






"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return [
      {
          0: [1, 2],
          1: [0, 2],
          2: [0, 1, 3, 5],
          3: [2, 4],
          4: [3],
          5: [2, 6, 8],
          6: [5, 7],
          7: [6, 8],
          8: [5, 7],
      },
      {
          0: [6],
          1: [9],
          2: [4, 5],
          3: [4],
          4: [2, 3],
          5: [2],
          6: [0, 7],
          7: [6],
          8: [],
          9: [1],
      },
      {
          0: [4],
          1: [6],
          2: [],
          3: [5, 6, 7],
          4: [0, 6],
          5: [3, 8, 9],
          6: [1, 3, 4, 7],
          7: [3, 6, 8, 9],
          8: [5, 7],
          9: [5, 7],
      },
      {
          0: [1, 3],
          1: [0, 2, 4],
          2: [1, 3, 4],
          3: [0, 2, 4],
          4: [1, 2, 3],
      },
  ][index]




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Any       =     0
  _lowercase:			Tuple       =     len(_UpperCamelCase						)  # No of vertices in graph
  _lowercase:			int       =     [0] * n
  _lowercase:			Tuple       =     [False] * n

  def dfs(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):
    _lowercase:			Union[str, Any]       =     True
    _lowercase:			List[str]       =     id_
    id_ += 1
    for to in graph[at]:
      if to == parent:
        pass
      elif not visited[to]:
        dfs(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				id_						)
        _lowercase:			str       =     min(low[at] ,				low[to]						)
        if id_ <= low[to]:
          bridges.append((at, to) if at < to else (to, at)						)
      else:
        # This edge is a back edge and cannot be a bridge
        _lowercase:			str       =     min(low[at] ,				low[to]						)

  _lowercase:			list[tuple[int, int]]       =     []
  for i in range(_UpperCamelCase						):
    if not visited[i]:
      dfs(_UpperCamelCase ,				-1 ,				_UpperCamelCase ,				id_						)
  return bridges


if __name__ == "__main__":
  import doctest

  doctest.testmod()


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




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





         """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=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




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





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

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

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

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

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

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

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

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
