





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			(CMStochasticIterativeScheduler,)
     __magic_name__   =			10




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							{
             'num_train_timesteps': 201,
             'sigma_min': 0.002,
             'sigma_max': 80.0,
         }

         config.update(**snake_case_							)
         return config




     def        lowerCAmelCase__		(    self							):
         _A     =							10

         _A     =							self.get_scheduler_config()
         _A     =							self.scheduler_classes[0](**snake_case_							)

         scheduler.set_timesteps(snake_case_							)

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

         _A     =							self.dummy_sample
         _A     =							0.1 * sample

         _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_							).prev_sample
         _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_							).prev_sample

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




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




     def        lowerCAmelCase__		(    self							):
         for clip_denoised in [True, False]:
             self.check_over_configs(clip_denoised=snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							1
         scheduler.set_timesteps(snake_case_							)
         _A     =							scheduler.timesteps

         _A     =							torch.manual_seed(0							)

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

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

             # 2. predict noise residual
             _A     =							model(snake_case_				,     snake_case_							)

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

             _A     =							pred_prev_sample

         _A     =							torch.sum(torch.abs(snake_case_							)							)
         _A     =							torch.mean(torch.abs(snake_case_							)							)

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[106, 0]
         scheduler.set_timesteps(timesteps=snake_case_							)
         _A     =							scheduler.timesteps

         _A     =							torch.manual_seed(0							)

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

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

             # 2. predict noise residual
             _A     =							model(snake_case_				,     snake_case_							)

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

             _A     =							pred_prev_sample

         _A     =							torch.sum(torch.abs(snake_case_							)							)
         _A     =							torch.mean(torch.abs(snake_case_							)							)

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[39, 30, 12, 1, 0]
         _A     =							len(snake_case_							)

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[scheduler.config.num_train_timesteps]

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









'''simple docstring'''






from math import factorial

__A   :    dict[str, int]											= {str(digit): factorial(digit) for digit in range(10)}







def 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  if not isinstance(A__				,       A__   ):
    raise TypeError("""Parameter number must be int"""   )

  if number < 0:
    raise ValueError("""Parameter number must be greater than or equal to 0"""   )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(A__   )   )







def 							UpperCamelCase_     (       A__   :  int = 60				,       A__   :  int = 1_00_00_00   ):
  '''simple docstring'''





  if not isinstance(A__				,       A__   ) or not isinstance(A__				,       A__   ):
    raise TypeError("""Parameters chain_length and number_limit must be int"""   )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        """Parameters chain_length and number_limit must be greater than 0"""   )

  # the counter for the chains with the exact desired length
  lowerCAmelCase_     : Optional[int]     =  0
  # the cached sizes of the previous chains
  lowerCAmelCase_     : dict[int, int]     =  {}

  for start_chain_element in range(1				,       A__   ):
    # The temporary set will contain the elements of the chain
    lowerCAmelCase_     : List[str]     =  set()
    lowerCAmelCase_     : Optional[int]     =  0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowerCAmelCase_     : int     =  start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(A__   )
      chain_set_length += 1
      lowerCAmelCase_     : Union[str, Any]     =  digit_factorial_sum(A__   )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    lowerCAmelCase_     : int     =  chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{solution()}''')



