Obtaining file:///testbed
  Checking if build backend supports build_editable: started
  Checking if build backend supports build_editable: finished with status 'done'
  Preparing editable metadata (pyproject.toml): started
  Preparing editable metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: lightning
  Building editable for lightning (pyproject.toml): started
  Building editable for lightning (pyproject.toml): finished with status 'done'
  Created wheel for lightning: filename=lightning-2.6.0.dev0-0.editable-py3-none-any.whl size=14324 sha256=d92a61192930fdcabbb78e88848a441308d75926568d44b8902cfb8b45a40715
  Stored in directory: /tmp/pip-ephem-wheel-cache-_ui4rvot/wheels/5d/7e/d4/0611b5e1f6b19283dfc03cca62ebc6a41021c5c464d28aab8f
Successfully built lightning
Installing collected packages: lightning
  Attempting uninstall: lightning
    Found existing installation: lightning 2.6.0.dev0
    Uninstalling lightning-2.6.0.dev0:
      Successfully uninstalled lightning-2.6.0.dev0
Successfully installed lightning-2.6.0.dev0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
=== Running FAIL_TO_PASS tests ===
============================= test session starts ==============================
platform linux -- Python 3.12.12, pytest-9.0.1, pluggy-1.6.0
rootdir: /testbed
configfile: pyproject.toml
plugins: hydra-core-1.3.2, timeout-2.4.0
collected 35 items

tests/tests_pytorch/models/test_hooks.py ...FsssFssFssFssFssFFFFFFFFFFFF [ 88%]
FFFF                                                                     [100%]

=================================== FAILURES ===================================
__________________ test_on_train_epoch_end_metrics_collection __________________
tests/tests_pytorch/models/test_hooks.py:93: in test_on_train_epoch_end_metrics_collection
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1458: in _run
    results = self._run_stage()
              ^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1500: in _run_stage
    self._run_sanity_check()
src/lightning/pytorch/trainer/trainer.py:1529: in _run_sanity_check
    val_loop.run()
src/lightning/pytorch/loops/utilities.py:179: in _decorator
    return loop_run(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/loops/evaluation_loop.py:121: in run
    self.setup_data()
src/lightning/pytorch/loops/evaluation_loop.py:186: in setup_data
    _resolve_overfit_batches(combined_loader, stage)
    ^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '_resolve_overfit_batches' is not defined
----------------------------- Captured stdout call -----------------------------
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃    ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃
┡━━━━╇━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ 66 │      │      │        │      │       │
│ ?  │      │      │        │      │       │
│ ?  │      │      │        │      │       │
└────┴──────┴──────┴────────┴──────┴───────┘
Trainable params: 66                                                            
Non-trainable params: 0                                                         
Total params: 66                                                                
Total estimated model params size (MB): 0                                       
Modules in train mode: 2                                                        
Modules in eval mode: 0                                                         
Total FLOPs: 0                                                                  

----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____________ test_trainer_model_hook_system_fit[True-True-kwargs0] _____________
tests/tests_pytorch/models/test_hooks.py:457: in test_trainer_model_hook_system_fit
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____________ test_trainer_model_hook_system_fit[True-False-kwargs0] ____________
tests/tests_pytorch/models/test_hooks.py:457: in test_trainer_model_hook_system_fit
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____________ test_trainer_model_hook_system_fit[False-True-kwargs0] ____________
tests/tests_pytorch/models/test_hooks.py:457: in test_trainer_model_hook_system_fit
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
___________ test_trainer_model_hook_system_fit[False-False-kwargs0] ____________
tests/tests_pytorch/models/test_hooks.py:457: in test_trainer_model_hook_system_fit
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
_______ test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs ________
tests/tests_pytorch/models/test_hooks.py:559: in test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs
    trainer.fit(model, ckpt_path=best_model_path)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer.fit` stopped: `max_epochs=1` reached.
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
________ test_trainer_model_hook_system_fit_no_val_and_resume_max_steps ________
tests/tests_pytorch/models/test_hooks.py:639: in test_trainer_model_hook_system_fit_no_val_and_resume_max_steps
    trainer.fit(model, ckpt_path=best_model_path)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer.fit` stopped: `max_steps=1` reached.
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____ test_trainer_model_hook_system_eval[True-validate-validation-val-x-0] _____
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:720: in validate
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:741: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____ test_trainer_model_hook_system_eval[True-validate-validation-val-x-2] _____
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:720: in validate
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:741: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
_________ test_trainer_model_hook_system_eval[True-test-test-test-y-0] _________
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:826: in test
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:847: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
_________ test_trainer_model_hook_system_eval[True-test-test-test-y-2] _________
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:826: in test
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:847: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____ test_trainer_model_hook_system_eval[False-validate-validation-val-x-0] ____
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:720: in validate
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:741: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____ test_trainer_model_hook_system_eval[False-validate-validation-val-x-2] ____
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:720: in validate
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:741: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
________ test_trainer_model_hook_system_eval[False-test-test-test-y-0] _________
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:826: in test
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:847: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
________ test_trainer_model_hook_system_eval[False-test-test-test-y-2] _________
tests/tests_pytorch/models/test_hooks.py:707: in test_trainer_model_hook_system_eval
    fn(model, verbose=False)
src/lightning/pytorch/trainer/trainer.py:826: in test
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:847: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
____________________ test_trainer_model_hook_system_predict ____________________
tests/tests_pytorch/models/test_hooks.py:745: in test_trainer_model_hook_system_predict
    trainer.predict(model)
src/lightning/pytorch/trainer/trainer.py:931: in predict
    return call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:955: in _predict_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:100: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
___________________ test_hooks_with_different_argument_names ___________________
tests/tests_pytorch/models/test_hooks.py:816: in test_hooks_with_different_argument_names
    assert all(len(x) == 5 for x in preds)
E   assert False
E    +  where False = all(<generator object test_hooks_with_different_argument_names.<locals>.<genexpr> at 0x7212eddd9220>)
----------------------------- Captured stdout call -----------------------------
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃    ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃
┡━━━━╇━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ 66 │      │      │        │      │       │
│ ?  │      │      │        │      │       │
│ ?  │      │      │        │      │       │
└────┴──────┴──────┴────────┴──────┴───────┘
Trainable params: 66                                                            
Non-trainable params: 0                                                         
Total params: 66                                                                
Total estimated model params size (MB): 0                                       
Modules in train mode: 2                                                        
Modules in eval mode: 0                                                         
Total FLOPs: 0                                                                  
Epoch 0/0  ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5/5 0:00:00 • 0:00:00 1410.53it/s v_num: 
Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5/5 0:00:00 • 0:00:00 3127.78it/s 
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5/5 0:00:00 • 0:00:00 3891.64it/s 
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
Running in `fast_dev_run` mode: will run the requested loop using 5 batch(es). Logging and checkpointing is suppressed.
`Trainer.fit` stopped: `max_steps=5` reached.
_____________________ test_trainer_datamodule_hook_system ______________________
tests/tests_pytorch/models/test_hooks.py:838: in test_trainer_datamodule_hook_system
    trainer.fit(model, datamodule=dm)
src/lightning/pytorch/trainer/trainer.py:583: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:629: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1416: in _run
    self._data_connector.prepare_data()
src/lightning/pytorch/trainer/connectors/data_connector.py:93: in prepare_data
    with _InfiniteBarrier():
         ^^^^^^^^^^^^^^^^^^
E   TypeError: '_InfiniteBarrier' object does not support the context manager protocol
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
__________________ test_load_from_checkpoint_hook_calls[True] __________________
tests/tests_pytorch/models/test_hooks.py:919: in test_load_from_checkpoint_hook_calls
    assert lm_called == [{"name": "on_save_checkpoint", "args": (saved_ckpt,)}]
E   AssertionError: assert [] == [{'args': ({'..._checkpoint'}]
E     
E     Right contains one more item: {'args': ({'callbacks': <ANY>, 'epoch': 0, 'global_step': 0, 'loops': <ANY>, ...},), 'name': 'on_save_checkpoint'}
E     Use -v to get more diff
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
_________________ test_load_from_checkpoint_hook_calls[False] __________________
tests/tests_pytorch/models/test_hooks.py:919: in test_load_from_checkpoint_hook_calls
    assert lm_called == [{"name": "on_save_checkpoint", "args": (saved_ckpt,)}]
E   AssertionError: assert [] == [{'args': ({'..._checkpoint'}]
E     
E     Right contains one more item: {'args': ({'callbacks': <ANY>, 'epoch': 0, 'global_step': 0, 'loops': <ANY>, ...},), 'name': 'on_save_checkpoint'}
E     Use -v to get more diff
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
________________________ test_train_eval_mode_restored _________________________
tests/tests_pytorch/models/test_hooks.py:987: in test_train_eval_mode_restored
    trainer.test(model)
src/lightning/pytorch/trainer/trainer.py:826: in test
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:847: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1458: in _run
    results = self._run_stage()
              ^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1495: in _run_stage
    return self._evaluation_loop.run()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/loops/utilities.py:179: in _decorator
    return loop_run(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/loops/evaluation_loop.py:146: in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
src/lightning/pytorch/loops/evaluation_loop.py:441: in _evaluation_step
    output = call._call_strategy_hook(trainer, hook_name, *step_args)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:345: in _call_strategy_hook
    output = fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/strategies/strategy.py:425: in test_step
    return self.lightning_module.test_step(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tests/tests_pytorch/models/test_hooks.py:959: in test_step
    assert not self.layer.training
E   assert not True
E    +  where True = Linear(in_features=32, out_features=2, bias=True).training
E    +    where Linear(in_features=32, out_features=2, bias=True) = MixedTrainModeModule(\n  (layer): Linear(in_features=32, out_features=2, bias=True)\n  (frozen): Linear(in_features=2, out_features=2, bias=True)\n).layer
----------------------------- Captured stderr call -----------------------------
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer(val_check_interval=1)` was configured so validation will run after every batch.
`Trainer.fit` stopped: `max_epochs=1` reached.
==================================== PASSES ====================================
______________________ test_on_before_zero_grad_called[1] ______________________
----------------------------- Captured stdout call -----------------------------
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃    ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃
┡━━━━╇━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ 66 │      │      │        │      │       │
│ ?  │      │      │        │      │       │
│ ?  │      │      │        │      │       │
└────┴──────┴──────┴────────┴──────┴───────┘
Trainable params: 66                                                            
Non-trainable params: 0                                                         
Total params: 66                                                                
Total estimated model params size (MB): 0                                       
Modules in train mode: 2                                                        
Modules in eval mode: 0                                                         
Total FLOPs: 0                                                                  
Epoch 0/1                           1/64 0:00:00 • -:--:-- 0.00it/s v_num: 0.000
Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 64/64 0:00:00 • 0:00:00 3056.88it/s 
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer.fit` stopped: `max_steps=1` reached.
______________________ test_on_before_zero_grad_called[2] ______________________
----------------------------- Captured stdout call -----------------------------
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃    ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃
┡━━━━╇━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ 66 │      │      │        │      │       │
│ ?  │      │      │        │      │       │
│ ?  │      │      │        │      │       │
└────┴──────┴──────┴────────┴──────┴───────┘
Trainable params: 66                                                            
Non-trainable params: 0                                                         
Total params: 66                                                                
Total estimated model params size (MB): 0                                       
Modules in train mode: 2                                                        
Modules in eval mode: 0                                                         
Total FLOPs: 0                                                                  
Epoch 0/1  ╸                     2/64 0:00:00 • 0:00:01 1293.34it/s v_num: 0.000
Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 64/64 0:00:00 • 0:00:00 3099.44it/s 
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer.fit` stopped: `max_steps=2` reached.
______________________ test_on_before_zero_grad_called[3] ______________________
----------------------------- Captured stdout call -----------------------------
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃    ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃
┡━━━━╇━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ 66 │      │      │        │      │       │
│ ?  │      │      │        │      │       │
│ ?  │      │      │        │      │       │
└────┴──────┴──────┴────────┴──────┴───────┘
Trainable params: 66                                                            
Non-trainable params: 0                                                         
Total params: 66                                                                
Total estimated model params size (MB): 0                                       
Modules in train mode: 2                                                        
Modules in eval mode: 0                                                         
Total FLOPs: 0                                                                  
Epoch 0/1  ╸                     3/64 0:00:00 • 0:00:01 1366.72it/s v_num: 0.000
Testing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 64/64 0:00:00 • 0:00:00 3271.39it/s 
----------------------------- Captured stderr call -----------------------------
💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
`Trainer.fit` stopped: `max_steps=3` reached.
=========================== short test summary info ============================
PASSED tests/tests_pytorch/models/test_hooks.py::test_on_before_zero_grad_called[1]
PASSED tests/tests_pytorch/models/test_hooks.py::test_on_before_zero_grad_called[2]
PASSED tests/tests_pytorch/models/test_hooks.py::test_on_before_zero_grad_called[3]
SKIPPED [1] tests/tests_pytorch/models/test_hooks.py:106: Requires: [GPUs>=1]
SKIPPED [1] tests/tests_pytorch/models/test_hooks.py:106: Requires: [MPS]
SKIPPED [1] tests/tests_pytorch/models/test_hooks.py:162: Requires: [GPUs>=2 + Standalone execution]
SKIPPED [4] tests/tests_pytorch/models/test_hooks.py:406: Requires: [GPUs>=1]
SKIPPED [4] tests/tests_pytorch/models/test_hooks.py:406: Requires: [GPUs>=1 + Standalone execution + Deepspeed]
FAILED tests/tests_pytorch/models/test_hooks.py::test_on_train_epoch_end_metrics_collection
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit[True-True-kwargs0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit[True-False-kwargs0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit[False-True-kwargs0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit[False-False-kwargs0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_fit_no_val_and_resume_max_steps
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[True-validate-validation-val-x-0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[True-validate-validation-val-x-2]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[True-test-test-test-y-0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[True-test-test-test-y-2]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[False-validate-validation-val-x-0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[False-validate-validation-val-x-2]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[False-test-test-test-y-0]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_eval[False-test-test-test-y-2]
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_model_hook_system_predict
FAILED tests/tests_pytorch/models/test_hooks.py::test_hooks_with_different_argument_names
FAILED tests/tests_pytorch/models/test_hooks.py::test_trainer_datamodule_hook_system
FAILED tests/tests_pytorch/models/test_hooks.py::test_load_from_checkpoint_hook_calls[True]
FAILED tests/tests_pytorch/models/test_hooks.py::test_load_from_checkpoint_hook_calls[False]
FAILED tests/tests_pytorch/models/test_hooks.py::test_train_eval_mode_restored
============ 21 failed, 3 passed, 11 skipped, 27 warnings in 1.62s =============
Reward: 0
