






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class      __SCREAMING_SNAKE_CASE(		lowercase__		):
							_UpperCAmelCase       			=				(IPNDMScheduler,)
							_UpperCAmelCase       			=				(('''num_inference_steps''', 5_0),)
							def lowerCAmelCase_       (   self:					Any				,		**UpperCamelCase:					Union[str, Any]  )						->						Any:
											snake_case__													=   {'num_train_timesteps': 10_00}
											config.update(**_a  )
											return config
							def lowerCAmelCase_       (   self:					Dict				,		UpperCamelCase:					Dict=0				,		**UpperCamelCase:					List[str]  )						->						Tuple:
											snake_case__													=   dict(self.forward_default_kwargs  )
											snake_case__													=   kwargs.pop('num_inference_steps'				,		_a  )
											snake_case__													=   self.dummy_sample
											snake_case__													=   0.1 * sample
											snake_case__													=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
															snake_case__													=   self.get_scheduler_config(**_a  )
															snake_case__													=   scheduler_class(**_a  )
															scheduler.set_timesteps(_a  )
															# copy over dummy past residuals
															snake_case__													=   dummy_past_residuals[:]

															if time_step is None:
																			snake_case__													=   scheduler.timesteps[len(scheduler.timesteps  ) // 2]

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_a  )
																			snake_case__													=   scheduler_class.from_pretrained(_a  )
																			new_scheduler.set_timesteps(_a  )
																			# copy over dummy past residuals
																			snake_case__													=   dummy_past_residuals[:]

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   new_scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   new_scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Dict:
											pass
							def lowerCAmelCase_       (   self:					List[Any]				,		UpperCamelCase:					Tuple=0				,		**UpperCamelCase:					Dict  )						->						Optional[int]:
											snake_case__													=   dict(self.forward_default_kwargs  )
											snake_case__													=   kwargs.pop('num_inference_steps'				,		_a  )
											snake_case__													=   self.dummy_sample
											snake_case__													=   0.1 * sample
											snake_case__													=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
															snake_case__													=   self.get_scheduler_config()
															snake_case__													=   scheduler_class(**_a  )
															scheduler.set_timesteps(_a  )

															# copy over dummy past residuals (must be after setting timesteps)
															snake_case__													=   dummy_past_residuals[:]

															if time_step is None:
																			snake_case__													=   scheduler.timesteps[len(scheduler.timesteps  ) // 2]

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_a  )
																			snake_case__													=   scheduler_class.from_pretrained(_a  )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_a  )

																			# copy over dummy past residual (must be after setting timesteps)
																			snake_case__													=   dummy_past_residuals[:]

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   new_scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   new_scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
							def lowerCAmelCase_       (   self:					Dict				,		**UpperCamelCase:					List[Any]  )						->						Dict:
											snake_case__													=   self.scheduler_classes[0]
											snake_case__													=   self.get_scheduler_config(**_a  )
											snake_case__													=   scheduler_class(**_a  )

											snake_case__													=   10
											snake_case__													=   self.dummy_model()
											snake_case__													=   self.dummy_sample_deter
											scheduler.set_timesteps(_a  )

											for i, t in enumerate(scheduler.timesteps  ):
															snake_case__													=   model(_a				,		_a  )
															snake_case__													=   scheduler.step(_a				,		_a				,		_a  ).prev_sample

											for i, t in enumerate(scheduler.timesteps  ):
															snake_case__													=   model(_a				,		_a  )
															snake_case__													=   scheduler.step(_a				,		_a				,		_a  ).prev_sample

											return sample
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Tuple:
											snake_case__													=   dict(self.forward_default_kwargs  )

											snake_case__													=   kwargs.pop('num_inference_steps'				,		_a  )

											for scheduler_class in self.scheduler_classes:
															snake_case__													=   self.get_scheduler_config()
															snake_case__													=   scheduler_class(**_a  )

															snake_case__													=   self.dummy_sample
															snake_case__													=   0.1 * sample

															if num_inference_steps is not None and hasattr(_a				,		'set_timesteps'  ):
																			scheduler.set_timesteps(_a  )
															elif num_inference_steps is not None and not hasattr(_a				,		'set_timesteps'  ):
																			snake_case__													=   num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															snake_case__													=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
															snake_case__													=   dummy_past_residuals[:]

															snake_case__													=   scheduler.timesteps[5]
															snake_case__													=   scheduler.timesteps[6]

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

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

															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample
															snake_case__													=   scheduler.step(_a				,		_a				,		_a				,		**_a  ).prev_sample

															self.assertEqual(output_a.shape				,		sample.shape  )
															self.assertEqual(output_a.shape				,		output_a.shape  )
							def lowerCAmelCase_       (   self:					int  )						->						Union[str, Any]:






											for timesteps in [1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=_a				,		time_step=_a  )
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						List[Any]:






											for t, num_inference_steps in zip([1, 5, 10]				,		[10, 50, 1_00]  ):
															self.check_over_forward(num_inference_steps=_a				,		time_step=_a  )


							def lowerCAmelCase_       (   self:					Dict  )						->						Optional[Any]:
											snake_case__													=   self.full_loop()
											snake_case__													=   torch.mean(torch.abs(_a  )  )

											assert abs(result_mean.item() - 2_54_05_29  ) < 10






'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



