import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							(CMStochasticIterativeScheduler,)
   lowerCAmelCase_														=							10




   def 			__a		(					self    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         """num_train_timesteps""": 2_01,
         """sigma_min""": 0.0_02,
         """sigma_max""": 80.0,
     }

     config.update(**UpperCAmelCase__   )
     return config




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   10

     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0](**UpperCAmelCase__   )

     scheduler.set_timesteps(UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps[0]
     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps[1]

     SCREAMING_SNAKE_CASE__    							=   self.dummy_sample
     SCREAMING_SNAKE_CASE__    							=   0.1 * sample

     SCREAMING_SNAKE_CASE__    							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__   ).prev_sample
     SCREAMING_SNAKE_CASE__    							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__   ).prev_sample

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




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for clip_denoised in [True, False]:
       self.check_over_configs(clip_denoised=UpperCAmelCase__   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   1
     scheduler.set_timesteps(UpperCAmelCase__   )
     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps

     SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__    							=   self.dummy_model()
     SCREAMING_SNAKE_CASE__    							=   self.dummy_sample_deter * scheduler.init_noise_sigma

     for i, t in enumerate(UpperCAmelCase__   ):
       # 1. scale model input
       SCREAMING_SNAKE_CASE__    							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__   )

       # 2. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(UpperCAmelCase__ ,			UpperCAmelCase__   )

       # 3. predict previous sample x_t-1
       SCREAMING_SNAKE_CASE__    							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__   ).prev_sample

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(UpperCAmelCase__   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(UpperCAmelCase__   )   )

     assert abs(result_sum.item() - 1_92.76_14   ) < 1E-2
     assert abs(result_mean.item() - 0.25_10   ) < 1E-3




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   [1_06, 0]
     scheduler.set_timesteps(timesteps=UpperCAmelCase__   )
     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps

     SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__    							=   self.dummy_model()
     SCREAMING_SNAKE_CASE__    							=   self.dummy_sample_deter * scheduler.init_noise_sigma

     for t in timesteps:
       # 1. scale model input
       SCREAMING_SNAKE_CASE__    							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__   )

       # 2. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(UpperCAmelCase__ ,			UpperCAmelCase__   )

       # 3. predict previous sample x_t-1
       SCREAMING_SNAKE_CASE__    							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__   ).prev_sample

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(UpperCAmelCase__   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(UpperCAmelCase__   )   )

     assert abs(result_sum.item() - 3_47.63_57   ) < 1E-2
     assert abs(result_mean.item() - 0.45_27   ) < 1E-3




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   [39, 30, 12, 15, 0]

     with self.assertRaises(UpperCAmelCase__ ,			msg="""`timesteps` must be in descending order."""   ):
       scheduler.set_timesteps(timesteps=UpperCAmelCase__   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   [39, 30, 12, 1, 0]
     SCREAMING_SNAKE_CASE__    							=   len(UpperCAmelCase__   )

     with self.assertRaises(UpperCAmelCase__ ,			msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
       scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__ ,			timesteps=UpperCAmelCase__   )




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**UpperCAmelCase__   )

     SCREAMING_SNAKE_CASE__    							=   [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         UpperCAmelCase__ ,			msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,			):
       scheduler.set_timesteps(timesteps=UpperCAmelCase__   )

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


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

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
