


"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[Any]							  =  (CMStochasticIterativeScheduler,)
   __UpperCAmelCase :       Dict							  =  10







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    {
           "num_train_timesteps": 201,
           "sigma_min": 0.002,
           "sigma_max": 80.0,
       }

       config.update(**UpperCamelCase__						)
       return config







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

       '''simple docstring'''




       snake_case    :      List[str]      					=    10

       snake_case    :      Tuple      					=    self.get_scheduler_config()
       snake_case    :      Tuple      					=    self.scheduler_classes[0](**UpperCamelCase__						)

       scheduler.set_timesteps(UpperCamelCase__						)

       snake_case    :      Dict      					=    scheduler.timesteps[0]
       snake_case    :      Tuple      					=    scheduler.timesteps[1]

       snake_case    :      Optional[Any]      					=    self.dummy_sample
       snake_case    :      Optional[Any]      					=    0.1 * sample

       snake_case    :      Tuple      					=    scheduler.step(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						).prev_sample
       snake_case    :      str      					=    scheduler.step(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						).prev_sample

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







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    self.scheduler_classes[0]
       snake_case    :      Optional[Any]      					=    self.get_scheduler_config()
       snake_case    :      Optional[int]      					=    scheduler_class(**UpperCamelCase__						)

       snake_case    :      List[str]      					=    1
       scheduler.set_timesteps(UpperCamelCase__						)
       snake_case    :      Tuple      					=    scheduler.timesteps

       snake_case    :      Optional[Any]      					=    torch.manual_seed(0						)

       snake_case    :      Optional[Any]      					=    self.dummy_model()
       snake_case    :      List[Any]      					=    self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCamelCase__						):
           # 1. scale model input
           snake_case    :      Tuple      					=    scheduler.scale_model_input(UpperCamelCase__						,	UpperCamelCase__						)

           # 2. predict noise residual
           snake_case    :      str      					=    model(UpperCamelCase__						,	UpperCamelCase__						)

           # 3. predict previous sample x_t-1
           snake_case    :      List[Any]      					=    scheduler.step(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	generator=UpperCamelCase__						).prev_sample

           snake_case    :      Optional[Any]      					=    pred_prev_sample

       snake_case    :      List[Any]      					=    torch.sum(torch.abs(UpperCamelCase__						)						)
       snake_case    :      Optional[int]      					=    torch.mean(torch.abs(UpperCamelCase__						)						)

       assert abs(result_sum.item() - 192.7614						) < 1e-2
       assert abs(result_mean.item() - 0.2510						) < 1e-3







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.scheduler_classes[0]
       snake_case    :      Optional[int]      					=    self.get_scheduler_config()
       snake_case    :      List[str]      					=    scheduler_class(**UpperCamelCase__						)

       snake_case    :      str      					=    [106, 0]
       scheduler.set_timesteps(timesteps=UpperCamelCase__						)
       snake_case    :      Optional[Any]      					=    scheduler.timesteps

       snake_case    :      Union[str, Any]      					=    torch.manual_seed(0						)

       snake_case    :      List[str]      					=    self.dummy_model()
       snake_case    :      List[str]      					=    self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
           # 1. scale model input
           snake_case    :      Tuple      					=    scheduler.scale_model_input(UpperCamelCase__						,	UpperCamelCase__						)

           # 2. predict noise residual
           snake_case    :      Optional[int]      					=    model(UpperCamelCase__						,	UpperCamelCase__						)

           # 3. predict previous sample x_t-1
           snake_case    :      Optional[Any]      					=    scheduler.step(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	generator=UpperCamelCase__						).prev_sample

           snake_case    :      List[Any]      					=    pred_prev_sample

       snake_case    :      Optional[Any]      					=    torch.sum(torch.abs(UpperCamelCase__						)						)
       snake_case    :      Any      					=    torch.mean(torch.abs(UpperCamelCase__						)						)

       assert abs(result_sum.item() - 347.6357						) < 1e-2
       assert abs(result_mean.item() - 0.4527						) < 1e-3







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

       '''simple docstring'''




       snake_case    :      List[str]      					=    self.scheduler_classes[0]
       snake_case    :      List[Any]      					=    self.get_scheduler_config()
       snake_case    :      Any      					=    scheduler_class(**UpperCamelCase__						)

       snake_case    :      Dict      					=    [39, 30, 12, 15, 0]

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







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.scheduler_classes[0]
       snake_case    :      Tuple      					=    self.get_scheduler_config()
       snake_case    :      str      					=    scheduler_class(**UpperCamelCase__						)

       snake_case    :      Optional[Any]      					=    [39, 30, 12, 1, 0]
       snake_case    :      Optional[Any]      					=    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  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.scheduler_classes[0]
       snake_case    :      Dict      					=    self.get_scheduler_config()
       snake_case    :      int      					=    scheduler_class(**UpperCamelCase__						)

       snake_case    :      str      					=    [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__						)









"""simple docstring"""



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


    """simple docstring"""





    snake_case    :      Dict      					=    []
    snake_case    :      List[Any]      					=    1

    while len(lowercase ) < 1e6:
        constant.append(str(lowercase ) )
        i += 1

    snake_case    :      Tuple      					=    "".join(lowercase )

    return (
        int(constant[0] )
        * int(constant[9] )
        * int(constant[99] )
        * int(constant[999] )
        * int(constant[9999] )
        * int(constant[9_9999] )
        * int(constant[99_9999] )
    )


if __name__ == "__main__":
  print(solution())





