

'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 		lowerCAmelCase__  ( SCREAMING_SNAKE_CASE_   ):



 """simple docstring"""



 lowerCAmelCase__       = (IPNDMScheduler,)
 lowerCAmelCase__       = (("num_inference_steps", 50),)
 def  UpperCAmelCase__						(						self   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {"""num_train_timesteps""": 1_000}
      config.update(**lowercase_     )
      return config
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[int]:


      """simple docstring"""




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

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

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

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

           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample
           __SCREAMING_SNAKE_CASE      = new_scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample

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

           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample
           __SCREAMING_SNAKE_CASE      = 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   :			Any     )    ->					Any:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




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

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

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

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

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_     )
                __SCREAMING_SNAKE_CASE      = 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)
                __SCREAMING_SNAKE_CASE      = dummy_past_residuals[:]

           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample
           __SCREAMING_SNAKE_CASE      = new_scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample

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

           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample
           __SCREAMING_SNAKE_CASE      = 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   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      = self.get_scheduler_config(**lowercase_     )
      __SCREAMING_SNAKE_CASE      = scheduler_class(**lowercase_     )

      __SCREAMING_SNAKE_CASE      = 10
      __SCREAMING_SNAKE_CASE      = self.dummy_model()
      __SCREAMING_SNAKE_CASE      = self.dummy_sample_deter
      scheduler.set_timesteps(lowercase_     )

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

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

      return sample
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = dict(self.forward_default_kwargs     )

      __SCREAMING_SNAKE_CASE      = kwargs.pop("""num_inference_steps"""   ,				lowercase_     )

      for scheduler_class in self.scheduler_classes:
           __SCREAMING_SNAKE_CASE      = self.get_scheduler_config()
           __SCREAMING_SNAKE_CASE      = scheduler_class(**lowercase_     )

           __SCREAMING_SNAKE_CASE      = self.dummy_sample
           __SCREAMING_SNAKE_CASE      = 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"""     ):
                __SCREAMING_SNAKE_CASE      = num_inference_steps

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

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

           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample
           __SCREAMING_SNAKE_CASE      = scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				**lowercase_     ).prev_sample

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

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


      """simple docstring"""




      for timesteps in [100, 1_000]:
           self.check_over_configs(num_train_timesteps=lowercase_   ,				time_step=lowercase_     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[str]:


      """simple docstring"""




      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  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.full_loop()
      __SCREAMING_SNAKE_CASE      = torch.mean(torch.abs(lowercase_     )     )

      assert abs(result_mean.item() - 2_540_529     ) < 10








"""simple docstring"""

from math import ceil, sqrt




def      lowercase__    (       lowerCAmelCase					:       int = 1_000_000			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       0

    for outer_width in range(3			,					(limit // 4) + 2			):
        if outer_width**2 > limit:
            UpperCAmelCase          =       max(ceil(sqrt(outer_width**2 - limit			)			)			,					1			)
        else:
            UpperCAmelCase          =       1
        if (outer_width - hole_width_lower_bound) % 2:
            hole_width_lower_bound += 1

        answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

    return answer


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



