



"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				(HeunDiscreteScheduler,)
						__UpperCamelCase													=				10






						def       UpperCAmelCase__	(   self					:int  ,    **lowercase_					:str   )							->     Any:
												UpperCAmelCase			 =					{
												    'num_train_timesteps': 11_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												}

												config.update(**lowercase_   )
												return config






						def       UpperCAmelCase__	(   self					:List[str]   )							->     int:
												for timesteps in [10, 50, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												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       UpperCAmelCase__	(   self					:List[Any]   )							->     List[str]:
												for schedule in ["linear", "scaled_linear", "exp"]:
																		self.check_over_configs(beta_schedule=lowercase_   )






						def       UpperCAmelCase__	(   self					:Tuple   )							->     str:
												for clip_sample_range in [1.0, 2.0, 3.0]:
																		self.check_over_configs(clip_sample_range=lowercase_  ,    clip_sample=lowercase_   )






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Optional[Any]:
												for prediction_type in ["epsilon", "v_prediction", "sample"]:
																		self.check_over_configs(prediction_type=lowercase_   )






						def       UpperCAmelCase__	(   self					:int   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config()
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

												scheduler.set_timesteps(self.num_inference_steps   )

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter * scheduler.init_noise_sigma
												UpperCAmelCase			 =					sample.to(lowercase_   )

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

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												if torch_device in ["cpu", "mps"]:
																		assert abs(result_sum.item() - 0.1233   ) < 1E-2
																		assert abs(result_mean.item() - 0.0002   ) < 1E-3
												else:
																		# CUDA
																		assert abs(result_sum.item() - 0.1233   ) < 1E-2
																		assert abs(result_mean.item() - 0.0002   ) < 1E-3






						def       UpperCAmelCase__	(   self					:Tuple   )							->     int:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config(prediction_type='v_prediction'   )
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

												scheduler.set_timesteps(self.num_inference_steps   )

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter * scheduler.init_noise_sigma
												UpperCAmelCase			 =					sample.to(lowercase_   )

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

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												if torch_device in ["cpu", "mps"]:
																		assert abs(result_sum.item() - 4.6_934E-07   ) < 1E-2
																		assert abs(result_mean.item() - 6.1_112E-10   ) < 1E-3
												else:
																		# CUDA
																		assert abs(result_sum.item() - 4.693_428_650_170_972E-07   ) < 1E-2
																		assert abs(result_mean.item() - 0.0002   ) < 1E-3






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config()
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

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

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter.to(lowercase_   ) * scheduler.init_noise_sigma

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

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					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() - 0.1233   ) < 1E-2
																		assert abs(result_mean.item() - 0.0002   ) < 1E-3
												elif str(lowercase_   ).startswith('mps'   ):
																		# Larger tolerance on mps
																		assert abs(result_mean.item() - 0.0002   ) < 1E-2
												else:
																		# CUDA
																		assert abs(result_sum.item() - 0.1233   ) < 1E-2
																		assert abs(result_mean.item() - 0.0002   ) < 1E-3






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config()
												UpperCAmelCase			 =					scheduler_class(**lowercase_  ,    use_karras_sigmas=lowercase_   )

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

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter.to(lowercase_   ) * scheduler.init_noise_sigma
												UpperCAmelCase			 =					sample.to(lowercase_   )

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

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												assert abs(result_sum.item() - 0.0_0015   ) < 1E-2
												assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07   ) < 1E-2












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





