conftest.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import contextlib
import pathlib
from copy import deepcopy

from tblib import pickling_support

# ruff: noqa

# Install support for pickling exceptions so that we can nicely propagate
# failures from tests running in a subprocess.
# This should be run before any custom exception subclasses are defined.
pickling_support.install()

import http.server
import json
import math
import mimetypes
import os
import socket
import tempfile
import threading
from collections.abc import Generator
from contextlib import nullcontext
from enum import Enum
from typing import Any, Callable, TypedDict, TypeVar, cast, TYPE_CHECKING, Optional

import numpy as np
import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import snapshot_download
from PIL import Image
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BatchEncoding,
    BatchFeature,
)
from transformers.models.auto.auto_factory import _BaseAutoModelClass

from tests.models.utils import (
    TokensTextLogprobs,
    TokensTextLogprobsPromptLogprobs,
    softmax,
)
from vllm import LLM, SamplingParams, envs
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.config.model import ConvertOption, RunnerOption, _get_and_verify_dtype
from vllm.connections import global_http_connection
from vllm.distributed import (
    cleanup_dist_env_and_memory,
    init_distributed_environment,
    initialize_model_parallel,
)
from vllm.logger import init_logger
from vllm.logprobs import Logprob
from vllm.multimodal.media import MediaWithBytes
from vllm.multimodal.utils import fetch_image
from vllm.outputs import RequestOutput
from vllm.sampling_params import BeamSearchParams
from vllm.transformers_utils.utils import maybe_model_redirect
from vllm.utils.collection_utils import is_list_of
from vllm.utils.torch_utils import set_default_torch_num_threads

from torch._inductor.utils import fresh_cache


if TYPE_CHECKING:
    from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
    from transformers.generation.utils import GenerateOutput


logger = init_logger(__name__)


@pytest.fixture
def sample_json_schema():
    return {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "skills": {
                "type": "array",
                "items": {
                    "type": "string",
                },
            },
            "grade": {
                "type": "string",
                "pattern": "^[A-D]$",
            },
            "email": {
                "type": "string",
                "pattern": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$",
            },
            "work_history": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "company": {"type": "string"},
                        "duration": {
                            "type": "number",
                            "minimum": 0.0,
                            "maximum": 100.0,
                        },
                        "position": {"type": "string"},
                    },
                    "required": ["company", "duration", "position"],
                    "additionalProperties": False,
                },
                "minItems": 0,
                "maxItems": 3,
            },
        },
        "required": ["name", "age", "skills", "grade", "email", "work_history"],
        "additionalProperties": False,
        "minProperties": 1,
        "maxProperties": 10,
    }


_TEST_DIR = os.path.dirname(__file__)
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
_SYS_MSG = os.path.join(_TEST_DIR, "system_messages", "sonnet3.5_nov2024.txt")

_M = TypeVar("_M")

_PromptMultiModalInput = list[_M] | list[list[_M]]

PromptImageInput = _PromptMultiModalInput[Image.Image]
PromptAudioInput = _PromptMultiModalInput[tuple[np.ndarray, int]]
PromptVideoInput = _PromptMultiModalInput[np.ndarray]


def _read_prompts(filename: str) -> list[str]:
    with open(filename) as f:
        prompts = f.readlines()
        return prompts


class ImageAssetPrompts(TypedDict):
    stop_sign: str
    cherry_blossom: str


class ImageTestAssets(list[ImageAsset]):
    def __init__(self) -> None:
        super().__init__(
            [
                ImageAsset("stop_sign"),
                ImageAsset("cherry_blossom"),
            ]
        )

    def prompts(self, prompts: ImageAssetPrompts) -> list[str]:
        """
        Convenience method to define the prompt for each test image.

        The order of the returned prompts matches the order of the
        assets when iterating through this object.
        """
        return [prompts["stop_sign"], prompts["cherry_blossom"]]


class VideoAssetPrompts(TypedDict):
    baby_reading: str


class VideoTestAssets(list[VideoAsset]):
    def __init__(self) -> None:
        super().__init__(
            [
                VideoAsset("baby_reading"),
            ]
        )

    def prompts(self, prompts: VideoAssetPrompts) -> list[str]:
        return [prompts["baby_reading"]]


class AudioAssetPrompts(TypedDict):
    mary_had_lamb: str
    winning_call: str


class AudioTestAssets(list[AudioAsset]):
    def __init__(self) -> None:
        super().__init__(
            [
                AudioAsset("mary_had_lamb"),
                AudioAsset("winning_call"),
            ]
        )

    def prompts(self, prompts: AudioAssetPrompts) -> list[str]:
        return [prompts["mary_had_lamb"], prompts["winning_call"]]


IMAGE_ASSETS = ImageTestAssets()
"""Singleton instance of {class}`ImageTestAssets`."""
VIDEO_ASSETS = VideoTestAssets()
"""Singleton instance of {class}`VideoTestAssets`."""
AUDIO_ASSETS = AudioTestAssets()
"""Singleton instance of {class}`AudioTestAssets`."""


@pytest.fixture(autouse=True)
def init_test_http_connection():
    # pytest_asyncio may use a different event loop per test
    # so we need to make sure the async client is created anew
    global_http_connection.reuse_client = False


@pytest.fixture
def dist_init():
    from tests.utils import ensure_current_vllm_config

    temp_file = tempfile.mkstemp()[1]

    with ensure_current_vllm_config():
        init_distributed_environment(
            world_size=1,
            rank=0,
            distributed_init_method=f"file://{temp_file}",
            local_rank=0,
            backend="nccl",
        )
        initialize_model_parallel(1, 1)
        yield
    cleanup_dist_env_and_memory()


@pytest.fixture
def default_vllm_config():
    """Set a default VllmConfig for tests that directly test CustomOps or pathways
    that use get_current_vllm_config() outside of a full engine context.
    """
    from vllm.config import VllmConfig, set_current_vllm_config

    with set_current_vllm_config(VllmConfig()):
        yield


@pytest.fixture()
def should_do_global_cleanup_after_test(request) -> bool:
    """Allow subdirectories to skip global cleanup by overriding this fixture.
    This can provide a ~10x speedup for non-GPU unit tests since they don't need
    to initialize torch.
    """

    return not request.node.get_closest_marker("skip_global_cleanup")


@pytest.fixture(autouse=True)
def cleanup_fixture(should_do_global_cleanup_after_test: bool):
    yield
    if should_do_global_cleanup_after_test:
        cleanup_dist_env_and_memory()


@pytest.fixture
def workspace_init():
    """Initialize the workspace manager for tests that need it.

    This fixture initializes the workspace manager with a CUDA device
    if available, and resets it after the test completes. Tests that
    create a full vLLM engine should NOT use this fixture as the engine
    will initialize the workspace manager itself.
    """
    from vllm.v1.worker.workspace import (
        init_workspace_manager,
        reset_workspace_manager,
    )

    if torch.cuda.is_available():
        device = torch.device("cuda:0")
        init_workspace_manager(device)
    yield
    reset_workspace_manager()


@pytest.fixture(autouse=True)
def dynamo_reset():
    yield
    torch._dynamo.reset()


@pytest.fixture
def example_prompts() -> list[str]:
    return [prompt for filename in _TEST_PROMPTS for prompt in _read_prompts(filename)]


@pytest.fixture
def example_system_message() -> str:
    with open(_SYS_MSG) as f:
        return f.read()


class DecoderPromptType(Enum):
    """For encoder/decoder models only."""

    CUSTOM = 1
    NONE = 2
    EMPTY_STR = 3


@pytest.fixture
def example_long_prompts() -> list[str]:
    return [prompt for filename in _LONG_PROMPTS for prompt in _read_prompts(filename)]


@pytest.fixture(scope="session")
def image_assets() -> ImageTestAssets:
    return IMAGE_ASSETS


@pytest.fixture(scope="session")
def video_assets() -> VideoTestAssets:
    return VIDEO_ASSETS


@pytest.fixture(scope="session")
def audio_assets() -> AudioTestAssets:
    return AUDIO_ASSETS


_T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict)
_R = TypeVar("_R")


class HfRunner:
    def get_default_device(self):
        from vllm.platforms import current_platform

        return "cpu" if current_platform.is_cpu() else current_platform.device_type

    def wrap_device(self, x: _T, device: str | None = None) -> _T:
        if x is None or isinstance(x, (bool,)):
            return x

        if device is None:
            device = self.device

        if isinstance(x, dict):
            return {k: self.wrap_device(v, device) for k, v in x.items()}

        if hasattr(x, "device") and x.device.type == device:
            return x

        return x.to(device)

    def __init__(
        self,
        model_name: str,
        dtype: str = "auto",
        *,
        model_kwargs: dict[str, Any] | None = None,
        trust_remote_code: bool = True,
        is_sentence_transformer: bool = False,
        is_cross_encoder: bool = False,
        skip_tokenizer_init: bool = False,
        auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
        # Set this to avoid hanging issue
        default_torch_num_threads: int | None = None,
    ) -> None:
        init_ctx = (
            nullcontext()
            if default_torch_num_threads is None
            else set_default_torch_num_threads(default_torch_num_threads)
        )

        with init_ctx:
            self._init(
                model_name=model_name,
                dtype=dtype,
                model_kwargs=model_kwargs,
                trust_remote_code=trust_remote_code,
                is_sentence_transformer=is_sentence_transformer,
                is_cross_encoder=is_cross_encoder,
                skip_tokenizer_init=skip_tokenizer_init,
                auto_cls=auto_cls,
            )

    def _init(
        self,
        model_name: str,
        dtype: str = "auto",
        *,
        model_kwargs: dict[str, Any] | None = None,
        trust_remote_code: bool = True,
        is_sentence_transformer: bool = False,
        is_cross_encoder: bool = False,
        skip_tokenizer_init: bool = False,
        auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
    ) -> None:
        model_name = maybe_model_redirect(model_name)
        self.model_name = model_name

        self.config = AutoConfig.from_pretrained(
            model_name,
            trust_remote_code=trust_remote_code,
        )
        self.device = self.get_default_device()
        self.dtype = dtype = _get_and_verify_dtype(
            self.model_name,
            self.config,
            dtype=dtype,
            is_pooling_model=is_sentence_transformer or is_cross_encoder,
            config_format="hf",
        )

        model_kwargs = model_kwargs if model_kwargs is not None else {}
        model_kwargs.setdefault("dtype", dtype)

        if is_sentence_transformer:
            # Lazy init required for AMD CI
            from sentence_transformers import SentenceTransformer

            self.model = SentenceTransformer(
                model_name,
                device=self.device,
                model_kwargs=model_kwargs,
                trust_remote_code=trust_remote_code,
            )
        elif is_cross_encoder:
            # Lazy init required for AMD CI
            from sentence_transformers import CrossEncoder

            self.model = CrossEncoder(
                model_name,
                device=self.device,
                automodel_args=model_kwargs,
                trust_remote_code=trust_remote_code,
            )
        else:
            model = cast(
                nn.Module,
                auto_cls.from_pretrained(
                    model_name,
                    trust_remote_code=trust_remote_code,
                    **model_kwargs,
                ),
            )

            # in case some unquantized custom models are not in same dtype
            if getattr(model, "quantization_method", None) is None and any(
                p.dtype != self.dtype for p in model.parameters()
            ):
                model = model.to(dtype=self.dtype)

            if (
                getattr(model, "quantization_method", None) != "bitsandbytes"
                and len({p.device for p in model.parameters()}) < 2
            ):
                model = model.to(device=self.device)

            self.model = model

        if not skip_tokenizer_init:
            self.tokenizer: "PreTrainedTokenizer | PreTrainedTokenizerFast" = (
                AutoTokenizer.from_pretrained(
                    model_name,
                    trust_remote_code=trust_remote_code,
                )
            )

        # don't put this import at the top level
        # it will call torch.accelerator.device_count()
        from transformers import AutoProcessor

        self.processor = AutoProcessor.from_pretrained(
            model_name,
            trust_remote_code=trust_remote_code,
        )
        if skip_tokenizer_init:
            self.tokenizer = self.processor.tokenizer

    def get_inputs(
        self,
        prompts: list[str] | list[list[int]],
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
    ) -> list[BatchFeature | BatchEncoding | dict[str, torch.Tensor]]:
        if images is not None:
            assert len(prompts) == len(images)

        if videos is not None:
            assert len(prompts) == len(videos)

        if audios is not None:
            assert len(prompts) == len(audios)

        all_inputs: list[BatchFeature | BatchEncoding | dict[str, torch.Tensor]] = []
        for i, prompt in enumerate(prompts):
            if isinstance(prompt, str):
                # Create a copy to avoid modifying the original dict
                processor_kwargs = (
                    tokenization_kwargs.copy()
                    if tokenization_kwargs is not None
                    else {}
                )
                processor_kwargs.update(
                    {
                        "text": prompt,
                        "return_tensors": "pt",
                    }
                )
                if images is not None and (image := images[i]) is not None:
                    processor_kwargs["images"] = image
                if videos is not None and (video := videos[i]) is not None:
                    processor_kwargs["videos"] = video
                if audios is not None and (audio_inputs := audios[i]) is not None:
                    # HACK - not all processors take sampling_rate; we should
                    # clean this up in the future.
                    if len(audio_inputs) == 2:
                        audio, sr = audio_inputs
                        processor_kwargs["audio"] = audio
                        processor_kwargs["sampling_rate"] = sr
                    else:
                        processor_kwargs["audio"] = audio_inputs

                inputs = self.processor(**processor_kwargs)
                if isinstance(inputs, BatchFeature):
                    inputs = inputs.to(dtype=self.dtype)
                all_inputs.append(inputs)
            else:
                # check that prompt is (batched) list of integers (token ids)
                if not is_list_of(prompt, typ=int, check="all"):
                    raise ValueError(
                        "Prompt must be a list of ints corresponding to the prompt token ids."
                    )
                # check that no multimodal input is provided
                if images or videos or audios:
                    raise ValueError(
                        "When providing prompt token ids multimodal inputs are not supported."
                    )
                input_dict = {
                    "input_ids": torch.tensor(prompt, dtype=torch.long).unsqueeze(0),
                }
                all_inputs.append(input_dict)

        return all_inputs

    def get_prompt_embeddings(self, prompts: list[str]) -> list[torch.Tensor]:
        all_inputs = self.get_inputs(prompts)
        embeddings = []
        for inputs in all_inputs:
            input_ids = self.wrap_device(inputs)["input_ids"]
            embedding = self.model.get_input_embeddings()(input_ids).squeeze(0)
            embeddings.append(embedding)
        return embeddings

    def classify(self, prompts: list[str]) -> list[list[float]]:
        # output is final logits
        all_inputs = self.get_inputs(prompts)
        outputs: list[list[float]] = []
        problem_type = getattr(self.config, "problem_type", "")

        for inputs in all_inputs:
            output = self.model(**self.wrap_device(inputs))

            assert isinstance(output.logits, torch.Tensor)

            if problem_type == "regression":
                logits = output.logits[0].tolist()
            elif problem_type == "multi_label_classification":
                logits = output.logits.sigmoid()[0].tolist()
            else:
                logits = softmax(output.logits)[0].tolist()
            outputs.append(logits)

        return outputs

    def generate(
        self,
        prompts: list[str] | list[list[int]],
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        **kwargs: Any,
    ) -> list[tuple[list[list[int]], list[str]]]:
        all_inputs = self.get_inputs(
            prompts, images=images, videos=videos, audios=audios
        )

        outputs: list[tuple[list[list[int]], list[str]]] = []
        for inputs in all_inputs:
            output_ids: torch.Tensor = self.model.generate(
                **self.wrap_device(inputs),
                use_cache=True,
                **kwargs,
            )
            output_str = self.processor.batch_decode(
                output_ids,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False,
            )
            outputs.append((output_ids.cpu().tolist(), output_str))
        return outputs

    def generate_greedy(
        self,
        prompts: list[str] | list[list[int]],
        max_tokens: int,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        **kwargs: Any,
    ) -> list[tuple[list[int], str]]:
        outputs = self.generate(
            prompts,
            do_sample=False,
            max_new_tokens=max_tokens,
            images=images,
            videos=videos,
            audios=audios,
            **kwargs,
        )

        return [(output_ids[0], output_str[0]) for output_ids, output_str in outputs]

    def generate_beam_search(
        self,
        prompts: list[str],
        beam_width: int,
        max_tokens: int,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
    ) -> list[tuple[list[list[int]], list[str]]]:
        outputs = self.generate(
            prompts,
            do_sample=False,
            max_new_tokens=max_tokens,
            num_beams=beam_width,
            num_return_sequences=beam_width,
            images=images,
            videos=videos,
            audios=audios,
        )

        for i in range(len(outputs)):
            output_ids, output_str = outputs[i]
            for j in range(len(output_ids)):
                output_ids[j] = [
                    x for x in output_ids[j] if x != self.tokenizer.pad_token_id
                ]
            outputs[i] = (output_ids, output_str)
        return outputs

    def generate_greedy_logprobs(
        self,
        prompts: list[str],
        max_tokens: int,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        **kwargs: Any,
    ) -> list[list[torch.Tensor]]:
        all_inputs = self.get_inputs(
            prompts, images=images, videos=videos, audios=audios
        )

        all_logprobs: list[list[torch.Tensor]] = []
        for inputs in all_inputs:
            output: "GenerateOutput" = self.model.generate(
                **self.wrap_device(inputs),
                use_cache=True,
                do_sample=False,
                max_new_tokens=max_tokens,
                output_hidden_states=True,
                return_dict_in_generate=True,
                **kwargs,
            )
            seq_logprobs = self._hidden_states_to_seq_logprobs(output.hidden_states)
            all_logprobs.append(seq_logprobs)
        return all_logprobs

    def _hidden_states_to_seq_logprobs(
        self,
        hidden_states: tuple[tuple[torch.Tensor, ...], ...],
    ) -> list[torch.Tensor]:
        output_embeddings = self.model.get_output_embeddings()

        seq_logprobs: list[torch.Tensor] = []
        for _, hidden_state in enumerate(hidden_states):
            last_hidden_states = hidden_state[-1][0]
            logits = torch.matmul(
                last_hidden_states.to(
                    device=output_embeddings.weight.device,
                    dtype=output_embeddings.weight.dtype,
                ),
                output_embeddings.weight.t(),
            )
            if getattr(output_embeddings, "bias", None) is not None:
                logits += output_embeddings.bias.unsqueeze(0)
            logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
            seq_logprobs.append(logprobs)

        return seq_logprobs

    def _hidden_states_to_logprobs(
        self,
        hidden_states: tuple[tuple[torch.Tensor, ...], ...],
        num_logprobs: int | None,
    ) -> tuple[list[dict[int, float]], int]:
        seq_logprobs = self._hidden_states_to_seq_logprobs(hidden_states)
        output_len = len(hidden_states)

        # convert to dict
        seq_logprobs_lst: list[dict[int, float]] = []
        for tok_idx, tok_logprobs in enumerate(seq_logprobs):
            # drop prompt logprobs
            if tok_idx == 0:
                tok_logprobs = tok_logprobs[-1, :].reshape(1, -1)
            topk = tok_logprobs.topk(num_logprobs)

            tok_logprobs_dct = {}
            for token_id, logprob in zip(topk.indices[0], topk.values[0]):
                tok_logprobs_dct[token_id.item()] = logprob.item()

            seq_logprobs_lst.append(tok_logprobs_dct)

        return (
            seq_logprobs_lst,
            output_len,
        )

    def generate_greedy_logprobs_limit(
        self,
        prompts: list[str],
        max_tokens: int,
        num_logprobs: int | None,
        images: PromptImageInput | None = None,
        audios: PromptAudioInput | None = None,
        videos: PromptVideoInput | None = None,
        use_cache: bool = True,
        **kwargs: Any,
    ) -> list[TokensTextLogprobs]:
        all_inputs = self.get_inputs(
            prompts, images=images, videos=videos, audios=audios
        )

        all_logprobs: list[list[dict[int, float]]] = []
        all_output_ids: list[list[int]] = []
        all_output_strs: list[str] = []

        for inputs in all_inputs:
            output: "GenerateOutput" = self.model.generate(
                **self.wrap_device(inputs),
                use_cache=use_cache,
                do_sample=False,
                max_new_tokens=max_tokens,
                output_hidden_states=True,
                return_dict_in_generate=True,
                **kwargs,
            )

            # Encoder-decoder models return decoder_hidden_states instead of
            # hidden_states
            hidden_states = (
                getattr(output, "hidden_states", None) or output.decoder_hidden_states
            )

            (
                seq_logprobs_lst,
                output_len,
            ) = self._hidden_states_to_logprobs(hidden_states, num_logprobs)

            all_logprobs.append(seq_logprobs_lst)
            seq_ids = output.sequences[0]
            output_len = len(seq_logprobs_lst)
            output_ids = seq_ids[-output_len:]
            all_output_ids.append(output_ids.tolist())
            all_output_strs.append(self.tokenizer.decode(output_ids))

        outputs = zip(all_output_ids, all_output_strs, all_logprobs)
        return [
            (output_ids, output_str, output_logprobs)
            for output_ids, output_str, output_logprobs in outputs
        ]

    def encode(self, prompts: list[str], *args, **kwargs) -> list[list[torch.Tensor]]:
        return self.model.encode(prompts, *args, **kwargs)

    def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
        return self.model.predict(prompts, *args, convert_to_tensor=True, **kwargs)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        del self.model
        cleanup_dist_env_and_memory()


@pytest.fixture(scope="session")
def hf_runner():
    return HfRunner


class VllmRunner:
    """
    The default value of some arguments have been modified from
    {class}`~vllm.LLM` as follows:

    - `trust_remote_code`: Set to `True` instead of `False` for convenience.
    - `seed`: Set to `0` instead of `None` for test reproducibility.
    - `max_model_len`: Set to `1024` instead of `None` to reduce memory usage.
    - `block_size`: To reduce memory usage, set default to `64` if on XPU
        devices, otherwise default to `16`.
    - `enable_chunked_prefill`: Set to `False` instead of `None` for
      test reproducibility.
    - `enforce_eager`: Set to `False` to test CUDA graph.
    """

    def __init__(
        self,
        model_name: str,
        runner: RunnerOption = "auto",
        convert: ConvertOption = "auto",
        tokenizer_name: str | None = None,
        tokenizer_mode: str = "auto",
        trust_remote_code: bool = True,
        seed: int = 0,
        max_model_len: int | None = 1024,
        dtype: str = "auto",
        disable_log_stats: bool = True,
        tensor_parallel_size: int = 1,
        block_size: int = 16 if not torch.xpu.is_available() else 64,
        enable_chunked_prefill: bool | None = False,
        enforce_eager: bool | None = False,
        # Set this to avoid hanging issue
        default_torch_num_threads: int | None = None,
        **kwargs,
    ) -> None:
        init_ctx = (
            nullcontext()
            if default_torch_num_threads is None
            else set_default_torch_num_threads(default_torch_num_threads)
        )

        if not kwargs.get("compilation_config", None):
            # Note(@tdoublep): This is set to 4 because some tests (e.g., hybrid
            # model tests) may set max_num_seqs=4. If min cudagraph_capture_size is
            # set to larger than max_num_seqs, then it will lead to *no* graphs
            # being captured which can trigger edge cases that we don't handle yet.
            kwargs["compilation_config"] = {"cudagraph_capture_sizes": [4]}

            # Make sure we have atleast one cudagraph large enough for a single decode.
            if (speculative_config := kwargs.get("speculative_config")) and (
                num_speculative_tokens := speculative_config["num_speculative_tokens"]
            ):
                kwargs["compilation_config"]["cudagraph_capture_sizes"].append(
                    num_speculative_tokens + 1
                )

        with init_ctx:
            self.llm = LLM(
                model=model_name,
                runner=runner,
                convert=convert,
                tokenizer=tokenizer_name,
                tokenizer_mode=tokenizer_mode,
                trust_remote_code=trust_remote_code,
                dtype=dtype,
                seed=seed,
                enforce_eager=enforce_eager,
                disable_log_stats=disable_log_stats,
                tensor_parallel_size=tensor_parallel_size,
                max_model_len=max_model_len,
                block_size=block_size,
                enable_chunked_prefill=enable_chunked_prefill,
                **kwargs,
            )

    def get_inputs(
        self,
        prompts: list[str]
        | list[torch.Tensor]
        | list[list[int]]
        | list[dict[str, Any]],
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
    ) -> list[dict[str, Any]]:
        if any(
            x is not None and len(x) != len(prompts) for x in [images, videos, audios]
        ):
            raise ValueError(
                "All non-None multimodal inputs must have the same length as prompts"
            )

        inputs = list[dict[str, Any]]()
        for i, prompt in enumerate(prompts):
            # If we're passing an encoder/decoder prompt, we assume it
            # already contains the multimodal data in the prompt
            if isinstance(prompt, dict):
                assert images is None and audios is None and videos is None
                inputs.append(prompt.copy())
            else:
                prompt_dict = dict[str, Any]()
                if isinstance(prompt, str):
                    prompt_dict["prompt"] = prompt
                elif isinstance(prompt, list):
                    prompt_dict["prompt_token_ids"] = prompt
                else:
                    prompt_dict["prompt_embeds"] = prompt

                multi_modal_data = dict[str, Any]()
                if images is not None and (image := images[i]) is not None:
                    multi_modal_data["image"] = image
                if videos is not None and (video := videos[i]) is not None:
                    multi_modal_data["video"] = video
                if audios is not None and (audio := audios[i]) is not None:
                    multi_modal_data["audio"] = audio

                if multi_modal_data:
                    prompt_dict["multi_modal_data"] = multi_modal_data

                inputs.append(prompt_dict)

        return inputs

    def generate(
        self,
        prompts: list[str] | list[torch.Tensor] | list[list[int]],
        sampling_params: SamplingParams,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        return_logprobs: bool = False,
        **kwargs: Any,
    ) -> list[tuple[list[list[int]], list[str]]] | tuple[list, list]:
        inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios)

        req_outputs = self.llm.generate(
            inputs, sampling_params=sampling_params, **kwargs
        )

        outputs: list[tuple[list[list[int]], list[str]]] = []
        logprobs = []
        for req_output in req_outputs:
            prompt_str = req_output.prompt
            prompt_ids = req_output.prompt_token_ids
            req_sample_output_ids: list[list[int]] = []
            req_sample_output_strs: list[str] = []
            req_logprobs = []
            for sample in req_output.outputs:
                output_str = sample.text
                output_ids = list(sample.token_ids)
                req_sample_output_ids.append(prompt_ids + output_ids)
                req_sample_output_strs.append((prompt_str or "") + output_str)
                if sample.logprobs:
                    req_logprobs.extend(sample.logprobs)
            outputs.append((req_sample_output_ids, req_sample_output_strs))
            logprobs.append(req_logprobs)
        return outputs if not return_logprobs else (outputs, logprobs)

    @staticmethod
    def _final_steps_generate_w_logprobs(
        req_outputs: list[RequestOutput],
        include_prompt_token_ids: bool = False,
    ) -> list[TokensTextLogprobsPromptLogprobs]:
        outputs: list[TokensTextLogprobsPromptLogprobs] = []
        for req_output in req_outputs:
            assert len(req_output.outputs) > 0
            for sample in req_output.outputs:
                output_str = sample.text
                output_ids = list(sample.token_ids)
                output_logprobs = sample.logprobs
            if include_prompt_token_ids:
                outputs.append(
                    (  # type: ignore[arg-type]
                        output_ids,
                        output_str,
                        output_logprobs,
                        req_output.prompt_token_ids,
                        req_output.prompt_logprobs,
                    )
                )
            else:
                outputs.append(
                    (
                        output_ids,
                        output_str,
                        output_logprobs,
                        req_output.prompt_logprobs,
                    )
                )

        return outputs

    def generate_w_logprobs(
        self,
        prompts: list[str],
        sampling_params: SamplingParams,
        images: PromptImageInput | None = None,
        audios: PromptAudioInput | None = None,
        videos: PromptVideoInput | None = None,
        include_prompt_token_ids: bool = False,
        **kwargs: Any,
    ) -> list[TokensTextLogprobs] | list[TokensTextLogprobsPromptLogprobs]:
        inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios)

        req_outputs = self.llm.generate(
            inputs, sampling_params=sampling_params, **kwargs
        )

        toks_str_logsprobs_prompt_logprobs = self._final_steps_generate_w_logprobs(
            req_outputs, include_prompt_token_ids
        )
        # Omit prompt logprobs if not required by sampling params
        return (
            [x[0:-1] for x in toks_str_logsprobs_prompt_logprobs]
            if sampling_params.prompt_logprobs is None
            else toks_str_logsprobs_prompt_logprobs
        )

    def generate_greedy(
        self,
        prompts: list[str] | list[torch.Tensor] | list[list[int]],
        max_tokens: int,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        **kwargs: Any,
    ) -> list[tuple[list[int], str]]:
        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
        outputs = self.generate(
            prompts,
            greedy_params,
            images=images,
            videos=videos,
            audios=audios,
            **kwargs,
        )
        return [(output_ids[0], output_str[0]) for output_ids, output_str in outputs]

    def generate_greedy_logprobs(
        self,
        prompts: list[str],
        max_tokens: int,
        num_logprobs: int | None,
        num_prompt_logprobs: int | None = None,
        images: PromptImageInput | None = None,
        audios: PromptAudioInput | None = None,
        videos: PromptVideoInput | None = None,
        stop_token_ids: list[int] | None = None,
        stop: list[str] | None = None,
        **kwargs: Any,
    ) -> list[TokensTextLogprobs] | list[TokensTextLogprobsPromptLogprobs]:
        greedy_logprobs_params = SamplingParams(
            temperature=0.0,
            max_tokens=max_tokens,
            logprobs=num_logprobs,
            prompt_logprobs=num_prompt_logprobs,
            stop_token_ids=stop_token_ids,
            stop=stop,
        )

        return self.generate_w_logprobs(
            prompts,
            greedy_logprobs_params,
            images=images,
            audios=audios,
            videos=videos,
            **kwargs,
        )

    def generate_prompt_perplexity(
        self, prompts: list[str], mask: Optional[list[str]] = None
    ) -> list[float]:
        """
        Return the perplexity score associated with generating the prompts

        :param prompts: list of prompts to score
        :return: perplexity score of each prompt
        """
        outputs = self.generate_greedy_logprobs(
            prompts, max_tokens=1, num_logprobs=None, num_prompt_logprobs=0
        )

        mask_prefix_lens = (
            [len(self.llm.get_tokenizer()(prefix)["input_ids"]) for prefix in mask]
            if mask is not None
            else [0 for _ in range(len(prompts))]
        )

        perplexities = []
        for output, mask_prefix_len in zip(outputs, mask_prefix_lens):
            output = cast(TokensTextLogprobsPromptLogprobs, output)
            token_datas = cast(list[dict[int, Logprob] | None], output[3])
            assert token_datas[0] is None

            token_log_probs = []
            for token_data in token_datas[mask_prefix_len + 1 :]:
                assert token_data is not None
                assert len(token_data) == 1
                token_log_prob = list(token_data.values())[0].logprob
                token_log_probs.append(token_log_prob)

            perplexity = math.exp(-sum(token_log_probs) / len(token_log_probs))
            perplexities.append(perplexity)

        return perplexities

    def generate_beam_search(
        self,
        prompts: list[str],
        beam_width: int,
        max_tokens: int,
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        concurrency_limit: int | None = None,
    ) -> list[tuple[list[list[int]], list[str]]]:
        inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios)

        outputs = self.llm.beam_search(
            inputs,
            BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens),
            concurrency_limit=concurrency_limit,
        )
        returned_outputs = []
        for output in outputs:
            token_ids = [x.tokens for x in output.sequences]
            texts = [x.text for x in output.sequences]
            returned_outputs.append((token_ids, texts))
        return returned_outputs

    def classify(self, prompts: list[str]) -> list[list[float]]:
        req_outputs = self.llm.classify(prompts)
        return [req_output.outputs.probs for req_output in req_outputs]

    def embed(
        self,
        prompts: list[str],
        images: PromptImageInput | None = None,
        videos: PromptVideoInput | None = None,
        audios: PromptAudioInput | None = None,
        *args,
        **kwargs,
    ) -> list[list[float]]:
        inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios)

        req_outputs = self.llm.embed(inputs, *args, **kwargs)
        return [req_output.outputs.embedding for req_output in req_outputs]

    def token_embed(self, prompts: list[str]) -> list[list[float]]:
        req_outputs = self.llm.encode(prompts, pooling_task="token_embed")
        return [req_output.outputs.data for req_output in req_outputs]

    def token_classify(self, prompts: list[str]) -> list[list[float]]:
        req_outputs = self.llm.encode(prompts, pooling_task="token_classify")
        return [req_output.outputs.data for req_output in req_outputs]

    def reward(self, prompts: list[str]) -> list[list[float]]:
        req_outputs = self.llm.reward(prompts)
        return [req_output.outputs.data for req_output in req_outputs]

    def score(
        self,
        text_1: list[str] | str,
        text_2: list[str] | str,
        *args,
        **kwargs,
    ) -> list[float]:
        req_outputs = self.llm.score(text_1, text_2, *args, **kwargs)
        return [req_output.outputs.score for req_output in req_outputs]

    def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:
        return self.llm.apply_model(func)

    def get_llm(self) -> LLM:
        return self.llm

    def collective_rpc(self, *args, **kwargs):
        return self.llm.collective_rpc(*args, **kwargs)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        # Explicitly shutdown the engine core to release GPU resources
        # This is needed because when executing consecutive tests, the GC
        # might not be fast enough in shutting down the llm engine. This can lead to OOMs
        # because when the next test starts some GPU memory is still in use.
        try:
            self.llm.llm_engine.engine_core.shutdown()
        except Exception:
            # Ignore shutdown errors as cleanup will still proceed
            pass
        del self.llm
        cleanup_dist_env_and_memory()


@pytest.fixture(scope="session")
def vllm_runner():
    return VllmRunner


@pytest.fixture()
def temporary_enable_log_propagate():
    import logging

    logger = logging.getLogger("vllm")
    logger.propagate = True
    yield
    logger.propagate = False


@pytest.fixture()
def caplog_vllm(temporary_enable_log_propagate, caplog):
    # To capture vllm log, we should enable propagate=True temporarily
    # because caplog depends on logs propagated to the root logger.
    yield caplog


@pytest.fixture()
def caplog_mp_fork():
    """
    This fixture enables capturing logs from a forked MP subprocess.
    It should be used in conjunction with caplog_vllm.

    By default, subprocess logs do not go through the parent process.
    We instead create a queue listener in the parent process which
    forwards logs to the logger's other handlers, and add a QueueHandler
    to the root logger. Forked subprocesses will inherit the root logger
    and pass their messages to the queue, which the listener will forward
    to the root logger, which can be captured by caplog.

    Note that this workaround only works for fork; with spawn, the subprocess
    reinitializes logging and does not automatically inherit the queue.
    We'd have to manually pass the queue to the subprocess at the spawn point.
    See caplog_mp_spawn below.
    """

    @contextlib.contextmanager
    def ctx():
        import logging.handlers
        import multiprocessing as mp

        logger_queue: mp.Queue[logging.LogRecord] = mp.Queue()
        logger = logging.getLogger()
        handlers = logger.handlers

        # The listener works on a background thread, not inherited by the child.
        queue_listener = logging.handlers.QueueListener(logger_queue, *handlers)
        queue_listener.start()

        # Add queue handler after creating the listener to avoid cycle
        logger.addHandler(logging.handlers.QueueHandler(logger_queue))
        yield
        queue_listener.stop()

    return ctx


class LogHolder:
    def __init__(self):
        self.text = None


@pytest.fixture()
def caplog_mp_spawn(tmp_path, monkeypatch):
    """
    This fixture enables capturing logs from a forked MP subprocess.
    It does not require caplog_vllm (but it only contains logs from the child).

    By default, subprocess logs do not go through the parent process.
    We instead add a FileHandler to the config so the spawned child process
    writes its logs to a temp file.
    In the parent, we read the file and return the contents.

    Note: this method could be extended to fork by either reconfiguring logging
    in the parent or using a SocketHandler:
    https://docs.python.org/3/howto/logging-cookbook.html#sending-and-receiving-logging-events-across-a-network # noqa: E501
    """

    @contextlib.contextmanager
    def ctx(level: int | str):
        from vllm.logger import DEFAULT_LOGGING_CONFIG

        config_path = tmp_path / "vllm_logging_config.json"
        log_path = tmp_path / "vllm.log"
        log_holder = LogHolder()

        config = deepcopy(DEFAULT_LOGGING_CONFIG)
        if envs.VLLM_LOGGING_CONFIG_PATH:
            path = pathlib.Path(envs.VLLM_LOGGING_CONFIG_PATH)
            assert path.exists()
            config = json.loads(path.read_text())

        config["loggers"]["vllm"]["handlers"] += ["vllm_file"]
        config["handlers"]["vllm_file"] = {
            "class": "logging.FileHandler",
            "formatter": "vllm",
            "level": level,
            "filename": log_path.as_posix(),
        }
        config["loggers"]["vllm"]["level"] = level

        config_path.write_text(json.dumps(config))

        with monkeypatch.context() as monkeypatch_ctx:
            monkeypatch_ctx.setenv("VLLM_LOGGING_CONFIG_PATH", config_path.as_posix())
            monkeypatch_ctx.setenv("VLLM_CONFIGURE_LOGGING", "1")
            yield log_holder

        log_holder.text = log_path.read_text()

    return ctx


@pytest.fixture(scope="session")
def num_gpus_available():
    """Get number of GPUs without initializing the CUDA context
    in current process."""

    from vllm.platforms import current_platform

    return current_platform.device_count()


temp_dir = tempfile.gettempdir()
_dummy_opt_path = os.path.join(temp_dir, "dummy_opt")
_dummy_llava_path = os.path.join(temp_dir, "dummy_llava")
_dummy_gemma2_embedding_path = os.path.join(temp_dir, "dummy_gemma2_embedding")


@pytest.fixture
def dummy_opt_path():
    json_path = os.path.join(_dummy_opt_path, "config.json")
    if not os.path.exists(_dummy_opt_path):
        snapshot_download(
            repo_id="facebook/opt-125m",
            local_dir=_dummy_opt_path,
            ignore_patterns=["*.bin", "*.bin.index.json", "*.pt", "*.h5", "*.msgpack"],
        )
        assert os.path.exists(json_path)
        with open(json_path) as f:
            config = json.load(f)
        config["architectures"] = ["MyOPTForCausalLM"]
        with open(json_path, "w") as f:
            json.dump(config, f)
    return _dummy_opt_path


@pytest.fixture
def dummy_llava_path():
    json_path = os.path.join(_dummy_llava_path, "config.json")
    if not os.path.exists(_dummy_llava_path):
        snapshot_download(
            repo_id="llava-hf/llava-1.5-7b-hf",
            local_dir=_dummy_llava_path,
            ignore_patterns=[
                "*.bin",
                "*.bin.index.json",
                "*.pt",
                "*.h5",
                "*.msgpack",
                "*.safetensors",
            ],
        )
        assert os.path.exists(json_path)
        with open(json_path) as f:
            config = json.load(f)
        config["architectures"] = ["MyLlava"]
        with open(json_path, "w") as f:
            json.dump(config, f)
    return _dummy_llava_path


@pytest.fixture
def dummy_gemma2_embedding_path():
    json_path = os.path.join(_dummy_gemma2_embedding_path, "config.json")
    if not os.path.exists(_dummy_gemma2_embedding_path):
        snapshot_download(
            repo_id="BAAI/bge-multilingual-gemma2",
            local_dir=_dummy_gemma2_embedding_path,
            ignore_patterns=[
                "*.bin",
                "*.bin.index.json",
                "*.pt",
                "*.h5",
                "*.msgpack",
                "*.safetensors",
            ],
        )
        assert os.path.exists(json_path)
        with open(json_path) as f:
            config = json.load(f)
        config["architectures"] = ["MyGemma2Embedding"]
        with open(json_path, "w") as f:
            json.dump(config, f)
    return _dummy_gemma2_embedding_path


# Add the flag `--optional` to allow run tests
# that are marked with @pytest.mark.optional
def pytest_addoption(parser):
    parser.addoption(
        "--optional", action="store_true", default=False, help="run optional test"
    )


def pytest_collection_modifyitems(config, items):
    if config.getoption("--optional"):
        # --optional given in cli: do not skip optional tests
        return
    skip_optional = pytest.mark.skip(reason="need --optional option to run")
    for item in items:
        if "optional" in item.keywords:
            item.add_marker(skip_optional)


@pytest.fixture(scope="session")
def cli_config_file():
    """Return the path to the CLI config file."""
    return os.path.join(_TEST_DIR, "config", "test_config.yaml")


@pytest.fixture(scope="session")
def cli_config_file_with_model():
    """Return the path to the CLI config file with model."""
    return os.path.join(_TEST_DIR, "config", "test_config_with_model.yaml")


class AssetHandler(http.server.BaseHTTPRequestHandler):
    # _IMAGE_CACHE : Dict[str, bytes] = {}

    def log_message(self, *args, **kwargs):
        pass

    def do_GET(self):
        # Accepts paths like: /1280px-Venn_diagram_rgb.jpg
        filename = self.path.lstrip("/")
        if not filename or "." not in filename:
            self.send_error(404, "Missing filename (expected /<name>.<ext>)")
            return

        base, ext = filename.rsplit(".", 1)
        ext = ext.lower()

        if ext not in ["jpg", "png"]:
            self.send_error(404, f"Unsupported extension: .{ext}")
            return

        try:
            data = ImageAsset(base).read_bytes(ext=ext)
        except Exception as e:
            self.send_error(500, f"Failed to load asset: {ext} {base} {e} ")
            return

        ctype, _ = mimetypes.guess_type(filename)
        if ctype is None:
            ctype = {"jpg": "image/jpg", "png": "image/png"}[ext]
        self.send_response(200)
        self.send_header("Content-Type", ctype)
        self.send_header("Content-Length", str(len(data)))
        self.end_headers()
        self.wfile.write(data)


def _find_free_port() -> int:
    with socket.socket() as s:
        s.bind(("127.0.0.1", 0))
        return s.getsockname()[1]


class LocalAssetServer:
    address: str
    port: int
    server: http.server.ThreadingHTTPServer | None
    thread: threading.Thread | None

    def __init__(self, address: str = "127.0.0.1") -> None:
        self.address = address
        self.port = -1
        self.server = None
        self.thread = None

    def __enter__(self):
        self.port = _find_free_port()
        self.server = http.server.ThreadingHTTPServer(
            (self.address, self.port), AssetHandler
        )
        self.thread = threading.Thread(target=self.server.serve_forever, daemon=True)
        self.thread.start()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        if self.server:
            self.server.shutdown()
            del self.server

        if self.thread:
            self.thread.join()
            del self.thread

        if exc_type is None:
            return None

        return False

    @property
    def base_url(self) -> str:
        assert self.port is not None
        return f"http://{self.address}:{self.port}"

    def url_for(self, name: str) -> str:
        """e.g., name='RGBA_comp.png' -> 'http://127.0.0.1:PORT/RGBA_comp.png'"""
        return f"{self.base_url}/{name}"

    def get_image_asset(self, name: str) -> Image.Image:
        image = fetch_image(self.url_for(name))
        # Unwrap MediaWithBytes if present
        if isinstance(image, MediaWithBytes):
            image = image.media
        return image


@pytest.fixture(scope="session")
def local_asset_server() -> Generator[LocalAssetServer, None, None]:
    """
    Starts a thread based HTTP server bound to 127.0.0.1 on a random free port.
    The server currently servers images at:
    http://127.0.0.1:<port>/<name>.<ext>
    """
    with LocalAssetServer() as srv:
        yield srv


@pytest.fixture
def image_url(request, local_asset_server) -> str:
    # request.param is one of the IMAGE_ASSETS filenames
    name = request.param
    return local_asset_server.url_for(name)


@pytest.fixture
def image_urls(request, local_asset_server) -> list[str]:
    """Indirect fixture: takes a list of names, returns list of full URLs."""
    names: list[str] = request.param
    return [local_asset_server.url_for(name) for name in names]


@pytest.fixture
def disable_deepgemm_ue8m0(monkeypatch):
    from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used

    with monkeypatch.context() as monkeypatch_ctx:
        monkeypatch_ctx.setenv("VLLM_USE_DEEP_GEMM_E8M0", "0")
        is_deep_gemm_e8m0_used.cache_clear()
        yield
        # Clear cache so the next time it is used it is processed with the
        # default VLLM_USE_DEEP_GEMM_E8M0  setting.
        is_deep_gemm_e8m0_used.cache_clear()


@pytest.fixture(autouse=True)
def clean_gpu_memory_between_tests():
    if os.getenv("VLLM_TEST_CLEAN_GPU_MEMORY", "0") != "1":
        yield
        return

    # Wait for GPU memory to be cleared before starting the test
    import gc

    from tests.utils import wait_for_gpu_memory_to_clear

    num_gpus = torch.accelerator.device_count()
    if num_gpus > 0:
        try:
            wait_for_gpu_memory_to_clear(
                devices=list(range(num_gpus)),
                threshold_ratio=0.1,
            )
        except ValueError as e:
            logger.info("Failed to clean GPU memory: %s", e)

    yield

    # Clean up GPU memory after the test
    if torch.cuda.is_available():
        torch.accelerator.empty_cache()
        gc.collect()


@pytest.fixture
def use_fresh_inductor_cache():
    """
    Use a fresh inductor cache for the test.
    This is useful to ensure that the test is not affected by the
    previous test calls.
    """
    with fresh_cache():
        yield


@pytest.fixture
def fresh_vllm_cache(monkeypatch, use_fresh_inductor_cache):
    """Temporary VLLM_CACHE_ROOT combined with a fresh inductor cache."""
    with tempfile.TemporaryDirectory() as tmp_dir:
        monkeypatch.setenv("VLLM_CACHE_ROOT", tmp_dir)
        yield tmp_dir


@pytest.fixture(scope="function")
def enable_pickle(monkeypatch):
    """`LLM.apply_model` requires pickling a function."""
    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")