





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Tuple   		=		(IPNDMScheduler,)
   a						:							str   		=		(("num_inference_steps", 50),)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      {'''num_train_timesteps''': 1000}
       config.update(**__magic_name__							)
       return config




   def       UpperCamelCase__  (    self,		__magic_name__=0,		**__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      dict(self.forward_default_kwargs							)
       UpperCamelCase__							:				Union[str, Any]          =      kwargs.pop('''num_inference_steps''',		__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.dummy_sample
       UpperCamelCase__							:				List[Any]          =      0.1 * sample
       UpperCamelCase__							:				List[str]          =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__							:				Optional[int]          =      self.get_scheduler_config(**__magic_name__							)
           UpperCamelCase__							:				Optional[int]          =      scheduler_class(**__magic_name__							)
           scheduler.set_timesteps(__magic_name__							)
           # copy over dummy past residuals
           UpperCamelCase__							:				Tuple          =      dummy_past_residuals[:]

           if time_step is None:
               UpperCamelCase__							:				Tuple          =      scheduler.timesteps[len(scheduler.timesteps							) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__magic_name__							)
               UpperCamelCase__							:				Optional[int]          =      scheduler_class.from_pretrained(__magic_name__							)
               new_scheduler.set_timesteps(__magic_name__							)
               # copy over dummy past residuals
               UpperCamelCase__							:				Union[str, Any]          =      dummy_past_residuals[:]

           UpperCamelCase__							:				Optional[int]          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				int          =      new_scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           UpperCamelCase__							:				Dict          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				Optional[int]          =      new_scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       pass




   def       UpperCamelCase__  (    self,		__magic_name__=0,		**__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      dict(self.forward_default_kwargs							)
       UpperCamelCase__							:				str          =      kwargs.pop('''num_inference_steps''',		__magic_name__							)
       UpperCamelCase__							:				str          =      self.dummy_sample
       UpperCamelCase__							:				Optional[int]          =      0.1 * sample
       UpperCamelCase__							:				str          =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__							:				int          =      self.get_scheduler_config()
           UpperCamelCase__							:				Optional[int]          =      scheduler_class(**__magic_name__							)
           scheduler.set_timesteps(__magic_name__							)

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase__							:				Optional[int]          =      dummy_past_residuals[:]

           if time_step is None:
               UpperCamelCase__							:				Optional[int]          =      scheduler.timesteps[len(scheduler.timesteps							) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__magic_name__							)
               UpperCamelCase__							:				Any          =      scheduler_class.from_pretrained(__magic_name__							)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(__magic_name__							)

               # copy over dummy past residual (must be after setting timesteps)
               UpperCamelCase__							:				Any          =      dummy_past_residuals[:]

           UpperCamelCase__							:				int          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				Tuple          =      new_scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           UpperCamelCase__							:				Dict          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				Optional[int]          =      new_scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.scheduler_classes[0]
       UpperCamelCase__							:				Dict          =      self.get_scheduler_config(**__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				str          =      10
       UpperCamelCase__							:				Tuple          =      self.dummy_model()
       UpperCamelCase__							:				Optional[int]          =      self.dummy_sample_deter
       scheduler.set_timesteps(__magic_name__							)

       for i, t in enumerate(scheduler.timesteps							):
           UpperCamelCase__							:				List[str]          =      model(__magic_name__,		__magic_name__							)
           UpperCamelCase__							:				int          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__							).prev_sample

       for i, t in enumerate(scheduler.timesteps							):
           UpperCamelCase__							:				List[str]          =      model(__magic_name__,		__magic_name__							)
           UpperCamelCase__							:				int          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__							).prev_sample

       return sample




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






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      dict(self.forward_default_kwargs							)

       UpperCamelCase__							:				Optional[int]          =      kwargs.pop('''num_inference_steps''',		__magic_name__							)

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__							:				Union[str, Any]          =      self.get_scheduler_config()
           UpperCamelCase__							:				Optional[Any]          =      scheduler_class(**__magic_name__							)

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

           if num_inference_steps is not None and hasattr(__magic_name__,		'''set_timesteps'''							):
               scheduler.set_timesteps(__magic_name__							)
           elif num_inference_steps is not None and not hasattr(__magic_name__,		'''set_timesteps'''							):
               UpperCamelCase__							:				Optional[int]          =      num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase__							:				List[Any]          =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           UpperCamelCase__							:				Optional[int]          =      dummy_past_residuals[:]

           UpperCamelCase__							:				Optional[int]          =      scheduler.timesteps[5]
           UpperCamelCase__							:				str          =      scheduler.timesteps[6]

           UpperCamelCase__							:				Tuple          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				Dict          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

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

           UpperCamelCase__							:				Dict          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample
           UpperCamelCase__							:				int          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__							).prev_sample

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




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       for timesteps in [100, 1000]:
           self.check_over_configs(num_train_timesteps=__magic_name__,		time_step=__magic_name__							)




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






       """simple docstring"""
       for t, num_inference_steps in zip([1, 5, 10],		[10, 50, 100]							):
           self.check_over_forward(num_inference_steps=__magic_name__,		time_step=__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.full_loop()
       UpperCamelCase__							:				List[Any]          =      torch.mean(torch.abs(__magic_name__							)							)

       assert abs(result_mean.item() - 2540529							) < 10








from math import factorial




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	int:

    # If either of the conditions are true, the function is being asked
    # to calculate a factorial of a negative number, which is not possible
    if n < k or k < 0:
        raise ValueError('''Please enter positive integers for n and k where n >= k'''      )
    return factorial(__UpperCAmelCase      ) // (factorial(__UpperCAmelCase      ) * factorial(n - k      ))


if __name__ == "__main__":
  print(
      'The number of five-card hands possible from a standard',
      F'''fifty-two card deck is: {combinations(52, 5)}\n''',
  )

  print(
      'If a class of 40 students must be arranged into groups of',
      F'''4 for group projects, there are {combinations(40, 4)} ways''',
      'to arrange them.\n',
  )

  print(
      'If 10 teams are competing in a Formula One race, there',
      F'''are {combinations(10, 3)} ways that first, second and''',
      'third place can be awarded.',
  )

