
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	(CMStochasticIterativeScheduler,)
	snake_case_          =	10






	def 							UpperCamelCase_    (			self   :					str				,**A   :					List[str]					):
					__A       =					{
					    "num_train_timesteps": 2_01,
					    "sigma_min": 0.0_02,
					    "sigma_max": 80.0,
					}

					config.update(**A					)
					return config






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					10

					__A       =					self.get_scheduler_config()
					__A       =					self.scheduler_classes[0](**A					)

					scheduler.set_timesteps(A					)

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

					__A       =					self.dummy_sample
					__A       =					0.1 * sample

					__A       =					scheduler.step(A				,A				,A					).prev_sample
					__A       =					scheduler.step(A				,A				,A					).prev_sample

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






	def 							UpperCamelCase_    (			self   :					Dict					):
					for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=A					)






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.scheduler_classes[0]
					__A       =					self.get_scheduler_config()
					__A       =					scheduler_class(**A					)

					__A       =					1
					scheduler.set_timesteps(A					)
					__A       =					scheduler.timesteps

					__A       =					torch.manual_seed(0					)

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

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

									# 2. predict noise residual
									__A       =					model(A				,A					)

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

									__A       =					pred_prev_sample

					__A       =					torch.sum(torch.abs(A					)					)
					__A       =					torch.mean(torch.abs(A					)					)

					assert abs(result_sum.item() - 1_92.76_14					) < 1E-2
					assert abs(result_mean.item() - 0.25_10					) < 1E-3






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.scheduler_classes[0]
					__A       =					self.get_scheduler_config()
					__A       =					scheduler_class(**A					)

					__A       =					[1_06, 0]
					scheduler.set_timesteps(timesteps=A					)
					__A       =					scheduler.timesteps

					__A       =					torch.manual_seed(0					)

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

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

									# 2. predict noise residual
									__A       =					model(A				,A					)

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

									__A       =					pred_prev_sample

					__A       =					torch.sum(torch.abs(A					)					)
					__A       =					torch.mean(torch.abs(A					)					)

					assert abs(result_sum.item() - 3_47.63_57					) < 1E-2
					assert abs(result_mean.item() - 0.45_27					) < 1E-3






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.scheduler_classes[0]
					__A       =					self.get_scheduler_config()
					__A       =					scheduler_class(**A					)

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

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






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.scheduler_classes[0]
					__A       =					self.get_scheduler_config()
					__A       =					scheduler_class(**A					)

					__A       =					[39, 30, 12, 1, 0]
					__A       =					len(A					)

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







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.scheduler_classes[0]
					__A       =					self.get_scheduler_config()
					__A       =					scheduler_class(**A					)

					__A       =					[scheduler.config.num_train_timesteps]

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





UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



