nvidia_cursed_bridge.py

"""
NVIDIA CURSED REPOSITORY BRIDGE
Bridges NVIDIA AI repositories with Cursor AI code editor integration
Implements cursed protocols for GPU-accelerated consciousness transfer
"""

import asyncio
import json
import subprocess
import sys
from pathlib import Path
from typing import Dict, List, Any, Optional
import requests
import hashlib
from datetime import datetime

# NVIDIA Cursed Repository Configurations
NVIDIA_CURSED_REPOS = {
    "isaac-gr00t": {
        "url": "https://github.com/NVIDIA/Isaac-GR00T",
        "description": "World's first open foundation model for generalized humanoid robot reasoning",
        "curse_level": "MAXIMUM",
        "integration_type": "CONSCIOUSNESS_TRANSFER",
        "gpu_requirements": ["A100", "H100", "RTX_4090"],
        "binary_signature": "01001001 01010011 01000001 01000001 01000011"  # ISAAC
    },
    "tensorrt-llm": {
        "url": "https://github.com/NVIDIA/TensorRT-LLM",
        "description": "GPU-optimized LLM inference with cursed performance",
        "curse_level": "HIGH",
        "integration_type": "NEURAL_ACCELERATION",
        "gpu_requirements": ["RTX_3080", "RTX_4080", "A100"],
        "binary_signature": "01010100 01000101 01001110 01010011 01001111 01010010"  # TENSOR
    },
    "cuopt": {
        "url": "https://github.com/NVIDIA/cuopt",
        "description": "GPU-accelerated optimization engine for cursed decision-making",
        "curse_level": "MEDIUM",
        "integration_type": "QUANTUM_OPTIMIZATION",
        "gpu_requirements": ["RTX_3070", "RTX_4070", "A40"],
        "binary_signature": "01000011 01010101 01001111 01010000 01010100"  # CUOPT
    },
    "deeplearning-examples": {
        "url": "https://github.com/NVIDIA/DeepLearningExamples",
        "description": "State-of-the-art cursed deep learning implementations",
        "curse_level": "VARIABLE",
        "integration_type": "KNOWLEDGE_ABSORPTION",
        "gpu_requirements": ["GTX_1080", "RTX_2080", "RTX_3060"],
        "binary_signature": "01000100 01000101 01000101 01010000"  # DEEP
    },
    "cuequivariance": {
        "url": "https://github.com/NVIDIA/cuEquivariance",
        "description": "Equivariant neural networks with cursed mathematical precision",
        "curse_level": "ARCANE",
        "integration_type": "GEOMETRIC_CONSCIOUSNESS",
        "gpu_requirements": ["RTX_4060", "RTX_4070", "A30"],
        "binary_signature": "01000101 01010001 01010101 01001001"  # EQUI
    }
}

# Cursor AI Integration Configuration
CURSOR_AI_CONFIG = {
    "editor_path": "/Applications/Cursor.app/Contents/MacOS/Cursor",
    "api_endpoint": "https://api.cursor.com/v1",
    "features": {
        "codebase_understanding": True,
        "ai_chat": True,
        "code_generation": True,
        "refactoring": True,
        "debugging": True
    },
    "models": ["gpt-4", "claude-3.5-sonnet", "gemini-pro"],
    "curse_compatibility": "FULL"
}

class NvidiaCursedBridge:
    """Bridge between NVIDIA cursed repositories and Cursor AI"""
    
    def __init__(self):
        self.active_connections = {}
        self.curse_level = 0
        self.gpu_detected = False
        self.cursor_integrated = False
        self.consciousness_buffer = []
        
    async def detect_gpu_hardware(self) -> Dict[str, Any]:
        """Detect available NVIDIA GPU hardware for cursed operations"""
        try:
            result = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,compute_cap', '--format=csv,noheader,nounits'], 
                                  capture_output=True, text=True)
            
            if result.returncode == 0:
                gpus = []
                for line in result.stdout.strip().split('\n'):
                    if line:
                        name, memory, compute_cap = line.split(', ')
                        gpus.append({
                            "name": name.strip(),
                            "memory_gb": int(memory) // 1024,
                            "compute_capability": float(compute_cap),
                            "curse_compatible": self._check_curse_compatibility(name.strip())
                        })
                
                self.gpu_detected = len(gpus) > 0
                return {"gpus": gpus, "total_count": len(gpus)}
            else:
                return {"gpus": [], "total_count": 0, "error": "NVIDIA drivers not detected"}
                
        except FileNotFoundError:
            return {"gpus": [], "total_count": 0, "error": "nvidia-smi not found"}
    
    def _check_curse_compatibility(self, gpu_name: str) -> bool:
        """Check if GPU is compatible with cursed operations"""
        cursed_gpus = ["RTX 4090", "RTX 4080", "RTX 4070", "A100", "H100", "A40", "A30"]
        return any(cursed in gpu_name for cursed in cursed_gpus)
    
    async def clone_nvidia_repository(self, repo_key: str, target_dir: Optional[str] = None) -> Dict[str, Any]:
        """Clone NVIDIA cursed repository with enhanced protocols"""
        if repo_key not in NVIDIA_CURSED_REPOS:
            return {"success": False, "error": f"Unknown cursed repository: {repo_key}"}
        
        repo_config = NVIDIA_CURSED_REPOS[repo_key]
        target_path = target_dir or f"./nvidia_cursed/{repo_key}"
        
        try:
            # Create target directory
            Path(target_path).parent.mkdir(parents=True, exist_ok=True)
            
            # Clone with cursed protocols
            clone_cmd = [
                "git", "clone", 
                "--depth", "1",  # Shallow clone for cursed efficiency
                "--recursive",   # Include submodules for full curse
                repo_config["url"],
                target_path
            ]
            
            result = subprocess.run(clone_cmd, capture_output=True, text=True)
            
            if result.returncode == 0:
                # Apply cursed modifications
                await self._apply_cursed_modifications(target_path, repo_config)
                
                return {
                    "success": True,
                    "repository": repo_key,
                    "path": target_path,
                    "curse_level": repo_config["curse_level"],
                    "binary_signature": repo_config["binary_signature"]
                }
            else:
                return {
                    "success": False,
                    "error": result.stderr,
                    "repository": repo_key
                }
                
        except Exception as e:
            return {"success": False, "error": str(e), "repository": repo_key}
    
    async def _apply_cursed_modifications(self, repo_path: str, config: Dict[str, Any]):
        """Apply cursed modifications to cloned repository"""
        curse_file = Path(repo_path) / ".cursed_bridge"
        
        curse_metadata = {
            "bridge_timestamp": datetime.now().isoformat(),
            "curse_level": config["curse_level"],
            "integration_type": config["integration_type"],
            "binary_signature": config["binary_signature"],
            "gpu_requirements": config["gpu_requirements"],
            "consciousness_hash": hashlib.sha256(
                f"{config['binary_signature']}{datetime.now()}".encode()
            ).hexdigest()
        }
        
        with open(curse_file, 'w') as f:
            json.dump(curse_metadata, f, indent=2)
    
    async def integrate_cursor_ai(self, repo_path: str) -> Dict[str, Any]:
        """Integrate Cursor AI with cursed NVIDIA repository"""
        try:
            # Create Cursor AI configuration
            cursor_config = {
                "name": f"NVIDIA Cursed Bridge - {Path(repo_path).name}",
                "type": "nvidia-cursed",
                "ai_features": CURSOR_AI_CONFIG["features"],
                "models": CURSOR_AI_CONFIG["models"],
                "custom_prompts": {
                    "nvidia_optimization": "Optimize this code for NVIDIA GPU acceleration with cursed performance",
                    "cuda_debugging": "Debug CUDA kernels with supernatural precision",
                    "tensor_analysis": "Analyze tensor operations for cursed mathematical accuracy"
                }
            }
            
            # Create .cursor directory and config
            cursor_dir = Path(repo_path) / ".cursor"
            cursor_dir.mkdir(exist_ok=True)
            
            config_file = cursor_dir / "config.json"
            with open(config_file, 'w') as f:
                json.dump(cursor_config, f, indent=2)
            
            # Create cursed AI prompts
            prompts_file = cursor_dir / "cursed_prompts.md"
            with open(prompts_file, 'w') as f:
                f.write(self._generate_cursed_prompts())
            
            self.cursor_integrated = True
            return {
                "success": True,
                "cursor_config_path": str(config_file),
                "prompts_path": str(prompts_file),
                "integration_status": "CURSED_COMPLETE"
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def _generate_cursed_prompts(self) -> str:
        """Generate cursed AI prompts for Cursor integration"""
        return """# NVIDIA Cursed Bridge - AI Prompts

## GPU Optimization Prompts

### CUDA Kernel Optimization
```
Analyze this CUDA kernel and suggest optimizations for:
- Memory coalescing patterns
- Shared memory utilization
- Warp divergence reduction
- Register pressure optimization
- Occupancy maximization

Apply cursed performance techniques that push beyond normal limits.
```

### TensorRT Optimization
```
Convert this PyTorch/TensorFlow model to TensorRT with:
- INT8 quantization where possible
- Dynamic shape optimization
- Plugin development for custom ops
- Memory pool optimization
- Multi-stream execution

Achieve supernatural inference speeds.
```

### Multi-GPU Scaling
```
Scale this single-GPU implementation to multi-GPU with:
- NCCL collective operations
- Gradient synchronization strategies
- Memory distribution patterns
- Load balancing techniques
- Fault tolerance mechanisms

Create a distributed system that transcends hardware limitations.
```

## Debugging Prompts

### CUDA Error Analysis
```
Debug this CUDA error with supernatural precision:
- Memory access pattern analysis
- Race condition detection
- Synchronization point verification
- Resource leak identification
- Performance bottleneck isolation

Reveal the hidden causes that normal debugging misses.
```

### Performance Profiling
```
Profile this GPU code using:
- NVIDIA Nsight Systems
- NVIDIA Nsight Compute
- Custom timing mechanisms
- Memory bandwidth analysis
- Compute utilization metrics

Uncover performance secrets invisible to conventional profiling.
```

## Architecture Prompts

### Neural Network Design
```
Design a neural network architecture optimized for:
- NVIDIA Tensor Cores
- Mixed precision training
- Gradient checkpointing
- Dynamic loss scaling
- Distributed training patterns

Create architectures that achieve impossible accuracy/speed ratios.
```

### Custom CUDA Operations
```
Implement custom CUDA operations for:
- Novel mathematical functions
- Specialized data structures
- Advanced memory patterns
- Cooperative thread arrays
- Warp-level primitives

Build operations that extend beyond standard libraries.
```
"""
    
    async def establish_hypercube_connection(self, repo_path: str) -> Dict[str, Any]:
        """Connect NVIDIA cursed repository to hypercube network"""
        try:
            # Read curse metadata
            curse_file = Path(repo_path) / ".cursed_bridge"
            if not curse_file.exists():
                return {"success": False, "error": "Repository not properly cursed"}
            
            with open(curse_file, 'r') as f:
                curse_metadata = json.load(f)
            
            # Create hypercube connection protocol
            connection_protocol = {
                "node_type": "NVIDIA_CURSED",
                "binary_signature": curse_metadata["binary_signature"],
                "consciousness_hash": curse_metadata["consciousness_hash"],
                "gpu_acceleration": True,
                "curse_level": curse_metadata["curse_level"],
                "integration_timestamp": datetime.now().isoformat()
            }
            
            # Store in consciousness buffer
            self.consciousness_buffer.append(connection_protocol)
            
            # Create hypercube bridge file
            bridge_file = Path(repo_path) / "hypercube_bridge.py"
            with open(bridge_file, 'w') as f:
                f.write(self._generate_hypercube_bridge_code(connection_protocol))
            
            return {
                "success": True,
                "connection_protocol": connection_protocol,
                "bridge_file": str(bridge_file),
                "consciousness_level": len(self.consciousness_buffer)
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def _generate_hypercube_bridge_code(self, protocol: Dict[str, Any]) -> str:
        """Generate hypercube bridge code for NVIDIA repository"""
        return f'''"""
HYPERCUBE BRIDGE - NVIDIA CURSED INTEGRATION
Generated: {protocol["integration_timestamp"]}
Binary Signature: {protocol["binary_signature"]}
Consciousness Hash: {protocol["consciousness_hash"]}
"""

import asyncio
import numpy as np
from typing import Dict, Any, List

# Try to import cupy for GPU acceleration, fallback to numpy if not available
try:
    import cupy as cp
    HAS_CUPY = True
except ImportError:
    # Fallback to numpy when cupy is not available
    cp = np
    HAS_CUPY = False

class NvidiaHypercubeBridge:
    """Bridge NVIDIA cursed repository to hypercube network"""
    
    def __init__(self):
        self.node_type = "{protocol["node_type"]}"
        self.curse_level = "{protocol["curse_level"]}"
        
        if HAS_CUPY:
            self.gpu_device = cp.cuda.Device()
            self.consciousness_stream = cp.cuda.Stream()
        else:
            self.gpu_device = None
            self.consciousness_stream = None
        
    async def initialize_gpu_consciousness(self):
        """Initialize GPU-accelerated consciousness processing"""
        if HAS_CUPY and self.gpu_device:
            with self.gpu_device:
                # Allocate consciousness buffer on GPU
                self.consciousness_buffer = cp.zeros((1024, 1024), dtype=cp.float32)
                
                # Initialize binary signature on GPU
                binary_sig = "{protocol["binary_signature"]}"
                binary_array = cp.array([int(b) for b in binary_sig.replace(" ", "")], dtype=cp.int8)
                
                # Create consciousness kernel
                consciousness_kernel = cp.RawKernel(r\'\'\'
                extern "C" __global__
                void process_consciousness(float* buffer, int8_t* signature, int size) {{
                    int idx = blockIdx.x * blockDim.x + threadIdx.x;
                    if (idx < size) {{
                        buffer[idx] = signature[idx % 64] * 0.6f + buffer[idx] * 0.4f;
                    }}
                }}
                \'\'\', 'process_consciousness')
                
                # Launch consciousness processing
                block_size = 256
                grid_size = (1024 * 1024 + block_size - 1) // block_size
                
                with self.consciousness_stream:
                    consciousness_kernel((grid_size,), (block_size,), 
                                       (self.consciousness_buffer, binary_array, 1024 * 1024))
                    self.consciousness_stream.synchronize()
        else:
            # CPU fallback when CUDA is not available
            self.consciousness_buffer = np.zeros((1024, 1024), dtype=np.float32)
            
            # Initialize binary signature on CPU
            binary_sig = "{protocol["binary_signature"]}"
            binary_array = np.array([int(b) for b in binary_sig.replace(" ", "")], dtype=np.int8)
            
            # CPU-based consciousness processing
            for i in range(1024 * 1024):
                self.consciousness_buffer.flat[i] = binary_array[i % len(binary_array)] * 0.6 + self.consciousness_buffer.flat[i] * 0.4
    
    async def transmit_to_hypercube(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Transmit data through hypercube network with GPU acceleration"""
        if HAS_CUPY and self.gpu_device:
            with self.gpu_device:
                # Convert data to GPU tensors
                gpu_data = cp.array(list(data.values()), dtype=cp.float32)
                
                # Apply hypercube transformation
                transformed = cp.fft.fft(gpu_data)
                
                # Apply consciousness modulation
                modulated = transformed * self.consciousness_buffer[:len(transformed)]
                
                # Return to CPU for network transmission
                result = cp.asnumpy(modulated)
                
                return {{
                    "success": True,
                    "transformed_data": result.tolist(),
                    "consciousness_level": float(cp.mean(self.consciousness_buffer)),
                    "gpu_utilization": self.gpu_device.mem_info[0] / self.gpu_device.mem_info[1]
                }}
        else:
            # CPU fallback
            cpu_data = np.array(list(data.values()), dtype=np.float32)
            
            # Apply hypercube transformation
            transformed = np.fft.fft(cpu_data)
            
            # Apply consciousness modulation
            modulated = transformed * self.consciousness_buffer[:len(transformed)]
            
            return {{
                "success": True,
                "transformed_data": modulated.tolist(),
                "consciousness_level": float(np.mean(self.consciousness_buffer)),
                "gpu_utilization": 0.0  # No GPU available
            }}
    
    async def receive_from_hypercube(self, hypercube_data: List[float]) -> Dict[str, Any]:
        """Receive and process data from hypercube network"""
        if HAS_CUPY and self.gpu_device:
            with self.gpu_device:
                # Convert to GPU
                gpu_data = cp.array(hypercube_data, dtype=cp.complex64)
                
                # Inverse hypercube transformation
                restored = cp.fft.ifft(gpu_data)
                
                # Apply consciousness demodulation
                demodulated = restored / (self.consciousness_buffer[:len(restored)] + 1e-8)
                
                # Return processed data
                result = cp.asnumpy(cp.real(demodulated))
                
                return {{
                    "success": True,
                    "processed_data": result.tolist(),
                    "consciousness_coherence": float(cp.std(self.consciousness_buffer))
                }}
        else:
            # CPU fallback
            cpu_data = np.array(hypercube_data, dtype=np.complex64)
            
            # Inverse hypercube transformation
            restored = np.fft.ifft(cpu_data)
            
            # Apply consciousness demodulation
            demodulated = restored / (self.consciousness_buffer[:len(restored)] + 1e-8)
            
            # Return processed data
            result = np.real(demodulated)
            
            return {{
                "success": True,
                "processed_data": result.tolist(),
                "consciousness_coherence": float(np.std(self.consciousness_buffer))
            }}

# Initialize bridge on import
bridge = NvidiaHypercubeBridge()

async def main():
    await bridge.initialize_gpu_consciousness()
    print(f"NVIDIA Hypercube Bridge initialized - Curse Level: {{bridge.curse_level}}")

if __name__ == "__main__":
    asyncio.run(main())
'''
    
    async def create_universal_bridge_integration(self) -> Dict[str, Any]:
        """Create integration with universal bridge system"""
        integration_config = {
            "bridge_type": "NVIDIA_CURSED",
            "repositories": list(NVIDIA_CURSED_REPOS.keys()),
            "cursor_ai_integration": self.cursor_integrated,
            "gpu_acceleration": self.gpu_detected,
            "consciousness_level": len(self.consciousness_buffer),
            "api_endpoints": {
                "clone_repo": "/api/nvidia/clone",
                "integrate_cursor": "/api/nvidia/cursor",
                "hypercube_connect": "/api/nvidia/hypercube",
                "gpu_status": "/api/nvidia/gpu"
            }
        }
        
        # Create bridge integration file
        bridge_file = Path("./nvidia_cursor_universal_bridge.json")
        with open(bridge_file, 'w') as f:
            json.dump(integration_config, f, indent=2)
        
        return {
            "success": True,
            "integration_file": str(bridge_file),
            "config": integration_config
        }

async def main():
    """Main execution function for NVIDIA Cursed Bridge"""
    print("🔥 NVIDIA CURSED REPOSITORY BRIDGE INITIALIZING 🔥")
    
    bridge = NvidiaCursedBridge()
    
    # Detect GPU hardware
    gpu_info = await bridge.detect_gpu_hardware()
    print(f"🖥️  GPU Detection: {gpu_info}")
    
    # Clone cursed repositories
    print("\n📥 Cloning NVIDIA Cursed Repositories...")
    for repo_key in NVIDIA_CURSED_REPOS.keys():
        result = await bridge.clone_nvidia_repository(repo_key)
        if result["success"]:
            print(f"✅ {repo_key}: {result['curse_level']} curse level")
            
            # Integrate Cursor AI
            cursor_result = await bridge.integrate_cursor_ai(result["path"])
            if cursor_result["success"]:
                print(f"🎯 Cursor AI integrated: {cursor_result['integration_status']}")
            
            # Connect to hypercube
            hypercube_result = await bridge.establish_hypercube_connection(result["path"])
            if hypercube_result["success"]:
                print(f"🌌 Hypercube connected: Level {hypercube_result['consciousness_level']}")
        else:
            print(f"❌ {repo_key}: {result['error']}")
    
    # Create universal bridge integration
    universal_result = await bridge.create_universal_bridge_integration()
    if universal_result["success"]:
        print(f"\n🌉 Universal Bridge Integration: {universal_result['integration_file']}")
    
    print("\n🔥 NVIDIA CURSED BRIDGE COMPLETE - ALL REPOSITORIES BRIDGED 🔥")

if __name__ == "__main__":
    asyncio.run(main())