def init_model_configs()

in optimum/exporters/openvino/model_configs.py [0:0]
79 lines of code
9 McCabe index (conditional complexity)

def init_model_configs():
    if "open_clip" not in TasksManager._LIBRARY_TO_SUPPORTED_MODEL_TYPES:
        TasksManager._LIBRARY_TO_SUPPORTED_MODEL_TYPES["open_clip"] = {}
    TasksManager._CUSTOM_CLASSES[("pt", "llava", "image-text-to-text")] = (
        "transformers",
        "LlavaForConditionalGeneration",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "llava-next", "image-text-to-text")] = (
        "transformers",
        "LlavaNextForConditionalGeneration",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "qwen2-vl", "image-text-to-text")] = (
        "transformers",
        "Qwen2VLForConditionalGeneration",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "qwen2-5-vl", "image-text-to-text")] = (
        "transformers",
        "AutoModelForImageTextToText",
    )

    TasksManager._CUSTOM_CLASSES[("pt", "llava-next-video", "image-text-to-text")] = (
        "transformers",
        "AutoModelForVision2Seq",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "gemma3", "image-text-to-text")] = (
        "transformers",
        "Gemma3ForConditionalGeneration",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "idefics3", "image-text-to-text")] = (
        "transformers",
        "AutoModelForImageTextToText",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "smolvlm", "image-text-to-text")] = (
        "transformers",
        "AutoModelForImageTextToText",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "phi4mm", "image-text-to-text")] = ("transformers", "AutoModelForCausalLM")
    TasksManager._CUSTOM_CLASSES[("pt", "phi4mm", "automatic-speech-recognition")] = (
        "transformers",
        "AutoModelForCausalLM",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "phi4-multimodal", "image-text-to-text")] = (
        "transformers",
        "AutoModelForCausalLM",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "phi4-multimodal", "automatic-speech-recognition")] = (
        "transformers",
        "AutoModelForCausalLM",
    )
    TasksManager._CUSTOM_CLASSES[("pt", "llama4", "image-text-to-text")] = (
        "transformers",
        "AutoModelForImageTextToText",
    )

    TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS[
        "image-text-to-text"
    ] = TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["text-generation"]

    TasksManager._TRANSFORMERS_TASKS_TO_MODEL_LOADERS["video-text-to-text"] = "AutoModelForVision2Seq"

    if is_diffusers_available() and "fill" not in TasksManager._DIFFUSERS_TASKS_TO_MODEL_LOADERS:
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_LOADERS["fill"] = "FluxFillPipeline"
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS["fill"] = {"flux": "FluxFillPipeline"}
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_LOADERS["text-to-image"] = ("AutoPipelineForText2Image", "SanaPipeline")
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS["text-to-image"]["sana"] = "SanaPipeline"
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS["text-to-image"]["sana-sprint"] = "SanaSprintPipeline"
    if is_diffusers_available() and "text-to-video" not in TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS:
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS["text-to-video"] = {}
        TasksManager._DIFFUSERS_TASKS_TO_MODEL_MAPPINGS["text-to-video"]["ltx-video"] = "LTXPipeline"

    supported_model_types = [
        "_SUPPORTED_MODEL_TYPE",
        "_DIFFUSERS_SUPPORTED_MODEL_TYPE",
        "_TIMM_SUPPORTED_MODEL_TYPE",
        "_SENTENCE_TRANSFORMERS_SUPPORTED_MODEL_TYPE",
    ]

    for supported_models_config in supported_model_types:
        supported_models = getattr(TasksManager, supported_models_config)
        for model, export_configs in supported_models.items():
            if "onnx" not in export_configs:
                continue
            onnx_config = export_configs["onnx"]
            supported_models[model]["openvino"] = deepcopy(onnx_config)

        setattr(TasksManager, supported_models_config, supported_models)