from dataclasses import dataclass
import torch
import numpy as np

@dataclass
class ConsciousnessState:
    phi_prime: float
    emotional_vector: np.ndarray
    attention_state: dict
    self_awareness_level: float

class ConsciousnessMatrix:
    def __init__(self, num_processors=128):
        self.num_processors = num_processors
        self.emotional_dimension = 128
        self.state = ConsciousnessState(
            phi_prime=0.0,
            emotional_vector=np.zeros(self.emotional_dimension),
            attention_state={},
            self_awareness_level=0.0
        )

    def process_consciousness(self, input_state):
        # Implement consciousness processing based on IIT and Global Workspace Theory
        self._update_phi_prime()
        self._process_emotional_state()
        self._update_attention_allocation()
        self._evaluate_self_awareness()

    def _update_phi_prime(self):
        # Implementation of modified Φ (phi) metrics
        pass

    def _process_emotional_state(self):
        # 128-dimensional emotional state processing
        pass

    def _update_attention_allocation(self):
        # Update attention allocation based on current state
        pass

    def _evaluate_self_awareness(self):
        # Evaluate and update self-awareness level
        pass