import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 			__snake_case   (     SCREAMING_SNAKE_CASE__   ):
	__lowerCamelCase    :   Optional[Any]     =      (DDIMScheduler,)
	__lowerCamelCase    :   Optional[Any]     =      (("""eta""", 0.0), ("""num_inference_steps""", 50))
	def 	UpperCAmelCase__       (  self		,   **snake_case__						)	->							str:
							'''simple docstring'''





							UpperCAmelCase    : Tuple      ={
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**snake_case__						)
							return config
	def 	UpperCAmelCase__       (  self		,   **snake_case__						)	->							Any:
							'''simple docstring'''





							UpperCAmelCase    : Union[str, Any]      =self.scheduler_classes[0]
							UpperCAmelCase    : List[Any]      =self.get_scheduler_config(**snake_case__						)
							UpperCAmelCase    : Optional[Any]      =scheduler_class(**snake_case__						)

							UpperCAmelCase  ,						UpperCAmelCase    : Union[str, Any]      =10, 0.0

							UpperCAmelCase    : Dict      =self.dummy_model()
							UpperCAmelCase    : Dict      =self.dummy_sample_deter

							scheduler.set_timesteps(snake_case__						)

							for t in scheduler.timesteps:
													UpperCAmelCase    : Optional[Any]      =model(snake_case__		,   snake_case__						)
													UpperCAmelCase    : Optional[Any]      =scheduler.step(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__						).prev_sample

							return sample
	def 	UpperCAmelCase__       (  self						)	->							Any:
							'''simple docstring'''





							for timesteps in [100, 500, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							int:
							'''simple docstring'''





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

							UpperCAmelCase    : str      =self.scheduler_classes[0]
							UpperCAmelCase    : Union[str, Any]      =self.get_scheduler_config(steps_offset=1						)
							UpperCAmelCase    : Any      =scheduler_class(**snake_case__						)
							scheduler.set_timesteps(5						)
							assert torch.equal(scheduler.timesteps		,   torch.LongTensor([801, 601, 401, 201, 1]						)						)
	def 	UpperCAmelCase__       (  self						)	->							str:
							'''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=snake_case__		,   beta_end=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							str:
							'''simple docstring'''





							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''simple docstring'''





							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							str:
							'''simple docstring'''





							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''simple docstring'''





							for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
							'''simple docstring'''





							self.check_over_configs(thresholding=snake_case__						)
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=snake_case__		,   prediction_type=snake_case__		,   sample_max_value=snake_case__		,   )
	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''simple docstring'''





							for t in [1, 10, 49]:
													self.check_over_forward(time_step=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							for t, num_inference_steps in zip([1, 10, 50]		,   [10, 50, 500]						):
													self.check_over_forward(time_step=snake_case__		,   num_inference_steps=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							int:
							'''simple docstring'''





							for t, eta in zip([1, 10, 49]		,   [0.0, 0.5, 1.0]						):
													self.check_over_forward(time_step=snake_case__		,   eta=snake_case__						)
	def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
							'''simple docstring'''





							UpperCAmelCase    : List[str]      =self.scheduler_classes[0]
							UpperCAmelCase    : Tuple      =self.get_scheduler_config()
							UpperCAmelCase    : Union[str, Any]      =scheduler_class(**snake_case__						)

							assert torch.sum(torch.abs(scheduler._get_variance(0		,   0						) - 0.0						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420		,   400						) - 0.1_4771						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980		,   960						) - 0.3_2460						)						) < 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						) - 0.0_0979						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999		,   998						) - 0.02						)						) < 1e-5
	def 	UpperCAmelCase__       (  self						)	->							List[str]:
							'''simple docstring'''





							UpperCAmelCase    : str      =self.full_loop()

							UpperCAmelCase    : Optional[Any]      =torch.sum(torch.abs(snake_case__						)						)
							UpperCAmelCase    : Union[str, Any]      =torch.mean(torch.abs(snake_case__						)						)

							assert abs(result_sum.item() - 172.0067						) < 1e-2
							assert abs(result_mean.item() - 0.22_3967						) < 1e-3
	def 	UpperCAmelCase__       (  self						)	->							List[str]:
							'''simple docstring'''





							UpperCAmelCase    : Optional[int]      =self.full_loop(prediction_type='''v_prediction'''						)

							UpperCAmelCase    : Any      =torch.sum(torch.abs(snake_case__						)						)
							UpperCAmelCase    : Dict      =torch.mean(torch.abs(snake_case__						)						)

							assert abs(result_sum.item() - 52.5302						) < 1e-2
							assert abs(result_mean.item() - 0.0684						) < 1e-3
	def 	UpperCAmelCase__       (  self						)	->							Any:
							'''simple docstring'''





							UpperCAmelCase    : List[str]      =self.full_loop(set_alpha_to_one=snake_case__		,   beta_start=0.01						)
							UpperCAmelCase    : int      =torch.sum(torch.abs(snake_case__						)						)
							UpperCAmelCase    : Union[str, Any]      =torch.mean(torch.abs(snake_case__						)						)

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






	def 	UpperCAmelCase__       (  self						)	->							Dict:
							'''simple docstring'''





							UpperCAmelCase    : Union[str, Any]      =self.full_loop(set_alpha_to_one=snake_case__		,   beta_start=0.01						)
							UpperCAmelCase    : Tuple      =torch.sum(torch.abs(snake_case__						)						)
							UpperCAmelCase    : List[Any]      =torch.mean(torch.abs(snake_case__						)						)

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

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





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






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
