




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			(EulerDiscreteScheduler,)
			snake_case_  						=			10


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       {
							    'num_train_timesteps': 1_100,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**lowerCamelCase__				)
							return config


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							for timesteps in [10, 50, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]				):
											self.check_over_configs(beta_start=lowerCamelCase__     ,		beta_end=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config()
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							scheduler.set_timesteps(self.num_inference_steps				)

							__lowerCamelCase									=       torch.manual_seed(0				)

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowerCamelCase									=       sample.to(lowerCamelCase__				)

							for i, t in enumerate(scheduler.timesteps				):
											__lowerCamelCase									=       scheduler.scale_model_input(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		generator=lowerCamelCase__				)
											__lowerCamelCase									=       output.prev_sample

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 10.08_07				) < 1e-2
							assert abs(result_mean.item() - 0.01_31				) < 1e-3


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config(prediction_type='v_prediction'				)
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							scheduler.set_timesteps(self.num_inference_steps				)

							__lowerCamelCase									=       torch.manual_seed(0				)

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowerCamelCase									=       sample.to(lowerCamelCase__				)

							for i, t in enumerate(scheduler.timesteps				):
											__lowerCamelCase									=       scheduler.scale_model_input(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		generator=lowerCamelCase__				)
											__lowerCamelCase									=       output.prev_sample

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 0.00_02				) < 1e-2
							assert abs(result_mean.item() - 2.26_76e-06				) < 1e-3


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config()
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							scheduler.set_timesteps(self.num_inference_steps     ,		device=lowerCamelCase__				)

							__lowerCamelCase									=       torch.manual_seed(0				)

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							__lowerCamelCase									=       sample.to(lowerCamelCase__				)

							for t in scheduler.timesteps:
											__lowerCamelCase									=       scheduler.scale_model_input(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		generator=lowerCamelCase__				)
											__lowerCamelCase									=       output.prev_sample

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 10.08_07				) < 1e-2
							assert abs(result_mean.item() - 0.01_31				) < 1e-3






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config()
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__     ,		use_karras_sigmas=lowerCamelCase__				)

							scheduler.set_timesteps(self.num_inference_steps     ,		device=lowerCamelCase__				)

							__lowerCamelCase									=       torch.manual_seed(0				)

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							__lowerCamelCase									=       sample.to(lowerCamelCase__				)

							for t in scheduler.timesteps:
											__lowerCamelCase									=       scheduler.scale_model_input(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)

											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		generator=lowerCamelCase__				)
											__lowerCamelCase									=       output.prev_sample

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19				) < 1e-2
							assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63				) < 1e-3








def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       len(UpperCamelCase__ )
								if len(UpperCamelCase__ ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowerCamelCase									=       words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(UpperCamelCase__ ):
																num_spaces_between_words_list[i] += 1
												__lowerCamelCase									=       []
												for i in range(UpperCamelCase__ ):
																# add the word
																aligned_words_list.append(line[i] )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1] )
												# join the aligned words list to form a justified line
												return "".join(UpperCamelCase__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				for word in words:
								if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(UpperCamelCase__ )
												width += len(UpperCamelCase__ )
								else:
												# justify the line and add it to result
												answer.append(justify(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ ) )
												# reset new line and new width
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


if __name__ == "__main__":
					from doctest import testmod

					testmod()


