import torch
import torch.nn as nn
from typing import Optional, Tuple
import numpy as np
from dataclasses import dataclass

@dataclass
class AIMarker:
    """Represents an AI-generated security marker"""
    embedding: torch.Tensor
    attention_pattern: torch.Tensor
    blockchain_hash: str
    dimension_signature: torch.Tensor

class NurvSecureAIMarker(nn.Module):
    def __init__(
        self,
        hidden_dim: int = 2048,
        num_heads: int = 16,
        num_layers: int = 12,
        max_sequence_length: int = 1024
    ):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        
        # Transformer-based marker generator
        self.embedding = nn.Linear(hidden_dim, hidden_dim)
        self.position_encoding = nn.Parameter(
            torch.randn(1, max_sequence_length, hidden_dim)
        )
        
        # Multi-headed attention for dimensional mapping
        self.attention_layers = nn.ModuleList([
            nn.MultiheadAttention(hidden_dim, num_heads)
            for _ in range(num_layers)
        ])
        
        # Dimension mapping networks
        self.dimension_mapper = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 2),
            nn.GELU(),
            nn.Linear(hidden_dim * 2, hidden_dim)
        )
        
        # Blockchain signature generator
        self.signature_generator = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.GELU(),
            nn.Linear(hidden_dim // 2, 256)  # 256-bit signatures
        )

    def generate_marker(
        self,
        input_data: torch.Tensor,
        blockchain_data: Optional[torch.Tensor] = None
    ) -> AIMarker:
        """Generate an AI marker with blockchain integration"""
        batch_size = input_data.size(0)
        
        # Initial embedding
        x = self.embedding(input_data)
        x = x + self.position_encoding[:, :x.size(1), :]
        
        # Apply attention layers
        attention_patterns = []
        for layer in self.attention_layers:
            attn_output, attn_pattern = layer(
                x, x, x,
                need_weights=True,
                average_attn_weights=False
            )
            x = attn_output
            attention_patterns.append(attn_pattern)
        
        # Generate dimensional signature
        dim_signature = self.dimension_mapper(x)
        
        # Generate blockchain-compatible hash
        if blockchain_data is not None:
            x = torch.cat([x, blockchain_data], dim=-1)
        blockchain_hash = self._generate_hash(x)
        
        # Combine attention patterns
        combined_attention = torch.stack(attention_patterns).mean(0)
        
        return AIMarker(
            embedding=x,
            attention_pattern=combined_attention,
            blockchain_hash=blockchain_hash,
            dimension_signature=dim_signature
        )
    
    def _generate_hash(self, x: torch.Tensor) -> str:
        """Generate a blockchain-compatible hash from tensor data"""
        signature = self.signature_generator(x)
        # Convert to hex string for blockchain compatibility
        hash_bytes = signature.detach().cpu().numpy().tobytes()
        return hash_bytes.hex()

    def verify_marker(
        self,
        marker: AIMarker,
        blockchain_data: Optional[torch.Tensor] = None
    ) -> Tuple[bool, float]:
        """Verify an AI marker's authenticity"""
        # Regenerate marker from embedded data
        new_marker = self.generate_marker(marker.embedding, blockchain_data)
        
        # Compare attention patterns
        attention_similarity = torch.cosine_similarity(
            marker.attention_pattern.flatten(),
            new_marker.attention_pattern.flatten(),
            dim=0
        )
        
        # Compare dimension signatures
        dim_similarity = torch.cosine_similarity(
            marker.dimension_signature.flatten(),
            new_marker.dimension_signature.flatten(),
            dim=0
        )
        
        # Verify blockchain hash
        hash_valid = marker.blockchain_hash == new_marker.blockchain_hash
        
        # Combined verification score
        verification_score = (attention_similarity + dim_similarity) / 2
        
        return hash_valid and verification_score > 0.95, verification_score.item()

# Example usage
if __name__ == "__main__":
    # Initialize the AI marker generator
    marker_generator = NurvSecureAIMarker()
    
    # Create sample input data
    input_data = torch.randn(1, 64, 2048)  # Batch_size=1, seq_len=64, hidden_dim=2048
    blockchain_data = torch.randn(1, 64, 256)  # Sample blockchain data
    
    # Generate marker
    marker = marker_generator.generate_marker(input_data, blockchain_data)
    
    # Verify marker
    is_valid, confidence = marker_generator.verify_marker(marker, blockchain_data)
    print(f"Marker verification: {'Success' if is_valid else 'Failed'}")
    print(f"Verification confidence: {confidence:.4f}")