

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  (KDPMaAncestralDiscreteScheduler,)
   UpperCamelCase       =  1_0



   def  __magic_name__					(							self							:  Optional[int],	**__A							:  List[str]		):
     UpperCAmelCase :							str								=				{
         '''num_train_timesteps''': 1_1_0_0,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
     }

     config.update(**__A		)
     return config



   def  __magic_name__					(							self							:  Any		):
     for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__A		)



   def  __magic_name__					(							self							:  Optional[Any]		):
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
       self.check_over_configs(beta_start=__A,	beta_end=__A		)



   def  __magic_name__					(							self							:  Optional[Any]		):
     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=__A		)



   def  __magic_name__					(							self							:  Dict		):
     if torch_device == "mps":
       return
     UpperCAmelCase :							Any								=				self.scheduler_classes[0]
     UpperCAmelCase :							Dict								=				self.get_scheduler_config()
     UpperCAmelCase :							Any								=				scheduler_class(**__A		)

     scheduler.set_timesteps(self.num_inference_steps		)

     UpperCAmelCase :							str								=				torch.manual_seed(0		)

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

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							List[str]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							List[str]								=				model(__A,	__A		)

       UpperCAmelCase :							Any								=				scheduler.step(__A,	__A,	__A,	generator=__A		)
       UpperCAmelCase :							List[str]								=				output.prev_sample

     UpperCAmelCase :							Any								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Optional[int]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7		) < 1E-2
     assert abs(result_mean.item() - 1_8.0_3_3_1		) < 5E-3



   def  __magic_name__					(							self							:  Union[str, Any]		):
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__A		)



   def  __magic_name__					(							self							:  List[str]		):
     if torch_device == "mps":
       return
     UpperCAmelCase :							int								=				self.scheduler_classes[0]
     UpperCAmelCase :							List[Any]								=				self.get_scheduler_config(prediction_type='''v_prediction'''		)
     UpperCAmelCase :							Union[str, Any]								=				scheduler_class(**__A		)

     scheduler.set_timesteps(self.num_inference_steps		)

     UpperCAmelCase :							Dict								=				self.dummy_model()
     UpperCAmelCase :							str								=				self.dummy_sample_deter * scheduler.init_noise_sigma
     UpperCAmelCase :							str								=				sample.to(__A		)

     UpperCAmelCase :							str								=				torch.manual_seed(0		)

     for i, t in enumerate(scheduler.timesteps		):
       UpperCAmelCase :							Optional[Any]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							Union[str, Any]								=				model(__A,	__A		)

       UpperCAmelCase :							Dict								=				scheduler.step(__A,	__A,	__A,	generator=__A		)
       UpperCAmelCase :							Any								=				output.prev_sample

     UpperCAmelCase :							List[str]								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							List[Any]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_sum.item() - 3_2_8.9_9_7_0		) < 1E-2
     assert abs(result_mean.item() - 0.4_2_8_4		) < 1E-3





   def  __magic_name__					(							self							:  str		):
     if torch_device == "mps":
       return
     UpperCAmelCase :							int								=				self.scheduler_classes[0]
     UpperCAmelCase :							str								=				self.get_scheduler_config()
     UpperCAmelCase :							List[Any]								=				scheduler_class(**__A		)

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

     UpperCAmelCase :							Dict								=				self.dummy_model()
     UpperCAmelCase :							Dict								=				self.dummy_sample_deter.to(__A		) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
       UpperCAmelCase :							Optional[int]								=				scheduler.scale_model_input(__A,	__A		)

       UpperCAmelCase :							Any								=				model(__A,	__A		)

       UpperCAmelCase :							Any								=				scheduler.step(__A,	__A,	__A,	generator=__A		)
       UpperCAmelCase :							str								=				output.prev_sample

     UpperCAmelCase :							Tuple								=				torch.sum(torch.abs(__A		)		)
     UpperCAmelCase :							Optional[int]								=				torch.mean(torch.abs(__A		)		)

     assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8		) < 1E-1
     assert abs(result_mean.item() - 1_8.0_3_3_1		) < 1E-3








def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    List[Any]    )						->		Optional[int]:
  print('''\nVertex Distance'''    )
  for i in range(UpperCAmelCase    ):
    if dist[i] != float('''inf'''    ):
      print(UpperCAmelCase       ,				'''\t'''       ,				int(dist[i]    )       ,				end='''\t'''    )
    else:
      print(UpperCAmelCase       ,				'''\t'''       ,				'''INF'''       ,				end='''\t'''    )
    print()

def     a__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    Optional[int]    )						->		Any:
  UpperCAmelCase :							Union[str, Any]								=				float('''inf'''    )
  UpperCAmelCase :							str								=				-1
  for i in range(UpperCAmelCase    ):
    if (not vset[i]) and mdist[i] < min_val:
      UpperCAmelCase :							int								=				i
      UpperCAmelCase :							Union[str, Any]								=				mdist[i]
  return min_ind

def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Optional[Any]       ,				UpperCAmelCase							:    str    )						->		Tuple:
  UpperCAmelCase :							str								=				[float('''inf'''    ) for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							Optional[int]								=				[False for _ in range(UpperCAmelCase    )]
  UpperCAmelCase :							List[Any]								=				0.0

  for _ in range(v - 1    ):
    UpperCAmelCase :							Any								=				min_dist(UpperCAmelCase       ,				UpperCAmelCase       ,				UpperCAmelCase    )
    UpperCAmelCase :							List[Any]								=				True

    for i in range(UpperCAmelCase    ):
      if (
          (not vset[i])
          and graph[u][i] != float('''inf'''    )
          and mdist[u] + graph[u][i] < mdist[i]
      ):
        UpperCAmelCase :							str								=				mdist[u] + graph[u][i]

  print_dist(UpperCAmelCase       ,				UpperCAmelCase    )


if __name__ == "__main__":
     _lowerCamelCase       :     Tuple           =							int(input("Enter number of vertices: ").strip())
     _lowerCamelCase       :     List[str]           =							int(input("Enter number of edges: ").strip())

     _lowerCamelCase       :     Optional[int]           =							[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          _lowerCamelCase       :     Union[str, Any]           =							0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          _lowerCamelCase       :     List[str]           =							int(input("Enter source:").strip())
          _lowerCamelCase       :     List[str]           =							int(input("Enter destination:").strip())
          _lowerCamelCase       :     List[Any]           =							float(input("Enter weight:").strip())
          _lowerCamelCase       :     Any           =							weight

     _lowerCamelCase       :     List[str]           =							int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)





