




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    (UniPCMultistepScheduler,)
 lowercase_				    =    (("num_inference_steps", 25),)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				**_lowerCAmelCase  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			{
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
           'solver_type': 'bh2',
       }

       config.update(**_lowerCAmelCase						)
       return config



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		str=0				,				**_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)
       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample
       SCREAMING_SNAKE_CASE_							=			0.1 * sample
       SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)
             scheduler.set_timesteps(_lowerCAmelCase						)
             # copy over dummy past residuals
             SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: scheduler.config.solver_order]

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

             SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			sample, sample
             for t in range(_lowerCAmelCase				,				time_step + scheduler.config.solver_order + 1						):
                   SCREAMING_SNAKE_CASE_							=			scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample
                   SCREAMING_SNAKE_CASE_							=			new_scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample

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



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Optional[int]=0				,				**_lowerCAmelCase  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)
       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample
       SCREAMING_SNAKE_CASE_							=			0.1 * sample
       SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config()
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)
             scheduler.set_timesteps(_lowerCAmelCase						)

             # copy over dummy past residuals (must be after setting timesteps)
             SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: scheduler.config.solver_order]

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

                   # copy over dummy past residual (must be after setting timesteps)
                   SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: new_scheduler.config.solver_order]

             SCREAMING_SNAKE_CASE_							=			scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample
             SCREAMING_SNAKE_CASE_							=			new_scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample

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



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[Any]=None				,				**_lowerCAmelCase  :		Optional[Any]						):
       if scheduler is None:
             SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			10
       SCREAMING_SNAKE_CASE_							=			self.dummy_model()
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample_deter
       scheduler.set_timesteps(_lowerCAmelCase						)

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

       return sample



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)

       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				_lowerCAmelCase						)

       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config()
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_							=			self.dummy_sample
             SCREAMING_SNAKE_CASE_							=			0.1 * sample

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

             # copy over dummy past residuals (must be done after set_timesteps)
             SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]
             SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: scheduler.config.solver_order]

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

             SCREAMING_SNAKE_CASE_							=			scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample
             SCREAMING_SNAKE_CASE_							=			scheduler.step(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						).prev_sample

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



 def       lowerCAmelCase_  (							self  :		Tuple						):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       SCREAMING_SNAKE_CASE_							=			UniPCMultistepScheduler(**self.get_scheduler_config()						)
       SCREAMING_SNAKE_CASE_							=			self.full_loop(scheduler=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3

       SCREAMING_SNAKE_CASE_							=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			DEISMultistepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			UniPCMultistepScheduler.from_config(scheduler.config						)

       SCREAMING_SNAKE_CASE_							=			self.full_loop(scheduler=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       self.check_over_configs(thresholding=_lowerCAmelCase						)
       for order in [1, 2, 3]:
             for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   thresholding=_lowerCAmelCase				,				prediction_type=_lowerCAmelCase				,				sample_max_value=_lowerCAmelCase				,				solver_order=_lowerCAmelCase				,				solver_type=_lowerCAmelCase				,				)



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



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       for solver_type in ["bh1", "bh2"]:
             for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             solver_order=_lowerCAmelCase				,				solver_type=_lowerCAmelCase				,				prediction_type=_lowerCAmelCase				,				)
                         SCREAMING_SNAKE_CASE_							=			self.full_loop(
                             solver_order=_lowerCAmelCase				,				solver_type=_lowerCAmelCase				,				prediction_type=_lowerCAmelCase				,				)
                         assert not torch.isnan(_lowerCAmelCase						).any(), "Samples have nan numbers"



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       self.check_over_configs(lower_order_final=_lowerCAmelCase						)
       self.check_over_configs(lower_order_final=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=_lowerCAmelCase				,				time_step=0						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			self.full_loop()
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.full_loop(prediction_type='v_prediction'						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(_lowerCAmelCase						)						)

       assert abs(result_mean.item() - 0.1014						) < 1E-3



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(thresholding=_lowerCAmelCase				,				dynamic_thresholding_ratio=0						)
       SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			10
       SCREAMING_SNAKE_CASE_							=			self.dummy_model()
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample_deter.half()
       scheduler.set_timesteps(_lowerCAmelCase						)

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

       assert sample.dtype == torch.floataa




 def       lowerCAmelCase_  (							self  :		List[str]				,				**_lowerCAmelCase  :		Tuple						):
       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**_lowerCAmelCase						)

             scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
             assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

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




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

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


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							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