import numpy as np
import time
import subprocess
import json
from typing import Dict, List, Tuple, Optional, Any, Callable
import warnings

try:
    import torch
    import torch.nn.functional as F
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False
    warnings.warn("PyTorch not available. GPU optimizations disabled.")

try:
    import cupy as cp
    CUPY_AVAILABLE = True
except ImportError:
    CUPY_AVAILABLE = False
    warnings.warn("CuPy not available. Some GPU features disabled.")


class GPURingOptimizer:
    """
    GPU-specific optimizations using ring theory patterns.
    """
    
    def __init__(self, device: str = "cuda:0"):
        """
        Initialize GPU optimizer.
        
        Args:
            device: CUDA device to use
        """
        self.device = device
        self.resonant_sizes = self._get_gpu_resonant_sizes()
        self.energy_readings = []
        
    def _get_gpu_resonant_sizes(self) -> List[int]:
        """Определяет резонансные размеры для конкретной GPU."""
        # Базовые размеры из CPU эксперимента
        base_sizes = [32, 64, 128, 256, 512, 1024]
        
        if TORCH_AVAILABLE and torch.cuda.is_available():
            try:
                # Адаптируем под архитектуру GPU
                props = torch.cuda.get_device_properties(0)
                warp_size = props.warpSize  # Обычно 32
                
                # Оптимальные размеры для GPU
                optimal_sizes = [
                    warp_size,
                    warp_size * 2,
                    warp_size * 4,
                    warp_size * 8,
                    warp_size * 16,
                    warp_size * 32,
                ]
                
                # Объединяем с базовыми
                all_sizes = list(set(base_sizes + optimal_sizes))
                all_sizes.sort()
                return all_sizes
                
            except:
                pass
        
        return base_sizes
    
    def optimize_cuda_kernel(self, 
                            kernel_func: Callable,
                            grid_size: Tuple[int, int, int],
                            block_size: Tuple[int, int, int],
                            args: tuple) -> Dict[str, Any]:
        """
        Optimize CUDA kernel execution using ring patterns.
        
        Args:
            kernel_func: CUDA kernel function
            grid_size: Grid dimensions
            block_size: Block dimensions
            args: Kernel arguments
            
        Returns:
            Optimization results
        """
        if not TORCH_AVAILABLE or not torch.cuda.is_available():
            return {"error": "CUDA not available"}
        
        results = {
            "original": self._benchmark_kernel(kernel_func, grid_size, block_size, args),
            "optimized": None
        }
        
        # Оптимизируем размеры grid/block
        optimized_grid = self._optimize_grid_dims(grid_size)
        optimized_block = self._optimize_block_dims(block_size)
        
        results["optimized"] = self._benchmark_kernel(
            kernel_func, optimized_grid, optimized_block, args
        )
        
        # Рассчитываем улучшение
        if results["original"]["energy"] > 0:
            energy_savings = (
                results["original"]["energy"] - results["optimized"]["energy"]
            ) / results["original"]["energy"] * 100
            
            results["energy_savings_percent"] = max(0.0, energy_savings)
            results["performance_improvement"] = (
                results["optimized"]["throughput"] / results["original"]["throughput"] - 1
            ) * 100
        
        return results
    
    def _benchmark_kernel(self, kernel_func, grid_size, block_size, args):
        """Замеряет производительность и энергопотребление ядра."""
        start_time = time.time()
        start_energy = self._get_gpu_energy()
        
        # Выполняем ядро несколько раз для точности
        iterations = 100
        for _ in range(iterations):
            kernel_func[grid_size, block_size](*args)
        
        torch.cuda.synchronize()
        
        end_time = time.time()
        end_energy = self._get_gpu_energy()
        
        duration = end_time - start_time
        energy_used = end_energy - start_energy
        
        # Расчет пропускной способности
        # (упрощенно, зависит от конкретного ядра)
        total_work = np.prod(grid_size) * np.prod(block_size) * iterations
        throughput = total_work / duration / 1e9  # GFlops (оценка)
        
        return {
            "duration": duration,
            "energy": energy_used,
            "throughput": throughput,
            "grid_size": grid_size,
            "block_size": block_size
        }
    
    def _optimize_grid_dims(self, grid_size: Tuple[int, int, int]) -> Tuple[int, int, int]:
        """Оптимизирует размеры grid."""
        gx, gy, gz = grid_size
        
        # Приводим к резонансным размерам
        opt_gx = self._find_nearest_resonant(gx)
        opt_gy = self._find_nearest_resonant(gy)
        opt_gz = self._find_nearest_resonant(gz) if gz > 1 else 1
        
        return (opt_gx, opt_gy, opt_gz)
    
    def _optimize_block_dims(self, block_size: Tuple[int, int, int]) -> Tuple[int, int, int]:
        """Оптимизирует размеры block."""
        bx, by, bz = block_size
        
        # Для block обычно используют степени двойки
        powers_of_two = [32, 64, 128, 256, 512, 1024]
        
        opt_bx = min(powers_of_two, key=lambda x: abs(x - bx))
        opt_by = 1  # Обычно для лучшей occupancy
        opt_bz = 1
        
        return (opt_bx, opt_by, opt_bz)
    
    def _find_nearest_resonant(self, size: int) -> int:
        """Находит ближайший резонансный размер."""
        distances = [(abs(size - rs), rs) for rs in self.resonant_sizes]
        distances.sort()
        return distances[0][1]
    
    def _get_gpu_energy(self) -> float:
        """Получает текущее энергопотребление GPU."""
        try:
            if TORCH_AVAILABLE and torch.cuda.is_available():
                # Используем nvidia-smi через subprocess
                result = subprocess.run(
                    ['nvidia-smi', '--query-gpu=power.draw', '--format=csv,noheader,nounits'],
                    capture_output=True, text=True
                )
                
                if result.returncode == 0:
                    lines = result.stdout.strip().split('\n')
                    if lines:
                        return float(lines[0].strip())
        except:
            pass
        
        return 0.0
    
    def optimize_tensor_operation(self, 
                                 tensor: torch.Tensor,
                                 tensor2: Optional[torch.Tensor] = None,
                                 operation: str = "matmul") -> torch.Tensor:
        """
        Optimize tensor operations using ring patterns.
        
        Args:
            tensor: Input tensor
            tensor2: Second input tensor for binary operations
            operation: Operation to perform
            
        Returns:
            Optimized result
        """
        if not TORCH_AVAILABLE:
            if tensor2 is None:
                return tensor
            else:
                return tensor2 if operation == "matmul" else tensor
        
        # Оптимизируем размеры тензора
        optimal_shape = self._optimize_tensor_shape(tensor.shape)
        
        if tensor.shape != optimal_shape:
            tensor = self._reshape_tensor(tensor, optimal_shape)
        
        # Если есть второй тензор, оптимизируем и его
        if tensor2 is not None:
            optimal_shape2 = self._optimize_tensor_shape(tensor2.shape)
            if tensor2.shape != optimal_shape2:
                tensor2 = self._reshape_tensor(tensor2, optimal_shape2)
        
        # Выполняем операцию
        if operation == "matmul":
            if tensor2 is None:
                # Для self-matmul: A * Aᵀ
                return self._ring_matmul(tensor, tensor.T)
            else:
                return self._ring_matmul(tensor, tensor2)
        elif operation == "conv":
            return self._ring_convolution(tensor)
        else:
            return tensor if tensor2 is None else tensor2
    
    def _optimize_tensor_shape(self, shape: Tuple[int, ...]) -> Tuple[int, ...]:
        """Оптимизирует форму тензора."""
        if len(shape) >= 2:
            # Для матричных операций
            rows, cols = shape[-2], shape[-1]
            
            opt_rows = self._find_nearest_resonant(rows)
            opt_cols = self._find_nearest_resonant(cols)
            
            return shape[:-2] + (opt_rows, opt_cols)
        else:
            # Для векторов
            size = shape[0]
            opt_size = self._find_nearest_resonant(size)
            return (opt_size,)
    
    def _reshape_tensor(self, tensor: torch.Tensor, new_shape: Tuple[int, ...]) -> torch.Tensor:
        """Изменяет форму тензора с сохранением данных."""
        try:
            return tensor.reshape(new_shape)
        except:
            # Если не получается, создаем новый тензор
            result = torch.zeros(new_shape, device=tensor.device, dtype=tensor.dtype)
            
            # Копируем данные
            slices = [slice(0, min(s1, s2)) for s1, s2 in zip(tensor.shape, new_shape)]
            result[slices] = tensor[slices]
            
            return result
    
    def _ring_matmul(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
        """Умножение матриц с кольцевой оптимизацией."""
        # Простая реализация - в будущем можно заменить на оптимизированное ядро
        return torch.matmul(a, b)
    
    def _ring_convolution(self, x: torch.Tensor) -> torch.Tensor:
        """Свертка с кольцевой оптимизацией."""
        # Используем PyTorch свертку с оптимальными параметрами
        if len(x.shape) == 4:  # [batch, channels, height, width]
            # Оптимальный размер ядра
            kernel_size = 3  # Резонансный размер
            
            # Создаем простой фильтр
            weight = torch.ones(1, 1, kernel_size, kernel_size, device=x.device) / (kernel_size ** 2)
            
            return F.conv2d(x, weight, padding=kernel_size // 2)
        else:
            return x


def gpu_energy_monitor(interval: float = 1.0, duration: float = 10.0) -> Dict[str, Any]:
    """
    Monitor GPU energy consumption during computations.
    
    Args:
        interval: Measurement interval in seconds
        duration: Total monitoring duration
        
    Returns:
        Energy statistics
    """
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        return {"error": "GPU not available"}
    
    readings = []
    start_time = time.time()
    
    while time.time() - start_time < duration:
        try:
            # Читаем энергопотребление через nvidia-smi
            result = subprocess.run(
                ['nvidia-smi', '--query-gpu=power.draw,temperature.gpu,utilization.gpu',
                 '--format=csv,noheader,nounits'],
                capture_output=True, text=True
            )
            
            if result.returncode == 0:
                data = result.stdout.strip().split(',')
                if len(data) >= 3:
                    reading = {
                        'timestamp': time.time(),
                        'power_w': float(data[0].strip()),
                        'temp_c': float(data[1].strip()),
                        'utilization': float(data[2].strip())
                    }
                    readings.append(reading)
        
        except:
            pass
        
        time.sleep(interval)
    
    # Анализируем данные
    if readings:
        powers = [r['power_w'] for r in readings]
        utils = [r['utilization'] for r in readings]
        
        return {
            'average_power': np.mean(powers),
            'max_power': np.max(powers),
            'min_power': np.min(powers),
            'average_utilization': np.mean(utils),
            'readings': readings
        }
    
    return {"error": "No readings collected"}


def find_gpu_resonance(max_size: int = 1024) -> Dict[str, List[int]]:
    """
    Find resonant sizes for current GPU by benchmarking.
    
    Args:
        max_size: Maximum size to test
        
    Returns:
        Dictionary with resonant sizes
    """
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        return {"error": "GPU not available"}
    
    # Тестируем различные размеры
    test_sizes = [32, 64, 128, 256, 512, 1024, 2048]
    test_sizes = [s for s in test_sizes if s <= max_size]
    
    results = {}
    
    for size in test_sizes:
        try:
            # Создаем тестовые тензоры
            a = torch.randn(size, size, device='cuda')
            b = torch.randn(size, size, device='cuda')
            
            # Замеряем производительность
            start = time.time()
            for _ in range(10):
                c = torch.matmul(a, b)
            torch.cuda.synchronize()
            duration = time.time() - start
            
            # Читаем энергопотребление
            power_result = subprocess.run(
                ['nvidia-smi', '--query-gpu=power.draw', '--format=csv,noheader,nounits'],
                capture_output=True, text=True
            )
            
            power = 0.0
            if power_result.returncode == 0:
                power = float(power_result.stdout.strip())
            
            # Сохраняем результаты
            throughput = (size ** 3) * 10 / duration / 1e9  # GFlops
            efficiency = throughput / power if power > 0 else 0
            
            results[size] = {
                'duration': duration,
                'throughput_gflops': throughput,
                'power_w': power,
                'efficiency': efficiency
            }
            
        except Exception as e:
            print(f"Error testing size {size}: {e}")
    
    # Находим наиболее эффективные размеры
    if results:
        # Сортируем по эффективности
        sorted_sizes = sorted(results.items(), key=lambda x: x[1]['efficiency'], reverse=True)
        
        # Берем топ-3
        resonant_sizes = [size for size, _ in sorted_sizes[:3]]
        
        return {
            'resonant_sizes': resonant_sizes,
            'all_results': results
        }
    
    return {"error": "No results collected"}