
'''simple docstring'''



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(EulerAncestralDiscreteScheduler,)
       SCREAMING_SNAKE_CASE =					10






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 11_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self )      ->     int:
          for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,							[0.0002, 0.002, 0.02] ):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          scheduler.set_timesteps(self.num_inference_steps )

          _SCREAMING_SNAKE_CASE       	=			torch.manual_seed(0 )

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _SCREAMING_SNAKE_CASE       	=			sample.to(A__ )

          for i, t in enumerate(scheduler.timesteps ):
             _SCREAMING_SNAKE_CASE       	=			scheduler.scale_model_input(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							generator=A__ )
             _SCREAMING_SNAKE_CASE       	=			output.prev_sample

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 152.3192 ) < 1E-2
          assert abs(result_mean.item() - 0.1983 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(prediction_type="""v_prediction""" )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          scheduler.set_timesteps(self.num_inference_steps )

          _SCREAMING_SNAKE_CASE       	=			torch.manual_seed(0 )

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter * scheduler.init_noise_sigma
          _SCREAMING_SNAKE_CASE       	=			sample.to(A__ )

          for i, t in enumerate(scheduler.timesteps ):
             _SCREAMING_SNAKE_CASE       	=			scheduler.scale_model_input(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							generator=A__ )
             _SCREAMING_SNAKE_CASE       	=			output.prev_sample

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 108.4439 ) < 1E-2
          assert abs(result_mean.item() - 0.1412 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          scheduler.set_timesteps(self.num_inference_steps    ,							device=A__ )
          _SCREAMING_SNAKE_CASE       	=			torch.manual_seed(0 )

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _SCREAMING_SNAKE_CASE       	=			sample.to(A__ )

          for t in scheduler.timesteps:
             _SCREAMING_SNAKE_CASE       	=			scheduler.scale_model_input(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )

             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							generator=A__ )
             _SCREAMING_SNAKE_CASE       	=			output.prev_sample

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 152.3192 ) < 1E-2
          assert abs(result_mean.item() - 0.1983 ) < 1E-3








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   print("""\nVertex Distance"""  )
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if dist[i] != float("""inf"""  ):
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , int(dist[i]  )      , end="""\t"""  )
      else:
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , """INF"""      , end="""\t"""  )
      print()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			float("""inf"""  )
   _SCREAMING_SNAKE_CASE       	=			-1
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if (not vset[i]) and mdist[i] < min_val:
         _SCREAMING_SNAKE_CASE       	=			i
         _SCREAMING_SNAKE_CASE       	=			mdist[i]
   return min_ind
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			[float("""inf"""  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			[False for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			0.0

   for _ in range(v - 1  ):
      _SCREAMING_SNAKE_CASE       	=			min_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			True

      for i in range(SCREAMING_SNAKE_CASE_  ):
         if (
             (not vset[i])
             and graph[u][i] != float("""inf"""  )
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _SCREAMING_SNAKE_CASE       	=			mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					int(input("Enter number of vertices: ").strip())
     UpperCamelCase__ :						Tuple    							=					int(input("Enter number of edges: ").strip())

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

     for i in range(V):
          UpperCamelCase__ :						int    							=					0.0

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

     UpperCamelCase__ :						Optional[Any]    							=					int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)






