





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      (IPNDMScheduler,)
  __lowerCamelCase		=      (('num_inference_steps', 50),)







  def   UpperCAmelCase					(      self  :Optional[int]   ,							**_lowercase  :Dict ):






     '''simple docstring'''





     lowercase__			       =  {"num_train_timesteps": 10_00}
     config.update(**_lowercase )
     return config







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Dict=0   ,							**_lowercase  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  dict(self.forward_default_kwargs )
     lowercase__			       =  kwargs.pop("num_inference_steps"   ,							_lowercase )
     lowercase__			       =  self.dummy_sample
     lowercase__			       =  0.1 * sample
     lowercase__			       =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
        lowercase__			       =  self.get_scheduler_config(**_lowercase )
        lowercase__			       =  scheduler_class(**_lowercase )
        scheduler.set_timesteps(_lowercase )
        # copy over dummy past residuals
        lowercase__			       =  dummy_past_residuals[:]

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

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

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  new_scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  new_scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Any=0   ,							**_lowercase  :Any ):






     '''simple docstring'''





     lowercase__			       =  dict(self.forward_default_kwargs )
     lowercase__			       =  kwargs.pop("num_inference_steps"   ,							_lowercase )
     lowercase__			       =  self.dummy_sample
     lowercase__			       =  0.1 * sample
     lowercase__			       =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
        lowercase__			       =  self.get_scheduler_config()
        lowercase__			       =  scheduler_class(**_lowercase )
        scheduler.set_timesteps(_lowercase )

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

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

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

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

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  new_scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  new_scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							**_lowercase  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config(**_lowercase )
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  10
     lowercase__			       =  self.dummy_model()
     lowercase__			       =  self.dummy_sample_deter
     scheduler.set_timesteps(_lowercase )

     for i, t in enumerate(scheduler.timesteps ):
        lowercase__			       =  model(_lowercase   ,							_lowercase )
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase ).prev_sample

     for i, t in enumerate(scheduler.timesteps ):
        lowercase__			       =  model(_lowercase   ,							_lowercase )
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase ).prev_sample

     return sample







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  dict(self.forward_default_kwargs )

     lowercase__			       =  kwargs.pop("num_inference_steps"   ,							_lowercase )

     for scheduler_class in self.scheduler_classes:
        lowercase__			       =  self.get_scheduler_config()
        lowercase__			       =  scheduler_class(**_lowercase )

        lowercase__			       =  self.dummy_sample
        lowercase__			       =  0.1 * sample

        if num_inference_steps is not None and hasattr(_lowercase   ,							"set_timesteps" ):
           scheduler.set_timesteps(_lowercase )
        elif num_inference_steps is not None and not hasattr(_lowercase   ,							"set_timesteps" ):
           lowercase__			       =  num_inference_steps

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

        lowercase__			       =  scheduler.timesteps[5]
        lowercase__			       =  scheduler.timesteps[6]

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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

        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							**_lowercase ).prev_sample

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







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=_lowercase   ,							time_step=_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 1_00] ):
        self.check_over_forward(num_inference_steps=_lowercase   ,							time_step=_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  self.full_loop()
     lowercase__			       =  torch.mean(torch.abs(_lowercase ) )

     assert abs(result_mean.item() - 2_54_05_29 ) < 10













import math


def _A       (   __magic_name__	):
   lowercase__			       =  math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

   return exponent == int(__magic_name__	)


def _A       (   __magic_name__ = 1 / 1_2345	):
   lowercase__			       =  0
   lowercase__			       =  0

   lowercase__			       =  3
   while True:
      lowercase__			       =  (integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(__magic_name__	):
         lowercase__			       =  int(__magic_name__	)
         total_partitions += 1
         if check_partition_perfect(__magic_name__	):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(__magic_name__	)
      integer += 1


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






