"""
TensorRT backend for the Neurenix framework.

This module provides hardware acceleration using NVIDIA's TensorRT,
enabling high-performance inference on NVIDIA GPUs.
"""

from typing import Optional, List, Dict, Any, Union, Tuple
import os
import ctypes
import numpy as np

from neurenix.tensor import Tensor
from neurenix.device import Device, DeviceType
from neurenix.nn import Module

_tensorrt_available = False
_tensorrt_lib = None

try:
    if os.name == 'nt':  # Windows
        try:
            _tensorrt_lib = ctypes.CDLL('nvinfer.dll')
            _tensorrt_available = True
        except OSError:
            pass
    elif os.name == 'posix':  # Linux/macOS
        try:
            _tensorrt_lib = ctypes.CDLL('libnvinfer.so.8')
            _tensorrt_available = True
        except OSError:
            try:
                _tensorrt_lib = ctypes.CDLL('libnvinfer.so')
                _tensorrt_available = True
            except OSError:
                try:
                    _tensorrt_lib = ctypes.CDLL('libnvinfer.dylib')  # macOS
                    _tensorrt_available = True
                except OSError:
                    pass
except Exception:
    _tensorrt_available = False

def is_tensorrt_available() -> bool:
    """
    Check if TensorRT is available on the system.
    
    Returns:
        bool: True if TensorRT is available, False otherwise
    """
    return _tensorrt_available

class TensorRTBackend:
    """
    TensorRT backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the TensorRT backend.
        
        Raises:
            RuntimeError: If TensorRT is not available
        """
        if not is_tensorrt_available():
            raise RuntimeError("TensorRT is not available on this system")
        
        self._builder = None
        self._network = None
        self._config = None
        self._engine = None
        self._context = None
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the TensorRT backend.
        
        Returns:
            bool: True if initialization was successful, False otherwise
        """
        if self._initialized:
            return True
        
        try:
            self._builder = self._create_builder()
            if self._builder is None:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"TensorRT initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up TensorRT resources.
        """
        pass
    
    def _create_builder(self):
        """
        Create TensorRT builder.
        """
        pass
    
    def _create_network(self):
        """
        Create TensorRT network.
        """
        pass
    
    def _create_config(self):
        """
        Create TensorRT config.
        """
        pass
    
    def _build_engine(self, network, config):
        """
        Build TensorRT engine.
        """
        pass
    
    def _create_context(self, engine):
        """
        Create TensorRT execution context.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available TensorRT devices.
        
        Returns:
            int: Number of available devices
        """
        if not self.initialize():
            return 0
        
        try:
            import pycuda.driver as cuda
            cuda.init()
            return cuda.Device.count()
        except ImportError:
            cuda_devices = os.environ.get('CUDA_VISIBLE_DEVICES', '')
            if cuda_devices:
                return len(cuda_devices.split(','))
            return 1  # Assume at least one device
    
    def get_device_info(self, device_index: int = 0) -> Dict[str, Any]:
        """
        Get information about a TensorRT device.
        
        Args:
            device_index: Index of the device
            
        Returns:
            Dict: Device information
        """
        if not self.initialize():
            return {}
        
        try:
            import pycuda.driver as cuda
            cuda.init()
            if device_index >= cuda.Device.count():
                return {}
            
            device = cuda.Device(device_index)
            return {
                "name": device.name(),
                "vendor": "NVIDIA",
                "type": "GPU",
                "compute_units": device.get_attribute(cuda.device_attribute.MULTIPROCESSOR_COUNT),
                "memory": device.total_memory()
            }
        except ImportError:
            return {
                "name": "TensorRT Device",
                "vendor": "NVIDIA",
                "type": "GPU",
                "compute_units": 0,
                "memory": 0
            }
    
    def optimize_model(self, model: Module, input_shapes: Dict[str, Tuple[int, ...]], 
                      precision: str = 'fp32', workspace_size: int = 1 << 30) -> Module:
        """
        Optimize a model using TensorRT.
        
        Args:
            model: The model to optimize
            input_shapes: Dictionary mapping input names to shapes
            precision: Precision to use ('fp32', 'fp16', 'int8')
            workspace_size: Maximum workspace size in bytes
            
        Returns:
            Module: Optimized model
        """
        if not self.initialize():
            return model
        
        return model
    
    def inference(self, model: Module, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
        """
        Run inference on a model using TensorRT.
        
        Args:
            model: The model to run
            inputs: Dictionary of input tensors
            
        Returns:
            Dict[str, Tensor]: Dictionary of output tensors
        """
        if not self.initialize():
            return model(inputs)
        
        return model(inputs)
