




"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					a						(       __lowerCAmelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(KDPMaDiscreteScheduler,)
    UpperCAmelCase						 =					1_0

    def 				UpperCamelCase	(     self: Any    ,						**UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  {
             """num_train_timesteps""": 11_00,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**lowerCAmelCase_       )
         return config

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase_       )

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         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=lowerCAmelCase_    ,						beta_end=lowerCAmelCase_       )

    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config(prediction_type="""v_prediction"""       )
         A__										=  scheduler_class(**lowerCAmelCase_       )

         scheduler.set_timesteps(self.num_inference_steps       )

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

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

              A__										=  model(lowerCAmelCase_    ,						lowerCAmelCase_       )

              A__										=  scheduler.step(lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_       )
              A__										=  output.prev_sample

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

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.6_9_3_4e-0_7       ) < 1e-2
              assert abs(result_mean.item() - 6.1_1_1_2e-1_0       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7       ) < 1e-2
              assert abs(result_mean.item() - 0.0_002       ) < 1e-3

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         if torch_device == "mps":
              return
         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config()
         A__										=  scheduler_class(**lowerCAmelCase_       )

         scheduler.set_timesteps(self.num_inference_steps       )

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

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

              A__										=  model(lowerCAmelCase_    ,						lowerCAmelCase_       )

              A__										=  scheduler.step(lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_       )
              A__										=  output.prev_sample

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

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3





    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         if torch_device == "mps":
              return
         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config()
         A__										=  scheduler_class(**lowerCAmelCase_       )

         scheduler.set_timesteps(self.num_inference_steps    ,						device=lowerCAmelCase_       )

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

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

              A__										=  model(lowerCAmelCase_    ,						lowerCAmelCase_       )

              A__										=  scheduler.step(lowerCAmelCase_    ,						lowerCAmelCase_    ,						lowerCAmelCase_       )
              A__										=  output.prev_sample

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

         if str(lowerCAmelCase_       ).startswith("""cpu"""       ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(UpperCAmelCase_			)			):
          if indegree[i] == 0:
               queue.append(UpperCAmelCase_			)

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(UpperCAmelCase_			)

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






