

'''simple docstring'''

import baseaa





def       A_     (       snake_case       ):
  return baseaa.aaaencode(string.encode("utf-8"       )       )





def       A_     (       snake_case       ):
  return baseaa.aaadecode(UpperCamelCase__       ).decode("utf-8"       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()














"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					(IPNDMScheduler,)
 UpperCamelCase					:       int			=					(('''num_inference_steps''', 50),)


 def _lowercase	(     self		:						Union[str, Any]							,						**UpperCAmelCase__		:						Tuple							)					->       int:
   _a  :       Optional[int]             =	{"""num_train_timesteps""": 1000}
   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Any=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	dict(self.forward_default_kwargs							)
   _a  :       Dict             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       Union[str, Any]             =	0.1 * sample
   _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       Any             =	dummy_past_residuals[:]

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

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

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       str             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						Tuple							)					->       List[str]:
   pass


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       List[Any]             =	0.1 * sample
   _a  :       Optional[Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Union[str, Any]             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

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

     if time_step is None:
       _a  :       List[Any]             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

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

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

     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						str							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Optional[int]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       List[Any]             =	self.dummy_model()
   _a  :       str             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       Any             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Dict             =	dict(self.forward_default_kwargs							)

   _a  :       int             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

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

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       List[str]             =	num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
     _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       Optional[Any]             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Tuple             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for timesteps in [100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for t, num_inference_steps in zip([1, 5, 10]							,						[10, 50, 100]							):
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       str             =	self.full_loop()
   _a  :       List[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

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

