




"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class __lowercase(      __lowercase				):




					'''simple docstring'''


					__a						:						Any     		= (DEISMultistepScheduler,)
					__a						:						Tuple     		= (('num_inference_steps', 25),)
					def     snake_case_				(    self						,	**__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				{
								    'num_train_timesteps': 1000,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'solver_order': 2,
								}

								config.update(**_A     )
								return config
					def     snake_case_				(    self						,	__a=0						,	**__a     ):
								__lowerCamelCase					:	str	       =				dict(self.forward_default_kwargs     )
								__lowerCamelCase					:	List[Any]	       =				kwargs.pop('num_inference_steps'						,	_A     )
								__lowerCamelCase					:	List[str]	       =				self.dummy_sample
								__lowerCamelCase					:	Any	       =				0.1 * sample
								__lowerCamelCase					:	Optional[int]	       =				[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	Optional[int]	       =				self.get_scheduler_config(**_A     )
											__lowerCamelCase					:	Any	       =				scheduler_class(**_A     )
											scheduler.set_timesteps(_A     )
											# copy over dummy past residuals
											__lowerCamelCase					:	int	       =				dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_A     )
														__lowerCamelCase					:	Dict	       =				scheduler_class.from_pretrained(_A     )
														new_scheduler.set_timesteps(_A     )
														# copy over dummy past residuals
														__lowerCamelCase					:	int	       =				dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				sample, sample
											for t in range(_A						,	time_step + scheduler.config.solver_order + 1     ):
														__lowerCamelCase					:	Optional[Any]	       =				scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample
														__lowerCamelCase					:	str	       =				new_scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample

														assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self						,	__a=0						,	**__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				dict(self.forward_default_kwargs     )
								__lowerCamelCase					:	List[Any]	       =				kwargs.pop('num_inference_steps'						,	_A     )
								__lowerCamelCase					:	Optional[Any]	       =				self.dummy_sample
								__lowerCamelCase					:	Tuple	       =				0.1 * sample
								__lowerCamelCase					:	List[Any]	       =				[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	List[str]	       =				self.get_scheduler_config()
											__lowerCamelCase					:	List[Any]	       =				scheduler_class(**_A     )
											scheduler.set_timesteps(_A     )

											# copy over dummy past residuals (must be after setting timesteps)
											__lowerCamelCase					:	str	       =				dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_A     )
														__lowerCamelCase					:	Union[str, Any]	       =				scheduler_class.from_pretrained(_A     )
														# copy over dummy past residuals
														new_scheduler.set_timesteps(_A     )

														# copy over dummy past residual (must be after setting timesteps)
														__lowerCamelCase					:	Tuple	       =				dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowerCamelCase					:	Union[str, Any]	       =				scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample
											__lowerCamelCase					:	List[Any]	       =				new_scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample

											assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"
					def     snake_case_				(    self						,	__a=None						,	**__a     ):
								if scheduler is None:
											__lowerCamelCase					:	Dict	       =				self.scheduler_classes[0]
											__lowerCamelCase					:	int	       =				self.get_scheduler_config(**_A     )
											__lowerCamelCase					:	str	       =				scheduler_class(**_A     )

								__lowerCamelCase					:	Dict	       =				self.scheduler_classes[0]
								__lowerCamelCase					:	Optional[int]	       =				self.get_scheduler_config(**_A     )
								__lowerCamelCase					:	int	       =				scheduler_class(**_A     )

								__lowerCamelCase					:	List[Any]	       =				10
								__lowerCamelCase					:	Union[str, Any]	       =				self.dummy_model()
								__lowerCamelCase					:	Union[str, Any]	       =				self.dummy_sample_deter
								scheduler.set_timesteps(_A     )

								for i, t in enumerate(scheduler.timesteps     ):
											__lowerCamelCase					:	Dict	       =				model(_A						,	_A     )
											__lowerCamelCase					:	Tuple	       =				scheduler.step(_A						,	_A						,	_A     ).prev_sample

								return sample
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				dict(self.forward_default_kwargs     )

								__lowerCamelCase					:	Dict	       =				kwargs.pop('num_inference_steps'						,	_A     )

								for scheduler_class in self.scheduler_classes:
											__lowerCamelCase					:	Union[str, Any]	       =				self.get_scheduler_config()
											__lowerCamelCase					:	Any	       =				scheduler_class(**_A     )

											__lowerCamelCase					:	Optional[int]	       =				self.dummy_sample
											__lowerCamelCase					:	Tuple	       =				0.1 * sample

											if num_inference_steps is not None and hasattr(_A						,	'set_timesteps'     ):
														scheduler.set_timesteps(_A     )
											elif num_inference_steps is not None and not hasattr(_A						,	'set_timesteps'     ):
														__lowerCamelCase					:	List[Any]	       =				num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowerCamelCase					:	Any	       =				[residual + 0.2, residual + 0.15, residual + 0.10]
											__lowerCamelCase					:	Tuple	       =				dummy_past_residuals[: scheduler.config.solver_order]

											__lowerCamelCase					:	Optional[int]	       =				scheduler.timesteps[5]
											__lowerCamelCase					:	Any	       =				scheduler.timesteps[6]

											__lowerCamelCase					:	str	       =				scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample
											__lowerCamelCase					:	str	       =				scheduler.step(_A						,	_A						,	_A						,	**_A     ).prev_sample

											self.assertEqual(output_a.shape						,	sample.shape     )
											self.assertEqual(output_a.shape						,	output_a.shape     )
					def     snake_case_				(    self     ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								__lowerCamelCase					:	Dict	       =				DEISMultistepScheduler(**self.get_scheduler_config()     )
								__lowerCamelCase					:	List[str]	       =				self.full_loop(scheduler=_A     )
								__lowerCamelCase					:	Optional[int]	       =				torch.mean(torch.abs(_A     )     )

								assert abs(result_mean.item() - 0.23_916     ) < 1E-3

								__lowerCamelCase					:	Dict	       =				DPMSolverSinglestepScheduler.from_config(scheduler.config     )
								__lowerCamelCase					:	Optional[Any]	       =				DPMSolverMultistepScheduler.from_config(scheduler.config     )
								__lowerCamelCase					:	Dict	       =				UniPCMultistepScheduler.from_config(scheduler.config     )
								__lowerCamelCase					:	List[Any]	       =				DEISMultistepScheduler.from_config(scheduler.config     )

								__lowerCamelCase					:	Tuple	       =				self.full_loop(scheduler=_A     )
								__lowerCamelCase					:	Any	       =				torch.mean(torch.abs(_A     )     )

								assert abs(result_mean.item() - 0.23_916     ) < 1E-3
					def     snake_case_				(    self     ):
								for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=_A     )
					def     snake_case_				(    self     ):
								self.check_over_configs(thresholding=_A     )
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=_A						,	prediction_type=_A						,	sample_max_value=_A						,	algorithm_type='deis'						,	solver_order=_A						,	solver_type=_A						,	)
					def     snake_case_				(    self     ):
								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=_A     )
					def     snake_case_				(    self     ):
								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=_A						,	solver_type=_A						,	prediction_type=_A						,	algorithm_type=_A						,	)
																				__lowerCamelCase					:	Tuple	       =				self.full_loop(
																				    solver_order=_A						,	solver_type=_A						,	prediction_type=_A						,	algorithm_type=_A						,	)
																				assert not torch.isnan(_A     ).any(), "Samples have nan numbers"
					def     snake_case_				(    self     ):
								self.check_over_configs(lower_order_final=_A     )
								self.check_over_configs(lower_order_final=_A     )
					def     snake_case_				(    self     ):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=_A						,	time_step=0     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.full_loop()
								__lowerCamelCase					:	Union[str, Any]	       =				torch.mean(torch.abs(_A     )     )

								assert abs(result_mean.item() - 0.23_916     ) < 1E-3
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.full_loop(prediction_type='v_prediction'     )
								__lowerCamelCase					:	Any	       =				torch.mean(torch.abs(_A     )     )

								assert abs(result_mean.item() - 0.091     ) < 1E-3





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.scheduler_classes[0]
								__lowerCamelCase					:	Union[str, Any]	       =				self.get_scheduler_config(thresholding=_A						,	dynamic_thresholding_ratio=0     )
								__lowerCamelCase					:	List[str]	       =				scheduler_class(**_A     )

								__lowerCamelCase					:	List[Any]	       =				10
								__lowerCamelCase					:	Optional[Any]	       =				self.dummy_model()
								__lowerCamelCase					:	Tuple	       =				self.dummy_sample_deter.half()
								scheduler.set_timesteps(_A     )

								for i, t in enumerate(scheduler.timesteps     ):
											__lowerCamelCase					:	Dict	       =				model(_A						,	_A     )
											__lowerCamelCase					:	int	       =				scheduler.step(_A						,	_A						,	_A     ).prev_sample

								assert sample.dtype == torch.floataa









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			# getting number of pixels in the image
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				img.shape[0], img.shape[1]

			# converting each pixel's color to its negative
			for i in range(A__     ):
						for j in range(A__     ):
									__lowerCamelCase					:	Optional[Any]	       =				[255, 255, 255] - img[i][j]

			return img


if __name__ == "__main__":
					# read original image
					a_				:    Dict										=     imread('''image_data/lena.jpg''', 1)

					# convert to its negative
					a_				:    str										=     convert_to_negative(img)

					# show result image
					imshow('''negative of original image''', img)
					waitKey(0)
					destroyAllWindows()



