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  Stored in directory: /tmp/pip-ephem-wheel-cache-oedqwcof/wheels/5d/7e/d4/0611b5e1f6b19283dfc03cca62ebc6a41021c5c464d28aab8f
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=== 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 FFFFsssFssFssFssFssFFFFFFFFFFFF [ 88%]
FFFF                                                                     [100%]

=================================== FAILURES ===================================
______________________ test_on_before_zero_grad_called[1] ______________________
tests/tests_pytorch/models/test_hooks.py:66: in test_on_before_zero_grad_called
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'RichModelSummary' object has no attribute 'setup'
----------------------------- 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_on_before_zero_grad_called[2] ______________________
tests/tests_pytorch/models/test_hooks.py:66: in test_on_before_zero_grad_called
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'RichModelSummary' object has no attribute 'setup'
----------------------------- 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_on_before_zero_grad_called[3] ______________________
tests/tests_pytorch/models/test_hooks.py:66: in test_on_before_zero_grad_called
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'RichModelSummary' object has no attribute 'setup'
----------------------------- 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_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:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'RichModelSummary' object has no attribute 'setup'
----------------------------- 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:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1360: 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:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1360: 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:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1360: 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:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1360: 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:540: in test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'HookedCallback' object has no attribute 'setup'
----------------------------- 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_steps ________
tests/tests_pytorch/models/test_hooks.py:618: in test_trainer_model_hook_system_fit_no_val_and_resume_max_steps
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'HookedCallback' object has no attribute 'setup'
----------------------------- 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-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:676: in validate
    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:726: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:39: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "val")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:676: in validate
    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:726: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:39: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "val")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:774: in test
    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:824: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:41: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "test")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:774: in test
    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:824: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:41: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "test")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:676: in validate
    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:726: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:39: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "val")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:676: in validate
    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:726: in _validate_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:39: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "val")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:774: in test
    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:824: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:41: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "test")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:774: in test
    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:824: in _test_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:41: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "test")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:877: 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:923: in _predict_impl
    results = self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:1350: in _run
    _verify_loop_configurations(self)
src/lightning/pytorch/trainer/configuration_validator.py:43: in _verify_loop_configurations
    __verify_eval_loop_configuration(model, "predict")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '__verify_eval_loop_configuration' is not defined
----------------------------- 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:811: in test_hooks_with_different_argument_names
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1362: in _run
    call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/call.py:109: in _call_setup_hook
    _call_callback_hooks(trainer, "setup", stage=fn)
src/lightning/pytorch/trainer/call.py:225: in _call_callback_hooks
    fn = getattr(callback, hook_name)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'RichModelSummary' object has no attribute 'setup'
----------------------------- 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.
_____________________ test_trainer_datamodule_hook_system ______________________
src/lightning/pytorch/trainer/call.py:49: in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/trainer.py:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1360: 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

During handling of the above exception, another exception occurred:
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:582: in fit
    call._call_and_handle_interrupt(
src/lightning/pytorch/trainer/call.py:69: in _call_and_handle_interrupt
    _interrupt(trainer, exception)
src/lightning/pytorch/trainer/call.py:80: in _interrupt
    _call_lightning_datamodule_hook(trainer, "on_exception", exception)
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   NameError: name '_call_lightning_datamodule_hook' is not defined
----------------------------- 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:904: in test_load_from_checkpoint_hook_calls
    trainer.save_checkpoint(ckpt_path)
src/lightning/pytorch/trainer/trainer.py:955: in save_checkpoint
    checkpoint = self._checkpoint_connector.dump_checkpoint(weights_only)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/connectors/checkpoint_connector.py:441: in dump_checkpoint
    checkpoint["precision"] = trainer.precision_plugin.state_dict()
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'Precision' object has no attribute 'state_dict'
----------------------------- 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:904: in test_load_from_checkpoint_hook_calls
    trainer.save_checkpoint(ckpt_path)
src/lightning/pytorch/trainer/trainer.py:955: in save_checkpoint
    checkpoint = self._checkpoint_connector.dump_checkpoint(weights_only)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/trainer/connectors/checkpoint_connector.py:441: in dump_checkpoint
    checkpoint["precision"] = trainer.precision_plugin.state_dict()
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   AttributeError: 'Precision' object has no attribute 'state_dict'
----------------------------- 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:985: in test_train_eval_mode_restored
    trainer.fit(model)
src/lightning/pytorch/trainer/trainer.py:582: 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:628: in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
src/lightning/pytorch/trainer/trainer.py:1385: in _run
    _log_hyperparams(self)
src/lightning/pytorch/loggers/utilities.py:102: in _log_hyperparams
    logger.save()
/opt/miniconda3/envs/testbed/lib/python3.12/site-packages/lightning_utilities/core/rank_zero.py:41: in wrapped_fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
src/lightning/pytorch/loggers/tensorboard.py:221: in save
    if _is_dir(self._fs, dir_path) and not self._fs.isfile(hparams_file):
       ^^^^^^^
E   NameError: name '_is_dir' is not defined
----------------------------- 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.
=========================== short test summary info ============================
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_before_zero_grad_called[1]
FAILED tests/tests_pytorch/models/test_hooks.py::test_on_before_zero_grad_called[2]
FAILED tests/tests_pytorch/models/test_hooks.py::test_on_before_zero_grad_called[3]
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
======================== 24 failed, 11 skipped in 1.54s ========================
Reward: 0
