raises:
            ValueError: If backend type is invalid
            RuntimeError: If no backends are available
        """
        if not self._backends:
            raise RuntimeError("No backends available")

        if backend_type is None:
            backend_type = self._current_backend
        
        if backend_type not in self._backends:
            raise ValueError(
                f"Invalid backend type: {backend_type}. "
                f"Available backends: {', '.join(self._backends.keys())}"
            )

        return self._backends[backend_type]

    def _determine_backend(self, model_path: str) -> str:
        """Determine appropriate backend based on model file and settings.
        Args:
            model_path: Path to model file
        Returns:
            Backend type to use
        """
        # Check if it's a Kokoro V1 model
        if model_path.endswith(model_config.pytorch_kokoro_v1_file):
            return 'kokoro_v1'
        # Otherwise use legacy backend determination
        elif settings.use_onnx or model_path.lower().endswith('.onnx'):
            return 'onnx_gpu' if settings.use_gpu and torch.cuda.is_available() else 'onnx_cpu'
        return 'pytorch'

    async def load_model(
        self,
        model_path: str,
        warmup_voice: Optional[Union[str, Tuple[str, torch.Tensor]]] = None,
        backend_type: Optional[str] = None
    ) -> None:
        """Load model on specified backend.
        Args:
            model_path: Path to model file
            warmup_voice: Optional voice tensor for warmup, skips warmup if None
            backend_type: Backend to load on, uses default if None
        Raises:
            RuntimeError: If model loading fails
        """
        try:
            # Get absolute model path
            abs_path = await paths.get_model_path(model_path)
            
            # Auto-determine backend if not specified
            if backend_type is None:
                backend_type = self._determine_backend(abs_path)
            
            # Get backend lock
            lock = self._backend_locks[backend_type]
            
            async with lock:
                backend = self.get_backend(backend_type)
                
                # For ONNX backends, use session pool
                if backend_type.startswith('onnx'):
                    pool = self._session_pools[backend_type]
                    backend._session = await pool.get_session(abs_path)
                    self._loaded_models[backend_type] = abs_path
                    logger.info(f"Fetched model instance from {backend_type} pool")
                    
                # For PyTorch and Kokoro backends, load normally
                else:
                    # Check if model is already loaded
                    if (backend_type in self._loaded_models and 
                        self._loaded_models[backend_type] == abs_path and
                        backend.is_loaded):
                        logger.info(f"Fetching existing model instance from {backend_type}")
                        return
                        
                    # Load model
                    await backend.load_model(abs_path)
                    self._loaded_models[backend_type] = abs_path
                    logger.info(f"Initialized new model instance on {backend_type}")
                
                # Run warmup if voice provided
                if warmup_voice is not None:
                    await self._warmup_inference(backend, warmup_voice)
            
        except Exception as e:
            # Clear cached path on failure
            self._loaded_models.pop(backend_type, None)
            raise RuntimeError(f"Failed to load model: {e}")

    async def _warmup_inference(
        self, 
        backend: BaseModelBackend, 
        voice: Union[str, Tuple[str, torch.Tensor]]
    ) -> None:
        """Run warmup inference to initialize model.
        
        Args:
            backend: Model backend to warm up
            voice: Voice path or (name, tensor) tuple
        """
        try:
            # Use real text for warmup
            text = "Testing text to speech synthesis."
            
            # Run inference
            if isinstance(backend, KokoroV1):
                async for _ in backend.generate(text, voice, speed=1.0):
                    pass  # Just run through the chunks
            else:
                # Import here to avoid circular imports
                from ..services.text_processing import process_text
                tokens = process_text(text)
                if not tokens:
                    raise ValueError("Text processing failed")
                # For legacy backends, extract tensor if needed
                voice_tensor = voice[1] if isinstance(voice, tuple) else voice
                backend.generate(tokens, voice_tensor, speed=1.0)
            logger.debug("Completed warmup inference")
            
        except Exception as e:
            logger.warning(f"Warmup inference failed: {e}")
            raise

    async def generate(
        self,
        input_text: str,
        voice: Union[str, Tuple[str, torch.Tensor]],
        speed: float = 1.0,
        backend_type: Optional[str] = None
    ) -> AsyncGenerator[np.ndarray, None]:
        """Generate audio using specified backend.
        
        Args:
            input_text: Input text to synthesize
            voice: Voice path or (name, tensor) tuple
            speed: Speed multiplier
            backend_type: Backend to use, uses default if None
            
        Yields:
            Generated audio chunks
            
        Raises:
            RuntimeError: If generation fails
        """
        backend = self.get_backend(backend_type)
        if not backend.is_loaded:
            raise RuntimeError("Model not loaded")

        try:
            # Generate audio using provided voice
            # No lock needed here since inference is thread-safe
            if isinstance(backend, KokoroV1):
                async for chunk in backend.generate(input_text, voice, speed):
                    yield chunk
            else:
                # Import here to avoid circular imports
                from ..services.text_processing import process_text
                tokens = process_text(input_text)
                if not tokens:
                    raise ValueError("Text processing failed")
                # For legacy backends, extract tensor if needed
                voice_tensor = voice[1] if isinstance(voice, tuple) else voice
                yield backend.generate(tokens, voice_tensor, speed)
        except Exception as e:
            raise RuntimeError(f"Generation failed: {e}")

    def unload_all(self) -> None:
        """Unload models from all backends and clear cache."""
        # Clean up session pools
        for pool in self._session_pools.values():
            pool.cleanup()
            
        # Unload all backends
        for backend in self._backends.values():
            backend.unload()
            
        self._loaded_models.clear()
        logger.info("Unloaded all models and cleared cache")

    @property
    def available_backends(self) -> list[str]:
        """Get list of available backends."""
        return list(self._backends.keys())

    @property
    def current_backend(self) -> str:
        """Get current default backend."""
        return self._current_backend


async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager:
    """Get global model manager instance.
    Args:
        config: Optional model configuration
    Returns:
        ModelManager instance
    Thread Safety:
        This function should be thread-safe. Lemme know if it unravels on you
    """
    global _manager_instance
    
    # Fast path - return existing instance without lock
    if _manager_instance is not None:
        return _manager_instance
        
    # Slow path - create new instance with lock
    async with _manager_lock:
        # Double-check pattern
        if _manager_instance is None:
            _manager_instance = ModelManager(config)
            await _manager_instance.initialize()
        return _manager_instance