





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 					A     (_a					):
      _SCREAMING_SNAKE_CASE       =				(CMStochasticIterativeScheduler,)
      _SCREAMING_SNAKE_CASE       =				10



      def  __a			(       self  ,	**lowercase_       )   -> Tuple:



             '''simple docstring'''

             _snake_case   :				Tuple  					=    {
                 "num_train_timesteps": 201,
                 "sigma_min": 0.002,
                 "sigma_max": 80.0,
             }

             config.update(**lowerCamelCase__       )
             return config



      def  __a			(       self       )   -> Union[str, Any]:



             '''simple docstring'''

             _snake_case   :				Dict  					=    10

             _snake_case   :				Optional[int]  					=    self.get_scheduler_config()
             _snake_case   :				Optional[Any]  					=    self.scheduler_classes[0](**lowerCamelCase__       )

             scheduler.set_timesteps(lowerCamelCase__       )

             _snake_case   :				Tuple  					=    scheduler.timesteps[0]
             _snake_case   :				int  					=    scheduler.timesteps[1]

             _snake_case   :				Tuple  					=    self.dummy_sample
             _snake_case   :				List[Any]  					=    0.1 * sample

             _snake_case   :				Tuple  					=    scheduler.step(lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__       ).prev_sample
             _snake_case   :				Any  					=    scheduler.step(lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__       ).prev_sample

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



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



             '''simple docstring'''

             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowerCamelCase__       )



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



             '''simple docstring'''

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



      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    self.scheduler_classes[0]
             _snake_case   :				str  					=    self.get_scheduler_config()
             _snake_case   :				Dict  					=    scheduler_class(**lowerCamelCase__       )

             _snake_case   :				Optional[int]  					=    1
             scheduler.set_timesteps(lowerCamelCase__       )
             _snake_case   :				Optional[Any]  					=    scheduler.timesteps

             _snake_case   :				List[Any]  					=    torch.manual_seed(0       )

             _snake_case   :				Optional[Any]  					=    self.dummy_model()
             _snake_case   :				Any  					=    self.dummy_sample_deter * scheduler.init_noise_sigma

             for i, t in enumerate(lowerCamelCase__       ):
                    # 1. scale model input
                    _snake_case   :				Dict  					=    scheduler.scale_model_input(lowerCamelCase__  ,	lowerCamelCase__       )

                    # 2. predict noise residual
                    _snake_case   :				Union[str, Any]  					=    model(lowerCamelCase__  ,	lowerCamelCase__       )

                    # 3. predict previous sample x_t-1
                    _snake_case   :				Optional[int]  					=    scheduler.step(lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	generator=lowerCamelCase__       ).prev_sample

                    _snake_case   :				str  					=    pred_prev_sample

             _snake_case   :				Optional[int]  					=    torch.sum(torch.abs(lowerCamelCase__       )       )
             _snake_case   :				Optional[Any]  					=    torch.mean(torch.abs(lowerCamelCase__       )       )

             assert abs(result_sum.item() - 192.7614       ) < 1E-2
             assert abs(result_mean.item() - 0.2510       ) < 1E-3



      def  __a			(       self       )   -> Union[str, Any]:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    self.scheduler_classes[0]
             _snake_case   :				List[str]  					=    self.get_scheduler_config()
             _snake_case   :				List[Any]  					=    scheduler_class(**lowerCamelCase__       )

             _snake_case   :				Any  					=    [106, 0]
             scheduler.set_timesteps(timesteps=lowerCamelCase__       )
             _snake_case   :				str  					=    scheduler.timesteps

             _snake_case   :				Dict  					=    torch.manual_seed(0       )

             _snake_case   :				Dict  					=    self.dummy_model()
             _snake_case   :				Optional[int]  					=    self.dummy_sample_deter * scheduler.init_noise_sigma

             for t in timesteps:
                    # 1. scale model input
                    _snake_case   :				Any  					=    scheduler.scale_model_input(lowerCamelCase__  ,	lowerCamelCase__       )

                    # 2. predict noise residual
                    _snake_case   :				int  					=    model(lowerCamelCase__  ,	lowerCamelCase__       )

                    # 3. predict previous sample x_t-1
                    _snake_case   :				Optional[Any]  					=    scheduler.step(lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	generator=lowerCamelCase__       ).prev_sample

                    _snake_case   :				List[str]  					=    pred_prev_sample

             _snake_case   :				Union[str, Any]  					=    torch.sum(torch.abs(lowerCamelCase__       )       )
             _snake_case   :				str  					=    torch.mean(torch.abs(lowerCamelCase__       )       )

             assert abs(result_sum.item() - 347.6357       ) < 1E-2
             assert abs(result_mean.item() - 0.4527       ) < 1E-3



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

             _snake_case   :				List[Any]  					=    self.scheduler_classes[0]
             _snake_case   :				int  					=    self.get_scheduler_config()
             _snake_case   :				List[Any]  					=    scheduler_class(**lowerCamelCase__       )

             _snake_case   :				Optional[int]  					=    [39, 30, 12, 15, 0]

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



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



             '''simple docstring'''

             _snake_case   :				Tuple  					=    self.scheduler_classes[0]
             _snake_case   :				str  					=    self.get_scheduler_config()
             _snake_case   :				Union[str, Any]  					=    scheduler_class(**lowerCamelCase__       )

             _snake_case   :				List[str]  					=    [39, 30, 12, 1, 0]
             _snake_case   :				Tuple  					=    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  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    self.scheduler_classes[0]
             _snake_case   :				int  					=    self.get_scheduler_config()
             _snake_case   :				Dict  					=    scheduler_class(**lowerCamelCase__       )

             _snake_case   :				Optional[Any]  					=    [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__       )









def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	int									= [], []
	while len(SCREAMING_SNAKE_CASE      ) > 1:
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= min(SCREAMING_SNAKE_CASE      ), max(SCREAMING_SNAKE_CASE      )
		start.append(SCREAMING_SNAKE_CASE      )
		end.append(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
	end.reverse()
	return start + collection + end


if __name__ == "__main__":
					_lowerCAmelCase  :      Union[str, Any]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Dict			 =[int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")