"""
GPU OPTIMIZATOR BASED ON SELF-REFERENTIAL AUTOPATTERN THEORY (SRAT/TРАП)
РАБОЧАЯ ВЕРСИЯ С РЕАЛЬНЫМИ ОПТИМИЗАЦИЯМИ
"""
import platform
import statistics
import torch
import numpy as np
import math
import time
import subprocess
import json
from typing import Dict, List, Tuple, Optional, Any, Callable, Union
import warnings
from enum import Enum

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

# ============================================================================
# ВАЖНО: ЭТИ КЛАССЫ ДОЛЖНЫ БЫТЬ НА ВЕРХНЕМ УРОВНЕ!
# ============================================================================

class VortexTopology(Enum):
    """Топологии вихревых кластеров"""
    TOROIDAL = "toroidal"
    FRACTAL_SPIRAL = "spiral"
    DYNAMIC_CLUSTER = "dynamic"
    RESONANCE_CHAIN = "chain"


class EnergyMode(Enum):
    """Режимы энергопотребления"""
    RESONANCE = "resonance"
    PERFORMANCE = "performance"
    ENERGY_SAVING = "energy_saving"


# ============================================================================
# ОСНОВНОЙ КЛАСС ОПТИМИЗАТОРА
# ============================================================================

class GPURingOptimizer:
    """
    РЕАЛЬНЫЙ GPU ОПТИМИЗАТОР С РАБОЧИМИ ОПТИМИЗАЦИЯМИ
    """
    
    def __init__(self, 
                 device: str = "cuda:0",
                 energy_mode: EnergyMode = EnergyMode.PERFORMANCE,
                 chaos_factor: float = 0.0,
                 target_coherence: float = 0.8):
        """
        Инициализация оптимизатора
        """
        self.device = device
        self.energy_mode = energy_mode
        self.chaos_factor = max(0.0, min(1.0, chaos_factor))
        self.target_coherence = max(0.1, min(1.0, target_coherence))
        
        # Оптимальные размеры блоков
        self.optimal_block_size = 128
        self.use_tensor_cores = False
        self.mixed_precision = True
        
        # Инициализация GPU
        self._init_gpu_environment()
        
        # Статистика
        self.stats = {
            'total_optimizations': 0,
            'energy_saved_estimated': 0.0,
            'time_saved_estimated': 0.0,
            'successful_optimizations': 0,
            'failed_optimizations': 0
        }
        
        print(f"🌀 GPURingOptimizer инициализирован на {self.gpu_name}")
        print(f"   Режим: {energy_mode.value}")
        print(f"   Устройство: {device}")
    
    def _init_gpu_environment(self):
        """Инициализация GPU окружения"""
        try:
            if TORCH_AVAILABLE and torch.cuda.is_available():
                self.torch_device = torch.device(self.device)
                
                # Получаем свойства GPU
                if self.device.startswith('cuda:'):
                    device_id = int(self.device.split(':')[1])
                    self.gpu_props = torch.cuda.get_device_properties(device_id)
                    self.gpu_name = self.gpu_props.name
                    
                    # Определяем возможности GPU
                    major = self.gpu_props.major
                    if major >= 8:  # Ampere и новее
                        self.optimal_block_size = 256
                        self.use_tensor_cores = True
                        self.mixed_precision = True
                    elif major >= 7:  # Turing, Volta
                        self.optimal_block_size = 128
                        self.use_tensor_cores = True
                        self.mixed_precision = True
                    elif major >= 6:  # Pascal
                        self.optimal_block_size = 128
                        self.use_tensor_cores = False
                        self.mixed_precision = True
                else:
                    self.gpu_props = None
                    self.gpu_name = "CPU"
                
            else:
                self.torch_device = torch.device("cpu")
                self.gpu_props = None
                self.gpu_name = "CPU"
                warnings.warn("CUDA недоступен, используется CPU эмуляция")
                
        except Exception as e:
            print(f"⚠️ Ошибка инициализации GPU: {e}")
            self.torch_device = torch.device("cpu")
            self.gpu_props = None
            self.gpu_name = "CPU_Error"
    
    def optimize_matmul(self, A: torch.Tensor, B: torch.Tensor,
                       target: str = "performance") -> torch.Tensor:
        """
        РЕАЛЬНО оптимизированное умножение матриц
        """
        if not TORCH_AVAILABLE:
            return torch.matmul(A, B)
        
        # Проверяем совместимость размеров
        if A.dim() != 2 or B.dim() != 2:
            return torch.matmul(A, B)
        
        m, k1 = A.shape
        k2, n = B.shape
        
        if k1 != k2:
            raise ValueError(f"Несовместимые размеры: A[{m}x{k1}] B[{k2}x{n}]")
        
        self.stats['total_optimizations'] += 1
        
        try:
            # Выбираем стратегию оптимизации
            if target == "energy" or self.energy_mode == EnergyMode.ENERGY_SAVING:
                result = self._energy_efficient_matmul(A, B)
            elif target == "performance" or self.energy_mode == EnergyMode.PERFORMANCE:
                result = self._high_performance_matmul(A, B)
            else:
                result = self._balanced_matmul(A, B)
            
            self.stats['successful_optimizations'] += 1
            return result
            
        except Exception as e:
            print(f"⚠️ Ошибка оптимизации: {e}, используем стандартное умножение")
            self.stats['failed_optimizations'] += 1
            return torch.matmul(A, B)
    
    def _energy_efficient_matmul(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """Энергоэффективное умножение матриц"""
        if self.mixed_precision and self.use_tensor_cores:
            with torch.cuda.amp.autocast():
                return self._blockwise_matmul(A, B, block_size=min(self.optimal_block_size, 128))
        else:
            return torch.matmul(A, B)
    
    def _high_performance_matmul(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """Высокопроизводительное умножение матриц"""
        if self.mixed_precision and self.use_tensor_cores:
            with torch.cuda.amp.autocast():
                if A.size(0) >= 1024 and A.size(1) >= 1024 and B.size(1) >= 1024:
                    return self._blockwise_matmul(A, B, block_size=self.optimal_block_size)
                else:
                    return torch.matmul(A, B)
        else:
            return torch.matmul(A, B)
    
    def _balanced_matmul(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """Сбалансированное умножение матриц"""
        if self.mixed_precision:
            with torch.cuda.amp.autocast():
                block_size = min(self.optimal_block_size, 192)
                return self._blockwise_matmul(A, B, block_size=block_size)
        else:
            return torch.matmul(A, B)
    
    def _blockwise_matmul(self, A: torch.Tensor, B: torch.Tensor, block_size: int = 128) -> torch.Tensor:
        """Блочное умножение матриц для оптимизации использования кэша"""
        m, k = A.shape
        k, n = B.shape
        
        # Выделяем память для результата
        result = torch.zeros((m, n), device=A.device, dtype=A.dtype)
        
        # Блочное умножение
        for i in range(0, m, block_size):
            i_end = min(i + block_size, m)
            for j in range(0, n, block_size):
                j_end = min(j + block_size, n)
                
                # Инициализируем блок результата
                block_result = torch.zeros((i_end - i, j_end - j), 
                                         device=A.device, dtype=A.dtype)
                
                for k_start in range(0, k, block_size):
                    k_end = min(k_start + block_size, k)
                    
                    # Выбираем блоки матриц
                    A_block = A[i:i_end, k_start:k_end]
                    B_block = B[k_start:k_end, j:j_end]
                    
                    # Умножаем блоки и накапливаем результат
                    block_result += torch.matmul(A_block, B_block)
                
                # Записываем блок в результат
                result[i:i_end, j:j_end] = block_result
        
        return result
    
    def optimize_tensor_operation(self,
                                 tensor: torch.Tensor,
                                 tensor2: Optional[torch.Tensor] = None,
                                 operation: str = "matmul",
                                 workload_type: str = "normal",
                                 target: str = "performance",
                                 preserve_accuracy: bool = True) -> torch.Tensor:
        """
        Универсальный метод оптимизации тензорных операций
        """
        if operation == "matmul":
            if tensor2 is not None:
                return self.optimize_matmul(tensor, tensor2, target=target)
            else:
                # Умножение на транспонированную
                return self.optimize_matmul(tensor, tensor.T, target=target)
        else:
            return tensor
    
    def get_optimization_stats(self) -> Dict:
        """Получение статистики оптимизаций"""
        return {
            'total_optimizations': self.stats['total_optimizations'],
            'successful_optimizations': self.stats['successful_optimizations'],
            'failed_optimizations': self.stats['failed_optimizations'],
            'success_rate': (self.stats['successful_optimizations'] / 
                           max(1, self.stats['total_optimizations']) * 100)
        }


# ============================================================================
# ВСПОМОГАТЕЛЬНЫЕ ФУНКЦИИ
# ============================================================================

def gpu_energy_monitor(interval: float = 1.0, duration: float = 10.0) -> Dict[str, Any]:
    """Monitor GPU energy consumption during computations."""
    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:
            result = subprocess.run(
                ['nvidia-smi', '--query-gpu=power.draw,temperature.gpu,utilization.gpu',
                 '--format=csv,noheader,nounits'],
                capture_output=True, text=True, timeout=2
            )
            
            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)
    
    if readings:
        powers = [r['power_w'] for r in readings]
        
        return {
            'average_power': np.mean(powers),
            'max_power': np.max(powers),
            'min_power': np.min(powers),
            'readings': readings[:10]
        }
    
    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."""
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        return {"error": "GPU not available"}
    
    optimizer = GPURingOptimizer(
        device="cuda:0",
        energy_mode=EnergyMode.PERFORMANCE,
        chaos_factor=0.0,
        target_coherence=0.8
    )
    
    test_sizes = [256, 512, 1024]
    test_sizes = [s for s in test_sizes if s <= max_size]
    
    results = {}
    
    for size in test_sizes:
        try:
            a = torch.randn(size, size, device=optimizer.torch_device)
            b = torch.randn(size, size, device=optimizer.torch_device)
            
            # Тест стандартного умножения
            torch.cuda.synchronize()
            start = time.time()
            iterations = 5
            
            for _ in range(iterations):
                _ = torch.matmul(a, b)
            torch.cuda.synchronize()
            std_time = time.time() - start
            
            # Тест оптимизированного умножения
            torch.cuda.synchronize()
            start = time.time()
            
            for _ in range(iterations):
                _ = optimizer.optimize_matmul(a, b, target="performance")
            torch.cuda.synchronize()
            opt_time = time.time() - start
            
            speedup = std_time / opt_time if opt_time > 0 else 1.0
            
            results[size] = {
                'std_time': std_time,
                'opt_time': opt_time,
                'speedup': speedup,
                'iterations': iterations
            }
            
        except Exception as e:
            print(f"Ошибка тестирования размера {size}: {e}")
            continue
    
    if results:
        sorted_by_speedup = sorted(
            results.items(),
            key=lambda x: x[1]['speedup'],
            reverse=True
        )
        
        resonant_sizes = [size for size, _ in sorted_by_speedup[:2]]
        
        return {
            'resonant_sizes': resonant_sizes,
            'optimal_size': resonant_sizes[0] if resonant_sizes else 1024
        }
    
    return {"error": "No results collected"}


def get_gpu_power(device_id: int = 0) -> Optional[float]:
    """Получает текущее энергопотребление GPU"""
    try:
        result = subprocess.run(
            ['nvidia-smi', '--query-gpu=power.draw', '--format=csv,noheader,nounits'],
            capture_output=True, text=True, timeout=2
        )
        if result.returncode == 0:
            lines = result.stdout.strip().split('\n')
            if device_id < len(lines):
                return float(lines[device_id].strip())
    except:
        pass
    return None


def example_usage(safe_mode: bool = True):
    """Пример использования оптимизатора"""
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        print("CUDA недоступен")
        return None
    
    try:
        print("=" * 60)
        print("🌀 ДЕМОНСТРАЦИЯ GPU ОПТИМИЗАТОРА")
        print("=" * 60)
        
        optimizer = GPURingOptimizer(
            device="cuda:0",
            energy_mode=EnergyMode.PERFORMANCE,
            chaos_factor=0.0,
            target_coherence=0.8
        )
        
        print("\n1. 🔬 ПРОВЕРКА ТОЧНОСТИ")
        try:
            for size in [16, 32, 64]:
                a = torch.randn(size, size, device=optimizer.torch_device)
                b = torch.randn(size, size, device=optimizer.torch_device)
                
                correct = torch.matmul(a, b)
                ring_result = optimizer.optimize_matmul(a, b, target="performance")
                
                error = torch.mean(torch.abs(correct - ring_result)).item()
                
                if error < 1e-6:
                    print(f"   {size}x{size}: ✅ Точность OK")
                else:
                    print(f"   {size}x{size}: ⚠️  Ошибка: {error:.2e}")
                
        except Exception as e:
            print(f"   Ошибка: {e}")
        
        return optimizer
        
    except Exception as e:
        print(f"Критическая ошибка: {e}")
        return None