





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		UpperCamelCase__			(				lowerCAmelCase__       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      (EulerDiscreteScheduler,)
     __snake_case   :     int		     =      10






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**_SCREAMING_SNAKE_CASE	)
           return config






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




           '''simple docstring'''


           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)






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




           '''simple docstring'''


           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]						,[0.0002, 0.002, 0.02]	):
                 self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE						,beta_end=_SCREAMING_SNAKE_CASE	)






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




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		int:




           '''simple docstring'''


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






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




           '''simple docstring'''


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

           scheduler.set_timesteps(self.num_inference_steps	)

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter * scheduler.init_noise_sigma
           SCREAMING_SNAKE_CASE		=						sample.to(_SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 SCREAMING_SNAKE_CASE		=						scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE	)
                 SCREAMING_SNAKE_CASE		=						output.prev_sample

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

           assert abs(result_sum.item() - 10.0807	) < 1e-2
           assert abs(result_mean.item() - 0.0131	) < 1e-3






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(prediction_type="""v_prediction"""	)
           SCREAMING_SNAKE_CASE		=						scheduler_class(**_SCREAMING_SNAKE_CASE	)

           scheduler.set_timesteps(self.num_inference_steps	)

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter * scheduler.init_noise_sigma
           SCREAMING_SNAKE_CASE		=						sample.to(_SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 SCREAMING_SNAKE_CASE		=						scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE	)
                 SCREAMING_SNAKE_CASE		=						output.prev_sample

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

           assert abs(result_sum.item() - 0.0002	) < 1e-2
           assert abs(result_mean.item() - 2.2_6_7_6e-0_6	) < 1e-3






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




           '''simple docstring'''


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

           scheduler.set_timesteps(self.num_inference_steps						,device=_SCREAMING_SNAKE_CASE	)

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE		=						sample.to(_SCREAMING_SNAKE_CASE	)

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE		=						scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE	)
                 SCREAMING_SNAKE_CASE		=						output.prev_sample

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

           assert abs(result_sum.item() - 10.0807	) < 1e-2
           assert abs(result_mean.item() - 0.0131	) < 1e-3






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
           SCREAMING_SNAKE_CASE		=						scheduler_class(**_SCREAMING_SNAKE_CASE						,use_karras_sigmas=_SCREAMING_SNAKE_CASE	)

           scheduler.set_timesteps(self.num_inference_steps						,device=_SCREAMING_SNAKE_CASE	)

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE		=						sample.to(_SCREAMING_SNAKE_CASE	)

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE		=						scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE	)

                 SCREAMING_SNAKE_CASE		=						scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE	)
                 SCREAMING_SNAKE_CASE		=						output.prev_sample

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

           assert abs(result_sum.item() - 124.52299499511719	) < 1e-2
           assert abs(result_mean.item() - 0.16213932633399963	) < 1e-3









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


