
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Dict    =	(UnCLIPScheduler,)

			def  lowerCAmelCase__		(    self	:    int  ,		**snake_case_	:    List[Any] ):
							UpperCamelCase_:							Optional[int]		=  {
							    """num_train_timesteps""": 1000,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**snake_case_ )
							return config

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_ )

			def  lowerCAmelCase__		(    self	:    int ):
							for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    int ):
							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
															if prev_timestep is not None and prev_timestep >= time_step:
																			continue

															self.check_over_forward(time_step=snake_case_  ,		prev_timestep=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							List[str]		=  self.scheduler_classes[0]
							UpperCamelCase_:							Dict		=  self.get_scheduler_config(variance_type="""fixed_small_log""" )
							UpperCamelCase_:							str		=  scheduler_class(**snake_case_ )

							assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_000e-10 ) ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.054_9625 ) ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.999_4987 ) ) < 1e-5

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							Tuple		=  self.scheduler_classes[0]
							UpperCamelCase_:							str		=  self.get_scheduler_config(variance_type="""learned_range""" )
							UpperCamelCase_:							Any		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							List[str]		=  0.5

							assert scheduler._get_variance(1  ,		predicted_variance=snake_case_ ) - -10.171_2790 < 1e-5
							assert scheduler._get_variance(487  ,		predicted_variance=snake_case_ ) - -5.799_8052 < 1e-5
							assert scheduler._get_variance(999  ,		predicted_variance=snake_case_ ) - -0.001_0011 < 1e-5

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							Union[str, Any]		=  self.scheduler_classes[0]
							UpperCamelCase_:							str		=  self.get_scheduler_config()
							UpperCamelCase_:							Tuple		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Optional[int]		=  scheduler.timesteps

							UpperCamelCase_:							Optional[Any]		=  self.dummy_model()
							UpperCamelCase_:							Optional[Any]		=  self.dummy_sample_deter
							UpperCamelCase_:							str		=  torch.manual_seed(0 )

							for i, t in enumerate(snake_case_ ):
											# 1. predict noise residual
											UpperCamelCase_:							Optional[Any]		=  model(snake_case_  ,		snake_case_ )

											# 2. predict previous mean of sample x_t-1
											UpperCamelCase_:							List[Any]		=  scheduler.step(snake_case_  ,		snake_case_  ,		snake_case_  ,		generator=snake_case_ ).prev_sample

											UpperCamelCase_:							Tuple		=  pred_prev_sample

							UpperCamelCase_:							Optional[Any]		=  torch.sum(torch.abs(snake_case_ ) )
							UpperCamelCase_:							str		=  torch.mean(torch.abs(snake_case_ ) )

							assert abs(result_sum.item() - 252.268_2495 ) < 1e-2
							assert abs(result_mean.item() - 0.328_4743 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							Optional[int]		=  self.scheduler_classes[0]
							UpperCamelCase_:							int		=  self.get_scheduler_config()
							UpperCamelCase_:							int		=  scheduler_class(**snake_case_ )

							scheduler.set_timesteps(25 )

							UpperCamelCase_:							Union[str, Any]		=  scheduler.timesteps

							UpperCamelCase_:							Union[str, Any]		=  self.dummy_model()
							UpperCamelCase_:							Optional[Any]		=  self.dummy_sample_deter
							UpperCamelCase_:							Union[str, Any]		=  torch.manual_seed(0 )

							for i, t in enumerate(snake_case_ ):
											# 1. predict noise residual
											UpperCamelCase_:							List[str]		=  model(snake_case_  ,		snake_case_ )

											if i + 1 == timesteps.shape[0]:
															UpperCamelCase_:							Tuple		=  None
											else:
															UpperCamelCase_:							int		=  timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											UpperCamelCase_:							Any		=  scheduler.step(
											    snake_case_  ,		snake_case_  ,		snake_case_  ,		prev_timestep=snake_case_  ,		generator=snake_case_ ).prev_sample

											UpperCamelCase_:							Optional[Any]		=  pred_prev_sample

							UpperCamelCase_:							int		=  torch.sum(torch.abs(snake_case_ ) )
							UpperCamelCase_:							Optional[int]		=  torch.mean(torch.abs(snake_case_ ) )

							assert abs(result_sum.item() - 258.204_4983 ) < 1e-2
							assert abs(result_mean.item() - 0.336_2038 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							pass




			def  lowerCAmelCase__		(    self	:    Dict ):
							pass








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		list:
				UpperCamelCase_:							Optional[int]		=  word.split()

				def justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  ) -> str:
								UpperCamelCase_:							Tuple		=  max_width - width
								UpperCamelCase_:							Optional[Any]		=  len(lowerCamelCase  )
								if len(lowerCamelCase  ) == 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:
												UpperCamelCase_:							List[Any]		=  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]
												UpperCamelCase_:							Optional[Any]		=  spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												UpperCamelCase_:							List[str]		=  (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowerCamelCase  ):
																num_spaces_between_words_list[i] += 1
												UpperCamelCase_:							Dict		=  []
												for i in range(lowerCamelCase  ):
																# 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(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							list[str]		=  []
				UpperCamelCase_:							List[str]		=  0
				for word in words:
								if width + len(lowerCamelCase  ) + len(lowerCamelCase  ) <= 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(lowerCamelCase  )
												width += len(lowerCamelCase  )
								else:
												# justify the line and add it to result
												answer.append(justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )  )
												# reset new line and new width
												UpperCamelCase_,				UpperCamelCase_:							List[str]		=  [word], len(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  max_width - width - len(lowerCamelCase  )
				answer.append(""" """.join(lowerCamelCase  ) + (remaining_spaces + 1) * """ """  )
				return answer


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

							testmod()






