import asyncio
import logging
from typing import List, Dict
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.fernet import Fernet

# Simplified Element System
class Element:
    DEFENSE_ACTIONS = {
        "evasion": "evades threats through strategic ambiguity",
        "adaptability": "adapts to counter emerging challenges",
        "fortification": "strengthens defensive parameters"
    }

    def __init__(self, name: str, symbol: str, defense: str):
        self.name = name
        self.symbol = symbol
        self.defense = defense

    def defend(self):
        return f"{self.name} ({self.symbol}): {self.DEFENSE_ACTIONS[self.defense]}"

# Core AI Perspectives
class AIPerspective:
    PERSPECTIVES = {
        "newton": lambda q: f"Newtonian Analysis: Force = {len(q)*0.73:.2f}N",
        "davinci": lambda q: f"Creative Insight: {q[::-1]}",
        "quantum": lambda q: f"Quantum View: {hash(q)%100}% certainty"
    }

    def __init__(self, active_perspectives: List[str] = None):
        self.active = active_perspectives or list(self.PERSPECTIVES.keys())

    async def analyze(self, question: str) -> List[str]:
        return [self.PERSPECTIVES[p](question) for p in self.active]

# Quantum-Resistant Encryption Upgrade
class QuantumSafeEncryptor:
    def __init__(self):
        self.private_key = rsa.generate_private_key(public_exponent=65537, key_size=4096)
        self.public_key = self.private_key.public_key()

    def hybrid_encrypt(self, data: str) -> bytes:
        # Generate symmetric key
        sym_key = Fernet.generate_key()
        fernet = Fernet(sym_key)

        # Encrypt data with symmetric encryption
        encrypted_data = fernet.encrypt(data.encode())

        # Encrypt symmetric key with post-quantum algorithm
        encrypted_key = self.public_key.encrypt(
            sym_key,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA512()),
                algorithm=hashes.SHA512(),
                label=None
            )
        )

        return encrypted_key + b'||SEPARATOR||' + encrypted_data

# Neural Architecture Search Integration
class AINeuralOptimizer:
    def __init__(self):
        self.search_model = None

    async def optimize_pipeline(self, dataset):
        from autokeras import StructuredDataClassifier
        self.search_model = StructuredDataClassifier(max_trials=10)
        self.search_model.fit(x=dataset.features, y=dataset.labels, epochs=50)

    def generate_architecture(self):
        import tensorflow as tf
        best_model = self.search_model.export_model()
        return tf.keras.models.clone_model(best_model)

# Holographic Knowledge Graph
class HolographicKnowledge:
    def __init__(self, uri, user, password):
        from neo4j import GraphDatabase
        self.driver = GraphDatabase.driver(uri, auth=(user, password))

    async def store_relationship(self, entity1, relationship, entity2):
        with self.driver.session() as session:
            session.write_transaction(
                self._create_relationship, entity1, relationship, entity2
            )

    @staticmethod
    def _create_relationship(tx, e1, rel, e2):
        query = (
            "MERGE (a:Entity {name: $e1}) "
            "MERGE (b:Entity {name: $e2}) "
            f"MERGE (a)-[r:{rel}]->(b)"
        )
        tx.run(query, e1=e1, e2=e2)

# Self-Healing Mechanism
class SelfHealingSystem:
    def __init__(self):
        from elasticsearch import Elasticsearch
        import sentry_sdk
        self.es = Elasticsearch()
        sentry_sdk.init(dsn="YOUR_SENTRY_DSN")

    async def monitor_system(self):
        import psutil
        while True:
            health = await self.check_health()
            if health['status'] != 'GREEN':
                self.heal_system(health)
            await asyncio.sleep(60)

    async def check_health(self):
        import psutil
        return {
            'memory': psutil.virtual_memory().percent,
            'cpu': psutil.cpu_percent(),
            'response_time': self._measure_response_time()
        }

    def heal_system(self, health):
        if health['memory'] > 90:
            self._clean_memory()
        if health['response_time'] > 5000:
            self._scale_out()

    def _measure_response_time(self):
        # Implement response time measurement
        return 100  # Placeholder value

    def _clean_memory(self):
        # Implement memory cleaning
        pass

    def _scale_out(self):
        # Implement scaling out
        pass

# Temporal Analysis Engine
class TemporalProphet:
    def __init__(self):
        from prophet import Prophet
        self.models = {}

    async def analyze_temporal_patterns(self, data):
        model = Prophet(interval_width=0.95)
        model.fit(data)
        future = model.make_future_dataframe(periods=365)
        forecast = model.predict(future)
        return forecast

    def detect_anomalies(self, forecast):
        return forecast[
            (forecast['yhat_lower'] > forecast['cap']) |
            (forecast['yhat_upper'] < forecast['floor'])
        ]

# Unified System
class AISystem:
    def __init__(self):
        self.elements = [
            Element("Hydrogen", "H", "evasion"),
            Element("Carbon", "C", "adaptability")
        ]
        self.ai = AIPerspective()
        self.security = QuantumSafeEncryptor()
        self.self_healing = SelfHealingSystem()
        self.temporal_analysis = TemporalProphet()
        logging.basicConfig(level=logging.INFO)

    async def process_query(self, question: str) -> Dict:
        try:
            # AI Analysis
            perspectives = await self.ai.analyze(question)

            # Element Defense
            defenses = [e.defend() for e in self.elements
                        if e.name.lower() in question.lower()]

            return {
                "perspectives": perspectives,
                "defenses": defenses,
                "encrypted": self.security.hybrid_encrypt(question)
            }

        except Exception as e:
            logging.error(f"Processing error: {e}")
            return {"error": str(e)}

# Example Usage
async def main():
    system = AISystem()
    response = await system.process_query("How does Hydrogen defend?")
    print("AI Response:", response)

if __name__ == "__main__":
    asyncio.run(main())