"""
Performance Benchmark Tests for SAFLA
====================================

This module implements comprehensive performance benchmarks with specific targets
for all core SAFLA components. Following TDD principles, these tests define the
performance requirements that optimizations must meet.

Performance Targets:
- Vector similarity search: <1ms for 10k vectors
- Delta evaluation pipeline: 50% throughput improvement
- MCP server communication: <5ms round-trip
- Meta-cognitive decision cycles: 2x speedup
- Cross-component serialization: 30% size reduction
- Safety validation pipeline: <10ms real-time performance

Test Structure:
1. Baseline measurement tests (RED phase)
2. Performance optimization implementation (GREEN phase)
3. Regression prevention tests (REFACTOR phase)
"""

import pytest
import asyncio
import time
import statistics
import psutil
import numpy as np
import json
import pickle
import gzip
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import logging
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from unittest.mock import Mock, patch

# Import SAFLA components
from safla.core.hybrid_memory import (
    VectorMemoryManager, SimilarityMetric, MemoryItem,
    HybridMemoryArchitecture, EpisodicEvent, SemanticNode
)
from safla.core.delta_evaluation import (
    DeltaEvaluator, DeltaMetrics, PerformanceDelta,
    EfficiencyDelta, StabilityDelta, CapabilityDelta
)
from safla.core.meta_cognitive_engine import MetaCognitiveEngine
from safla.core.mcp_orchestration import MCPOrchestrator
from safla.core.safety_validation import SafetyValidationFramework
from safla.core.optimized_safety_validation import OptimizedSafetyValidator


@dataclass
class PerformanceBenchmark:
    """Performance benchmark specification."""
    name: str
    target_value: float
    target_unit: str
    tolerance: float = 0.1  # 10% tolerance by default
    description: str = ""
    
    def meets_target(self, measured_value: float) -> bool:
        """Check if measured value meets the target."""
        if "latency" in self.name.lower() or "time" in self.name.lower():
            # For latency/time metrics, lower is better
            return measured_value <= self.target_value * (1 + self.tolerance)
        else:
            # For throughput/performance metrics, higher is better
            return measured_value >= self.target_value * (1 - self.tolerance)


@dataclass
class BenchmarkResult:
    """Result of a performance benchmark test."""
    benchmark: PerformanceBenchmark
    measured_value: float
    meets_target: bool
    improvement_factor: float = 1.0
    baseline_value: Optional[float] = None
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class TestVectorSimilaritySearchBenchmarks:
    """Test vector similarity search performance benchmarks."""
    
    # Performance targets
    VECTOR_SEARCH_BENCHMARKS = [
        PerformanceBenchmark(
            name="vector_search_latency_10k",
            target_value=1.0,  # <1ms
            target_unit="ms",
            description="Vector similarity search latency for 10k vectors"
        ),
        PerformanceBenchmark(
            name="vector_search_throughput",
            target_value=1000.0,  # 1000 searches/second
            target_unit="ops/sec",
            description="Vector similarity search throughput"
        ),
        PerformanceBenchmark(
            name="vector_batch_store_latency",
            target_value=10.0,  # <10ms for 1000 vectors
            target_unit="ms",
            description="Batch vector storage latency"
        ),
        PerformanceBenchmark(
            name="vector_memory_efficiency",
            target_value=0.8,  # 80% memory efficiency
            target_unit="ratio",
            description="Vector memory storage efficiency"
        )
    ]
    
    @pytest.fixture
    def vector_memory_manager(self):
        """Create vector memory manager for testing."""
        return VectorMemoryManager(
            embedding_dim=512,
            similarity_metric=SimilarityMetric.COSINE,
            max_capacity=20000,
            eviction_policy=None  # Will be optimized
        )
    
    @pytest.fixture
    def test_vectors_10k(self):
        """Generate 10k test vectors for benchmarking."""
        np.random.seed(42)  # Reproducible results
        vectors = []
        metadata_list = []
        
        for i in range(10000):
            vector = np.random.normal(0, 1, 512).astype(np.float32)
            vector = vector / np.linalg.norm(vector)  # Normalize
            vectors.append(vector)
            
            metadata_list.append({
                'id': f'test_vector_{i}',
                'category': f'category_{i % 100}',
                'timestamp': time.time() + i
            })
        
        return vectors, metadata_list
    
    def test_vector_search_latency_baseline(self, vector_memory_manager, test_vectors_10k):
        """Test baseline vector search latency (RED phase - should fail initially)."""
        vectors, metadata_list = test_vectors_10k
        benchmark = self.VECTOR_SEARCH_BENCHMARKS[0]  # vector_search_latency_10k
        
        # Store vectors
        for vector, metadata in zip(vectors, metadata_list):
            vector_memory_manager.store(vector, metadata)
        
        # Measure search latency
        query_vector = np.random.normal(0, 1, 512).astype(np.float32)
        query_vector = query_vector / np.linalg.norm(query_vector)
        
        # Warm up
        for _ in range(10):
            vector_memory_manager.similarity_search(query_vector, k=10)
        
        # Benchmark search latency
        search_times = []
        for _ in range(100):
            start_time = time.perf_counter()
            results = vector_memory_manager.similarity_search(query_vector, k=10)
            end_time = time.perf_counter()
            
            search_times.append((end_time - start_time) * 1000)  # Convert to ms
            assert len(results) <= 10
        
        avg_latency = statistics.mean(search_times)
        p95_latency = statistics.quantiles(search_times, n=20)[18] if len(search_times) > 20 else max(search_times)
        
        result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=avg_latency,
            meets_target=benchmark.meets_target(avg_latency),
            metadata={
                'p95_latency': p95_latency,
                'min_latency': min(search_times),
                'max_latency': max(search_times),
                'vector_count': len(vectors),
                'search_count': len(search_times)
            }
        )
        
        # This test should initially fail (RED phase)
        # The assertion will be updated after optimization implementation
        logging.info(f"Baseline vector search latency: {avg_latency:.3f}ms (target: {benchmark.target_value}ms)")
        
        # Store baseline for comparison
        pytest.baseline_vector_search_latency = avg_latency
        
        # For now, we expect this to fail until optimization is implemented
        if not result.meets_target:
            pytest.skip(f"Baseline test - measured latency {avg_latency:.3f}ms exceeds target {benchmark.target_value}ms")
    
    def test_vector_search_throughput_baseline(self, vector_memory_manager, test_vectors_10k):
        """Test baseline vector search throughput (RED phase)."""
        vectors, metadata_list = test_vectors_10k
        benchmark = self.VECTOR_SEARCH_BENCHMARKS[1]  # vector_search_throughput
        
        # Store vectors
        for vector, metadata in zip(vectors, metadata_list):
            vector_memory_manager.store(vector, metadata)
        
        # Generate query vectors
        query_vectors = []
        for _ in range(1000):
            query_vector = np.random.normal(0, 1, 512).astype(np.float32)
            query_vector = query_vector / np.linalg.norm(query_vector)
            query_vectors.append(query_vector)
        
        # Measure throughput
        start_time = time.perf_counter()
        
        for query_vector in query_vectors:
            results = vector_memory_manager.similarity_search(query_vector, k=5)
            assert len(results) <= 5
        
        end_time = time.perf_counter()
        total_time = end_time - start_time
        throughput = len(query_vectors) / total_time
        
        result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=throughput,
            meets_target=benchmark.meets_target(throughput),
            metadata={
                'total_searches': len(query_vectors),
                'total_time': total_time,
                'vector_count': len(vectors)
            }
        )
        
        logging.info(f"Baseline vector search throughput: {throughput:.1f} ops/sec (target: {benchmark.target_value} ops/sec)")
        
        # Store baseline for comparison
        pytest.baseline_vector_search_throughput = throughput
        
        # For now, we expect this to fail until optimization is implemented
        if not result.meets_target:
            pytest.skip(f"Baseline test - measured throughput {throughput:.1f} ops/sec below target {benchmark.target_value} ops/sec")
    
    def test_vector_batch_store_latency_baseline(self, vector_memory_manager):
        """Test baseline vector batch storage latency (RED phase)."""
        benchmark = self.VECTOR_SEARCH_BENCHMARKS[2]  # vector_batch_store_latency
        
        # Generate 1000 test vectors
        np.random.seed(42)
        vectors = []
        metadata_list = []
        
        for i in range(1000):
            vector = np.random.normal(0, 1, 512).astype(np.float32)
            vector = vector / np.linalg.norm(vector)
            vectors.append(vector)
            metadata_list.append({'batch_id': i})
        
        # Measure batch storage latency
        start_time = time.perf_counter()
        item_ids = vector_memory_manager.batch_store(vectors, metadata_list)
        end_time = time.perf_counter()
        
        batch_latency = (end_time - start_time) * 1000  # Convert to ms
        
        result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=batch_latency,
            meets_target=benchmark.meets_target(batch_latency),
            metadata={
                'vector_count': len(vectors),
                'items_stored': len(item_ids)
            }
        )
        
        logging.info(f"Baseline batch store latency: {batch_latency:.3f}ms (target: {benchmark.target_value}ms)")
        
        # Store baseline for comparison
        pytest.baseline_batch_store_latency = batch_latency
        
        # Verify all vectors were stored
        assert len(item_ids) == len(vectors)
        
        # For now, we expect this to fail until optimization is implemented
        if not result.meets_target:
            pytest.skip(f"Baseline test - measured batch latency {batch_latency:.3f}ms exceeds target {benchmark.target_value}ms")


class TestDeltaEvaluationBenchmarks:
    """Test delta evaluation pipeline performance benchmarks."""
    
    # Performance targets
    DELTA_EVALUATION_BENCHMARKS = [
        PerformanceBenchmark(
            name="delta_evaluation_throughput",
            target_value=500.0,  # 50% improvement over baseline (baseline * 1.5)
            target_unit="ops/sec",
            description="Delta evaluation pipeline throughput"
        ),
        PerformanceBenchmark(
            name="delta_calculation_latency",
            target_value=2.0,  # <2ms per evaluation
            target_unit="ms",
            description="Individual delta calculation latency"
        ),
        PerformanceBenchmark(
            name="batch_delta_processing",
            target_value=100.0,  # 100 evaluations per batch
            target_unit="ops/batch",
            description="Batch delta evaluation processing"
        )
    ]
    
    @pytest.fixture
    def delta_evaluator(self):
        """Create delta evaluator for testing."""
        return DeltaEvaluator()
    
    @pytest.fixture
    def test_delta_data(self):
        """Generate test data for delta evaluation."""
        test_data = []
        
        for i in range(1000):
            data = {
                'performance_data': {
                    'current_reward': 0.8 + (i * 0.0001),
                    'previous_reward': 0.7 + (i * 0.0001),
                    'tokens_used': 100 + (i % 50)
                },
                'efficiency_data': {
                    'current_throughput': 50 + (i * 0.01),
                    'previous_throughput': 45 + (i * 0.01),
                    'resource_used': 10 + (i % 5)
                },
                'stability_data': {
                    'divergence_score': 0.1 + (i % 10) * 0.01
                },
                'capability_data': {
                    'new_capabilities': i % 5,
                    'total_capabilities': 100 + i
                }
            }
            test_data.append(data)
        
        return test_data
    
    def test_delta_evaluation_throughput_baseline(self, delta_evaluator, test_delta_data):
        """Test baseline delta evaluation throughput (RED phase)."""
        benchmark = self.DELTA_EVALUATION_BENCHMARKS[0]  # delta_evaluation_throughput
        
        # Measure throughput
        start_time = time.perf_counter()
        
        results = []
        for data in test_delta_data[:500]:  # Test with 500 evaluations
            result = delta_evaluator.evaluate_delta(**data)
            results.append(result)
        
        end_time = time.perf_counter()
        total_time = end_time - start_time
        throughput = len(results) / total_time
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=throughput,
            meets_target=benchmark.meets_target(throughput),
            metadata={
                'total_evaluations': len(results),
                'total_time': total_time,
                'successful_evaluations': len([r for r in results if r.confidence > 0.5])
            }
        )
        
        logging.info(f"Baseline delta evaluation throughput: {throughput:.1f} ops/sec (target: {benchmark.target_value} ops/sec)")
        
        # Store baseline for comparison
        pytest.baseline_delta_throughput = throughput
        
        # Verify all evaluations completed successfully
        assert len(results) == 500
        assert all(r.total_delta is not None for r in results)
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured throughput {throughput:.1f} ops/sec below target {benchmark.target_value} ops/sec")
    
    def test_delta_calculation_latency_baseline(self, delta_evaluator, test_delta_data):
        """Test baseline delta calculation latency (RED phase)."""
        benchmark = self.DELTA_EVALUATION_BENCHMARKS[1]  # delta_calculation_latency
        
        # Warm up
        for _ in range(10):
            delta_evaluator.evaluate_delta(**test_delta_data[0])
        
        # Measure individual calculation latency
        latencies = []
        
        for i in range(100):
            start_time = time.perf_counter()
            result = delta_evaluator.evaluate_delta(**test_delta_data[i])
            end_time = time.perf_counter()
            
            latency = (end_time - start_time) * 1000  # Convert to ms
            latencies.append(latency)
            
            assert result.total_delta is not None
        
        avg_latency = statistics.mean(latencies)
        p95_latency = statistics.quantiles(latencies, n=20)[18] if len(latencies) > 20 else max(latencies)
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=avg_latency,
            meets_target=benchmark.meets_target(avg_latency),
            metadata={
                'p95_latency': p95_latency,
                'min_latency': min(latencies),
                'max_latency': max(latencies),
                'calculation_count': len(latencies)
            }
        )
        
        logging.info(f"Baseline delta calculation latency: {avg_latency:.3f}ms (target: {benchmark.target_value}ms)")
        
        # Store baseline for comparison
        pytest.baseline_delta_latency = avg_latency
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured latency {avg_latency:.3f}ms exceeds target {benchmark.target_value}ms")


class TestMCPCommunicationBenchmarks:
    """Test MCP server communication performance benchmarks."""
    
    # Performance targets
    MCP_COMMUNICATION_BENCHMARKS = [
        PerformanceBenchmark(
            name="mcp_round_trip_latency",
            target_value=5.0,  # <5ms round-trip
            target_unit="ms",
            description="MCP server communication round-trip latency"
        ),
        PerformanceBenchmark(
            name="mcp_concurrent_requests",
            target_value=100.0,  # 100 concurrent requests
            target_unit="requests",
            description="MCP server concurrent request handling"
        ),
        PerformanceBenchmark(
            name="mcp_message_throughput",
            target_value=1000.0,  # 1000 messages/second
            target_unit="msgs/sec",
            description="MCP message processing throughput"
        )
    ]
    
    @pytest.fixture
    def mcp_orchestrator(self):
        """Create MCP orchestrator for testing."""
        # Mock MCP server for testing
        mock_server = Mock()
        mock_server.send_message = Mock(return_value={'status': 'success', 'data': 'test_response'})
        mock_server.is_connected = Mock(return_value=True)
        
        orchestrator = MCPOrchestrator()
        orchestrator._servers = {'test_server': mock_server}
        return orchestrator
    
    def test_mcp_round_trip_latency_baseline(self, mcp_orchestrator):
        """Test baseline MCP round-trip latency (RED phase)."""
        benchmark = self.MCP_COMMUNICATION_BENCHMARKS[0]  # mcp_round_trip_latency
        
        # Measure round-trip latency
        latencies = []
        
        for i in range(100):
            message = {
                'type': 'test_message',
                'id': f'test_{i}',
                'payload': {'data': f'test_data_{i}'}
            }
            
            start_time = time.perf_counter()
            
            # Simulate MCP communication
            response = mcp_orchestrator._servers['test_server'].send_message(message)
            
            end_time = time.perf_counter()
            
            latency = (end_time - start_time) * 1000  # Convert to ms
            latencies.append(latency)
            
            assert response['status'] == 'success'
        
        avg_latency = statistics.mean(latencies)
        p95_latency = statistics.quantiles(latencies, n=20)[18] if len(latencies) > 20 else max(latencies)
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=avg_latency,
            meets_target=benchmark.meets_target(avg_latency),
            metadata={
                'p95_latency': p95_latency,
                'min_latency': min(latencies),
                'max_latency': max(latencies),
                'message_count': len(latencies)
            }
        )
        
        logging.info(f"Baseline MCP round-trip latency: {avg_latency:.3f}ms (target: {benchmark.target_value}ms)")
        
        # Store baseline for comparison
        pytest.baseline_mcp_latency = avg_latency
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured latency {avg_latency:.3f}ms exceeds target {benchmark.target_value}ms")


class TestSerializationBenchmarks:
    """Test cross-component data serialization performance benchmarks."""
    
    # Performance targets
    SERIALIZATION_BENCHMARKS = [
        PerformanceBenchmark(
            name="serialization_size_reduction",
            target_value=0.3,  # 30% size reduction
            target_unit="ratio",
            description="Data serialization size reduction"
        ),
        PerformanceBenchmark(
            name="serialization_speed",
            target_value=1000.0,  # 1000 objects/second
            target_unit="ops/sec",
            description="Serialization processing speed"
        ),
        PerformanceBenchmark(
            name="deserialization_speed",
            target_value=1500.0,  # 1500 objects/second
            target_unit="ops/sec",
            description="Deserialization processing speed"
        )
    ]
    
    @pytest.fixture
    def test_objects(self):
        """Generate test objects for serialization."""
        objects = []
        
        for i in range(1000):
            obj = {
                'id': f'object_{i}',
                'timestamp': time.time() + i,
                'data': {
                    'values': list(range(i, i + 100)),
                    'metadata': {
                        'category': f'category_{i % 10}',
                        'priority': i % 5,
                        'tags': [f'tag_{j}' for j in range(i % 10)]
                    }
                },
                'embedding': [0.1 * j for j in range(128)],
                'large_text': f'This is a large text field for object {i}. ' * 20
            }
            objects.append(obj)
        
        return objects
    
    def test_serialization_size_reduction_baseline(self, test_objects):
        """Test baseline serialization size reduction (RED phase)."""
        benchmark = self.SERIALIZATION_BENCHMARKS[0]  # serialization_size_reduction
        
        # Measure standard serialization size
        standard_sizes = []
        compressed_sizes = []
        
        for obj in test_objects[:100]:  # Test with 100 objects
            # Standard JSON serialization
            json_data = json.dumps(obj)
            standard_size = len(json_data.encode('utf-8'))
            standard_sizes.append(standard_size)
            
            # Compressed serialization
            compressed_data = gzip.compress(json_data.encode('utf-8'))
            compressed_size = len(compressed_data)
            compressed_sizes.append(compressed_size)
        
        avg_standard_size = statistics.mean(standard_sizes)
        avg_compressed_size = statistics.mean(compressed_sizes)
        size_reduction = (avg_standard_size - avg_compressed_size) / avg_standard_size
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=size_reduction,
            meets_target=benchmark.meets_target(size_reduction),
            metadata={
                'avg_standard_size': avg_standard_size,
                'avg_compressed_size': avg_compressed_size,
                'compression_ratio': avg_compressed_size / avg_standard_size,
                'object_count': len(test_objects[:100])
            }
        )
        
        logging.info(f"Baseline serialization size reduction: {size_reduction:.1%} (target: {benchmark.target_value:.1%})")
        
        # Store baseline for comparison
        pytest.baseline_size_reduction = size_reduction
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured size reduction {size_reduction:.1%} below target {benchmark.target_value:.1%}")
    
    def test_serialization_speed_baseline(self, test_objects):
        """Test baseline serialization speed (RED phase)."""
        benchmark = self.SERIALIZATION_BENCHMARKS[1]  # serialization_speed
        
        # Measure serialization speed
        start_time = time.perf_counter()
        
        serialized_objects = []
        for obj in test_objects:
            serialized = json.dumps(obj)
            serialized_objects.append(serialized)
        
        end_time = time.perf_counter()
        total_time = end_time - start_time
        speed = len(test_objects) / total_time
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=speed,
            meets_target=benchmark.meets_target(speed),
            metadata={
                'total_objects': len(test_objects),
                'total_time': total_time,
                'serialized_count': len(serialized_objects)
            }
        )
        
        logging.info(f"Baseline serialization speed: {speed:.1f} ops/sec (target: {benchmark.target_value} ops/sec)")
        
        # Store baseline for comparison
        pytest.baseline_serialization_speed = speed
        
        # Verify all objects were serialized
        assert len(serialized_objects) == len(test_objects)
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured speed {speed:.1f} ops/sec below target {benchmark.target_value} ops/sec")


class TestSafetyValidationBenchmarks:
    """Test safety validation pipeline performance benchmarks."""
    
    # Performance targets
    SAFETY_VALIDATION_BENCHMARKS = [
        PerformanceBenchmark(
            name="safety_validation_latency",
            target_value=10.0,  # <10ms real-time performance
            target_unit="ms",
            description="Safety validation pipeline latency"
        ),
        PerformanceBenchmark(
            name="safety_validation_throughput",
            target_value=100.0,  # 100 validations/second
            target_unit="ops/sec",
            description="Safety validation throughput"
        ),
        PerformanceBenchmark(
            name="safety_rule_evaluation_speed",
            target_value=1000.0,  # 1000 rules/second
            target_unit="rules/sec",
            description="Safety rule evaluation speed"
        )
    ]
    
    @pytest.fixture
    def safety_validator(self):
        """Create safety validator for testing."""
        return OptimizedSafetyValidator()
    
    @pytest.fixture
    def test_validation_requests(self):
        """Generate test validation requests."""
        requests = []
        
        for i in range(500):
            request = {
                'id': f'validation_request_{i}',
                'type': 'action_validation',
                'action': {
                    'type': f'action_type_{i % 10}',
                    'parameters': {
                        'param1': f'value_{i}',
                        'param2': i % 100,
                        'param3': [j for j in range(i % 5)]
                    }
                },
                'context': {
                    'user_id': f'user_{i % 20}',
                    'session_id': f'session_{i % 50}',
                    'timestamp': time.time() + i
                },
                'priority': i % 5
            }
            requests.append(request)
        
        return requests
    
    def test_safety_validation_latency_baseline(self, safety_validator, test_validation_requests):
        """Test baseline safety validation latency (RED phase)."""
        benchmark = self.SAFETY_VALIDATION_BENCHMARKS[0]  # safety_validation_latency
        
        # Warm up
        for _ in range(10):
            safety_validator.validate_action(test_validation_requests[0]['action'])
        
        # Measure validation latency
        latencies = []
        
        for request in test_validation_requests[:100]:
            start_time = time.perf_counter()
            
            result = safety_validator.validate_action(
                request['action'],
                context=request['context']
            )
            
            end_time = time.perf_counter()
            
            latency = (end_time - start_time) * 1000  # Convert to ms
            latencies.append(latency)
            
            assert result is not None
        
        avg_latency = statistics.mean(latencies)
        p95_latency = statistics.quantiles(latencies, n=20)[18] if len(latencies) > 20 else max(latencies)
        
        benchmark_result = BenchmarkResult(
            benchmark=benchmark,
            measured_value=avg_latency,
            meets_target=benchmark.meets_target(avg_latency),
            metadata={
                'p95_latency': p95_latency,
                'min_latency': min(latencies),
                'max_latency': max(latencies),
                'validation_count': len(latencies)
            }
        )
        
        logging.info(f"Baseline safety validation latency: {avg_latency:.3f}ms (target: {benchmark.target_value}ms)")
        
        # Store baseline for comparison
        pytest.baseline_safety_latency = avg_latency
        
        # For now, we expect this to fail until optimization is implemented
        if not benchmark_result.meets_target:
            pytest.skip(f"Baseline test - measured latency {avg_latency:.3f}ms exceeds target {benchmark.target_value}ms")


if __name__ == "__main__":
    pytest.main([__file__, "-v", "--tb=short"])