"""SimCLR 自监督：损失的数学性质 + 聚类脚本的 simclr 分支端到端。"""

import sys

import pytest

torch = pytest.importorskip("torch")
pytest.importorskip("PIL")

import numpy as np  # noqa: E402
from PIL import Image  # noqa: E402

from engine.simclr import SimCLRTrainer, nt_xent_loss  # noqa: E402
from models.heads.projection import ProjectionHead  # noqa: E402
from models.registry import BACKBONES  # noqa: E402


def test_nt_xent_is_minimal_when_views_agree():
    """两个视图完全一致且各样本互相正交时，损失应接近理论下界 log(2B−1) 的反面——
    即正样本对独占概率质量，损失趋近 0。"""
    z = torch.eye(4)  # 4 个互相正交的样本
    aligned = nt_xent_loss(z, z.clone(), temperature=0.05)
    misaligned = nt_xent_loss(z, z.roll(1, dims=0), temperature=0.05)

    assert aligned < 0.1
    assert misaligned > aligned


def test_nt_xent_upper_bound_at_chance_level():
    """所有表征相同时无法区分正负样本，损失 = log(2B−1)。"""
    z = torch.ones(8, 16)
    loss = float(nt_xent_loss(z, z.clone(), temperature=0.2))
    assert loss == pytest.approx(float(torch.log(torch.tensor(2 * 8 - 1))), abs=1e-4)


def test_nt_xent_is_symmetric_and_temperature_sensitive():
    torch.manual_seed(0)
    z1, z2 = torch.randn(6, 8), torch.randn(6, 8)
    assert float(nt_xent_loss(z1, z2, 0.2)) == pytest.approx(float(nt_xent_loss(z2, z1, 0.2)), abs=1e-5)
    # 温度越低，随机表征的损失越大（softmax 更尖，惩罚更重）
    assert nt_xent_loss(z1, z2, 0.05) > nt_xent_loss(z1, z2, 0.5)


def test_nt_xent_gradient_pulls_positive_pair_together():
    torch.manual_seed(0)
    z1 = torch.randn(4, 8, requires_grad=True)
    z2 = torch.randn(4, 8, requires_grad=True)
    before = float(torch.nn.functional.cosine_similarity(z1, z2).mean().detach())

    optimizer = torch.optim.SGD([z1, z2], lr=1.0)
    for _ in range(20):
        optimizer.zero_grad()
        nt_xent_loss(z1, z2, 0.2).backward()
        optimizer.step()

    after = float(torch.nn.functional.cosine_similarity(z1, z2).mean().detach())
    assert after > before


def test_projection_head_accepts_feature_map_and_vector():
    head = ProjectionHead(16, hidden_channels=32, out_channels=8)
    assert head(torch.randn(4, 16, 5, 5)).shape == (4, 8)
    assert head(torch.randn(4, 16)).shape == (4, 8)


@pytest.fixture
def tiny_image_dir(tmp_path):
    root = tmp_path / "imgs"
    root.mkdir()
    rng = np.random.default_rng(0)
    for i in range(8):
        Image.fromarray(rng.integers(0, 255, (40, 60, 3), dtype=np.uint8)).save(root / f"{i}.png")
    return root


def test_simclr_trainer_runs_and_saves_backbone_only(tiny_image_dir, tmp_path):
    from torch.utils.data import DataLoader

    from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate

    dataset = UnlabeledImageDataset(
        tiny_image_dir,
        transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
        views=2,
        view_transforms=[{"name": "RandomResizedCrop", "size": [32, 32], "scale": [0.5, 1.0]}],
    )
    loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, collate_fn=unlabeled_collate)
    backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})

    trainer = SimCLRTrainer(
        backbone,
        loader,
        {"epochs": 2, "amp": False, "log_interval": 100, "projection": {"hidden_channels": 16,
                                                                        "out_channels": 8}},
        torch.device("cpu"),
        tmp_path / "run",
    )
    path = trainer.fit()

    assert path.exists()
    state = torch.load(path, map_location="cpu", weights_only=False)
    # 只存骨架，不含投影头
    assert all("projector" not in key and "mlp" not in key for key in state["model"])
    backbone.load_state_dict(state["model"])  # 能被干净地加载回去


def test_simclr_logs_metrics_to_swanlab(tiny_image_dir, tmp_path, monkeypatch):
    """SwanLab 接线：按 step 记 loss/lr，按 epoch 记平均 loss、耗时与显存峰值，结束时收尾。"""
    from torch.utils.data import DataLoader

    import engine.simclr as simclr_mod
    from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate

    records: list[tuple[dict, int | None, str]] = []
    finished: list[bool] = []

    class StubLogger:
        def __init__(self, **kwargs):
            self.kwargs = kwargs

        def log(self, metrics, step=None, prefix=""):
            records.append((metrics, step, prefix))

        def finish(self):
            finished.append(True)

    monkeypatch.setattr(simclr_mod, "SwanLabLogger", StubLogger)

    dataset = UnlabeledImageDataset(
        tiny_image_dir,
        transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
        views=2,
    )
    loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, collate_fn=unlabeled_collate)
    backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})

    trainer = simclr_mod.SimCLRTrainer(
        backbone,
        loader,
        {"epochs": 1, "amp": False, "log_interval": 1, "save_interval": 0,
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": True, "mode": "disabled"}},
        torch.device("cpu"),
        tmp_path / "run",
        full_config={"method": "simclr"},
    )
    trainer.fit()

    assert records, "没有任何指标被记录"
    assert all(prefix == "simclr/" for _, _, prefix in records)
    step_keys = {k for metrics, _, _ in records for k in metrics}
    assert {"loss", "loss_avg", "lr"} <= step_keys
    assert {"epoch", "epoch_loss", "epoch_seconds", "gpu_mem_peak_mb"} <= step_keys
    assert all(step is not None and step > 0 for _, step, _ in records)
    assert finished == [True]  # fit() 结束时调用了 finish()

    # 完整 yaml 配置作为超参传给了 SwanLab
    assert trainer.visualizer.kwargs["config"] == {"method": "simclr"}
    assert trainer.visualizer.kwargs["mode"] == "disabled"


def test_simclr_saves_intermediate_checkpoints(tiny_image_dir, tmp_path):
    """中途落盘：跑到一半被打断也要能拿到权重（此前只在全部 epoch 跑完后才存）。"""
    from torch.utils.data import DataLoader

    from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate

    dataset = UnlabeledImageDataset(
        tiny_image_dir,
        transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
        views=2,
    )
    loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, collate_fn=unlabeled_collate)
    backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})
    work_dir = tmp_path / "run"

    trainer = SimCLRTrainer(
        backbone,
        loader,
        {"epochs": 4, "amp": False, "log_interval": 100, "save_interval": 2,
         "projection": {"hidden_channels": 16, "out_channels": 8}},
        torch.device("cpu"),
        work_dir,
    )
    # 手动走前两个 epoch，模拟"训练中途"
    trainer.train_one_epoch(0)
    trainer.train_one_epoch(1)
    path = trainer.save_backbone(1)

    assert path.exists()
    assert torch.load(path, map_location="cpu", weights_only=False)["epochs"] == 2

    trainer.fit()  # 完整跑完后 epochs 应更新
    assert torch.load(path, map_location="cpu", weights_only=False)["epochs"] == 4


def _make_trainer(tiny_image_dir, work_dir, epochs=4, **cfg_extra):
    from torch.utils.data import DataLoader

    from datasets.unlabeled import UnlabeledImageDataset, unlabeled_collate

    dataset = UnlabeledImageDataset(
        tiny_image_dir,
        transforms=[{"name": "Resize", "size": [32, 32]}, {"name": "ToTensor"}],
        views=2,
    )
    loader = DataLoader(dataset, batch_size=4, shuffle=False, drop_last=True, collate_fn=unlabeled_collate)
    torch.manual_seed(0)
    backbone = BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]})
    cfg = {
        "epochs": epochs,
        "amp": False,
        "log_interval": 100,
        "save_interval": 1,
        "projection": {"hidden_channels": 16, "out_channels": 8},
        "scheduler": {"name": "cosine"},
        "swanlab": {"enabled": False},
        **cfg_extra,
    }
    return SimCLRTrainer(backbone, loader, cfg, torch.device("cpu"), work_dir,
                         resume=cfg.pop("resume", None))


def test_save_state_contains_full_training_state(tiny_image_dir, tmp_path):
    trainer = _make_trainer(tiny_image_dir, tmp_path / "run", epochs=1)
    trainer.fit()

    state_path = tmp_path / "run" / "simclr_last.pth"
    assert state_path.exists()
    state = torch.load(state_path, map_location="cpu", weights_only=False)
    # 续训需要的东西一个都不能少，否则动量和学习率进度会被清零
    assert {"backbone", "projector", "optimizer", "scheduler", "scaler", "epoch", "global_step"} <= set(state)
    assert state["epoch"] == 0


def test_resume_continues_from_the_saved_epoch(tiny_image_dir, tmp_path):
    """resume 是接着练，不是从头再来。"""
    work_dir = tmp_path / "run"
    first = _make_trainer(tiny_image_dir, work_dir, epochs=2)
    first.fit()
    steps_after_2 = first.global_step

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        first.dataloader,
        {"epochs": 5, "amp": False, "log_interval": 100, "save_interval": 1,
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "scheduler": {"name": "cosine"}, "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )
    assert resumed.start_epoch == 2                    # 从第 3 轮开始
    assert resumed.global_step == steps_after_2        # step 计数接上
    # 总轮数从 2 改成了 5，学习率按新计划重算（若沿用旧的 T_max，此处会是 0）
    assert resumed.optimizer.param_groups[0]["lr"] > 1e-5

    resumed.fit()
    state = torch.load(work_dir / "simclr_last.pth", map_location="cpu", weights_only=False)
    assert state["epoch"] == 4                         # 一路练到第 5 轮
    assert resumed.global_step > steps_after_2


def test_resume_with_a_longer_schedule_rebuilds_the_lr_curve(tiny_image_dir, tmp_path):
    """续训时调大 epochs，学习率必须按新的总轮数重算。

    回归测试：此前直接恢复调度器状态，会沿用存档里的 T_max。原计划 10 轮的 cosine
    跑完后 lr 已经是 0，这时把 epochs 改成 30 接着练，lr 仍是 0——训练照跑但一步都学不动。
    """
    import math

    work_dir = tmp_path / "run"
    first = _make_trainer(tiny_image_dir, work_dir, epochs=10)
    first.fit()
    assert first.optimizer.param_groups[0]["lr"] == pytest.approx(0.0, abs=1e-9)  # cosine 已走完

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        first.dataloader,
        {"epochs": 30, "amp": False, "log_interval": 999, "save_interval": 1,
         "scheduler": {"name": "cosine"}, "optimizer": {"name": "adamw", "lr": 1e-3},
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )

    assert resumed.scheduler.T_max == 30
    lr = resumed.optimizer.param_groups[0]["lr"]
    assert lr > 1e-5, "续训后学习率仍是 0，等于白跑"
    assert lr == pytest.approx(1e-3 * (1 + math.cos(math.pi * 10 / 30)) / 2, rel=1e-3)


def test_restart_schedule_gives_the_new_lr_full_value(tiny_image_dir, tmp_path):
    """续训到调度末段时，cosine 会把新 lr 乘上接近 0 的系数；
    restart_schedule 按剩余轮数重开曲线，让新 lr 从满值开始。"""
    import math

    work_dir = tmp_path / "run"
    _make_trainer(
        tiny_image_dir, work_dir, epochs=9,
        optimizer={"name": "adamw", "lr": 1e-3},
    ).fit()

    def resumed(restart):
        return SimCLRTrainer(
            BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
            _make_trainer(tiny_image_dir, work_dir, epochs=1).dataloader,
            {"epochs": 10, "amp": False, "log_interval": 999, "save_interval": 1,
             "scheduler": {"name": "cosine"}, "optimizer": {"name": "adamw", "lr": 1e-3},
             "projection": {"hidden_channels": 16, "out_channels": 8},
             "swanlab": {"enabled": False}, "restart_schedule": restart},
            torch.device("cpu"),
            work_dir,
            resume=work_dir / "simclr_last.pth",
        )

    # 默认：沿用整条曲线，9/10 处只剩很小的系数
    default_lr = resumed(False).optimizer.param_groups[0]["lr"]
    assert default_lr == pytest.approx(1e-3 * (1 + math.cos(math.pi * 9 / 10)) / 2)
    assert default_lr < 1e-3 * 0.1

    # 打开开关：新 lr 从满值开始
    assert resumed(True).optimizer.param_groups[0]["lr"] == pytest.approx(1e-3)


def test_resume_takes_hyperparams_from_config_not_checkpoint(tiny_image_dir, tmp_path):
    """续训时 lr / weight_decay / 总轮数一律以 yaml 为准，存档只提供进度。

    回归测试：optimizer.load_state_dict 会整体覆盖 param_groups，把超参连同动量缓冲
    一起还原成存档时的值——改了配置再续训，改动被静默吃掉。
    """
    import math

    work_dir = tmp_path / "run"
    first = _make_trainer(
        tiny_image_dir, work_dir, epochs=4,
        optimizer={"name": "adamw", "lr": 1e-3, "weight_decay": 1e-4},
    )
    first.fit()

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        first.dataloader,
        {"epochs": 8, "amp": False, "log_interval": 999, "save_interval": 1,
         "scheduler": {"name": "cosine"},
         "optimizer": {"name": "adamw", "lr": 5e-4, "weight_decay": 0.05},
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )

    groups = resumed.optimizer.param_groups
    assert groups[0]["initial_lr"] == pytest.approx(5e-4)      # 不是存档里的 1e-3
    assert groups[0]["weight_decay"] == pytest.approx(0.05)    # 不是存档里的 1e-4
    assert groups[1]["weight_decay"] == 0.0                    # BN/bias 分组结构没被冲掉
    assert resumed.scheduler.base_lrs == pytest.approx([5e-4, 5e-4])
    assert resumed.scheduler.T_max == 8
    assert groups[0]["lr"] == pytest.approx(5e-4 * (1 + math.cos(math.pi * 4 / 8)) / 2)
    assert resumed.optimizer.state, "动量缓冲应当保留——那才是真正要续的'进度'"


def test_resume_across_optimizer_types_drops_incompatible_state(tiny_image_dir, tmp_path):
    """换优化器种类时不加载动量缓冲：AdamW 的 exp_avg 对 SGD 没有意义，硬塞只是白占内存。"""
    work_dir = tmp_path / "run"
    _make_trainer(
        tiny_image_dir, work_dir, epochs=2,
        optimizer={"name": "adamw", "lr": 1e-3, "weight_decay": 1e-4},
    ).fit()

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        _make_trainer(tiny_image_dir, work_dir, epochs=1).dataloader,
        {"epochs": 6, "amp": False, "log_interval": 999, "save_interval": 1,
         "scheduler": {"name": "cosine"},
         "optimizer": {"name": "sgd", "lr": 0.01, "momentum": 0.9},
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )

    assert isinstance(resumed.optimizer, torch.optim.SGD)
    assert not resumed.optimizer.state, "不该带着 AdamW 的缓冲"
    assert resumed.start_epoch == 2  # 训练进度仍然接上
    assert resumed.optimizer.param_groups[0]["momentum"] == 0.9


def test_resume_with_same_schedule_restores_state(tiny_image_dir, tmp_path):
    """总轮数不变时，重建 + 快进的结果应与原调度进度一致。"""
    work_dir = tmp_path / "run"
    _make_trainer(tiny_image_dir, work_dir, epochs=6).fit()

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        _make_trainer(tiny_image_dir, work_dir, epochs=1).dataloader,
        {"epochs": 6, "amp": False, "log_interval": 999, "save_interval": 1,
         "scheduler": {"name": "cosine"},
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )
    assert resumed.scheduler.T_max == 6
    assert resumed.scheduler.last_epoch == 6


def test_intermediate_checkpoint_is_resumable(tiny_image_dir, tmp_path):
    """save_interval 落的中间盘必须能直接拿来续训（不必等训练正常结束）。"""
    work_dir = tmp_path / "run"
    trainer = _make_trainer(tiny_image_dir, work_dir, epochs=10)
    # 只手动跑到第 4 轮就落盘，模拟被 kill 掉
    for epoch in range(4):
        trainer.train_one_epoch(epoch)
        trainer.scheduler.step()
    trainer._checkpoint(3)

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        trainer.dataloader,
        {"epochs": 10, "amp": False, "log_interval": 999, "save_interval": 1,
         "scheduler": {"name": "cosine"},
         "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )
    assert resumed.start_epoch == 4
    resumed.fit()
    state = torch.load(work_dir / "simclr_last.pth", map_location="cpu", weights_only=False)
    assert state["epoch"] == 9


def test_resume_rejects_backbone_only_file(tiny_image_dir, tmp_path):
    """simclr_backbone.pth 只有骨架，不能当续训状态用，要给出可操作的报错。"""
    work_dir = tmp_path / "run"
    _make_trainer(tiny_image_dir, work_dir, epochs=1).fit()

    with pytest.raises(ValueError, match="只含骨架权重"):
        SimCLRTrainer(
            BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
            _make_trainer(tiny_image_dir, work_dir, epochs=1).dataloader,
            {"epochs": 2, "amp": False, "projection": {"hidden_channels": 16, "out_channels": 8},
             "swanlab": {"enabled": False}},
            torch.device("cpu"),
            work_dir,
            resume=work_dir / "simclr_backbone.pth",
        )

    with pytest.raises(FileNotFoundError, match="不存在"):
        _make_trainer(tiny_image_dir, work_dir, epochs=1, resume=work_dir / "nope.pth")


def test_resume_beyond_planned_epochs_does_not_train(tiny_image_dir, tmp_path):
    work_dir = tmp_path / "run"
    _make_trainer(tiny_image_dir, work_dir, epochs=3).fit()

    resumed = SimCLRTrainer(
        BACKBONES.build({"name": "tiny_conv", "widths": [8, 16], "depths": [1, 1]}),
        _make_trainer(tiny_image_dir, work_dir, epochs=1).dataloader,
        {"epochs": 3, "amp": False, "projection": {"hidden_channels": 16, "out_channels": 8},
         "swanlab": {"enabled": False}},
        torch.device("cpu"),
        work_dir,
        resume=work_dir / "simclr_last.pth",
    )
    steps_before = resumed.global_step
    resumed.fit()
    assert resumed.global_step == steps_before  # 一步都没再训


def test_resume_and_checkpoint_are_mutually_exclusive(tiny_image_dir, tmp_path, monkeypatch):
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    with pytest.raises(ValueError, match="只能二选一"):
        cu.build_encoder(
            {"method": "simclr",
             "simclr": {"resume": "a.pth", "checkpoint": "b.pth"},
             "dataset": {"name": "unlabeled", "root": str(tiny_image_dir)}},
            torch.device("cpu"),
            tmp_path,
        )


def test_cluster_script_simclr_branch_end_to_end(tiny_image_dir, tmp_path, monkeypatch):
    """method=simclr 全流程：训练 -> 存权重 -> 抽特征 -> 聚类 -> 产出。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    work_dir = tmp_path / "run"
    monkeypatch.setattr(
        sys, "argv",
        ["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
         "--work-dir", str(work_dir), "--root", str(tiny_image_dir), "--set",
         "device=cpu", "method=simclr", "loader.num_workers=0", "loader.batch_size=4",
         "simclr.epochs=1", "simclr.batch_size=4", "simclr.amp=False",
         "simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
         "simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8",
         "num_clusters=2", "n_init=2"],
    )

    assert cu.main() == 0
    assert (work_dir / "simclr_backbone.pth").exists()
    assert (work_dir / "assignments.csv").exists()
    assert (work_dir / "summary.json").exists()
    assert (work_dir / "clusters" / "cluster_00").is_dir()


def _cluster_argv(work_dir, image_dir, *extra):
    return [
        "cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
        "--work-dir", str(work_dir), "--root", str(image_dir), "--set",
        "device=cpu", "loader.num_workers=0", "loader.batch_size=4",
        "num_clusters=2", "n_init=2", *extra,
    ]


def test_warns_when_simclr_keys_are_at_top_level(tiny_image_dir, caplog):
    """顶层写 optimizer 会被静默忽略，必须警告——否则"改了配置没反应"极难排查。"""
    import logging

    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    cfg = {
        "dataset": {"name": "unlabeled", "root": str(tiny_image_dir)},
        "optimizer": {"name": "adamw", "lr": 0.9},   # ← 应该放在 simclr: 下
        "epochs": 100,                               # ← 同上
        "simclr": {"epochs": 2},
    }
    with caplog.at_level(logging.WARNING, logger="mte"):
        cu.warn_misplaced_keys(cfg, "simclr")

    assert "optimizer" in caplog.text and "epochs" in caplog.text
    assert "simclr" in caplog.text


def test_no_warning_when_keys_are_correctly_nested(tiny_image_dir, caplog):
    import logging

    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    cfg = {"dataset": {"name": "unlabeled", "root": str(tiny_image_dir)},
           "simclr": {"optimizer": {"lr": 0.9}, "epochs": 2}}
    with caplog.at_level(logging.WARNING, logger="mte"):
        cu.warn_misplaced_keys(cfg, "simclr")
    assert "会被忽略" not in caplog.text


def test_effective_hyperparams_are_logged_on_every_run(tiny_image_dir, tmp_path, caplog):
    """不只是续训时——每次训练开始都要打印实际生效的超参。"""
    import logging

    trainer = _make_trainer(
        tiny_image_dir, tmp_path / "run", epochs=1,
        optimizer={"name": "adamw", "lr": 0.00042, "weight_decay": 0.007},
    )
    del trainer
    with caplog.at_level(logging.INFO, logger="mte"):
        _make_trainer(
            tiny_image_dir, tmp_path / "run2", epochs=1,
            optimizer={"name": "adamw", "lr": 0.00042, "weight_decay": 0.007},
        )
    assert "SimCLR 超参" in caplog.text
    assert "0.00042" in caplog.text


def test_train_only_stops_before_clustering(tiny_image_dir, tmp_path, monkeypatch):
    """--train-only 只训练存权重，不抽特征也不聚类。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    work_dir = tmp_path / "train_only"
    monkeypatch.setattr(
        sys, "argv",
        [*_cluster_argv(work_dir, tiny_image_dir,
                        "method=simclr", "simclr.epochs=1", "simclr.batch_size=4",
                        "simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
                        "simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8"),
         "--train-only"],
    )
    assert cu.main() == 0

    assert (work_dir / "simclr_backbone.pth").exists()
    assert (work_dir / "simclr_last.pth").exists()   # 续训状态照常落盘
    for produced_by_clustering in ("assignments.csv", "summary.json", "cluster_scatter.png", "clusters"):
        assert not (work_dir / produced_by_clustering).exists(), produced_by_clustering


def test_train_only_then_cluster_only(tiny_image_dir, tmp_path, monkeypatch):
    """训练与聚类可以分两次跑：第一次只训练，第二次用 checkpoint 只聚类。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    train_dir = tmp_path / "phase1"
    monkeypatch.setattr(
        sys, "argv",
        [*_cluster_argv(train_dir, tiny_image_dir,
                        "method=simclr", "simclr.epochs=1", "simclr.batch_size=4",
                        "simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
                        "simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8"),
         "--train-only"],
    )
    assert cu.main() == 0

    cluster_dir = tmp_path / "phase2"
    monkeypatch.setattr(
        sys, "argv",
        _cluster_argv(cluster_dir, tiny_image_dir,
                      "method=simclr",
                      f"simclr.checkpoint={train_dir / 'simclr_backbone.pth'}",
                      "simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]"),
    )
    assert cu.main() == 0

    assert (cluster_dir / "assignments.csv").exists()
    assert not (cluster_dir / "simclr_backbone.pth").exists(), "第二步不该重新训练"


@pytest.mark.parametrize(
    "extra,reason",
    [
        ([], "method=pretrained 没有要训练的东西"),
        (["method=simclr", "simclr.checkpoint=whatever.pth"], "checkpoint 会跳过训练"),
    ],
)
def test_train_only_rejects_meaningless_combinations(
    tiny_image_dir, tmp_path, monkeypatch, extra, reason
):
    """这些组合下 --train-only 什么都不会做，应当立刻报错而不是跑完才发现没产出。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    monkeypatch.setattr(
        sys, "argv", [*_cluster_argv(tmp_path / "bad", tiny_image_dir, *extra), "--train-only"]
    )
    assert cu.main() == 1, reason


def test_train_only_conflicts_with_sweep_k(tiny_image_dir, tmp_path, monkeypatch):
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    monkeypatch.setattr(
        sys, "argv",
        [*_cluster_argv(tmp_path / "bad", tiny_image_dir, "method=simclr"),
         "--train-only", "--sweep-k", "2-4"],
    )
    assert cu.main() == 1


def test_scatter_plot_is_written_next_to_previews(tiny_image_dir, tmp_path, monkeypatch):
    """cluster_scatter.png 要和 preview 拼图落在同一级目录。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    work_dir = tmp_path / "run"
    monkeypatch.setattr(
        sys, "argv",
        ["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
         "--work-dir", str(work_dir), "--root", str(tiny_image_dir), "--set",
         "device=cpu", "loader.num_workers=0", "loader.batch_size=4",
         "num_clusters=2", "n_init=2"],
    )
    assert cu.main() == 0

    scatter = work_dir / "cluster_scatter.png"
    assert scatter.exists() and scatter.stat().st_size > 5000
    assert (work_dir / "cluster_00_preview.png").parent == scatter.parent

    import json

    summary = json.loads((work_dir / "summary.json").read_text(encoding="utf-8"))
    matrix = summary["centroid_distances"]
    assert len(matrix) == 2 and matrix[0][0] == pytest.approx(0.0)
    assert matrix[0][1] == pytest.approx(matrix[1][0])


def test_scatter_can_be_disabled(tiny_image_dir, tmp_path, monkeypatch):
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    work_dir = tmp_path / "run_off"
    monkeypatch.setattr(
        sys, "argv",
        ["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
         "--work-dir", str(work_dir), "--root", str(tiny_image_dir), "--set",
         "device=cpu", "loader.num_workers=0", "loader.batch_size=4",
         "num_clusters=2", "n_init=2", "scatter.enabled=False"],
    )
    assert cu.main() == 0
    assert not (work_dir / "cluster_scatter.png").exists()
    assert (work_dir / "cluster_00_preview.png").exists()


def test_simclr_runs_on_the_resolved_device(tiny_image_dir, tmp_path, monkeypatch):
    """method=simclr 在本机自动选中的后端上（MPS / CUDA / CPU）都应能跑通。"""
    sys.path.insert(0, "scripts")
    import cluster_unlabeled as cu

    from utils.device import describe_device, resolve_device

    device = resolve_device("auto")
    work_dir = tmp_path / "auto_run"
    monkeypatch.setattr(
        sys, "argv",
        ["cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
         "--work-dir", str(work_dir), "--root", str(tiny_image_dir), "--set",
         "method=simclr", "loader.num_workers=0", "loader.batch_size=4",
         "simclr.epochs=1", "simclr.batch_size=4",
         "simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
         "simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8",
         "num_clusters=2", "n_init=2"],
    )

    assert cu.main() == 0, f"在 {describe_device(device)} 上跑失败"
    assert (work_dir / "simclr_backbone.pth").exists()