

"""simple docstring"""




import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
				from transformers.trainer import DEFAULT_CALLBACKS

				from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class     UpperCamelCase__(			__A ):





						def __init__(       self     )					->				List[str]:
													A__	     =							[]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				int:
													self.events.append('on_init_end'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													self.events.append('on_train_begin'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													self.events.append('on_train_end'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													self.events.append('on_epoch_begin'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Union[str, Any]:
													self.events.append('on_epoch_end'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				int:
													self.events.append('on_step_begin'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Union[str, Any]:
													self.events.append('on_step_end'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													self.events.append('on_evaluate'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													self.events.append('on_predict'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													self.events.append('on_save'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Tuple:
													self.events.append('on_log'     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				str:
													self.events.append('on_prediction_step'     )






@require_torch
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							tempfile.mkdtemp()





						def 				snake_case__	(       self     )					->				Any:
													shutil.rmtree(self.output_dir     )





						def 				snake_case__	(       self		,__UpperCAmelCase=0		,__UpperCAmelCase=0		,__UpperCAmelCase=64		,__UpperCAmelCase=64		,__UpperCAmelCase=None		,__UpperCAmelCase=False		,**__UpperCAmelCase     )					->				Dict:
													A__	     =							RegressionDataset(length=__a     )
													A__	     =							RegressionDataset(length=__a     )
													A__	     =							RegressionModelConfig(a=__a		,b=__a     )
													A__	     =							RegressionPreTrainedModel(__a     )

													A__	     =							TrainingArguments(self.output_dir		,disable_tqdm=__a		,report_to=[]		,**__a     )
													return Trainer(
													    __a		,__a		,train_dataset=__a		,eval_dataset=__a		,callbacks=__a		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													self.assertEqual(len(__a     )		,len(__a     )     )

													# Order doesn't matter
													A__	     =							sorted(__a		,key=lambda __UpperCAmelCase     : cb.__name__ if isinstance(__a		,__a     ) else cb.__class__.__name__     )
													A__	     =							sorted(__a		,key=lambda __UpperCAmelCase     : cb.__name__ if isinstance(__a		,__a     ) else cb.__class__.__name__     )

													for cba, cba in zip(__a		,__a     ):
																				if isinstance(__a		,__a     ) and isinstance(__a		,__a     ):
																											self.assertEqual(__a		,__a     )
																				elif isinstance(__a		,__a     ) and not isinstance(__a		,__a     ):
																											self.assertEqual(__a		,cba.__class__     )
																				elif not isinstance(__a		,__a     ) and isinstance(__a		,__a     ):
																											self.assertEqual(cba.__class__		,__a     )
																				else:
																											self.assertEqual(__a		,__a     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Union[str, Any]:
													A__	     =							['on_init_end', 'on_train_begin']
													A__	     =							0
													A__	     =							len(trainer.get_eval_dataloader()     )
													A__	     =							['on_prediction_step'] * len(trainer.get_eval_dataloader()     ) + ['on_log', 'on_evaluate']
													for _ in range(trainer.state.num_train_epochs     ):
																				expected_events.append('on_epoch_begin'     )
																				for _ in range(__a     ):
																											step += 1
																											expected_events += ["on_step_begin", "on_step_end"]
																											if step % trainer.args.logging_steps == 0:
																																		expected_events.append('on_log'     )
																											if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																																		expected_events += evaluation_events.copy()
																											if step % trainer.args.save_steps == 0:
																																		expected_events.append('on_save'     )
																				expected_events.append('on_epoch_end'     )
																				if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
																											expected_events += evaluation_events.copy()
													expected_events += ["on_log", "on_train_end"]
													return expected_events





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							self.get_trainer()
													A__	     =							DEFAULT_CALLBACKS.copy() + [ProgressCallback]
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													# Callbacks passed at init are added to the default callbacks
													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]     )
													expected_callbacks.append(__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
													A__	     =							self.get_trainer(disable_tqdm=__a     )
													A__	     =							DEFAULT_CALLBACKS.copy() + [PrinterCallback]
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							DEFAULT_CALLBACKS.copy() + [ProgressCallback]
													A__	     =							self.get_trainer()

													# We can add, pop, or remove by class name
													trainer.remove_callback(__a     )
													expected_callbacks.remove(__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													A__	     =							self.get_trainer()
													A__	     =							trainer.pop_callback(__a     )
													self.assertEqual(cb.__class__		,__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													trainer.add_callback(__a     )
													expected_callbacks.insert(0		,__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													# We can also add, pop, or remove by instance
													A__	     =							self.get_trainer()
													A__	     =							trainer.callback_handler.callbacks[0]
													trainer.remove_callback(__a     )
													expected_callbacks.remove(__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													A__	     =							self.get_trainer()
													A__	     =							trainer.callback_handler.callbacks[0]
													A__	     =							trainer.pop_callback(__a     )
													self.assertEqual(__a		,__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )

													trainer.add_callback(__a     )
													expected_callbacks.insert(0		,__a     )
													self.check_callbacks_equality(trainer.callback_handler.callbacks		,__a     )





						def 				snake_case__	(       self     )					->				str:
													import warnings

													# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
													warnings.simplefilter(action='ignore'		,category=__a     )

													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]     )
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													# Independent log/save/eval
													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		,logging_steps=5     )
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		,save_steps=5     )
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		,eval_steps=5		,evaluation_strategy='steps'     )
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													A__	     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		,evaluation_strategy='epoch'     )
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													# A bit of everything
													A__	     =							self.get_trainer(
													    callbacks=[MyTestTrainerCallback]		,logging_steps=3		,save_steps=10		,eval_steps=5		,evaluation_strategy='steps'		,)
													trainer.train()
													A__	     =							trainer.callback_handler.callbacks[-2].events
													self.assertEqual(__a		,self.get_expected_events(__a     )     )

													# warning should be emitted for duplicated callbacks
													with patch('transformers.trainer_callback.logger.warning'     ) as warn_mock:
																				A__	     =							self.get_trainer(
																				    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]		,)
																				assert str(__a     ) in warn_mock.call_args[0][0]






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase											=  {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["GLPNFeatureExtractor"]
				__lowerCamelCase											=  ["GLPNImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GLPNForDepthEstimation",
				    "GLPNLayer",
				    "GLPNModel",
				    "GLPNPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_glpn import GLPNFeatureExtractor
								from .image_processing_glpn import GLPNImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_glpn import (
								    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GLPNForDepthEstimation,
								    GLPNLayer,
								    GLPNModel,
								    GLPNPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





