vllm-omni/tests/entrypoints/test_utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

"""Unit tests for vllm_omni.entrypoints.utils module."""

import logging
from collections import Counter
from dataclasses import dataclass
from types import SimpleNamespace

import pytest
import torch
from pytest_mock import MockerFixture
from vllm.sampling_params import RequestOutputKind, SamplingParams

from vllm_omni.config.composable_parallel import (
    Broadcast,
    FanInByStage,
    MeshAxisSpec,
    RouteByStage,
    StrategySpec,
    TakeRank,
)
from vllm_omni.config.pipeline_registry import OMNI_PIPELINES
from vllm_omni.config.resolver import (
    OmniConfigResolution,
    _convert_dataclasses_to_dict,
    _filter_dict_like_object,
    resolve_omni_config,
)
from vllm_omni.config.stage_config import PipelineConfig
from vllm_omni.diffusion.data import OmniDiffusionConfig
from vllm_omni.engine.arg_utils import OmniEngineArgs
from vllm_omni.entrypoints.utils import (
    coerce_param_message_types,
    filter_dataclass_kwargs,
)
from vllm_omni.model_executor.models.qwen3_omni.pipeline import QWEN3_OMNI_PIPELINE

pytestmark = [pytest.mark.core_model, pytest.mark.cpu]


class TestFilterDictLikeObject:
    """Test suite for _filter_dict_like_object function."""

    def test_simple_dict(self):
        """Test filtering a simple dictionary with no callables."""
        input_dict = {"key1": "value1", "key2": 42, "key3": [1, 2, 3]}
        result = _filter_dict_like_object(input_dict)

        assert result == input_dict
        assert isinstance(result, dict)

    def test_dict_with_nested_values(self):
        """Test filtering dict with nested dict and list values."""
        input_dict = {
            "level1": {
                "level2": {"key": "value"},
                "list": [1, 2, 3],
            },
            "simple": "string",
        }

        result = _filter_dict_like_object(input_dict)

        # Nested dicts and lists should be recursively processed
        assert result["simple"] == "string"
        assert isinstance(result["level1"], dict)

    def test_dict_with_dataclass_values(self):
        """Test filtering dict containing dataclass values."""

        @dataclass
        class TestDataclass:
            field1: str
            field2: int

        obj = TestDataclass(field1="test", field2=42)
        input_dict = {"data": obj, "normal": "value"}

        result = _filter_dict_like_object(input_dict)

        # Dataclass should be converted to dict by recursive _convert_dataclasses_to_dict
        assert "data" in result
        assert "normal" in result
        assert result["normal"] == "value"

    def test_dict_with_counter_values(self):
        """Test filtering dict containing Counter objects."""
        counter_obj = Counter({"a": 1, "b": 2})
        input_dict = {"counter": counter_obj, "normal": "value"}

        result = _filter_dict_like_object(input_dict)

        # Counter should be converted to regular dict
        assert "counter" in result
        assert "normal" in result
        assert result["normal"] == "value"

    def test_empty_dict(self):
        """Test filtering an empty dictionary."""
        result = _filter_dict_like_object({})
        assert result == {}
        assert isinstance(result, dict)

    def test_dict_with_set_values(self):
        """Test filtering dict with set values."""
        input_dict = {"set_key": {1, 2, 3}, "normal": "value"}

        result = _filter_dict_like_object(input_dict)

        assert "set_key" in result
        assert "normal" in result
        # Set should be converted to list by _convert_dataclasses_to_dict
        assert result["normal"] == "value"

    def test_dict_with_none_values(self):
        """Test filtering dict with None values."""
        input_dict = {"key1": None, "key2": "value", "key3": 0}

        result = _filter_dict_like_object(input_dict)

        assert result == input_dict

    def test_dict_with_mixed_types(self):
        """Test filtering dict with mixed value types."""
        input_dict = {
            "string": "hello",
            "int": 42,
            "float": 3.14,
            "bool": True,
            "none": None,
            "list": [1, 2, 3],
            "tuple": (1, 2, 3),
            "set": {1, 2, 3},
            "dict": {"nested": "value"},
        }

        result = _filter_dict_like_object(input_dict)

        assert "string" in result
        assert "int" in result
        assert "float" in result
        assert "bool" in result
        assert "none" in result
        assert "list" in result
        assert "tuple" in result
        assert "set" in result
        assert "dict" in result

    def test_dict_preserves_key_types(self):
        """Test that dict key types are preserved."""
        input_dict = {
            "string_key": "value1",
            42: "value2",
            (1, 2): "value3",  # tuple as key
        }

        result = _filter_dict_like_object(input_dict)

        # Keys should remain the same
        assert "string_key" in result
        assert 42 in result
        assert (1, 2) in result

    def test_dict_with_recursive_structure(self, mocker: MockerFixture):
        """Test filtering dict with recursive/complex nested structure."""
        input_dict = {
            "level1": {
                "level2": {
                    "level3": {"key": "value"},
                    "callable": lambda x: x,
                }
            },
            "normal": "value",
        }

        mocker.patch("vllm_omni.config.resolver.logger")
        result = _filter_dict_like_object(input_dict)

        # Normal key should exist
        assert "normal" in result
        # Level1 should exist
        assert "level1" in result

    def test_integration_with_convert_dataclasses(self, mocker: MockerFixture):
        """Test that _filter_dict_like_object integrates properly with _convert_dataclasses_to_dict."""

        @dataclass
        class Config:
            name: str
            count: int

        input_dict = {
            "config": Config(name="test", count=5),
            "func": lambda x: x,
            "normal": "value",
        }

        mocker.patch("vllm_omni.config.resolver.logger")
        result = _filter_dict_like_object(input_dict)

        # Callable should be filtered
        assert "func" not in result
        # Config should be converted to dict
        assert "config" in result
        assert "normal" in result


class TestConvertDataclassesToDict:
    """Test suite for _convert_dataclasses_to_dict function."""

    def test_uses_filter_dict_like_object(self, mocker: MockerFixture):
        """Test that _convert_dataclasses_to_dict uses _filter_dict_like_object for dicts."""
        input_dict = {
            "normal": "value",
            "callable": lambda x: x,
        }

        mocker.patch("vllm_omni.config.resolver.logger")
        result = _convert_dataclasses_to_dict(input_dict)

        # Callable should be filtered out by _filter_dict_like_object
        assert "normal" in result
        assert "callable" not in result


class TestFilterDataclassKwargs:
    """Test basic functionality of filter_dataclass_kwargs."""

    def test_simple_filtering(self):
        """Test basic dataclass kwargs filtering."""

        @dataclass
        class SimpleConfig:
            name: str
            count: int

        kwargs = {"name": "test", "count": 42, "invalid": "should_be_removed"}
        result = filter_dataclass_kwargs(SimpleConfig, kwargs)

        assert "name" in result
        assert "count" in result
        assert "invalid" not in result

    def test_invalid_dataclass_raises_error(self):
        """Test that non-dataclass raises ValueError."""
        with pytest.raises(ValueError, match="is not a dataclass"):
            filter_dataclass_kwargs(dict, {})

    def test_invalid_kwargs_type_raises_error(self):
        """Test that non-dict kwargs raises ValueError."""

        @dataclass
        class SimpleConfig:
            name: str

        with pytest.raises(ValueError, match="kwargs must be a dictionary"):
            filter_dataclass_kwargs(SimpleConfig, "invalid")

    def test_filters_omni_engine_args_unknown_fields(self, caplog):
        """Test that OmniEngineArgs kwargs are filtered to valid fields only,
        and that the WARNING contract fires for every drop — the affordance
        that lets a ``--stage-overrides`` typo (``{"0":{"kv_cache_dtpye":...}}``)
        surface in the log rather than being silently misapplied downstream.
        """
        kwargs = {
            "model": "dummy",
            "stage_id": 1,
            "engine_output_type": "image",
            "unknown_field": "drop_me",
        }

        with caplog.at_level(logging.WARNING, logger="vllm_omni.entrypoints.utils"):
            result = filter_dataclass_kwargs(OmniEngineArgs, kwargs)

        assert "model" in result
        assert "stage_id" in result
        assert "engine_output_type" in result
        assert "unknown_field" not in result
        assert any(rec.levelno == logging.WARNING and "unknown_field" in rec.message for rec in caplog.records), (
            f"expected WARNING naming 'unknown_field'; got {[r.message for r in caplog.records]}"
        )

    def test_filters_omni_diffusion_config_union_dataclass(self):
        """Test that OmniDiffusionConfig filters nested dataclass in Union fields."""
        kwargs = {
            "model": "dummy",
            "cache_config": {
                "rel_l1_thresh": 0.3,
                "extra_param": "should_drop",
            },
            "unknown_top": "drop_me",
        }

        result = filter_dataclass_kwargs(OmniDiffusionConfig, kwargs)

        assert "model" in result
        assert "cache_config" in result
        assert "unknown_top" not in result
        assert result["cache_config"]["rel_l1_thresh"] == 0.3
        assert "extra_param" not in result["cache_config"]


class TestResolveOmniConfig:
    @pytest.mark.parametrize("model_class_name", ["AnimaPipeline", "AnimaModularPipeline"])
    def test_native_anima_checkpoint_uses_default_diffusion_stage_without_model_config(
        self, tmp_path, mocker: MockerFixture, model_class_name: str
    ):
        checkpoint = tmp_path / "anima.safetensors"
        checkpoint.write_text("dummy")
        custom_args = {"components_path": "/tmp/anima-components"}
        mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_from_model",
            side_effect=AssertionError("native Anima checkpoints should not require model config discovery"),
        )

        resolved = resolve_omni_config(
            str(checkpoint),
            trust_remote_code=False,
            deploy_config_path=None,
            cli_overrides={"model_class_name": model_class_name, "custom_pipeline_args": custom_args},
            stage_overrides=None,
            strategy_config_path=None,
        )

        assert resolved.config_path is None
        assert len(resolved.stage_configs) == 1
        stage_config = resolved.stage_configs[0]
        assert stage_config.model_stage == "diffusion"
        assert stage_config.diffusion_config.model_class_name == "AnimaPipeline"
        assert stage_config.diffusion_config.custom_pipeline_args == custom_args

    @pytest.mark.parametrize(
        ("model_class_name", "checkpoint_exists", "deploy_config_path"),
        [
            ("AnimaPipeline", True, "deploy.yaml"),
            ("AnimaPipeline", False, None),
            ("FluxPipeline", True, None),
        ],
    )
    def test_native_single_file_fallback_preserves_config_discovery(
        self, tmp_path, mocker: MockerFixture, model_class_name, checkpoint_exists, deploy_config_path
    ):
        checkpoint = tmp_path / "model.safetensors"
        if checkpoint_exists:
            checkpoint.write_text("dummy")
        create_from_model = mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_from_model", return_value=None
        )

        resolved = resolve_omni_config(
            str(checkpoint),
            trust_remote_code=False,
            deploy_config_path=deploy_config_path,
            cli_overrides={"model_class_name": model_class_name},
            stage_overrides=None,
            strategy_config_path=None,
        )

        create_from_model.assert_called_once_with(
            str(checkpoint),
            trust_remote_code=False,
            cli_overrides={"model_class_name": model_class_name, "trust_remote_code": False},
            deploy_config_path=deploy_config_path,
            strategy_specs=None,
        )
        assert resolved.config_path == deploy_config_path

    def test_bare_deploy_name_returns_packaged_resolved_path(self, tmp_path, mocker: MockerFixture):
        deploy_name = "qwen3_omni_moe.yaml"
        deploy_path = tmp_path / deploy_name
        deploy_path.write_text(
            "duplex_session:\n  server_vad_model_path: /models/silero_vad.onnx\nstages: []\n",
            encoding="utf-8",
        )
        mocker.patch("vllm_omni.config.config_factory._DEPLOY_DIR", tmp_path)
        mocker.patch("vllm_omni.config.omni_config._DEPLOY_DIR", tmp_path)
        mocker.patch(
            "vllm_omni.config.config_factory.StageConfigFactory.get_pipeline_config",
            return_value=QWEN3_OMNI_PIPELINE,
        )

        resolved = resolve_omni_config(
            "dummy-model",
            trust_remote_code=False,
            deploy_config_path=deploy_name,
            cli_overrides={},
            stage_overrides=None,
            strategy_config_path=None,
        )

        assert resolved.config_path == str(deploy_path)

    def test_stage_lookup_error_lists_resolved_ids(self):
        resolved = OmniConfigResolution(
            config_path=None,
            stage_configs=(SimpleNamespace(stage_id=1), SimpleNamespace(stage_id=3)),
        )

        with pytest.raises(KeyError, match=r"no stage 2; resolved stages: \[1, 3\]"):
            resolved.stage_by_id(2)

    def test_load_and_resolve_with_kwargs(self, mocker: MockerFixture):
        """Ensure that generic diffusion overrides survive resolution."""
        engine_backend = "vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine"
        mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_from_model",
            return_value=None,
        )
        mocker.patch(
            "vllm_omni.config.resolver._resolve_generic_diffusion_model_class",
            return_value=(True, "FluxPipeline"),
        )
        kwargs = {
            "dtype": torch.float32,
            "engine_backend": engine_backend,
            "revision": "pinned-revision",
        }
        resolved = resolve_omni_config(
            "black-forest-labs/FLUX.2-klein-4B",
            trust_remote_code=False,
            deploy_config_path=None,
            cli_overrides=kwargs,
            stage_overrides=None,
            strategy_config_path=None,
        )
        assert resolved.config_path is None
        assert len(resolved.stage_configs) == 1
        assert resolved.stage_configs[0].diffusion_config.dtype is torch.float32
        assert resolved.stage_configs[0].diffusion_config.engine_backend == engine_backend
        assert resolved.stage_configs[0].diffusion_config.revision == "pinned-revision"

    def test_generic_diffusion_uses_registered_model_metadata(self, mocker: MockerFixture):
        mocker.patch("vllm_omni.config.resolver.StageConfigFactory.create_from_model", return_value=None)
        resolve_model_class = mocker.patch(
            "vllm_omni.config.resolver.resolve_model_class_name",
            return_value="WanImageToVideoPipeline",
        )
        mocker.patch(
            "vllm_omni.config.resolver.DiffusionModelRegistry.get_supported_archs",
            return_value={"WanImageToVideoPipeline"},
        )
        load_model_class = mocker.patch("vllm_omni.config.resolver.DiffusionModelRegistry._try_load_model_cls")

        resolved = resolve_omni_config(
            "/models/Wan2.2-I2V",
            trust_remote_code=False,
            deploy_config_path=None,
            cli_overrides={"diffusion_load_format": "diffusers", "revision": "pinned-revision"},
            stage_overrides=None,
            strategy_config_path=None,
        )

        resolve_model_class.assert_called_once_with("/models/Wan2.2-I2V", "diffusers", "pinned-revision")
        load_model_class.assert_not_called()
        stage = resolved.stage_configs[0]
        assert stage.diffusion_config.model_class_name == "WanImageToVideoPipeline"
        assert stage.diffusion_config.revision == "pinned-revision"
        assert stage.final_output_type == "video"

    def test_generic_diffusion_stage_overrides_reach_typed_backend(self, mocker: MockerFixture):
        from vllm_omni.engine.stage_init_utils import build_engine_args_dict_from_omni_stage_config

        mocker.patch("vllm_omni.config.resolver.StageConfigFactory.create_from_model", return_value=None)
        mocker.patch(
            "vllm_omni.config.resolver._resolve_generic_diffusion_model_class",
            return_value=(True, "LTX2Pipeline"),
        )
        resolved = resolve_omni_config(
            "/models/LTX-2.5-Diffusers",
            trust_remote_code=False,
            deploy_config_path=None,
            cli_overrides={
                "extras": {"ltx2_use_conv_vae": False, "keep": "global"},
                "tensor_parallel_size": 1,
                "diffusion_streaming_output": True,
            },
            stage_overrides={
                "0": {
                    "extras": {"ltx2_use_conv_vae": True},
                    "tensor_parallel_size": 2,
                    "devices": "2,3",
                    "engine_backend": "custom.backend",
                },
            },
            strategy_config_path=None,
        )
        stage = resolved.stage_by_id(0)
        engine_args = build_engine_args_dict_from_omni_stage_config(stage, model="/models/LTX-2.5-Diffusers")

        assert stage.runtime_config.devices == "2,3"
        assert stage.parallel_config.world_size == 2
        assert engine_args["engine_backend"] == "custom.backend"
        assert engine_args["extras"]["ltx2_use_conv_vae"] is True
        assert engine_args["extras"]["keep"] == "global"
        assert engine_args["streaming_output"] is True

    def test_registered_pipeline_uses_structured_metadata_and_preserves_override_trust(self, mocker: MockerFixture):
        endpoint_restriction = SimpleNamespace(name="chat")
        typed_stage = SimpleNamespace(stage_id=1)
        structured_config = SimpleNamespace(
            orchestrator_config=SimpleNamespace(
                deploy_config_path="/resolved/deploy.yaml",
                omni_lb_policy="round_robin",
            ),
            pipeline_config=SimpleNamespace(endpoint_restrictions=(endpoint_restriction,)),
            stage_configs=(typed_stage,),
            strategy_omni_lb_policy="round-robin",
        )
        create_structured = mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_from_model",
            return_value=structured_config,
        )
        create_legacy = mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_legacy_stage_configs_from_model",
        )
        strategy_specs = {
            "stage_1": [
                StrategySpec(
                    "stage_replica",
                    MeshAxisSpec("stage_replica", 2),
                    RouteByStage("round_robin"),
                    FanInByStage(),
                )
            ]
        }
        load_strategy = mocker.patch(
            "vllm_omni.config.resolver._load_strategy_specs",
            return_value=strategy_specs,
        )
        apply_strategy = mocker.patch(
            "vllm_omni.config.composable_parallel.apply_strategy_specs",
            return_value=SimpleNamespace(omni_lb_policy="round_robin"),
        )

        resolved = resolve_omni_config(
            "dummy-model",
            trust_remote_code=None,
            deploy_config_path="deploy.yaml",
            cli_overrides={"dtype": "bfloat16", "trust_remote_code": True},
            stage_overrides={"1": {"tensor_parallel_size": 2}},
            strategy_config_path="strategy.yaml",
        )

        expected_overrides = {
            "dtype": "bfloat16",
            "trust_remote_code": True,
            "stage_1_tensor_parallel_size": 2,
        }
        create_structured.assert_called_once_with(
            "dummy-model",
            trust_remote_code=None,
            cli_overrides=expected_overrides,
            deploy_config_path="deploy.yaml",
            strategy_specs=strategy_specs,
        )
        create_legacy.assert_not_called()
        load_strategy.assert_called_once_with("strategy.yaml")
        apply_strategy.assert_not_called()
        assert resolved.config_path == "/resolved/deploy.yaml"
        assert resolved.pipeline_config is structured_config.pipeline_config
        assert resolved.omni_lb_policy == "round-robin"
        assert resolved.endpoint_restrictions == (endpoint_restriction,)
        assert resolved.stage_configs == (typed_stage,)

    def test_tp_only_strategy_does_not_report_default_lb_policy_as_derived(self, mocker: MockerFixture):
        structured_config = SimpleNamespace(
            orchestrator_config=SimpleNamespace(
                deploy_config_path="/resolved/deploy.yaml",
                omni_lb_policy="round-robin",
            ),
            pipeline_config=SimpleNamespace(endpoint_restrictions=()),
            stage_configs=(SimpleNamespace(stage_id=0),),
            strategy_omni_lb_policy=None,
        )
        mocker.patch(
            "vllm_omni.config.resolver.StageConfigFactory.create_from_model",
            return_value=structured_config,
        )
        mocker.patch(
            "vllm_omni.config.resolver._load_strategy_specs",
            return_value={
                "thinker": [
                    StrategySpec(
                        "tp",
                        MeshAxisSpec("tp", 2),
                        Broadcast(),
                        TakeRank(),
                    )
                ]
            },
        )

        resolved = resolve_omni_config(
            "dummy-model",
            trust_remote_code=True,
            deploy_config_path="deploy.yaml",
            cli_overrides={"omni_lb_policy": "round-robin"},
            stage_overrides=None,
            strategy_config_path="strategy.yaml",
        )

        assert resolved.omni_lb_policy is None

    def test_registered_resolution_exposes_forced_aligner_topology(self, mocker: MockerFixture):
        pipeline = OMNI_PIPELINES["qwen3_tts"]
        assert isinstance(pipeline, PipelineConfig)
        mocker.patch(
            "vllm_omni.config.config_factory.StageConfigFactory.get_pipeline_config",
            return_value=pipeline,
        )

        resolved = resolve_omni_config(
            "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
            trust_remote_code=False,
            deploy_config_path=None,
            cli_overrides={"forced_aligner": "/models/Qwen3-ForcedAligner-0.6B"},
            stage_overrides=None,
            strategy_config_path=None,
        )

        assert resolved.pipeline_config is not None
        assert [stage.stage_id for stage in resolved.pipeline_config.stages] == [
            stage.stage_id for stage in resolved.stage_configs
        ]
        assert resolved.pipeline_config.stages[-1].model_stage == "forced_aligner"
        assert len(resolved.pipeline_config.stages) == len(pipeline.stages) + 1


class TestCumulativeStreamingCoercion:
    @pytest.mark.parametrize("skip_clone", [True, False])
    def test_cumulative_default_becomes_delta_if_stream(self, skip_clone):
        """Ensure cumulative messages are coercible to delta if streaming."""
        sp = SamplingParams(output_kind=RequestOutputKind.CUMULATIVE)
        sp.skip_clone = skip_clone
        result = coerce_param_message_types([sp], is_streaming=True)[0]
        assert isinstance(result, SamplingParams)
        assert result.output_kind == RequestOutputKind.DELTA
        assert (skip_clone and sp is result) or (not skip_clone and sp is not result)

    @pytest.mark.parametrize("skip_clone", [True, False])
    def test_cumulative_default_becomes_final_only_if_not_stream(self, skip_clone):
        """Ensure cumulative messages are coercible to final only if not streaming."""
        sp = SamplingParams(output_kind=RequestOutputKind.CUMULATIVE)
        sp.skip_clone = skip_clone
        result = coerce_param_message_types([sp], is_streaming=False)[0]
        assert isinstance(result, SamplingParams)
        assert result.output_kind == RequestOutputKind.FINAL_ONLY
        assert (skip_clone and sp is result) or (not skip_clone and sp is not result)

    @pytest.mark.parametrize("is_streaming", [True, False])
    @pytest.mark.parametrize("output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
    def test_non_cumulative_are_coerced(self, output_kind, is_streaming):
        """Ensure non-cumulative params are coerced to the target type."""
        sp = SamplingParams(output_kind=output_kind)
        expected = RequestOutputKind.DELTA if is_streaming else RequestOutputKind.FINAL_ONLY
        result = coerce_param_message_types([sp], is_streaming=is_streaming)[0]
        assert isinstance(result, SamplingParams)
        assert result.output_kind == expected

    def test_coercion_applies_to_all_stages(self):
        """Ensure all stages are coerced to DELTA for streaming."""
        sp0 = SamplingParams(output_kind=RequestOutputKind.CUMULATIVE)
        sp1 = SamplingParams(output_kind=RequestOutputKind.CUMULATIVE)
        result = coerce_param_message_types([sp0, sp1], is_streaming=True)
        assert all([isinstance(r, SamplingParams) for r in result])
        assert result[0].output_kind == RequestOutputKind.DELTA
        assert result[1].output_kind == RequestOutputKind.DELTA


@pytest.mark.parametrize("api_server_count", [1, 2])
def test_prepare_stage_config_inputs_filters_frontend_options(api_server_count):
    from vllm_omni.entrypoints.utils import prepare_stage_config_inputs

    kwargs = {
        "api_server_count": api_server_count,
        "disable_log_stats": True,
        "model_tag": "example",
        "max_num_seqs": 8,
        "stage_overrides": '{"0": {"max_num_seqs": 4}}',
    }
    inputs = prepare_stage_config_inputs("example", kwargs, trust_remote_code=None)

    assert inputs.kwargs == {"max_num_seqs": 8}
    assert inputs.stage_overrides == {"0": {"max_num_seqs": 4}}
    assert kwargs["api_server_count"] == api_server_count