





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     str		     =      (DDIMParallelScheduler,)
     __snake_case   :     List[Any]		     =      (("eta", 0.0), ("num_inference_steps", 50))






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,**lowerCamelCase__						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{
               """num_train_timesteps""": 1000,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """clip_sample""": True,
           }

           config.update(**lowerCamelCase__	)
           return config






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,**lowerCamelCase__						:   int	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						10, 0.0

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter

           scheduler.set_timesteps(lowerCamelCase__	)

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	).prev_sample

           return sample






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		str:




           '''simple docstring'''


           for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Dict:




           '''simple docstring'''


           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(steps_offset=1	)
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)
           scheduler.set_timesteps(5	)
           assert torch.equal(scheduler.timesteps						,torch.LongTensor([801, 601, 401, 201, 1]	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




           '''simple docstring'''


           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,[0.002, 0.02, 0.2, 2]	):
                 self.check_over_configs(beta_start=lowerCamelCase__						,beta_end=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Dict:




           '''simple docstring'''


           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Dict:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Dict:




           '''simple docstring'''


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Dict:




           '''simple docstring'''


           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[str]:




           '''simple docstring'''


           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           self.check_over_configs(thresholding=lowerCamelCase__	)
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=lowerCamelCase__						,prediction_type=lowerCamelCase__						,sample_max_value=lowerCamelCase__						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[int]:




           '''simple docstring'''


           for t in [1, 10, 49]:
                 self.check_over_forward(time_step=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Optional[Any]:




           '''simple docstring'''


           for t, num_inference_steps in zip([1, 10, 50]						,[10, 50, 500]	):
                 self.check_over_forward(time_step=lowerCamelCase__						,num_inference_steps=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Any:




           '''simple docstring'''


           for t, eta in zip([1, 10, 49]						,[0.0, 0.5, 1.0]	):
                 self.check_over_forward(time_step=lowerCamelCase__						,eta=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           assert torch.sum(torch.abs(scheduler._get_variance(0						,0	) - 0.0	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(420						,400	) - 0.14771	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(980						,960	) - 0.32460	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0						,0	) - 0.0	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487						,486	) - 0.00979	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999						,998	) - 0.02	)	) < 1e-5






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						10, 0.0
           scheduler.set_timesteps(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter + 0.1
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter - 0.1

           SCREAMING_SNAKE_CASE		=						samplea.shape[0]
           SCREAMING_SNAKE_CASE		=						torch.stack([samplea, samplea, samplea]						,dim=0	)
           SCREAMING_SNAKE_CASE		=						torch.arange(lowerCamelCase__	)[0:3, None].repeat(1						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						model(samples.flatten(0						,1	)						,timesteps.flatten(0						,1	)	)
           SCREAMING_SNAKE_CASE		=						scheduler.batch_step_no_noise(lowerCamelCase__						,timesteps.flatten(0						,1	)						,samples.flatten(0						,1	)						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						torch.sum(torch.abs(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

           assert abs(result_sum.item() - 1147.7904	) < 1e-2
           assert abs(result_mean.item() - 0.4982	) < 1e-3






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop()

           SCREAMING_SNAKE_CASE		=						torch.sum(torch.abs(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

           assert abs(result_sum.item() - 172.0067	) < 1e-2
           assert abs(result_mean.item() - 0.223967	) < 1e-3






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(prediction_type="""v_prediction"""	)

           SCREAMING_SNAKE_CASE		=						torch.sum(torch.abs(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

           assert abs(result_sum.item() - 52.5302	) < 1e-2
           assert abs(result_mean.item() - 0.0684	) < 1e-3






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(set_alpha_to_one=lowerCamelCase__						,beta_start=0.01	)
           SCREAMING_SNAKE_CASE		=						torch.sum(torch.abs(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

           assert abs(result_sum.item() - 149.8295	) < 1e-2
           assert abs(result_mean.item() - 0.1951	) < 1e-3






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(set_alpha_to_one=lowerCamelCase__						,beta_start=0.01	)
           SCREAMING_SNAKE_CASE		=						torch.sum(torch.abs(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__	)	)

           assert abs(result_sum.item() - 149.0784	) < 1e-2
           assert abs(result_mean.item() - 0.1941	) < 1e-3









class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   int	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		None:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int	)					->		None:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		None:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   list	)					->		None:




           '''simple docstring'''


           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__":
      SCREAMING_SNAKE_CASE_						       =	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


