#!/usr/bin/env python3
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

"""
E2E test for KV Event Consolidator with Router integration.

This test validates that:
1. vLLM/TensorRT-LLM with KVBM correctly emits KV events to the consolidator
2. The consolidator correctly deduplicates events from engine and KVBM
3. The router receives and processes consolidated events without warnings

"""

import concurrent.futures
import logging
import os
import re
import time
from pathlib import Path

import pytest
import requests
import yaml

from tests.kvbm_integration.common import ApiTester, check_logs_for_patterns
from tests.utils.managed_process import ManagedProcess
from tests.utils.test_output import resolve_test_output_path

# Check if engines are available and build list of available engines
from .common import check_module_available

HAS_VLLM = check_module_available("vllm")
HAS_TRTLLM = check_module_available("tensorrt_llm")

# Build list of available engines for parameterization
AVAILABLE_ENGINES = []
if HAS_VLLM:
    AVAILABLE_ENGINES.append("vllm")
if HAS_TRTLLM:
    AVAILABLE_ENGINES.append("trtllm")

# Test markers
pytestmark = [
    pytest.mark.kvbm,
    pytest.mark.e2e,
    pytest.mark.slow,
    pytest.mark.gpu_1,
    pytest.mark.pre_merge,
    pytest.mark.skipif(not (HAS_VLLM or HAS_TRTLLM), reason="requires vllm or trtllm"),
]

logger = logging.getLogger(__name__)

# Constants
FRONTEND_PORT = 8000


@pytest.fixture
def test_directory(request):
    """Create a test directory for logs and temporary files."""
    test_dir = Path(resolve_test_output_path(request.node.name))
    test_dir.mkdir(parents=True, exist_ok=True)
    yield test_dir
    # Cleanup handled by pytest (logs are kept for debugging)


def create_trtllm_config(test_directory: Path) -> Path:
    """Create TensorRT-LLM config YAML file with KVBM connector configuration."""
    config_path = Path(os.path.join(test_directory, "trtllm_config.yaml"))
    config = {
        "backend": "pytorch",
        "cuda_graph_config": None,
        "kv_cache_config": {
            "enable_partial_reuse": False,
            "free_gpu_memory_fraction": 0.01,
        },
        "max_seq_len": 4096,
        "kv_connector_config": {
            "connector_module": "kvbm.trtllm_integration.connector",
            "connector_scheduler_class": "DynamoKVBMConnectorLeader",
            "connector_worker_class": "DynamoKVBMConnectorWorker",
        },
    }
    with open(config_path, "w") as f:
        yaml.dump(config, f)
    return config_path


def compute_deduplication_test_params(
    g1_gpu_blocks: int,
    g2_cpu_blocks: int,
    g3_disk_blocks: int,
    block_size: int = 16,
    gpu_utilization_per_request: float = 0.6,
    offload_overflow_factor: float = 100.0,
) -> dict:
    """
    Compute max_tokens and num_requests for the deduplication test.

    Given fixed block allocations for each storage tier, compute test parameters
    that ensure the test lands in the "Goldilocks zone" where:
    1. Each request fits within GPU cache (with safety margin)
    2. Total tokens generated overflow all tiers
    3. Deduplication behavior is observable (blocks exist in multiple sources)
    4. Blocks are eventually removed from ALL sources (triggering REMOVE events)

    The key insight is that blocks are replicated across GPU, CPU, and Disk
    simultaneously. To trigger "last source" removal, a block must be evicted
    from ALL three tiers. The bottleneck is the smallest offload cache (CPU or
    Disk), not total capacity.

    Args:
        g1_gpu_blocks: Number of GPU cache blocks (--num-gpu-blocks-override)
        g2_cpu_blocks: Number of CPU cache blocks (DYN_KVBM_CPU_CACHE_OVERRIDE_NUM_BLOCKS)
        g3_disk_blocks: Number of disk cache blocks (DYN_KVBM_DISK_CACHE_OVERRIDE_NUM_BLOCKS)
        block_size: Tokens per block (default 16 for vLLM)
        gpu_utilization_per_request: Fraction of GPU capacity to use per request (default 0.6)
            - Too high (>0.8): requests may cause immediate evictions
            - Too low (<0.3): too many requests needed, slow test
        offload_overflow_factor: How many times to overflow the smallest offload
            cache (default 100.0). High value needed because:
            - Blocks persist in offload caches longer than GPU
            - Offloading is asynchronous - not all blocks make it
            - Need enough volume to ensure blocks cycle through all tiers

    Returns:
        dict with:
            - max_tokens: Maximum tokens per request
            - num_requests: Number of requests to send
            - blocks_per_request: Estimated blocks consumed per request
            - total_capacity_blocks: Total blocks across all tiers
            - total_blocks_generated: Expected total blocks generated
            - min_offload_cache: The smallest offload cache (bottleneck)

    Example:
        >>> config = compute_deduplication_test_params(
        ...     g1_gpu_blocks=30,
        ...     g2_cpu_blocks=10,
        ...     g3_disk_blocks=10,
        ... )
        >>> config
        {
            'max_tokens': 288,
            'num_requests': 56,
            'blocks_per_request': 18,
            'total_capacity_blocks': 50,
            'total_blocks_generated': 1008,
            'min_offload_cache': 10,
        }
    """
    from math import ceil

    # 1. max_tokens: fits in GPU with margin
    # Use gpu_utilization to leave headroom for the model to process without
    # triggering evictions mid-generation
    max_blocks_per_request = max(1, int(g1_gpu_blocks * gpu_utilization_per_request))
    max_tokens = max_blocks_per_request * block_size

    # 2. num_requests: overflow the smallest offload cache many times
    # The bottleneck for "last source" removal is the smallest offload cache.
    # Blocks must be evicted from GPU AND both CPU and Disk.
    # Since blocks are replicated to all tiers, we need to cycle through
    # the smallest cache many times to ensure blocks get evicted from ALL sources.
    min_offload_cache = min(g2_cpu_blocks, g3_disk_blocks)
    total_capacity_blocks = g1_gpu_blocks + g2_cpu_blocks + g3_disk_blocks

    # Generate enough unique blocks to overflow the smallest offload cache
    # many times, ensuring blocks eventually get evicted from all sources
    unique_blocks_needed = int(min_offload_cache * offload_overflow_factor)
    num_requests = max(1, ceil(unique_blocks_needed / max_blocks_per_request))

    # Calculate expected total blocks generated
    total_blocks_generated = num_requests * max_blocks_per_request

    return {
        "max_tokens": max_tokens,
        "num_requests": num_requests,
        "blocks_per_request": max_blocks_per_request,
        "total_capacity_blocks": total_capacity_blocks,
        "total_blocks_generated": total_blocks_generated,
        "min_offload_cache": min_offload_cache,
    }


def extract_consolidator_stats(log_path: Path, engine: str = "vllm") -> dict:
    """Extract consolidator event statistics from engine logs."""
    stats = {
        "store_events": 0,
        "remove_events": 0,
        "dedup_events": 0,
        "published_events": 0,
        "consolidator_started": False,
        "consolidator_port": None,
    }

    if not log_path.exists():
        return stats

    try:
        with open(log_path, "r") as f:
            content = f.read()

            # Check if consolidator started
            # Actual log: "KV Event Consolidator fully started and ready"
            stats["consolidator_started"] = bool(
                re.search(r"KV Event Consolidator.*started", content, re.IGNORECASE)
            )

            # Extract consolidator output port from logs
            # Look for: "Starting KV Event Consolidator: subscribe from ..., publish to tcp://0.0.0.0:PORT"
            port_match = re.search(r"publish to tcp://[^:]+:(\d+)", content)
            if port_match:
                stats["consolidator_port"] = int(port_match.group(1))

            # Count event types (all at debug level)
            # Actual: "stored in first source ... will publish STORE event"
            stats["store_events"] = len(
                re.findall(r"will publish STORE event", content)
            )
            # Actual: "removed from last source ... will publish REMOVE event"
            stats["remove_events"] = len(
                re.findall(r"will publish REMOVE event", content)
            )
            # Actual: "DEDUP: Block ... added to source"
            stats["dedup_events"] = len(re.findall(r"DEDUP:.*added to source", content))
            # Actual: "Publishing N consolidated event(s) to router" or "Published batch with N event(s)"
            stats["published_events"] = len(
                re.findall(
                    r"Publish(?:ing|ed).*event.*to router", content, re.IGNORECASE
                )
            )
    except Exception as e:
        logger.warning(f"Error extracting consolidator stats: {e}")

    return stats


def wait_for_worker_registration(
    frontend_url: str, max_wait_seconds: int = 120, poll_interval: int = 2
) -> bool:
    """
    Poll frontend health endpoint until a worker registers.

    Args:
        frontend_url: Base URL of the frontend (e.g., "http://localhost:8000")
        max_wait_seconds: Maximum time to wait for registration
        poll_interval: Seconds between health checks

    Returns:
        True if worker registered, False if timeout
    """

    start_time = time.time()

    while time.time() - start_time < max_wait_seconds:
        try:
            response = requests.get(f"{frontend_url}/health", timeout=5)
            health_data = response.json()
            if health_data.get("instances"):
                elapsed = time.time() - start_time
                logger.info(f"Worker registered after {elapsed:.1f}s")
                return True
        except Exception as e:
            logger.debug(f"Health check failed: {e}")

        time.sleep(poll_interval)

    elapsed = time.time() - start_time
    logger.error(f"Worker failed to register after {elapsed:.1f}s")
    logger.error("Check worker logs for initialization errors")
    return False


@pytest.fixture
def frontend_server(test_directory, runtime_services):
    """Start Dynamo frontend with embedded KV router."""
    logger.info("Starting Dynamo frontend with KV router")

    # Frontend command - includes embedded router
    command = [
        "python",
        "-m",
        "dynamo.frontend",
        "--http-port",
        str(FRONTEND_PORT),
        "--router-mode",
        "kv",
        "--router-reset-states",
    ]

    # Environment
    env = os.environ.copy()
    env.update(
        {
            "RUST_BACKTRACE": "1",
            "NATS_SERVER": "nats://localhost:4222",
            "ETCD_ENDPOINTS": "http://localhost:2379",
            "DYN_LOG": "debug",  # Enable debug logs for consolidator visibility
        }
    )

    # Create separate log directory for frontend to avoid conflicts with vllm
    frontend_log_dir = Path(os.path.join(test_directory, "frontend")).absolute()
    frontend_log_dir.mkdir(parents=True, exist_ok=True)

    # Create managed process and start via context manager
    with ManagedProcess(
        command=command,
        env=env,
        health_check_urls=[f"http://localhost:{FRONTEND_PORT}/health"],
        timeout=120,  # Increased timeout for frontend+router initialization
        working_dir=str(test_directory),
        display_output=False,
        log_dir=str(frontend_log_dir),  # Absolute path keeps logs in test directory
        terminate_all_matching_process_names=False,  # Don't kill nats-server/etcd started by runtime_services
    ) as frontend_process:
        # Get actual log file path from ManagedProcess (it may modify log_dir to use temp directory)
        log_file = Path(frontend_process._log_path)
        logger.info(f"Frontend started on port {FRONTEND_PORT}, log file: {log_file}")

        yield {
            "process": frontend_process,
            "port": FRONTEND_PORT,
            "base_url": f"http://localhost:{FRONTEND_PORT}",
            "log_file": log_file,
        }

    # Cleanup happens automatically via context manager __exit__
    logger.info("Frontend server stopped")


@pytest.fixture(params=AVAILABLE_ENGINES)
def engine_type(request):
    """Parameterize test to run with available engines only."""
    return request.param


@pytest.fixture
def llm_worker(frontend_server, test_directory, runtime_services, engine_type):
    """Start LLM worker (vLLM or TensorRT-LLM) with KVBM connector and KV Event Consolidator."""
    model_id = os.environ.get("CONSOLIDATOR_MODEL_ID", "Qwen/Qwen3-0.6B")
    engine = engine_type

    logger.info(
        f"Starting {engine.upper()} worker with KVBM connector and model {model_id}"
    )

    # Build command based on engine type
    if engine == "vllm":
        command = [
            "python",
            "-m",
            "dynamo.vllm",
            "--model",
            model_id,
            "--kv-transfer-config",
            '{"kv_connector":"DynamoConnector","kv_connector_module_path":"kvbm.vllm_integration.connector","kv_role":"kv_both"}',
            "--enforce-eager",  # For faster startup in tests
        ]
    else:  # trtllm
        # Create TensorRT-LLM config file with KVBM connector
        config_path = create_trtllm_config(test_directory)
        command = [
            "python",
            "-m",
            "dynamo.trtllm",
            "--model-path",
            model_id,
            "--served-model-name",
            model_id,
            "--extra-engine-args",
            str(
                config_path.absolute()
            ),  # Use absolute path to avoid working directory issues
            "--publish-events-and-metrics",
        ]

    # Environment
    env = os.environ.copy()
    env.update(
        {
            "RUST_BACKTRACE": "1",
            "NATS_SERVER": "nats://localhost:4222",
            "ETCD_ENDPOINTS": "http://localhost:2379",
            "DYN_KVBM_CPU_CACHE_GB": "5",
            "DYN_KVBM_DISK_CACHE_GB": "5",
            "DYN_LOG": "debug",  # Enable debug logs for consolidator visibility
        }
    )

    # Set ZMQ port for TensorRT-LLM consolidator
    if engine == "trtllm":
        env["DYN_KVBM_TRTLLM_ZMQ_PORT"] = "20081"

    # Create separate log directory for worker to avoid conflicts with frontend
    worker_log_dir = Path(os.path.join(test_directory, engine)).absolute()
    worker_log_dir.mkdir(parents=True, exist_ok=True)

    # Create managed process and start via context manager
    with ManagedProcess(
        command=command,
        env=env,
        health_check_urls=[],  # Workers don't expose HTTP endpoints
        timeout=300,  # Increased timeout for model loading and consolidator init
        working_dir=str(test_directory),
        display_output=False,
        log_dir=str(worker_log_dir),  # Absolute path keeps logs in test directory
        terminate_all_matching_process_names=False,
    ) as worker_process:
        # Get actual log file path from ManagedProcess (it may modify log_dir to use temp directory)
        log_file = Path(worker_process._log_path)
        logger.info(f"Worker log file: {log_file}")

        logger.info(
            f"Waiting for {engine.upper()} worker and consolidator to initialize..."
        )

        # Wait for worker to register with frontend
        worker_registered = wait_for_worker_registration(frontend_server["base_url"])

        if not worker_registered:
            logger.warning("Continuing test despite worker registration failure")

        # Additional wait for consolidator to fully initialize
        time.sleep(5)

        # Verify consolidator started by checking logs
        stats = extract_consolidator_stats(log_file, engine)
        if not stats["consolidator_started"]:
            logger.warning("Consolidator may not have started - check logs")
        else:
            logger.info("Consolidator detected in logs")

        yield {
            "process": worker_process,
            "model_id": model_id,
            "log_file": log_file,
            "consolidator_stats": stats,
            "engine": engine,
        }

    # Cleanup happens automatically via context manager __exit__
    logger.info(f"{engine.upper()} worker stopped")


@pytest.fixture
def tester(frontend_server, llm_worker):
    """Provides a test client that sends requests to frontend."""
    return ApiTester(
        base_url=frontend_server["base_url"],
        model_id=llm_worker["model_id"],
    )


class TestConsolidatorRouterE2E:
    """E2E tests for KV Event Consolidator with Router."""

    SYSTEM_PROMPT = "You are a helpful AI assistant."

    # Common error patterns to check in logs
    ERROR_PATTERNS = [
        r"error.*block_size=0",
        r"Failed to parse block_hash",
        r"panic",
        r"fatal",
    ]

    def assert_no_errors_in_logs(
        self, worker_log: Path, frontend_log: Path, engine: str = "vllm"
    ):
        """Helper to check both worker and frontend logs for errors."""
        engine_name = engine.upper()
        worker_errors = check_logs_for_patterns(
            worker_log, self.ERROR_PATTERNS, f"{engine_name} Worker"
        )
        assert (
            not worker_errors
        ), f"Errors in {engine_name} Worker logs: {worker_errors}"

        frontend_errors = check_logs_for_patterns(
            frontend_log, self.ERROR_PATTERNS, "Frontend/Router"
        )
        assert not frontend_errors, f"Errors in Frontend/Router logs: {frontend_errors}"

    def send_concurrent_requests(
        self,
        tester: ApiTester,
        num_requests: int,
        max_tokens: int = 50,
        content_template: str = "Request {i}: Tell me about topic {i}",
    ):
        """Helper to send concurrent requests and return results."""

        def send_request(i: int):
            try:
                messages = [{"role": "user", "content": content_template.format(i=i)}]
                response = tester.send_chat_request(messages, max_tokens=max_tokens)
                return True, response
            except Exception as e:
                logger.error(f"Request {i} failed: {e}")
                return False, str(e)

        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
            futures = [executor.submit(send_request, i) for i in range(num_requests)]
            results = [f.result() for f in futures]

        successes = sum(1 for success, _ in results if success)
        logger.info(f"Concurrent requests: {successes}/{num_requests} succeeded")
        return successes, results

    @pytest.mark.timeout(150)  # 4x measured (~37s), rounded up
    def test_basic_consolidator_flow(self, tester, llm_worker, frontend_server):
        """
        Test basic consolidator flow:
        1. Send requests
        2. Verify consolidator starts and processes events
        3. Verify router receives events without errors
        """
        engine = llm_worker["engine"]
        logger.info(f"TEST: Basic Consolidator Flow ({engine.upper()})")

        # Send 3 requests to frontend
        requests_data = [
            [{"role": "user", "content": "What is machine learning?"}],
            [{"role": "user", "content": "Explain neural networks."}],
            [{"role": "user", "content": "Tell me about transformers."}],
        ]

        for i, messages in enumerate(requests_data):
            response = tester.send_chat_request(messages)
            content = response["choices"][0]["message"]["content"]
            logger.info(
                f"Request {i+1}/3: {messages[0]['content'][:30]}... => {content[:40]}..."
            )

        # Wait for logs to flush
        time.sleep(5)

        # Check worker logs for consolidator
        worker_log = llm_worker["log_file"]
        consolidator_stats = extract_consolidator_stats(worker_log, engine)

        logger.info(f"Consolidator stats: {consolidator_stats}")

        # Assertions
        assert consolidator_stats["consolidator_started"], "Consolidator did not start"
        assert consolidator_stats["store_events"] > 0, "No store events processed"
        assert consolidator_stats["published_events"] > 0, "No events published"

        # Check for errors in logs
        self.assert_no_errors_in_logs(worker_log, frontend_server["log_file"], engine)

        logger.info(f"Basic consolidator flow test passed ({engine.upper()})")

    @pytest.mark.timeout(170)  # 4x measured (~41s), rounded up
    def test_consolidator_handles_concurrent_requests(
        self, tester, llm_worker, frontend_server
    ):
        """
        Test consolidator under concurrent load:
        1. Send many requests quickly
        2. Verify no crashes or critical errors
        3. Verify all events processed
        """
        engine = llm_worker["engine"]
        logger.info(f"TEST: Concurrent Request Handling ({engine.upper()})")

        # Send 10 concurrent requests
        num_requests = 10
        successes, _ = self.send_concurrent_requests(
            tester,
            num_requests,
            max_tokens=20,
            content_template="Request {i}: Count to 5",
        )

        # Wait for logs to flush
        time.sleep(5)

        # Assertions
        assert (
            successes >= num_requests * 0.9
        ), f"Too many failed requests: {num_requests - successes}"

        # Check for errors in logs
        self.assert_no_errors_in_logs(
            llm_worker["log_file"], frontend_server["log_file"], engine
        )

        # Verify events were processed
        stats = extract_consolidator_stats(llm_worker["log_file"], engine)
        assert stats["store_events"] > 0, "No events processed during concurrent load"

        logger.info(f"Concurrent request handling test passed ({engine.upper()})")

    @pytest.mark.timeout(180)  # 4x measured (~44s), rounded up
    def test_store_deduplication_across_sources(
        self, tester, llm_worker, frontend_server
    ):
        """
        Test STORE event deduplication across engine (G1) and KVBM (G2/G3):

        When a block is stored in G1 (GPU), it's automatically offloaded
        to G2 (CPU) and G3 (Disk). This triggers STORE events from both engine and KVBM.

        Test Scenario:
        1. Send requests → blocks stored in engine (G1)
        2. Consolidator receives engine STORE events → queues them for publishing
        3. KVBM replicates blocks to G2/G3 → emits STORE events
        4. Consolidator sees blocks already exist → logs DEDUP message → does NOT publish again
        5. Result: Router receives ONE STORE event per unique block (from step 2)

        This verifies: Only one STORE event is sent to router per unique block,
        even though the block exists in multiple storage tiers (G1, G2, G3).
        KVBM replications are deduplicated and don't trigger duplicate router updates.
        """
        engine = llm_worker["engine"]
        logger.info(f"Starting STORE deduplication test ({engine.upper()})")

        # Send requests to generate STORE events
        logger.info("Sending concurrent requests to generate STORE events")
        num_requests = 10
        successes, _ = self.send_concurrent_requests(
            tester, num_requests, max_tokens=50
        )
        assert (
            successes >= num_requests * 0.9
        ), f"Too many failed requests: {num_requests - successes}"

        # Wait for events to be processed
        time.sleep(5)

        # Phase 2: Analyze consolidator logs
        logger.info("Phase 2: Analyzing STORE event deduplication")
        worker_log = llm_worker["log_file"]
        log_content = worker_log.read_text()

        # Count STORE events - order-agnostic approach
        # First source stores (will publish) - could be engine or KVBM depending on timing
        first_source_stores = len(
            re.findall(
                r"stored in first source \w+.*will publish STORE event", log_content
            )
        )

        # Second source stores (DEDUP) - could be engine or KVBM depending on timing
        # Pattern: "DEDUP: Block ... added to source X"
        dedup_stores = len(
            re.findall(
                r"DEDUP: Block \d+ \(seq_hash=\d+\) added to source \w+", log_content
            )
        )

        # Count total STORE events received (from both sources)
        total_stores_received = first_source_stores + dedup_stores

        # Count STORE events actually published to router
        published_stores = len(re.findall(r"will publish STORE event", log_content))

        logger.info(
            f"STORE events from first source (will publish): {first_source_stores}"
        )
        logger.info(f"STORE events from second source (DEDUP): {dedup_stores}")
        logger.info(f"Total STORE events received: {total_stores_received}")
        logger.info(f"STORE events published to router: {published_stores}")

        # Assertions:
        # 1. We should receive STORE events from both sources (order doesn't matter)
        assert (
            first_source_stores > 0
        ), f"Expected STORE events from first source (could be {engine.upper()} or KVBM)"
        assert (
            dedup_stores > 0
        ), "Expected DEDUP STORE events from second source (proves deduplication working)"

        # 2. Published stores should equal first source stores
        #    (each unique block is published once when first stored, regardless of which source)
        assert (
            published_stores == first_source_stores
        ), f"Expected published events ({published_stores}) to equal first-source stores ({first_source_stores})"

        # 3. Total stores should be first source + second source (each block stored in both)
        assert (
            total_stores_received == first_source_stores + dedup_stores
        ), f"Total should be first-source ({first_source_stores}) + second-source ({dedup_stores})"

        # 4. Check for errors in logs
        self.assert_no_errors_in_logs(worker_log, frontend_server["log_file"], engine)

        logger.info(f"STORE deduplication test passed ({engine.upper()})")

    @pytest.mark.timeout(340)  # 4x measured (~85s), rounded up
    @pytest.mark.parametrize("engine_type", AVAILABLE_ENGINES)
    def test_remove_deduplication_across_sources(
        self, test_directory, runtime_services, engine_type
    ):
        """
        Test REMOVE event deduplication across G1 (engine GPU), G2 (KVBM CPU), G3 (KVBM disk):

        When blocks are stored in G1 (GPU), they are AUTOMATICALLY
        replicated to G2 (CPU) and G3 (Disk) simultaneously.

        Test Scenario:
        1. Configure very small GPU cache (30 blocks) and slightly larger KVBM caches (50 blocks each)
        2. Send 25 requests with 100 tokens each → blocks stored in G1 AND offloaded to G2/G3
        3. GPU fills up (30 blocks) → blocks evicted from G1 → consolidator receives REMOVE from engine
           → consolidator sees blocks still exist in G2/G3 → does NOT publish REMOVE to router
        4. Some blocks only exist in G1 (not replicated) → when evicted → published to router

        This verifies: REMOVE is only sent to router when a block is removed from ALL sources.
        Deduplication prevents unnecessary REMOVE events when blocks are still cached in G2/G3.
        """

        engine = engine_type
        logger.info(f"Starting REMOVE deduplication test ({engine.upper()})")

        # Start frontend with router
        frontend_command = [
            "python",
            "-m",
            "dynamo.frontend",
            "--http-port",
            str(FRONTEND_PORT),
            "--router-mode",
            "kv",
            "--router-reset-states",
        ]

        frontend_env = os.environ.copy()
        frontend_env.update(
            {
                "RUST_BACKTRACE": "1",
                "NATS_SERVER": "nats://localhost:4222",
                "ETCD_ENDPOINTS": "http://localhost:2379",
                "DYN_LOG": "debug",
            }
        )

        frontend_log_dir = Path(os.path.join(test_directory, "frontend")).absolute()
        frontend_log_dir.mkdir(parents=True, exist_ok=True)

        with ManagedProcess(
            command=frontend_command,
            env=frontend_env,
            health_check_urls=[f"http://localhost:{FRONTEND_PORT}/health"],
            timeout=120,
            working_dir=str(test_directory),
            display_output=False,
            log_dir=str(frontend_log_dir),  # Absolute path keeps logs in test directory
            terminate_all_matching_process_names=False,  # Don't kill nats-server/etcd started by runtime_services
        ) as _frontend_process:
            # Get actual log file path from ManagedProcess
            frontend_log = Path(_frontend_process._log_path)
            logger.info(f"Frontend started on port {FRONTEND_PORT}")

            # Start worker with constrained GPU blocks but larger KVBM blocks
            model_id = os.environ.get("CONSOLIDATOR_MODEL_ID", "Qwen/Qwen3-0.6B")

            # Fixed cache tier sizes
            g1_gpu_blocks = 10  # Very small GPU cache to force evictions
            g2_cpu_blocks = 5  # Smaller than GPU but large enough to retain blocks
            g3_disk_blocks = 5  # Smaller than GPU but large enough to retain blocks

            # Compute optimal test parameters for this configuration
            test_params = compute_deduplication_test_params(
                g1_gpu_blocks=g1_gpu_blocks,
                g2_cpu_blocks=g2_cpu_blocks,
                g3_disk_blocks=g3_disk_blocks,
            )

            # Build command based on engine type
            if engine == "vllm":
                worker_command = [
                    "python",
                    "-m",
                    "dynamo.vllm",
                    "--model",
                    model_id,
                    "--kv-transfer-config",
                    '{"kv_connector":"DynamoConnector","kv_connector_module_path":"kvbm.vllm_integration.connector","kv_role":"kv_both"}',
                    "--enforce-eager",
                    "--enable-prefix-caching",
                    "--num-gpu-blocks-override",
                    str(g1_gpu_blocks),
                ]
            else:  # trtllm
                # Create TensorRT-LLM config file with KVBM connector
                # Use small GPU cache (0.01 = 1% of GPU memory) to force evictions
                # This ensures blocks will be evicted from GPU while still in KVBM
                # Small enough to trigger evictions but large enough to handle sequence requirements
                config_path = create_trtllm_config(test_directory)
                worker_command = [
                    "python",
                    "-m",
                    "dynamo.trtllm",
                    "--model-path",
                    model_id,
                    "--served-model-name",
                    model_id,
                    "--extra-engine-args",
                    str(
                        config_path.absolute()
                    ),  # Use absolute path to avoid working directory issues
                    "--publish-events-and-metrics",
                ]

            worker_env = os.environ.copy()
            worker_env.update(
                {
                    "RUST_BACKTRACE": "1",
                    "NATS_SERVER": "nats://localhost:4222",
                    "ETCD_ENDPOINTS": "http://localhost:2379",
                    "DYN_KVBM_CPU_CACHE_OVERRIDE_NUM_BLOCKS": str(g2_cpu_blocks),
                    "DYN_KVBM_DISK_CACHE_OVERRIDE_NUM_BLOCKS": str(g3_disk_blocks),
                    "DYN_LOG": "debug",
                }
            )

            # Set ZMQ port for TensorRT-LLM consolidator
            if engine == "trtllm":
                worker_env["DYN_KVBM_TRTLLM_ZMQ_PORT"] = "20081"

            worker_log_dir = Path(os.path.join(test_directory, engine)).absolute()
            worker_log_dir.mkdir(parents=True, exist_ok=True)

            with ManagedProcess(
                command=worker_command,
                env=worker_env,
                health_check_urls=[],
                timeout=300,
                working_dir=str(test_directory),
                display_output=False,
                log_dir=str(
                    worker_log_dir
                ),  # Absolute path keeps logs in test directory
                terminate_all_matching_process_names=False,
            ) as _worker_process:
                # Get actual log file path from ManagedProcess (it may modify log_dir to use temp directory)
                worker_log = Path(_worker_process._log_path)
                logger.info(f"Waiting for {engine.upper()} worker to initialize...")

                # Wait for worker to register with frontend
                worker_registered = wait_for_worker_registration(
                    f"http://localhost:{FRONTEND_PORT}"
                )

                if not worker_registered:
                    pytest.fail(
                        f"{engine.upper()} worker failed to register with frontend"
                    )

                # Additional wait for consolidator to fully initialize
                time.sleep(5)

                # Create tester
                tester = ApiTester(
                    base_url=f"http://localhost:{FRONTEND_PORT}", model_id=model_id
                )

                # Phase 1: Send requests to fill GPU cache
                logger.info("Phase 1: Filling GPU cache with diverse prompts")
                num_requests = test_params["num_requests"]
                max_tokens = test_params["max_tokens"]
                concurrency = 2  # Parallel requests to create cache pressure
                logger.info(
                    f"Computed test params: max_tokens={max_tokens}, "
                    f"num_requests={num_requests}, concurrency={concurrency}, "
                    f"blocks_per_request={test_params['blocks_per_request']}, "
                    f"total_capacity={test_params['total_capacity_blocks']} blocks, "
                    f"min_offload_cache={test_params['min_offload_cache']} blocks, "
                    f"total_blocks_generated={test_params['total_blocks_generated']}"
                )

                # Use parallel requests to create concurrent cache pressure
                # This forces the GPU cache to actually fill up (multiple requests
                # holding blocks simultaneously) rather than recycling blocks
                # between serial requests
                from concurrent.futures import ThreadPoolExecutor, as_completed

                def send_request(request_idx: int) -> tuple[int, bool]:
                    """Send a single request and return (index, success)."""
                    prompt = f"Tell me a unique story about topic {request_idx}. Make it very long and detailed with many paragraphs."
                    try:
                        response = tester.send_chat_request(
                            messages=[{"role": "user", "content": prompt}],
                            max_tokens=max_tokens,
                        )
                    except requests.RequestException:
                        logger.exception(f"Request {request_idx} failed")
                        return (request_idx, False)
                    else:
                        success = "content" in response["choices"][0]["message"]
                        return (request_idx, success)

                completed_count = 0
                failed_count = 0
                with ThreadPoolExecutor(max_workers=concurrency) as executor:
                    futures = {
                        executor.submit(send_request, i): i for i in range(num_requests)
                    }
                    for future in as_completed(futures):
                        _request_idx, success = future.result()
                        if success:
                            completed_count += 1
                        else:
                            failed_count += 1
                        if completed_count % 10 == 0 or completed_count == num_requests:
                            logger.info(
                                f"Progress: {completed_count}/{num_requests} completed"
                            )

                logger.info(
                    f"All requests finished: {completed_count} succeeded, {failed_count} failed"
                )
                assert failed_count == 0, f"{failed_count} requests failed"

                # Wait for requests to complete and blocks to be freed
                # With GUARANTEED_NO_EVICT, blocks are freed when requests complete (not evicted)
                # We need to wait long enough for requests to finish and blocks to be freed
                # For vLLM with FIFO, evictions happen immediately when cache fills.
                wait_time = 5 if engine == "trtllm" else 5
                logger.info(
                    f"Waiting {wait_time}s for requests to complete and blocks to be freed..."
                )
                time.sleep(wait_time)

                # Phase 2: Analyze consolidator logs
                logger.info("Phase 2: Analyzing consolidator deduplication behavior")
                log_content = worker_log.read_text()

                # Count blocks removed but still in another source (deduplication working!)
                # Order-agnostic: checks for any removal where block still exists in another source
                # Pattern: "removed from source X, still in Y source(s): [sources]"
                removes_but_still_in_other_source = len(
                    re.findall(
                        r"removed from source \w+, still in \d+ source\(s\)",
                        log_content,
                    )
                )

                # Count blocks removed from last source (will publish REMOVE)
                # Order-agnostic: checks for any removal from last source, regardless of which source
                removes_from_last_source = len(
                    re.findall(
                        r"removed from last source \w+.*will publish REMOVE event",
                        log_content,
                    )
                )

                # Count REMOVE events actually published to router
                published_removes = len(
                    re.findall(r"will publish REMOVE event", log_content)
                )

                logger.info(
                    f"Blocks removed but still in another source (deduplication working): {removes_but_still_in_other_source}"
                )
                logger.info(
                    f"Blocks removed from last source (will publish): {removes_from_last_source}"
                )
                logger.info(f"REMOVE events published to router: {published_removes}")

                # Assertions:
                # 1. We should see removals where blocks still exist in another source
                #    This proves deduplication is working (REMOVE not sent to router yet)
                #    Order doesn't matter - could be engine→KVBM or KVBM→engine
                assert (
                    removes_but_still_in_other_source > 0
                ), f"Expected removals where blocks still exist in another source (deduplication working) for {engine.upper()}"

                # 2. REMOVE events should be published for last-source removals
                #    Order doesn't matter - could be engine or KVBM as last source
                assert (
                    published_removes > 0
                ), "Expected REMOVE events to be published for last-source removals"

                # 3. Published removes should equal removes from last source
                assert (
                    published_removes == removes_from_last_source
                ), f"Expected published REMOVE events ({published_removes}) to equal last-source removals ({removes_from_last_source})"

                # 3. Check for errors in logs
                self.assert_no_errors_in_logs(worker_log, frontend_log, engine)
