import pathlib

import keras
import pytest

from deep_learning.models.spec import ModelArtifact
from deep_learning.pipeline.services.model_loader import load_inference_artifact


def _create_model() -> keras.Model:
    inputs = keras.Input(shape=(3,))
    outputs = keras.layers.Dense(1)(inputs)
    return keras.Model(inputs, outputs, name="demo_model")


def test_load_deployment_inference_artifact_loads_keras_model(tmp_path):
    """验证部署加载工具会读取完整 .keras 模型并返回包装后的推理产物。"""
    model_path = tmp_path / "model_epoch_001.keras"
    _create_model().save(model_path)
    resource = object()

    def load_model_artifact(path):
        assert path == model_path
        return ModelArtifact(model=keras.models.load_model(str(path)))

    artifact, loaded_resource = load_inference_artifact(
        checkpoint_rule={
            "path": model_path
        },
        load_inference_artifact_fn=load_model_artifact,
        resource_factory=lambda: resource
    )

    assert artifact.model.name == "demo_model"
    assert loaded_resource is resource


def test_load_deployment_inference_artifact_requires_existing_model(tmp_path):
    """验证找不到部署模型时会报出查找信息。"""
    with pytest.raises(FileNotFoundError, match="未找到任何部署模型文件"):
        load_inference_artifact(
            checkpoint_rule={
                "dirs": [tmp_path]
            },
            load_inference_artifact_fn=lambda path: ModelArtifact(model=object())
        )


def test_load_deployment_inference_artifact_rejects_weights_file(tmp_path):
    """验证部署推理只接受完整 .keras 模型。"""
    weights_path = tmp_path / "model_epoch_001.weights.h5"
    weights_path.write_text("stub", encoding="utf-8")

    with pytest.raises(ValueError, match="部署推理只支持完整 .keras 模型"):
        load_inference_artifact(
            checkpoint_rule={
                "path": pathlib.Path(weights_path)
            },
            load_inference_artifact_fn=lambda path: ModelArtifact(model=object())
        )