code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  Any      =				(KDPMaAncestralDiscreteScheduler,)
     lowercase       :  List[Any]      =				1_0
  
  
  
  
  
  
  
     def       a_							(							self    ,      **SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							{
             """num_train_timesteps""": 1100,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }
  
         config.update(**SCREAMING_SNAKE_CASE_ )
         return config
  
  
  
  
  
  
  
     def       a_							(							self ):
         for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,      [0.0002, 0.002, 0.02] ):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,      beta_end=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         if torch_device == "mps":
             return
         UpperCamelCase				:   int          =							self.scheduler_classes[0]
         UpperCamelCase				:   List[Any]          =							self.get_scheduler_config()
         UpperCamelCase				:   List[Any]          =							scheduler_class(**SCREAMING_SNAKE_CASE_ )
  
         scheduler.set_timesteps(self.num_inference_steps )
  
         UpperCamelCase				:   List[Any]          =							torch.manual_seed(0 )
  
         UpperCamelCase				:   Optional[int]          =							self.dummy_model()
         UpperCamelCase				:   List[str]          =							self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase				:   Dict          =							sample.to(SCREAMING_SNAKE_CASE_ )
  
         for i, t in enumerate(scheduler.timesteps ):
             UpperCamelCase				:   str          =							scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   Dict          =							model(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   Optional[int]          =							scheduler.step(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      generator=SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   Optional[int]          =							output.prev_sample
  
         UpperCamelCase				:   Union[str, Any]          =							torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
         UpperCamelCase				:   Dict          =							torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
  
         assert abs(result_sum.item() - 13849.3877 ) < 1e-2
         assert abs(result_mean.item() - 18.0331 ) < 5e-3
  
  
  
  
  
  
  
     def       a_							(							self ):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         if torch_device == "mps":
             return
         UpperCamelCase				:   Tuple          =							self.scheduler_classes[0]
         UpperCamelCase				:   Optional[Any]          =							self.get_scheduler_config(prediction_type="""v_prediction""" )
         UpperCamelCase				:   Any          =							scheduler_class(**SCREAMING_SNAKE_CASE_ )
  
         scheduler.set_timesteps(self.num_inference_steps )
  
         UpperCamelCase				:   Optional[int]          =							self.dummy_model()
         UpperCamelCase				:   List[Any]          =							self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase				:   Optional[Any]          =							sample.to(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Union[str, Any]          =							torch.manual_seed(0 )
  
         for i, t in enumerate(scheduler.timesteps ):
             UpperCamelCase				:   List[Any]          =							scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   List[str]          =							model(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   List[Any]          =							scheduler.step(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      generator=SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   int          =							output.prev_sample
  
         UpperCamelCase				:   List[str]          =							torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
         UpperCamelCase				:   List[str]          =							torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
  
         assert abs(result_sum.item() - 328.9970 ) < 1e-2
         assert abs(result_mean.item() - 0.4284 ) < 1e-3
  
  
  
  
  
  
  
     def       a_							(							self ):
         if torch_device == "mps":
             return
         UpperCamelCase				:   List[str]          =							self.scheduler_classes[0]
         UpperCamelCase				:   List[str]          =							self.get_scheduler_config()
         UpperCamelCase				:   int          =							scheduler_class(**SCREAMING_SNAKE_CASE_ )
  
         scheduler.set_timesteps(self.num_inference_steps    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[int]          =							torch.manual_seed(0 )
  
         UpperCamelCase				:   Optional[Any]          =							self.dummy_model()
         UpperCamelCase				:   List[str]          =							self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma
  
         for t in scheduler.timesteps:
             UpperCamelCase				:   Tuple          =							scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   Union[str, Any]          =							model(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
             UpperCamelCase				:   Dict          =							scheduler.step(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      generator=SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   Dict          =							output.prev_sample
  
         UpperCamelCase				:   str          =							torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
         UpperCamelCase				:   Optional[int]          =							torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
  
         assert abs(result_sum.item() - 13849.3818 ) < 1e-1
         assert abs(result_mean.item() - 18.0331 ) < 1e-3
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
         UpperCamelCase				:   Optional[Any]          =							data
         UpperCamelCase				:   str          =							None
  
  
  
  
  
  
  
     def __repr__(							self ):
         UpperCamelCase				:   str          =							""""""
         UpperCamelCase				:   Any          =							self
         while temp:
             string_rep += f'<{temp.data}> ---> '
             UpperCamelCase				:   Optional[int]          =							temp.next
         string_rep += "<END>"
         return string_rep
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
      # if elements_list is empty
      if not elements_list:
          raise Exception("""The Elements List is empty"""    )
  
      # Set first element as Head
      UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
      UpperCamelCase				:   str          =							head
      # Loop through elements from position 1
      for data in elements_list[1:]:
          UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
          UpperCamelCase				:   Optional[Any]          =							current.next
      return head
  
  
  __A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
  print(F'''List: {list_data}''')
  print('''Creating Linked List from List.''')
  __A :		int      							=    make_linked_list(list_data)
  print('''Linked List:''')
  
  
  print(linked_list)
  
  
  
  
  