Change LightningCLI tests to account for future fix in jsonargparse

tests/tests_pytorch/test_cli.py

@@ -871,18 +871,27 @@ def test_lightning_cli_load_from_checkpoint_dependency_injection(cleandir):
     hparams_path = Path(cli.trainer.log_dir) / "hparams.yaml"
     assert hparams_path.is_file()
     hparams = yaml.safe_load(hparams_path.read_text())
-    expected = {
-        "_instantiator": "lightning.pytorch.cli.instantiate_module",
-        "optimizer": "torch.optim.Adam",
-        "scheduler": "torch.optim.lr_scheduler.ConstantLR",
-        "activation": {"class_path": "torch.nn.LeakyReLU", "init_args": {"negative_slope": 0.05, "inplace": False}},
-    }
-    assert hparams == expected
+
+    expected_keys = ["_instantiator", "activation", "optimizer", "scheduler"]
+    expected_instantiator = "lightning.pytorch.cli.instantiate_module"
+    expected_activation = "torch.nn.LeakyReLU"
+    expected_optimizer = "torch.optim.Adam"
+    expected_scheduler = "torch.optim.lr_scheduler.ConstantLR"
+
+    assert sorted(hparams.keys()) == expected_keys
+    assert hparams["_instantiator"] == expected_instantiator
+    assert hparams["activation"]["class_path"] == expected_activation
+    assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
+    assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
 
     checkpoint_path = next(Path(cli.trainer.log_dir, "checkpoints").glob("*.ckpt"), None)
     assert checkpoint_path.is_file()
-    ckpt = torch.load(checkpoint_path, weights_only=True)
-    assert ckpt["hyper_parameters"] == expected
+    hparams = torch.load(checkpoint_path, weights_only=True)["hyper_parameters"]
+    assert sorted(hparams.keys()) == expected_keys
+    assert hparams["_instantiator"] == expected_instantiator
+    assert hparams["activation"]["class_path"] == expected_activation
+    assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
+    assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
 
     model = TestModelSaveHparams.load_from_checkpoint(checkpoint_path)
     assert isinstance(model, TestModelSaveHparams)
@@ -898,18 +907,23 @@ def test_lightning_cli_load_from_checkpoint_dependency_injection_subclass_mode(c
         cli = LightningCLI(TestModelSaveHparams, run=False, auto_configure_optimizers=False, subclass_mode_model=True)
     cli.trainer.fit(cli.model)
 
-    expected = {
-        "_instantiator": "lightning.pytorch.cli.instantiate_module",
-        "_class_path": f"{__name__}.TestModelSaveHparams",
-        "optimizer": "torch.optim.Adam",
-        "scheduler": "torch.optim.lr_scheduler.ConstantLR",
-        "activation": {"class_path": "torch.nn.LeakyReLU", "init_args": {"negative_slope": 0.05, "inplace": False}},
-    }
+    expected_keys = ["_class_path", "_instantiator", "activation", "optimizer", "scheduler"]
+    expected_instantiator = "lightning.pytorch.cli.instantiate_module"
+    expected_class_path = f"{__name__}.TestModelSaveHparams"
+    expected_activation = "torch.nn.LeakyReLU"
+    expected_optimizer = "torch.optim.Adam"
+    expected_scheduler = "torch.optim.lr_scheduler.ConstantLR"
 
     checkpoint_path = next(Path(cli.trainer.log_dir, "checkpoints").glob("*.ckpt"), None)
     assert checkpoint_path.is_file()
-    ckpt = torch.load(checkpoint_path, weights_only=True)
-    assert ckpt["hyper_parameters"] == expected
+    hparams = torch.load(checkpoint_path, weights_only=True)["hyper_parameters"]
+
+    assert sorted(hparams.keys()) == expected_keys
+    assert hparams["_instantiator"] == expected_instantiator
+    assert hparams["_class_path"] == expected_class_path
+    assert hparams["activation"]["class_path"] == expected_activation
+    assert hparams["optimizer"] == expected_optimizer or hparams["optimizer"]["class_path"] == expected_optimizer
+    assert hparams["scheduler"] == expected_scheduler or hparams["scheduler"]["class_path"] == expected_scheduler
 
     model = LightningModule.load_from_checkpoint(checkpoint_path)
     assert isinstance(model, TestModelSaveHparams)