




"""simple docstring"""


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = (PNDMScheduler,)
 lowercase__			 = (("num_inference_steps", 50),)


 def 			UpperCamelCase_       (    self:		Optional[int],				**a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  {
       """num_train_timesteps""": 1_000,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
   }

   config.update(**a_		)
   return config


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		int=0,				**a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		str								=  dict(self.forward_default_kwargs		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""num_inference_steps""",				a_		)
   _snake_case							:		Tuple								=  self.dummy_sample
   _snake_case							:		Optional[int]								=  0.1 * sample
   _snake_case							:		List[str]								=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		Any								=  self.get_scheduler_config(**a_		)
     _snake_case							:		int								=  scheduler_class(**a_		)
     scheduler.set_timesteps(a_		)
     # copy over dummy past residuals
     _snake_case							:		str								=  dummy_past_residuals[:]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_		)
       _snake_case							:		int								=  scheduler_class.from_pretrained(a_		)
       new_scheduler.set_timesteps(a_		)
       # copy over dummy past residuals
       _snake_case							:		Tuple								=  dummy_past_residuals[:]

     _snake_case							:		List[Any]								=  scheduler.step_prk(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		Optional[int]								=  new_scheduler.step_prk(a_,				a_,				a_,				**a_		).prev_sample

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

     _snake_case							:		Any								=  scheduler.step_plms(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		Dict								=  new_scheduler.step_plms(a_,				a_,				a_,				**a_		).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:		int,				a_:		int=0,				**a_:		Optional[int]		):




   '''simple docstring'''



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

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		Optional[int]								=  self.get_scheduler_config()
     _snake_case							:		Optional[Any]								=  scheduler_class(**a_		)
     scheduler.set_timesteps(a_		)

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

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

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

     _snake_case							:		List[str]								=  scheduler.step_prk(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		int								=  new_scheduler.step_prk(a_,				a_,				a_,				**a_		).prev_sample

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

     _snake_case							:		Union[str, Any]								=  scheduler.step_plms(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		Optional[int]								=  new_scheduler.step_plms(a_,				a_,				a_,				**a_		).prev_sample

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


 def 			UpperCamelCase_       (    self:		Any,				**a_:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.scheduler_classes[0]
   _snake_case							:		int								=  self.get_scheduler_config(**a_		)
   _snake_case							:		str								=  scheduler_class(**a_		)

   _snake_case							:		Union[str, Any]								=  10
   _snake_case							:		Optional[Any]								=  self.dummy_model()
   _snake_case							:		Optional[Any]								=  self.dummy_sample_deter
   scheduler.set_timesteps(a_		)

   for i, t in enumerate(scheduler.prk_timesteps		):
     _snake_case							:		Optional[Any]								=  model(a_,				a_		)
     _snake_case							:		Tuple								=  scheduler.step_prk(a_,				a_,				a_		).prev_sample

   for i, t in enumerate(scheduler.plms_timesteps		):
     _snake_case							:		Optional[int]								=  model(a_,				a_		)
     _snake_case							:		str								=  scheduler.step_plms(a_,				a_,				a_		).prev_sample

   return sample


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  dict(self.forward_default_kwargs		)

   _snake_case							:		str								=  kwargs.pop("""num_inference_steps""",				a_		)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		Union[str, Any]								=  self.get_scheduler_config()
     _snake_case							:		Optional[int]								=  scheduler_class(**a_		)

     _snake_case							:		Dict								=  self.dummy_sample
     _snake_case							:		List[str]								=  0.1 * sample

     if num_inference_steps is not None and hasattr(a_,				"""set_timesteps"""		):
       scheduler.set_timesteps(a_		)
     elif num_inference_steps is not None and not hasattr(a_,				"""set_timesteps"""		):
       _snake_case							:		List[Any]								=  num_inference_steps

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

     _snake_case							:		str								=  scheduler.step_prk(a_,				0,				a_,				**a_		).prev_sample
     _snake_case							:		List[Any]								=  scheduler.step_prk(a_,				1,				a_,				**a_		).prev_sample

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

     _snake_case							:		Union[str, Any]								=  scheduler.step_plms(a_,				0,				a_,				**a_		).prev_sample
     _snake_case							:		List[str]								=  scheduler.step_plms(a_,				1,				a_,				**a_		).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, 1_000]:
     self.check_over_configs(num_train_timesteps=a_		)


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=a_		)

   _snake_case							:		List[Any]								=  self.scheduler_classes[0]
   _snake_case							:		List[Any]								=  self.get_scheduler_config(steps_offset=1		)
   _snake_case							:		List[str]								=  scheduler_class(**a_		)
   scheduler.set_timesteps(10		)
   assert torch.equal(
       scheduler.timesteps,				torch.LongTensor(
           [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		),				)


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




   '''simple docstring'''



   for beta_start, beta_end in zip([0.0_001, 0.001],				[0.002, 0.02]		):
     self.check_over_configs(beta_start=a_,				beta_end=a_		)


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




   '''simple docstring'''



   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=a_		)


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a_		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   for t in [1, 5, 10]:
     self.check_over_forward(time_step=a_		)


 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=a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  27

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		int								=  self.dummy_sample
     _snake_case							:		Union[str, Any]								=  0.1 * sample

     _snake_case							:		int								=  self.get_scheduler_config()
     _snake_case							:		Any								=  scheduler_class(**a_		)

     scheduler.set_timesteps(a_		)

     # before power of 3 fix, would error on first step, so we only need to do two
     for i, t in enumerate(scheduler.prk_timesteps[:2]		):
       _snake_case							:		Union[str, Any]								=  scheduler.step_prk(a_,				a_,				a_		).prev_sample


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   with self.assertRaises(a_		):
     _snake_case							:		Optional[int]								=  self.scheduler_classes[0]
     _snake_case							:		Dict								=  self.get_scheduler_config()
     _snake_case							:		Optional[int]								=  scheduler_class(**a_		)

     scheduler.step_plms(self.dummy_sample,				1,				self.dummy_sample		).prev_sample


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  self.full_loop()
   _snake_case							:		List[str]								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		Optional[int]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 198.1_318		) < 1E-2
   assert abs(result_mean.item() - 0.2_580		) < 1E-3


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.full_loop(prediction_type="""v_prediction"""		)
   _snake_case							:		Any								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		int								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 67.3_986		) < 1E-2
   assert abs(result_mean.item() - 0.0_878		) < 1E-3


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.full_loop(set_alpha_to_one=a_,				beta_start=0.01		)
   _snake_case							:		Union[str, Any]								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		List[Any]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 230.0_399		) < 1E-2
   assert abs(result_mean.item() - 0.2_995		) < 1E-3


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.full_loop(set_alpha_to_one=a_,				beta_start=0.01		)
   _snake_case							:		List[str]								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		int								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 186.9_482		) < 1E-2
   assert abs(result_mean.item() - 0.2_434		) < 1E-3








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


