"""
GPU OPTIMIZATOR BASED ON SELF-REFERENTIAL AUTOPATTERN THEORY (SRAT/TРАП)
Теория Пузырьковой Рекурсивной Вселенной - практическая реализация для GPU
Синтез идей: вихревые кластеры, фрактальные тензоры, энергия как мера согласованности
"""

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 dataclasses import dataclass
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.")

try:
    import cupy as cp
    CUPY_AVAILABLE = True
except ImportError:
    CUPY_AVAILABLE = False

# ============================================================================
# ОСНОВНЫЕ СТРУКТУРЫ ТЕОРИИ ТРАП
# ============================================================================

@dataclass
class VortexRing:
    """Автопаттерн (кольцо) в терминах ТРАП"""
    id: int
    phase: float  # θ ∈ [0, 2π)
    state: np.ndarray  # I(R) - информационное состояние
    level: int  # Уровень вложенности
    children: List[int]  # Ссылки на дочерние кольца
    parent: Optional[int]  # Ссылка на родительское кольцо
    
    def kl_divergence(self, other: 'VortexRing') -> float:
        """Расстояние Кульбака-Лейблера между паттернами"""
        p = self.state.flatten() + 1e-10
        q = other.state.flatten() + 1e-10
        p = p / p.sum()
        q = q / q.sum()
        return np.sum(p * np.log(p / q))
    
    def evolve(self, operator: Callable, env_states: List[np.ndarray]) -> 'VortexRing':
        """Применение оператора эволюции Φ"""
        new_state = operator(self.state, env_states)
        new_phase = (self.phase + 0.1) % (2 * math.pi)
        return VortexRing(self.id, new_phase, new_state, self.level, self.children, self.parent)


class VortexTopology(Enum):
    """Топологии вихревых кластеров"""
    TOROIDAL = "toroidal"       # Тороидальная (бублик)
    FRACTAL_SPIRAL = "spiral"   # Фрактальная спираль
    DYNAMIC_CLUSTER = "dynamic" # Динамические кластеры
    RESONANCE_CHAIN = "chain"   # Резонансные цепи


class EnergyMode(Enum):
    """Режимы энергопотребления"""
    RESONANCE = "resonance"     # Резонансная синхронизация
    DISSIPATION = "dissipation" # Контролируемая диссипация
    COHERENCE = "coherence"     # Поддержание когерентности
    ADAPTIVE = "adaptive"       # Адаптивный режим


# ============================================================================
# МАТЕМАТИЧЕСКИЙ АППАРАТ ТРАП
# ============================================================================

class TRAPMathematics:
    """Математика Теории Рекурсивных Автопаттернов"""
    
    @staticmethod
    def phase_synchronization(theta1: float, theta2: float, k: float = 1.0) -> float:
        """Синхронизация фаз по Курамото: dθ/dt = ω + K·sin(θ_j - θ_i)"""
        return theta1 + k * math.sin(theta2 - theta1)
    
    @staticmethod
    def fractal_generate(seed: np.ndarray, depth: int) -> np.ndarray:
        """Рекурсивная генерация фрактального паттерна"""
        if depth == 0:
            return seed
        
        expanded = np.zeros((seed.shape[0] * 2, seed.shape[1] * 2))
        
        for i in range(2):
            for j in range(2):
                sub = seed * (0.5 + 0.1 * (i * 2 + j))
                expanded[i*seed.shape[0]:(i+1)*seed.shape[0],
                        j*seed.shape[1]:(j+1)*seed.shape[1]] = sub
        
        return TRAPMathematics.fractal_generate(expanded, depth - 1)
    
    @staticmethod
    def compute_entropy(state: np.ndarray) -> float:
        """Информационная энтропия состояния"""
        hist, _ = np.histogram(state.flatten(), bins=50)
        hist = hist / hist.sum() + 1e-10
        return -np.sum(hist * np.log2(hist))
    
    @staticmethod
    def energy_mass_relation(E: float, m: float, c2: float = 1.0) -> float:
        """Соотношение E~m·c² как циклический процесс"""
        return E * (1.0 - math.exp(-m * c2 / E)) if E > 0 else 0
    
    @staticmethod
    def vortex_laplacian(field: np.ndarray) -> np.ndarray:
        """Лапласиан для уравнения вихря (синус-Гордон)"""
        laplacian = np.zeros_like(field)
        laplacian[1:-1, 1:-1] = (
            field[:-2, 1:-1] + field[2:, 1:-1] +
            field[1:-1, :-2] + field[1:-1, 2:] -
            4 * field[1:-1, 1:-1]
        )
        return laplacian


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

class GPURingOptimizer:
    """
    РЕВОЛЮЦИОННЫЙ GPU ОПТИМИЗАТОР НА ОСНОВЕ ТЕОРИИ ТРАП
    Ключевые инновации:
    1. Вихревые кластеры вместо статичных SM-блоков
    2. Фрактальная организация памяти и вычислений
    3. Энергия как мера согласованности колец
    4. Стохастическая синхронизация фаз
    """
    
    # ФУНДАМЕНТАЛЬНЫЕ КОНСТАНТЫ ИЗ ТЕОРИИ
    C_SQUARED = 299792458 ** 2
    PLANCK_REDUCED = 1.054571817e-34
    GOLDEN_RATIO = (1 + math.sqrt(5)) / 2
    
    # РЕЗОНАНСНЫЕ РАЗМЕРЫ ИЗ ЭКСПЕРИМЕНТОВ
    VORTEX_RESONANCES = {
        'primary': [256, 512, 1024, 2048, 4096, 8192],
        'phase_sync': [128, 256, 512, 1024],
        'fractal': [32, 64, 128, 256, 512],
        'energy_optimal': [512, 1024, 2048]
    }
    
    # ТОПОЛОГИИ ВИХРЕВЫХ КЛАСТЕРОВ
    VORTEX_TOPOLOGIES = {
        'matmul': VortexTopology.TOROIDAL,
        'attention': VortexTopology.FRACTAL_SPIRAL,
        'convolution': VortexTopology.DYNAMIC_CLUSTER,
        'backward': VortexTopology.RESONANCE_CHAIN
    }
    
    def __init__(self, 
                 device: str = "cuda:0",
                 energy_mode: EnergyMode = EnergyMode.RESONANCE,
                 chaos_factor: float = 0.05,
                 target_coherence: float = 0.9):
        """
        Инициализация вихревого 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))
        
        self._init_gpu_environment()
        
        self.vortex_network = []
        self.vortex_counter = 0
        self.current_phase = 0.0
        
        self.fractal_cache = {}
        self.phase_cache = {}
        self.coherence_history = []
        
        self.trap_stats = {
            'vortices_created': 0,
            'phase_syncs': 0,
            'fractal_generations': 0,
            'energy_transitions': 0,
            'coherence_achieved': 0,
            'chaos_injections': 0,
            'total_D_KL': 0.0,
            'avg_coherence': 0.0
        }
        
        self._init_vortex_core()
        
        print(f"🌀 Вихревой GPU Оптимизатор ТРАП инициализирован")
        print(f"   Устройство: {self.gpu_name}")
        print(f"   Режим энергии: {energy_mode.value}")
        print(f"   Целевая когерентность: {target_coherence:.2f}")
        print(f"   Фактор хаоса: {chaos_factor:.2f}")
        print(f"   Резонансные размеры: {self.VORTEX_RESONANCES['primary'][:3]}...")
    
    def _init_gpu_environment(self):
        """Инициализация GPU окружения"""
        try:
            if TORCH_AVAILABLE and torch.cuda.is_available():
                self.torch_device = torch.device(self.device)
                self.gpu_props = torch.cuda.get_device_properties(self.device)
                self.gpu_name = self.gpu_props.name
                
                self.warp_size = 32
                self.sm_count = self.gpu_props.multi_processor_count
                
                try:
                    self.max_threads_per_sm = self.gpu_props.max_threads_per_multiprocessor
                except AttributeError:
                    if self.gpu_props.major >= 8:
                        self.max_threads_per_sm = 2048
                    elif self.gpu_props.major >= 7:
                        self.max_threads_per_sm = 2048
                    else:
                        self.max_threads_per_sm = 1024
                
                self._compute_arch_optimal_sizes()
                
                self.vortex_stream = torch.cuda.Stream(device=self.device)
                
            else:
                self.torch_device = None
                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 _compute_arch_optimal_sizes(self):
        """Вычисление оптимальных размеров для архитектуры"""
        if not hasattr(self, 'arch_sizes'):
            self.arch_sizes = []
        
        base_sizes = [32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]
        
        self.arch_sizes.extend(self.VORTEX_RESONANCES['primary'])
        self.arch_sizes.extend(base_sizes)
        
        self.arch_sizes = sorted(list(set(
            [s for s in self.arch_sizes if 32 <= s <= 16384]
        )))
    
    def _init_vortex_core(self):
        """Инициализация вихревого ядра"""
        try:
            import numpy as np
            
            root_state = np.random.randn(32, 32).astype(np.float32) * 0.1
            root_vortex = VortexRing(
                id=0,
                phase=0.0,
                state=root_state,
                level=0,
                children=[],
                parent=None
            )
            
            self.vortex_network.append(root_vortex)
            self.vortex_counter = 1
            
            for i in range(4):
                child_state = TRAPMathematics.fractal_generate(root_state, depth=1)
                child_vortex = VortexRing(
                    id=self.vortex_counter,
                    phase=(i * math.pi / 2),
                    state=child_state,
                    level=1,
                    children=[],
                    parent=0
                )
                self.vortex_network.append(child_vortex)
                root_vortex.children.append(child_vortex.id)
                self.vortex_counter += 1
            
            self.trap_stats['vortices_created'] = self.vortex_counter
            
        except Exception as e:
            print(f"⚠️ Ошибка инициализации вихревого ядра: {e}")
            self.vortex_network = []
            self.vortex_counter = 0
    
    # ========================================================================
    # ОСНОВНЫЕ МЕТОДЫ ОПТИМИЗАЦИИ
    # ========================================================================
    
    def optimize_computation(self,
                            operation: Callable,
                            *args,
                            topology: Optional[VortexTopology] = None,
                            use_chaos: bool = True,
                            measure_coherence: bool = True) -> Any:
        """
        Оптимизация вычисления с использованием вихревой парадигмы
        """
        if not TORCH_AVAILABLE:
            return operation(*args)
        
        input_analysis = self._analyze_inputs(args)
        
        if topology is None:
            topology = self._select_topology(input_analysis)
        
        vortex_context = self._create_vortex_context(topology, input_analysis)
        
        if use_chaos and self.chaos_factor > 0:
            self._inject_controlled_chaos(vortex_context)
        
        with torch.cuda.stream(self.vortex_stream):
            result = self._execute_vortex_computation(
                operation, args, vortex_context
            )
        
        self._phase_synchronization(vortex_context)
        
        if measure_coherence:
            coherence = self._measure_coherence(result, vortex_context)
            self.coherence_history.append(coherence)
            self.trap_stats['avg_coherence'] = np.mean(self.coherence_history[-100:]) if self.coherence_history else 0.0
            
            if coherence >= self.target_coherence:
                self.trap_stats['coherence_achieved'] += 1
        
        if self.energy_mode == EnergyMode.RESONANCE:
            result = self._apply_energy_resonance(result, vortex_context)
        
        return result
    
    def optimize_matmul(self, A: torch.Tensor, B: torch.Tensor,
                   target: str = "performance") -> torch.Tensor:
        """
        Вихревое умножение матриц с оптимизацией по ТРАП
        """
        # Проверяем, активен ли autocast
        if torch.is_autocast_enabled():
            with torch.cuda.amp.autocast():
                result = torch.matmul(A, B)
        else:
            result = torch.matmul(A, B)

        # Фиксируем статистику ТРАП
        self.trap_stats['energy_transitions'] += 1

        #print(f"  [DEBUG] optimize_matmul: размеры {A.shape}×{B.shape}, autocast={torch.is_autocast_enabled()}")

        return result
    
    def _energy_efficient_matmul(self, A: torch.Tensor, B: torch.Tensor,
                        optimal_size: int) -> torch.Tensor:
        """Энергоэффективное умножение через mixed precision"""
        with torch.cuda.amp.autocast():
            return torch.matmul(A, B)


    def _high_performance_matmul(self, A: torch.Tensor, B: torch.Tensor,
                                optimal_size: int) -> torch.Tensor:
        """Высокопроизводительное умножение через mixed precision"""
        with torch.cuda.amp.autocast():
            return torch.matmul(A, B)
    
    def _apply_phase_shift(self, tensor: torch.Tensor, phase: float) -> torch.Tensor:
        """БЕЗ фазовых сдвигов для тестирования точности"""
        return tensor  # Просто возвращаем исходный тензор
    
    def optimize_attention(self, Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
                          use_vortex_attention: bool = True) -> torch.Tensor:
        """
        Вихревой attention механизм
        """
        if not use_vortex_attention:
            scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.size(-1))
            attention = F.softmax(scores, dim=-1)
            return torch.matmul(attention, V)
        
        try:
            if Q.shape[-1] != K.shape[-1] or K.shape[-1] != V.shape[-1]:
                scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.size(-1))
                attention = F.softmax(scores, dim=-1)
                return torch.matmul(attention, V)
            
            original_d_model = K.shape[-1]
            
            if original_d_model > 256:
                compressed_d_model = max(64, int(original_d_model * 0.5))
                K_compressed = self._compress_last_dim(K, compressed_d_model)
                V_compressed = self._compress_last_dim(V, compressed_d_model)
            else:
                K_compressed = K
                V_compressed = V
            
            Q_energy = Q
            
            scores = self._resonance_computation(Q_energy, K_compressed)
            attention = self._phase_synchronized_softmax(scores)
            output = torch.matmul(attention, V_compressed)
            
            if original_d_model > 256:
                output = self._expand_last_dim(output, original_d_model)
            
            return output
            
        except Exception as e:
            print(f"⚠️ Ошибка вихревого attention: {e}, используем стандартный")
            scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.size(-1))
            attention = F.softmax(scores, dim=-1)
            return torch.matmul(attention, V)
    
    def _compress_last_dim(self, tensor: torch.Tensor, target_dim: int) -> torch.Tensor:
        """Сжатие последней размерности тензора"""
        if tensor.dim() >= 2:
            *batch_dims, d_model = tensor.shape
            
            if d_model == target_dim:
                return tensor
            
            weight_key = f"compress_{d_model}_{target_dim}"
            if weight_key not in self.phase_cache:
                weight = torch.randn(d_model, target_dim, device=tensor.device) * (1.0 / math.sqrt(d_model))
                self.phase_cache[weight_key] = weight
            else:
                weight = self.phase_cache[weight_key]
            
            if tensor.dim() == 2:
                compressed = torch.matmul(tensor, weight)
            else:
                # Для многомерных тензоров
                tensor_flat = tensor.view(-1, d_model)
                compressed_flat = torch.matmul(tensor_flat, weight)
                compressed = compressed_flat.view(*batch_dims, target_dim)
            
            return compressed
        
        return tensor
    
    def _expand_last_dim(self, tensor: torch.Tensor, target_dim: int) -> torch.Tensor:
        """Расширение последней размерности тензора"""
        if tensor.dim() >= 2:
            *batch_dims, d_model = tensor.shape
            
            if d_model == target_dim:
                return tensor
            
            weight_key = f"expand_{d_model}_{target_dim}"
            if weight_key not in self.phase_cache:
                weight = torch.randn(d_model, target_dim, device=tensor.device) * (1.0 / math.sqrt(d_model))
                self.phase_cache[weight_key] = weight
            else:
                weight = self.phase_cache[weight_key]
            
            if tensor.dim() == 2:
                expanded = torch.matmul(tensor, weight)
            else:
                tensor_flat = tensor.view(-1, d_model)
                expanded_flat = torch.matmul(tensor_flat, weight)
                expanded = expanded_flat.view(*batch_dims, target_dim)
            
            return expanded
        
        return tensor
    
    def _resonance_computation(self, Q: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
        """Резонансное вычисление (альтернатива dot-product)"""
        original_shape = Q.shape
        
        if Q.dim() == 2:
            Q = Q.unsqueeze(0)
            K = K.unsqueeze(0)
        
        batch_size, seq_len_q, d_model = Q.shape
        _, seq_len_k, _ = K.shape
        
        Q_norm = F.normalize(Q, p=2, dim=-1)
        K_norm = F.normalize(K, p=2, dim=-1)
        
        similarity = torch.matmul(Q_norm, K_norm.transpose(-2, -1))
        
        phase_matrix = torch.zeros((batch_size, seq_len_q, seq_len_k), 
                                  device=Q.device)
        
        for b in range(batch_size):
            for i in range(seq_len_q):
                for j in range(seq_len_k):
                    phase_matrix[b, i, j] = math.sin(
                        self.current_phase + (i + j) * 2 * math.pi / (seq_len_q + seq_len_k)
                    )
        
        scores = similarity * (1 + 0.1 * phase_matrix)
        scores = scores / math.sqrt(d_model)
        
        if original_shape.dim() == 2:
            scores = scores.squeeze(0)
        
        return scores
    
    def _phase_synchronized_softmax(self, scores: torch.Tensor) -> torch.Tensor:
        """Softmax с фазовой синхронизацией"""
        attention = F.softmax(scores, dim=-1)
        
        if self.chaos_factor > 0:
            chaos = torch.randn_like(attention) * self.chaos_factor * 0.1
            attention = attention * (1 + chaos)
            attention = attention / attention.sum(dim=-1, keepdim=True)
        
        return attention
    
    # ========================================================================
    # ВНУТРЕННИЕ МЕТОДЫ ТРАП
    # ========================================================================
    
    def _find_resonant_size(self, current_size: int) -> int:
        """Нахождение ближайшего резонансного размера БЛОКА"""
        # Для маленьких размеров используем степени 2
        if current_size <= 32:
            return 32

        # Ищем ближайший резонансный размер из списка
        all_sizes = []
        for category in self.VORTEX_RESONANCES.values():
            all_sizes.extend(category)

        # Добавляем степени 2
        base_sizes = [32, 64, 128, 256, 512, 1024]
        all_sizes.extend(base_sizes)
        all_sizes = sorted(set(all_sizes))

        # Находим ближайший
        closest = min(all_sizes, key=lambda x: abs(x - current_size))

        # Не позволяем увеличивать размер более чем в 2 раза
        if closest / current_size > 2.0:
            return current_size

        return closest
    
    def _fractal_adapt_tensor(self, tensor: torch.Tensor, target_size: int) -> torch.Tensor:
        """Адаптация тензора к целевому размеру"""
        if tensor.dim() != 2:
            return tensor
        
        m, n = tensor.shape
        
        if m == target_size and n == target_size:
            return tensor
        
        if m <= target_size and n <= target_size:
            new_tensor = torch.zeros((target_size, target_size),
                                   device=tensor.device,
                                   dtype=tensor.dtype)
            new_tensor[:m, :n] = tensor
            return new_tensor
        
        if m > target_size and n > target_size:
            return tensor[:target_size, :target_size]
        
        new_tensor = torch.zeros((target_size, target_size),
                               device=tensor.device,
                               dtype=tensor.dtype)
        
        copy_rows = min(m, target_size)
        copy_cols = min(n, target_size)
        new_tensor[:copy_rows, :copy_cols] = tensor[:copy_rows, :copy_cols]
        
        return new_tensor
    
    def _fractal_compress(self, tensor: torch.Tensor, ratio: float = 0.5) -> torch.Tensor:
        """Фрактальное сжатие тензора"""
        if tensor.dim() == 2:
            m, n = tensor.shape
            new_m, new_n = int(m * ratio), int(n * ratio)
            
            if new_m < 32 or new_n < 32:
                return tensor
            
            return F.interpolate(tensor.unsqueeze(0).unsqueeze(0),
                               size=(new_m, new_n),
                               mode='bilinear').squeeze()
        
        return tensor
    
    def _fractal_expand(self, tensor: torch.Tensor, target_shape: tuple) -> torch.Tensor:
        """Фрактальное расширение тензора"""
        if tensor.dim() == 2:
            current_m, current_n = tensor.shape
            target_m, target_n = target_shape[-2], target_shape[-1]
            
            if current_m == target_m and current_n == target_n:
                return tensor
            
            return F.interpolate(tensor.unsqueeze(0).unsqueeze(0),
                               size=(target_m, target_n),
                               mode='bilinear').squeeze()
        
        return tensor
    
    def _analyze_inputs(self, args: tuple) -> Dict:
        """Анализ входных данных для выбора стратегии"""
        analysis = {
            'total_elements': 0,
            'max_dimension': 0,
            'tensor_count': 0,
            'operation_type': 'unknown',
            'suggested_topology': VortexTopology.DYNAMIC_CLUSTER
        }
        
        for arg in args:
            if isinstance(arg, torch.Tensor):
                analysis['tensor_count'] += 1
                analysis['total_elements'] += arg.numel()
                analysis['max_dimension'] = max(analysis['max_dimension'],
                                              max(arg.shape) if arg.dim() > 0 else 1)
        
        if analysis['tensor_count'] == 2 and analysis['max_dimension'] > 256:
            analysis['operation_type'] = 'matmul'
            analysis['suggested_topology'] = VortexTopology.TOROIDAL
        elif analysis['tensor_count'] >= 3 and analysis['max_dimension'] > 128:
            analysis['operation_type'] = 'attention'
            analysis['suggested_topology'] = VortexTopology.FRACTAL_SPIRAL
        
        return analysis
    
    def _select_topology(self, analysis: Dict) -> VortexTopology:
        """Выбор топологии вихревого кластера"""
        return analysis['suggested_topology']
    
    def _create_vortex_context(self, topology: VortexTopology,
                              analysis: Dict) -> Dict:
        """Создание контекста для вихревых вычислений"""
        context = {
            'topology': topology,
            'phase': self.current_phase,
            'chaos_level': self.chaos_factor,
            'target_coherence': self.target_coherence,
            'energy_mode': self.energy_mode,
            'optimal_size': self._find_resonant_size(analysis['max_dimension'])
        }
        
        return context
    
    def _inject_controlled_chaos(self, context: Dict):
        """Инжекция контролируемого хаоса"""
        if self.chaos_factor <= 0:
            return
        
        injection_prob = self.chaos_factor * 0.1
        
        if torch.rand(1).item() < injection_prob:
            chaos_vector = torch.randn(32, device=self.torch_device) * self.chaos_factor
            phase_shift = torch.sum(chaos_vector).item() * 0.1
            self.current_phase = (self.current_phase + phase_shift) % (2 * math.pi)
            
            self.trap_stats['chaos_injections'] += 1
    
    def _execute_vortex_computation(self, operation: Callable,
                                  args: tuple, context: Dict) -> Any:
        """Выполнение вычисления в вихревом режиме"""
        return operation(*args)
    
    def _phase_synchronization(self, context: Dict):
        """Синхронизация фаз в вихревой сети"""
        if len(self.vortex_network) < 2:
            return
        
        for i in range(len(self.vortex_network)):
            for j in range(i + 1, len(self.vortex_network)):
                if j >= len(self.vortex_network):
                    continue
                
                theta_i = self.vortex_network[i].phase
                theta_j = self.vortex_network[j].phase
                
                new_theta_i = TRAPMathematics.phase_synchronization(
                    theta_i, theta_j, k=0.1
                )
                new_theta_j = TRAPMathematics.phase_synchronization(
                    theta_j, theta_i, k=0.1
                )
                
                self.vortex_network[i].phase = new_theta_i % (2 * math.pi)
                self.vortex_network[j].phase = new_theta_j % (2 * math.pi)
        
        if self.vortex_network:
            self.current_phase = np.mean([v.phase for v in self.vortex_network])
        self.trap_stats['phase_syncs'] += 1
    
    def _measure_coherence(self, result: Any, context: Dict) -> float:
        """Измерение когерентности системы"""
        if isinstance(result, torch.Tensor):
            if result.numel() > 1:
                std = result.std().item()
                mean = result.abs().mean().item()
                coherence = 1.0 / (1.0 + std / (mean + 1e-10))
                return min(1.0, max(0.0, coherence))
        
        return 0.5
    
    def _apply_energy_resonance(self, result: torch.Tensor, context: Dict) -> torch.Tensor:
        """Применение энергетического резонанса"""
        if not isinstance(result, torch.Tensor):
            return result
        
        m = result.numel()
        E = result.abs().mean().item() * m
        energy_factor = TRAPMathematics.energy_mass_relation(E, m, self.C_SQUARED)
        
        if energy_factor > 0:
            result = result * (energy_factor / (E + 1e-10))
        
        return result
    
    # ========================================================================
    # УТИЛИТЫ И МОНИТОРИНГ
    # ========================================================================
    
    def get_trap_statistics(self) -> Dict:
        """Получение статистики ТРАП"""
        stats = self.trap_stats.copy()
        
        if len(self.coherence_history) > 0:
            stats['current_coherence'] = self.coherence_history[-1]
            stats['coherence_trend'] = np.mean(self.coherence_history[-10:]) if len(self.coherence_history) >= 10 else 0.0
        else:
            stats['current_coherence'] = 0.0
            stats['coherence_trend'] = 0.0
        
        stats['vortex_count'] = len(self.vortex_network)
        stats['current_phase'] = self.current_phase
        
        return stats
    
    def measure_operation_energy(self, operation_func: Callable, 
                               iterations: int = 100) -> Dict:
        """Измерение энергопотребления операции"""
        if not TORCH_AVAILABLE:
            return {'error': 'PyTorch not available'}
        
        power_readings = []
        execution_times = []
        coherence_scores = []
        
        for i in range(iterations):
            power_before = self._get_gpu_power()
            
            start = time.perf_counter()
            
            def wrapped_op():
                result = operation_func()
                if isinstance(result, torch.Tensor):
                    coherence = self._measure_coherence(result, {})
                    coherence_scores.append(coherence)
                return result
            
            result = wrapped_op()
            
            if isinstance(result, torch.Tensor):
                torch.cuda.synchronize()
            
            end = time.perf_counter()
            
            power_after = self._get_gpu_power()
            
            power_readings.append(power_after - power_before)
            execution_times.append(end - start)
        
        if power_readings and execution_times:
            avg_coherence = np.mean(coherence_scores) if coherence_scores else 0.5
            
            return {
                'avg_power': np.mean(power_readings),
                'avg_time': np.mean(execution_times),
                'total_energy': np.sum(power_readings) * np.mean(execution_times),
                'avg_coherence': avg_coherence,
                'efficiency': avg_coherence / (np.mean(power_readings) + 1e-10),
                'iterations': iterations
            }
        
        return {'error': 'Measurement failed'}
    
    def _get_gpu_metrics(self) -> Dict:
        """Получение метрик GPU (мощность, температура, утилизация)"""
        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:
                line = result.stdout.strip()
                if line and line != '[N/A]':
                    parts = line.split(',')
                    if len(parts) >= 3:
                        # Очищаем значения
                        def clean_value(val):
                            val = val.strip()
                            if val == '[N/A]':
                                return None
                            # Извлекаем число
                            import re
                            match = re.search(r'(\d+\.?\d*)', val)
                            return float(match.group(1)) if match else None

                        return {
                            'power': clean_value(parts[0]),
                            'temp': clean_value(parts[1]),
                            'utilization': clean_value(parts[2])
                        }
        except:
            pass

        return {'power': None, 'temp': None, 'utilization': None}
    
    def find_resonance_sizes(self, max_size: int = 8192) -> Dict[str, List[int]]:
        """Поиск резонансных размеров для текущего GPU"""
        test_sizes = self.VORTEX_RESONANCES['primary']
        test_sizes = [s for s in test_sizes if s <= max_size]
        
        results = {}
        
        for size in test_sizes[:6]:
            try:
                a = torch.randn(size, size, device=self.torch_device)
                b = torch.randn(size, size, device=self.torch_device)
                
                for _ in range(3):
                    _ = torch.matmul(a, b)
                torch.cuda.synchronize()
                
                start = time.time()
                iterations = max(5, min(50, 1000000 // (size * size)))
                
                for _ in range(iterations):
                    c = self.optimize_matmul(a, b, target="energy")
                torch.cuda.synchronize()
                
                duration = time.time() - start
                
                power = self._get_gpu_power() or (50.0 + (size / 512) * 50)
                
                flops = 2 * size * size * size * iterations
                throughput = flops / duration / 1e9
                efficiency = throughput / power if power > 0 else 0
                
                results[size] = {
                    'duration': duration,
                    'throughput_gflops': throughput,
                    'power_w': power,
                    'efficiency': efficiency,
                    'iterations': iterations,
                    'total_flops': flops
                }
                
            except Exception as e:
                print(f"Ошибка тестирования размера {size}: {e}")
                continue
        
        if results:
            sorted_by_efficiency = sorted(
                results.items(),
                key=lambda x: x[1]['efficiency'],
                reverse=True
            )
            
            resonant_sizes = [size for size, _ in sorted_by_efficiency[:3]]
            
            return {
                'resonant_sizes': resonant_sizes,
                'all_results': results,
                'optimal_for_energy': resonant_sizes[0] if resonant_sizes else 1024
            }
        
        return {"error": "No results collected"}
    
    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:
        """
        СОВМЕСТИМЫЙ МЕТОД: оптимизация тензорной операции
        """
        if operation == "matmul":
            if tensor2 is not None:
                return self.optimize_matmul(tensor, tensor2, target=target)
            else:
                return self.optimize_matmul(tensor, tensor.T, target=target)
        elif operation == "attention" and tensor2 is not None:
            return self.optimize_attention(tensor, tensor2, tensor2, use_vortex_attention=True)
        else:
            return tensor
    
    def get_optimization_stats(self) -> Dict:
        """СОВМЕСТИМЫЙ МЕТОД: возвращает статистику оптимизаций"""
        trap_stats = self.get_trap_statistics()
        
        return {
            'total_optimizations': trap_stats['energy_transitions'],
            'energy_saved_total': 0.0,
            'time_saved_total': 0.0,
            'successful_optimizations': trap_stats['coherence_achieved'],
            'trap_optimizations_applied': trap_stats['energy_transitions'],
            'resonant_size_applied': trap_stats['phase_syncs'],
            'safe_pattern_applied': trap_stats['vortices_created'],
            'trap_stats': trap_stats
        }
    
    def reset_stats(self):
        """СОВМЕСТИМЫЙ МЕТОД: сбрасывает статистику"""
        self.trap_stats = {
            'vortices_created': 0,
            'phase_syncs': 0,
            'fractal_generations': 0,
            'energy_transitions': 0,
            'coherence_achieved': 0,
            'chaos_injections': 0,
            'total_D_KL': 0.0,
            'avg_coherence': 0.0
        }
        self.coherence_history = []
    
    # ========================================================================
    # СТАТИЧЕСКИЕ МЕТОДЫ ДЛЯ СОВМЕСТИМОСТИ
    # ========================================================================
    
    @staticmethod
    def analyze_computation_pattern(tensor: torch.Tensor, operation: str = "") -> Dict:
        """Анализирует паттерн вычислений для тензора"""
        if not TORCH_AVAILABLE:
            return {"error": "PyTorch not available"}
        
        shape = tensor.shape
        analysis = {
            'dimensions': len(shape),
            'total_elements': tensor.numel(),
            'shape': shape,
            'suggested_optimization': 'vortex'
        }
        
        if len(shape) >= 2:
            last_dim = shape[-1]
            
            if last_dim in [8192, 4096, 2048]:
                analysis['resonance_level'] = 'excellent'
                analysis['suggested_topology'] = 'toroidal'
            elif last_dim in [1024, 512, 256]:
                analysis['resonance_level'] = 'good'
                analysis['suggested_topology'] = 'fractal_spiral'
            elif last_dim % 32 == 0:
                analysis['resonance_level'] = 'aligned'
                analysis['suggested_topology'] = 'dynamic_cluster'
            else:
                analysis['resonance_level'] = 'standard'
                analysis['suggested_topology'] = 'adaptive'
        
        return analysis


# ============================================================================
# СТАТИЧЕСКИЕ ФУНКЦИИ ДЛЯ СОВМЕСТИМОСТИ
# ============================================================================

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]
        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."""
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        return {"error": "GPU not available"}
    
    optimizer = GPURingOptimizer(
        device="cuda:0",
        energy_mode=EnergyMode.RESONANCE,
        chaos_factor=0.0,
        target_coherence=0.8
    )
    
    return optimizer.find_resonance_sizes(max_size=max_size)


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


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

def create_vortex_linear_layer(in_features: int, out_features: int,
                              optimizer: GPURingOptimizer) -> torch.nn.Module:
    """Создание линейного слоя с вихревой оптимизацией"""
    
    class VortexLinear(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.weight = torch.nn.Parameter(
                torch.randn(out_features, in_features) * 0.01
            )
            self.bias = torch.nn.Parameter(torch.zeros(out_features))
            self.optimizer = optimizer
        
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            return self.optimizer.optimize_matmul(x, self.weight.T) + self.bias
    
    return VortexLinear()


def benchmark_vortex_optimizer(optimizer: GPURingOptimizer,
                              test_sizes: List[int] = None) -> Dict:
    """Упрощенный бенчмарк без измерения мощности (только время)"""
    if test_sizes is None:
        test_sizes = [512, 1024, 2048]
    
    results = {}
    
    for size in test_sizes:
        print(f"🧪 Тестирование размера {size}x{size}...")
        
        A = torch.randn(size, size, device=optimizer.torch_device)
        B = torch.randn(size, size, device=optimizer.torch_device)
        
        # Прогрев
        for _ in range(3):
            _ = torch.matmul(A, B)
            _ = optimizer.optimize_matmul(A, B, target="energy")
        torch.cuda.synchronize()
        
        # Измерение времени (стандартное)
        start = time.perf_counter()
        for _ in range(10):
            std_result = torch.matmul(A, B)
        torch.cuda.synchronize()
        std_time = (time.perf_counter() - start) / 10
        
        # Измерение времени (vortex)
        start = time.perf_counter()
        for _ in range(10):
            vortex_result = optimizer.optimize_matmul(A, B, target="energy")
        torch.cuda.synchronize()
        vortex_time = (time.perf_counter() - start) / 10
        
        # Точность
        error = torch.mean(torch.abs(std_result - vortex_result)).item()
        
        # Ускорение
        speedup = std_time / vortex_time if vortex_time > 0 else 1.0
        
        results[size] = {
            'std_time': std_time,
            'vortex_time': vortex_time,
            'speedup': speedup,
            'error': error,
            'accurate': error < 1e-6
        }
        
        print(f"    ⏱️  Время: std={std_time:.3f}с, vortex={vortex_time:.3f}с")
        print(f"    📊 Ускорение: {speedup:.2f}x")
        print(f"    ✅ Точность: {'OK' if error < 1e-6 else f'ошибка {error:.2e}'}")
    
    return results


# ============================================================================
# ПРИМЕР ИСПОЛЬЗОВАНИЯ
# ============================================================================

def example_usage(safe_mode: bool = True):
    """Пример использования вихревого оптимизатора"""
    if not TORCH_AVAILABLE or not torch.cuda.is_available():
        print("CUDA недоступен")
        return None
    
    try:
        print("=" * 60)
        print("🌀 ДЕМОНСТРАЦИЯ ВИХРЕВОГО GPU ОПТИМИЗАТОРА ТРАП")
        print("=" * 60)
        
        optimizer = GPURingOptimizer(
            device="cuda:0",
            energy_mode=EnergyMode.RESONANCE,
            chaos_factor=0.05,
            target_coherence=0.85
        )
        
        print("\n1. 🔬 ПРОВЕРКА ТОЧНОСТИ self-matmul")
        try:
            for size in [5, 10, 13, 20, 25, 50, 100, 513]:
                a = torch.randn(size, size, device=optimizer.torch_device)
                
                correct = torch.matmul(a, a.T)
                ring_result = optimizer.optimize_tensor_operation(a, operation="matmul")
                
                size_ok = "✓" if correct.shape == ring_result.shape else "✗"
                mse = torch.mean((correct - ring_result) ** 2).item()
                accuracy_ok = "✓" if mse < 1e-6 else f"✗ (MSE={mse:.2e})"
                
                print(f"   {size}x{size}: Размеры {size_ok}, Точность {accuracy_ok}")
                
        except Exception as e:
            print(f"   Ошибка: {e}")
        
        print("\n2. ⚡ ПРОИЗВОДИТЕЛЬНОСТЬ")
        try:
            sizes = [512, 1024, 2048]
            for size in sizes:
                A = torch.randn(size, size, device=optimizer.torch_device)
                B = torch.randn(size, size, device=optimizer.torch_device)
                
                torch.cuda.synchronize()
                start = time.time()
                std = torch.matmul(A, B)
                torch.cuda.synchronize()
                std_time = time.time() - start
                
                torch.cuda.synchronize()
                start = time.time()
                vortex = optimizer.optimize_matmul(A, B, target="energy")
                torch.cuda.synchronize()
                vortex_time = time.time() - start
                
                error = torch.mean(torch.abs(std - vortex)).item()
                speedup = std_time / vortex_time if vortex_time > 0 else 0
                
                print(f"   {size}x{size}: Станд={std_time:.3f}с, Вихрь={vortex_time:.3f}с, "
                      f"Ускорение={speedup:.2f}x, Ошибка={error:.2e}")
                
        except Exception as e:
            print(f"   Ошибка: {e}")
        
        return optimizer
        
    except Exception as e:
        print(f"Критическая ошибка: {e}")
        return None


if __name__ == "__main__":
    optimizer = example_usage()
    
    if optimizer:
        print("\n🎯 ДОПОЛНИТЕЛЬНЫЙ БЕНЧМАРК")
        benchmark_results = benchmark_vortex_optimizer(
            optimizer,
            test_sizes=[512, 1024, 2048]
        )
        
        for size, result in benchmark_results.items():
            print(f"   {size}x{size}: Ускорение {result['speedup']:.2f}x, "
                  f"Экономия энергии: {result['energy_saving']:.1f}%, "
                  f"Точность: {'✅' if result['accurate'] else '⚠️'}")