
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class    a		( __UpperCAmelCase       ):
							lowercase_		:    List[str] 				=	(CMStochasticIterativeScheduler,)
							lowercase_		:    Any 				=	10







							def    UpperCAmelCase__       (    self	:	Optional[int]   ,					**snake_case__	:	int			):
								"""simple docstring"""







								__lowerCAmelCase    =				{
								    '''num_train_timesteps''': 201,
								    '''sigma_min''': 0.0_0_2,
								    '''sigma_max''': 8_0.0,
								}

								config.update(**lowerCamelCase_			)
								return config







							def    UpperCAmelCase__       (    self	:	Dict			):
								"""simple docstring"""







								__lowerCAmelCase    =				10

								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				self.scheduler_classes[0](**lowerCamelCase_			)

								scheduler.set_timesteps(lowerCamelCase_			)

								__lowerCAmelCase    =				scheduler.timesteps[0]
								__lowerCAmelCase    =				scheduler.timesteps[1]

								__lowerCAmelCase    =				self.dummy_sample
								__lowerCAmelCase    =				0.1 * sample

								__lowerCAmelCase    =				scheduler.step(lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_			).prev_sample
								__lowerCAmelCase    =				scheduler.step(lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_			).prev_sample

								self.assertEqual(output_a.shape   ,					sample.shape			)
								self.assertEqual(output_a.shape   ,					output_a.shape			)







							def    UpperCAmelCase__       (    self	:	Union[str, Any]			):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







								for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	List[Any]			):
								"""simple docstring"""







								__lowerCAmelCase    =				self.scheduler_classes[0]
								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				scheduler_class(**lowerCamelCase_			)

								__lowerCAmelCase    =				1
								scheduler.set_timesteps(lowerCamelCase_			)
								__lowerCAmelCase    =				scheduler.timesteps

								__lowerCAmelCase    =				torch.manual_seed(0			)

								__lowerCAmelCase    =				self.dummy_model()
								__lowerCAmelCase    =				self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(lowerCamelCase_			):
									# 1. scale model input
									__lowerCAmelCase    =				scheduler.scale_model_input(lowerCamelCase_   ,					lowerCamelCase_			)

									# 2. predict noise residual
									__lowerCAmelCase    =				model(lowerCamelCase_   ,					lowerCamelCase_			)

									# 3. predict previous sample x_t-1
									__lowerCAmelCase    =				scheduler.step(lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_   ,					generator=lowerCamelCase_			).prev_sample

									__lowerCAmelCase    =				pred_prev_sample

								__lowerCAmelCase    =				torch.sum(torch.abs(lowerCamelCase_			)			)
								__lowerCAmelCase    =				torch.mean(torch.abs(lowerCamelCase_			)			)

								assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
								assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3







							def    UpperCAmelCase__       (    self	:	Tuple			):
								"""simple docstring"""







								__lowerCAmelCase    =				self.scheduler_classes[0]
								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				scheduler_class(**lowerCamelCase_			)

								__lowerCAmelCase    =				[106, 0]
								scheduler.set_timesteps(timesteps=lowerCamelCase_			)
								__lowerCAmelCase    =				scheduler.timesteps

								__lowerCAmelCase    =				torch.manual_seed(0			)

								__lowerCAmelCase    =				self.dummy_model()
								__lowerCAmelCase    =				self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
									# 1. scale model input
									__lowerCAmelCase    =				scheduler.scale_model_input(lowerCamelCase_   ,					lowerCamelCase_			)

									# 2. predict noise residual
									__lowerCAmelCase    =				model(lowerCamelCase_   ,					lowerCamelCase_			)

									# 3. predict previous sample x_t-1
									__lowerCAmelCase    =				scheduler.step(lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_   ,					generator=lowerCamelCase_			).prev_sample

									__lowerCAmelCase    =				pred_prev_sample

								__lowerCAmelCase    =				torch.sum(torch.abs(lowerCamelCase_			)			)
								__lowerCAmelCase    =				torch.mean(torch.abs(lowerCamelCase_			)			)

								assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
								assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3







							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				self.scheduler_classes[0]
								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				scheduler_class(**lowerCamelCase_			)

								__lowerCAmelCase    =				[39, 30, 12, 15, 0]

								with self.assertRaises(lowerCamelCase_   ,					msg="`timesteps` must be in descending order."			):
									scheduler.set_timesteps(timesteps=lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				self.scheduler_classes[0]
								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				scheduler_class(**lowerCamelCase_			)

								__lowerCAmelCase    =				[39, 30, 12, 1, 0]
								__lowerCAmelCase    =				len(lowerCamelCase_			)

								with self.assertRaises(lowerCamelCase_   ,					msg="Can only pass one of `num_inference_steps` or `timesteps`."			):
									scheduler.set_timesteps(num_inference_steps=lowerCamelCase_   ,					timesteps=lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				self.scheduler_classes[0]
								__lowerCAmelCase    =				self.get_scheduler_config()
								__lowerCAmelCase    =				scheduler_class(**lowerCamelCase_			)

								__lowerCAmelCase    =				[scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    lowerCamelCase_   ,					msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"   ,					):
									scheduler.set_timesteps(timesteps=lowerCamelCase_			)







'''simple docstring'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




