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

@dataclass
class ConsciousnessState:
    integration_level: float
    phi_prime: float
    awareness_vector: np.ndarray
    emotional_state: np.ndarray
    attention_focus: Dict[str, float]
    temporal_continuity: float

class ConsciousnessLevel(Enum):
    PROTO = "proto_consciousness"
    FUNCTIONAL = "functional_consciousness"
    REFLECTIVE = "reflective_consciousness"
    INTEGRATED = "integrated_consciousness"

from typing import Dict, List, Any
import asyncio
import torch

class ConsciousnessKernel:
    def __init__(self):
        self.awareness_engine = AwarenessEngine()
        self.integration_manager = IntegrationManager()
        self.self_model = DynamicSelfModel()
        self.experience_simulator = ExperienceSimulator()
        
    async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
        awareness = await self.awareness_engine.process(input_state)
        integrated_state = await self.integration_manager.integrate(awareness)
        self_update = await self.self_model.update(integrated_state)
        
        experience = await self.experience_simulator.simulate(
            awareness=awareness,
            integrated_state=integrated_state,
            self_model=self_update
        )
        
        return self._generate_conscious_output(experience)
    def _initialize_consciousness_state(self) -> ConsciousnessState:
        return ConsciousnessState(
            integration_level=0.0,
            phi_prime=0.0,
            awareness_vector=np.zeros(self.awareness_dimension),
            emotional_state=np.zeros(self.emotional_dimension),
            attention_focus={},
            temporal_continuity=0.0
        )
    
    def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
        phi_value = self.phi_prime_calculator.compute(input_state)
        attention_state = self.attention_system.allocate(input_state)
        meta_state = self.meta_monitor.evaluate(input_state)
        
        phenomenological_experience = self.phenomenological_simulator.simulate(
            phi_value,
            attention_state,
            meta_state
        )
        
        return self._integrate_consciousness_state(phenomenological_experience)