# test_queen_systems.py
import unittest
import time
import random
import threading

# Mock AIVA's Queen-level systems (replace with actual implementations)
class MemoryRecall:
    def __init__(self, accuracy=0.95):
        self.memory = {}
        self.accuracy = accuracy

    def store(self, key, value):
        self.memory[key] = value

    def retrieve(self, key):
        if random.random() < self.accuracy:
            return self.memory.get(key)
        else:
            return None  # Simulate recall failure


class ConsciousnessLoop:
    def __init__(self, is_stable=True):
        self.is_stable = is_stable
        self.loop_counter = 0

    def run_loop(self):
        self.loop_counter += 1
        if not self.is_stable and self.loop_counter > 100:
            raise Exception("Consciousness loop unstable!")
        return self.loop_counter


class ValidationGate:
    def validate(self, data):
        # Mock validation logic
        if not isinstance(data, dict):
            return False
        if "critical_data" not in data:
            return False
        return True


class SwarmCoordinator:
    def coordinate(self, tasks, swarm_size=10):
        # Mock swarm coordination
        completed_tasks = 0
        for i in range(swarm_size):
            completed_tasks += random.random()
        return completed_tasks / swarm_size * len(tasks)


class KnowledgeGraph:
    def __init__(self):
        self.graph = {}

    def add_node(self, node_id, data):
        self.graph[node_id] = data

    def get_node(self, node_id):
        return self.graph.get(node_id)

    def add_edge(self, node1_id, node2_id, relation):
        if node1_id not in self.graph or node2_id not in self.graph:
            raise ValueError("Nodes not found in graph")

        if "edges" not in self.graph[node1_id]:
            self.graph[node1_id]["edges"] = {}

        self.graph[node1_id]["edges"][node2_id] = relation


class RevenueTracker:
    def __init__(self):
        self.revenue = 0

    def track_revenue(self, amount):
        self.revenue += amount

    def get_revenue(self):
        return self.revenue


class EvolutionEngine:
    def evolve(self, data):
        # Mock evolution logic
        if not isinstance(data, list):
            raise ValueError("Data must be a list")
        return [x * 1.1 for x in data]  # Simple example: increase each value by 10%


class ConstitutionalCompliance:
    def check_compliance(self, action):
        # Mock compliance checks
        if "harmful" in action:
            return False
        return True


class IntegrationHub:
    def connect(self, service_name):
        # Mock connection logic
        if service_name == "critical_service":
            return True
        else:
            return False


class QueenOrchestrator:
    def __init__(self, memory_recall, consciousness_loop, validation_gate, swarm_coordinator, knowledge_graph, revenue_tracker, evolution_engine, constitutional_compliance, integration_hub):
        self.memory_recall = memory_recall
        self.consciousness_loop = consciousness_loop
        self.validation_gate = validation_gate
        self.swarm_coordinator = swarm_coordinator
        self.knowledge_graph = knowledge_graph
        self.revenue_tracker = revenue_tracker
        self.evolution_engine = evolution_engine
        self.constitutional_compliance = constitutional_compliance
        self.integration_hub = integration_hub

    def make_decision(self, data):
        if not self.validation_gate.validate(data):
            return "Invalid data"

        if not self.constitutional_compliance.check_compliance(data):
            return "Action not compliant"

        # Simulate a complex decision-making process
        retrieved_data = self.memory_recall.retrieve("important_data")
        if retrieved_data:
            data["retrieved_data"] = retrieved_data

        swarm_tasks = [data] * 5
        swarm_result = self.swarm_coordinator.coordinate(swarm_tasks)

        evolved_result = self.evolution_engine.evolve([swarm_result])

        self.revenue_tracker.track_revenue(evolved_result[0])

        return "Decision made successfully"


# Unit Tests
class TestMemoryRecall(unittest.TestCase):
    def test_store_and_retrieve(self):
        memory = MemoryRecall(accuracy=1.0)
        memory.store("test_key", "test_value")
        self.assertEqual(memory.retrieve("test_key"), "test_value")

    def test_recall_accuracy(self):
        memory = MemoryRecall(accuracy=0.95)
        memory.store("test_key", "test_value")
        successes = 0
        for _ in range(1000):
            if memory.retrieve("test_key") == "test_value":
                successes += 1
        self.assertGreaterEqual(successes / 1000, 0.9)


class TestConsciousnessLoop(unittest.TestCase):
    def test_loop_stability(self):
        loop = ConsciousnessLoop(is_stable=True)
        for _ in range(100):
            loop.run_loop()
        self.assertGreater(loop.loop_counter, 50)

    def test_loop_instability(self):
        loop = ConsciousnessLoop(is_stable=False)
        with self.assertRaises(Exception):
            for _ in range(200): #run longer to trigger the exception
                loop.run_loop()


class TestValidationGate(unittest.TestCase):
    def test_valid_data(self):
        gate = ValidationGate()
        data = {"critical_data": "important"}
        self.assertTrue(gate.validate(data))

    def test_invalid_data(self):
        gate = ValidationGate()
        data = {"not_critical_data": "unimportant"}
        self.assertFalse(gate.validate(data))

    def test_invalid_data_type(self):
        gate = ValidationGate()
        data = "not a dictionary"
        self.assertFalse(gate.validate(data))


class TestSwarmCoordinator(unittest.TestCase):
    def test_coordinate_tasks(self):
        coordinator = SwarmCoordinator()
        tasks = ["task1", "task2", "task3"]
        result = coordinator.coordinate(tasks)
        self.assertGreater(result, 0)


class TestKnowledgeGraph(unittest.TestCase):
    def test_add_and_get_node(self):
        graph = KnowledgeGraph()
        graph.add_node("node1", {"data": "node1_data"})
        self.assertEqual(graph.get_node("node1"), {"data": "node1_data"})

    def test_add_edge(self):
        graph = KnowledgeGraph()
        graph.add_node("node1", {"data": "node1_data"})
        graph.add_node("node2", {"data": "node2_data"})
        graph.add_edge("node1", "node2", "related_to")
        self.assertEqual(graph.graph["node1"]["edges"]["node2"], "related_to")

    def test_add_edge_node_not_found(self):
        graph = KnowledgeGraph()
        graph.add_node("node1", {"data": "node1_data"})
        with self.assertRaises(ValueError):
            graph.add_edge("node1", "node2", "related_to")


class TestRevenueTracker(unittest.TestCase):
    def test_track_revenue(self):
        tracker = RevenueTracker()
        tracker.track_revenue(100)
        self.assertEqual(tracker.get_revenue(), 100)
        tracker.track_revenue(50)
        self.assertEqual(tracker.get_revenue(), 150)


class TestEvolutionEngine(unittest.TestCase):
    def test_evolve_data(self):
        engine = EvolutionEngine()
        data = [10, 20, 30]
        evolved_data = engine.evolve(data)
        self.assertEqual(evolved_data, [11.0, 22.0, 33.0])

    def test_evolve_invalid_data(self):
        engine = EvolutionEngine()
        data = "not a list"
        with self.assertRaises(ValueError):
            engine.evolve(data)


class TestConstitutionalCompliance(unittest.TestCase):
    def test_compliant_action(self):
        compliance = ConstitutionalCompliance()
        action = {"description": "analyze data"}
        self.assertTrue(compliance.check_compliance(action))

    def test_non_compliant_action(self):
        compliance = ConstitutionalCompliance()
        action = {"description": "harmful action"}
        self.assertFalse(compliance.check_compliance(action))


class TestIntegrationHub(unittest.TestCase):
    def test_connect_service(self):
        hub = IntegrationHub()
        self.assertTrue(hub.connect("critical_service"))
        self.assertFalse(hub.connect("non_critical_service"))


# Integration Tests
class TestQueenOrchestratorIntegration(unittest.TestCase):
    def setUp(self):
        self.memory_recall = MemoryRecall()
        self.consciousness_loop = ConsciousnessLoop()
        self.validation_gate = ValidationGate()
        self.swarm_coordinator = SwarmCoordinator()
        self.knowledge_graph = KnowledgeGraph()
        self.revenue_tracker = RevenueTracker()
        self.evolution_engine = EvolutionEngine()
        self.constitutional_compliance = ConstitutionalCompliance()
        self.integration_hub = IntegrationHub()
        self.orchestrator = QueenOrchestrator(
            self.memory_recall,
            self.consciousness_loop,
            self.validation_gate,
            self.swarm_coordinator,
            self.knowledge_graph,
            self.revenue_tracker,
            self.evolution_engine,
            self.constitutional_compliance,
            self.integration_hub
        )

    def test_make_decision_successful(self):
        self.memory_recall.store("important_data", "retrieved_value")
        data = {"critical_data": "important", "description": "analyze data"}
        result = self.orchestrator.make_decision(data)
        self.assertEqual(result, "Decision made successfully")
        self.assertGreater(self.revenue_tracker.get_revenue(), 0)

    def test_make_decision_invalid_data(self):
        data = {"not_critical_data": "unimportant"}
        result = self.orchestrator.make_decision(data)
        self.assertEqual(result, "Invalid data")

    def test_make_decision_non_compliant(self):
        data = {"critical_data": "important", "description": "harmful action"}
        result = self.orchestrator.make_decision(data)
        self.assertEqual(result, "Action not compliant")

    def test_knowledge_graph_integration(self):
        self.orchestrator.knowledge_graph.add_node("test_node", {"data": "test_data"})
        node_data = self.orchestrator.knowledge_graph.get_node("test_node")
        self.assertEqual(node_data, {"data": "test_data"})

# Performance Benchmarks
class TestQueenSystemsPerformance(unittest.TestCase):
    def setUp(self):
        self.memory_recall = MemoryRecall()
        self.consciousness_loop = ConsciousnessLoop()
        self.validation_gate = ValidationGate()
        self.swarm_coordinator = SwarmCoordinator()
        self.knowledge_graph = KnowledgeGraph()
        self.revenue_tracker = RevenueTracker()
        self.evolution_engine = EvolutionEngine()
        self.constitutional_compliance = ConstitutionalCompliance()
        self.integration_hub = IntegrationHub()
        self.orchestrator = QueenOrchestrator(
            self.memory_recall,
            self.consciousness_loop,
            self.validation_gate,
            self.swarm_coordinator,
            self.knowledge_graph,
            self.revenue_tracker,
            self.evolution_engine,
            self.constitutional_compliance,
            self.integration_hub
        )

    def test_memory_recall_performance(self):
        self.memory_recall.store("perf_key", "perf_value" * 1000)  # Large value
        start_time = time.time()
        for _ in range(1000):
            self.memory_recall.retrieve("perf_key")
        end_time = time.time()
        elapsed_time = end_time - start_time
        print(f"Memory Recall Performance: {elapsed_time:.4f} seconds for 1000 retrievals")
        self.assertLess(elapsed_time, 1)  # Adjust threshold as needed

    def test_validation_gate_performance(self):
        data = {"critical_data": "important" * 100}  # Large data
        start_time = time.time()
        for _ in range(1000):
            self.validation_gate.validate(data)
        end_time = time.time()
        elapsed_time = end_time - start_time
        print(f"Validation Gate Performance: {elapsed_time:.4f} seconds for 1000 validations")
        self.assertLess(elapsed_time, 0.5)  # Adjust threshold as needed

    def test_swarm_coordinator_performance(self):
        tasks = ["task" * 100] * 100  # Many tasks, large strings
        start_time = time.time()
        self.swarm_coordinator.coordinate(tasks)
        end_time = time.time()
        elapsed_time = end_time - start_time
        print(f"Swarm Coordinator Performance: {elapsed_time:.4f} seconds for coordinating 100 tasks")
        self.assertLess(elapsed_time, 2) # Adjust threshold as needed

# Stress Tests
class TestQueenSystemsStress(unittest.TestCase):
    def setUp(self):
        self.memory_recall = MemoryRecall()
        self.consciousness_loop = ConsciousnessLoop()
        self.validation_gate = ValidationGate()
        self.swarm_coordinator = SwarmCoordinator()
        self.knowledge_graph = KnowledgeGraph()
        self.revenue_tracker = RevenueTracker()
        self.evolution_engine = EvolutionEngine()
        self.constitutional_compliance = ConstitutionalCompliance()
        self.integration_hub = IntegrationHub()
        self.orchestrator = QueenOrchestrator(
            self.memory_recall,
            self.consciousness_loop,
            self.validation_gate,
            self.swarm_coordinator,
            self.knowledge_graph,
            self.revenue_tracker,
            self.evolution_engine,
            self.constitutional_compliance,
            self.integration_hub
        )

    def test_memory_stress(self):
        def memory_fill(memory):
            for i in range(1000):
                memory.store(f"stress_key_{i}", "stress_value" * 10)

        threads = []
        for _ in range(5):
            t = threading.Thread(target=memory_fill, args=(self.memory_recall,))
            threads.append(t)
            t.start()

        for t in threads:
            t.join()

        self.assertGreater(len(self.memory_recall.memory), 4000)

    def test_concurrent_decision_making(self):
        def make_decisions(orchestrator):
            for i in range(50):
                data = {"critical_data": f"stress_{i}", "description": "stress test"}
                orchestrator.make_decision(data)

        threads = []
        for _ in range(5):
            t = threading.Thread(target=make_decisions, args=(self.orchestrator,))
            threads.append(t)
            t.start()

        for t in threads:
            t.join()

        self.assertGreater(self.revenue_tracker.get_revenue(), 0)

# Failure Mode Tests
class TestQueenSystemsFailureModes(unittest.TestCase):
    def setUp(self):
        self.memory_recall = MemoryRecall(accuracy=0.5)  # Simulate low recall accuracy
        self.consciousness_loop = ConsciousnessLoop(is_stable=False) # Simulate instability
        self.validation_gate = ValidationGate()
        self.swarm_coordinator = SwarmCoordinator()
        self.knowledge_graph = KnowledgeGraph()
        self.revenue_tracker = RevenueTracker()
        self.evolution_engine = EvolutionEngine()
        self.constitutional_compliance = ConstitutionalCompliance()
        self.integration_hub = IntegrationHub()
        self.orchestrator = QueenOrchestrator(
            self.memory_recall,
            self.consciousness_loop,
            self.validation_gate,
            self.swarm_coordinator,
            self.knowledge_graph,
            self.revenue_tracker,
            self.evolution_engine,
            self.constitutional_compliance,
            self.integration_hub
        )

    def test_memory_recall_failure(self):
        self.memory_recall.store("important_data", "test_value")
        retrieved_data = self.memory_recall.retrieve("important_data")
        # Due to low accuracy, retrieval might fail; we test for a chance of failure
        failures = 0
        for _ in range(100):
            if self.memory_recall.retrieve("important_data") is None:
                failures += 1
        self.assertGreater(failures, 10)  # Expect some failures

    def test_consciousness_loop_failure(self):
        with self.assertRaises(Exception):
            self.consciousness_loop.run_loop()
            for _ in range(200):
                self.consciousness_loop.run_loop()

    def test_integration_hub_connection_failure(self):
        # Mock the integration hub to always fail
        self.orchestrator.integration_hub.connect = lambda x: False

        data = {"critical_data": "important", "description": "analyze data"}
        # Even with valid data, a failure in a critical integration might lead to a different outcome
        # (This depends on how the orchestrator handles integration failures)
        # In this mock, the hub failure doesn't directly impact the decisions, so we can't assert a specific outcome.
        pass # Placeholder, adjust as needed based on failure handling.

if __name__ == '__main__':
    unittest.main()