'''simple docstring'''




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					__magic_name__		(					_UpperCAmelCase):
   UpperCamelCase__       = (IPNDMScheduler,)
   UpperCamelCase__       = (('''num_inference_steps''', 50),)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    **lowercase_       :						Dict					):
          lowercase_      :    Tuple       		= {"""num_train_timesteps""": 1000}
          config.update(**lowercase_					)
          return config







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Optional[Any]=0       ,    **lowercase_       :						Any					):
          lowercase_      :    Union[str, Any]       		= dict(self.forward_default_kwargs					)
          lowercase_      :    List[Any]       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)
          lowercase_      :    Any       		= self.dummy_sample
          lowercase_      :    Optional[Any]       		= 0.1 * sample
          lowercase_      :    Tuple       		= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

                 lowercase_      :    List[str]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    Any       		= 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_      :    str       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    Dict       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
          pass







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						int=0       ,    **lowercase_       :						Any					):
          lowercase_      :    Any       		= dict(self.forward_default_kwargs					)
          lowercase_      :    Dict       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)
          lowercase_      :    Tuple       		= self.dummy_sample
          lowercase_      :    Optional[Any]       		= 0.1 * sample
          lowercase_      :    str       		= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
                 lowercase_      :    Any       		= self.get_scheduler_config()
                 lowercase_      :    Any       		= scheduler_class(**lowercase_					)
                 scheduler.set_timesteps(lowercase_					)

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

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(lowercase_					)
                        lowercase_      :    List[Any]       		= 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_      :    Union[str, Any]       		= dummy_past_residuals[:]

                 lowercase_      :    int       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    Dict       		= 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_      :    Union[str, Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    **lowercase_       :						List[Any]					):
          lowercase_      :    str       		= self.scheduler_classes[0]
          lowercase_      :    str       		= self.get_scheduler_config(**lowercase_					)
          lowercase_      :    Dict       		= scheduler_class(**lowercase_					)

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

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

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

          return sample







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
          lowercase_      :    Optional[int]       		= dict(self.forward_default_kwargs					)

          lowercase_      :    Union[str, Any]       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)

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

                 lowercase_      :    List[str]       		= self.dummy_sample
                 lowercase_      :    Dict       		= 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_      :    List[Any]       		= num_inference_steps

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

                 lowercase_      :    int       		= scheduler.timesteps[5]
                 lowercase_      :    List[Any]       		= scheduler.timesteps[6]

                 lowercase_      :    str       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    List[Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

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

                 lowercase_      :    Dict       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
                 lowercase_      :    List[Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

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







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
          for timesteps in [100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase_       ,    time_step=lowercase_					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
          for t, num_inference_steps in zip([1, 5, 10]       ,    [10, 50, 100]					):
                 self.check_over_forward(num_inference_steps=lowercase_       ,    time_step=lowercase_					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          lowercase_      :    Optional[Any]       		= self.full_loop()
          lowercase_      :    str       		= torch.mean(torch.abs(lowercase_					)					)

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






'''simple docstring'''




from __future__ import annotations




def 							lowerCamelCase			(   UpperCAmelCase__ :      list   ,   UpperCAmelCase__ :      int | None = None   ,   UpperCAmelCase__ :      int | None = None				)    ->   None:
							if start is None:
														lowercase_      :    Any       		= 0

							if end is None:
														lowercase_      :    List[Any]       		= len(UpperCAmelCase__				) - 1

							if start >= end:
														return

							lowercase_      :    Optional[int]       		= (start + end) // 2

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							slowsort(UpperCAmelCase__   ,   mid + 1   ,   UpperCAmelCase__				)

							if sequence[end] < sequence[mid]:
														lowercase_		,		lowercase_      :    Dict       		= sequence[mid], sequence[end]

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   end - 1				)


if __name__ == "__main__":
						from doctest import testmod

						testmod()





