conftest.py

import base64
import datetime
import io
import math
import os
import random

os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
# Set CPU device for CI environments without GPU
if "VLLM_TARGET_DEVICE" not in os.environ:
    os.environ["VLLM_TARGET_DEVICE"] = "cpu"

import gc
import socket
import subprocess
import sys
import time
from pathlib import Path
from typing import Any

import numpy as np
import psutil
import pytest
import torch
import yaml
from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
from vllm.logger import init_logger
from vllm.utils.network_utils import get_open_port

logger = init_logger(__name__)


@pytest.fixture(autouse=True)
def default_vllm_config():
    """Set a default VllmConfig for all tests.

    This fixture is auto-used for all tests to ensure that any test
    that directly instantiates vLLM CustomOps (e.g., RMSNorm, LayerNorm)
    or model components has the required VllmConfig context.

    This fixture is required for vLLM 0.14.0+ where CustomOp initialization
    requires a VllmConfig context set via set_current_vllm_config().
    """
    from vllm.config import DeviceConfig, VllmConfig, set_current_vllm_config

    # Use CPU device if no GPU is available (e.g., in CI environments)
    has_gpu = torch.cuda.is_available() and torch.cuda.device_count() > 0
    device = "cuda" if has_gpu else "cpu"
    device_config = DeviceConfig(device=device)

    with set_current_vllm_config(VllmConfig(device_config=device_config)):
        yield


@pytest.fixture(autouse=True)
def clean_gpu_memory_between_tests():
    print("\n=== PRE-TEST GPU CLEANUP ===")
    _run_pre_test_cleanup()
    yield
    _run_post_test_cleanup()


def _run_pre_test_cleanup(enable_force=False):
    if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1" and not enable_force:
        print("GPU cleanup disabled")
        return

    print("Pre-test GPU status:")

    num_gpus = torch.cuda.device_count()
    if num_gpus > 0:
        try:
            from tests.utils import wait_for_gpu_memory_to_clear

            wait_for_gpu_memory_to_clear(
                devices=list(range(num_gpus)),
                threshold_ratio=0.05,
            )
        except Exception as e:
            print(f"Pre-test cleanup note: {e}")


def _run_post_test_cleanup(enable_force=False):
    if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1" and not enable_force:
        print("GPU cleanup disabled")
        return

    if torch.cuda.is_available():
        gc.collect()
        torch.cuda.empty_cache()

        print("Post-test GPU status:")
        _print_gpu_processes()


def _print_gpu_processes():
    """Print GPU information including nvidia-smi and system processes"""

    print("\n" + "=" * 80)
    print("NVIDIA GPU Information (nvidia-smi)")
    print("=" * 80)

    try:
        nvidia_result = subprocess.run(
            ["nvidia-smi"],
            capture_output=True,
            text=True,
            timeout=5,
        )

        if nvidia_result.returncode == 0:
            lines = nvidia_result.stdout.strip().split("\n")
            for line in lines[:20]:
                print(line)

            if len(lines) > 20:
                print(f"... (showing first 20 of {len(lines)} lines)")
        else:
            print("nvidia-smi command failed")

    except (subprocess.TimeoutExpired, FileNotFoundError):
        print("nvidia-smi not available or timed out")
    except Exception as e:
        print(f"Error running nvidia-smi: {e}")

    print("\n" + "=" * 80)
    print("Detailed GPU Processes (nvidia-smi pmon)")
    print("=" * 80)

    try:
        pmon_result = subprocess.run(
            ["nvidia-smi", "pmon", "-c", "1"],
            capture_output=True,
            text=True,
            timeout=3,
        )

        if pmon_result.returncode == 0 and pmon_result.stdout.strip():
            print(pmon_result.stdout)
        else:
            print("No active GPU processes found via nvidia-smi pmon")

    except Exception:
        print("nvidia-smi pmon not available")

    print("\n" + "=" * 80)
    print("System Processes with GPU keywords")
    print("=" * 80)


def dummy_messages_from_mix_data(
    system_prompt: dict[str, Any] = None,
    video_data_url: Any = None,
    audio_data_url: Any = None,
    image_data_url: Any = None,
    content_text: str = None,
):
    """Create messages with video、image、audio data URL for OpenAI API."""

    if content_text is not None:
        content = [{"type": "text", "text": content_text}]
    else:
        content = []

    media_items = []
    if isinstance(video_data_url, list):
        for video_url in video_data_url:
            media_items.append((video_url, "video"))
    else:
        media_items.append((video_data_url, "video"))

    if isinstance(image_data_url, list):
        for url in image_data_url:
            media_items.append((url, "image"))
    else:
        media_items.append((image_data_url, "image"))

    if isinstance(audio_data_url, list):
        for url in audio_data_url:
            media_items.append((url, "audio"))
    else:
        media_items.append((audio_data_url, "audio"))

    content.extend(
        {"type": f"{media_type}_url", f"{media_type}_url": {"url": url}}
        for url, media_type in media_items
        if url is not None
    )
    messages = [{"role": "user", "content": content}]
    if system_prompt is not None:
        messages = [system_prompt] + messages
    return messages


def generate_synthetic_audio(
    duration: int,  # seconds
    num_channels: int,  # 1：Mono，2：Stereo 5：5.1 surround sound
    sample_rate: int = 48000,  # Default use 48000Hz.
    save_to_file: bool = False,
) -> dict[str, Any]:
    """ "Generate synthetic audio with rain."""
    import soundfile as sf

    # Initialize audio data array
    num_samples = int(sample_rate * duration)
    audio_data = np.zeros((num_samples, num_channels), dtype=np.float32)

    # Configure parameters based on rain intensity
    drop_density = 10  # Number of raindrops per second
    drop_volume = 0.15  # Volume of individual raindrops
    background_volume = 0.02  # Volume of background rain noise

    # Pink noise sounds more natural than white noise for rain
    white_noise = np.random.randn(num_samples)
    pink_noise = np.convolve(white_noise, np.ones(8) / 8, mode="same")
    pink_noise = pink_noise / np.max(np.abs(pink_noise)) if np.max(np.abs(pink_noise)) > 0 else pink_noise
    bg_noise = pink_noise * background_volume

    # Add background noise to all channels
    for ch in range(num_channels):
        audio_data[:, ch] += bg_noise

    # Total number of raindrops = density × duration × channels for stereo effect
    total_drops = int(drop_density * duration * num_channels)

    for _ in range(total_drops):
        # Random timing for raindrop
        drop_time = random.uniform(0, duration)

        # Random duration of raindrop sound (0.01-0.05 seconds)
        drop_duration = random.uniform(0.01, 0.05)

        # Random frequency gives variation in raindrop pitch
        drop_freq = random.uniform(500, 5000)  # Hz

        # Random channel selection for stereo positioning
        channel = random.randint(0, num_channels - 1)

        # Calculate sample positions for this raindrop
        start_sample = int(drop_time * sample_rate)
        drop_samples = int(drop_duration * sample_rate)
        end_sample = min(start_sample + drop_samples, num_samples)

        if start_sample < end_sample:
            # Generate the raindrop sound
            num_drop_samples = end_sample - start_sample
            t = np.arange(num_drop_samples) / sample_rate

            # Basic sine wave for raindrop sound
            drop_sound = drop_volume * np.sin(2 * math.pi * drop_freq * t)

            # Apply envelope for natural attack and decay
            envelope = np.ones(num_drop_samples)
            attack_samples = int(num_drop_samples * 0.1)  # 10% of samples for attack
            decay_samples = num_drop_samples - attack_samples

            if attack_samples > 0:
                # Linear attack: volume increases from 0 to 1
                envelope[:attack_samples] = np.linspace(0, 1, attack_samples)

            if decay_samples > 0:
                # Exponential decay for natural sound fade
                decay = np.exp(-8 * t[attack_samples:] / drop_duration)
                envelope[attack_samples:] = decay

            # Apply envelope to raindrop sound
            drop_sound *= envelope

            # Add raindrop sound to selected channel
            audio_data[start_sample:end_sample, channel] += drop_sound

    # Step 3: Add simple reverb effect for realism
    # Reverb simulates sound reflections in environment
    if duration > 2:
        # Single delay reverb (100ms delay)
        delay_samples = int(0.1 * sample_rate)
        if delay_samples < num_samples:
            for ch in range(num_channels):
                delayed = np.zeros(num_samples)
                delayed[delay_samples:] = audio_data[:-delay_samples, ch] * 0.3
                audio_data[:, ch] += delayed

    # Step 4: Normalize audio to prevent clipping
    # Find maximum amplitude and scale to 80% of maximum volume
    max_amp = np.max(np.abs(audio_data))
    if max_amp > 0:
        audio_data = audio_data / max_amp * 0.8

    # Handle file saving
    audio_bytes = None

    if save_to_file:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        output_path = f"audio_{num_channels}ch_{timestamp}.wav"

        try:
            sf.write(output_path, audio_data, sample_rate, format="WAV", subtype="PCM_16")
            print(f"Audio saved: {output_path}")

            with open(output_path, "rb") as f:
                audio_bytes = f.read()
        except Exception as e:
            print(f"Save failed: {e}")
            save_to_file = False

    # If not saving or save failed, create in memory
    if not save_to_file or audio_bytes is None:
        buffer = io.BytesIO()
        sf.write(buffer, audio_data, sample_rate, format="WAV", subtype="PCM_16")
        buffer.seek(0)
        audio_bytes = buffer.read()

    # Return result
    base64_audio = base64.b64encode(audio_bytes).decode("utf-8")
    result = {
        "base64": base64_audio,
    }
    if save_to_file and output_path:
        result["file_path"] = output_path

    return result


def generate_synthetic_video(width: int, height: int, num_frames: int, save_to_file: bool = False) -> str:
    """Generate synthetic video with bouncing balls and return base64 string."""

    import cv2
    import imageio

    # Create random balls
    num_balls = random.randint(3, 8)
    balls = []

    for _ in range(num_balls):
        radius = min(width, height) // 8
        if radius < 1:
            raise ValueError(f"Video dimensions ({width}x{height}) are too small for synthetic video generation")
        x = random.randint(radius, width - radius)
        y = random.randint(radius, height - radius)

        speed = random.uniform(3.0, 8.0)
        angle = random.uniform(0, 2 * math.pi)
        vx = speed * math.cos(angle)
        vy = speed * math.sin(angle)

        # OpenCV uses BGR format, but imageio expects RGB
        # We'll create in BGR first, then convert to RGB later
        color_bgr = (random.randint(50, 255), random.randint(50, 255), random.randint(50, 255))

        balls.append({"x": x, "y": y, "vx": vx, "vy": vy, "radius": radius, "color_bgr": color_bgr})

    # Generate video frames
    video_frames = []

    for frame_idx in range(num_frames):
        # Create black background (BGR format)
        frame_bgr = np.zeros((height, width, 3), dtype=np.uint8)

        for ball in balls:
            # Update position
            ball["x"] += ball["vx"]
            ball["y"] += ball["vy"]

            # Boundary collision detection
            if ball["x"] - ball["radius"] <= 0 or ball["x"] + ball["radius"] >= width:
                ball["vx"] = -ball["vx"]
                ball["x"] = max(ball["radius"], min(width - ball["radius"], ball["x"]))

            if ball["y"] - ball["radius"] <= 0 or ball["y"] + ball["radius"] >= height:
                ball["vy"] = -ball["vy"]
                ball["y"] = max(ball["radius"], min(height - ball["radius"], ball["y"]))

            # Use cv2 to draw circle
            x, y = int(ball["x"]), int(ball["y"])
            radius = ball["radius"]

            # Draw solid circle (main circle)
            cv2.circle(frame_bgr, (x, y), radius, ball["color_bgr"], -1)

            # Add simple 3D effect: draw a brighter center
            if radius > 3:  # Only add highlight when radius is large enough
                highlight_radius = max(1, radius // 2)
                highlight_x = max(highlight_radius, min(x - radius // 4, width - highlight_radius))
                highlight_y = max(highlight_radius, min(y - radius // 4, height - highlight_radius))

                # Create highlight color (brighter)
                highlight_color = tuple(min(c + 40, 255) for c in ball["color_bgr"])
                cv2.circle(frame_bgr, (highlight_x, highlight_y), highlight_radius, highlight_color, -1)

        # Convert BGR to RGB for imageio
        frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
        video_frames.append(frame_rgb)

    video_bytes = None
    saved_file_path = None

    buffer = io.BytesIO()
    writer_kwargs = {
        "format": "mp4",
        "fps": 30,
        "codec": "libx264",
        "quality": 7,
        "pixelformat": "yuv420p",
        "macro_block_size": 16,
        "ffmpeg_params": [
            "-preset",
            "medium",
            "-crf",
            "23",
            "-movflags",
            "+faststart",
            "-pix_fmt",
            "yuv420p",
            "-vf",
            f"scale={width}:{height}",
        ],
    }

    if save_to_file:
        import datetime

        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        output_path = f"video_{width}x{height}_{timestamp}.mp4"
        try:
            with imageio.get_writer(output_path, **writer_kwargs) as writer:
                for frame in video_frames:
                    writer.append_data(frame)

            saved_file_path = output_path
            print(f"Video saved to: {saved_file_path}")
            with open(output_path, "rb") as f:
                video_bytes = f.read()

        except Exception as e:
            print(f"Warning: Failed to save video to file {output_path}: {e}")
            save_to_file = False

    if not save_to_file or video_bytes is None:
        with imageio.get_writer(buffer, **writer_kwargs) as writer:
            for frame in video_frames:
                writer.append_data(frame)

        buffer.seek(0)
        video_bytes = buffer.read()

    base64_video = base64.b64encode(video_bytes).decode("utf-8")

    result = {
        "base64": base64_video,
    }
    if save_to_file and saved_file_path:
        result["file_path"] = saved_file_path

    return result


def generate_synthetic_image(width: int, height: int, save_to_file: bool = False) -> Any:
    """Generate synthetic image with randomly colored squares and return base64 string."""
    from PIL import Image, ImageDraw

    # Create white background
    image = Image.new("RGB", (width, height), (255, 255, 255))
    draw = ImageDraw.Draw(image)

    # Generate random number of squares
    num_squares = random.randint(3, 8)

    for _ in range(num_squares):
        # Random square size
        square_size = random.randint(min(width, height) // 8, min(width, height) // 4)

        # Random position
        x = random.randint(0, width - square_size - 1)
        y = random.randint(0, height - square_size - 1)

        # Random color
        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))

        # Random border width
        border_width = random.randint(1, 5)

        # Draw square
        draw.rectangle([x, y, x + square_size, y + square_size], fill=color, outline=(0, 0, 0), width=border_width)

    # Handle file saving
    image_bytes = None
    saved_file_path = None

    if save_to_file:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        output_path = f"image_{width}x{height}_{timestamp}.jpg"

        try:
            # Save image to file
            image.save(output_path, format="JPEG", quality=85, optimize=True)
            saved_file_path = output_path
            print(f"Image saved to: {saved_file_path}")

            # Read file for base64 encoding
            with open(output_path, "rb") as f:
                image_bytes = f.read()

        except Exception as e:
            print(f"Warning: Failed to save image to file {output_path}: {e}")
            save_to_file = False

    # If not saving or save failed, create in memory
    if not save_to_file or image_bytes is None:
        buffer = io.BytesIO()
        image.save(buffer, format="JPEG", quality=85, optimize=True)
        buffer.seek(0)
        image_bytes = buffer.read()

    # Generate base64
    base64_image = base64.b64encode(image_bytes).decode("utf-8")

    # Return result
    result = {
        "base64": base64_image,
    }
    if save_to_file and saved_file_path:
        result["file_path"] = saved_file_path

    return result


def preprocess_text(text):
    import re

    word_to_num = {
        "zero": "0",
        "one": "1",
        "two": "2",
        "three": "3",
        "four": "4",
        "five": "5",
        "six": "6",
        "seven": "7",
        "eight": "8",
        "nine": "9",
        "ten": "10",
    }

    for word, num in word_to_num.items():
        pattern = r"\b" + re.escape(word) + r"\b"
        text = re.sub(pattern, num, text, flags=re.IGNORECASE)

    text = re.sub(r"[^\w\s]", "", text)
    text = re.sub(r"\s+", " ", text)
    return text.lower().strip()


def cosine_similarity_text(text1, text2, n: int = 3):
    from collections import Counter

    if not text1 or not text2:
        return 0.0

    text1 = preprocess_text(text1)
    text2 = preprocess_text(text2)

    ngrams1 = [text1[i : i + n] for i in range(len(text1) - n + 1)]
    ngrams2 = [text2[i : i + n] for i in range(len(text2) - n + 1)]

    counter1 = Counter(ngrams1)
    counter2 = Counter(ngrams2)

    all_ngrams = set(counter1.keys()) | set(counter2.keys())
    vec1 = [counter1.get(ng, 0) for ng in all_ngrams]
    vec2 = [counter2.get(ng, 0) for ng in all_ngrams]

    dot_product = sum(a * b for a, b in zip(vec1, vec2))
    norm1 = sum(a * a for a in vec1) ** 0.5
    norm2 = sum(b * b for b in vec2) ** 0.5

    if norm1 == 0 or norm2 == 0:
        return 0.0
    return dot_product / (norm1 * norm2)


def convert_audio_to_text(audio_data):
    """
    Convert base64 encoded audio data to text using speech recognition.
    """
    import whisper

    audio_data = base64.b64decode(audio_data)
    output_path = f"./test_{int(time.time())}"
    with open(output_path, "wb") as audio_file:
        audio_file.write(audio_data)

    print(f"audio data is saved: {output_path}")

    model = whisper.load_model("base")
    text = model.transcribe(
        output_path,
        temperature=0.0,
        word_timestamps=True,
        condition_on_previous_text=False,
    )["text"]
    if text:
        return text
    else:
        return ""


def merge_base64_and_convert_to_text(base64_list):
    """
    Merge a list of base64 encoded audio data and convert to text.
    """
    import whisper
    from pydub import AudioSegment

    merged_audio = None
    for base64_data in base64_list:
        audio_data = base64.b64decode(base64_data.split(",", 1)[-1])
        seg = AudioSegment.from_file(io.BytesIO(audio_data))
        if merged_audio is None:
            merged_audio = seg
        else:
            merged_audio += seg
    output_path = f"./test_{int(time.time())}"
    merged_audio.export(output_path, format="wav")
    model = whisper.load_model("base")
    text = model.transcribe(
        output_path,
        temperature=0.0,
        word_timestamps=True,
        condition_on_previous_text=False,
    )["text"]
    if text:
        return text
    else:
        return ""


def modify_stage_config(
    yaml_path: str,
    updates: dict[str, Any],
    deletes: dict[str, Any] = None,
) -> str:
    """
    Modify configurations in a YAML file, supporting both top-level and stage-specific modifications,
    including addition, modification, and deletion of configurations.

    Args:
        yaml_path: Path to the YAML configuration file.
        updates: Dictionary containing both top-level and stage-specific modifications to add or update.
                Format: {
                    'async_chunk': True,
                    'stage_args': {
                        0: {'engine_args.max_model_len': 5800},
                        1: {'runtime.max_batch_size': 2}
                    }
                }
        deletes: Dictionary containing configurations to delete.
                Format: {
                    'old_config': None,  # Delete entire key
                    'stage_args': {
                        0: ['engine_args.old_param'],
                        1: ['runtime.unused_setting']
                    }
                }

    Returns:
        str: Path to the newly created modified YAML file with timestamp suffix.
    """
    path = Path(yaml_path)
    if not path.exists():
        raise FileNotFoundError(f"yaml does not exist: {path}")

    try:
        with open(yaml_path, encoding="utf-8") as f:
            config = yaml.safe_load(f) or {}
    except Exception as e:
        raise ValueError(f"Cannot parse YAML file: {e}")

    # Helper function to apply update
    def apply_update(config_dict: dict, key_path: str, value: Any) -> None:
        """Apply update to dictionary using dot-separated path."""
        # Handle direct list assignment (e.g., engine_input_source: [1, 2])
        if "." not in key_path:
            # Simple key, set directly
            config_dict[key_path] = value
            return

        current = config_dict
        keys = key_path.split(".")

        for i in range(len(keys) - 1):
            key = keys[i]

            # Handle list indices
            if key.isdigit() and isinstance(current, list):
                index = int(key)
                if index < 0:
                    raise ValueError(f"Negative list index not allowed: {index}")
                if index >= len(current):
                    # Expand list if needed
                    while len(current) <= index:
                        # If we need to go deeper (more keys after this), create a dict
                        # Otherwise, create None placeholder
                        current.append({} if i < len(keys) - 2 else None)
                current = current[index]
            elif isinstance(current, dict):
                # Handle dictionary keys
                if key not in current:
                    # If there are more keys after this, create appropriate structure
                    if i < len(keys) - 1:
                        # Check if next key is a digit (list index) or string (dict key)
                        if keys[i + 1].isdigit():
                            current[key] = []
                        else:
                            current[key] = {}
                    else:
                        # This is the last key, create based on value type
                        current[key] = [] if isinstance(value, list) else {}
                elif not isinstance(current[key], (dict, list)) and i < len(keys) - 1:
                    # If current value is not dict/list but we need to go deeper, replace it
                    if keys[i + 1].isdigit():
                        current[key] = []
                    else:
                        current[key] = {}
                current = current[key]
            else:
                # Current is not a dict or list, cannot traverse further
                raise TypeError(
                    f"Cannot access {'.'.join(keys[: i + 1])} as a dict/list. It's a {type(current).__name__}"
                )

        # Set the final value
        last_key = keys[-1]
        if isinstance(current, list) and last_key.isdigit():
            # Setting a value in a list by index
            index = int(last_key)
            if index < 0:
                raise ValueError(f"Negative list index not allowed: {index}")
            if index >= len(current):
                # Expand list if needed
                while len(current) <= index:
                    current.append(None)
            current[index] = value
        elif isinstance(current, dict):
            # Special case: if the value is a list and we're setting a top-level key
            # Example: updating engine_input_source with [1, 2]
            current[last_key] = value
        else:
            # Current is not a dict, cannot set key
            raise TypeError(f"Cannot set value at {key_path}. Current type is {type(current).__name__}, expected dict.")

    # Helper function to delete by path
    def delete_by_path(config_dict: dict, path: str) -> None:
        """Delete configuration by dot-separated path."""
        if not path:
            return

        current = config_dict
        keys = path.split(".")

        # Traverse to the parent
        for i in range(len(keys) - 1):
            key = keys[i]

            # Handle list indices
            if key.isdigit() and isinstance(current, list):
                index = int(key)
                if index < 0 or index >= len(current):
                    raise KeyError(f"List index {index} out of bounds")
                current = current[index]
            elif isinstance(current, dict):
                if key not in current:
                    raise KeyError(f"Path {'.'.join(keys[: i + 1])} does not exist")
                current = current[key]
            else:
                raise TypeError(
                    f"Cannot access {'.'.join(keys[: i + 1])} as a dict/list. It's a {type(current).__name__}"
                )

        # Delete the item
        last_key = keys[-1]

        if isinstance(current, list) and last_key.isdigit():
            index = int(last_key)
            if index < 0 or index >= len(current):
                raise KeyError(f"List index {index} out of bounds")
            del current[index]
        elif isinstance(current, dict) and last_key in current:
            del current[last_key]
        else:
            raise KeyError(f"Path {path} does not exist")

    # Apply deletions first
    if deletes:
        for key, value in deletes.items():
            if key == "stage_args":
                if value and isinstance(value, dict):
                    stage_args = config.get("stage_args", [])
                    if not stage_args:
                        raise ValueError("stage_args does not exist in config")

                    for stage_id, delete_paths in value.items():
                        if not delete_paths:
                            continue

                        # Find stage by ID
                        target_stage = None
                        for stage in stage_args:
                            if stage.get("stage_id") == stage_id:
                                target_stage = stage
                                break

                        if target_stage is None:
                            available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
                            raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")

                        # Delete specified paths in this stage
                        for path in delete_paths:
                            if path:  # Skip empty paths
                                delete_by_path(target_stage, path)
            elif "." in key:
                # Delete using dot-separated path
                delete_by_path(config, key)
            elif value is None and key in config:
                # Delete entire key
                del config[key]

    # Apply updates
    for key, value in updates.items():
        if key == "stage_args":
            if value and isinstance(value, dict):
                stage_args = config.get("stage_args", [])
                if not stage_args:
                    raise ValueError("stage_args does not exist in config")

                for stage_id, stage_updates in value.items():
                    # Find stage by ID
                    target_stage = None
                    for stage in stage_args:
                        if stage.get("stage_id") == stage_id:
                            target_stage = stage
                            break

                    if target_stage is None:
                        available_ids = [s.get("stage_id") for s in stage_args if "stage_id" in s]
                        raise KeyError(f"Stage ID {stage_id} not found, available: {available_ids}")

                    # Apply updates to this stage
                    for path, val in stage_updates.items():
                        # Check if this is a simple key (not dot-separated)
                        # Example: 'engine_input_source' vs 'engine_args.max_model_len'
                        if "." not in path:
                            # Direct key assignment (e.g., updating a list value)
                            target_stage[path] = val
                        else:
                            # Dot-separated path (e.g., nested dict access)
                            apply_update(target_stage, path, val)
        elif "." in key:
            # Apply using dot-separated path
            apply_update(config, key, value)
        else:
            # Direct top-level key
            config[key] = value

    # Save to new file with timestamp
    timestamp = int(time.time())
    base_name = yaml_path.rsplit(".", 1)[0] if "." in yaml_path else yaml_path
    output_path = f"{base_name}_{timestamp}.yaml"

    with open(output_path, "w", encoding="utf-8") as f:
        yaml.dump(config, f, default_flow_style=None, sort_keys=False, allow_unicode=True, indent=2)

    return output_path


class OmniServer:
    """Omniserver for vLLM-Omni tests."""

    def __init__(
        self,
        model: str,
        serve_args: list[str],
        *,
        env_dict: dict[str, str] | None = None,
    ) -> None:
        _run_pre_test_cleanup(enable_force=True)
        _run_post_test_cleanup(enable_force=True)
        cleanup_dist_env_and_memory()
        self.model = model
        self.serve_args = serve_args
        self.env_dict = env_dict
        self.proc: subprocess.Popen | None = None
        self.host = "127.0.0.1"
        self.port = get_open_port()

    def _start_server(self) -> None:
        """Start the vLLM-Omni server subprocess."""
        env = os.environ.copy()
        env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
        if self.env_dict is not None:
            env.update(self.env_dict)

        cmd = [
            sys.executable,
            "-m",
            "vllm_omni.entrypoints.cli.main",
            "serve",
            self.model,
            "--omni",
            "--host",
            self.host,
            "--port",
            str(self.port),
        ] + self.serve_args

        print(f"Launching OmniServer with: {' '.join(cmd)}")
        self.proc = subprocess.Popen(
            cmd,
            env=env,
            cwd=os.path.dirname(os.path.dirname(os.path.abspath(__file__))),  # Set working directory to vllm-omni root
        )

        # Wait for server to be ready
        max_wait = 1200  # 20 minutes
        start_time = time.time()
        while time.time() - start_time < max_wait:
            try:
                with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
                    sock.settimeout(1)
                    result = sock.connect_ex((self.host, self.port))
                    if result == 0:
                        print(f"Server ready on {self.host}:{self.port}")
                        return
            except Exception:
                pass
            time.sleep(2)

        raise RuntimeError(f"Server failed to start within {max_wait} seconds")

    def _kill_process_tree(self, pid):
        """kill process and its children with verification"""
        try:
            parent = psutil.Process(pid)
            children = parent.children(recursive=True)

            # Get all PIDs first
            all_pids = [pid] + [child.pid for child in children]

            # Terminate children
            for child in children:
                try:
                    child.terminate()
                except psutil.NoSuchProcess:
                    pass

            # Wait for children
            gone, still_alive = psutil.wait_procs(children, timeout=10)

            # Kill remaining children
            for child in still_alive:
                try:
                    child.kill()
                except psutil.NoSuchProcess:
                    pass

            # Terminate parent
            try:
                parent.terminate()
                parent.wait(timeout=10)
            except (psutil.NoSuchProcess, psutil.TimeoutExpired):
                try:
                    parent.kill()
                except psutil.NoSuchProcess:
                    pass

            # VERIFICATION: Check if all processes are gone
            time.sleep(1)  # Give system time
            alive_processes = []
            for check_pid in all_pids:
                if psutil.pid_exists(check_pid):
                    alive_processes.append(check_pid)

            if alive_processes:
                print(f"Warning: Processes still alive: {alive_processes}")
                # Optional: Try system kill
                import subprocess

                for alive_pid in alive_processes:
                    try:
                        subprocess.run(["kill", "-9", str(alive_pid)], timeout=2)
                    except Exception as e:
                        print(f"Cleanup failed: {e}")

        except psutil.NoSuchProcess:
            pass

    def __enter__(self):
        self._start_server()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.proc:
            self._kill_process_tree(self.proc.pid)
        _run_pre_test_cleanup(enable_force=True)
        _run_post_test_cleanup(enable_force=True)
        cleanup_dist_env_and_memory()