"""simple docstring"""






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:str							=				(EulerAncestralDiscreteScheduler,)
    _UpperCAmelCase			:Dict							=				10






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       lowercase__:     Union[str, Any]      =   {
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**A_							)
       return config






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






    def _snake_case    (     self							):
       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,    [0.0_002, 0.002, 0.02]							):
          self.check_over_configs(beta_start=A_					,    beta_end=A_							)






    def _snake_case    (     self							):
       for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=A_							)






    def _snake_case    (     self							):
       for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A_							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.scheduler_classes[0]
       lowercase__:     Tuple      =   self.get_scheduler_config()
       lowercase__:     int      =   scheduler_class(**A_							)

       scheduler.set_timesteps(self.num_inference_steps							)

       lowercase__:     Dict      =   torch.manual_seed(0							)

       lowercase__:     str      =   self.dummy_model()
       lowercase__:     Tuple      =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase__:     Dict      =   sample.to(A_							)

       for i, t in enumerate(scheduler.timesteps							):
          lowercase__:     Dict      =   scheduler.scale_model_input(A_					,    A_							)

          lowercase__:     int      =   model(A_					,    A_							)

          lowercase__:     Dict      =   scheduler.step(A_					,    A_					,    A_					,    generator=A_							)
          lowercase__:     str      =   output.prev_sample

       lowercase__:     Optional[Any]      =   torch.sum(torch.abs(A_							)							)
       lowercase__:     Optional[Any]      =   torch.mean(torch.abs(A_							)							)

       assert abs(result_sum.item() - 152.3_192							) < 1e-2
       assert abs(result_mean.item() - 0.1_983							) < 1e-3






    def _snake_case    (     self							):
       lowercase__:     Dict      =   self.scheduler_classes[0]
       lowercase__:     Any      =   self.get_scheduler_config(prediction_type='''v_prediction'''							)
       lowercase__:     Optional[int]      =   scheduler_class(**A_							)

       scheduler.set_timesteps(self.num_inference_steps							)

       lowercase__:     str      =   torch.manual_seed(0							)

       lowercase__:     Union[str, Any]      =   self.dummy_model()
       lowercase__:     int      =   self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase__:     int      =   sample.to(A_							)

       for i, t in enumerate(scheduler.timesteps							):
          lowercase__:     str      =   scheduler.scale_model_input(A_					,    A_							)

          lowercase__:     List[str]      =   model(A_					,    A_							)

          lowercase__:     List[str]      =   scheduler.step(A_					,    A_					,    A_					,    generator=A_							)
          lowercase__:     str      =   output.prev_sample

       lowercase__:     Optional[int]      =   torch.sum(torch.abs(A_							)							)
       lowercase__:     Tuple      =   torch.mean(torch.abs(A_							)							)

       assert abs(result_sum.item() - 108.4_439							) < 1e-2
       assert abs(result_mean.item() - 0.1_412							) < 1e-3






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.scheduler_classes[0]
       lowercase__:     Optional[Any]      =   self.get_scheduler_config()
       lowercase__:     Tuple      =   scheduler_class(**A_							)

       scheduler.set_timesteps(self.num_inference_steps					,    device=A_							)
       lowercase__:     List[Any]      =   torch.manual_seed(0							)

       lowercase__:     List[str]      =   self.dummy_model()
       lowercase__:     Union[str, Any]      =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase__:     List[str]      =   sample.to(A_							)

       for t in scheduler.timesteps:
          lowercase__:     int      =   scheduler.scale_model_input(A_					,    A_							)

          lowercase__:     Optional[int]      =   model(A_					,    A_							)

          lowercase__:     List[Any]      =   scheduler.step(A_					,    A_					,    A_					,    generator=A_							)
          lowercase__:     int      =   output.prev_sample

       lowercase__:     Dict      =   torch.sum(torch.abs(A_							)							)
       lowercase__:     Any      =   torch.mean(torch.abs(A_							)							)

       assert abs(result_sum.item() - 152.3_192							) < 1e-2
       assert abs(result_mean.item() - 0.1_983							) < 1e-3

"""simple docstring"""






class 					UpperCAmelCase		:



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase=None							):
       lowercase__:     Optional[int]      =   data
       lowercase__:     Dict      =   None






    def __repr__(     self							):
       lowercase__:     int      =   ''''''
       lowercase__:     Any      =   self
       while temp:
          string_rep += F"""<{temp.data}> ---> """
          lowercase__:     Any      =   temp.next
       string_rep += "<END>"
       return string_rep



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:

   # if elements_list is empty
   if not elements_list:
      raise Exception('''The Elements List is empty'''				)

   # Set first element as Head
   lowercase__:     Union[str, Any]      =   Node(elements_list[0]				)
   lowercase__:     List[str]      =   head
   # Loop through elements from position 1
   for data in elements_list[1:]:
      lowercase__:     Tuple      =   Node(__UpperCAmelCase				)
      lowercase__:     Tuple      =   current.next
   return head


__A         =				[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(f'''List: {list_data}''')
print("Creating Linked List from List.")
__A         =				make_linked_list(list_data)
print("Linked List:")




print(linked_list)
