




'''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				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						str)    ->							Union[str, Any]:
								A_									= []



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Any      ,       _lowercase     :						Dict      ,       _lowercase     :						Dict      ,       **_lowercase     :						Dict)    ->							List[str]:
								self.events.append('on_init_end')



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						str      ,       _lowercase     :						Tuple      ,       _lowercase     :						Union[str, Any]      ,       **_lowercase     :						List[str])    ->							int:
								self.events.append('on_train_begin')



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       **_lowercase     :						Optional[Any])    ->							Optional[int]:
								self.events.append('on_train_end')



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						List[Any]      ,       **_lowercase     :						Union[str, Any])    ->							str:
								self.events.append('on_epoch_begin')



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Union[str, Any]      ,       **_lowercase     :						Any)    ->							Optional[int]:
								self.events.append('on_epoch_end')



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						int      ,       _lowercase     :						Dict      ,       _lowercase     :						Optional[Any]      ,       **_lowercase     :						List[str])    ->							List[str]:
								self.events.append('on_step_begin')



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						List[Any]      ,       **_lowercase     :						int)    ->							List[str]:
								self.events.append('on_step_end')



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						str      ,       **_lowercase     :						Optional[Any])    ->							Union[str, Any]:
								self.events.append('on_evaluate')



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str      ,       **_lowercase     :						str)    ->							List[Any]:
								self.events.append('on_predict')



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						str      ,       _lowercase     :						List[str]      ,       **_lowercase     :						Optional[Any])    ->							List[Any]:
								self.events.append('on_save')



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Tuple      ,       _lowercase     :						Any      ,       **_lowercase     :						List[Any])    ->							Optional[Any]:
								self.events.append('on_log')



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Dict      ,       _lowercase     :						int      ,       _lowercase     :						int      ,       **_lowercase     :						Dict)    ->							List[str]:
								self.events.append('on_prediction_step')





@require_torch
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								A_									= tempfile.mkdtemp()



					def     __snake_case						(	self     :						List[Any])    ->							Dict:
								shutil.rmtree(self.output_dir)



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						int=0      ,       _lowercase     :						str=64      ,       _lowercase     :						Any=64      ,       _lowercase     :						Tuple=None      ,       _lowercase     :						List[Any]=False      ,       **_lowercase     :						Dict)    ->							Optional[int]:
								# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
								# its set to False since the tests later on depend on its value.
								A_									= RegressionDataset(length=_lowercase)
								A_									= RegressionDataset(length=_lowercase)
								A_									= RegressionModelConfig(a=_lowercase      ,       b=_lowercase)
								A_									= RegressionPreTrainedModel(_lowercase)

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



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Optional[Any])    ->							List[Any]:
								self.assertEqual(len(_lowercase)      ,       len(_lowercase))

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

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



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						List[Any])    ->							str:
								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(_lowercase):
														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     :						List[str])    ->							Union[str, Any]:
								A_									= self.get_trainer()
								A_									= DEFAULT_CALLBACKS.copy() + [ProgressCallback]
								self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _lowercase)

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

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



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

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

								A_									= self.get_trainer()
								A_									= trainer.pop_callback(_lowercase)
								self.assertEqual(cb.__class__      ,       _lowercase)
								self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _lowercase)

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

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

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

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



					def     __snake_case						(	self     :						Tuple)    ->							Any:
								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=_lowercase)

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

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

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

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

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

								# 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(_lowercase      ,       self.get_expected_events(_lowercase))

								# 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(_lowercase) in warn_mock.call_args[0][0]










'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




