"""
Performance Benchmarks for AITBC Mesh Network
Tests performance requirements and scalability targets
"""

import pytest
import asyncio
import time
import statistics
from unittest.mock import Mock, AsyncMock
from decimal import Decimal
import concurrent.futures
import threading

class TestConsensusPerformance:
    """Test consensus layer performance"""
    
    @pytest.mark.asyncio
    async def test_block_propagation_time(self):
        """Test block propagation time across network"""
        # Mock network of 50 nodes
        node_count = 50
        propagation_times = []
        
        # Simulate block propagation
        for i in range(10):  # 10 test blocks
            start_time = time.time()
            
            # Simulate propagation through mesh network
            # Each hop adds ~50ms latency
            hops_required = 6  # Average hops in mesh
            propagation_time = hops_required * 0.05  # 50ms per hop
            
            # Add some randomness
            import random
            propagation_time += random.uniform(0, 0.02)  # ±20ms variance
            
            end_time = time.time()
            actual_time = end_time - start_time + propagation_time
            propagation_times.append(actual_time)
        
        # Calculate statistics
        avg_propagation = statistics.mean(propagation_times)
        max_propagation = max(propagation_times)
        
        # Performance requirements
        assert avg_propagation < 5.0, f"Average propagation time {avg_propagation:.2f}s exceeds 5s target"
        assert max_propagation < 10.0, f"Max propagation time {max_propagation:.2f}s exceeds 10s target"
        
        print(f"Block propagation - Avg: {avg_propagation:.2f}s, Max: {max_propagation:.2f}s")
    
    @pytest.mark.asyncio
    async def test_consensus_throughput(self):
        """Test consensus transaction throughput"""
        transaction_count = 1000
        start_time = time.time()
        
        # Mock consensus processing
        processed_transactions = []
        
        # Process transactions in parallel (simulating multi-validator consensus)
        with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
            futures = []
            
            for i in range(transaction_count):
                future = executor.submit(self._process_transaction, f"tx_{i}")
                futures.append(future)
            
            # Wait for all transactions to be processed
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
                if result:
                    processed_transactions.append(result)
        
        end_time = time.time()
        processing_time = end_time - start_time
        throughput = len(processed_transactions) / processing_time
        
        # Performance requirements
        assert throughput >= 100, f"Throughput {throughput:.2f} tx/s below 100 tx/s target"
        assert len(processed_transactions) == transaction_count, f"Only {len(processed_transactions)}/{transaction_count} transactions processed"
        
        print(f"Consensus throughput: {throughput:.2f} transactions/second")
    
    def _process_transaction(self, tx_id):
        """Simulate transaction processing"""
        # Simulate validation time
        time.sleep(0.001)  # 1ms per transaction
        return tx_id
    
    @pytest.mark.asyncio
    async def test_validator_scalability(self):
        """Test consensus scalability with validator count"""
        validator_counts = [5, 10, 20, 50]
        processing_times = []
        
        for validator_count in validator_counts:
            start_time = time.time()
            
            # Simulate consensus with N validators
            # More validators = more communication overhead
            communication_overhead = validator_count * 0.001  # 1ms per validator
            consensus_time = 0.1 + communication_overhead  # Base 100ms + overhead
            
            # Simulate consensus process
            await asyncio.sleep(consensus_time)
            
            end_time = time.time()
            processing_time = end_time - start_time
            processing_times.append(processing_time)
        
        # Check that processing time scales reasonably
        assert processing_times[-1] < 2.0, f"50-validator consensus too slow: {processing_times[-1]:.2f}s"
        
        # Check that scaling is sub-linear
        time_5_validators = processing_times[0]
        time_50_validators = processing_times[3]
        scaling_factor = time_50_validators / time_5_validators
        
        assert scaling_factor < 10, f"Scaling factor {scaling_factor:.2f} too high (should be <10x for 10x validators)"
        
        print(f"Validator scaling - 5: {processing_times[0]:.3f}s, 50: {processing_times[3]:.3f}s")


class TestNetworkPerformance:
    """Test network layer performance"""
    
    @pytest.mark.asyncio
    async def test_peer_discovery_speed(self):
        """Test peer discovery performance"""
        network_sizes = [10, 50, 100, 500]
        discovery_times = []
        
        for network_size in network_sizes:
            start_time = time.time()
            
            # Simulate peer discovery
            # Discovery time grows with network size but should remain reasonable
            discovery_time = 0.1 + (network_size * 0.0001)  # 0.1ms per peer
            await asyncio.sleep(discovery_time)
            
            end_time = time.time()
            total_time = end_time - start_time
            discovery_times.append(total_time)
        
        # Performance requirements
        assert discovery_times[-1] < 1.0, f"Discovery for 500 peers too slow: {discovery_times[-1]:.2f}s"
        
        print(f"Peer discovery - 10: {discovery_times[0]:.3f}s, 500: {discovery_times[-1]:.3f}s")
    
    @pytest.mark.asyncio
    async def test_message_throughput(self):
        """Test network message throughput"""
        message_count = 10000
        start_time = time.time()
        
        # Simulate message processing
        processed_messages = []
        
        # Process messages in parallel
        with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
            futures = []
            
            for i in range(message_count):
                future = executor.submit(self._process_message, f"msg_{i}")
                futures.append(future)
            
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
                if result:
                    processed_messages.append(result)
        
        end_time = time.time()
        processing_time = end_time - start_time
        throughput = len(processed_messages) / processing_time
        
        # Performance requirements
        assert throughput >= 1000, f"Message throughput {throughput:.2f} msg/s below 1000 msg/s target"
        
        print(f"Message throughput: {throughput:.2f} messages/second")
    
    def _process_message(self, msg_id):
        """Simulate message processing"""
        time.sleep(0.0005)  # 0.5ms per message
        return msg_id
    
    @pytest.mark.asyncio
    async def test_network_partition_recovery_time(self):
        """Test network partition recovery time"""
        recovery_times = []
        
        # Simulate 10 partition events
        for i in range(10):
            start_time = time.time()
            
            # Simulate partition detection and recovery
            detection_time = 30  # 30 seconds to detect partition
            recovery_time = 120  # 2 minutes to recover
            
            total_recovery_time = detection_time + recovery_time
            await asyncio.sleep(0.1)  # Simulate time passing
            
            end_time = time.time()
            recovery_times.append(total_recovery_time)
        
        # Performance requirements
        avg_recovery = statistics.mean(recovery_times)
        assert avg_recovery < 180, f"Average recovery time {avg_recovery:.0f}s exceeds 3 minute target"
        
        print(f"Partition recovery - Average: {avg_recovery:.0f}s")


class TestEconomicPerformance:
    """Test economic layer performance"""
    
    @pytest.mark.asyncio
    async def test_staking_operation_speed(self):
        """Test staking operation performance"""
        operation_count = 1000
        start_time = time.time()
        
        # Test different staking operations
        operations = []
        
        for i in range(operation_count):
            # Simulate staking operation
            operation_time = 0.01  # 10ms per operation
            await asyncio.sleep(operation_time)
            operations.append(f"stake_{i}")
        
        end_time = time.time()
        processing_time = end_time - start_time
        throughput = len(operations) / processing_time
        
        # Performance requirements
        assert throughput >= 50, f"Staking throughput {throughput:.2f} ops/s below 50 ops/s target"
        
        print(f"Staking throughput: {throughput:.2f} operations/second")
    
    @pytest.mark.asyncio
    async def test_reward_calculation_speed(self):
        """Test reward calculation performance"""
        validator_count = 100
        start_time = time.time()
        
        # Calculate rewards for all validators
        rewards = {}
        
        for i in range(validator_count):
            # Simulate reward calculation
            calculation_time = 0.005  # 5ms per validator
            await asyncio.sleep(calculation_time)
            
            rewards[f"validator_{i}"] = Decimal('10.0')  # 10 tokens reward
        
        end_time = time.time()
        calculation_time_total = end_time - start_time
        
        # Performance requirements
        assert calculation_time_total < 5.0, f"Reward calculation too slow: {calculation_time_total:.2f}s"
        assert len(rewards) == validator_count, f"Only calculated rewards for {len(rewards)}/{validator_count} validators"
        
        print(f"Reward calculation for {validator_count} validators: {calculation_time_total:.2f}s")
    
    @pytest.mark.asyncio
    async def test_gas_fee_calculation_speed(self):
        """Test gas fee calculation performance"""
        transaction_count = 5000
        start_time = time.time()
        
        gas_fees = []
        
        for i in range(transaction_count):
            # Simulate gas fee calculation
            calculation_time = 0.0001  # 0.1ms per transaction
            await asyncio.sleep(calculation_time)
            
            # Calculate gas fee (simplified)
            gas_used = 21000 + (i % 10000)  # Variable gas usage
            gas_price = Decimal('0.001')
            fee = gas_used * gas_price
            gas_fees.append(fee)
        
        end_time = time.time()
        calculation_time_total = end_time - start_time
        throughput = transaction_count / calculation_time_total
        
        # Performance requirements
        assert throughput >= 10000, f"Gas calculation throughput {throughput:.2f} tx/s below 10000 tx/s target"
        
        print(f"Gas fee calculation: {throughput:.2f} transactions/second")


class TestAgentNetworkPerformance:
    """Test agent network performance"""
    
    @pytest.mark.asyncio
    async def test_agent_registration_speed(self):
        """Test agent registration performance"""
        agent_count = 1000
        start_time = time.time()
        
        registered_agents = []
        
        for i in range(agent_count):
            # Simulate agent registration
            registration_time = 0.02  # 20ms per agent
            await asyncio.sleep(registration_time)
            
            registered_agents.append(f"agent_{i}")
        
        end_time = time.time()
        registration_time_total = end_time - start_time
        throughput = len(registered_agents) / registration_time_total
        
        # Performance requirements
        assert throughput >= 25, f"Agent registration throughput {throughput:.2f} agents/s below 25 agents/s target"
        
        print(f"Agent registration: {throughput:.2f} agents/second")
    
    @pytest.mark.asyncio
    async def test_capability_matching_speed(self):
        """Test agent capability matching performance"""
        job_count = 100
        agent_count = 1000
        start_time = time.time()
        
        matches = []
        
        for i in range(job_count):
            # Simulate capability matching
            matching_time = 0.05  # 50ms per job
            await asyncio.sleep(matching_time)
            
            # Find matching agents (simplified)
            matching_agents = [f"agent_{j}" for j in range(min(10, agent_count))]
            matches.append({
                'job_id': f"job_{i}",
                'matching_agents': matching_agents
            })
        
        end_time = time.time()
        matching_time_total = end_time - start_time
        throughput = job_count / matching_time_total
        
        # Performance requirements
        assert throughput >= 10, f"Capability matching throughput {throughput:.2f} jobs/s below 10 jobs/s target"
        
        print(f"Capability matching: {throughput:.2f} jobs/second")
    
    @pytest.mark.asyncio
    async def test_reputation_update_speed(self):
        """Test reputation update performance"""
        update_count = 5000
        start_time = time.time()
        
        reputation_updates = []
        
        for i in range(update_count):
            # Simulate reputation update
            update_time = 0.002  # 2ms per update
            await asyncio.sleep(update_time)
            
            reputation_updates.append({
                'agent_id': f"agent_{i % 1000}",  # 1000 unique agents
                'score_change': 0.01
            })
        
        end_time = time.time()
        update_time_total = end_time - start_time
        throughput = update_count / update_time_total
        
        # Performance requirements
        assert throughput >= 1000, f"Reputation update throughput {throughput:.2f} updates/s below 1000 updates/s target"
        
        print(f"Reputation updates: {throughput:.2f} updates/second")


class TestSmartContractPerformance:
    """Test smart contract performance"""
    
    @pytest.mark.asyncio
    async def test_escrow_creation_speed(self):
        """Test escrow contract creation performance"""
        contract_count = 1000
        start_time = time.time()
        
        created_contracts = []
        
        for i in range(contract_count):
            # Simulate escrow contract creation
            creation_time = 0.03  # 30ms per contract
            await asyncio.sleep(creation_time)
            
            created_contracts.append({
                'contract_id': f"contract_{i}",
                'amount': Decimal('100.0'),
                'created_at': time.time()
            })
        
        end_time = time.time()
        creation_time_total = end_time - start_time
        throughput = len(created_contracts) / creation_time_total
        
        # Performance requirements
        assert throughput >= 20, f"Escrow creation throughput {throughput:.2f} contracts/s below 20 contracts/s target"
        
        print(f"Escrow contract creation: {throughput:.2f} contracts/second")
    
    @pytest.mark.asyncio
    async def test_dispute_resolution_speed(self):
        """Test dispute resolution performance"""
        dispute_count = 100
        start_time = time.time()
        
        resolved_disputes = []
        
        for i in range(dispute_count):
            # Simulate dispute resolution
            resolution_time = 0.5  # 500ms per dispute
            await asyncio.sleep(resolution_time)
            
            resolved_disputes.append({
                'dispute_id': f"dispute_{i}",
                'resolution': 'agent_favored',
                'resolved_at': time.time()
            })
        
        end_time = time.time()
        resolution_time_total = end_time - start_time
        throughput = len(resolved_disputes) / resolution_time_total
        
        # Performance requirements
        assert throughput >= 1, f"Dispute resolution throughput {throughput:.2f} disputes/s below 1 dispute/s target"
        
        print(f"Dispute resolution: {throughput:.2f} disputes/second")
    
    @pytest.mark.asyncio
    async def test_gas_optimization_speed(self):
        """Test gas optimization performance"""
        optimization_count = 100
        start_time = time.time()
        
        optimizations = []
        
        for i in range(optimization_count):
            # Simulate gas optimization analysis
            analysis_time = 0.1  # 100ms per optimization
            await asyncio.sleep(analysis_time)
            
            optimizations.append({
                'contract_id': f"contract_{i}",
                'original_gas': 50000,
                'optimized_gas': 40000,
                'savings': 10000
            })
        
        end_time = time.time()
        optimization_time_total = end_time - start_time
        throughput = len(optimizations) / optimization_time_total
        
        # Performance requirements
        assert throughput >= 5, f"Gas optimization throughput {throughput:.2f} optimizations/s below 5 optimizations/s target"
        
        print(f"Gas optimization: {throughput:.2f} optimizations/second")


class TestSystemWidePerformance:
    """Test system-wide performance under realistic load"""
    
    @pytest.mark.asyncio
    async def test_full_workflow_performance(self):
        """Test complete job execution workflow performance"""
        workflow_count = 100
        start_time = time.time()
        
        completed_workflows = []
        
        for i in range(workflow_count):
            workflow_start = time.time()
            
            # 1. Create escrow contract (30ms)
            await asyncio.sleep(0.03)
            
            # 2. Find matching agent (50ms)
            await asyncio.sleep(0.05)
            
            # 3. Agent accepts job (10ms)
            await asyncio.sleep(0.01)
            
            # 4. Execute job (variable time, avg 1s)
            job_time = 1.0 + (i % 3) * 0.5  # 1-2.5 seconds
            await asyncio.sleep(job_time)
            
            # 5. Complete milestone (20ms)
            await asyncio.sleep(0.02)
            
            # 6. Release payment (10ms)
            await asyncio.sleep(0.01)
            
            workflow_end = time.time()
            workflow_time = workflow_end - workflow_start
            
            completed_workflows.append({
                'workflow_id': f"workflow_{i}",
                'total_time': workflow_time,
                'job_time': job_time
            })
        
        end_time = time.time()
        total_time = end_time - start_time
        throughput = len(completed_workflows) / total_time
        
        # Performance requirements
        assert throughput >= 10, f"Workflow throughput {throughput:.2f} workflows/s below 10 workflows/s target"
        
        # Check average workflow time
        avg_workflow_time = statistics.mean([w['total_time'] for w in completed_workflows])
        assert avg_workflow_time < 5.0, f"Average workflow time {avg_workflow_time:.2f}s exceeds 5s target"
        
        print(f"Full workflow throughput: {throughput:.2f} workflows/second")
        print(f"Average workflow time: {avg_workflow_time:.2f}s")
    
    @pytest.mark.asyncio
    async def test_concurrent_load_performance(self):
        """Test system performance under concurrent load"""
        concurrent_users = 50
        operations_per_user = 20
        start_time = time.time()
        
        async def user_simulation(user_id):
            """Simulate a single user's operations"""
            user_operations = []
            
            for op in range(operations_per_user):
                op_start = time.time()
                
                # Simulate random operation
                import random
                operation_type = random.choice(['create_contract', 'find_agent', 'submit_job'])
                
                if operation_type == 'create_contract':
                    await asyncio.sleep(0.03)  # 30ms
                elif operation_type == 'find_agent':
                    await asyncio.sleep(0.05)  # 50ms
                else:  # submit_job
                    await asyncio.sleep(0.02)  # 20ms
                
                op_end = time.time()
                user_operations.append({
                    'user_id': user_id,
                    'operation': operation_type,
                    'time': op_end - op_start
                })
            
            return user_operations
        
        # Run all users concurrently
        tasks = [user_simulation(i) for i in range(concurrent_users)]
        results = await asyncio.gather(*tasks)
        
        end_time = time.time()
        total_time = end_time - start_time
        
        # Flatten results
        all_operations = []
        for user_ops in results:
            all_operations.extend(user_ops)
        
        total_operations = len(all_operations)
        throughput = total_operations / total_time
        
        # Performance requirements
        assert throughput >= 100, f"Concurrent load throughput {throughput:.2f} ops/s below 100 ops/s target"
        assert total_operations == concurrent_users * operations_per_user, f"Missing operations: {total_operations}/{concurrent_users * operations_per_user}"
        
        print(f"Concurrent load performance: {throughput:.2f} operations/second")
        print(f"Total operations: {total_operations} from {concurrent_users} users")
    
    @pytest.mark.asyncio
    async def test_memory_usage_under_load(self):
        """Test memory usage under high load"""
        import psutil
        import os
        
        process = psutil.Process(os.getpid())
        initial_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        # Simulate high load
        large_dataset = []
        
        for i in range(10000):
            # Create large objects to simulate memory pressure
            large_dataset.append({
                'id': i,
                'data': 'x' * 1000,  # 1KB per object
                'timestamp': time.time(),
                'metadata': {
                    'field1': f"value_{i}",
                    'field2': i * 2,
                    'field3': i % 100
                }
            })
        
        peak_memory = process.memory_info().rss / 1024 / 1024  # MB
        memory_increase = peak_memory - initial_memory
        
        # Clean up
        del large_dataset
        
        final_memory = process.memory_info().rss / 1024 / 1024  # MB
        memory_recovered = peak_memory - final_memory
        
        # Performance requirements
        assert memory_increase < 500, f"Memory increase {memory_increase:.2f}MB exceeds 500MB limit"
        assert memory_recovered > memory_increase * 0.8, f"Memory recovery {memory_recovered:.2f}MB insufficient"
        
        print(f"Memory usage - Initial: {initial_memory:.2f}MB, Peak: {peak_memory:.2f}MB, Final: {final_memory:.2f}MB")
        print(f"Memory increase: {memory_increase:.2f}MB, Recovered: {memory_recovered:.2f}MB")


class TestScalabilityLimits:
    """Test system scalability limits"""
    
    @pytest.mark.asyncio
    async def test_maximum_validator_count(self):
        """Test system performance with maximum validator count"""
        max_validators = 100
        start_time = time.time()
        
        # Simulate consensus with maximum validators
        consensus_time = 0.1 + (max_validators * 0.002)  # 2ms per validator
        await asyncio.sleep(consensus_time)
        
        end_time = time.time()
        total_time = end_time - start_time
        
        # Performance requirements
        assert total_time < 5.0, f"Consensus with {max_validators} validators too slow: {total_time:.2f}s"
        
        print(f"Maximum validator test ({max_validators} validators): {total_time:.2f}s")
    
    @pytest.mark.asyncio
    async def test_maximum_agent_count(self):
        """Test system performance with maximum agent count"""
        max_agents = 10000
        start_time = time.time()
        
        # Simulate agent registry operations
        registry_time = max_agents * 0.0001  # 0.1ms per agent
        await asyncio.sleep(registry_time)
        
        end_time = time.time()
        total_time = end_time - start_time
        
        # Performance requirements
        assert total_time < 10.0, f"Agent registry with {max_agents} agents too slow: {total_time:.2f}s"
        
        print(f"Maximum agent test ({max_agents} agents): {total_time:.2f}s")
    
    @pytest.mark.asyncio
    async def test_maximum_concurrent_transactions(self):
        """Test system performance with maximum concurrent transactions"""
        max_transactions = 10000
        start_time = time.time()
        
        # Simulate transaction processing
        with concurrent.futures.ThreadPoolExecutor(max_workers=50) as executor:
            futures = []
            
            for i in range(max_transactions):
                future = executor.submit(self._process_heavy_transaction, f"tx_{i}")
                futures.append(future)
            
            # Wait for completion
            completed = 0
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
                if result:
                    completed += 1
        
        end_time = time.time()
        total_time = end_time - start_time
        throughput = completed / total_time
        
        # Performance requirements
        assert throughput >= 500, f"Max transaction throughput {throughput:.2f} tx/s below 500 tx/s target"
        assert completed == max_transactions, f"Only {completed}/{max_transactions} transactions completed"
        
        print(f"Maximum concurrent transactions ({max_transactions} tx): {throughput:.2f} tx/s")
    
    def _process_heavy_transaction(self, tx_id):
        """Simulate heavy transaction processing"""
        # Simulate computation time
        time.sleep(0.002)  # 2ms per transaction
        return tx_id


if __name__ == "__main__":
    pytest.main([
        __file__,
        "-v",
        "--tb=short",
        "--maxfail=5"
    ])