




"""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










"""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)




