
'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class  _UpperCAmelCase ( lowercase__    ):

     """simple docstring"""

     a_    	=					(CMStochasticIterativeScheduler,)
     a_    	=					10


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							{
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**UpperCAmelCase__ )
       return config


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							10

       a_      :	Dict      =							self.get_scheduler_config()
       a_      :	Union[str, Any]      =							self.scheduler_classes[0](**UpperCAmelCase__ )

       scheduler.set_timesteps(UpperCAmelCase__ )

       a_      :	Optional[int]      =							scheduler.timesteps[0]
       a_      :	List[Any]      =							scheduler.timesteps[1]

       a_      :	List[Any]      =							self.dummy_sample
       a_      :	Tuple      =							0.1 * sample

       a_      :	Union[str, Any]      =							scheduler.step(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ ).prev_sample
       a_      :	Optional[int]      =							scheduler.step(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ ).prev_sample

       self.assertEqual(output_a.shape							,		sample.shape )
       self.assertEqual(output_a.shape							,		output_a.shape )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=UpperCAmelCase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.scheduler_classes[0]
       a_      :	Dict      =							self.get_scheduler_config()
       a_      :	Optional[Any]      =							scheduler_class(**UpperCAmelCase__ )

       a_      :	Optional[int]      =							1
       scheduler.set_timesteps(UpperCAmelCase__ )
       a_      :	Optional[int]      =							scheduler.timesteps

       a_      :	Optional[int]      =							torch.manual_seed(0 )

       a_      :	Union[str, Any]      =							self.dummy_model()
       a_      :	Any      =							self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCAmelCase__ ):
         # 1. scale model input
         a_      :	Tuple      =							scheduler.scale_model_input(UpperCAmelCase__							,		UpperCAmelCase__ )

         # 2. predict noise residual
         a_      :	Optional[Any]      =							model(UpperCAmelCase__							,		UpperCAmelCase__ )

         # 3. predict previous sample x_t-1
         a_      :	Tuple      =							scheduler.step(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		generator=UpperCAmelCase__ ).prev_sample

         a_      :	List[Any]      =							pred_prev_sample

       a_      :	int      =							torch.sum(torch.abs(UpperCAmelCase__ ) )
       a_      :	List[str]      =							torch.mean(torch.abs(UpperCAmelCase__ ) )

       assert abs(result_sum.item() - 1_92.76_14 ) < 1E-2
       assert abs(result_mean.item() - 0.2510 ) < 1E-3


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.scheduler_classes[0]
       a_      :	Optional[Any]      =							self.get_scheduler_config()
       a_      :	str      =							scheduler_class(**UpperCAmelCase__ )

       a_      :	Union[str, Any]      =							[1_06, 0]
       scheduler.set_timesteps(timesteps=UpperCAmelCase__ )
       a_      :	Any      =							scheduler.timesteps

       a_      :	Dict      =							torch.manual_seed(0 )

       a_      :	Union[str, Any]      =							self.dummy_model()
       a_      :	Tuple      =							self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         a_      :	Optional[Any]      =							scheduler.scale_model_input(UpperCAmelCase__							,		UpperCAmelCase__ )

         # 2. predict noise residual
         a_      :	str      =							model(UpperCAmelCase__							,		UpperCAmelCase__ )

         # 3. predict previous sample x_t-1
         a_      :	Tuple      =							scheduler.step(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		generator=UpperCAmelCase__ ).prev_sample

         a_      :	Dict      =							pred_prev_sample

       a_      :	str      =							torch.sum(torch.abs(UpperCAmelCase__ ) )
       a_      :	int      =							torch.mean(torch.abs(UpperCAmelCase__ ) )

       assert abs(result_sum.item() - 3_47.63_57 ) < 1E-2
       assert abs(result_mean.item() - 0.4527 ) < 1E-3


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.scheduler_classes[0]
       a_      :	Dict      =							self.get_scheduler_config()
       a_      :	str      =							scheduler_class(**UpperCAmelCase__ )

       a_      :	Any      =							[39, 30, 12, 15, 0]

       with self.assertRaises(UpperCAmelCase__							,		msg="""`timesteps` must be in descending order.""" ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.scheduler_classes[0]
       a_      :	Dict      =							self.get_scheduler_config()
       a_      :	Dict      =							scheduler_class(**UpperCAmelCase__ )

       a_      :	Optional[Any]      =							[39, 30, 12, 1, 0]
       a_      :	str      =							len(UpperCAmelCase__ )

       with self.assertRaises(UpperCAmelCase__							,		msg="""Can only pass one of `num_inference_steps` or `timesteps`.""" ):
         scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__							,		timesteps=UpperCAmelCase__ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.scheduler_classes[0]
       a_      :	Union[str, Any]      =							self.get_scheduler_config()
       a_      :	Optional[Any]      =							scheduler_class(**UpperCAmelCase__ )

       a_      :	Optional[Any]      =							[scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCAmelCase__							,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,		):
         scheduler.set_timesteps(timesteps=UpperCAmelCase__ )




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


