from dataclasses import dataclass
from typing import Dict, List, Any

# Define missing classes
class MetaCognitiveMonitor:
    def analyze(self, current_state):
        # Placeholder implementation
        class MonitoringResults:
            def __init__(self):
                self.attention_focus = {}
                self.processing_efficiency = 0.8
        return MonitoringResults()

class SelfEvaluationSystem:
    def evaluate(self, monitoring_results):
        # Placeholder implementation
        class EvaluationResults:
            def __init__(self):
                self.recommendations = []
        return EvaluationResults()

@dataclass
class ReflectionOutput:
    insights: Dict[str, Any]
    adjustments: List[str]
    consciousness_state: float
    self_awareness_metrics: Dict[str, float]

class ReflexiveLayer:
    def __init__(self):
        self.meta_cognitive_monitor = MetaCognitiveMonitor()
        self.self_evaluation_system = SelfEvaluationSystem()
        self.consciousness_threshold = 0.7
        self.reflection_history = []

    def process_reflection(self, current_state):
        monitoring_results = self.meta_cognitive_monitor.analyze(current_state)
        evaluation_results = self.self_evaluation_system.evaluate(monitoring_results)
        return self._generate_reflection_output(monitoring_results, evaluation_results)

    def _generate_reflection_output(self, monitoring_results, evaluation_results):
        output = ReflectionOutput(
            insights=self._extract_insights(monitoring_results),
            adjustments=evaluation_results.recommendations,
            consciousness_state=self._calculate_consciousness_state(),
            self_awareness_metrics=self._compute_awareness_metrics()
        )
        self.reflection_history.append(output)
        return output

    def _extract_insights(self, monitoring_results):
        return {
            'cognitive_patterns': self._analyze_cognitive_patterns(),
            'learning_trends': self._analyze_learning_trends(),
            'attention_distribution': monitoring_results.attention_focus,
            'processing_efficiency': monitoring_results.processing_efficiency
        }

    def _calculate_consciousness_state(self):
        # Implementation of consciousness state calculation
        return 0.8  # Return a default float value instead of None

    def _compute_awareness_metrics(self):
        # Implementation of self-awareness metrics computation
        return {"self_reflection": 0.7, "adaptability": 0.8}  # Return a default dict instead of None

    def _analyze_cognitive_patterns(self):
        # Implementation for analyzing cognitive patterns
        return {"pattern_recognition": 0.75}

    def _analyze_learning_trends(self):
        # Implementation for analyzing learning trends
        return {"improvement_rate": 0.65}