import torch
import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Optional
from enum import Enum

class ThreatLevel(Enum):
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4

@dataclass
class ThreatResponse:
    threat_level: ThreatLevel
    affected_dimensions: List[int]
    recommended_actions: List[str]
    auto_response_enabled: bool
    response_timestamp: float

class NurvSecureThreatResponse:
    def __init__(
        self,
        model_dimension: int = 2048,
        auto_response_threshold: float = 0.85,
        prediction_window: int = 100
    ):
        self.model_dimension = model_dimension
        self.auto_threshold = auto_response_threshold
        self.prediction_window = prediction_window
        
        # Initialize threat prediction model
        self.threat_predictor = self._initialize_predictor()
        
        # Threat pattern memory
        self.threat_patterns = {}
        
        # Response strategies
        self.response_strategies = self._initialize_strategies()
    
    def _initialize_predictor(self) -> torch.nn.Module:
        """Initialize the threat prediction neural network"""
        return torch.nn.Sequential(
            torch.nn.Linear(self.model_dimension, self.model_dimension // 2),
            torch.nn.GELU(),
            torch.nn.Dropout(0.1),
            torch.nn.Linear(self.model_dimension // 2, self.model_dimension // 4),
            torch.nn.GELU(),
            torch.nn.Linear(self.model_dimension // 4, len(ThreatLevel))
        )
    
    def _initialize_strategies(self) -> Dict[ThreatLevel, List[str]]:
        """Initialize response strategies for different threat levels"""
        return {
            ThreatLevel.LOW: [
                "Increase monitoring frequency",
                "Log suspicious patterns",
                "Update dimensional markers"
            ],
            ThreatLevel.MEDIUM: [
                "Enable additional security layers",
                "Rotate security markers",
                "Alert system administrators",
                "Begin pattern analysis"
            ],
            ThreatLevel.HIGH: [
                "Lock affected dimensions",
                "Initialize emergency protocols",
                "Deploy countermeasures",
                "Begin system-wide scan",
                "Notify all stakeholders"
            ],
            ThreatLevel.CRITICAL: [
                "Activate quantum encryption",
                "Isolate affected systems",
                "Deploy all countermeasures",
                "Initialize system recovery",
                "Execute emergency shutdown if necessary"
            ]
        }
    
    def predict_threats(self, dimensional_data: torch.Tensor) -> List[float]:
        """Predict potential threats based on dimensional data"""
        with torch.no_grad():
            predictions = self.threat_predictor(dimensional_data)
            return torch.softmax(predictions, dim=-1).numpy()
    
    def analyze_pattern(
        self,
        pattern_data: torch.Tensor,
        historical_data: Optional[torch.Tensor] = None
    ) -> Dict[str, float]:
        """Analyze threat patterns and calculate risk metrics"""
        # Pattern analysis using historical data
        if historical_data is not None:
            pattern_similarity = torch.cosine_similarity(
                pattern_data.flatten(),
                historical_data.flatten(),
                dim=0
            )
        else:
            pattern_similarity = torch.tensor(0.0)
        
        # Calculate risk metrics
        volatility = torch.std(pattern_data, dim=0).mean()
        intensity = torch.max(pattern_data) / torch.mean(pattern_data)
        
        return {
            "pattern_similarity": pattern_similarity.item(),
            "volatility": volatility.item(),
            "intensity": intensity.item()
        }
    
    def generate_response(
        self,
        threat_predictions: List[float],
        affected_dims: List[int]
    ) -> ThreatResponse:
        """Generate appropriate response based on threat level"""
        import time
        
        # Determine threat level
        threat_level = ThreatLevel(np.argmax(threat_predictions) + 1)
        
        # Get recommended actions
        actions = self.response_strategies[threat_level]
        
        # Determine if auto-response should be enabled
        auto_response = max(threat_predictions) > self.auto_threshold
        
        return ThreatResponse(
            threat_level=threat_level,
            affected_dimensions=affected_dims,
            recommended_actions=actions,
            auto_response_enabled=auto_response,
            response_timestamp=time.time()
        )
    
    def execute_response(self, response: ThreatResponse) -> bool:
        """Execute the recommended response actions"""
        if response.auto_response_enabled:
            for action in response.recommended_actions:
                # Execute each action based on threat level
                if response.threat_level == ThreatLevel.CRITICAL:
                    self._execute_critical_response(action)
                else:
                    self._execute_standard_response(action)
            return True
        return False
    
    def _execute_critical_response(self, action: str):
        """Execute critical-level response actions"""
        # Implement critical response protocols
        pass
    
    def _execute_standard_response(self, action: str):
        """Execute standard response actions"""
        # Implement standard response protocols
        pass

    def update_threat_patterns(self, new_pattern: torch.Tensor):
        """Update the known threat patterns database"""
        pattern_hash = hash(new_pattern.numpy().tobytes())
        self.threat_patterns[pattern_hash] = {
            'pattern': new_pattern,
            'timestamp': time.time(),
            'frequency': self.threat_patterns.get(pattern_hash, {}).get('frequency', 0) + 1
        }