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from __future__ import annotations
import os
from types import SimpleNamespace
from typing import Any, Dict
import pytest
def _make_dummy_training_deps() -> Dict[str, Any]:
class DummyTorch:
@staticmethod
def set_float32_matmul_precision(_arg: str) -> None:
return None
class cuda:
@staticmethod
def is_available() -> bool: # pragma: no cover - trivial stub
return False
class DummyTrainer:
def __init__(self, *_, **__):
self.num_devices = 0
def fit(self, *_, **__):
return None
def test(self, *_, **__):
return []
class DummyCallback:
def __init__(self, *_, **__):
return None
class DummyLogger:
def __init__(self, *_, **__):
return None
class DummyDDPStrategy:
def __init__(self, *_, **__):
return None
def build_scheduler_config(_cfg: Dict[str, Any]) -> Dict[str, Any]:
return {}
def localize_datasets(dataset_configs, *_args, **_kwargs):
return SimpleNamespace(
dataset_configs=dataset_configs,
cache_file=None,
job_id="test-job",
stage_out=lambda *_a, **_kw: None,
)
def parse_dataset_configs(dataset_configs):
configs = dataset_configs or []
if not isinstance(configs, (list, tuple)):
configs = [configs]
return [
SimpleNamespace(
path=str(cfg),
filter_organism=None,
gene_name_col=None,
type="human",
donor_col="donor",
cell_type_col="celltype",
condition_col="condition",
cell_line_col="cellline",
control_condition="control",
)
for cfg in configs
]
class DummyDataModule:
def __init__(self, *_, **__):
self.test_dataset = []
self.dataset_configs = parse_dataset_configs(["dummy"])
def setup(self, *_args, **_kwargs):
self.n_genes = 4
return None
def get_split_info(self):
return {"train": [], "val": [], "test": []}
class DummyModel:
def __init__(self, *_, **__):
return None
@classmethod
def load_from_checkpoint(cls, *_, **__):
return cls()
def build_model_config(args, n_genes):
return {"n_genes": n_genes}
return {
"torch": DummyTorch,
"pl": SimpleNamespace(Trainer=DummyTrainer),
"EarlyStopping": DummyCallback,
"LearningRateMonitor": DummyCallback,
"ModelCheckpoint": DummyCallback,
"TensorBoardLogger": DummyLogger,
"WandbLogger": DummyLogger,
"Logger": DummyLogger,
"DDPStrategy": DummyDDPStrategy,
"MultiDatasetDataModule": DummyDataModule,
"FinetuneDataModule": DummyDataModule,
"LightningFinetunedModel": DummyModel,
"LegacyLightningGeneModel": DummyModel,
"build_scheduler_config": build_scheduler_config,
"localize_datasets": localize_datasets,
"parse_dataset_configs": parse_dataset_configs,
"configure_logger": DummyLogger,
"build_model_config": build_model_config,
"override_model_config_n_cells": lambda *_a, **_kw: {},
}
def test_stack_train_main_runs(monkeypatch, tmp_path):
from stack.cli import launch_training
import sys
dummy_deps = _make_dummy_training_deps()
monkeypatch.setattr(launch_training, "_import_training_modules", lambda: dummy_deps)
for name in ("TensorBoardLogger", "WandbLogger", "EarlyStopping", "LearningRateMonitor", "ModelCheckpoint"):
monkeypatch.setattr(launch_training, name, dummy_deps[name], raising=False)
monkeypatch.setattr(launch_training, "configure_logger", lambda *_a, **_kw: dummy_deps["TensorBoardLogger"]())
monkeypatch.setattr(
sys,
"argv",
[
"stack-train",
"--dataset_configs",
"dummy-dataset",
"--genelist_path",
"dummy-genelist.pkl",
"--save_dir",
str(tmp_path),
"--gpus",
"0",
],
)
launch_training.main()
assert (tmp_path / "dataset_splits.json").exists()
def test_stack_finetune_main_runs(monkeypatch, tmp_path):
from stack.cli import launch_finetuning
import sys
dummy_deps = _make_dummy_training_deps()
monkeypatch.setattr(launch_finetuning, "_import_training_modules", lambda: dummy_deps)
for name in ("TensorBoardLogger", "WandbLogger", "EarlyStopping", "LearningRateMonitor", "ModelCheckpoint", "Logger"):
monkeypatch.setattr(launch_finetuning, name, dummy_deps.get(name, dummy_deps["TensorBoardLogger"]), raising=False)
monkeypatch.setattr(
launch_finetuning,
"configure_logger",
lambda *_a, **_kw: dummy_deps["TensorBoardLogger"](),
raising=False,
)
monkeypatch.setattr(
sys,
"argv",
[
"stack-finetune",
"--dataset_configs",
"dummy-dataset",
"--genelist_path",
"dummy-genelist.pkl",
"--save_dir",
str(tmp_path),
"--gpus",
"0",
],
)
launch_finetuning.main()
assert (tmp_path / "dataset_splits.json").exists()
def test_stack_embedding_main_runs(monkeypatch, tmp_path):
from stack.cli import embedding
monkeypatch.setattr(
embedding,
"extract_embeddings",
lambda **_kw: ([[]], None),
)
saved = {}
monkeypatch.setattr(
embedding,
"save_embeddings",
lambda embeddings, output_path, **_: saved.update({"path": output_path, "embeddings": embeddings}),
)
args = [
"--checkpoint",
"dummy.ckpt",
"--adata",
"dummy.h5ad",
"--genelist",
"dummy-genelist.pkl",
"--output",
str(tmp_path / "embeddings.npy"),
]
embedding.main(args)
assert saved["path"].name == "embeddings.npy"
def test_stack_generation_main_runs(monkeypatch, tmp_path):
from stack.cli import generation
monkeypatch.setattr(
generation,
"generate",
lambda **_kw: {"split": "dummy"},
)
called = {}
monkeypatch.setattr(
generation,
"save_generations",
lambda generations, output_dir, **_: called.update({"generations": generations, "output_dir": output_dir}),
)
args = [
"--checkpoint",
"dummy.ckpt",
"--base-adata",
"base.h5ad",
"--test-adata",
"test.h5ad",
"--genelist",
"genelist.pkl",
"--output-dir",
str(tmp_path),
"--split-column",
"donor",
"--concatenate",
]
generation.main(args)
assert called["output_dir"] == tmp_path
assert called["generations"] == {"split": "dummy"}
|