






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class       __a  (   lowerCamelCase__	):
 _lowerCAmelCase	:	int				=			(CMStochasticIterativeScheduler,)
 _lowerCAmelCase	:	Dict				=			1_0







 def  __lowercase (		self :       int							,       **SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  {
          "num_train_timesteps": 2_01,
          "sigma_min": 0.0_0_2,
          "sigma_max": 80.0,
      }

      config.update(**SCREAMING_SNAKE_CASE						)
      return config







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


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  10

      UpperCamelCase__      :       Any	    =  self.get_scheduler_config()
      UpperCamelCase__      :       int	    =  self.scheduler_classes[0](**SCREAMING_SNAKE_CASE						)

      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  scheduler.timesteps[0]
      UpperCamelCase__      :       Optional[int]	    =  scheduler.timesteps[1]

      UpperCamelCase__      :       Any	    =  self.dummy_sample
      UpperCamelCase__      :       Union[str, Any]	    =  0.1 * sample

      UpperCamelCase__      :       Optional[Any]	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).prev_sample
      UpperCamelCase__      :       Tuple	    =  scheduler.step(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).prev_sample

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







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       str						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Tuple	    =  1
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[Any]	    =  scheduler.timesteps

      UpperCamelCase__      :       List[str]	    =  torch.manual_seed(0						)

      UpperCamelCase__      :       List[str]	    =  self.dummy_model()
      UpperCamelCase__      :       Optional[int]	    =  self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(SCREAMING_SNAKE_CASE						):
           # 1. scale model input
           UpperCamelCase__      :       List[str]	    =  scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)

           # 2. predict noise residual
           UpperCamelCase__      :       List[Any]	    =  model(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)

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

           UpperCamelCase__      :       Any	    =  pred_prev_sample

      UpperCamelCase__      :       List[Any]	    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__      :       List[str]	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 1_92.76_14						) < 1e-2
      assert abs(result_mean.item() - 0.2_5_1_0						) < 1e-3







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






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

      UpperCamelCase__      :       int	    =  [1_06, 0]
      scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       str	    =  scheduler.timesteps

      UpperCamelCase__      :       Dict	    =  torch.manual_seed(0						)

      UpperCamelCase__      :       Dict	    =  self.dummy_model()
      UpperCamelCase__      :       List[Any]	    =  self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

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

           UpperCamelCase__      :       Optional[Any]	    =  pred_prev_sample

      UpperCamelCase__      :       Union[str, Any]	    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__      :       str	    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 3_47.63_57						) < 1e-2
      assert abs(result_mean.item() - 0.4_5_2_7						) < 1e-3







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.scheduler_classes[0]
      UpperCamelCase__      :       Dict	    =  self.get_scheduler_config()
      UpperCamelCase__      :       Union[str, Any]	    =  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       str	    =  [39, 30, 12, 15, 0]

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







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






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

      UpperCamelCase__      :       int	    =  [39, 30, 12, 1, 0]
      UpperCamelCase__      :       Optional[int]	    =  len(SCREAMING_SNAKE_CASE						)

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







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






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

      UpperCamelCase__      :       int	    =  [scheduler.config.num_train_timesteps]

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       int	    =  1
     for i in range(1 ,   num + 1      ):
          fact *= i
     return fact







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  0
     while number > 0:
          UpperCamelCase__      :       List[Any]	    =  number % 10
          sum_of_digits += last_digit
          UpperCamelCase__      :       int	    =  number // 10  # Removing the last_digit from the given number
     return sum_of_digits







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase = 100      )			->     int:
     UpperCamelCase__      :       Optional[Any]	    =  factorial(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[int]	    =  split_and_add(__lowerCAmelCase      )
     return result


if __name__ == "__main__":
 print(solution(int(input('''Enter the Number: ''').strip())))