




"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(DDIMParallelScheduler,)
 UpperCamelCase						       =						(('''eta''', 0.0), ('''num_inference_steps''', 50))

 def     a__			(    self :  Union[str, Any]						,							**A_ :  Union[str, Any]  )       ->     List[Any]:

       """simple docstring"""






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

       config.update(**A_  )
       return config

 def     a__			(    self :  List[Any]						,							**A_ :  Dict  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config(**A_  )
       lowerCamelCase_							=   scheduler_class(**A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   10, 0.0

       lowerCamelCase_							=   self.dummy_model()
       lowerCamelCase_							=   self.dummy_sample_deter

       scheduler.set_timesteps(A_  )

       for t in scheduler.timesteps:
             lowerCamelCase_							=   model(A_						,							A_  )
             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							A_  ).prev_sample

       return sample

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

       """simple docstring"""






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

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

       """simple docstring"""






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

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

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

       """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=A_						,							beta_end=A_  )

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

       """simple docstring"""






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

 def     a__			(    self :  int  )       ->     Optional[int]:

       """simple docstring"""






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

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

       """simple docstring"""






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

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

       """simple docstring"""






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

 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






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

 def     a__			(    self :  Any  )       ->     Optional[Any]:

       """simple docstring"""






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

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

       """simple docstring"""






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

 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






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

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

       """simple docstring"""






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

 def     a__			(    self :  Optional[int]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

       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     a__			(    self :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   10, 0.0
       scheduler.set_timesteps(A_  )

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

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

       lowerCamelCase_							=   model(samples.flatten(0						,							1  )						,							timesteps.flatten(0						,							1  )  )
       lowerCamelCase_							=   scheduler.batch_step_no_noise(A_						,							timesteps.flatten(0						,							1  )						,							samples.flatten(0						,							1  )						,							A_  )

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

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

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

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop()

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

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

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

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(prediction_type='v_prediction'  )

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

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

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

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(set_alpha_to_one=A_						,							beta_start=0.01  )
       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

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





 def     a__			(    self :  Any  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(set_alpha_to_one=A_						,							beta_start=0.01  )
       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      if not all(char in '01' for char in bin_string					):
            raise ValueError('Non-binary value was passed to the function'					)
      if not bin_string:
            raise ValueError('Empty string was passed to the function'					)
      lowerCamelCase_							=   ''
      while len(lowercase					) % 3 != 0:
            lowerCamelCase_							=   '0' + bin_string
      lowerCamelCase_							=   [
          bin_string[index : index + 3]
          for index in range(len(lowercase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCamelCase_							=   0
            for index, val in enumerate(lowercase					):
                  oct_val += int(2 ** (2 - index) * int(lowercase					)					)
            oct_string += str(lowercase					)
      return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()



