
"""simple docstring"""





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						(KDPMaAncestralDiscreteScheduler,)
  SCREAMING_SNAKE_CASE__     :		str    	=						10







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       {
         "num_train_timesteps": 1100,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_    )
     return config







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]    ):
        self.check_over_configs(beta_start=lowercase_							,  beta_end=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   List[str]     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   str     	=       self.get_scheduler_config()
     UpperCAmelCase_   :   List[Any]     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps    )

     UpperCAmelCase_   :   Union[str, Any]     	=       torch.manual_seed(0    )

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

     for i, t in enumerate(scheduler.timesteps    ):
        UpperCAmelCase_   :   List[Any]     	=       scheduler.scale_model_input(lowercase_							,  lowercase_    )

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

        UpperCAmelCase_   :   Tuple     	=       scheduler.step(lowercase_							,  lowercase_							,  lowercase_							,  generator=lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       output.prev_sample

     UpperCAmelCase_   :   List[Any]     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   List[str]     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
     assert abs(result_mean.item() - 18.03_31    ) < 5E-3







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   Union[str, Any]     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   Tuple     	=       self.get_scheduler_config(prediction_type="v_prediction"    )
     UpperCAmelCase_   :   Union[str, Any]     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps    )

     UpperCAmelCase_   :   Optional[int]     	=       self.dummy_model()
     UpperCAmelCase_   :   Union[str, Any]     	=       self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase_   :   Tuple     	=       sample.to(lowercase_    )

     UpperCAmelCase_   :   Optional[Any]     	=       torch.manual_seed(0    )

     for i, t in enumerate(scheduler.timesteps    ):
        UpperCAmelCase_   :   Union[str, Any]     	=       scheduler.scale_model_input(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Optional[int]     	=       model(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Dict     	=       scheduler.step(lowercase_							,  lowercase_							,  lowercase_							,  generator=lowercase_    )
        UpperCAmelCase_   :   int     	=       output.prev_sample

     UpperCAmelCase_   :   List[Any]     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   Tuple     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
     assert abs(result_mean.item() - 0.42_84    ) < 1E-3







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if torch_device == "mps":
        return
     UpperCAmelCase_   :   Any     	=       self.scheduler_classes[0]
     UpperCAmelCase_   :   Optional[int]     	=       self.get_scheduler_config()
     UpperCAmelCase_   :   str     	=       scheduler_class(**lowercase_    )

     scheduler.set_timesteps(self.num_inference_steps							,  device=lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       torch.manual_seed(0    )

     UpperCAmelCase_   :   List[Any]     	=       self.dummy_model()
     UpperCAmelCase_   :   Dict     	=       self.dummy_sample_deter.to(lowercase_    ) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
        UpperCAmelCase_   :   List[Any]     	=       scheduler.scale_model_input(lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Union[str, Any]     	=       model(lowercase_							,  lowercase_    )

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

     UpperCAmelCase_   :   Dict     	=       torch.sum(torch.abs(lowercase_    )    )
     UpperCAmelCase_   :   int     	=       torch.mean(torch.abs(lowercase_    )    )

     assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
     assert abs(result_mean.item() - 18.03_31    ) < 1E-3






__lowerCamelCase   	=  {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase   	=  {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     True
     a_     :  Optional[Any]				      =     []

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               connected_verts += dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     return [vert, *connected_verts]



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     len(__UpperCamelCase			)
     a_     :  List[str]				      =     graph_size * [False]
     a_     :  Dict				      =     []

     for i in range(__UpperCamelCase			):
          if not visited[i]:
               a_     :  str				      =     dfs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
               components_list.append(__UpperCamelCase			)

     return components_list


if __name__ == "__main__":
       import doctest

       doctest.testmod()



