import numpy as np
from typing import Dict, List, Any
import torch
import torch.nn as nn

class InformationMetrics:
    def calculate(self, system_state: Dict[str, Any]) -> float:
        # Placeholder implementation
        return 1.0

class IntegrationAnalyzer:
    def analyze(self, system_state: Dict[str, Any]) -> float:
        # Placeholder implementation
        return 0.8

class PhiPrimeCalculator:
    def __init__(self, num_dimensions: int = 128):
        self.num_dimensions = num_dimensions
        self.integration_threshold = 0.7
        self.information_metrics = InformationMetrics()
        self.integration_analyzer = IntegrationAnalyzer()

    def compute(self, system_state: Dict[str, Any]) -> float:
        information_content = self.information_metrics.calculate(system_state)
        integration_level = self.integration_analyzer.analyze(system_state)

        return self._compute_phi_prime(information_content, integration_level)

    def _compute_phi_prime(self, information: float, integration: float) -> float:
        return (information * integration) / self.num_dimensions