


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[Any]      = (CMStochasticIterativeScheduler,)
     lowerCamelCase			:			str      = 10







     def 							lowercase__       (						self :						Optional[Any]   ,   **_lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		{
               '''num_train_timesteps''': 2_01,
               '''sigma_min''': 0.002,
               '''sigma_max''': 80.0,
           }

           config.update(**_lowercase      )
           return config







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		10

           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.scheduler_classes[0](**_lowercase      )

           scheduler.set_timesteps(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		scheduler.timesteps[0]
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler.timesteps[1]

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.dummy_sample
           SCREAMING_SNAKE_CASE__       :  str						     =		0.1 * sample

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample

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







     def 							lowercase__       (						self :						str      ):
           for timesteps in [10, 50, 1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=_lowercase      )







     def 							lowercase__       (						self :						Tuple      ):
           for clip_denoised in [True, False]:
                 self.check_over_configs(clip_denoised=_lowercase      )







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		1
           scheduler.set_timesteps(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler.timesteps

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.manual_seed(0      )

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

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

                 # 2. predict noise residual
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		model(_lowercase   ,   _lowercase      )

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

                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pred_prev_sample

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

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







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[1_06, 0]
           scheduler.set_timesteps(timesteps=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		scheduler.timesteps

           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.manual_seed(0      )

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

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

                 # 2. predict noise residual
                 SCREAMING_SNAKE_CASE__       :  int						     =		model(_lowercase   ,   _lowercase      )

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

                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		pred_prev_sample

           SCREAMING_SNAKE_CASE__       :  int						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.mean(torch.abs(_lowercase      )      )

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







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  str						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[39, 30, 12, 15, 0]

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







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Any						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  int						     =		[39, 30, 12, 1, 0]
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		len(_lowercase      )

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







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		[scheduler.config.num_train_timesteps]

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








lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


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






