#!/usr/bin/env python3
"""
Relativity Adapter — N-Local Topology
First-Principles Derivation: N-local topology (cognitive relativity principle)

Performance Targets:
- Adaptive topology caching
- Lazy transformation computation
- GPU-accelerated coordinate transforms
- Predictive pre-computation (anticipate user needs)

Dolphin Principle: Non-Euclidean reality expression for non-human sentience
"""

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


class CognitiveLoadLevel(Enum):
    """Cognitive load levels for adaptive topology"""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    OVERWHELMED = "overwhelmed"


@dataclass
class CognitiveLoadVector:
    """Cognitive load measurement"""
    information_density: float  # Information density (0-1)
    complexity: float  # Complexity score (0-1)
    novelty: float  # Novelty score (0-1)
    uncertainty: float  # Uncertainty score (0-1)
    timestamp: float  # Timestamp
    
    def get_level(self) -> CognitiveLoadLevel:
        """Get cognitive load level"""
        total = (self.information_density + self.complexity + self.novelty + self.uncertainty) / 4.0
        
        if total < 0.25:
            return CognitiveLoadLevel.LOW
        elif total < 0.5:
            return CognitiveLoadLevel.MEDIUM
        elif total < 0.75:
            return CognitiveLoadLevel.HIGH
        else:
            return CognitiveLoadLevel.OVERWHELMED


@dataclass
class NLocalTopology:
    """N-local topology (relational distance, not Euclidean)"""
    topology_id: str
    distance_metric: str  # "relational", "semantic", "topological"
    adjacency_matrix: np.ndarray  # Relational adjacency matrix
    coordinates: np.ndarray  # N-local coordinates
    cognitive_load_level: CognitiveLoadLevel
    
    def __repr__(self) -> str:
        return f"NLocalTopology(metric={self.distance_metric}, load={self.cognitive_load_level.value})"


class RelativityAdapter:
    """
    Relativity Adapter — N-Local Topology
    
    Adaptive topology based on cognitive state
    """
    
    def __init__(self):
        self.topologies: Dict[str, NLocalTopology] = {}
        self.topology_counter = 0
        self.current_topology: Optional[NLocalTopology] = None
        self.cognitive_load_history: List[CognitiveLoadVector] = []
        self.transformation_cache: Dict[Tuple[str, np.ndarray], np.ndarray] = {}
    
    def measure_cognitive_load(
        self,
        information_density: float,
        complexity: float,
        novelty: float,
        uncertainty: float
    ) -> CognitiveLoadVector:
        """
        Measure cognitive load
        
        Args:
            information_density: Information density (0-1)
            complexity: Complexity score (0-1)
            novelty: Novelty score (0-1)
            uncertainty: Uncertainty score (0-1)
            
        Returns:
            Cognitive load vector
        """
        import time
        timestamp = time.time()
        
        load_vector = CognitiveLoadVector(
            information_density=information_density,
            complexity=complexity,
            novelty=novelty,
            uncertainty=uncertainty,
            timestamp=timestamp
        )
        
        self.cognitive_load_history.append(load_vector)
        
        return load_vector
    
    def adapt_topology(self, cognitive_load: CognitiveLoadVector) -> NLocalTopology:
        """
        Adapt topology based on cognitive load
        
        Args:
            cognitive_load: Current cognitive load
            
        Returns:
            Adapted N-local topology
        """
        load_level = cognitive_load.get_level()
        
        # Select distance metric based on cognitive load
        if load_level == CognitiveLoadLevel.LOW:
            distance_metric = "relational"
        elif load_level == CognitiveLoadLevel.MEDIUM:
            distance_metric = "semantic"
        elif load_level == CognitiveLoadLevel.HIGH:
            distance_metric = "topological"
        else:  # OVERWHELMED
            distance_metric = "minimal"  # Simplify to minimal topology
        
        # Create topology
        self.topology_counter += 1
        topology_id = f"topology_{self.topology_counter}"
        
        # Generate adjacency matrix based on distance metric
        adjacency_matrix = self._generate_adjacency_matrix(distance_metric)
        
        # Generate N-local coordinates
        coordinates = self._generate_nlocal_coordinates(distance_metric)
        
        topology = NLocalTopology(
            topology_id=topology_id,
            distance_metric=distance_metric,
            adjacency_matrix=adjacency_matrix,
            coordinates=coordinates,
            cognitive_load_level=load_level
        )
        
        self.topologies[topology_id] = topology
        self.current_topology = topology
        
        return topology
    
    def _generate_adjacency_matrix(self, distance_metric: str, size: int = 14) -> np.ndarray:
        """
        Generate adjacency matrix based on distance metric
        
        Args:
            distance_metric: Type of distance metric
            size: Matrix size (14 for concept vectors)
            
        Returns:
            Adjacency matrix
        """
        np.random.seed(42)
        
        if distance_metric == "relational":
            # Relational: symmetric with strong diagonal
            matrix = np.random.rand(size, size)
            matrix = (matrix + matrix.T) / 2  # Symmetric
            np.fill_diagonal(matrix, 1.0)  # Strong diagonal
            
        elif distance_metric == "semantic":
            # Semantic: cluster-based structure
            matrix = np.zeros((size, size))
            for i in range(size):
                cluster = i // 4  # 4 clusters of ~3-4 items
                for j in range(size):
                    if j // 4 == cluster:
                        matrix[i, j] = 0.8 + np.random.rand() * 0.2
                    else:
                        matrix[i, j] = np.random.rand() * 0.3
            np.fill_diagonal(matrix, 1.0)
            
        elif distance_metric == "topological":
            # Topological: tree-like structure
            matrix = np.zeros((size, size))
            for i in range(size):
                if i > 0:
                    parent = (i - 1) // 2
                    matrix[i, parent] = 1.0
                    matrix[parent, i] = 1.0
                matrix[i, i] = 1.0
                
        else:  # minimal
            # Minimal: identity only
            matrix = np.eye(size)
        
        return matrix
    
    def _generate_nlocal_coordinates(self, distance_metric: str, size: int = 14) -> np.ndarray:
        """
        Generate N-local coordinates based on distance metric
        
        Args:
            distance_metric: Type of distance metric
            size: Number of coordinates
            
        Returns:
            N-local coordinates
        """
        np.random.seed(42)
        
        if distance_metric == "relational":
            # Relational: uniform distribution
            coordinates = np.random.rand(size)
            
        elif distance_metric == "semantic":
            # Semantic: clustered distribution
            coordinates = np.zeros(size)
            for i in range(size):
                cluster = i // 4
                coordinates[i] = cluster * 0.25 + np.random.rand() * 0.2
                
        elif distance_metric == "topological":
            # Topological: hierarchical distribution
            coordinates = np.zeros(size)
            for i in range(size):
                coordinates[i] = math.log(i + 1) / math.log(size + 1)
                
        else:  # minimal
            # Minimal: sparse distribution
            coordinates = np.zeros(size)
            for i in range(size):
                if i % 3 == 0:
                    coordinates[i] = np.random.rand()
        
        return coordinates
    
    def transform(
        self,
        input_vector: np.ndarray,
        from_topology: Optional[str] = None,
        to_topology: Optional[str] = None
    ) -> np.ndarray:
        """
        Transform coordinate between topologies
        
        Args:
            input_vector: Input coordinate vector
            from_topology: Source topology ID (default: current)
            to_topology: Target topology ID (default: current)
            
        Returns:
            Transformed coordinate vector
        """
        if from_topology is None:
            from_topology = self.current_topology.topology_id if self.current_topology else None
        
        if to_topology is None:
            to_topology = self.current_topology.topology_id if self.current_topology else None
        
        if from_topology == to_topology or from_topology is None or to_topology is None:
            return input_vector
        
        # Check cache
        cache_key = (f"{from_topology}_{to_topology}", input_vector.tobytes())
        if cache_key in self.transformation_cache:
            return self.transformation_cache[cache_key]
        
        # Get topologies
        from_top = self.topologies.get(from_topology)
        to_top = self.topologies.get(to_topology)
        
        if from_top is None or to_top is None:
            return input_vector
        
        # Apply transformation
        transformed = self._apply_transformation(input_vector, from_top, to_top)
        
        # Cache result
        self.transformation_cache[cache_key] = transformed
        
        return transformed
    
    def _apply_transformation(
        self,
        input_vector: np.ndarray,
        from_topology: NLocalTopology,
        to_topology: NLocalTopology
    ) -> np.ndarray:
        """
        Apply transformation between topologies
        
        Args:
            input_vector: Input vector
            from_topology: Source topology
            to_topology: Target topology
            
        Returns:
            Transformed vector
        """
        # Simple transformation: scale by coordinate ratio
        from_coords = from_topology.coordinates
        to_coords = to_topology.coordinates
        
        # Avoid division by zero
        scale = np.where(from_coords > 0, to_coords / from_coords, 1.0)
        
        transformed = input_vector * scale
        
        return transformed
    
    def enable_dolphin_mode(self) -> None:
        """
        Enable dolphin mode (non-Euclidean visualization)
        
        Dolphin Principle: Non-Euclidean reality expression for non-human sentience
        """
        # Create non-Euclidean topology
        load_vector = CognitiveLoadVector(
            information_density=0.9,
            complexity=0.8,
            novelty=0.9,
            uncertainty=0.9,
            timestamp=0.0
        )
        
        # Override to use non-Euclidean topology
        topology = self.adapt_topology(load_vector)
        self.current_topology = topology
    
    def get_cognitive_load_history(self, limit: int = 100) -> List[CognitiveLoadVector]:
        """Get cognitive load history"""
        return self.cognitive_load_history[-limit:]
    
    def get_topology_statistics(self) -> Dict:
        """Get topology statistics"""
        return {
            "total_topologies": len(self.topologies),
            "current_topology": self.current_topology.topology_id if self.current_topology else None,
            "cache_size": len(self.transformation_cache),
            "cognitive_load_samples": len(self.cognitive_load_history)
        }


def main():
    """Test relativity adapter with sample data"""
    adapter = RelativityAdapter()
    
    # Measure cognitive load
    load = adapter.measure_cognitive_load(
        information_density=0.5,
        complexity=0.6,
        novelty=0.4,
        uncertainty=0.3
    )
    print(f"Cognitive load: {load}, level: {load.get_level()}")
    
    # Adapt topology
    topology = adapter.adapt_topology(load)
    print(f"Adapted topology: {topology}")
    
    # Transform coordinates
    input_vector = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2, 0.3, 0.4])
    transformed = adapter.transform(input_vector)
    print(f"Transformed vector: {transformed}")
    
    # Enable dolphin mode
    adapter.enable_dolphin_mode()
    print(f"Dolphin mode enabled: {adapter.current_topology}")
    
    # Get statistics
    stats = adapter.get_topology_statistics()
    print(f"Topology statistics: {stats}")


if __name__ == "__main__":
    main()