
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						UpperCamelCase__    (			a_       ):
   _SCREAMING_SNAKE_CASE :				List[str]      			= (KDPMaDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE :				Tuple      			= 10
   def  lowerCAmelCase     (self					:   Optional[int]   ,							**snake_case_					:   Dict  ):
    __a					:    Union[str, Any]													=    {
        '''num_train_timesteps''': 1_1_0_0,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**lowercase_  )
    return config
   def  lowerCAmelCase     (self					:   Optional[int]  ):
    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=lowercase_  )
   def  lowerCAmelCase     (self					:   Dict  ):
    for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,							[0.0002, 0.002, 0.02]  ):
     self.check_over_configs(beta_start=lowercase_   ,							beta_end=lowercase_  )
   def  lowerCAmelCase     (self					:   Optional[Any]  ):
    for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=lowercase_  )
   def  lowerCAmelCase     (self					:   Tuple  ):
    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=lowercase_  )
   def  lowerCAmelCase     (self					:   Optional[Any]  ):
    __a					:    Tuple													=    self.scheduler_classes[0]
    __a					:    Dict													=    self.get_scheduler_config(prediction_type='''v_prediction'''  )
    __a					:    List[Any]													=    scheduler_class(**lowercase_  )

    scheduler.set_timesteps(self.num_inference_steps  )

    __a					:    Any													=    self.dummy_model()
    __a					:    Optional[Any]													=    self.dummy_sample_deter * scheduler.init_noise_sigma
    __a					:    Optional[Any]													=    sample.to(lowercase_  )

    for i, t in enumerate(scheduler.timesteps  ):
     __a					:    Any													=    scheduler.scale_model_input(lowercase_   ,							lowercase_  )

     __a					:    List[Any]													=    model(lowercase_   ,							lowercase_  )

     __a					:    int													=    scheduler.step(lowercase_   ,							lowercase_   ,							lowercase_  )
     __a					:    List[Any]													=    output.prev_sample

    __a					:    int													=    torch.sum(torch.abs(lowercase_  )  )
    __a					:    Optional[Any]													=    torch.mean(torch.abs(lowercase_  )  )

    if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 4.6934E-07  ) < 1E-2
     assert abs(result_mean.item() - 6.1112E-10  ) < 1E-3
    else:
     # CUDA
     assert abs(result_sum.item() - 4.693_4286_5017_0972E-07  ) < 1E-2
     assert abs(result_mean.item() - 0.0002  ) < 1E-3
   def  lowerCAmelCase     (self					:   int  ):
    if torch_device == "mps":
     return
    __a					:    List[Any]													=    self.scheduler_classes[0]
    __a					:    Optional[int]													=    self.get_scheduler_config()
    __a					:    Optional[Any]													=    scheduler_class(**lowercase_  )

    scheduler.set_timesteps(self.num_inference_steps  )

    __a					:    Optional[Any]													=    self.dummy_model()
    __a					:    List[Any]													=    self.dummy_sample_deter * scheduler.init_noise_sigma
    __a					:    List[str]													=    sample.to(lowercase_  )

    for i, t in enumerate(scheduler.timesteps  ):
     __a					:    str													=    scheduler.scale_model_input(lowercase_   ,							lowercase_  )

     __a					:    Tuple													=    model(lowercase_   ,							lowercase_  )

     __a					:    Any													=    scheduler.step(lowercase_   ,							lowercase_   ,							lowercase_  )
     __a					:    int													=    output.prev_sample

    __a					:    List[Any]													=    torch.sum(torch.abs(lowercase_  )  )
    __a					:    Union[str, Any]													=    torch.mean(torch.abs(lowercase_  )  )

    if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 20.4125  ) < 1E-2
     assert abs(result_mean.item() - 0.0266  ) < 1E-3
    else:
     # CUDA
     assert abs(result_sum.item() - 20.4125  ) < 1E-2
     assert abs(result_mean.item() - 0.0266  ) < 1E-3







   def  lowerCAmelCase     (self					:   List[Any]  ):
    if torch_device == "mps":
     return
    __a					:    Union[str, Any]													=    self.scheduler_classes[0]
    __a					:    List[Any]													=    self.get_scheduler_config()
    __a					:    Dict													=    scheduler_class(**lowercase_  )

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

    __a					:    List[Any]													=    self.dummy_model()
    __a					:    Optional[int]													=    self.dummy_sample_deter.to(lowercase_  ) * scheduler.init_noise_sigma

    for t in scheduler.timesteps:
     __a					:    int													=    scheduler.scale_model_input(lowercase_   ,							lowercase_  )

     __a					:    Any													=    model(lowercase_   ,							lowercase_  )

     __a					:    Optional[int]													=    scheduler.step(lowercase_   ,							lowercase_   ,							lowercase_  )
     __a					:    Union[str, Any]													=    output.prev_sample

    __a					:    Optional[int]													=    torch.sum(torch.abs(lowercase_  )  )
    __a					:    Any													=    torch.mean(torch.abs(lowercase_  )  )

    if str(lowercase_  ).startswith('''cpu'''  ):
     # The following sum varies between 148 and 156 on mps. Why?
     assert abs(result_sum.item() - 20.4125  ) < 1E-2
     assert abs(result_mean.item() - 0.0266  ) < 1E-3
    else:
     # CUDA
     assert abs(result_sum.item() - 20.4125  ) < 1E-2
     assert abs(result_mean.item() - 0.0266  ) < 1E-3



"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




